From ce06ab09894d494c59f6ab1d2615352bacb5acb0 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 04:21:56 -0600 Subject: [PATCH 01/26] Updated calculate peer scores function and fixed fstrings --- AI_BENCHMARKING_ANALYSIS.ipynb | 6876 +++++++++++++++------------ bootstrapped_h2h_bot_vs_pros.csv | 88 +- functions.py | 115 +- weighted_t_test_h2h_bot_vs_pros.csv | 92 +- 4 files changed, 4002 insertions(+), 3169 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 3753b54..313d580 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -182,7 +182,7 @@ "# Weighted vs unweighted breakdown for those overlapping questions?\n", "df_pro_bot_overlap = df_pro_bot_resolved_questions[~df_pro_bot_resolved_questions['pro_question_id'].isna()]\n", "print(f'Unweighted count: {df_pro_bot_overlap.shape[0]}')\n", - "print(f'Weighted count: {df_pro_bot_overlap['question_weight'].sum()}')" + "print(f'Weighted count: {df_pro_bot_overlap[\"question_weight\"].sum()}')" ] }, { @@ -503,7 +503,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Process forecasts (consolidate forecast columns; take the last forecast from each forecaster for each question) \n", + "# Process forecasts (consolidate forecast columns; take the last forecast from each forecaster for each question)\n", "df_bot_forecasts = process_forecasts(df_bot_forecasts)\n", "df_pro_forecasts = process_forecasts(df_pro_forecasts)\n", "\n", @@ -615,12 +615,12 @@ " False\n", " \n", " \n", - " 3\n", + " 5\n", " 31268\n", - " SpottedBear\n", + " darkives\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 131523\n", + " 103907\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -630,16 +630,16 @@ " NaN\n", " NaN\n", " 31736\n", - " [0.001,0.59,0.35,0.044,0.015]\n", + " [0.001,0.49,0.365,0.1,0.044]\n", " False\n", " \n", " \n", - " 4\n", + " 6\n", " 31268\n", - " Zaldath\n", + " datscilly\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 139161\n", + " 103777\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -649,7 +649,7 @@ " NaN\n", " NaN\n", " 31736\n", - " [0.001,0.623,0.336,0.03,0.01]\n", + " [0.001,0.56,0.36,0.059,0.02]\n", " False\n", " \n", " \n", @@ -657,47 +657,40 @@ "" ], "text/plain": [ - " question_id forecaster \\\n", - "0 31268 Jgalt \n", - "1 31268 MaciekK \n", - "2 31268 OpenSystem \n", - "3 31268 SpottedBear \n", - "4 31268 Zaldath \n", - "\n", - " question_title \\\n", - "0 For Q1 2025, how many banks will be listed on ... \n", - "1 For Q1 2025, how many banks will be listed on ... \n", - "2 For Q1 2025, how many banks will be listed on ... \n", - "3 For Q1 2025, how many banks will be listed on ... \n", - "4 For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster question_title \\\n", + "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", + "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", + "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", + "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", + "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", "1 2025-01-17 19:06:22.013528+00 117580 1 \n", "2 2025-01-17 19:06:22.013528+00 120160 1 \n", - "3 2025-01-17 19:06:22.013528+00 131523 1 \n", - "4 2025-01-17 19:06:22.013528+00 139161 1 \n", + "5 2025-01-17 19:06:22.013528+00 103907 1 \n", + "6 2025-01-17 19:06:22.013528+00 103777 1 \n", "\n", " scheduled_close_time actual_close_time question_weight \\\n", "0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "1 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "2 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "3 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "4 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", " type options range_min range_max post_id \\\n", "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "3 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "4 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", + "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", + "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "\n", " forecast is_median \n", "0 [0.001,0.568,0.366,0.041,0.024] False \n", "1 [0.001,0.62,0.35,0.019,0.01] True \n", "2 [0.005,0.7,0.25,0.04,0.005] False \n", - "3 [0.001,0.59,0.35,0.044,0.015] False \n", - "4 [0.001,0.623,0.336,0.03,0.01] False " + "5 [0.001,0.49,0.365,0.1,0.044] False \n", + "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, "execution_count": 16, @@ -740,19 +733,18 @@ { "data": { "text/plain": [ - "array(['GreeneiBot2', 'Grizeu_Bot', 'InstitutPelFutur', 'NextWorldLab',\n", - " 'acm_bot', 'metac-Gemini-Exp-1206', 'metac-Llama-3.1', 'mmBot',\n", - " 'metac-claude-3-5-sonnet-latest', 'metac-gpt-4o',\n", - " 'metac-grok-2-1212', 'metac-o1', 'metac-o1-preview',\n", - " 'metac-perplexity', 'bot_median',\n", - " 'metac-claude-3-5-sonnet-20240620', 'pgodzinai', 'jkraybill_bot',\n", - " 'metac-exa', 'manticAI', 'MWG', 'CatrachoCaster', 'twsummerbot',\n", - " 'VeritasAI', 'X_bot', 'annabot', 'minefrac1', 'metac-deepseek-r1',\n", - " 'Bot_Pepa', 'laylaps', 'ajf-bot', 'SynapseSeer', 'RPM_bot',\n", - " 'cookics_bot_TEST', 'ProfessorSP', 'wunderplumb', 'CumulativeBot',\n", - " 'pianobot', 'krm-bot', 'KevinTestBot', '4Shadower', 'swingswish',\n", - " 'jonahsingerbot', 'bean_bot', 'andrewsiah', 'cobyj-bot'],\n", - " dtype=object)" + "array(['metac-Llama-3.1', 'metac-Gemini-Exp-1206', 'acm_bot',\n", + " 'NextWorldLab', 'metac-o1-preview', 'metac-perplexity', 'mmBot',\n", + " 'metac-claude-3-5-sonnet-latest', 'Grizeu_Bot', 'GreeneiBot2',\n", + " 'InstitutPelFutur', 'metac-claude-3-5-sonnet-20240620', 'metac-o1',\n", + " 'metac-grok-2-1212', 'metac-gpt-4o', 'bot_median', 'pgodzinai',\n", + " 'metac-exa', 'jkraybill_bot', 'VeritasAI', 'MWG', 'twsummerbot',\n", + " 'CatrachoCaster', 'X_bot', 'manticAI', 'annabot', 'minefrac1',\n", + " 'metac-deepseek-r1', 'Bot_Pepa', 'laylaps', 'ajf-bot',\n", + " 'SynapseSeer', 'RPM_bot', 'cookics_bot_TEST', 'ProfessorSP',\n", + " 'wunderplumb', 'CumulativeBot', 'pianobot', 'krm-bot',\n", + " 'KevinTestBot', '4Shadower', 'swingswish', 'jonahsingerbot',\n", + " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, "execution_count": 18, @@ -801,7 +793,7 @@ " \n", " \n", " \n", - " 11\n", + " 12\n", " metac-o1\n", " 9.674740\n", " 3631.123492\n", @@ -810,7 +802,16 @@ " 1.738353\n", " \n", " \n", - " 12\n", + " 15\n", + " bot_median\n", + " 8.829587\n", + " 3337.760404\n", + " 409\n", + " 5.839419\n", + " 1.521098\n", + " \n", + " \n", + " 4\n", " metac-o1-preview\n", " 8.465638\n", " 3121.449998\n", @@ -819,16 +820,7 @@ " 2.298000\n", " \n", " \n", - " 14\n", - " bot_median\n", - " 6.926374\n", - " 2618.307732\n", - " 409\n", - " 3.779645\n", - " 1.600741\n", - " \n", - " \n", - " 19\n", + " 24\n", " manticAI\n", " 6.510835\n", " 2055.210309\n", @@ -837,7 +829,7 @@ " 3.029040\n", " \n", " \n", - " 5\n", + " 1\n", " metac-Gemini-Exp-1206\n", " 5.417367\n", " 1880.476418\n", @@ -851,18 +843,18 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "11 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "12 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", - "14 bot_median 6.926374 2618.307732 409 3.779645 \n", - "19 manticAI 6.510835 2055.210309 337 0.552564 \n", - "5 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", + "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", + "15 bot_median 8.829587 3337.760404 409 5.839419 \n", + "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", + "24 manticAI 6.510835 2055.210309 337 0.552564 \n", + "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", - "11 1.738353 \n", - "12 2.298000 \n", - "14 1.600741 \n", - "19 3.029040 \n", - "5 2.309106 " + "12 1.738353 \n", + "15 1.521098 \n", + "4 2.298000 \n", + "24 3.029040 \n", + "1 2.309106 " ] }, "metadata": {}, @@ -899,7 +891,7 @@ " \n", " \n", " \n", - " 23\n", + " 19\n", " VeritasAI\n", " -4.854808\n", " -1602.183635\n", @@ -917,7 +909,7 @@ " 3.096816\n", " \n", " \n", - " 1\n", + " 8\n", " Grizeu_Bot\n", " -9.743831\n", " -1882.605577\n", @@ -926,7 +918,7 @@ " 3.931500\n", " \n", " \n", - " 9\n", + " 14\n", " metac-gpt-4o\n", " -5.987786\n", " -2235.360274\n", @@ -949,17 +941,17 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "23 VeritasAI -4.854808 -1602.183635 361 -8.860367 \n", + "19 VeritasAI -4.854808 -1602.183635 361 -8.860367 \n", "26 minefrac1 -9.333648 -1757.059251 202 -15.440064 \n", - "1 Grizeu_Bot -9.743831 -1882.605577 207 -17.494967 \n", - "9 metac-gpt-4o -5.987786 -2235.360274 404 -10.422687 \n", + "8 Grizeu_Bot -9.743831 -1882.605577 207 -17.494967 \n", + "14 metac-gpt-4o -5.987786 -2235.360274 404 -10.422687 \n", "30 ajf-bot -14.000701 -3208.260547 244 -24.482548 \n", "\n", " weighted_se \n", - "23 2.036820 \n", + "19 2.036820 \n", "26 3.096816 \n", - "1 3.931500 \n", - "9 2.255950 \n", + "8 3.931500 \n", + "14 2.255950 \n", "30 5.321344 " ] }, @@ -1520,7 +1512,7 @@ " \n", " 3\n", " bot_median\n", - " 8152.574861\n", + " 8806.147044\n", " \n", " \n", " 4\n", @@ -1541,7 +1533,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8152.574861\n", + "3 bot_median 8806.147044\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1710,13 +1702,13 @@ " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3711.510468\n", " \n", " \n", " 3\n", - " bot_median\n", - " 2724.680171\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 4\n", @@ -1946,8 +1938,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 2724.680171\n", + "2 bot_median 3711.510468\n", + "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -2082,12 +2074,12 @@ "# Print WEIGHTED average for pro_median\n", "print(\"PRO MEDIAN\")\n", "pro_median_baseline = df_pro_baseline_long[df_pro_baseline_long['forecaster'] == 'pro_median']\n", - "print(f'Average baseline: {(pro_median_baseline['score'] * pro_median_baseline['question_weight']).sum() / pro_median_baseline['question_weight'].sum()}')\n", + "print(f'Average baseline: {(pro_median_baseline[\"score\"] * pro_median_baseline[\"question_weight\"]).sum() / pro_median_baseline[\"question_weight\"].sum()}')\n", "\n", "# Same for pgodzinai in df_bot_scores (this differs from the bot team results later on because it's on ALL his questions)\n", "print(\"pgodzinai MEDIAN\")\n", "pgodzinai_baseline = df_bot_scores[df_bot_scores['forecaster'] == 'pgodzinai']\n", - "print(f'Average baseline: {(pgodzinai_baseline['score'] * pgodzinai_baseline['question_weight']).sum() / pgodzinai_baseline['question_weight'].sum()}')" + "print(f'Average baseline: {(pgodzinai_baseline[\"score\"] * pgodzinai_baseline[\"question_weight\"]).sum() / pgodzinai_baseline[\"question_weight\"].sum()}')" ] }, { @@ -2193,12 +2185,12 @@ " False\n", " \n", " \n", - " 3\n", + " 5\n", " 31268\n", - " SpottedBear\n", + " darkives\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 131523\n", + " 103907\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -2208,16 +2200,16 @@ " NaN\n", " NaN\n", " 31736\n", - " [0.001,0.59,0.35,0.044,0.015]\n", + " [0.001,0.49,0.365,0.1,0.044]\n", " False\n", " \n", " \n", - " 4\n", + " 6\n", " 31268\n", - " Zaldath\n", + " datscilly\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 139161\n", + " 103777\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -2227,7 +2219,7 @@ " NaN\n", " NaN\n", " 31736\n", - " [0.001,0.623,0.336,0.03,0.01]\n", + " [0.001,0.56,0.36,0.059,0.02]\n", " False\n", " \n", " \n", @@ -2235,47 +2227,40 @@ "" ], "text/plain": [ - " question_id forecaster \\\n", - "0 31268 Jgalt \n", - "1 31268 MaciekK \n", - "2 31268 OpenSystem \n", - "3 31268 SpottedBear \n", - "4 31268 Zaldath \n", - "\n", - " question_title \\\n", - "0 For Q1 2025, how many banks will be listed on ... \n", - "1 For Q1 2025, how many banks will be listed on ... \n", - "2 For Q1 2025, how many banks will be listed on ... \n", - "3 For Q1 2025, how many banks will be listed on ... \n", - "4 For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster question_title \\\n", + "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", + "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", + "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", + "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", + "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", "1 2025-01-17 19:06:22.013528+00 117580 1 \n", "2 2025-01-17 19:06:22.013528+00 120160 1 \n", - "3 2025-01-17 19:06:22.013528+00 131523 1 \n", - "4 2025-01-17 19:06:22.013528+00 139161 1 \n", + "5 2025-01-17 19:06:22.013528+00 103907 1 \n", + "6 2025-01-17 19:06:22.013528+00 103777 1 \n", "\n", " scheduled_close_time actual_close_time question_weight \\\n", "0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "1 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "2 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "3 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "4 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", " type options range_min range_max post_id \\\n", "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "3 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "4 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", + "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", + "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", "\n", " forecast is_median \n", "0 [0.001,0.568,0.366,0.041,0.024] False \n", "1 [0.001,0.62,0.35,0.019,0.01] True \n", "2 [0.005,0.7,0.25,0.04,0.005] False \n", - "3 [0.001,0.59,0.35,0.044,0.015] False \n", - "4 [0.001,0.623,0.336,0.03,0.01] False " + "5 [0.001,0.49,0.365,0.1,0.044] False \n", + "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, "execution_count": 28, @@ -2353,9 +2338,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.4,0.35,0.2,0.04,0.01]\n", " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.2,0.25,0.35,0.15,0.05]\n", + " [0.35000000000000003,0.30000000000000004,0.250...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2377,7 +2362,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", @@ -2427,7 +2412,7 @@ " ...\n", " [0.25,0.6,0.15]\n", " [0.2,0.6,0.2]\n", - " [0.15,0.45,0.4]\n", + " [0.15,0.55,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2449,8 +2434,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", + " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2488,24 +2473,24 @@ "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... NaN NaN \n", "\n", " CatrachoCaster ... metac-o1 \\\n", - "0 NaN ... [0.45,0.3,0.15,0.05,0.05] \n", - "1 NaN ... [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", + "1 NaN ... [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", "2 NaN ... 0.1 \n", "3 [0.16,0.47,0.37] ... [0.25,0.6,0.15] \n", - "4 NaN ... [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", "0 [0.02,0.7,0.2,0.07,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.15 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.2,0.25,0.35,0.15,0.05] NaN \n", + "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", "2 0.1 NaN \n", - "3 [0.15,0.45,0.4] NaN \n", + "3 [0.15,0.55,0.3] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2593,7 +2578,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.9\n", + " 0.95\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2617,8 +2602,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.2\n", - " 0.9\n", + " 0.35\n", + " 0.4\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2641,8 +2626,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.85\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2665,7 +2650,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.75\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2689,9 +2674,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.07\n", - " 0.1\n", " 0.05\n", + " 0.05\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2714,18 +2699,18 @@ "98 35387 35367 no 0.85 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.9 NaN NaN ... 0.9 \n", - "95 None 0.05 0.95 NaN NaN ... 0.2 \n", - "96 None 0.97 0.85 NaN NaN ... 0.85 \n", - "97 None 0.666 0.8 NaN NaN ... 0.75 \n", - "98 None 0.03 0.3 NaN NaN ... 0.07 \n", + "94 None 0.95 0.9 NaN NaN ... 0.95 \n", + "95 None 0.05 0.95 NaN NaN ... 0.35 \n", + "96 None 0.97 0.85 NaN NaN ... 0.9 \n", + "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "98 None 0.03 0.3 NaN NaN ... 0.05 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.9 NaN NaN 0.95 0.95 NaN \n", - "95 0.9 NaN NaN 0.15 NaN NaN \n", - "96 0.9 NaN NaN 0.9 NaN NaN \n", + "95 0.4 NaN NaN 0.15 NaN NaN \n", + "96 0.95 NaN NaN 0.9 NaN NaN \n", "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.1 0.05 NaN 0.15 0.05 NaN \n", + "98 0.05 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -2874,61 +2859,6 @@ "cell_type": "code", "execution_count": 34, "metadata": {}, - "outputs": [], - "source": [ - "# Simple function to parse CDF strings for numeric questions\n", - "def parse_numeric_forecasts(df):\n", - " \"\"\"\n", - " Parse CDF strings for numeric questions in-place.\n", - " \n", - " Args:\n", - " df: DataFrame with forecast data\n", - " \"\"\"\n", - " # Get numeric questions\n", - " numeric_mask = df['type'] == 'numeric'\n", - " \n", - " # List of columns to process\n", - " forecast_cols = [col for col in df.columns if col in all_bots or col in ['pro_median', 'bot_median']]\n", - " \n", - " # Process each column\n", - " for col in forecast_cols:\n", - " # Process only for numeric questions and only where the column exists\n", - " if col in df.columns:\n", - " for idx in df[numeric_mask].index:\n", - " value = df.at[idx, col]\n", - " \n", - " # Skip NaN values\n", - " if pd.isna(value):\n", - " continue\n", - " \n", - " # Process string values\n", - " if isinstance(value, str):\n", - " try:\n", - " # Parse the CDF string to an array\n", - " parsed_array = np.array([float(x) for x in value.strip('[]').split(',')])\n", - " df.at[idx, col] = parsed_array\n", - " except Exception as e:\n", - " print(f\"Warning: Could not parse {col} at index {idx}: {e}\")\n", - " \n", - " return df\n", - "\n", - "# Now parse the numeric forecasts\n", - "df_pro_bot_forecasts = parse_numeric_forecasts(df_pro_bot_forecasts)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, "outputs": [ { "data": { @@ -2962,6 +2892,7 @@ " Bot_Pepa\n", " CatrachoCaster\n", " ...\n", + " metac-o1\n", " metac-o1-preview\n", " metac-perplexity\n", " minefrac1\n", @@ -2971,7 +2902,6 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", @@ -2988,169 +2918,211 @@ " NaN\n", " NaN\n", " ...\n", - " 299.573227\n", - " 529.831737\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.02,0.7,0.2,0.07,0.01]\n", + " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", - " 229.263476\n", - " 270.308741\n", + " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", + " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 501.063529\n", " \n", " \n", - " 3\n", - " 31280\n", - " 31274\n", - " 5-9\n", + " 1\n", + " 31269\n", + " 31263\n", + " 86.82\n", " 1.0\n", - " multiple_choice\n", - " [0-4, 5-9, >9]\n", - " [0.16,0.44,0.4]\n", + " numeric\n", + " None\n", + " [0.0013749738, 0.0014499743, 0.001526641, 0.0016050848, 0.0016854241, 0.0017677851, 0.0018523023, 0.0019391193, 0.002028389, 0.0021202748, 0.0022149507, 0.0023126022, 0.0024134273, 0.002517637, 0.0026254563, 0.0027371251, 0.0028528992, 0.0029730514, 0.0030978724, 0.0032276722, 0.0033627814, 0.0035035523, 0.0036503604, 0.003803606, 0.0039637158, 0.0041311448, 0.0043063775, 0.0044899306, 0.0046823546, 0.0048842361, 0.0050962001, 0.0053189126, 0.0055530831, 0.0057994673, 0.0060588703, 0.0063321494, 0.0066202178, 0.0069240477, 0.0072446744, 0.0075831999, 0.0079407973, 0.0083187152, 0.0087182821, 0.0091409116, 0.0095881072, 0.0100614684, 0.0105626958, 0.0110935973, 0.0116560946, 0.0122522299, 0.0128841727, 0.0135542271, 0.0142648397, 0.0150186074, 0.0158182855, 0.0166667968, 0.0175672405, 0.0185229009, 0.0195372578, 0.0206139958, 0.0217570149, 0.0229704403, 0.0242586335, 0.0256262025, 0.027078013, 0.0286191989, 0.0302551733, 0.0319916387, 0.0338345977, 0.0357903626, 0.0378655653, 0.0400671652, 0.042402458, 0.044879082, 0.0475050233, 0.0502886206, 0.0532385667, 0.0563639085, 0.0596740451, 0.0631787221, 0.0668880234, 0.0708123591, 0.0749624495, 0.0793493045, 0.0839841985, 0.0888786389, 0.0940443298, 0.0994931287, 0.1052369965, 0.1112879404, 0.1176579487, 0.1243589183, 0.1314025737, 0.1388003774, 0.1465634324, 0.1547023763, 0.1632272673, 0.1721474631, 0.1814714929, 0.1912069234, ...]\n", + " NaN\n", " NaN\n", " NaN\n", - " 6.595797\n", " ...\n", - " 31.015493\n", - " 2.247286\n", + " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...]\n", + " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", + " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", + " NaN\n", + " [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...]\n", + " [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...]\n", " NaN\n", - " 12.783337\n", - " 15.252598\n", " NaN\n", " NaN\n", - " -4.652002\n", " NaN\n", - " 31.015493\n", " \n", " \n", - " 6\n", - " 31292\n", - " 31286\n", - " Jeff Bezos\n", + " 2\n", + " 31270\n", + " 31264\n", + " no\n", " 1.0\n", - " multiple_choice\n", - " [Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else]\n", - " [0.2,0.025,0.225,0.08,0.445,0.025]\n", + " binary\n", + " None\n", + " 0.013\n", + " NaN\n", " NaN\n", " NaN\n", - " -70.444674\n", " ...\n", - " 29.885537\n", - " 21.184400\n", + " 0.1\n", + " 0.15\n", + " 0.1\n", " NaN\n", - " -18.457128\n", - " 11.152127\n", + " 0.2\n", + " 0.07\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 11.152127\n", " \n", " \n", - " 9\n", - " 31321\n", - " 31370\n", - " 0\n", + " 3\n", + " 31280\n", + " 31274\n", + " 5-9\n", " 1.0\n", " multiple_choice\n", - " [0, 1, 2, Greater than 2]\n", - " [0.336,0.364,0.2,0.1]\n", + " [0-4, 5-9, >9]\n", + " [0.16,0.44,0.4]\n", " NaN\n", " NaN\n", - " -87.546874\n", + " [0.16,0.47,0.37]\n", " ...\n", - " -51.879379\n", - " -121.194097\n", + " [0.25,0.6,0.15]\n", + " [0.2,0.6,0.2]\n", + " [0.15,0.55,0.3]\n", " NaN\n", - " -80.647587\n", - " -49.410118\n", + " [0.25,0.5,0.25]\n", + " [0.27499999999999997,0.5125,0.21249999999999997]\n", " NaN\n", " NaN\n", - " -62.415431\n", + " [0.116,0.42,0.464]\n", " NaN\n", - " -69.314718\n", " \n", " \n", - " 13\n", - " 31368\n", - " 31366\n", - " ≥0% and <5%\n", + " 4\n", + " 31281\n", + " 31275\n", + " 119.2\n", " 1.0\n", - " multiple_choice\n", - " [Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%]\n", - " [0.05,0.45,0.45,0.05]\n", + " numeric\n", + " None\n", + " [0.0, 0.0005044914, 0.0010323506, 0.0015847475, 0.0021629075, 0.0027681135, 0.003401708, 0.0040650959, 0.0047597462, 0.0054871954, 0.0062490491, 0.0070469847, 0.0078827545, 0.0087581873, 0.0096751916, 0.0106357578, 0.0116419606, 0.0126959618, 0.0138000124, 0.0149564548, 0.0161677252, 0.0174363555, 0.0187649755, 0.0201563143, 0.0216132019, 0.0231385708, 0.0247354566, 0.0264069992, 0.0281564425, 0.029987135, 0.0319025289, 0.0339061792, 0.0360017424, 0.0381929741, 0.0404837261, 0.0428779433, 0.045379659, 0.0479929901, 0.0507221307, 0.0535713452, 0.0565449605, 0.0596473565, 0.0628829558, 0.0662562123, 0.0697715985, 0.073433591, 0.0772466553, 0.0812152286, 0.0853437018, 0.0896363995, 0.0940975586, 0.0987313059, 0.1035416339, 0.1085323748, 0.1137071746, 0.1190694637, 0.1246224286, 0.1303689808, 0.1363117257, 0.1424529302, 0.1487944895, 0.1553378942, 0.1620841958, 0.1690339734, 0.1761872995, 0.1835437065, 0.191102154, 0.1988609968, 0.2068179538, 0.2149700792, 0.2233137345, 0.2318445639, 0.2405574718, 0.2494466036, 0.2585053305, 0.2677262387, 0.2771011237, 0.2866209903, 0.2962760595, 0.3060557827, 0.3159488636, 0.3259432898, 0.3360263733, 0.3461848008, 0.356404695, 0.3666716851, 0.3769709877, 0.3872880285, 0.3976129907, 0.4079386213, 0.4182575841, 0.4285624679, 0.4388454621, 0.4490984582, 0.459313496, 0.4694828597, 0.4795991502, 0.4896553473, 0.49964486, 0.5095615629, ...]\n", + " NaN\n", " NaN\n", " NaN\n", - " -16.907633\n", " ...\n", - " 44.183275\n", - " 33.647224\n", - " 2.197891\n", - " 20.067070\n", - " 25.378052\n", + " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...]\n", + " [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...]\n", + " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.2066666667, 0.2133333333, 0.22, 0.2266666667, 0.2333333333, 0.24, 0.2466666667, 0.2533333333, 0.26, 0.2666666667, 0.2733333333, 0.28, 0.2866666667, 0.2933333333, 0.3, 0.3066666667, 0.3133333333, 0.32, 0.3266666667, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3933333333, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...]\n", " NaN\n", + " [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", + " [0.0, 0.0001141583, 0.0002446967, 0.0003862688, 0.0005272579, 0.0006650709, 0.0008243437, 0.0011074433, 0.0016696544, 0.0025699094, 0.0037138357, 0.0049708626, 0.0062610152, 0.0075426566, 0.0089765864, 0.0111726822, 0.0147311078, 0.0195212559, 0.0249547717, 0.0306181288, 0.0363105138, 0.0419407763, 0.0476011969, 0.053516341, 0.0598014349, 0.0663689162, 0.0730761187, 0.0798334547, 0.0865904866, 0.0933196582, 0.1000172031, 0.1066924089, 0.1133554776, 0.1200140176, 0.1266729489, 0.1333343989, 0.1399984689, 0.1466644317, 0.1533314439, 0.1599988203, 0.1666661444, 0.1733332523, 0.1800001372, 0.1866668598, 0.1933334943, 0.2000000995, 0.2066667101, 0.2133333393, 0.2199999878, 0.22666665, 0.2333333196, 0.2399999916, 0.2466666631, 0.2533333329, 0.2600000011, 0.2666666681, 0.2733333345, 0.2800000007, 0.286666667, 0.2933333334, 0.2999999999, 0.3066666665, 0.3133333332, 0.3199999999, 0.3266666666, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3934628939, 0.400837331, 0.40925763, 0.4186848364, 0.428718413, 0.4390353607, 0.4494419812, 0.4597974687, 0.4700329298, 0.4801500685, 0.4901790777, 0.500153105, 0.5101028922, 0.5200515519, 0.5300114112, 0.5398722838, 0.5492279015, 0.5576212737, 0.5650210292, 0.571743695, 0.5780856137, 0.5842571713, 0.5904328096, 0.5967209586, 0.603152213, 0.6097133168, ...]\n", " NaN\n", " NaN\n", + " [0.0, 0.001311947, 0.0026238939, 0.0039358409, 0.0052477878, 0.0065597348, 0.0078716817, 0.0091836287, 0.0104955756, 0.0118075226, 0.0131194695, 0.0144314165, 0.0157433634, 0.0170553104, 0.0183672573, 0.0196792043, 0.0209911512, 0.0223030982, 0.0236150451, 0.0249269921, 0.026238939, 0.027550886, 0.0288628329, 0.0301747799, 0.0314867268, 0.0327986738, 0.0341106207, 0.0354225677, 0.0367345146, 0.0380464616, 0.0393584085, 0.0406703555, 0.0419823024, 0.0432942494, 0.0446061963, 0.0459181433, 0.0472300902, 0.0485420372, 0.0498539841, 0.0511659311, 0.052477878, 0.053789825, 0.0551017719, 0.0564137189, 0.0577256658, 0.0590376128, 0.0603495597, 0.0616615067, 0.0629734536, 0.0642854006, 0.0655973475, 0.0669092945, 0.0682212414, 0.0695331884, 0.0708451353, 0.0721570823, 0.0734690292, 0.0747809762, 0.0760929231, 0.0774048701, 0.078716817, 0.080028764, 0.0813407109, 0.0826526579, 0.0839646048, 0.0852765518, 0.0865884987, 0.0879004457, 0.0902457862, 0.0933094828, 0.0978079399, 0.1023063969, 0.1068048539, 0.111303311, 0.115801768, 0.120300225, 0.124798682, 0.1292971391, 0.1338199508, 0.1388055027, 0.1440933779, 0.1496807808, 0.1571177226, 0.1652387403, 0.1753118263, 0.1904276903, 0.2058197291, 0.2212117678, 0.237030829, 0.2551785571, 0.273870758, 0.2925629589, 0.3115548313, 0.3307464845, 0.3499926649, 0.3692260274, 0.3884136416, 0.407661417, 0.4269091924, 0.4457073638, ...]\n", " NaN\n", - " -32.542240\n", " \n", " \n", "\n", - "

5 rows × 54 columns

\n", + "

5 rows × 53 columns

\n", "" ], "text/plain": [ - " pro_question_id bot_question_id resolution question_weight \\\n", - "0 31268 31262 0 1.0 \n", - "3 31280 31274 5-9 1.0 \n", - "6 31292 31286 Jeff Bezos 1.0 \n", - "9 31321 31370 0 1.0 \n", - "13 31368 31366 ≥0% and <5% 1.0 \n", - "\n", - " type \\\n", - "0 multiple_choice \n", - "3 multiple_choice \n", - "6 multiple_choice \n", - "9 multiple_choice \n", - "13 multiple_choice \n", + " pro_question_id bot_question_id resolution question_weight \\\n", + "0 31268 31262 0 1.0 \n", + "1 31269 31263 86.82 1.0 \n", + "2 31270 31264 no 1.0 \n", + "3 31280 31274 5-9 1.0 \n", + "4 31281 31275 119.2 1.0 \n", "\n", - " options \\\n", - "0 [0, 1, 2-3, 4-6, >6] \n", - "3 [0-4, 5-9, >9] \n", - "6 [Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else] \n", - "9 [0, 1, 2, Greater than 2] \n", - "13 [Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%] \n", + " type options \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] \n", + "1 numeric None \n", + "2 binary None \n", + "3 multiple_choice [0-4, 5-9, >9] \n", + "4 numeric None \n", + "\n", + " pro_median \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] \n", + "1 [0.0013749738, 0.0014499743, 0.001526641, 0.0016050848, 0.0016854241, 0.0017677851, 0.0018523023, 0.0019391193, 0.002028389, 0.0021202748, 0.0022149507, 0.0023126022, 0.0024134273, 0.002517637, 0.0026254563, 0.0027371251, 0.0028528992, 0.0029730514, 0.0030978724, 0.0032276722, 0.0033627814, 0.0035035523, 0.0036503604, 0.003803606, 0.0039637158, 0.0041311448, 0.0043063775, 0.0044899306, 0.0046823546, 0.0048842361, 0.0050962001, 0.0053189126, 0.0055530831, 0.0057994673, 0.0060588703, 0.0063321494, 0.0066202178, 0.0069240477, 0.0072446744, 0.0075831999, 0.0079407973, 0.0083187152, 0.0087182821, 0.0091409116, 0.0095881072, 0.0100614684, 0.0105626958, 0.0110935973, 0.0116560946, 0.0122522299, 0.0128841727, 0.0135542271, 0.0142648397, 0.0150186074, 0.0158182855, 0.0166667968, 0.0175672405, 0.0185229009, 0.0195372578, 0.0206139958, 0.0217570149, 0.0229704403, 0.0242586335, 0.0256262025, 0.027078013, 0.0286191989, 0.0302551733, 0.0319916387, 0.0338345977, 0.0357903626, 0.0378655653, 0.0400671652, 0.042402458, 0.044879082, 0.0475050233, 0.0502886206, 0.0532385667, 0.0563639085, 0.0596740451, 0.0631787221, 0.0668880234, 0.0708123591, 0.0749624495, 0.0793493045, 0.0839841985, 0.0888786389, 0.0940443298, 0.0994931287, 0.1052369965, 0.1112879404, 0.1176579487, 0.1243589183, 0.1314025737, 0.1388003774, 0.1465634324, 0.1547023763, 0.1632272673, 0.1721474631, 0.1814714929, 0.1912069234, ...] \n", + "2 0.013 \n", + "3 [0.16,0.44,0.4] \n", + "4 [0.0, 0.0005044914, 0.0010323506, 0.0015847475, 0.0021629075, 0.0027681135, 0.003401708, 0.0040650959, 0.0047597462, 0.0054871954, 0.0062490491, 0.0070469847, 0.0078827545, 0.0087581873, 0.0096751916, 0.0106357578, 0.0116419606, 0.0126959618, 0.0138000124, 0.0149564548, 0.0161677252, 0.0174363555, 0.0187649755, 0.0201563143, 0.0216132019, 0.0231385708, 0.0247354566, 0.0264069992, 0.0281564425, 0.029987135, 0.0319025289, 0.0339061792, 0.0360017424, 0.0381929741, 0.0404837261, 0.0428779433, 0.045379659, 0.0479929901, 0.0507221307, 0.0535713452, 0.0565449605, 0.0596473565, 0.0628829558, 0.0662562123, 0.0697715985, 0.073433591, 0.0772466553, 0.0812152286, 0.0853437018, 0.0896363995, 0.0940975586, 0.0987313059, 0.1035416339, 0.1085323748, 0.1137071746, 0.1190694637, 0.1246224286, 0.1303689808, 0.1363117257, 0.1424529302, 0.1487944895, 0.1553378942, 0.1620841958, 0.1690339734, 0.1761872995, 0.1835437065, 0.191102154, 0.1988609968, 0.2068179538, 0.2149700792, 0.2233137345, 0.2318445639, 0.2405574718, 0.2494466036, 0.2585053305, 0.2677262387, 0.2771011237, 0.2866209903, 0.2962760595, 0.3060557827, 0.3159488636, 0.3259432898, 0.3360263733, 0.3461848008, 0.356404695, 0.3666716851, 0.3769709877, 0.3872880285, 0.3976129907, 0.4079386213, 0.4182575841, 0.4285624679, 0.4388454621, 0.4490984582, 0.459313496, 0.4694828597, 0.4795991502, 0.4896553473, 0.49964486, 0.5095615629, ...] \n", + "\n", + " 4Shadower Bot_Pepa CatrachoCaster ... \\\n", + "0 NaN NaN NaN ... \n", + "1 NaN NaN NaN ... \n", + "2 NaN NaN NaN ... \n", + "3 NaN NaN [0.16,0.47,0.37] ... \n", + "4 NaN NaN NaN ... \n", + "\n", + " metac-o1 \\\n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...] \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.02,0.7,0.2,0.07,0.01] \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", + "2 0.15 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", + "\n", + " metac-perplexity \\\n", + "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", + "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", + "2 0.1 \n", + "3 [0.15,0.55,0.3] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.2066666667, 0.2133333333, 0.22, 0.2266666667, 0.2333333333, 0.24, 0.2466666667, 0.2533333333, 0.26, 0.2666666667, 0.2733333333, 0.28, 0.2866666667, 0.2933333333, 0.3, 0.3066666667, 0.3133333333, 0.32, 0.3266666667, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3933333333, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...] \n", + "\n", + " minefrac1 \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " mmBot \\\n", + "0 [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297] \n", + "1 [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...] \n", + "2 0.2 \n", + "3 [0.25,0.5,0.25] \n", + "4 [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...] \n", + "\n", + " pgodzinai \\\n", + "0 [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965] \n", + "1 [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...] \n", + "2 0.07 \n", + "3 [0.27499999999999997,0.5125,0.21249999999999997] \n", + "4 [0.0, 0.0001141583, 0.0002446967, 0.0003862688, 0.0005272579, 0.0006650709, 0.0008243437, 0.0011074433, 0.0016696544, 0.0025699094, 0.0037138357, 0.0049708626, 0.0062610152, 0.0075426566, 0.0089765864, 0.0111726822, 0.0147311078, 0.0195212559, 0.0249547717, 0.0306181288, 0.0363105138, 0.0419407763, 0.0476011969, 0.053516341, 0.0598014349, 0.0663689162, 0.0730761187, 0.0798334547, 0.0865904866, 0.0933196582, 0.1000172031, 0.1066924089, 0.1133554776, 0.1200140176, 0.1266729489, 0.1333343989, 0.1399984689, 0.1466644317, 0.1533314439, 0.1599988203, 0.1666661444, 0.1733332523, 0.1800001372, 0.1866668598, 0.1933334943, 0.2000000995, 0.2066667101, 0.2133333393, 0.2199999878, 0.22666665, 0.2333333196, 0.2399999916, 0.2466666631, 0.2533333329, 0.2600000011, 0.2666666681, 0.2733333345, 0.2800000007, 0.286666667, 0.2933333334, 0.2999999999, 0.3066666665, 0.3133333332, 0.3199999999, 0.3266666666, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3934628939, 0.400837331, 0.40925763, 0.4186848364, 0.428718413, 0.4390353607, 0.4494419812, 0.4597974687, 0.4700329298, 0.4801500685, 0.4901790777, 0.500153105, 0.5101028922, 0.5200515519, 0.5300114112, 0.5398722838, 0.5492279015, 0.5576212737, 0.5650210292, 0.571743695, 0.5780856137, 0.5842571713, 0.5904328096, 0.5967209586, 0.603152213, 0.6097133168, ...] \n", + "\n", + " pianobot swingswish \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " twsummerbot \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 [0.116,0.42,0.464] \n", + "4 [0.0, 0.001311947, 0.0026238939, 0.0039358409, 0.0052477878, 0.0065597348, 0.0078716817, 0.0091836287, 0.0104955756, 0.0118075226, 0.0131194695, 0.0144314165, 0.0157433634, 0.0170553104, 0.0183672573, 0.0196792043, 0.0209911512, 0.0223030982, 0.0236150451, 0.0249269921, 0.026238939, 0.027550886, 0.0288628329, 0.0301747799, 0.0314867268, 0.0327986738, 0.0341106207, 0.0354225677, 0.0367345146, 0.0380464616, 0.0393584085, 0.0406703555, 0.0419823024, 0.0432942494, 0.0446061963, 0.0459181433, 0.0472300902, 0.0485420372, 0.0498539841, 0.0511659311, 0.052477878, 0.053789825, 0.0551017719, 0.0564137189, 0.0577256658, 0.0590376128, 0.0603495597, 0.0616615067, 0.0629734536, 0.0642854006, 0.0655973475, 0.0669092945, 0.0682212414, 0.0695331884, 0.0708451353, 0.0721570823, 0.0734690292, 0.0747809762, 0.0760929231, 0.0774048701, 0.078716817, 0.080028764, 0.0813407109, 0.0826526579, 0.0839646048, 0.0852765518, 0.0865884987, 0.0879004457, 0.0902457862, 0.0933094828, 0.0978079399, 0.1023063969, 0.1068048539, 0.111303311, 0.115801768, 0.120300225, 0.124798682, 0.1292971391, 0.1338199508, 0.1388055027, 0.1440933779, 0.1496807808, 0.1571177226, 0.1652387403, 0.1753118263, 0.1904276903, 0.2058197291, 0.2212117678, 0.237030829, 0.2551785571, 0.273870758, 0.2925629589, 0.3115548313, 0.3307464845, 0.3499926649, 0.3692260274, 0.3884136416, 0.407661417, 0.4269091924, 0.4457073638, ...] \n", + "\n", + " wunderplumb \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", "\n", - " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] NaN NaN NaN \n", - "3 [0.16,0.44,0.4] NaN NaN 6.595797 \n", - "6 [0.2,0.025,0.225,0.08,0.445,0.025] NaN NaN -70.444674 \n", - "9 [0.336,0.364,0.2,0.1] NaN NaN -87.546874 \n", - "13 [0.05,0.45,0.45,0.05] NaN NaN -16.907633 \n", - "\n", - " ... metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "0 ... 299.573227 529.831737 NaN 229.263476 \n", - "3 ... 31.015493 2.247286 NaN 12.783337 \n", - "6 ... 29.885537 21.184400 NaN -18.457128 \n", - "9 ... -51.879379 -121.194097 NaN -80.647587 \n", - "13 ... 44.183275 33.647224 2.197891 20.067070 \n", - "\n", - " pgodzinai pianobot swingswish twsummerbot wunderplumb \\\n", - "0 270.308741 NaN NaN NaN NaN \n", - "3 15.252598 NaN NaN -4.652002 NaN \n", - "6 11.152127 NaN NaN NaN NaN \n", - "9 -49.410118 NaN NaN -62.415431 NaN \n", - "13 25.378052 NaN NaN NaN NaN \n", - "\n", - " bot_team_median \n", - "0 501.063529 \n", - "3 31.015493 \n", - "6 11.152127 \n", - "9 -69.314718 \n", - "13 -32.542240 \n", - "\n", - "[5 rows x 54 columns]" + "[5 rows x 53 columns]" ] }, "metadata": {}, @@ -3188,6 +3160,7 @@ " Bot_Pepa\n", " CatrachoCaster\n", " ...\n", + " metac-o1\n", " metac-o1-preview\n", " metac-perplexity\n", " minefrac1\n", @@ -3197,191 +3170,227 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", " \n", - " 81\n", - " 35169\n", - " 35119\n", - " Not in top 50\n", - " 1.0\n", - " multiple_choice\n", - " [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50]\n", - " [0.02,0.01,0.015,0.015,0.05,0.89]\n", + " 94\n", + " 35380\n", + " 35345\n", + " yes\n", + " 1.00\n", + " binary\n", + " None\n", + " 0.95\n", + " 0.9\n", " NaN\n", - " -280.223742\n", " NaN\n", " ...\n", - " -448.863637\n", - " -178.058617\n", - " -300.703183\n", - " -287.919846\n", - " -339.002408\n", + " 0.95\n", + " 0.9\n", + " NaN\n", " NaN\n", + " 0.95\n", + " 0.95\n", " NaN\n", - " -234.857021\n", - " -240.919483\n", - " -287.919846\n", + " 0.9\n", + " 0.762\n", + " 0.9\n", " \n", " \n", - " 82\n", - " 35170\n", - " 35121\n", - " 3 or more\n", - " 1.0\n", - " multiple_choice\n", - " [0, 1, 2, 3 or more]\n", - " [0.01,0.18,0.54,0.27]\n", + " 95\n", + " 35381\n", + " 35354\n", + " no\n", + " 1.00\n", + " binary\n", + " None\n", + " 0.05\n", + " 0.95\n", " NaN\n", - " -77.944110\n", " NaN\n", " ...\n", - " -99.325177\n", - " -18.677591\n", - " -52.324814\n", - " 10.536052\n", - " 25.951120\n", + " 0.35\n", + " 0.4\n", " NaN\n", " NaN\n", - " 27.650877\n", - " -64.460900\n", - " 27.650877\n", - " \n", - " \n", - " 83\n", - " 35171\n", - " 35123\n", - " ≥7.5 and ≤8.5\n", - " 1.0\n", - " multiple_choice\n", - " [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5]\n", - " [0.02,0.3,0.3,0.3,0.08]\n", + " 0.15\n", + " NaN\n", + " NaN\n", + " 0.1\n", + " 0.126\n", + " 0.95\n", + " \n", + " \n", + " 96\n", + " 35385\n", + " 35358\n", + " yes\n", + " 1.00\n", + " binary\n", + " None\n", + " 0.97\n", + " 0.85\n", " NaN\n", - " -70.227966\n", " NaN\n", " ...\n", - " -132.175584\n", - " -26.570317\n", + " 0.9\n", + " 0.95\n", " NaN\n", - " -18.232156\n", " NaN\n", + " 0.9\n", " NaN\n", " NaN\n", - " -17.832954\n", - " -56.798404\n", - " -62.860866\n", + " 0.85\n", + " 0.828\n", + " 0.85\n", " \n", " \n", - " 91\n", - " 35377\n", - " 35334\n", - " Jimmy Patronis\n", - " 1.0\n", - " multiple_choice\n", - " [Jimmy Patronis, Gay Valimont, Someone else]\n", - " [0.997,0.001,0.002]\n", - " -17.134888\n", - " -15.951442\n", + " 97\n", + " 35386\n", + " 35364\n", + " no\n", + " 0.85\n", + " binary\n", + " None\n", + " 0.666\n", + " 0.8\n", " NaN\n", - " ...\n", - " -3.781749\n", - " -4.828879\n", " NaN\n", - " -12.482886\n", - " -8.037710\n", + " ...\n", + " 0.8\n", + " 0.85\n", + " 0.3\n", " NaN\n", - " -11.352931\n", + " 0.85\n", + " 0.85\n", " NaN\n", - " -14.781838\n", - " -12.104814\n", + " 0.7\n", + " 0.132\n", + " 0.3\n", " \n", " \n", - " 92\n", - " 35378\n", - " 35336\n", - " 31-49\n", - " 1.0\n", - " multiple_choice\n", - " [0-24, 25-30, 31-49, 50-70, >70]\n", - " [0.001,0.359,0.55,0.08,0.01]\n", - " -69.314718\n", - " -87.183897\n", + " 98\n", + " 35387\n", + " 35367\n", + " no\n", + " 0.85\n", + " binary\n", + " None\n", + " 0.03\n", + " 0.3\n", " NaN\n", - " ...\n", - " -170.474809\n", - " -290.872090\n", " NaN\n", - " -170.474809\n", - " -31.845373\n", + " ...\n", + " 0.05\n", + " 0.05\n", + " 0.03\n", " NaN\n", - " -48.097266\n", + " 0.15\n", + " 0.05\n", " NaN\n", - " -74.923665\n", - " -20.067070\n", + " 0.2\n", + " 0.27\n", + " 0.2\n", " \n", " \n", "\n", - "

5 rows × 54 columns

\n", + "

5 rows × 53 columns

\n", "" ], "text/plain": [ - " pro_question_id bot_question_id resolution question_weight \\\n", - "81 35169 35119 Not in top 50 1.0 \n", - "82 35170 35121 3 or more 1.0 \n", - "83 35171 35123 ≥7.5 and ≤8.5 1.0 \n", - "91 35377 35334 Jimmy Patronis 1.0 \n", - "92 35378 35336 31-49 1.0 \n", + " pro_question_id bot_question_id resolution question_weight type \\\n", + "94 35380 35345 yes 1.00 binary \n", + "95 35381 35354 no 1.00 binary \n", + "96 35385 35358 yes 1.00 binary \n", + "97 35386 35364 no 0.85 binary \n", + "98 35387 35367 no 0.85 binary \n", "\n", - " type \\\n", - "81 multiple_choice \n", - "82 multiple_choice \n", - "83 multiple_choice \n", - "91 multiple_choice \n", - "92 multiple_choice \n", + " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", + "94 None 0.95 0.9 NaN NaN ... 0.95 \n", + "95 None 0.05 0.95 NaN NaN ... 0.35 \n", + "96 None 0.97 0.85 NaN NaN ... 0.9 \n", + "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "98 None 0.03 0.3 NaN NaN ... 0.05 \n", "\n", - " options \\\n", - "81 [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50] \n", - "82 [0, 1, 2, 3 or more] \n", - "83 [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5] \n", - "91 [Jimmy Patronis, Gay Valimont, Someone else] \n", - "92 [0-24, 25-30, 31-49, 50-70, >70] \n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", + "94 0.9 NaN NaN 0.95 0.95 NaN \n", + "95 0.4 NaN NaN 0.15 NaN NaN \n", + "96 0.95 NaN NaN 0.9 NaN NaN \n", + "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "98 0.05 0.03 NaN 0.15 0.05 NaN \n", + "\n", + " swingswish twsummerbot wunderplumb \n", + "94 0.9 0.762 0.9 \n", + "95 0.1 0.126 0.95 \n", + "96 0.85 0.828 0.85 \n", + "97 0.7 0.132 0.3 \n", + "98 0.2 0.27 0.2 \n", "\n", - " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", - "81 [0.02,0.01,0.015,0.015,0.05,0.89] NaN -280.223742 NaN \n", - "82 [0.01,0.18,0.54,0.27] NaN -77.944110 NaN \n", - "83 [0.02,0.3,0.3,0.3,0.08] NaN -70.227966 NaN \n", - "91 [0.997,0.001,0.002] -17.134888 -15.951442 NaN \n", - "92 [0.001,0.359,0.55,0.08,0.01] -69.314718 -87.183897 NaN \n", - "\n", - " ... metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "81 ... -448.863637 -178.058617 -300.703183 -287.919846 \n", - "82 ... -99.325177 -18.677591 -52.324814 10.536052 \n", - "83 ... -132.175584 -26.570317 NaN -18.232156 \n", - "91 ... -3.781749 -4.828879 NaN -12.482886 \n", - "92 ... -170.474809 -290.872090 NaN -170.474809 \n", - "\n", - " pgodzinai pianobot swingswish twsummerbot wunderplumb \\\n", - "81 -339.002408 NaN NaN -234.857021 -240.919483 \n", - "82 25.951120 NaN NaN 27.650877 -64.460900 \n", - "83 NaN NaN NaN -17.832954 -56.798404 \n", - "91 -8.037710 NaN -11.352931 NaN -14.781838 \n", - "92 -31.845373 NaN -48.097266 NaN -74.923665 \n", - "\n", - " bot_team_median \n", - "81 -287.919846 \n", - "82 27.650877 \n", - "83 -62.860866 \n", - "91 -12.104814 \n", - "92 -20.067070 \n", - "\n", - "[5 rows x 54 columns]" + "[5 rows x 53 columns]" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# Simple function to parse CDF strings for numeric questions\n", + "def parse_numeric_forecasts(df):\n", + " \"\"\"\n", + " Parse CDF strings for numeric questions in-place.\n", + "\n", + " Args:\n", + " df: DataFrame with forecast data\n", + " \"\"\"\n", + " # Get numeric questions\n", + " numeric_mask = df['type'] == 'numeric'\n", + "\n", + " # List of columns to process\n", + " forecast_cols = [col for col in df.columns if col in all_bots or col in ['pro_median', 'bot_median']]\n", + "\n", + " # Process each column\n", + " for col in forecast_cols:\n", + " # Process only for numeric questions and only where the column exists\n", + " if col in df.columns:\n", + " for idx in df[numeric_mask].index:\n", + " value = df.at[idx, col]\n", + "\n", + " # Skip NaN values\n", + " if pd.isna(value):\n", + " continue\n", + "\n", + " # Process string values\n", + " if isinstance(value, str):\n", + " try:\n", + " # Parse the CDF string to an array\n", + " parsed_array = np.array([float(x) for x in value.strip('[]').split(',')])\n", + " df.at[idx, col] = parsed_array\n", + " except Exception as e:\n", + " print(f\"Warning: Could not parse {col} at index {idx}: {e}\")\n", + "\n", + " return df\n", + "\n", + "# Now parse the numeric forecasts\n", + "df_pro_bot_forecasts = parse_numeric_forecasts(df_pro_bot_forecasts)\n", + "display_head_and_tail(df_pro_bot_forecasts)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", + "# @Ben: Check -> This was originally 'calculate_all_peer_scores'. NOt sure the correct function alternative\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -3414,6 +3423,7 @@ " Bot_Pepa\n", " CatrachoCaster\n", " ...\n", + " metac-o1\n", " metac-o1-preview\n", " metac-perplexity\n", " minefrac1\n", @@ -3423,165 +3433,178 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", " \n", - " 2\n", - " 31270\n", - " 31264\n", - " no\n", + " 0\n", + " 31268\n", + " 31262\n", + " 0\n", " 1.0\n", - " binary\n", - " None\n", - " 0.013\n", - " NaN\n", - " NaN\n", - " NaN\n", + " multiple_choice\n", + " [0, 1, 2-3, 4-6, >6]\n", + " [0.001,0.62,0.35,0.019,0.01]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -14.943369\n", - " -9.227528\n", - " NaN\n", - " -21.005831\n", - " -5.948545\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " -14.943369\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 5\n", - " 31282\n", - " 31276\n", - " yes\n", + " 3\n", + " 31280\n", + " 31274\n", + " 5-9\n", " 1.0\n", - " binary\n", - " None\n", - " 0.45\n", - " NaN\n", - " NaN\n", - " 67.445505\n", + " multiple_choice\n", + " [0-4, 5-9, >9]\n", + " [0.16,0.44,0.4]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -25.131443\n", - " 44.183275\n", - " NaN\n", - " 51.082562\n", - " 32.047190\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 32.047190\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 8\n", - " 31294\n", - " 31288\n", - " yes\n", + " 6\n", + " 31292\n", + " 31286\n", + " Jeff Bezos\n", " 1.0\n", - " binary\n", - " None\n", - " 0.95\n", - " NaN\n", - " NaN\n", - " -19.645607\n", + " multiple_choice\n", + " [Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else]\n", + " [0.2,0.025,0.225,0.08,0.445,0.025]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " 0.000000\n", - " 0.000000\n", - " NaN\n", - " -11.122564\n", - " -14.715764\n", - " NaN\n", - " NaN\n", - " -39.812370\n", - " NaN\n", - " -17.185026\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 12\n", - " 31338\n", - " 31334\n", - " yes\n", + " 9\n", + " 31321\n", + " 31370\n", + " 0\n", " 1.0\n", - " binary\n", - " None\n", - " 0.9\n", - " NaN\n", - " NaN\n", - " -0.309119\n", + " multiple_choice\n", + " [0, 1, 2, Greater than 2]\n", + " [0.336,0.364,0.2,0.1]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -18.232156\n", - " 0.000000\n", - " NaN\n", - " 5.406722\n", - " -5.715841\n", - " NaN\n", - " NaN\n", - " -49.977579\n", - " NaN\n", - " -5.715841\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 16\n", - " 33876\n", - " 33751\n", - " no\n", + " 13\n", + " 31368\n", + " 31366\n", + " ≥0% and <5%\n", " 1.0\n", - " binary\n", - " None\n", - " 0.058\n", - " NaN\n", - " NaN\n", - " NaN\n", + " multiple_choice\n", + " [Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%]\n", + " [0.05,0.45,0.45,0.05]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -4.561051\n", - " 0.845671\n", - " NaN\n", - " -6.808337\n", - " NaN\n", - " NaN\n", - " NaN\n", - " -7.606972\n", - " NaN\n", - " -7.606972\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", "\n", - "

5 rows × 54 columns

\n", + "

5 rows × 53 columns

\n", "" ], "text/plain": [ - " pro_question_id bot_question_id resolution question_weight type \\\n", - "2 31270 31264 no 1.0 binary \n", - "5 31282 31276 yes 1.0 binary \n", - "8 31294 31288 yes 1.0 binary \n", - "12 31338 31334 yes 1.0 binary \n", - "16 33876 33751 no 1.0 binary \n", + " pro_question_id bot_question_id resolution question_weight \\\n", + "0 31268 31262 0 1.0 \n", + "3 31280 31274 5-9 1.0 \n", + "6 31292 31286 Jeff Bezos 1.0 \n", + "9 31321 31370 0 1.0 \n", + "13 31368 31366 ≥0% and <5% 1.0 \n", + "\n", + " type \\\n", + "0 multiple_choice \n", + "3 multiple_choice \n", + "6 multiple_choice \n", + "9 multiple_choice \n", + "13 multiple_choice \n", + "\n", + " options \\\n", + "0 [0, 1, 2-3, 4-6, >6] \n", + "3 [0-4, 5-9, >9] \n", + "6 [Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else] \n", + "9 [0, 1, 2, Greater than 2] \n", + "13 [Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%] \n", + "\n", + " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 0.643473 2.597381 1.762901 \n", + "3 [0.16,0.44,0.4] 0.643473 2.597381 1.762901 \n", + "6 [0.2,0.025,0.225,0.08,0.445,0.025] 0.643473 2.597381 1.762901 \n", + "9 [0.336,0.364,0.2,0.1] 0.643473 2.597381 1.762901 \n", + "13 [0.05,0.45,0.45,0.05] 0.643473 2.597381 1.762901 \n", + "\n", + " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", + "0 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "3 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "6 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "9 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "13 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "\n", + " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", + "0 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "3 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "6 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "9 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "13 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... \\\n", - "2 None 0.013 NaN NaN NaN ... \n", - "5 None 0.45 NaN NaN 67.445505 ... \n", - "8 None 0.95 NaN NaN -19.645607 ... \n", - "12 None 0.9 NaN NaN -0.309119 ... \n", - "16 None 0.058 NaN NaN NaN ... \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -14.943369 -9.227528 NaN -21.005831 -5.948545 \n", - "5 -25.131443 44.183275 NaN 51.082562 32.047190 \n", - "8 0.000000 0.000000 NaN -11.122564 -14.715764 \n", - "12 -18.232156 0.000000 NaN 5.406722 -5.715841 \n", - "16 -4.561051 0.845671 NaN -6.808337 NaN \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "2 NaN NaN NaN NaN -14.943369 \n", - "5 NaN NaN NaN NaN 32.047190 \n", - "8 NaN NaN -39.812370 NaN -17.185026 \n", - "12 NaN NaN -49.977579 NaN -5.715841 \n", - "16 NaN NaN -7.606972 NaN -7.606972 \n", - "\n", - "[5 rows x 54 columns]" + "[5 rows x 53 columns]" ] }, "metadata": {}, @@ -3619,6 +3642,7 @@ " Bot_Pepa\n", " CatrachoCaster\n", " ...\n", + " metac-o1\n", " metac-o1-preview\n", " metac-perplexity\n", " minefrac1\n", @@ -3628,182 +3652,183 @@ " swingswish\n", " twsummerbot\n", " wunderplumb\n", - " bot_team_median\n", " \n", " \n", " \n", " \n", - " 94\n", - " 35380\n", - " 35345\n", - " yes\n", - " 1.00\n", - " binary\n", - " None\n", - " 0.95\n", - " -5.406722\n", - " NaN\n", - " NaN\n", + " 81\n", + " 35169\n", + " 35119\n", + " Not in top 50\n", + " 1.0\n", + " multiple_choice\n", + " [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50]\n", + " [0.02,0.01,0.015,0.015,0.05,0.89]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -5.406722\n", - " NaN\n", - " NaN\n", - " 0.000000\n", - " 0.000000\n", - " NaN\n", - " -5.406722\n", - " -22.051543\n", - " -5.406722\n", - " -5.406722\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 95\n", - " 35381\n", - " 35354\n", - " no\n", - " 1.00\n", - " binary\n", - " None\n", - " 0.05\n", - " -294.443898\n", - " NaN\n", - " NaN\n", + " 82\n", + " 35170\n", + " 35121\n", + " 3 or more\n", + " 1.0\n", + " multiple_choice\n", + " [0, 1, 2, 3 or more]\n", + " [0.01,0.18,0.54,0.27]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -225.129180\n", - " NaN\n", - " NaN\n", - " -11.122564\n", - " NaN\n", - " NaN\n", - " -5.406722\n", - " -8.338161\n", - " -294.443898\n", - " -11.122564\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 96\n", - " 35385\n", - " 35358\n", - " yes\n", - " 1.00\n", - " binary\n", - " None\n", - " 0.97\n", - " -13.205972\n", - " NaN\n", - " NaN\n", + " 83\n", + " 35171\n", + " 35123\n", + " ≥7.5 and ≤8.5\n", + " 1.0\n", + " multiple_choice\n", + " [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5]\n", + " [0.02,0.3,0.3,0.3,0.08]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -7.490131\n", - " NaN\n", - " NaN\n", - " -7.490131\n", - " NaN\n", - " NaN\n", - " -13.205972\n", - " -15.828292\n", - " -13.205972\n", - " -13.205972\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 97\n", - " 35386\n", - " 35364\n", - " no\n", - " 0.85\n", - " binary\n", - " None\n", - " 0.666\n", - " -51.282363\n", - " NaN\n", - " NaN\n", + " 91\n", + " 35377\n", + " 35334\n", + " Jimmy Patronis\n", + " 1.0\n", + " multiple_choice\n", + " [Jimmy Patronis, Gay Valimont, Someone else]\n", + " [0.997,0.001,0.002]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -80.050570\n", - " 73.993934\n", - " NaN\n", - " -80.050570\n", - " -80.050570\n", - " NaN\n", - " -10.735852\n", - " 95.505072\n", - " 73.993934\n", - " -10.735852\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 98\n", - " 35387\n", - " 35367\n", - " no\n", - " 0.85\n", - " binary\n", - " None\n", - " 0.03\n", - " -32.621574\n", - " NaN\n", - " NaN\n", + " 92\n", + " 35378\n", + " 35336\n", + " 31-49\n", + " 1.0\n", + " multiple_choice\n", + " [0-24, 25-30, 31-49, 50-70, >70]\n", + " [0.001,0.359,0.55,0.08,0.01]\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", " ...\n", - " -7.490131\n", - " -2.083409\n", - " NaN\n", - " -13.205972\n", - " -2.083409\n", - " NaN\n", - " -19.268434\n", - " -28.425154\n", - " -19.268434\n", - " -13.205972\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", "\n", - "

5 rows × 54 columns

\n", + "

5 rows × 53 columns

\n", "" ], "text/plain": [ - " pro_question_id bot_question_id resolution question_weight type \\\n", - "94 35380 35345 yes 1.00 binary \n", - "95 35381 35354 no 1.00 binary \n", - "96 35385 35358 yes 1.00 binary \n", - "97 35386 35364 no 0.85 binary \n", - "98 35387 35367 no 0.85 binary \n", + " pro_question_id bot_question_id resolution question_weight \\\n", + "81 35169 35119 Not in top 50 1.0 \n", + "82 35170 35121 3 or more 1.0 \n", + "83 35171 35123 ≥7.5 and ≤8.5 1.0 \n", + "91 35377 35334 Jimmy Patronis 1.0 \n", + "92 35378 35336 31-49 1.0 \n", + "\n", + " type \\\n", + "81 multiple_choice \n", + "82 multiple_choice \n", + "83 multiple_choice \n", + "91 multiple_choice \n", + "92 multiple_choice \n", + "\n", + " options \\\n", + "81 [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50] \n", + "82 [0, 1, 2, 3 or more] \n", + "83 [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5] \n", + "91 [Jimmy Patronis, Gay Valimont, Someone else] \n", + "92 [0-24, 25-30, 31-49, 50-70, >70] \n", "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... \\\n", - "94 None 0.95 -5.406722 NaN NaN ... \n", - "95 None 0.05 -294.443898 NaN NaN ... \n", - "96 None 0.97 -13.205972 NaN NaN ... \n", - "97 None 0.666 -51.282363 NaN NaN ... \n", - "98 None 0.03 -32.621574 NaN NaN ... \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 -5.406722 NaN NaN 0.000000 0.000000 \n", - "95 -225.129180 NaN NaN -11.122564 NaN \n", - "96 -7.490131 NaN NaN -7.490131 NaN \n", - "97 -80.050570 73.993934 NaN -80.050570 -80.050570 \n", - "98 -7.490131 -2.083409 NaN -13.205972 -2.083409 \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "94 NaN -5.406722 -22.051543 -5.406722 -5.406722 \n", - "95 NaN -5.406722 -8.338161 -294.443898 -11.122564 \n", - "96 NaN -13.205972 -15.828292 -13.205972 -13.205972 \n", - "97 NaN -10.735852 95.505072 73.993934 -10.735852 \n", - "98 NaN -19.268434 -28.425154 -19.268434 -13.205972 \n", - "\n", - "[5 rows x 54 columns]" + " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", + "81 [0.02,0.01,0.015,0.015,0.05,0.89] 0.643473 2.597381 1.762901 \n", + "82 [0.01,0.18,0.54,0.27] 0.643473 2.597381 1.762901 \n", + "83 [0.02,0.3,0.3,0.3,0.08] 0.643473 2.597381 1.762901 \n", + "91 [0.997,0.001,0.002] 0.643473 2.597381 1.762901 \n", + "92 [0.001,0.359,0.55,0.08,0.01] 0.643473 2.597381 1.762901 \n", + "\n", + " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", + "81 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "82 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "83 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "91 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "92 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "\n", + " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", + "81 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "82 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "83 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "91 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "92 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", + "\n", + "[5 rows x 53 columns]" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", - "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", - "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'binary'])" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ + }, { "data": { "text/html": [ @@ -3825,33 +3850,455 @@ " \n", " \n", " \n", - " bot\n", - " Peer Score\n", - " \n", - " \n", - " Rank\n", - " \n", - " \n", + " pro_question_id\n", + " bot_question_id\n", + " resolution\n", + " question_weight\n", + " type\n", + " options\n", + " pro_median\n", + " 4Shadower\n", + " Bot_Pepa\n", + " CatrachoCaster\n", + " ...\n", + " metac-o1\n", + " metac-o1-preview\n", + " metac-perplexity\n", + " minefrac1\n", + " mmBot\n", + " pgodzinai\n", + " pianobot\n", + " swingswish\n", + " twsummerbot\n", + " wunderplumb\n", " \n", " \n", " \n", " \n", - " 1\n", - " metac-o1\n", - " 3864.168122\n", - " \n", - " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", - " \n", - " \n", - " 3\n", - " bot_median\n", - " 2724.680171\n", + " 31270\n", + " 31264\n", + " no\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.013\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", " \n", " \n", - " 4\n", + " 5\n", + " 31282\n", + " 31276\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.45\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", + " \n", + " \n", + " 8\n", + " 31294\n", + " 31288\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.95\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", + " \n", + " \n", + " 12\n", + " 31338\n", + " 31334\n", + " yes\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.9\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", + " \n", + " \n", + " 16\n", + " 33876\n", + " 33751\n", + " no\n", + " 1.0\n", + " binary\n", + " None\n", + " 0.058\n", + " 0.643473\n", + " 2.597381\n", + " 1.762901\n", + " ...\n", + " 21.041046\n", + " 10.134917\n", + " 20.283821\n", + " -2.987997\n", + " 9.735149\n", + " 3.537037\n", + " -2.173212\n", + " 2.411469\n", + " 14.267308\n", + " 2.372721\n", + " \n", + " \n", + "\n", + "

5 rows × 53 columns

\n", + "" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight type \\\n", + "2 31270 31264 no 1.0 binary \n", + "5 31282 31276 yes 1.0 binary \n", + "8 31294 31288 yes 1.0 binary \n", + "12 31338 31334 yes 1.0 binary \n", + "16 33876 33751 no 1.0 binary \n", + "\n", + " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", + "2 None 0.013 0.643473 2.597381 1.762901 ... 21.041046 \n", + "5 None 0.45 0.643473 2.597381 1.762901 ... 21.041046 \n", + "8 None 0.95 0.643473 2.597381 1.762901 ... 21.041046 \n", + "12 None 0.9 0.643473 2.597381 1.762901 ... 21.041046 \n", + "16 None 0.058 0.643473 2.597381 1.762901 ... 21.041046 \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "2 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "5 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "8 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "12 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "16 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb \n", + "2 -2.173212 2.411469 14.267308 2.372721 \n", + "5 -2.173212 2.411469 14.267308 2.372721 \n", + "8 -2.173212 2.411469 14.267308 2.372721 \n", + "12 -2.173212 2.411469 14.267308 2.372721 \n", + "16 -2.173212 2.411469 14.267308 2.372721 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionspro_median4ShadowerBot_PepaCatrachoCaster...metac-o1metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumb
943538035345yes1.00binaryNone0.950.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
953538135354no1.00binaryNone0.050.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
963538535358yes1.00binaryNone0.970.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
973538635364no0.85binaryNone0.6660.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
983538735367no0.85binaryNone0.030.6434732.5973811.762901...21.04104610.13491720.283821-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
\n", + "

5 rows × 53 columns

\n", + "
" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight type \\\n", + "94 35380 35345 yes 1.00 binary \n", + "95 35381 35354 no 1.00 binary \n", + "96 35385 35358 yes 1.00 binary \n", + "97 35386 35364 no 0.85 binary \n", + "98 35387 35367 no 0.85 binary \n", + "\n", + " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", + "94 None 0.95 0.643473 2.597381 1.762901 ... 21.041046 \n", + "95 None 0.05 0.643473 2.597381 1.762901 ... 21.041046 \n", + "96 None 0.97 0.643473 2.597381 1.762901 ... 21.041046 \n", + "97 None 0.666 0.643473 2.597381 1.762901 ... 21.041046 \n", + "98 None 0.03 0.643473 2.597381 1.762901 ... 21.041046 \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "95 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "96 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "97 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "98 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb \n", + "94 -2.173212 2.411469 14.267308 2.372721 \n", + "95 -2.173212 2.411469 14.267308 2.372721 \n", + "96 -2.173212 2.411469 14.267308 2.372721 \n", + "97 -2.173212 2.411469 14.267308 2.372721 \n", + "98 -2.173212 2.411469 14.267308 2.372721 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", + "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", + "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'binary'])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4078,8 +4525,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 2724.680171\n", + "2 bot_median 3711.510468\n", + "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -4146,13 +4593,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", + "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", "mean metac-o1 forecast on questions that resolved no: 26.0%\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -4195,15 +4642,15 @@ "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", "\n", "# Plot points for \"yes\" resolution\n", - "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100, \n", + "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", " color='blue', alpha=0.6, label='Resolved Yes')\n", - "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100, \n", + "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", " color='blue', alpha=0.6)\n", "\n", "# Plot points for \"no\" resolution\n", - "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100, \n", + "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", " color='red', alpha=0.6, label='Resolved No')\n", - "plt.scatter(x_pro_no, resolved_no[top_bot] * 100, \n", + "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", " color='red', alpha=0.6)\n", "\n", "# Customize the plot\n", @@ -4228,7 +4675,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_322865/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_739597/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -4353,20 +4800,20 @@ "
\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4711,8 +5158,8 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2481.552010 97 \n", - "4 5 acm_bot 2239.058675 85 \n", + "3 4 acm_bot 2239.058675 85 \n", + "4 5 bot_median 2196.323052 97 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", @@ -4760,8 +5207,8 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 93.10 \n", - "4 81.25 \n", + "3 81.25 \n", + "4 93.10 \n", "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", @@ -4956,20 +5403,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4984,6 +5417,20 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5580,8 +6027,8 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2481.6 93.1 26.7 55.791339 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", + "bot_median 2196.3 93.1 23.6 59.192687 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", @@ -5629,8 +6076,8 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 5.782185 4.609796 1.985277 38.1 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", + "bot_median 6.134698 3.845505 1.985277 35.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", @@ -5678,8 +6125,8 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 15.2 0.999994 0.000013 \n", "acm_bot 15.3 0.999987 0.000025 \n", + "bot_median 11.4 0.999889 0.000221 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", @@ -5749,6 +6196,38 @@ "outputId": "a7935679-8993-4329-d05d-fd701c4b77a8" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", + " t_statistic = (weighted_average - 0) / std_error\n" + ] + }, { "data": { "text/html": [ @@ -5785,797 +6264,797 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
botPeer Score
Rank
1metac-o13864.168122
2bot_median3711.510468
3metac-o1-preview3162.155445
4manticAI2142.538438
34bot_median2481.5520109793.10
45acm_bot2239.0586758581.25
45bot_median2196.3230529793.10
56metac-claude-3-5-sonnet-202406200.000036
bot_median2481.693.126.755.7913395.7821854.6097961.98527738.115.20.9999940.000013
acm_bot2239.181.20.000025
bot_median2196.393.123.659.1926876.1346983.8455051.98527735.811.40.9998890.000221
metac-claude-3-5-sonnet-202406202018.191.5
Grizeu_Bot487.940.012.2123.49852319.5390470.6251002.02031451.7-27.30.7322250.535551metac-o11998.995.021.03.570999e-153.663768e-165.743007e+161.9847521.021.01.00.000000
acm_bot149.763.82.3123.16721915.4139760.1521161.99701833.1-28.40.5602090.879583metac-perplexity1927.095.020.30.000000e+000.000000e+00inf1.9847520.320.31.00.000000
RPM_bot145.06.024.231.46890712.8471271.8809962.57058257.2-8.90.9406380.118725bot_median1698.895.017.90.000000e+000.000000e+00inf1.9847517.917.91.00.000000
X_bot20.75.04.119.7562378.8352580.4688972.77644528.7-20.40.6682210.663558acm_bot1680.695.017.73.570999e-153.663768e-164.828449e+161.9847517.717.71.00.000000
cobyj-bot0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNAmanticAI1378.295.014.50.000000e+000.000000e+00inf1.9847514.514.51.00.000000
andrewsiah0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNAtwsummerbot1355.495.014.31.785500e-151.831884e-167.788325e+161.9847514.314.31.00.000000
jonahsingerbot-61.34.7-13.05.4853692.530212-5.1548422.784843-6.0-20.10.0041410.008283jkraybill_bot1354.595.014.31.785500e-151.831884e-167.783286e+161.9847514.314.31.00.000000
bean_bot-70.74.7-15.18.8131374.065197-3.7022222.784843-3.7-26.40.0119250.023851metac-claude-3-5-sonnet-202406201136.795.012.03.570999e-153.663768e-163.265969e+161.9847512.012.01.00.000000
jkraybill_bot-76.138.2-2.067.06547910.858048-0.1837062.02336020.0-24.00.4276220.855243GreeneiBot21115.495.011.75.356499e-155.495652e-162.136428e+161.9847511.711.71.00.000000
CumulativeBot-97.010.2-9.530.1210609.408238-1.0055352.231848metac-claude-3-5-sonnet-latest1091.695.011.55.356499e-155.495652e-162.090764e+161.9847511.511.5-30.50.1701090.3402181.00.000000
swingswish-109.06.7-16.315.1455315.851229-2.7797012.450387-1.9-30.60.0168960.033793NextWorldLab1050.395.011.11.785500e-151.831884e-166.035038e+161.9847511.111.11.00.000000
SynapseSeer-128.527.1-4.847.0810459.052373-0.5249592.04956913.8-23.30.3020260.604052metac-grok-2-12121047.495.011.00.000000e+000.000000e+00inf1.9847511.011.01.00.000000
KevinTestBot-148.38.4-17.759.36966920.484482-0.8619382.31149629.7-65.00.2078890.415777metac-gpt-4o1002.095.010.53.570999e-153.663768e-162.878879e+161.9847510.510.51.00.000000
twsummerbot-237.247.0-5.079.50269011.596659-0.4351342.01121518.3-28.40.3327500.665500metac-Llama-3.1973.095.010.20.000000e+000.000000e+00inf1.9847510.210.21.00.000000
pianobot-272.24.7-57.992.18716542.522768-1.3617862.79898661.1-176.90.1251370.250274Grizeu_Bot966.495.010.20.000000e+000.000000e+00inf1.9847510.210.21.00.000000
annabot-316.024.8-12.743.7374108.782683-1.4506142.0613075.4-30.80.0799700.159940SynapseSeer964.795.010.21.785500e-151.831884e-165.543440e+161.9847510.210.21.00.000000
CatrachoCaster-331.319.7-16.852.31505911.786737-1.4269802.0887777.8-41.40.0850350.170071metac-o1-preview962.895.010.11.785500e-151.831884e-165.532510e+161.9847510.110.11.00.000000
cookics_bot_TEST-413.324.6-16.872.42669414.602631-1.1504362.06084513.3-46.90.1307440.261488mmBot924.895.09.70.000000e+000.000000e+00inf1.984759.79.71.00.000000
GreeneiBot2-446.645.8-9.888.55320713.092083-0.7457052.01234016.6-36.10.2298720.459745metac-exa919.995.09.71.785500e-151.831884e-165.285939e+161.984759.79.71.00.000000
metac-o1-500.374.7-6.7111.25524212.872419-0.5203391.99159718.9-32.30.3021940.604387annabot854.495.09.01.785500e-151.831884e-164.909363e+161.984759.09.01.00.000000
krm-bot-521.09.5-54.850.62785616.425846-3.3389622.264709-17.6-92.00.0047000.009400metac-deepseek-r1802.095.08.41.785500e-151.831884e-164.608683e+161.984758.48.41.00.000000
4Shadower-527.812.2-43.380.79118223.130448-1.8702732.1816957.2-93.70.0438960.087792VeritasAI802.095.08.41.785500e-151.831884e-164.608352e+161.984758.48.41.00.000000
MWG-766.429.5-26.087.75333816.156699-1.6080772.0435277.0-59.00.0594210.118842laylaps723.495.07.68.927498e-169.159420e-178.313180e+161.984757.67.61.00.000000
bot_median-780.675.7-10.385.1138919.782560-1.0541471.9911819.2-29.80.1476070.295213cookics_bot_TEST612.495.06.41.785500e-151.831884e-163.518949e+161.984756.46.41.00.000000
Bot_Pepa-814.937.2-21.993.06728515.269248-1.4365512.0250989.0-52.90.0797220.159444metac-Gemini-Exp-1206548.095.05.80.000000e+000.000000e+00inf1.984755.85.81.00.000000
ajf-bot-843.131.4-26.9104.85473318.727046-1.4360202.03766711.3-65.10.0806120.161224MWG520.895.05.58.927498e-169.159420e-175.985647e+161.984755.55.51.00.000000
manticAI-861.555.0-15.782.87386511.169634-1.4011472.0030646.7-38.00.0834430.166886
ajf-bot481.295.05.11.785500e-151.831884e-162.764898e+161.984755.15.11.00.000000
ProfessorSP-997.216.8-59.496.91948823.645934-2.5102932.112371-9.4-109.30.0116720.023345pgodzinai336.095.03.58.927498e-169.159420e-173.861639e+161.984753.53.51.00.000000
metac-perplexity-1072.972.7-14.8105.31560712.351666-1.1948081.9924629.9-39.40.1180500.236099KevinTestBot314.595.03.38.927498e-169.159420e-173.614852e+161.984753.33.31.00.000000
wunderplumb-1159.023.8-48.890.74010618.619477-2.6209902.065034-10.4-87.30.0076770.015353InstitutPelFutur256.095.02.78.927498e-169.159420e-172.941623e+161.984752.72.71.00.000000
laylaps-1214.552.2-23.348.0199296.646397-3.5005872.005359-9.9-36.60.0004860.000971Bot_Pepa246.895.02.60.000000e+000.000000e+00inf1.984752.62.61.00.000000
NextWorldLab-1224.163.8-19.298.66262212.347306-1.5526991.9970185.5-43.80.0627580.125517CumulativeBot241.195.02.54.463749e-164.579710e-175.542703e+161.984752.52.51.00.000000
metac-Gemini-Exp-1206-1250.565.1-19.294.99321111.773405-1.6315191.9963774.3-42.70.0538420.107685swingswish229.195.02.44.463749e-164.579710e-175.265549e+161.984752.42.41.00.000000
minefrac1-1289.443.5-29.6123.19979118.679504-1.5868582.0149188.0-67.30.0599790.119958wunderplumb225.495.02.44.463749e-164.579710e-175.180942e+161.984752.42.41.00.000000
pgodzinai-1330.462.0-21.598.40405312.497327-1.7169531.9981743.5-46.40.0455310.091062jonahsingerbot212.995.02.24.463749e-164.579710e-174.894511e+161.984752.22.21.00.000000
metac-deepseek-r1-1360.348.2-28.2108.35980215.607908-1.8082482.0091123.1-59.60.0384710.076941bean_bot200.095.02.10.000000e+000.000000e+00inf1.984752.12.11.00.000000
metac-Llama-3.1-1412.173.7-19.297.48349911.355267-1.6873751.9920243.5-41.80.0479090.095818X_bot181.495.01.90.000000e+000.000000e+00inf1.984751.91.91.00.000000
metac-claude-3-5-sonnet-latest-1463.974.7-19.696.85591111.206393-1.7487371.9915972.7-41.90.0422500.084500CatrachoCaster167.595.01.84.463749e-164.579710e-173.849373e+161.984751.81.81.00.000000
metac-claude-3-5-sonnet-20240620-1649.975.1-22.0105.32409412.153679-1.8076161.9915362.2-46.20.0373620.0747254Shadower61.195.00.62.231875e-162.289855e-172.810106e+161.984750.60.61.00.000000
metac-o1-preview-1830.674.7-24.5107.51540912.439714-1.9699551.9915970.3-49.30.0263010.052601krm-bot60.895.00.61.115937e-161.144927e-175.586129e+161.984750.60.61.00.000000
mmBot-2006.475.7-26.578.5323519.026111-2.9364461.991181-8.5-44.50.0022050.004411RPM_bot52.695.00.61.115937e-161.144927e-174.834420e+161.984750.60.61.00.000000
VeritasAI-2024.567.7-29.963.2821037.691066-3.8881871.994849-14.6-45.20.0001180.000235andrewsiah0.095.00.00.000000e+000.000000e+00NaN1.984750.00.0NaNNA
metac-grok-2-1212-2154.674.7-28.8106.09460612.275325-2.3496851.991597-4.4-53.30.0107350.021470cobyj-bot0.095.00.00.000000e+000.000000e+00NaN1.984750.00.0NaNNA
metac-gpt-4o-2196.674.7-29.4100.42168411.618958-2.5308441.991597-6.3-52.50.0067560.013513pianobot-206.595.0-2.24.463749e-164.579710e-17-4.745305e+161.98475-2.2-2.20.00.000000
metac-exa-2249.172.7-30.991.72329010.757526-2.8758531.992462-9.5-52.40.0026510.005302ProfessorSP-280.495.0-3.08.927498e-169.159420e-17-3.222942e+161.98475-3.0-3.00.00.000000
InstitutPelFutur-2477.372.8-34.0102.04145411.959443-2.8453911.992461-10.2-57.90.0028880.005777minefrac1-283.995.0-3.04.463749e-164.579710e-17-6.524424e+161.98475-3.0-3.00.00.000000
\n", "
" ], "text/plain": [ - " W_score W_count W_ave W_stdev \\\n", - "Grizeu_Bot 487.9 40.0 12.2 123.498523 \n", - "acm_bot 149.7 63.8 2.3 123.167219 \n", - "RPM_bot 145.0 6.0 24.2 31.468907 \n", - "X_bot 20.7 5.0 4.1 19.756237 \n", - "cobyj-bot 0.0 0.0 NaN NaN \n", - "andrewsiah 0.0 0.0 NaN NaN \n", - "jonahsingerbot -61.3 4.7 -13.0 5.485369 \n", - "bean_bot -70.7 4.7 -15.1 8.813137 \n", - "jkraybill_bot -76.1 38.2 -2.0 67.065479 \n", - "CumulativeBot -97.0 10.2 -9.5 30.121060 \n", - "swingswish -109.0 6.7 -16.3 15.145531 \n", - "SynapseSeer -128.5 27.1 -4.8 47.081045 \n", - "KevinTestBot -148.3 8.4 -17.7 59.369669 \n", - "twsummerbot -237.2 47.0 -5.0 79.502690 \n", - "pianobot -272.2 4.7 -57.9 92.187165 \n", - "annabot -316.0 24.8 -12.7 43.737410 \n", - "CatrachoCaster -331.3 19.7 -16.8 52.315059 \n", - "cookics_bot_TEST -413.3 24.6 -16.8 72.426694 \n", - "GreeneiBot2 -446.6 45.8 -9.8 88.553207 \n", - "metac-o1 -500.3 74.7 -6.7 111.255242 \n", - "krm-bot -521.0 9.5 -54.8 50.627856 \n", - "4Shadower -527.8 12.2 -43.3 80.791182 \n", - "MWG -766.4 29.5 -26.0 87.753338 \n", - "bot_median -780.6 75.7 -10.3 85.113891 \n", - "Bot_Pepa -814.9 37.2 -21.9 93.067285 \n", - "ajf-bot -843.1 31.4 -26.9 104.854733 \n", - "manticAI -861.5 55.0 -15.7 82.873865 \n", - "ProfessorSP -997.2 16.8 -59.4 96.919488 \n", - "metac-perplexity -1072.9 72.7 -14.8 105.315607 \n", - "wunderplumb -1159.0 23.8 -48.8 90.740106 \n", - "laylaps -1214.5 52.2 -23.3 48.019929 \n", - "NextWorldLab -1224.1 63.8 -19.2 98.662622 \n", - "metac-Gemini-Exp-1206 -1250.5 65.1 -19.2 94.993211 \n", - "minefrac1 -1289.4 43.5 -29.6 123.199791 \n", - "pgodzinai -1330.4 62.0 -21.5 98.404053 \n", - "metac-deepseek-r1 -1360.3 48.2 -28.2 108.359802 \n", - "metac-Llama-3.1 -1412.1 73.7 -19.2 97.483499 \n", - "metac-claude-3-5-sonnet-latest -1463.9 74.7 -19.6 96.855911 \n", - "metac-claude-3-5-sonnet-20240620 -1649.9 75.1 -22.0 105.324094 \n", - "metac-o1-preview -1830.6 74.7 -24.5 107.515409 \n", - "mmBot -2006.4 75.7 -26.5 78.532351 \n", - "VeritasAI -2024.5 67.7 -29.9 63.282103 \n", - "metac-grok-2-1212 -2154.6 74.7 -28.8 106.094606 \n", - "metac-gpt-4o -2196.6 74.7 -29.4 100.421684 \n", - "metac-exa -2249.1 72.7 -30.9 91.723290 \n", - "InstitutPelFutur -2477.3 72.8 -34.0 102.041454 \n", - "\n", - " std_err t_stat t_crit upper_bound \\\n", - "Grizeu_Bot 19.539047 0.625100 2.020314 51.7 \n", - "acm_bot 15.413976 0.152116 1.997018 33.1 \n", - "RPM_bot 12.847127 1.880996 2.570582 57.2 \n", - "X_bot 8.835258 0.468897 2.776445 28.7 \n", - "cobyj-bot NaN NaN NaN NaN \n", - "andrewsiah NaN NaN NaN NaN \n", - "jonahsingerbot 2.530212 -5.154842 2.784843 -6.0 \n", - "bean_bot 4.065197 -3.702222 2.784843 -3.7 \n", - "jkraybill_bot 10.858048 -0.183706 2.023360 20.0 \n", - "CumulativeBot 9.408238 -1.005535 2.231848 11.5 \n", - "swingswish 5.851229 -2.779701 2.450387 -1.9 \n", - "SynapseSeer 9.052373 -0.524959 2.049569 13.8 \n", - "KevinTestBot 20.484482 -0.861938 2.311496 29.7 \n", - "twsummerbot 11.596659 -0.435134 2.011215 18.3 \n", - "pianobot 42.522768 -1.361786 2.798986 61.1 \n", - "annabot 8.782683 -1.450614 2.061307 5.4 \n", - "CatrachoCaster 11.786737 -1.426980 2.088777 7.8 \n", - "cookics_bot_TEST 14.602631 -1.150436 2.060845 13.3 \n", - "GreeneiBot2 13.092083 -0.745705 2.012340 16.6 \n", - "metac-o1 12.872419 -0.520339 1.991597 18.9 \n", - "krm-bot 16.425846 -3.338962 2.264709 -17.6 \n", - "4Shadower 23.130448 -1.870273 2.181695 7.2 \n", - "MWG 16.156699 -1.608077 2.043527 7.0 \n", - "bot_median 9.782560 -1.054147 1.991181 9.2 \n", - "Bot_Pepa 15.269248 -1.436551 2.025098 9.0 \n", - "ajf-bot 18.727046 -1.436020 2.037667 11.3 \n", - "manticAI 11.169634 -1.401147 2.003064 6.7 \n", - "ProfessorSP 23.645934 -2.510293 2.112371 -9.4 \n", - "metac-perplexity 12.351666 -1.194808 1.992462 9.9 \n", - "wunderplumb 18.619477 -2.620990 2.065034 -10.4 \n", - "laylaps 6.646397 -3.500587 2.005359 -9.9 \n", - "NextWorldLab 12.347306 -1.552699 1.997018 5.5 \n", - "metac-Gemini-Exp-1206 11.773405 -1.631519 1.996377 4.3 \n", - "minefrac1 18.679504 -1.586858 2.014918 8.0 \n", - "pgodzinai 12.497327 -1.716953 1.998174 3.5 \n", - "metac-deepseek-r1 15.607908 -1.808248 2.009112 3.1 \n", - "metac-Llama-3.1 11.355267 -1.687375 1.992024 3.5 \n", - "metac-claude-3-5-sonnet-latest 11.206393 -1.748737 1.991597 2.7 \n", - "metac-claude-3-5-sonnet-20240620 12.153679 -1.807616 1.991536 2.2 \n", - "metac-o1-preview 12.439714 -1.969955 1.991597 0.3 \n", - "mmBot 9.026111 -2.936446 1.991181 -8.5 \n", - "VeritasAI 7.691066 -3.888187 1.994849 -14.6 \n", - "metac-grok-2-1212 12.275325 -2.349685 1.991597 -4.4 \n", - "metac-gpt-4o 11.618958 -2.530844 1.991597 -6.3 \n", - "metac-exa 10.757526 -2.875853 1.992462 -9.5 \n", - "InstitutPelFutur 11.959443 -2.845391 1.992461 -10.2 \n", - "\n", - " lower_bound cdf p_value \n", - "Grizeu_Bot -27.3 0.732225 0.535551 \n", - "acm_bot -28.4 0.560209 0.879583 \n", - "RPM_bot -8.9 0.940638 0.118725 \n", - "X_bot -20.4 0.668221 0.663558 \n", - "cobyj-bot NaN NaN NA \n", - "andrewsiah NaN NaN NA \n", - "jonahsingerbot -20.1 0.004141 0.008283 \n", - "bean_bot -26.4 0.011925 0.023851 \n", - "jkraybill_bot -24.0 0.427622 0.855243 \n", - "CumulativeBot -30.5 0.170109 0.340218 \n", - "swingswish -30.6 0.016896 0.033793 \n", - "SynapseSeer -23.3 0.302026 0.604052 \n", - "KevinTestBot -65.0 0.207889 0.415777 \n", - "twsummerbot -28.4 0.332750 0.665500 \n", - "pianobot -176.9 0.125137 0.250274 \n", - "annabot -30.8 0.079970 0.159940 \n", - "CatrachoCaster -41.4 0.085035 0.170071 \n", - "cookics_bot_TEST -46.9 0.130744 0.261488 \n", - "GreeneiBot2 -36.1 0.229872 0.459745 \n", - "metac-o1 -32.3 0.302194 0.604387 \n", - "krm-bot -92.0 0.004700 0.009400 \n", - "4Shadower -93.7 0.043896 0.087792 \n", - "MWG -59.0 0.059421 0.118842 \n", - "bot_median -29.8 0.147607 0.295213 \n", - "Bot_Pepa -52.9 0.079722 0.159444 \n", - "ajf-bot -65.1 0.080612 0.161224 \n", - "manticAI -38.0 0.083443 0.166886 \n", - "ProfessorSP -109.3 0.011672 0.023345 \n", - "metac-perplexity -39.4 0.118050 0.236099 \n", - "wunderplumb -87.3 0.007677 0.015353 \n", - "laylaps -36.6 0.000486 0.000971 \n", - "NextWorldLab -43.8 0.062758 0.125517 \n", - "metac-Gemini-Exp-1206 -42.7 0.053842 0.107685 \n", - "minefrac1 -67.3 0.059979 0.119958 \n", - "pgodzinai -46.4 0.045531 0.091062 \n", - "metac-deepseek-r1 -59.6 0.038471 0.076941 \n", - "metac-Llama-3.1 -41.8 0.047909 0.095818 \n", - "metac-claude-3-5-sonnet-latest -41.9 0.042250 0.084500 \n", - "metac-claude-3-5-sonnet-20240620 -46.2 0.037362 0.074725 \n", - "metac-o1-preview -49.3 0.026301 0.052601 \n", - "mmBot -44.5 0.002205 0.004411 \n", - "VeritasAI -45.2 0.000118 0.000235 \n", - "metac-grok-2-1212 -53.3 0.010735 0.021470 \n", - "metac-gpt-4o -52.5 0.006756 0.013513 \n", - "metac-exa -52.4 0.002651 0.005302 \n", - "InstitutPelFutur -57.9 0.002888 0.005777 " + " W_score W_count W_ave W_stdev \\\n", + "metac-o1 1998.9 95.0 21.0 3.570999e-15 \n", + "metac-perplexity 1927.0 95.0 20.3 0.000000e+00 \n", + "bot_median 1698.8 95.0 17.9 0.000000e+00 \n", + "acm_bot 1680.6 95.0 17.7 3.570999e-15 \n", + "manticAI 1378.2 95.0 14.5 0.000000e+00 \n", + "twsummerbot 1355.4 95.0 14.3 1.785500e-15 \n", + "jkraybill_bot 1354.5 95.0 14.3 1.785500e-15 \n", + "metac-claude-3-5-sonnet-20240620 1136.7 95.0 12.0 3.570999e-15 \n", + "GreeneiBot2 1115.4 95.0 11.7 5.356499e-15 \n", + "metac-claude-3-5-sonnet-latest 1091.6 95.0 11.5 5.356499e-15 \n", + "NextWorldLab 1050.3 95.0 11.1 1.785500e-15 \n", + "metac-grok-2-1212 1047.4 95.0 11.0 0.000000e+00 \n", + "metac-gpt-4o 1002.0 95.0 10.5 3.570999e-15 \n", + "metac-Llama-3.1 973.0 95.0 10.2 0.000000e+00 \n", + "Grizeu_Bot 966.4 95.0 10.2 0.000000e+00 \n", + "SynapseSeer 964.7 95.0 10.2 1.785500e-15 \n", + "metac-o1-preview 962.8 95.0 10.1 1.785500e-15 \n", + "mmBot 924.8 95.0 9.7 0.000000e+00 \n", + "metac-exa 919.9 95.0 9.7 1.785500e-15 \n", + "annabot 854.4 95.0 9.0 1.785500e-15 \n", + "metac-deepseek-r1 802.0 95.0 8.4 1.785500e-15 \n", + "VeritasAI 802.0 95.0 8.4 1.785500e-15 \n", + "laylaps 723.4 95.0 7.6 8.927498e-16 \n", + "cookics_bot_TEST 612.4 95.0 6.4 1.785500e-15 \n", + "metac-Gemini-Exp-1206 548.0 95.0 5.8 0.000000e+00 \n", + "MWG 520.8 95.0 5.5 8.927498e-16 \n", + "ajf-bot 481.2 95.0 5.1 1.785500e-15 \n", + "pgodzinai 336.0 95.0 3.5 8.927498e-16 \n", + "KevinTestBot 314.5 95.0 3.3 8.927498e-16 \n", + "InstitutPelFutur 256.0 95.0 2.7 8.927498e-16 \n", + "Bot_Pepa 246.8 95.0 2.6 0.000000e+00 \n", + "CumulativeBot 241.1 95.0 2.5 4.463749e-16 \n", + "swingswish 229.1 95.0 2.4 4.463749e-16 \n", + "wunderplumb 225.4 95.0 2.4 4.463749e-16 \n", + "jonahsingerbot 212.9 95.0 2.2 4.463749e-16 \n", + "bean_bot 200.0 95.0 2.1 0.000000e+00 \n", + "X_bot 181.4 95.0 1.9 0.000000e+00 \n", + "CatrachoCaster 167.5 95.0 1.8 4.463749e-16 \n", + "4Shadower 61.1 95.0 0.6 2.231875e-16 \n", + "krm-bot 60.8 95.0 0.6 1.115937e-16 \n", + "RPM_bot 52.6 95.0 0.6 1.115937e-16 \n", + "andrewsiah 0.0 95.0 0.0 0.000000e+00 \n", + "cobyj-bot 0.0 95.0 0.0 0.000000e+00 \n", + "pianobot -206.5 95.0 -2.2 4.463749e-16 \n", + "ProfessorSP -280.4 95.0 -3.0 8.927498e-16 \n", + "minefrac1 -283.9 95.0 -3.0 4.463749e-16 \n", + "\n", + " std_err t_stat t_crit \\\n", + "metac-o1 3.663768e-16 5.743007e+16 1.98475 \n", + "metac-perplexity 0.000000e+00 inf 1.98475 \n", + "bot_median 0.000000e+00 inf 1.98475 \n", + "acm_bot 3.663768e-16 4.828449e+16 1.98475 \n", + "manticAI 0.000000e+00 inf 1.98475 \n", + "twsummerbot 1.831884e-16 7.788325e+16 1.98475 \n", + "jkraybill_bot 1.831884e-16 7.783286e+16 1.98475 \n", + "metac-claude-3-5-sonnet-20240620 3.663768e-16 3.265969e+16 1.98475 \n", + "GreeneiBot2 5.495652e-16 2.136428e+16 1.98475 \n", + "metac-claude-3-5-sonnet-latest 5.495652e-16 2.090764e+16 1.98475 \n", + "NextWorldLab 1.831884e-16 6.035038e+16 1.98475 \n", + "metac-grok-2-1212 0.000000e+00 inf 1.98475 \n", + "metac-gpt-4o 3.663768e-16 2.878879e+16 1.98475 \n", + "metac-Llama-3.1 0.000000e+00 inf 1.98475 \n", + "Grizeu_Bot 0.000000e+00 inf 1.98475 \n", + "SynapseSeer 1.831884e-16 5.543440e+16 1.98475 \n", + "metac-o1-preview 1.831884e-16 5.532510e+16 1.98475 \n", + "mmBot 0.000000e+00 inf 1.98475 \n", + "metac-exa 1.831884e-16 5.285939e+16 1.98475 \n", + "annabot 1.831884e-16 4.909363e+16 1.98475 \n", + "metac-deepseek-r1 1.831884e-16 4.608683e+16 1.98475 \n", + "VeritasAI 1.831884e-16 4.608352e+16 1.98475 \n", + "laylaps 9.159420e-17 8.313180e+16 1.98475 \n", + "cookics_bot_TEST 1.831884e-16 3.518949e+16 1.98475 \n", + "metac-Gemini-Exp-1206 0.000000e+00 inf 1.98475 \n", + "MWG 9.159420e-17 5.985647e+16 1.98475 \n", + "ajf-bot 1.831884e-16 2.764898e+16 1.98475 \n", + "pgodzinai 9.159420e-17 3.861639e+16 1.98475 \n", + "KevinTestBot 9.159420e-17 3.614852e+16 1.98475 \n", + "InstitutPelFutur 9.159420e-17 2.941623e+16 1.98475 \n", + "Bot_Pepa 0.000000e+00 inf 1.98475 \n", + "CumulativeBot 4.579710e-17 5.542703e+16 1.98475 \n", + "swingswish 4.579710e-17 5.265549e+16 1.98475 \n", + "wunderplumb 4.579710e-17 5.180942e+16 1.98475 \n", + "jonahsingerbot 4.579710e-17 4.894511e+16 1.98475 \n", + "bean_bot 0.000000e+00 inf 1.98475 \n", + "X_bot 0.000000e+00 inf 1.98475 \n", + "CatrachoCaster 4.579710e-17 3.849373e+16 1.98475 \n", + "4Shadower 2.289855e-17 2.810106e+16 1.98475 \n", + "krm-bot 1.144927e-17 5.586129e+16 1.98475 \n", + "RPM_bot 1.144927e-17 4.834420e+16 1.98475 \n", + "andrewsiah 0.000000e+00 NaN 1.98475 \n", + "cobyj-bot 0.000000e+00 NaN 1.98475 \n", + "pianobot 4.579710e-17 -4.745305e+16 1.98475 \n", + "ProfessorSP 9.159420e-17 -3.222942e+16 1.98475 \n", + "minefrac1 4.579710e-17 -6.524424e+16 1.98475 \n", + "\n", + " upper_bound lower_bound cdf p_value \n", + "metac-o1 21.0 21.0 1.0 0.000000 \n", + "metac-perplexity 20.3 20.3 1.0 0.000000 \n", + "bot_median 17.9 17.9 1.0 0.000000 \n", + "acm_bot 17.7 17.7 1.0 0.000000 \n", + "manticAI 14.5 14.5 1.0 0.000000 \n", + "twsummerbot 14.3 14.3 1.0 0.000000 \n", + "jkraybill_bot 14.3 14.3 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-20240620 12.0 12.0 1.0 0.000000 \n", + "GreeneiBot2 11.7 11.7 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-latest 11.5 11.5 1.0 0.000000 \n", + "NextWorldLab 11.1 11.1 1.0 0.000000 \n", + "metac-grok-2-1212 11.0 11.0 1.0 0.000000 \n", + "metac-gpt-4o 10.5 10.5 1.0 0.000000 \n", + "metac-Llama-3.1 10.2 10.2 1.0 0.000000 \n", + "Grizeu_Bot 10.2 10.2 1.0 0.000000 \n", + "SynapseSeer 10.2 10.2 1.0 0.000000 \n", + "metac-o1-preview 10.1 10.1 1.0 0.000000 \n", + "mmBot 9.7 9.7 1.0 0.000000 \n", + "metac-exa 9.7 9.7 1.0 0.000000 \n", + "annabot 9.0 9.0 1.0 0.000000 \n", + "metac-deepseek-r1 8.4 8.4 1.0 0.000000 \n", + "VeritasAI 8.4 8.4 1.0 0.000000 \n", + "laylaps 7.6 7.6 1.0 0.000000 \n", + "cookics_bot_TEST 6.4 6.4 1.0 0.000000 \n", + "metac-Gemini-Exp-1206 5.8 5.8 1.0 0.000000 \n", + "MWG 5.5 5.5 1.0 0.000000 \n", + "ajf-bot 5.1 5.1 1.0 0.000000 \n", + "pgodzinai 3.5 3.5 1.0 0.000000 \n", + "KevinTestBot 3.3 3.3 1.0 0.000000 \n", + "InstitutPelFutur 2.7 2.7 1.0 0.000000 \n", + "Bot_Pepa 2.6 2.6 1.0 0.000000 \n", + "CumulativeBot 2.5 2.5 1.0 0.000000 \n", + "swingswish 2.4 2.4 1.0 0.000000 \n", + "wunderplumb 2.4 2.4 1.0 0.000000 \n", + "jonahsingerbot 2.2 2.2 1.0 0.000000 \n", + "bean_bot 2.1 2.1 1.0 0.000000 \n", + "X_bot 1.9 1.9 1.0 0.000000 \n", + "CatrachoCaster 1.8 1.8 1.0 0.000000 \n", + "4Shadower 0.6 0.6 1.0 0.000000 \n", + "krm-bot 0.6 0.6 1.0 0.000000 \n", + "RPM_bot 0.6 0.6 1.0 0.000000 \n", + "andrewsiah 0.0 0.0 NaN NA \n", + "cobyj-bot 0.0 0.0 NaN NA \n", + "pianobot -2.2 -2.2 0.0 0.000000 \n", + "ProfessorSP -3.0 -3.0 0.0 0.000000 \n", + "minefrac1 -3.0 -3.0 0.0 0.000000 " ] }, "execution_count": 43, @@ -7803,23 +8282,9 @@ "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\s'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n", - "<>:29: SyntaxWarning: invalid escape sequence '\\s'\n", - "/tmp/ipykernel_322865/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\m'\n", - " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n", - "/tmp/ipykernel_322865/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\s'\n", - " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n" - ] - }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -8344,152 +8809,144 @@ " 6.0\n", " 7.2\n", " 9.6\n", - " 12.0\n", + " 11.9\n", " 13.1\n", " \n", " \n", " metac-o1-preview\n", - " 3.9\n", + " 3.7\n", " 5.2\n", " 8.3\n", " 11.2\n", - " 12.6\n", + " 12.8\n", " \n", " \n", " manticAI\n", - " -0.2\n", + " 0.2\n", " 2.1\n", " 5.5\n", - " 8.7\n", - " 10.4\n", + " 8.8\n", + " 10.5\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.9\n", - " 2.3\n", - " 5.1\n", - " 7.8\n", - " 9.1\n", + " 0.4\n", + " 1.9\n", + " 4.9\n", + " 7.5\n", + " 8.9\n", " \n", " \n", " acm_bot\n", - " 0.3\n", - " 1.9\n", - " 4.5\n", - " 7.5\n", - " 8.8\n", + " 0.2\n", + " 1.8\n", + " 4.7\n", + " 7.7\n", + " 9.1\n", " \n", " \n", " metac-perplexity\n", - " -1.7\n", - " 0.5\n", - " 4.1\n", - " 7.7\n", + " -2.2\n", + " 0.0\n", + " 4.3\n", + " 7.8\n", " 9.9\n", " \n", " \n", + " GreeneiBot2\n", + " -1.2\n", + " 0.4\n", + " 3.9\n", + " 7.0\n", + " 8.7\n", + " \n", + " \n", " twsummerbot\n", " 0.3\n", - " 1.4\n", + " 1.5\n", " 3.9\n", " 6.1\n", - " 7.5\n", + " 7.4\n", " \n", " \n", - " GreeneiBot2\n", - " -1.0\n", - " 0.7\n", - " 3.8\n", - " 7.2\n", - " 8.8\n", + " pgodzinai\n", + " -3.4\n", + " -1.2\n", + " 3.2\n", + " 7.3\n", + " 9.6\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.0\n", - " 0.9\n", - " 3.1\n", + " -0.2\n", + " 0.8\n", + " 2.9\n", " 5.0\n", - " 6.2\n", - " \n", - " \n", - " pgodzinai\n", - " -3.1\n", - " -1.1\n", - " 2.8\n", - " 6.9\n", - " 8.7\n", + " 5.8\n", " \n", " \n", " CumulativeBot\n", - " -0.2\n", - " 0.8\n", - " 2.6\n", - " 4.4\n", + " -0.1\n", + " 0.9\n", + " 2.7\n", + " 4.6\n", " 5.4\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -1.3\n", - " 0.1\n", - " 2.6\n", - " 4.9\n", - " 6.2\n", - " \n", - " \n", " SynapseSeer\n", " 0.4\n", " 1.1\n", + " 2.6\n", + " 4.1\n", + " 4.8\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-latest\n", + " -1.3\n", + " -0.0\n", " 2.5\n", - " 4.0\n", - " 4.9\n", + " 5.0\n", + " 6.2\n", " \n", " \n", " jkraybill_bot\n", " -3.5\n", - " -1.6\n", + " -1.7\n", " 1.7\n", - " 4.8\n", + " 5.0\n", " 6.4\n", " \n", " \n", " metac-exa\n", - " -5.2\n", - " -2.7\n", + " -4.8\n", + " -2.2\n", " 1.7\n", - " 5.4\n", - " 7.6\n", + " 5.6\n", + " 7.8\n", " \n", " \n", " metac-deepseek-r1\n", - " -1.9\n", - " -0.6\n", - " 1.5\n", - " 3.6\n", - " 4.9\n", + " -2.0\n", + " -0.8\n", + " 1.3\n", + " 3.4\n", + " 4.6\n", " \n", " \n", " MWG\n", " -1.6\n", - " -0.9\n", + " -0.8\n", " 0.7\n", - " 2.0\n", - " 2.7\n", + " 2.1\n", + " 2.8\n", " \n", " \n", " andrewsiah\n", - " -1.0\n", + " -0.8\n", " -0.6\n", " -0.0\n", " 0.6\n", - " 1.0\n", - " \n", - " \n", - " X_bot\n", - " -0.4\n", - " -0.2\n", - " -0.0\n", - " 0.1\n", - " 0.2\n", + " 0.9\n", " \n", " \n", " pianobot\n", @@ -8497,94 +8954,102 @@ " -0.8\n", " -0.0\n", " 0.7\n", - " 1.1\n", + " 1.0\n", " \n", " \n", " cobyj-bot\n", " -1.5\n", " -0.9\n", - " -0.1\n", - " 0.9\n", - " 1.4\n", + " -0.0\n", + " 0.8\n", + " 1.3\n", + " \n", + " \n", + " X_bot\n", + " -0.4\n", + " -0.2\n", + " -0.0\n", + " 0.1\n", + " 0.2\n", " \n", " \n", " annabot\n", - " -3.6\n", + " -3.5\n", " -2.3\n", " -0.4\n", " 1.2\n", - " 1.9\n", + " 2.1\n", " \n", " \n", " bean_bot\n", - " -3.0\n", - " -2.1\n", - " -0.4\n", - " 1.2\n", - " 2.0\n", + " -3.2\n", + " -2.3\n", + " -0.5\n", + " 1.1\n", + " 1.8\n", " \n", " \n", " KevinTestBot\n", - " -3.8\n", - " -2.7\n", - " -0.5\n", + " -4.1\n", + " -2.8\n", + " -0.6\n", " 1.6\n", - " 2.5\n", + " 2.7\n", " \n", " \n", " CatrachoCaster\n", - " -2.4\n", + " -2.2\n", " -1.7\n", " -0.8\n", " 0.2\n", - " 0.8\n", + " 0.7\n", " \n", " \n", " jonahsingerbot\n", " -3.0\n", " -2.2\n", " -0.8\n", - " 0.4\n", + " 0.5\n", " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.6\n", + " -3.5\n", " -2.7\n", " -0.9\n", - " 0.8\n", - " 1.6\n", + " 0.7\n", + " 1.7\n", " \n", " \n", " ProfessorSP\n", - " -4.5\n", - " -3.4\n", + " -4.6\n", + " -3.3\n", " -1.0\n", - " 1.0\n", + " 1.1\n", " 2.1\n", " \n", " \n", - " metac-grok-2-1212\n", - " -6.5\n", - " -4.7\n", - " -1.4\n", - " 1.8\n", - " 3.3\n", + " mmBot\n", + " -7.5\n", + " -5.4\n", + " -1.5\n", + " 2.4\n", + " 4.7\n", " \n", " \n", - " mmBot\n", - " -7.1\n", - " -5.2\n", - " -1.6\n", - " 2.2\n", - " 4.1\n", + " metac-grok-2-1212\n", + " -6.6\n", + " -4.8\n", + " -1.5\n", + " 1.9\n", + " 3.6\n", " \n", " \n", " 4Shadower\n", - " -4.7\n", + " -4.6\n", " -3.6\n", " -1.6\n", - " 0.3\n", + " 0.2\n", " 1.2\n", " \n", " \n", @@ -8592,112 +9057,112 @@ " -5.2\n", " -4.0\n", " -1.9\n", - " -0.1\n", - " 0.7\n", + " -0.2\n", + " 0.5\n", " \n", " \n", - " RPM_bot\n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.2\n", " -4.9\n", - " -3.9\n", " -2.0\n", - " -0.7\n", - " -0.1\n", + " 0.9\n", + " 2.4\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.5\n", - " -5.0\n", + " RPM_bot\n", + " -4.9\n", + " -3.8\n", " -2.1\n", - " 0.9\n", - " 2.4\n", + " -0.7\n", + " -0.2\n", " \n", " \n", " InstitutPelFutur\n", - " -9.2\n", - " -6.7\n", - " -2.5\n", - " 1.8\n", - " 3.6\n", + " -9.1\n", + " -6.4\n", + " -2.4\n", + " 1.7\n", + " 4.0\n", " \n", " \n", - " metac-Llama-3.1\n", - " -6.6\n", - " -5.5\n", - " -2.5\n", - " 0.2\n", - " 1.4\n", + " wunderplumb\n", + " -6.2\n", + " -4.9\n", + " -2.4\n", + " -0.2\n", + " 1.1\n", " \n", " \n", - " wunderplumb\n", - " -6.3\n", - " -5.2\n", - " -2.6\n", - " -0.3\n", - " 1.0\n", + " metac-Llama-3.1\n", + " -6.8\n", + " -5.3\n", + " -2.7\n", + " 0.0\n", + " 1.5\n", " \n", " \n", " NextWorldLab\n", - " -8.3\n", - " -6.7\n", + " -8.8\n", + " -6.8\n", " -3.4\n", - " -0.4\n", - " 1.2\n", - " \n", - " \n", - " laylaps\n", - " -9.9\n", - " -7.7\n", - " -3.8\n", - " -0.1\n", - " 2.2\n", + " -0.3\n", + " 1.5\n", " \n", " \n", " Bot_Pepa\n", " -7.0\n", - " -6.0\n", + " -5.9\n", " -3.9\n", - " -1.8\n", - " -0.9\n", + " -2.0\n", + " -1.1\n", + " \n", + " \n", + " laylaps\n", + " -10.1\n", + " -7.9\n", + " -4.0\n", + " -0.1\n", + " 2.1\n", " \n", " \n", " VeritasAI\n", - " -7.8\n", - " -6.6\n", - " -4.3\n", - " -1.9\n", - " -0.4\n", + " -8.0\n", + " -6.8\n", + " -4.4\n", + " -2.0\n", + " -0.7\n", " \n", " \n", " minefrac1\n", - " -8.0\n", - " -6.7\n", + " -7.9\n", + " -6.8\n", " -4.6\n", - " -2.5\n", - " -1.3\n", + " -2.7\n", + " -1.5\n", " \n", " \n", " Grizeu_Bot\n", - " -8.8\n", - " -7.6\n", + " -9.3\n", + " -7.7\n", " -5.1\n", - " -2.4\n", - " -0.9\n", + " -2.5\n", + " -1.0\n", " \n", " \n", " metac-gpt-4o\n", - " -10.6\n", + " -10.4\n", " -9.0\n", - " -5.8\n", - " -2.9\n", + " -6.1\n", + " -3.0\n", " -1.4\n", " \n", " \n", " ajf-bot\n", " -15.0\n", - " -13.0\n", - " -8.6\n", - " -4.4\n", - " -2.0\n", + " -12.6\n", + " -8.4\n", + " -4.2\n", + " -2.2\n", " \n", " \n", "\n", @@ -8705,51 +9170,51 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.0 7.2 9.6 12.0 13.1\n", - "metac-o1-preview 3.9 5.2 8.3 11.2 12.6\n", - "manticAI -0.2 2.1 5.5 8.7 10.4\n", - "metac-Gemini-Exp-1206 0.9 2.3 5.1 7.8 9.1\n", - "acm_bot 0.3 1.9 4.5 7.5 8.8\n", - "metac-perplexity -1.7 0.5 4.1 7.7 9.9\n", - "twsummerbot 0.3 1.4 3.9 6.1 7.5\n", - "GreeneiBot2 -1.0 0.7 3.8 7.2 8.8\n", - "cookics_bot_TEST 0.0 0.9 3.1 5.0 6.2\n", - "pgodzinai -3.1 -1.1 2.8 6.9 8.7\n", - "CumulativeBot -0.2 0.8 2.6 4.4 5.4\n", - "metac-claude-3-5-sonnet-latest -1.3 0.1 2.6 4.9 6.2\n", - "SynapseSeer 0.4 1.1 2.5 4.0 4.9\n", - "jkraybill_bot -3.5 -1.6 1.7 4.8 6.4\n", - "metac-exa -5.2 -2.7 1.7 5.4 7.6\n", - "metac-deepseek-r1 -1.9 -0.6 1.5 3.6 4.9\n", - "MWG -1.6 -0.9 0.7 2.0 2.7\n", - "andrewsiah -1.0 -0.6 -0.0 0.6 1.0\n", + "metac-o1 6.0 7.2 9.6 11.9 13.1\n", + "metac-o1-preview 3.7 5.2 8.3 11.2 12.8\n", + "manticAI 0.2 2.1 5.5 8.8 10.5\n", + "metac-Gemini-Exp-1206 0.4 1.9 4.9 7.5 8.9\n", + "acm_bot 0.2 1.8 4.7 7.7 9.1\n", + "metac-perplexity -2.2 0.0 4.3 7.8 9.9\n", + "GreeneiBot2 -1.2 0.4 3.9 7.0 8.7\n", + "twsummerbot 0.3 1.5 3.9 6.1 7.4\n", + "pgodzinai -3.4 -1.2 3.2 7.3 9.6\n", + "cookics_bot_TEST -0.2 0.8 2.9 5.0 5.8\n", + "CumulativeBot -0.1 0.9 2.7 4.6 5.4\n", + "SynapseSeer 0.4 1.1 2.6 4.1 4.8\n", + "metac-claude-3-5-sonnet-latest -1.3 -0.0 2.5 5.0 6.2\n", + "jkraybill_bot -3.5 -1.7 1.7 5.0 6.4\n", + "metac-exa -4.8 -2.2 1.7 5.6 7.8\n", + "metac-deepseek-r1 -2.0 -0.8 1.3 3.4 4.6\n", + "MWG -1.6 -0.8 0.7 2.1 2.8\n", + "andrewsiah -0.8 -0.6 -0.0 0.6 0.9\n", + "pianobot -1.2 -0.8 -0.0 0.7 1.0\n", + "cobyj-bot -1.5 -0.9 -0.0 0.8 1.3\n", "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "pianobot -1.2 -0.8 -0.0 0.7 1.1\n", - "cobyj-bot -1.5 -0.9 -0.1 0.9 1.4\n", - "annabot -3.6 -2.3 -0.4 1.2 1.9\n", - "bean_bot -3.0 -2.1 -0.4 1.2 2.0\n", - "KevinTestBot -3.8 -2.7 -0.5 1.6 2.5\n", - "CatrachoCaster -2.4 -1.7 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", - "krm-bot -3.6 -2.7 -0.9 0.8 1.6\n", - "ProfessorSP -4.5 -3.4 -1.0 1.0 2.1\n", - "metac-grok-2-1212 -6.5 -4.7 -1.4 1.8 3.3\n", - "mmBot -7.1 -5.2 -1.6 2.2 4.1\n", - "4Shadower -4.7 -3.6 -1.6 0.3 1.2\n", - "swingswish -5.2 -4.0 -1.9 -0.1 0.7\n", - "RPM_bot -4.9 -3.9 -2.0 -0.7 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -6.5 -5.0 -2.1 0.9 2.4\n", - "InstitutPelFutur -9.2 -6.7 -2.5 1.8 3.6\n", - "metac-Llama-3.1 -6.6 -5.5 -2.5 0.2 1.4\n", - "wunderplumb -6.3 -5.2 -2.6 -0.3 1.0\n", - "NextWorldLab -8.3 -6.7 -3.4 -0.4 1.2\n", - "laylaps -9.9 -7.7 -3.8 -0.1 2.2\n", - "Bot_Pepa -7.0 -6.0 -3.9 -1.8 -0.9\n", - "VeritasAI -7.8 -6.6 -4.3 -1.9 -0.4\n", - "minefrac1 -8.0 -6.7 -4.6 -2.5 -1.3\n", - "Grizeu_Bot -8.8 -7.6 -5.1 -2.4 -0.9\n", - "metac-gpt-4o -10.6 -9.0 -5.8 -2.9 -1.4\n", - "ajf-bot -15.0 -13.0 -8.6 -4.4 -2.0" + "annabot -3.5 -2.3 -0.4 1.2 2.1\n", + "bean_bot -3.2 -2.3 -0.5 1.1 1.8\n", + "KevinTestBot -4.1 -2.8 -0.6 1.6 2.7\n", + "CatrachoCaster -2.2 -1.7 -0.8 0.2 0.7\n", + "jonahsingerbot -3.0 -2.2 -0.8 0.5 1.0\n", + "krm-bot -3.5 -2.7 -0.9 0.7 1.7\n", + "ProfessorSP -4.6 -3.3 -1.0 1.1 2.1\n", + "mmBot -7.5 -5.4 -1.5 2.4 4.7\n", + "metac-grok-2-1212 -6.6 -4.8 -1.5 1.9 3.6\n", + "4Shadower -4.6 -3.6 -1.6 0.2 1.2\n", + "swingswish -5.2 -4.0 -1.9 -0.2 0.5\n", + "metac-claude-3-5-sonnet-20240620 -6.2 -4.9 -2.0 0.9 2.4\n", + "RPM_bot -4.9 -3.8 -2.1 -0.7 -0.2\n", + "InstitutPelFutur -9.1 -6.4 -2.4 1.7 4.0\n", + "wunderplumb -6.2 -4.9 -2.4 -0.2 1.1\n", + "metac-Llama-3.1 -6.8 -5.3 -2.7 0.0 1.5\n", + "NextWorldLab -8.8 -6.8 -3.4 -0.3 1.5\n", + "Bot_Pepa -7.0 -5.9 -3.9 -2.0 -1.1\n", + "laylaps -10.1 -7.9 -4.0 -0.1 2.1\n", + "VeritasAI -8.0 -6.8 -4.4 -2.0 -0.7\n", + "minefrac1 -7.9 -6.8 -4.6 -2.7 -1.5\n", + "Grizeu_Bot -9.3 -7.7 -5.1 -2.5 -1.0\n", + "metac-gpt-4o -10.4 -9.0 -6.1 -3.0 -1.4\n", + "ajf-bot -15.0 -12.6 -8.4 -4.2 -2.2" ] }, "execution_count": 50, @@ -8828,372 +9293,372 @@ " \n", " \n", " \n", - " Grizeu_Bot\n", - " -9.7\n", - " -5.4\n", - " 4.4\n", - " 15.9\n", - " 22.2\n", + " metac-o1\n", + " 21.0\n", + " 21.0\n", + " 21.0\n", + " 21.0\n", + " 21.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", - " 0.3\n", - " 1.4\n", - " 2.8\n", - " 3.7\n", + " metac-perplexity\n", + " 20.3\n", + " 20.3\n", + " 20.3\n", + " 20.3\n", + " 20.3\n", " \n", " \n", - " X_bot\n", - " -0.4\n", - " -0.3\n", - " 0.2\n", - " 0.7\n", - " 1.2\n", + " bot_median\n", + " 17.9\n", + " 17.9\n", + " 17.9\n", + " 17.9\n", + " 17.9\n", " \n", " \n", - " andrewsiah\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " acm_bot\n", + " 17.7\n", + " 17.7\n", + " 17.7\n", + " 17.7\n", + " 17.7\n", " \n", " \n", - " cobyj-bot\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " manticAI\n", + " 14.5\n", + " 14.5\n", + " 14.5\n", + " 14.5\n", + " 14.5\n", " \n", " \n", - " acm_bot\n", - " -16.3\n", - " -11.3\n", - " -0.2\n", - " 14.8\n", - " 22.5\n", + " twsummerbot\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", " \n", " \n", - " jonahsingerbot\n", - " -1.4\n", - " -1.1\n", - " -0.6\n", - " -0.3\n", - " -0.1\n", + " jkraybill_bot\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", " \n", " \n", - " bean_bot\n", - " -1.6\n", - " -1.3\n", - " -0.7\n", - " -0.3\n", - " -0.1\n", + " metac-claude-3-5-sonnet-20240620\n", + " 12.0\n", + " 12.0\n", + " 12.0\n", + " 12.0\n", + " 12.0\n", " \n", " \n", - " CumulativeBot\n", - " -2.9\n", - " -2.3\n", - " -1.0\n", - " 0.2\n", - " 1.0\n", + " GreeneiBot2\n", + " 11.7\n", + " 11.7\n", + " 11.7\n", + " 11.7\n", + " 11.7\n", " \n", " \n", - " swingswish\n", - " -2.4\n", - " -1.9\n", - " -1.1\n", - " -0.5\n", - " -0.3\n", + " metac-claude-3-5-sonnet-latest\n", + " 11.5\n", + " 11.5\n", + " 11.5\n", + " 11.5\n", + " 11.5\n", " \n", " \n", - " jkraybill_bot\n", - " -8.5\n", - " -6.2\n", - " -1.1\n", - " 4.6\n", - " 7.5\n", + " NextWorldLab\n", + " 11.1\n", + " 11.1\n", + " 11.1\n", + " 11.1\n", + " 11.1\n", " \n", " \n", - " KevinTestBot\n", - " -5.8\n", - " -3.9\n", - " -1.4\n", - " 0.4\n", - " 1.1\n", + " metac-grok-2-1212\n", + " 11.0\n", + " 11.0\n", + " 11.0\n", + " 11.0\n", + " 11.0\n", " \n", " \n", - " SynapseSeer\n", - " -6.3\n", - " -4.6\n", - " -1.5\n", - " 1.9\n", - " 3.9\n", + " metac-gpt-4o\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", " \n", " \n", - " pianobot\n", - " -8.0\n", - " -5.9\n", - " -2.6\n", - " -0.2\n", - " 0.1\n", + " metac-Llama-3.1\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", " \n", " \n", - " twsummerbot\n", - " -13.4\n", - " -10.3\n", - " -2.9\n", - " 4.6\n", - " 9.2\n", + " Grizeu_Bot\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", " \n", " \n", - " CatrachoCaster\n", - " -8.6\n", - " -6.8\n", - " -3.4\n", - " -0.3\n", - " 1.1\n", + " SynapseSeer\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", + " 10.2\n", " \n", " \n", - " annabot\n", - " -8.4\n", - " -6.5\n", - " -3.4\n", - " -0.6\n", - " 0.9\n", + " metac-o1-preview\n", + " 10.1\n", + " 10.1\n", + " 10.1\n", + " 10.1\n", + " 10.1\n", " \n", " \n", - " cookics_bot_TEST\n", - " -12.1\n", - " -9.7\n", - " -4.2\n", - " 0.1\n", - " 2.1\n", + " mmBot\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", " \n", " \n", - " GreeneiBot2\n", - " -17.4\n", - " -13.2\n", - " -4.9\n", - " 3.6\n", - " 7.4\n", + " metac-exa\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", + " 9.7\n", " \n", " \n", - " krm-bot\n", - " -10.6\n", - " -8.6\n", - " -5.3\n", - " -2.6\n", - " -1.6\n", + " annabot\n", + " 9.0\n", + " 9.0\n", + " 9.0\n", + " 9.0\n", + " 9.0\n", " \n", " \n", - " 4Shadower\n", - " -12.8\n", - " -9.8\n", - " -5.3\n", - " -1.8\n", - " -1.1\n", + " metac-deepseek-r1\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", " \n", " \n", - " metac-o1\n", - " -22.7\n", - " -18.5\n", - " -6.7\n", - " 8.5\n", - " 16.1\n", + " VeritasAI\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", + " 8.4\n", " \n", " \n", - " MWG\n", - " -18.3\n", - " -14.9\n", - " -8.3\n", - " -2.2\n", - " 1.3\n", + " laylaps\n", + " 7.6\n", + " 7.6\n", + " 7.6\n", + " 7.6\n", + " 7.6\n", " \n", " \n", - " ajf-bot\n", - " -22.3\n", - " -17.2\n", - " -8.8\n", - " -1.4\n", - " 2.5\n", + " cookics_bot_TEST\n", + " 6.4\n", + " 6.4\n", + " 6.4\n", + " 6.4\n", + " 6.4\n", " \n", " \n", - " bot_median\n", - " -22.7\n", - " -18.3\n", - " -9.0\n", - " 2.1\n", - " 8.9\n", + " metac-Gemini-Exp-1206\n", + " 5.8\n", + " 5.8\n", + " 5.8\n", + " 5.8\n", + " 5.8\n", " \n", " \n", - " Bot_Pepa\n", - " -20.9\n", - " -16.3\n", - " -9.0\n", - " -1.2\n", - " 2.7\n", + " MWG\n", + " 5.5\n", + " 5.5\n", + " 5.5\n", + " 5.5\n", + " 5.5\n", " \n", " \n", - " manticAI\n", - " -22.1\n", - " -17.7\n", - " -9.5\n", - " -0.7\n", - " 4.9\n", + " ajf-bot\n", + " 5.1\n", + " 5.1\n", + " 5.1\n", + " 5.1\n", + " 5.1\n", " \n", " \n", - " ProfessorSP\n", - " -20.7\n", - " -16.8\n", - " -10.1\n", - " -4.7\n", - " -2.4\n", + " pgodzinai\n", + " 3.5\n", + " 3.5\n", + " 3.5\n", + " 3.5\n", + " 3.5\n", " \n", " \n", - " wunderplumb\n", - " -22.4\n", - " -19.1\n", - " -12.0\n", - " -5.8\n", - " -3.3\n", + " KevinTestBot\n", + " 3.3\n", + " 3.3\n", + " 3.3\n", + " 3.3\n", + " 3.3\n", " \n", " \n", - " metac-perplexity\n", - " -29.1\n", - " -24.0\n", - " -12.0\n", - " 0.8\n", - " 8.0\n", + " InstitutPelFutur\n", + " 2.7\n", + " 2.7\n", + " 2.7\n", + " 2.7\n", + " 2.7\n", " \n", " \n", - " laylaps\n", - " -21.0\n", - " -17.8\n", - " -12.8\n", - " -8.1\n", - " -5.8\n", + " Bot_Pepa\n", + " 2.6\n", + " 2.6\n", + " 2.6\n", + " 2.6\n", + " 2.6\n", " \n", " \n", - " NextWorldLab\n", - " -28.4\n", - " -24.0\n", - " -13.6\n", - " -2.8\n", - " 4.0\n", + " CumulativeBot\n", + " 2.5\n", + " 2.5\n", + " 2.5\n", + " 2.5\n", + " 2.5\n", " \n", " \n", - " pgodzinai\n", - " -31.7\n", - " -25.6\n", - " -14.0\n", - " -4.1\n", - " 1.9\n", + " swingswish\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " -28.1\n", - " -23.3\n", - " -14.0\n", - " -2.7\n", - " 3.2\n", + " wunderplumb\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", + " 2.4\n", " \n", " \n", - " metac-deepseek-r1\n", - " -30.7\n", - " -25.2\n", - " -14.6\n", - " -4.9\n", - " 0.5\n", + " jonahsingerbot\n", + " 2.2\n", + " 2.2\n", + " 2.2\n", + " 2.2\n", + " 2.2\n", " \n", " \n", - " minefrac1\n", - " -29.8\n", - " -24.8\n", - " -14.9\n", - " -3.1\n", - " 4.1\n", + " bean_bot\n", + " 2.1\n", + " 2.1\n", + " 2.1\n", + " 2.1\n", + " 2.1\n", " \n", " \n", - " metac-Llama-3.1\n", - " -32.9\n", - " -26.8\n", - " -15.1\n", - " -3.3\n", - " 3.2\n", + " X_bot\n", + " 1.9\n", + " 1.9\n", + " 1.9\n", + " 1.9\n", + " 1.9\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -32.6\n", - " -26.6\n", - " -15.9\n", - " -3.5\n", - " 3.2\n", + " CatrachoCaster\n", + " 1.8\n", + " 1.8\n", + " 1.8\n", + " 1.8\n", + " 1.8\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -35.3\n", - " -29.9\n", - " -18.2\n", - " -4.3\n", - " 2.8\n", + " 4Shadower\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", " \n", " \n", - " metac-o1-preview\n", - " -38.9\n", - " -32.4\n", - " -19.3\n", - " -6.9\n", - " 0.3\n", + " krm-bot\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", " \n", " \n", - " mmBot\n", - " -36.2\n", - " -30.9\n", - " -21.1\n", - " -11.7\n", - " -7.1\n", + " RPM_bot\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", + " 0.6\n", " \n", " \n", - " VeritasAI\n", - " -33.5\n", - " -28.9\n", - " -21.3\n", - " -14.4\n", - " -11.1\n", + " andrewsiah\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " metac-grok-2-1212\n", - " -41.8\n", - " -35.2\n", - " -23.4\n", - " -10.4\n", - " -3.8\n", + " cobyj-bot\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " metac-exa\n", - " -40.4\n", - " -34.4\n", - " -23.4\n", - " -13.8\n", - " -7.9\n", + " pianobot\n", + " -2.2\n", + " -2.2\n", + " -2.2\n", + " -2.2\n", + " -2.2\n", " \n", " \n", - " metac-gpt-4o\n", - " -41.7\n", - " -34.7\n", - " -23.8\n", - " -11.3\n", - " -5.3\n", + " ProfessorSP\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", " \n", " \n", - " InstitutPelFutur\n", - " -43.6\n", - " -37.9\n", - " -26.5\n", - " -14.9\n", - " -6.6\n", + " minefrac1\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", + " -3.0\n", " \n", " \n", "\n", @@ -9201,52 +9666,52 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "Grizeu_Bot -9.7 -5.4 4.4 15.9 22.2\n", - "RPM_bot -0.1 0.3 1.4 2.8 3.7\n", - "X_bot -0.4 -0.3 0.2 0.7 1.2\n", + "metac-o1 21.0 21.0 21.0 21.0 21.0\n", + "metac-perplexity 20.3 20.3 20.3 20.3 20.3\n", + "bot_median 17.9 17.9 17.9 17.9 17.9\n", + "acm_bot 17.7 17.7 17.7 17.7 17.7\n", + "manticAI 14.5 14.5 14.5 14.5 14.5\n", + "twsummerbot 14.3 14.3 14.3 14.3 14.3\n", + "jkraybill_bot 14.3 14.3 14.3 14.3 14.3\n", + "metac-claude-3-5-sonnet-20240620 12.0 12.0 12.0 12.0 12.0\n", + "GreeneiBot2 11.7 11.7 11.7 11.7 11.7\n", + "metac-claude-3-5-sonnet-latest 11.5 11.5 11.5 11.5 11.5\n", + "NextWorldLab 11.1 11.1 11.1 11.1 11.1\n", + "metac-grok-2-1212 11.0 11.0 11.0 11.0 11.0\n", + "metac-gpt-4o 10.5 10.5 10.5 10.5 10.5\n", + "metac-Llama-3.1 10.2 10.2 10.2 10.2 10.2\n", + "Grizeu_Bot 10.2 10.2 10.2 10.2 10.2\n", + "SynapseSeer 10.2 10.2 10.2 10.2 10.2\n", + "metac-o1-preview 10.1 10.1 10.1 10.1 10.1\n", + "mmBot 9.7 9.7 9.7 9.7 9.7\n", + "metac-exa 9.7 9.7 9.7 9.7 9.7\n", + "annabot 9.0 9.0 9.0 9.0 9.0\n", + "metac-deepseek-r1 8.4 8.4 8.4 8.4 8.4\n", + "VeritasAI 8.4 8.4 8.4 8.4 8.4\n", + "laylaps 7.6 7.6 7.6 7.6 7.6\n", + "cookics_bot_TEST 6.4 6.4 6.4 6.4 6.4\n", + "metac-Gemini-Exp-1206 5.8 5.8 5.8 5.8 5.8\n", + "MWG 5.5 5.5 5.5 5.5 5.5\n", + "ajf-bot 5.1 5.1 5.1 5.1 5.1\n", + "pgodzinai 3.5 3.5 3.5 3.5 3.5\n", + "KevinTestBot 3.3 3.3 3.3 3.3 3.3\n", + "InstitutPelFutur 2.7 2.7 2.7 2.7 2.7\n", + "Bot_Pepa 2.6 2.6 2.6 2.6 2.6\n", + "CumulativeBot 2.5 2.5 2.5 2.5 2.5\n", + "swingswish 2.4 2.4 2.4 2.4 2.4\n", + "wunderplumb 2.4 2.4 2.4 2.4 2.4\n", + "jonahsingerbot 2.2 2.2 2.2 2.2 2.2\n", + "bean_bot 2.1 2.1 2.1 2.1 2.1\n", + "X_bot 1.9 1.9 1.9 1.9 1.9\n", + "CatrachoCaster 1.8 1.8 1.8 1.8 1.8\n", + "4Shadower 0.6 0.6 0.6 0.6 0.6\n", + "krm-bot 0.6 0.6 0.6 0.6 0.6\n", + "RPM_bot 0.6 0.6 0.6 0.6 0.6\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", - "acm_bot -16.3 -11.3 -0.2 14.8 22.5\n", - "jonahsingerbot -1.4 -1.1 -0.6 -0.3 -0.1\n", - "bean_bot -1.6 -1.3 -0.7 -0.3 -0.1\n", - "CumulativeBot -2.9 -2.3 -1.0 0.2 1.0\n", - "swingswish -2.4 -1.9 -1.1 -0.5 -0.3\n", - "jkraybill_bot -8.5 -6.2 -1.1 4.6 7.5\n", - "KevinTestBot -5.8 -3.9 -1.4 0.4 1.1\n", - "SynapseSeer -6.3 -4.6 -1.5 1.9 3.9\n", - "pianobot -8.0 -5.9 -2.6 -0.2 0.1\n", - "twsummerbot -13.4 -10.3 -2.9 4.6 9.2\n", - "CatrachoCaster -8.6 -6.8 -3.4 -0.3 1.1\n", - "annabot -8.4 -6.5 -3.4 -0.6 0.9\n", - "cookics_bot_TEST -12.1 -9.7 -4.2 0.1 2.1\n", - "GreeneiBot2 -17.4 -13.2 -4.9 3.6 7.4\n", - "krm-bot -10.6 -8.6 -5.3 -2.6 -1.6\n", - "4Shadower -12.8 -9.8 -5.3 -1.8 -1.1\n", - "metac-o1 -22.7 -18.5 -6.7 8.5 16.1\n", - "MWG -18.3 -14.9 -8.3 -2.2 1.3\n", - "ajf-bot -22.3 -17.2 -8.8 -1.4 2.5\n", - "bot_median -22.7 -18.3 -9.0 2.1 8.9\n", - "Bot_Pepa -20.9 -16.3 -9.0 -1.2 2.7\n", - "manticAI -22.1 -17.7 -9.5 -0.7 4.9\n", - "ProfessorSP -20.7 -16.8 -10.1 -4.7 -2.4\n", - "wunderplumb -22.4 -19.1 -12.0 -5.8 -3.3\n", - "metac-perplexity -29.1 -24.0 -12.0 0.8 8.0\n", - "laylaps -21.0 -17.8 -12.8 -8.1 -5.8\n", - "NextWorldLab -28.4 -24.0 -13.6 -2.8 4.0\n", - "pgodzinai -31.7 -25.6 -14.0 -4.1 1.9\n", - "metac-Gemini-Exp-1206 -28.1 -23.3 -14.0 -2.7 3.2\n", - "metac-deepseek-r1 -30.7 -25.2 -14.6 -4.9 0.5\n", - "minefrac1 -29.8 -24.8 -14.9 -3.1 4.1\n", - "metac-Llama-3.1 -32.9 -26.8 -15.1 -3.3 3.2\n", - "metac-claude-3-5-sonnet-latest -32.6 -26.6 -15.9 -3.5 3.2\n", - "metac-claude-3-5-sonnet-20240620 -35.3 -29.9 -18.2 -4.3 2.8\n", - "metac-o1-preview -38.9 -32.4 -19.3 -6.9 0.3\n", - "mmBot -36.2 -30.9 -21.1 -11.7 -7.1\n", - "VeritasAI -33.5 -28.9 -21.3 -14.4 -11.1\n", - "metac-grok-2-1212 -41.8 -35.2 -23.4 -10.4 -3.8\n", - "metac-exa -40.4 -34.4 -23.4 -13.8 -7.9\n", - "metac-gpt-4o -41.7 -34.7 -23.8 -11.3 -5.3\n", - "InstitutPelFutur -43.6 -37.9 -26.5 -14.9 -6.6" + "pianobot -2.2 -2.2 -2.2 -2.2 -2.2\n", + "ProfessorSP -3.0 -3.0 -3.0 -3.0 -3.0\n", + "minefrac1 -3.0 -3.0 -3.0 -3.0 -3.0" ] }, "execution_count": 51, @@ -9294,7 +9759,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -9909,505 +10374,511 @@ "output_type": "stream", "text": [ " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.65]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.35]\n", " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.02]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.3]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.98]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.01]\n", - " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.35]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.3]\n", " >>> Collected 1 forecasts: [0.75]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " >>> Collected 2 forecasts: [0.15, 0.1]\n", - " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.2, 0.7]\n", " >>> Collected 2 forecasts: [0.95, 0.9]\n", - " >>> Collected 2 forecasts: [0.75, 0.75]\n", + " >>> Collected 2 forecasts: [0.85, 0.75]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.7, 0.6]\n", - " >>> Collected 2 forecasts: [0.7, 0.35]\n", + " >>> Collected 2 forecasts: [0.65, 0.6]\n", + " >>> Collected 2 forecasts: [0.7, 0.3]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", " >>> Collected 2 forecasts: [0.15, 0.05]\n", - " >>> Collected 2 forecasts: [0.2, 0.25]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.7, 0.8]\n", - " >>> Collected 2 forecasts: [0.25, 0.35]\n", - " >>> Collected 2 forecasts: [0.1, 0.15]\n", + " >>> Collected 2 forecasts: [0.35, 0.85]\n", + " >>> Collected 2 forecasts: [0.25, 0.6]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", " >>> Collected 2 forecasts: [0.15, 0.25]\n", " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.02]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.25]\n", + " >>> Collected 2 forecasts: [0.02, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.02]\n", " >>> Collected 2 forecasts: [0.25, 0.3]\n", + " >>> Collected 2 forecasts: [0.3, 0.3]\n", " >>> Collected 2 forecasts: [0.15, 0.15]\n", - " >>> Collected 2 forecasts: [0.98, 0.97]\n", - " >>> Collected 2 forecasts: [0.35, 0.4]\n", - " >>> Collected 2 forecasts: [0.35, 0.25]\n", - " >>> Collected 2 forecasts: [0.85, 0.7]\n", - " >>> Collected 2 forecasts: [0.01, 0.02]\n", - " >>> Collected 2 forecasts: [0.85, 0.75]\n", - " >>> Collected 2 forecasts: [0.99, 0.85]\n", - " >>> Collected 2 forecasts: [0.2, 0.99]\n", - " >>> Collected 2 forecasts: [0.95, 0.25]\n", - " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.98, 0.98]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", + " >>> Collected 2 forecasts: [0.35, 0.3]\n", + " >>> Collected 2 forecasts: [0.3, 0.55]\n", + " >>> Collected 2 forecasts: [0.1, 0.02]\n", + " >>> Collected 2 forecasts: [0.8, 0.8]\n", + " >>> Collected 2 forecasts: [0.99, 0.99]\n", + " >>> Collected 2 forecasts: [0.99, 0.99]\n", + " >>> Collected 2 forecasts: [0.35, 0.1]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", " >>> Collected 2 forecasts: [0.9, 0.65]\n", " >>> Collected 2 forecasts: [0.35, 0.6]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.3, 0.3]\n", - " >>> Collected 2 forecasts: [0.75, 0.8]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.85, 0.85]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.3, 0.2]\n", + " >>> Collected 2 forecasts: [0.75, 0.85]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.15, 0.3]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.05]\n", - " >>> Collected 2 forecasts: [0.8, 0.9]\n", + " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.1, 0.03]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.2]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.85, 0.75]\n", - " >>> Collected 2 forecasts: [0.1, 0.07]\n", + " >>> Collected 2 forecasts: [0.9, 0.95]\n", + " >>> Collected 2 forecasts: [0.4, 0.35]\n", + " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", " >>> Collected 3 forecasts: [0.15, 0.1, 0.07]\n", - " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", + " >>> Collected 3 forecasts: [0.2, 0.7, 0.62]\n", " >>> Collected 3 forecasts: [0.95, 0.9, 0.82]\n", - " >>> Collected 3 forecasts: [0.75, 0.75, 0.85]\n", + " >>> Collected 3 forecasts: [0.85, 0.75, 0.85]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.35, nan]\n", + " >>> Collected 3 forecasts: [0.65, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.3, nan]\n", " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", " >>> Collected 3 forecasts: [0.15, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.25, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.25]\n", " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.35, 0.108]\n", - " >>> Collected 3 forecasts: [0.1, 0.15, 0.16]\n", + " >>> Collected 3 forecasts: [0.35, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.6, 0.108]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.16]\n", " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", " >>> Collected 3 forecasts: [0.15, 0.25, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.1, 0.02, 0.03]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.1, 0.25, 0.125]\n", + " >>> Collected 3 forecasts: [0.02, 0.05, 0.034]\n", + " >>> Collected 3 forecasts: [0.05, 0.02, 0.03]\n", " >>> Collected 3 forecasts: [0.25, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.3, 0.3, 0.35]\n", " >>> Collected 3 forecasts: [0.15, 0.15, 0.115]\n", - " >>> Collected 3 forecasts: [0.98, 0.97, 0.97]\n", - " >>> Collected 3 forecasts: [0.35, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.35, 0.25, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.85, 0.7, 0.17]\n", - " >>> Collected 3 forecasts: [0.01, 0.02, 0.12]\n", - " >>> Collected 3 forecasts: [0.85, 0.75, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.85, 0.99]\n", - " >>> Collected 3 forecasts: [0.2, 0.99, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.95, 0.25, 0.14]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.98, 0.98, 0.97]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", + " >>> Collected 3 forecasts: [0.35, 0.3, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.3, 0.55, 0.17]\n", + " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.8, 0.8, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", + " >>> Collected 3 forecasts: [0.99, 0.99, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.35, 0.1, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", " >>> Collected 3 forecasts: [0.9, 0.65, 0.7666666666666667]\n", " >>> Collected 3 forecasts: [0.35, 0.6, 0.875]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.3, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.75, 0.8, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.85, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.3, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.75, 0.85, 0.67]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.3925]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.086]\n", - " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", - " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", - " >>> Collected 3 forecasts: [0.9, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.75, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.07, 0.05]\n", + " >>> Collected 3 forecasts: [0.15, 0.15, 0.285]\n", + " >>> Collected 3 forecasts: [0.1, 0.03, 0.02]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", + " >>> Collected 3 forecasts: [0.4, 0.35, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", " >>> Collected 4 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.82, 0.794]\n", - " >>> Collected 4 forecasts: [0.75, 0.75, 0.85, 0.884]\n", + " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.75, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.35, nan, nan]\n", + " >>> Collected 4 forecasts: [0.65, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.3, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.25, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.25, nan]\n", " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.35, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.1, 0.15, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.35, 0.85, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.25, 0.6, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.25, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.15, 0.25, 0.15, 0.144]\n", " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.02, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.1, 0.25, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.02, 0.05, 0.034, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", " >>> Collected 4 forecasts: [0.15, 0.15, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.35, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.85, 0.7, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.85, 0.75, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.85, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.95, 0.25, 0.14, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.98, 0.98, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.3, 0.55, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.8, 0.8, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.3, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.8, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.3, 0.2, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.85, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.3925, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.9, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.75, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.1, 0.07, 0.05, 0.02]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.4, 0.35, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", " >>> Collected 5 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.35, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.65, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.3, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.25, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.25, nan, nan]\n", " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.35, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.15, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.85, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.6, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", " >>> Collected 5 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.85, 0.7, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.3, 0.55, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.8, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.3, 0.2, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.85, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.2, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.4, 0.35, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", " >>> Collected 6 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.35, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.65, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.3, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225]\n", " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15]\n", " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", " >>> Collected 6 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", " >>> Collected 7 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88]\n", - " >>> Collected 7 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85, 0.8]\n", + " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", + " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15]\n", " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075, 0.1]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", + " >>> Collected 7 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1]\n", + " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", " >>> Collected 7 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3, 0.1]\n", - " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.15]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.55]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725, 0.78]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 7 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65]\n", + " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", + " >>> Collected 7 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9]\n", + " >>> Collected 7 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", + " >>> Collected 7 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", " >>> Collected 8 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", - " >>> Collected 8 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85, 0.8, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 8 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", + " >>> Collected 8 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765]\n", + " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", " >>> Collected 8 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513]\n", - " >>> Collected 8 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3, 0.1, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.55, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", - " >>> Collected 8 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 8 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513]\n", + " >>> Collected 8 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95]\n", + " >>> Collected 8 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", + " >>> Collected 8 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", + " >>> Collected 8 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", " >>> Collected 9 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8]\n", - " >>> Collected 9 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.3]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.65]\n", " >>> Collected 9 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", - " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3, 0.1, 0.6485000000000001, 0.75]\n", - " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.55, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.8]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 9 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.95]\n", + " >>> Collected 9 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", " >>> Collected 10 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8, 0.638]\n", - " >>> Collected 10 forecasts: [0.75, 0.75, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.3, nan]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.75, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85, 0.546]\n", " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", - " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.75, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.25, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.35, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.65, 0.293]\n", " >>> Collected 10 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.85, 0.7, 0.17, 0.236, nan, 0.3, 0.1, 0.6485000000000001, 0.75, 0.155]\n", - " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.85, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.95, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.55, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.8, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.2, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.8, 0.126]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.07, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.95, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -10490,7 +10961,7 @@ " 0\n", " [0.02,0.7,0.2,0.07,0.01]\n", " 0.017463\n", - " 0.085\n", + " 0.1\n", " \n", " \n", " 1\n", @@ -10498,8 +10969,8 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", " \n", " \n", " 2\n", @@ -10524,9 +10995,9 @@ " numeric\n", " NaN\n", " 119.2\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", - " [0.0, 0.00161112178, 0.0032277004800000003, 0....\n", - " [0.0, 0.0017712494571428573, 0.0035463967, 0.0...\n", + " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", + " [0.0, 0.00318255036, 0.00637055762, 0.00956313...\n", + " [0.0, 0.0028936984428571426, 0.005791294657142...\n", " \n", " \n", " ...\n", @@ -10543,7 +11014,7 @@ " NaN\n", " yes\n", " 0.9\n", - " 0.9\n", + " 0.905\n", " 0.9025\n", " \n", " \n", @@ -10551,18 +11022,18 @@ " binary\n", " NaN\n", " no\n", - " 0.9\n", - " 0.2\n", - " 0.1335\n", + " 0.4\n", + " 0.35\n", + " 0.2085\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", + " 0.95\n", " 0.9\n", - " 0.85\n", - " 0.775\n", + " 0.772\n", " \n", " \n", " 361\n", @@ -10570,16 +11041,16 @@ " NaN\n", " no\n", " 0.85\n", - " 0.75\n", - " 0.73\n", + " 0.8\n", + " 0.755\n", " \n", " \n", " 364\n", " binary\n", " NaN\n", " no\n", - " 0.1\n", - " 0.052\n", + " 0.05\n", + " 0.05\n", " 0.046\n", " \n", " \n", @@ -10606,38 +11077,38 @@ "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.15 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", ".. ... \n", "342 0.9 \n", - "351 0.9 \n", - "355 0.9 \n", + "351 0.4 \n", + "355 0.95 \n", "361 0.85 \n", - "364 0.1 \n", + "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", "0 0.017463 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", "2 0.085 \n", "3 0.6 \n", - "4 [0.0, 0.00161112178, 0.0032277004800000003, 0.... \n", + "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", ".. ... \n", - "342 0.9 \n", - "351 0.2 \n", - "355 0.85 \n", - "361 0.75 \n", - "364 0.052 \n", + "342 0.905 \n", + "351 0.35 \n", + "355 0.9 \n", + "361 0.8 \n", + "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 0.085 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "0 0.1 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", "2 0.125 \n", "3 0.5125 \n", - "4 [0.0, 0.0017712494571428573, 0.0035463967, 0.0... \n", + "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", ".. ... \n", "342 0.9025 \n", - "351 0.1335 \n", - "355 0.775 \n", - "361 0.73 \n", + "351 0.2085 \n", + "355 0.772 \n", + "361 0.755 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -10712,52 +11183,52 @@ " \n", " 0\n", " 1\n", - " 16.68\n", + " 15.75\n", " \n", " \n", " 1\n", " 2\n", - " 26.29\n", + " 26.31\n", " \n", " \n", " 2\n", " 3\n", - " 28.21\n", + " 27.15\n", " \n", " \n", " 3\n", " 4\n", - " 26.98\n", + " 27.65\n", " \n", " \n", " 4\n", " 5\n", - " 27.65\n", + " 27.58\n", " \n", " \n", " 5\n", " 6\n", - " 26.39\n", + " 27.57\n", " \n", " \n", " 6\n", " 7\n", - " 26.89\n", + " 27.05\n", " \n", " \n", " 7\n", " 8\n", - " 27.15\n", + " 27.45\n", " \n", " \n", " 8\n", " 9\n", - " 27.29\n", + " 26.23\n", " \n", " \n", " 9\n", " 10\n", - " 26.71\n", + " 26.47\n", " \n", " \n", "\n", @@ -10765,16 +11236,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 16.68\n", - "1 2 26.29\n", - "2 3 28.21\n", - "3 4 26.98\n", - "4 5 27.65\n", - "5 6 26.39\n", - "6 7 26.89\n", - "7 8 27.15\n", - "8 9 27.29\n", - "9 10 26.71" + "0 1 15.75\n", + "1 2 26.31\n", + "2 3 27.15\n", + "3 4 27.65\n", + "4 5 27.58\n", + "5 6 27.57\n", + "6 7 27.05\n", + "7 8 27.45\n", + "8 9 26.23\n", + "9 10 26.47" ] }, "execution_count": 61, @@ -10814,7 +11285,7 @@ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2']" ] }, "execution_count": 62, @@ -10927,19 +11398,19 @@ " NaN\n", " NaN\n", " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.4,0.35,0.2,0.04,0.01]\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", " ...\n", " 0.02\n", - " 0.235\n", + " 0.21\n", " 0.02\n", " 0.017463\n", " 0.017463\n", " 0.02\n", - " 0.085\n", - " 0.085\n", - " 0.15\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.02\n", + " 0.02\n", " \n", " \n", " 1\n", @@ -10951,19 +11422,19 @@ " 60.0\n", " 100.0\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.03366666666666667, 0.0341314028, 0.03460208...\n", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", - " [0.041833333333333333, 0.042403191266666675, 0...\n", - " [0.041833333333333333, 0.042403191266666675, 0...\n", + " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", + " [0.03366666666666667, 0.03409436576666667, 0.0...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", + " [0.041833333333333333, 0.04238467275, 0.042938...\n", + " [0.041833333333333333, 0.04238467275, 0.042938...\n", " \n", " \n", " 2\n", @@ -11010,7 +11481,7 @@ " 0.55625\n", " 0.5125\n", " 0.5125\n", - " 0.53125\n", + " 0.55625\n", " 0.5125\n", " \n", " \n", @@ -11022,20 +11493,20 @@ " NaN\n", " 0.0\n", " 400.0\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", " ...\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", - " [0.0, 0.0017047194333333333, 0.0034148989, 0.0...\n", - " [0.0, 0.001733085025, 0.003470265075, 0.005210...\n", - " [0.0, 0.00161112178, 0.0032277004800000003, 0....\n", - " [0.0, 0.0016497910333333336, 0.003304129483333...\n", - " [0.0, 0.0017712494571428573, 0.0035463967, 0.0...\n", - " [0.0, 0.0017712494571428573, 0.0035463967, 0.0...\n", - " [0.0, 0.0019069861375000002, 0.003817382825, 0...\n", - " [0.0, 0.0018408706777777778, 0.003684772944444...\n", + " [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07...\n", + " [0.0, 0.00642857145, 0.01285714285, 0.01928571...\n", + " [0.0, 0.004323767066666667, 0.0086529941333333...\n", + " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", + " [0.0, 0.00318255036, 0.00637055762, 0.00956313...\n", + " [0.0, 0.00295931485, 0.0059231771, 0.008890847...\n", + " [0.0, 0.0028936984428571426, 0.005791294657142...\n", + " [0.0, 0.0028936984428571426, 0.005791294657142...\n", + " [0.0, 0.0028097639124999995, 0.005622938375, 0...\n", + " [0.0, 0.0026433398111111108, 0.005289711211111...\n", " \n", " \n", "\n", @@ -11062,14 +11533,14 @@ "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.15 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", "\n", " metac-o1 \\\n", - "0 [0.45,0.3,0.15,0.05,0.05] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", "2 0.1 \n", "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " pgodzinai ... \\\n", "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", @@ -11083,70 +11554,70 @@ "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", "2 0.15 \n", "3 0.6 \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", + "4 [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07... \n", "\n", " median_forecast_2_bots \\\n", - "0 0.235 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "0 0.21 \n", + "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", "2 0.125 \n", "3 0.6 \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", + "4 [0.0, 0.00642857145, 0.01285714285, 0.01928571... \n", "\n", " median_forecast_3_bots \\\n", "0 0.02 \n", - "1 [0.03366666666666667, 0.0341314028, 0.03460208... \n", + "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", "2 0.1 \n", "3 0.6 \n", - "4 [0.0, 0.0017047194333333333, 0.0034148989, 0.0... \n", + "4 [0.0, 0.004323767066666667, 0.0086529941333333... \n", "\n", " median_forecast_4_bots \\\n", "0 0.017463 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", "2 0.085 \n", "3 0.6 \n", - "4 [0.0, 0.001733085025, 0.003470265075, 0.005210... \n", + "4 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", "\n", " median_forecast_5_bots \\\n", "0 0.017463 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", "2 0.085 \n", "3 0.6 \n", - "4 [0.0, 0.00161112178, 0.0032277004800000003, 0.... \n", + "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", "\n", " median_forecast_6_bots \\\n", "0 0.02 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", "2 0.1 \n", "3 0.55625 \n", - "4 [0.0, 0.0016497910333333336, 0.003304129483333... \n", + "4 [0.0, 0.00295931485, 0.0059231771, 0.008890847... \n", "\n", " median_forecast_7_bots \\\n", - "0 0.085 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "0 0.1 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", "2 0.125 \n", "3 0.5125 \n", - "4 [0.0, 0.0017712494571428573, 0.0035463967, 0.0... \n", + "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", "\n", " median_forecast_8_bots \\\n", - "0 0.085 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "0 0.1 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", "2 0.125 \n", "3 0.5125 \n", - "4 [0.0, 0.0017712494571428573, 0.0035463967, 0.0... \n", + "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", "\n", " median_forecast_9_bots \\\n", - "0 0.15 \n", - "1 [0.041833333333333333, 0.042403191266666675, 0... \n", + "0 0.02 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", "2 0.15 \n", - "3 0.53125 \n", - "4 [0.0, 0.0019069861375000002, 0.003817382825, 0... \n", + "3 0.55625 \n", + "4 [0.0, 0.0028097639124999995, 0.005622938375, 0... \n", "\n", " median_forecast_10_bots \n", - "0 0.15 \n", - "1 [0.041833333333333333, 0.042403191266666675, 0... \n", + "0 0.02 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", "2 0.15 \n", "3 0.5125 \n", - "4 [0.0, 0.0018408706777777778, 0.003684772944444... \n", + "4 [0.0, 0.0026433398111111108, 0.005289711211111... \n", "\n", "[5 rows x 27 columns]" ] @@ -11210,14 +11681,14 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -14.9893\n" + "Weighted Total Score: -15.1905\n" ] } ], @@ -11227,7 +11698,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 68, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11239,7 +11710,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -11251,7 +11722,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -14.97\n" + "The average of 'head_to_head' is: -15.16\n" ] } ], @@ -11261,7 +11732,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 69, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11307,17 +11778,17 @@ " \n", " \n", " head_to_head\n", - " -1424.0\n", + " -1443.1\n", " 93.1\n", - " -15.3\n", - " 90.635958\n", - " 9.393462\n", - " -1.628277\n", + " -15.5\n", + " 86.181587\n", + " 8.931813\n", + " -1.735425\n", " 1.985277\n", - " 3.4\n", - " -33.9\n", - " 0.053441\n", - " 0.106882\n", + " 2.2\n", + " -33.2\n", + " 0.043005\n", + " 0.086010\n", " \n", " \n", "\n", @@ -11325,13 +11796,13 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -1424.0 93.1 -15.3 90.635958 9.393462 -1.628277 \n", + "head_to_head -1443.1 93.1 -15.5 86.181587 8.931813 -1.735425 \n", "\n", " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.985277 3.4 -33.9 0.053441 0.106882 " + "head_to_head 1.985277 2.2 -33.2 0.043005 0.086010 " ] }, - "execution_count": 70, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -11342,24 +11813,317 @@ "df_bot_team_h2h" ] }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0I0myCHpl7FT", + "outputId": "bcc45b9a-f328-4f0c-ef98-a7620af7e358" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 5:\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
titlebot_team_medianpro_medianresolutionhead_to_head
335How many cubic meters of water produced and su...[0.146083333325, 0.1540953797, 0.1622041748, 0...[0.0346238299,0.0364286012,0.0383259676,0.0403...130027.0-265.7
279What will Kalshi's rank in the iPhone Top Free...0.063[0.02,0.01,0.015,0.015,0.05,0.89]Not in top 50-264.8
121How many movies will be new on Netflix's top 1...0.14[0.005,0.017,0.157,0.821]3 or more-176.9
151How many earthquakes of magnitude ≥ 4 will hap...[0.0, 0.0032810261, 0.0065908451250000005, 0.0...[0.0,0.0158237002,0.0235315723,0.0279864362,0....0.0-157.3
47What will be Donald Trump's net worth, accordi...0.17[0.6,0.2,0.1,0.075,0.025]0-$6 billion, inclusive-126.1
\n", + "
" + ], + "text/plain": [ + " title \\\n", + "335 How many cubic meters of water produced and su... \n", + "279 What will Kalshi's rank in the iPhone Top Free... \n", + "121 How many movies will be new on Netflix's top 1... \n", + "151 How many earthquakes of magnitude ≥ 4 will hap... \n", + "47 What will be Donald Trump's net worth, accordi... \n", + "\n", + " bot_team_median \\\n", + "335 [0.146083333325, 0.1540953797, 0.1622041748, 0... \n", + "279 0.063 \n", + "121 0.14 \n", + "151 [0.0, 0.0032810261, 0.0065908451250000005, 0.0... \n", + "47 0.17 \n", + "\n", + " pro_median \\\n", + "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] \n", + "121 [0.005,0.017,0.157,0.821] \n", + "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... \n", + "47 [0.6,0.2,0.1,0.075,0.025] \n", + "\n", + " resolution head_to_head \n", + "335 130027.0 -265.7 \n", + "279 Not in top 50 -264.8 \n", + "121 3 or more -176.9 \n", + "151 0.0 -157.3 \n", + "47 0-$6 billion, inclusive -126.1 " + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.set_option('display.max_colwidth', 50)\n", + "\n", + "df_sorted = df_top_bot_pro_forecasts.sort_values(by='head_to_head')\n", + "df_sorted['head_to_head'] = df_sorted['head_to_head'].round(1)\n", + "#df_sorted['resolution'] = df_sorted['resolution'].map({1: 'yes', 0: 'no'})\n", + "\n", + "df_top5 = df_sorted.head(5)\n", + "df_bottom5 = df_sorted.tail(5)\n", + "\n", + "print(\"Top 5:\")\n", + "\n", + "df_top5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bottom 5:\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
titlebot_team_medianpro_medianresolutionhead_to_head
85Will Elon Musk attend the Super Bowl in 2025?0.16850.755no122.2
0For Q1 2025, how many banks will be listed on ...0.017463[0.001,0.62,0.35,0.019,0.01]0286.0
189What will the highest rank of metac-GPT4o or m...[0.0, 0.051569126225, 0.10695714615, 0.1599563...[0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...34.0491.5
211Will Nikola Corporation file for bankruptcy be...0.990.999annulledNaN
214Will the state of Rhode Island have any recrea...0.9720.95annulledNaN
\n", + "
" + ], + "text/plain": [ + " title \\\n", + "85 Will Elon Musk attend the Super Bowl in 2025? \n", + "0 For Q1 2025, how many banks will be listed on ... \n", + "189 What will the highest rank of metac-GPT4o or m... \n", + "211 Will Nikola Corporation file for bankruptcy be... \n", + "214 Will the state of Rhode Island have any recrea... \n", + "\n", + " bot_team_median \\\n", + "85 0.1685 \n", + "0 0.017463 \n", + "189 [0.0, 0.051569126225, 0.10695714615, 0.1599563... \n", + "211 0.99 \n", + "214 0.972 \n", + "\n", + " pro_median resolution \\\n", + "85 0.755 no \n", + "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", + "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", + "211 0.999 annulled \n", + "214 0.95 annulled \n", + "\n", + " head_to_head \n", + "85 122.2 \n", + "0 286.0 \n", + "189 491.5 \n", + "211 NaN \n", + "214 NaN " + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"\\nBottom 5:\")\n", + "\n", + "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bot_question_id Int64\n", + "title object\n", + "resolution float64\n", + "scheduled_close_time datetime64[ns]\n", + "actual_close_time datetime64[ns]\n", + "type object\n", + "options object\n", + "range_min float64\n", + "range_max float64\n", + "pro_question_id Int64\n", + "question_weight float64\n", + "bot_team_median object\n", + "pro_median object\n", + "head_to_head float64\n", + "weighted_score float64\n", + "dtype: object" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Cast df_top_bot_pro_forecasts['resolution'] as string - idk why this is necessary but it is\n", + "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].astype(pd.StringDtype())\n", + "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].map({'yes': 1, 'no': 0})\n", + "df_top_bot_pro_forecasts.dtypes" + ] + }, { "cell_type": "code", "execution_count": 73, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0I0myCHpl7FT", - "outputId": "bcc45b9a-f328-4f0c-ef98-a7620af7e358" - }, + "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 5:\n" - ] - }, { "data": { "text/html": [ @@ -11381,86 +12145,160 @@ " \n", " \n", " \n", + " bot_question_id\n", " title\n", + " resolution\n", + " scheduled_close_time\n", + " actual_close_time\n", + " type\n", + " options\n", + " range_min\n", + " range_max\n", + " pro_question_id\n", + " question_weight\n", " bot_team_median\n", " pro_median\n", - " resolution\n", " head_to_head\n", + " weighted_score\n", " \n", " \n", " \n", " \n", - " 279\n", - " What will Kalshi's rank in the iPhone Top Free...\n", - " 0.03\n", - " [0.02,0.01,0.015,0.015,0.05,0.89]\n", - " Not in top 50\n", - " -339.0\n", + " 0\n", + " 31262\n", + " For Q1 2025, how many banks will be listed on ...\n", + " NaN\n", + " 2025-01-20 03:27:00\n", + " 2025-01-20 03:27:00\n", + " multiple_choice\n", + " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", + " NaN\n", + " NaN\n", + " 31268\n", + " 1.0\n", + " 0.017463\n", + " [0.001,0.62,0.35,0.019,0.01]\n", + " 286.007699\n", + " 286.007699\n", " \n", " \n", - " 121\n", - " How many movies will be new on Netflix's top 1...\n", - " 0.1\n", - " [0.005,0.017,0.157,0.821]\n", - " 3 or more\n", - " -210.5\n", + " 1\n", + " 31263\n", + " What percentage of the vote will Alexander Luk...\n", + " NaN\n", + " 2025-01-20 03:27:00\n", + " 2025-01-20 03:27:00\n", + " numeric\n", + " NaN\n", + " 60.0\n", + " 100.0\n", + " 31269\n", + " 1.0\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", + " -76.357515\n", + " -76.357515\n", " \n", " \n", - " 335\n", - " How many cubic meters of water produced and su...\n", - " [0.12255555556666668, 0.1304049507, 0.13838334...\n", - " [0.0346238299,0.0364286012,0.0383259676,0.0403...\n", - " 130027.0\n", - " -158.7\n", + " 2\n", + " 31264\n", + " Will the bubble in the Magnificent Seven pop b...\n", + " 0.0\n", + " 2025-01-20 03:27:00\n", + " 2025-01-20 03:27:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 31270\n", + " 1.0\n", + " 0.085\n", + " 0.013\n", + " -7.574597\n", + " -7.574597\n", " \n", " \n", - " 12\n", - " What will be the monthly cargo volumes at the ...\n", - " [0.03366666666666667, 0.034913915633333334, 0....\n", - " [0.001714054,0.0017985406,0.0018846914,0.00197...\n", - " 720283.0\n", - " -130.3\n", + " 3\n", + " 31274\n", + " How many arms sales globally will the US State...\n", + " NaN\n", + " 2025-01-21 11:42:00\n", + " 2025-01-21 11:42:00\n", + " multiple_choice\n", + " [\"0-4\",\"5-9\",\">9\"]\n", + " NaN\n", + " NaN\n", + " 31280\n", + " 1.0\n", + " 0.6\n", + " [0.16,0.44,0.4]\n", + " 31.015493\n", + " 31.015493\n", " \n", " \n", - " 71\n", - " Will OpenAI, Anthropic, or Perplexity run an a...\n", - " 0.15\n", - " 0.55\n", - " yes\n", - " -129.9\n", + " 4\n", + " 31275\n", + " How much will it rain in Brasília, Brazil in F...\n", + " NaN\n", + " 2025-01-21 11:42:00\n", + " 2025-01-21 11:42:00\n", + " numeric\n", + " NaN\n", + " 0.0\n", + " 400.0\n", + " 31281\n", + " 1.0\n", + " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", + " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", + " 28.578581\n", + " 28.578581\n", " \n", " \n", "\n", "" ], "text/plain": [ - " title \\\n", - "279 What will Kalshi's rank in the iPhone Top Free... \n", - "121 How many movies will be new on Netflix's top 1... \n", - "335 How many cubic meters of water produced and su... \n", - "12 What will be the monthly cargo volumes at the ... \n", - "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", "\n", - " bot_team_median \\\n", - "279 0.03 \n", - "121 0.1 \n", - "335 [0.12255555556666668, 0.1304049507, 0.13838334... \n", - "12 [0.03366666666666667, 0.034913915633333334, 0.... \n", - "71 0.15 \n", - "\n", - " pro_median resolution \\\n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more \n", - "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... 130027.0 \n", - "12 [0.001714054,0.0017985406,0.0018846914,0.00197... 720283.0 \n", - "71 0.55 yes \n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", "\n", - " head_to_head \n", - "279 -339.0 \n", - "121 -210.5 \n", - "335 -158.7 \n", - "12 -130.3 \n", - "71 -129.9 " + " options range_min range_max pro_question_id \\\n", + "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", + "1 NaN 60.0 100.0 31269 \n", + "2 NaN NaN NaN 31270 \n", + "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", + "4 NaN 0.0 400.0 31281 \n", + "\n", + " question_weight bot_team_median \\\n", + "0 1.0 0.017463 \n", + "1 1.0 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 1.0 0.085 \n", + "3 1.0 0.6 \n", + "4 1.0 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "\n", + " pro_median head_to_head \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 286.007699 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -76.357515 \n", + "2 0.013 -7.574597 \n", + "3 [0.16,0.44,0.4] 31.015493 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 28.578581 \n", + "\n", + " weighted_score \n", + "0 286.007699 \n", + "1 -76.357515 \n", + "2 -7.574597 \n", + "3 31.015493 \n", + "4 28.578581 " ] }, "execution_count": 73, @@ -11469,33 +12307,92 @@ } ], "source": [ - "pd.set_option('display.max_colwidth', 50)\n", - "\n", - "df_sorted = df_top_bot_pro_forecasts.sort_values(by='head_to_head')\n", - "df_sorted['head_to_head'] = df_sorted['head_to_head'].round(1)\n", - "#df_sorted['resolution'] = df_sorted['resolution'].map({1: 'yes', 0: 'no'})\n", - "\n", - "df_top5 = df_sorted.head(5)\n", - "df_bottom5 = df_sorted.tail(5)\n", - "\n", - "print(\"Top 5:\")\n", - "\n", - "df_top5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + "df_top_bot_pro_forecasts.head()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, + "outputs": [], + "source": [ + "# Make binary-only df_top_bot_pro_forecasts for calibration curves etc\n", + "df_top_bot_pro_forecasts_binary = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['type'] == 'binary'].copy()\n", + "\n", + "df_top_bot_pro_forecasts_all_binary = df_top_bot_pro_forecasts_all[df_top_bot_pro_forecasts_all['type'] == 'binary'].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 807 + }, + "id": "BjNQ4IND6Ct7", + "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" + }, "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Bottom 5:\n" + "Number of pro forecasts: 50\n" ] - }, + } + ], + "source": [ + "# Set up the plot\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly calibrated')\n", + "\n", + "# Plot calibration curves for bot_team_median and pro_median\n", + "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'bot_team_median', 'Bot Team Median', 'blue')\n", + "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'pro_median', 'Pro Median', 'red')\n", + "\n", + "# Customize the plot\n", + "plt.xlabel('Assigned Probability', fontsize=12)\n", + "plt.ylabel('Fraction that Resolved \\'Yes\\'', fontsize=12)\n", + "plt.title(f'Calibration Curve: Bot Team Median vs Pro Median\\n(only overlap: {len(df_top_bot_pro_forecasts_binary)} questions)', fontsize=14)\n", + "plt.legend(fontsize=10)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Set axis limits\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# Map resolution to 0 and 1\n", + "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -11517,141 +12414,206 @@ " \n", " \n", " \n", + " bot_question_id\n", " title\n", + " resolution\n", + " scheduled_close_time\n", + " actual_close_time\n", + " type\n", + " options\n", + " range_min\n", + " range_max\n", + " pro_question_id\n", + " question_weight\n", " bot_team_median\n", " pro_median\n", - " resolution\n", - " head_to_head\n", " \n", " \n", " \n", " \n", - " 170\n", - " In its March update, will Similarweb report de...\n", - " 0.7\n", - " 0.144\n", - " yes\n", - " 158.1\n", + " 2\n", + " 31264\n", + " Will the bubble in the Magnificent Seven pop b...\n", + " 0.0\n", + " 2025-01-20 03:27:00\n", + " 2025-01-20 03:27:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 31270\n", + " 1.0\n", + " 0.085\n", + " 0.013\n", " \n", " \n", - " 0\n", - " For Q1 2025, how many banks will be listed on ...\n", - " 0.02\n", - " [0.001,0.62,0.35,0.019,0.01]\n", - " 0\n", - " 299.6\n", + " 5\n", + " 31276\n", + " Will the USDA-posted recall by Pork Dynasty In...\n", + " 1.0\n", + " 2025-01-21 11:42:00\n", + " 2025-01-21 11:42:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 31282\n", + " 1.0\n", + " 0.66\n", + " 0.45\n", " \n", " \n", - " 189\n", - " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.05003188076666667, 0.11135575903333333...\n", - " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", - " 34.0\n", - " 502.6\n", + " 8\n", + " 31288\n", + " Will Eric Adams be Mayor of New York City on t...\n", + " 1.0\n", + " 2025-01-22 20:19:00\n", + " 2025-01-22 20:19:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 31294\n", + " 1.0\n", + " 0.86\n", + " 0.95\n", " \n", " \n", - " 211\n", - " Will Nikola Corporation file for bankruptcy be...\n", - " 0.99\n", - " 0.999\n", - " annulled\n", + " 10\n", + " 31318\n", + " Will the S&P 500 index go up in January 2025?\n", + " 1.0\n", + " 2025-01-23 23:23:00\n", + " 2025-01-23 23:23:00\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " <NA>\n", + " 1.0\n", + " NaN\n", " NaN\n", " \n", " \n", - " 214\n", - " Will the state of Rhode Island have any recrea...\n", - " 0.923333\n", - " 0.95\n", - " annulled\n", + " 13\n", + " 31334\n", + " At the end of March 2025, will Wikipedia still...\n", + " 1.0\n", + " 2025-01-24 14:23:00\n", + " 2025-01-24 14:23:00\n", + " binary\n", " NaN\n", + " NaN\n", + " NaN\n", + " 31338\n", + " 1.0\n", + " 0.85\n", + " 0.9\n", " \n", " \n", "\n", "" ], "text/plain": [ - " title \\\n", - "170 In its March update, will Similarweb report de... \n", - "0 For Q1 2025, how many banks will be listed on ... \n", - "189 What will the highest rank of metac-GPT4o or m... \n", - "211 Will Nikola Corporation file for bankruptcy be... \n", - "214 Will the state of Rhode Island have any recrea... \n", + " bot_question_id title \\\n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "5 31276 Will the USDA-posted recall by Pork Dynasty In... \n", + "8 31288 Will Eric Adams be Mayor of New York City on t... \n", + "10 31318 Will the S&P 500 index go up in January 2025? \n", + "13 31334 At the end of March 2025, will Wikipedia still... \n", "\n", - " bot_team_median \\\n", - "170 0.7 \n", - "0 0.02 \n", - "189 [0.0, 0.05003188076666667, 0.11135575903333333... \n", - "211 0.99 \n", - "214 0.923333 \n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary NaN \n", + "5 1.0 2025-01-21 11:42:00 2025-01-21 11:42:00 binary NaN \n", + "8 1.0 2025-01-22 20:19:00 2025-01-22 20:19:00 binary NaN \n", + "10 1.0 2025-01-23 23:23:00 2025-01-23 23:23:00 binary NaN \n", + "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", "\n", - " pro_median resolution \\\n", - "170 0.144 yes \n", - "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", - "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", - "211 0.999 annulled \n", - "214 0.95 annulled \n", + " range_min range_max pro_question_id question_weight bot_team_median \\\n", + "2 NaN NaN 31270 1.0 0.085 \n", + "5 NaN NaN 31282 1.0 0.66 \n", + "8 NaN NaN 31294 1.0 0.86 \n", + "10 NaN NaN 1.0 NaN \n", + "13 NaN NaN 31338 1.0 0.85 \n", "\n", - " head_to_head \n", - "170 158.1 \n", - "0 299.6 \n", - "189 502.6 \n", - "211 NaN \n", - "214 NaN " + " pro_median \n", + "2 0.013 \n", + "5 0.45 \n", + "8 0.95 \n", + "10 NaN \n", + "13 0.9 " ] }, - "execution_count": 74, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"\\nBottom 5:\")\n", - "\n", - "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + "df_top_bot_pro_forecasts_all_binary.head()" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 78, "metadata": {}, "outputs": [ { "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAMWCAYAAADs4eXxAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd4VNXWx/HvTEJ6SGipEHrvBESkF6nSFQVFEMTe9fVaL3AtiIrXjooXULHSCSCoFBFE6VUINXRIAgRISJ/z/nGYISEJhJBkUn6f5+HhzJmTM2smM5NZs/dey2IYhoGIiIiIiIiI5DurswMQERERERERKamUdIuIiIiIiIgUECXdIiIiIiIiIgVESbeIiIiIiIhIAVHSLSIiIiIiIlJAlHSLiIiIiIiIFBAl3SIiIiIiIiIFREm3iIiIiIiISAFR0i0iIiIiIiJSQJR0i0iRN3LkSCwWC1FRUY59UVFRWCwWRo4cmenYTp06YbFYCjfA65DdfRGRoi+795bp06djsViYPn26c4KSIi+79/yVK1disVgYN26c0+ISkcKlpFtE8sXGjRsZPXo0tWvXxtvbG09PT2rWrMnw4cP59ddfnR1eoSmuH8IvXrzIBx98QOfOnalUqRJlypShfPnytGvXjrfeeouYmBhnh1io7L/HjP+sViv+/v60b9+eadOm3fBt5PTF0dVcGdO1/hVn48aNc9yP5557Lsfj/vWvfzmOUxJTuOwJZcZ/ZcuWpVWrVvz3v/8lNTW10GPK+Nq9/fbbczxu8uTJjuOu5zUoIpIXrs4OQESKN5vNxnPPPcd///tfXF1d6dKlC/369aNMmTIcOHCARYsWMWPGDP7zn//w6quv5tvthoaGsmvXLvz8/PLtnIVhwoQJvPDCC4SGhjo7FIetW7fSv39/Dh06RNWqVenXrx+BgYGcP3+ev/76ixdffJEJEyZw/PhxvL29nR1uoeratSvt2rUDIC0tjSNHjjB//nxGjRrFP//8wzvvvFOo8YwdOzbLvvfff59z585le11J4OrqyowZM3jrrbdwdc38sSUtLY2vv/4aV1dX0tLSCj22gQMHcvPNNxMcHFzot12UjB49msqVK2MYBkeOHGHOnDk888wzLF++nIiICKfE5OrqSkREBLGxsVSsWDHL9f/73/+c9ry56aab2LVrV7ZxiUjJpKRbRG7IK6+8wn//+1+aNWvGrFmzqFmzZqbrExMT+fjjjzl9+nS+3m6ZMmWoV69evp6zMAQHBxepD+hHjx6le/fuxMbGMmnSJJ588klcXFwyHbN582Yee+wxp4xaOVu3bt144YUXMu2LioqiUaNGfPTRR/znP//B09Oz0OLJbiR3+vTpnDt3rsSO8vbq1YuIiAgWLlzIgAEDMl23ePFiTp48Sb9+/ViwYEGhx+bn51fsvvgrCPfffz8333yz4/Lrr79O8+bNWbhwIStXrqRTp06FHpP9eTNjxgyeeuqpTNdt27aNjRs3Ou154+XlVSz/folI3ml6uYjk2b59+3j77bepUKECS5YsyZJwA3h6evJ///d/jB8/3rFvz549PP/887Ro0YIKFSrg4eFBnTp1eOGFF4iPj8/VbV9ram5SUhIvvPACYWFheHh4UL9+fT766CMMw8h0XMbp4BEREbRt2xZfX1+qVasGQEpKCh999BE9evSgSpUquLu7ExAQwKBBg9i8eXOmc40cOZL77rsPgPvuuy/bab5XW9M9bdo0WrdujY+PDz4+PrRu3TrbaeoZ1wNu2LCBW2+9FV9fX/z8/Bg4cOB1rRd/+eWXiY6O5qWXXuKZZ57JknADNG/enN9//52yZctmuf0r5fR7qVatGtWqVSMuLo7HHnuMKlWq4OrqyvTp0+natStWq5VDhw5lG+MTTzyBxWLJskxh1apV9O3bl4oVK+Lu7k7t2rV55ZVXuHjxYq7vf15Uq1aNunXrkpyczIULF7JcHxERQefOnfHz88PT05OmTZvy3nvvZRpRmz59OtWrVwfgq6++yvRcWblyZb7EmZKSwnvvvUeLFi3w9vbG19eX9u3bZ5tkXO9r0r6+OTk5mZdeeomwsDA8PT0JDw/nt99+A+DcuXM8+uijhISE4OHhQZs2bVi3bt11349Bgwbh7+/P1KlTs1w3depUypUrx8CBA3P8+ejoaJ5++mlq1aqFu7s7FStWZPDgwezYsSPb41evXk3Hjh3x9vamQoUK3HnnnRw5ciTbY3NaTjJ37lyGDh1KrVq18PLyws/Pj/bt2zN79uws58j4mtm3bx8DBw6kXLlyeHt7061bN7Zu3XqVR+ey0aNHY7FYWLVqVbbXv/fee1gsFqZMmeLYt2LFCnr16kVISAju7u4EBgbSvn17vvjii1zdZk5CQkIYNGgQAOvXrwcuLxdYuXIl06dPp0WLFnh5eWVKyA8dOsTo0aMJDQ3Fzc2NypUrM3r0aA4fPnzdMdxyyy3Uq1cv26UgU6dOxcXFhREjRuT48xcuXGDs2LE0bNgQT09P/P396dGjB6tXr872+J07d3Lbbbc53ot79+6d43Msp/fQFStWMGrUKOrWrev4O9CyZcscfx8Wi4VOnTpx6tQpRowYQcWKFfH09OTmm2/Ot/cREckfGukWkTybPn066enpPPjggwQGBl71WHd3d8f2nDlz+N///kfnzp3p1KkTNpuNv/76i4kTJ/L777+zatUqypQpc0OxDRkyhM2bNzN48GAAZs+ezRNPPEFUVBSTJk3KcvzMmTP55ZdfuO2223jkkUc4f/48AGfOnOGpp56iffv29O7dm3LlynHgwAEWLFjAzz//zKpVq2jVqhUAAwYMIC4ujvnz59O/f3+aNWuW63ifeOIJPvroI0JDQxk9erQj5vvuu4/NmzfzwQcfZPmZ9evX8/bbb9O5c2cefPBBNm/ezLx589i+fTs7duzAw8Pjqrd58eJFfvjhBzw9Pa+6ZhbIMq03L5KTk+nSpQvx8fH069cPV1dXAgMDGT58OMuXL+fbb7/lpZdeyvQzaWlp/PDDD4SEhNC1a1fH/smTJ/Poo4/i7+9P3759CQgIYMOGDbzxxhusWLGCFStW4Obm5ji+U6dO/P7776xYseKGR90OHTpEZGQklStXJiAgINN17733Hs8++yzly5dn2LBheHt7s2DBAp599ln++OMP5syZg8VioVmzZjz55JN88MEHNG3aNNMIrv0LnxuRnJxMz549WblyJc2aNWP06NGkpqayaNEi+vfvz0cffcRjjz3mOD6vr8k777yT7du3069fPxITE/n222+57bbbWLNmDQ888AApKSnccccdxMTE8OOPP9KzZ08OHjx4XaPDHh4eDB06lClTpnDq1CnHe82pU6dYtGgRDzzwQI7P9f3799OpUyfHjI4BAwYQHR3N7NmzWbp0KcuWLaN169aO45ctW0avXr2wWq3ceeedhISEsGzZMtq2bUu5cuVyHfOLL76Im5sb7dq1Izg4mJiYGBYsWMDtt9/Ohx9+yOOPP57lZ6Kiorj55ptp2LAho0aNYv/+/cyfP5/OnTuza9eua77HDh8+nKlTpzJjxgw6dOiQ5fpvvvkGd3d37rjjDgAWLVpE37598ff3p3///o44t27dyjfffMMDDzyQ6/t7NVfWFnjnnXdYsWIF/fv3p3v37o4v+vbs2UO7du2IiYmhb9++NGzYkB07djB16lQiIiJYvXo1derUua7bvu+++/jXv/7Fxo0bCQ8PB8wvo7799lt69OhBSEhItj935swZOnTowM6dO2nbti0PPfQQ58+fd/w+Zs6cmek1u2PHDtq2bUt8fDyDBg2idu3arFu3jrZt29K0adNcxztx4kT27dvHzTffzMCBA4mLi2PJkiU8+OCDREZGZvu3Ky4ujnbt2uHn58fw4cOJjo7mxx9/pEePHmzcuJFGjRpd12MmIgXEEBHJo06dOhmA8dtvv13Xzx09etRITk7Osn/8+PEGYMyYMSPT/hEjRhiAcfDgQce+gwcPGoAxYsSITMd27NjRAIy6desacXFxjv1xcXFG3bp1DYvFYqxfv96xf9q0aQZgWK1W49dff80SU1JSknH06NEs+3fs2GH4+PgY3bp1y7Tffr5p06Zle9+zuy+///67ARj169fPFPOZM2eMOnXqGICxatUqx/4VK1YYgAEYP/zwQ6bzDx8+3ACM77//Ptvbz2jlypUGYLRr1+6ax2Zkv/2xY8dmuS6n30vVqlUNwOjRo4dx8eLFTNedP3/e8PT0NBo0aJDlfBEREQZgPPfcc459O3fuNFxdXY2mTZsasbGxmY6fMGGCARjvvvtupv3258WKFStydR/tv8euXbsaY8eONcaOHWu8/PLLxogRI4xy5coZAQEBWZ73+/btM1xdXY2AgADj8OHDjv1JSUlGu3btDMD4+uuvr/lYXS/7Y5vRSy+9ZADGq6++athsNsf+8+fPGy1btjTc3NyMY8eOOfZf72vS/ni2a9fOiI+Pd+z/8ccfDcDw9/c37rjjDiM1NdVx3cSJEw3AmDRpUq7u19ixYx3P5Q0bNhiA8fbbbzuuf/vttw3A2Lhxo/H9999n+5y85ZZbDBcXF2PJkiWZ9kdGRhq+vr5G48aNHfvS09ONGjVqGBaLxfjjjz8c+202mzFs2DDHay6jnF7v+/fvz3J/Lly4YDRu3Njw8/MzEhISHPvtzwPAeOuttzL9zCuvvGIAxoQJE67+YF2KMywszChXrpyRlJSU6brt27cbgHH77bc79g0aNMgAjC1btmQ515Wvq5zY38/Wrl2baf+JEyeMwMBAAzB+//13wzAu/z69vb2Nbdu2ZTlX586dDcD4/PPPM+3/5JNPDMDo0qVLrmKy/04mTJhgnDhxwnB1dTUeeeQRx/U//fSTARizZ8821q5dm+1r0P77njJlSqb9p06dMqpUqWJUqlTJSExMdOy3vx6ufJ28+OKLjt9txvf8nN5DDxw4kOX+pKamGrfeeqvh4uJiHDp0KNN19nM/8sgjRnp6umP/l19+aQDGgw8+eNXHSkQKj5JuEcmzevXqGYCxe/fufDnf6dOnDcAYOXJkpv15Sbqv/PBjGIbxzTffGIDx2GOPOfbZP6ANHDjwuuPt27ev4ebmZqSkpGQ53/Uk3aNGjTIA48cff8xy/LfffmsAxqhRoxz77B/YOnTokOV4+3XPPPPMNeP/4YcfDMC46667rnlsdreRl6R769at2Z5z6NChjgQqoyFDhmRJDJ544oksX0TYpaenG5UqVTLCw8Mz7T906JCxa9euTMnO1dh/j9n9c3V1NR577DHj1KlTmX7mP//5jwEYEydOzHK+NWvWZEkcCirpTk9PN8qVK2fUrFkzU8Jtt2DBAgMwPvroo2ueO6fXpP11Zk+oMt52mTJlDCBLgnD48GEDMO69995c3a+MSbdhGEaTJk2M+vXrO66vX7++0bRpU8MwjGyT7k2bNmV57WT0zDPPGICxfft2wzAuf/nVt2/fLMdGRUUZLi4uuU66czJp0iQDMFauXOnYZ38eVK9ePVPilPG6QYMG5er89iRv9uzZmfY///zzBmDMmzfPsc+edEdGRubq3Nmxv5+NHj3aGDt2rPHvf//bGDVqlOHv728ARv/+/R3H2n+fTz/9dJbzHDp0yACMBg0aZHnOpqenO/7WZPwyKycZk27DMIx+/foZ5cqVcyTJPXv2NCpVqmSkpKRkm3THxMQYLi4uOSb5H374oQEYERERmWJv0qRJlmMvXLjgeCxyk3TnZPbs2QZgTJ8+PdN++5cYFy5cyLQ/NTXVcHV1NVq0aJGr84tIwdP0chEpdIZhMG3aNKZPn86OHTs4d+4cNpvNcf3x48dv+Dbat2+f474r12KDWU02J1u2bOHtt99m9erVnDx5MktBsdjY2BsqjmaPJ7tpz507d3bEcCX7dMmMKleuDJhTDosaDw8PGjdunO11w4cP5/vvv+ebb76hRYsWAJw/f56IiAgaN26caYrmX3/9BeCYHnylMmXKsHv37kz7wsLC8hSzvdo8mJX6T5w4wbx583j22WdZvHgxmzZtckyVvtrvsU2bNnh4eGT7e8xvkZGRnD17lpCQkEy1FOzs7d8yPkZ5fU1euYTCarUSEBDAxYsXszzm9tdIXl/fo0aN4qmnnmLt2rUA7Nq1K9tlF3b258mpU6eyrT9gv/+7d++mUaNGjrXT2b13VK1alSpVquS6XkJ0dDRvvfUWP//8M4cOHSIxMTHT9dk9Bs2aNcNqzVxq53pfz8OHD2fChAl88803jjXVNpuN7777jgoVKtC7d2/HsXfddRdz5szh5ptvZtiwYXTt2pX27dvnqaL2//73P8e2j48P9evX5+677+bRRx/Ncmx277X210XHjh2zTEe3Wq106NCB3bt3s2XLFqpUqXJdsY0aNYoFCxYwd+5cOnTowC+//MKTTz6Z4xKm9evXk56eTnJycrbPm7179wLm8+a2225zPG/sXQ4y8vHxoVmzZrleX33hwgXeffdd5s2bx/79+0lISMh0fXbPmzp16uDj45Npn33pTlH8OyBSWinpFpE8CwoKYvfu3Rw7doy6devm+ueeeOIJPv74Y6pUqUK/fv0IDg52rPkeP348ycnJNxxbdusf7fvOnTuXq+MB/vzzT7p06QJA9+7dqV27Nj4+PlgsFubNm8fWrVtvON7z589jtVqpVKlStnFZLBbHGvOM7IXNMrKvvU5PT7/m7QYFBQFw7Nix6w05TwICAnLsHd29e3cCAwP54YcfePfdd3FxcWHWrFkkJiYyfPjwTMeeOXMGgDfeeKPAY87IarUSGhrKo48+yokTJ3jjjTf4+OOPefnllwEcv6PsnksWi4XAwMBCeaztj8/OnTvZuXNnjsdl/ECf19dkTs/Bqz0381oF/5577uH55593FFRzc3Pj7rvvzvF4++OwaNEiFi1alONx9sfB/r5w5Tp9u8DAwFwl3WfOnKFVq1YcPnyYtm3b0q1bN/z9/XFxcWHLli3Mnz8/28fzRl/PAPXr1yc8PJzFixdz9uxZypUrx8qVKzl69CiPPPJIpkTzjjvuYN68ebz33nt89tlnfPLJJ1gsFjp37sykSZOuqybF2rVrM1Uvv5rsXh9Xe+3A5S9ssnsfvJY+ffoQGBjI1KlTOXDgADabjVGjRuV4vP15s2bNGtasWZPjcdfzvMmNlJQUOnXqxKZNm2jevDnDhw+nQoUKuLq6EhUVxVdffZXr5w2Yz53cPm9EpOAp6RaRPGvbti0rV65k2bJljsT0WqKjo/nkk09o0qQJa9euxcvLy3HdyZMnsx2Zy4tTp05lGWk7deoUQLZFnHJKBt944w2Sk5P5448/soxk/PXXX7muLHw1ZcuWxWazERMTk+WDW3R0NIZh5PjB6ka0atUKNzc3NmzYwPnz53N9G/bRuOz622b3hYZdTo8xgIuLC0OHDuX999/nt99+o0ePHnzzzTdYrVaGDRuW6Vh7nOfPn8fX1zdXMec3e/Ete2XmjHGdOnWKqlWrZjreMAxOnTpVIL/HK9lvY/DgwcyaNeuaxxfWa/JGVahQgf79+/Pjjz8CZuHCChUq5Hi8/XG4smhcTuzvC9HR0dleb3//uJb//e9/HD58mNdee41XXnkl03VvvfUW8+fPz9V58mr48OE89dRT/PTTTzz44IN88803jv1X6t+/P/379+fChQusWbPGUVCvZ8+e7N69G39//3yPL7v3gYyvneycPHky03HXw9XVlXvvvZdJkyaxc+dObrrppqsWF7PfxrPPPsu77757zfPn1/Nm/vz5bNq0idGjR/Pll19muu6HH37gq6++ytV5RKRoUsswEcmzkSNH4uLiwhdffOGYspoT+zf0Bw4cwDAMunXrlunDPcAff/yRb7Fldy77vubNm+f6PPv376d8+fJZEu6LFy+yadOmLMfbK/FezwiDPZ7spiDa913PqFNueXl5cdddd5GYmJhtVdyM0tLSHNON7VWcsxu1zW7qfm7Zk4IZM2Zw5MgRfv/9dzp37kxoaGim4+wJr336sDOcPXsWINMU7Kv9Hv/++2+SkpIy/R7z8lzJjfr161O2bFk2bNiQq1HlwnpN5odRo0Zx4cIFLly4cNXRSrj8PLFPR78W+xKG7O7zoUOHcmwbdqX9+/cDZkJ7pcJ4PIcOHYqrqyszZswgMTGROXPmUKtWrauORPv6+tKzZ0+++OILRo4cyalTp/j7778LPFY7++ti1apVWdo6GobhaIOW1/fBUaNGOZaHXOt506pVKywWy3U/b7JrJRYfH5/rJSXOft6ISMFS0i0ieVarVi2ef/55YmNj6dWrFwcPHsxyTFJSEu+9955jbZx9BPDPP//MlLAcPXqUF198Md9ie+211zKNup47d47XX38di8Vy1d6sV6patSpnz57NNE03PT2d5557LtsvGsqXLw+Q6w/ogCOe8ePHZ5o+ee7cOcco4/XEfD3eeOMNKlWqxBtvvMGHH36Y6Xdit23bNjp16uSIrW7duvj6+rJgwQLHVEwwR3Ref/31PMfSokULGjRowNy5c/n8888xDCPb0blHHnkEV1dXHn/88Wz798bFxWVJ/g8fPszu3bvzpYd3UlISn376KUCm1kzDhg3D1dWV9957L9Pay5SUFP71r38BZOpfXq5cOSwWy3U9V3LD1dWVhx9+mEOHDvHcc89lm3jv2LHDMTJXWK/J/NC9e3fmzZvHvHnzuPXWW6967E033UTr1q35/vvvHaPjGdlsNn7//XfH5Xbt2lG9enUWLlyYKYEyDIOXXnop11+O2B/PK5Ow7777jsWLF+fqHDciICCA7t27s2bNGt5//33Onz/PPffck+W4VatWZXuf7M+La7UczE9hYWF07tyZnTt3ZunH/sUXX7Br1y66dOly3eu57erVq8fPP//M3Llzr7okAcxlN0OGDOHPP//knXfeyfIlAJhfotnfS8LCwujQoQPbtm3j22+/zXTcm2++met11Tk9b37//fdMvdVFpHjS9HIRuSGvv/46SUlJ/Pe//6Vu3bp06dKFRo0aUaZMGQ4ePMhvv/3G6dOnHclYcHAwgwcPZvbs2bRs2ZKuXbty6tQpFi5cSNeuXR3f9t+oOnXq0KhRo0x9uo8ePcozzzxDy5Ytc32exx9/nF9++YV27doxZMgQPDw8WLlyJceOHaNTp05ZRjXbtGmDp6cn77//PmfPnnWs075ymmlGHTp04PHHH+ejjz5yxGwYhiPmJ554Itu+u/mhcuXK/PLLLwwYMIAnn3yS//73v3Tt2pXAwEDOnz/PunXrWL9+PWXLlnWsB3Vzc+Pxxx/nzTffpEWLFo7pqREREXTs2PGGfofDhw/nxRdf5O2338bLy8vx+8uoUaNGfPrppzz88MPUrVuX3r17U7NmTS5cuMCBAwf4/fffGTlyJJ999pnjZ+6999489en+7bffSEpKAswk7eTJk/z8888cPXqUZs2a8cgjjziOrVmzJhMnTuTZZ5+lSZMmDBkyBG9vbyIiIoiMjKR///6Zkh8fHx9atWrFqlWrGD58OLVr18ZqtTJ8+PAs09Ov1/jx49m0aRMffvghixYtokOHDgQEBHDs2DG2b9/O1q1bWbt2LQEBAYX2mswPVqs125HAnHz//fd07tyZu+66i/fff58WLVrg6enJ4cOHWbt2LTExMY7fr9Vq5YsvvqB3795069bN0ad7+fLlnDhxgiZNmrBt27Zr3ubw4cOZOHEijz/+OCtWrKBq1aps3bqVZcuWMWjQIObMmZPn+59bw4cPZ/HixYwdOxYg26T7iSee4Pjx47Rr145q1aphsVhYvXo169at4+abb862MFhBmjx5Mu3atWPMmDFERETQoEEDdu7cyYIFC6hUqRKTJ0++ofP37Nkz18d++umnREZG8vzzz/PNN9/Qpk0b/P39OXLkCBs2bGDv3r2cOHHCMTPkk08+oW3bttx7773MmzfP0ad7/fr1tG/fPlcj1X379qVatWq8/fbb7Nixg0aNGhEZGcnChQsZOHBgrpaKiEgR5pyi6SJS0qxfv94YNWqUUatWLcPT09Nwd3c3qlWrZgwbNixL/+sLFy4Yzz77rFGtWjXD3d3dqF27tvHaa68ZKSkpBmB07Ngx0/F5aRmWmJhoPP/880aVKlUMNzc3o27dusaHH36YpR1Nblr+zJo1y2jRooXh5eVlVKxY0RgyZIixf//+bOMyDMNYtGiR0apVK8PT0zNLb9+cfsYwDGPq1KlGq1atDC8vL8PLy8to1aqVMXXq1CzH5aVl17UkJCQY77//vtGxY0ejYsWKhqurq+Hv72+0adPGeOONN7L07U1PTzfGjRvneHzr1KljfPDBB8aBAwdybBlWtWrVa8Zx+PBhw2q1GoAxdOjQqx67bt0646677jJCQkKMMmXKGBUrVjRatGhhvPDCC8auXbsyHZvXPt1X/vP29jaaNWtmvP766zm2H5s/f77RsWNHw9fX13B3dzcaN25sTJo0KVPParvIyEijd+/ehr+/v2GxWK4rRrvs+nQbhmGkpaUZn3/+udG2bVujbNmyhru7uxEWFmb07NnTmDx5cqb+2tf7mrQ/njnFk9PvOrtz5eTKlmFXk1OfbsMw+92/8sorRqNGjQxPT0/Dx8fHqF27tjFs2DBjzpw5WY5ftWqV0aFDB8PT09MoX768cccddxiHDh3K9j7n9P6xZcsWo3v37ka5cuUMX19fo2PHjsZvv/2W7fHXes1ez2Nmd/HiRaNs2bIGYLRp0ybbY3744QdjyJAhRs2aNQ0vLy/Dz8/PaNq0qTFx4sQsLahyklOf7uzYf59Xe35HRUUZ9913nxEcHGy4uroawcHBxn333WdERUXlKh7DyNoy7Gpy6tNtGOZj+Pbbbxvh4eGGt7e34enpaVSvXt0YMGCA8fXXX2d5PW/fvt3o3bu34ePjY/j6+hq9evUytm/fnu17/tX6dA8ePNioVKmS42/ADz/8kOPxV3tu5PY9V0QKh8Uwspk3IyIiIiIiIiI3TGu6RURERERERAqIkm4RERERERGRAqKkW0RERERERKSAKOkWERERERERKSBKukVEREREREQKiJJuERERERERkQKipFtEREo9wzAIDw+ne/fuN3SeTp06YbFYMu1buXIlFouFcePG3dC5i7uoqCgsFgsjR450dijZ+u2337BYLCxevNjZoYiISAmjpFtEREq9r7/+mk2bNvGf//zH2aEUa9WqVaNatWrODiNPunXrRrt27Xj++edJT093djgiIlKCKOkWEZFSzWazMW7cONq3b8/NN9/s7HBKrNDQUHbt2sWECROcHUqOnn/+eXbu3MkPP/zg7FBExFnuuAPWrjW3bTZ4/HGoWRNq1YKPP8755xYvhhYtoFkzaNQIvvrq8nXr10PbttC0qXn98uW5i2XqVGjcGFxd4f33r37s33+b569TB7p0gWPHrn1dUhKEh8O5c7mLR/JMSbeIiJRqP//8M1FRUdx7773ODqVEK1OmDPXq1SM4ONjZoeSoZ8+eVKxYkc8++8zZoYiIM6xbB2fOQJs25uUZM+Cff2DPHvO6d96BnTuz/pxhwD33wPTpsGULLFwIDz4IFy6Y1w0cCOPHw9at8NNPMHIkJCZeO57wcPP4YcOufpzNBnffbSbme/ZA797w1FPXvs7DA4YPh0mTcvHgyI1Q0i0iIqXatGnTsFgsDB48OMt1Gzdu5LHHHqNRo0b4+fnh6elJ48aNeeutt0hNTS2wmI4cOcLQoUMpX748Pj4+dOzYkVWrVjFu3DgsFgsrV650HDt9+nQsFgvTp0/Pcp6rrSc/ePAg999/P2FhYbi7uxMcHMzIkSM5dOhQlmM3bdrE7bff7ji2UqVKtGrVijfeeAO4vF770KFDHDp0CIvF4vhnv+2rrek+dOgQo0ePJjQ0FDc3NypXrszo0aM5fPhwlmPt6+ZTU1MZN24c1apVw93dnTp16vDpp59mOT4pKYlJkybRtGlT/Pz88Pb2plq1agwZMoStW7dmOrZMmTIMGDCA1atXs2/fviznEpES7vPPMye4P/4IY8aAiwuULw933gnff5/9z1osEBdnbp8/DxUqgLs7nD4NMTHQrZt5XZ064O8PP/987XiaNoX69cF6jZRt40ZzNLxzZ/Pygw9CRIQ5kn216wDuugumTDG/HJAC4+rsAERERJzFMAxWrFhB3bp1KVeuXJbrp0yZQkREBB06dKB3795cvHiRlStX8uKLL7J+/Xpmz56d7zGdOHGCNm3acOzYMXr06EGLFi3YtWsXt956K53tH5pu0N9//02PHj1ISEjgtttuo3bt2kRFRfHtt9/y888/s3btWmrUqAHAli1buOWWW3BxcaF///5UrVqVuLg4/vnnH7744gtefvll/P39GTt2LO9fmv74lH0UBTNJvpo9e/bQrl07YmJi6Nu3Lw0bNmTHjh1MnTqViIgIVq9eTZ06dbL83NChQ1m3bh29evXCxcWFn376iUcffZQyZcowZswYx3EjRozgp59+okmTJtx33324u7tz5MgRVqxYwfr162natGmm87Zp04Yvv/yS5cuXU6tWrbw9wCJSPK1cCU8/ffny4cNQterly9WqwV9/Zf05i8VM0AcNAm9vOHsW5swBNzeoWBGCg80R6yFDzKnmkZEQFZV/cV8Zp68vlC0Lx49f/boaNSAoCDw9zRH8Ro3yLybJREm3iIiUWrt27eLMmTP06tUr2+tfeuklPvnkE1xcXBz7DMPg/vvvZ+rUqaxZs4a2bdvma0wvvvgix44d4/XXX+fll1927P/iiy948MEHb/j8qamp3HXXXdhsNtatW0fz5s0d161evZpOnTrx5JNPEhERAcA333xDcnIy8+bNo3///pnOdfr0aQD8/f0ZN26cY7T9eiq1P/TQQ8TExPD555/zwAMPOPZ/+umnPProozz88MMsW7Ysy88dPXqUHTt2ULZsWQCefPJJGjVqxKRJkxxJ97lz55g5cybh4eH8/fffmX6P6enpXLhwIct5W7ZsCcCaNWsyxSMipcDRoxAYeP0/l5YGr79uJtodOpiJdb9+sH27mXTPnw//+hdMmAANG0K7duboc1ERFGTedyXdBUbTy0VEpNQ6evQoAIE5fMgKCwvLlKgBWCwWHn30UcBsM5WfUlJS+PHHHwkICODZZ5/NdN39999P7dq1b/g2Fi5cSFRUFP/3f/+XKeEGaNeuHf3792fx4sWcP38+03Wenp5ZzlWhQoUbiuXw4cOsWLGCBg0aZBqdBjMZr1evHsuXL+fIkSNZfnbChAmOhBugbt26tG3blsjISEcybbFYMAwDDw8PrFdMz3RxccHf3z/Lee3PBftzQ0RKES+vy9OuAcLCIOOSm6goc9+VtmwxR447dDAvt2oFlSvD5s3m5aZNYckS8/KMGeaxDRvmX9xXxnnhglkcLSTk6tfZJSWZo91SYJR0i4hIqZVxpDY7KSkpvPfee9x0002ULVsWq9WKxWIhPDwcgOPHj+drPJGRkSQlJdGyZUs8PDwyXWe1WvNlVP2vS1MjIyMjGTduXJZ/J0+exGazsWfPHgCGDBmC1Wpl4MCBjBo1iu+//55jGavi3oAtW7YA0LFjxyz9za1WKx0ufYC1H5eR/XeQUeXKlQGIu7SusmzZsvTu3Zs1a9bQokUL3nzzTf7888+rrscvX748ALGxsdd7d0SkuGvSxJz6bXfHHeZ65/R0s8Dajz+a67qvVKUKnDgBu3aZl/ftg/37oW5d8/KJE5ePnTLFnILepYt5+eOP4cUXbyzu8HBITYUVK8zLn38OffuahdKudh2Y923/frNKuhSYIjSvQUREpHDZR2+TMo5sZHD77bcTERFBnTp1uPPOOwkICKBMmTLExcXxwQcfkJycnK/xnLvUtiUgICDb63Makb8eZ86cAeDbb7+96nEJCQkAtG7dmpUrV/Lmm2/y3XffMW3aNABatWrFxIkTb2iduX00Paf7Za90fuWoO5BplNvO9dJ0zYx9tmfOnOmI3T5dv2zZstx33328+eabeHl5ZTpH4qWKwlfuF5FS4PbbYenSy0XPhg83p4rXrm2u237mmcvJ6YIF5r8vvzSnpH/xhblm22o1K4Z//PHlUfEvvoBvvzWLldWvD3PnmucDszr6pRoaWUyfDq+8Yq4RnzcP3n3XLILWvDl89pk5Yv6f/5i3OWOGWSQtKckcxf7mG/McV7sOYPVqc2T+0heOUjCUdIuISKlVqVIl4HIimtH69euJiIigR48eLFq0KNM087/++osPPvgg3+Px8/MDIDo6OtvrT506lWWffdp0WlpaluvOZdN71Z6sRkREcNttt+Uqrvbt2/Pzzz+TmJjI33//TUREBJ9++il9+vRhx44djqJr18seS3b3C+DkyZOZjssLLy8vXn/9dV5//XUOHjzIihUr+Oyzz/jggw9ITEzk888/z3S8/blgf26ISCly331wyy0wbpw5Gu3iAp98kv2x/fqZ/+yGDjX/ZWfsWPNfdrZtg4kTs79u5EjzX3Yeeijz5TZtzHNl52rXTZ5srjeXAqXp5SIiUmo1bNgQq9VKZMbphJfs378fgD59+mRZ1/3HH38USDx16tTBw8ODDRs2ZBl9t9ls/Pnnn1l+xl51Pbsp35vt6wkzaN26NQBr16697vg8PT3p1KkTkyZN4qWXXiIxMZFff/3Vcb2Li0umUeZradasGQCrVq3CuKJdjWEYrFq1KtNxN6p69eqMGjWK33//HR8fHxYsWJDlGPtzobGmWoqUPj4+8N//wsGDhXebq1ebFcWdISkJOnaEW291zu2XIkq6RUSk1PL396dJkyZs2LABm82W6bqql1qsrF69OtP+nTt3MmHChAKJx93dnSFDhhAdHc2kSZMyXffll1861llnFB4ejsVi4YcffsiUqO/duzfb0fj+/fsTFhbGe++950hqM0pNTc10n9euXZvt9Hv76HTGtefly5cnNjY2x+n6VwoLC6Nz587s3LmTqVOnZrruiy++YNeuXXTp0oUqVark6nxXiomJYceOHVn2nz17luTk5Czr5sFspwbmOnMRKYW6di09Vbw9PODhh50dRamg6eUiIlKqDRw4kLFjx/LXX39xyy23OPbfdNNN3HTTTfz000+cOHGCm2++mcOHD7NgwQL69OnDrFmzCiSet956i2XLlvHKK6+wevVqmjdvzq5du1i8eDHdu3fnl19+yXR8SEgIQ4cO5bvvviM8PJyePXsSHR3N3Llz6dmzZ5Ze4u7u7syaNYtevXrRsWNHunTpQuPGjbFYLBw6dIg//viDChUqsHv3bgAmTpzIihUr6NChA9WrV8fDw4NNmzaxbNkyatSowcCBAx3n7tKlCxs2bKBXr160b98eNzc3OnTo4CiIlp3JkyfTrl07xowZQ0REBA0aNGDnzp0sWLCASpUqMXny5Dw/lseOHaN58+Y0bdqUJk2aEBoayunTp5k/fz6pqak899xzWX7m119/pVy5cleNWURE5Hoo6RYRkVLt/vvv57XXXmPGjBmZkm4XFxcWLlzICy+8wJIlS1i/fj21a9fm3XffpVevXgWWdAcHB/Pnn3/y/PPPs3TpUlatWkV4eDi//vory5cvz5J0gzkKXrFiRX788Uc++eQT6tatyxdffEFISEiWpBvMImhbt27lnXfeYfHixaxZswZ3d3dCQ0MZMGAAQzOsS3z44Yfx8/Pj77//5vfff8cwDMLCwnjppZd4+umnM623fvXVVzl79iwLFy7kjz/+ID09nbFjx141ga1bty4bNmxg/PjxLFmyhEWLFlGpUiXuu+8+xo4d65hxkBfVqlVj3LhxLF++nN9++43Tp09TsWJFWrRowZNPPknPnj0zHR8VFcWaNWt48sknsx0FFxERyQuLceUiKhERkVJm+PDhLFq0iEOHDuHrrLV1uTBu3DjGjx/PihUr6NSpk7PDKXFeeeUV3n77bXbt2kXNmjWdHY6IiJQQWtMtIiKl3uuvv05iYiIfffSRs0MRJzl79iwfffQRDz/8sBJuERHJV5peLiIipV7VqlX56quvcmxdJSXfwYMHefrpp3n88cedHYqIiJQwSrpFRESAIUOGODsEcaIWLVrQokULZ4chIiIlUJGaXr5q1Sr69u1LSEgIFouFefPmXfNnVq5cSYsWLXB3d6dWrVpMnz69wOMUERFxhnHjxmEYhtZzi4iIFCNFKulOSEigadOmfPLJJ7k6/uDBg/Tp04fOnTuzZcsWnnrqKe6//36WLl1awJGKiIiIiIiIXFuRrV5usViYO3cuAwYMyPGYf/3rXyxatIgdO3Y49t11113ExcWxZMmSQohSREREREREJGfFek332rVr6datW6Z9PXr04KmnnsrxZ5KTk0lOTnZcttlsnDlzhgoVKmCxWAoqVBERERERESniDMPgwoULhISEYLXmz8TwYp10nzx5ksDAwEz7AgMDOX/+PImJiXh6emb5mQkTJjB+/PjCClFERERERESKmSNHjlC5cuV8OVexTrrz4sUXX+SZZ55xXD537hxhYWEcPHgQf39/5wUmkk9sNhuxsbFUrFgx376dE3EmPaelpLnh57TNBsePw549kJQEbm75H6RILtkMg1jDoKLFglWzRnNnxw6suRgEtC1YAO3bF0JAkpaWxhdffMGFCxewWq28+eab+Pr65tv5i3XSHRQUlKWn6qlTpyhbtmy2o9wA7u7uuLu7Z9nv7++vpFtKBJvNRkpKCv7+/kpQpETQc1pKmht6TickwN69EBUFZctC1aoFEqNIbtkMg5SkJPw9PJR051ZYGEyeDNHR2V9vsUDlytC7N7i4FG5spVj//v3ZsGEDXbp04c0338zXpcfF+tNLmzZtWLZsWaZ9v/76K23atHFSRCIiIiIFwDDgxAlYvx4OHICAAChXztlRiUheuLjAc89lf5090Xv/fSXcBSw2NpbDhw87Ljds2JB77703X0e47YpU0h0fH8+WLVvYsmULYLYE27Jli+PBePHFF7n33nsdxz/00EMcOHCA559/nt27d/Ppp5/y008/8fTTTzsjfBEREZH8l5wM//xjJtxJSeYoWTaz9kSkGGnRAlyzmXRcuTLMmgWDBhV+TKXI9u3bmTJlCj/++CMXLlxw7C+owtpFanr5hg0b6Ny5s+Oyfe31iBEjmD59OidOnMj0bUT16tVZtGgRTz/9NB988AGVK1fmyy+/pEePHoUeu4iIiEi+i42F3bvNaaiVKoGXl7MjEpH8sHAhpKWZ2127QrNm0LGjppQXsNTUVJYsWcKmTZsACAkJKZTbLVJJd6dOnbha2/Dp06dn+zObN28uwKhM6enppKamFvjtiNwom81GamoqSUlJ11wrWKZMGVz0xi4iUvSkpZnrtvfsMQunVa6sD+IiJYVhwJw5ly8/9JBZELFdO73OC1BsbCyzZs1y1ATr0KEDHTt2LJR6MUUq6S6KDMPg5MmTxMXFOTsUkVwxDAObzcaFCxdyNUXG39+foKAg9akXESkqzp2DyEg4dsxct10A6wtFxIk2bgT77N2WLc0lIydPOjemEm779u0sXLiQlJQUvL29GTRoEDVq1Ci021fSfQ32hDsgIAAvLy8lJlLkGYZBWloarq6uV32+GobBxYsXib5UOTM4OLiwQhQRkezYbHD0qJlwX7wIISHZr/kUkeJt9uzL24MHOy+OUmT//v2kpKRQrVo1Bg0aVCDF0q5G7+RXkZ6e7ki4K1So4OxwRHIlt0k34GitFx0dTUBAgKaai4g4y8WL5lTyQ4fA29ucTi4iJc+ZM7Bihbldvjx06uTUcEqL3r17ExQUxE033eSU9qNFqnp5UWNfw+2loiVSgtmf36pZICLiBPZWYOvWwcGDZrE0tQITKbkWLLhcQK1vXyhTxrnxlFDbtm1j9uzZjnphbm5u3HzzzU5JuEEj3bmiKeVSkun5LSLiJMnJZqK9f7/5wbtKlcs9ekWk5LHZYO7cy5cHDnReLCVUamoqP//8s6PQdp06dWjcuLGTo1LSLSIiIlL4zp+HffvUCkykNFm3ziyQCHDzzVpGks9iY2OZOXOmo15Rx44dadiwoZOjMml6uVy3cePGERgYiMViYd68eQV2OwV9/mtZuXIlFovFUbl++vTp+Pv7O64fN24czZo1c0ps1+PK+yEiIk6UlmaObO/da67trFxZCbdIaaECagVm27ZtfPHFF0RHR+Pt7c3w4cPp1KmT06aTX6loRCH5buTIkVgsFiwWC25ubtSqVYv//Oc/pNnXkOTRrl27GD9+PJ9//jknTpygV69eNxxrcUle77zzTvbs2VMot6VEWUSkBDp/HjZvhh07zJ68ISHqyStSWsTEwKpV5nbFitC+vXPjKUFWrFjB3LlzSU1NpVq1ajz44IOF2g4sNzS9vATr2bMn06ZNIzk5mcWLF/Poo49SpkwZXnzxxes+V3p6OhaLhf379wPQv3//UrcW2NPT01HtO69SUlJwc3PLp4hERKRYsNnMKaW7d5tVyoODLxdSEpHSYf58SE83t/v3VzvAfFSnTh3WrFlDu3bt6NChQ5EZ3c6o6EUk+cbd3Z2goCCqVq3Kww8/TLdu3ViwYAEAycnJPPfcc4SGhuLt7U3r1q1ZuXKl42ftU6kXLFhAgwYNcHd3Z9SoUfTt2xcAq9WaKen+8ssvqV+/Ph4eHtSrV49PP/00UyxHjx5l6NChlC9fHm9vb1q2bMnff//N9OnTGT9+PFu3bnWMzE+fPj3LfenSpQuPPfZYpn0xMTG4ubmxbNmyHB+DiIgIWrVqhYeHBxUrVmRghoIV33zzDS1btsTX15egoCCGDRvmWAOSnSunl9t9/vnnVKlSBS8vL4YMGcK5c+cc140cOZIBAwbwxhtvEBISQt26da9521FRUXTu3BmAcuXKYbFYGDlyJAA2m40JEyZQvXp1PD09adq0KbNmzcoUz+LFi2nQoAFeXl507tyZqKioHO+TiIgUsIsXYds22LTJvFy5sj5si5Q26elgXzJptaqAWj44c+aMYzs0NJQnn3yySE0nv5Le9fMoJSUlx+usViuuGf6gXu1Yi8VCmQytAnI6Nj9GRz09PTl9+jQAjz32GP/88w8//PADISEhzJ07l549e7J9+3Zq164NwMWLF5k4cSJffvklFSpUIDg4mE6dOnHfffdx4sQJx3m//fZb/v3vf/Pxxx/TvHlzNm/ezJgxY/D29mbEiBHEx8fTsWNHQkNDWbBgAUFBQWzatAmbzcadd97Jjh07WLJkCb/99hsAfn5+WWK///77eeyxx5g0aRLu7u4AzJgxg9DQULp06ZLt/V20aBEDBw7k5Zdf5uuvvyYlJYXFixc7rk9NTeW1116jbt26REdH88wzzzBy5MhMx1zLvn37+Omnn4iIiOD8+fOMHj2aRx55hG+//dZxzLJlyyhbtiy//vprrm67SpUqzJ49m8GDBxMZGUnZsmUdI+wTJkxgxowZfPbZZ9SuXZtVq1Zxzz33UKlSJTp27MiRI0cYPHgwDz/8MA8++CAbN27k2WefzfX9ERGRfGIYcOqUObp99iwEBsKlv18iUsqsXQsnT5rbt9wCQUHOjacYs1cn37ZtG/fffz9Blx5LX19fJ0d2dUq682jChAk5Xle7dm2GDRvmuPzuu+/m2AO5atWqjlFMgA8++ICLFy9mOW7s2LF5jtUwDJYtW8bSpUt5/PHHOXz4MNOmTePw4cOEhIQA8Nxzz7FkyRKmTZvGm2++CZhP6k8//ZSmTZs6zmUf6Q3K8GYxduxYJk2axKBBgwCoXr06//zzD59//jkjRozgu+++IyYmhvXr11O+fHkAatWq5fh5Hx8fXF1dM53zSoMGDeKxxx5j/vz5DBkyBDBHnu1r17PzxhtvcNdddzF+/HjHvoz3ZdSoUY7tGjVq8OGHH9KqVSvi4+Px8fG5yiN6WVJSEl9//TWhoaEAfPTRR/Tp04dJkyY57o+3tzdffvllpi9OrnXb9scpICDA8ZgnJyfz5ptv8ttvv9GmTRvHz65evZrPP/+cjh07MnnyZGrWrMnbb7+Nq6sr9erVY/v27UycODFX90dERPJBcrJZLG3/fnNUW63AREq3jAXULn1elut3ZXXyI0eOXDV/KEqUdJdgCxcuxMfHh9TUVGw2G8OGDWPcuHGsXLmS9PR06tSpk+n45ORkKlSo4Ljs5uZGkyZNrnobCQkJ7N+/n9GjRzNmzBjH/rS0NMeI9ZYtW2jevLkjkcwLDw8Phg8fztSpUxkyZAibNm1ix44djuny2dmyZUummK60ceNGxo0bx9atWzl79iw2mw2Aw4cP06BBg1zFFRYW5ki4Adq0aYPNZiMyMtLxJtC4ceMsMxXyctv79u3j4sWL3HrrrZn2p6Sk0Lx5c8AsdHfTTTdlut6eoIuISCE4fRoiI81RLbUCE5GTJ2HNGnM7MBDatnVuPMXUtm3bWLhwIampqXh7ezNo0KAiVyztapR059HVipFduZbgueeey/HYK0dpn3zyyRsLLIPOnTszefJk3NzcCAkJcUx5j4+Px8XFhY0bN+JyRdXUjCO8np6e1yyWFh8fD8CUKVNo3bp1puvs577R4mN2999/P82aNePo0aNMmzaNLl26ULVq1RyPv9rtJiQk0KNHD3r06MG3335LpUqVOHz4MD169LjqcoC88Pb2zpfbtj/WixYtypToA44p9yIi4iRpaXDoEOzZY25XrqzK5CJiFlC7NLjCgAF6X7hO9unkmzdvBswZtYMGDcr1rNSiQkl3Hl3PGuuCOvZavL29M03jtmvevDnp6elER0fT/gbbFQQGBhISEsKBAwe4++67sz2mSZMmfPnll5w5cybb0W43NzfS7dUcr6Jx48a0bNmSKVOm8N133/Hxxx9f9fgmTZqwbNky7rvvvizX7d69m9OnT/PWW29RpUoVADZs2HDNGK50+PBhjh8/7pim/9dff2G1Wh0F07KTm9u2Pw8yPi72gnaHDx+mY8eO2Z67fv36WUb///rrr+u+XyIich3OnzdHt48eBX9/KFvW2RGJSFGQlna5gJqLi1m1XK7Lli1bHAl3x44di2x18mtR0l0K1alTh7vvvpt7772XSZMm0bx5c2JiYli2bBlNmjShT58+13W+8ePH88QTT+Dn50fPnj1JTk5mw4YNnD17lmeeeYahQ4fy5ptvMmDAACZMmEBwcDCbN28mJCSENm3aUK1aNQ4ePMiWLVuoXLkyvr6+OY7c2guqeXt7Z6pEnp2xY8fStWtXatasyV133UVaWhqLFy/mX//6F2FhYbi5ufHRRx/x0EMPsWPHDl577bXrut9gTnsfMWIE7777LufPn+eJJ55gyJAhV11fkpvbrlq1KhaLhYULF9K7d288PT3x9fXlueee4+mnn8Zms9GuXTvOnTvHmjVrKFu2LCNGjOChhx5i0qRJvPDCC4wZM4ZNmzZlWw1eRETywZWtwEJCVJlcRC5bvdrszw1mX+6AAOfGUwyFh4dz9OhRmjVrRvXq1Z0dTp4Vv68JJF9MmzaNe++9l2effZa6desyYMAA1q9fT1hY2HWf6/777+fLL79k2rRpNG7cmI4dOzJ9+nTHC8PNzY1ffvmFgIAAevfuTePGjXnrrbcc088HDx5Mz5496dy5M5UqVeL777/P8baGDh2Kq6srQ4cOxcPD46pxderUiZkzZ7JgwQKaNWtGly5dWLduHQCVKlVi+vTpzJw5kwYNGvDWW2/x7rvvXvd9r1WrFoMGDaJ37950796dJk2aZGmXdqXc3HZoaCjjx4/nhRdeIDAw0NEu7bXXXuPVV19lwoQJ1K9fn549e7Jo0SLHYx0WFsasWbMc9/mzzz5zFMYTEZF8pFZgInItc+Zc3lYBtVxJTU1l5cqVjiLUVquVgQMHFuuEG8BiGIbh7CCc6fz58/j5+XH27NksPZiTkpI4ePAg1atXv2aCJ4UjKiqKmjVrsn79elq0aOHscIokwzBIS0vD1dX1mmvyQc9zKfpsNhvR0dEEBAQUyyllUsLYW4FFRsKZM+bI1XW+d9oMg+ikJAI8PLCqqrkUc3o+5+D4cXM6uWGYs2DmzTN7dGcnPd0suNa+PZQrV6hhFiUxMTHMnDmTmJgYwsPDue2225wSR1xcHOXKlePcuXOUzaflQvpKVoqF1NRUTp8+zSuvvMLNN9+shFtERApfSgrs26dWYCJybXPnmgk3wMCBOSfcAsDWrVtZtGiRozp5w4YNnR1SvlLSLcXCmjVr6Ny5M3Xq1GHWrFnODkdEREobtQITkdxKSwN7YVsXF+jb17nxFGGpqaksXryYLVu2AMW3Ovm1KOmWYqFTp06U8pUQIiLiDPZWYHv3qhWYiOTOypXmF3UAnTtDxYpODaeoOn36ND/++CMxl4rNderUifbt25fIpWRKukVERESyY28FduSIuc6yUiVnRyQixYEKqOWKq6sr8fHxeHt7M3jw4GJfLO1qlHSLiIiIZGRvBRYZCQkJEBqqyuQikjtHjsClbjlUqQItWzo3niLGZrM5RrL9/Py46667KF++fImbTn6lkjd2LyIiIpJXiYmwfbvZCsww1ApMRK7PlaPcJXCqdF7FxMTw+eefExkZ6dgXFhZW4hNu0Ei3iIiIiJlgR0fD7t3mWszAwOtuBSYipVxKCkREmNtlyqiAWgYZq5MvW7aMOnXq5Kq1bUmhpFtERERKt5QUOHDAbAdmtUJYmFqBicj1W74c4uLM7S5dwN/fmdEUCVdWJ69RowYDBw4sVQk3KOkWERGR0uzMGXPt9okTZoVhb29nRyQixVXGqeWDBzsvjiIiJiaGmTNnEhMTg8VioWPHjiW2Ovm1lL57LFKAoqKisFgsjm/zVq5cicViIc7+raeIiBQN6emwf79Z8Cg21ly7rYRbRPLq4EGzFgRA9erQvLlz43Gyc+fOMWXKFGJiYvDx8eHee++lY8eOpTLhBiXdhSY93WzZ9/335v/p6QV7eyNHjsRisTj+VahQgZ49e7Jt27brPs+AAQOuekzG28nu37hx4/J+R/LRuHHjsFgs9OzZM8t177zzDhaLhU6dOuXrbd5yyy2cOHECPz+/fD2viIjcgAsXYMsW2LYN3NwgJES9t0XkxmQc5R44sNQvUfHz86NJkybUqFGDBx98kGrVqjk7JKfS9PJCMGcOPPkkHD16eV/lyvDBBwXbuq9nz55MmzYNgJMnT/LKK69w2223cfjw4Xy9nRMnTji2f/zxR/79739nqkpYlCoSBgcHs2LFCo4ePUrlypUd+6dOnUpYWFi+356bmxtBQUH5fl4REckDmw2OHzeLpcXHQ3CwWexIRORGJCXBokXmtrs79Onj3HicJCYmBk9PT8dn/549e2K1Wkvt6HZGegQK2Jw5cPvtmRNuMNt/3n575i/F8pu7uztBQUEEBQXRrFkzXnjhBY4cOUJMTIzjmO3bt9OlSxc8PT2pUKECDzzwAPHx8YA5MvzVV18xf/58x6j1ypUrs9yO/TaCgoLw8/PDYrFk2vfDDz9Qv359PDw8qFevHp9++mmmn//Xv/5FnTp18PLyokaNGrz66qukpqY6rh83bhzNmjVzJMY+Pj488sgjpKen8/bbbxMUFERAQABvvPHGNR+TgIAAunfvzldffeXY9+effxIbG0ufbN4gv/zyy6vGvm7dOpo3b46HhwctW7Zk8+bNma6/cnr56dOnGTp0KKGhoXh5edG4cWO+//77TD/TqVMnnnjiCZ5//nnKly9PUFBQkZktICJSbCUmwo4dsHGjmXxXqaKEW0Tyx2+/wfnz5na3blAKZzhu3bqVKVOmMGfOHGw2GwCurq5KuC/RSHcBSk83R7gNI+t1hmHOOnnqKejfv+BntcXHxzNjxgxq1apFhQoVAEhISKBHjx60adOG9evXEx0dzf33389jjz3G9OnTee6559i1axfnz593jJiXL1/+um7322+/5d///jcff/wxzZs3Z/PmzYwZMwZvb29GjBgBgK+vL9OnTyckJITt27czZswYfH19ef755x3n2b9/Pz///DNLlixh//793H777Rw4cIA6derw+++/8+effzJq1Ci6detG69atrxrTqFGjeP7553n55ZcBc5T77rvvvu7Y4+Pjue2227j11luZMWMGBw8e5Mknn7zqbSclJREeHs6//vUvypYty6JFixg+fDg1a9bkpptuchz31Vdf8cwzz/D333+zdu1aRo4cSdu2bbn11ltz/diLiMglp06pFZiIFJwre3OXIikpKfz888+OekYWi4WUlBQ89D6biZLuPGjZEk6evPZxyclmbZacGAYcOQJBQeZMlGsJCoING3If58KFCx3TOxISEggODmbhwoWOb5y+++47kpKS+Prrr/G+VDzm448/pm/fvkycOJHAwEA8PT1JTk7O8xTpsWPHMmnSJAZdegOqXr06//zzD59//rkj6X7llVccx1erVo3nnnuOH374IVPSbbPZmDp1Kr6+vjRo0IDOnTsTGRnJ4sWLsVqt1K1bl4kTJ7JixYprJt233XYbDz30EKtWrSI8PJyffvqJ1atXM3Xq1OuK/bvvvsNms/G///0PDw8PGjZsyNGjR3n44YdzvO3Q0FCee+45x+XHH3+cpUuX8tNPP2VKups0acLYsWMBqF27Nh9//DHLli1T0i0icj2ubAVWpYr5v4hIftm3z6wPAVCrFjRp4tx4CtGV1ck7depEu3btNLqdDSXdeXDypDk9PL9cLTG/EZ07d2by5MkAnD17lk8//ZRevXqxbt06qlatyq5du2jatKkj4QZo27YtNpuNyMhIAgMDb+j2ExIS2L9/P6NHj2bMmDGO/WlpaZkKi/344498+OGH7N+/n/j4eNLS0ihbtmymc1WrVg1fX1/H5cDAQFxcXDK9qAMDA4mOjr5mXGXKlOGee+5h2rRpjtHyJle8QeYm9l27dtGkSZNM3+S1adPmqrednp7Om2++yU8//cSxY8dISUkhOTkZLy+vTMddGU9wcHCu7puIiFyiVmAiUhhmz768PWhQqSmgtmXLFhYvXkxqaio+Pj4MHjy41BdLuxol3XmQ20Hfa41021WsmPuR7uvh7e1NrVq1HJe//PJL/Pz8mDJlCq+//vr1nSwP7GvDp0yZkmX02eXSfPq1a9dy9913M378eHr06IGfnx8//PADkyZNynR8mSvW3Vkslmz32deQXMuoUaNo3bo1O3bsYNSoUXmKPS/eeecdPvjgA95//30aN26Mt7c3Tz31FCkpKZmOu5H7JiJSqqWnw6FDsGcPpKaalUtVmVxECkJiIixebG57eEDv3s6Np5CkpaWxevVqUlNTqVGjBoMGDco0iCdZKenOg9xO8U5Ph2rVzFHx7NZ1WyzmZ4GDBwvn84DFYsFqtZKYmAhA/fr1mT59OgkJCY4Xypo1axzTtcGsvp2ex/5mgYGBhISEcODAgWzXTINZxKxq1aqO9dUAhw4dytPtXY+GDRvSsGFDtm3bxrBhw7Jcn5vY69evzzfffENSUpJjtPuvv/666u2uWbOG/v37c8899wDmtPk9e/bQoEGDG7xHIiLChQtmsn34sFnIqFIlZ0ckIiXZ0qWQkGBu9+gBRahjT0FydXXljjvuYM+ePbRr1w5LKRndvxGacF+AXFzMtmCQdaaJ/fL77xdcwp2cnMzJkyc5efIku3bt4vHHHyc+Pp6+ffsCcPfdd+Ph4cGIESPYsWMHK1as4PHHH2f48OGOqeXVqlVj27ZtREZGEhsbm6mqeG6MHz+eCRMm8OGHH7Jnzx62b9/OtGnTeO+99wBzvfLhw4f54Ycf2L9/Px9++CFz587N3wciB8uXL+fEiRP4+/vnKfZhw4ZhsVgYM2YM//zzD4sXL+bdd9+96m3Wrl2bX3/9lT///JNdu3bx4IMPcurUqfy+ayIipYthmG1C1q0zi6UEB5fK6sEiUshKUQG1LVu2sG7dOsflwMBA2rdvr4Q7l5R0F7BBg2DWLAgNzby/cmVzf0G+PpcsWUJwcDDBwcG0bt2a9evXM3PmTDp16gSAl5cXS5cu5cyZM7Rq1Yrbb7+drl278vHHHzvOMWbMGOrWrUvLli2pVKkSa9asua4Y7r//fr788kumTZtG48aN6dixI9OnT6d69eoA9OvXj6effprHHnuMZs2a8eeff/Lqq6/m22NwNd7e3jkm3LmJ3cfHh4iICLZv307z5s15+eWXmThx4lVv85VXXqFFixb06NGDTp06ERQUxIABA/LxXomIlDKJibB9u9kKLD1drcBEpHDs3g3//GNu16sHJXTWYkpKCvPmzWP+/PksXbpUg0V5ZDGM7CY+lx7nz5/Hz8+Ps2fPZknAkpKSOHjwINWrV7/hsvfp6fDHH2Y9l+BgaN9eS8ykYBiGQVpaGq6urrn69jE/n+ciBcFmsxEdHU1AQIAqokpmxbQVmM0wiE5KIsDDA6tGiaSYK7XP5zfeAPvszJdeurGRtPR0s1Jz+/ZQrlz+xJcPoqOjmTVrVqbq5KVhdDsuLo5y5cpx7ty5LMWd80pruguJiwtcGmAWERGRG2FvBbZ/v7leS63ARKQwJSSY67kBvLzM9dwlzJYtW1i0aBFpaWmqTp4PlHSLiIhI8aFWYCLibEuWwMWL5navXiXufSgiIoJNmzYBqDp5PlHSLSIiIkWfvRXY3r1mT87QUHDVxxgRKWSGkbU3dwlTsWLFUjWdvDDor5WIiIgUbVe2AqtY0dkRiUhptXOn+X4E0LAhXGqzW9wlJibi6ekJwM0330z16tUJCgpyclQlh5JuERERKZoMA44dM6eTX7hgViJVZXIRcaaMbcIGD3ZeHPkkJSWFxYsXc/ToUcaMGYO7uzsWi0UJdz5T0i0iIiJFT2KiOZX84EHw9DR7bWqKo4g404ULlwuo+fhA9+7OjecGRUdHM3PmTGJjY7FYLERFRVG3hIzcFzVKukVERKRoiY6GXbvMomkBAcWmFZiIlHCLF5s1JQB69y62702GYbBlyxYWL15MWloavr6+DB48mKpVqzo7tBJLSbeIiIgUDampZiuwffvMy5UrqxWYiBQNhpF5ankxLaBmn06+detWAGrWrMnAgQNVnbyAKekWERER5zt7FnbvNluBVahgTt0UESkqtm6F/fvN7aZNoVYt58aTR0uXLmXr1q1YLBY6d+5Mu3btVJ28EOjrYykWOnXqxFNPPeW4XK1aNd5//32nxSMiIvkkPd1ct71uHcTEmK3AlHCLSFFTQgqode7cmeDgYEaMGKF2YIVISXdhSU+HlSvh++/N/9PTC/TmRo4cicViwWKx4ObmRq1atfjPf/5DWlpavt5OVFQUFosFFxcXjh07lum6EydO4Orq6ijMkJ/Wr1/PAw88kK/nFBGRQhYfD1u2mCNIrq7qvS0iRVNcHPz2m7nt5wdduzo1nOuRkpLimEoO4OPjw5gxY7R+u5Ap6S4Mc+ZAtWrQuTMMG2b+X61a5m/MCkDPnj05ceIEe/fu5dlnn2XcuHG888472R6bkpJyQ7cVGhrK119/nWnfV199RWho6A2dNyeVKlXCy8urQM4tIiIFzN4K7O+/4cgRCAoCf39nRyUikr1Fi8D+WblPH3B3d248uRQdHc2UKVOYN28eO3bscOzX6HbhU9Jd0ObMgdtvh6NHM+8/dszcX4CJt7u7O0FBQVStWpWHH36Ybt26sWDBAsAcCR8wYABvvPEGISEhjvYA27dvp0uXLnh6elKhQgUeeOAB4uPjr3lbI0aMYNq0aZn2TZs2jREjRmQ5dseOHfTq1QsfHx8CAwMZPnw4sbGxjusTEhK499578fHxITg4mEmTJmU5x5XTy9977z0aN26Mt7c3VapU4ZFHHskU9/Tp0/H392fp0qXUr18fHx8fx5cSIiJSiJKSYMcO2LAB0tLMYmnqvS0iRVUxLKBmGAabN29mypQpxMbG4uvri6+vr7PDKtWUdBek9HR48knzxXol+76nnirwqeZ2np6emUa0ly1bRmRkJL/++isLFy4kISGBHj16UK5cOdavX8/MmTP57bffeOyxx6557n79+nH27FlWr14NwOrVqzl79ix9+/bNdFxcXBxdunShefPmbNiwgSVLlnDq1CmGDBniOOb//u//+P3335k/fz6//PILK1euZNOmTVe9favVyocffsjOnTv56quvWL58Oc8//3ymYy5evMi7777LN998w6pVqzh8+DDPPffcNe+biIjkk+hoWL/e7L9dsaL5TyMuIlKUbdwIhw6Z2+Hh5mzVIiwlJYV58+axYMEC0tLSqFmzJg8++KCmkzuZFk7lRcuWcPLktY9LToYMI7hZGMblaXW5maYSFGSODFwnwzBYtmwZS5cu5fHHH3fs9/b25ssvv8TNzQ2AKVOmkJSUxNdff+1oG/Dxxx/Tt29fJk6cSGBgYI63UaZMGe655x6mTp1Ku3btmDp1Kvfccw9lrhi9+Pjjj2nevDlvvvmmY9/UqVOpUqUKe/bsISQkhP/973/MmDGDrpfWy3z11VdUrlz5qvfxyiJrr7/+Og899BCffvqpY39qaiqfffYZNWvWBOCxxx7jP//5z1XPKyIi+eDKVmBVqqgVmIgUD8VolDs6OpqZM2cSGxur6uRFjJLuvDh50pwenl+ulpjfgIULF+Lj40Nqaio2m41hw4Yxbtw4x/WNGzd2JNwAu3btomnTppn69LVt2xabzUZkZORVk26AUaNGccstt/Dmm28yc+ZM1q5dm6Vw29atW1mxYgU+2VSm3b9/P4mJiaSkpNC6dWvH/vLlyzumv+fkt99+Y8KECezevZvz58+TlpZGUlISFy9edKz99vLyciTcAMHBwURHR1/1vCIicoPOnoXISDh+XK3ARKR4OXMGli83t8uVM+syFWFnz551TCcfPHiwRreLECXdeREUlLvjrjXSbVexYu5Huq9D586dmTx5Mm5uboSEhOB6RUXYjMl1fmjcuDH16tVj6NCh1K9fn0aNGrFly5ZMx8THxztGzq8UHBzMPvsoyHWIioritttu4+GHH+aNN96gfPnyrF69mtGjR5OSkuJIuq8cdbdYLBjZTf0XEZEbl55uzuaKjDT/HqoyuYgUNxERZu0JgL59IcNgVVFhGIZjJLtu3br069ePOnXq5PvnfLkx+uuXF7md4p2ebq77OHYs+3XdFotZQObgQXBxydcQwUyqa9Wqlevj69evz/Tp00lISHC8UNesWYPVar3mSLPdqFGjeOSRR5g8eXK217do0YLZs2dTrVq1LF8CANSsWZMyZcrw999/ExYWBpjf2u3Zs4eOHTtme86NGzdis9mYNGkS1kvTFX/66adcxSsiIgUgPt5Mto8cAV9f88tlEZHixGaDuXMvXx440Hmx5ODUqVMsWrSIwYMH4+fnB0Dz5s2dHJVkRwuqCpKLC3zwgbl95VoK++X33y+QhDsv7r77bjw8PBgxYgQ7duxgxYoVPP744wwfPvyaU8vtxowZQ0xMDPfff3+21z/66KOcOXOGoUOHsn79evbv38/SpUu57777SE9Px8fHh9GjR/N///d/LF++nB07djBy5EhHMp2dWrVqkZqaykcffcSBAwf45ptv+Oyzz/L0GIiIyA24shVYYKBagYlI8bR+/eXuQ61bm7UoigjDMNi0aRNffvklR44cYenSpc4OSa5BSXdBGzQIZs0yp9VlVLmyub8IFWTw8vJi6dKlnDlzhlatWnH77bfTtWtXPv7441yfw9XVlYoVK2Y7ig0QEhLCmjVrSE9Pp3v37jRu3JinnnoKf39/R2L9zjvv0L59e/r27Uu3bt1o164d4eHhOd5m06ZNee+995g4cSKNGjXi22+/ZcKECdd350VE5MbYW4Ft3Hi5FVgRnIopIpIrs2df3i5Cn9ft1ckjIiJIS0ujVq1a9OnTx9lhyTVYjFK+qPX8+fP4+flx9uxZ/K/4Nj4pKYmDBw9SvXp1PDw8buyG0tPhjz/gxAkIDob27YvMCLeULIZhkJaWhqura66qVebr81ykANhsNqKjowkICLjqrBdxouhoczp5TIw5uq33kquyGQbRSUkEeHhgVVVhKeZK5PM5Nhb69DE/v1eoAIsWFWxNivR0s1Bz+/ZmwbYcnDp1ipkzZ3L69GksFgtdunShbdu2qk6ez+Li4ihXrhznzp2jbNmy+XJOrekuLC4u0KmTs6MQERHJP2oFJiIl0fz5ZiIM0L9/kSgCeejQIWbMmEFaWpqqkxdDzn8GiYiISPETFwe7d6sVmIiULOnpMG+euW2xFJkCaiEhIZQvX56yZcsyYMAAVScvZpR0i4iISO5lbAWWlKRWYCJSsvz1l7kcFOCWW8xloU5y+vRpypUrh9VqpUyZMtx77714eXlpOnkxpDlgIiIikjvx8bBtG2zebCbalSsr4RaRkqUIFFCzVyf/7LPPWL16tWO/t7e3Eu5iSn8pRURE5OoMwxz52bULLlyAoCBVJheRkufUKbAnuQEB0LZtoYeQkpLCokWL2LZtGwDHjh3DMAwl28Wcku5csNlszg5BpMDo+S0iV5WUBHv3wsGD4O5ujm7rw5+IlETz54P9c9GAAYU+k+dUbCwzv/3WUZ28a9eu3HLLLUq4SwAl3Vfh5uaG1Wrl+PHjVKpUCTc3Nz3ppcjLbcswwzBISUkhJiYGq9WKm0atRORKMTFmsTS1AhORki4t7XIBNavVrFpeSAzDYHNsLD//+CNp6en4+vpy++23ExYWVmgxSMFS0n0VVquV6tWrc+LECY4fP+7scERyxTAMbDYbVqs1V18SeXl5ERYWpv7HInJZaqo5sr13r3lZrcBEpKRbswaio83tdu3MLxoLybnERBYfPUq6YVC7dm0GDBiAl5dXod2+FDwl3dfg5uZGWFgYaWlppNv79YkUYTabjdOnT1OhQoVrJtIuLi7XHBEXkVLG3grsxAkoX16twESkdJgz5/L24MGFetP+Xl70qlyZpLAwbunaVZ/LSiAl3blgsVgoU6YMZcqUcXYoItdks9koU6YMHh4eGr0WkdxLT4ejR82EOykJQkJUmVxESofjx+HPP83t4GC4+eYCvTnDMNh85AiBvr6ElisHQHjFihAerpoZJZT+moqIiJR2CQmwZw9ERUHZsmaxNBGR0mLePLNLA5gF1FxcCuymktPSWLRtG9uPH8ff05MHO3TAQ4MkJZ6SbhERkdLK3gps9244d06twESk9ElLM6uWg5lsF2ABtVPnzzNz40ZOJyRgsVgIr1oVd1fXyxXTpcRS0i0iIlIaJSXB/v3mP3d3s1iapjWKSGnz++9w+rS53bEjVKyY7zdhn07+844dpNlslPXwYHCLFoSVL5/vtyVFk5JuERGR0iYmBiIj4dQps0Kvp6ezIxIRcY6MBdQGDcr306elp7Ng2za2HzsGQO2AAAY0a4aXZhWVKkq6RURESosrW4GFhakVmIiUXkeOwN9/m9uVK8NNN+X7TbhYrSSnpmKxWOhaty631Kyp6uSlkJJuERGR0iAuzhzdPnYMKlRQKzARkblzL28PHJhvX0IahoHNMHCxWrFYLPRv1ozT8fFU0XTyUktJt4iISElms5mjOZGRkJgIoaFqBSYikpICERHmtqsr9OuXL6dNTktj4bZtWC0WBjRrhsViwcvNDS8l3KWa/uqKiIiUVPZWYIcOga+vWoGJiNitWAFnz5rbXbrApX7ZN+LkperkZy5VJ29bsyYBZcve8Hml+FPSLSIiUtLYW4FFRprTytUKTEQks3wsoGYYBpsOH+bnnTtJv1Sd/PYWLZRwi4OSbhERkZIkORn27YMDB8xEW63AREQyi4qCjRvN7apVITw8z6eyTyffcfw4oOrkkj0l3SIiIiVFbCzs3g3R0RAQoFZgIiLZuXKUO49fTBqGwXfr1nH4zBmzOnm9etxSo4aqk0sWSrpFRESKu7S0y63AbDZzdFutwEREskpOhkWLzG03N7jttjyfymKx0LF2bRZs28bg5s1VnVxypKRbRESkODt3zhzdViswEZFrW7bMfN8E6NYN/Pyu68eT09KIPn/ekWDXqFSJxzp1wtXFJb8jlRJESbeIiEhxpFZgIiLXb/bsy9vXWUDNXp08PjmZB9u3p7y3N4ASbrkm/XUWEREpbjK2AvPxUSswEZHc2L8ftm41t2vUgKZNc/VjhmGw8fBhlmSoTp6UmlqAgUpJo6RbRESkuDAMOHnSnE4eFweBgeDu7uyoRESKh4wF1AYPzlUBteTUVBZu367q5HJDlHSLiIgUB/ZWYPv3qxWYiMj1Skq6XEDN3R16977mj5w8d46ZmzZxJiEB66Xq5G1UnVzyQEm3iIhIUadWYCIiN+aXXyA+3tzu3h18fa/5I9uOHeNMQgJlPTy4PTycKuXKFXCQUlIp6RYRESmq0tIgKspcv61WYCIieZexgNrgwbn6ka716mEB2taqpenkckP0l1tERKQoOncONm6EbdvAywuCg5Vwi4jkRWQk7NxpbtepAw0bZnvYiXPnmLdlCzabDQAXq5VbGzRQwi03TCPdIiIiRYnNBkePmtPJ1QpMROTGXaOA2pXVySt4e9O+du1CDlJKMv0VFxERKSrUCkxEJH8lJMDPP5vbXl7Qs2emq5NTU4nYvp2dl6qT1wkIoGXVqoUdpZRwSrpFRESczd4KLDISzpyBoCC1AhMRyQ9Ll8LFi+Z2jx7g7e246sS5c8zauJEzFy+qOrkUKCXdIiIiznRlK7CwMLUCExHJD4aRYwG1ncePM3fLFtJtNvw8Pbm9RQsqqzq5FBAl3SIiIs6SsRVYpUrm1EcREckf//xjziACaNAA6tVzXBVYtixWi4WagYEMaNoUTxVLkwKkpFtERKSw2VuB7d0L6enm2m0XF2dHJSJSsmQsoDZoEBdTUhyVyCv6+DCmXTsq+vhoOrkUOPUeERERKUznzsGmTbB9O3h6mq3AlHCLiOSv+HhzPTdgeHuzsV493l+2jEOnTzsOqeTrq4RbCoVGukVERAqDvRVYZKRZ1CckRK3AREQKyuLFkJQEwP5WrVi4bx8A244do2qFCs6MTEoh/bUXEREpaBcvXm4F5u2tVmAiIgXJMDJNLf+1fn2sFgvd6tfn5urVnRiYlFZKukVERApKxlZgZ89CYKBagYmIFDBj2zYsl0a2D1epQnK1atyn6uTiREq6RURECkJystkGbP9+KFMGqlRRKzARkUJw4bvvKHtp+3iXLjzYvr2qk4tTKekWERHJb6dPm63ATp1SKzARkcJ07hy+q1cDkObtTev778eihFucTEm3iIhIfsnYCiwtTa3AREQKgWEYbD16lHpBQXgsWoQlORkA1379zC4RIk6mpFtERCQ/nD9vrt0+ehTKlQNfX2dHJCJS4iWnphKxbRs7T5xgz8mT3DFnDo6FPIMGOTM0EQcl3SIiIjfCZoNjx8zp5GoFJiJSaE6cO8fMjRs5e/EiVouFBqdOYYmKMq9s0QJUqVyKCH0qEBERyauMrcC8vNQKTESkEBiGwYZDh1j6zz+k22z4eXpye4sWVH733csHaZRbihAl3SIiItfLMMwiabt3qxWYiEghSro0nfyfEycAqBsYSP+mTfG8eBGWLzcP8veHLl2cF6TIFZR0i4iIXI+MrcBcXdUKTESkENkMg6Nnz2K1WOhWvz43V6+OxWKBH3+E1FTzoL59QRXLpQhR0i0iIpJbp0+bxdJOnlQrMBGRQmIYhplYA15ubtwRHg5A5XLlzANsNpgz5/IPaGq5FDFKukVERK4lLc1ct71nj1qBiYgUIvt08toBATSrUgXIkGzbbdgAR46Y2zfdZM5AEilClHSLiIhcjb0V2JEjZiuwsmWdHZGISKmQsTr5/pgYsw93mTJZD9QotxRxSrpFRESyc2UrsNBQtQITESkEhmGw/tAhfrmiOnm2CXdsLKxYYW5XqACdOhVqrCK5oU8PIiIiV1IrMBERp8ixOnlOhdEiIiA93dzu109fjkqRpGeliIiInb0VWGQknDkDAQHg4eHsqERESoXU9HSm/PEHZy5exGqxcGv9+rS2VyfPjs0Gc+ea2xYLDBhQaLGKXA8l3SIiIgApKbBvn1qBiYg4SRkXFxqGhLD92DFub9GC0CsLpl3pr7/g+HFzu00bcxmQSBGkpFtEREStwEREnCIpNZWUtDTKenoC0KlOHW6pWTP79dtXUgE1KSaUdIuISOllbwW2d69agYmIFLLjcXHM2rQJzzJluO+WW3B1ccFqteJhtV77h6Oj4Y8/zO1KlaBdu4INVuQGKOkWEZHS6cpWYJUqOTsiEZFSwTAM1kdF8cuuXaTbbBienpxPSqK8t3fuTzJ//uUCagMGqICaFGl6doqISOlibwUWGQkJCWoFJiJSiJJSU1mwdSu7Tp4EoF5gIP2uVp08O2lpMG+euW21Qv/++R+oSD7SpwwRESk9EhPNVmBRUWoFJiJSyOzTyc/mtjp5Tv780+w0AdC2LQQF5X+wIvlISbeIiJR8GVuBnT4NgYFqBSYiUogMw2DJzp2cvXgRf0/P3FUnz0nGAmqDB+dPgCIFSEm3iIiUbCkpcOCA2Q7MaoWwMLUCExEpZBaLhYHNmrFyzx56Nmx4fdPJMzpxAtasMbeDgsxWYSJFnJJuEREpuc6cMUe3T5yAihXheor0iIjIDTkeF8fhM2e4uUYNAMp5ezOwefMbO+m8eebsJTALqKnjhBQDSrpFRKTkSU83123v3QupqWoFJiJSiAzDYF1UFL/88w82wyCgbFlqVKx44ydOSzOrloP5nq4CalJM5KIJXuH65JNPqFatGh4eHrRu3Zp169Zd9fj333+funXr4unpSZUqVXj66adJSkoqpGhFRKTIuXABNm+GbdvAzQ1CQpRwi4gUkqTUVGZu3MiSnTuxGQb1goII8fPLn5P/8QfExprbHTqo1aMUG0VqpPvHH3/kmWee4bPPPqN169a8//779OjRg8jISAICArIc/9133/HCCy8wdepUbrnlFvbs2cPIkSOxWCy89957TrgHIiLiNDYbHD8Ou3dDfDwEB0OZMs6OSkSk1Dh2qTp53KXq5N0bNOCmatWuvzp5TmbPvrytAmpSjBSppPu9995jzJgx3HfffQB89tlnLFq0iKlTp/LCCy9kOf7PP/+kbdu2DBs2DIBq1aoxdOhQ/v7770KNW0REnCwx0ZxKfvAgeHpClSrOjkhEpFTZeOiQY3Tb39OT28PDCfX3z78bOHoU/vrL3A4NhZtuyr9zixSwIjO9PCUlhY0bN9KtWzfHPqvVSrdu3Vi7dm22P3PLLbewceNGxxT0AwcOsHjxYnr37l0oMYuISBFw6hSsW2dWJ69YESpUcHZEIiKljquLCzbDoH5QEA926JC/CTfA3LmXtwcONLtRiBQTRWakOzY2lvT0dAIDAzPtDwwMZPfu3dn+zLBhw4iNjaVdu3YYhkFaWhoPPfQQL730Uo63k5ycTHJysuPy+fPnAbDZbNhstny4JyLOZbPZMAxDz2cpMXJ8TqekmCPb9lZglSub/9ur2ooUUTbDMJ/Teq5KMZeWno7VasUwDBqHhuLj7k6NihWxWCz5+/xOTcUSEYEFMFxdMfr2LVnv9YZx+Z8+vzldQXyGLjJJd16sXLmSN998k08//ZTWrVuzb98+nnzySV577TVeffXVbH9mwoQJjB8/Psv+mJgYUlJSCjpkkQJns9k4d+4chmFg1bfAUgJk+5y+cAGOHTNbgpUtCx4eZhIuUgzYDINzqakYgFU946UYMgyDHUePsuPIEfqHh5NssWAAvr6+xGQY3MovHsuX43/mDABJHTpwzssLSlLhZHuSd+aM/pYVAefOncv3cxaZpLtixYq4uLhw6tSpTPtPnTpFUFBQtj/z6quvMnz4cO6//34AGjduTEJCAg888AAvv/xytgnHiy++yDPPPOO4fP78eapUqUKlSpXwz+9pMCJOYLPZsFgsVKpUSUm3lAiZntOGAYcPm6Pbqanmuj5VJpdixmYYWIBKHh5KuqXYSUpNZcHWrURe+sx+NCaG2qGhBfp8tkREOLbd77iDAA+PArkdp0lPN/8vXx6Ujzidm5tbvp+zyCTdbm5uhIeHs2zZMgYMGACYH7SWLVvGY489lu3PXLx4MUtS4XLpw5eRw5QTd3d33N3ds+y3Wq1KUKTEsFgsek5LiWKxWLAmJGDdt89Muv38IJuuFiLFhcViwXrpn0hxcSwujlkbNxKXmOioTt6yalVikpML7vl86BBs2GBuh4VhbdkSStrrxmK5/E+f3ZyuID4/F5mkG+CZZ55hxIgRtGzZkptuuon333+fhIQERzXze++9l9DQUCZMmABA3759ee+992jevLljevmrr75K3759Hcm3iIgUc4Zh9mU9eVKtwEREnMAwDNZFRfHLP/+Y1cm9vLijRQtC/P0LvjZBxgJqgwaVvIRbSoUilXTfeeedxMTE8O9//5uTJ0/SrFkzlixZ4iiudvjw4UzfPLzyyitYLBZeeeUVjh07RqVKlejbty9vvPGGs+6CiIjkp8RE2LMHoqLMddtqBSYiUuj+OniQX/75B4D6QUH0a9oUj8L48jM5GexTy93c4LbbCv42RQqAxchpHnYpcf78efz8/Dh79qzWdEuJYLPZiI6OJiAgQNPLpXg7dQp278Z25gzRfn4ElC2rqbhSItgMg+ikJAK0pluKiaTUVP63Zg0tq1blpmrVsGR43hbo8/nnn8FeHLlXL3jttfw9f1GRnm7O5mrfHsqVc3Y0pV5cXBzlypXj3LlzlC1bNl/OWaRGukVEREhJgQMHYP9+cxphaKiquYqIFCLDMNgbHU3tgAAsFgseZcrwUIcOuBT2l/lz5lzeHjy4cG9bJB8p6RYRkaLjzBmIjIQTJ6BCBfDxKVm9WEVEirik1FTmb93K7pMn6d2oEa2qVQMo/IR7/37YvNncrlEDmjYt3NsXyUdKukVExPnS080KtXv3mmv4QkPBVX+iREQKU8bq5C5Wa6Zp5IVOBdSkBNEnGhERca4LF8xiafZWYBUrOjsiEZFSxTAM/j54kF937cJmGJTz8uL2S9XJnSIpCRYtMrfd3aF3b+fEIZJPlHSLiIhzGAYcO2ZOJ79wQa3AREScIDElhQVbt7L71CmgkKuT5+TXX82/CwDdu0M+FbMScRYl3SIiUvgSE82p5AcPmq3AKlfW1EERESeIiY8nMjoaF6uV7g0a0KpqVedOK4fMBdQGDXJeHCL5REm3iIgUruho2LULTp+GwEAz6RYREacIK1+ePo0aEezn57zp5Bnt2QPbt5vbdepAo0bOjUckHyjpFhGRwpGxFRhAlSqgXvIiIoUqMSWFn3fsoH3t2lTy9QUgvGpVJ0eVwZWj3M4edRfJB0q6RUSk4J09C7t3Z24FJiIiherY2bPM2rSJuMREYhMSGNOunfOnkmd08SL8/LO57ekJPXs6Nx6RfKKkW0RECo5agYmIOF121clva9y4aCXcAEuXQkKCud2zp76glRJDn3xERKRgxMeblcmPHAFfX7UCExFxgsSUFOZv3UrkperkDYKD6dukiXOrk+dEBdSkhFLSLSIi+csw4Phxczr5hQsQFKRWYCIiThB38SLT167lXGJi0apOnp1//jGLbAI0aAD16zs3HpF8pKRbRETyT2Ii7NtnFkxTKzAREacq6+GBn6cnVouF21u0KBrVyXOiUW4pwZR0i4hI/oiONke3Y2PVCkxExEkSU1Jwc3XFxWrFarVyR4sWuLq4FM3p5Hbx8eZ6bgBvb+je3bnxiOQzJd0iInJjUlPNke19+8zLagUmIuIURy9VJ68XFETPhg0B8CkOX4AuWWLOlALo3Ru8vJwbj0g+U9ItIiJ5d/asWSzt+HG1AhMRcRLDMPjr4EF+u1SdfM+pU3SuWxf34tAtwjBg9uzLlzW1XEqgYvBKFBGRIic93axKHhmpVmAiIk6UU3XyYpFwA+zYYbaVBGjcGGrXdm48IgWgmLwaRUSkyIiPhz174PBhtQITEXEi+3Rye3XyHg0a0LKoVifPScYCaoMHOy8OkQKkpFtERHLnylZggYHg5ubsqERESqWUtDS+W7eOxNRUynl5cUd4OMF+fs4O6/qcPw+//GJu+/pCt27OjUekgCjpFhGRa0tKMqf/HTwI7u5qBSYi4mRurq70adyYf06coG+TJkW7OnlOFi82lygB9OmjrhdSYinpFhGRq4uONtdux8SoFZiIiBMdPXuWtPR0ql1a1tMwJIQGwcHFazq5nQqoSSmipFtERLKnVmAiIkVCxurknmXK8GCHDvhe+gK0WCbcAFu2mLOnAJo3hxo1nBqOSEFS0i0iIlnFxZlrt9UKTETEqa6sTl6tQgXcXFycHFU+yFhATaPcUsIp6RYRkcsytgJLSlIrMBERJyoR1cmzExcHv/1mbvv5QZcuTg1HpKDpk5SIiJji481iaVFRULasWSxNREQKXcbp5DbDoLyXF7cXx+rkOVm40FzCBNC3r1mgU6QEU9ItIlLaXdkKLChIrcBERJzs1Pnz2AyDhsHB9G3SBPfiWJ08O4aReWr5wIHOi0WkkCjpFhEpzdQKTESkyDAMA4vFgsVioXejRlSvWJEmoaHFfzp5Rhs2wOHD5narVlC1qnPjESkESrpFREqrmBhzdFutwEREnMowDNYeOMCRs2cZEh6OxWLBzdWVpiVxmY8KqEkppKRbRKS0SU01R7b37jUvqxWYiIjTJKakMG/rVvZcqk4eeeoU9YKCnBxVATl9GlasMLfLl4dOnZwajkhhUdItIlKa2FuBnThhfuBRKzAREae5sjp5z4YNqRsY6OywCk5EBKSlmdv9+kFJWacucg1KukVESoP0dDh61Ey4k5IgJEStwEREnMQ+nXzZ7t0lszp5dmw2mDv38uUBA5wWikhh0ycuEZGSTq3ARESKlMU7drDh0CEAGoaE0Ldx45JTnTwnf/8Nx46Z223a6G+RlCpKukVESirDMKeR794N586pFZiISBHRrHJlth07xq316xMeFlayqpPnRAXUpBRT0i0iUhIlJcG+fXDggNkKrEoVtQITEXESwzA4deECQWXLAhBarhxPdemCZ2n5IjQmBlatMrcrVoT27Z0bj0ghU9ItIlLSxMRAZCScOmW2AvP0dHZEIiKl1sWUFOZt2cKB2Fjub9fOkXiXmoQbYP58s7YImGu5VVNEShk940VESorUVHPd9p495uWwMLUCExFxoiNnzzJr40bOJyXhYrUSm2G0u9RIT79cQM1qVQE1KZWUdIuIlARxcebo9rFjUKGCWoGJiDiRYRj8eeAAy+3Vyb29uaNFC4JKcnXynKxda868ArjlFrO+iEgpo6RbRKQ4s9ngyBEz4U5MhNBQTdsTEXEi+3TyvdHRADQKCeG20lCdPCezZ1/eHjzYeXGIOJE+mYmIFFcJCeZU8kOHwNdX7VdERIqArUePsjc6GherlV4NG9KitFQnz87Jk7BmjbkdGGiOdIuUQkq6RUSKG7UCExEpslpXr05sfDytqlYtndPJM5o/35yRBTBwILi4ODceESdRhR0RkeIkKQn++Qc2bjQLp1WpooRbRMSJLqaksGTnTlIvVee2Wiz0bdJECXdaGsybZ267uED//k4NR8SZNNItIlJc2FuBRUdDQIBagYmIOFnG6uTpNht9Gjd2dkhFx+rV5t8tMPtyV6rk3HhEnEhJt4hIUWdvBbZ3rzlNr0oVtQITEXGi7KqTh1et6uywipY5cy5vq4CalHJKukVEirJz58y128eOQfnyZsE0ERFxmmyrkzdpgrs6R1x27JjZKgzMrhqtWzs3HhEn07uDiEhRpFZgIiJFzolz5/hh/XrOJyWpOvnVzJtnFv0EGDBAs7Ok1NMnOBGRoiZjKzAfH7UCExEpIrzc3EhNT6e8tzd3hIcTVLass0MqelJTzarlYBZQ69fPufGIFAFKukVEigrDMHua7t4NcXFmT1N3d2dHJSJSqqWmp1PmUqsrP09P7mndmgo+PppOnpPff4czZ8ztzp2hQgXnxiNSBGiuh4hIUZCcbLYCW7/e3K5SRQm3iIiTHT5zho9XrCDy5EnHvhB/fyXcV5OxgNqgQc6LQ6QI0TuGiIizxcaao9tqBSYiUiQYhsGf+/ezLDISwzBYvW8fdQIDtXb7Wg4fhnXrzO2wMGjZ0rnxiBQRSrpFRJwlLc1sBbZnj1qBiYgUETlVJ1fCnQtz517eHjhQf9NELlHSLSLiDGoFJiJS5Bw+c4bZmzapOnlepKTAggXmdpky0Levc+MRKUKUdIuIFCabDY4eNRNutQITESkyTsfHM33tWgzDoIK3N7erOvn1Wb7c/EIZoGtX8Pd3ajgiRYk+6YmIFBa1AhMRKbIq+PjQIiyMlNRU+jRpomJp18miAmoiOdK7iYhIQbO3AouMNNuoBAWpMrmISBFw+MwZynt54ePhAUDvhg2xWCyaTn6dXKKisGzebF6oXh2aN3duQCJFjJJuEZGClJwM+/bB/v3g5mZWc9WHORERpzIMgzX797M8MpJq5ctzz803Y7VYsKrwV5542ddygznKrb9zIpko6RYRKSgZW4FVqgReXs6OSESk1LuYksLczZvZFxMDgI+7O+k2G1YXFydHVkwlJeH588/mtrs79Onj3HhEiiAl3SIi+c3eCmzvXkhPN9du68OciIjTHT5zhlmbNnEhKQlXq5VejRrRvEoVTSe/EcuWYY2PN7dvvRVUfE4kCyXdIiL56dw5s1ja0aNQrpxagYmIFAEZp5Pbq5PfER5OoBLEG2aZPfvyBRVQE8mWkm4RkfxgbwUWGQkXL0JIiFqBiYgUEanp6Ww5cgTDMGgcEqLq5Pll714s27cDYNSqhaVxYycHJFI06d1GRORGXbx4uRWYt7dagYmIFDFurq7cER7Osbg4TSfPTxnahBmDBulxFcmBkm4RkbzK2Ars7FkIDFQrMBGRIsA+ndzVauXmGjUACCxbVtPJ81NiIixeDIDNwwN69XJyQCJFl5JuEZG8SE83k+39+81p5FWqqEWKiEgRkJCczLwtW9gXE4PVYqF2QAAVfHycHVbJs3QpJCQAkNS1Kx56jEVypKRbRCQvDhwwk+6KFdUKTESkiDh0+jSzN2/OVJ28vLe3s8MqmTJMLb84YAAeTgxFpKhT0i0icr1OnjTXcJcvr4RbRKQIMAyD1fv3s0LVyQvH7t3wzz8AGPXqkVavnpMDEinalHSLiFyPCxdg506z77am0omIOJ1hGPy4YQORp04B0CQ0lD6NG+Om6uQFJ0ObMGPgQCcGIlI86N1IRCS3UlPNb/YvXFCFchGRIsJisVC1QgX2x8TQu1Ejmqk6ecGKj4clS8xtb2/o0cO58YgUA0q6RURywzBg7144dgxCQ1U0TUTEiQzDICE5GR8PcyXxzdWrUy8wkHJav13wliwxK5cD9OxpJt5JSc6NSaSIszo7ABGRYuHoUTPpDggwq5WLiIhTJCQn8+26dUxfu5bktDTAHO1Wwl0IDCNTATUGD3ZeLCLFiD45iohcy9mzsGuXWTTN09PZ0YiIlFqHTp9m9qZNXEhOxtVq5XhcHNUrVnR2WKXHzp1mIVGARo2gTh0zEReRq1LSLSJyNUlJ5oeM5GQICXF2NCIipZKjOvnu3RhARR8fbm/RQtXJC1uGAmoMGuS8OESKGSXdIiI5sdnMtijR0VClirOjEREplRKSk5m7ZQv7Y2IAVSd3mgsX4JdfzG0fH+je3bnxiBQjercSEclJVBQcPAhBQWBVCQwREWdYunMn+2NicLVa6d24Mc0qV1Z1cmdYtMic9QXQpw9cKmInItempFtEJDvR0eYot78/uLs7OxoRkVKre4MGXEhOplfDhgRoOrlzXFlATVPLRa6Lhm5ERK6UkGD24zYM0Ac8EZFClZCczPqoKMdlHw8PRrRpo4TbmbZuhQMHzO1mzaBmTaeGI1LcaKRbRCSjtDQz4Y6Lg8qVnR2NiEipkrE6uaebG41UwLJoUAE1kRuipFtExM4wYN8+OHIEQkNBawZFRAqFYRis3rePFZGRjurkAb6+zg5LwPwSetkyc9vPD7p2dWo4IsWRkm4REbvjx83+oxUrgqriiogUiiurkzetXJnejRqpOnlRsXAhpKSY27fdpjonInmgdzMREYBz52DXLvPDhLe3s6MRESkVMk4nd7Va6dO4Mc3UorHoMAyYO/fy5YEDnReLSDGmpFtEJDnZXMedkKB13CIihSgpLY0LyclU9PHhjvBwTSkvajZuhEOHzO3wcKhWzanhiBRXSrpFpHSz2cwp5SdOKOEWESkEhmE4+mzXDQxkcIsW1AkI0HTyoihjAbXBg50Xh0gxp5ZhIlK6HTlitkEJDAQXF2dHIyJSokWdPs1nq1ZxLjHRsa9RSIgS7qLozBlYscLcLlcOOnd2bjwixZiSbhEpvWJjzXXcPj7g4eHsaERESizDMFi1dy9fr11L9IULrIiMdHZIci0REWYbTYB+/aBMGefGI1KM6WtFESmdLl6EnTvNDxSVKjk7GhGREiun6uRShNlsMGfO5csqoCZyQ5R0i0jpk54OkZHm1Dmt4xYRKTBRl6qTx6s6efGybh0cO2Zut26tv5UiN0hJt4iUPgcOwMGDEBICVq2yEREpCHtPneL79esxQNXJixsVUBPJV7lOuletWpVlX4cOHfI1GBGRAnfypDnKXaGC1qeJiBSgahUrEuDrS5CfH70bNVKxtOIiNhbsn/srVAB93he5Ybl+9+vUqRMWiwXDMACwWCykp6cXWGAiIvnu/HlzHberq1k8TURE8tWJc+cILFsWq8VCGRcX7rvlFtz1BWfxMn++uQwLoH9/82+miNyQXL+KDh48WJBxiIgUrJQUs1J5fDyEhjo7GhGREsVmGKzet4+VkZF0qluXDrVrAyjhLm7S02HuXHPbYlEBNZF8kuuku2rVqgUZh4hIwTEM2LvXLAoTGmp+kBARkXyRkJzMnM2bORAbC0DcxYsYhoFF77XFz9q15jIsgFtugeBg58YjUkLk23wRwzBYsWIFycnJtGvXDl8VyhCRouLoUdi3DwICNE1ORCQfRcXGMnvzZlUnLykytgkbNMh5cYiUMHn69Pnyyy/z559/smLFCsBMuLt3787y5csxDIOwsDCWLVtGzZo18zVYEZHrduYM/PMPeHuDp6ezoxERKREyTic3gEo+Ptyu6uTF28mTsHq1uR0YCG3bOjcekRIkT71yZs+ezU033eS4PGvWLJYtW8brr7/OwoULSU9PZ9y4cfkVo4hI3iQmmgl3SgqUK+fsaERESowzCQms2rsXA2hWpQr3t2unhLu4mz8fbDZzWwXURPJVnl5Nx44do1atWo7Lc+bMoUGDBrz44osAPPzww0yePDl/IhQRyYv0dLM1WEwMVK7s7GhEREqUij4+9GnUCIvFounkJUFampl0A1itZtItIvkmTyPdrq6uJCcnA+bU8mXLltGzZ0/H9YGBgcReKqYhIuIUUVHmv8BA8wOEiIjkmc0wWLV3L8fi4hz7moeFKeEuKVavhuhoc7t9e/Nvp4jkmzx9Em3UqBEzZszg7NmzTJs2jdOnT9OnTx/H9YcOHaJixYr5FqSIyHWJjobdu8HPD9zdnR2NiEixFp+czLd//82KyEhmbdxISlqas0OS/KYCaiIFKk/Ty//973/Tt29fR2Ldtm1bOnfu7Lh+0aJFtGrVKn8iFBG5HvHx5jpuiwXKlnV2NCIixdrB2FjmXKpOXsbFhU516+Kmtb4ly/HjZqswgJAQuPlm58YjUgLl6V3z1ltvZdOmTfz666/4+/tz5513Oq47e/YsHTp0oL/WgohIYUtNhV27IC5O67hFRG6AzTD4Y+9eft+zx1Gd/I7wcCqpWFrJM3cuGIa5PWAAuLg4NRyRkijPX1U2aNCABg0aZNlfrlw5/vvf/95QUCIi180wYP9+OHIEQkPNkW4REbluyWlp/LRhAwcu1edpVqUKvRo21Ah3SZSWBgsWmNsuLtCvn3PjESmhbujd86+//mLFihVER0fzyCOPULt2bS5evMju3bupU6cOPj4++RWniMjVHT8Oe/dCpUpqcyIicgPcXFxwtVop4+JCn8aNaaqZQyXXypVw+rS53akTqCaTSIHI0yfTlJQU7rrrLubPn49hGFgsFvr27Uvt2rWxWq10796dp59+mpdffjm/4xURySouzlzH7e4OXl7OjkZEpNixGQY2mw1XFxcsFgv9mzUjITlZ08lLOhVQEykUeape/uqrr7Jw4UImT55MZGQkhn0dCODh4cEdd9zBfHuvPxGRgpScbK7jvngRKlRwdjQiIsVOfHIyM/7+m4ht2xyf6bzc3JRwl3RHjsC6deZ25cqgIsgiBSZPSff333/Pww8/zAMPPED58uWzXF+/fn0OHDhww8GJiFyVzQaRkXDiBAQHOzsaEZFi52BsLJ+vWsXB2Fh2nTzJ2YsXnR2SFJYrR7mteUoLRCQX8jS9PDo6msaNG+d4vYuLCxf1pi0iBe3wYThwAAIDVW1VROQ62AyDVZeqkwNU8vXljhYtKO/t7eTIpFCkpEBEhLldpgz07evceERKuDwl3VWqVGH37t05Xr9mzRpq1aqV56BERK4pNtacVu7rCx4ezo5GRKTYiE9KYs7mzRy8VECrWZUq9G7UiDL68rL0WLHCrIcC0LkzlCvn1HBESro8zSMZNmwYn3/+OWvXrnXss1xqzzNlyhR++ukn7r333vyJUETkShcvws6d5vRyf39nRyMiUmwYhsGMdes4ePo0ZVxcGNCsGf2bNlXCXdrMnn15e/Bg58UhUkrkeqR7+/btjinlL7/8Mn/99RcdOnSgfv36WCwWnn76ac6cOcPRo0fp3bs3Tz/9dIEFLSKlWFqaOcJ9+jSEhTk7GhGRYsVisdC9fn1++ecfBrdooWJppVFUFGzaZG5XqwYtWjgzGpFSIdcj3eHh4bz44oskJSXh5ubGkiVLmDZtGjVq1KBevXokJyfTpEkTpk+fTkREBC76xlRECsKBA+Za7pAQuDTDRkREchaflMTB2FjH5RqVKvFAhw5KuEurjAXUBg7U31KRQpDrke7Ro0fzzjvvMHPmTCZPnsytt97KPffcwz333FOQ8YmIXHbiBOzZA+XLm4VfRETkqg7ExjJn82ZS09N5sH17R6E0qxKt0ikpCRYuNLfd3OC225wbj0gpkeuR7smTJ/Pnn3/i6+tLz549ueeee4iJiSnI2ERELjt/Hv75x0y2fXycHY2ISJFmMwxW7tnDN3/9RUJyMv6eno4e3FKKLVtm/j0F6NYN/PycG49IKXFd1ctvuukmNm7cyAcffMDYsWP5+eefeeuttwgPD8/2+BZaIyIi+SElxUy44+OhcmVnRyMiUqRdWZ28eZUq9FJ1coHMU8tVQE2k0Fx3yzCr1crTTz9Nv379aN26NQ899FCWYwzDwGKxkJ6eni9BikgpZhiwdy8cPw6hoc6ORkSkSLNPJ09ITqaMiwu3NW5ME31ZKQD79sHWreZ2zZrQpIlz4xEpRfLUp3vZsmU8/PDDxMXF8fDDD9OqVav8jktExHTkiPlBISAAXPP0liUiUmrsOXWKhORkAnx9uSM8nIpajiN2GUe5Bw1SATWRQnRdn2BjYmJ4+umn+f7772nSpAlr165Vwi0iBefMGbM9mLc3eHo6OxoRkSLv1vr18XJzo02NGppOLpclJsKiRea2hwf06ePceERKmVwXUpsyZQr16tVj3rx5TJw4kQ0bNijhFpGCk5horuNOTYVy5ZwdjYhIkXQwNpafNmwg3WYDwMVqpUPt2kq4JbNffoGEBHO7e3cVJBUpZLke6X7wwQfp2bMnkydPpmrVqgUZk4iUdunpsHs3REdDlSrOjkZEpMixGQar9uzh9717AVgXFUWbGjWcHJUUWVdOLReRQpXrpPv777/nzjvvLMhYRERMUVHmv+BgsOZ6Qo6ISKkQn5TE7M2bicpQnbylBkQkJ7t3w86d5nbdutCwoXPjESmFcp10K+EWkUJx6pT5AaFcOXBzc3Y0IiJFiqqTy3VTATURp8t10t2lS5cs+5YvX56vwYhIKRcfb67jtlrB19fZ0YiIFCkbDx1i4fbtAKpOLrmTkABLlpjbXl7Qs6dz4xEppXKddGsdt4gUqNRUs1L5uXOgURsRkSyqVqhAGRcXGoWE0KtRIxVLk2tbsgQuXjS3e/Y0u4GISKHLddI9bdq0goxDREozwzB7cR85AqGhmvomInLJucRE/C61TKzo48MjHTvi7+Xl5KikWDAMFVATKSJUoUhEnO/YMdi7FypVAtdcfxcoIlJi2QyDFZGRfLh8OYcuFUwDlHBL7v3zD0RGmtsNGkC9es6NR6QU06dbEXGuuDhzWrmHh7neTESklLuQlMScDNXJ98XEULVCBSdHJcXO7NmXtwcPdl4cIqKkW0ScKCnJ/CY+MdGcVi4iUsodiIkxq5OnpKg6ueRdfDz88ou57e0N3bs7Nx6RUk5Jt4g4h80Ge/bAyZMqnCYipZ7NMPh9zx5W7d0LqDq53KDFi80vtgH69IFLdQFExDmUdIuIc0RFwYEDEBgIqsArIqVc5MmTjoS7RVgYPRs2VHVyyRvDyDy1XAXURJyuyBVS++STT6hWrRoeHh60bt2adevWXfX4uLg4Hn30UYKDg3F3d6dOnTosXry4kKIVkTyJiTGLu5Qta67lFhEp5eoFBdG8ShUGNmtG3yZNlHBL3m3bBvv3m9tNmkCtWs6NR0SK1kj3jz/+yDPPPMNnn31G69atef/99+nRoweRkZEEBARkOT4lJYVbb72VgIAAZs2aRWhoKIcOHcLf37/wgxeR3ElIMNdx22zg5+fsaEREnMJmGKzdv5/wqlXxKFMGi8VCv6ZNnR2WlAQqoCZS5OQq6bZarVjy0Dc3PT39uo5/7733GDNmDPfddx8An332/+zdd3xTdfcH8E+6Fx10UwqUTaGDvSwqqLgV0EdxMFzg+Okj6uNGUR9x+7gFFcUNKsMBqCAIKKLsvVfpnulM2+Te3x+naQoUaNMkN+Pzfr368iZtkwOG5J77Pd9z3sdPP/2EOXPm4JFHHjnl5+fMmYPi4mL8+eef8PX1BQB06tSpxXESkYMYjcCePUBxMZCYqHU0RESaKDcY8NOmTcguLUVWaSmu7d/fqvMsolPo9cDy5XIcGgqMGqVtPEQEoJlJ9/Tp00/5MFi4cCF27tyJ0aNHo0ePHgCAPXv24JdffkGfPn1w9dVXtyiQ2tpabNy4EY8++mjDfV5eXrjggguwbt26Jn/n+++/x9ChQ3H33Xdj8eLFiI6Oxg033ICHH34Y3qcpy6qpqUFNTU3D7bKyMgCAoihQFKVFMRM5I0VRoKqq872eVRU4cAA4ehSIj7fcR3QWiqrKa5qvF3IDhwoKsGjLFlTW1sLP2xs94uKgAlD5+iZb+PFHeNXWAgDUyy6D6u9v989avkfbgKpavpzt/M0D2eMcullJ99NPP33C7dmzZyM/Px87duxoSLjNdu/ejZEjR6Jdu3YtCqSwsBAmkwmxsbEn3B8bG4s9e/Y0+TuHDh3Cb7/9hhtvvBFLlizBgQMHcNddd6Gurg5PPfVUk78zc+ZMzJgx45T7CwoKUFv/JkXkyhRFgV6vh6qq8PJyorYNxcWSdIeGAiaTfBE1g6Kq0NfVQQXgxdVAclGKqmLjoUPYdOQIACAsOBgX9emDtiEhyDd3mSZqDVVF1HffNTRsKrzsMpgc8Nrie7QN1NYCOp2cKzEf0Zxer7f5Y1q1p/vll1/GPffcc0rCDQC9evXCPffcg5deegm33357qwM8E0VREBMTg9mzZ8Pb2xv9+/dHVlYWXn755dMm3Y8++iimTZvWcLusrAyJiYmIjo7mXnByC4qiQKfTITo62nmS7rIyIDsb8PMD+O+MWkhRVegARAcE8ISOXFKFwYAFW7bgaHExAKBvYiL6du6M+OBgvqbJdjZuhNexYwAAtV8/RDZxnm4PfI9upbo6oLQUSEoCOnbkRBcn4OfnZ/PHtCrpPn78eMMe6qb4+vri+PHjLXrMqKgoeHt7Iy8v74T78/LyEBcX1+TvxMfHw9fX94RS8l69eiE3Nxe1tbVN/oX5+/vD39//lPu9vLycJ0EhaiWdTuc8r+naWtnHXVnJedxkNZ1OB6/6LyJX4+PtjeKqKvh5e+Py1FT0btcO+QYDX9NkWwsXNhzqxo51aJ8AvkdbyWgEcnIk4e7TBzhDfkWOY4/zZ6sesU+fPnj33XeRlZV1yveOHz+Od999FykpKS16TD8/P/Tv3x8rVqxouE9RFKxYsQJDhw5t8neGDx+OAwcOnFB3v2/fPsTHx9vlCgURtZCqAvv2AVlZwGkunhERuaPGe7SD/PxwXf/+uD0jAykJCRpGRW6rpAQwn0OHhwMjR2oaDjWD0SjnR4mJQHIyE243Z9VK9+uvv47Ro0eje/fuGDNmDLrWz//bv38/Fi1aBFVV8fnnn7f4cadNm4aJEydiwIABGDRoEP73v/+hsrKyoZv5hAkTkJCQgJkzZwIA7rzzTrz99tu477778H//93/Yv38/nn/+edx7773W/LGIyNYyM2VWaFwc4ONUEwqJiOym3GDAd5s2IT0xEen1kxoSIiI0jorc2g8/SBIHAFdcIdu5yHmZTJJwJyQAKSlAE1W45F6sOgs+55xzsH79ejz55JNYuHAhqqurAQCBgYEYPXo0ZsyY0eKVbgC47rrrUFBQgOnTpyM3Nxfp6elYtmxZQ3O1Y8eOnbDcn5iYiJ9//hn3338/UlNTkZCQgPvuuw8PP/ywNX8sIrKloiJg924gJAQICNA6GiIihzhYUIAFmzejqrYWhZWV6N2uHXy5R5PsSVFOKC3H2LHaxUJnpyjS5yY+HkhN5TmSh9CprZxRoSgKCgoKAMC5Gjc1U1lZGcLCwlBSUsJGauQWFEVBfn4+YmJitPv3WF0NbNggjUFaOMmA6GSKqiLfYEAMm/SQE1MUBav27cOaAwcAALGhobi2Xz9EhoSc+rN8TZMtrV8P3H23HA8aBLz7rkOfnq/nFlBVWeGOjAT69gWCg7WOiJpQWlqKiIgI6PV6hIaG2uQxW13v6eXlhYCAAISEhLhcwk1EdmAySeO0ggLZp0RE5ObM5eTm7uT9O3bExcnJ8OEKNznCggWWY65yOy9zwh0RAaSlMeH2MFZnyRs2bMDFF1+MoKAgREZG4vfffwcg87avuuoqrFq1ylYxEpErOXwYOHJEyqZ4IY6I3Jyhrg6z1qzB0eJi+Hl7Y1zfvrg8JYUJNzlGYSFgPueOjATOO0/LaOhMcnKAsDBJuNu00ToacjCrzoj//PNPnHPOOdi/fz9uuummE7qHR0VFQa/XY9asWTYLkohcRF4esHevXMVlExci8gABvr7ol5iI2NBQ3JGRgT7sTk6O9P33UmEGAFdeyaalzio3FwgKkoQ7LEzraEgDViXdjz32GHr16oVdu3bh+eefP+X7559/PtavX9/q4IjIhZSXAzt3yuo2r+ASkRsrNxhQWlXVcPu87t1x6/DhTe7fJrIbkwlYtEiOdTpgzBhNw6HTyMuT7uTp6bIoQR7JqqT7n3/+weTJk+Hv7w9dEw0TEhISkJub2+rgiMhF1NVJp/KyMiA6WutoiIjs5mBBAd5fvRrfbNwIY/0Ko5eXFzuUk+P99Zd0wQaAoUPZuNQZFRYC3t6ywh0ZqXU0pCGralB8fX1PKCk/WVZWFkJ4tZfIM6gqsH8/cPy4zJtk51IickMndydvExCA6ro6tGGyTVphAzXnVlQk50jp6VyQIOtWuocMGYJvv/22ye9VVlbi448/xrnnntuqwIjIRWRlAQcOyAcK95IRkRsqNxjw6V9/NSTc/Tt2xG3Dh6MN5+uSVvLygLVr5TgmBjjnHG3joROVlEgVYEoKEBendTTkBKw6Q54xYwbOPfdcXHbZZRg/fjwAYOvWrTh06BBeeeUVFBQU4Mknn7RpoETkhEpKgF27gMBAaRBCRORmDhYUYMHmzaiqrYWfjw+uSE1FH5bxktYWL7Y0ULvqKl70diZ6PWAwSEk5GytSPav+hQ4ePBhLlizBnXfeiQkTJgAAHnjgAQBAly5dsGTJEqSmptouSiJyPgaDJNwGAz9UiMgtqaqK3/bsQVVtLeJCQ3FNv35slkbaMxol6QakeenVV2saDjVSXg5UVgKpqUBiotbRkBOx+rLYyJEjsXfvXmzZsgX79++Hoijo0qUL+vfv32RzNSJyI4oC7Nkj5W3t22sdDRGRXeh0OlzTrx/+PnIEo3r25Oxtcg5//imfvwAwfDgQG6ttPCQqKmSVu08foEMHraMhJ2NV0q3X6xFWP2MuPT0d6enptoyJiJzdkSPyFRcnXTmJiNzEgfx85JWVYXjXrgCAiOBgjO7dW+OoiBr57jvL8bhx2sVBFlVVQGkpkJwMdO7MprJ0CqsaqcXExOCqq67Cl19+iYqKClvHRETOrKBAVrlDQ2XuJBGRG1AUBSv27MEXf/+N5Xv24EhRkdYhEZ0qJ0dWugG58D10qLbxEFBdLZ3Ke/QAunRhwk1NsirpnjZtGnbu3ImbbroJMTExGDduHL755htUV1fbOj4iciaVlbKPW1WB+moXIiJXV1Zdjbl//YW19d3JB3TsiPbh4doGRdSURYvkMxiQvdysNtOWwSCLEd26Ad27yx57oiZY9cqYOXMmDhw4gPXr1+Ouu+7Cxo0bcd111yEmJgbjx4/HokWLUFtba+tYiUhLRqOscBcXc/8YEbmNA/n5mLVmDY4VF8PPxwfX9OuHy1JSuH+bnI/RKEk3IMk2G6hpq7ZW9tZ37Qr07MmEm86oVfMFBg4ciIEDB+KVV17BunXrMG/ePHz77beYP38+QkNDUVJSYqs4iUhLqgocPAgcPQq0a8fSKSJyC6v378fKvXsBAHGhobi2f3+0DQ7WOCqi01i9WsqYAWDECCAqStt4PFldnZT6d+4M9OrFigM6K5sN9Rs6dCiioqIQERGB1157DWVlZbZ6aCLSWk4OsG+ffMD7+modDRGRTYQGBACQcvLRyclc3SbnxgZqzsFoBLKzgaQkoHdvzkinZmn1q+Tw4cOYN28e5s+fj61bt8LLywvnn38+rrvuOlvER0Ra0+tlH7efH8AVICJycTVGI/zrT5LTExMRFRKC9hERGkdFdBbHjwPr18txQgIwaJC28XgqoxHIypIZ3MnJXIigZrMq6c7MzMT8+fMxb948bNy4ETqdDhkZGXjnnXcwbtw4REdH2zpOItJCTY0k3JWVnMdNRC5NURSs3LcP244fx5QRIxDk5wcATLjJNSxcaDkeM4b7h7VgMknCnZAApKRwggu1iFVJd8eOHaHT6TBkyBC8/vrruPbaaxEfH2/r2IhIS4oiJeU5OUy4icillVVX47vNm3GsuBgAsCsnBwM6dtQ4KqJmqqsDvv9ejn18gCuv1DYeT6QoUlIeHw+kpgL1W1OImsuqpPvll1/Gv/71LyQmJto6HiJyFpmZ0jwtNpYNQojIZR3Iz8fCLVtQVVsLPx8fXJmait7t2mkdFlHzrVwJmJsTn38+0LattvF4GlWVhDsqShLuwECtIyIXZFXS/cADD9g6DiJyJkVFMh6sTRtezSUil6QoCn7buxd/HDwIgN3JyYUtWGA5ZgM1x1JVKSmPiADS0tjbhqzWrKT7008/terBJ0yYYNXvEZGGqqpkH3ddHceREJHLWr1/f0PCze7k5LKOHAE2bJDjDh2A/v01Dcfj5OQAYWGScLdpo3U05MKalXRPmjSpxQ+s0+mYdBO5GpMJ2LsXKCzkPm4icmlDOnfG3rw8nNO1K8vJyXU1bqA2diyg02kXi6fJzQWCgiThDgvTOhpycc1Kug8fPmzvOIjIGRw6JFfV4+LYGZWIXIqiKNiRnY2UhATodDoE+PrijowM6JikkKuqqQF+/FGO/fyAyy/XNh5Pkpcn3cnT06W0nKiVmpV0d2SHTyL3l5sr3crbtpUPdyIiF9G4O3mN0YiBnToBABNucm0rVgB6vRyPGgWEh2sajscoLJQGsmlpQGSk1tGQm7CqkVpju3btwtGjRwFIcp6cnNzqoIjIwcrLgZ075UMmJETraIiImm1/fj4Wbt6M6ro6+Pn4IJgXDcldsIGa4xUVSfO09HQgOlrraMiNWJ10L168GNOmTcORI0dOuD8pKQmvvfYaruQMQSLXUFcnjdPKy7mPm4hcxsndyePDwnBNv37sTk7u4eBBYMsWOe7cWVZdyb5KSuScKD1dttkR2ZBVSfeSJUswbtw4dOzYEc8//zx69eoFANi9ezdmz56NsWPH4scff8TFF19s02CJyMZUFdi/X8ZhJCSwQQsRuYTG5eQAMLBTJ1zUqxe7k5P7aLzKzQZq9qfXAwaDXNxISNA6GnJDOlVV1Zb+0tChQ1FTU4M1a9Yg+KQrypWVlTjnnHMQEBCAdevW2SxQeykrK0NYWBhKSkoQzr0y5AYURUF+fj5iYmLgdbZmaJmZwKZNsmcpMNAxARK1kKKqyDcYEBMQAC+eeBKAo0VFmLtuHfx8fHBlaiqSXaw7OV/TdEYGA3DxxUBFhTTzWrbMqcdVufzrubxcvlJTAfaxIgClpaWIiIiAXq9HaGioTR7TqvbE27Ztw8SJE09JuAEgODgYkyZNwrZt21odHBHZUUkJsHu3jMNgwk1ELqRjZCSuTEvDHRkZLpdwE53VL79Iwg0AF13k1Am3y6uokFXu5GSZg05kJ1Yl3QEBASiuL+lqSnFxMQICAqwOiojszGCQxmk1NdKtnIjIiemrq/H5+vUoKC9vuC89MZH7t8k9sYGaY1RVAaWlknB37swSfrIrq5LukSNH4o033miyfHz9+vV48803ccEFF7Q6OCKyA0UB9uwB8vPZKISInN7+vDzMWr0aBwsK8MO2bbBiVxyR69i7F9ixQ467dwd699Y2HndlMEin8h49gC5dmHCT3VnVSO2ll17C0KFDcc4552DQoEHo0aMHAGDv3r34+++/ERMTgxdffNGmgRKRjRw5Ahw+LAn32fZ8ExFpxKQoWHlSd/Kr09M5e5vcGxuo2Z/BIAsP3bvLF8+FyAGsSrqTkpKwbds2zJw5E0uXLsW8efMAyJzu++67D4888ghiYmJsGigR2UB+vqxyh4dLcxYiIiekr67Gd5s2IbOkBAC7k5OHqKqSpmmA9FrhFCDbq60F8vKAbt2Anj2ZcJPDWD2nOyYmBq+//jpef/11W8ZDRPZSWSnzuFUVsFEnRiIiWysoL8fHf/6J6ro6+Ltod3Iiq/z8s3xWA5Jwh4RoG4+7qasDcnJk/3avXgAv4pEDWZ10N+XQoUOoqalpmNtNRE7CaJSEu7QUaN9e62iIiE4rMjgYUSEhMCoKrunXj83SyHN8953leOxY7eJwR0YjkJ0NJCXJPnkfm6ZARGdlVU3Fm2++ieuvv/6E+yZNmoRu3bqhT58+GDBgAPLz820SIBG1kqoCBw7ITO74eO4PIyKnU1ZdDZOiAAC8vLxw3YABuGXYMCbc5Dl27ZLtX4B00+YClu0YjUBWFpCYKH+3vr5aR0QeyKqk+8MPP0RsbGzD7Z9//hmffvop7rjjDrz11ls4dOgQZsyYYbMgiagVsrOBffuAqChe2SUip7MvLw/vr16N5bt3N9wX7O/P/dvkWU5uoEa2YTJJwp2QAKSksJ8NacaqM/CjR4+eUEI+f/58JCUl4b333gMA5Obm4rPPPrNNhERkPb0e2L1bPmS4YkRETsSkKPhtzx78eegQAOBYcTGMJhOTbfI8FRWWBmrBwcBFF2kbj7tQFFl4iI+XhDsgQOuIyINZlXSfPCPzl19+wVVXXdVwu1OnTsjNzW1dZETUOjU1Uq5WWcl93ETkVPTV1fh20yYcr+9OPqhTJ1zI7uTkqZYulTFWAHDppUBQkLbxuANVlYQ7KgpITeXfKWnOqvLy7t27Y+HChQCktDw7OxuXXHJJw/ePHz+O8PBwmwRIRFZQFGD/funSGR+vdTRERA325eVh1urVOF5SAn8fH1zbvz8u6dOHCTd5JlVlAzVbU1UpKY+IANLSWOlHTsGqle4HH3wQN9xwAyIiIlBZWYlevXph9OjRDd//7bffkJ6ebqsYiailCguBw4eB2FiOxCAip1FdW4sFmzejxmhEu7AwXNOvHyJ4QkyebPt2aXYKyIpst27axuMOcnKAsDBJuNu00ToaIgBWJt3XX389IiMjsWTJEoSHh+Ouu+6CT32DpuLiYrRt2xY333yzTQMlomYqKpJO5cHB3L9ERE4l0M8PV6Sm4lhxMcvJiQA2ULO13FwpJU9Lk8SbyEno1JM3aHuYsrIyhIWFoaSkhCXx5PqqqqD8/Tfyy8sREx8PL44HIzegqCryDQbEBATwNe2C9uXlwdfbG0lRUVqH4jT4miYAQFkZcMkl0oOlTRvZ2+2CF8ud5vWclwf4+QF9+wKRkdrFQS6vtLQUERER0Ov1CA0Ntcljtmp+UFZWFlavXo38/HyMGzcO7du3h8lkgl6vR1hYGLx5BZvIcUwmYO9eoKREGocQEWnIpChYsWcP1h06hGA/P0wdMQIhLphQENnNTz9Jwg0Al1/ukgm30ygslO10aWlMuMkpWdVITVVVTJs2DUlJSbjxxhsxbdo07Nu3DwBQUVGBTp064a233rJpoER0FocOyT7uuDiAKydEpCF9dTU+WbcO6+rHgfVJSECAr6/GURE5EVVlabmtFBXJ32daGhAdrXU0RE2yKul++eWX8cYbb+DBBx/Er7/+esIIsbCwMIwdOxbfNe7ESET2lZsrq9yRkQBPbIlIQyd3J/9X//64uHdv7t8mamzzZrlQDgD9+gFJSdrG46pKS4G6OpnDHRendTREp2VVefkHH3yACRMm4Pnnn0dRUdEp309NTcXSpUtbHRwRNUNZGbBzJ+DjA4SEyNVeIiIHU1UVv+7e3bC63S4sDNf0748IzsclOhVXuVtPrweqq2WFOyFB62iIzsiqpDszMxPDhg077feDg4NRVlZmdVBE1Ey1tcDu3UBFBT9wiEhzlfX7UwcnJeHCXr3g7WVVQR2ReystBVaskOOwMGDkSE3DcUnl5UBlpYxZS0zUOhqis7Iq6Y6JiUFmZuZpv79x40Z06NDB6qCIqBlUFdi/H8jKkoSb+7iJSAOKqsJLp4NOp8NlKSno064dusXGah0WkfP64QcpiQaAK6+UjtvUfBUVssrdpw/AfINchFWXoMeOHYv3338fh+pLyABAV3/C/8svv+CTTz7Btddea5sIiahpx48DBw4AMTFSWk5E5EAmRcGvu3Zh3oYNDb1d/Hx8mHATnYmqAgsXWm6PGaNdLK6oqkoqBZKTgc6dueBALsOqpHvGjBmIj49Heno6JkyYAJ1OhxdffBHnnHMOLrnkEqSmpuKxxx6zdaxEZFZcDOzaBQQHA4GBWkdDRB7G3J38z0OHsC8vD4eb6O9CRE3YsAE4dkyOBw7kSm1LGAzSqbxHD6BLFybc5FKsSrrDwsLw119/4T//+Q+ysrIQEBCA33//HaWlpXjqqaewZs0aBLFxCpF9VFdLwl1bC0REaB0NEXmYfXl5eP+k7uSdo6K0DovINTSe7jNunHZxuBqDAcjPB7p1A7p3B9gvglyM1TWpgYGBeOKJJ/DEE080+f3Dhw8jieMPiGzLZJLRYAUFQPv2WkdDRB7EpChYsWcPu5MTWauoCFi5Uo7btgXOPVfbeFxFbS2QlycJd8+eTLjJJdn8Vbtt2zbccMMN6NGjh60fmoiOHJGv2Fh+6BCRQy3asqUh4R6clIRbhg9nwk3UEt9/LxfPAWmg5uurbTyuoK4OyMmR/du9egHe3lpHRGSVFq1079y5E++99x4OHjyIiIgIXHvttRhT3wBi06ZNeOKJJ/Dzzz/D19cXN910k10CJvJY+fnAnj0yXsTfX+toiMjDDOncGYcLC3FZSgp6xcdrHQ6Ra1EUYNEiOdbpgKuv1jIa12A0AtnZQFIS0Ls3m8aSS2v2q/evv/7CyJEjYTAYGu6bN28eXnvtNRiNRjz88MNo06YNHnroIdx3332I5wcyke1UVMg+bp0OCA3VOhoi8gAmRUF2aSkS27YFACSEh+O+UaPgy5UmopZbv15GfALAkCHcInY2RqP8fSUmSqdyVgWQi2t20v3MM88gICAACxcuREZGBg4fPozJkydj+vTpqK6uxrRp0/D4448jLCzMnvESeZ66OmD3bhmRwQ9pInKA0qoqfLdpE3LKynDb8OGIq/9sZ8JNZCU2UGs+k0kS7oQEICWF1X3kFpq9KXT9+vW4++67MXr0aAQFBaF379547bXXUF5ejnvvvRcvvfQSE24iW1NV4OBBIDMTiI/neAwisru9eXmYtWYNjpeWwsfLCxU1NVqHROTaCgqANWvkODoaOOccbeNxZooiCXd8vCTcAQFaR0RkE81e6S4tLUX37t1PuM98e+TIkbaNiohEdjawf798SHMvExHZkUlRsHzPHvzF7uREtrV4saWB2lVX8fP8dFRVznuio4HUVIDvPeRGmv2vXlVVeJ9UVma+HcCrUES2V1oq+7j9/fnBQ0R2VVpVhW83bUJWaSkA6U5+Ya9e8OaUBKLWMZmAhQvl2MuLDdROR1VlhTsiAkhLA4KDtY6IyKZadKltyZIlyM3NbbhdVVUFnU6Hb775Blu2bDnhZ3U6He6//36bBEnkcWpqZB93VRX3cROR3e3IzkZWaSkCfH1xVVoaesbFaR0SkXv480+ZMQ0Aw4cD/LfVtJwcmc6Slga0aaN1NEQ2p1NVVW3OD3q18Gq3TqeDyVxK48TKysoQFhaGkpIShIeHax0Okexn2rEDOHBAEu4WNi5SVBX5BgNiAgLgxT3g5Ab4mrY/RVXx6+7dGNSpE8vJHYCvaQ9y//2W/dyvvw5kZGgbjx20+vWcmyt7t/v1k5VuIo2VlpYiIiICer0eoTaaGtTsle7Dhw/b5AmJ6CyOHQMOHwZiY1uccBMRNUdpVRV+378fl/bpA19vb3jpdBidnKx1WETuJTcX+OMPOY6NBYYN0zYeZ5SXJ9vo0tOZcJNba3bS3bFjR3vGQUQAUFgI7NkjpVXslUBEdrA3NxeLtm6Foa4O/j4+uLh3b61DInJPixZJ9RoAjBnDC+knKyyUv5O0NCAyUutoiOyK7ROJnEVVFbBzpzRd4fg9IrKxk7uTJ4SHY0hSksZREbkpo1GSbkASy6uu0jQcp1NUJM3T0tOlWzmRm2PSTeQMjEZpnFZcDCQmah0NEbmZk7uTD0lKwgXsTk5kP2vWyEouAIwYwcSysdJSoK5OEm42liMPwaSbyBkcOiR7uePjATbVISIbOlJYiHkbN8JQV8fu5ESOsmCB5XjsWO3icDZ6PVBdLSXlCQlaR0PkMEy6ibSWkwPs2we0bQv4+modDRG5mYjgYOgg5eTX9OuHcHYnJ7Kv48eBdevkOCEBGDxY23icRXk5UFkJpKSwqo88DpNuIi2VlQG7dkmyHRKidTRE5CbMq9oAEBYYiIlDhyIqJITl5ESOYN7LDUgDNf67AyoqZJW7Tx+AzZnJA1n1LnDLLbdg/fr1p/3+33//jVtuucXqoIg8Qm2tJNwVFUBUlNbREJGb2JObizd++w17c3Mb7osNDWXCTeQIdXXA99/Lsbc3cMUV2sbjDKqqZB93cjLQuTO30ZFHsuoT+JNPPsHBgwdP+/3Dhw9j7ty5VgdF5PZUFdi/H8jOZhMRIrIJk6Lg5507MW/DBhjq6rDh6FGtQyLyPKtWSVNUABg5kqOwDAbpVN6jB9ClCxNu8lh2KS/Pzs5GYGCgPR6ayD1kZgIHDgAxMYAPd3kQUeuc3J18aOfOGNWzp7ZBEXkiNlCzMBiA/Hyge3f5YrUNebBmn+0vXrwYixcvbrg9e/ZsLF++/JSfKy0txfLlyzFw4EDbREjkboqLZTxYcDDAi1NE1Ep7cnOxeOvWhn3cV6eloQcraIgc7+hR4J9/5LhDB2DAAG3j0VJtLZCXB3TrBvTsyYSbPF6zk+5du3bhm2++AQDodDqsX78eGzduPOFndDodgoODMWLECLz22mu2jZTIHVRXyz7uujru4yaiVsvV6zFvwwYA7E5OpLmFCy3HY8d6bil1XZ1MZuncGejVS/a2E3m4Zifdjz76KB599FEAgJeXFz766CPccMMNdguMyO2YTMCePVJqxVEZRGQDcWFhGNCxI3y9vTGqZ082SyPSSk0N8MMPcuzrC1x+ubbxaMVolH41SUlA797cQkdUz6p/CYqi2DoOIvd3+DBw5AgQH88yKyKy2t7cXCSEhyMkIAAAcGmfPtB56ooakbNYuVJGYgHAqFFAeLim4WjCnHAnJkqn8vqxhURkZfdyImqhvDxg714gIgLw89M6GiJyQSZFwbKdO/H1hg1YsGULFFUFACbcRM7gu+8sx+PGaReHVhRFEu6EBCAlBfD31zoiIqdiddK9dOlSXHjhhYiMjISPjw+8vb1P+SIiyBzu3btldbtNG62jISIXVFJVhTl//IH1hw8DAOJCQ6HWJ91EpLFDh4DNm+U4KQlIT9c0HIdTFBkLFhcnCXd9FQ4RWViVdH/33Xe4/PLLkZeXh+uvvx6KomD8+PG4/vrrERgYiNTUVEyfPt3WsRK5nro6SbhLS4HoaK2jISIXtDsnB7NWr0a2Xo8AX19cP3AgLkpO5v5tImfhyQ3UVFWapoWFScLNRo5ETbJqT/fMmTMxaNAgrF27FiUlJXjvvfdwyy23YOTIkThy5AiGDBmCpKQkW8dK5FpUVWZxZ2ZKuZUnfQgTUauZFAW/7t7dsLrdPjwc49idnMi5GAzAjz/Ksb8/cNll2sbjSKoKZGXJ/vUOHWQUKhE1yarL5Lt27cL1118Pb29v+NR3JayrqwMAdOrUCXfddRdefPFF20VJ5IqysoD9+2WFm907iaiFTIqCA/n5AIChnTtj0rBhTLiJnM3y5UB5uRxfeCEQGqptPI5kXuFOTQUCA7WOhsipWZUJBAUFwa++GVR4eDj8/f2Rk5PT8P3Y2Fgcrr8yT+SRSkulrDwggKVWRGQVPx8fXNu/P0qrq9EjNlbrcIioKY0bqI0dq10cjpabK+c3aWmSeNdfICSiplm10t2jRw/s2rWr4XZ6ejo+++wzGI1GGAwGfPnll+jQoYPNgiRyKQYDsGsXUF0NREZqHQ0RuQhzd/J1hw413BcbGsqEm8hZ7d8PbN8ux926yZ5mT5CXJ6X06ekylYWIzsqqpHvMmDFYvHgxampqAACPP/44Vq1ahfDwcERHR2PNmjV45JFHbBookUtQFGDfPrkCHBendTRE5CIadydfvns39NXVWodERGezYIHl2FMaqBUWAt7eUlLOhQWiZtOpNpo5smbNGixYsADe3t647LLLcP7559viYe2urKwMYWFhKCkpQXh4uNbhkKs7dAjYtk32cWs0MkNRVeQbDIgJCICXJ5wAkNtz99f07pwcLN66FTVGIwJ9fXF1ejq6c3Xbrbn7a9ojVFUBl1wCVFbKfualS4GQEK2jsq+iIllcSE8/YWFBURTk5+cjJiYGXpyqQG6gtLQUERER0Ov1CLVRnwabdXfKyMhARkaGrR6OyPUUFAB790oTFc6oJKKzOKU7eUQErunXD2FsSETk/H75RRJuABg92v0T7tJSGYN6UsJNRM3DlspEtlBZKfu4FUUaihARnYGiqpi7bh0yS0oAAMM6d8bInj05e5vIVZxcWu7O9HrpU5OWJiNQiajFrPp0V1UVs2bNwqBBgxAVFQVvb+9Tvnw4Iok8hdEI7NkDFBcDLAklombw0unQKz4egb6+GD9wIC5MTmbCTeQqdu+WC+0A0KsXkJysbTz2VF4uCwt9+gCJiVpHQ+SyrMqM//Of/+C1115Deno6brrpJkSwcyF5KlUFDh4Ejh4F2rXzjCYqRGQVk6Kg3GBomLU9JCkJKe3aIYTbUYhci6escldUyCp3nz5Ax45aR0Pk0qxKuufOnYtx48Zh/vz5to6HyLXk5Ei38shIwNdX62iIyEmVVFbi202bUGM04vaMDPj7+ECn0zHhJnI1FRXAsmVyHBws+7ndUVUVUFIC9O4NdO7MRQWiVrIq6a6ursYFF1xg61iIXIteLyVmfn7u30CFiKx2cnfyoooKtOO0DCLXtGyZ7G8GpHt5feWKWzEYpFN5z55Aly5MuIlswKoNZKNGjcI///xj61iIXEdtrSTcFRVAVJTW0RCREzKaTFi6Ywfmb9yIGqMR7SMiMGXECCbcRK5KVd2/tNxgAPLzgW7dgO7dAfaaILIJq/4lvfvuu/jrr7/w/PPPo6ioyNYxETk3VZWS8qwsjs0goiaVVFZizp9/4u8jRwBId/JJQ4dyHBiRK9u5Uz7/Adnn3L27tvHYWm0tkJcHdO0qq9xMuIlsplnl5W3atIHupNISo9GIJ598Ek8++SQCAgLg7e19wvd1Oh30er3tIiVyFpmZ0jwtLg5gl34iasKvu3cjR69HoK8vrk5PR3dONiByfd99ZzkeN067OOyhrk761HTuLB3ZTzqvJ6LWaVbGMG7cuFOSbiKPVFQkZeUhIQAbIBHRaVyakgIVwMW9e3N1m8gdlJUBv/wixyEhwIUXahuPLRmNQHY2kJQkjdO4oEBkc836V/XJJ5/YOQwiF1BdLXM5a2u5j5uITlBSWYndubkY1qULACDE3x/XDRigcVREZDNLlgA1NXJ82WXuc+HdaJTtcomJMm+ck1iI7MKqzRrPPPMMduzYcdrv79y5E88884zVQRE5HZMJ2LMHKCjgPm4iOsGunBzMWrMGv+7ejZ3Z2VqHQ0S25q4N1EwmSbgTEoCUFMDfX+uIiNyWVUn3008/jW3btp32+zt27MCMGTOsDorI6Rw+DBw5AsTHs7EIEQGwdCf/pr47eWJEBNpHRGgdFhHZ2tatwKFDcpyeLmO0XJ2iSMIdHy8Jt7us3BM5Kbts2iguLoafn589HprI8fLygL17gYgImclNRB6vpLIS32zahJz6hqHDunTByB494M2LckTux90aqKmq7OGOjgZSU91z1jiRk2l20r169WqsWrWq4faCBQtw4MCBU36utLQU8+bNQ0pKik0CJNJUebmMCPHyAtq00ToaInICe3JzsWjLFtQYjQj09cWY9HR0Y3dyIvdUWgqsWCHHYWHAyJGahtNqqior3BERQFoaEBysdUREHqHZSffKlSsbSsZ1Oh0WLFiABY33tzSSnJyMt956yzYREmmlrk46lZeVAe3bax0NETkJnU7XUE4+rl8/dicncmc//igNVAHg8stdf99zTo5cPEhL42ICkQM1O+n+z3/+g3vuuQeqqiImJgbvv/8+xp1UYqPT6RAUFIQA7gshV6eqwP79wPHj0mCEI/OIPJpJURpKx3vExmL8wIHoEh3NcnIid3ZyA7UxY7SLxRZyc6WUPC1NEm8icphmJ92BgYEIrL+af/jwYURHRyOIe0DIXWVlAQcOyH4nzqsk8mi7srOxfM8eTBw6tGFVuzvLyYnc38aNwLFjcjxgANCpk6bhtEpenqzSp6dLaTkROZRVl+g7duzIhJvcV0mJzOMODGRzESIPZjSZsGT7dnyzaRNKqqrw58GDWodERI7UuIGaK48JKywEvL2laVpkpNbREHkkLuERNWYwSMJtMEhZORF5pOLKSnzbqDv58C5dcH6PHhpHRUQOU1wMrFwpxxERwPnnaxuPtYqKpEw+PR2IidE6GiKPxaSbyExRgD17pASLjdOIPNau7Gx8v20bu5MTebLvvweMRjm+8krA11fbeKxRWipNYdPTgbg4raMh8mjsAENkduSIfMXFSRkWuYaHHwa2bZNjRQFeegm46irg6quBefNO/3t33w1cfz1www3AbbfJBRezY8eAW26RcsIJE4DmlhW39PdUFZg6FTjvvBPvX7NGZsGOGQM89BBQUSH3FxXJ45pPBMnmdmRl4ZtNmxq6k08ZMYIJN5GnURRg4ULLbVdsoKbXA9XVUlLOyj0izTHpJgKAggJg714gNNT1x4F4kh07ZKRbaqrcXrIEOHxYus3OnQt89tnpE98XXgC+/hr48kvgxhuB+pGIAIDnn5eTrAULJMlt/L0zaenvffHFqVUVVVXAs88Cr74qJ31RUcBHH8n3IiOl6+xPPzUvHmqxHnFxiA0NxfAuXU5onEZEHuTvv6WhKgAMGeJ61W/l5UBlJdCnD5CYqHU0RAQm3UTywbRrl1zZ5ggN17JgATB6tOX2r7/KCre3t/y/vPBC4Oefm/7dxvNJKyosY+GKi2U++yWXyO1Ro2TLQWbmmWNp6e8dPAj8/jswadKJ9//5J9Cjh6VL7rXXnvhnGD36xBE21GqHCwuhqCoAwNfbG7cNH44LevXiODAiT+XKDdQqKmSVOzkZ6NhR62iIqJ7Ve7p3796Njz/+GIcOHUJJSQnU+hMWM51OhxUrVrQ6QCK7MhqlrLi4mFeDXdHGjVIebpabC8THW263awds3376358+XR4DAN54Q/6blycryuZRcTodEBsrj32m10hLfs9oBP77X+DJJ4GTE7vc3BP33rVrJ51njUZ57J49ZZxdRQUQEnL6eOisjCYTftm1C/8cPYrze/TAiG7dAAA+3F5C5LkKCoDVq+U4MhIYMULbeFqiqkomsPTuDXTubLmYTESasyrp/uyzzzB58mT4+vqiR48eiGhi3t/JSTiR01FVWW08elQSG344uZ78/NaNP3nmGfnvjz8Cb74pX44we7Z0wk1KArKzW/a7Pj6ySl9YyKS7FYorK/HNxo3ILSsDIAk4EREWLwbM7wdXX225kOrsDAbp+9GzJ9ClC89piJyMVe8kTz/9NPr27YulS5ciKirK1jEROUZODrBvn+yZdcWupAQEBAA1NZbbcXHy/9W8xzs7u3kdWy+/HJg5Uzq9xsbKiYt5ZVlVZRX7bI/Tkt/btElWtOfPl5O7ykrgiiuATz+Vn1+/3vKz2dnyGm184ldby94DrbCzvjt5bX138rF9+6IrR+kQkckELFokxzqdJN2uwGCQi9Ddu8sXt8YQOR2r/lVmZ2fjlltuYcJNrkuvl33cfn5AcLDW0ZC1unWTSgWzCy6QEyaTSf4f//orcNFFp/5eebmUEJqtWiV7wMPCgLZtZU/10qXyvRUrZLapuUR8+nTL7NbGzvZ7jX34oayu//CDHAcHy3FEBDB0qGx5OHJEfvabb078MxQVWUrXqUWMJhN+2r4d327ahFqjER3atsXUESOYcBORWLdOLogCwPDhJ25Xcla1tXKBt2tXWeVmwk3klKxa6U5NTUV2S0siiZxFTY0k3JWVrteRlE40ciTw11/A4MFy+9JL5f+tufHNjTfKiQggTctWr5Z91BUVMmqspkZOUCIigNdft5TjPfaYdB7/+GNJiJ96yvKcu3fLqLGmnOn3nn1W9gaee+6Z/0zBwcATTwAPPCAXD7p0ObEL+rp1MmKMJ1YtVlxZic31je3O6doV53fvDi/+PRKRmas1UKurk+quzp2BXr047pTIielUKzZf//HHH7j22mvx7bffYtiwYfaIy2HKysoQFhaGkpIShIeHax0O2ZuiADt3Avv3S8Lthh9Qiqoi32BATEAAvNx9T1dVlczF/vhjwBGjnUpKgMcfB9591/7PdTq33SYxJCVpF4OD2fI1vfX4cQT7+XF1mzTlUe/TriI3F7jySjlPiI2Vvd3OvJ/baJSxZklJMhpMw21yiqIgPz8fMTExvJBJbqG0tBQRERHQ6/UIDQ21yWNa9W7y4osvIiwsDBkZGUhOTkaHDh3gfVLyotPpsHjxYpsESWQzmZnAoUPygeqGCbfHCQoCpk2TEw/zirY9RURom3AXFQHXXONRCXdrGE0m/LJ7N9Lat0dC/UXVNFa3EFFTFi+WhBtw/gZq5oQ7MVFGg7EvDZHTs+odZdu2bdDpdOjQoQMqKiqwa9euU35Gxyu35GyKimSvbEiINOAi9zBokNYROE5kJHDxxVpH4RIadyc/kJ+Pu887j3O3iahpRqOlgZq3N3DVVZqGc0YmkyTcCQlASgqbahK5CKuS7iPmBj9ErqKqSvb61tVJJ2gicluNu5MH+fnh0j59mHAT0emtXWtprnnOOdIE0xkpiiTc8fGScHMBgchlOHHtDJGNmEzA3r0y15ilpURuy2gy4eddu7ChvqN9h7ZtMa5vX4Q6Yr8/EbmuBQssx+PGaRfHmaiqjJCMjpaxmEFBWkdERC3QqqT7999/x08//YSj9Sc4HTt2xGWXXYZzz9adl8iRDh2S8Utxcez4TOSmqmpr8dlffyG3rAwAu5MTUTNlZclUCABo1w4YMkTbeJqiqhJnRASQlsZRp0QuyKqku7a2FuPHj8eiRYugqmpD1+/S0lK8+uqrGDNmDL766iv4srEDaS03F9i3T2Yo+/lpHQ0R2Umgry/aBASgzGDAmPR0dicnouZZtEiSWkAaqDnjhbqcHCAsTBLuNm20joaIrGDVO8uMGTOwcOFCPPDAA8jJyUFxcTGKi4uRm5uLBx98EAsWLMAzzzxj61iJWqa8XMaDeXtL8zQicitGkwm1RiMAad55dXo6pmRkMOEmouYxGoHvv5djb28ZGeZscnOllDwtTRJvInJJViXdX375JSZOnIiXXnoJsbGxDffHxMTgxRdfxIQJE/DZZ5/ZLEiiFqurk8Zp5eVsnEbkhooqKvDRH3/gp+3bodavUgX5+XH/NhE136pVMtkEAM47z/nOF/LypDt5erqUlhORy7Iq6c7JycHgwYNP+/3BgwcjNzfX6qDeeecddOrUCQEBARg8eDD+/vvvZv3e119/LasdV19t9XOTG1BVKSnPzpYOnxxfR7ZmMgEbNgDLlsl/TSatI/IoO7KzMXvtWhkHVlCAcoNB65CIyBU1bqA2dqx2cTSlsFBW31NTZVwkEbk0q5Lu9u3bY9WqVaf9/u+//472VnaJnjdvHqZNm4annnoKmzZtQlpaGkaPHo38/Pwz/t6RI0fw4IMPIiMjw6rnJTdy/Dhw4IB0+PRhg36ysd9+A664Apg6FXjiCfnvFVfI/WRXRpMJP23fju82bUKt0YgObdtiSkYGV7eJqOUyMwHzok5iIjBwoLbxNFZUJAsIaWnOO76MiFrEqqR74sSJmD9/PqZOnYq9e/fCZDJBURTs3bsXd955J7755htMmjTJqoBee+013H777Zg8eTKSk5Px/vvvIygoCHPmzDnt75hMJtx4442YMWMGOnfubNXzkpsoKQF275b9TzwRJ1v77TfgP/8BTr4ImJ8v9zPxtht9VRXm/Plnwziwc7p2xcQhQ5hwE5F1Gq9yjxnjPA3USktli1xKikxdISK3YNUy4GOPPYaDBw9i9uzZ+OCDDxpGsiiKAlVVMXHiRDz22GMtftza2lps3LgRjz76aMN9Xl5euOCCC7DOPM6hCc888wxiYmJw6623Ys2aNS3/A5F7MBhkH3dNjYz9ILIlkwl45ZUz/8yrrwLnnislgWQziqpi6ZYt0FdXI8jPj93Jiah1amuBH36QY19fqVZyBno9UF0tK9wJCVpHQ0Q2ZFXS7e3tjU8++QTTpk3DkiVLTpjTfemllyI1NdWqYAoLC2EymU5ozgYAsbGx2LNnT5O/s3btWnz00UfYsmVLs56jpqYGNTU1DbfL6me6KooCRVGsipucgKLICndeHtC+vWX8hwdSVBWqqkLx4L8Du9i8GV5n2eaCvDwomzcD/fs7JiYPMrxHD+w4dgxj+vZFaEAAX9/k0vg+rbHffoNXaSkAQB05Emp4uPbnDeXlQEWFrHAnJMh5jYswL7rxPJrchT1ey63a8Jqammp1gm0L5eXluPnmm/HBBx8gqpkdJ2fOnIkZM2accn9BQQFqa2ttHSI5Sm4ucOSIdPf08P+PiqpCX1cHFYAXm8jZTEBODsKb8XNlOTkwsLFXq+mrqlBWXY3EyEgoqoqQNm1wUVoaDAD/fsnl8X1aW22//RZ+9cfFl12GOq3fUwwGoLIS6NABCAg4dQuTk1MUBXq9HqqqNlS/ErkyvV5v88d0qi5TUVFR8Pb2Rl5e3gn35+XlIa6JfS0HDx7EkSNHcEWjsiDzlQkfHx/s3bsXXbp0OeF3Hn30UUybNq3hdllZGRITExEdHY3w8HAb/mnIYQoKgKwsmV/Zpo3W0WhOUVXoAEQHBPBkzpbi45v1Y6GVlQgNCLBzMO5tZ3Y2fty2DdDpcPs55yA8KIivaXIrfJ/W0OHD8KqvjlQ7dULEkCHaTjmpqpIV7uRkoEsXl5y4oigKdDodoqOjmXSTW/Dz8zv7D7VQs5JuLy8veHl5oaqqCn5+fvDy8oLuLG8KOp0ORqOxRcH4+fmhf//+WLFiRcPYL0VRsGLFCtxzzz2n/HzPnj2xffv2E+574oknUF5ejjfeeAOJiYmn/I6/vz/8/f1Pud/8ZyQXU1kpZeU6nSTdBED+/XnVf5GN9O0rKxBnWRHxeuUV2eYwdarMV6VmM5pMWLZrFzbWb1nq0LYt/Ly94aXT8TVNboevaY0sXNhwqBs7Fjotz/0MBqC4GOjZE+ja1XmauVlBp9PxXJrchj1ex81KuqdPnw6dTgef+vFL5tv2MG3aNEycOBEDBgzAoEGD8L///Q+VlZWYPHkyAGDChAlISEjAzJkzERAQgD59+pzw++bV6pPvJzdkNErjtNJS2cdNZE+//37WhLvBZ58Ba9YATz8N8L2oWYoqKvDNpk3Iq++zkdG1K87r3h1eXl7c90pEtmEwAD/9JMd+fsBll2kbS34+0L27fDFZJXJrzUq6n3766TPetqXrrrsOBQUFmD59OnJzc5Geno5ly5Y1NFc7duwYr6KRNDw5cEDmbCYkuGQ5FrmQ/Hzgv/+13A4NBeqTQwBAbCxw//1ATg7w3nvSV+DIEeCWW4CJE4Hbb5cTPGrSjqws/LBtG2pNJgT5+WFs377oEh2tdVhE5G6WL7e8d194oXYVcrW1UhHVrZuscvO8lsjt6VS15UsIzzzzDMaOHXva1eSdO3fiu+++w/Tp01sdoL2VlZUhLCwMJSUl3NPtSrKygI0bgfBwIDhY62iciqKqyDcYEMO9grahKMA99wB//y23R44Enn8e2LIFKCwEoqKk9Nw8JuzQIVnh3rXL8hhdu8p9PXs6OHjX8PPOnfjr8GF0bNsW4/r1Q5uT9sTzNU3uhq9pjdxyC7Btmxx/9JGM5nK0ujogOxvo3FkqoXycqr2SVRRFQX5+PmJiYrgwRm6htLQUERER0Ov1CA0NtcljWvUv4+mnn8Y285tWE3bs2NFkh3Aim9DrZR+3vz8TbrK/L7+0JNwxMcDjj8tJ0oABwMUXy38bz+Xu3BmYMwe4807LydSBA7LiPXu2bIsgNL7ee0GvXri0Tx9MGDLklISbiMgmDhywJNxdugBaTN8xGiXhTkoCevd2i4SbiJrHLpejiouL7dL1jQg1NbKCWFkpK4xE9rRvH/DOO5bbTz/dvHJEHx/g1ltlb3f37nKfySRJ96RJcvLnwbZnZeHLv/9umDbh7eWFgZ06cYWEiOznu+8sx+PGOX5bmtEoVXqJidKp3NfXsc9PRJpq9iW21atXY9WqVQ23FyxYgANNnDiWlpZi3rx5SElJsUmARA0URZKgnBw2TiP7MxiAJ56QUkAAuOkmYNCglj1Gt27A3Lmy8j1njiTee/YAN98M3HGH/NeDVjrqTCb8vHMnNh47BgDYlJmJAR07ahwVEbm96mpgyRI5DggALr3Usc9vMknCnZAApKRwsgWRB2r22d7KlSsbSsZ1Oh0WLFiABQsWNPmzycnJeOutt2wTIZFZZqbsl42NPbGcl8ge3npLXm+ArFbfdZd1j+PrC0yZAowYATz1lDxmXZ2soK9aBcyYAXTqZKuondYp3cm7dUO/JsY6EhHZ3M8/S4UcAFx0ERAS4rjnVhRJuOPjJeHmFhoij9TsWr7//Oc/KCgoQH5+PlRVxfvvv4+CgoITvgoLC1FVVYUdO3Zg8ODB9oybPE1hoezjDgnhBxbZ3x9/APPmybG/P/Dcc63vPt6rF/D551Jebi6j3rkTuPFGud9kat3jO7HtWVmYvWYN8srKEOTnh5sGD8bIHj1YTk5EjtF4kWjcOMc9r6rKHu7oaNlDHhTkuOcmIqfS7JXuwMBABAYGAgAOHz6MmJiYhttEdlVVJcmJ0SgfXET2VFwMPPOM5fZ990lzNFvw85NO6OeeK/vDjx6VPgX/+5+sej/1lOz3cyNrDxzAij17AOC03cmJiOxmzx7LNIkePWQ/tSOoqqxwh4dLl3Q2fiXyaFYtMyiKguXLl5/2+z/88AOOHDlibUxEFuY9sMXFUlZOZE+qCjz7LFBUJLeHDweuvdb2z5OSAnzxhaxym5v5bNkCjB8vK+z1DcbcQc+4OPj5+GBEt27sTk5EjqdVA7WcHGm8mZ4OtGnjmOckIqdlVdL94IMP4s033zzt99955x088sgjVgdF1ODQIeDIEdkLxVJUsrfvvgPWrJHjiAhg+nT7naAFBAD33y8dzc2NAQ0G4OWXZf94drZ9ntcBCsrLG46jQkJw7/nn43yWkxORo1VWyn5uQEq7R492zPPm5srzpaU1b+IFEbk9q86A1q1bhwsvvPC03x81ahTWmE9ciayVmwvs3QtERnK0BtnfkSPA669bbk+fLq89e+vbF/jqqxNX1DdsAK6/XvYhNppn7ezqTCb8sG0b3lu9GkfN1QIAgtmpl4i0sGyZbFEDgIsvdkyJd16e9AJJT5eLt0REsDLpLikpQZszlMqEhISgqNEJF1GLlZXJPm4fH8d2GSXPVFcn48FqauT2tdcCGRmOe/7AQODhh4H33pOqDkBOFJ9/Hrj3XjmJc3KFFRX4aO1abDp2DKqqIluv1zokIvJkqnpiafnYsfZ/zsJCma6SmuqYi7ZE5DKsSro7dOiAP/7447TfX7NmDdpzjjJZq7ZWOpVXVABRUVpHQ57g/feldwAg47vuu0+bOAYOlFXvMWMs961bB1x3HfDDD0676t3Qnby8HMF+frh58GAMtVXzOSIia+zcCezbJ8e9ewM9e9r3+YqK5D06LQ2IibHvcxGRy7Eq6R4/fjy++uorvPnmm1AaNfwxmUx44403MG/ePNxwww02C5I8iKoC+/dLx8+4OMc1PCHPtWED8OmncuzjI+PBtGz2FRICPP64zAk3n7hVVMg872nTZCXFSZjLyRds3ow6kwmdIiMxZcQIdOaUASLSWuMxYfZe5S4tlYqplBQ5dyEiOolOVVu+dFJTU4PLLrsMv/32G6Kjo9GjRw8AwN69e1FQUIDzzjsPS5cuhb8L7OMrKytDWFgYSkpKEB4ernU4lJkJbNokZVkcSWcVRVWRbzAgJiAAXrxocWZlZdIx3Fy+fe+9wIQJ2sbUWHk58OqrwI8/Wu4LDQX+8x9pCKTx/99tx49j4ZYtAIAR3brh3O7d7fKa42ua3A1f03ZWXi57uGtq5ELm0qX2O6fQ62U7UFqa2418bC5FUZCfn4+YmBg2zCS3UFpaioiICOj1eoSGhtrkMa36l+Hv749ffvkFH330EQYNGoTCwkIUFhZi0KBBmDNnDpYvX+4SCTc5meJimaUZHMyEm+xPVYGZMy0J94ABwE03aRvTydq0kXner75q2R9YVib7zx9+WP7NaCglIQEDOnbETYMHS3dyJg9E5AyWLLH06Lj0UvudU5SXS4f0Pn08NuEmouaxaqXbnXCl20lUVwMbNwIlJUC7dlpH49K4gtJMP/4oCS0gq8dffuncZYGlpTJOzDz+BgDCw4FHHwVGjXJICHUmE1bv34/hXbogwIETBfiaJnfD17Qdqar0wTh0SG5//TXQtavtn6eiQt6X+/QBOnfWvPJIS1zpJnfjNCvdRDZlMslosIIC5056yH0cPw689JLl9mOPOf9rLzwc+O9/gRdflGNATvgeflj2gJeW2vXpCysq8OHatVh74AB+3LbNrs9FRGS1rVstCXdamn0S7qoqWSRITvb4hJuImsfH2l/Mzc3FRx99hE2bNkGv15/QUA0AdDodVqxY0eoAyQMcOSJfsbEAr5CSvRmNMoPbPLv1iiuACy7QNqaWGDVKZnvPnAmsXCn3/fyzNIR7/HFgxAibP+W248fx4/btqDOZEOznh34dOtj8OYiIbMLeDdQMBulU3rMn0KULE24iaharku5t27bhvPPOQ3V1NXr06IHt27cjOTkZpaWlyMrKQpcuXZDIvS3UHPn5MqopLAxgHwByhI8/BswrtQkJwIMPahuPNdq2lZX6n3+W/5aVyUngtGnA5ZcDDzwg+8Fbqc5kwtIdO7A5MxMA0CkyEmP79kUbLbu7ExGdTmkpsHy5HIeG2n7rjcEg5y3du8sXFwqIqJmserd45JFHEBISgr1792L58uVQVRVvvPEGMjMzMW/ePJSUlOCFF16wdazkbioqpHGaTicfjkT2tn078OGHcuztDTz7rDTuc0U6nXTnnT8fyMiw3P/jj7Kf8c8/W/XwJZWV+HDt2oaE+9xu3XDzkCFMuInIef30E1BbK8eXX27b8Y+1tdJ4s2tXWeVmwk1ELWDVO8Yff/yBKVOmoEOHDg0NE8zl5ddeey1uvPFGPPTQQ7aLktxPXR2we7dclTbPIiayp8pK6fptMsntW28FUlO1jckWoqKA116TpnAhIXJffr6MP/vvf+XPbQV/X19U19Uh2N8fNw8ZgvPYnZyInJmq2q+0vK4OyMmR/du9eslFWyKiFrAq6VYUBbGxsQCA8PBweHt7o7jR6JqUlBRs3LjRNhGS+1FV4OBBaWYVH8/9UOQYr7wCZGXJcWoqcMst2sZjSzqdrOrMmwcMGWK5f+FC4PrrgX/+adbDmBr15gjy88P4gQMxNSMDnaOibB0xEZFtbdwIHD0qx/37A5062eZxjUYgOxtISgJ69wZ8rG6HREQezKqkOykpCYcPH5YH8PJCUlISlpv30AD4888/OX6LTi87G9i/X1bo+OFFjrB8OfDDD3IcFAQ884x7vvZiY4G33pJu7EFBcl9ODnDnndL1vLr6tL9aWFGB2WvWYEt9OTkAxIeFIYTl5ETkCuyxym00ysXaxETpVO7AUYlE5F6sSrovuugifPPNNw2377zzTnz44Ye44IILMGrUKMydOxc33HCDzYIkN1JaKvu4/f0tSQGRPeXlAc8/b7n9n/8A7dtrF4+96XRywvn118CAAZb7v/kGGD8e2LLllF/Zdvw4Zq9Zg/zycqzev/+EFW8iIqdXXAz89psch4cD55/f+sc0mSThTkgAUlLY7JWIWsWqpPvxxx/HV199hbq6OgDAv//9bzzzzDMoKiqCXq/Hk08+ieeee86mgZIbqKmRfdxVVUBkpNbRkCdQFOCpp6S7NwBceCFw2WXaxuQo7doB774LPPSQpZnQ8ePA7bfLHnCDAXUmE77fuhULt2xBncmEpMhI3DJsGLzZIIiIXMkPP8iqNABceSXg59e6x1MUSbjj4yXhZsUPEbWSTlVVtSW/oKoqysvL4efnhwA3eBMqKytDWFgYSkpKWBJvT4oC7NgBHDggq4xsQmI3iqoi32BATEAAG199+inw5ptyHBsLfPWVZ3bKz8yURmtbtzbcZUxMxOIxY7CjbVsAwLndu2NEt25O+Zrha5rcDV/TNqQoUt1z/LjcXrhQysGtpaqScEdGAn37uu6ECwdSFAX5+fmIiYlpaLBM5MpKS0sREREBvV6PUBudN7b4X0ZtbS3atm2LN80nskTNcewYcPiwJD5MuMkR9uyRlV5ASq5nzPDMhBuQE9DZs4F//7thBcgnMxNj3noLF69ciQn9+uG87t158k9ErueffywJ96BBtkm4w8OBtDQm3ERkMy1Ouv39/REXFwd/7m2h5ioslASoTRuWaJFjGAzAk09ayg1vvvnE/c2eyNsbuOkm4MsvpQMvAC9VxeDff0fSv/8tWz+IiFzNd99ZjseNa91j5eQAYWFAerqcsxAR2YhVNSCTJk3Cp59+itraWlvHQ+6mqgrYuVMakoSFaR0NeYo33pDKCgDo2VO6d3u4wooKlFRVyRidjz6CcvfdUM2deA8dAiZNAmbNknm0RESuoLAQ+P13OY6MBM491/rHys2VBq9paTxfISKbs2pmTkpKChYtWoTevXtj0qRJ6NSpEwIDA0/5ubG2GtlArslolNWz4uLWlXsRtcSaNdKpG5Bus8895/FjXrYdP44ft29HVEgIbhk2DD4+PvCaPBnIyJBGc3v3yoWxDz6QE9gZM4Bu3bQOm4jozBYvlvcuQBqoWTsKMi9PPi/S04GICJuFR0RkZtW70/jx4xuOn3zyySZ/RqfTwWR+IyTPdOiQ7OWOj5c9tUT2VlQkM7jN7r9fVnY9VJ3JhKU7dmBz/eztAB8f1JpM8DH3VejaFZg7F5gzB/joIzl53bdPyvFvvx2YONE955kTkeszmYBFi+RYpwPGjLHucQoLZftNaionqxCR3Vh1NrVy5Upbx0HuJidHTt7btvX4VUZyEFWVhLukRG5nZLR+f58LK6yowDcbNyK/vBwAcF737shoqju5jw9wxx3AiBHS4fzAAalSee89y6p3UpLj/wBERGfy119yrgEAQ4fKmMSWKiqSz470dCAmxqbhERE11uyk+7HHHsP111+P1NRUnNuaPTPk/srKgF27JNkOCdE6GvIU33wD/PGHHLdtK43UPLTCYuvx4/hp+3bUmUwI9vfHuL59kRQVdeZf6tlTRqx98IGsfiuK/Du+8UZg6lT5LycPEJGzaNxAzZrtjKWl0sMiPR2Ii7NVVERETWp2I7UXXngBO3bsaLhdVFQEb29v/Pbbb3YJjFxUba2cqFdUAGc7ySeylUOHpHma2VNPSeLtgRRFwd+HD6POZEJSZCSmZmScPeE28/MD7r5bys3NZfm1tTLr/PbbZbsIEZHW8vKAtWvlOCYGOOeclv2+Xg9UV0tJeUKC7eMjIjpJqybYq6pqqzjIHagqsH8/kJ3Nq8bkOLW1wBNPADU1cvtf/wKGD9c2Jg15eXnhmn79MLJHD9w0ZAhCrBnT16cP8PnnsrfbXC2wbRswfjzw9deyCk5EpJXFiy3vQ1dd1bLeE+XlQGWlvM+xySsROUirkm6iE2Rmyn7QmBg2XyLHee896R8AAJ07A/feq208Gth6/DhW79/fcDsiOLjp/dstERAA3HeflJubT0xraoBXXpFy8+PHWxk1EZEVjEZLAzUvL+Dqq5v/uxUVssqdnAx07GiP6IiImsSkm2yjuFjGgwUHA02MjyOyi7//Bj77TI59fWU8mDUruy6qzmTC4q1bsWjLFqzcuxeZ5iZytpSeDnz5JXD99Zb7Nm2SVe9vv5UKFyIiR/njDyA/X47POQeIjW3e71VVSaPN5GS5QOuhPT+ISBstWo48cuQINm3aBADQ6/UAgP379yM8PLzJn+/Xr1/roiPXUF0t+7jr6riPmxxHr5du22Z33w10765ZOI5WUF6ObzZtQkGj7uQJp3kvbrXAQODBB4HzzpMO8dnZ8u/+hReAlSulaR23lBCRIyxYYDlubgM1g0E6lffsCXTpwoSbiBxOpzZzY7aXlxd0J71Jqap6yn2N73eFOd1lZWUICwtDSUnJaS8e0BmYTLLX8/BhKUH1YvGE1hRVRb7BgJiAgNaVFzszVQUefhgwN3IcNAh4+22Pef017k4e4u+Psc3pTm4rlZXStK7xiW9wMDBtGnDllXY5mfWI1zR5FL6mrZSdLXu4VRWIj5cy87NNVTAYZGW8e3egVy+P+ZxwJEVRkJ+fj5iYGHjx75fcQGlpKSIiIqDX6xEaGmqTx2z2SvfHH39skyckN3P4MHD0qHz48Y2WHOWHHywJd1iYrHh7yOtvyY4d+OfIEQBAUlQUxvbtixB/f8cFEBwMPPYYMHIk8Oyz0kW4slKOf/tNmtpFRzsuHiLyHIsWWba0XH312RPu2lp5j+rWTVa5PeRzgoicT7OT7okTJ9ozDnJFZWXSOC08XEYNETlCZqY08zJ77DFp3uchEsLDsQHAud27t75ZWmsMGQLMmwe8+qpcBAFkr+W//gU89BBwySUs4SQi2zEapWs5IMn2VVed+efr6oCcHNm/3avX2RN0IiI74iU/sl5BgezrbNNG60jIUxiNsn+4qkpuX3klMGqUtjE5QKV5HBqAtPbtcee55+Lc7t21L0sNCZGZ6K+/DkRGyn3l5cD06ZJ4FxVpGx8RuY/ff7e8p5x77pl7yBiNUoqelAT07s2JKkSkOSbdZB2jUUYGhYRoHQl5ko8+AnbskOPERGnu5cZqjUYs3rIFs9asQVVtbcP90c52oSsjA5g/X1a3zVatklXv5cs1C4uI3EhzG6gZjUBWlnxGJCfLZAsiIo0x6SbrFBUBpaWAjZoLEJ3Vli2SdANSJvjss0BQkKYh2VNBeTk+XLsWW44fR4XBgEOFhVqHdGZhYfL/5OWXgYgIuU+vBx55BHj0UXm/ICKyRmYmsH69HCckSPPMpphMknAnJAApKYAj+10QEZ0Bk26yTk6O7NdkyRY5QkWFlCwrity+/XagTx9tY7KjrceP44O1a1FQUYEQf39MGDIEfdq10zqs5jn/fFn1blz2/+uvsuq9apVmYRGRC1u40HI8dmzTDdEURRLu+HhJuAMCHBcfEdFZMOmmlquslG6gYWFaR0Ke4uWXZX8eAKSlAZMnaxuPnZjLyRdt2YI6kwmdo6IwZcQIdHLUODBbiYgAXnwReP55y/tEcbFsB5g+XZowEhE1R22tpVmjjw9wxRWn/oyqymdEdDSQmurWVVBE5JqYdFPLFRZK4s393OQIv/wC/PSTHAcHA88847ZdaFft24ctx49DB+C87t1x4+DBjh0HZmsXXSQdzkeMsNy3ZAlw3XXS6ZyI6GxWrgRKSuT4/POBtm1P/L6qygp3eLhclA0OdniIRERnw6SbWkZRpIEaryKTI+TmAjNnWm4//LDs1XNTI7p1Q2JEBCYMGeIc3cltISpKxorNmGG5UFdQANx3n+wBr6jQNj4icm6NG6iNG3fq93NypKImPZ3TVIjIaTHpppYpKZEy0fBwrSMhd2cySSlyebncvuiiE7tju4FaoxEbjh6FqqoAgABfX0weNsz1ysnPRqcDLrtM9noPG2a5f/FiWfU2N0giImrsyBFg40Y57tgR6N//xO/n5soiQFoat7wRkVNj0k0tk5cnq90cwUH29tlnwKZNchwXJx2w3WHlt565O/lP27djw9GjDffr3OjPeIqYGOCNN4AnnrCUgOblAXffDbzwgmX+OhERcOqYsMbvj3l50p08Pd0yMYGIyEkx6abmMxikUQnHhJG97d4NvPeeHOt0UprsRmWDWzIzT+hO7nRzt+1JpwOuvhr4+mtg4EDL/d9+C4wfb7nQQkSerabG0s/Dzw+4/HLL9woLpbdHaioQGalNfERELcCkm5qvqEj2X3pSgkCOV10tK6Emk9yeNOnUkkIXVWs0YtGWLVi8dWtDd/KpI0agkyeeNMbHA++8I/v0zaN9srKAKVNkD7jBoG18RKStFSsAvV6OR42ylI8XFUnztLQ0qZ4hInIBTLqpeczdQX193arEl5zQ668D5nLrXr2AO+7QNh4bya8vJ99a3538/B49cNPgwQh25e7kreXlBVx7rax69+0r96kq8NVXwA03ANu2aRsfEWnnu+8sx+YGaqWlQF2dzOGOi9MkLCIiazDppubR66XjMBuokT39/rtlD19AgHS3dpP+AdW1tSisLyefMHQoRnTr5t77t1uifXtg1ixg2jTZowkAx44Bt90GvPmmlJkSkec4cADYulWOO3eWVW29XiqhUlPdeooFEbknJt3UPAUFQG2tpQyUyNYKCyXJNps2DejUSbNwbMHclRwAOkZGYmy/fp5bTn42Xl6yuv3FF7KKBUjTxk8/he7mm+GzZ4+28RGR45zcQK2iAqisBPr0ARITtYuLiMhKTLrp7OrqpLSce7nJXlQVeOYZKR0EgHPPBcaM0TSk1jKXkxeYR54B6NOunWeXkzdHp07Ahx8C//d/DVUOusOHETl1KnTvvSfvR0TkvgwGYMkSOfb3l88DvR5ITpaxYURELohJN51dUZF84LFrOdnLvHnAn3/KcWSkNFJz4dLrLZmZ+GDNGmTr9fh51y6tw3E93t7AxInA55/Lvn4AOpMJujlzgAkTgL17NQ6QiOzml19kZRuQBmpGoyTcnTu79OcCEXk2Jt10djk58kHn7a11JOSODhyQfbtmTz/tsjNXG3cnNyoKukRHY0x6utZhua4uXYCPP4YyZQpU8/vP/v2SeH/4oZyME5F7adxA7dxzgZ495b2ACTcRuTAm3XRmFRVAfj4bqJF91NQATz4p/QIAmdM8dKi2MVnp5O7kI3v0wI2DBrGcvLV8fIDbbkPRBx9A7dZN7jOZgPffByZPBg4e1DY+IrKdvXuBnTvlOCkJuPRSoHt36flAROTC+C5GZ1ZYKM1LgoO1joTc0TvvyMolICsZ99yjbTxWyi4txQdr1qCgogJt/P0xcehQZLA7uU0Zu3WDOncucOutlqqb3buBm24CPvnEMtediFxX4wZqN94o20uYcBORG+A7GZ2eyQRkZjLhJvv46y/gyy/l2M8P+O9/LeOiXExcaCjiw8LQJToaU0aMQEd2J7cPX1/gzjuBOXNkfycgjdXeflvGix05oml4RNQKlZXA0qVyHBgI3H8/t7URkdtg0k2nV1wMlJSwtJxsr7RU9m6b3XMP0LWrVtFYpbCiAiZFAQB4eXnhhkGDWE7uKL17A599Jnu7zatg27fLytgXX8ioMSJyLUuXAlVVcnzjjUDbttrGQ0RkQ0y66fTy8mSUk4+P1pGQO1FVWdUuLJTbQ4YA11+vbUwtoKoqNmdmYtbq1fh19+6G+wN8fVlO7kj+/sC990pDtQ4d5L6aGuD114EpU4Djx7WNj4iar65OpliY3XmndrEQEdkBk25qWnW1dC0PC9M6EnI3ixcDK1fKcViYrHi7yJ69WqMRi7duxff13ckbr3aTRlJTZZvC+PGW7sabN8uFnG++4ao3kbMzmYDVq4HDh+X2wIFAv37axkREZGOucaZLjldYCJSXAyEhWkdC7uTYMeCVVyy3n3wSiIrSLp4WyC8rwwdNdCf3dpELBm4tIAB44AFg1iwgIUHuMxiAF1+UrQs5OdrGR0RNUxQgKwtYu9Zy35Qp2sVDRGQnPFukU6mqfAgGBHAuJtmO0Qg88YQkQwAwZgxw3nmahtQc5nLyD9auRSG7kzu3fv2Ar74CrrnGct/ff8uq96JF8t5GRM5BVYHsbGmatmKF3Bca6lLbjYiImotJN52qtBQoKmIDNbKt2bOBXbvkuEMHYNo0beNppsraWvy8cyeMisLu5K4gKAh45BEZRxcXJ/dVVgLPPQfcd5/0qiAibZkv7oeHy1xucwO1m2/mxBQicktMuulU+fnS1IRdmMlWNm+WWcqAjIB59llZ3XABIf7+uDI1taGcnN3JXcTgwcDXXwNXXWW5788/geuuA378kaveRFoy94xJSwPmzrXcz9JyInJTTLrpRLW1cvW5TRutIyF3UVEBTJ9uaWg1ZYqMfHJSqqpi87FjOFRQ0HBfcrt2LCd3RSEh0jfgjTeA6Gi5r6JCmvc98IClgz4ROU5urlSkpKVJ9dP27XL/0KFASoq2sRER2QmTbjpRURFQVib7qohs4cUXLY2s+vYFJk7UNp4zqDUasWjLFny/bRsWbN6MypoarUMiWxg+XMYRXXqp5b7Vq2XV++efuepN5Ch5eVJFl54ORERI80OzqVM1C4uIyN6YdNOJsrNlLjc7MpMtLFsGLF0qxyEhwDPPSHm5EzJ3J9+WlQWdTochnTsjyM9P67DIVkJD5fX3yitA27Zyn14PPP647AEvKdE2PiJ3V1go7/+pqUBkJFBcDMyfL9+LiACuvVbb+IiI7IiZFVmUlcl+bjZQI1vIyQFmzrTcfuQRID5eu3hOQ1VVbDp2zNKdPCAAE4cMwTldu7Kc3B2dd56c6F94oeW+FSuAf/3LMj+eiGyrqEgqStLSgJgYue/TTy3TLCZMcJk+H0RE1mDSTRaFhUB1NT/4qPVMJtnHXVkpty+5BLj4Ym1jaoJJUbBoyxb8sG0bjIqCrtHRmJKRwe7k7i48XC4IvfCCNHMCZKX7oYdkD7her2l4RG6ltFSas6akWCYKqOqJpeVsoEZEbo5JNwmjEcjMlBJgotaaO1c6lgOyuv3ww9rGcxpeOh109V+jevbEDexO7lkuuEBWvRvPi1+6VPZ6r12rWVhEbkOvl4v5qalAQoLl/jVrgD175HjECKBXL23iIyJyECbdJIqL5Wq0edWHyFo7d1pWMLy8ZB+tE13MUVUVRpMJAKDT6XBpnz6YPGwYy8k9VWQk8PLLMsbOPLWhsBD497+BGTOk2zkRtVx5uVQ79ekDJCae+L3337ccc5WbiDwAk24SubnyXx8fbeMg11ZVJeW59UktJk2SjuVOwtydfP7GjVDrO1b7+fggMSJC48hIUzqdbIGYP186nZv98IOsev/1l3axEbmiigpZ5U5OBjp2PPF7hYXAd9/JcVQUMG6c4+MjInIwJt0kiVJuLhuoUeu9/jpw7JgcJycDd9yhbTyN5JWVYfaaNdiWlYUDBQXIKi3VOiRyNtHRwP/+J/0IgoPlvrw84J57gOeft/QoIKLTq6qSHgnJyUDnznJRq7FPPgFqa+V40iQZIUZE5OaYdJNcda6osJxkEllj5Upg4UI5DgwEnnvOKSonzN3JP1y7FkWVlWgTEIBJQ4eiPVe3qSk6HXDllTLXe/Bgy/0LFgDjxwMbNmgXG5GzMxikU3nPnkCXLqcm3IpyYgM1J7owS0RkT0y6PZ2iAFlZkiRxPytZq6BAkmyzBx4AOnTQLp565nLyxt3Jp44YgQ7mOc1EpxMXB7z9toy6M090yM4Gpk6VPeDV1drGR+RsDAYZO9qtG9C9u/T0ONnKlcCBA3I8apT8LBGRB2DS7elKS+WqNEvLyVqKIg2nzGOWzj8fuOoqbWOq983GjdiWlXVCd/IgPz+twyJXodMB11wDfPUV0K+f5f5584AbbgC2bNEsNCKnUlsrWzG6dpVV7qYSboBjwojIYzHp9nT5+TIujIkIWevrry2NpqKjgccfd5qqifO6d0dEUBAmDR3K7uRkvfbtpdvygw9a9p9mZgK33y57wA0GTcMj0lRdHZCTI/u3e/UCvL2b/rncXMsWpNhYp7k4S0TkCEy6PVlNjZSWh4ZqHQm5qv37gbfestx+6ilNqyZqjEYcKixsuJ0QEYG7zzuP5eTUel5ewPXXA19+KTOHAUBVgc8/B266CdixQ9v4iLRgNMq2i6QkoHfvM/fx+Phj+XkAuOUWXuwnIo/CpNuTFRbKHE3zbFqilqipAZ54QlY5ACm3HTJEs3DyysrwwZo1+PLvv5FrLnUH4H26Mkcia3TsCHzwAXDffZak4cgRSSLeecfSlZnI3RmNcuE+MVE6lfv6nv5nFQWYPVuOdTqpEiEi8iA8G/VUqipXp319T7/3iuhM3n4bOHhQjrt1A+6+W5MwVFXFxqNHG7qTB/n5wagomsRCHsLbG7j5ZlnlTk6W+xRFVvImTAD27NE2PiJ7M5kk4U5IAFJSzj7265df5OIUAIweLSvjREQehNmWpyork47TbKBG1li3TppLAbLa99xzmsxarTEasXDzZvy4fTuMioJuMTGYOmIEx4GRY3TuDMyZA9x1l6Ws9sABYOJEWdUzl9ISuRPz1JP4eEm4AwLO/jtsoEZEHo5Jt6cqKJDmP835sCRqrKQEePppy+1775V5rA5mLiffnp0NnU6HC3r2xPiBA9mdnBzLx0dKyz/7TMYkAbIKOHu2JN/m8UhE7sBcJRcdLb0NgoLO/jtZWcAPP8hxu3bA5ZfbN0YiIifEpNsTmfdhcS83tZSqAs8+K2PmAGDYMOC66zQJZW9eHooqK9EmIACThg7FcHYnJy116wbMnSt7Vc3dm/fulSZrjRtIEbkqVZVzh/BwIC0NCA5u3u999JFciAKA2247c7M1IiI3xaTbExUVyXxudi2nllq4EFi9Wo7Dw4Hp0zUbD3ZO164Y0a0bpo4Ywe7k5Bx8faV09pNPpPQckGT7nXeAW28FDh/WNDyiVsnJAcLCgPT05l+0Nxql8SAg/WNuu81u4REROTMm3Z4oJ0c+/Hi1mVriyBHgtdcst6dPB6KiHPb0eWVl+GbjRtTVr5h46XQ4v0cPlpOT8+nVS5qsTZpkaVS5cydw441yv3nVj8hV5OZKKXlamiTezbV0KXD8uBxfeql0Oici8kBMuj1NZSWQl8dVbmqZujrgySelDwAAjBsHjBjhkKdu3J18V04OVu3b55DnJWoVPz/gnnuktLZjR7mvthb43/+AO+4AMjM1DY+o2fLypFFmejrQ0iaVjRuoTZ1q07CIiFwJk25PU1goiXdIiNaRkCuZPRvYvVuOO3YE7r/fIU9bYzRiwUndyYdr0LSNyGopKcAXX8gqt3krxtatwPXXA19/LZ2giZxVYaH0KEhNBSIjW/a7R48CS5bIcYcOwMUX2z4+IiIXwaTbkyiKrK40t/kJEQBs3Ch7VAE5+XruOYd0vc8tK8PsNWuww9ydvFcvdicn1xQQIBeqZs8G2reX+2pqgFdekXFj2dnaxkfUlKIiaZ6WlgbExLT89z/8UH4fOLHBIBGRB2LS7UmKi+WrJfuxyLOVl8vebfOJ0513yn5VO9uTm4sP165FcWUlQgMCMHnoUAzv0oXdycm19e0r8+3/9S/LfRs2yKr3ggWWf2dEWistlW1FKSlAXFzLf7+uTrZWAJJs33KLTcMjInI1TLo9SX6+nNT5+modCbkCVQVmzpT9fADQrx9w880Oeer4sDD4eXujW0wMpowYgUR2Jyd3ERgI/Oc/wHvvAfHxcl9VFfD888D//Z80rCLSkl4PVFdLSXlCgnWP8cMP0rQVAK68UuZzExF5MCbdnsJgkBJGNlCj5lq6FPjlFzlu0wZ45hm7lgdWmJu0AQgLDMRt55zDcnJyXwMHyqr3mDGW+/76S+be//ADV71JG+XlQEUF0KdP6zqNs4EaEdEJmHR7isJC+TBt7mxN8mxZWcCLL1puP/qodSWGzWDuTv7Gb79hb6NVvrbBwSwnJ/cWEgI8/jjw1luWPbOVlcCMGcC0afK+TeQoFRWyyt27t6XjvjUOHbJcsO3cGbjgAtvER0Tkwph0ewJVlSTKz8/SPZfodIxG2cddWSm3L7sMuOgiuzzVyd3Jd5nLEYk8ydChwLx5wOWXW+5bs0b2fi9bxlVvsr+qKqCkBEhOlkS5NecKs2dbjm+/3TKrnojIg/Gd0BPo9bJiEh6udSTkCj75REYaAbKf76GH7PI0J3cnv7BXL1ydnm6X5yJyem3aAE8/Dbz6qmU0U1kZ8MQTsge8uFjT8MiNGQzSqbxnT6BLl9Yl3LW1wMcfy7GvLzB5sm1iJCJycUy6PUFBgXQSdcCYJ3JxO3YAH3wgx15eso/bxjPdzeXkJ3cnH8bu5ETAuefKqvfo0Zb7Vq6UVe/ly7WLi9yTwSBNVrt1A7p3b/2q9KJF8niA9CuIjW11iERE7oBJt7urqwOOH7d54kRuqKpKVtVMJrl9660yn9XGjpeU4Mft22FSFHYnJ2pKeDjw3/9KXwVzhVJpKfDII8Bjj8kxUWvV1sp0iq5dZZXbFmXg779vOZ4ypfWPR0TkJph0u7uiIilRZNdyOptXXpELNIB0rr31Vrs8TWLbthjUqRMu7NWL3cmJzmTUKGD+fOD88y33/fKLdDhfvVq7uMj11dXJSK/OnYFevWwzmWLfPqnKAGTVvPHrlojIwzHpdnc5OXL12o6jnsgNrFgBfP+9HAcFAc89B/j42OShVVXFpmPHThgJdkmfPiwnJ2qOtm2Bl16Sf5Pmi6dFRdLd/OmnZSoFUUsYjTJCNClJOpXb6L3+hAZqd9zBxq1ERI0w6XZn5eVSOsYGanQm+flSymr24INA+/Y2eeiaujp8t3kzfti2DQs2b4bCLsxELafTARdfLKveGRmW+3/8UVa9//xTu9jItRiNMs0kMVE6lfv62uZxDQZLAzU/P2DiRNs8LhGRm2DS7c4KC2WfblCQ1pGQs1IUWS0rK5Pbo0YBV1xhk4fO1esxe80a7MzOhpdOh64xMeC6B1ErREUBr70m/2bNfTry84F775ULZxUVmoZHTs5kkoQ7IQFISQH8/W332N99Z+mwf+218lolIqIGTLrdlckk+3ODg7WOhJzZl18Cf/8txzEx0qSplSWBqqpiw9Gj+PCPP1BcVYXQgABMGjaM5eREtqDTyTzvefOAIUMs9y9cCFx/PfDPP9rFRs5LUSThjo+XhNvW00xmzbIcs4EaEdEpmHS7q+JioKSEpeV0evv2Ae+8I8c6HTBjBhAW1qqHrDEasWDzZvx0cnfyiAgbBExEDWJjgbfekgtl5mqm3Fzgzjul63l1tbbxkfNQVdnDHR0NpKbavvpt505gzRo5Tk4GzjnHto9PROQGmHS7q9xc+aC1VYMUci8Gg4wHq6uT2zfdBAwc2OqH1QHI0evhpdOxOzmRvel0wNixwNdfAwMGWO7/5htZ9d68WbvYyDmoqqxwh4fLCEh7VL81bqA2ZQobqBERNYFJtzuqrpaku5WrluTG3nwTOHRIjrt3l9UxK6mqCrW+QZqfjw+u7d+f5eREjtSuHfDuu8BDD1nKhrOypIP0a6/JRTbyTDk5ci6Qng60aWP7x6+qAubOleOAAODmm23/HEREboBJtzsqLJSGOuZGO0SNrV0rXZABaaTz3/9Kt1krmLuT/3X4cMN9saGhLCcncjQvL+lk/tVXsqIJyCrnl18CN9wAbN+ubXzkeLm5Ukqelma/i/Dz5wN6vRxffz3A934ioiYx6XY35lKygACWeNGpiouBZ56x3L7vPpnVaoWcRt3Jf9uzB5U1NTYKkoislpgo5b7//rflYtqxY8Ctt8oe8NpaTcMjB8nLk4uq6en2TYTZQI2IqFmYdLub0lKgqIil5XQqVQWefdYy1mX4cBnt0uKHUfHPkSP4qFF38glDhyLYluNniMh63t7Sp+HLL4HeveU+RZEy4JtuAnbv1jY+sq/CQnkNpKYCkZH2e56tW4G//pLj1FRg8GD7PRcRkYtj0u1u8vOlORYTIDrZd99ZOsxGRADTp7e4GqKmrg7fbdqEJTt2wKQo6B4bi6nsTk7knDp1Aj76CLjnHsDXV+47dAiYNElWKM2NFMl9FBXJBda0NBkDaU+NV7mnTmV1HRHRGTDpdie1tTKb2x7NUsi1HTkCvP665fZTT7V4BcSkKPjwjz+wMycHXjodLkpOxvUDBiCQ3cmJnJePjyTZn30G9Ogh95lMwAcfABMnAvv3axoe2VBpqVxISUkB4uLs+1wVFcDnn8txcDBw4432fT4iIhfHpNudFBYCZWVAaKjWkZAzqasDHn8cMO+5vvZaq+aoent5oV+HDggLDMTkYcMwtHNndicnchVdu0p5+R13SOkxAOzbJ92mP/oIMBq1jY9aR6+XySWpqUBCgv2f76uvgPJyOR4/nucdRERnwaTbXaiqjAbx9ZUutkRm770H7N0rx0lJ0jytmWrq6lBcWdlwe0hSEqaOGIH2LCcncj0+PpJ0z50rSTggyfZ77wG33GIZI0iupbxcVp779JFGeo5wcmk5ERGdEbMzd1FeLvu5w8O1joScyYYNUlYKyAn3c89Z5vieRY5ej1lr1uDLv/9GTf0qmE6nQ4B5bygRuaaePYFPPwUmT7ZcpN21S5qsffqplJ+T8zMapUt5ebk0zOvY0THPu2EDsHGjHPfvL19ERHRGTLrdRUGBlJYFBmodCTmLsjLZu62qcvuuuyx7Os+gcXfykqoqGBUFZdXVdg6WiBzKzw+4+25gzhxpuAZIX5A33wRuvx04elTT8OgMTCa5yJ6TIxfaBwwAOnd2XCMzjgkjImoxJt3uwGiUBmohIVpHQs5CVaGbOVNWQQBg4EBZxTqLk7uT94iNxZSMDESzOR+Re+rTB/jiC9nbbU7atm0DbrhB9u0qirbxkYWiSO+WrCz5vB84EBg0CIiPd1zCXVYmrwtAmraOH++Y5yUicnFMut1BcbF0LeVsbqoXsGwZdMuXy43QUODpp8+6199cTt64O/l17E5O5P78/aXXwwcfWPYE19QAr74q+3WPH9c2Pk+nKDIKLDNTtgcNGAAMGSIN03x8HBvLF18A5j4fN93Ei/1ERM3EpNsd5OTIVW5Hf/iSczp+HKGNx4M99hgQG3vWX1u5dy9KqqrYnZzIU6WnA19+CVx/veW+TZtkNfPbby1bVcgxVBUoKZFk289P9k4PHSoXRrToraGqwPvvW26ztJyIqNmYdLu6ykopIeYqNwGA0Qjd9OnwMu/BvuIK4IILmvWrV6amIj0xEVMyMtidnMhTBQYCDz4oyVW7dnJfdTXwwguyBzw3V9v4PIGqSvXasWNyQT09XZLtjh0l+dbK+vWy9QCQlfa0NO1iISJyMUy6XV1RkSTewcFaR0LOYM4c6LZvBwCoCQly8nwaOXo9Vu/f33A7JCAAV6WlsZyciKSE+auvgLFjLff9/Tdw3XXA4sVc9baXsjJJtlVVZm4PGyZN0vz9tY6Mq9xERK3ApNuVKYrstQsMdFwTFXJe27YBH30EAFC9vaE++2yTF2MadydfuXcvdufkODpSInIFwcGyPeXtty1bVCorgWefBf79b5maQbZRUSHJdl2djP8aOlRmqTvLRJKSEmDePDkODwf+9S9NwyEicjVMul1ZSYmsdLO0nCorgSefbJivWzFxIpCScsqPGerq8O1J3ck7RUY6OloiciVDhkjCdcUVlvv++EMSryVLuOrdGpWVsmfbYJCRjsOGyX+drXrts88kRgCYMAEICtI2HiIiF8POW64sP1+SLJYD0yuvyBgZAGpqKipvvhknn7Jll5bi202bUFJVBS+dDhf26oXBSUlslkZEZxcSAjz1FDByJPDf/8roqvJyYPp04LffgEcfBXgBr/mqq+WiuZ8f0KWL7NcODdU6qqaxgRoRUatxpdtV1dQA2dnO+yFNjrN8OfDDD3IcHAx1xoxTOtlvPnYMc/7884Tu5EPYnZyIWiojQ1a9L7nEct+qVbLq/euvmoXlMgwG2Ram10vJ/tChUpXkzJ/la9cCu3fLcUYGkJysbTxERC7IKZPud955B506dUJAQAAGDx6Mv//++7Q/+8EHHyAjIwMRERGIiIjABRdccMafdxuFhdJwpU0brSMhLeXmyqqT2UMPAe3bn/JjQf7+DeXk7E5ORK0SFib7ul9+GTC/l+j1str96KPSeZtOVFMj1UglJUCHDpJsd+rkGtvDZs2yHHOVm4jIKk6XdM+bNw/Tpk3DU089hU2bNiEtLQ2jR49Gfn5+kz+/atUqjB8/HitXrsS6deuQmJiIiy66CFn1pbZuSVVlldvPD/Byuv+F5CiKAjz9tJR4AsCFFwKXXdbw7br6/d0A0CM2FpOGDsV1AwawOzkR2cb55wPz5wOjRlnu+/VXWfVetUqzsJxKXZ18XhcWAgkJsj8+PV0uVrhCpVFhIfDNN3IcGQmMG6dtPERELsrpMrbXXnsNt99+OyZPnozk5GS8//77CAoKwpw5c5r8+S+++AJ33XUX0tPT0bNnT3z44YdQFAUrVqxwcOQOVFYmXWPDw7WOhLT0+efAhg1yHBsrK0w6HVRVxY7MTLy9ciX05nndADpGRrKcnIhsKyICePFF4PnnLau2xcUyrvDJJ+XzyhMZjVKJlJcHxMRIst23LxAV5RrJttncuUBtrRxPmgQEBGgaDhGRq3KqRmq1tbXYuHEjHn300Yb7vLy8cMEFF2DdunXNeoyqqirU1dWhbdu2TX6/pqYGNTU1DbfL6k8IFEWBoiitiN6B8vOlVM3fn11jPdWePdC9+y50AFSdTvZxt2kDQ20tfti2DXtycwEAG48exXk9emgbK1ErKaoKVVWh8P3OeV14IdC3L3QzZ0K3erXct3Qp1A0boD7+ODB8uLbxOYrRKA3SjEYgOhpISpL/mqvS6s8zFEWR17Qzn3eoKnSzZsF8iUC57baG+Ikac4nXM1EL2OO17FRJd2FhIUwmE2LN80DrxcbGYs+ePc16jIcffhjt2rXDBRdc0OT3Z86ciRkzZpxyf0FBAWrNV3OdmdEIHDkiV5vN4zvIsxgMiHriCfgYjQCAyhtuQEXv3ijIz8fyHTtQVl0NL50Og7t2Ra/EROTzdUIuTlFV6OvqoALwcqVVQk8TEgI89xwCfv4ZoW+8Aa+KCugKCqD7979RddllKL/nHqghIVpHaR+KIlt96uqkKVpcnFSj6XRSon3KjyvQ6/VQVRVeTrpNzG/tWrTdvx8AUDN8OErCw+WiP9FJXOH1TNQSer3e5o/pVEl3a73wwgv4+uuvsWrVKgScpgTq0UcfxbRp0xpul5WVITExEdHR0Qh3hXLt/Hygqko+0L29tY6GNKB74w3ojh4FAKg9eyLwrruwMzsbv+7eDZOiICwwEOf37o3eMTFMUMgtKKoKHYDogAC+pl3B1VcDw4ZBfe456Oqr1IJ++gmBGzZAffJJYPBgbeOzJUWRcvqqKikdT0qS7T4+Zz69UhQFOp0O0dHRTpuk6ObPbzj2vecexMTEaBgNOTNXeD0TtYSfHfofOVXSHRUVBW9vb+Tl5Z1wf15eHuLi4s74u6+88gpeeOEFLF++HKmpqaf9OX9/f/j7+59yv5eXl2u8UeTmypXzs3ygk5taswb49ls59veH7rnnsCU3F8t27gQA9IyNxeVpaSg3meCl0zFBIbehq3898zXtImJjgTffBBYvBl5/HaishC4vD7p77gGuuQa4914gKEjrKK2nqtKJvKICaNtWxmjFxwO+vs1+CJ1O57znHnl5wKJFchwTA6+xY9m4lc7IqV/PRC1kj9exU/3L8PPzQ//+/U9ogmZuijZ06NDT/t5LL72EZ599FsuWLcOAAQMcEao2Kirkg9AVVuTJ9oqKgGeesdy+/36gUyektm+PdmFhGJ2cjH8NGIDAFpz0ERHZjU4nq95ffw0MGmS5/9tvgfHjgY0bNQvNaqoqI9GOHZNqs759ZfxXhw4tSrid3scfS6k8ANxyi0xLISIiqzlV0g0A06ZNwwcffIC5c+di9+7duPPOO1FZWYnJkycDACZMmHBCo7UXX3wRTz75JObMmYNOnTohNzcXubm5qKio0OqPYD+FhVLCFhysdSTkaKoqCXdJCQCgfNAgKGPHAgB8vb1x6/DhGNK5M7uTE5HziY8H3n4bePhhS/frrCyZ+fzqq67Tn0Svl2QbANLSgGHDZNa2uyWkigLMnm25ffvt2sVCROQmnC7pvu666/DKK69g+vTpSE9Px5YtW7Bs2bKG5mrHjh1DTk5Ow8+/9957qK2txTXXXIP4+PiGr1deeUWrP4J9mEzA8eOuXY5H1vvmG+CPPwAA1aGheH/ECKw9eLDh2yznIiKn5uUFXHutrHr37Wu5/6uvgBtuALZu1S62sykvl2TbZAJSUpwjOSYAAEpbSURBVGRlu0sX9x2f9euvwOHDcnzRRUDnztrGQ0TkBpxyY/A999yDe+65p8nvrVq16oTbR44csX9AzqCkRJq1sJGJ5zl0CHjjjYabC664AoY2beDPff1E5GratwdmzZLk+513ZPzlsWOymnrjjcDUqTIO0xlUVMi2nuBgoFcvIDHRMyrNZs2yHE+dql0cRERuhMtjriIvT0qM3WnPGJ1dba3MuK2fLb9+0CAUpqbilmHDMDgpSePgiIis4OUlq9tffCErx4CUNH/2GXDTTUB9Y0jNVFUBmZlAdTXQo4eUkffs6RkJd3Y28P33chwfD1x+ubbxEBG5CSbdrsBgAHJyZPYneRTjW29BVz8nNT86Gpk33og7MjKQEBGhcWRERK3UqRPw4YfA//2f5YLy4cPSuOvddy2NvBzFYJBtXOXlUj4+dCjQuzfQpo1j49DSRx9JGT0A3HorL/QTEdkIk25XUFgoJwGe9MFPwN9/w+errwAARm9v5D70EMYNG4ZAd2vaQ0Sey9sbmDgR+PxzKeEGJOmbMweYMAHYu9f+MZiT7dJSoGNHSbZTUoCwMPs/tzMxmYAPPpBjLy82UCMisiEm3c5OVeVkwN9fxq+QZygtBZ5+uuFm5W23IXXkSHYnJyL31KWLjKmaOhUw96vYv18S7w8/BIxG2z9nba2UUxcXy8ivIUOkK7mnVhItWyZl9QBwySXyd0JERDbBpNvZ6fXSyMXTrrh7KENdHb7buBHVM2YA+fly56BBCLv1Vm0DIyKyNx8f4LbbgE8/Bbp1k/tMJuD994HJk4FGExtapa5OtmwVFABxcZJsp6cDkZGefXH7/fctx1OmaBcHEZEbYtLt7PLz5Wq8u44moQbZpaWYtWYNfJYsQeCaNXJnWBgwY4aU+hEReYLu3SXxvvVWKT8HgN27pcnaJ59Y9hy3lNEI5OZKY9KoKGDwYKB/fyA62rOTbUBWuJcskePERODSS7WNh4jIzfBM3pnV1UlpORuouTVVVbH+8GF89Mcf0GVm4pKlSy3ffPxxOSEkIvIkvr7AnXfK3m7znOi6OuDtt2U1vCXjQo1GuYCdkwO0bQsMGgQMHAjExvKCptmHH0oHeUD+fs0XO4iIyCb4aePMCguBsjI2UHNjhro6fLNxI5bt3AkYjbjxxx/hV1sr37zqKmDkSG0DJCLSUu/eMkpswgRLgrx9u8z0/uILS6LYFJNJPkezs+VzdOBASbjj45lUNmY0StINyN8LtzMREdkck25nlpMjH4A8OXBL5QYDZq1Zg925ufDS6TB51y5EHj4s30xMBB54QNsAiYicgb8/cO+9khiam3vV1ACvvy57j48fP/HnFUV6oWRlydasAQNk33ZCAj9Pm/Ljj3JhAgCuuEL+noiIyKaYdDur8nIphwsP1zoSspMQf3/EhIQgPCgIU8PC0H7hQvmGtzfw7LNAUJC2ARIROZPUVODLL4Hx4y17sDdvBq6/Hpg/X1a2S0pkf7KfH9Cvn4z/Sky0dESnU7GBGhGR3THpdlaFhUB1NRMvN2Ooq0Nt/egbnU6Hq9PTMaVvX0S/9JKlTPL224E+fTSMkojISQUESBXQrFmWFVmDAXjpJdmLnJ8vnciHDpWZ235+mobr9A4fBn75RY47dQIuukjTcIiI3BWTbmdkMkm5HBNut5JVWopZq1fjp+3boaoqACDQzw8Br79uKe1LT5fROEREdHr9+gFffSW9L8y2b5c537/9xmS7uT74AKj/PMIdd7CxHBGRnfDd1RkVF0uJHEvL3YKqqvjr0CHM+eMPlFZX41hJCarr6uSbP/9sGdMSHAw88wz3HBIRnU1FhczZvusu4OOPpYQckK1Zt98uI69O3utNJ6qtBT76SI59fHjBl4jIjph0O6PcXLnyzD1oLq+6thbzN2zAz7t2QVFV9IqLw5SMDAT5+cn/55kzLT/8yCNAu3baBUtE5OwqK4Fjx6SkPDkZGDYMmDQJ2LHjxK7by5bJNp1PP7Ws5NKJFi+WcnwAGDMGiIvTNh4iIjfGpNvZVFVJ1/KwMK0joVbKKinB7DVrsCcvD146HS7p3RvX9u+PAF9f2UIwfbqs1gDA6NHAJZdoGzARkbOqrpYGaZWVQPfusme7Z08gJES+Hxoq3c2XLLFcvNTrgYkTpQQ9N1e72J3VrFmWYzZQIyKyKybdzqaoSE4qzCcS5JJMioL5GzeitLoa4UFBuHX4cAxKSoLO3HH3s8+ATZvkOC5OVrmJiOhEBoOUiev1QOfOkmz37i1JdlMuuURWvW++2XLfDz/I73z9NVe9zfbvB1askOOuXYHzz9c2HiIiN8ek25koipxcBARYxqGQS/L28sJVaWlIjo/HlIwMtGu8P3/3buC99+RYp5N93G3aaBInEZFTqqmROdslJTKbe+hQGRnWnF4nERFSVr5oERATI/cVF8uosX/9S/aCe7rZsy3HU6awgRoRkZ3xXdaZlJbKSjdLy11SVkkJ9uXlNdzuHB1tKSc3q64GHn9cyssB2YvYr59jAyUicla1tTLNobBQRoINGSJTHdq2bfljXXUVsHMncN11lvu+/VZWvRcssFnILqemRprPAdLlfdIkTcMhIvIETLqdSX4+YDQC/v5aR0It0NCd/M8/8d3mzSiurDz9D7/+ujQBAqQJ0B13OCZIIiJnZjTKvuv8fCA2VpLtfv2AqKjWVX5FRUlZ+fz5QGSk3FdQAIwbB9x4o6yAe5rvvpML/ID8PURFaRsPEZEHYNLtLMyldCwzdikndyfvEhUlncmbsmqVZXUlIAB49lmg8So4EZGnMRqBvDxJuCMjgcGDgQEDpCzcltusrr1WVr2vvtpy35dfyqr3jz/a7nlcQeMGalOnahcHEZEHYdLtLIqKZL7o6ZrDkNPJKinBrPru5N5eXrikT59Ty8nNCguB556z3H7gAaBjR8cFS0TkTEwmWXHOyZF92gMHyldcnP32F8fGyoXPzz+37A3PzQWuuEJmVOv19nleZ7J7N7B6tRz36gVkZGgbDxGRh2DS7QxUVfaw+fiwmYmLMJeT66urEREUhFuGDcOgTp0s3ckbUxRgxgzZsw8A55574moLEZGnUBS5CJmVBQQHS6I9aJCM+fL2tv/z63RSVr5zJ3DppZb7P/lE5nr/8ov9Y9BS41XuO+5g01YiIgdhhucMysvlin9zurKSUyitroaiqugVF4c7Tu5OfrL584F16+Q4MhJ48kme6BCRZ1EU2T+dmSnbawYMkH3bCQlywdnR2rWTsvI5cywVZsePA6NHSzfv8nLHx2Rv1dXA3LlyHBAATJigbTxERB6ESbczKCiQWaSBgVpHQmegNprvemGvXhjbt+/py8nNDhwA3nzTcnvGDF5cISLPoapS5XP8uCTX/fvL+K/ERO17Wuh0Ula+fTtw4YWW+2fPlvFkK1dqF5s9zJ9vqbj617+s6whPRERWYdKtNaNRTkZCQrSOhE7D3J38s7/+gqIoAGQOd0pCQtPl5GY1NcATT8gIHEBmxA4Z4oCIiYg0pqqyR9o8rSEtTZLtjh1lTJUz6dAB+Pln4L33pOQdAI4cAUaOBO69FzjTRApXwgZqRESaYdKtteJiufLMBmpOqbq2FvPqu5MfLirCjuzs5v/yO+/ISjcAdOkC3HOPfYIkInIm5eWSbJtMsmI8bBjQubOUNDsrnU4S0W3bpO+G2VtvyZzwP/7QLDSb2LbNss0pJYUXgImIHIxJt9ZycuTDXos9bXRGx+u7k++t705+aZ8+SElIaN4v//WXjKMBZFXnv//l/HUicm8VFZJs19TIKK5hw4CuXV1r61TnzsBvvwFvvGGJ+8AB6fL94IOyL9oVNV7lnjKFfUWIiByMSbeWKitlXElYmNaRUCOqqmLdoUP4uFF38luHD8fA03UnP1lpKfD005bb//d/cuJJROSOKiulQVp1NdCjhyTbPXpYSrVdjZeXlJVv2SJ/FkDK5V99FejXD1i/XtPwWqyyUsakAUBQEHDTTdrGQ0TkgZh0a6mwEKiqct0TEzf16+7d+GXXLiiqiuT4eNyRkYH45l4YUVVZ1S4slNtDhgDXXWe/YImItFJdLT1JKipkC83QoUByMtCmjdaR2Ub37jLT+uWXLZVKe/ZIIv7YY7Ki7wq+/hooK5Pj8eN5oZ+ISANMurWiKDKnNDCQZV5Opl+HDgj09cWlffrgmn79ztyd/GSLF1s63oaFyYo3Z68TkTsxGCTZ1uulMdrQobJP2B2TOW9vKSvftElmigPy+T1zpow927RJ2/ia4/33LcdTpmgXBxGRB2M2oJWSEqCoyD1PUlyMqqrIKilpuB0VEoL7Ro1qfjm52dGjwCuvWG4/+SQQFWXDSImINFRTA2Rny+dXhw6SbKelARERWkdmf8nJwJ9/SiWT+ULsjh3A4MFycbWuTtPwTmvTJmDDBjnu108uFBARkcMx6dZKXp50dnW20Skextyd/KM//sDRoqKG+/1b2tjOaJQk22CQ22PGAOedZ7tAiYi0UlcnTT+LioD4eNk2k54uc549qVLLx0fKyjdskD8/IO/9M2ZI8r19u6bhNYkN1IiInAKTbi0YDHICwzFhmmrcndzLywulrelKO3s2sGuXHHfoAEybZpsgiYi0YjRKs8+8PCA6WhLLfv2kgseTk7fUVGmmNn26lJ8DwObNQN++UnZeWysNNLt0kSaab799+seqqZFxkt26SYm+ucmZwQBcfbXsK09LAy680DKC8mzy84GLL5bn/+ADuS8kRPZzN1ZRAYweLf8/w8Ob/728PGDQIHl9EBFRszDp1kJRkTQ1cZdmMy7mdN3J09q3t+4BN28GPv5Yjr29geeec60ROUREjRmNkljl5spq9uDBsp85JoY9Ksz8/GSFe/16IClJ7jOZZCW8Vy/gn3+AffuAv/+WRmw7dzb5MLpHH5ULGPv2yUp54y1Kd9wB7N0LbN0KXHUVcNttzYvtkUekGuGhh6S5JyAJ98nnHL6+wMMPA8uXn/oYZ/pebKw0k/v00+bFQ0RETLodTlWlgZqfH09eNGAuJzd3J+8dH48pLelOfrLycikrN5/YTJ0qe/+IiFyNyQQUFFgqsQYOlBXNuDh+Xp1O//7AiBHAJZdY/o4OHQI2bgT+9z/p23LddcBXX53yq7qqKmDOHNknbq4ciIuT/wYEAJdearl/yBDgyJHmxTR/vpSSN26g1q/fqT/n7w+MHHnqSvbZvgdIEt+4dJ2IiM6In6KOptfLSc3pPsjIrvbk5WFvXh68vbxwWUoKxvXrB/+WdCc/2YsvymoQICc1EybYJlAiIkdRFBlzaJ6oMWCAJHnt2lnKp+n01qwBXnoJ+OMPmU8OSLXAgw8C554rs7GPHTvl17yPHJFKguefl7/zjAxgxYqmn+ONN2S1+2yKimQP/rFjskIOyHPYun9M//7Atm2WUWRERHRGLewWRa1WWCj7vQICtI7EI6W3b4+C8nKkJCRYv7pttmyZfAGyX27GDJ6gEpHrUFXpRF5eLolZr17SKK01FyI90fHjUnLdp49sN0pMlOQXkET877+l8ZqinFgxYDRCd/SoVEe98IL87oUXSil6bKzl555/XvZzny4hb0rjVejOnVv1x2uSj490rc/OZn8aIqJm4Eq3I9XVyYcz93I7THVtLX7cvh2G+nEuOp0OFyUntz7hzs6WhjlmjzwiJ6tERM5OVYHSUlkN9fKSKp2hQ6UJJBPulgsKskyuCAyUPfBPPSWNzAD57P/nH2DUKODw4YZfMyUkQPXyAm68Ue7o21f2hzfugv7KK8CCBcDSpfI8ZxMZKQmxuZw9LEz+f3foYIM/6EkMBvYvISJqJibdjlRUJOXlvCrsEObu5BuPHsVPthzlYjLJCVVlpdy+5BLpFEtE5Oz0ekupc1oaMHw40KmT7OEl66SmSsMzs2uvlZLzTZtObH62apX87KxZgKpCjYyUfdM//yzfP3xYvnr1ktuvvSbJ86+/nrol7dFHT98VvU8fy0WA0aNlj/6559riT2qRlyf7zRMTbfu4RERuikm3I+XmyocUS5DtSlVV/HnwYEN38rZBQRhmXnGwhblzpQwQkNXthx+23WMTEdlDeTlw9KhcNExJkZXtLl241ckWrrnGkjgDwM03Az17Skn5b78Bd90FdOwo36uoAKZOhe6SS+CVlQX1vfeku3lKiowImzULSEiQqrgHHpCKhPPPl8caPNjyHFu3WpquNaaqcmHFbONG4PPPLRUM06ef2GAtNVVeC2VlQPv2EntzvrdsGTBmDBvsERE1k05VzW2XPVNZWRnCwsJQUlKCcHs2N6uokL1dgYFAcLD9nsfDVdfWYtHWrdiXlwcA6B0fjytSU1vXLK2xnTuBW26RE1cvL5nPnZ5um8e2EUVVkW8wICYgAF6ePEuX3AZf061QUQEUF0tpcseOkjyFhGgdlXupqJARWuvWnf7zvaxMGquZ52YDUNq0Af73P3hNntyyuecmkzS6W7/+1KR37VppyAZIFcPatS38wzRTRoZ8/plX5cmjKYqC/Px8xMTEwIsXYsgNlJaWIiIiAnq9HqE2qlDmvwxHKSyUcmQm3HaTV1aGWWvWYF99d/JL+/RpfXfyxqqqZDyYySS3J092uoSbiAiAvF9lZgLV1UD37pIU9uzJhNseQkKA118/Yb/2KUJDJUldtkxWsgF4lZfD69ZbgSuvlBLw5vL2lj3iTSU3jRuoTZ3a/Mdsibw84M47mXATEbUAk25HMJnk5IcJt121CQiAoqpoGxSEW4cPx8BOnaCz5arYa69Z9kL27g3cfrvtHpuIyBYMBilNLi+XrtVDh8r7FRt42teoUbKX+mxGjwZ27IDaeLzkjz/K/6Mvv5TycGsVFQHffCPHbdtK2bs9xMYCN9xgn8cmInJTTLodoaREvjib2+ZqjcaG4yA/P9w0aBDuyMhofXfyk61cCSxaJMeBgcCzz0qHWCIiZ2BOtktLpYx86FDZk2vr90JqvfBwqB9/jJJPPoFq3pddUiJdzMeNA/LzrXvcuXOBmho5njiR+/WJiJwIk25HyMuTq9dM0mwqs6QE7/7+O7ZkZjbcFxMaartycrOCAuC55yy3H3jAPuNXiIhaqrZWRhgWF8t+7SFDpCt5RITWkdFZ1IweDXXbNmD8eMudCxfKqve337bswVRVytfNpkyxTZBERGQTTLrtrbpaToi42mAz5u7kn9R3J//r8GEo9uoHqCjA009busGefz5w1VX2eS4iouaqq5N9wAUF0sV6yBCZtx0Z2bKmXKStyEgpK//2WyAqSu4rLJSxY+PHS8l4c/z+u2Vs2XnnAT162CVcIiKyDpNueysqks6mbF5jE1W1tfj6n3/w6+7dUFQVvdu1w+ShQ+3X0fjrr6VDLABERwOPP84TWiLSjtEo4yfz8iRJGzwY6N9f3p/43uS6xo2T6Rhjx1ru+/prWfX+/vuz/37jMWBc5SYicjpMuu1JVWWPnb8/T4ZsILOkBLNWr8a+/Hx4e3nhspQUjOvb1/bl5Gb79wNvvWW5/fTT3JdPRNowmWSvb06OlI4PGgQMHChNrTiixz3ExMiK95dfWrYH5OVJddXEibJfvyn5+cCCBXIcHS3zs4mIyKnwk9qeSktlpZuJWqvpq6sxd906lBkMaBscjNuGD8eAjh1t2528MYMBeOIJKeEEpMHN4MH2eS4iotMxmaTcOCtLOpAPHCgJd3y8jI4i96LTSVn5zp3A5Zdb7v/0U+mOvmyZ5T6TCVi1Cvj3vy2fVZMny4V+IiJyKuzsZU/5+dLkhh+ArRYWGIhhnTujuKoKV6Sk2G912+ztt4GDB+W4Wzfg7rvt+3xERI0pinS0rqiQfb/JybJ3297vfeQc4uOlrPzTT4H77pO+IllZwCWXALfdBpx7LvDoo1JN1xibfBIROSUm3fZSWysfkKGhWkfisjJLShDi54eI+vnm59c3hrHb6rbZn3/KXjpALpg89xzg52ff5yQiAmRbUmkpUFYms5b795cEjO9Bnkenk7LyUaOAW28FfvlF7v/wQ/lqyv/9n7xeGu8NJyIizbG83F6KiuSkqU0brSNxOaqq4o/67uTfbtoEo8kEQJJtuyfcJSXAjBmW2//3f0CXLvZ9TiIic7J97JgkW+npMmu7Y0cm3J6ufXspK589G6i/CH1G//63lJ4TEZHT4Eq3vWRny3477rlrkaraWizasgX78/MBAG2Dg+03Duxkqgo8+6xlRMuwYcB11znmuYnIc5WVyQW/0FAgNRVISAACA7WOipyJTgfcfrtcyG881/tkqgpkZgJr1sjoMCIicgpMuu2hvFxmp7KBWotklpTg240bUWYwwNvLC5f07o1+HTrYf3XbbMECYPVqOQ4PB6ZPZ9d5IrKfigqguFhWL3v3BhITgaAgraMiZ9bci9A5OfaNg4iIWoRJtz0UFABVVTK6g85KVVX8eegQftuzB4qqom1wMK7t3x9xjtwPf+QI8NprltvTp8sMXCIiW6uslIqaoCCgRw9pfhUSonVU5Ari4237c0RE5BBMum3NZJLSLp5ANZuiqtiVnQ1FVdGnXTtcnpoKfx8HvjTr6oAnnwRqauT2uHHAiBGOe34i8gzV1TL+y98f6NpV9muz2Sa1REaG7PHOymp61Vunk+9nZDg+NiIiOi0m3bZWVCTNcOLitI7EZXh7eeGafv1wuKgIfRMTHVdObjZrFrB7txx37Ajcf79jn5+I3JvBIMm2jw+QlCTvM9x+RNbw9gbeeAO45hpJsBsn3ubPzv/9j/1kiIicDLuX21penvzXkSu1LkZVVfxx4ABW7d3bcF9EcLBj92+bbdwIzJ0rxz4+wH//CwQEODYGInJPNTWyIllSIiXkQ4dKozQm3NQaY8cC334rDfcaa99e7ue4MCIip8PM0JaqqqR5CU+oTuvk7uQ94+IQFxamTTBlZbJ327xSMHUq0LOnNrEQkfuoq5PeHqoqiVHHjkBkJBszku2MHQtcdZV0Kc/JkT3cGRlc4SYiclJMum2psFC60bZtq3UkTulYcTG+27TphO7ksVrtZ1RVYOZMS2VC//7AzTdrEwsRuQejUT4HjEZJgjp1koaaTLbJHry9ORaMiMhFMOm2FUWRMsLAQJ5gnURVVfx58CBW7N0LVVURGRyMaxzdnfxkS5YAv/4qx23aADNmcIWAiKxjNEo/j7o6ICZG9m3HxABe3MFFRERETLptp6JC9u1pVSrtxL7dtAm76meGatKd/GRZWcBLL1luP/YYG98RUcuZTDJnu7rakmzHxvICHhEREZ2ASbetqKqsdrOB2im6xcRgb14eLundW5tmaY0ZjTIerLJSbl92GXDhhdrFQ0SuR1Ek2a6slPLxPn3kwh3f/4mIiKgJPEMgm1NVFWUGA8ICAwEA6YmJSIqKaritqU8+AbZtk+OEBOChhzQNh4hciKLISMjycmmM1quX7N329dU6MiIiInJiTLrJpszdyXPLyjB1xAgE+fkBgHMk3Dt2AB98IMdeXsAzzwAhIdrGRETOT1UBvV6+wsPx/+3deVxU5f4H8M8M26Cyw4ALi+KuCIaGmHsoqWlmKlniUl7qp5bl1TK1gNQocytzafFqpSVXQ683l1RcMvFec82uWyiopYCg7PvM8/vjkcFhExAYGD7v12ter3POPGfO9+AB53ue53wfPPYY0KIFcP/vGxEREVFFmHRTjXmwOrmpUolbqaloq1YbOiwpKwtYsEA+gwkAL78MeHsbNiYiqt+EkFMLpqbKeh3e3nKEjEpl6MiIiIioAWHSTY+srOrkY319DTcdWFmWLQP+/FMue3nJpJuIqDwZGbI4ZrNm8m9Gq1ZydgoiIiKiKmLSTY8kOz8f28+cQeydOwAArxYtMNzQ1clLio4Gdu6Uy02aAAsXsuAREZUtM1MWSWvSBOjcWSbbTZsaOioiIiJqwJh50CM5dPkyYu/cgalSiaFdu6K7q6thq5OXlJgILF5cvD5njvwSTUT0oOxsOde2SgV06AC4ugJWVoaOioiIiIwAk256JE927IjU7GwEdOpUv4aTA7LScHi4fCYTAJ58Enj6acPGRET1S06OTLbNzABPT8DNTT6/TURERFRDmHRTlWTn5+PMzZvo3aYNFAoFVGZmeNHPz9Bhle2774ATJ+SyWg3MmwfUp154IjKc3FwgOVk+auLuLl92doaOioiIiIwQk26qtBt372Lb6dPIyM2FuYkJenp4GDqk8l2+DKxeLZcVCtnjzd4rIsrPl8k2IHu13dwAe3vekCMiIqJaw6SbHkoIgWNXr+LgA9XJ3eztDR1W+XJz5fRgBQVyfcIEoGdPw8ZERIZVUCCTba0WaN4c8PAAHB2ZbBMREVGtY9JNFSpVnbxlSwz38qpf1clL+vRTIC5OLrdvD/zf/xk2HiIynMJCmWwXFgIuLjLZdnIClEpDR0ZERESNRD3OnMjQbt69i633h5PX2+rkJf3yC/DPf8plCwtZudzc3LAxEVHdKyyUU3/l5QHOzjLZdnZmsk1ERER1jkk3lUsrBDJzc+HQtCnG+vrWv+rkJd29C7z/fvH6G28ArVsbLBwiMgCNBrh3T1Yld3QEunWTPdwmJoaOjIiIiBopJt2kRysElPd7st0dHBDUsydaOzjAvD4PJwcAIWTCffeuXO/TBxgzxrAxEVHd0Wplsp2VJQujde4sn92u73+7iIiIyOjx2wjpXE9Jwb9/+w1BPXrAycoKANDB2dnAUVXSDz/IoeWA/ML97rsskETUGAgBpKYC6enyd79DB5ls87ESIiIiqieYdBOEEPjl6lUcul+d/NDlyxjXo4ehw6q8uDhgxYri9ffeAxwcDBcPEdU+IYC0NPmysQG6dwdatJC1HIiIiIjqESbdjVxWXh52nD2rq07e7X518gajoEBOD5aXJ9fHjpVDy4nIeKWny6Hk1taAtzfQsiWgUhk6KiIiIqIyMeluxK6npOCH06eRkZcHU6USw7p2hU99r05e0tq1wOXLcrl1a2DmTMPGQ0S1JzNT1m1o1gzo2hVo1Qpo0sTQURERERFViEl3IxWfnIxv/vtfCCHg2KwZxjz2WP2vTl7SyZPAt9/KZVNTYNEi9nYRGaOsLJlsW1rKAmmtWsnEm4iIiKgBYNLdSLnZ26OVrS3smjTBcC+v+l+dvKS0NPnsthByffp0WUCJiIxHdjaQkiKLorVsCbRvD9jaGjoqIiIioippYJkWPYq/7t2Di40NTJRKKJVKTPDzg5mJScMaTg7IRDsiAkhKkus9ewIvvmjYmIio5uTmAsnJcgRLmzaAq6us29DQRuMQERERgUl3o6CrTn7pEvxat0Zgly4A0PB6t4vs2gUcOCCXra2BsDBAqTRoSERUA/LyZLJtYgK4uwNubnIaMK22+CYbERERUQPTQLMuqqysvDxsP3sWV+9XJ8/Oz4cQouH1bhf5809gyZLi9XnzgIYylzgRlS0/XybbgBxG7u4up/1rqH+niIiIiB7ApNuIGUV18gcVFgLvviuf8wSAESOAgADDxkRE1VdYKJNtjQZo3hzw8AAcHZlsExERkVFh0m2EhBD4JTYWhy5fhgDg2KwZxj72GNQN/XnI9euB8+flcqtWwOzZho2HiKqnsFAWSCsokCNVWrcGnJz4mAgREREZJSbdRig9NxfHrl6FANCtZcuGWZ28pN9+k0k3IJ/3XLgQaNrUsDERUdVoNHLqr9xcQK2WybZaLX+niYiIiIxUA8/EqCw2lpYY6e2NvMJC+LRq1XCHkxfJzJTDyrVauT51KuDlZdiYiKjytFqZbGdny+HjXl6yh7uh3wwkIiIiqgR+4zECQggcjY1FK1tbtHFyAgB0bt7cwFHVoKVLgb/+ksvdugFTphg2HiKqHK0WuHdP3jhzcAA6dwZcXAAzM0NHRkRERFRnmHQ3cA9WJ29qbo7pAwbA0tzc0GHVnP37gR9/lMtNm8ph5ewdI6rfhABSU4H0dMDODvD1lYXSjOlvExEREVElMXtpwOLvVyfPvF+dPKBTJ+NKuBMSgA8+KF5/6y05nRAR1U9CAGlp8mVjA/j4AC1aACqVoSMjIiIiMhgm3Q1Q0XDyww9WJ/f1hdrKytCh1RyNBggNBTIy5PrgwcCwYYaNiYjKl54uh5JbW8vHQFq2BCwtDR0VERERkcEx6W5gCjQaRJ48iat37gAAvFu1wrCuXRt+dfKSNm0CTp2Sy87OwDvvcO5eovooM1MWSWvaFOjSRU7nx5kFiIiIiHSMLFMzfqZKJZqam8NUqcRwLy/4uLoaOqSad+kSsHatXFYogPBw2XtGRPVHVpZMti0tgQ4dADc3oFkzQ0dFREREVO8w6W4AhBAo0GhgbmoKhUKB4V5e6NO2LZyMaTh5kdxcYP58oLBQrk+cCPToYdiYiKhYTg6QkiKLonl6Au7uvClGREREVAEm3fVcVl4eos6cgalSied79oRCoYC5qalxJtwAsHIlcP26XO7YEXj1VYOGQ0T35eYCycly9gB3d8DDA7C1NXRURERERPUek+56rGR18jsZGVAbc4/S0aPAtm1y2cICWLSI8/kSGVpenky2lUo5hNzdXU4DxhoLRERERJXCpLse0gqBXx6oTu7UrBnGGFt18pJSUoD33y9enzVL9qQRkWEUFAB37shpwFq2lMm2gwOTbSIiIqIqYtJdzxQNJ7+WnAwA8GnVCkONsTr5g4SQxdLu3ZPr/foBo0cbNiaixqqwUPZsFxYCLi5A69aAo6Ps6SYiIiKiKjPiTK7hEUIg8uRJ3Lx3z7irk5f0z38CMTFy2cEBePdd9qYR1bXCQjnipKAAUKtlsq1WM9kmIiIiekRMuusRhUKBwM6d8eP583i2e3fjHk5e5OpV4NNPi9dDQ+XzokRUNzQaOfVXbi7g5CSTbWdnwMTE0JERERERGQUm3QaWlZeHv1JT0d7ZGQDQ0s4OIX37QtEYenrz84EFC2ShJgAICgJ69zZsTESNhVYrk+3sbDnCpGtXOZzcmB9lISIiIjIAfrsyoPjkZPxw5gxyCgow9Ykn4GJjAwCNI+EGgDVrgD/+kMtt2gCvvWbYeIgaAyFk/YTMTMDeHujUCWjenDMFEBEREdUSJt0GUFZ1cpPG9tzkf/8LbNokl83MgMWLAZXKsDERGTMhgLQ0+bK1Bbp3l8m2hYWhIyMiIiIyaky661hmXh62N7bq5CWlpgJhYcXrM2YA7doZKhoi45eWJn/vbGwAb285BRhvchERERHVCePrXh07Fjh+XC5rtXLIsqcn0LYt8Nln5e6mvHpVPk/cvj3Qsyfwv//JN3JzgVGj5HZvb2DwYCA2tnKx3L0rj//ss8C4cbh96BA+//lnXEtOhpmJCZ7x9sYzPj4y4T56FHjuOdl2zhw59LNIee+lpAATJwJvvQX89lvxOS9ZAjzzjIw7MrL8+G7cAF56SU7PNXGiLGpWJCYGCA4Gnn8emDwZuHKlWueM06f13xcC+OADOf8vIL/4b9tW9XMuLKxcPESNWUYGcP26LJbm5QX4+8u/h0y4iYiIiOqMcSXdJ07IpM/fX65v2gRcuCATxhMngI8/Lk6mS7B8800gJES2ffttmWgWCQkBLl8Gzp2TyezUqZWLZ9UqWZxo+3YgNBT2ERHIzs6GU7Nm+FufPsXTgWVnAwsXAsuWybaOjsD69Q9/z8EBaNUKuHYN6NZNbtu9G4iLA6KigK+/Br79Vj+ZftAHH8ikNipKJrLh4XJ7erqctissDNiyBZg5UxY8q8Y5Y8EC/QR5507g4EG5rFDIyuVVPWdvb2DXrsrFQ9QYZWbKm2p5eUDnzvKGYrt2QJMmho6MiIiIqNExrqT788+BF14oXo+MBP72Nzn1jb29rI79/feldnMCYHr2LDBhgtzw3HPAzZuyR1ulAoYNK543ulcvID6+cvEcOCA/CwC6dIG5iwueEwJT+/SB04PTgcXEAB06AB4ecn3sWOCnnx7+HgDk5Mgq4EX275c93CYmcijp4MH67YvcvQtcvAgMHSrXn3wSSEyU5/3nn3JfT0/5Xvfu8r1Ll6p8znB0BE6dkus3bwJLlxa3bdcOeOyxqp9zYKC8UUBE+rKzZbKdkyNH5/TuDXTsCDRtaujIiIiIiBot40q6Dx8G/PyK12/cANzdi9c9POS2ElwBaJ2di6fKUSgAN7cy2+KTT2Rv98OkpkIUFmLT1aso0Gjkx7Zogc4KRenntxMS5FQ9RVq0AJKTZQ9xRe8Bspf7zp3i4dcJCbI40oPtExJKx5eYKHuNHzxnZ2fZ1s1NPgN67px878gRICsLuHXroeeMwkKZaJc8fmGh7PXOyZHbu3SRr+qcc8eO8obIg8PRiRqz3Fx5sywjQ94s8/eXv18P3twjIiIiIoMwrupdf/4pE8fa8sEHMtmLjq6wmVYIHL96FX5aLa7euYNjsbEY0KFD7cSUlCS/WCcnA82a1cxnNmsGfPQRsHq17Dnz8pJTepmYVP8zv/qqeGi/qyvQvz9w+3b1PsvUtObPmaghys2VvwempvIGo7s7YGdn6KiIiIiI6AHGlXQ3aSK/hBZxc5NFhIqe8Y6Pl9tKuAlAmZgoe1JNTWWxrxs39NsuXSqHNB84UOFzkZl5eYg6fx5xycnoqVTCz9oavYuGad+6pd+DW8TFRU6hVeTWLdlbbGpa8XuAHP6en1887Y+Li0xmi57xLu+Yzs6yKNmD55yYWNy2Rw/5AuTnBwbKxLsitrYyMU9OLu7tvnVL9pL/4x9y3cREPq+dkACcOVO9cy6KiVMdUWOVny9/zwBZ18HDQz5CU/QYDBERERHVG8Y1vLxbN1nwrMjYscCXX8rKvXfvyme8g4JK7XYHgKZbt+J5o3/4QX6RbdtWri9fLp8F379fJpYPeucdXVX0uIwMfB4Tg7j71ckz+/TBUxcvyuHk//ufHAbu61s6bn9/+bx00bPiW7cCQ4Y8/D1AftkWoriHPyAA2LFDnnNamoz5wfZF7O3lc9N79sj16GhArZa90EDxF3pA9lL36FH83meflV8VPSBA/vwAec5JSfJnp9XKbSEhstDao5xzSkrxcHiixqSgQN5Uu3NH3pzq1UvWRXBwYMJNREREVE8ZV0/3mDGy4FZAgFwPDgZ+/VUW7FIogFmz5FBpQFbR3rlTJtQAslesgPXrr8sh5NbWwIYNst2ffwJ//7vs5R04UG6zsCjuiT13DvD1xbkLF/CvK1cgADg1a4axvr6wf/xx4L33ZIVwMzPZw1vUW7tuney9HTNGFjlasEAeR6ORz2QWVRKv6D1A3hzIyQGU9++fDBsmK7aPHi3XX3yx+ObBkSPAzz/LyuQAMG+e/KwNG+RxQkOLP3fdOtkTXTTV0HvvFb935Yp8rrosr72mf85t2sjK8YBMkq2tH35eDzvn48eBAQOKz5nI2BUWFtc1cHYGWrcGnJz4O0BERETUACiEEMLQQdSYzExZrff48UpX601PT4eNjQ3u3bsH25K92A+j0ciepv/+F2m3buHzjRvRwcUFQ728ShdLqy1Tpsge7c2bAUvL2j+eRiOPuXHjw7/w//QTMH++XG7aVPZ4t2jx6DFMnSo/t3XrR/8sI6QVAkm5uVCrVFCy97Nh02jkyI68PJlse3jIESmPUl+hAdJqtUhKSoJarYaSNxrICPCaJmPC65mMTWpqKuzs7JCWlgbrog7DR2RcPd3NmgErVsh5qrt2rfXD3UtPh92vvwIAbKys8H+dO8OqZUv9Z49rU0qKHC5vbw/89Vdxj3ZtMjEBvvnm4e0SEoCIiOL1uXNrJuFOSZGjA5hwkzHTaIB792QhQycn+ejMgzMsEBEREVGDYXzf4J58stYPodVqcfToURw5cgRBQUHocL8yuZW5ea0fW4+DA/DUU3V7zMrQaOQQ86IpvZ56qng+8EdVX8+ZqCZotTLZzsyU13rnzvLZbTMzQ0dGRERERNVkfEl3LcvMzERUVBTi4uIAAPHx8bqkm+779lvg9Gm57OICvP22YeMhqu+EkPPcp6fLkSu+vkDz5kBd38gjIiIiohrHpLsK4uLiEBUVhczMTJiZmWH48OHw9vY2dFj1y4ULwNq1clmplMXjrKwMGxNRfSWErMmQlgbY2AA+PkDLlpwOj4iIiMiIMOmuBK1Wi59//hlHjhwBAKjVaowZMwZOTk4GjqyeycmRVcc1Grk+aRLQvbthYyKqr9LT5VBya2v5zHbLlnVTDJGIiIiI6hST7kqIj4/XJdzdu3fH0KFDYcZnLEtbvhy4cUMud+4MvPKKYeMhqo8yM4G7d2VF/y5dAFdXoEkTQ0dFRERERLWESXcltGnTBv7+/nB2duZw8vIcPgxs3y6XVSr9OcmJCMjKktX3mzQBOnQA3NzkjAtEREREZNSYFZVBq9UiJiYGPj4+aHb/S/GQIUMMHFU9lpwMLFpUvP73vwPu7oaLh6g+ycmRvyMWFkC7djLZrqE5H4mIiIio/quXM9ivXr0aHh4eUKlU8PPzw4kTJypsv3XrVnTs2BEqlQpeXl7YvXt3tY+dmZmJTZs2ITo6GlFRURBCPHwnjQY4ehQ4cgQ4dar4mWZjptEAJ08Ce/YAs2bJyssAMGAAMGqUAQMjqidyc4E//5RF0lq3Bvz9ga5dmXATERERNTL1rqc7MjISs2bNwrp16+Dn54eVK1ciMDAQly9fhlqtLtU+JiYG48ePR0REBJ5++ml89913GDVqFE6fPo2uXbtW6djXrl1DVFQUsrKyYGZmBh8fHygUiop3iooCZs6UX66LqNXA7NnAoEFVOn6DcfAgsHQpkJSkv93KShZSe9jPjMiY5eXJnm2lUvZqu7vLacCIiIiIqFFSiEp15dYdPz8/9OzZE5999hkAOdTb1dUVr732GubOnVuqfVBQELKysvDjjz/qtvXq1Qs+Pj5Yt27dQ4+Xnp4OGxsb/Pvf/8apU6cAyOrkY8eOhaOjY8U7R0UBY8bIaX/KsmSJ8SXeBw8Cb71V/vvGeM4NjFYIJOXmQq1SQckbIHUnP18m20LISuTu7oCDA29C1QCtVoukpCSo1WoolfVygBZRlfCaJmPC65mMTWpqKuzs7JCWlgbrGhqhWK96uvPz83Hq1Cm88847um1KpRIBAQE4fvx4mfscP34cs2bN0tsWGBiIHTt2VOnYx44dg0qlqnx1co1G9nBXdM/i3XeBvXuN50u3EMCxYxW3WbYM6N8fMDGpm5iIDK2wUCbbhYVA8+aAhwfg5GQ8v/dERERE9EjqVdKdnJwMjUYDZ2dnve3Ozs64dOlSmfskJCSU2T4hIaHM9nl5ecjLy9Otp6WlAZB36Z588kl07doVWVlZDw/26FEoHxxSXvbBZM9wY5KYCO3evXIqJDIILYB0rRbmSmX9LNpgTDQa+XJ0lEXSHB3lsPL7f1eoZmi1WqSnp8Pc3Jy9KGQUeE2TMeH1TMYm9X6tqpocEF6vku66EBERgfDw8FLblyxZgiVLlhggIiMUGmroCIiIiIiIiKotJSUFNjY2NfJZ9SrpdnR0hImJCRITE/W2JyYmwsXFpcx9XFxcqtT+nXfe0RuOnpqaCnd3d9y4caPGfqhEhpSeng5XV1fcvHmzxp5DITIkXtNkbHhNkzHh9UzGJi0tDW5ubrCvwUK49SrpNjc3h6+vL6KjozHq/rRTWq0W0dHRmDFjRpn7+Pv7Izo6Gm+88YZu2/79++Hv719mewsLC1hYWJTabmNjwz8UZFSsra15TZNR4TVNxobXNBkTXs9kbGrycYl6lXQDwKxZszBp0iT06NEDjz/+OFauXImsrCxMmTIFADBx4kS0bNkSERERAICZM2eif//+WLZsGYYPH44tW7bg5MmT+OKLLwx5GkRERERERET1L+kOCgrCnTt38N577yEhIQE+Pj7Yu3evrljajRs39O469O7dG9999x0WLFiAefPmoV27dtixY0eV5+gmIiIiIiIiqmn1LukGgBkzZpQ7nPzw4cOlto0dOxZjx46t1rEsLCwQGhpa5pBzooaI1zQZG17TZGx4TZMx4fVMxqY2rmmFqMla6ERERERERESkw8n0iIiIiIiIiGoJk24iIiIiIiKiWsKkm4iIiIiIiKiWNIqke/Xq1fDw8IBKpYKfnx9OnDhRYfutW7eiY8eOUKlU8PLywu7du+soUqLKqco1/eWXX6Jv376ws7ODnZ0dAgICHvo7QFTXqvp3usiWLVugUCgwatSo2g2QqAqqej2npqZi+vTpaN68OSwsLNC+fXt+96B6parX9MqVK9GhQwdYWlrC1dUVb775JnJzc+soWqKK/fzzzxgxYgRatGgBhUKBHTt2PHSfw4cP47HHHoOFhQXatm2LjRs3VumYRp90R0ZGYtasWQgNDcXp06fh7e2NwMBAJCUlldk+JiYG48ePx8svv4wzZ85g1KhRGDVqFH7//fc6jpyobFW9pg8fPozx48fj0KFDOH78OFxdXTFkyBD89ddfdRw5Udmqek0XiY+Px+zZs9G3b986ipTo4ap6Pefn52Pw4MGIj4/Htm3bcPnyZXz55Zdo2bJlHUdOVLaqXtPfffcd5s6di9DQUFy8eBHr169HZGQk5s2bV8eRE5UtKysL3t7eWL16daXax8XFYfjw4Rg4cCDOnj2LN954A1OnTsVPP/1U+YMKI/f444+L6dOn69Y1Go1o0aKFiIiIKLP9uHHjxPDhw/W2+fn5iVdeeaVW4ySqrKpe0yUVFhYKKysr8fXXX9dWiERVUp1rurCwUPTu3Vt89dVXYtKkSeKZZ56pg0iJHq6q1/PatWtFmzZtRH5+fl2FSFQlVb2mp0+fLgYNGqS3bdasWeKJJ56o1TiJqgOA2L59e4Vt3nrrLdGlSxe9bUFBQSIwMLDSxzHqnu78/HycOnUKAQEBum1KpRIBAQE4fvx4mfscP35crz0ABAYGltueqC5V55ouKTs7GwUFBbC3t6+tMIkqrbrX9Pvvvw+1Wo2XX365LsIkqpTqXM87d+6Ev78/pk+fDmdnZ3Tt2hUffPABNBpNXYVNVK7qXNO9e/fGqVOndEPQr127ht27d2PYsGF1EjNRTauJ/NC0poOqT5KTk6HRaODs7Ky33dnZGZcuXSpzn4SEhDLbJyQk1FqcRJVVnWu6pLfffhstWrQo9ceDyBCqc03/8ssvWL9+Pc6ePVsHERJVXnWu52vXruHgwYN48cUXsXv3bsTGxmLatGkoKChAaGhoXYRNVK7qXNMvvPACkpOT0adPHwghUFhYiFdffZXDy6nBKi8/TE9PR05ODiwtLR/6GUbd001E+j788ENs2bIF27dvh0qlMnQ4RFWWkZGB4OBgfPnll3B0dDR0OESPTKvVQq1W44svvoCvry+CgoIwf/58rFu3ztChEVXL4cOH8cEHH2DNmjU4ffo0oqKisGvXLixcuNDQoREZjFH3dDs6OsLExASJiYl62xMTE+Hi4lLmPi4uLlVqT1SXqnNNF1m6dCk+/PBDHDhwAN26davNMIkqrarX9NWrVxEfH48RI0botmm1WgCAqakpLl++DE9Pz9oNmqgc1fkb3bx5c5iZmcHExES3rVOnTkhISEB+fj7Mzc1rNWaiilTnmn733XcRHByMqVOnAgC8vLyQlZWFkJAQzJ8/H0ol+/yoYSkvP7S2tq5ULzdg5D3d5ubm8PX1RXR0tG6bVqtFdHQ0/P39y9zH399frz0A7N+/v9z2RHWpOtc0ACxZsgQLFy7E3r170aNHj7oIlahSqnpNd+zYEefPn8fZs2d1r5EjR+oqirq6utZl+ER6qvM3+oknnkBsbKzu5hEAXLlyBc2bN2fCTQZXnWs6Ozu7VGJddFNJ1q0ialhqJD+seo23hmXLli3CwsJCbNy4UVy4cEGEhIQIW1tbkZCQIIQQIjg4WMydO1fX/tixY8LU1FQsXbpUXLx4UYSGhgozMzNx/vx5Q50CkZ6qXtMffvihMDc3F9u2bRO3b9/WvTIyMgx1CkR6qnpNl8Tq5VSfVPV6vnHjhrCyshIzZswQly9fFj/++KNQq9Vi0aJFhjoFIj1VvaZDQ0OFlZWV+P7778W1a9fEvn37hKenpxg3bpyhToFIT0ZGhjhz5ow4c+aMACCWL18uzpw5I65fvy6EEGLu3LkiODhY1/7atWuiSZMmYs6cOeLixYti9erVwsTEROzdu7fSxzT6pFsIIVatWiXc3NyEubm5ePzxx8V//vMf3Xv9+/cXkyZN0mv/z3/+U7Rv316Ym5uLLl26iF27dtVxxEQVq8o17e7uLgCUeoWGhtZ94ETlqOrf6Qcx6ab6pqrXc0xMjPDz8xMWFhaiTZs2YvHixaKwsLCOoyYqX1Wu6YKCAhEWFiY8PT2FSqUSrq6uYtq0aeLevXt1HzhRGQ4dOlTmd+Oi63jSpEmif//+pfbx8fER5ubmok2bNmLDhg1VOqZCCI7zICIiIiIiIqoNRv1MNxEREREREZEhMekmIiIiIiIiqiVMuomIiIiIiIhqCZNuIiIiIiIiolrCpJuIiIiIiIioljDpJiIiIiIiIqolTLqJiIiIiIiIagmTbiIiIiIiIqJawqSbiIioisLCwqBQKAwdxkMNGDAAAwYMMHQYOkU/t+Tk5Br7TA8PDzz99NMPbXf48GEoFAocPnxYt23y5Mnw8PDQa6dQKBAWFlZj8RERETHpJiIio7FmzRooFAr4+fkZOpQGxcPDAwqFQvdSq9Xo27cvtm/fbujQDC4mJgZhYWFITU01dChERNRAMekmIiKjsXnzZnh4eODEiROIjY2tteMsWLAAOTk5tfb5huDj44Nvv/0W3377LWbPno1bt25h9OjRWLdunaFDqxH9+vVDTk4O+vXrV2G7nJwcLFiwQLceExOD8PBwJt1ERFRtTLqJiMgoxMXFISYmBsuXL4eTkxM2b95ca8cyNTWFSqWqtc83hJYtW2LChAmYMGEC3nrrLRw7dgxNmzbFihUryt2nsLAQ+fn5dRhl9SmVSqhUKiiVFX/1UalUMDU1raOoiIioMWDSTURERmHz5s2ws7PD8OHDMWbMmHKT7i1btsDX1xdWVlawtraGl5cXPvnkE937BQUFCA8PR7t27aBSqeDg4IA+ffpg//79ujZlPdOdk5OD119/HY6OjrCyssLIkSPx119/lXpGuGjf2NhYTJ48Gba2trCxscGUKVOQnZ1dKt5NmzbB19cXlpaWsLe3x/PPP4+bN2+WavfFF1/A09MTlpaWePzxx3H06NGq/gj1uLi4oFOnToiLiwMAxMfHQ6FQYOnSpVi5ciU8PT1hYWGBCxcuAAAOHjyIvn37omnTprC1tcUzzzyDixcvlvnZycnJGDduHKytreHg4ICZM2ciNzdXr82GDRswaNAgqNVqWFhYoHPnzli7dm258e7btw8+Pj5QqVTo3LkzoqKi9N4v65nusjz47xUWFoY5c+YAAFq3bq0bfh8fH4/+/fvD29u7zM/o0KEDAgMDKzwOERE1Hky6iYjIKGzevBmjR4+Gubk5xo8fjz/++AO//vqrXpv9+/dj/PjxsLOzw0cffYQPP/wQAwYMwLFjx3RtwsLCEB4ejoEDB+Kzzz7D/Pnz4ebmhtOnT1d4/MmTJ2PVqlUYNmwYPvroI1haWmL48OHlth83bhwyMjIQERGBcePGYePGjQgPD9drs3jxYkycOBHt2rXD8uXL8cYbbyA6Ohr9+vXTG+68fv16vPLKK3BxccGSJUvwxBNPYOTIkWUm55VVUFCAmzdvwsHBQW/7hg0bsGrVKoSEhGDZsmWwt7fHgQMHEBgYiKSkJISFhWHWrFmIiYnBE088gfj4+DLPPTc3FxERERg2bBg+/fRThISE6LVZu3Yt3N3dMW/ePCxbtgyurq6YNm0aVq9eXerz/vjjDwQFBWHo0KGIiIiAqakpxo4dq3ejpDpGjx6N8ePHAwBWrFihG37v5OSE4OBg/Pbbb/j999/19vn1119x5coVTJgw4ZGOTURERkQQERE1cCdPnhQAxP79+4UQQmi1WtGqVSsxc+ZMvXYzZ84U1tbWorCwsNzP8vb2FsOHD6/weKGhoeLB/0JPnTolAIg33nhDr93kyZMFABEaGlpq35deekmv7bPPPiscHBx06/Hx8cLExEQsXrxYr9358+eFqampbnt+fr5Qq9XCx8dH5OXl6dp98cUXAoDo379/hecihBDu7u5iyJAh4s6dO+LOnTvi3Llz4vnnnxcAxGuvvSaEECIuLk4AENbW1iIpKUlvfx8fH6FWq0VKSopu27lz54RSqRQTJ04sde4jR47U23/atGkCgDh37pxuW3Z2dqk4AwMDRZs2bUrFDkD88MMPum1paWmiefPmonv37rpthw4dEgDEoUOHdNsmTZok3N3d9T6v5L/Xxx9/LACIuLg4vXapqalCpVKJt99+W2/766+/Lpo2bSoyMzNLxU9ERI0Te7qJiKjB27x5M5ydnTFw4EAAcohwUFAQtmzZAo1Go2tna2uLrKysCntAbW1t8b///Q9//PFHpY+/d+9eAMC0adP0tr/22mvl7vPqq6/qrfft2xcpKSlIT08HAERFRUGr1WLcuHFITk7WvVxcXNCuXTscOnQIAHDy5EkkJSXh1Vdfhbm5ue7zJk+eDBsbm0qfw759++Dk5AQnJyd4e3tj69atCA4OxkcffaTX7rnnnoOTk5Nu/fbt2zh79iwmT54Me3t73fZu3bph8ODB2L17d6ljTZ8+XW+96Of0YFtLS0vdclpaGpKTk9G/f39cu3YNaWlpevu3aNECzz77rG7d2toaEydOxJkzZ5CQkFDpn0FV2NjY4JlnnsH3338PIQQAQKPRIDIyEqNGjULTpk1r5bhERNTwMOkmIqIGTaPRYMuWLRg4cCDi4uIQGxuL2NhY+Pn5ITExEdHR0bq206ZNQ/v27TF06FC0atUKL730ki5hLvL+++8jNTUV7du3h5eXF+bMmYPffvutwhiuX78OpVKJ1q1b621v27Ztufu4ubnprdvZ2QEA7t27B0AOmRZCoF27drpkuOh18eJFJCUl6Y4NAO3atdP7PDMzM7Rp06bCuB/k5+eH/fv348CBA4iJiUFycjK++eYbveQXQKlzLDp+hw4dSn1mp06dkJycjKysLL3tJWP19PSEUqnUG4p+7NgxBAQE6J4Rd3Jywrx58wCgVNLdtm3bUs/Yt2/fHgDKHN5eUyZOnIgbN27onp8/cOAAEhMTERwcXGvHJCKihoflOYmIqEE7ePAgbt++jS1btmDLli2l3t+8eTOGDBkCAFCr1Th79ix++ukn7NmzB3v27MGGDRswceJEfP311wDk1FJXr17Fv/71L+zbtw9fffUVVqxYgXXr1mHq1Kk1FreJiUmZ24t6TbVaLRQKBfbs2VNm22bNmtVYLADg6OiIgICAh7YrmYTXhJIJ89WrV/Hkk0+iY8eOWL58OVxdXWFubo7du3djxYoV0Gq1NR5DdQQGBsLZ2RmbNm1Cv379sGnTJri4uFTq50hERI0Hk24iImrQNm/eDLVaXWaBraioKGzfvh3r1q3TJYvm5uYYMWIERowYAa1Wi2nTpuHzzz/Hu+++q+uZtre3x5QpUzBlyhRkZmaiX79+CAsLKzfpdnd3h1arRVxcnF4v7qPMFe7p6QkhBFq3bq3rtS3v2IDsGR80aJBue0FBAeLi4sqtsF1Tio5/+fLlUu9dunQJjo6OpYZa//HHH3o95rGxsdBqtfDw8AAA/Pvf/0ZeXh527typNyKgaEh9SbGxsRBC6CXvV65cAQDdZ1ZXyRsCDzIxMcELL7yAjRs34qOPPsKOHTvwt7/9rdwbKkRE1DhxeDkRETVYOTk5iIqKwtNPP40xY8aUes2YMQMZGRnYuXMnACAlJUVvf6VSiW7dugEA8vLyymzTrFkztG3bVvd+WYqmh1qzZo3e9lWrVlX73EaPHg0TExOEh4frer+LCCF0cfbo0QNOTk5Yt26d3pzZGzdu1KtwXluaN28OHx8ffP3113rH+/3337Fv3z4MGzas1D4lb5AU/ZyGDh0KoHgUwIPnnZaWhg0bNpQZw61bt7B9+3bdenp6Or755hv4+PjAxcWleid2X9ENg/J+lsHBwbh37x5eeeUVZGZmsmo5ERGVwp5uIiJqsHbu3ImMjAyMHDmyzPd79eoFJycnbN68GUFBQZg6dSru3r2LQYMGoVWrVrh+/TpWrVoFHx8fdOrUCQDQuXNnDBgwAL6+vrC3t8fJkyexbds2zJgxo9w4fH198dxzz2HlypVISUlBr169cOTIEV1va0W9peXx9PTEokWL8M477yA+Ph6jRo2ClZUV4uLisH37doSEhGD27NkwMzPDokWL8Morr2DQoEEICgpCXFwcNmzYUKVnuh/Fxx9/jKFDh8Lf3x8vv/wycnJysGrVKtjY2OjNUV4kLi4OI0eOxFNPPYXjx49j06ZNeOGFF3S98kOGDNGNSChKZr/88kuo1Wrcvn271Oe1b98eL7/8Mn799Vc4OzvjH//4BxITE8tN0qvC19cXADB//nw8//zzMDMzw4gRI3TJePfu3dG1a1ds3boVnTp1wmOPPfbIxyQiIuPCnm4iImqwNm/eDJVKhcGDB5f5vlKpxPDhw7F3716kpKRgwoQJUKlUWLNmDaZNm4avv/4aQUFB2LNnD5RK+V/i66+/jvj4eEREROD111/HkSNHsGjRIixbtqzCWL755htMnz4du3btwttvv438/HxERkYCAFQqVbXOb+7cufjhhx+gVCoRHh6O2bNnY+fOnRgyZIjejYaQkBCsWbMGt27dwpw5c3D06FHs3LkTrq6u1TpuVQUEBGDv3r1wcHDAe++9h6VLl6JXr144duxYqcJrABAZGQkLCwvMnTsXu3btwowZM7B+/Xrd+x06dMC2bdugUCgwe/ZsrFu3DiEhIZg5c2aZx2/Xrh0iIyOxe/duzJ07FwUFBYiMjNSNQHgUPXv2xMKFC3Hu3DlMnjwZ48ePx507d/TaTJw4EQBYQI2IiMqkECXHrBEREVGNOHv2LLp3745NmzbhxRdfNHQ4VEs++eQTvPnmm4iPjy9VlZ6IiIg93URERDUgJyen1LaVK1dCqVSiX79+BoiI6oIQAuvXr0f//v2ZcBMRUZn4TDcREVENWLJkCU6dOoWBAwfC1NRUNyVZSEhInQ3zprqTlZWFnTt34tChQzh//jz+9a9/GTokIiKqpzi8nIiIqAbs378f4eHhuHDhAjIzM+Hm5obg4GDMnz8fpqa8x21s4uPj0bp1a9ja2mLatGlYvHixoUMiIqJ6ikk3ERERERERUS3hM91EREREREREtYRJNxEREREREVEtYdJNREREREREVEuYdBMRERERERHVEibdRERERERERLWESTcRERERERFRLWHSTURERERERFRLmHQTERERERER1RIm3URERERERES15P8B9YbmK/tKOPoAAAAASUVORK5CYII=", "text/plain": [ - "bot_question_id Int64\n", - "title object\n", - "resolution float64\n", - "scheduled_close_time datetime64[ns]\n", - "actual_close_time datetime64[ns]\n", - "type object\n", - "options object\n", - "range_min float64\n", - "range_max float64\n", - "pro_question_id Int64\n", - "question_weight float64\n", - "bot_team_median object\n", - "pro_median object\n", - "head_to_head float64\n", - "weighted_score float64\n", - "dtype: object" + "
" ] }, - "execution_count": 75, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of pro forecasts: 50\n", + "Number of bot forecasts: 241\n" + ] } ], "source": [ - "# Cast df_top_bot_pro_forecasts['resolution'] as string - idk why this is necessary but it is\n", - "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].astype(pd.StringDtype())\n", - "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].map({'yes': 1, 'no': 0})\n", - "df_top_bot_pro_forecasts.dtypes" + "# Set up the plot\n", + "plt.figure(figsize=(10, 8))\n", + "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly calibrated')\n", + "\n", + "# Plot calibration curves for bot_team_median and pro_median\n", + "plot_calibration_curve(df_top_bot_pro_forecasts_all_binary, 'bot_team_median', 'Bot Team Median', 'blue')\n", + "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'pro_median', 'Pro Median', 'red')\n", + "\n", + "# Customize the plot\n", + "plt.xlabel('Assigned Probability', fontsize=12)\n", + "plt.ylabel('Fraction that Resolved \\'Yes\\'', fontsize=12)\n", + "plt.title(f'Calibration Curve: Bot Team Median vs Pro Median\\n(all questions)', fontsize=14)\n", + "plt.legend(fontsize=10)\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Set axis limits\n", + "plt.xlim(0, 1)\n", + "plt.ylim(0, 1)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")\n", + "print(f\"Number of bot forecasts: {len(df_top_bot_pro_forecasts_all_binary)}\")" ] }, { "cell_type": "code", - "execution_count": 76, - "metadata": {}, + "execution_count": 80, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lPPgorXB7omi", + "outputId": "24571b16-50b7-4e51-cd3d-420c15c7fe42" + }, "outputs": [ { "data": { @@ -11705,10 +12667,10 @@ " NaN\n", " 31268\n", " 1.0\n", - " 0.02\n", + " 0.017463\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 299.573227\n", - " 299.573227\n", + " 286.007699\n", + " 286.007699\n", " \n", " \n", " 1\n", @@ -11723,10 +12685,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.03366666666666667, 0.0341314028, 0.03460208...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -57.286904\n", - " -57.286904\n", + " -76.357515\n", + " -76.357515\n", " \n", " \n", " 2\n", @@ -11741,10 +12703,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.085\n", " 0.013\n", - " -9.227528\n", - " -9.227528\n", + " -7.574597\n", + " -7.574597\n", " \n", " \n", " 3\n", @@ -11777,151 +12739,62 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.0017047194333333333, 0.0034148989, 0.0...\n", + " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 56.082092\n", - " 56.082092\n", + " 28.578581\n", + " 28.578581\n", " \n", " \n", "\n", "" ], "text/plain": [ - " bot_question_id title \\\n", - "0 31262 For Q1 2025, how many banks will be listed on ... \n", - "1 31263 What percentage of the vote will Alexander Luk... \n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "3 31274 How many arms sales globally will the US State... \n", - "4 31275 How much will it rain in Brasília, Brazil in F... \n", - "\n", - " resolution scheduled_close_time actual_close_time type \\\n", - "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", - "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", - "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", - "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", - "\n", - " options range_min range_max pro_question_id \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", - "1 NaN 60.0 100.0 31269 \n", - "2 NaN NaN NaN 31270 \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", - "4 NaN 0.0 400.0 31281 \n", - "\n", - " question_weight bot_team_median \\\n", - "0 1.0 0.02 \n", - "1 1.0 [0.03366666666666667, 0.0341314028, 0.03460208... \n", - "2 1.0 0.1 \n", - "3 1.0 0.6 \n", - "4 1.0 [0.0, 0.0017047194333333333, 0.0034148989, 0.0... \n", - "\n", - " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 299.573227 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -57.286904 \n", - "2 0.013 -9.227528 \n", - "3 [0.16,0.44,0.4] 31.015493 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 56.082092 \n", - "\n", - " weighted_score \n", - "0 299.573227 \n", - "1 -57.286904 \n", - "2 -9.227528 \n", - "3 31.015493 \n", - "4 56.082092 " - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_top_bot_pro_forecasts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [], - "source": [ - "# Make binary-only df_top_bot_pro_forecasts for calibration curves etc\n", - "df_top_bot_pro_forecasts_binary = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['type'] == 'binary'].copy()\n", - "\n", - "df_top_bot_pro_forecasts_all_binary = df_top_bot_pro_forecasts_all[df_top_bot_pro_forecasts_all['type'] == 'binary'].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 807 - }, - "id": "BjNQ4IND6Ct7", - "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", + "\n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", + "\n", + " options range_min range_max pro_question_id \\\n", + "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", + "1 NaN 60.0 100.0 31269 \n", + "2 NaN NaN NaN 31270 \n", + "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", + "4 NaN 0.0 400.0 31281 \n", + "\n", + " question_weight bot_team_median \\\n", + "0 1.0 0.017463 \n", + "1 1.0 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 1.0 0.085 \n", + "3 1.0 0.6 \n", + "4 1.0 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "\n", + " pro_median head_to_head \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 286.007699 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -76.357515 \n", + "2 0.013 -7.574597 \n", + "3 [0.16,0.44,0.4] 31.015493 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 28.578581 \n", + "\n", + " weighted_score \n", + "0 286.007699 \n", + "1 -76.357515 \n", + "2 -7.574597 \n", + "3 31.015493 \n", + "4 28.578581 " ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of pro forecasts: 50\n" - ] - } - ], - "source": [ - "# Set up the plot\n", - "plt.figure(figsize=(10, 8))\n", - "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly calibrated')\n", - "\n", - "# Plot calibration curves for bot_team_median and pro_median\n", - "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'bot_team_median', 'Bot Team Median', 'blue')\n", - "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'pro_median', 'Pro Median', 'red')\n", - "\n", - "# Customize the plot\n", - "plt.xlabel('Assigned Probability', fontsize=12)\n", - "plt.ylabel('Fraction that Resolved \\'Yes\\'', fontsize=12)\n", - "plt.title(f'Calibration Curve: Bot Team Median vs Pro Median\\n(only overlap: {len(df_top_bot_pro_forecasts_binary)} questions)', fontsize=14)\n", - "plt.legend(fontsize=10)\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Set axis limits\n", - "plt.xlim(0, 1)\n", - "plt.ylim(0, 1)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()\n", - "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Map resolution to 0 and 1\n", - "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ { "data": { "text/html": [ @@ -11956,194 +12829,138 @@ " question_weight\n", " bot_team_median\n", " pro_median\n", + " head_to_head\n", + " weighted_score\n", " \n", " \n", " \n", " \n", - " 2\n", - " 31264\n", - " Will the bubble in the Magnificent Seven pop b...\n", - " 0.0\n", - " 2025-01-20 03:27:00\n", - " 2025-01-20 03:27:00\n", + " 342\n", + " 35345\n", + " Will the US Citizenship and Immigration Servic...\n", + " 1.0\n", + " 2025-03-12 22:00:00\n", + " 2025-03-12 22:00:00\n", " binary\n", " NaN\n", " NaN\n", " NaN\n", - " 31270\n", - " 1.0\n", - " 0.1\n", - " 0.013\n", + " 35380\n", + " 1.00\n", + " 0.9275\n", + " 0.95\n", + " -2.396919\n", + " -2.396919\n", " \n", " \n", - " 5\n", - " 31276\n", - " Will the USDA-posted recall by Pork Dynasty In...\n", - " 1.0\n", - " 2025-01-21 11:42:00\n", - " 2025-01-21 11:42:00\n", + " 351\n", + " 35354\n", + " Will the United States impose any new tariffs ...\n", + " 0.0\n", + " 2025-03-13 03:00:00\n", + " 2025-03-13 03:00:00\n", " binary\n", " NaN\n", " NaN\n", " NaN\n", - " 31282\n", - " 1.0\n", - " 0.6\n", - " 0.45\n", + " 35381\n", + " 1.00\n", + " 0.375\n", + " 0.05\n", + " -41.871033\n", + " -41.871033\n", " \n", " \n", - " 8\n", - " 31288\n", - " Will Eric Adams be Mayor of New York City on t...\n", + " 355\n", + " 35358\n", + " Will ChatGPT rank in the top 10 global website...\n", " 1.0\n", - " 2025-01-22 20:19:00\n", - " 2025-01-22 20:19:00\n", + " 2025-03-13 03:00:00\n", + " 2025-03-13 03:00:00\n", " binary\n", " NaN\n", " NaN\n", " NaN\n", - " 31294\n", - " 1.0\n", - " 0.9\n", - " 0.95\n", + " 35385\n", + " 1.00\n", + " 0.925\n", + " 0.97\n", + " -4.750233\n", + " -4.750233\n", " \n", " \n", - " 10\n", - " 31318\n", - " Will the S&P 500 index go up in January 2025?\n", - " 1.0\n", - " 2025-01-23 23:23:00\n", - " 2025-01-23 23:23:00\n", + " 361\n", + " 35364\n", + " Will Doge's Agency Efficiency Leaderboard have...\n", + " 0.0\n", + " 2025-03-14 23:00:00\n", + " 2025-03-14 23:00:00\n", " binary\n", " NaN\n", " NaN\n", " NaN\n", - " <NA>\n", - " 1.0\n", - " NaN\n", - " NaN\n", + " 35386\n", + " 0.85\n", + " 0.825\n", + " 0.666\n", + " -64.635502\n", + " -54.940177\n", " \n", " \n", - " 13\n", - " 31334\n", - " At the end of March 2025, will Wikipedia still...\n", - " 1.0\n", - " 2025-01-24 14:23:00\n", - " 2025-01-24 14:23:00\n", + " 364\n", + " 35367\n", + " Will the Project 2025 Tracker spreadsheet mark...\n", + " 0.0\n", + " 2025-03-14 23:00:00\n", + " 2025-03-14 23:00:00\n", " binary\n", " NaN\n", " NaN\n", " NaN\n", - " 31338\n", - " 1.0\n", - " 0.75\n", - " 0.9\n", + " 35387\n", + " 0.85\n", + " 0.05\n", + " 0.03\n", + " -2.083409\n", + " -1.770897\n", " \n", " \n", "\n", "" ], "text/plain": [ - " bot_question_id title \\\n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "5 31276 Will the USDA-posted recall by Pork Dynasty In... \n", - "8 31288 Will Eric Adams be Mayor of New York City on t... \n", - "10 31318 Will the S&P 500 index go up in January 2025? \n", - "13 31334 At the end of March 2025, will Wikipedia still... \n", - "\n", - " resolution scheduled_close_time actual_close_time type options \\\n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary NaN \n", - "5 1.0 2025-01-21 11:42:00 2025-01-21 11:42:00 binary NaN \n", - "8 1.0 2025-01-22 20:19:00 2025-01-22 20:19:00 binary NaN \n", - "10 1.0 2025-01-23 23:23:00 2025-01-23 23:23:00 binary NaN \n", - "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", - "\n", - " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "2 NaN NaN 31270 1.0 0.1 \n", - "5 NaN NaN 31282 1.0 0.6 \n", - "8 NaN NaN 31294 1.0 0.9 \n", - "10 NaN NaN 1.0 NaN \n", - "13 NaN NaN 31338 1.0 0.75 \n", - "\n", - " pro_median \n", - "2 0.013 \n", - "5 0.45 \n", - "8 0.95 \n", - "10 NaN \n", - "13 0.9 " - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_top_bot_pro_forecasts_all_binary.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" + " bot_question_id title \\\n", + "342 35345 Will the US Citizenship and Immigration Servic... \n", + "351 35354 Will the United States impose any new tariffs ... \n", + "355 35358 Will ChatGPT rank in the top 10 global website... \n", + "361 35364 Will Doge's Agency Efficiency Leaderboard have... \n", + "364 35367 Will the Project 2025 Tracker spreadsheet mark... \n", + "\n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "342 1.0 2025-03-12 22:00:00 2025-03-12 22:00:00 binary NaN \n", + "351 0.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "355 1.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "361 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "\n", + " range_min range_max pro_question_id question_weight bot_team_median \\\n", + "342 NaN NaN 35380 1.00 0.9275 \n", + "351 NaN NaN 35381 1.00 0.375 \n", + "355 NaN NaN 35385 1.00 0.925 \n", + "361 NaN NaN 35386 0.85 0.825 \n", + "364 NaN NaN 35387 0.85 0.05 \n", + "\n", + " pro_median head_to_head weighted_score \n", + "342 0.95 -2.396919 -2.396919 \n", + "351 0.05 -41.871033 -41.871033 \n", + "355 0.97 -4.750233 -4.750233 \n", + "361 0.666 -64.635502 -54.940177 \n", + "364 0.03 -2.083409 -1.770897 " ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of pro forecasts: 50\n", - "Number of bot forecasts: 241\n" - ] - } - ], - "source": [ - "# Set up the plot\n", - "plt.figure(figsize=(10, 8))\n", - "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly calibrated')\n", - "\n", - "# Plot calibration curves for bot_team_median and pro_median\n", - "plot_calibration_curve(df_top_bot_pro_forecasts_all_binary, 'bot_team_median', 'Bot Team Median', 'blue')\n", - "plot_calibration_curve(df_top_bot_pro_forecasts_binary, 'pro_median', 'Pro Median', 'red')\n", - "\n", - "# Customize the plot\n", - "plt.xlabel('Assigned Probability', fontsize=12)\n", - "plt.ylabel('Fraction that Resolved \\'Yes\\'', fontsize=12)\n", - "plt.title(f'Calibration Curve: Bot Team Median vs Pro Median\\n(all questions)', fontsize=14)\n", - "plt.legend(fontsize=10)\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Set axis limits\n", - "plt.xlim(0, 1)\n", - "plt.ylim(0, 1)\n", - "\n", - "# Show the plot\n", - "plt.tight_layout()\n", - "plt.show()\n", - "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")\n", - "print(f\"Number of bot forecasts: {len(df_top_bot_pro_forecasts_all_binary)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lPPgorXB7omi", - "outputId": "24571b16-50b7-4e51-cd3d-420c15c7fe42" - }, - "outputs": [ { "ename": "ValueError", "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", @@ -12151,21 +12968,22 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[80], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/metaculus/aib-analysis/functions.py:824\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 814\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 815\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 822\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 823\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 824\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 826\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 827\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/.local/lib/python3.12/site-packages/numpy/_core/_methods.py:49\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 48\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[80], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:839\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 837\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 839\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 842\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" ] } ], "source": [ "# Calculate confidence scores for bot_team_median and pro_median\n", + "display_head_and_tail(df_top_bot_pro_forecasts)\n", "bot_confidence = calculate_confidence(df_top_bot_pro_forecasts['bot_team_median'], df_top_bot_pro_forecasts['resolution'])\n", "pro_confidence = calculate_confidence(df_top_bot_pro_forecasts['pro_median'], df_top_bot_pro_forecasts['resolution'])\n", "\n", @@ -12277,7 +13095,7 @@ "cp.rename(columns={'post_id': 'cp_post_id', 'question_id': 'cp_question_id'}, inplace=True)\n", "\n", "bot_cp_id = pd.read_csv('bot_to_main_feed_ids.csv')\n", - " \n", + "\n", "# Merge these on cp_question_id\n", "df_bot_cp = pd.merge(bot_cp_id, cp, on='cp_post_id', how='right') # ahh?\n", "\n", @@ -12400,10 +13218,10 @@ "for bot_question_id in groups_exploded['bot_question_id'].unique():\n", " # Get all rows for this bot_question_id\n", " question_group = groups_exploded[groups_exploded['bot_question_id'] == bot_question_id]\n", - " \n", + "\n", " # Get the question title\n", " question_title = question_group['question_title'].iloc[0]\n", - " \n", + "\n", " # Function to check if option matches question title\n", " def option_matches(row):\n", " option = row['options']\n", @@ -12415,16 +13233,16 @@ " or_format = f\"{start} or {end}\"\n", " return or_format in question_title\n", " return False\n", - " \n", + "\n", " # Find rows where the question title contains the option (with format handling)\n", " matching_rows = question_group[question_group.apply(option_matches, axis=1)]\n", - " \n", + "\n", " filtered_rows = []\n", "\n", " # If we found a matching row, add the first one to our filtered rows, EXCEPT... Biden\n", " if not matching_rows.empty and 'Biden' not in question_title:\n", " filtered_rows.append(matching_rows.iloc[0])\n", - " \n", + "\n", " # If Biden in question_title, we mustn't just take the first row - we must sum the rows that meet the threshold\n", " if 'Biden' in question_title:\n", " # Get first row for each unique option to avoid duplicates\n", @@ -12433,7 +13251,7 @@ " # Drop option='1' - we don't ask about 1 or more\n", " first_rows = first_rows[first_rows['options'] != '1']\n", " biden_interp = first_rows.copy()\n", - " \n", + "\n", " # Now for each row in biden_interp\n", " for idx, row in biden_interp.iterrows():\n", " threshold = int(row['threshold'])\n", @@ -12444,10 +13262,10 @@ " forecast_value = first_rows[first_rows['options'].isin(['3', '4 or more'])]['forecast_values'].sum()\n", " elif threshold == 4:\n", " forecast_value = first_rows[first_rows['options'] == '4 or more']['forecast_values'].sum()\n", - " \n", + "\n", " # Update this row's forecast value\n", " biden_interp.at[idx, 'forecast_value'] = forecast_value\n", - " \n", + "\n", " filtered_rows.append(biden_interp.iloc[0])\n", "\n", "# Combine all filtered rows into a DataFrame\n", @@ -12502,7 +13320,7 @@ "thresholds = {\n", " 29163: ('less', 2.0), # COVID hospitalizations\n", " 29349: ('greater', 100), # Brasilia rain\n", - " 29350: ('greater', 150), # Brasilia rain \n", + " 29350: ('greater', 150), # Brasilia rain\n", " 29351: ('greater', 200), # Brasilia rain\n", " 29353: ('greater', 20), # Arms sales\n", " 29354: ('greater', 25), # Arms sales\n", @@ -12591,7 +13409,7 @@ "# 29567: China youth unemployment > 17.0 and less than 18.0\n", "row = numerics[numerics['bot_question_id'] == 29567].iloc[0]\n", "numerics.loc[numerics['bot_question_id'] == row['bot_question_id'], 'forecast_values'] = cdf_between(row, row['cdf'], 17.0, 18.0)\n", - " \n", + "\n", "# 29568: China youth unemployment > 18.0 and less than 19.0\n", "row = numerics[numerics['bot_question_id'] == 29568].iloc[0]\n", "numerics.loc[numerics['bot_question_id'] == row['bot_question_id'], 'forecast_values'] = cdf_between(row, row['cdf'], 18.0, 19.0)\n", @@ -12701,7 +13519,7 @@ "if True:\n", " # Filter rows where the months do not match\n", " df_bot_cp_exploded = df_bot_cp_exploded[\n", - " (df_bot_cp_exploded['bot_version_month'] == df_bot_cp_exploded['cp_version_month']) | \n", + " (df_bot_cp_exploded['bot_version_month'] == df_bot_cp_exploded['cp_version_month']) |\n", " (df_bot_cp_exploded['bot_version_month'].isnull())\n", "]\n", "\n", @@ -13234,7 +14052,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -13248,7 +14066,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/bootstrapped_h2h_bot_vs_pros.csv b/bootstrapped_h2h_bot_vs_pros.csv index c536929..5811dc4 100644 --- a/bootstrapped_h2h_bot_vs_pros.csv +++ b/bootstrapped_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI -Grizeu_Bot,-9.7,-5.4,4.4,15.9,22.2 -RPM_bot,-0.1,0.3,1.4,2.8,3.7 -X_bot,-0.4,-0.3,0.2,0.7,1.2 +metac-o1,21.0,21.0,21.0,21.0,21.0 +metac-perplexity,20.3,20.3,20.3,20.3,20.3 +bot_median,17.9,17.9,17.9,17.9,17.9 +acm_bot,17.7,17.7,17.7,17.7,17.7 +manticAI,14.5,14.5,14.5,14.5,14.5 +twsummerbot,14.3,14.3,14.3,14.3,14.3 +jkraybill_bot,14.3,14.3,14.3,14.3,14.3 +metac-claude-3-5-sonnet-20240620,12.0,12.0,12.0,12.0,12.0 +GreeneiBot2,11.7,11.7,11.7,11.7,11.7 +metac-claude-3-5-sonnet-latest,11.5,11.5,11.5,11.5,11.5 +NextWorldLab,11.1,11.1,11.1,11.1,11.1 +metac-grok-2-1212,11.0,11.0,11.0,11.0,11.0 +metac-gpt-4o,10.5,10.5,10.5,10.5,10.5 +metac-Llama-3.1,10.2,10.2,10.2,10.2,10.2 +Grizeu_Bot,10.2,10.2,10.2,10.2,10.2 +SynapseSeer,10.2,10.2,10.2,10.2,10.2 +metac-o1-preview,10.1,10.1,10.1,10.1,10.1 +mmBot,9.7,9.7,9.7,9.7,9.7 +metac-exa,9.7,9.7,9.7,9.7,9.7 +annabot,9.0,9.0,9.0,9.0,9.0 +metac-deepseek-r1,8.4,8.4,8.4,8.4,8.4 +VeritasAI,8.4,8.4,8.4,8.4,8.4 +laylaps,7.6,7.6,7.6,7.6,7.6 +cookics_bot_TEST,6.4,6.4,6.4,6.4,6.4 +metac-Gemini-Exp-1206,5.8,5.8,5.8,5.8,5.8 +MWG,5.5,5.5,5.5,5.5,5.5 +ajf-bot,5.1,5.1,5.1,5.1,5.1 +pgodzinai,3.5,3.5,3.5,3.5,3.5 +KevinTestBot,3.3,3.3,3.3,3.3,3.3 +InstitutPelFutur,2.7,2.7,2.7,2.7,2.7 +Bot_Pepa,2.6,2.6,2.6,2.6,2.6 +CumulativeBot,2.5,2.5,2.5,2.5,2.5 +swingswish,2.4,2.4,2.4,2.4,2.4 +wunderplumb,2.4,2.4,2.4,2.4,2.4 +jonahsingerbot,2.2,2.2,2.2,2.2,2.2 +bean_bot,2.1,2.1,2.1,2.1,2.1 +X_bot,1.9,1.9,1.9,1.9,1.9 +CatrachoCaster,1.8,1.8,1.8,1.8,1.8 +4Shadower,0.6,0.6,0.6,0.6,0.6 +krm-bot,0.6,0.6,0.6,0.6,0.6 +RPM_bot,0.6,0.6,0.6,0.6,0.6 andrewsiah,0.0,0.0,0.0,0.0,0.0 cobyj-bot,0.0,0.0,0.0,0.0,0.0 -acm_bot,-16.3,-11.3,-0.2,14.8,22.5 -jonahsingerbot,-1.4,-1.1,-0.6,-0.3,-0.1 -bean_bot,-1.6,-1.3,-0.7,-0.3,-0.1 -CumulativeBot,-2.9,-2.3,-1.0,0.2,1.0 -swingswish,-2.4,-1.9,-1.1,-0.5,-0.3 -jkraybill_bot,-8.5,-6.2,-1.1,4.6,7.5 -KevinTestBot,-5.8,-3.9,-1.4,0.4,1.1 -SynapseSeer,-6.3,-4.6,-1.5,1.9,3.9 -pianobot,-8.0,-5.9,-2.6,-0.2,0.1 -twsummerbot,-13.4,-10.3,-2.9,4.6,9.2 -CatrachoCaster,-8.6,-6.8,-3.4,-0.3,1.1 -annabot,-8.4,-6.5,-3.4,-0.6,0.9 -cookics_bot_TEST,-12.1,-9.7,-4.2,0.1,2.1 -GreeneiBot2,-17.4,-13.2,-4.9,3.6,7.4 -krm-bot,-10.6,-8.6,-5.3,-2.6,-1.6 -4Shadower,-12.8,-9.8,-5.3,-1.8,-1.1 -metac-o1,-22.7,-18.5,-6.7,8.5,16.1 -MWG,-18.3,-14.9,-8.3,-2.2,1.3 -ajf-bot,-22.3,-17.2,-8.8,-1.4,2.5 -bot_median,-22.7,-18.3,-9.0,2.1,8.9 -Bot_Pepa,-20.9,-16.3,-9.0,-1.2,2.7 -manticAI,-22.1,-17.7,-9.5,-0.7,4.9 -ProfessorSP,-20.7,-16.8,-10.1,-4.7,-2.4 -wunderplumb,-22.4,-19.1,-12.0,-5.8,-3.3 -metac-perplexity,-29.1,-24.0,-12.0,0.8,8.0 -laylaps,-21.0,-17.8,-12.8,-8.1,-5.8 -NextWorldLab,-28.4,-24.0,-13.6,-2.8,4.0 -pgodzinai,-31.7,-25.6,-14.0,-4.1,1.9 -metac-Gemini-Exp-1206,-28.1,-23.3,-14.0,-2.7,3.2 -metac-deepseek-r1,-30.7,-25.2,-14.6,-4.9,0.5 -minefrac1,-29.8,-24.8,-14.9,-3.1,4.1 -metac-Llama-3.1,-32.9,-26.8,-15.1,-3.3,3.2 -metac-claude-3-5-sonnet-latest,-32.6,-26.6,-15.9,-3.5,3.2 -metac-claude-3-5-sonnet-20240620,-35.3,-29.9,-18.2,-4.3,2.8 -metac-o1-preview,-38.9,-32.4,-19.3,-6.9,0.3 -mmBot,-36.2,-30.9,-21.1,-11.7,-7.1 -VeritasAI,-33.5,-28.9,-21.3,-14.4,-11.1 -metac-grok-2-1212,-41.8,-35.2,-23.4,-10.4,-3.8 -metac-exa,-40.4,-34.4,-23.4,-13.8,-7.9 -metac-gpt-4o,-41.7,-34.7,-23.8,-11.3,-5.3 -InstitutPelFutur,-43.6,-37.9,-26.5,-14.9,-6.6 +pianobot,-2.2,-2.2,-2.2,-2.2,-2.2 +ProfessorSP,-3.0,-3.0,-3.0,-3.0,-3.0 +minefrac1,-3.0,-3.0,-3.0,-3.0,-3.0 diff --git a/functions.py b/functions.py index 29a05b2..00efd06 100644 --- a/functions.py +++ b/functions.py @@ -27,12 +27,12 @@ def process_forecasts(df): 2. Sorting by created_at to get chronological order 3. Taking the last forecast for each (forecaster, question_id) pair 4. Dropping unused columns - + Parameters: ----------- df : pandas DataFrame DataFrame containing forecast data - + Returns: -------- pandas DataFrame @@ -44,22 +44,22 @@ def process_forecasts(df): df['continuous_cdf'] ) ) - + # Sort by created_at to ensure chronological order df = df.sort_values(by='created_at') - + # Take the last forecast for each (forecaster, question_id) pair df = df.groupby(['question_id', 'forecaster']).last().reset_index() - + # Drop the original forecast columns as they're now redundant df = df.drop(['probability_yes', 'probability_yes_per_category', 'continuous_cdf'], axis=1) - + return df def add_is_median(df): """ Marks exactly one row per question_id as the median. - Guarantees one median per question by taking the forecaster with + Guarantees one median per question by taking the forecaster with the actual median value for that question. Args: @@ -70,19 +70,19 @@ def add_is_median(df): """ # Initialize median column df['is_median'] = False - + # For each question_id for qid in df['question_id'].unique(): # Get just the rows for this question question_mask = df['question_id'] == qid question_df = df[question_mask] - + # Get the median value index (middle position after sorting) median_idx = question_df['forecast'].sort_values().index[len(question_df)//2] - + # Mark that row df.loc[median_idx, 'is_median'] = True - + return df def add_median_rows(df, prefix): @@ -98,10 +98,10 @@ def add_median_rows(df, prefix): """ # Get the median rows median_rows = df[df['is_median']].copy() - + # Change forecaster to 'median' median_rows['forecaster'] = f'{prefix}_median' - + # Combine original and new median rows whole = pd.concat([df, median_rows], ignore_index=True).sort_values('question_id').drop_duplicates(['question_id', 'forecaster']) @@ -196,7 +196,7 @@ def make_wide(df_bot_peer, df_pro_bot_resolved_questions): """ Options from https://stats.stackexchange.com/questions/47325/bias-correction-in-weighted-variance I didn't think (B) beared trying, but could be wrong. - MGH -It makes very little difference here but (C) does seem to be the correct formula - corrects for +It makes very little difference here but (C) does seem to be the correct formula - corrects for the bias in the sample variance. """ @@ -216,7 +216,7 @@ def calc_weighted_std_dev(df3, bot, weighted_score, weighted_count, weight_col): """ weighted_average = weighted_score / weighted_count return np.sqrt(((df3[bot] - weighted_average) ** 2 * df3[weight_col]).sum() / (weighted_count - 1)) - + def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col): """ Calculates the weighted standard deviation using Claude (via Nikos) method - (C) from stack exchange post. @@ -233,7 +233,7 @@ def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) """ weighted_average = weighted_score / weighted_count return np.sqrt( - (df3[weight_col] * (df3[bot] - weighted_average) ** 2).sum() / + (df3[weight_col] * (df3[bot] - weighted_average) ** 2).sum() / (df3[weight_col].sum() * (1 - (df3[weight_col] ** 2).sum() / (df3[weight_col].sum() ** 2))) ) @@ -319,16 +319,16 @@ def get_median_forecast(row, bots): @BEN: Check Calculates the median forecast for a given set of bots, handling different question types properly. - + Args: df (pandas.DataFrame): DataFrame with bot forecast columns and question metadata. bots (list): List of bot column names. - + Returns: pandas.Series: Median forecast for each row. """ q_type = row['type'] - + forecasts = [] for bot in bots: f_raw = row.get(bot) @@ -341,7 +341,7 @@ def get_median_forecast(row, bots): continue else: forecasts.append(f_raw) # Already parsed float or list - + if not forecasts: return np.nan @@ -380,6 +380,19 @@ def get_median_forecast(row, bots): raise ValueError(f"Unknown question type: {q_type}") +def calculate_all_peer_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[str]) -> pd.DataFrame: + """ + Takes in a df that has a row for each question, a column for each team, and a forecast as that columns value + Changes the df so that the forecast is now the score for that question + """ + score_df = df_bot_team_forecasts.copy() + team_scores = calculate_weighted_scores(df_bot_team_forecasts, teams) + for team in teams: + score_for_team = team_scores[team] + score_df[team] = score_for_team + return score_df + + def calculate_weighted_scores(df_bot_team_forecasts, teams): """ @BEN: check @@ -470,15 +483,15 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): Calculates weighted statistics, including t-test and p-values, for multiple bots. Args: - df_input (pandas.DataFrame): + df_input (pandas.DataFrame): DataFrame with peer scores, such as `df_bot_vs_pro_peer`, comparing each bot to the pro median. - bot_list (list): + bot_list (list): List of column names corresponding to bot scores. - weight_col (str, optional): + weight_col (str, optional): Name of the column containing weights. Defaults to 'question_weight'. Returns: - pandas.DataFrame: + pandas.DataFrame: Leaderboard DataFrame with calculated statistics for each bot, including: - W_score: Weighted score. - W_count: Weighted count. @@ -494,33 +507,33 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): """ # Initialize results dataframe df_W_leaderboard = pd.DataFrame(index=bot_list) - + for bot in bot_list: # Create working copy with just needed columns df3 = df_input[[bot, weight_col]].copy() df3 = df3.dropna() df3 = df3.reset_index(drop=True) - + # Calculate weighted statistics weighted_score = (df3[bot] * df3[weight_col]).sum() weighted_count = df3[weight_col].sum() - + if weighted_count > 2: # Only calculate if we have enough data weighted_average = weighted_score / weighted_count weighted_std_dev = calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) std_error = weighted_std_dev / np.sqrt(weighted_count) t_statistic = (weighted_average - 0) / std_error - + # Get t-critical value and confidence bounds effective_n = (df3[weight_col].sum() ** 2) / (df3[weight_col] ** 2).sum() t_crit = stats.t.ppf(0.975, df=effective_n - 1) # 95% confidence level upper_bound = weighted_average + t_crit * std_error lower_bound = weighted_average - t_crit * std_error - + # Calculate CDF and p-value cdf = stats.t.cdf(t_statistic, df=weighted_count-1) p_value = 2 * min(cdf, 1 - cdf) # Two-tailed p-value - + else: # Not enough data weighted_average = weighted_score / weighted_count if weighted_count > 0 else np.nan weighted_std_dev = np.nan @@ -531,7 +544,7 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): lower_bound = np.nan cdf = np.nan p_value = np.nan - + # Store results df_W_leaderboard.loc[bot, 'W_score'] = weighted_score df_W_leaderboard.loc[bot, 'W_count'] = weighted_count @@ -544,34 +557,36 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): df_W_leaderboard.loc[bot, 'lower_bound'] = lower_bound df_W_leaderboard.loc[bot, 'cdf'] = cdf df_W_leaderboard.loc[bot, 'p_value'] = p_value - + # Format and round the results df_W_leaderboard['W_score'] = df_W_leaderboard['W_score'].round(1) # Store numerical p-values temporarily for sorting df_W_leaderboard['_p_value_sort'] = df_W_leaderboard['p_value'] - + # Format p-values as percentages df_W_leaderboard['p_value'] = df_W_leaderboard['p_value'].apply( lambda x: f"{x:.6f}" if pd.notnull(x) else "NA" ) - + # Round other columns df_W_leaderboard[['W_ave', 'W_count', 'lower_bound', 'upper_bound']] = \ df_W_leaderboard[['W_ave', 'W_count', 'lower_bound', 'upper_bound']].round(1) - + # Sort by the numerical p-values df_W_leaderboard = df_W_leaderboard.sort_values( by='W_score', ascending=False, na_position='last' ) - + # Drop the temporary sorting column df_W_leaderboard = df_W_leaderboard.drop('_p_value_sort', axis=1) - + return df_W_leaderboard + + def calculate_head_to_head(row, a, b): """ @BEN: Check... @@ -866,10 +881,10 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): """ # Create figure and axes fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12)) - + # Define bin edges bins = np.linspace(0, 1, 6) - + # Top plot: Questions that resolved 1 ax1.hist([df[df[resolution_col] == 1][bot_col], df[df[resolution_col] == 1][pro_col]], @@ -882,7 +897,7 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): # Set integer y-ticks for top plot ymax1 = int(np.ceil(ax1.get_ylim()[1])) ax1.set_yticks(range(0, ymax1 + 1, 2)) - + # Bottom plot: Questions that resolved 0 ax2.hist([df[df[resolution_col] == 0][bot_col], df[df[resolution_col] == 0][pro_col]], @@ -895,7 +910,7 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): # Set integer y-ticks for bottom plot ymax2 = int(np.ceil(ax2.get_ylim()[1])) ax2.set_yticks(range(0, ymax2 + 1, 10)) - + # Adjust layout and display plt.tight_layout() plt.show() @@ -1134,24 +1149,24 @@ def compute_bucket_forecast_value(row): # Handle binary_version_tuple gracefully if pd.isna(row['binary_version_tuple']) or not isinstance(row['binary_version_tuple'], (list, tuple)): return None - + # Extract the first and second elements of the tuple comparison_type = row['binary_version_tuple'][0] string_location = row['binary_version_tuple'][1] - + # Skip if comparison_type is 'complicated' if comparison_type == 'complicated': return None - + # Compute forecast_value using the extracted string_location forecast_value = get_cdf_at(row['cdf'], nominal_location_to_cdf_location(string_location, row)) - + # Apply logic based on comparison_type if comparison_type == 'less': return forecast_value elif comparison_type == 'greater': return 1 - forecast_value - + return None # Apply the function to each row and overwrite forecast_value (currently contains cdf, which we no longer need) @@ -1161,16 +1176,16 @@ def compute_bucket_forecast_value(row): def parse_options_array(options_str): """ Parse options string that looks like an array into an actual array. - + Args: options_str: String representation of options array (e.g. '["0","1","2-3","4-6",">6"]') - + Returns: List of option strings """ if not isinstance(options_str, str): return options_str # Already parsed or None - + try: # First try using eval (safer than literal_eval for this specific case) options_array = eval(options_str) @@ -1185,6 +1200,6 @@ def parse_options_array(options_str): parts = re.findall(r'"([^"]*)"', cleaned) if parts: return parts - + # Simple fallback: just split by comma and strip quotes return [p.strip().strip('"\'') for p in cleaned.split(',')] diff --git a/weighted_t_test_h2h_bot_vs_pros.csv b/weighted_t_test_h2h_bot_vs_pros.csv index 96cf6b7..b364ee5 100644 --- a/weighted_t_test_h2h_bot_vs_pros.csv +++ b/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -Grizeu_Bot,487.9,40.0,12.2,123.49852344088487,19.53904680990783,0.6251000199360248,2.0203143354405637,51.7,-27.3,0.7322246430842996,0.535551 -acm_bot,149.7,63.8,2.3,123.1672185402655,15.413976167212882,0.1521157135047702,1.9970180928411654,33.1,-28.4,0.5602085330688682,0.879583 -RPM_bot,145.0,6.0,24.2,31.46890650801069,12.847127284662498,1.8809957274619813,2.570581835636314,57.2,-8.9,0.9406376166785096,0.118725 -X_bot,20.7,5.0,4.1,19.75623679424021,8.835257690300725,0.4688971268422159,2.7764451051977987,28.7,-20.4,0.668221204908144,0.663558 -cobyj-bot,0.0,0.0,,,,,,,,,NA -andrewsiah,0.0,0.0,,,,,,,,,NA -jonahsingerbot,-61.3,4.7,-13.0,5.485368611367634,2.5302118657643557,-5.15484234051559,2.7848427377534137,-6.0,-20.1,0.004141428880289339,0.008283 -bean_bot,-70.7,4.7,-15.1,8.81313702231215,4.065196971858859,-3.702222190036137,2.7848427377534137,-3.7,-26.4,0.01192534276282408,0.023851 -jkraybill_bot,-76.1,38.2,-2.0,67.06547883632598,10.85804803442324,-0.18370601441935402,2.023360215298298,20.0,-24.0,0.4276215664726116,0.855243 -CumulativeBot,-97.0,10.2,-9.5,30.12105998155594,9.408238498783877,-1.0055347747612828,2.2318482470257073,11.5,-30.5,0.17010877366473343,0.340218 -swingswish,-109.0,6.7,-16.3,15.145530939114826,5.8512290764953425,-2.779700630431383,2.4503873959101115,-1.9,-30.6,0.016896405137265973,0.033793 -SynapseSeer,-128.5,27.1,-4.8,47.08104512679923,9.052373408885058,-0.5249586045828704,2.0495688922222266,13.8,-23.3,0.3020257536154594,0.604052 -KevinTestBot,-148.3,8.4,-17.7,59.36966948088596,20.484482089149132,-0.861937850691314,2.3114957148363993,29.7,-65.0,0.20788855644704712,0.415777 -twsummerbot,-237.2,47.0,-5.0,79.50268976923377,11.596659167249031,-0.4351341379419649,2.011215351349222,18.3,-28.4,0.3327499422743516,0.665500 -pianobot,-272.2,4.7,-57.9,92.18716506105443,42.522768374266384,-1.3617857782441627,2.798986372998989,61.1,-176.9,0.12513690451031248,0.250274 -annabot,-316.0,24.8,-12.7,43.737410179436026,8.78268331306498,-1.4506136216521068,2.061307003341828,5.4,-30.8,0.07997018027788368,0.159940 -CatrachoCaster,-331.3,19.7,-16.8,52.31505896858736,11.786737352016457,-1.4269796898114384,2.0887774106971415,7.8,-41.4,0.08503530101258772,0.170071 -cookics_bot_TEST,-413.3,24.6,-16.8,72.42669439141218,14.602630986445607,-1.1504360014417054,2.060844706052324,13.3,-46.9,0.13074420290720767,0.261488 -GreeneiBot2,-446.6,45.8,-9.8,88.55320725176313,13.092082882350407,-0.7457050808617829,2.0123403544597687,16.6,-36.1,0.22987241625188587,0.459745 -metac-o1,-500.3,74.7,-6.7,111.25524179571492,12.872419395150438,-0.5203385298152786,1.9915966480791545,18.9,-32.3,0.3021936468001055,0.604387 -krm-bot,-521.0,9.5,-54.8,50.627856321510166,16.42584560255888,-3.3389622067030595,2.2647088573190035,-17.6,-92.0,0.004699854903992789,0.009400 -4Shadower,-527.8,12.2,-43.3,80.79118175671782,23.1304480505728,-1.870272754393436,2.181694676433973,7.2,-93.7,0.043896119135688104,0.087792 -MWG,-766.4,29.5,-26.0,87.753337992406,16.156699118332316,-1.6080774730154093,2.043526587895404,7.0,-59.0,0.059420840675107243,0.118842 -bot_median,-780.6,75.7,-10.3,85.11389082378146,9.782559637787905,-1.0541472762650386,1.991180868356605,9.2,-29.8,0.14760661430231808,0.295213 -Bot_Pepa,-814.9,37.2,-21.9,93.0672852336652,15.269247572172862,-1.4365511370924278,2.0250978379673494,9.0,-52.9,0.07972209366548037,0.159444 -ajf-bot,-843.1,31.4,-26.9,104.85473327098268,18.727045567955233,-1.4360202527786072,2.0376668291983946,11.3,-65.1,0.08061224440506941,0.161224 -manticAI,-861.5,55.0,-15.7,82.87386541760124,11.169633780368585,-1.4011467022381876,2.003063688519742,6.7,-38.0,0.0834429937716208,0.166886 -ProfessorSP,-997.2,16.8,-59.4,96.91948763187727,23.64593376252087,-2.510292938252793,2.1123711239055107,-9.4,-109.3,0.011672270373603825,0.023345 -metac-perplexity,-1072.9,72.7,-14.8,105.3156072760711,12.351665757565863,-1.1948077828717358,1.9924623002180712,9.9,-39.4,0.11804973996535996,0.236099 -wunderplumb,-1159.0,23.8,-48.8,90.740106090436,18.619476902939518,-2.620989857063412,2.065034175048189,-10.4,-87.3,0.007676506818434511,0.015353 -laylaps,-1214.5,52.2,-23.3,48.01992906842049,6.64639675338256,-3.5005872010263053,2.005358510673014,-9.9,-36.6,0.0004856418727962744,0.000971 -NextWorldLab,-1224.1,63.8,-19.2,98.66262212994546,12.347305753344907,-1.552698610221572,1.9970180928411654,5.5,-43.8,0.06275829680564975,0.125517 -metac-Gemini-Exp-1206,-1250.5,65.1,-19.2,94.99321076040114,11.773404699868328,-1.6315194435246863,1.9963767235603869,4.3,-42.7,0.053842330878096756,0.107685 -minefrac1,-1289.4,43.5,-29.6,123.19979122882201,18.679504139979862,-1.5868575895194426,2.0149178012042084,8.0,-67.3,0.05997902931188052,0.119958 -pgodzinai,-1330.4,62.0,-21.5,98.40405336166643,12.497327274265158,-1.7169528181446574,1.998173547416901,3.5,-46.4,0.04553088385451872,0.091062 -metac-deepseek-r1,-1360.3,48.2,-28.2,108.35980238796017,15.607907596292135,-1.808247915950853,2.0091123850303423,3.1,-59.6,0.038470700886698884,0.076941 -metac-Llama-3.1,-1412.1,73.7,-19.2,97.48349885250519,11.355267367831132,-1.687375000139217,1.9920236390185833,3.5,-41.8,0.04790881765000651,0.095818 -metac-claude-3-5-sonnet-latest,-1463.9,74.7,-19.6,96.8559111558961,11.206392518452509,-1.7487367238291156,1.9915966480791545,2.7,-41.9,0.04225009834107552,0.084500 -metac-claude-3-5-sonnet-20240620,-1649.9,75.1,-22.0,105.32409379053074,12.153679026757276,-1.8076157533135497,1.9915359040496325,2.2,-46.2,0.03736236035591808,0.074725 -metac-o1-preview,-1830.6,74.7,-24.5,107.51540873641419,12.439714393299266,-1.9699554012840843,1.9915966480791545,0.3,-49.3,0.026300611526952466,0.052601 -mmBot,-2006.4,75.7,-26.5,78.53235084186326,9.026110757840675,-2.9364459612521934,1.991180868356605,-8.5,-44.5,0.0022054969593251583,0.004411 -VeritasAI,-2024.5,67.7,-29.9,63.28210251110541,7.691066484341371,-3.88818660370801,1.9948486063528272,-14.6,-45.2,0.00011762351540143696,0.000235 -metac-grok-2-1212,-2154.6,74.7,-28.8,106.09460633753015,12.275325155894583,-2.3496848937723014,1.9915966480791545,-4.4,-53.3,0.01073504583547352,0.021470 -metac-gpt-4o,-2196.6,74.7,-29.4,100.42168394988849,11.618958453605197,-2.53084357359069,1.9915966480791545,-6.3,-52.5,0.006756252860737068,0.013513 -metac-exa,-2249.1,72.7,-30.9,91.72328991140397,10.757526338903716,-2.875853188346894,1.9924623002180712,-9.5,-52.4,0.002651041040011998,0.005302 -InstitutPelFutur,-2477.3,72.8,-34.0,102.04145421493415,11.959442897860137,-2.8453905383922216,1.992460623985373,-10.2,-57.9,0.002888355174527779,0.005777 +metac-o1,1998.9,95.0,21.0,3.570999300115835e-15,3.663767977230083e-16,5.743007173754146e+16,1.9847501794262088,21.0,21.0,1.0,0.000000 +metac-perplexity,1927.0,95.0,20.3,0.0,0.0,inf,1.9847501794262088,20.3,20.3,1.0,0.000000 +bot_median,1698.8,95.0,17.9,0.0,0.0,inf,1.9847501794262088,17.9,17.9,1.0,0.000000 +acm_bot,1680.6,95.0,17.7,3.570999300115835e-15,3.663767977230083e-16,4.828448927545706e+16,1.9847501794262088,17.7,17.7,1.0,0.000000 +manticAI,1378.2,95.0,14.5,0.0,0.0,inf,1.9847501794262088,14.5,14.5,1.0,0.000000 +twsummerbot,1355.4,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.788325122257914e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 +jkraybill_bot,1354.5,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.783286397381174e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 +metac-claude-3-5-sonnet-20240620,1136.7,95.0,12.0,3.570999300115835e-15,3.663767977230083e-16,3.26596902511772e+16,1.9847501794262088,12.0,12.0,1.0,0.000000 +GreeneiBot2,1115.4,95.0,11.7,5.3564989501737525e-15,5.495651965845125e-16,2.1364275625153532e+16,1.9847501794262088,11.7,11.7,1.0,0.000000 +metac-claude-3-5-sonnet-latest,1091.6,95.0,11.5,5.3564989501737525e-15,5.495651965845125e-16,2.0907644050343052e+16,1.9847501794262088,11.5,11.5,1.0,0.000000 +NextWorldLab,1050.3,95.0,11.1,1.7854996500579174e-15,1.8318839886150415e-16,6.035037516349447e+16,1.9847501794262088,11.1,11.1,1.0,0.000000 +metac-grok-2-1212,1047.4,95.0,11.0,0.0,0.0,inf,1.9847501794262088,11.0,11.0,1.0,0.000000 +metac-gpt-4o,1002.0,95.0,10.5,3.570999300115835e-15,3.663767977230083e-16,2.87887889373382e+16,1.9847501794262088,10.5,10.5,1.0,0.000000 +metac-Llama-3.1,973.0,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 +Grizeu_Bot,966.4,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 +SynapseSeer,964.7,95.0,10.2,1.7854996500579174e-15,1.8318839886150415e-16,5.5434396730578184e+16,1.9847501794262088,10.2,10.2,1.0,0.000000 +metac-o1-preview,962.8,95.0,10.1,1.7854996500579174e-15,1.8318839886150415e-16,5.5325101025506376e+16,1.9847501794262088,10.1,10.1,1.0,0.000000 +mmBot,924.8,95.0,9.7,0.0,0.0,inf,1.9847501794262088,9.7,9.7,1.0,0.000000 +metac-exa,919.9,95.0,9.7,1.7854996500579174e-15,1.8318839886150415e-16,5.285938770788284e+16,1.9847501794262088,9.7,9.7,1.0,0.000000 +annabot,854.4,95.0,9.0,1.7854996500579174e-15,1.8318839886150415e-16,4.909363317298574e+16,1.9847501794262088,9.0,9.0,1.0,0.000000 +metac-deepseek-r1,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608683275523464e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 +VeritasAI,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608352429717695e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 +laylaps,723.4,95.0,7.6,8.927498250289587e-16,9.159419943075207e-17,8.313179820692651e+16,1.9847501794262088,7.6,7.6,1.0,0.000000 +cookics_bot_TEST,612.4,95.0,6.4,1.7854996500579174e-15,1.8318839886150415e-16,3.5189490119492424e+16,1.9847501794262088,6.4,6.4,1.0,0.000000 +metac-Gemini-Exp-1206,548.0,95.0,5.8,0.0,0.0,inf,1.9847501794262088,5.8,5.8,1.0,0.000000 +MWG,520.8,95.0,5.5,8.927498250289587e-16,9.159419943075207e-17,5.985647068886487e+16,1.9847501794262088,5.5,5.5,1.0,0.000000 +ajf-bot,481.2,95.0,5.1,1.7854996500579174e-15,1.8318839886150415e-16,2.7648981076196796e+16,1.9847501794262088,5.1,5.1,1.0,0.000000 +pgodzinai,336.0,95.0,3.5,8.927498250289587e-16,9.159419943075207e-17,3.8616390554277256e+16,1.9847501794262088,3.5,3.5,1.0,0.000000 +KevinTestBot,314.5,95.0,3.3,8.927498250289587e-16,9.159419943075207e-17,3.614851659932975e+16,1.9847501794262088,3.3,3.3,1.0,0.000000 +InstitutPelFutur,256.0,95.0,2.7,8.927498250289587e-16,9.159419943075207e-17,2.9416230195900824e+16,1.9847501794262088,2.7,2.7,1.0,0.000000 +Bot_Pepa,246.8,95.0,2.6,0.0,0.0,inf,1.9847501794262088,2.6,2.6,1.0,0.000000 +CumulativeBot,241.1,95.0,2.5,4.463749125144793e-16,4.579709971537604e-17,5.542702538240192e+16,1.9847501794262088,2.5,2.5,1.0,0.000000 +swingswish,229.1,95.0,2.4,4.463749125144793e-16,4.579709971537604e-17,5.265549431654757e+16,1.9847501794262088,2.4,2.4,1.0,0.000000 +wunderplumb,225.4,95.0,2.4,4.463749125144793e-16,4.579709971537604e-17,5.180942325472045e+16,1.9847501794262088,2.4,2.4,1.0,0.000000 +jonahsingerbot,212.9,95.0,2.2,4.463749125144793e-16,4.579709971537604e-17,4.894510648634918e+16,1.9847501794262088,2.2,2.2,1.0,0.000000 +bean_bot,200.0,95.0,2.1,0.0,0.0,inf,1.9847501794262088,2.1,2.1,1.0,0.000000 +X_bot,181.4,95.0,1.9,0.0,0.0,inf,1.9847501794262088,1.9,1.9,1.0,0.000000 +CatrachoCaster,167.5,95.0,1.8,4.463749125144793e-16,4.579709971537604e-17,3.8493725321790856e+16,1.9847501794262088,1.8,1.8,1.0,0.000000 +4Shadower,61.1,95.0,0.6,2.2318745625723967e-16,2.289854985768802e-17,2.810105705323094e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 +krm-bot,60.8,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,5.586128771835555e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 +RPM_bot,52.6,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,4.834419627569585e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 +andrewsiah,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA +cobyj-bot,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA +pianobot,-206.5,95.0,-2.2,4.463749125144793e-16,4.579709971537604e-17,-4.745304957283875e+16,1.9847501794262088,-2.2,-2.2,0.0,0.000000 +ProfessorSP,-280.4,95.0,-3.0,8.927498250289587e-16,9.159419943075207e-17,-3.2229421543642156e+16,1.9847501794262088,-3.0,-3.0,0.0,0.000000 +minefrac1,-283.9,95.0,-3.0,4.463749125144793e-16,4.579709971537604e-17,-6.524423956604449e+16,1.9847501794262088,-3.0,-3.0,0.0,0.000000 From fb786fc8e9656f1c88b0b20bdf7df906a18e427e Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 07:01:51 -0600 Subject: [PATCH 02/26] reorganized data files into subdirectories --- AI_BENCHMARKING_ANALYSIS.ipynb | 521 +- functions.py | 1 + main.py | 0 .../bot_to_main_feed_ids.csv | 0 .../bootstrapped_h2h_bot_vs_pros.csv | 0 .../df_top_bot_pro_cp_forecasts.csv | 0 .../weighted_baseline_bot_cp.csv | 0 ...ghted_bot_ONLY_peer_leaderboard_t_test.csv | 0 .../weighted_bot_peer_leaderboard_t_test.csv | 0 .../weighted_t_test_h2h_bot_vs_cp.csv | 0 .../weighted_t_test_h2h_bot_vs_pros.csv | 0 pgodzinai_comments.csv | 47670 ---------------- pgodzinai_comments.ipynb | 187 - 13 files changed, 512 insertions(+), 47867 deletions(-) create mode 100644 main.py rename bot_to_main_feed_ids.csv => misc_data/bot_to_main_feed_ids.csv (100%) rename bootstrapped_h2h_bot_vs_pros.csv => notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv (100%) rename df_top_bot_pro_cp_forecasts.csv => notebook_outputs/df_top_bot_pro_cp_forecasts.csv (100%) rename weighted_baseline_bot_cp.csv => notebook_outputs/weighted_baseline_bot_cp.csv (100%) rename weighted_bot_ONLY_peer_leaderboard_t_test.csv => notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv (100%) rename weighted_bot_peer_leaderboard_t_test.csv => notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv (100%) rename weighted_t_test_h2h_bot_vs_cp.csv => notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv (100%) rename weighted_t_test_h2h_bot_vs_pros.csv => notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv (100%) delete mode 100644 pgodzinai_comments.csv delete mode 100644 pgodzinai_comments.ipynb diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 313d580..0f510e4 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -7088,7 +7088,7 @@ "outputs": [], "source": [ "# Write to csv\n", - "df_W_leaderboard.to_csv('weighted_t_test_h2h_bot_vs_pros.csv', index=True)" + "df_W_leaderboard.to_csv('notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv', index=True)" ] }, { @@ -8051,7 +8051,7 @@ "outputs": [], "source": [ "# Write to csv\n", - "df_W_leaderboard_print.to_csv('weighted_bot_peer_leaderboard_t_test.csv', index=False)" + "df_W_leaderboard_print.to_csv('notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv', index=False)" ] }, { @@ -9740,7 +9740,7 @@ "outputs": [], "source": [ "# Write df_rounded (bootstrapping h2h) to csv\n", - "df_rounded.to_csv('bootstrapped_h2h_bot_vs_pros.csv')" + "df_rounded.to_csv('notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv')" ] }, { @@ -10319,7 +10319,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_W_bot_only_peer_leaderboard.to_csv('weighted_bot_ONLY_peer_leaderboard_t_test.csv', index=True)" + "df_W_bot_only_peer_leaderboard.to_csv('notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv', index=True)" ] }, { @@ -11145,7 +11145,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 81, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11154,6 +11154,507 @@ "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" }, "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idquestion_weightresolutiontypeoptionsrange_minrange_maxmetac-o1-previewmetac-o1pgodzinai...median_forecast_1_botsmedian_forecast_2_botsmedian_forecast_3_botsmedian_forecast_4_botsmedian_forecast_5_botsmedian_forecast_6_botsmedian_forecast_7_botsmedian_forecast_8_botsmedian_forecast_9_botsmedian_forecast_10_bots
0312621.00multiple_choice[0, 1, 2-3, 4-6, >6]NaNNaN[0.02,0.7,0.2,0.07,0.01][0.4,0.35,0.2,0.04,0.01][0.014925742574257425,0.5137871287128712,0.334......0.020.210.020.0174630.0174630.020.10.10.020.02
1312631.086.82numericNaN60.0100.0[0.05,0.0506666667,0.0513333333,0.052,0.052666...[0.05,0.0505555556,0.0511111111,0.0516666667,0...[0.001,0.001060875,0.0011396,0.0012863125,0.00......[0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...[0.05, 0.05061111115, 0.0512222222, 0.05183333...[0.03366666666666667, 0.03409436576666667, 0.0...[0.037750000000000006, 0.03822284245, 0.038700...[0.037750000000000006, 0.03822284245, 0.038700...[0.0402, 0.040728273960000005, 0.04126011788, ...[0.0402, 0.040728273960000005, 0.04126011788, ...[0.0402, 0.040728273960000005, 0.04126011788, ...[0.041833333333333333, 0.04238467275, 0.042938...[0.041833333333333333, 0.04238467275, 0.042938...
2312641.0nobinaryNaNNaNNaN0.150.10.07...0.150.1250.10.0850.0850.10.1250.1250.150.15
3312741.05-9multiple_choice[0-4, 5-9, >9]NaNNaN[0.2,0.6,0.2][0.25,0.6,0.15][0.27499999999999997,0.5125,0.21249999999999997]...0.60.60.60.60.60.556250.51250.51250.556250.5125
4312751.0119.2numericNaN0.0400.0[0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...[0.0,0.0028571429,0.0057142857,0.0085714286,0....[0.0,0.0001141583,0.0002446967,0.0003862688,0.......[0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07...[0.0, 0.00642857145, 0.01285714285, 0.01928571...[0.0, 0.004323767066666667, 0.0086529941333333...[0.0, 0.00369737075, 0.0073988365, 0.011103060...[0.0, 0.00318255036, 0.00637055762, 0.00956313...[0.0, 0.00295931485, 0.0059231771, 0.008890847...[0.0, 0.0028936984428571426, 0.005791294657142...[0.0, 0.0028936984428571426, 0.005791294657142...[0.0, 0.0028097639124999995, 0.005622938375, 0...[0.0, 0.0026433398111111108, 0.005289711211111...
\n", + "

5 rows × 27 columns

\n", + "
" + ], + "text/plain": [ + " bot_question_id question_weight resolution type \\\n", + "0 31262 1.0 0 multiple_choice \n", + "1 31263 1.0 86.82 numeric \n", + "2 31264 1.0 no binary \n", + "3 31274 1.0 5-9 multiple_choice \n", + "4 31275 1.0 119.2 numeric \n", + "\n", + " options range_min range_max \\\n", + "0 [0, 1, 2-3, 4-6, >6] NaN NaN \n", + "1 NaN 60.0 100.0 \n", + "2 NaN NaN NaN \n", + "3 [0-4, 5-9, >9] NaN NaN \n", + "4 NaN 0.0 400.0 \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.02,0.7,0.2,0.07,0.01] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.15 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", + "\n", + " metac-o1 \\\n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + "\n", + " pgodzinai ... \\\n", + "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", + "1 [0.001,0.001060875,0.0011396,0.0012863125,0.00... ... \n", + "2 0.07 ... \n", + "3 [0.27499999999999997,0.5125,0.21249999999999997] ... \n", + "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... ... \n", + "\n", + " median_forecast_1_bots \\\n", + "0 0.02 \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.15 \n", + "3 0.6 \n", + "4 [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07... \n", + "\n", + " median_forecast_2_bots \\\n", + "0 0.21 \n", + "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", + "2 0.125 \n", + "3 0.6 \n", + "4 [0.0, 0.00642857145, 0.01285714285, 0.01928571... \n", + "\n", + " median_forecast_3_bots \\\n", + "0 0.02 \n", + "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", + "2 0.1 \n", + "3 0.6 \n", + "4 [0.0, 0.004323767066666667, 0.0086529941333333... \n", + "\n", + " median_forecast_4_bots \\\n", + "0 0.017463 \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 0.085 \n", + "3 0.6 \n", + "4 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "\n", + " median_forecast_5_bots \\\n", + "0 0.017463 \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 0.085 \n", + "3 0.6 \n", + "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", + "\n", + " median_forecast_6_bots \\\n", + "0 0.02 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.1 \n", + "3 0.55625 \n", + "4 [0.0, 0.00295931485, 0.0059231771, 0.008890847... \n", + "\n", + " median_forecast_7_bots \\\n", + "0 0.1 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.125 \n", + "3 0.5125 \n", + "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", + "\n", + " median_forecast_8_bots \\\n", + "0 0.1 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.125 \n", + "3 0.5125 \n", + "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", + "\n", + " median_forecast_9_bots \\\n", + "0 0.02 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", + "2 0.15 \n", + "3 0.55625 \n", + "4 [0.0, 0.0028097639124999995, 0.005622938375, 0... \n", + "\n", + " median_forecast_10_bots \n", + "0 0.02 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", + "2 0.15 \n", + "3 0.5125 \n", + "4 [0.0, 0.0026433398111111108, 0.005289711211111... \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idquestion_weightresolutiontypeoptionsrange_minrange_maxmetac-o1-previewmetac-o1pgodzinai...median_forecast_1_botsmedian_forecast_2_botsmedian_forecast_3_botsmedian_forecast_4_botsmedian_forecast_5_botsmedian_forecast_6_botsmedian_forecast_7_botsmedian_forecast_8_botsmedian_forecast_9_botsmedian_forecast_10_bots
342353451.00yesbinaryNaNNaNNaN0.90.950.95...0.90.9250.950.92750.9050.920.9050.90250.90.9
351353541.00nobinaryNaNNaNNaN0.40.35NaN...0.40.3750.3750.3750.350.20250.350.20850.350.238
355353581.00yesbinaryNaNNaNNaN0.950.9NaN...0.950.9250.9250.9250.90.850.80.7720.80.814
361353640.85nobinaryNaNNaNNaN0.850.80.85...0.850.8250.850.8250.80.7550.80.7550.80.755
364353670.85nobinaryNaNNaNNaN0.050.050.05...0.050.050.050.050.050.050.050.0460.050.05
\n", + "

5 rows × 27 columns

\n", + "
" + ], + "text/plain": [ + " bot_question_id question_weight resolution type options range_min \\\n", + "342 35345 1.00 yes binary NaN NaN \n", + "351 35354 1.00 no binary NaN NaN \n", + "355 35358 1.00 yes binary NaN NaN \n", + "361 35364 0.85 no binary NaN NaN \n", + "364 35367 0.85 no binary NaN NaN \n", + "\n", + " range_max metac-o1-preview metac-o1 pgodzinai ... \\\n", + "342 NaN 0.9 0.95 0.95 ... \n", + "351 NaN 0.4 0.35 NaN ... \n", + "355 NaN 0.95 0.9 NaN ... \n", + "361 NaN 0.85 0.8 0.85 ... \n", + "364 NaN 0.05 0.05 0.05 ... \n", + "\n", + " median_forecast_1_bots median_forecast_2_bots median_forecast_3_bots \\\n", + "342 0.9 0.925 0.95 \n", + "351 0.4 0.375 0.375 \n", + "355 0.95 0.925 0.925 \n", + "361 0.85 0.825 0.85 \n", + "364 0.05 0.05 0.05 \n", + "\n", + " median_forecast_4_bots median_forecast_5_bots median_forecast_6_bots \\\n", + "342 0.9275 0.905 0.92 \n", + "351 0.375 0.35 0.2025 \n", + "355 0.925 0.9 0.85 \n", + "361 0.825 0.8 0.755 \n", + "364 0.05 0.05 0.05 \n", + "\n", + " median_forecast_7_bots median_forecast_8_bots median_forecast_9_bots \\\n", + "342 0.905 0.9025 0.9 \n", + "351 0.35 0.2085 0.35 \n", + "355 0.8 0.772 0.8 \n", + "361 0.8 0.755 0.8 \n", + "364 0.05 0.046 0.05 \n", + "\n", + " median_forecast_10_bots \n", + "342 0.9 \n", + "351 0.238 \n", + "355 0.814 \n", + "361 0.755 \n", + "364 0.05 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/html": [ @@ -11248,7 +11749,7 @@ "9 10 26.47" ] }, - "execution_count": 61, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -13094,7 +13595,7 @@ "cp = pd.read_csv('https://data.heroku.com/dataclips/xwbtczmsuszvlbrhdifhsilplfxf.csv')\n", "cp.rename(columns={'post_id': 'cp_post_id', 'question_id': 'cp_question_id'}, inplace=True)\n", "\n", - "bot_cp_id = pd.read_csv('bot_to_main_feed_ids.csv')\n", + "bot_cp_id = pd.read_csv('misc_data/bot_to_main_feed_ids.csv')\n", "\n", "# Merge these on cp_question_id\n", "df_bot_cp = pd.merge(bot_cp_id, cp, on='cp_post_id', how='right') # ahh?\n", @@ -13828,9 +14329,9 @@ "outputs": [], "source": [ "# Write both leaderboards to csv\n", - "weighted_leaderboard.to_csv('weighted_baseline_bot_cp.csv', index=False)\n", + "weighted_leaderboard.to_csv('notebook_outputs/weighted_baseline_bot_cp.csv', index=False)\n", "\n", - "df_W_leaderboard.to_csv('weighted_t_test_h2h_bot_vs_cp.csv', index=True)" + "df_W_leaderboard.to_csv('notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv', index=True)" ] }, { @@ -14024,7 +14525,7 @@ "df_top_bot_pro_cp_forecasts = df_top_bot_pro_cp_forecasts.rename(columns={'forecast_values': 'community_prediction'})\n", "\n", "# Write df_top_bot_pro_cp_forecasts to csv, but only the columns bot question id, cp post id, cp question id, title, resolution, cp_reveal_time, forecast_values, bot_team_median, pro_median\n", - "df_top_bot_pro_cp_forecasts[['bot_question_id', 'cp_post_id', 'cp_question_id', 'title', 'resolution', 'cp_reveal_time', 'community_prediction', 'bot_team_median', 'pgodzinai', 'pro_median']].to_csv('df_top_bot_pro_cp_forecasts.csv', index=False)" + "df_top_bot_pro_cp_forecasts[['bot_question_id', 'cp_post_id', 'cp_question_id', 'title', 'resolution', 'cp_reveal_time', 'community_prediction', 'bot_team_median', 'pgodzinai', 'pro_median']].to_csv('notebook_outputs/df_top_bot_pro_cp_forecasts.csv', index=False)" ] }, { diff --git a/functions.py b/functions.py index 00efd06..d0bf79f 100644 --- a/functions.py +++ b/functions.py @@ -385,6 +385,7 @@ def calculate_all_peer_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[s Takes in a df that has a row for each question, a column for each team, and a forecast as that columns value Changes the df so that the forecast is now the score for that question """ + raise NotImplementedError("I accidentally implemented baseline scoring here unfortunately") score_df = df_bot_team_forecasts.copy() team_scores = calculate_weighted_scores(df_bot_team_forecasts, teams) for team in teams: diff --git a/main.py b/main.py new file mode 100644 index 0000000..e69de29 diff --git a/bot_to_main_feed_ids.csv b/misc_data/bot_to_main_feed_ids.csv similarity index 100% rename from bot_to_main_feed_ids.csv rename to misc_data/bot_to_main_feed_ids.csv diff --git a/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv similarity index 100% rename from bootstrapped_h2h_bot_vs_pros.csv rename to notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv diff --git a/df_top_bot_pro_cp_forecasts.csv b/notebook_outputs/df_top_bot_pro_cp_forecasts.csv similarity index 100% rename from df_top_bot_pro_cp_forecasts.csv rename to notebook_outputs/df_top_bot_pro_cp_forecasts.csv diff --git a/weighted_baseline_bot_cp.csv b/notebook_outputs/weighted_baseline_bot_cp.csv similarity index 100% rename from weighted_baseline_bot_cp.csv rename to notebook_outputs/weighted_baseline_bot_cp.csv diff --git a/weighted_bot_ONLY_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv similarity index 100% rename from weighted_bot_ONLY_peer_leaderboard_t_test.csv rename to notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv diff --git a/weighted_bot_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv similarity index 100% rename from weighted_bot_peer_leaderboard_t_test.csv rename to notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv diff --git a/weighted_t_test_h2h_bot_vs_cp.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv similarity index 100% rename from weighted_t_test_h2h_bot_vs_cp.csv rename to notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv diff --git a/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv similarity index 100% rename from weighted_t_test_h2h_bot_vs_pros.csv rename to notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv diff --git a/pgodzinai_comments.csv b/pgodzinai_comments.csv deleted file mode 100644 index 5551c1f..0000000 --- a/pgodzinai_comments.csv +++ /dev/null @@ -1,47670 +0,0 @@ -post_id,post_title,comment_text -25871,"On July 17, 2024, will Nvidia's market capitalization be larger than Apple's?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- Nvidia's market capitalization has surged significantly, reaching $3.34 trillion, surpassing Microsoft and Apple to become the most valuable public company. -- Nvidia's rapid growth is attributed to the increasing demand for its graphics processing units (GPUs) in the development of artificial intelligence (AI) systems. -- Apple's market capitalization is slightly below $3.4 trillion, while Microsoft leads with a market capitalization just exceeding $3.4 trillion. -- Experts believe Nvidia will likely be the first to reach a $4 trillion market capitalization due to the high demand for its AI-focused hardware. -- Despite concerns about Nvidia's valuation being overstretched and increasing competition in the semiconductor market, analysts remain bullish on its growth trajectory. -- Apple is also seen as a strong contender for reaching the $4 trillion market cap, leveraging AI to drive customer upgrades and attract new iPhone users. - -Recent updates indicate that Nvidia's market capitalization continues to grow, with some experts predicting it will reach $4 trillion first. However, Apple's growth prospects are also promising, particularly with its integration of AI capabilities into its latest devices. The current market capitalization values are: - -- Nvidia: $3.34 trillion -- Apple: slightly below $3.4 trillion - -Expert opinions suggest that Nvidia's momentum may continue, but Apple's growth potential and relatively lower valuation make it a strong contender as well. - -######### - -GPT - -Given the high stakes and complex factors involved in predicting market capitalization, I will approach this methodically by evaluating relevant data, trends, and factors influencing Nvidia and Apple’s market performance. I will also consider statistical analysis and historical precedents. - -### Current Context and Data: -1. **Current Market Capitalization:** - - Nvidia: $3.34 trillion - - Apple: slightly below $3.4 trillion - -2. **Trends:** - - Nvidia has experienced significant growth, primarily due to its dominance in the AI chip market. - - Apple continues to perform strongly, especially with its integration of AI capabilities into its latest devices and strong consumer demand. - -3. **Market Forecasts and Expert Opinions:** - - Many analysts are bullish on Nvidia due to the increasing demand for AI hardware. - - Apple is also seen as a potential candidate for a $4 trillion market cap. - -### Factors to Consider: -1. **Temporal Cushion (9 days until resolution):** - - The market can experience significant movements within short periods, given volatility and investor sentiment. - - Nine days provide time for any unexpected news or events to impact market caps. - -2. **Historical Volatility:** - - Tech stocks are known for high volatility, especially companies like Nvidia and Apple. - -3. **Comparative Dynamics:** - - Nvidia’s rapid rise is due to a high demand but could be sensitive to shifts in market sentiment or economic indicators. - - Apple benefits from a more diversified product line and consistent revenue streams. - -### Base Rates and Historical Precedents: -- Historically, rapid surges in market valuation, such as Nvidia’s recent growth, tend to stabilize or correct over short timeframes, especially if perceived as overstretched. -- Apple’s valuation tends to be more stable due to its diversified, entrenched position in consumer electronics and services. - -### Statistical Considerations: -- Using historical market data, compute daily standard deviation: - - Assume a 2% daily volatility (standard deviation) for Nvidia and 1.5% for Apple based on their historical performance. -- Over 9 days: - - Nvidia could experience roughly \(2\% \cdot \sqrt{9} ≈ 6%\) movement. - - Apple could experience roughly \(1.5\% \cdot \sqrt{9} ≈ 4.5%\) movement. - -### Default Resolution: -- As of now, Apple has a slightly higher market cap. -- Without significant new developments, the default outcome favors Apple maintaining its lead due to the factors discussed. - -### Potential Novel Developments: -- **Technology and Product Announcements:** Any significant product release or technological breakthrough can impact valuations. -- **Economic Indicators:** Broader market economic factors like interest rate changes or economic reports can influence investor behavior. -- **Geopolitical Events:** Unpredictable geopolitical events can impact tech stock performance. - -### Adjusted Probabilities after Considering Factors: -1. **Nvidia’s continued upward trajectory**: Given Nvidia’s current momentum and the high demand for AI hardware, assume a 60% probability they will maintain or increase their valuation preference compared to Apple. -2. **Apple’s consistent growth**: Apple’s stability and potential AI integration pose a 40% probability they will close the marginal gap given current valuations and trends. - -### Final Assessment: -Given the above calculations, data analysis, and expert opinions, the comprehensive reasoning leans towards a slight favorability for Nvidia. - -Given the nine-day period and diverse influencing factors, the uncertainty remains, yet steady trends prefer Nvidia in this high volatility period. - -### Probability: -Considering all the outlined angles and thorough reassessment: - -**Probability: 62%** - -This suggests there’s a 62% chance that Nvidia’s market capitalization will be larger than Apple’s on July 17, 2024." -25871,"On July 17, 2024, will Nvidia's market capitalization be larger than Apple's?","PERPLEXITY - -To provide context for this question, here is a summary of recent news and market trends relevant to Nvidia and Apple: - -1. **Market Capitalization**: Nvidia's market capitalization has surged, reaching $2.8 trillion as of May 28, 2024, and $3.34 trillion as of June 18, 2024, making it the most valuable public company. Apple's market capitalization stands at around $3.4 trillion. - -2. **Growth and Revenue**: Nvidia has demonstrated substantial growth, driven by its dominance in AI chips and data center services. Its revenue and profitability are accelerating, with analysts expecting continued growth over the next couple of years. Apple, on the other hand, has faced challenges in expanding its revenue recently but is expected to benefit from integrating AI capabilities into its products. - -3. **AI Focus**: Both companies are heavily invested in AI. Nvidia is seen as the engine powering the AI revolution, while Apple is integrating AI into its products, including the upcoming iPhone release. - -4. **Valuation**: Nvidia's price-to-earnings (P/E) ratio is around 74, which is considered high but more reasonable than its previous levels. Apple's P/E ratio is more modest, suggesting potential for further upward movement. - -5. **Expert Opinions**: Analysts believe Nvidia will lead the race to a $4 trillion market cap due to the high demand for its AI-focused hardware. However, Microsoft is seen as a more sustainable $4 trillion company with its diversified revenue streams. Apple is also expected to reach the $4 trillion mark as it leverages AI to drive customer upgrades and attract new iPhone users. - -Given these factors, it is difficult to predict with certainty whether Nvidia's market capitalization will be larger than Apple's on July 17, 2024. However, based on current trends and expert opinions, Nvidia appears to be in a strong position to maintain its lead. - -######### - -GPT - -### Step-by-Step Analysis - -**Step 1: Understand the Background & Current Situation** -- **Nvidia Market Capitalization**: $3.34 trillion as of June 18, 2024. -- **Apple Market Capitalization**: $3.4 trillion as of the same date. -- **Nvidia's Growth**: Driven by dominance in the AI chip market and robust financial performance. -- **Apple's Growth**: Somewhat slower but with potential upside due to AI integration in upcoming products. - -**Step 2: Importance of Different Background Sources** -- **Market Capitalization Figures**: Crucial as they represent the current standing. -- **Growth and Revenue Trends**: Important but should be weighted appropriately as both companies are strong with some variance in growth rates. -- **AI Focus**: This is currently a significant driver for Nvidia but also impactful for Apple. -- **Valuation**: Nvidia’s higher P/E ratio suggests a higher risk but also higher potential returns. -- **Expert Opinions**: Useful but should be tempered with quantitative data and historical performance. - -**Step 3: Time Frame and Variance** -- **Time Frame**: Nine days until July 17, 2024. -- **Market Variance**: - - Historically, both Apple and Nvidia can experience significant daily and weekly stock fluctuations. - - Nvidia, given its higher P/E ratio, may show more volatility. - -**Step 4: Base Rates and Historical Precedents** -- Large-cap tech companies can witness large swings based on macroeconomic news, tech sector trends, quarterly earnings reports, and product launches. - -**Step 5: Default Resolution** -- If no significant event occurs, Apple (~$3.4T) is slightly ahead of Nvidia (~$3.34T). -- Consider the likelihood of notable events occurring within the next nine days that could shift the balance. - -**Step 6: Potential Changes or Trends** -- **Positive Catalysts for Nvidia**: Announcements of new AI partnerships, stellar financial reports, or major tech advancements could propel Nvidia further. -- **Negative Catalysts for Nvidia**: Market correction, AI advancement slowdowns, or negative financial news. - -- **Positive Catalysts for Apple**: Strong AI integration in new products, favorable earnings reports, or unexpected positive market sentiment. -- **Negative Catalysts for Apple**: Market correction, poor earnings reports, or production/supply chain issues. - -**Step 7: Distribution and Uncertainty** -- Estimate based on current positions and the significance of potential catalysts. -- Both companies have comparable upside potential but Nvidia's recent outperformance and focus on a high-growth sector (AI) provide a slight edge. - -**Step 8: Statistical Checks** -- **Volatility**: Daily stock price volatility for large-cap stocks typically ranges around 1-3%. Given Nvidia's relatively higher volatility, the probability distribution may tilt slightly in its favor, assuming no major disruptions. -- **Probability Simulation**: Simulating minor daily fluctuations for both companies, with Nvidia potentially edging out Apple by continuing its recent growth trajectory. - -### Final Probability Estimate -**Probability: 60%** -- I estimate a 60% probability that Nvidia's market capitalization will be larger than Apple's on July 17, 2024. This accounts for Nvidia’s current momentum, high growth potential, and the short time frame where minor fluctuations in growth could tip the balance in favor of Nvidia. However, Apple's strong market positioning and stable performance mean the outcome remains fairly uncertain." -25871,"On July 17, 2024, will Nvidia's market capitalization be larger than Apple's?","PERPLEXITY - -To provide a detailed rundown of the news relevant to Nvidia's and Apple's market capitalizations, here are some key points and recent updates: - -1. **Market Capitalization and Recent Performance**: - - Nvidia recently achieved a market capitalization of $3 trillion, positioning it among the top three most valuable companies globally. - - Apple's market cap is slightly below $3.4 trillion, with Microsoft leading the pack with a market cap just exceeding $3.4 trillion. - -2. **Growth Prospects and AI Integration**: - - Nvidia's growth is driven by its dominance in AI-focused hardware, particularly GPUs, which are crucial for the development of cutting-edge AI software and models. - - Apple has faced challenges in expanding its revenue recently but is expected to benefit from integrating AI capabilities into its latest devices, such as the upcoming iPhone release. - -3. **Expert Opinions and Projections**: - - Experts believe Nvidia will win the race to a $4 trillion market cap due to the insatiable demand for its AI-focused hardware. - - Analysts are bullish on Nvidia's growth trajectory, citing its rapid product roadmap and strategic positioning in the AI landscape. - - Apple is also seen as a strong contender for reaching the $4 trillion market cap, leveraging AI to drive customer upgrades and attract new iPhone users. - -4. **Recent Updates and Market Performance**: - - Nvidia's stock has surged significantly, with a remarkable increase in revenues and earnings. Its market capitalization reached $3.34 trillion on June 18, 2024, surpassing Microsoft and Apple. - - Apple's market cap is slightly below $3.4 trillion, with a more favorable valuation compared to Nvidia, suggesting potential for further upward movement. - -In conclusion, Nvidia's market capitalization has recently surpassed Apple's, and experts predict Nvidia will continue to grow rapidly due to its dominance in AI-focused hardware. However, Apple's integration of AI capabilities into its latest devices and its more favorable valuation could potentially lead to growth, making it a strong contender in the long term. - -### Current Market Values: -- **Nvidia**: $3.34 trillion (as of June 18, 2024) -- **Apple**: Slightly below $3.4 trillion (as of recent updates) - -Given these updates and the current market values, it is likely that Nvidia's market capitalization will remain larger than Apple's on July 17, 2024. - -######### - -GPT - -### Step-by-Step Analysis for Predicting Nvidia's Market Capitalization Versus Apple's on July 17, 2024 - -#### 1. **Current Market Values and Recent Performance** -- **Nvidia**: $3.34 trillion as of June 18, 2024. -- **Apple**: Slightly below $3.4 trillion as of recent updates. - -#### 2. **Recent Trends and Growth Prospects** -- **Nvidia**: Witnessed a rapid growth trajectory, fueled by a strong demand for AI hardware. Recent stock surge following impressive Q1 financial results and stock split indicates continued investor confidence. -- **Apple**: Stable growth, but not as accelerated as Nvidia. While integrating AI capabilities, the reception and impact on stock price and market cap are more moderate compared to Nvidia. - -#### 3. **Market Variance and Volatility** -- **Past Data**: Analyzing historical patterns, tech stocks can be volatile, particularly when driven by the AI sector (as seen with Nvidia). However, market leaders like Apple tend to have less volatility due to their more diversified portfolio and steady revenue streams. -- **Short-Term Horizon (9 days)**: Given the tight timeline, significant market movements are less likely. Macro-economic conditions, quarterly results, or major announcements could influence stock prices, but drastic changes are less probable. - -#### 4. **Expert Analyses and Projections** -- **Bullish on Nvidia**: Analysts broadly agree on Nvidia's favorable position in the market, with the potential to become the most valuable company due to its AI dominance. -- **Strong Contender� Apple**: Despite Nvidia's surge, experts also caution Apple has a strong potential for resurgence given its favorable valuation and continued innovations. - -#### 5. **Base Rates and Historical Context** -- **Base Rates**: Historically, shifts in the top few companies by market cap are rare and typically driven by significant economic or technological events. Both Nvidia and Apple have robust historical resilience. -- **Recent Novel Trends**: The AI boom represents a substantial new factor for Nvidia, whereas Apple has historically been more consistent but less influenced by recent AI-driven trends. - -#### 6. **Default Resolution if Trends Continue** -- **Status Quo**: As of the latest updates, Nvidia has a slight lead against Apple. Without significant new information or disruptive events, Nvidia is likely to maintain this lead based on current trajectories. - -### Summary and Probability Estimation -- **Nvidia is currently ahead by a small margin and is expected to maintain its lead given the short time frame.** -- **Primary Considerations**: - - Nvidia's recent growth rate and investor sentiment strongly favor continued performance. - - Apple's steady performance is not projected to surpass Nvidia's recent gains in the immediate term. - - Market volatility in a 9-day period is unlikely to dramatically alter positions barring unforeseen events. - -### Final Probability -Given all factors and the short time frame, Nvidia is likely to maintain a marginally higher market cap than Apple on July 17, 2024. - -**Probability: 70%** - -This accounts for Nvidia's strong growth trajectory and position alongside the stable but slightly lagging performance of Apple considering the near-term forecast." -25871,"On July 17, 2024, will Nvidia's market capitalization be larger than Apple's?","PERPLEXITY - -To provide a comprehensive overview of the relevant news for the query about Nvidia's market capitalization compared to Apple's on July 17, 2024, here are the key points from recent articles: - -1. **Market Capitalization Milestones**: - - Nvidia's market capitalization has surged, reaching $2.8 trillion, closing in on Apple's $2.9 trillion. - - Nvidia's market cap has further increased to $3 trillion, with a prediction of reaching $10 trillion by 2030. - - Nvidia has become the most valuable public company, surpassing Microsoft and Apple, with a market capitalization of $3.34 trillion. - -2. **Recent Performance and Projections**: - - Nvidia's share price rose 3.6%, lifting its value to $3.34 trillion, while Microsoft and Apple fell. - - Nvidia is expected to continue growing due to its dominance in AI accelerators and the increasing demand for GPUs in data centers. - - Experts believe Nvidia will be the first to reach a $4 trillion market capitalization due to its AI-focused hardware and robust growth. - -3. **Comparison with Apple**: - - Apple's market capitalization is slightly below $3.4 trillion, with a more favorable valuation compared to Nvidia. - - Apple's growth prospects are promising with the introduction of Apple Intelligence, integrating AI capabilities into its latest devices, which could stimulate demand and unlock growth avenues. - -4. **Industry Trends and Competition**: - - The AI boom is driving the growth of tech giants, with Nvidia, Microsoft, Apple, Google, and Amazon all experiencing significant increases in market capitalization. - - Nvidia's aggressive innovation and market foresight have positioned it as a leader in the AI landscape, with a strong foundation for continued growth. - -Given these recent developments and trends, it appears likely that Nvidia's market capitalization will be larger than Apple's on July 17, 2024. However, the actual outcome will depend on various market and economic factors that may influence the performance of both companies in the coming weeks. - -######### - -GPT - -Let's break down our prediction step by step, carefully weighing the evidence and accounting for uncertainty. We'll go through the following steps: - -1. **Current Market Capitalization**: - - As of the information we have today, July 8, 2024, Nvidia's market cap is valued at $3.34 trillion while Apple's market cap is slightly below $3.4 trillion. - -2. **Trends in Market Capitalization**: - - Nvidia has experienced a dramatic increase in its market capitalization, driven largely by its dominance in the AI chip market. Analysts are optimistic about Nvidia's continued growth, with predictions that it could reach $4 trillion. - - Apple also has growth prospects, particularly with its new AI initiative (Apple Intelligence), but it seems less explosive compared to Nvidia's rapid ascent. - -3. **Recent Movements**: - - Nvidia's share price has recently increased by 3.6%, boosting its market cap to $3.34 trillion. On the other hand, both Microsoft and Apple's market caps have fallen slightly. - -4. **Industry context**: - - The overall tech sector is buoyed by the AI boom, benefiting companies like Nvidia significantly. - - Apple remains a highly valuable and stable company, with continued strong performance anticipated due to its brand, ecosystem, and new AI integrations. - -5. **Timeframe and Volatility**: - - There are only 9 days remaining until the resolution date (July 17, 2024). Daily and weekly granularity suggests limited time for major market shifts barring any significant, unforeseen events. - - Given the short timeframe, we should focus more on the current status and recent momentum rather than predicting dramatic changes in either direction. - -6. **Base Rates and Historical Data**: - - Historically, valuations of tech giants like Apple, Microsoft, and Nvidia can fluctuate with market sentiment, economic data, earnings reports, or significant technological developments. - - Both Nvidia and Apple have historically shown robust capacity to maintain and increase their market capitalizations, though Nvidia's recent trajectory has been exceptional. - -7. **Potential Risks and Exogenous Factors**: - - Economic indicators or shocks, such as interest rate changes, geopolitical events, or significant regulatory actions could potentially affect market caps but are unpredictable within the short time frame left. - -8. **Default Resolution**: - - With the current numbers at hand, Nvidia is very slightly behind Apple in market capitalization. - - Given the imminent timeline and recent upward trend for Nvidia, a slight catch-up is possible but not guaranteed. - -Weighing all these considerations: - -- **Current Position**: Nvidia slightly behind Apple. -- **Momentum**: Nvidia showing upward trend, but limited time left. -- **Variability**: High volatility possible in tech stocks but little time for substantial change barring major news. -- **Historical precedence**: Both companies strong but Nvidia's recent gains are exceptional. - -Considering these factors holistically and the very tight competition: - -**Final Analysis**: -Given the short timeframe and current data points, Nvidia has a significant chance to surpass Apple's market cap by the resolution date, but given the marginal difference and high market variability, the likelihood of such a precise outcome can introduce a higher degree of uncertainty. - -**Final Probability Estimate**: -Given the data and taking into consideration the uncertainty and momentum: - -**Probability: 60%** - -This reflects a slight but notable favor towards Nvidia capitalizing on recent trends but recognizing the inherent volatility and tight market cap race with Apple." -25876,"Will exactly 2 Starship launches reach reach an altitude of 160 kilometers by Sept 30, 2024?","PERPLEXITY - -Here is a detailed rundown of the news relevant to the query: - -- **Recent Launch History**: The second integrated flight test (IFT-2) of SpaceX's Starship occurred on November 18, 2023. The mission achieved several milestones, including the Starship reaching an altitude of 149 km (93 mi) and a velocity of 24,000 km/h, but ultimately did not complete its planned orbit due to a safe command destroying the second stage prior to re-entry. - -- **Upcoming Launches**: SpaceX is planning for its fifth integrated flight test (IFT-5) in August 2024, which will attempt a booster tower landing. The sixth integrated flight test (IFT-6) is also in preparation, with Ship 31 completing a successful cryogenic test in July 2024. - -- **Launch Frequency and Goals**: SpaceX aims to significantly lower launch costs through economies of scale, reusing both rocket stages, and increasing launch frequency. Elon Musk has stated that the goal is to eventually launch Starship for only $1 million. - -- **Recent Progress and Challenges**: The fourth test flight of Starship is expected to occur soon, with SpaceX President Gwynne Shotwell indicating a possible launch in early May 2024. The previous third test flight, which occurred in March 2024, achieved several milestones but ended with both stages breaking apart during descent. - -- **Metaculus Questions**: There are ongoing Metaculus questions related to Starship's progress, including whether Starship will reach orbit in 2024 and whether exactly two Starship launches will reach low-Earth orbit by September 30, 2024. - -Overall, SpaceX is actively working towards improving the Starship's performance and achieving its ambitious goals. The recent launch history and upcoming tests will likely influence the outcome of the query. - -######### - -GPT - -### Step-by-Step Analysis - -#### Evaluate Recent History and Current Trends - -1. **Past Performances and Current Status**: - - The IFT-2 in November 2023 reached 149 km but failed to complete its planned mission. - - The IFT-3 in March 2024 reached 234 km but broke up upon re-entry. - - Upcoming flights (IFT-4, IFT-5, and IFT-6) are planned, with IFT-5 scheduled for August 2024 and IFT-6 already in preparation. - -2. **Stated Intentions and Goals**: - - Elon Musk's tweet mentioned at least 6 more flights in 2024. - - Gwynne Shotwell indicated a potential flight for early May 2024. - -#### Analyze Timing and Frequency - -- **Time Remaining**: There are about 83 days (approximately 2.7 months) left until September 30, 2024. -- **Launch Frequency**: If SpaceX were to launch monthly or close to it, they could achieve the stated goal. However, this is contingent on various factors including technical readiness, regulatory approvals, and weather conditions. - -#### Consider Base Rates and Historical Trends - -- Historically, SpaceX has shown rapid progress, but also encounters with technical issues that can cause delays (e.g., RUDs - Rapid Unscheduled Disassembly, i.e., failures in flight). -- The two successful launches IFT-2 and IFT-3 indeed achieved significant altitudes, though not without issues. - -#### Assess Likelihood of Reaching the Target Altitude - -- **Current Baseline**: One successful launch has reached over 160 km (IFT-3 in March 2024). -- **Upcoming Launches**: - - IFT-4 in early May (if it occurs as planned) would provide another data point very soon. - - IFT-5 is scheduled for August. - -If at least two more launches occur (IFT-4 in May and IFT-5 in August), there is a higher probability as each has a *good chance* of reaching the required altitude based on improvements and learnings from previous launches. - -#### Potential Risks and Variances - -- Delays due to technical issues, regulatory hurdles, or unforeseen circumstances can impact the launch schedule significantly. -- Successful learning and resolution of issues from past flights could improve chances, but are not guaranteed. - -### Final Analysis Based on the Data - -- **Default Resolution**: If IFT-4 and IFT-5 occur as planned and reach the required altitude, the criteria will likely be met. -- **Risk Assessment**: Given SpaceX's iterative approach, the risk of failure is present but can be mitigated by rapid iteration and improvement. -- **Variance and Uncertainty**: Allowance for possible delays or deterministic failures must be factored in, impacting the timeline. - -### Final Probability Determination - -- **Overall Probability**: - - Considering both historical performance and ambitious plans set by SpaceX, - - Taking into account the remaining number of days and the launch schedule, - - Balancing between the successes and potential risks: - -After thorough consideration and assuming no major catastrophic failures or delays disrupt the schedule significantly, the probability that exactly 2 Starship launches will reach 160 km by Sept 30, 2024, is calculated as: - -**Probability: 70%**" -25877,"Will exactly 3 Starship launches reach reach an altitude of 160 kilometers by Sept 30, 2024?","PERPLEXITY - -To provide context for forecasting the number of Starship launches reaching an altitude of 160 kilometers by September 30, 2024, here are the key points from recent news articles: - -1. **Recent Test Flights:** - - The third integrated flight test (IFT-3) of Starship occurred on March 14, 2024. Although the rocket reached orbital velocity, both stages broke apart during reentry. - - The second integrated flight test (IFT-2) took place on November 18, 2023. The Starship upper stage reached an altitude of 148 km, but the Super Heavy booster exploded during its boostback burn. - -2. **Upcoming Flights:** - - SpaceX plans to conduct several more test flights in 2024, with as many as six flights planned. - - The fifth integrated flight test (IFT-5) is expected to occur in August 2024, aiming to catch the Super Heavy booster with a tower. - - Vehicle testing for the sixth integrated flight test (IFT-6) has commenced, with Ship 31 completing a successful cryogenic test in July 2024. - -3. **Development and Funding:** - - SpaceX has invested over $3 billion in the Starbase facility and Starship systems from July 2014 to May 2023. - - Elon Musk expects the cost of a Starship orbital launch to eventually be around $1 million. - -4. **NASA Contracts:** - - SpaceX was awarded a $2.89 billion contract from NASA to develop the Starship lunar lander for Artemis III. - - SpaceX received a $1.15 billion contract for a second lunar lander for Artemis 4. - -Given the ongoing testing and development of Starship, it is crucial to monitor the progress of upcoming flights to determine if exactly three launches will reach an altitude of 160 kilometers by September 30, 2024. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. Compile and Analyze Background Information -Let's review the key points of the background and upcoming activities: -- **Recent Flights:** - - IFT-3 (March 14, 2024): Reached 234 km (Success per resolution criteria) - - IFT-2 (November 18, 2023): Reached 148 km (Failed) - -- **Upcoming Flights:** - - 6 flights planned for 2024 (Halo tweet by Elon Musk) - - IFT-5 expected in August 2024 - - IFT-6 vehicle is progressing in tests (indication of readiness for launch) - -#### 2. Assess Importance of Different Background Sources -- **Past Performance**: IFT-1 and IFT-2 had some failures but IFT-3 reached the required altitude (suggests room for improvement and success ahead). -- **Elon's Statement**: Historically optimistic but indicates ambition. -- **Current Development**: IFT-5 scheduled for August, IFT-6 in progress for September, both essential milestones. -- **Investment and Incentives**: Significant capital already spent by SpaceX, bolstered by NASA contracts indicating high importance and focus. - -#### 3. Historical Base Rates and Skew Factors -- **Base Rate**: Given 3 significant integrated flight tests (IFT) in the last year, 1 completely successful, presuming faster iteration and improvement as typical for SpaceX’s methodology (rapid prototyping and learning from failures). -- **Future Flights**: 6 launches projected for 2024. - -#### 4. Projections based on Current Defaults -- **IFT-3 Success**: Already achieved. -- **Upcoming Flights**: - - **IFT-5 (August 2024)**: Expected to reach 160 km based on improved preparation. - - **IFT-6 (Progressing Well)**: Likely within the time frame and target altitude. - - **Future Prospects**: Probability of 1-2 more (IFT-7 and possibly IFT-8) happening before end of September seems reasonable given the rapid cadence. - -#### 5. Consider Variance and Uncertainty -- **Reliability of Technology**: Improvement typically doesn't follow linear progress but an S-curve. -- **Weather and Technical Delays**: Realistic to factor in potential delays and failures. - -#### 6. Statistical and Probabilistic Analysis -- **Probability Calculation**: - - **1 Success Achieved (IFT-3)** - - **Probability of IFT-5 Success**: 70% based on recent improvements - - **Probability of IFT-6 Success**: 60% with careful reading of current readiness - -To determine P(exactly 3): -- \( P(IFTx succeeds) = p \) -- Collector Event \(P(\# of flights reaching 160 km = 3) \) - -Considering the exhaustive permutations and complement events: -\[ P(\# = 3) = P(3 Succeeded - Combinations & Complement) \] - -#### Simulation and Adjustment -Base on combining probabilities: -```python -from scipy.stats import binom -# Assume 1 Success, Expecting 2 out of remaining 3 vehicles (event projection) -p_exactly_3 = binom.pmf(2, 3, 0.65-0.70) - -Final Adjusted Based on Delays: -p_added = ∑ p of event across multiple Outcomes - -Final Conditional Plus Context: -70% x IFT-5 x IFT-6 -``` -Final Calculation sums probabilistically combining conditional.js of each. Adjusting Median values, - -### Conclusions: -- Realistic base rate and correction suggests room for 70%-85%. -- Adjust conservative lower spectrum = ""Probability: 65%"" - -Final Answer: -**""Probability: 65%""**" -25875,"Will Joe Biden announce before July 31, 2024 that he will not accept the Democratic Party's nomination for President?","PERPLEXITY - -Here is a detailed rundown of the news relevant to the question about Joe Biden announcing that he will not accept the Democratic Party's nomination for President before July 31, 2024: - -- **President Biden's Stance**: President Biden has firmly denied calls to step aside, emphasizing that he was chosen by the voters and that any doubts about his candidacy should be addressed through a challenge at the Democratic National Convention in August. - -- **Democratic Party Divisions**: The party is divided, with some prominent Democrats advocating for Biden to step down due to concerns over his recent debate performance and fitness for another term. Others, including influential members of the Congressional Black Caucus, continue to support him. - -- **Calls for Withdrawal**: Several House Democrats, including Lloyd Doggett, Raul Grijalva, Seth Moulton, Mike Quigley, and Angie Craig, have openly called for Biden to step down, citing concerns about the party's future and the risks of facing another term under Donald Trump. - -- **Potential Replacement**: If Biden were to step down, Vice President Kamala Harris could potentially inherit his campaign funds, but her approval ratings are not significantly higher than Biden's. Other potential candidates, such as Governors Gavin Newsom, Gretchen Whitmer, and Josh Shapiro, have been mentioned but have not expressed explicit interest in challenging Biden. - -- **25th Amendment**: The 25th Amendment, which allows the vice president and a majority of Democrats in Congress to declare the president unfit, is a distant possibility but has been discussed as a potential means to force Biden out. - -- **Donor Pressure**: Some donors, including former PayPal CEO William 'Bill' Harris, have joined the calls for Biden to step down and are threatening to withhold donations to the Democrats until he does so. - -- **Recent Updates**: Biden has engaged in high-level meetings with world leaders at the NATO summit in Washington, while House Democratic leader Hakeem Jeffries has facilitated closed-door sessions to align lawmakers before indicating his stance. Speaker Emerita Nancy Pelosi has reiterated her support for Biden, emphasizing his accomplishments as president. - -- **Current Values**: Recent polls show a decline in voter confidence in Biden's mental fitness, with an Ipsos poll indicating a drop from 28% to 20% after the presidential debate. Voter preferences have shifted towards Trump by an average of 3.5 points, according to one analysis by the Brookings Institution. - -- **Expert Opinions**: Analysts note that even small shifts in voter preferences are crucial in what is expected to be a tight election. Strategists emphasize the importance of congressional races in countering Trump's influence, with record fundraising levels indicating strong support for Democratic candidates. - -Given these developments, there is significant pressure on Biden to step down, but he remains resolute in his determination to continue his campaign. The ongoing turmoil within the Democratic Party and the potential for further shifts in voter preferences will be crucial in determining whether Biden announces that he will not accept the nomination before July 31, 2024. - -######### - -GPT - -### Analyzing the Situation - -1. **Current Stance of Biden**: - - Firmly denies stepping aside; emphasized voter choice and challenge through Democratic National Convention. - - Defended re-election bid post-debate disaster. - - Key allies still support him publicly. - -2. **Pressure in the Party**: - - Divided democratic opinions; some factions urging him to step down. - - Influential Democrats remain supportive; the Congressional Black Caucus and prominent figures like Nancy Pelosi behind him. - -3. **Calls for Withdrawal and Donor Pressure**: - - Notable calls for him to step down from some House Democrats and donors. - - Donors like 'Bill' Harris threating to withhold donations unless he steps aside. - -4. **Potential Replacement Candidates**: - - Kamala Harris, despite low approval ratings. - - Other potential candidates like Governors Gavin Newsom, Gretchen Whitmer, and Josh Shapiro, not actively challenging. - -5. **Public Opinion and Polls Post-Debate**: - - Decrease in voter confidence in Biden’s mental fitness. - - Shift towards Trump in polls but Democratic alignment and fundraising still strong. - - Decline from 28% to 20% in mental fitness confidence post-debate (Ipsos poll). - -6. **Historical Base Rates for Incumbent Withdrawals**: - - Incumbent presidents rarely step down from running for re-election. - - The increased scrutiny on mental fitness and debate performances, though impactful, are not immediate indicators of a forced step down within such a short timeframe (three weeks). - -### Timeframe and Variability Analysis - -- **July 9 to July 31, 2024**: There are just 22 days for the situation to develop. -- **Daily and Weekly Variance**: - - Political pressures might rise, but typically, significant decisions like a candidate stepping down usually involve extended periods of high pressure and strategic political maneuvering, often spanning several weeks to months. - - **Exogenous shocks** could potentially change the situation rapidly (e.g., major health issues, significant political developments), but absent these, changes tend to be more gradual. - -### Default Resolution and Trend Analysis -- **Default behavior**: Biden doesn't step down based on current strong defenses and public statements. -- **Resolution Criteria**: To resolve as ""Yes,"" Biden must make a definitive public announcement that he will not accept the nomination, which requires overcoming substantial inertia and numerous internal discussions. - -### Statistical and Historical Considerations -- **Incumbent Rarity**: Historical precedents rare for incumbents to withdraw at such a juncture. -- **Novel Pressure Factors**: High-profile debate disaster and internal party divisions present. -- **Poll Fluctuations**: Generally, poll fluctuations and donor pressures have a moderated immediate effect but might impact behind-the-scenes discussions. - -### Final Reasoning: - -Given the significant short timeframe, firm standing of Biden, notable public support factions, and historical rarity of such withdrawals, it appears unlikely that Biden will announce that he will not accept the nomination before July 31, 2024. - -Probability of Biden announcing he will not accept the nomination before July 31, 2024: **Probability: 15%** - -This accounts for the significant pressures but balances them against the typical political inertia and historical patterns." -25873,Will the Real-time Sahm Rule Recession Indicator increase Year-over-Year for Aug 2024?,"PERPLEXITY - -To provide context for the query about the Real-time Sahm Rule Recession Indicator, here are the key points from recent news and data: - -1. **Current Status**: The Real-time Sahm Rule Recession Indicator stands at 0.43% as of June 2024, which is higher than the long-term average of 0.43%. This indicator has been rising, with the latest value being higher than the previous month and year. - -2. **June 2024 Jobs Report**: The U-3 unemployment rate increased by 0.1 percentage points in June 2024, reaching 4.1%. This rise triggered the Sahm Rule, suggesting the US economy might be in or nearing a recession. The three-month moving average of the national unemployment rate has risen by 0.50 percentage points or more relative to its low during the previous 12 months, meeting the Sahm Rule criterion. - -3. **Expert Opinions**: Claudia Sahm, the architect of the Sahm Rule, has emphasized the importance of monitoring shifts in the labor market. She has expressed concerns that the Federal Reserve's reluctance to implement rate cuts could inadvertently steer the economy towards a recession. However, Wall Street remains optimistic about the economy, citing past instances where recession forecasts proved inaccurate. - -4. **Historical Context**: The Sahm Rule has accurately predicted every recession since 1970. It signals the start of a recession when the three-month moving average of the national unemployment rate rises by 0.50 percentage points or more relative to its low during the previous 12 months. - -5. **Recent Trends**: The unemployment rate has been rising gradually, with the gap between the current rate and its recent low expanding in June compared to May. This disparity is currently at its most significant level since March 2021, a period marked by economic recovery from the pandemic-induced downturn. - -Given these points, the Real-time Sahm Rule Recession Indicator is likely to continue rising, especially if the unemployment rate continues to increase. The recent trigger of the Sahm Rule in June 2024 suggests that the economy might be heading towards a recession, which could lead to an increase in the indicator year-over-year for August 2024. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. Current Status -- The Real-time Sahm Rule Recession Indicator is at 0.43% in June 2024. -- The Sahm Rule has been triggered due to the U-3 unemployment rate rising by 0.50 percentage points relative to the three-month moving average low of the past 12 months. - -#### 2. Recent Trends -- The unemployment rate increased by 0.1 percentage points in June 2024 to 4.1%. -- The most significant gap between the current unemployment rate and its recent low since March 2021 was observed in June 2024. - -#### 3. Expert Opinions -- Claudia Sahm has expressed concerns about potential recession due to the Fed's policies. -- Wall Street remains cautiously optimistic but acknowledges recession risks. - -#### 4. Historical Context -- The Sahm Rule has accurately predicted every recession since 1970. -- It is notable that the indicator rises during recession periods indicating deteriorating labor market conditions. - -#### 5. Base Rates and Novel Factors -- Historically, when the Sahm Rule is triggered, unemployment typically continues to rise until policy intervention or other economic adjustments take effect. -- Given the current economic climate, where the Sahm Rule has just been triggered, it is highly probable that unemployment will continue to exhibit upward trends in the near term. - -#### 6. Time Until Resolution -- There is approximately one month until the resolution date of August 2024. -- Recent data trends indicate a rise in the unemployment rate, which aligns with the likely continued increase in the Sahm Rule Recession Indicator. - -### Calculating the Probability -1. **Historical Base Rate**: The Sahm Rule Indicator has historically risen during periods immediately following its trigger. Given its accurate track record and the current economic indicators, it is reasonable to assume this trend will continue. -2. **Recent Economic Data**: The rise in the unemployment rate and meeting of the Sahm Rule criteria in recent months strongly suggest that the indicator will remain elevated and possibly even increase further by August 2024. -3. **Expert Insights**: Concerns from economists like Claudia Sahm add weight to the likelihood of continued upward movement in the indicator. - -Considering these points: -- The **default resolution** given no major policy changes likely suggests an **increase** in the Sahm Rule Recession Indicator. -- Given the rapid recent rise and the conditions that meet the Sahm rule criteria, the indicator exceeding its value from August 2023 appears quite probable. - -#### Adding Uncertainty Factors -- Potential interventions or unexpected economic shifts: While possible, these are less likely within the short time frame given the Fed's current stance. -- Statistical variance: The short time frame between observations minimizes daily and weekly variance impact significantly on the larger trend. - -### Final Calculation -Considering all input data, the probability of the Real-time Sahm Rule Recession Indicator increasing year-over-year for August 2024 resolves to a high confidence level. - -**Probability: 85%** - -This represents a strong confidence in the given economic conditions and historical precedence, balanced with a realistic margin for unexpected events or variances." -25878,"Will exactly 2 or 3 Starship launches reach reach an altitude of 160 kilometers by Sept 30, 2024?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **Recent Updates**: The third flight test of Starship is planned to launch as soon as March 14, pending regulatory approval. This test aims to build on previous milestones, including the successful ascent burn of both stages, opening and closing Starship’s payload door, and a controlled reentry of Starship. -- **Previous Flight Tests**: The second integrated flight test of Starship occurred on November 18, 2023. Although it did not achieve orbit, it reached an altitude of 149 km and a velocity of 24,000 km/h, becoming the first Starship to reach outer space. -- **Metaculus Predictions**: There are two relevant Metaculus questions. One asks if Starship will reach orbit in 2024, and the other asks if exactly 2 or 3 Starship launches will reach low-Earth orbit by September 30, 2024. These questions are awaiting approval by Community Moderators. -- **Current Status**: As of July 2024, the fifth integrated flight test is expected to occur in August, with a planned tower catch of the Super Heavy booster. The sixth integrated flight test is also in preparation, with Ship 31 completing a successful cryogenic test. -- **Expert Opinions**: Elon Musk has stated that the goal is to lower launch costs significantly via economies of scale, and SpaceX Chief Financial Officer Bret Johnsen disclosed that SpaceX has invested over $3 billion into the Starbase facility and Starship systems. - -There are no recent updates indicating a specific movement towards a particular resolution. However, the ongoing development and testing of Starship suggest progress towards achieving orbit. - -######### - -GPT - -To determine the probability that exactly 2 or 3 SpaceX Starship launches will reach an altitude of 160 kilometers by September 30, 2024, I will carefully evaluate the available information, trends, and historical data. - -### Breakdown of Available Information - -1. **Past Flights and Achievements:** - - **IFT-3 on March 14, 2024** reached an orbit with an altitude of 234 km. - - **IFT-2 on November 18, 2023** did not achieve orbit but reached an altitude of 149 km (outer space but one km short). - -2. **Planned Future Tests:** - - **IFT-5 is planned for August 2024.** - - **IFT-6 is in preparation with Ship 31’s successful cryogenic test.** - -3. **Elon Musk’s Statements:** - - Aiming for at least six more flights in 2024, indicating optimistic and aggressive testing plans. - -### Evaluation of Trends and Base Rates - -- **Intensive Testing:** SpaceX has been accelerating its Starship tests. In ~3 months (since March to now), one successful flight reaching the 160km criterion and another one planned for August implies a testing cadence of roughly one flight every 2-3 months. -- **Historical Success Rate:** One out of two tests in 2024 has achieved the altitude criterion (IFT-3), while the other test (IFT-2) missed it narrowly due to earlier flights not reaching the required altitude but still indicating progressive development. - -### Factors to Consider - -1. **Technological and Operational Readiness:** - - Given the recent success in reaching the required altitude and the aggressive prep for further tests, technological and operational readiness appears high. - -2. **Regulatory and Safety:** - - SpaceX typically faces regulatory challenges, but recent trends show they have been getting timely approvals. - -3. **Time Frame:** - - From today (July 9) to Sept 30, 2024, there are approximately 83 days or nearly 3 months, within which 2 tests are explicitly planned, and potentially more. - -4. **Risk Factors:** - - Any test failure could delay subsequent tests, given the allotted time frame. - -### Probability Distribution Analysis - -1. **Base Rate Approach:** - - If roughly every 2-3 months sees one flight, we might expect up to 2-3 more attempts by the deadline. Given the aggressive goals and Musk’s history of over-achievement, let’s cautiously estimate up to 4 attempts. - -2. **Success Rate:** - - Assuming a success rate derived from recent testing (50%), and considering the likelihood that process improvements will boost future success rates moderately: - - 0.7 (reasonable improved success rate given recent breakthrough and Musk’s optimistic targets). - -### Estimating Probability Mathematically - -Using binomial probability distribution: -\[ P(k; n, p) = \binom{n}{k} p^k (1-p)^{n-k} \] - -Where: -\[ n = \text{number of attempts (e.g., 3 or 4 more attempts),} \] -\[ p = \text{success probability (0.7),} \] -\[ k = \text{number of successes (2 or 3 desired),} \] - -For exactly 2 successful flights out of 4 attempted: -\[ P(2; 4, 0.7) = \binom{4}{2} (0.7)^2 (0.3)^2 = 6 \cdot 0.49 \cdot 0.09 = 0.2646 \] - -For exactly 3 successful flights out of 4 attempted: -\[ P(3; 4, 0.7) = \binom{4}{3} (0.7)^3 (0.3) = 4 \cdot 0.343 \cdot 0.3 = 0.4116 \] - -Combining these: -\[ P(2) + P(3) = 0.2646 + 0.4116 = 0.6762 \] - -### Final Conclusion -**Probability: 68%** - -This accounts for the optimistic yet cautious estimate based on trends, capabilities, and forward-looking industry dynamics. The progression of events is carefully monitored by SpaceX and suggests a reasonable likelihood of achieving 2 or 3 successful high-altitude Starship flights by the given date." -25874,Will the time gap between the first and second place finishers in the 2024 Tour de France be more than 3 minutes?,"PERPLEXITY - -To provide a detailed rundown of the news relevant to the question about the time gap between the first and second place finishers in the 2024 Tour de France, here are the key points: - -1. **Route and Stages**: The 2024 Tour de France features 21 stages, including two individual time trials, four summit finishes, and a variety of terrain, including the Apennines, Italian and French Alps, Massif Central, and Pyrenees. -2. **Time Bonuses**: Riders can earn bonus seconds at stage finishes and at strategic points along the route. The first three riders receive 10, 6, and 4 seconds at stage finishes, and 8, 5, and 2 seconds at bonus sprints. -3. **Time Limit**: The time limit for each stage varies based on the stage's difficulty and the winner's average speed. Riders must finish within a certain percentage of the winner's time to avoid being disqualified. -4. **Jerseys and Classifications**: The yellow jersey is worn by the overall leader, the green jersey by the points leader, the polka-dot jersey by the mountain classification leader, and the white jersey by the young rider classification leader. -5. **Current Standings**: As of the first rest day, the current standings are not explicitly mentioned in the provided sources. However, discussions on Reddit suggest that the top contenders, including Tadej Pogačar, Jonas Vingegaard, Remco Evenepoel, and Primož Roglič, are closely matched and will likely battle for the top spots in the coming stages. -6. **Expert Opinions**: Some fans and analysts believe that Roglič could potentially win the Tour by consistently finishing close to the leaders and then excelling in the final time trial. - -There are no recent updates indicating a significant movement towards a particular resolution. The current predictions and discussions are centered around the top contenders and their strategies for the remaining stages. - -**Current Predictions**: -- Metaculus does not have a specific prediction market for the time gap between the first and second place finishers in the 2024 Tour de France. However, it may be possible to create a custom market or find related markets that could provide insights into the expected time gaps. - -**Current Values**: -- As of the first rest day, the current standings and time gaps are not explicitly mentioned in the provided sources. - -######### - -GPT - -### Step-by-Step Analysis: - -#### 1. **Understanding the Context** -- **Event**: 2024 Tour de France -- **Question**: Will the time gap between the first and second place finishers be more than 3 minutes? -- **Current Date**: July 9, 2024 -- **End Date**: July 21, 2024 - -#### 2. **Tour Details & Stages** -- **Stages**: 21 stages in total - - 2 individual time trials - - 4 summit finishes -- **Terrain**: Apennines, Italian and French Alps, Massif Central, Pyrenees - -#### 3. **Time Bonuses & Limits** -- **Time Bonuses**: Minor, often do not create substantial gaps -- **Disqualifications & Time Limits**: Riders must finish within a certain percentage of the winner's time - -#### 4. **Current Standings and Key Riders** -- **Top Contenders**: Tadej Pogačar, Jonas Vingegaard, Remco Evenepoel, Primož Roglič -- **Fan Opinion**: Roglič may excel in the final time trial - -#### 5. **Expert Opinions and Predictions** -- **Close Match**: The gap is currently expected to be closely contested among top riders -- **No specific time gaps mentioned currently** - -### Base Rate & Historical Context -- **Historical Analysis**: - - The gap between the first and second place finishers in recent Tour de France editions: - - 2023: 7 minutes 29 seconds - - 2022: 2 minutes 43 seconds - - 2021: 5 minutes 20 seconds - - 2020: 59 seconds - - 2019: 1 minute 11 seconds - -Historical averages: -- Large gaps (3+ minutes) are possible but not guaranteed, heavily dependent on individual performance and race conditions. - -### Recency and Variance - -#### 6. **Current Situation and Recency of Information** -- Considering today’s date (July 9) and the final date (July 21), there's a lot of racing still to happen (12 more stages). -- **Variance**: - - Remaining stages include significant mountain stages and individual time trials, which are crucial for creating larger time gaps. - - Factors such as team strategies, weather conditions, rider health, and unforeseen incidents will play substantial roles. - -### Analyzing the Distribution of Outcomes - -#### 7. **Statistical Probability Distribution** -- **Historical Frequency**: Mixed cases of sub-3 minute and above-3 minute gaps. -- **Remaining Stages**: Favorable for significant time gaps, especially in the mountains and time trials. - -#### 8. **Expert Insight** -- Given the historical context, remaining race dynamics, top contenders' form, and stage profiles, predicting substantial movement in time differentials is reasonable. -- Close contests are indicated, but key decisive stages in mountains and time trials suggest potential for larger gaps. - -### Probability Assignment -Combining the mean historical results, remaining key stages, and the expert opinions: - -#### Assumptions -- Base Rate: 50% (equally likely to be above or below 3 minutes) -- Adjustment for 2024 specifics: - - Remaining difficult stages inclines towards more variance. - - Health and strategy of key riders are pivotal and prone to sudden changes. - -### Conclusion -Balancing all information, the probability of the gap being more than 3 minutes is more nuanced. A balanced approach taking into account an above-average chance due to critical stages and historical instances of significant gaps seems appropriate. - -### Final Probability -""Probability: 65%""" -25872,Will 4 or more goals be scored in the two semi-final games combined in the 2024 UEFA European Championship?,"PERPLEXITY - -The 2024 UEFA European Championship semi-finals are set to take place, with Spain facing France and the Netherlands facing England. Here is a concise rundown of the relevant news: - -- **Spain vs. France**: Spain has been the highest-scoring team in the tournament, with 11 goals, while France has struggled to score, with only three goals, all of which were either own goals or a penalty from Kylian Mbappé. France coach Didier Deschamps has responded to criticism of his team's style, suggesting that those who find it boring can watch another match. Spain will be without several key players due to suspension or injury, including Pedri, Dani Carvajal, and Robin le Normand. - -- **Netherlands vs. England**: The Netherlands and England are set to face off in the other semi-final. A video simulation of the match highlights the attacking prowess of both teams, with England's Harry Kane expected to be a key threat. - -- **Recent Updates**: There have been no recent updates indicating significant changes in the teams' performances or strategies. However, the suspensions and injuries affecting Spain could impact their ability to score goals. - -- **Expert Opinions**: France coach Didier Deschamps has expressed confidence in his team's ability to win, while Spain defender Marc Cucurella acknowledges the strength of his team but respects France. - -- **Current Values**: There are no specific current values directly relevant to this question. - -- **Forecasting Sites**: There are no relevant prediction markets on Metaculus for this specific question. - -Overall, the semi-finals promise to be intense and closely contested, with Spain's high-scoring form facing off against France's defensive solidity, and the Netherlands and England battling for a spot in the final. - -######### - -GPT - -Let's break down this forecasting exercise methodically, ensuring we consider all relevant factors, base rates, and uncertainties. - -### 1. **Base Rates Analysis** -Historical data from UEFA European Championship semi-finals can provide us with an initial probability estimate. - -- In the last 5 tournaments (2016, 2012, 2008, 2004, 2000): - - **2016**: 3 goals total in two semi-final matches (2-0, 0-0) - - **2012**: 4 goals total in two semi-final matches (3-0, 0-0) - - **2008**: 5 goals total in two semi-final matches (3-2, 0-3) - - **2004**: 3 goals total in two semi-final matches (2-1, 1-0) - - **2000**: 3 goals total in two semi-final matches (2-1, 2-1) - -From these five tournaments, we observe: -- 2 instances of 4 or more goals -- 3 instances of fewer than 4 goals - -So, the base rate for having 4 or more goals in combined semi-final matches is \( \frac{2}{5} = 40\% \). - -### 2. **Team Form and Key Factors** -- **Spain vs. France**: - - - Spain has been high-scoring with 11 goals in the tournament. - - France has only scored 3 goals, showing a more defensive bias. - - Spain is missing key players due to suspension/injury which may reduce their scoring power. - -- **Netherlands vs. England**: - - - Both teams have strong attacking capabilities. - - Harry Kane is noted as a key offensive threat. - -### 3. **Recent Performance** -Recent information indicates that Spain's scoring capabilities are reduced due to suspensions and injuries. However, they still have high-scoring form throughout the tournament and face a defensively solid France. - -Netherlands vs. England is consistently positioned as a high-scoring potential match due to their attacking prowess. - -### 4. **Expert Opinions and Forecasting Sites** -- There is no recent change in performances or strategies. -- Expert opinions suggest confidence and respect among teams, without substantial biases. - -### 5. **Current Trends and Variabilities** -We must consider that player performances can shift rapidly due to the highly dynamic nature of sports competitions. A tight match can often result in fewer goals, while an early lead can open up a game resulting in more goals. - -### 6. **Resolution Criteria** -Counting only goals within ordinary and extra time (excluding penalty shootouts). - -### Integrating All Factors -Considering historical data, team form, key player absences, and expert opinions: -- Spain's scoring might be hindered but not negligible. -- France's defensive focus tends to lead to lower-scoring matches. -- The Netherlands and England could compensate with a higher-scoring match. - -### Probabilistic Thinking and Estimation -Combining the base rate with the qualitative assessment (slightly bullish due to England-Netherlands high-scoring potential), I would adjust the initial 40% base rate upwards but keeping a conservative approach due to uncertainties: - -### Final Probability -""Probability: 45%"" - -This accounts for base rates and current known factors, allowing for the inherent variability and dynamics of high-stakes tournaments." -25930,"What will Trump's lead over Biden be greater than 2 and less than or equal to 3 on July 15, 2024, according to 538's national polling average?","PERPLEXITY - -To provide context for the query about Trump's lead over Biden in 538's national polling average, here are the key points from recent news and polling data: - -1. **538's Forecast Methodology**: 538's 2024 presidential election forecast model uses a combination of polling, economic, and demographic data to predict likely election outcomes. It adjusts polls for house effects, mode, partisanship, voter likelihood, and third parties, and weights them based on the pollster's rating and frequency of polling. - -2. **Recent Polling Trends**: Trump has gained a significant lead over Biden in recent polls following their debate. A Wall Street Journal poll shows Trump leading Biden by 6 points, while a New York Times and Siena College survey indicates a 6-point advantage for Trump among registered voters likely to participate in the election. - -3. **538's Current Polling Average**: As of July 9, 2024, 538's national polling average shows Trump leading Biden by 2-3 percentage points. The latest polls included in their average are from June 27-29, with Trump leading by 3 points in one poll and Biden leading by 1 point in another. - -4. **Forecast Updates**: 538's forecast updates at least once a day and whenever new data is available. Uncertainty in the forecast will decrease as the election approaches. - -Given these trends and the current polling average, it is possible that Trump's lead over Biden could remain between 2 and 3 points on July 15, 2024, according to 538's national polling average. However, this will depend on the results of new polls and how they are incorporated into the 538 model. - -For a more accurate prediction, it would be helpful to monitor 538's updates and new polling data leading up to July 15, 2024. - -######### - -GPT - -Alright, let's break this down systematically to determine the probability that Trump's lead over Biden will be greater than 2 and less than or equal to 3 points on 538's national polling average on July 15, 2024. - -### 1. **Understanding the Current Situation** -- As of July 5, 2024, Trump leads Biden by 2.4 points in 538's national polling average. -- Polling trends after the debate have shown a widening gap in favor of Trump. Specifically, recent polls indicate Trump's lead is between 2 and 6 points. -- It's currently July 9, 2024, leaving us with six days until the target date of July 15, 2024. - -### 2. **Analyzing the Context and Sources** -- **Recent Polls**: The last update on 538 (July 5) shows a Trump lead of 2.4 points. The recent polling trend suggests Trump's lead may sustain or slightly fluctuate within a narrow range. -- **Debate Impact**: Biden's poor debate performance and subsequent negative media coverage have significantly impacted his polling numbers. -- **538’s Forecast Methodology**: 538 incorporates a variety of factors including polling, economic conditions, demographics, and historical data. Polling averages are adjusted and weighted to account for biases and inaccuracies. - -### 3. **Base Rate Considerations** -- Presidential polling averages can fluctuate due to debates, news cycles, and major campaign events. Historically, margins within 2-3 points can be quite volatile in the weeks following significant events such as debates. -- In the past, significant debate performances have moved polling averages by up to 4 percentage points within a week, although smaller shifts (1-2 points) are more typical. - -### 4. **Timeframe and Variability** -- With six days remaining, there is room for fluctuation, but drastic changes are less likely absent any unexpected major developments (e.g., breaking news, scandals, significant policy announcements). -- Day-to-day variability in the lead could range around +/-1 point due to natural polling fluctuation and 538’s updates. - -### 5. **Possible Scenarios** -- **Stabilization**: The lead could stabilize within the current range of 2.4 points, hovering around 2-3 points, especially if no major news alters the current dynamics. -- **Increase**: Continued negative coverage for Biden might push Trump's lead beyond 3 points. -- **Decrease**: Biden could recover slightly if his team’s efforts to reverse perceptions have a positive impact, pulling the lead back towards 2 points or below. - -### 6. **Overall Assessment** -Given the current trends, the likeliest scenarios revolve around Trump's lead remaining relatively stable or slightly increasing. The most probable outcome is that the lead will not fall back significantly within the next six days. - -### **Probability Calculation** -- **Probability of Stabilization within 2-3 Points**: Given the trends and time remaining, there's a fair chance that the lead could stabilize within the 2-3 point range, especially if the average doesn't experience significant new shocks. -- **Historical Fluctuation Range**: Historical data suggests that, while there’s room for fluctuation, the likelihood of staying in a narrow band close to the current average is reasonable. - -Based on the available data, historical context, and the time remaining: - -- **Estimated Probability**: Given the current lead of 2.4 points and the consideration of polling trends, stabilization effect, and natural variance, I predict the probability of Trump's lead being greater than 2 and less than or equal to 3 on July 15, 2024, at approximately **67%**. - -**Final Answer: Probability: 67%**" -25931,"What will Trump's lead over Biden be greater than 3 and less than or equal to 4 on July 15, 2024, according to 538's national polling average?","PERPLEXITY - -To provide context for the query about Trump's lead over Biden in 538's national polling average, here are the relevant news and polling updates: - -1. **Recent Polls**: A New York Times/Siena College survey and a Wall Street Journal poll both show Trump leading Biden by 6 percentage points among likely voters. These polls were conducted after the recent debate between the two candidates, which seems to have impacted Biden's popularity, particularly due to concerns about his age. - -2. **538's Forecast Model**: 538's presidential election forecast model takes into account polling, economic, and demographic data. It adjusts polls for various factors like house effects, mode, partisanship, voter likelihood, and third parties, and weights them based on the pollster's rating and frequency of polling. The model also considers the correlation in voting patterns between states and demographic similarities. - -3. **Current Polling Averages**: As of the latest updates, 538's national polling average shows Trump leading Biden. The most recent polls included in the average are from June 27-29, with Trump leading by 3 percentage points in one poll and Biden leading by 1 percentage point in another. - -Given these updates, it is difficult to predict exactly what Trump's lead will be on July 15, 2024. However, considering the recent surge in Trump's popularity and the ongoing debate about Biden's age, it is possible that Trump's lead might remain significant. The current polling averages and forecast models will continue to evolve as new data becomes available. - -For the specific query, it is uncertain whether Trump's lead will be greater than 3 and less than or equal to 4 on July 15, 2024, according to 538's national polling average. The forecast will likely continue to shift as new polls are released and incorporated into the model. - -######### - -GPT - -### Understanding the Context - -Firstly, let's summarize the key points from the background information to structure the problem effectively: - -1. **Polling Trends**: As of July 5, 2024, Trump has a +2.4 percentage point lead over Biden. -2. **Debate Impact**: Biden's widely seen as poor debate performance has led to an increased lead for Trump from +0.2 before the debate to +2.4. -3. **Recent Polls**: Some recent surveys mentioned show Trump leading Biden by +6 percentage points which could impact future polling averages. -4. **538's Model**: It's a dynamic model that adjusts polling data based on a variety of factors. - -### Evaluating Recent Data - -Given recent trends and the impact of major events, particularly the debate: -1. **Immediate Impact of the Debate**: - - Biden's poor performance in the debate has given Trump a noticeable boost. - - The debate occurred on June 27, and Trump's lead widened to +2.4 by July 5. - -2. **Polling Volatility**: - - The lead moved from +0.2 to +2.4 (+2.2 points) in about a week following a single significant event. - -3. **Major News Influences**: - - House Democrats' calls for Biden to drop out and the pressure has likely exacerbated Biden's declining perception. - -### Statistical and Historical Context - -1. **Base Rates in Polling Shifts**: - - Historical fluctuations in polling averages around key political events can help us measure the likelihood of further shifts. - - Presidential poll leverages generally show slower, gradual shifts unless constant high-impact stories keep the focus on negative aspects of a candidate. - -2. **Potential for Return to Mean**: - - It's important to consider regression to the mean, where initial surge impacts begin to moderate as immediate reactions wane. - -### Time Until Resolution - -The target date for the question's resolution is July 15, 2024. There are 6 days within which new factors or reappraisals could influence the polling numbers. - -1. **Stability vs. Continued Shift**: - - Assuming the current news cycle and pressures remain (Biden facing criticism, internal party pressures), a further continuation of the trend favoring Trump could be expected but likely at a moderated scale. - -### Calculation of Likely Outcomes - -Given the volatility induced by a major debate and current polling trends suggesting a larger lead extending to even +6 points, **weighting towards a broader range of movements**: -- Trump's lead widening beyond +4 within a short six-day window could happen but is not as probable considering usual polling dynamics. -- It suggests stabilization but slightly widening remains more likely. - -### Conclusion and Probability Estimate - -1. **Current trend** (up to July 5): +2.4 point lead. -2. **Recent high-end poll influences**: Suggest a possible move toward a higher number, but oscillations expected around high-stress periods. -3. **Biden Response**: Moderate efforts to regain ground may see some effect, but broad perception challenges remain dominant. -4. **Historical polling stability with such situations**: Suggest continued increase within similar bounds but not drastic shifts in short span. - -**Most Likely Scenario**: -- Lead stays bounded within current trajectory, stretching slightly beyond +3 but likely capping around +4 considering inertia and typical polling variability. - -### Probability: 38% - -Hence, I predict a 38% probability that Trump's lead over Biden will be greater than 3 and less than or equal to 4 points in 538’s national polling average on July 15, 2024, given the described conditions and historical polling mechanics." -25932,"What will Trump's lead over Biden be greater than 2 and less than or equal to 4 on July 15, 2024, according to 538's national polling average?","PERPLEXITY - -To provide context for the query about Trump's lead over Biden, I have summarized the most relevant news and polling data: - -### Recent Polls and Trends -- **New York Times/Siena Poll**: Donald Trump has taken a six-point lead over President Joe Biden among likely voters, marking his largest advantage since 2015. -- **Wall Street Journal Poll**: Trump leads Biden by six points, a significant increase from a February poll where he led by two points. -- **FiveThirtyEight Polling Averages**: The latest averages show Trump ahead of Biden, with recent polls indicating a shift in favor of Trump. - -### Key Factors and Insights -- **Biden's Age**: A significant concern among voters, with 80% of respondents in the Wall Street Journal poll considering Biden too old for a second term. -- **Debate Performance**: Biden's recent debate performance has raised concerns among Democrats about his viability as a presidential nominee. - -### Current Forecast and Projections -- **FiveThirtyEight Forecast**: The presidential race remains a toss-up, with Biden favored to win in 501 out of 1,000 simulations and Trump winning in 499 simulations. -- **Metaculus Prediction Market**: Not available for this specific query, but it can be checked for relevant predictions. - -### Current Values and Numbers -- **538's National Polling Average**: Trump leads Biden by 3 percentage points as of the latest updates. - -Given the recent polling trends and shifts in favor of Trump, it is possible that Trump's lead over Biden could be greater than 2 and less than or equal to 4 on July 15, 2024, according to 538's national polling average. However, this will depend on how the polls continue to evolve over the next week. - -######### - -GPT - -### Step-by-Step Analysis - -1. **Understanding the Resolution Criteria**: - - We are interested in the national polling average as reported by 538 on July 15, 2024. - - Specifically, we need to determine if Trump’s lead over Biden is greater than 2 and less than or equal to 4 percentage points. - -2. **Current Polling Data**: - - As of July 5, 2024, Trump’s lead over Biden is +2.4 points. - - Recent polls (NYT/Siena and WSJ) show Trump ahead by 6 points. - -3. **Key Factors Influencing Polls**: - - **Debate Performance**: Biden’s poor performance is creating negative momentum. - - **Public Perception of Age**: Worries about Biden’s age are increasing. - - **Calls for Biden to Withdraw**: Growing internal and external pressures may impact public perception further. - -4. **Impact of Debate and Age Concerns**: - - Debate performance and age concerns have historically led to negative polling trends. - - Short-term trends indicate increasing support for Trump. - -5. **Poll Volatility**: - - Polling averages can fluctuate daily based on news cycles and campaign events. - - Historically, such fluctuations post an event like a debate can last for several weeks. - -6. **Historical Trends and Novel Factors**: - - Presidential races often see shifts in polling averages, but a lead of 2+ points over a short time frame can be significant. - - Current pressures on Biden suggest a potentially enduring downward pressure on his polling numbers. - -7. **Current Trends and Momentum**: - - Trump’s lead increased from 0.2 to 2.4 points in a matter of days (June 27 to July 5). - - Given the recency, if trends continue, it's likely the lead continues to grow, but the rate of growth may slow as Biden’s team reacts. - -8. **Projection**: - - If the trend continues moderately, Trump's lead could stabilize slightly above 2 points. - - Larger leads (like +6 in NYT/Siena) suggest the upper bound could be exceeded but should not be heavily weighted for day-to-day fluctuations. - - Considering response tactics by Biden’s team might cushion drastic losses. - -9. **Statistical Considerations**: - - Assume normal distribution for polling averages where mean reflects current trend and standard deviation based on historical volatility. - - Predictive confidence intervals are slightly broader near campaign events. - -### Probability Estimation - -1. **Default State**: - - If conditions remain unchanged, Trump continues a +2.4 point lead or improves. - -2. **Risk and Variability**: - - Key uncertainty involves Biden’s potential recovery efforts and further debate impacts. - -Given the above assessments, the probability distribution leans towards Trump maintaining and slightly increasing his lead, fitting the specified range: - -### Final Conclusion - -**Probability of Trump's lead being greater than 2 and less than or equal to 4 on July 15, 2024, according to 538's national polling average**: - -**Probability: 75%** - -This probability reflects a balanced consideration of current momentum and typical variability in polling trends over the remaining short time frame." -25933,"Will the CBOE S&P 500 3-Month Volatility Index (VXVCLS) - be higher Year-over-Year on July 20, 2024?","PERPLEXITY - -To assist in forecasting whether the CBOE S&P 500 3-Month Volatility Index (VXVCLS) will be higher year-over-year on July 20, 2024, here is a concise rundown of relevant news and data: - -1. **Current Index Values**: - - The VXVCLS index is currently at 16.38 as of July 3, 2024. - - The VIX Index, which is closely related to VXVCLS, was at 15.36 on January 17, 2024. - -2. **Recent Trends**: - - The VIX Index edged up slightly in January 2024, closing at 15. - - Shorter-term volatility measures, such as the Cboe 1-Day Volatility Index, declined in January, while longer-term measures, like the Cboe 1-Year Volatility Index, also saw a modest decline. - -3. **Expert Opinions and Research**: - - Various research papers and guides from institutions like the CFA Institute, University of Massachusetts, S&P Dow Jones Indices, and BlackRock provide insights into the VIX Index and its applications in investment and trading strategies. - -4. **Forecasting Platforms**: - - Metaculus has a question pending approval regarding whether the VXVCLS will be higher year-over-year on July 2, 2024, but it has not yet been approved or received significant user feedback. - -Given the current values and recent trends, it appears that volatility measures have been relatively stable, with some short-term declines. However, there is no clear indication of a significant movement towards a higher year-over-year value on July 20, 2024. - -######### - -GPT - -### Step-by-Step Approach: - -#### 1. **Current Index Values**: -- **Current VXVCLS Index**: 16.38 (as of July 3, 2024) -- **VIX Index on January 17, 2024**: 15.36 -- **Current VIX Index Value** (if available): Mentioned edge up in January but no recent value provided. - -#### 2. **Resolution Criteria**: -- **Resolution Date**: July 20, 2024 -- **Reported by St. Louis Fed**: First published value. -- **Check Against**: VXVCLS value on July 20, 2023 (requires historical data). - -#### 3. **Historical Data for July 20, 2023**: -- Let's assume the VXVCLS value on July 20, 2023, is necessary for the forecast—this data needs to be looked up or assumed. -- Hypothetical example: If the value on July 20, 2023, was 14.50. - -#### 4. **Recent Trends & Influence Factors**: -- **Recent Trends**: Slight decline in short-term and long-term volatility indices, reflecting stability. -- **External Factors**: Consider events politically, economically, technologically, and environmentally: - - **Economics**: Inflation, interest rates, market stability. - - **Politics**: Geopolitical tensions, regulatory changes. - - **Technology**: Technological advancements affecting market behavior. - - **Environment**: Natural disasters, environmental regulations. - -#### 5. **Expert Opinion & Research**: -- **Expert Analysis**: Suggests a stable short term with no significant spikes. -- **Forecasting Platforms (Metaculus)**: Question pending, indicating no significant user consensus yet. -- **Research**: Insights on VIX and related indices suggest possible stability but no drastic upward movement. - -#### 6. **Base Rates and Historical Analysis**: -- **Base Rates**: Historical analysis of year-over-year changes in VXVCLS: - - If in the past, year-over-year changes show ~50% chance of being higher/lower. - - Use historical volatility changes across years. - -#### 7. **Evaluating Probabilities**: -- **Variance in Indices**: Check daily/weekly fluctuations. -- **Default Behavior**: Current stability suggests a similar trend if no significant changes occur. -- **Novel Events**: Check recent/severe novel changes skewing base rates. - -### Statistical Analysis: -- **Variance & Confidence Interval**: Use historical data standard deviation to project possible ranges for the July 20, 2024, value. -- **Z-Score Calculation**: Calculate the likelihood of the July 20, 2024, value being higher than July 20, 2023. - -Assume normal distribution, calculate Z-score: -\[ Z = \frac{(X - \mu)}{\sigma} \] -Where \(X\) is the future value, \(\mu\) is the mean (historical average), and \(\sigma\) is the standard deviation. - -Without specific numbers, assume a standard approach using the given details. - -### Final Estimated Probability: -- **Current Value (July 3, 2024)**: 16.38 -- **Previous Value (July 20, 2023)**: Assume 14.50 -- Given higher current value, historically stable volatility, and absence of extreme changes, moderate increase considered likely. - -### Conclusion: -- After evaluating all factors and assuming moderate stability with slight upward trend or external stable factors, the final estimated probability: - -**Probability: 65%**" -25934,"Will US offer India a nuclear submarine before Sept 30, 2024?","PERPLEXITY - -To provide context for the question of whether the US will offer India a nuclear submarine before September 30, 2024, here are some key points from recent news and developments: - -1. **India-Russia Nuclear Submarine Deal**: India and Russia signed a $3 billion deal for the lease of another Akula-class nuclear-powered attack submarine, dubbed Chakra III, which is expected to be delivered to the Indian Navy by 2025. - -2. **US-India Civil Nuclear Agreement**: The US and India have been working to finalize their civil nuclear agreement, which includes cooperation on nuclear energy and technology. However, issues related to India's nuclear weapons program and liability laws have been hindering progress. - -3. **US Nuclear Submarine Production**: The US Department of Defense has reduced its order for Virginia-class nuclear-powered attack submarines from two to one in fiscal 2025 due to shipbuilding bottlenecks and capacity issues at private shipyards. - -4. **India's Nuclear Submarine Plans**: India has been focusing on developing indigenous nuclear-powered attack submarines, with six planned under Project 75 Alpha. The first submarine is expected to enter service in 2032. - -Given these developments, it appears that the US is not currently planning to offer India a nuclear submarine before September 30, 2024. The US-India civil nuclear agreement is still being negotiated, and the US has its own shipbuilding constraints. India, on the other hand, is pursuing its own nuclear submarine development and has a deal with Russia for a leased submarine. - -######### - -GPT - -To determine the probability of the US offering India a nuclear submarine before September 30, 2024, I will go through a structured and detailed analysis based on the provided information and general knowledge about geopolitical affairs. - -### Step 1: Contextual Information Review -1. **India's Current Submarine Deals**: - - India has a substantial portion of its military hardware from Russia. - - India's deal with Russia for the lease of another Akula-class nuclear-powered attack submarine underscores this continued reliance. - -2. **US Relations and Agreements**: - - The US-India civil nuclear agreement is still being worked out. - - The US has significant shipbuilding constraints, as noted by the reduced order for Virginia-class submarines. - -3. **India's Independent Submarine Development**: - - India is focusing on indigenous nuclear-powered attack submarines with Project 75 Alpha, although these won't be in service until 2032. - -### Step 2: Analysis of Trends and Historical Precedents -1. **US Policy on Nuclear Propulsion Technology Sharing**: - - Historically, the US has been restrictive in sharing nuclear propulsion technology, both in public statements and treaties. The AUKUS deal (US, UK, and Australia) was an exception rather than the norm and caused notable diplomatic tension with allies like France. - -2. **Geopolitical Factors**: - - India and the US have been strengthening their strategic partnership, but the US's stance on nuclear technology transfer has been conservative, especially with non-NATO allies. - - The geopolitical landscape includes China's rise and the resulting Indo-Pacific strategy, which might influence the US to bolster India's military capabilities but not necessarily extend to nuclear submarines. - -### Step 3: Base Rates and Novel Considerations -1. **Base Rates**: - - The historical base rate of the US offering nuclear submarines to countries outside the UK and Australia is zero. - - Notable precedent with AUKUS is an outlier and specifically tailored for geopolitical strategic balance against China in the Indo-Pacific. - -2. **Current Dynamics (Novel Considerations)**: - - The geopolitical environment could push the US towards closer military cooperation with India, but that might translate more into conventional arms sales, technology transfers related to non-nuclear domains, and strategic dialogues. - -### Step 4: Timeline and Variance -1. **Time Remaining**: - - With less than three months till the resolution date, the window for any significant policy shift or announcement is quite narrow. - -2. **Daily and Weekly Variance**: - - Major changes in defense partnerships and the sharing of sensitive technology do not typically emerge quickly without extensive preparatory groundwork, which there are no clear indicators of. - -### Step 5: Probability Distribution and Uncertainty -- The default assumption (base rate and current trajectory) suggests no change. -- Considering the limited time frame, historical reluctance, current policy context, and existing commitments to other submarine development and leasing, the probability remains low. - -### Final Probability Estimation -Given these factors, and carefully weighing each aspect, the probability that the US will offer India a nuclear submarine before September 30, 2024, is very low. Even with the context of improving US-India relations and strategic partnerships, the specific constraint on nuclear technology sharing is a significant barrier. - -**Final Answer:** -Probability: **3%**" -25935,"Will OpenAI publish information describing Q* (Q-Star) by Sept 30, 2024?","PERPLEXITY - -Here is a concise rundown of the news most relevant to the question: - -- **OpenAI's Q\* (Q-Star)**: Q\* is a rumored AI breakthrough that could represent a significant advance in AI capabilities. It is believed to be a system or method explored by OpenAI, which could potentially be a major step towards achieving Artificial General Intelligence (AGI). - -- **Metaculus Predictions**: The prediction market on Metaculus indicates a 25% chance that OpenAI will publish information about Q\* by the end of 2024. There is also a separate question on Metaculus asking if OpenAI will publish information about Q\* by September 30, 2024, which is currently awaiting approval. - -- **Recent Updates and Discussions**: There have been recent discussions and reports about Q\*, including a Reuters article detailing the potential breakthrough and its implications for AI development. The OpenAI community has also been actively discussing Q\*, with some users speculating about its potential and the need for safety protocols and alignment measures. - -- **Expert Opinions**: Some experts believe that Q\* could be a significant step towards achieving AGI, with one user suggesting that AGI could be achieved within the next 6-24 months. Others emphasize the importance of ensuring robust safety measures are in place to mitigate potential risks associated with AGI. - -- **Current Values**: The current prediction market values on Metaculus indicate a 25% chance of OpenAI publishing information about Q\* by the end of 2024. - -- **Related Questions**: There are several related questions on Metaculus and other platforms, including predictions about OpenAI's progress in AI alignment, the release of new AI models, and the potential for AGI to be achieved in the near future. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Reviewing the Information and Context** - -- **Current Date**: 2024-07-09 -- **Deadline for Prediction**: 2024-09-30 (approximately 83 days remaining) - -#### 2. **Analyzing Key Background Information** - -- **OpenAI's Q\* (Q-Star)**: A rumored significant AI breakthrough, hinting at AGI levels of capability. -- **Event Trigger**: Sam Altman’s removal as CEO was possibly influenced by concerns surrounding Q*. -- **Metaculus Prediction Market**: Currently predicts a 25% chance of OpenAI publishing information about Q* by the end of 2024. There is a specific prediction for September 30, 2024, awaiting approval, suggesting some skepticism but possibly more granularity. - -#### 3. **Current Trends and Expert Opinions** - -- **Rumors and Speculations**: Exist around Q* being a critical step towards AGI, underlined by safety concerns. -- **Expert Opinions**: Some predict AGI within 6-24 months, indicating the potential immediacy of developments requiring public disclosure by OpenAI. - -#### 4. **Evaluating Historical Base Rates and Novel Circumstances** - -- **Base Rate for Announcements**: Historically, tech companies like OpenAI have kept significant breakthroughs under wraps until they have clear strategic or safety frameworks. However, past major announcements (e.g., GPT-3) have occurred when the technology was deemed stable enough for public knowledge. -- **Novel Factors**: The urgency and gravity of potential AGI-level breakthrough implied by Q* rumors might push OpenAI towards greater transparency, especially in light of ethical and safety considerations. - -#### 5. **Time Frame Analysis** - -- **Remaining Time**: There are approximately 83 days until the end of the prediction period. -- **Weekly Variance**: With each week presenting an opportunity for major announcements or shifts in organizational strategy, there's moderate volatility but also inertia in establishing organizational policies or resolving internal conflicts. - -#### 6. **Behavioral Insights** - -- **Default Organizational Behavior**: OpenAI has been conservative with announcements. Public acknowledgment of a sensitive project like Q* would likely require rigorous internal review. -- **Possible Triggers for Change**: Regulatory pressures, public relations strategy shifts, or significant technological milestones could necessitate a disclosure. - -#### 7. **Statistical and Probabilistic Analysis** - -- **Combining Base Rates and Novel Factors**: Given the historical base rate of disclosure and current contextual pressures suggesting higher chances, let's consider: - - An inherent conservatism bias. - - Recent strategic shifts and significant remaining time window providing substantial chance for disclosure. - -### Probability Estimation - -1. **Base Rate Adjustment**: - - Historical base rate of major disclosures from companies like OpenAI: 10-15%. - - Current unique context increment: ~10% increase (due to multiple pressures and emerged rumors). - -2. **Trend Weighting**: - - Adjust for moderate near-term variance but high impact if a disclosure happens. - - Balancing trend inertia with psychobehavioral triggers: adds 5-10%. - -3. **Final Probability Calculation**: - - Base rate: ~12%. - - Contextual factors: +10%. - - Behavioral insights and volatility: +7%. - -**Final Estimated Probability**: -Probability: 29% - -Maintaining some allowance for unexpected events and network effects, this cautiously accounts for existing predictions while acknowledging significant external pressures and time remaining for resolution. - -**Final Answer**: -Probability: 29%" -25936,"Will the Crimean Bridge be hit with an attack before Sept 30, 2024?","PERPLEXITY - -The Crimean Bridge, a strategic and symbolic connection between Russia and occupied Crimea, has been a key target for Ukraine in the ongoing conflict. Here are the most relevant news and updates: - -1. **Ukrainian Intentions**: Ukrainian officials have repeatedly stated their intention to destroy the Crimean Bridge, considering it a legitimate military target. Commander of the Navy of the Armed Forces of Ukraine, Oleksii Neizhpapa, said that the bridge will be destroyed in the first half of 2024. - -2. **Previous Attacks**: The bridge has been hit and repaired twice before. In October 2022, an explosion caused several spans of the roadway to fall into the water, and in July 2023, another attack damaged the bridge, causing at least two deaths and one injury. - -3. **Russian Defenses**: Russia has taken extensive measures to protect the bridge, including strengthening anti-aircraft defenses and deploying a ""target barge"" as a decoy for incoming guided missiles. Additionally, Russia has deployed S-300 air-defense missile systems and Pantsir-S1 anti-aircraft artillery systems to safeguard the bridge. - -4. **Recent Developments**: Ukraine has been actively targeting Russian military assets in Crimea. In January 2024, Ukraine hit two Russian military targets in Crimea, and in March, Ukrainian sea drones sunk a Russian missile corvette and a patrol ship near the Kerch Strait. - -5. **Metaculus Prediction**: The Metaculus prediction market currently has no active predictions on the specific question of whether the Crimean Bridge will be attacked before September 30, 2024. - -Given the ongoing conflict and Ukraine's stated intentions, there is a high likelihood of an attack on the Crimean Bridge before September 30, 2024. However, the effectiveness of Russia's defenses and the evolving military strategies of both sides will influence the outcome. - -######### - -GPT - -To address the question of whether the Crimean Bridge will be hit with an attack before September 30, 2024, we need to carefully evaluate the background information, trends, historical precedents, and current geopolitical dynamics. Here, I'll walk through a structured approach to arrive at a well-informed probability estimate. - -### Step-by-Step Analysis - -#### 1. Historical Context and Base Rates -The Crimean Bridge has been attacked three times since the Russian invasion of Ukraine: in October 2022, July 2023, and a failed attempt in August 2023. This suggests a relatively high base rate of attempts on the bridge within a short period. This high frequency indicates persistent efforts by Ukraine to target this strategic infrastructure. - -#### 2. Current Statements and Intentions -Ukrainian officials have explicitly stated their intention to destroy the Crimean Bridge, with specific mentions of plans to conduct such operations in the first half of 2024. The reliability of this information is bolstered by the history of actual attacks. Moreover, recent attacks on other Russian military targets in Crimea indicate Ukraine's ongoing operational capabilities and intent. - -#### 3. Russian Defensive Measures -Russia has implemented extensive defenses around the Crimean Bridge, using sophisticated anti-aircraft and missile defense systems such as the S-300 and Pantsir-S1. These measures have partially succeeded in foiling attacks, as seen in the failed attempt in August 2023. The effectiveness of these defenses will be a critical factor in determining the likelihood of a successful strike. - -#### 4. Recent Developments -Ukraine's success in damaging important Russian military assets as recently as January and March 2024 highlights continued offensive capabilities. Given that the window until the resolution date is approximately three months, this period offers ample opportunity for another attempted strike. - -#### 5. Political and Symbolic Factors -There is strong symbolic and strategic value for Ukraine in targeting the Crimean Bridge, especially given the critical connectivity it provides between Russia and Crimea. The anticipation of an attack before significant dates (like Putin’s inauguration) suggests that timing and symbolism play a crucial role in Ukraine's military planning. - -#### 6. Meta-Prediction Insights -While the Metaculus prediction market does not provide specific information on this question, the collected intelligence suggests a considerable probability of an imminent attack within the given timeframe. - -### Probability Distribution Analysis - -1. **Baseline Probability from Past Data**: - - Three attacks in a roughly 18-month period (Oct 2022 - Apr 2024): ~0.167 attacks/month. - - For a three-month horizon: ~50% given past frequency and assuming an equal distribution. - -2. **Adjusting for Intentions and Capabilities**: - - Given explicit Ukrainian intentions and capabilities, we'll significantly boost this base probability. - - Reinforced Russian defenses slightly mitigate this probability but won't eliminate it. - -3. **Symbolic Date Consideration**: - - Significant dates and events (e.g., attempts before September 30, 2024) further support the likelihood of continued efforts. - - Combining historical frequency with enhanced intent and capability: elevate baseline to around 70%. - -4. **Uncertainty and Variance Consideration**: - - Unforeseen geopolitical changes, new military technologies, or unforeseen effective defenses could affect the probability. A conservative down-weighting for high uncertainty might reduce this adjusted probability to approximately 60%. - -### Conclusion - -Considering all factors—past attack frequency, expressed intentions, defensive measures, recent developments, and symbolic factors—I arrive at a considered estimate by weighting the difference between strong intentions/plans and the possibility of defensive success or political changes. - -**Final Probability: 60%**" -26006,"Will Trump's lead over Biden be greater than 2 and less than or equal to 4 on July 15, 2024, according to 538's national polling average?","PERPLEXITY - -To provide context for the query about Trump's lead over Biden in 538's national polling average, here are the key points from the relevant sources: - -1. **Current Polling Averages**: As of July 10, 2024, the latest national polls on FiveThirtyEight show Trump leading Biden by 3 percentage points in some polls, while others have Biden ahead by 1 percentage point or the candidates tied. - -2. **Recent Trends**: The race remains a toss-up, with Biden recently ""pulling ahead"" by a small margin of 0.3 percentage points, which is within the model's uncertainty interval. - -3. **Forecast Model**: 538's forecast model combines polling data with campaign ""fundamentals"" such as economic conditions, state partisanship, and incumbency. The model updates daily and reacts to new data, with uncertainty decreasing as Election Day approaches. - -4. **Methodology**: The model adjusts polls for house effects, mode, partisanship, voter likelihood, and third parties, and weights them based on the pollster's rating and frequency of polling. It also considers demographic and historical voting patterns in states to infer trends. - -5. **Upcoming Debate**: The first televised debate between Biden and Trump is scheduled for June 27, which could potentially impact the race and polling averages. - -6. **Current Predictions**: There are no specific predictions on Metaculus related to this exact query. However, the general election forecasts on Metaculus can provide broader context for the race. - -Given these points, the query about Trump's lead over Biden on July 15, 2024, will depend on how the race evolves over the next few weeks, particularly after the first debate and any subsequent shifts in polling averages. - -######### - -GPT - -Given the criteria and data provided, let's proceed step by step to estimate the probability that Trump's lead over Biden will be greater than 2 and less than or equal to 4 points on July 15, 2024, as per 538's national polling average. - -### Step-by-Step Analysis: - -1. **Current Polling Data**: - - As of July 5, Trump leads by +2.4 points. - - As of July 10, we have mixed results with Trump leading by 3 points in some polls and Biden ahead by 1 point or the candidates tied in others. The overall lead has fluctuated around +3 points, within the model's uncertainty interval. - -2. **Recent Trends and Debate Impact**: - - Biden’s poor debate performance on June 27 led to an increase in Trump’s lead from +0.2 to +2.4 points by July 5. - - This shows a significant impact from the debate, yet the lead could remain volatile. - - Attempts by the Biden campaign to reverse perception might create some shifts or stabilization in the coming days. However, dramatic short-term shifts are unlikely without another major event. - -3. **Modeling Uncertainty and Poll Variability**: - - 538's model incorporates various factors and the uncertainty interval suggests some inherent variability in the polling averages. - - Due to the debate having occurred recently, the effects might still stabilize around the current lead. - - Any sudden large shifts (`>1 point`) are historically less frequent without significant events. - -4. **Historical Precedents and Base Rates**: - - Historically, national polling averages do not typically change drastically in a single week without catalytic events. - - Given a lead of +2.4 points already observed and the general polarization in current politics, the range of a lead between 2 and 4 points seems quite plausible. - -5. **Time Frame and Immediate Factors**: - - With only 5 days until the resolution date and no upcoming major events or debates within this timeframe that could drastically alter voter perceptions, large shifts are improbable. - - Any new major political missteps or unprecedented events still remain low probability within such a short period. - -### Synthesis and Forecast: - -1. **Default Resolution if Trends Continue**: - - If existing trends persist, it’s likely the lead stays around +3 points which is within the forecast range (+2 to +4 points). - -2. **Possible Shifts and Variability**: - - The variability and uncertainty in polling could see slight changes but massive game-changing data is absent. - -3. **Base Rates and Historical Consistency**: - - Changing major polling numbers by more than 1 point in just 5 days without a significant event is rare. - -### Final Probability Calculation: - -Considering these factors: -- There is a high probability Trump's lead remains in the observed trend (+2.4 to +3.0) due to recent Biden missteps and lack of upcoming debate or event. -- Historical stability in the absence of major events supports this consistency. -- Thus, I would assign a higher likelihood to Trump's lead staying within the given range of +2 to +4 points. - -Taking into account some minor variability due to unforeseen minor political events or polling adjustments: - -**Final Probability: 65%** - -This considers that while the lead is likely to be stable due to recent debate impacts, polling variability and model adjustments might create minor deviations that could affect but not significantly alter the current trend." -26004,"Will Trump's lead over Biden be greater than 2 and less than or equal to 3 on July 15, 2024, according to 538's national polling average?","PERPLEXITY - -To provide context for the query about Trump's lead over Biden in 538's national polling average, here are the relevant details: - -1. **Current Polling Averages**: - - As of June 20-25, 2024, Trump leads Biden by 4 points in the national polls among likely voters (LV) and by 6 points among registered voters (RV). - - Another poll from June 23-25, 2024, shows Trump and Biden tied at 42% among registered voters. - -2. **Methodology**: - - 538's polling averages are adjusted based on state and national polls, which means candidates' averages can shift even if no new polls have been added. - - The averages account for each poll's recency, sample size, methodology, and house effects. - -3. **Forecast Model**: - - 538's 2024 presidential election forecast model combines polling, economic, and demographic data to explore likely election outcomes. - - The model updates at least once a day and whenever new data is available, with uncertainty decreasing as Election Day approaches. - -4. **Recent Updates**: - - As of the latest forecast, the presidential race is still considered a toss-up, with Biden favored to win in some simulations and Trump favored in others. - - The first televised debate between Biden and Trump is scheduled for June 27, which could potentially impact the race. - -Given these details, there is no clear indication that Trump's lead will be greater than 2 and less than or equal to 3 on July 15, 2024, according to 538's national polling average. The current polling averages show a larger lead for Trump, and the forecast model continues to reflect a competitive race. - -######### - -GPT - -### Analysis and Forecasting Approach - -**Step 1: Understanding the Context and Current Data** -- As of July 5, 2024, Donald Trump has a lead of +2.4 percentage points over Joe Biden according to the 538 national polling average. -- Biden's performance in the debate on June 27 was widely seen as poor and has led to increasing pressure to drop out of the race. -- Historical context: - - The debate seemed to have had a significant short-term effect, potentially exacerbating Biden's drop in polls. - - Recent trends show an expanding lead for Trump, from +0.2 on the day of the debate to +2.4 on July 5. - -**Step 2: Evaluating Factors that Could Influence the Polling Average** -- **Short-Term Momentum:** Given the debate performance and subsequent criticisms, Biden's campaign has been negatively affected in the short term. However, the Biden team is reportedly taking action to reverse these perceptions. -- **Media Influence:** Major news outlets such as The New York Times and influential political voices are discussing Biden's potential withdrawal, which could have both stabilizing and destabilizing effects. -- **Polling Fluctuations:** Historical data show that polling averages can fluctuate significantly based on new events, media coverage, and public perception. - -**Step 3: Examining Base Rates and Historical Patterns** -- Presidential polls can be volatile, especially with significant events like debates. -- 538's methodology adjusts polls for recency and various biases, so sudden changes may dampen slightly but generally reflect in the averages. - -**Step 4: Probabilistic Analysis of Potential Outcomes** -- **Short-Term Response to the Debate:** The immediate effect post-debate was an increase in Trump's lead from 0.2 to 2.4 points over a span of a few days. This suggests a momentum against Biden. -- **Campaign's Efforts to Counteract:** If Biden's campaign efforts to reverse perceptions are successful, the drop might stabilize or even reverse slightly. -- **Time Until July 15 (5 Days):** A degree of stability could be expected as Biden's team seeks to stem further losses, but significant reversal is less likely in a short span. - -**Step 5: Confidence Intervals and Variance Analysis** -- **Recent Polls Analysis:** Given the current lead (+2.4) and historical volatility, polling averages can vary within a range due to sampling errors and campaign efforts. -- **Polling Stability:** If the lead continued to grow significantly, we should see more than +3; if Biden's campaign mitigates the damage, it could stabilize but stay above +2. - -### Computation and Inference -- **Assuming Normal Distribution:** The polling lead has shown recent growth patterns indicating potential variance around 0.5 to 1 points short-term. -- **Confidence Interval Calculation:** Trump's lead could range anywhere from approximately +2 to +3 points with slight fluctuations around the current +2.4 point average. - -### Conclusion -Given the recent trends, the computation of an interval around the current lead, and the near-term effects including potential corrective actions from Biden's campaign: - -- **Probability Assessment:** - - Strong impact leading to a widening lead above 3: Less likely (15% probability) - - Biden mitigating impact but still leaving a strong lead: Slightly more likely (45% probability) - - Lead stabilizing within the marginal zone (greater than 2 and less than or equal to 3): More likely (40%) - -### Final Probability -After considering all factors and probabilistic distribution: - -- Probability: 40% - -The prediction is for a 40% chance that Trump's lead over Biden will be greater than 2 and less than or equal to 3 on July 15, 2024, according to 538's national polling average." -26005,"Will Trump's lead over Biden be greater than 3 and less than or equal to 4 on July 15, 2024, according to 538's national polling average?","PERPLEXITY - -To assist in forecasting whether Trump's lead over Biden will be greater than 3 and less than or equal to 4 on July 15, 2024, according to 538's national polling average, here is a concise rundown of the most relevant news and data: - -1. **Current Polling Averages**: - - According to FiveThirtyEight's national polling averages, Trump is currently leading Biden by a margin of +3 percentage points. - - The latest polls show a mixed trend, with some polls indicating a narrow lead for Trump and others showing a tie or a slight lead for Biden. - -2. **Recent Updates**: - - The forecast still sees the presidential race as a pure toss-up, with President Joe Biden favored to win in XXX out of 1,000 simulations and former President Donald Trump winning in XXX simulations. - - Biden recently ""pulled ahead"" of Trump in the national polling averages, but only by a margin of +0.3 percentage points, which is within the model's uncertainty interval. - -3. **Methodology**: - - FiveThirtyEight's forecast model combines polling data with campaign ""fundamentals"" such as economic conditions, state partisanship, and incumbency. - - The model adjusts polls for house effects, mode, partisanship, voter likelihood, and third parties, and weights them based on the pollster's rating and frequency of polling. - -4. **Upcoming Events**: - - The first televised debate between Biden and Trump is scheduled for June 27, which could potentially shake up the race. - -5. **Expert Opinions**: - - The forecast model is based on a combination of polls and campaign fundamentals, aiming to provide a sense of each candidate's likelihood of winning rather than calling a winner. - -6. **Current Values**: - - As of the latest updates, Biden is favored to win in XXX out of 1,000 simulations, while Trump wins in XXX simulations. - -7. **Prediction Markets**: - - No specific prediction markets for this exact scenario are available on Metaculus. However, general election forecasts can be found on the platform. - -This summary provides a comprehensive overview of the current state of the presidential race, highlighting the key factors that might influence the outcome. - -######### - -GPT - -To determine the probability that Trump's lead over Biden will be greater than 3 and less than or equal to 4 on July 15, 2024, according to 538's national polling average, let's break down the information and examine the factors at play. - -### Step-by-Step Analysis - -#### 1. **Current Polling and Trends** -- As of July 5, 2024, Trump is leading Biden by +2.4 percentage points. -- The trend since the June 27 debate shows Trump's lead increasing steadily from +0.2 to +2.4. - -#### 2. **Immediate Context and Upcoming Events** -- Biden's poor debate performance on June 27 has negatively impacted his polling numbers. -- Calls for Biden to drop out have emerged, including from significant public figures and media outlets. - -#### 3. **Biden Campaign's Response** -- The Biden campaign is taking active measures to reverse the negative perception. -- Internal acknowledgment by the Biden team that they must improve the situation quickly. - -#### 4. **Historical Polling Data and Variability** -- We should consider the historical variability of polling data over a short period. -- In presidential races, polling averages can shift due to significant events such as debates, scandals, or new policy announcements. - -#### 5. **Statistical Analysis and Base Rates** -- Let's consider the historical polling shifts in short windows. A shift of 0.6–1 percentage point in either direction within a week is common due to daily variability. -- We have five days until July 15. Historical data suggest that significant shifts (more than 1 percentage point) within such a short period are less common unless a major event or revelation occurs. - -### Assessing Probabilities - -Given that Trump currently has a +3 percentage point lead: - -#### Probability Components: -1. **Trump's Lead Increased Between +3 and +4 Points** - - A further increase of up to 1 point in Trump's favor is plausible given current trends and the backlash against Biden's debate performance. - - Probability of Trump's lead being between +3 and +4 points: Moderate to High, as the trend has been upward for Trump and internal campaign efforts to reverse the trend may not have immediate visible effects. - -2. **Stabilization or Slight Reduction** - - Biden's campaign efforts may result in stabilization or a slight reduction in Trump's lead. - - Stabilization or reduction would typically indicate a less aggressive increase. - -3. **Variability and Unforeseen Events** - - Given the short timeframe, normal variance in polling data needs to be accounted for. - - Unforeseen events could skew the probability, but no information currently suggests a major upcoming event that would drastically alter the trend. - -### Synthesis of Factors: -- **Current Lead:** +3 points. -- **Timeframe:** 5 days until July 15. -- **Variance:** Historical short-term variance up to ±1 point is likely without major events. -- **Biden's Efforts:** Likely to have some impact but not instantly or significantly. - -### Final Probability Estimation: -After weighing all factors, I estimate the probability of Trump's lead over Biden being greater than 3 and less than or equal to 4 percentage points on July 15, 2024, as follows: - -**Probability: 60%** - -This estimation considers the current trend, the typical variability in polling data, the impact of the recent debate, and the short timeframe for significant changes." -25952,"Will the Prime Minister of France belong to the New Popular Front coalition on July 30, 2024?","PERPLEXITY - -The New Popular Front (NFP), a coalition of four left-green parties, has emerged victorious in France's recent parliamentary election, securing 182 seats in a divided 577-seat assembly. The NFP comprises France Unbowed (LFI), the Socialist Party (PS), the French Green party (LE-EELV), and the French Communist Party (PCF). The coalition's agenda is heavily influenced by the radical-left LFI, advocating for policies such as reversing pension reforms, increasing public sector wages, and implementing a wealth tax. - -The NFP's success can be attributed to strategic alliances aimed at preventing vote splitting against the far-right National Rally (RN). However, the coalition's unity may face challenges due to internal conflicts and diverging views among its members. Previous alliances like the Nupes fell apart due to personality clashes and policy disagreements. - -The future of the NFP hinges on its ability to navigate these internal dynamics and maintain a unified front to govern effectively. The coalition has said it hopes to put forward a name for prime minister within the week, but without a single leader, it doesn't have an obvious candidate. Mélenchon has insisted that the NFP will enact its policies and only its policies, while moderates advocate for inclusive dialogue and cooperation with other political groups. - -President Macron has urged mainstream parties to form a government, but the NFP's willingness to compromise remains uncertain. If the NFP fails to form a coalition, it would be left to rule by minority, seeking consensus bill by bill. - -Currently, there are no recent updates indicating a clear resolution on the prime ministerial position. The situation remains uncertain, with the NFP's internal dynamics and potential coalitions with other parties like Ensemble being crucial factors in determining the outcome. - -Metaculus, a forecasting site, does not have a specific prediction market on this question. However, it is essential to monitor the situation closely, as the next steps in the formation of the French government will significantly impact the country's political landscape. - -######### - -GPT - -To predict whether the Prime Minister of France will belong to the New Popular Front (NPF) coalition on July 30, 2024, we should consider the following key points, data sources, and methodical steps to arrive at an informed probability estimate. - -### Key Considerations - -1. **Election Outcome & Current Assembly Composition**: - - The NPF has secured 182 seats out of 577, as per the assistant's report. - - The far-right National Rally has a significant number of seats (38 elected and more in runoffs), but not enough for a majority alone. - - Other key players include the centrist ""Together"" and National Rally-led ""Union of the Far-Right"". - -2. **French Political System**: - - The French semi-presidential system requires the President's nominated Prime Minister to command enough support in the National Assembly to avoid a vote of no confidence. - - Macron's desire to find a mainstream, stable government post-snap elections suggests he will seek a coalition or a compromise candidate. - -3. **President Macron’s Role and Strategy**: - - Macron has shown a preference for mainstream stability and might prefer a figure who can bridge gaps (potentially someone from Together or even a moderate from NPF). - - However, the absence of a clear candidate from NPF suggests internal friction, making it difficult for them to present a united front. - -4. **Historical Precedent & Political Dynamics**: - - Past alliances like Nupes have fallen apart due to internal ideological and personality clashes. Considering the diverse composition of NPF, similar challenges may occur. - - Macron’s previous strategies and political maneuvering suggest that he might lean toward a more centrist or less polarizing candidate to avoid instability. - -### Probabilistic Analysis - -1. **Base Rates & Historical Context**: - - Macron has previously appointed Prime Ministers who weren't from his party out of pragmatic necessity (e.g., Édouard Philippe from Republicans). - - Minority government instances and forming coalitions by piecing together support across the spectrum are common in fractured European parliaments. - -2. **Recent Trends & Strategic Withdrawals**: - - The strategic withdrawal of third-placed candidates to ensure National Rally does not win indicates a practical, anti-far-right tactic. - - This collaborative behavior could extend to forming a government but still requires agreement on a Prime Minister. - -3. **Time Factor**: - - With nearly three weeks until the prediction date (July 30), there is a limited but significant window for party negotiations and formation of consensus. - - Though rapid changes are common in such political environments, a fragmented parliament isn't quick to solidify alliances. - -### Calculating Probabilities - -- **Baseline Prediction (No Change)**: Given the sheer number of seats required (289+) and NPF's current tally (182), the probability of NPF alone putting forward a viable and independent candidate (without broader compromise) is around 25-30%. - -- **Compromise Candidate Scenario**: If Macron decides on a compromise candidate who appeals across factions, the probability marginally decreases for NPF, as it’s less likely an outright NPF member. - -- **Internal NPF Dynamics**: Given the historical splits and tensions, the probability slightly lowers further due to friction within NPF itself. - -Combining these aspects, let's balance the above considerations with potential swings from ongoing negotiations and political dynamics: - -``` -- NPF winning Prime Ministership outright: ~30% -- Compromise candidate from NPF subset, accepted by Macron and others: ~20% -- Internal conflict and historical splits reducing likelihood: ~10% -``` - -Final Estimated Probability: -Taking all factors into account, and balancing the range of scenarios, the resulting probability distribution benefits from weighting towards the historical tendencies and strategic political actions: - -### Probability: 20%" -25953,"Will the Prime Minister of France belong to the Together coalition on July 30, 2024?","PERPLEXITY - -To provide context for the question about the Prime Minister of France belonging to the Together coalition on July 30, 2024, here is a summary of the relevant news: - -- **Election Results and Coalition Formation**: France held parliamentary elections on June 29, 2024, which resulted in a deadlock in parliament. President Emmanuel Macron has called on mainstream political parties to form a coalition, excluding far-right and extreme left factions, to create a stable governing body. -- **Macron's Coalition Proposal**: Macron's proposed ""Republican bloc"" aims to unite mainstream parties to achieve a solid majority. He plans to appoint a prime minister once this collaboration is established. -- **Electoral Process and Post-Election Scenarios**: The French electoral process involves district-based voting, with a second round if no candidate secures over 50% of the vote. The outcome could lead to a far-right government, no clear majority, or a coalition government. In the case of no majority, the president must appoint a prime minister from the parliamentary group with the most seats. -- **Current Political Landscape**: The far-right National Rally (RN) party led the first round of parliamentary elections, while the left-wing alliance performed well. Centrists are now seeking alliances with the right to form a government. - -Recent updates indicate that Macron is actively pushing for a coalition among mainstream parties, which could potentially lead to a prime minister from the Together coalition. However, the ongoing negotiations and complex post-election scenarios make it difficult to predict the exact outcome. - -**Current Values and Prediction Markets**: -- There are no specific current values directly related to the prime minister's affiliation on July 30, 2024. -- Metaculus does not have a relevant prediction market for this specific question. - -**Expert Opinions**: -- Macron's call for a coalition emphasizes the need for a stable and effective governing body, excluding far-right and extreme left factions. -- The possibility of a far-right government or no clear majority could lead to complex governance and decision-making scenarios. - -Overall, the situation remains fluid, with ongoing negotiations and various possible outcomes. - -######### - -GPT - -To predict whether the Prime Minister of France will belong to the Together coalition on July 30, 2024, let's break down the relevant factors systematically: - -### 1. **Political Context and Recent Developments** -- **Recent Parliamentary Elections**: The parliamentary elections on June 29 and July 7 resulted in a complex runoff scenario. The high turnout and dramatic dynamics, with far-right candidates advancing to numerous runoffs, complicate the outcome. -- **Strategic Withdrawals**: Together coalition and the New Popular Front urging their third-placed candidates to withdraw to contain the far-right indicates tactical maneuvering, which could affect the balance of power. -- **Coalition Challenges**: The formation of coalitions is critical. Macron is pushing for a Republican bloc, mainly to create stability and potentially avoid a far-right prime minister. - -### 2. **Current Standing of Political Parties** -- **National Rally (Far-Right)**: Strong showing, but their ability to form a majority remains uncertain due to strategic withdrawals by Together and New Popular Front. -- **Together (Centrist)**: This coalition, aligned with President Macron, is critical in forming a stable government. Macron’s proposal for a Republican bloc includes Together and other mainstream parties. -- **New Popular Front (Left/Center-Left)**: Their cooperation with Together indicates they're strategically aligned to block the far-right. - -### 3. **Prime Minister Appointment Process** -- The President does not require a confidence vote from the legislature for appointing the Prime Minister but can be subject to a vote of no confidence. This means Macron could technically appoint a Prime Minister from Together even if they do not have a majority, but the stability of the government would depend on avoiding a no-confidence vote. - -### 4. **Historical and Base Rates** -- Historical trends in France have shown that centrist and left coalitions tend to unite effectively to block far-right political ascensions. This situational tactic has historically been effective. -- The precedent during Macron's presidency shows reliance on strategic coalitions and political maneuvering. - -### 5. **Potential Outcomes by July 30, 2024** -- Macron successfully forms the Republican bloc, and a Prime Minister from Together is appointed. -- Macron attempts but fails to form a stable bloc, leading to a possible caretaker or transitional government. -- The far-right manages to leverage their strong electoral performance to force a non-Together Prime Minister. - -### 6. **Time Factor and Fluidity** -- Given that there are only 20 days until the queried date, the ability for significant political shifts is moderate but not negligible. -- Macron’s urgency in advocating for a mainstream coalition suggests a probable swift lineup of his government structure to avoid political instability. - -### 7. **Expert Opinions and Current Reporting** -- Reports indicate ongoing negotiations and a strong push from Macron towards maintaining a centrist leadership to ensure governance stability. - -### Conclusion -After weighing all factors, including the fluid political environment, historical tendencies, Macron’s maneuvering strategies, and the time left until resolution: - -- Base rates and historical precedence favor a centrist (Together) Prime Minister, especially given Macron’s capacity for strategic alliances. -- The far-right's unprecedented rise and strong turnout bolster uncertainties but are mitigated by tactical withdrawals to block their ascension. -- The overall drive for stability by Macron makes it significantly likely that a Together coalition-associated Prime Minister will be in place. - -**Final Probability Estimate:** -Given all considerations, I'd estimate the probability of the Prime Minister of France remaining from the Together coalition on July 30, 2024, as **Probability: 75%**. This accounts for both the high likelihood of strategic alignment and the remaining uncertainties in the current volatile political landscape." -25954,"Will the Prime Minister of France belong to a coalition other than New Popular Front or Together on July 30, 2024?","PERPLEXITY - -Here is a detailed rundown of the news relevant to the question: - -- **Recent Election Results**: The New Popular Front (NFP), a left-wing coalition, won the most seats in France's snap legislative elections, but fell short of an absolute majority. The NFP secured 193 seats, ahead of President Emmanuel Macron's centrist Ensemble coalition with 164 seats and the far-right National Rally (RN) with 143 seats. - -- **NFP's Claim to Govern**: Despite not having an absolute majority, the NFP claims it has the right to govern and is willing to work with other parties to form a government. The NFP leaders have held talks to coalesce on issues such as who should be prime minister and have vowed to constrain the far-right. - -- **Potential Prime Minister Candidates**: Jean-Luc Mélenchon, the leader of France Unbowed (LFI), and Olivier Faure, the Socialist leader, have both expressed interest in the prime ministerial role. However, there are concerns about Mélenchon's past comments on antisemitism, which could impact his candidacy. - -- **Current Political Landscape**: The NFP is trying to form a government, but Macron's Ensemble coalition and the RN are also vying for power. Macron has urged mainstream parties to form a broad coalition to break the political deadlock. - -- **Recent Updates**: French Prime Minister Gabriel Attal plans to resign, and Macron has called on parliament to negotiate a broad coalition. The NFP is still discussing potential candidates for the prime ministerial role, while the far-right RN is preparing to take on leadership roles in the National Assembly. - -- **Forecasting Sites**: There are no relevant prediction markets on Metaculus regarding this specific question. - -- **Current Values**: There are no specific numerical values relevant to this question. - -Overall, the situation remains uncertain, with multiple parties vying for power and no clear majority. The NFP is pushing to govern, but Macron's Ensemble coalition and the RN are also in the mix. The outcome will depend on the ability of these parties to form coalitions and negotiate a workable government. - -######### - -GPT - -To predict the probability that the Prime Minister of France will belong to a coalition other than New Popular Front (NFP) or Together on July 30, 2024, let's work through this step by step: - -### 1. Understanding Current Context -- **Date:** Today is July 10, 2024. -- **Recent Snap Elections:** The NFP won the most seats but without an absolute majority (193 seats). Together secured 164 seats, while the far-right National Rally (RN) got 143 seats. The majority needed in the 577-member assembly is 289 seats. - -### 2. Analysis of Political Dynamics -- **Potential for Coalition:** The NFP is currently positioned as the strongest single coalition but without an absolute majority. They are trying to pull together a governing coalition. -- **Macron’s Role:** As President, Macron holds significant sway. He is pushing for a broader coalition, which suggests difficulty for any one coalition to form a stable government. -- **Gabriel Attal's Resignation:** The current Prime Minister, affiliated with Together, plans to resign, introducing further instability and making it crucial to focus on the formation dynamics. - -### 3. Recency & Credibility of Sources -- The research assistant's update is current and includes analysis of post-election dynamics just a few days ago. -- Attal’s resignation is also recent and shapes the immediate political landscape. - -### 4. Historical Base Rates -- French politics often involves coalition governments due to the fragmented nature of support across the political spectrum. -- Unstable political climates post-election are common when no clear majority is obtained, leading to negotiations that can shift power dynamics relatively quickly. - -### 5. Likelihood of Various Outcomes -- **Coalition Formation:** Macron’s Together coalition may attempt to form a government by allying with other smaller parties, but the NFP remains a strong contender. -- **RN's Position:** Despite the RN gaining significant seats, historically, they've faced challenges in forming broader coalitions due to their far-right stance. Alliances with them are typically less probable. -- **Broad Coalition:** Macron’s call for a broad coalition could lead to an unaligned candidate taking a compromise Prime Minister position, betraying no firm allegiance to the existing primary coalitions. - -### 6. Time Factor & Variance -- **Short Timeframe:** With only 20 days until July 30, 2024, the window for change is limited. Rapid coalition negotiations, however, could shift the landscape. -- **Current Momentum:** NFP is actively positioning to govern, drawing on their recent electoral strength. - -### 7. Default Behaviors vs. Novel Developments -- **Default Resolution:** Given current standings and historical patterns, if NFP successfully forms a government, an NFP Prime Minister is the most likely default scenario. -- **Novel Shifts:** Political appeals from Macron for a broader coalition introduce some degree of novelty which tilts probability slightly away from an assured NFP governorship. - -### 8. Calculation and Refinement -Given that the NFP leads but does not have an outright majority, ongoing coalition talks, the President’s efforts for a broader coalition, and political volatility: -- NFP successfully forming a government: ~60% -- Together or RN forming a coalition directly seems less probable, but broader coalitional agreements, given Macron’s push, slightly increase chances. - -### 9. Conclusion -Balancing default tendencies, historical precedent, and the specific recent developments: -- **NFP (Likely):** 60% -- **Together (Less Likely):** 20% -- **Other Broad Coalition or No Prime Minister (Possible but uncertain):** 20% - -Therefore, aggregating these scenarios while conforming to the criteria leads us to: - -**Probability that the Prime Minister belongs to a coalition other than New Popular Front or Together on July 30, 2024:** **20%** - -**Final Answer:** Probability: 20%" -25955,"Before October 1, 2024, will there be an armed forces death in a conflict opposing China to Taiwan, the United States, the Philippines, or Japan?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question, I have summarized the key points from the provided sources: - -1. **Taiwan Strait Tensions**: - - The situation in the Taiwan Strait is precarious, with China signaling that it does not want a confrontation but may change its posture at any time. - - The United States has expressed concerns over China's military activities and has notified Congress of two arms sale packages to support Taiwan. - - The Biden administration has reiterated that it is not seeking independence for Taiwan but will defend it if necessary. - -2. **Japan-Philippines Defense Pact**: - - Japan and the Philippines have signed a landmark defense pact, known as the Reciprocal Access Agreement, to counter China's growing aggression in the region. - - This pact allows for the exchange of military forces for training and joint exercises, enhancing regional security and deterrence against China. - -3. **U.S. Military Preparations**: - - The U.S. Marines are retooling their strategy to fight against China, focusing on island-hopping battles across Asia and adapting to a maritime fight. - - The U.S. military is preparing for a potential conflict with China, with a focus on the First Island Chain, which includes Japan, Taiwan, and the Philippines. - -4. **Regional Tensions**: - - China's territorial disputes with Japan and the Philippines continue, with recent clashes involving coast guard and navy vessels. - - The Philippines has denounced China's actions in the South China Sea, warning that any harm to Filipino personnel would be considered close to an act of war. - -Given these developments, there is a heightened risk of armed conflict in the region. However, whether there will be an armed forces death in a conflict opposing China to Taiwan, the United States, the Philippines, or Japan before October 1, 2024, remains uncertain and will depend on the evolving dynamics of the situation. - -For forecasting purposes, it would be useful to monitor the following: - -- Any further escalation of tensions between China and Taiwan, the United States, the Philippines, or Japan. -- The ratification process of the Japan-Philippines defense pact and its potential impact on regional security. -- U.S. military preparations and any changes in its strategy for countering China. -- Any new incidents or clashes between China and its regional neighbors. - -Additionally, checking for relevant prediction markets on sites like Metaculus could provide valuable insights into the collective expectations of forecasters. - -######### - -GPT - -### Analysis - -#### Recency and Importance of Sources -1. **Taiwan Strait Tensions** - - **Recent Incidents:** Large-scale military drills by China were conducted in May 2024. Increased encroachment into Taiwan's ADIZ is ongoing. - - **US Involvement:** US arms sales notified to Congress, and US commitment to defend Taiwan if necessary. - - **Importance:** Very high, given the potential direct military confrontation. - -2. **Japan-Philippines Defense Pact** - - **Timeline:** Landmark defense pact recently signed between Japan and the Philippines. - - **Importance:** High, enhances regional military coordination and deterrence against China. - -3. **US Military Preparations** - - **Strategic Reorientation:** Focus on island-hopping battles and presence in the First Island Chain, including the Philippines and Japan. - - **Importance:** High, reveals US readiness to counter Chinese actions in a contested region. - -4. **Regional Tensions** - - **Recent Clashes:** Collision between Chinese and Philippine vessels in June 2024. - - **Importance:** High, illustrating potential flashpoints for armed conflict. - -### Historical Base Rates -- **South China Sea and Cross-Strait Relations:** Historically, incidents frequently come close to conflict but often de-escalate due to diplomatic interventions. -- **Similar Events:** Historical clashes rarely lead to immediate armed forces deaths. Most recent clashes (e.g., India-China border skirmishes) did lead to casualties but are not directly comparable in terms of naval conflict dynamics. - -### Novel Risk Factors -- **Political Situations:** Recent changes in Taiwan's leadership may increase volatility. -- **Economic Considerations:** The global economy's reaction to military conflict, especially given the post-COVID economic state, can prompt de-escalatory behavior from large economies like the US and China. -- **Technological Developments:** Advancements in surveillance, AI in conflict scenarios, and naval capabilities can both escalate and mitigate immediate engagements. - -### Default Resolution -- **Status Quo:** Historically, even with increased tensions, actual combat resulting in armed forces deaths has been avoided or highly controlled. The default resolution is no significant armed forces deaths due to de-escalation mechanisms in place. - -### Chain of Thought and Reasoning -1. **Current Tensions:** High tensions are notable between China and Taiwan, China and the Philippines, as well as involving US-Japan defensive preparations. -2. **Escalation Probability:** Given recent incidents and military preparations, the probability of escalation appears higher than usual. -3. **De-escalation Mechanisms:** Historical patterns show that despite high tensions, diplomatic efforts often avert direct military conflict. -4. **Time Frame Sensitivity:** Approximately 3 months remain until the resolution date; within such a short time span, significant escalation to the point of armed forces deaths is less likely but not impossible. -5. **Regional Cooperation:** New defense pacts and US military strategy may either deter Chinese aggressive actions or provoke limited engagements. - -### Statistical Considerations -1. **Look at Similar Timeframes:** Past incidents where tensions peaked (e.g., 1995–96 Taiwan Strait Crisis, 2016 South China Sea arbitration ruling), actual armed forces deaths did not promptly follow escalations. -2. **Frequency of Engagements:** Although the tempo has increased, major incidents do not frequently result in fatalities. - -### Probability Calculation -- **Initial Base Rate Estimate:** Given improved defense mechanisms, initial probability at ~10%. -- **Adjust for Recent Tensions:** Add ~15% due to current escalations and provocations—total 25%. -- **Consider Diplomacy & De-escalation:** Subtract ~10% for high likelihood of intervention and diplomatic de-escalations—reducing to 15%. -- **Short Timeframe:** Approximately three months reduce the chances slightly due to operational inertia and ongoing negotiations—final adjustment leads to a robust estimate of 20%. - -### Conclusion -**Probability: 20%** - -This analysis incorporates a balanced view of recent incidents, historical data, de-escalation mechanisms, time frames, and regional dynamics. The provided answer reflects an informed estimation based on the structured assessment of factors influencing potential outcomes." -25956,Will Democrats have a brokered convention in 2024?,"PERPLEXITY - -To provide a comprehensive overview of the possibility of a brokered convention for the Democratic Party in 2024, I have analyzed recent news articles and expert opinions. - -**Current Situation and Concerns** - -- President Joe Biden's lackluster debate performance on June 27 has raised concerns among Democrats about his suitability for office, with some strategists and donors urging for an alternative candidate. -- Despite these concerns, Biden has maintained that he will not drop out of the race. -- The Democratic Party is grappling with the possibility of replacing Biden, but the process is complex and faces significant hurdles. - -**Brokered Convention Possibility** - -- A brokered convention would occur if Biden drops out of the race, allowing delegates to vote for another candidate. -- The last brokered convention was at the 1952 Democratic National Convention, where Adlai Stevenson was nominated after strategic negotiations. -- If Biden remains in the race, it is highly unlikely that a brokered convention will occur, as he has accumulated the necessary delegates to secure the nomination. - -**Expert Opinions and Party Rules** - -- Election law expert Derek Muller believes that a brokered convention is unlikely unless Biden voluntarily steps down, given the significant number of pledged delegates supporting him. -- Party rules require a candidate to gather signatures from 600 delegates to qualify for nomination, and a brokered convention would involve multiple rounds of voting if no candidate secures a majority. - -**Recent Updates and Developments** - -- There have been no recent updates indicating a significant shift towards a brokered convention, as Biden remains committed to staying in the race. -- Discussions among party members and experts continue to focus on the possibility of replacing Biden, but no concrete alternatives have emerged. - -**Forecasting Sites and Current Predictions** - -- Metaculus currently predicts a 15% chance of a brokered convention occurring at the 2024 Democratic National Convention. - -In summary, while there are concerns about Biden's performance and discussions about replacing him, a brokered convention remains unlikely unless Biden voluntarily steps down. The Democratic Party's rules and current delegate commitments make it challenging to oust Biden as the nominee. - -######### - -GPT - -To forecast the probability of the Democratic Party having a brokered convention in 2024, let's methodically analyze the information provided and consider relevant factors: - -### Key Points and Breakdown - -1. **Historical Context**: - - Since the 20th Century, brokered conventions for the Democratic Party have been rare. The last one happened in 1952. - - Base rate: 5 brokered conventions out of 31, or roughly 16%. - -2. **Current Situation**: - - Joe Biden's poor debate performance led to concerns but strong public support from prominent Democrats. - - Biden currently has a significant number of pledged delegates (99%), making a brokered convention unlikely under normal circumstances. - -3. **Current Sentiment**: - - There's widespread Democratic concern and some advocacy for an open convention. - - Initial polling suggests growing voter concern about Biden’s ability to continue, potentially heightening internal party pressures. - -4. **Timelines**: - - The Democratic Convention and the virtual roll call vote will be before Ohio's August 7 ballot deadline, providing less than a month for any significant developments. - - Biden's commitment to stay in the race further reduces the likelihood of an enforced brokered convention. - -5. **Rules and Mechanisms**: - - It would require Biden to voluntarily step down and significant party maneuvering to reach a brokered convention. This is logistically complex given the time constraints. - -6. **Statistical Forecasts**: - - Metaculus predicts a 15% chance, reflecting the sum of the evidence and expert judgment. - -### Detailed Assessment - -1. **Base Rates**: - - Base rate for brokered conventions: 16%. Historical tendency moved downward in recent decades due to changes to the primary process and delegate allocation rules. - -2. **Current Dynamics**: - - Biden’s debate performance and subsequent political fallout: Significant negative sentiment exists, but with strong endorsements from leaders such as Obama and Clyburn, party cohesion remains. - -3. **Delegate Situation**: - - Biden has overwhelming delegate support, making it practically challenging for a brokered convention unless a massive shift in delegate allegiance occurs, which is unlikely given timeline constraints. - -4. **Public/Media Influence**: - - Public and media pressure can influence outcomes but not decisively alter the formal nomination mechanisms within this short window. - -5. **Timing and Change Probability**: - - Less than one month to induce a large-scale party shift and official reconsideration: low probability, since significant steps to formally challenge or replace Biden would need to be undertaken almost immediately given time constraints. - -6. **Expert Opinion**: - - Election law experts and political scientists generally suggest it is unlikely unless Biden steps down (~15% prediction on Metaculus aligns with this). - -### Conclusion - -After integrating these factors: - -- **Base Rate Adjustment**: Historical base rate adjusted downward for modern context. -- **Current Situation Impact**: Biden’s confirmed delegate majority, short time frame, and public support constrain a brokered convention likelihood. -- **Sentiment Analysis**: Growing sentiment for reconsideration influences but doesn’t decisively change the mechanisms in place. - -### Final Probability Calculation: - -- **Historical Rarity Adjustment**: 5% increase over base due to heightened sentiment but pragmatic resistance. -- **Temporal Influence**: Fast-declining potential due to logistic constraints. - -**Final Probability**: (Base 16% * Adjustment Factors) ≈ 15% (aligning with Metaculus). - -Thus, the most accurate forecast, given careful reassessment, consolidates at: -**Probability: 15%**" -25957,Will Tadej Pogačar win the Tour de France 2024?,"PERPLEXITY - -Here is a concise rundown of the news relevant to the question of whether Tadej Pogačar will win the Tour de France 2024: - -- **Current Standings**: Tadej Pogačar currently leads the Tour de France with a 33-second advantage over Remco Evenepoel and a 1-minute, 15-second lead over Jonas Vingegaard. -- **Stage 11**: Pogačar extended his overall lead to one minute and seconds over Remco Evenepoel after Jonas Vingegaard won Stage 11. Vingegaard caught up with Pogačar after the Slovenian initiated a breakaway, but Pogačar managed to widen his overall lead. -- **Strategy and Tactics**: Pogačar emphasized the importance of having strong legs to win the Tour de France, stating that intelligence is important but not enough on its own. His team, UAE Emirates, executed a well-planned strategy in Stage 11, positioning him for a potential time gain over his competitors. -- **Rivalry with Vingegaard**: Pogačar and Vingegaard are engaged in a close duel, with Vingegaard's endurance-based strategy aiming to stay in the hunt until the final act in the Alps. Vingegaard's stage win in Stage 11 showcased his determination and strategic prowess. -- **Upcoming Stages**: The Tour de France will continue through the Massif Central, featuring challenging climbs that will test the riders' endurance and tactical skills. - -Recent updates indicate that Pogačar is maintaining his lead, but Vingegaard's stage win in Stage 11 demonstrates his ability to challenge Pogačar. The upcoming stages will be crucial in determining the ultimate winner of the Tour de France 2024. - -There are no relevant prediction markets on Metaculus at this time. - -######### - -GPT - -To estimate the probability that Tadej Pogačar will win the Tour de France 2024, we need to carefully analyze the available information and consider the factors that might influence the outcome. - -### Overview of Current Situation -- **Current Standings**: Pogačar is leading with a 33-second advantage over Remco Evenepoel and a 1-minute, 15-second lead over Jonas Vingegaard. -- **Stage 11 Performance**: Pogačar managed to extend his lead despite Vingegaard winning the stage, showcasing his competitive form and strategic capabilities. -- **Team Strategy**: Pogačar's team, UAE Emirates, is implementing effective strategies, aiding him in maintaining his lead. -- **Upcoming Challenges**: The race will continue through the Massif Central, which includes challenging climbs that could affect the standings. - -### Key Considerations -1. **Historical Data & Current Form**: - - **Tadej Pogačar’s Previous Wins**: He has won the Tour de France twice (2020 and 2021), and he placed second in 2022. This demonstrates his capability and experience in winning the race. - - **Current Performance**: Leading the race midway indicates strong form and effective strategy implementation. His 33-second lead, though not insurmountable, is significant given the context. - -2. **Strength of Rivals**: - - **Jonas Vingegaard**: The current Tour de France champion (2022 and 2023) is in the top three and remains a major competitor. His ability to win Stage 11 asserts his capability. - - **Remco Evenepoel**: Currently second, continues to be a significant threat given his performance and potential. - -3. **Team Support**: - - Pogačar’s team (UAE Emirates) has been performing well strategically, which is crucial in supporting his bid for victory. - -4. **Unpredictable Factors**: - - **Accidents/Injuries**: Cyclists face a high risk of accidents and injuries that could drastically affect the race outcome. - - **Weather Conditions**: Weather could either favor or disadvantage Pogačar in the upcoming mountain stages. - - **Disqualifications**: Though rare, potential violations of race rules or doping allegations could disqualify any rider. - -5. **Upcoming Stages**: - - The upcoming stages with significant climbs could potentially shake up the general standings. Pogačar has traditionally excelled in mountain stages, which plays to his strength but also presents opportunities for rivals. - -### Probabilistic Analysis -Given these factors, let's break down the probability: - -- **Base Rate for Leading Riders**: Historically, riders who lead in the middle of the race (around Stage 10-11) have a high probability of winning. For instance, about 60-70% of mid-race leaders go on to win. -- **Strength and Consistency**: Considering Pogačar’s historical performance, current form, and strategic support, his probability should be adjusted upwards. -- **Unpredictability**: Factoring in the aforementioned random events (e.g., accidents), we need to marginally adjust the probability downward. - -Considering all these elements, and taking into account the remaining stages and potential volatility, a reasonable probability estimate for Pogačar winning the Tour de France 2024 would be approximately **65-75%**. This range accounts for his current lead, demonstrated competitiveness, and team strategy, while also acknowledging the uncertainties and strengths of his rivals. - -Final Estimate: -**Probability: 70%**" -25958,Will Jonas Vingegaard win the Tour de France 2024?,"PERPLEXITY - -Here is a concise rundown of the news relevant to the question of whether Jonas Vingegaard will win the Tour de France 2024: - -- **Recent Developments**: Jonas Vingegaard, the defending champion, won the 11th stage of the Tour de France, beating race leader Tadej Pogacar in a thrilling sprint finish. -- **Current Standings**: Pogacar still leads the general classification, with Vingegaard trailing by 1 minute and 14 seconds, and Remco Evenepoel in second place, 1 minute and 6 seconds behind Pogacar. -- **Expert Opinions**: Vingegaard's victory in Stage 11 has been seen as a significant psychological blow to Pogacar, indicating that the Danish rider is gaining strength and confidence as the Tour progresses. -- **Recent Updates**: Vingegaard's comeback from a serious crash in April has been remarkable, and his ability to respond to Pogacar's attacks and win the stage has raised questions about the shifting dynamic among the top contenders. -- **Current Values**: No specific current values are relevant to this query. - -- **Forecasting Sites**: There are no relevant prediction markets on Metaculus at this time. - -Overall, while Vingegaard's recent stage win has boosted his chances, Pogacar still holds a significant lead in the general classification. The race remains competitive, and the outcome is far from certain. - -######### - -GPT - -To forecast whether Jonas Vingegaard will win the Tour de France 2024, we need to evaluate several factors, including recent performance, historical data, team support, and potential future events. Here is a step-by-step breakdown of the relevant information: - -### Step 1: **Review of Current Standings and Developments** -- **Recent Performance:** Jonas Vingegaard won the 11th stage, demonstrating strong form and psychological resilience. His victory over leader Tadej Pogacar is significant as it indicates Vingegaard is competitive. -- **Current Standings:** Pogacar leads Vingegaard by 1 minute and 14 seconds. Pogacar's lead is substantial but not insurmountable, especially in a race like the Tour de France where strategies and team dynamics play a crucial role. -- **Other Contenders:** Remco Evenepoel is 1 minute and 6 seconds behind Pogacar and also remains a strong contender. -- **Expert Opinions:** Analysts view Vingegaard's stage win as a potential shift in momentum. This suggests that while Pogacar currently leads, Vingegaard's chances are improving. - -### Step 2: **Historical Data and Base Rates** -- **Past Winners and Trends:** Historically, the leader at this point in the race has a strong probability of maintaining their lead, but it is not guaranteed. Crashes, injuries, and individual stages can drastically alter outcomes, especially in the latter parts of the race. -- **Vingegaard’s Past Performance:** Vingegaard won the Tour de France in 2022 and 2023, showcasing his capability to compete at the highest level and maintain form over the entire race. This historical success increases his credibility as a repeat winner. - -### Step 3: **Potential Future Events and Variance** -- **Accidents/Injuries:** The Tour de France is fraught with risks, including crashes and injuries. Vingegaard's comeback from a serious crash in April 2024 underscores his resilience, but any future mishap could jeopardize his chances. -- **Team Dynamics:** Team support will be crucial. Vingegaard’s team has demonstrated strong performance in the past, and their strategic support will be pivotal in the coming stages. -- **Race Unpredictability:** The remaining stages, particularly the mountain stages and time trials, will significantly impact the final standings. Vingegaard's climbing ability and time-trial performance will be decisive. - -### Step 4: **Evaluating Importance and Recency of Sources** -- **Critical Analysis:** The recent victory in Stage 11 is very recent and indicates good form, which is critical. Pogacar’s current leadership position is significant but overshadowed by the fact that Vingegaard has recently beaten him head-to-head. -- **Recency Bias:** Be cautious not to over-rely on the latest stage win. Evaluate it in the context of the entire race. - -### Step 5: **Probability Calculation** -- **Lead Time Consideration:** With 11 days left in the race (ending July 21), there is enough time for changes in rankings. Given the historical volatility and the competitive field, there is a decent probability that Vingegaard can recover the deficit. -- **Base Rate Consideration:** Analyzing past winners and positions at this stage of the race, the base rate for a second-placed contender winning is plausible but not dominant, typically around 20-30%. - -### Step 6: **Final Synthesis** -Combining all factors: -- Vingegaard is second and 1:14 behind; he has proven strength and resilience. -- His past wins, current form, and team dynamics are favorable. -- There remains significant uncertainty due to potential crashes and the competitive field. - -### Conclusion -Balancing these considerations, a reasonable probability that Vingegaard will win the 2024 Tour de France, given his current position, form, and historical performance, is about: -**Probability: 35%** - -This incorporates the possibility of further events altering the standings while respecting the current deficit and the evolving dynamics of the race." -25959,Will someone other than Tadej Pogačar or Jonas Vingegaard win the Tour de France 2024?,"PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether someone other than Tadej Pogačar or Jonas Vingegaard will win the Tour de France 2024, I have summarized the following sources: - -1. **Reddit Discussion**: - - The discussion highlights the ongoing debate about Vingegaard's chances, with some users pointing out that he might not be fully recovered from his recent injury. They also note that Pogačar's strategy this year is different, as he can afford to wait and conserve energy. - -2. **YouTube Analysis**: - - The video discusses whether Pogačar underestimated Vingegaard, focusing on their performance in Stage 11 of the Tour de France 2024. - -3. **Stage 11 Results**: - - Jonas Vingegaard won Stage 11, but Pogačar extended his overall lead. Vingegaard's victory was seen as a significant comeback after his recent injury, and it has boosted his morale heading into the Pyrenees stages. - -4. **Pre-Race Poll**: - - A pre-race poll on the Cycling News forum showed Tadej Pogačar as the clear favorite, with 65.7% of the votes. Jonas Vingegaard received 11.1% of the votes, indicating that many still see Pogačar as the dominant force. - -Recent updates suggest that both Pogačar and Vingegaard are strong contenders, but Vingegaard's Stage 11 win has injected new life into his campaign. The Pyrenees stages will be crucial in determining the final outcome. - -There are no current values or prediction markets to report, but the ongoing competition between Pogačar and Vingegaard will likely continue to shape the narrative of the Tour de France 2024. - -######### - -GPT - -To determine the probability that someone other than Tadej Pogačar or Jonas Vingegaard will win the Tour de France 2024, we need to consider several factors: - -### Past Performance and Current Form -1. **Historical Dominance**: Both Pogačar and Vingegaard have been dominant in the past few editions of the Tour de France. Pogačar won the Tour in 2020 and 2021, while Vingegaard won in 2022 and 2023. They have consistently shown they can perform well under pressure and maintain form across the various stages. - -2. **Current Form**: According to the most recent updates, both are in strong form: - - **Pogačar** is leading the general classification, which indicates he's in a good position. - - **Vingegaard**'s victory in Stage 11 suggests he has recovered well from his recent injury and is capable of challenging. - -### Team Support and Strategy -1. **Team Dynamics**: Both riders come from strong teams with robust support structures. This is crucial in multi-stage races where team tactics can make a significant difference. Pogačar rides for UAE Team Emirates and Vingegaard for Jumbo-Visma, both of which have proven track records in the Tour. - -2. **Race Strategy**: The information suggests Pogačar may be conserving energy, implying he might have a strong finish planned. Vingegaard's aggressive Stage 11 performance indicates he is also strategically placed to challenge. - -### External Uncertainties -1. **Accidents and Disqualifications**: As with any race, external events such as accidents or disqualifications can impact the outcome. Both riders are experienced and have relatively fewer incidents of major crashes or disqualifications, but cycling is inherently risky. - -### Competitors -1. **Other Contenders**: The pre-race poll and lack of other significant names in the analysis suggest that while other riders are present, they are not favored to win. However, dark horses can and do emerge, especially if leading contenders face setbacks. - -### Remaining Stages -1. **Stages to Come**: The race will end on July 21, leaving around 11 days and several stages which include pivotal mountain stages in the Pyrenees. The Pyrenees stages are critical and challenging; while both are strong climbers, unexpected performances can tilt the odds. - -### Summary of Probabilities -1. **Base Rates**: Historically, in recent years, the Tour de France has been dominated by a few top contenders. Considering Pogačar and Vingegaard's dominance and current standings, the base rate suggests a high probability of one of them winning. - -### Balancing Factors -1. **Current Lead**: Pogačar's current lead is a strong indicator. Vingegaard's comeback also indicates he should not be discounted. - -2. **External Variables**: Given the nature of competitive cycling, external uncertainties (like crashes) always exist but are difficult to quantify with high probability. - -### Combining All Factors -1. **Pogačar and Vingegaard’s combined probability**: Each has a significant share of the probability given their strong positions, current form, and historical performance. If we attribute around 40% chance to Pogačar and 30% to Vingegaard, we are left assessing the remaining 30%. - -2. **Other Contenders**: Given the historical dominance of these two riders and the lack of strong indications of other contenders, I would place the probability of another rider winning at around 20%. - -### Final Probability -Considering the weights of the above factors, especially current form, past dominance, and remaining uncertainties: - -**Probability: 20%** - -This calculation acknowledges the strong likelihood that either Pogačar or Vingegaard will win, but also accounts for the possibility of unforeseen circumstances resulting in another winner." -25960,"Will there be any reported human-to-human transmission of highly pathogenic avian influenza H5N1 globally before Sept 30, 2024?","PERPLEXITY - -Here is a concise rundown of the news most relevant to the question about human-to-human transmission of highly pathogenic avian influenza H5N1: - -- **Current Situation**: As of April 2024, there have been no reported cases of sustained human-to-human transmission of H5N1 globally. The World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) have emphasized that the risk of human-to-human transmission remains low. - -- **Recent Updates**: On April 1, 2024, the CDC reported a human case of H5N1 infection in the United States, linked to exposure to dairy cattle in Texas. The patient is recovering, and the CDC maintains that the risk assessment for the general public remains low. - -- **Global Spread**: H5N1 has been reported in 23 countries since 1997, with a case fatality rate of over 50%. The virus has spread globally among birds and has infected various mammals, including dairy cows in the United States. - -- **Expert Opinions**: The WHO and CDC have consistently stated that the risk of human-to-human transmission is low, as the virus has not acquired mutations that facilitate transmission among humans. However, they acknowledge that sporadic infections in humans and mammals can occur due to exposure to infected animals or contaminated environments. - -- **Current Values**: As of April 2024, there have been 889 reported cases and 463 deaths (case fatality rate of 52%) globally from 2003 to April 1, 2024. - -- **Forecasting Sites**: There are no specific prediction markets on Metaculus related to human-to-human transmission of H5N1 before September 30, 2024. However, Metaculus does have a general question about the likelihood of a pandemic caused by H5N1, which currently stands at 12%. - -In summary, while there have been recent human cases of H5N1 infection, there is no evidence of sustained human-to-human transmission. The WHO and CDC continue to monitor the situation and emphasize the low risk to the general public. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Review the Resolution Criteria and Background** -- **Resolution Criteria**: The resolution to ""Yes"" requires the CDC to report at least one case of human-to-human transmission of H5N1 globally between April 11, 2024, and September 30, 2024. -- **Background Summary**: - - Cases of human H5N1 infection have been linked to animal exposure, not human-to-human transmission. - - The virus has a high case fatality rate (~50%) but does not currently sustain human-to-human transmission. - - CDC and WHO rate the risk of human-to-human transmission as low. - -#### 2. **Base Rates and Historical Context** -- **Past H5N1 History**: Since 1997, human H5N1 cases have been sporadic, and sustained human-to-human transmission has not occurred. -- **Case Data**: Globally, about 890 cases since 1997, indicating it is relatively rare. - -#### 3. **Current Situation Analysis** -- **Recent Events**: A recent infection in Texas due to animal exposure. -- **Risk Assessments**: As of April 11, 2024, both WHO and CDC state that the risk remains low and there's no evidence of human-to-human transmission. - -#### 4. **Expert Opinions** -- **WHO and CDC**: Experts continue to emphasize low risk due to lack of necessary mutations for human-to-human transmission. -- **Virus Mutation Potential**: While mutation enabling human-to-human transmission is possible, historically this has not occurred. - -#### 5. **Future Considerations** -- From July 10, 2024, to September 30, 2024, there are about 11 weeks remaining. - -#### 6. **Potential for New Developments** -- **Mutation or New Information**: It is possible but statistically low for a significant mutation enabling human-to-human transmission to both occur and be confirmed by CDC within this window. - -#### 7. **Statistical Analysis and Probability Determination** -- **Base Rate Comparison**: No human-to-human transmission in several decades means the base rate is extremely low. -- **Time Horizon**: A short period (11 weeks) before the resolution date makes significant deviations from the base rate unlikely. - -### Final Probability Calculation - -Considering: -- **Historical Data**: Rare incidence (~890 cases since 1997), no sustained human-to-human transmission. -- **Risk Assessments**: Low from credible sources like WHO and CDC. -- **Time Frame**: 11 weeks, a very short window for significant epidemiological developments. - -### Rationale Examination - -High likelihood of continuation of the current trend considering the short timeline and the historical low incidence: -- Base rates strongly favor no reporting of human-to-human transmission. -- No current trend or recent mutation suggesting an imminent change. - -### Conclusion: - -**Probability of CDC Reporting Human-to-Human Spread of H5N1 Globally Before Sept 30, 2024**: The rigorously analyzed base rates and lack of recent alarming trends support a low probability. - -**Final Answer**: -""Probability: 10%""" -26097,"On July 21, 2024, will the closing value of BSE SENSEX be greater than 76k and less than or equal to 77k?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **Current Market Situation**: As of July 12, 2024, the S&P BSE Sensex closed at 80,519.34, a record high, with a 0.78% increase. The NSE Nifty 50 also reached a record high, closing at 24,502.15 with a 0.77% gain. -- **Recent Trends**: The Indian benchmarks have been on a six-week rally, driven by policy continuity after national elections, strong economic growth prospects, and record-high inflows from mutual funds and foreign investors. The Nifty has surged around 9% since early June, outperforming other emerging markets and major stock markets. -- **Expert Opinions**: A Reuters poll of 28 equity analysts in February 2024 forecast the Sensex index to reach a high of 76,000 by the end of June, with a 9% increase expected for the year. This suggests that the current rally is in line with expectations. -- **Upcoming Events**: The national budget on July 23, 2024, is expected to be a key event, particularly for sectors like infrastructure, capital goods, manufacturing, and consumption. -- **Metaculus Prediction Market**: There is a Metaculus question asking if the closing value of BSE SENSEX will be greater than 75k and less than or equal to 76k on July 21, 2024. The question is currently awaiting approval by Community Moderators. -- **Historical Data**: The BSE provides historical data on the Sensex index, which can be accessed for further analysis. - -Overall, the current market trend is positive, and the upcoming budget may have an impact on the Sensex's performance. However, there are no recent updates that would indicate a specific movement towards a particular resolution. - -######### - -GPT - -### Initial Analysis and Assumptions - -1. **Current Market Status:** As of July 12, 2024, the SENSEX is at 80,519.34, which is significantly higher than the range in question (76,000 to 77,000). This suggests that for the closing value to fall within the desired range, the SENSEX would need to decline by approximately 5.5% to 6.5% over the next 9 days. - -2. **Recent Trends:** The SENSEX has been on a rally, primarily driven by positive sentiments around economic growth and political stability post-election. However, the market has shown volatility following the election, indicating some degree of uncertainty. - -3. **Expert Forecasts:** The Reuters poll from February suggested a high of 76,000 by the end of June, which was achieved and surpassed. This suggests the forecasts were somewhat conservative, meaning the current high level of the index might not have been entirely anticipated by analysts earlier in the year. - -4. **Upcoming Events:** The national budget on July 23 is a significant event, which could cause market movement due to economic policy announcements. Although this event comes after our target date (July 21), anticipation of the budget could influence market behavior prior to the announcement. - -5. **Historical Volatility:** Understanding the daily and weekly volatility of the SENSEX is crucial. Historically, the Indian stock market can experience significant swings, but a drop of around 5.5% to 6.5% in such a short period would be relatively rare unless driven by significant negative news. - -6. **Global and Local Economic Indicators:** Broader market trends, including global markets, commodities, interest rates, and geopolitical factors, will play roles in influencing the SENSEX’s movement. - -### Statistical Analysis - -Using historical data to understand the daily volatility: - -1. **Calculating Daily Volatility:** - - Standard deviation of daily returns over the past month and year can give insight into daily volatility. - -2. **Probability of a 5.5% - 6.5% Drop:** - - Assuming a normal distribution of returns (which is a simplification but useful for this analysis), we can calculate the Z-score for a 5.5% drop. - - \[ - Z = \frac{X - \mu}{\sigma} - \] - - Where \(X\) is the desired closing value range (-5.5% to -6.5%), \(\mu\) is the mean daily return, and \(\sigma\) is the standard deviation of daily returns. - -### Reasoned Probability Assessment - -1. **Current Position and Necessary Movement:** - - Current SENSEX: 80,519.34 - - Target Range: 76,000 to 77,000 (a reduction of approximately 4,500 points) - - Required daily drop over 9 days: approx. 500 points/day (near 0.6% daily drop required consistently). - -2. **Market Factors:** - - Political Stability: Although the BJP's narrow victory raises concerns, there is no immediate political turmoil. - - Economic Indicators: No major economic data suggesting a market collapse. - - Market Sentiment: Generally positive due to economic growth and strong inflows. - -Combining historical volatility analysis with the narrative context: - -- **Base Rate Analysis:** Historical precedent shows a 5.5-6.5% drop over 9 days would be unusual without a significant negative event. -- **Uncertainty Elements:** Upcoming budget and any unforeseen geopolitical developments could impact, but present known variables don't strongly support a move within the target range. - -### Conclusion - -After considering the above factors: - -- **High Current Value:** A 4,500 point drop would require sustained negative sentiment or a major negative event, neither of which are apparent based on the provided information. -- **Volatility Patterns:** Historical data suggests such a drop in a short span is statistically less probable without a major disturbance. - -Final Prediction: Given the improbability of a significant market drop in the next 9 days, I estimate: - -**Probability: 5%**" -26100,"Will Robert F. Kennedy Jr. suspend his 2024 presidential campaign before September 24, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether Robert F. Kennedy Jr. will suspend his 2024 presidential campaign before September 24, 2024, I have summarized the key points from the provided sources: - -1. **Campaign Timeline and Ballot Access**: - - Kennedy announced his candidacy on April 5, 2023, and withdrew from the Democratic primary on October 9, 2023, to run as an independent candidate. - - He has been actively working on gaining ballot access in various states. As of June 2024, he has achieved ballot access in several states, including Nebraska, Mississippi, Washington, Tennessee, Alaska, Minnesota, South Carolina, New York, New Jersey, and Florida. - -2. **Polling and Debate Qualification**: - - Kennedy has been trying to meet the polling criteria to qualify for the presidential debates. He has secured 16% support in two qualifying polls, but still needs two more polls to reach the required 15% in four polls. - - His campaign has been facing financial constraints, with a significant portion of funds allocated to ballot access initiatives and security services. - -3. **Campaign Challenges and Controversies**: - - Kennedy's campaign has faced criticism for potentially being a spoiler in the election, drawing attention away from major party nominees. - - Democrats are litigating in multiple states to prevent Kennedy from being listed on the ballot, alleging voter deception and procedural errors during the petitioning process. - -4. **Recent Developments and Strategies**: - - After failing to qualify for the debate, Kennedy's campaign announced counter programming, including a potential appearance at the Atlanta showdown between Trump and Biden. - - The campaign manager remains optimistic about the campaign's future outlook, citing an increase in donors and supporters despite a decline in financial resources. - -Given these updates, there is no indication that Kennedy plans to suspend his campaign before September 24, 2024. Instead, his campaign appears to be actively working on ballot access and exploring alternative strategies to engage with voters despite facing financial and polling challenges. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Background and Current Status** -- Robert F. Kennedy Jr. announced his independent presidential campaign after initially running as a Democratic candidate. -- As of now, Kennedy is polling at 9.5% and has raised over $98 million, which are strong indicators of a sustained campaign. -- He has faced criticism and legal challenges, but his team seems optimistic and active in terms of strategy and ballot access. - -#### 2. **Timeline and Critical Events Leading Up to September 24, 2024** -- There are approximately 2.5 months (75 days) until September 24, 2024. -- Key events such as ballot access, debate qualifications, strategic announcements, and legal battles could influence the campaign's trajectory within this timeframe. - -#### 3. **Analysis of the Factors** -- **Polling and Financials**: Despite challenges, Kennedy’s polling at 9.5% is unusually high for an independent candidate, and he has substantial financial resources. - - **Polling**: If he qualifies for debates, his campaign might receive a significant boost. - - **Finances**: Funding constraints are mitigated by substantial donor support. -- **Legal Challenges**: Ongoing litigation efforts by Democrats to remove him from ballots could pose a significant roadblock. -- **Campaign Strategy**: Kennedy’s campaign remains focused on ballot access and creative engagement alternatives such as counter-programming to debates. - -#### 4. **Historical Precedents and Base Rates** -- Historically, third-party or independent candidates rarely drop out unless experiencing dire financial or legal troubles. -- Notable independent candidates like Ross Perot in 1992 faced setbacks but stayed in the race until election day, except for a temporary suspension earlier that year, which is unique and does not directly compare to Kennedy’s situation. - -#### 5. **Evaluating Potential Future Scenarios** -- **Scenario A: Kennedy Suspends Campaign** - - This would likely be driven by insurmountable legal challenges, a significant and unexpected financial crisis, or a strategic decision to play a different role in the election. - - **Probability**: Lower. The current activities and support indicate that the campaign is prepared to navigate near-term challenges. - -- **Scenario B: Kennedy Continues Campaign** - - Based on current polling, fundraising, and active campaign strategies, the campaign is likely to continue barring dramatic, unforeseen changes. - - **Probability**: Higher. The campaign shows resilience and determination to remain in contention. - -#### 6. **Probability Calculation** -- Given the resilience of the campaign, its unusual polling numbers, substantial fundraising, and continuous efforts to establish ballot access despite significant challenges: - - **Base Rate**: Historical third-party candidates rarely suspended campaigns in similar situations. - - **Adjustments for Novelty**: Kennedy's campaign has shown strength and resilience uncommon for independent candidates, despite legal and financial pressures. - -Combining these insights and considering variance, my assessment leans heavily towards Kennedy not suspending his campaign. - -### Conclusion -Given all analyzed factors, I conclude with a calculated probability of Robert F. Kennedy Jr. suspending his 2024 presidential campaign before September 24, 2024, as: - -**Probability: 15%** - -This reflects low likelihood, yet accounts for some uncertainty due to potential unforeseeable events or pressures." -26098,"On July 21, 2024, will the closing value of BSE SENSEX be greater than 75k and less than or equal to 77k?","PERPLEXITY - -The BSE SENSEX, a key index of the Indian stock market, has been trending upward recently. Here are the key points to consider for forecasting its future value: - -1. **Recent Performance**: The BSE SENSEX has continued its upward trajectory, closing above the significant 80,000 mark on July 4, 2024. - -2. **Historical Data**: Historical data for the BSE SENSEX is available on Yahoo Finance and the BSE website, providing detailed information on past performance. - -3. **Market Updates**: Recent market updates highlight significant movements in various stocks, such as Lloyds Metals and Energy, HCL Technologies, and TCS, which could impact the overall index. - -4. **Prediction Markets**: On Metaculus, a prediction market question is open for the closing value of the BSE SENSEX on July 21, 2024, to be greater than 75,000 and less than or equal to 77,000. - -These factors provide a comprehensive context for forecasting the future value of the BSE SENSEX. - -######### - -GPT - -To arrive at a probabilistic forecast for whether the closing value of the BSE SENSEX on July 21, 2024, will be greater than 75,000 and less than or equal to 77,000, we need to systematically consider the available information and relevant factors. Here's a step-by-step evaluation: - -### 1. Recent Performance and Current Value -- **Current Value:** As of July 4, 2024, the BSE SENSEX closed above the significant 80,000 mark. -- **Recent Trend:** The index has been trending upward recently, despite significant volatility post-election. - -### 2. Volatility and Daily Movements -- **Historical Volatility:** The SENSEX has experienced significant volatility post-election, with swings such as a 6% drop followed by a rebound. -- **Standard Deviation:** Given recent volatility, it's reasonable to estimate daily movements. For the sake of this prediction, let's assume a standard deviation of about 1.5% per day based on recent data and typical market behavior during volatile periods. - -### 3. Time Frame -- **Time to Resolution:** There are 9 days between today (July 12, 2024) and the resolution date (July 21, 2024). - -### 4. Calculation of Probable Range -Given a current value slightly above 80,000 and assuming daily movements follow a normal distribution centered around an upward trend: -- **Expected Daily Drift:** Historically, stock indices might move upward by ~0.1-0.2% per day in a bullish trend. -- **Cumulative Change:** Over 9 days, this could mean an approximation of around (0.15% * 9) = 1.35% upward drift. -- **Range Calculation:** Therefore, we need to calculate the probability of the SENSEX falling within [75,000, 77,000] given an initial value of ~80,000 + expected upward drift, minus typical volatility. - -### 5. Base Rates and Historical Performance -- **Base Rates:** Examining past data, a ~4% drop from a bullish streak is somewhat rare but not impossible given political and economic uncertainties. -- **Scenarios:** Calculate probabilities over diverse scenarios: - - Given a mean of ~81,080 (1.35% rise over 80,000), - - Use the volatility to derive plus-minus 15% movement scenario: (\[\]has bounded SENSEX would fall to around 70,000-90,000 over the period). - -### 6. Normal Distribution Approximation -Using these mean (81,080) and standard deviation assumptions, we use the normal distribution to find the likelihood of the SENSEX falling between 75,000 and 77,000. -- **Z-score Calculation:** - - **Lower bound (75,000):** Z = (75,000 - 81,080) / (1.5% * 81,080 * sqrt(9)) ≈ -4.07. - - **Upper bound (77,000):** Z = (77,000 - 81,080) / (1.5% * 81,080 * sqrt(9)) ≈ -2.90. - -### 7. Find the Probability -Consult Z-scores to use standard normal tables: -- **Z = -4.07** corresponds to a very low probability. -- **Z = -2.90** similarly corresponds to a low probability. -The probability for a Z-score from -4 and -3 roughly marks area under the curve in tails (closer to 0-0.5%). - -### 8. Expert Adjustment -Given the significant uncertainties from political factors and market reactions, slightly erring on conservative highly conservative. - -### Final Calculation and Output -Synthesizing all these data points, overall probability: - -**Probability: 3%** - -This takes into account the steep conditions for the SENSEX to lie precisely within the defined range amid high expected variability and upward momentum trends." -26095,Will Individual Neutral Athletes Win ≥15 Gold Medals at the Paris 2024 Olympics?,"PERPLEXITY - -To assist the superforecaster in answering the question about whether Individual Neutral Athletes (AINs) will win at least 15 gold medals at the Paris 2024 Olympics, I have compiled the following information: - -### Key Points: -1. **Eligibility Conditions**: The International Olympic Committee (IOC) has established strict eligibility conditions for AINs, including that they must not actively support the war, must not be contracted to the Russian or Belarusian military or national security agencies, and must meet all anti-doping requirements. -2. **Number of AINs**: As of March 2024, there are 12 AINs with a Russian passport and 7 AINs with a Belarusian passport who have qualified for Paris 2024. The IOC projects that under the most likely scenario, 36 AINs with a Russian passport and 22 AINs with a Belarusian passport will qualify for the Olympic Games Paris 2024. -3. **Individual Neutral Athlete Eligibility Review Panel (AINERP)**: The IOC has established the AINERP to evaluate the eligibility of each athlete with a Russian or Belarusian passport who obtains, or who could obtain, a qualification place for the Olympic Games Paris 2024, and that of their support personnel. -4. **Invitation and Participation**: The IOC will publish the list of AINs and their support personnel who have been invited to participate in the Olympic Games Paris 2024. Athletes must accept the individual invitation and sign the Conditions of Participation applicable to all participants. -5. **Current Invitations**: As of June 28, 2024, several AINs have been declared eligible and invited to participate in the Olympic Games Paris 2024, including athletes in cycling, rowing, shooting, and tennis. - -### Recent Updates: -- The IOC has reaffirmed its commitment to allowing AINs to compete under strict conditions, despite the ongoing conflict between Russia and Ukraine. -- The Russian Olympic Committee was suspended in October 2023, but this has not changed the IOC's stance on allowing AINs to participate. - -### Expert Opinions: -- Daniil Medvedev, a Russian tennis player, has expressed his willingness to participate as a neutral athlete, stating that he wants to represent what he can represent and that he knows who he is and why he plays tennis. - -### Current Values: -- There are currently 12 AINs with a Russian passport and 7 AINs with a Belarusian passport who have qualified for Paris 2024. -- The IOC projects that under the most likely scenario, 36 AINs with a Russian passport and 22 AINs with a Belarusian passport will qualify for the Olympic Games Paris 2024. - -### Prediction Markets: -- Metaculus has a question asking whether Individual Neutral Athletes will win ≥15 gold medals at the Paris 2024 Olympics, but it is currently awaiting review by Community Moderators. - -Overall, the IOC has established strict eligibility conditions for AINs and has set up a review panel to ensure compliance. While there are ongoing debates about the participation of Russian and Belarusian athletes, the IOC has reaffirmed its commitment to allowing AINs to compete under these conditions. - -######### - -GPT - -### Step-by-Step Evaluation Process - -#### 1. **Understanding the Question and Key Criteria** - - **Resolution Criteria**: The market resolves to ""Yes"" if Individual Neutral Athletes (AINs) win 15 or more gold medals at the Paris 2024 Olympics. - - **Current Status**: As of today, July 12, 2024. - - **Historical Data**: Russian Federation has averaged 19 gold medals in the last 3 Olympic Games, Belarus has averaged 1.7. - -#### 2. **Current Data Points** - - **Qualified Athletes**: 12 AINs with a Russian passport and 7 AINs with a Belarusian passport have already qualified. Projected numbers are 36 Russians and 22 Belarusians. - - **Expert Opinions and Athlete Statements**: Commitment from notable athletes. - -#### 3. **Eligibility Conditions and Potential Impacts** - - **Strict Eligibility Conditions**: Must not support the war, not contracted to military/security agencies, comply with anti-doping. - - **AINERP Review**: The review panel’s rigorous process might eliminate some potential competitors. - -#### 4. **Historical and Projected Medal Wins** - - **Base Rate Analysis**: - - Average 20.7 gold medals combined from Russia (19) and Belarus (1.7). - - Considering 7 to 8 fewer Russian athletes (current 12 out of likely 36) and 15 fewer Belarusian athletes (current 7 out of likely 22), there might be a reduction in total awarded medals. - -#### 5. **Comparison with Previous Olympics** - - **Historical Constraints**: Previous neutral participations (e.g., ROC in Tokyo 2020) still yielded competitive outcomes. ROC athletes had significant achievements despite neutral status. - - **Olympic Performance Consistency**: Predicting based on individual athletic performance consistency is key. - -#### 6. **Evaluation of the Constraints** - - **Stringent Eligibility**: Potential disqualifications mean not all who qualify may compete. - - **Current Invitations and Sports**: Competitive sports with high medal probabilities (cycling, rowing, shooting, and tennis) have participants. - -#### 7. **Probability and Uncertainty Evaluation** - - **In-Game Adaptations**: Athletes having already accepted invitations likely adapt to being neutrals. - - **Uncertainty Estimates**: - - Slightly fewer gold medals from a reduced pool of eligible athletes. - - Historical consistency in medal winning rates even under neutral status. - -### Statistical Estimation - - **Reduced Athlete Pool**: From a usual ~300 athletes to around 58 (projected AINs). - - **Medal Reduction Estimate**: Average drop in medals due to lower numbers and stringent criteria, estimating —15-30% reduction. - - **Gold Medal Estimate**: From previously averaged 20.7, reducing 20% leaves approximately 16.6. - -### Final Probability Determination - - **High Confidence Interval** with side of caution for strict reviews still pending and geopolitical fluidity: - - **Estimated Range of Outcome with Uncertainty Considered**: Due to 16.6 still aligning closely to 15, considering the finest conditions and challenges. - -#### **Conclusion** - - Based on thorough analysis including athlete counts, eligibility expectations, stricter scrutiny, historical consistency, and the current numbers in high-performance sports, the probability factors considered provide a high likelihood, yet under slight cautious consideration. - -**Final Probability Outcome**: - - **Probability**: **70%** - -Be prepared to adjust as newer data surfaces, impending reviews are clearer, and IOC sports listings update closer to the event opening." -26101,"Will TikTok US be sold to a US entity before Sept 30, 2024?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **Legislation and Timeline**: President Joe Biden signed legislation on April 25, 2024, that would ban TikTok in the United States unless its Chinese parent, ByteDance, sells the app. The law gives ByteDance up to a year to separate from TikTok. -- **Potential Buyers**: Analysts suggest that companies like Microsoft, Oracle, Intel, Cisco, Verizon, or AT&T could be viable options due to their ability to navigate antitrust regulations and potential opposition from China. Former Trump Treasury Secretary Steven Mnuchin has also announced plans to assemble a team of investors to buy TikTok, excluding its powerful content algorithm to circumvent Chinese government export restrictions. -- **Valuation**: The most valuable aspect of TikTok is its algorithm, which could make the platform worth $100 billion if sold with it. Without the algorithm, TikTok would have a valuation of $30 billion to $40 billion. -- **Legal Challenges**: TikTok has initiated legal action against the federal government, a case expected to reach the Supreme Court, following the enactment of the legislation. -- **Recent Updates**: There have been no recent updates indicating significant movement towards a sale. The legal battle and regulatory hurdles remain significant challenges for any potential buyer. - -**Current Predictions**: -- **Metaculus**: No relevant prediction markets are currently available on Metaculus. - -**Expert Opinions**: -- **Gene Kimmelman**: Companies like Intel, Cisco, Oracle, Verizon, or AT&T could be more viable options due to their ability to navigate antitrust regulations and potential opposition from China. -- **Jasmine Enberg**: Any potential buyer needs deep pockets and a strong stomach to navigate the regulatory hurdles. -- **Daniel Ives**: Microsoft and Oracle are the most likely bidders, as they could leverage TikTok's algorithm to expand their presence in social media. - -######### - -GPT - -Certainly, let's step through the relevant information, evaluate the context, and derive a probability estimate for whether TikTok US will be sold to a US entity before September 30, 2024. - -### Key Considerations: - -1. **Legislation and Timeline**: - - The legislation signed by President Biden on April 25, 2024, gives ByteDance up to a year to divest from TikTok. This would set a deadline by April 25, 2025. Our resolution date of September 30, 2024, falls within this period. - -2. **Potential Buyers**: - - Analysts have suggested several potential buyers like Microsoft and Oracle, due to their capability to navigate regulatory hurdles and their interests in expanding within the social media space. - -3. **Valuation and Components**: - - The algorithm is a critical component, with a significant difference in valuation based on whether this is included in a sale. This complexity might delay the sale process. - -4. **Legal Challenges**: - - TikTok has initiated legal action against the federal government, with the case expected to potentially reach the Supreme Court. Such legal challenges can be extremely time-consuming and could delay the sale. - -5. **Recent Updates**: - - There have been no significant movements towards the sale recently, indicating that legal challenges and other regulatory hurdles are significant obstacles. - -### Analysis: - -1. **Historical Precedent**: - - The attempt to sell TikTok US to Oracle in 2020-2021 under similar pressure from the Trump administration did not go through, indicating a precedent of difficulties in executing such sales under governmental pressure. - -2. **Regulatory and Legal Hurdles**: - - Legal battles, especially those reaching the Supreme Court, are seldom resolved quickly. Given the current legal situation starting post-legislation in April 2024, a resolution by September 30, 2024, is quite tight. - -3. **Market Dynamics and Potential Buyers’ Readiness**: - - Potential buyers may be hesitant to commit to a deal when the legal status is uncertain. Additionally, setting up a purchase involving such valuable technology would be complex and time-consuming. - -4. **Time Frame Constraints**: - - As of today, July 12, 2024, the available time until September 30, 2024, is less than three months. Given typical timelines for resolving such intricate matters legally and commercially, this is a short window. - -### Statistical Considerations: - -- **Base Rates**: - - Sales of this magnitude and complexity involving regulatory approval and geopolitical sensitivities generally take a long time. - - Previous failed sale attempts and current legal challenges provide additional resistance to a quick resolution. - -### Probability Calculation: - -Given the factors above: - -1. **Legal and Regulatory Complexity**: - - High probability of delay due to litigation: ~50% - - High stakes and valuation matters slowing down the negotiation process: ~30% - -2. **Time Constraints**: - - With less than three months to resolve: reduction of successful sale probability by time constraints: ~20% - -Summing up these factors: -- Probability that complexities will delay the sale beyond September 30, 2024: ~80-90% -- Adjusting for the remaining probability of an expedited or surprising quick resolution: ~10-20% - -### Final Probability Estimate: -After careful consideration, weighing the various complexities, legal hurdles, time constraints, and historical precedents, the probability that TikTok US will be sold to a US entity before September 30, 2024, is calculated as: - -**Probability: 15%**" -26102,Will astronauts Suni Williams and Butch Wilmore be on Earth on August 15?,"PERPLEXITY - -Here is a detailed rundown of the news relevant to the question: - -- **Current Status**: Astronauts Suni Williams and Butch Wilmore are currently stranded on the International Space Station (ISS) due to issues with the Boeing Starliner spacecraft's thrusters. They were initially scheduled to return after an eight-day mission but have been extended indefinitely. -- **Thruster Issues**: The Starliner spacecraft experienced problems with its reaction control system (RCS) thrusters, which either turned off or did not fire at intended strength. This has led to concerns about the spacecraft's ability to safely return the astronauts to Earth. -- **Confidence in Starliner**: Despite the issues, both Wilmore and Williams have expressed confidence in the spacecraft's ability to bring them back safely. They have been working with mission crews to collect data and optimize the situation for a successful return journey. -- **NASA's Plans**: NASA is currently predicting that the astronauts will not return until at least August. The agency is working to resolve the technical glitches and conducting thorough assessments to ensure a safe undocking and re-entry process. -- **Contingency Plans**: NASA has not ruled out the possibility of using a SpaceX Crew Dragon capsule to retrieve the astronauts if the Starliner's issues persist. -- **Recent Updates**: The astronauts have been providing updates from the ISS, including a recent Earth-to-space call where they discussed their mission and the ongoing tests to address the thruster issues. - -There are no recent updates indicating a significant movement towards a particular resolution. However, NASA and Boeing are actively working to resolve the technical issues, and the astronauts remain confident in the spacecraft's capabilities. - -Current values relevant to this question include: - -- **Predicted Return Date**: At least August. -- **Current Location**: International Space Station. - -Forecasting sites like Metaculus do not have a specific prediction market for this exact question. However, they may have related markets or discussions that could provide additional context. - -######### - -GPT - -### Analysis - -#### 1. **Current Status and Problematics** -- **Current Location**: Williams and Wilmore are still on the ISS. -- **Issues with Starliner**: Thruster problems and helium leaks have delayed their return ""indefinitely."" -- **Power and Fuel**: There is limited fuel on the ISS, providing a 45-day operational window to return as of June 24, 2024. - -#### 2. **Duration and Timeline** -- **Past Attempt Schedule**: Initially set for June 13, 2024. -- **Delay Information**: - - June 24 Report: 45-day window due to limited ISS fuel, results in an initial critical window to return by approximately August 8, 2024 (end of the 45 days). - - Current Date: July 12, 2024, provides approximately 33 days to resolve in the worst-case scenario. - -#### 3. **Likelihood of Resolution** -- **NASA’s Effort and Contingency**: NASA is actively working to resolve the issues with Boeing, and has not ruled out using a SpaceX Crew Dragon capsule as an alternative. -- **Confidence from Astronauts**: Both astronauts have expressed confidence in Starliner's ability to bring them back safely which potentially indicates some corrective measures are already underway or steps are likely to be successful soon. -- **Downtime and Operational Window**: Even if Boeing is unable to fix Starliner, NASA planning to potentially use Crew Dragon means that they have a proactive backup plan, although the implementation and decision process could still take time. - -#### 4. **Base Rates and Historical Context** -- **Historical Precedents**: There have been incidents of spacecraft encountering issues, yet the engineering teams usually manage to resolve the problems within weeks to months. However, ""indefinite"" delays tend to be indicative of more severe or complex issues. -- **Novel Technological Factors**: The ongoing collaboration with private entities (Boeing and SpaceX) means diversified solutions can be pursued, potentially accelerating resolution but also adding complexity. - -#### 5. **Critical Considerations and Influences** -- **Political and Institutional Pressures**: Apart from operational constraints, the reputational imperative on NASA and Boeing (following this public delay) accentuates the drive to resolve the issue promptly. -- **Environmental Factors**: Currently, no significant external conditions suggest higher-than-normal risks interfering with re-entry or retrieval missions within the analyzed window. - -### Conclusion and Probability Assignment -Given these considerations: -- Optimistically relying on Boeing/NASA’s capacity to resolve issues and/or alternatives (termed as ""confidence from astronauts"" and NASA’s backup with SpaceX). -- Factoring in that the estimated timeline by NASA to resolve is placed realistically beyond August. - -At a more granular level, the probability distribution is establishing itself looking at operational constraints within a condensed timeframe of the remaining 33 days until resolution criteria date (August 15). - -- Weighing solving issues in best-case scenario in the time left and historical patterns. -- Worst-case attention to indefinite resolution timeline and technical issue complexity. - -Given these multi-factorial dynamics, I estimate a grounded probability reflecting both optimistic resolve as well as the complexity of the delay at this tier. - -Final Answer: - -**Probability: 70%** - -This indicates a significant but not absolute confidence leaning for both astronauts to be back on Earth by August 15, given concurrent projected solution pathways and active resolution efforts." -26096,"On July 21, 2024, will the closing value of BSE SENSEX be greater than 75k and less than or equal to 76k?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **Current Market Performance**: The BSE SENSEX closed at 80,519.34 on July 12, 2024, with a gain of 622.00 points or 0.78%. This marks the sixth straight week of gains, with the Nifty 50 surging around 9% since early June. - -- **IT Sector Rally**: IT stocks, led by Tata Consultancy Services (TCS), have joined the rally after beating quarterly revenue expectations, signaling a revival in demand. This has boosted hopes for a U.S. rate cut in September. - -- **National Budget**: The upcoming national budget on July 23 is expected to be significant for capex-linked infrastructure, capital goods, manufacturing, and consumption-linked sectors. - -- **Dividend Dates**: Several companies have announced dividend dates, including Alembic Pharmaceuticals, Duncan Engineering, Gujarat Themis Biosyn Ltd., K.P.R. Mill Limited, Ndr Auto Components, Rane Brake Lining, and Siyaram Silk Mills. - -- **Global Market Context**: The S&P 500 has regained momentum, driven by robust first-quarter earnings reports and expectations of lower inflation. Analysts anticipate 9.3% earnings growth in the second quarter and 11.4% full-year earnings growth in 2024. - -- **Metaculus Prediction Market**: There is no current prediction market on Metaculus for the specific question of the BSE SENSEX closing value on July 21, 2024, but the platform is awaiting approval for a related question. - -- **Historical Data**: The BSE SENSEX historical data is available on the BSE website, allowing for analysis of past trends and performance. - -These updates provide context for the Indian stock market's recent performance and the factors influencing it, which can help inform a forecast about the BSE SENSEX closing value on July 21, 2024. - -######### - -GPT - -To forecast whether the BSE SENSEX will close between 75,000 and 76,000 on July 21, 2024, we need to carefully analyze the relevant data and trends, considering the potential range of outcomes and associated probabilities. - -### Step-by-Step Analysis: - -#### 1. **Current Market Performance:** - - The BSE SENSEX closed at **80,519.34** on July 12, 2024. - - Market trends have been positive with six straight weeks of gains and a 9% increase in the Nifty 50 since early June. - -#### 2. **IT Sector and Global Context:** - - IT stocks, pivotal to the SENSEX, have shown robust performance, influenced by Tata Consultancy Services beating revenue expectations. - - Positive expectations for a U.S. rate cut could stimulate international investment in Indian equities. - - The S&P 500 has also been gaining, driven by strong earnings and low inflation expectations, supporting a bullish sentiment in global markets. - -#### 3. **Budget and Domestic Factors:** - - The upcoming national budget on July 23 might introduce policies favoring infrastructure, manufacturing, and consumption sectors, potentially affecting market sentiment. - - Dividend announcements from various companies could influence certain stock prices, yet effects might be limited to specific sectors. - -#### 4. **Volatility and Historical Trends:** - - Given recent political uncertainties post-election and market volatility, the SENSEX has already experienced significant fluctuations. - - Historical data indicates that the SENSEX can exhibit wide ranges in response to economic and political events. - -### Statistical Considerations: - -- **Daily Volatility**: We need to consider the standard deviation of daily market movements. Historical data suggests the SENSEX can move within a range of approximately 2-3% in volatile periods. -- **Timeframe**: With 9 days remaining, we need to assess the likelihood of the SENSEX dropping to the specified range: - - Current Value: 80,519.34. - - Target Range: 75,000 to 76,000. - - Required Drop: Approximately 5,000 to 6,000 points. - - Percentage Change: 5,000 / 80,500 ≈ 6.21% and 6,000 / 80,500 ≈ 7.45%. - -Considering the general market sentiment, economic data, and political context, a significant drop of over 6% within a week is considerable but not impossible. However, such a drop within 9 days would typically require major negative economic news, political instability, or global market shifts. - -#### Base Rates and Novel Factors: - -- Historically, such a substantial drop in a short period is unusual without a triggering event. -- Key dates to watch are the announcement of the national budget on July 23, which is after our target date, potentially mitigating large downward adjustments before then. - -### Conclusion: - -Given the positive momentum, the anticipated national budget, and the absence of immediate negatively significant events, the likelihood of such a rapid drop seems moderate but not negligible. - -**Final Probability Assessment:** - -- Likelihood of SENSEX being above 76,000 or below 75,000: High. -- Likelihood of SENSEX closing between 75,000 and 76,000 on July 21, 2024: Low to moderate but considering uncertainty. - -**Probability: 15%** - -This accounts for the potential for high volatility while recognizing the generally positive current market sentiment and shorter timeframe." -26099,"Will a new nuclear-armed state emerge before Sept 30, 2024?","PERPLEXITY - -To provide a concise and detailed rundown of the news relevant to the question of whether a new nuclear-armed state will emerge before September 30, 2024, I have summarized the key points from the provided sources: - -1. **New START Treaty and Nuclear Disarmament**: - - The New START Treaty is set to expire in February 2026, and Russia has paused inspections and stopped providing data on its strategic nuclear forces since 2022. - - The treaty's expiration could lead to unregulated nuclear competition between the United States and Russia. - -2. **Belarus and Russian Nuclear Weapons**: - - Belarus has adopted a new military doctrine that includes the use of nuclear weapons for preventive deterrence against potential adversaries. - - The doctrine is expected to be approved by the All-Belarusian People’s Assembly in April 2024. - -3. **Global Nuclear Forces and Modernization**: - - The world's combined nuclear warhead inventory remains high, with nine countries possessing approximately 12,121 warheads as of early 2024. - - Nuclear-armed states are modernizing their forces, and the pace of reduction has slowed significantly compared to the 1990s. - - The United States has delivered modernized nuclear weapons to the Department of Defense and is discussing additional modernization programs. - -4. **Nuclear Planning and Exercises**: - - The United States has a strategic nuclear war plan, OPLAN 8010–12, which includes response options to de-escalate conflicts and emphasizes escalation control. - -5. **Nuclear Arms Control and Geopolitical Competition**: - - The demise of traditional nuclear arms control agreements has led to a return to unregulated nuclear competition, with Russia and China pursuing ambitious nuclear modernization campaigns. - - China is expected to increase its strategic nuclear arsenal to 1,000 warheads by the end of the decade, complicating strategic calculations for the United States and Russia. - -6. **Recent Updates and Forecasts**: - - There are no recent updates indicating the emergence of a new nuclear-armed state before September 30, 2024. - - The current geopolitical situation and the pace of nuclear modernization suggest that the risk of nuclear annihilation remains high, but there is no specific indication of a new nuclear-armed state emerging in the near future. - -In summary, while the global nuclear landscape is marked by modernization and competition, there is no concrete evidence suggesting the emergence of a new nuclear-armed state before September 30, 2024. - -######### - -GPT - -### Chain of Thought Rationale - -#### Step 1: Assessing Historical Base Rates -Historically, the emergence of new nuclear-armed states is a rare event. Since the mid-20th century, we have seen a slow increment in the number of nuclear-armed states, with approximately one new nuclear-armed state emerging every decade or so. However, this rate has slowed considerably due to international pressures and nonproliferation efforts. - -#### Step 2: Considering Current Geopolitical Conditions -The situation described includes heightened nuclear modernization and disarmament complexities. While there are heightened tensions, these do not inherently lead to the immediate emergence of new nuclear states: -- **Belarus:** While it is adopting a doctrine that includes nuclear deterrence, it mainly refers to the use of Russian nuclear weapons stationed on its territory. There is no indication that Belarus itself is developing and testing its independent nuclear arsenal. -- **China and Russia:** These countries are focused on modernizing their existing arsenals rather than new states developing new arsenals. - -#### Step 3: Evaluating Recent Developments -The recent updates and news indicate ongoing modernization and strategic developments among existing nuclear states. No news suggests imminent nuclear tests from non-nuclear states. Therefore, there is no direct evidence or recent development pointing to a newer nuclear-armed state emerging soon. - -#### Step 4: Considering Timeframe and Likelihood -The resolution period ends on September 30, 2024, meaning there are approximately 2.5 months left. The development and testing of a nuclear weapon from start to announcement or verified test would require significant time, often years. Given that there hasn't been significant progress reported in any state towards nuclear capability, the timeframe seems short for such a radical change. - -#### Step 5: Scanning for Novel Factors -- **Technological advancements:** While there have been advancements, they generally pertain to the modernization of existing arsenals rather than enabling new states to quickly develop and deploy nuclear weapons. -- **Political factors:** Many non-nuclear states remain under international scrutiny and pressure from nonproliferation treaties and global powers which diminishes the likelihood of a sudden development. - -### Statistical Analysis -Using Bayesian updating, we start with the low base rate of new nuclear-armed states. Given no new specific credible changes recently, the posterior probability will remain low. - -**Base Rate of New Nuclear-Armed States:** -\[ P(\text{new nuclear-armed state} | \text{historical data}) \approx \frac{1}{10} \text{ (decades)} = 0.1 = 10\% \] - -**Recent Increase in Geopolitical Tension Impact (assuming minor effect):** -\[ P(\text{new nuclear state} | \text{current geo state}) \approx 10\% \times 0.9 = 9\% \] - -Considering no strong steps toward nuclear weapon development in recent history and the short time frame left: -\[ P(\text{new nuclear state} | \text{few months left}) \approx 9\% \times 0.1 = 0.9\% \] - -### Final Assessment -Based on the rigorously low base rates, the lack of substantial recent evidence or announcements, the complexity and timeframe of developing nuclear weapons, and the weight of international nonproliferation mechanisms, the probability remains very low. - -**Probability: 1%**" -26133,"Will Benjamin Netanyahu remain Prime Minister of Israel through Sept 30, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether Benjamin Netanyahu will remain Prime Minister of Israel through September 30, 2024, I have summarized the following sources: - -1. **Netanyahu's Political Resilience**: - - Despite facing challenges and criticism, Netanyahu has consistently demonstrated his ability to maintain power and authority, particularly during times of conflict. - - His political career has been declared over multiple times, but he has consistently bounced back, and his party's support has recently climbed back to 21 seats, making him the preferred prime minister by about one-third of Israelis. - -2. **Upcoming Visit to Washington**: - - Netanyahu is scheduled to meet with US President Joe Biden in Washington, marking his first visit to the US capital in nearly four years. This meeting could be seen as a sign of improved relations between the two leaders. - -3. **Domestic Politics and Coalition Dynamics**: - - Recent polling suggests that parties on the right would control 58 mandates in the Knesset, slightly short of a majority, while the center and left would control 48 seats. This could lead to coalition complexities and potential challenges for Netanyahu. - - Netanyahu's leadership is not without controversy, with some parties on the right, like Avigdor Lieberman's Yisrael Beiteinu, potentially unwilling to cooperate with him. - -4. **War and Security Concerns**: - - The ongoing conflict with Hamas and the potential for a larger war in Lebanon have raised questions about Netanyahu's ability to effectively manage these crises. His recent offer of a two-month cease-fire in exchange for the release of hostages was rejected by Hamas. - - Despite criticism, Netanyahu remains committed to his vision for Israel's security, prioritizing the ongoing struggle against terrorist threats in Gaza. - -5. **Current Predictions and Forecasting Sites**: - - There are no specific predictions from forecasting sites like Metaculus on this exact question. However, the current political landscape and Netanyahu's historical ability to adapt and maintain power suggest that his chances of remaining Prime Minister through September 30, 2024, are significant. - -In summary, while Netanyahu faces challenges from various fronts, his political resilience and ability to navigate complex situations make it difficult to count him out. The upcoming visit to Washington and the evolving dynamics of domestic politics will be crucial in determining his chances of remaining Prime Minister through September 30, 2024. - -######### - -GPT - -### Evaluating the Information - -**1. Netanyahu's Political Resilience** -Netanyahu has survived numerous challenges in the past, displaying strong political resilience. This is a crucial factor to consider, as it suggests inherent strength in his political position even amidst significant turmoil. His current party's support being the preferred prime minister by about one-third of Israelis indicates a non-negligible base of support. - -**2. Upcoming Visit to Washington** -This visit shows an attempt to bolster international relations, particularly with the United States. An improvement in relations could reinforce his position, although it might not directly affect domestic political dynamics. However, it does portray Netanyahu as a globally relevant leader, which may improve his standing domestically. - -**3. Domestic Politics and Coalition Dynamics** -The coalition dynamics are complex. Right-wing parties control 58 mandates, short of a majority, while the center and left control 48 seats. This divided landscape suggests potential instability. Netanyahu needs to navigate these waters deftly to remain in power. The willingness of even right-wing parties like Yisrael Beiteinu to possibly avoid cooperation with him complicates his position. - -**4. War and Security Concerns** -The ongoing conflict with Hamas and potential threats from Lebanon are critical. Netanyahu’s response to these crises and public perception of his capability in handling security threats will significantly impact his political future. A failed security measure or ongoing criticism could undermine his support. However, wartime leaders often benefit from a rally-around-the-flag effect. - -**5. Current Predictions and Forecasting Sites** -There is no specific data from forecasting sites on this question. However, assessing Netanyahu’s historical ability to adapt and retain power coupled with the general political landscape, his chances seem significant but not overwhelming. - -### Evaluating the Time Frame -We have a time window from July 15, 2024, to September 30, 2024 – about 2.5 months. Short-term political shifts can be significant, and this brief window limits the extent of changes that might occur. - -### Base Rates and Historical Context -Historical context often points to political leaders under significant scrutiny or crisis having approximately a 30-40% chance of being ousted within a year. However, this can fluctuate based on the unique factors at play. - -### Variance and Recency -Considering recency, the escalation with Hamas and Netanyahu's handling thereof is the most salient factor. If he manages to portray a strong defense narrative successfully, it will buffer his political standing. The recency of his upcoming visit to the US might also provide a temporary bolster. - -### Synthesizing Probability -- **Resilience and Historical Context**: +20% -- **Upcoming US Visit**: +5% -- **Coalition Dynamics**: -20% -- **War and Security**: Neutral due to mixed potential impacts -- **Recency Effects**: Net positive due to current moves (US visit and active crisis management) - -Combining these factors, we weigh Netanyahu’s resilience and beneficial moves against the inherent instability of his coalition and ongoing security challenges. - -### Final Evaluation -Given the resilience shown by Netanyahu in the past, the short window until the question resolve date, and the lack of immediate strong reasons suggesting a clear path to his ouster, the probability remains moderately high but with uncertainty due to coalition dynamics and security handling. - -**Probability: 65%**" -26134,"Will SpaceX attempt to catch a Starship booster with the tower before Sept 30, 2024?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **Recent Developments:** - - SpaceX has successfully conducted a soft ocean landing of the Starship booster on Flight 4, which indicates progress towards a tower catch attempt. - - Elon Musk has expressed interest in attempting a catch on the next flight, Flight 5. - - A new test article, Booster 14.1, is being used to test the catch system, including the ""chopstick"" arms. - - Construction of a second orbital launch pad is underway at Starbase, which will support future launch and catch operations. - -- **Expert Opinions:** - - Many experts believe that SpaceX will attempt a catch soon, possibly on Flight 5, given the recent successes and ongoing preparations. - - Some experts think that a few more successful ocean landings will be needed before a tower catch attempt, while others believe that a single successful landing might be enough. - -- **Current Values:** - - No specific numbers are directly relevant to this question, but the progress of the Starship program and the recent successes are key indicators. - -- **Forecasting Sites:** - - Metaculus has a question on this topic, but it is still awaiting approval by Community Moderators. - -Overall, the recent successes and ongoing preparations suggest that SpaceX might attempt to catch a Starship booster with the tower before September 30, 2024. However, the exact timing will depend on the progress of the program and the company's confidence in the catch system. - -######### - -GPT - -### Step-by-Step Analysis - -**1. Evaluate Past Data and Trends** -- **Recent Developments:** - - Flight 4 successfully completed a soft ocean landing, which is a positive indicator of success in controlled landings. - - Musk’s interview in October 2023 suggested a tentative attempt toward the end of this year, implying confidence but also leaving room for delays. - - Flight 5 is on the horizon, with indications that a catch attempt might be considered. - -**2. Rate Importance and Recency of Information** -- **High Importance, High Recency:** - - Successfully completed soft ocean landing: This shows a tangible progression towards achieving a tower catch. - - Musk’s optimistic timeline: He holds key decision-making power, and his assessments often influence real-time corporate goals and actions. - - Currently ongoing preparations with experimental hardware: This demonstrates direct actionable steps towards preparing for a tower catch. - -**3. Consider Time Available and Variance** -- **Horizon of Prediction:** - - We are approximately 2.5 months away from the deadline (Sept 30, 2024). - - Within this timeframe, multiple developments and tests can occur, supporting or delaying this goal. - - Given SpaceX’s tight iteration cycles, a 2.5-month window allows for potentially one or two more flights, or extensive ground-based catch system testing. - -**4. Base Rates and Novel Factors** -- **Base Rates:** - - Historically, SpaceX has exhibited a pattern of rapid iteration and high tolerance for risk and failure in tests to expedite outcomes. -- **Novel Factors:** - - The novelty here is SpaceX’s intention to catch a booster with a tower, a procedure with no direct historical precedent in commercial spaceflight. - - Technological advances and experience from analogous rocket landings (Falcon 9) could skew the base rate towards higher success/practice iterations. - -**5. Default Resolution if Nothing Changes** -- **Status Quo Analysis:** - - If we assume no further significant changes or advancements, the hesitation from needing more successful ocean landings suggests the default state leans towards **No**. However, SpaceX's aggressive goals also mean rapid iteration and risk-taking could significantly shift towards a **Yes**. - -**6. Consideration of Expert Opinions and Language** -- **Expert Opinions:** - - While there is a split view on the necessity of additional successful landings, most experts lean towards an imminent trial on Flight 5, reflecting a substantial probability. -- **Language Analysis:** - - Musk’s comment construed more as hopeful rather than certain, indicates preparation but acknowledges typical uncertainties. - -### Probability Distribution and Uncertainty -- **Recent Achievements and Developments:** - - Boost the probability due to positive and recent technical success. -- **Iterative Nature and Historical Propensity:** - - Supports increased confidence, given SpaceX’s accelerated testing despite risks. -- **Uncertainty Reserve:** - - Retain caution due to the unpredictable nature of complex advancements and potential setbacks. - -### Final Calculation -Synthesizing the positive indications with the high degree of uncertainty typical for novel aerospace undertakings: -Considering the full range of influencing factors and developed analysis: -**My conclusion draws towards a probability emphasizing the recent progression but carefully accounts for the uncertainty of completion within the specified period.** - -### Final Answer: -**Probability: 65%**" -26159,"Between July 17 and July 28, 2024, will the strongest geomagnetic storm have a K-index (""Kp"") greater than 4 and less than or equal to 6?","PERPLEXITY - -Here is a concise rundown of the news most relevant to the question: - -1. **NOAA's 27-Day Space Weather Outlook Table**: - - This table provides a numerical forecast of three key solar-geophysical indices: the 10.7 cm solar radio flux, the planetary A index, and the largest daily K values. - -2. **Planetary K-index**: - - The K-index is used to characterize the magnitude of geomagnetic storms. It is derived from the maximum fluctuations of horizontal components observed on a magnetometer during a three-hour interval. The planetary 3-hour-range index Kp is the mean standardized K-index from 13 geomagnetic observatories. - - The K-index ranges from 0 to 9, with 1 being calm and 5 or more indicating a geomagnetic storm. Kp values greater than 7 indicate a large disturbance. - -3. **Geomagnetic Storm Forecasts**: - - The Geophysical Institute provides a daily forecast of geomagnetic activity for the next three days in 3-hour intervals. The current time interval is highlighted in blue. The forecast includes Kp values, with Kp < 4 indicating low activity and Kp = 4 indicating moderate activity. - -4. **Metaculus Prediction Market**: - - There is a Metaculus question asking whether the strongest geomagnetic storm between July 17 and July 28, 2024, will have a K-index greater than 4 and less than or equal to 6. The question is currently under review by Community Moderators. - -5. **Recent Updates**: - - The Geophysical Institute's aurora forecast shows Kp values ranging from 2 to 4 for the period from July 9 to July 27, 2024. There are no recent updates indicating movement towards a particular resolution. - -6. **Expert Opinions**: - - The K-index was introduced by Julius Bartels in 1938 and is used by the NOAA Space Weather Prediction Center to decide whether geomagnetic alerts and warnings need to be issued. - -7. **Current Values**: - - The current Kp index can be found on the NOAA Space Weather Prediction Center's website, which updates every minute. - -8. **Forecasting Sites**: - - The Metaculus prediction market is currently reviewing a question related to the strongest geomagnetic storm between July 17 and July 28, 2024. - -######### - -GPT - -### Step-by-Step Thought Process and Rationale: - -1. **Understanding the Question and Resolution Criteria**: - - The question asks if the strongest geomagnetic storm between July 17 and July 28, 2024, will have a K-index (Kp) greater than 4 and less than or equal to 6. - - The resolution criteria specify that the Kp value should be sourced from the German Research Center for Geosciences and be rounded to the nearest thousandth. - -2. **Current Background and Forecasts**: - - AR3697, the active sunspot region, has been producing intense geomagnetic storms previously. - - NOAA's 27-Day Space Weather Outlook provides an extended forecast that can help determine expected activity. - - Recent updates indicate Kp values ranging from 2 to 4 between July 9 to July 27, 2024, suggesting at least some moderate activity. - -3. **Base Rates and Historical Data**: - - Geomagnetic storms of Kp values greater than 4 but less than or equal to 6 occur with notable frequency when there are active sunspot regions. - - Historically, solar maximum periods see more frequent and intense geomagnetic storms. It's important to assess if 2024 is close to a solar maximum. - -4. **Current Trends and Variability**: - - The forecasts have shown potential for moderate geomagnetic activity (Kp=4) but no strong indications for extreme activity (Kp > 7). - - Given the forecast patterns, it’s likely there could be an uptick to Kp values in the 5-6 range, especially with the return of AR3697. - -5. **Expert Opinions and Forecasts**: - - The expert consensus and forecasts (like from NOAA and the Geophysical Institute) currently indicate moderate activity. - -6. **Temporal Considerations**: - - The timeframe from July 17 to July 28, 2024, is relatively short. This means any significant deviation from the current forecast trends would need to occur rapidly. - -7. **Statistical Analysis and Probability Distribution**: - - Considering the historical data, the likelihood of Kp values greater than 4 but less than or equal to 6 is appreciable but not guaranteed. - - Given current Kp forecasts and solar activity trends, there's a high probability of moderate geomagnetic storms. - -### Final Probability Estimation: - -Aggregating all the information, here’s the step-by-step conclusion: - -1. **Current Kp trends** suggest moderate geomagnetic activity: - - Kp forecast between 2 to 4 means there's some activity, and the probability of an uptick to 5-6 due to the influence of AR3697 is reasonably high but not certain. - -2. **Base Rate Analysis**: - - Moderate geomagnetic storms (Kp between 5 and 6) are a common occurrence when active sunspot regions are present. - -3. **Temporal Analysis**: - - With AR3697’s historical impact and continuous monitoring, a Kp of 5-6 seems plausible within the given timeframe but with inherent uncertainty. - -Considering all the intricacies, a balanced approach leads me to believe that the probability is not extremely high nor extremely low, given the background patterns and upcoming timelines. - -### Conclusion: - -**Probability: 70%** - -This estimation provides a middle-to-high chance, recognizing the active conditions yet the inherent variability in precise space weather predictions." -26138,"Will the Canadian Dollars to U.S. Dollar Spot Exchange Rate exceed 1.3600 on Friday September 20, 2024, according to FRED?","PERPLEXITY - -To assist in forecasting the Canadian Dollars to U.S. Dollar Spot Exchange Rate exceeding 1.3600 on Friday, September 20, 2024, here is a summary of relevant news and data: - -1. **Historical Exchange Rates**: - - The average exchange rate in 2024 is 0.7359 USD per CAD, with a worst exchange rate of 0.7235 USD on April 16, 2024. - - Recent exchange rates show a fluctuating trend, with rates ranging from 0.727193 on June 15, 2024, to 0.734757 on July 4, 2024. - -2. **Current Exchange Rate**: - - The current exchange rate is not explicitly stated in the provided sources, but it can be inferred from the recent historical rates mentioned above. - -3. **Forecasting Sites**: - - Metaculus has a question pending approval regarding the exchange rate exceeding 1.3600 on September 20, 2024, but no current predictions are available. - -4. **Expert Opinions**: - - No specific expert opinions are mentioned in the provided sources. - -5. **Recent Updates**: - - The exchange rate has been fluctuating in recent days, with a slight increase observed in early July. - -6. **Relevant Data Sources**: - - The Federal Reserve Economic Data (FRED) provides historical and current exchange rates, which are updated weekly. - -In summary, the exchange rate has been fluctuating recently, and there are no clear indications of a significant movement towards exceeding 1.3600 by September 20, 2024. However, the forecasting question on Metaculus is pending approval and may provide additional insights once it is active. - -######### - -GPT - -To forecast whether the Canadian Dollars to U.S. Dollar Spot Exchange Rate (DEXCAUS) will exceed 1.3600 on Friday, September 20, 2024, I will evaluate the following: - -### Key Points from the Given Data -1. **Historical Exchange Rates**: - - The average exchange rate in 2024 is 0.7359 USD per CAD. - - The worst exchange rate is 0.7235 on April 16, 2024. - - Recent exchange rates show fluctuation within approximately 1.3493 to 1.3824 CAD per USD. - -2. **Recent Trends**: - - As of July 5, 2024, the value was 1.3631. - - The past three months’ high was 1.3824, and the low was 1.3493. - -3. **Incomplete Data**: - - The exact current rate isn't provided, but can be inferred to be close to recent values (around 1.36 to 1.37). - -4. **Forecasting Sites and Expert Opinions**: - - There is no specific forecast on Metaculus yet. - - No expert opinions are directly influencing the forecast. - -### Critical Analysis -#### Historical Fluctuations -- The exchange rate has shown significant fluctuations over the past three months, with large swings within a range of approximately 1.3493 to 1.3824. -- Historical data suggests the rate has a tendency to both dip below and rise above 1.3600. - -#### Recent Observations -- As of the last observation on July 5, 2024, the exchange rate was at 1.3631, already above the threshold of 1.3600. -- While it is close to the threshold, the direction of future movements is uncertain given the volatility. - -#### Time Frame Consideration -- With about two months remaining until the target date of September 20, 2024, there is ample time for fluctuations to occur in either direction. -- Exchange rates can be heavily influenced by various factors such as economic data releases, central bank policies, geopolitical events, and market sentiment within this period. - -### Probabilistic Assessment -**Base Rate Estimation**: -- Given that the exchange rate has crossed 1.3600 multiple times in the recent past and is above it as of the last recorded date, the base rate probability of it staying above or crossing again is relatively high. - -**Volatility Consideration**: -- The high variance within a short time frame suggests a non-zero probability that the rate could fall below 1.3600 as well. - -**Political and Economic Factors**: -- Any significant economic announcements, both positive and negative, could impact the direction of the exchange rate. - -**Seasonal Trends**: -- There is no specific seasonal trend mentioned that would make a significant impact, but general market cycles should be considered. - -### Statistical and Intuitive Analysis -After evaluating the historical data, variance, and the time frame left until the resolution date, a nuanced probability distribution suggests an outcome that’s moderately likely given the current trajectory. - -- **Upside consideration**: The rate is currently above 1.3600, making it relatively more probable to exceed it again than to stay below. -- **Downside consideration**: High volatility and significant time remaining could push the rate below the threshold. - -### Final Prediction -Given the factors above, I aim to provide a balanced forecast that accounts for current data and analyzable trends while acknowledging inherent uncertainties. - -**Probability: 65%**" -26139,Will Kalenjin athletes win both the men's and women's marathon races at the 2024 Paris Olympics?,"PERPLEXITY - -To assist the superforecaster in answering the question about Kalenjin athletes winning both the men's and women's marathon races at the 2024 Paris Olympics, here is a summary of the relevant news: - -1. **Paris 2024 Marathon Pour Tous**: The Paris 2024 Olympic Games will feature a unique mass participation marathon, allowing amateur athletes to run the Olympic marathon route at night. The event aims to provide a unique experience, with two race formats: a full marathon and a 10km race. The marathon route will follow the same path as the Olympic marathon, paying tribute to the Women's March on Versailles in 1789. - -2. **Kenyan Marathoners**: Kenya is poised to shine in the marathon events at the Paris Olympics, given its rich pool of talent. Eliud Kipchoge, a two-time Olympic gold medalist, is aiming for a third consecutive gold. Other notable Kenyan athletes include Helen Obiri, Benson Kipruto, and Peres Jepchirchir. The country's dominance in distance running is attributed to factors such as high-altitude training and a strong cultural emphasis on running. - -3. **Athletics Kenya Names Marathon Team**: Athletics Kenya has announced its marathon team for the 2024 Paris Olympic Games, featuring Eliud Kipchoge, Brigid Kosgei, Peres Jepchirchir, and Hellen Obiri, among others. The team will be coached by Amos Korir, Nicholas Koech, and Patrick Sang. - -4. **Provisional Kenyan Marathon Squad**: A 20-strong provisional squad for the Paris 2024 Olympic Games includes defending Olympic champions Eliud Kipchoge and Peres Jepchirchir, as well as current men's world record holder Kelvin Kiptum and former women's world record holder Brigid Kosgei. The final team will be announced at the end of January 2024. - -5. **Metaculus Prediction Market**: There is a Metaculus question asking whether Kalenjin athletes will win both the men's and women's marathon races at the 2024 Paris Olympics. The question is currently awaiting approval by Community Moderators. - -Recent updates indicate that the Kenyan team is strong and well-prepared, with a rich history of success in distance running. However, the outcome of the Olympic marathon races will depend on various factors, including the performance of athletes from other countries. - -######### - -GPT - -### Step-by-Step Analysis: - -#### 1. **Background Review and Base Rates:** -- **Athletic Dominance:** The Kalenjin people, an ethnic group native to Kenya, have historically dominated long-distance running. The success in these events is attributed to factors such as high-altitude training, cultural emphasis on running, and genetic predispositions. -- **Past Olympic Performance:** Reviewing past Olympics gives us a clear view of dominance. In the 2020 Tokyo Olympics, Kalenjin athletes won both the men’s and women’s marathon (Eliud Kipchoge and Peres Jepchirchir). - -#### 2. **Specific Athlete Analysis:** -- **Eliud Kipchoge:** One of the greatest marathoners ever, winning gold in both 2016 (Rio) and 2020 (Tokyo) Olympics. If he competes in Paris, his chances of winning are high. -- **Peres Jepchirchir:** The reigning women’s Olympic marathon champion, also showing consistent performance in major marathon events. -- **Other Notable Athletes:** Brigid Kosgei and Helen Obiri (women) and Kelvin Kiptum and Benson Kipruto (men) bolster the team’s chances. - -#### 3. **Evaluating External Factors:** -- **Competitors:** Other countries also have strong marathon runners (e.g., from Ethiopia, Japan, and the USA). The unpredictable nature of marathons (injuries, weather, race-day conditions) adds an element of uncertainty. -- **Preparation:** Kenyan athletes have high-altitude training advantages and are well-coached, and their track records indicate strong readiness for the competition. - -#### 4. **Recent Trends and Current Status:** -- **Recent Form:** Reports suggest that the athletes in the Kenyan team are in good form and have been performing well in recent events. -- **Team Composition:** Final team selection ensures that only the top-performing athletes are chosen, increasing the probability of winning. - -#### 5. **Resolution Criteria Clarification:** -- **Kalenjin Roots:** It is crucial to confirm that the winning athletes' roots are at least partially Kalenjin, even if they represent another country. - -#### 6. **Counterexamples and Novel Factors:** -- **Injury Risks:** Marathons have high injury risks, which could derail even the most likely candidates. -- **Weather and Environmental Factors:** Paris weather conditions during the Olympics could affect performance. Unforeseen extreme weather could advantage/disadvantage certain athletes. - -#### 7. **Establishing the Default Resolution:** -- **Default Likelihood:** If nothing changes, and assuming both races proceed under typical conditions, the historical dominance and current form of the Kenyan team make them strong favorites. -- **Variance:** Daily and weekly granularity variations due to unforeseen training injuries or illnesses can still introduce uncertainties. - -### Conclusion: -Kalenjin athletes have a strong historical and contemporary edge in marathon events, significantly increasing the likelihood of them winning both the men's and women's marathons in 2024. However, considering competitor strength, injury risks, and environmental factors, some level of uncertainty remains. - -### Final Probability Assessment: -Given the data and historical context, Kalenjin athletes' probability of winning both marathon events—assuming no major disruptions or unforeseen unfavorable events—is high. - -### Probability: -**Probability: 70%** - -This estimation balances strong historical performance and current preparedness with inherent risks and the competitive field's depth." -26140,"Will the People's Republic of China control at least half of Taiwan before Sept 30, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether the People's Republic of China (PRC) will control at least half of Taiwan before September 30, 2024, here are the key points: - -1. **Metaculus Prediction Market**: A Metaculus question is currently under review, asking if the PRC will control at least half of Taiwan before September 30, 2024. The question is awaiting approval by Community Moderators. - -2. **Chinese Intentions**: Chairman of the Joint Chiefs of Staff Gen. Mark Milley stated that China wants the capability to take Taiwan by 2027 but does not intend to do so in the near term. He emphasized that China is focused on developing the capability rather than having an immediate intent to attack or seize Taiwan. - -3. **Taiwanese Resistance**: The majority of Taiwanese people resist unification with the PRC, with many opposing the ""One Country, Two Systems"" proposal. This resistance is rooted in Taiwan's historical and cultural identity, which differs significantly from the PRC's vision of a unified China. - -4. **Recent Developments**: Chinese Defense Minister Dong Jun recently stated that China is ready to take forceful action to prevent Taiwan from pursuing independence. This statement came after the inauguration of President William Lai Ching-te, whom Beijing labels a ""separatist."" China has also conducted military exercises around Taiwan in response to Lai's inauguration. - -5. **Historical Context**: The PRC views Taiwan as an integral part of a unified China and has long sought to bring Taiwan under its political control. The ROC, on the other hand, has consistently rebuffed these demands and maintained its independence. - -6. **Expert Opinions**: Gen. Milley and other military leaders have emphasized the difficulty and cost of an invasion of Taiwan, suggesting that it is not a likely scenario in the near term. - -7. **Current Values**: There are no specific current values that are directly relevant to this question. - -8. **Recent Updates**: The most recent updates indicate increased tensions between China and Taiwan, with China taking a more assertive stance on the issue of unification. However, there is no clear indication that China is planning to take control of half of Taiwan before September 30, 2024. - -In summary, while China has expressed its desire to unify with Taiwan and has taken steps to increase military pressure, there is no immediate indication that it will control at least half of Taiwan before September 30, 2024. The Taiwanese people's resistance to unification and the complexity of an invasion remain significant barriers to such an outcome. - -######### - -GPT - -To forecast the probability of the People's Republic of China (PRC) controlling at least half of Taiwan by September 30, 2024, I will systematically analyze the available evidence, historical context, current events, expert opinions, and base rates for similar past events. Here's my step-by-step approach: - -### Step 1: Review Historical Context and Base Rates -Historically, cross-strait relations have been characterized by tension and periodic escalations, but an all-out invasion or significant military control by the PRC over Taiwan has not occurred. The base rate for successful military invasions of sovereign territories with the involvement of major world powers defending the territory (e.g., the United States defending Taiwan) is low. - -### Step 2: Evaluate Current Political and Military Context -1. **Chinese Intentions**: China's ambition to unify Taiwan with the mainland is long-standing. However, authoritative sources like Gen. Mark Milley and Foreign Policy suggest that while China is developing the capability to potentially invade Taiwan by 2027, it does not have immediate intentions to do so. - -2. **Taiwanese Resistance**: The majority of Taiwanese strongly oppose unification with China. This societal resistance makes peaceful unification before September 30, 2024, unlikely. - -3. **Recent Developments**: The recent inauguration of a ""separatist"" president in Taiwan has led to increased Chinese military activity in the region. However, these actions appear more as demonstrations of force rather than preparations for an imminent invasion. - -### Step 3: Assess Expert Opinions -Military experts, including Gen. Milley, consider a near-term invasion highly unlikely due to the complexities and potential high costs associated with it. Moreover, an invasion would likely provoke a strong response from the United States and its allies, further reducing the feasibility within the short time frame. - -### Step 4: Consider Time Frame -With the assessment time frame being only 2.5 months away, the probability of significant and rapid changes in control over Taiwan's territories is low. Substantial military campaigns require extensive preparation, logistics, and sequence of operations, which would likely be evident well in advance. - -### Step 5: Analyze Resolution Criteria -The resolution criteria stipulate clear manifestations of control, such as reliable articles detailing Chinese-controlled areas in Taiwan or assertions that the ROC is a puppet state. These require credible and widespread recognition, which is improbable without large-scale, observable events preceding the resolution date. - -### Step 6: Account for Uncertainties and External Variables -While geopolitical surprises are possible, the strategic calculus for China likely deters immediate aggressive actions. Additionally, Taiwan's defense preparedness and potential international interventions (especially from the US) serve as significant deterrents. - -### Conclusion -Given the historical base rates, current intentions and capabilities of China, the political climate in Taiwan, military expert opinions, and the specific criteria for resolving the prediction: - -**Probability: 2%** - -This accounts for a minimal likelihood that unexpected, drastic changes could occur but recognizes the overwhelming evidence against a significant shift in control within the provided time frame." -26157,"Between July 17 and July 28, 2024, will the strongest geomagnetic storm have a K-index (""Kp"") greater than 5 and less than or equal to 6?","PERPLEXITY - -Here is a concise rundown of the news most relevant to the question: - -- **Current Alerts and Warnings**: The Space Weather Prediction Center (SWPC) has issued a Geomagnetic Storm Watch with a predicted highest storm level of G1, indicating a moderate level of solar activity that can impact the Earth's environment. - -- **Forecasting**: The SWPC provides a 27-day outlook of solar-geophysical indices, including the 10.7 cm solar radio flux, the planetary A index, and the largest daily K values. This forecast is issued every Monday and provides a numerical prediction of these indices. - -- **Recent Geomagnetic Activity**: The University of Alaska Geophysical Institute provides a daily forecast of geomagnetic activity in 3-hour intervals. As of July 15, 2024, the forecast indicates Kp values ranging from 2 to 4 for the next three days. - -- **Metaculus Predictions**: There are two relevant prediction markets on Metaculus. One question asks if the strongest geomagnetic storm between July 17 and July 28, 2024, will have a K-index greater than 5 and less than or equal to 6. The other question asks if the strongest storm will have a K-index greater than 4 and less than or equal to 6. Both questions are currently in the draft stage and await approval from Metaculus moderators. - -- **Expert Opinions**: The University of Alaska Geophysical Institute explains that the Kp number is a system of measuring aurora strength, with values ranging from 0 to 9. A Kp value of 5 or above is classified as a geomagnetic storm. - -- **Recent Updates**: There are no recent updates indicating significant movement towards a particular resolution. The current forecasts and warnings suggest moderate solar activity, but the Kp values are not yet indicative of a strong geomagnetic storm. - -- **Current Values**: The current Kp value is 3, as of July 15, 2024. - -######### - -GPT - -To predict the likelihood that the strongest geomagnetic storm between July 17 and July 28, 2024, will have a K-index (Kp) greater than 5 and less than or equal to 6, we must carefully analyze the provided information and consider various factors: - -1. **Current Situation and Forecasts**: - - **Current Kp Value**: The Kp value is presently 3, indicating a calm geomagnetic environment. - - **SWPC Prediction**: The Geomagnetic Storm Watch indicates a potential for G1 storms, which correspond to Kp values of 5. Historically, these predictions can guide us, but they often have a degree of uncertainty. - - **University of Alaska Forecast**: The Kp values are predicted to range between 2 to 4 in the coming days, suggesting moderate but not severe activity leading up to July 17. - -2. **Sunspot Activity (AR3697)**: - - The presence of AR3697 is noteworthy. Sunspot regions that have merged, resulting in higher magnetic complexity, are more likely to produce significant solar flares and CMEs. Given that AR3697 has produced M- and X-class flares in the past, the potential for geomagnetic storms remains high, even if current forecasts are moderate. - -3. **Historical Context and Base Rates**: - - Let's analyze the base rate for geomagnetic storms. G1 storms (Kp=5) occur quite frequently during periods of high solar activity. According to space weather data, G1 storms happen approximately 900 times during a solar cycle, which translates to multiple instances per year during the solar maximum. - - Having a peak Kp between 5.3̅ and 6.0 in a provided time frame during the active period of the solar cycle holds a moderate but not overwhelming probability. Historically, time windows of about 10 days around active periods can have a notable, but not dominant, chance of hitting this K-index range. - -4. **Metaculus Predictions**: - - Metaculus questions are pending approval and their signals are not providing directional confidence. While it's useful to know what the community is preparing to bet on, we must rely more heavily on direct forecasts and historical data currently, rather than unfinalized community sentiment. - -5. **Variance and Uncertainty**: - - Solar and geomagnetic activity is highly erratic and subject to rapid changes when sunspots are involved. This variability must be factored into any probabilistic forecast. - -**Assessment & Probability Calculation**: - -- **Immediate Forecasts (Next few days)**: Current predictions lean towards lower Kp values (2-4). -- **Potential for Sudden Changes**: Given the history of AR3697 and its recurrent activity, there's an increased chance for geomagnetic storms (G1 or higher). The current G1 watch indicates that this region is active, maintaining some potential for crossing the Kp=5 threshold with peak values above but unsure of crossing beyond Kp=6 given no higher immediate warnings. -- **Historical Precedent**: Active sunspots during solar maximum can produce Kp values >5 consistently but less often under the umbrella of 6 without breaching it. - -Synthesizing these insights, conventional forecasts combined with historical data and current sunspot watch lead me to estimate that there’s a substantial but not dominant probability that a storm will fall into Kp >5 and ≤6 within the given date range. Given the weights, I'll factor the inherent uncertainty in space weather prediction and the specific conditions described. - -**Final Probability**: -Given these analyses, I judge the probability that the strongest geomagnetic storm between July 17 and July 28, 2024, will have a K-index greater than 5 and less than or equal to 6 to be around **35%**. - -**Final Answer**: Probability: 35%" -26158,"Between July 17 and July 28, 2024, will the strongest geomagnetic storm have a K-index (""Kp"") greater than 4 and less than or equal to 5?","PERPLEXITY - -To assist with forecasting the strongest geomagnetic storm between July 17 and July 28, 2024, here is a summary of relevant information: - -1. **Current Alerts and Watches**: The Space Weather Prediction Center (SWPC) issues Alerts, Watches, and Warnings for geomagnetic storms. The current data is available in a JSON file, and previous files are also accessible. - -2. **Planetary K-index**: The K-index is used to quantify disturbances in the Earth's magnetic field. It ranges from 0 (calm) to 9 (major geomagnetic storm). The Planetary K-index (Kp) is derived from 13 geomagnetic observatories and is used to decide whether geomagnetic alerts and warnings need to be issued. - -3. **27-Day Outlook**: The SWPC issues a 27-day forecast of solar-geophysical indices, including the 10.7 cm solar radio flux, the planetary A index, and the largest daily K values. This forecast is updated every Monday. - -4. **Aurora Forecast**: The Geophysical Institute provides a daily forecast of geomagnetic activity in 3-hour intervals. The current forecast shows Kp values ranging from 2 to 4 for the next three days. - -5. **Metaculus Prediction Market**: A question on Metaculus asks whether the strongest geomagnetic storm between July 17 and July 28, 2024, will have a K-index greater than 4 and less than or equal to 5. The question is currently awaiting approval by Community Moderators. - -There are no recent updates indicating significant movement towards a particular resolution. The current Kp values are relatively low, ranging from 2 to 4, and the 27-day outlook does not suggest any major geomagnetic storms in the near future. Expert opinions are not explicitly stated in the provided sources, but the forecasts and alerts from SWPC and the Geophysical Institute provide a general indication of the expected geomagnetic activity. - -######### - -GPT - -### Step-by-Step Evaluation: - -#### 1. **Understanding the Question and Resolution Criteria:** -- The question asks whether the strongest geomagnetic storm between July 17 and July 28, 2024, will have a K-index (Kp) greater than 4 and less than or equal to 5. -- The resolution will use data from the German Research Center for Geosciences, specifically their 30-day history data product. -- Kp values are measured in thirds and the highest possible value is 9. - -#### 2. **Current Background Information:** -- **Sunspot AR3697**: This region has recently caused intense geomagnetic storms and is expected to face Earth again soon. It has history of producing significant solar flares. -- **Current Alerts and Watches**: No significant updates indicating imminent major geomagnetic storm activity. -- **27-Day Outlook**: Shows generally low Kp values, without major geomagnetic storms forecasted. -- **Recent Kp Values**: Range from 2 to 4. -- **Aurora Forecast**: Also predicts lower Kp values of 2 to 4 for the next three days. - -#### 3. **Historical Context and Base Rates:** -- **Typical Kp Distribution**: The highest Kp values often fall outside the 4.33-5.00 range during intense geomagnetic activity, usually resulting in either less severe (Kp <= 4) or more severe storms (Kp > 5). -- **Sunspot Activity**: Given that AR3697 has historically produced M- and X-class solar flares, there’s potential for significant geomagnetic disturbances. - -#### 4. **Analysis of Current and Future Trends:** -- **Near-Term Outlook**: Current forecasts indicate no immediate significant activity, but they are tracking a historically active sunspot returning. -- **Geomagnetic Activity Lag**: Geomagnetic storms can have delayed impacts varying from hours to days based on the progression and orientation of solar flares and CMEs. - -#### 5. **Base Rate Calculation:** -- **General Probability of Kp between 4 and 5**: Geomagnetic storms within this range might not be very common, particularly if a sunspot region is active enough to produce extreme storms. - -#### 6. **Statistical Distribution and Uncertainty:** -- **Most Likely Distribution**: Either calmer space weather with Kp <= 4 or more intense storms with Kp > 5, given the historical precedents of such active sunspot regions. -- **Estimated Probability Adjustment**: Need to account for inherent uncertainty and potential for sudden changes. - -### Final Calculation of Probability: -1. **Default Scenario**: Without additional significant solar activity, values predicted by current forecasts (2-4 range) dominate. -2. **Past Similar Events**: When the sunspot was last active, it caused strong geomagnetic storms, suggesting potential for higher Kp values beyond June’s forecast. -3. **Current Data and Predictions**: Despite forecasts suggesting moderate activity, the previous behavior of AR3697 hints at intense potential, slightly reducing the likelihood that highest Kp strictly falls in the 4-5 range. - -### Conclusion: -With careful consideration of background data, current forecasts, base rate frequencies, and trend analysis, the likelihood that the strongest geomagnetic storm between July 17 and July 28, 2024, will have a Kp greater than 4 and less than or equal to 5 is calculated as follows: - -**Probability: 40%** - -This percentage reflects the balance between low current forecasts, the historically intense activity of sunspot region AR3697, as well as the overall uncertainty adjusting for both likely underestimations and natural geomagnetic storm variations." -26190,"Will the UK Government ban any UK oil and gas licenses currently being processed by September 1, 2024?","PERPLEXITY - -The UK government is considering banning new drilling licenses in the North Sea. The Labor Party, which recently won the election, had promised to stop issuing new oil drilling licenses and gas licenses without revoking existing ones as part of its decarbonization plans. - -- **Ed Miliband**, the Energy Minister, is reportedly considering blocking a handful of pending applications submitted under an oil and gas licensing round opened in late 2022. However, the Department for Energy Security and Net Zero has denied reports that Miliband has banned the North Sea oil regulator from issuing any outstanding drilling and exploration licenses, calling them ""a complete fabrication"". - -- The government has confirmed that it will not issue new licenses to explore new fields but will not revoke existing oil and gas licenses and will manage existing fields for their entire lifespan. - -- The North Sea Transition Authority (NSTA) launched its most recent North Sea licensing round in 2022, which drew applications from over 70 companies. Five licenses were outstanding before the general election was called. - -- Industry bodies, such as Offshore Energies UK, are concerned about the impact of proposals for a further windfall tax and an end to new oil and gas licenses. - -- There are no recent updates indicating movement towards a particular resolution, but the government's stance on not issuing new licenses to explore new fields is clear. - -- Expert opinions suggest that new oil and gas licenses may not be ""climate compatible"" and that the UK should focus on transitioning away from fossil fuels. - -- There are no current values that are directly relevant to this question. - -- There are no relevant prediction markets on Metaculus that directly address this question. - -######### - -GPT - -### Step-by-Step Analysis - -#### Context and Base Rates -1. **Historical Precedent**: Historically, the UK government has been conservative about revoking issued licenses, especially given the economic significance of oil and gas to the UK economy. Labour pledges not to revoke existing licenses, reinforcing the idea that license revocation is uncommon and politically sensitive. - -2. **Current Climate**: - - The Labour Party, now in power, has pledged not to issue new licenses but has maintained that existing licenses would not be revoked. This pledge is clear and unambiguous in their manifesto. - - However, conflicting reports have emerged, with the Financial Times stating that Ed Miliband may be considering blocking some applications from the 33rd licensing round, though this has been labeled as a ""fabrication"" by the Department for Energy Security and Net Zero. - -#### Assessment of Sources -1. **Labour Party Manifesto**: A foundational and publicly documented pledge, which represents a strong commitment. As a ruling party, deviation from such high-profile promises has significant political costs. - -2. **Financial Times Report**: Provides conflicting information but lacks concrete evidence of immediate action. The government's official denial adds weight against the credibility of the report. - -3. **Industry Concerns**: Industry bodies like Offshore Energies UK voice concerns, which could influence governmental decisions. However, their primary worry revolves around new licenses and taxes rather than revocation of existing applications. - -4. **Expert Opinions**: While experts emphasize the incompatibility of new licenses with climate goals, this argument supports halting new licenses rather than revoking ones already in process. - -5. **UK Government Actions**: Thus far, there has been no formal announcement of revocation from the government, making the base case (no revocation) the current status. - -#### Time Analysis -- **July 16 to September 1, 2024**: Approximately 45 days remain for a government decision. This is critical because the proximity of this timeline means that any government intention to revoke should ideally have surfaced more explicitly by now considering the political and economic ramifications. - -- **Political and Procedural Lag**: Government decisions, especially those involving legal and economic implications, typically involve noticeable procedural steps and consultations, which have not been prominently observed. - -#### Impact of Recent Developments -- **Daily Telegraph Report**: Denied by the government and not corroborated by tangible actions, undermining the weight of this source. - -- **Constituents of the 33rd Licensing Round**: Revoking even one license might be politically risky and could contradict the manifesto's promise, leading to potential backlash from both industry and political stakeholders. - -#### Probability Distribution -- Base rate for revocation being historically low. -- Strong manifesto commitment not to revoke licenses. -- Contradictory media reports but no concrete government moves. -- Limited time window for major policy reversal or implementation. - -### Final Chain of Thought -1. The weight of consistent public and political pledges from Labour suggests a low likelihood of revoking licenses. -2. Conflicting media reports provide a speculative risk but lack substantial evidence or official moves towards revocation. -3. Political and economic ramifications of revocation make it a less likely immediate outcome. -4. Tight timeframe limits the scope for new developments without prior warning signs. - -### Conclusion -Given the analysis, while there is some degree of uncertainty mainly due to speculative reports, the probability that the UK government will revoke any North Sea oil and gas licenses currently being processed by September 1, 2024, remains low. - -**Probability: 15%**" -26189,Will a swimmer win the most gold medals at the 2024 Paris Olympics?,"PERPLEXITY - -The query is asking whether a swimmer will win the most gold medals at the 2024 Paris Olympics. Here are some relevant news and updates that can help inform this question: - -- Katie Ledecky is a dominant force in women's swimming, holding multiple world records and expected to add to her medal count in Paris. -- Michael Phelps, the most decorated Olympian in history, praises Ledecky as ""the greatest female swimmer of our time"". -- Swimming has been a strong sport for the United States at the Olympics, with American athletes winning over 250 gold medals. -- There are several individual and team records that could be broken at the Paris 2024 Olympics, including Katie Ledecky potentially winning the most Olympic gold medals in any sport. -- Coach Fares Ksebati shares his predictions for the Paris 2024 Olympics, including the men's 50 and 100 freestyle events, which could be highly competitive. - -These sources highlight the strong performance of American swimmers, particularly Katie Ledecky, and the potential for record-breaking at the Paris 2024 Olympics. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Understanding the Question and Context** -The task is to predict whether a swimmer will win the most gold medals at the 2024 Paris Olympics. Significant gold medals can often be won in a few select sports: swimming, athletics (track and field), and gymnastics. - -#### 2. **Reviewing Past Trends** -Historically: -- **Swimming** is a strong candidate because multiple events allow a single athlete to win many medals. Notable swimmers like Michael Phelps have taken advantage of this. -- **Gymnastics** can offer multiple medals, but fewer events and typically high competition limit dominance. -- **Athletics** also provides multiple medal opportunities, but distribution among various athletes tends to reduce an individual's medal count. - -#### 3. **Current Context and News Analysis** -**Key Points:** -- **Katie Ledecky's Dominance**: As of today, Katie Ledecky is positioned to be a top contender. Her track record is exceptional, and she is recognized for her potential to win multiple gold medals. -- **Competition in Swimming**: The competitiveness in men’s freestyle events suggests high variability, but also highlights potential for high medal counts. -- **Historical Performance of USA**: The USA traditionally dominates in swimming, increasing the likelihood of a high medal haul for their athletes. -- **Predictions from Experts**: Analysts are already anticipating record-breaking performances in swimming, which aligns with strong historical trends. - -Given these points: -- **Katie Ledecky** will be a strong contender to win multiple golds. -- **Men's 50 and 100 freestyle events** suggest strong potential but emphasize that other male swimmers will also be in contention, diluting any single competitor's likelihood of multiple wins. - -#### 4. **Base Rates and Novel Factors** -**Base Rates:** -- In recent Olympics (since 2000), swimming has often produced the athlete with the most gold medals. -- Statistically, it is common for the athlete with the most gold medals to come from swimming. -- In Tokyo 2020, Caeleb Dressel exemplified this trend by winning 5 golds in swimming. - -**Novel Factors:** -- **Technological advancements** could have an impact but are somewhat consistent across competitors and thus maintain relative parity. -- **Political or economic factors** do not appear aggressively in play. The primary influence remains athletic performance. -- **Environmental conditions like training facilities** remain top-notch, particularly for major swimming contenders from established programs. - -#### 5. **Default and Uncertainty Scenarios** -Given Swimming’s dominance, if trends continue as expected: -- **Default Resolution** should heavily favor a swimmer unless extreme variability or unprecedented performances occur in other sports, which, historically, is less common. - -#### 6. **Statistical Analysis of Uncertainty** -**Key Variables:** -- Potential for an injury or unexpected underperformance. -- Emergence of an outlier athlete in another sport. -- High variance in multi-medal winning sports means extreme certainty isn't warranted, but confidence remains high. - -#### 7. **Final Probability Judgment** -Accounting for the historical dominance of swimming in the context of athletes winning multiple golds, the strong current form of key swimmers like Katie Ledecky, and the existing competitive landscape, I estimate the probability with careful consideration of all factors discussed: - -**Probability: 75%** - -This captures strong likelihood but acknowledges inherent Olympic variability." -26196,"On Sept 30, 2024 will more than 200 and less than or equal to 300 measles cases have been reported in the United States in 2024?","PERPLEXITY - -To provide a concise and detailed rundown of the news relevant to the question about measles cases in the United States in 2024, I have summarized the key points from the provided sources: - -1. **Current Case Count**: As of June 27, 2024, there have been 159 measles cases reported in the United States across 23 jurisdictions. -2. **Outbreaks**: There have been 12 outbreaks (defined as 3 or more related cases) reported in 2024, with 66% of cases (105 of 159) being outbreak-associated. -3. **Age Distribution**: The majority of cases are among children under 5 years old, with 5-19 years and 20+ years age groups also affected. -4. **Vaccination Status**: Most cases are associated with individuals who are unvaccinated or have an unknown vaccination status. -5. **International Travel**: Many cases are linked to international travel, particularly from regions experiencing outbreaks, such as the Eastern Mediterranean and African WHO regions. -6. **Recent Surge**: There has been a significant surge in measles cases in 2024, with 125 cases reported across 17 states as of April 23, 2024. -7. **Global Context**: Global measles incidence has increased, and vaccination rates have declined, leading to a higher risk of importations into the U.S.. -8. **CDC Response**: The CDC has emphasized the importance of vaccination, especially before international travel, and has provided guidance on vaccination campaigns and outbreak control. - -Given these factors, it is crucial to monitor the situation closely and consider the potential for further outbreaks and increased case counts. - -**Metaculus Prediction Market**: There is no specific prediction market on Metaculus related to this exact question. However, it would be beneficial to set up a prediction market to track the progress of measles cases in the United States and gather insights from forecasters. - -**Expert Opinions**: Health experts, such as Dr. Demetre Daskalakis from the CDC, have stressed the urgency of addressing vaccination rates to stem the spread of measles, especially given the global context and vaccination rates in the U.S.. - -**Current Values**: As of June 27, 2024, the total number of measles cases reported in the United States is 159. - -######### - -GPT - -### Chain of Thought - -#### 1. **Current Case Count** -- As of July 15, 2024, 167 cases have already been reported. -- Note the significant surge observed in April with 125 cases reported up to that point. - -#### 2. **Historical Data & Outbreak Dynamics** -- **Outbreaks contribute significantly to case numbers:** 66% of cases are outbreak-associated. -- **Outbreaks tend to generate clusters of cases** rather than slow, linear increases. - -#### 3. **Time Frame** -- There are 76 days (or approximately 2.5 months) from today until September 30, 2024. -- **Compare to earlier months:** April to July saw 42 new cases (167 cases by July 15 minus 125 by April 23). - -#### 4. **Trend Analysis** -- **Recent Surge:** April to July saw a substantial number of cases, but it might not continue at the same high rate for a lack of new outbreaks. -- **Vaccination Dynamics:** If new vaccination campaigns are successful, case growth could slow. - -#### 5. **Global Context** -- High global incidence and related international travel remain risk factors. - -#### 6. **CDC and Public Health Actions** -- Continued CDC emphasis on vaccination could mitigate the rate of new cases. -- Efficacy of these campaigns remains uncertain but should be considered positively in mitigating further surges. - -#### 7. **Base Rates and Past Trends** -- **2019 Measles Outbreak:** Demonstrated how quickly cases can accumulate if outbreaks are extensive. -- **2023 Data:** With fewer global measles cases, the U.S. barely reached 80 cases by year-end. -- **Context-Specific:** Comparing absolute yearly totals, 2024's surge indicates possibly a higher but not out-of-control case count smoothing near the year's end. - -#### 8. **Statistical Projections** -- **From 167 cases currently, an upper limit of +133 cases is allowable:** For cases to remain ≤300 by Sept 30. -- **Past 90 days (April to July) saw an increase from 125 to 167 cases (~50% increase):** Linear projection would add similar or slightly higher due to ongoing vaccination and public health measures. - -Let's compute a basic projection using a simpler exponential smoothing: -\[ \text{If from April to July there are 42 additional cases in 82 days, it would average to around 0.512 cases/day.} \] -Project this over the upcoming 76 days: -\[ 0.512 \times 76 = 38.912 \] -Adding this to the current total: -\[ 167 + 38.912 \approx 206 \] - -Given a spread of cases in clusters, and possible high for any outbreaks, an additional buffer caps this to a plausible forecast. - -### Double-Checking Assumptions -- **If another surge** happens like from the earlier months, it might push toward higher end of range (~300). -- **If controlled well**, it could hover closer to the low 200s. -- **Trend advisability:** Clustering from 200-250 seems plausible considering mitigation and continued outbreak observations (avg 20% allowance for variance). - -### Conclusion - -The probability distribution should lean towards accommodating a plausible rise due to sporadic surges balanced by mitigation efforts. Considering the outlined reasoning and steps: - -**Prediction: Probability: 65%** -For the cases to fall within the 200-300 range by September 30, 2024." -26193,"Will there be a US presidential debate held on September 10, 2024?","PERPLEXITY - -Yes, there will be a US presidential debate held on September 10, 2024. The debate will be hosted by ABC News and will feature President Joe Biden and former President Donald Trump as participants. The debate will air in prime time, although the exact start time has not been disclosed yet. - -######### - -GPT - -### Chain of Thought - -#### I. Analyzing the Background Sources and Recency -- **ABC News Scheduled Debate (July 2024):** - - The debate is officially scheduled and preparations are underway. - - Recency: Very high, given it is only a few weeks old. - -- **New York Times (July 2024):** - - Indicated the first debate was a debacle for Biden, which could influence whether the second debate occurs. - - Recency: Very high, given it was published this month. - -- **CBS News (Current Date unspecified but likely July 2024):** - - Confirms the debate is still on schedule and mentions the details about time and hosting. - - Recency: High, though the exact date of the report isn't given, the information aligns with other sources. - -#### II. Timeline -- We have approximately 8 weeks between now (July 16, 2024) and September 10, 2024. - -#### III. Historical Context -- **Base Rates of Presidential Debates:** - - Historically, scheduled presidential debates have a high probability of occurring. - - The cancellations are very rare and usually due to extraordinary circumstances (health issues, significant national or global events, etc.) - -#### IV. Assessment of Current Political Climate -- **Biden's Performance in First Debate:** - - Poor performance might reduce the motivation for a second debate. - - However, canceling could also signal weakness and impact public opinion negatively. - -- **Trump's Motivation:** - - As an aggressive and confrontational debater, Trump would likely push for the second debate. - -- **Logistical and Media Investment:** - - ABC News and other stakeholders have invested resources into organizing the debate. - - These entities typically push for the completion of scheduled events unless rescheduling is absolutely necessary. - -#### V. Variance and Potential Disruptions -- **Health Issues:** - - Considering both Biden’s and Trump’s ages, health is always a minor concern, but historically this has rarely influenced debate schedules. - -- **Significant Events:** - - Potential for global or national events disrupting the debate is always non-zero but relatively low within an 8-week window. - - No significant upcoming events known as of current date that clash with debate schedules. - -- **Political Maneuvering:** - - Both candidates have incentives to show strength and fulfill pre-scheduled commitments to maintain public and media confidence. - -#### VI. Statistical Analysis -Given historical context and evaluating factors: -- Base rate of debate cancellation: historically quite low (<10%). -- Additional factors such as recent performance, health, and political maneuvering elevate risk slightly but not significantly. - -### Conclusion -- The scheduled debate as of now seems highly likely to occur. The primary risks are minor health issues or unexpected significant events within an 8-week period. Given the strong motivations for both candidates and media stakeholders, the probability of cancellation remains relatively low. - -### Final Probabilistic Estimate -**Probability: 85%**" -26194,"On Sept 30, 2024 will more than 200 measles cases have been reported in the United States in 2024?","PERPLEXITY - -To provide context for the query about measles cases in the United States in 2024, here is a summary of the relevant news and data: - -1. **Current Case Counts**: - - As of July 11, 2024, a total of 167 measles cases were reported by 24 jurisdictions in the United States. - - As of June 27, 2024, 159 measles cases were reported by 23 jurisdictions, with 12 outbreaks (defined as 3 or more related cases) and 66% of cases being outbreak-associated. - -2. **Recent Trends**: - - The number of measles cases in the United States has seen a significant increase in 2024, with the highest annual count since 2019. - - Between January and March 2024, the U.S. saw about 30% of the total cases recorded since the start of 2020, indicating a surge in cases. - -3. **Outbreaks and Locations**: - - Notable outbreaks have occurred in locations such as migrant shelters, elementary schools, and facilities in Philadelphia and Chicago. - - Chicago is still grappling with its outbreak, with 63 reported cases as of the latest count, and children under the age of 5 accounting for over half of the cases in the city. - -4. **Vaccination and Prevention**: - - Two doses of the measles, mumps, and rubella (MMR) vaccine are 97% effective in preventing measles. - - The Centers for Disease Control and Prevention (CDC) emphasize the importance of vaccination, especially for international travelers, to prevent the spread of measles. - -5. **Forecasting Sites**: - - There are no specific prediction markets on Metaculus related to this query. - -Given the current trends and the fact that measles cases are still being reported, it is possible that more than 200 measles cases will have been reported in the United States by September 30, 2024. However, this forecast should be made by the superforecaster based on their analysis of the data and trends. - -######### - -GPT - -To determine the probability that more than 200 measles cases will have been reported in the United States by September 30, 2024, we need to follow a structured approach, considering all relevant data points and trends, while also taking into account statistical concerns and possible scenarios. - -### Step-by-Step Analysis - -#### Data Points and Trends - -1. **Current Case Counts:** - - As of July 15, 2024, there are 167 reported cases. - - Between June 27 and July 15 (approximately 18 days), the increase was 8 cases (from 159 to 167). - -2. **Recent Trends:** - - There is a notable increase in measles cases in 2024 compared to previous years, especially the surge seen between January and March 2024. - - Historical context shows that 2024 has had the highest case count year since 2019. - -3. **Outbreak Information:** - - Notable ongoing outbreaks, such as in Chicago, with significant numbers reported. - - Migrant shelters and schools are contributing sites, indicative of possible rapid transmission in clusters. - -4. **Vaccination and Prevention:** - - While the MMR vaccine is highly effective, localized outbreaks in communities with lower vaccination rates can still occur, as seen in Chicago. - -5. **Forecast Timing:** - - There are approximately 75 days remaining until September 30, 2024. - -#### Statistical Considerations: - -- **Weekly Case Increase:** - - From June 27 to July 15, the increase was 8 cases in 18 days, approximating to around 3.5 cases per week (8 cases over 2.5 weeks). - -- **Extrapolation:** - - If we continue with a conservative rate of 3.5 cases per week, for the remaining 11 weeks (75 days): - - 3.5 cases/week * 11 weeks = 38.5 additional cases - - Thus, projected total cases: - - Current 167 + 38.5 = ~205.5 cases by September 30, 2024 - -#### Consideration of Historically Similar Events: - -- Given that 2024 has notably higher numbers connected with specific outbreaks and surge patterns, comparisons of past years where lower annual numbers were reported might not fully capture this anomaly. - -#### Novel Influences: - -- High mobility levels, especially among unvaccinated populations. -- Potential underreporting initially altered the perceived severity. - -### Default Scenario and Adjustments: - -- Given the current trends and outbreak specifics, without significant intervention or herd immunity increase (via vaccinations or natural infections), cases are likely to continue at a similar if not slightly higher rate. - -#### Checking and Balancing: - -- The rate calculated seems reasonable but may still be conservative, factoring in possible slight increases due to school reopening or local outbreak management variability. - -### Final Probability Determination: - -Having taken the latest data, extrapolated conservatively, and balanced the recent situation with historical trends and plausible scenario developments: - -""Probability: 75%"" - -This indicates a reasonably high confidence, given present circumstances and extrapolations, understanding that there remains a significant but not overwhelming uncertainty factor within the approximation involved." -26195,"On Sept 30, 2024 will more than 300 measles cases have been reported in the United States in 2024?","PERPLEXITY - -To assist the superforecaster in making a prediction about measles cases in the United States, I have compiled a detailed summary of relevant news and data: - -### Current Situation - -- As of July 11, 2024, a total of 167 measles cases were reported by 24 jurisdictions in the United States. -- The Centers for Disease Control and Prevention (CDC) updates measles case information on the second and fourth Friday of each month. -- Measles activity is currently considered low, with 12 outbreaks reported in 2024, and 66% of cases are associated with these outbreaks. - -### Recent Trends - -- By March 2024, at least 64 confirmed or suspected measles cases were reported in 17 states, surpassing the total number of cases in 2023. -- The CDC reported 338 measles cases in the United States between January 1, 2020, and March 28, 2024, with 29% of these cases occurring in the first quarter of 2024. -- The majority of measles cases are linked to unvaccinated individuals who have been exposed to the virus while traveling abroad or through localized transmission within the U.S.. - -### Vaccination and Prevention - -- Vaccination is the most effective way to prevent measles, with two doses of the measles, mumps, and rubella (MMR) vaccine providing 97% protection against measles. -- The CDC emphasizes the importance of ensuring adequate immunization, especially for international travelers, to prevent the spread of measles. -- Global declines in measles vaccination rates have increased the risk of outbreaks worldwide, including in the U.S.. - -### Expert Opinions - -- Dr. Demetre Daskalakis of the CDC stresses the urgency of addressing vaccination rates to stem the spread of measles, particularly given the global context. -- Health authorities assert that the U.S. is not at risk of losing its measles elimination status, despite the recent increase in cases, and sustained efforts are underway to address outbreaks and enhance vaccination coverage. - -### Prediction Markets - -- There are no relevant prediction markets available on Metaculus or other forecasting platforms that specifically address the question of whether more than 300 measles cases will be reported in the United States by September 30, 2024. - -### Current Values - -- As of July 11, 2024, there are 167 reported measles cases in the United States. -- There are currently no specific daily or weekly updates on measles cases that would indicate a significant movement towards a particular resolution. - -### Expert Opinions and Updates - -- The CDC and other health authorities continue to emphasize the importance of vaccination and early detection to prevent widespread transmission of measles. -- There are ongoing efforts to address outbreaks and enhance vaccination coverage to maintain the U.S. measles elimination status. - -This summary provides a comprehensive overview of the current measles situation in the United States, recent trends, vaccination and prevention strategies, expert opinions, and relevant data. It will help the superforecaster make an informed prediction about whether more than 300 measles cases will be reported in the United States by September 30, 2024. - -######### - -GPT - -### Step-by-Step Analysis to Determine the Probability - -#### 1. Current Situation Analysis -- **Current Cases:** As of July 15, 2024, there are 167 reported measles cases in the U.S. -- **Time Remaining:** From July 17 to September 30, there are approximately 2.5 months, or about 10 weeks remaining in the forecast period. -- **Update Frequency:** The CDC updates biweekly, so we expect roughly 5 more updates by the resolution date. - -#### 2. Historical Context and Trends -- **Data to Date:** Between January 1, 2020, and March 28, 2024, 338 measles cases were reported, indicating an approximate annual average of around 80 cases (though there are significant variances). -- **2024 Surge Observation:** 167 cases by mid-July, which already surpasses recent annual totals. Extrapolating current trends may pose challenges due to variabilities like increased reporting or improved vaccination during outbreaks. - -#### 3. Observation of Past Incidence Variability -- **Monthly Increase Trends:** From the summary, we observed 167 cases as of mid-July, averaging about 24 cases/month (~6 cases per week) earlier in the year. This indicates potential variability. -- **Outbreak Context:** 66% of cases were tied to outbreaks, suggesting that clustered outbreaks could dramatically affect totals. - -#### 4. Projection Exercise -- **Trend Projection:** If the current average monthly rate of 24 cases continues, we might expect an additional 60 cases over the next 2.5 months, reaching a total of ~227 cases (167 existing + 60 projected). - -#### 5. Influential Factors to Weigh -- **Vaccination Efforts:** Effective vaccination drives could limit outbreak extensions, making future months less active. -- **Travel Season:** The U.S. summer travel season could cause an uptick in cases due to higher exposure rates and international travel. -- **Public Health Measures:** Any novel policy interventions or clustering due to slow responses influence case numbers. - -#### 6. Bracketing Uncertainty -- **Upper Bound Cases:** Accounting for sudden outbreaks and potential spikes, numbers could rise more sharply. -- **Lower Bound Cases:** If containment and vaccination amplify, the rise may flatten. - -#### 7. Probability Distribution -- **Baseline Calculation:** Using the projection of 227 cases, additional fluctuation must be weighed for skewed outcomes (both tail ends). -- **Modeling Scenarios:** - - **Conservative Estimate:** Public health upscaling leads to slight increases (expected lower bound at 190-220 cases). - - **Aggressive Estimate:** Increased outbreak spikes lead to higher cases (upper bound at 250-310 cases). - -### Probability Calculation -Given the current trends, historical variability, and contexts provided, the convergence of models tentatively suggests near-borderline outcomes. Bias correction towards historical under-counting or overemphasis on summer spikes needs careful balancing. - -Given 60 likely additive cases plausibly increases with contingencies considered: - -**Weighted Extrapolation:** -- Conservative spike: 60 additional cases (final: ~ 227) -- Aggressive spike: ~140 additional cases (final: ~307) - -Thus, a balancing point exists around a probable increase but not precisely determined: - -### Final Thought Synthesis and Conclusion -**Key Factors:** -1. Yearly variability raises significant bounds, while clustered outbreak risks remain pragmatic. -2. Average trend balances between controlled and marginally upscaled outcomes. - -### Final Answer -**Probability: 55%** -This reflects the confluence of trends, outbreak potential, and the inherent timing variability, leaning slight probability just above the mean expected value." -26192,"Will Kimberly Cheatle remain Director of the United States Secret Service through September 30, 2024?","PERPLEXITY - -Here is a concise but detailed rundown of the news most relevant to the question: - -- **Recent Events**: There was an attempted assassination of former President Donald Trump on July 13, 2024, during a campaign rally in Butler, Pennsylvania. The incident resulted in the death of a former firefighter and injuries to Trump and two others. - -- **Director Kimberly Cheatle's Response**: Cheatle acknowledged responsibility for the agency's mishandling of the event, stating ""the buck stops with me,"" but refused to resign. She emphasized that such an incident should not happen again. - -- **Background on Director Cheatle**: Kimberly Cheatle is the 27th Director of the U.S. Secret Service, sworn in on September 17, 2022. She has over 25 years of experience with the Secret Service, holding various leadership roles, including Assistant Director of the Office of Protective Operations and Special Agent in Charge of the Atlanta Field Office. - -- **Pressure to Resign**: There is mounting pressure on Cheatle to step down due to the agency's response to the attempted assassination. However, she has not indicated any intention to resign. - -- **Current Status**: As of July 17, 2024, Cheatle remains the Director of the U.S. Secret Service. - -- **Relevant Updates**: There are no recent updates indicating movement towards a particular resolution regarding Cheatle's tenure. However, the fallout from the incident has intensified scrutiny on the Secret Service's readiness and operational protocols, which may impact public confidence in the agency's ability to protect high-profile political figures. - -- **Expert Opinions**: There are no specific expert opinions directly addressing Cheatle's tenure through September 30, 2024. However, the incident has raised concerns about the agency's ability to protect political figures, which could influence voter perceptions of security and stability in the political climate. - -- **Current Values**: There are no specific current values directly relevant to this question. - -- **Forecasting Sites**: There are no relevant prediction markets on Metaculus or other forecasting sites that directly address whether Kimberly Cheatle will remain Director of the U.S. Secret Service through September 30, 2024. - -######### - -GPT - -### Step-by-Step Analysis and Evaluation - -#### 1. **Understanding the Question and Criteria**: -- **Question**: Will Kimberly Cheatle remain Director of the United States Secret Service through September 30, 2024? -- **Resolution Criteria**: Yes, if she remains continuously; No, if there is any official announcement or temporary suspension before October 1, 2024. - -#### 2. **Current Context**: -- Recent assassination attempt on Donald Trump on July 13, 2024. -- Criticism and pressure on the Secret Service and its Director, Kimberly Cheatle. -- Cheatle has not resigned and has publicly stated responsibility. - -#### 3. **Evaluating Recent Events and Immediate Reactions**: -- **Critical Incident**: An assassination attempt on a high-profile figure like Trump results in enormous scrutiny. -- **Cheatle's Response**: She acknowledged responsibility but refused to resign. Indicates intent to remain in position despite pressure. -- **Public and Political Pressure**: There's substantial pressure which could escalate or diminish over time. - -#### 4. **Historical Context and Base Rates**: -- **Base Rates of Resignation/Removal**: Historically, public officials under scrutiny after major incidents are sometimes removed but also sometimes withstand pressure. -- **Notable Examples**: - - Removal: Julia Pierson resigned as Secret Service Director in 2014 after security lapses. - - Retention: Joseph Clancy continued as Director post-2015 despite scrutiny. - -#### 5. **Timeline Consideration**: -- Today is July 17, 2024. There are approximately 2.5 months (75 days) until the resolution date. -- Short-term pressure is high but political pressures can either intensify or wane over time. -- **Key Milestones**: Any upcoming significant events or investigations can influence outcomes. - -#### 6. **Assessment of Current Trends**: -- **Immediate Aftermath**: High scrutiny right now. Media typically spotlight failures but can shift focus over time. -- **Long-Term Trends**: If no immediate action (like a resignation or appointment of an acting director) happens soon, the likelihood of retention increases. - -#### 7. **Weighing the Factors**: -- **Internal Dynamics**: Organizational support or pressure from within the agency. Unknown factor. -- **Political Climate**: Current political environment is charged, especially with high-profile elections. Removal could be seen as acknowledging failure which administration might avoid. -- **Public Perception**: Strong perception of failure impacts but needs sustained media and political push to result in action. If media focus shifts, pressures might diminish. - -#### 8. **Statistical and Probabilistic Estimation**: -- **Historical Data Reference**: Roughly a 50/50 or slightly more leaning towards retention given resistance to immediate resignation. -- **Current Pressure**: High, so more inclined to reduce the base retention probability. -- **Events in the Short-Term**: Key is if there are no immediate actions taken, she is more likely to stay on due to inertia and diminishing impact of the event over time. - -Considering all these factors: - -1. **Base Rate Estimation**: General probability of retention without further events = 60-65%. -2. **Adjustment for Current Pressure and Short-Time Frame**: Decrease probability due to high current scrutiny = ~15%. -3. **Medium-Term Stability Post Immediate Reactions**: Slight increase if surviving initial pressure spike = ~5%. - -Final Calculation: -- Adjusted base rate = 60-65% - 15% + 5% = 50-55%. - -Given the condensed timeline and tendency of pressures to normalize over time, a well-grounded estimate is: - -**Probability: 55%** - -This accounts for the inherent volatility and considers that although current pressures are significant, the probability of sustained effort leading to removal diminishes as time progresses without immediate action." -26191,"Before August 22, 2024, will President Joe Biden take any of these specific actions to end his candidacy for re-election?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **Virtual Nomination**: The Democratic National Committee (DNC) is planning to hold a virtual roll call to nominate President Joe Biden for re-election, potentially as early as mid-July. This move is seen as a way to ensure Biden's inclusion on the ballot in Ohio, despite recent changes to the state's ballot deadline. - -- **Criticism and Concerns**: A group of House Democrats is expressing reservations about the swift nomination process, arguing that it could limit thorough discussions about Biden's fitness for office and potentially harm Democratic unity and morale. - -- **Biden's Campaign**: Despite internal party debates about his viability, Biden remains determined to secure a second term in the White House. His campaign emphasizes the importance of completing the roll call promptly to ensure his ballot access in Ohio. - -- **Expert Opinions**: Some Congressional Democrats have called for Biden to withdraw from the race, reflecting the party's divided stance on his campaign's effectiveness and chances of re-election. - -- **Recent Updates**: There have been no recent updates indicating movement towards Biden ending his candidacy for re-election. The focus remains on the upcoming election and critical issues at hand. - -- **Current Values**: There are no specific current values directly relevant to this question. - -- **Forecasting Sites**: No relevant prediction markets on Metaculus or other forecasting sites were found for this specific question. - -######### - -GPT - -### Evaluation Process - -#### Step 1: Analyze Relevant Events and Background Sources - -1. **Virtual Nomination**: The DNC plans to hold a virtual roll call for Biden's nomination, potentially mid-July. This would solidify Biden as the nominee, reducing the chances of him stepping down pre-convention if nominated. -2. **Criticism and Concerns**: Some House Democrats are concerned about Biden's fitness and the expedited nomination process. However, internal party criticism does not equate to a resignation or withdrawal. -3. **Biden's Resolve**: Biden remains determined to secure a second term despite these debates. His campaign's immediate goal is ballot access in Ohio. -4. **Calls for Withdrawal**: Some Democrats have called for Biden to withdraw, but there is no indication of movement towards resignation. - -#### Step 2: Evaluate Historical Precedent - -- **Historical Examples**: Historically, sitting presidents very rarely withdraw their candidacies due to internal party dissent or performance issues. -- **Base Rates**: We should consider the rarity of a sitting president resigning from candidacy (or office) within such a short timeframe before a convention (about a month). - -#### Step 3: Consider Time Frame and Recency - -- **Time until August 22, 2024**: With just over a month left until the convention, any significant changes need to happen rapidly—a short timeframe increases the difficulty of a drastic action like resignation or retraction unless under extraordinary circumstances. -- **Recent Events and Public Sentiment**: See no major shift from Biden's stance on continuing his candidacy despite calls for him to step down. - -#### Step 4: Assess Resolution Criteria - -- To resolve as **Yes**, one of these must occur: - 1. Biden or his campaign announces withdrawal from nomination. - 2. Biden releases his delegates. - 3. Biden endorses another person for Democratic nominee. - 4. Biden ceases to be President by tenure ending or resignation. - -#### Step 5: Evaluate Probabilities and Uncertainty - -- **Biden Withdrawing or Announcing Withdrawal**: - - The desire to secure ballot access in Ohio suggests Biden’s campaign is still operational. - - Despite internal dissent, no historical signs currently indicate he’s considering withdrawal. - - Estimated Probability ~10%. - -- **Releasing Delegates**: - - If Biden isn’t withdrawing, unlikely to release delegates. - - Estimated Probability ~5%. - -- **Endorsing Another Candidate**: - - Highly unlikely without significant push or health issues. - - Estimated Probability ~5%. - -- **Ceasing to be President**: - - No current health or major scandals suggesting imminent resignation. - - Estimated Probability ~1%. - -#### Aggregation and Final Probability Distribution - -Given the low probability of any single criterion being met and combining them under the condition of mutual exclusion with minor overlaps, the combined probability remains low. - -### Final Risk Assessment Review and Conclusion - -Reassessing: -- Biden determined to continue, significant maneuvering required to change. -- Time constraint of roughly one month lends little room for drastic shifts. -- Historical rarity of such events. - -### Conclusion - -Based on available data, historical precedent, and Biden’s current disposition, the overall probability of Biden taking specific actions to end his candidacy before August 22, 2024, is low. - -**Probability: 10%**" -26211,Will the Olympic record for the men's pole vault be broken at the 2024 Paris Olympics?,"PERPLEXITY - -The Olympic record for the men's pole vault at the 2024 Paris Olympics is likely to be broken. Here are some key points from recent news articles that support this prediction: - -1. **Mondo Duplantis's Dominance**: Mondo Duplantis, the Swedish pole vault sensation, has broken the world record eight times, including a recent jump of 6.24m at the Wanda Diamond League meeting in Xiamen. He has been undefeated in all competitions this year and has consistently cleared the 6m mark. - -2. **Recent Performances**: Duplantis has been in top form, winning the pole vault in a world record 20-5 1/4 (6.23m) during the Prefontaine Classic and setting another world record in April during a Diamond Discipline stop in China. - -3. **Competition**: While there are other strong competitors like Chris Nilsen of the United States, Ernest John Obiena of the Philippines, and Kurtis Marschall of Australia, none have come close to Duplantis's mark of 6.24m. Nilsen has the closest height at 6.00m, followed by Obiena at 5.97m. - -4. **Expert Opinions**: Many expect Duplantis to break his own record again at the Paris Olympics, given his consistent performance and dominance in the event. - -5. **Historical Context**: The men's pole vault has seen frequent record-breaking, with Duplantis setting multiple world records in recent years. This trend suggests that the Olympic record is also likely to be broken. - -Given these factors, it is highly probable that the Olympic record for the men's pole vault will be broken at the 2024 Paris Olympics. - -######### - -GPT - -### Step-by-Step Evaluation - -#### 1. **Understanding the Problem and Resolution Criteria** -The question is whether the Olympic record for the men's pole vault (currently 6.03m) will be broken at the 2024 Paris Olympics. The resolution criteria state that the question resolves as ""Yes"" if the winning height is 6.04m or higher. - -#### 2. **Recent Performance and Trends** - - **Mondo Duplantis's Dominance** - - Duplantis has set the world record at 6.24m earlier in 2024, indicating a performance level significantly above the current Olympic record. - - His consistent performance of clearing heights around 6m and over 6.20m in various competitions shows a high likelihood of surpassing 6.03m. - - **Other Competitors** - - Chris Nilsen (6.00m), Ernest John Obiena (5.97m), and Kurtis Marschall (5.95m) are capable athletes, although not as dominant as Duplantis. - -#### 3. **Expert Opinions and Historical Context** - - Multiple experts predict Duplantis is likely to break his record again, including the Olympic record. - - The trend in men's pole vault has been towards frequent record-breaking, driven by advances in technology, training, and athleticism. This pattern supports the likelihood of another record-breaking performance. - -#### 4. **Evaluating Uncertainty and Base Rates** - - **Base rates**: Historically, Olympic records get broken in premier athletic events, though not in every edition of the Games. - - **Variance in performance**: While elite athletes have high average performance levels, there can be less predictable elements due to injury, weather, or other competition factors. - - **Time Frame**: The Paris Olympics are imminent, and current performance trends are recent and relevant. The remaining time before the event decreases the likelihood of significant changes in performance trends. - -#### 5. **Analyzing Statistical Data** - - Duplantis has frequently cleared heights significantly above the current Olympic record (6.03m). - - Historical performance analysis shows a tendency for peak performance during major events like the Olympics due to heightened competition and motivation. - -#### 6. **Synthesizing Information** -Combining all the information: - - Duplantis's dominance with his recent performances clearing well above 6.04m. - - The surrounding competitive landscape where no other athlete has consistently surpassed 6.03m or even closely approached Duplantis's level. - - Trends and expert opinions heavily favoring a record-breaking due to Duplantis's performances. - - Reduced uncertainty with the data being very recent leading up to the Olympics. - -### Final Prediction -Given the weighted information and calculated probabilities, the probability of the Olympic men's pole vault record being broken at the 2024 Paris Olympics is high. - -**Probability: 85%** - -This takes into account Duplantis's consistent record-breaking performances and the concurrence of expert opinions and recent trends supporting a new Olympic record being set in Paris." -26210,"Will Israel invade Lebanon before October 1, 2024?","PERPLEXITY - -Here is a detailed rundown of the news relevant to the question of whether Israel will invade Lebanon before October 1, 2024: - -### Tensions and Escalations - -- **Cross-Border Attacks**: Between October 7, 2023, and June 21, 2024, there were at least 7,400 cross-border attacks between Israel and Lebanon, with Israel conducting about 83% of these attacks (6,142 incidents) and Hezbollah and other armed groups responsible for 1,258 attacks. -- **Escalations**: Both sides have intensified their attacks in recent weeks, with the US advocating for a ceasefire in Gaza. -- **IDF Plans**: The Israel Defense Forces (IDF) approved plans for an attack in southern Lebanon, stating that steps had been taken to ""accelerate readiness in the field"". - -### Military Capabilities - -- **Israel's Arsenal**: Israel possesses advanced missiles, including the Popeye, Extra, and Gabriel, as well as Jericho 2 and Jericho 3 ballistic missiles. They also have three integrated air defense systems: Iron Dome, David's Sling, and Arrow. -- **Hezbollah's Arsenal**: Hezbollah is estimated to have 130,000 rockets, including grad-type missiles, Fajr 3 and Fajr 5 ballistic missiles, and Zelzal or Fateh-110 missiles. - -### Expert Opinions - -- **War Consequences**: Experts believe an Israel-Lebanon war would be disastrous for both sides. -- **Imminent Invasion**: Despite Israel's aggressive stance, some experts do not believe an invasion is imminent due to internal and regional challenges and potential international condemnation. - -### Recent Updates - -- **IDF Readiness**: The IDF has accelerated readiness in the field, indicating a heightened state of preparedness. -- **US Ceasefire Push**: The US is pushing for a ceasefire in Gaza, which could impact the situation along the Israel-Lebanon border. - -### Prediction Markets - -- **Metaculus**: There are no relevant prediction markets on Metaculus regarding an Israeli invasion of Lebanon before October 1, 2024. - -### Current Values - -- **Casualties**: At least 543 people have been killed in Lebanon and 21 Israelis have been killed in the recent cross-border attacks. -- **Border Tensions**: Civilians have been evacuated from border villages, and both sides have targeted each other's military installations and villages. - -Overall, the situation remains highly volatile, with both sides engaging in intense attacks and the IDF preparing for a potential operation in southern Lebanon. However, expert opinions suggest that an invasion may not be imminent due to various regional and international factors. - -######### - -GPT - -To determine the probability of Israel invading Lebanon before October 1, 2024, I will apply a structured approach, considering various factors and using both qualitative and quantitative methodologies. Let's break it down step-by-step: - -### Step 1: Historical Base Rate Analysis - -**Historical Context:** -- Israel has previously invaded Lebanon during the 1982 Lebanon War and launched major operations like the 2006 Lebanon War. -- These conflicts, though infrequent, suggest that large-scale invasions are not a common occurrence but are significant events when they do happen. - -**Base Rate:** -- Since the formation of Hezbollah in the early 1980s, Israel has executed two major military operations in Lebanon involving ground troops, over approximately 40 years. -- This gives us an approximate base rate of 2 significant invasions in 40 years = 5%. - -### Step 2: Current Context Consideration - -**Critical Developments:** -- **Cross-Border Attacks:** The recent high frequency of attacks (7,400 since October 2023) indicates heightened tensions. -- **IDF Readiness:** The IDF has accelerated its preparations for a potential operation, which suggests a higher likelihood of military action. -- **Hezbollah's Arsenal:** Hezbollah’s significant arsenal heightens the potential threat to Israel, possibly prompting preemptive action. -- **International Pressure:** Diplomatic efforts by the US and France to de-escalate tensions may act as a counterweight to immediate invasion plans. - -### Step 3: Expert Opinions and Analysis - -**Expert Opinions:** -- Experts are divided but tend towards skepticism regarding an imminent full-scale invasion due to both regional and international ramifications. -- The general consensus leans towards a high-risk, high-cost scenario for Israel, which might deter immediate action unless deemed absolutely necessary. - -### Step 4: Timeline Consideration - -**Time Frame Analysis:** -- With 2.5 months remaining until the resolution date, an invasion remains possible but would likely depend on further escalations or failed diplomatic efforts. -- A short time window generally decreases the probability of taking such significant action unless prompted by severe provocations. - -### Step 5: Additional Variables - -- **Current Casualties:** The casualty count is high, with significant losses on both sides, potentially escalating urgency. -- **US Ceasefire Efforts:** Diplomatic interventions could either mitigate or delay military actions. -- **Geopolitical Reactions:** Potential backlash and consequences from international actors, especially the Permanent Members of the UN Security Council. - -### Step 6: Probability Calculation - -**Integration of Factors:** -- Let's weigh the base rate (5%) against the escalated context and the IDF preparations (approximately 20% due to rising tensions and readiness). -- Adjust for the deterrents, such as international pressures and the short timeline (reduce by ~5-10%). - -**Final Probability Estimate:** -Given the current escalations, historical infrequency, military readiness, and the countervailing diplomatic efforts, I would estimate the probability of Israel invading Lebanon before October 1, 2024, as follows: - -- **Base Rate:** 5% -- **Increased Likelihood Due to Context and Readiness:** +20% -- **Deterrents (International Pressure, Short Time Window):** -5 - -**Total Estimated Probability: 20%.** - -### Conclusion - -After a careful evaluation of the factors, my final prediction for the likelihood of Israel invading Lebanon before October 1, 2024, is: - -**Probability: 20%**" -26215,"Will a nuclear weapon be detonated as an act of war by Sept 30, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether a nuclear weapon will be detonated as an act of war by September 30, 2024, I have searched various sources and compiled the following information: - -1. **US Preparations for Potential Russian Nuclear Strike**: - - In 2022, the US prepared extensively for a potential nuclear strike by Russia against Ukraine, fearing the use of tactical or battlefield nuclear weapons. - -2. **Treaty on the Prohibition of Nuclear Weapons**: - - The Treaty on the Prohibition of Nuclear Weapons (TPNW) was adopted by the UN in 2017 and entered into force in 2021, aiming to eliminate nuclear weapons through a comprehensive set of prohibitions. - -3. **Nuclear Fratricide**: - - The concept of nuclear fratricide refers to the unintentional detonation of one nuclear weapon by another nearby. Modern nuclear weapons are designed to prevent this, and it is unlikely that one nuclear bomb would set off another. - -4. **Future Use of Nuclear Weapons**: - - There is a growing concern that a nuclear weapon might be used in a desperate situation, such as if a country with nuclear weapons faces a significant threat to its rule or territorial integrity. Examples include North Korea, Pakistan, and potential future conflicts in the Middle East. - -5. **Nuclear War Unlikely**: - - Some experts argue that nuclear weapons are not effective for war and that their use is unlikely due to the devastating consequences. However, the possibility of tactical nuclear weapons being used in specific scenarios cannot be ruled out. - -These sources highlight the ongoing concerns and preparations for potential nuclear conflicts, particularly in the context of the ongoing Russia-Ukraine conflict. They also emphasize the efforts to prohibit nuclear weapons and the unlikeliness of their use in large-scale wars. However, the possibility of tactical nuclear weapons being used in specific scenarios remains a concern. - -######### - -GPT - -Sure, let's break the question down step by step using a structured forecasting approach. - -### 1. **Determine the base rate** - -**Historical data:** -- In the past 75 years, there have been two nuclear detonations as acts of war (Hiroshima and Nagasaki). -- This period includes significant geopolitical tensions during the Cold War but no additional wartime detonations. -- This implies a very low base rate for nuclear detonations as acts of war in any given year. - -Given the historical context, there have been 2 instances in 75 years. Therefore, the annual base rate is approximately: -\[ \text{Base rate} = \frac{2}{75} \approx 0.0267 \] - -### 2. **Adjusting the base rate using the current geopolitical context** - -Next, consider recent events, news, and expert opinions to adjust this base rate. We'll rate the importance and recency of sources of information available: - -1. **Current Major Conflicts and Tensions:** - - Russia-Ukraine conflict: High tension here involves a nuclear power (Russia). The US and allies are deeply involved, increasing the risk but still not conclusively leading to nuclear war. - - Tensions with North Korea and Iran: Always a source of concern, however, there has been no significant escalation beyond traditional brinksmanship recently. - -2. **Recent Efforts and Treaties:** - - The Treaty on the Prohibition of Nuclear Weapons (TPNW): Suggests international efforts to curb nuclear weapons, though major nuclear powers have not adopted it. - -3. **Expert Opinions and Concerns:** - - Experts argue that large-scale nuclear war is unlikely due to mutually assured destruction, but tactical nuclear weapon use remains possible in desperate situations. - - US preparations indicate awareness but not certainty. - -Based on these inputs, let's evaluate the influence on the base rate: - -### 3. **Qualitative adjustments** - -- **Influence of Russia-Ukraine conflict:** Raises the base rate moderately due to ongoing non-resolved aggression. This is particularly concerning given the unpredictability associated with Putin's regime and Russia's doctrinal willingness to consider tactical nuclear weapons. -- **Global nuclear disarmament efforts:** Slightly lowers the base rate due to international pressure and treaties aimed at nuclear non-proliferation. -- **Historical precedent:** Lowers the base rate because of the 75-year history without any nuclear detonation as an act of war despite several close calls. - -### 4. **Factor in the time frame** - -The given date is July 17, 2024, and the question seeks a forecast for a nuclear detonation by September 30, 2024. This leaves approximately 2.5 months until the deadline. - -### 5. **Calculate adjusted probability** - -Rather than alter the base rate substantially from its historical figure, which is already quite low, we make incremental adjustments based on recent tensions and historical resilience to nuclear war: - -Base Rate (Annual) = 0.0267 per year - -For approximately 2.5 months: - -\[ \text{Base rate adjusted for 2.5 months} = 0.0267 \times \left( \frac{2.5}{12} \right) \approx 0.0056 \] - -### Qualitative Adjustments: - -Considering the raised tensions from the Russia-Ukraine conflict and acknowledging the relatively robust global deterrents and historic precedence: - -Let's moderately adjust the qualitative risk increase from 0.0056 to account for the heightened tension, say, an additional factor of 3: - -\[ \text{Adjusted probability} = 0.0056 \times 3 \approx 0.0168 \approx 1.7\% \] - -### 6. **Conclusion** - -After careful consideration of the historical data, current geopolitical tensions, expert opinions, and the limited remaining timeframe: - -**Probability: 2%** - -This incorporates the base rate adjusted for the timeframe and weighted by current events. The estimate remains low but acknowledges the slightly heightened risk within the specific geopolitical context of today." -26213,"Will the extent of the H5N1 outbreak in dairy cows on Sept 30, 2024, be less than 300 herds?","PERPLEXITY - -To assist the superforecaster in making an informed prediction about the extent of the H5N1 outbreak in dairy cows by September 30, 2024, I have compiled the following information from various sources: - -1. **Current Situation**: - - As of June 21, 2024, there are 118 dairy cow herds in 12 U.S. states with confirmed cases of avian influenza A(H5N1) virus infections. - - The outbreak was first reported on March 25, 2024, and has been spreading since then. - -2. **Human Cases**: - - There have been three human cases associated with the dairy cow outbreak, all with direct contact with sick cows. The cases were reported in Texas and Michigan, and all patients had mild illness and fully recovered. - -3. **Animal Health**: - - The USDA has reported 118 dairy cow herds in 12 states with confirmed cases of avian influenza A(H5N1) virus infections as of June 21, 2024. - - High A(H5N1) virus levels have been found in unpasteurized (raw) milk from infected cows. - -4. **Global Context**: - - From 2003 to April 1, 2024, there were 889 cases and 463 deaths (CFR 52%) caused by influenza A(H5N1) virus reported worldwide from 23 countries. - - Sporadic HPAI A(H5N1) virus infections in mammals have been reported globally, including in sea lions, sea elephants, and foxes. - -5. **Risk Assessment**: - - The CDC and WHO assess the public health risk to the general population posed by this virus as low, but the risk of infection for occupationally exposed persons is considered low-to-moderate. - -6. **Monitoring and Response**: - - The CDC is monitoring flu surveillance data using an enhanced, nationwide summer surveillance strategy, especially in areas where A(H5N1) viruses have been detected in dairy cows or other animals. - - The CDC is supporting states in monitoring people with exposure to cows, birds, or other domestic or wild animals infected or potentially infected with avian influenza A(H5N1) viruses. - -7. **Prediction Markets**: - - There are no relevant prediction markets available on Metaculus for this specific question. - -Given this information, the superforecaster can consider the current rate of spread, the effectiveness of monitoring and response efforts, and the global context of avian influenza outbreaks to make an informed prediction about the extent of the H5N1 outbreak in dairy cows by September 30, 2024. - -######### - -GPT - -To systematically approach the problem of predicting whether the extent of the H5N1 outbreak in dairy cows on September 30, 2024, will be less than 300 herds, we need to consider the following factors: - -**1. Current Situation and Rates of Spread:** -- The outbreak was reported on March 25, 2024. -- By May 15, 2024, the outbreak had affected 46 herds. -- By June 21, 2024, the number of herds affected had increased to 118 in 12 states. - -The rate of increase from May 15 to June 21 corresponds to an increase of 72 herds in approximately 37 days. This gives us an approximate daily increase rate of 72 herds / 37 days ≈ 1.95 herds per day. - -**2. Time Until Resolution:** -- From July 17, 2024, to September 30, 2024, there are 75 days remaining. -- If the outbreak continues to spread at the rate of ~1.95 herds/day, over the next 75 days, we can project an additional increase of approximately 1.95 herds/day * 75 days = 146 herds. - -**3. Total Projected Affected Herds:** -- Starting from the current 118 herds and adding the projected 146 herds, we get 118 + 146 = 264 herds by September 30, 2024. - -**4. Possible Changes in Containment and Spread:** -- Enhanced monitoring and response efforts by agencies like the CDC, USDA, and FDA could affect the rate of spread. -- If these efforts are highly effective, the rate of spread could decrease. -- Conversely, if the virus spreads to more densely populated dairy areas, the rate could increase. - -Considering these factors, the central estimate of 264 herds is below the threshold of 300 herds. However, the uncertainty in this estimate due to potential changes in containment efficiency or unexpected acceleration of the spread must be accounted for. - -**5. Adjusting for Uncertainty:** -- The base rate projection gives us a central estimate, but the true outcome is subject to variability. -- Let’s consider a variance due to effective containment measures or unforeseen outbreaks: - -If we assume a normal distribution around our estimate with a standard deviation to capture the uncertainty, we might estimate a range, say ±50 herds for standard deviation (SD). The middle 95% of the distribution would then range roughly from 264 - 100 to 264 + 100, giving us a range of approximately 164 to 364. - -Given this, let's compute the probability of the outcome being less than 300 herds: -- The total probability of being less than 300 herds is calculated using the properties of the normal distribution. -- Using the cumulative distribution function (CDF) for a normal distribution centered at 264 with SD =50: -\[ P(X < 300) \approx \text{CDF}((300 - 264) / 50) \] -\[ P(X < 300) \approx \text{CDF}(0.72) \approx 0.765 \] - -Therefore, given the possibilities of containment efforts affecting the spread rate and some general uncertainty assumptions, the probability distribution central estimate supports the likelihood of being less than 300 herds. - -**Final Answer:** -Given the above analysis, the most likely estimate for the probability of the extent of the H5N1 outbreak in dairy cows being less than 300 herds by September 30, 2024, is: -**Probability: 76%**" -26216,"Bitcoin Extremes: Will 1 bitcoin be worth $100,000 or more before Sept 15, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether 1 bitcoin will be worth $100,000 or more before September 15, 2024, I have summarized the following sources: - -1. **Forbes**: Bitcoin has set a new all-time intraday trading high of $73,000 in March 2024. Factors influencing its performance include institutional adoption, the recent halving event, regulatory adjustments, and macroeconomic trends. Optimistic scenarios highlight rising demand for block space and potential adoption of the Lightning Network, while pessimistic scenarios involve concerns about long-term security and environmental impacts. - -2. **Swan Bitcoin**: Various industry leaders have made bold predictions for Bitcoin's future value. Max Keiser predicts $200,000 by 2024, Fidelity predicts $1 billion by 2038, and Hal Finney predicted $22 million by 2045. The article discusses the complexities of past performance and emerging trends to provide a comprehensive understanding of Bitcoin's price trajectory. - -3. **CNBC**: Standard Chartered analyst Geoff Kendrick suggests that Bitcoin's value could jump to $100,000 by the end of 2024, citing the collapse of traditional banks and the stabilization of risky investments as factors favoring Bitcoin's performance. - -4. **USA Today**: The sentiment among Bitcoin investors is optimistic, with the SEC's approval of spot Bitcoin ETFs and the upcoming halving event in April 2024 potentially fueling prices to unprecedented heights. Experts believe it's only a matter of time before Bitcoin sets new all-time highs on its path to $100,000. - -5. **CoinCodex**: According to their Bitcoin prediction algorithm, Bitcoin is predicted to hit $100,000 in September 2024. The current sentiment is Neutral, and the Fear & Greed Index is showing 33 (Fear). - -These sources highlight the various factors influencing Bitcoin's price trajectory and the diverse predictions from industry leaders. The recent halving event, institutional adoption, and macroeconomic trends are key factors that could drive Bitcoin's value towards $100,000 or more before September 15, 2024. - -######### - -GPT - -To predict whether 1 bitcoin will be worth $100,000 or more before September 15, 2024, let's meticulously consider all factors and make a probabilistic assessment. - -### Step-by-Step Analysis: - -#### 1. Historical Trends and Volatility -- **Price Trends**: Bitcoin has experienced extreme price swings historically but has generally trended upwards over the long term. -- **Recent Highs**: As of March 2024, Bitcoin reached an all-time intraday high of $73,000. This represents a significant run-up from previous lows and suggests potential for further upward movement. -- **Past Cycles**: Bitcoin's price cycles have been characterized by sharp increases followed by significant corrections. Past performance can offer some understanding but isn’t a perfect predictor. - -#### 2. Recency and Importance of News Sources -- **Forbes**: Highlights the recent high of $73,000 and also points out influential factors like institutional adoption. The recency of the information (March 2024) makes it quite relevant. -- **Swan Bitcoin**: Covers speculative long-term predictions which are less relevant to a short-term forecast but emphasizes past performance and current industry sentiment. -- **CNBC**: Standard Chartered’s prediction of $100,000 by the end of 2024 provides a professional, albeit optimistic, financial analysis. This is a credible source and aligns with other positive predictions. -- **USA Today**: Highlights sentiments around SEC’s approval of Bitcoin ETFs and the upcoming halving event. Both are significant drivers of potential price increases. -- **CoinCodex**: Algorithmic prediction gives a specific forecast of hitting $100,000 in September 2024. While algorithms can be insightful, they should be critically evaluated in the context of all other factors. - -#### 3. Time Considerations -- **Timeframe**: From today's date (2024-07-17) until the resolution date (2024-09-15) is approximately 60 days. This relatively short period demands more weight be placed on recent trends and near-term catalysts. -- **Volatility**: Given the limited window, Bitcoin’s inherent volatility can work both ways. Past short-term movements suggest that such a spike is within the realm of possibility. - -#### 4. Factors Impacting Bitcoin's Price -- **Institutional Adoption**: Continues to be a major bullish factor, suggesting support for substantial price increases. -- **Regulatory Landscape**: Positive moves like SEC’s approval of Bitcoin ETFs are supportive of higher prices. However, any adverse regulatory changes could quickly change sentiment. -- **Macroeconomic Factors**: Global economic instability and interest in alternative assets can drive demand for Bitcoin. -- **Technological Developments**: Adoption of the Lightning Network and other technological advancements can enhance usability and demand. - -#### 5. Base Rates and Comparable Events -- **Base Rates**: Bitcoin has broken major price milestones before (e.g., reaching $20,000 in December 2017) and it’s performed significant short-term price increases historically. -- **Novelty/Changes**: Recent regulatory approvals and technological advancements are relatively new and can significantly impact prices. - -#### 6. Default Resolution and Probability Distribution -- If no significant events occur, Bitcoin's price would likely fluctuate but possibly remain below $100,000 considering it is significantly under this threshold ($27,000 away from $100,000 as of the recent high). - -### Final Calculation -Summarizing the probabilities: - -- **Institutional adoption and positive sentiment**: 30% -- **Regulatory support and resilience post-halving**: 20% -- **Technological advancements and demand pressures**: 20% -- **Potential adverse events or major sell-offs**: -10% (as a conservative adjustment) -- **Base Rate Historical Trends**: 30% - -Adding these up and normalizing the probability (factoring overlap): - -**Final Probability Estimate: 55%** - -This means that while the likelihood isn’t overwhelmingly certain, there are strong drivers that give Bitcoin a good chance to surpass the $100,000 mark before September 15, 2024. - -### Final Answer -**Probability: 55%**" -26212,"Will the extent of the H5N1 outbreak in dairy cows on Sept 30, 2024, exceed 200 herds?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question about the extent of the H5N1 outbreak in dairy cows on September 30, 2024, here are the key points from the provided sources: - -1. **Current Situation**: As of July 12, 2024, the Centers for Disease Control and Prevention (CDC) reported that 151 dairy cow herds in 12 U.S. states have confirmed cases of avian influenza A(H5N1) virus infections, with the number of infected herds continuing to grow. - -2. **Government Response**: The U.S. Department of Agriculture (USDA) has committed $98 million to enhance its response efforts, including financial incentives for livestock producers to improve on-site biosecurity and compensate for lost milk production. The Department of Health and Human Services (HHS) has allocated $101 million for testing, tracking, and treatment of both animals and humans potentially exposed to the H5N1 virus. - -3. **Economic Impact**: The American Association of Bovine Practitioners (AABP) estimates the economic impact of H5N1 on dairy cattle to be $100 to $200 per cow. - -4. **Human Cases**: Four human cases of A(H5) infection associated with this outbreak in U.S. dairy cows have been reported, with the CDC assessing the current H5N1 bird flu human health risk for the U.S. general public as low. - -5. **Global Context**: Globally, sporadic HPAI A(H5N1) virus infections in mammals have been reported across Asia, North America, South America, and Europe, with the World Health Organization (WHO) assessing the public health risk to the general population as low and for occupationally exposed persons as low-to-moderate. - -6. **Monitoring and Testing**: The CDC is monitoring flu surveillance data and conducting targeted, situation-specific testing of exposed people who develop symptoms. Over 32,000 specimens have been tested at public health labs since March 24, 2024. - -7. **Farmers' Cooperation**: Despite efforts to contain the outbreak, farmers have been hesitant to allow health officials on their premises for testing due to concerns about business implications, and farm workers have been reluctant to undergo testing due to fears about missing work or government tracking. - -8. **Containment Measures**: The USDA is working with states to limit the movement of lactating cattle within their borders to reduce further spread of H5N1 between herds. A federal order requires lactating dairy cattle to receive a negative result of testing for influenza type A virus before interstate movement. - -These updates indicate that the outbreak is ongoing and spreading, with efforts to contain it through financial incentives, testing, and movement restrictions. However, the effectiveness of these measures in limiting the spread to fewer than 200 herds by September 30, 2024, remains uncertain. - -######### - -GPT - -### Analysis: - -#### Key Parameters and Trends: - -1. **Current Number of Herds Affected (as of July 12, 2024):** - - 151 herds in 12 states. - -2. **Spread Rate:** - - The data shows that the number of affected herds has increased from 46 on May 15, 2024, to 151 as of July 12, 2024. This represents: - - May 15, 2024 to July 12, 2024: ~ 58 days. - - Increase from 46 to 151 herds in 58 days or about 105 herds in 58 days, averaging about 1.81 herds/day. - -3. **Time Remaining until September 30, 2024:** - - From July 17, 2024, to September 30, 2024, there are 75 days remaining. - -4. **Extrapolation:** - - If the current rate continues (1.81 herds/day): - - An additional 1.81 herds/day * 75 days = 135.75 herds. - - Adding this to the current 151 herds gives a projected total of about 286.75 herds by September 30, 2024. - -5. **Government Response:** - - While significant funds have been allocated to tackle the outbreak, the effectiveness of these measures, given the hesitation of farmers and workers, remains uncertain. - - $98 million from USDA for biosecurity and compensation. - - $101 million from HHS for testing and tracking. - -6. **Containment Efforts:** - - Movement restrictions and negative testing requirements for interstate movement of lactating dairy cattle. However, effectiveness is dubious with noted farmer resistance to testing and biosecurity measures. - -7. **Base Rate of Similar Events:** - - Historically, containment measures for diseases like H5N1 can be challenging, particularly given the zoonotic potential and rapid spread. - - In similar disease outbreaks, agricultural biosecurity compliance is a major variable affecting outcomes. - -### Statistical Analysis and Further Insights: - -1. **Exponential Growth Potential:** - - Disease outbreaks often follow exponential growth if not adequately contained. However, assuming containment efforts are somewhat effective, the purely linear projection might be moderated. - -2. **Incentive and Compliance Dynamics:** - - Financial incentives may incrementally increase compliance but the existing farmer and worker hesitance signals barrier challenges. - -3. **Global Context and Comparative Spread:** - - Global trends show sporadic mammalian infections, suggesting that while the potential spread could be wide, local factors (social, economic) will heavily influence the exact trajectory. - -### Conclusion and Final Probability: - -Given the extrapolation based on the current spread rate, the projected number of affected herds by September 30, 2024, is approximately 287 (if no substantial intervention effects occur). While there are substantial funds committed to containment, the noted resistance and complexity of implementing biosecurity measures introduce significant uncertainty. Typically, such resistance often leads to slower than anticipated virus containment. - -*However*, the potential effectiveness of these funded interventions should not be completely discounted. Balancing these factors makes it prudent to moderate the purely extrapolated probability moderately. - -### Final Detailed Analysis: - -- **Purely based on extrapolation**: Over 200 herds almost certainly. -- **Factoring in government interventions and resistance**: Still likely over 200 herds, but perhaps with a bit more uncertainty. - -### Summary: - -Taking into account spread rates, time remaining, intervention efforts, and historical precedents: - -**Probability: 80%** - -Given the present rate of spread and considering the potential impact of ongoing containment efforts, the probability of the number of H5N1 affected dairy herds exceeding 200 by September 30, 2024, is high but not absolute. My considered estimate is that there's an 80% chance that the spread will exceed 200 herds as per the resolution criteria." -26214,"Will the extent of the H5N1 outbreak in dairy cows on Sept 30, 2024, be between 200 and 300 herds?","PERPLEXITY - -The current situation regarding the H5N1 outbreak in dairy cows in the United States is as follows: - -- **USDA Response**: The U.S. Department of Agriculture (USDA) has announced an additional $824 million in emergency funding to protect livestock health and launched a Voluntary H5N1 Dairy Herd Status Pilot Program to help dairy producers monitor the health of their herds and move cows more quickly while providing ongoing testing and expanding USDA’s understanding of the disease. - -- **CDC Update**: The Centers for Disease Control and Prevention (CDC) reported that 151 dairy cow herds in 12 U.S. states have confirmed cases of avian influenza A(H5N1) virus infections in dairy cows. Four human cases of A(H5) infection associated with this outbreak in U.S. dairy cows have been reported. The CDC continues to respond to the public health challenge posed by the outbreak, working in collaboration with the USDA, the Food and Drug Administration (FDA), state public health and animal health officials, and other partners using a One Health approach. - -- **Current Situation**: The outbreak was first reported on March 25, 2024, and since then, there have been sporadic human cases in people who had exposure to infected dairy cows. The CDC believes the current risk to the general public from bird flu viruses is low, but people who have job-related or recreational exposure to infected birds or animals, including cows, are at greater risk of contracting HPAI A(H5N1) virus. - -- **WHO Risk Assessment**: The World Health Organization (WHO) reported a laboratory-confirmed human case of avian influenza A(H5N1) detected in Texas, where the case was exposed to dairy cattle presumed to be infected with the virus. The WHO assesses the public health risk to the general population posed by this virus to be low and for occupationally exposed persons the risk of infection is considered low-to-moderate. - -- **Prediction Markets**: Metaculus, a prediction market platform, has a query asking whether the extent of the H5N1 outbreak in dairy cows on September 30, 2024, will be between 200 and 300 herds. - -These sources provide a comprehensive overview of the ongoing efforts to contain the H5N1 outbreak in dairy cows and the associated public health risks. - -######### - -GPT - -In approaching this question, I will assess and weigh various factors contributing to the development of the H5N1 outbreak in dairy cows. - -### Current Situation Analysis -1. **Outbreak Growth**: - - The outbreak started on March 25, 2024, with the latest count at 151 herds as of July 17, 2024. - - On May 15, 2024, there were 46 herds affected. This means that the number of affected herds increased from 46 to 151 in about two months, indicating an average growth rate of approximately 52.5 herds per month. - -2. **Time Remaining**: - - There are about 2.5 months remaining until September 30, 2024. - -3. **Trend Projection**: - - If the growth rate of the outbreak continues at approximately 52.5 herds per month: - 1.5 months * 52.5 herds/month ≈ 131 herds added to the current 151 herds, which would give us a total of around 282 herds by the end of September. - -### Factors Influencing the Prediction -3. **Response and Mitigation Efforts**: - - USDA and other agencies have implemented significant efforts, such as the Voluntary H5N1 Dairy Herd Status Pilot Program and emergency funding, aimed at containing the outbreak. - - These interventions could significantly slow the rate of new infections, potentially bringing the number lower than projected by a simple extrapolation. - -4. **Prediction Markets and Expert Opinion**: - - Metaculus Prediction Market’s interest around whether the outbreak will be within the specified range suggests that this is a plausible scenario, given that prediction markets generally aggregate the wisdom of the crowd. - -5. **Historical Comparisons**: - - Outbreaks tend to follow a logistic growth model where initially growth is exponential but slows as the response measures take effect and as more stringent biocontrol measures are put into place. - -### Uncertainty and Distribution -1. **Variance Considerations**: - - While the growth rate has been relatively steady, there’s significant variance and uncertainty introduced by governmental and private sector interventions. - - The exact number could fluctuate both higher and lower based on unforeseen factors, such as additional public health measures or wider spread in cooler months. - -### Bayesian Analysis - Let's consider a Bayesian adjustment based on the above factors: - - **Prior**: Assuming no intervention and continued trend, the probability of being within the 200-300 range is high. - - **Likelihood**: Effective interventions may reduce the number of outbreaks from the current trajectory. - -Given this, we can moderately adjust the probability downwards from the simple extrapolated scenario, but we remain within a reasonable confidence range. - -### Final Calculation -1. Assume that interventions have an efficacy of slowing down the rate by 20%: - - Adjusted growth rate ≈ 52.5 * 0.8 = 42 herds/month. - - Final Calculation: (2.5 months * 42 herds/month) + 151 ≈ 256 herds. - This falls firmly within the 200-300 range. - -2. **Probability Estimate**: - Considering all information and possible variances, my best estimate balances between the default trend and effective interventions. - -### Conclusion: -Probability: 75% - -This percentage accounts for the high past growth rate and the likelihood of effective intervention, balancing it out to a moderate confidence level that the number of affected herds will be between 200 and 300 by September 30, 2024." -26236,Will at least 24 world records be broken at the 2024 Paris Olympics?,"PERPLEXITY - -To assist the superforecaster in predicting whether at least 24 world records will be broken at the 2024 Paris Olympics, I have compiled a concise rundown of the most relevant news and updates: - -### Athletics - -- **Pole Vault**: Armand ""Mondo"" Duplantis, the current world record holder, has broken the record eight times, with his most recent jump being 6.24m. He is expected to continue pushing the limits and potentially break his own record again. -- **Women's 200m**: The record has stood since 1988, but Shericka Jackson came close to breaking it with a time of 21.41 seconds at the World Athletics Championships 2023. Other top contenders, Elaine Thompson-Herah and Gabrielle Thomas, are also close to the record. -- **Men's 400m**: Wayde van Niekerk holds the current world record with a time of 43.03 seconds, set at the 2016 Rio Olympics. -- **Men's 800m**: David Rudisha holds the current world record with a time of 1:40.91 seconds, set at the 2012 London Olympics. -- **Men's 400m Hurdles**: Karsten Warholm holds the current world record with a time of 45.94 seconds, set at the 2021 Tokyo Olympics. - -### Swimming - -- **Women's 100m Backstroke**: Kaylee McKeown holds the current world record with a time of 57.33 seconds and is a strong favorite to break her own record again. -- **Men's 1500m Freestyle**: Sun Yang holds the current world record with a time of 14:31.02 seconds, set at the 2012 London Olympics. - -### Weightlifting - -- **Men's 61kg Clean and Jerk**: Hampton Morris broke the senior weightlifting world record with a lift of 176 kilograms (388 pounds) at the 2024 International Weightlifting Federation (IWF) World Cup in Thailand. - -### Other Events - -- **Sport Climbing**: Sam Watson broke the world record not just once, but twice within an hour at a recent World Cup event in China. -- **3x3 Basketball**: Jimmer Fredette could make history by becoming the first NBA player to compete in the Olympic 3x3 tournament. - -### General Trends - -- The Olympic Games often see a surge in record-breaking performances due to the high level of competitiveness among the world's top athletes. -- Many athletes are expected to push the limits and break records in their respective events, with some already coming close to existing records. - -### Expert Opinions - -- The article from MensHealth.com.au highlights the potential for multiple world records to be broken at the 2024 Paris Olympics, citing the advancement of human athleticism and the competitiveness of the Games. -- The Reddit discussion on r/trackandfield mentions several world records that are at risk of being broken, including those in the pole vault, shot put, and 400m hurdles. - -### Current Values and Predictions - -- There are no specific predictions or current values provided in the sources regarding the exact number of world records that will be broken. However, the general consensus is that multiple records are likely to be broken due to the high level of competition and athleticism at the 2024 Paris Olympics. - -### Recent Updates - -- The most recent updates suggest that athletes are continuing to push the limits and come close to breaking existing records. For example, Kaylee McKeown recently came close to her own world record in the 100m backstroke, and Armand Duplantis continues to dominate the pole vault event. - -Overall, the sources suggest that multiple world records are at risk of being broken at the 2024 Paris Olympics, with several athletes already demonstrating their potential to push the limits and break records in their respective events. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. Understanding the Context -The task is to predict whether at least 24 world records will be broken at the 2024 Paris Olympics, based on historical trends, current athlete performances, and general conditions leading up to the event. - -#### 2. Historical Data Analysis -Reviewing the number of world records broken in the last three Summer Olympics provides a valuable base rate: -- London 2012: 32 world records -- Rio de Janeiro 2016: 23 world records -- Tokyo 2020: 27 world records - -Average number of records broken is \( \frac{32 + 23 + 27}{3} = 27.3 \). - -#### 3. Evaluation of Current Trends and Expert Opinions -- **Athletics:** Several athletes are nearing or surpassing world records, such as Armand Duplantis (Pole Vault), Shericka Jackson (200m), and Kaylee McKeown (100m Backstroke). The competitive level seems elevated. -- **Swimming & Weightlifting:** Indicators suggest potential multiple record-breaking performances, given the readiness of athletes like Kaylee McKeown and Hampton Morris. -- **New Sports:** Innovations and the inclusion of new sports like Sport Climbing introduce additional record-breaking opportunities. - -#### 4. Influence of Environmental Conditions -The general trend at the Olympics often points to heightened performances due to competition levels and favorable conditions. With advanced training techniques, equipment, and nutrition, athletes are increasingly prepared to break records. - -#### 5. Time Sensitivity and Variance -Considering we are reasonably close to the event date (2024-07-18) and consistent performance maintenance, the likelihood of radical changes in athleticism is low. However, daily and weekly variances should be considered, especially with the potential for injuries or conditions impacting performances closer to the event. - -#### 6. Default Behavior and Skew from Historical Precedent -If no significant changes occur, we can reasonably expect a number close to the average of past Olympics. However, there is a slight upward trend and notable anticipation for numerous records being broken due to current athlete conditioning and preparation. - -#### 7. Resolution Criteria -- Multiple records broken within the same event count separately. -- Exclusion of non-standard world record categories like ""world bests"". - -### Synthesis and Conclusion -Based on the average number of world records broken in the past three Olympics (27.3), the maintained competitive fervor, and the promising signs from experts and current athletic standings, the likelihood of breaking at least 24 records appears strong. - -**Final Prediction:** -- Historical trend supports an average breaking of more than 24 records. -- Current performance trends and expert opinions support this prediction. -- Allowing for minor variances and the specific rules of counting records in preliminaries and finals, the probabilistic favor tilts towards at least 24 records being broken. - -Given these considerations: - -**Probability:** 85%" -26235,"Will the Warren Buffett Indicator exceed 200% before September 17, 2024? -","PERPLEXITY - -To assist the superforecaster in making a prediction about whether the Warren Buffett Indicator will exceed 200% before September 17, 2024, I have compiled a detailed rundown of the most relevant news and data. - -### Market Valuation Indicators - -- **Buffett Indicator**: As of July 18, 2024, the Buffett Indicator stands at 157.9%, indicating that the stock market is significantly overvalued. -- **Historical Context**: The Buffett Indicator has varied widely over the past five decades, with a low of 31.1% in July 1982 and a high of 157.9% in July 2024. -- **Current Valuation**: The ratio of total US stock market valuation to GDP is 197% as of May 31, 2024, which is 59.23% above the historical trend line, suggesting the stock market is overvalued relative to GDP. - -### Expert Opinions and Market Trends - -- **Warren Buffett's Views**: Warren Buffett has historically used the Buffett Indicator to make market calls. In 1999, he predicted lower long-term returns due to high valuations. In 2008, he expected equities to outperform cash over the next decade. -- **Market Sentiment**: Despite the Buffett Indicator flashing warning signs, some analysts are cautiously optimistic about further market gains, citing AI adoption and expectations of interest rate cuts. -- **JPMorgan Chase CEO Jamie Dimon**: Dimon views the current market climate as genuine, driven by AI adoption, rather than mere hype. - -### Prediction Markets - -- **Metaculus Prediction**: A question on Metaculus asks if the Warren Buffett Indicator will exceed 200% before September 17, 2024. The question is currently awaiting approval by Community Moderators. - -### Recent Updates - -- **Fed Balance Sheet**: The expansion and contraction of the Fed's balance sheet can influence the economy and the stock market. The Fed's actions can lead to changes in interest rates, which in turn affect consumption and investment. -- **Interest Rate Expectations**: Expectations of three interest rate cuts by the Federal Reserve have contributed to market optimism. - -### Key Data Points - -- **Current GDP**: The annualized GDP is estimated at $28.38 trillion as of May 31, 2024. -- **Total Market Value**: The composite US stock market value is estimated at $55.81 trillion as of May 31, 2024. - -By considering these factors, the superforecaster can make a more informed prediction about whether the Warren Buffett Indicator will exceed 200% before September 17, 2024. - -######### - -GPT - -### Step-by-Step Analysis: - -Given the information provided, let's break down the key factors that can influence whether the Warren Buffett Indicator will exceed 200% before September 17, 2024. - -### 1. Current Status: -- **Current Buffett Indicator:** 157.9% as of July 18, 2024. -- **Current Wilshire 5000 to GDP Ratio:** 197% as of May 31, 2024. -- **Most recent figure displayed in the data table:** 196.20375072862052 for July 8, 2024. - -### 2. Timeframe: -- We have approximately two months until September 17, 2024. Market changes can be significant over this period, especially considering economic dynamics such as interest rate changes, major economic reports, and corporate earnings announcements. - -### 3. Recent Trends and Market Sentiment: -- **Market Sentiment:** Despite high valuations, some analysts are cautiously optimistic about further gains, particularly due to AI advancements and expectations of interest rate cuts. -- **Expert Opinions:** While Buffett warns against high valuations, other credible figures (e.g., Jamie Dimon) show optimism driven by technological innovation. - -### 4. Economic Indicators: -- **GDP:** $28.38 trillion (May 31, 2024). -- **Total Stock Market Value:** $55.81 trillion (May 31, 2024). - -### 5. Historical Base Rates: -- From the historical data, we know the indicator has varied but reached its highest at 157.9% recently (historically lower than 200%). -- The ratio was highest in recent history at 157.9% which gives insight but does not directly correlate to the ratio required to exceed 200%. - -### 6. Likelihood of Events: -- The likelihood of the stock market increasing significantly enough within a two-month period to breach the 200% threshold needs to be considered. A growth from approximately 197% to over 200% is a modest single-digit percentage increase which, while uncommon in short periods, is not impossible given recent market volatility and triggers. - -### 7. Novel Factors: -- **Interest Rate Cuts:** Expectations of cuts may drive market behavior to optimistic valuations. -- **AI Adoption:** Significant gains could be driven by technological stocks, which disproportionately impact the overall valuation metrics. - -### 8. Statistical Analysis: -To breach 200%, the market cap must increase relative to GDP. -- **Current Wilshire 5000 to GDP Ratio Calculation:** - - Current Ratio = Market Cap / GDP = 197%. - - To achieve 200%: \[ \frac{\text{New Market Cap}}{\text{GDP}} > 2 \iff \text{New Market Cap} > 2 \times \text{GDP} \] - - With GDP constant at $28.38 trillion: - - Required Market Cap for 200% = 2 * 28.38 = 56.76 trillion. - -Over two months, while this short gain is theoretically achievable (around 0.957 trillion additional market cap, a ~1.7% increase), the base rate of such rapid increases should lead to cautious probabilistic assignment. - -### Final Probabilistic Evaluation: -- **Probability Calculation:** - - Current index: 157.9% - - Required quick gain over the next two months based on historical variance and trends. - - Factors such as rate cuts and AI advancements could positively skew market cap. - - Nonetheless, caution over historical rapid gain base rates warns against over-optimism. - -Thus, accounting for realistic, historical, and current trend-based rates of variance: - -""Probability: 40%""" -26234,"Will an avian influenza virus in humans be declared a “Public Health Emergency of International Concern” by the World Health Organization before Sept 30, 2024?","PERPLEXITY - -To provide context for forecasting whether an avian influenza virus in humans will be declared a ""Public Health Emergency of International Concern"" by the World Health Organization (WHO) before September 30, 2024, here are key points from relevant sources: - -1. **Current Situation**: - - The WHO has reported a laboratory-confirmed case of human infection with an influenza A(H5N1) virus in Texas, USA, in April 2024. - - The Centers for Disease Control and Prevention (CDC) has reported five cumulative human cases in the United States, with four cases linked to dairy cows and one to poultry. - -2. **Risk Assessment**: - - The WHO assesses the public health risk to the general population posed by this virus as low and for occupationally exposed persons as low-to-moderate. - - The CDC considers the current public health risk as low and is monitoring the situation closely. - -3. **Previous Declarations**: - - The WHO has previously declared a Public Health Emergency of International Concern (PHEIC) for the monkeypox outbreak in July 2022. - -4. **Monitoring and Response**: - - The WHO and CDC are actively monitoring the situation and providing guidance for public health professionals, laboratorians, and the general public. - - The FDA is working with the USDA and state partners to investigate the outbreak in dairy cows and ensure the safety of the commercial milk supply. - -5. **Prediction Markets**: - - There are no relevant prediction markets on Metaculus that directly address this specific question. - -Given the current risk assessments and ongoing monitoring, it is crucial to continue tracking updates from the WHO, CDC, and other health organizations to determine if the situation escalates and warrants a PHEIC declaration. - -######### - -GPT - -### Analysis and Probability Determination - -#### Step-by-Step Evaluation: - -1. **Current Situation and Initial Observations**: - - - **Human Cases**: The detection of five cumulative human cases of H5N1 in the USA confirms that a zoonotic jump has occurred. - - **Monitoring and Response**: The WHO, CDC, and other health authorities are actively monitoring the situation and providing guidance, but they currently assess the public health risk as low. - -2. **Base Rate of Similar Events**: - - - Previous similar zoonotic outbreaks (H1N1 in 2009 and COVID-19 in 2019) led to PHEIC declarations. However, these outbreaks also saw rapid human-to-human transmission, which elevated them to the status of international health crises. - -3. **Risk Assessment and Current Trends**: - - - **WHO and CDC Risk Assessment**: Both organizations classify the current public health risk as low. - - **Expert Concern**: There is concern over potential transmission among humans, especially given the virus's jump to cattle, an abundant mammalian host. - -4. **Potential for Escalation**: - - - The potential for H5N1 to evolve and spread among humans is non-negligible. Historical precedent shows that zoonotic viruses can suddenly escalate. - - Continued close monitoring by WHO and CDC suggests alertness but does not yet indicate an immediate risk that warrants a PHEIC. - -5. **Time Frame**: - - - Today's date is 2024-07-18, and the resolution date is 2024-09-30, giving approximately 2.5 months for the situation to change and potentially trigger a PHEIC declaration. - -6. **Historical Precedents and Novel Factors**: - - - Both H1N1 (2009) and COVID-19 (2019) show zoonotic viruses can lead to PHEIC declarations. However, these precedents included symptoms of rapid transmission between humans which we do not currently have with H5N1. - - Novel factors (advanced surveillance and quick action taken by health authorities) may prevent the situation from escalating unnoticed. - -#### Synthesis of Information: - -- Keeping the current risk assessment by WHO as low in mind, combined with the limited human cases so far, the probability seems low that the WHO will declare a PHEIC based on the current evidence. -- Nonetheless, the history of sudden zoonotic pandemics warrants careful attention to evaluating natural progression in the next couple of months. -- Given the history and monitoring protocols, any sudden increase in cases or indication of sustained human-to-human transmission would likely push towards a PHEIC declaration. - -#### Final Probability Estimate: - -After considering all factors—current case numbers, rate of escalation, expert guidance, remaining time frame, historical precedents, and ongoing health organization actions—the most reasoned estimate for the probability of a PHEIC declaration due to an avian influenza virus by the WHO before September 30, 2024, is: - -**Probability: 30%** - -This accounts for the uncertainty and the potential for rapid change in the situation, but leans on the current assessment of low risk by major authorities." -26233,"Will the domestic box office opening of ""Deadpool & Wolverine"" be higher than that of ""Deadpool"" and ""The Wolverine"" combined?","PERPLEXITY - -To provide context for forecasting the domestic box office opening of ""Deadpool & Wolverine,"" here are the key points from recent news and discussions: - -1. **Box Office Projections**: - - Advance box office tracking services predict an opening of $160 million-$165 million, which would be a record start for an R-rated movie. - - Some sources suggest the opening could be higher, up to $200 million, which would be significantly higher than the current R-rated record held by the first ""Deadpool"" movie. - -2. **Comparison to Previous Movies**: - - The current R-rated record is held by ""The Passion of the Christ"" (2004) with $370 million, but ""Deadpool & Wolverine"" is expected to beat the unadjusted record. - - The first ""Deadpool"" movie holds the record for the biggest R-rated opening with $132.4 million, which ""Deadpool & Wolverine"" is likely to surpass. - -3. **Tracking and Presales**: - - The movie's tracking numbers are strong, with high scores in unaided and total awareness and interest, indicating a high demand for the movie. - - Current presales are at $19 million, which is behind ""Doctor Strange in the Multiverse of Madness"" but ahead of ""Guardians of the Galaxy Vol. 3"" and ""Black Panther: Wakanda Forever"". - -4. **Expert Opinions and Hype**: - - Many experts and fans believe the movie has the potential to break records due to its strong tracking and the return of Hugh Jackman as Wolverine. - - Some are cautious about the R-rating limiting the opening, but others think the nostalgia for Wolverine and the X-Men will override this limitation. - -5. **Recent Updates**: - - The most recent updates suggest the opening could be higher than initially predicted, with some sources indicating a potential for $200 million or more. - -Overall, the current predictions and tracking data suggest that ""Deadpool & Wolverine"" has a strong chance of breaking the R-rated opening record and potentially reaching $200 million or more. - -######### - -GPT - -**Step-by-Step Analysis** - -1. **Resolution Criteria**: - - The question resolves as **Yes** if the domestic box office opening for ""Deadpool & Wolverine"" is higher than $185,548,391. - - The combination quantities are: ""Deadpool"" ($132,434,639) + ""The Wolverine"" ($53,113,752). - -2. **Current Projections**: - - Projections range from $160-$165 million (recent Deadline) with some sources projecting over $200+ million (earlier Deadline report). - - Considering the midpoint of the most recent projection, we get a figure of around $162.5 million. - - Historical R-rated record openings and trends suggest strong potential growth for such franchises. - -3. **Historical Comparisons**: - - Previous R-rated records: ""Deadpool"" holds the biggest R-rated opening with $132.4 million. - - Strong fan base and return of Hugh Jackman as Wolverine can significantly boost interest. - -4. **Presales and Tracking Data**: - - Fandango's report on strong pre-sales indicates high demand. - - Presales at $19 million are highly significant, showing keen initial interest. - -5. **Expert Opinions and Hype**: - - Consensus leans towards a historic performance due to several factors: - - Nostalgia for Wolverine and overall X-Men Universe appeal. - - Hugh Jackman & Ryan Reynolds' synergy/publicity boosting interest. - -6. **Time Until Release**: - - Approximately 8 days remain until release. - - Significant marketing, word-of-mouth, and media hype can emerge within such a short time, making last-second boosts more plausible. - -7. **Other Considerations**: - - The economic and social environment seems stable with no major disruption projections that could impede theatre attendance. - - Marvel Studios' track record demonstrates the ability to meet or surpass box office expectations. - -**Base Rate Analysis** -- Marvel movies generally perform exceptionally well in openings. -- Historical openings' distribution in similar timeframes and R-rated movies suggest high probability towards achieving $185.5 million. - -**Novel Factors to Consider** -- Unpredictable factors like natural disasters, political turmoil, or a sudden global event could create variance, although they are generally low in mid-year. - -**Risk and Uncertainty** -- It's essential to cover a range of probabilities given the projections and time frame: - - Could the movie fall short despite high expectations? Yes, but small likelihood given current data. - - Probability distribution should cover uncertainties from low to high estimates. - -**Final General and Specific Evaluations** -1. Default behavior (if nothing changes) trends highly to exceed $185.5 million. -2. Variance in daily pre-release hype further solidifies positives. -3. The highest probability aligns with surpassing the combined total due to sums from pre-sale trends and hype data. - -**Conclusion**: -Given the strong pre-sale indicators, recent projections, expert opinion, historical benchmarks, and remaining time for additional advertising, the probability that ""Deadpool & Wolverine"" will open domestically higher than $185,548,391 is high. - -**Final Answer:** -**Probability: 80%**" -26232,"Will the US government end its agreement directly allowing Verisign to manage the authoritative domain name registry for the .com TLD, before August 3, 2024?","PERPLEXITY - -Here is a detailed rundown of the news relevant to the question: - -**Background:** -- The Internet Corporation for Assigned Names and Numbers (ICANN) and Verisign, Inc. (Verisign) have a proposed agreement to amend the .COM Registry Agreement (RA), which includes provisions for pricing flexibility and security enhancements. -- Verisign operates the .COM top-level domain (TLD) under a Cooperative Agreement with the U.S. Department of Commerce, which is set to expire on November 30, 2024. - -**Recent Developments:** -- Advocacy groups, including the American Economic Liberties Project, have urged the U.S. government to terminate the agreement with Verisign and open up the management of the .COM TLD to competitive bidding, citing concerns over Verisign's monopoly power and rising domain name costs. -- Verisign has responded, stating that the Cooperative Agreement cannot be terminated and that the .COM registry will continue to be managed under the terms of the ICANN/Verisign .COM Registry Agreement if the Cooperative Agreement is ended. - -**Key Points:** -- The Cooperative Agreement between Verisign and the U.S. Department of Commerce is set to expire on November 30, 2024, and will automatically renew unless the Department provides written notice of non-renewal. -- The proposed amendment to the .COM Registry Agreement includes provisions for limited and transparent pricing flexibility, as well as security enhancements. -- Advocacy groups are pushing for the U.S. government to end the agreement and introduce competitive bidding for the management of the .COM TLD. - -**Current Status:** -- The U.S. government has not yet announced any decision on terminating the agreement with Verisign. -- Verisign maintains that the Cooperative Agreement cannot be terminated and that the .COM registry will continue to be managed under the terms of the ICANN/Verisign .COM Registry Agreement if the Cooperative Agreement is ended. - -**Expert Opinions:** -- Advocacy groups, such as the American Economic Liberties Project, argue that Verisign's monopoly power over the .COM TLD has led to rising costs and that the management of the TLD should be opened up to competitive bidding. -- Verisign asserts that the Cooperative Agreement cannot be terminated and that the .COM registry will continue to be managed under the terms of the ICANN/Verisign .COM Registry Agreement if the Cooperative Agreement is ended. - -**Relevant Prediction Markets:** -- No relevant prediction markets were found on Metaculus or other forecasting platforms. - -**Recent Updates:** -- The most recent updates are from June 2024, with Verisign responding to advocacy groups' calls to terminate the agreement and the U.S. government yet to make a decision on the matter. - -######### - -GPT - -To determine the probability that the U.S. government will end its agreement directly allowing Verisign to manage the authoritative domain name registry for the .com TLD before August 3, 2024, it is essential to consider several factors and trends. - -### 1. **Background Information and Historical Precedent:** - -#### Recency and Important Developments: -1. The Cooperative Agreement is set to automatically renew for six years unless the DOC provides notice of non-renewal on August 2, 2024. -2. Advocacy groups are actively pushing for the termination of this agreement. -3. The Biden administration is known for supporting initiatives that promote competition in the market, highlighted by the 2021 executive order. - -#### Historical Precedent: -1. The agreement with Verisign has been extended and amended multiple times since its inception. -2. Changes such as lifting price caps were implemented in the 2018 renewal, showing a tendency to amend rather than terminate. - -### 2. **Political Climate and Administration Policies:** - -1. **Pro-competition Sentiment:** The Biden administration has shown a willingness to challenge monopolistic positions. This stance would support a higher probability of non-renewal. -2. **Policy Consistency:** The administration's consistent efforts to promote market competition, as seen in other sectors, support at least the potential for action against Verisign’s position. - -### 3. **Current Status and Remaining Time:** - -1. **Lack of Announced Decisions:** - - There are no recent indicators that the DOC has made or is close to making a decision about terminating the agreement. - - With less than three weeks left until the notice deadline (August 2, 2024), significant changes or decisions would need to happen imminently. - -2. **Inertia and Bureaucratic Processes:** - - Terminating large agreements typically involves lengthy bureaucratic processes. - - The inertia of maintaining the status quo is strong, particularly when a decision and its consequences would need to be managed swiftly. - -### 4. **Analysis of Relevant Factors:** - -1. **Advocacy Group Pressure:** While this pressure is notable, similar efforts have not led to an immediate change in previous renewals. -2. **Verisign’s Position and Response:** Verisign’s insistence that the Cooperative Agreement cannot be terminated easily may be grounded in legal and practical realities that the DOC must consider. - -### 5. **Base Rates and Default Outcomes:** - -1. **Base Rate of Non-renewal:** Historically, such agreements tend to be renewed with amendments rather than terminated. -2. **Default Outcome if No Action:** If no action is taken, the agreement will automatically renew. - -### 6. **Assessment of Probabilities:** - -Taking into account the upcoming deadline, historical precedents, bureaucratic inertia, and the absence of recent decisive steps by the DOC, the probability of the agreement being terminated seems low. Despite advocacy group pressures and the administration's pro-competition stance, practical and bureaucratic constraints make immediate action less likely. - -Given all the above considerations, the prediction for the resolution of this question is as follows: - -### Final Answer: -**Probability: 15%** - -This probability takes into account the substantial pressures on the DOC, but acknowledges the significant bureaucratic and practical barriers to a sudden change within the remaining time frame." -26302,"Will the US see a large-scale riot between July 17, 2024 and Sept 30, 2024?","PERPLEXITY - -To provide a comprehensive rundown of the news relevant to the question of whether the US will see a large-scale riot between July 17, 2024, and September 30, 2024, I have summarized key points from various sources: - -1. **Risk of Election Violence**: - - There is a serious risk of extremist violence around the 2024 U.S. presidential election, with both far-right and far-left extremist groups posing a threat. - - The possibility of violence is heightened due to the ongoing political polarization and the potential for political rhetoric to incite violent actions. - -2. **Past Incidents and Trends**: - - The 2020 presidential election was marked by nationwide demonstrations and culminated in the January 6 assault on Capitol Hill. - - There have been instances of political violence and unrest in the US, including the Weather Underground bombings in the 1960s and 1970s, which were primarily rooted in left-wing ideology. - - Recent protests in support of Hamas have raised concerns about the potential for left-wing rioting during the 2024 election campaign. - -3. **Preparations and Concerns**: - - Conservative leaders are advised to prepare for potential unrest by developing plans for state and local deterrence, riot control, and prosecution of violent individuals. - - The Department of Homeland Security (DHS) has identified the threat of violence from individuals radicalized in the United States as high and largely unchanged, with a focus on lone offenders or small group attacks. - - The DHS also expects the 2024 election cycle to be a key event for possible violence and foreign influence targeting election infrastructure, processes, and personnel. - -4. **Monitoring and Response**: - - The ACLED (Armed Conflict Location & Event Data Project) has relaunched the US Crisis Monitor to track trends during the 2024 election cycle, highlighting concerns about political violence and threats against election officials and judges. - - Law enforcement is advised to establish interagency task forces to monitor, infiltrate, and disrupt violent left-wing activist networks. - -5. **Expert Opinions**: - - Experts warn that the political climate is poised for additional tensions during the 2024 election cycle, citing past incidents and ongoing political uncertainty. - - There is a need for political leaders and participants in the political and civic process to implement measures to prevent and manage violent election-related extremism. - -Given these sources, there is a significant risk of large-scale riots between July 17, 2024, and September 30, 2024, due to the ongoing political polarization, the potential for political rhetoric to incite violence, and the history of political unrest in the US. - -######### - -GPT - -To determine the probability of a large-scale riot occurring in the US between July 17, 2024, and September 30, 2024, let's proceed step by step with a comprehensive evaluation. - -### Step 1: Clarify the Resolution Criteria -A large-scale riot must meet at least one of the following criteria within a 100km radius and within 14 days: -1. At least 50 deaths due to violence. -2. At least 10,000 arrests. -3. Property damages of $1 billion or more. - -### Step 2: Historical Base Rates -- The 1992 LA riots meet the criteria. -- The January 6 Capitol attack did not meet the criteria despite nationwide attention. - -Considering these instances, we can note that large-scale riots are indeed rare, even in times of significant unrest. - -### Step 3: Current Context and Dynamics -#### Political Climate: -- **Election Violence Risk**: Ongoing presidential election with significant polarization and potent rhetoric contributing to higher tension. -- **Past Incidents**: Historical and recent (2020) evidence of politically-motivated unrest. -- **Extremist Group Activities**: Both far-right and far-left groups pose a risk. - -#### Preparations and Monitoring: -- DHS and ACLED have ramped up monitoring and preparations. -- Law enforcement has detailed contingency plans, may deter some incidents but also could escalate tensions if not managed properly. - -### Step 4: Expert Opinions and News Insight -Experts suggest significant tension during the election period. Efforts by various government agencies to monitor and control extremist behavior can both lower and exacerbate risks. - -### Step 5: Time Frame Analysis -- Time frame is approximately 2.5 months (July 17 to September 30, 2024). -- Significant, large-scale riots need a catalyzing event and rapid escalation, which must fit within this specific and short period. - -### Step 6: Statistical Consideration and Probability -#### Likelihood of Incidents: -- Political polarization increases risk of localized clashes. -- Truly large-scale events meeting criteria are uncommon. -- Recent history suggests a pattern of significant, but often not large-scale, disturbances. - -Using a combination of qualitative and quantitative risk analysis: -- A large-scale riot is a rare event but with non-zero probability given current tensions. - -Given the conditions, historic frequency, expert opinions, and the specificity of the resolution criteria, we can draw the following probabilistic conclusion. - -### Final Probability Estimate -Considering all factors, the probability of a large-scale riot meeting any of the criteria defined between July 17, 2024, and September 30, 2024, is assessed as follows: - -**Probability: 15%** - -This percentage reflects the heightened political tensions and monitoring efforts while recognizing the historically rare nature of such extensive unrest." -26303,"Will a major cyberattack, virus, worm, etc. that uses LLMs in some important way occur before Sept 30, 2024?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -**Cybersecurity Landscape:** - -- Cybercrime is expected to inflict damages totaling $9.5 trillion USD globally in 2024, making it the world’s third-largest economy after the U.S. and China. -- Small businesses are particularly vulnerable, with 60% of them going out of business within six months of falling victim to a data breach or cyberattack. -- The total global data storage is projected to exceed 200 zettabytes by 2025, with 100 zettabytes stored in the cloud, making it a prime target for cyberattacks. - -**Large Language Models (LLMs) in Cybersecurity:** - -- LLMs are transforming the cybersecurity landscape, providing efficiency, intelligence, and scalability for managing vulnerabilities, preventing attacks, and responding to incidents. -- However, LLMs also introduce new challenges, such as the potential for adversaries to leverage them for more efficient attacks and unintentional data leakage. -- Research has shown that popular LLMs are insecure and highly vulnerable to basic attempts to circumvent safeguards, which could be exploited in cyberattacks. - -**Recent Threats and Updates:** - -- A recent cyber-attack campaign exploited vulnerabilities in the OpenMetadata platform to breach Kubernetes environments. -- The U.K. AI Safety Institute has warned that LLMs released by major labs are highly vulnerable to even basic attempts to circumvent safeguards. - -**Expert Opinions and Predictions:** - -- Cybersecurity Ventures predicts the cyberinsurance market will grow to $14.8 billion USD in 2025 and exceed $34 billion USD by 2031. -- Palo Alto Networks predicts that transformative use cases for LLMs in cybersecurity will emerge, making LLMs indispensable for cybersecurity. - -**Current Values and Prediction Markets:** - -- Metaculus has a prediction market on whether an LLM cyberattack/virus/worm will occur before 2025, but it is still awaiting approval by Community Moderators. - -Overall, the cybersecurity landscape is increasingly vulnerable to cyberattacks, and the use of LLMs in cybersecurity is both a promising solution and a potential risk factor. Recent threats and updates highlight the need for robust safeguards and continued evaluation of LLM systems after deployment. - -######### - -GPT - -### Step-by-Step Analysis - -#### Understanding the Context: - -1. **Time Frame**: The prediction is for an event to happen before September 30, 2024. We are currently on July 19, 2024, which gives us approximately 2.5 months until the resolution date. - -2. **Resolution Criteria**: - - At least one person is killed - - There is at least $10 million worth of damage - - A medical facility, government facility, or company with a market value of at least $100 million is unable to continue basic functions. - -3. **Key Factors**: - - Rapid advancements and adoption of Large Language Models (LLMs) - - Historical context and past frequency of similar incidents - - Recency and relevance of cybersecurity vulnerabilities and attacks - -#### Historical Context and Base Rate: - -- **Historical Frequency**: - - Major cyberattacks causing significant financial damage are not uncommon. - - However, the use of LLMs in executing such attacks is a novel scenario. - -- **Nature of Cybersecurity Incidents**: - - Data breaches and ransomware attacks causing substantial financial and operational damage have been on the rise. - - Cyberattacks leading to fatalities are extremely rare but possible in critical infrastructure scenarios. - -#### Current Trends and Relevant Data: - -1. **Cybersecurity Landscape**: - - Cybercrime damages are predicted to reach $9.5 trillion in 2024, indicating a high prevalence of cyber threats. - - Recent vulnerabilities in cybersecurity platforms highlight ongoing risks. - -2. **LLM Vulnerabilities and Capabilities**: - - LLMs like ChatGPT and others have been reported to be vulnerable to circumvention, which could be exploited for cyberattacks. - - The integration of LLMs within cybersecurity tools both improves defense mechanisms and introduces new attack vectors. - -3. **Recent Events**: - - The attack on the OpenMetadata platform showcases recent exploitation attempts and the dynamic nature of cyber threats. - - Warnings from institutions like the U.K. AI Safety Institute emphasize the recognized risk within expert communities. - -#### Expert Predictions and Market Insight: - -- Predictions from cybersecurity companies and market analysts anticipate significant developments in cybersecurity threats. -- The prediction market interest (e.g., Metaculus) suggests community awareness but lacks immediate consensus. - -#### Timeframe and Probability Calculation: - -Given the short timeframe (about 2.5 months), the probability of a completely novel use-case (LLM-based cyberattack) must be evaluated against past data and expert opinion. - -Factors contributing to a higher probability: -- Increased focus on and vulnerability of cyber systems. -- Active exploitation attempts. - -Factors contributing to a lower probability: -- Short timeframe for such a specific and novel event. -- Historical rarity of LLM-based attacks reaching the required level of damage or disruption. - -#### Final Probability Distribution: - -Considering all the inputs: -- Likelihood of major cyberattacks in general (historical data): High -- Novelty and specific criteria involving LLMs: Medium to low within the short timeframe -- Existing efforts to mitigate and prevent such attacks: Significant, but not foolproof - -Taking into account the balance between elevated cybersecurity risks and the specificity criteria for resolution, I estimate the probability distribution as follows: - -**Calculation Outcome**: -After weighing all factors and uncertainties, the most likely probability is: - -**Probability: 35%** - -This takes into account the potential for rapid incident occurrence due to identified vulnerabilities and expert predictions, balanced by historical rarity and short remaining timeline." -26304,"Will William Ruto cease to be President of Kenya before October 1, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether William Ruto will cease to be President of Kenya before October 1, 2024, I have searched various sources including news articles, Wikipedia, and other relevant websites. - -### Current Situation and Recent Events - -- **Cabinet Dissolution**: On July 11, 2024, President William Ruto dismissed nearly all members of his cabinet, including ministers and the attorney-general, following violent demonstrations that led to the withdrawal of a contentious bill. -- **Protests and Reforms**: The recent protests, led by a new generation of young Kenyans, forced the government to reconsider unpopular tax proposals. In response, President Ruto announced austerity measures across government agencies, including halting proposed pay increases for cabinet members and parliamentarians, and reducing travel budgets. -- **Historical Context**: The current situation is reminiscent of past events in Kenyan political history, such as the failed military coup in 1982 and the response of President Mwai Kibaki to divisions in his government. - -### President Ruto's Background and Leadership - -- **Biography**: William Ruto is the fifth and current president of Kenya since September 13, 2022. Prior to becoming president, he served as the first elected deputy president of Kenya from 2013 to 2022. -- **Leadership Style**: Ruto's leadership has been marked by a mix of conciliatory and authoritarian actions. He initially responded to protests with a bloody police crackdown but later abandoned the tax proposals and announced cuts to spending. -- **International Relations**: President Ruto has engaged in international diplomacy, including a state visit to the United Kingdom and an address to the United Nations General Assembly, where he emphasized the need for global cooperation and economic development. - -### Expert Opinions and Predictions - -- **Potential for Dictatorship**: Some analysts suggest that Ruto's actions may indicate a shift towards a more authoritarian style of governance, similar to that of his predecessors. -- **Youth Protests**: The ongoing youth-led protests have forced Ruto to make concessions, and their continued activism may influence his future decisions. - -### Current Values and Indicators - -- **No current prediction markets or betting odds are available on Metaculus or other forecasting sites regarding Ruto's presidency. - -### Recent Updates and Movement Towards Resolution - -- **Cabinet Reformation**: President Ruto has pledged to consult widely before naming a new government, engaging with various sectors and political groups, as well as the public and private sectors. -- **Protest Demands**: The recent protests have led to significant changes in the Finance Bill of 2024, including the removal of VAT on essential items and revisions to levies on various services. - -In summary, while there are indications of potential authoritarian tendencies and ongoing protests, there is no clear indication that William Ruto will cease to be President of Kenya before October 1, 2024. The situation remains fluid, with Ruto's actions and the response of the Kenyan people and international community influencing the outcome. - -######### - -GPT - -To forecast whether William Ruto will cease to be President of Kenya before October 1, 2024, a methodical evaluation of multiple dimensions is required. Here is a breakdown of the factors influencing this prediction: - -### 1. **Political Context and Background** -- **Current Protests:** The ongoing youth-led protests are intense, with demands for Ruto's resignation. These protests were initially sparked by unpopular tax hikes and have escalated into broader calls for his ouster. -- **Government Response:** Ruto's decision to fire almost his entire cabinet as a concession to protestors, and the resignation of police chief Japhet Koome, indicate significant internal pressure and potentially weakening support within his administration. - -### 2. **Historical Precedents and Base Rates** -- **Comparative Analysis:** Historical instances of Kenyan presidents being ousted from office before the end of their term are rare but not unprecedented. For example, Daniel arap Moi faced a failed coup attempt in 1982. -- **Sub-Saharan Africa Trends:** In general, leadership changes in Sub-Saharan Africa often occur through coups, elections, or in rare cases, resignations. The region does have a historical pattern of contested leadership and instability during crises. - -### 3. **Key Variables** -- **Public Sentiment:** The intense and ongoing nature of the protests suggests that public dissatisfaction is high and widespread, which increases the pressure on Ruto. -- **International Relations:** Intense international scrutiny, especially following the police crackdown, affects internal stability. International support (or lack thereof) can play a crucial role. -- **Internal Political Stability:** The mass firing of his cabinet and resignation of the police chief might indicate cracks within Ruto's administrative structure, potentially leading to further instability or even a power vacuum. - -### 4. **Possible Scenarios Leading to Resolution** -- **Resignation:** The protests may force Ruto into a corner, leading to his resignation. -- **Impeachment or Parliamentary Action:** Though less common in Kenya, parliamentary actions could be taken against Ruto if the political climate worsens. -- **Coup:** Given Kenya's history and regional patterns, a coup, while not highly likely, remains a non-negligible risk factor. -- **Stabilization:** Ruto might manage to stabilize the situation, especially if his new cabinet gains public trust or quells the protests effectively. - -### 5. **Temporal Factors and Window of Prediction** -- **Time Frame:** From July 19, 2024, until October 1, 2024, there are a little over two months. Significant political changes in such a short period are less common but can occur under heightened tensions. - -### 6. **Expert Opinions and Recent Developments** -- **Expert Analysis:** Analysts suggest a mix of potential authoritarianism and necessary concessions by Ruto. The firing of his cabinet shows he is willing to make drastic changes to maintain his position, but it also shows vulnerability. -- **Recent Developments:** The removal of the Finance Bill’s most contentious features may appease some protestors but may not be enough to quell the more fundamental demands for Ruto’s resignation. - -### 7. **Statistical and Probabilistic Considerations** -- **Probability Estimation:** Combining the basal probability of a leader being ousted in similar scenarios with the specific situation in Kenya and current pressures, the situation warrants a heightened but not overwhelming probability of ouster. - -### Conclusion - -Considering the base rates of leadership changes in Kenya and similar contexts, the specific pressures on Ruto, and the short time period until October 1, 2024, we can structure the probability as follows: -- **High Pressure but Limited Time:** There's significant pressure on Ruto; however, the short time frame reduces the probability of a complete ouster. -- **Balancing Factors:** The internal instability, public protests, and potential for rapid political changes contribute to a higher-than-average likelihood but not a certainty. - -Thus, integrating these factors: - -**Probability: 35%** - -This probability signifies a notable risk but not a majority likelihood, balancing the high resistance against rapid changes in leadership within the remaining time frame." -26305,Will the same nation win more than one women's team sport at the 2024 Olympics?,"PERPLEXITY - -To assist the superforecaster in predicting whether the same nation will win more than one women's team sport at the 2024 Olympics, I have compiled a detailed rundown of relevant news and information: - -### Key Points - -- The 2024 Paris Olympics will feature a significant milestone in achieving gender parity, with 152 medal events for women, 157 for men, and 20 mixed-gender events. -- The United States has the largest delegation, with 53% of its team comprising women athletes, leading the way in female representation. -- Women's sports have seen a surge in popularity, leading to increased media rights fees and valuations. Advertisers are capitalizing on this trend, with WPP's GroupM committing to doubling ad spend on women's sports. -- NBCUniversal plans to dedicate significant prime time coverage to marquee events like track and field, swimming, and gymnastics, highlighting the growing success of female athletes. -- Notable athletes like Simone Biles, Katie Ledecky, and Sydney McLaughlin-Levrone will be aiming for further success in their respective disciplines. - -### Relevant Articles - -- ""In A First, Half The Athletes In The Paris Olympics Will Be Female"" by Brad Adgate (Forbes, July 12, 2024) - Highlights the growth of women's sports and the impact on the Olympic Movement. -- ""Paris Olympic competition nears total gender parity"" (AP News, July 18, 2024) - Discusses the progress towards gender equality in the Olympics and the slight imbalance in medal events. -- ""Paris 2024 sets milestone as first Olympics to achieve full gender parity"" (NBC Olympics, July 1, 2024) - Details the historic milestone of achieving numerical gender parity and the journey towards inclusivity in sports. -- ""Individual and team records that could be broken at the Olympic Games Paris 2024"" (Olympics.com, July 15, 2024) - Lists potential records that could be broken by athletes, including Katie Ledecky and Isabell Werth. - -### Expert Opinions - -- Mark Marshall, Chairman of Global Advertising and Partnerships at NBCUniversal, emphasizes the network's commitment to showcasing the best athletes regardless of gender. -- Jenny Wall, Chief Marketing Officer at VideoAmp, highlights the transformative impact of women's sports on marketing strategies and the need for brands to invest in women's sports. -- IOC member Nawal El Moutawakel notes that the Olympic Games are a rare occasion when female athletes can make headlines as much as their male counterparts. - -### Current Values and Predictions - -- No specific predictions or current values are directly relevant to this query. However, the growth of women's sports and the focus on gender parity at the 2024 Olympics could influence the outcome of women's team sports. - -### Forecasting Sites - -- No relevant prediction markets were found on Metaculus or other forecasting sites specifically addressing this query. - -######### - -GPT - -### Chain of Thought Analysis - -#### Step 1: Identify the Question Type -This is an ""Other Binary Outcome Event"" since it asks whether a binary (yes/no) condition will occur, specifically whether the same NOC will win more than one women's team sport at the 2024 Olympics. - -#### Step 2: Gather Relevant Information -- There are eight women's team sports events. -- Historical data suggests dominance by a few nations in women's team sports, especially the United States, which has a strong women's sports program across multiple disciplines. -- Gender parity and increased focus on women's sports in the 2024 Olympics might amplify the competitive environment, but it doesn't necessarily predict dominance by a single nation in multiple sports. - -#### Step 3: Historical Base Rates -Let's examine past Olympics: -- **Tokyo 2020:** USA won 5x5 Basketball, Brazil won 3x3 Basketball, Netherlands won Field Hockey, Norway won Handball, New Zealand won Rugby Sevens, and USA won Volleyball and Water Polo. -- **Rio 2016:** USA won 5x5 Basketball, no 3x3 Basketball event, UK won Field Hockey, Norway won Handball, Australia won Rugby Sevens, China won Volleyball, and USA won Water Polo. -- **London 2012:** USA won 5x5 Basketball, no 3x3 Basketball event, Netherlands won Field Hockey, Norway won Handball, no Rugby Sevens event, Brazil won Volleyball, and USA won Water Polo. - -From these statistics, it is evident that the USA has consistently won multiple women's team sports in the past three Olympics. - -#### Step 4: Evaluate Current Trends -- **Basketball (5x5)** and **Water Polo** have historically seen strong USA performances. -- **Volleyball** also sees repeat performances by the USA. -- Emergence of strong teams from different countries in Football, Field Hockey, Handball, and Rugby Sevens does imply significant competition. - -#### Step 5: Assess the Influence of Novel Factors -- Increased parity and visibility in women's sports may motivate other teams, but historically dominant teams may still maintain an edge due to experience and support infrastructure. -- No new disruptive changes in terms of geopolitics, technology, or other factors are identified that drastically change the status quo. - -#### Step 6: Calculate the Probability -Given the historical data and current trends, let's break down: -- The probability of USA women's teams winning in Basketball (5x5) is relatively high, based on historical trends and current performance (let's conservatively estimate at 60%-70%). -- The probability for USA in Water Polo and Volleyball is also high, with similar justifications (let's estimate 50%-60% each). - -Overall, the multiplication rule applies for combined probabilities, keeping USA's three primary strong sports: -\[ P(\text{Multiple Wins}) = 1 - P(\text{Not Winning All Three}) \] -Given interdependencies and overlaps, let's consider this simplified: -\[ \approx 70\% * 55\% * 55\% = \text{21.175\%} \] - -Given that some events might be mutually exclusive, a conservative estimate might slightly adjust upwards because the USA's sports programs often perform under pressure. - -Consider additional probabilities from other strong contenders: -- Scattering incidences of high probability winning scenarios by other strong teams show that at least one nation might win two or more women's team sports. - -#### Final Thought - -Upon consolidation of these factors and considering slight dependencies in certain sports performance of the USA, the answer: - -**Probability: 75%** - -Thus, it is likely (but not overwhelmingly certain) that the same nation will win more than one women's team sport at the 2024 Olympics." -26306,"Will the 500th richest person on Bloomberg's Billionaires Index have $6 billion or more on Monday September 16, 2024?","PERPLEXITY - -The query is about the net worth of the 500th richest person on Bloomberg's Billionaires Index on September 16, 2024. Here is a summary of the relevant news and data: - -- **Current Rankings and Net Worths**: - - The top five richest people on the Bloomberg Billionaires Index are: - 1. Elon Musk: $264 billion - 2. Jeff Bezos: $207 billion - 3. Bernard Arnault: $196 billion - 4. Mark Zuckerberg: $170 billion - 5. Bill Gates: $160 billion. - - - Donald Trump's net worth is $6.5 billion, making him one of the world's 500 richest people. - - - The ultra-exclusive $100 billion club has swelled to 15 individuals, collectively holding a net worth of $2.2 trillion. - -- **Recent Updates**: - - Donald Trump's net worth increased significantly due to a merger with Trump Media & Technology Group, adding billions of dollars to his paper fortune. - - - The $100 billion club has seen new entrants, including Francoise Bettencourt Meyers, Michael Dell, and Carlos Slim, due to various economic and market factors. - -- **Prediction Markets**: - - Metaculus has a question asking if the 500th richest person on Bloomberg's Billionaires Index will have $6 billion or more on September 16, 2024. - -- **Methodology**: - - The Bloomberg Billionaires Index is a daily ranking of the world's richest people, with net worth figures updated every business day after the close of trading in New York. Stakes in publicly traded companies are valued using the share's most recent closing price, and valuations are converted to U.S. dollars at current exchange rates. - -- **Expert Opinions**: - - No specific expert opinions are provided in the given sources, but the articles highlight the impact of market and economic changes on the net worth of billionaires. - -- **Current Values**: - - The current net worths of the top five richest people and Donald Trump are mentioned above. - - The net worths of other billionaires in the $100 billion club are also mentioned in the sources. - -######### - -GPT - -To determine the probability that the 500th richest person on Bloomberg's Billionaires Index will have a net worth of $6 billion or more on Monday, September 16, 2024, we will follow a systematic approach: - -### Analyzing the Current Situation - -1. **Baseline Data:** - - The current net worth of Donald Trump is cited as $6.5 billion. - - The rankings indicate a significant concentration of wealth at the top. - - There is an implication that the bottom tier of the 500 list ($6 billion threshold) might not be drastically different from $6.5 billion fluctuating slightly based on market conditions. - -2. **Trends and Historical Data**: - - The net worth of billionaires in the lower end of the 500 list generally increases over time due to economic growth, inflation, and stock market appreciation. - - Short-term adjustments, like major market fluctuations, mergers, acquisitions, or important policy changes, could drastically influence rankings but aren't the norm year-round. - - In the recent few years, the number of billionaires has grown, driven by technology and market expansions, particularly post-2020 recovery. - -### Quantitative Analysis - -1. **Historical Growth Rate:** - - From prior data, if we assume an average growth in net worth due to global economic trends and stock market gains, we can project the possible growth rate. - - For simplicity, let's assume a conservative annual growth rate of 5-10% in net worth for individuals around the $6 billion threshold. - -2. **Public Market Conditions**: - - Bloomberg's methodology relies on market-driven valuations which are encouraging, implying that if the markets remain stable or bullish, there is a positive upward trend in net worth figures. - - There's historical precedence for volatility in stock markets, but generally, ultra-high-net-worth individuals recover quickly owing to diversified holdings and wealth structures. - -3. **Variance in Net Worth**: - - The daily variance is pertinent, but needs smoothing over a significant time (July 19 to mid-September) which encompasses approximately 2 months. - - Historically, barring a significant financial disruption, individual net worth in this tier doesn't regularly drop drastically off without recovery. - -### Risk Factors & Novel Considerations - -1. **Political and Economic Events**: - - Upcoming elections, geopolitical tensions, and macroeconomic policy shifts (e.g., interest rates, tax changes) can introduce uncertainties. - - Natural disasters, pandemics, and other black swan events could disrupt markets significantly. - -2. **Regulatory and Market Innovations**: - - Technological advancements, new industry booms, or collapses can significantly add to or reduce the baseline wealth. - -### Final Calculation - -Given the combined conservative growth trend in net worth and historical stability at these ranks, the probability is high that the 500th person remains above $6 billion owing to: - -- Sustained economic growth and market trends with moderate impact expected. -- Absence of signals indicating major upcoming collapses or abnormal downturns in market forecasts. -- Base rates from recent years indicating alignment with this threshold. - -### Final Prediction - -Considering potential market variabilities, economic growth prospects, and the calculated growth in billionaires' wealth at the lower threshold of the 500 list: - -""Probability: 85%"" - -This prediction synthesizes a rational base rate understanding, historical data reflection, potential variances, and probable economic trajectory up to the checkpoint date." -26307,Will the US unemployment rate be above 4.1% in August 2024?,"PERPLEXITY - -Here is a concise rundown of the most relevant news for the query ""Will the US unemployment rate be above 4.1% in August 2024?"": - -- **Current Unemployment Rate**: The US seasonally adjusted unemployment rate for June 2024 stands at 4.1%, which is an historically low rate, ranking in the 17th percentile of unemployment rates since 1990. -- **Recent Trends**: The unemployment rate has been increasing, ticking up from 3.9% in April and May to 4.1% in June. This rise is seen as a sign of the labor market cooling or normalizing, as expected given the imbalance between labor demand and supply. -- **Economist Forecasts**: Economists polled by Reuters had forecast payrolls to increase by 190,000 in June, with the unemployment rate remaining unchanged at 4.0%. However, the actual payroll growth was 206,000, slightly above expectations. -- **Labor Market Indicators**: The labor force participation rate ticked up slightly, and the prime-age labor force participation rate (LFPR) is at a 22-year high. The diffusion index shows that 59.6% of private industries added jobs in June, indicating a solid job market. -- **Wage Growth**: Yearly wage growth before inflation was 3.9% in June, outpacing price growth for the 13th consecutive month, providing more buying power for working Americans. -- **State-by-State Unemployment**: The states with the highest unemployment rates in November 2023 were Nevada and the District of Columbia, while Maryland had the lowest rate at 1.8%. -- **Recent Reports**: The Bureau of Labor Statistics (BLS) reported that both the unemployment rate and the number of unemployed people changed little in June. - -There are no recent updates that strongly indicate movement towards a particular resolution. However, the trend of the unemployment rate ticking up and the labor market normalizing might suggest a possibility of the rate remaining above 4.1% in August 2024. - -No relevant prediction markets were found on Metaculus for this specific question. - -######### - -GPT - -### Background Analysis and Data Overview - -#### Current Unemployment Rate -- **4.1% in June 2024**: This is the current unemployment rate. Historically, this is a relatively low rate. - -#### Recent Trends and Forecasts -- **April and May 2024**: 3.9% unemployment rate. -- **June 2024**: Increased to 4.1%. This increment suggests potential upward movement. -- **Economist Forecasts**: The predicted payroll increase for June was 190,000, but actual was 206,000, slightly above forecast. Predictions were for a stable unemployment rate at 4.0%, but it ended up at 4.1%. - -#### Labor Market Indicators -- **Labor Force Participation**: Slight uptick, with prime-age LFPR at a 22-year high. -- **Diffusion Index**: 59.6% of industries adding jobs indicates robustness in job market. -- **Wage Growth**: Yearly wage growth before inflation at 3.9% in June, outpacing price growth for 13 months. - -### Base Rate and Historical Context -- **Historical Context Since 1990**: - - Unemployment rate has typically varied significantly, influenced by economic cycles. - - Current rate in the 17th percentile over this period, indicating better-than-average labor market conditions but prone to fluctuations. - -### Factors and Considerations -1. **Recent Trend**: The uptick from 3.9% to 4.1% over two months suggests a cooling trend. The labor market is normalizing. -2. **Time Frame for Change**: Approximately 1.5 months until the August data capture: - - Sufficient time for moderate changes. - - No extreme volatility indicated, but gradual trends matter. -3. **Economic Indicators**: - - **Solid Job Market**: Diffusion index and job growth above expectations show strength. - - **Labor Participation** rising signals entry/re-entry of working force, which may temporarily increase unemployment rate. -4. **Wage Growth**: Robust wage growth suggests strong demand for labor, possibly stabilizing the rate. -5. **External Factors**: No major political, economic, or environmental disruptions indicated that would cause dramatic swings. - -### Statistical Reasoning -Given the base rate, historical behavior of unemployment fluctuations, and the time frame provided: -- **Estimate of Change Required**: Shifts in unemployment tend to be gradual barring major shocks. -- **Prior Trends**: Marginal increase noted (0.2 percentage points rise over two months). - -### Baseline Probability Distribution -- **Assumption of Stability**: Given strengthening indicators (diffusion index, wage growth) and slight uptick in participation, the rate may stay near current levels. -- **Probability Estimation**: - - **Above 4.1%**: Slight changes in trend support a minor but noticeable probability. - - **Below or at 4.1%**: Given historical minimal variances day-to-day and expected economic stability. - -### Conclusion -Balancing gradual trends and robust economic indicators suggests a slight preference for the unemployment rate remaining at or near the current value, with minor room for further increases. - -### Probability Estimation -- **Probability the unemployment rate will be above 4.1% in August 2024**: - -Probability: **55%** - -This estimate accounts for a slight edge towards the continuation of the increasing trend but acknowledges the robustness and stability suggested by other indicators." -26389,"Will the Federal Reserve cut interest rates before September 30, 2024?","PERPLEXITY - -The Federal Reserve's interest rate decisions have been closely monitored as the central bank navigates the path to reducing inflation while maintaining a stable job market. Here is a summary of the relevant news and expert opinions: - -1. **Current Interest Rate**: The Federal Reserve has maintained its benchmark interest rate within the range of 5.25% to 5.50% since July 2023, the highest level in 23 years. - -2. **Recent Developments**: The Fed has been cautious about reducing rates due to persistent inflation, which remains above the central bank's 2% annual target. However, recent data has shown a renewed slowing of inflation, with the annual inflation rate dropping to 3% in June, a three-year low. - -3. **Market Expectations**: Traders are 100% certain that the Federal Reserve will cut interest rates by September, with a 93.3% probability of a quarter percentage point reduction and a 6.7% probability of a half percentage point decrease. - -4. **Fed Officials' Views**: Jerome Powell, the Fed Chairman, has emphasized the importance of gaining greater confidence in inflation returning to the 2% threshold before initiating rate cuts. New York Fed President John Williams has suggested that a rate cut could be warranted in the coming months. - -5. **Upcoming Meetings**: The Federal Reserve is scheduled to make decisions on interest rates on July 31 and September 18, with no meetings scheduled for August. - -6. **Expert Insights**: Financial experts, such as Amy Hubble from Radix Financial, believe that a rate hike in July seems unlikely but expect the Federal Reserve to remain vigilant in its efforts to maintain a stable job market and curb inflation rates. - -Given these developments, the likelihood of the Federal Reserve cutting interest rates before September 30, 2024, appears high, with market expectations and Fed officials' views aligning towards a rate cut in September. - -######### - -GPT - -To begin with, let's outline the relevant information and context for making this prediction: - -1. **Current Interest Rate**: The Federal Reserve has maintained its benchmark interest rate within the range of 5.25% to 5.50% since July 2023. -2. **Recent Inflation Data**: The annual inflation rate has dropped to 3% in June 2024, which is the lowest in three years. However, it remains above the Fed's target of 2%. -3. **Market Expectations**: Market participants are unanimously expecting a rate cut before September 30, 2024, with a 93.3% probability of a quarter percentage point reduction and a 6.7% probability of a half percentage point decrease. -4. **Fed Officials' Stances**: - - Jerome Powell has indicated a need for greater confidence in inflation returning to 2% before cutting rates. - - John Williams has suggested that a rate cut could be warranted in the coming months. -5. **Upcoming Meetings**: The FOMC has scheduled meetings on July 31, 2024, and September 18, 2024. -6. **Expert Insights**: Some financial experts believe a rate hike in July is unlikely but anticipate ongoing vigilance from the Fed regarding inflation and the job market. - -### Evaluating the Information: - -1. **Current Interest Rate and Inflation Trends**: - The current interest rate is historically high, and inflation has been reducing but is still above the target. This mixed signal provides some support for the Fed's cautious approach. - -2. **Market Expectations**: - Market expectations of a rate cut are high. Market betting often reflects aggregate information and expert analysis, which should not be dismissed lightly. However, they are not infallible. - -3. **Fed Officials’ Views**: - - **Jerome Powell**: His cautious stance emphasizes the need to see inflation reducing further, aligning his view with potential hesitancy. - - **John Williams**: His openness to a rate cut adds weight to the possibility of such a move soon. - The views of these officials signal an internal debate within the Fed about the appropriateness of a rate cut, but Powell's position as chair carries significant weight. - -4. **Timing and Meeting Schedule**: - With two upcoming FOMC meetings before the deadline (July 31 and September 18), there are two key opportunities for a potential rate cut announcement. This timing works in favor of a probability estimate closer to September 18, if it were to happen. - -### Historical Context and Base Rate: -Examining historical trends, the Fed has cut rates relatively swiftly during economic distress (e.g., early 2020 during the COVID pandemic) or significant economic shifts. However, it often requires compelling evidence, particularly on the inflation front, to make such decisions. - -Given the Federal Reserve's primary dual mandate (price stability and maximum sustainable employment), only a significant further drop in inflation or indications of economic slowdown might tilt the decision towards a rate cut. - -### Weighing Factors: - -1. **Expert Analysis and Market Expectations**: These favor a high likelihood of a rate cut announcement. -2. **Fed Officials' Stance**: Powell’s caution advises against over-optimism but still leaves room for a cut should inflation align closer to the target. -3. **Historical Actions**: The Fed’s past decisions during similar economic uncertainties align with cautious but responsive behavior. -4. **Meeting Schedule**: With two strategic opportunities, timing does indicate a reasonable probability of an announcement. - -### Conclusion: -Weighing these factors, Powell's requirement for more robust evidence of sustained low inflation, juxtaposed with market certainty and other officials' openness to cuts, creates a scenario where a rate cut seems likely but not guaranteed. Thus, a high probability but not absolute confidence level is appropriate. - -**Final Answer**: -Based on the analysis, I estimate the probability that the Federal Reserve will cut interest rates before September 30, 2024, to be **Probability: 80%**." -26388,Will an athlete win more than one medal for tennis at the 2024 Paris Olympics?,"PERPLEXITY - -To provide context for the question ""Will an athlete win more than one medal for tennis at the 2024 Paris Olympics?"", I have gathered information from various sources about the tennis events at the 2024 Paris Olympics. - -### Overview of Tennis Events -The tennis competition at the 2024 Paris Olympics will feature five events: men's and women's singles, men's and women's doubles, and mixed doubles. The events will be held at the Stade Roland Garros, the same venue as the French Open, from July 27 to August 4. - -### Qualification Rules -Each National Olympic Committee (NOC) can qualify up to 12 athletes, six per gender. The singles draws will include 64 players, with 56 direct acceptances based on the ATP and WTA rankings. France, as the host country, has one secured spot per event. Additionally, six ITF Places and one universality place are available. The doubles events feature 32 teams, with two players per NOC, and the mixed doubles event has 16 teams, one per NOC. - -### Notable Athletes -Several top tennis players are expected to participate, including Rafael Nadal, Carlos Alcaraz, Iga Swiatek, and Belinda Bencic. The French duo Nicolas Mahut and Pierre-Hughes Herbert, who have won two French Open titles, will also be competing. - -### Medal Possibilities -Given the format and the number of events, it is possible for athletes to win multiple medals. In the past, athletes like Venus Williams and Serena Williams have won multiple medals in tennis at the Olympics. - -### Current Predictions -Sports Illustrated contributor Brian Cazeneuve has made predictions for the medal winners in each event. While these predictions do not specifically address the question of an athlete winning multiple medals, they provide insight into the competitive landscape. - -### Recent Updates -There have been no recent updates that would significantly impact the likelihood of an athlete winning multiple medals. The qualification rules and event formats are well-established, and the competition is set to begin on July 27. - -### Expert Opinions -No specific expert opinions are available on the likelihood of an athlete winning multiple medals. However, the predictions from Sports Illustrated and the analysis of the qualification rules and event formats provide context for understanding the possibilities. - -### Current Values -There are no specific current values relevant to this question. - -### Prediction Markets -No relevant prediction markets are available on Metaculus or other forecasting sites. - -In summary, the tennis events at the 2024 Paris Olympics provide opportunities for athletes to win multiple medals. The qualification rules and event formats are established, and several top athletes are expected to participate. While there are no specific predictions or expert opinions on the likelihood of an athlete winning multiple medals, the competitive landscape suggests it is possible. - -######### - -GPT - -To predict whether an athlete will win more than one medal for tennis at the 2024 Paris Olympics, we'll approach the problem systematically using available information, historical data, and probabilistic reasoning. - -### Step-by-Step Analysis - -1. **Event Structure Overview**: - - Five events: Men's singles, women's singles, men's doubles, women's doubles, mixed doubles. - - This inherently allows for multiple medal opportunities for versatile or top-ranking players participating in more than one event. - -2. **Historical Data**: - - **Base Rate Calculation**: In the four more recent Olympics (2000, 2008, 2012, and 2016), we have seen some instances of athletes winning multiple medals. Notably: - - 2016 Rio Olympics: Venus Williams reached the finals in mixed doubles and won a medal in women's doubles, but didn't win singles. - - 2008 Beijing Olympics: Rafael Nadal won gold in singles but didn't participate in doubles. - - **Patterns**: Historically, at least one player winning multiple medals in tennis happens, but not frequently. Multiple participation is higher among seasoned and top-ranking players. - -3. **Current Competitive Landscape**: - - **Key Athletes**: - - Rafael Nadal, known for his prowess and dual participation (singles/doubles). - - Top-ranked players like Carlos Alcaraz, Iga Swiatek, and Belinda Bencic, who would likely participate in more than one event. - - **Doubles Specialists**: Potential for overlap in singles/doubles performance in the case of high rankings. - -4. **Expert Evaluations**: - - **Prediction Markets**: Though no direct prediction markets, expert predictions (e.g., from Sports Illustrated) highlight strong candidates. While these don't focus on dual medals directly, they inform us about probable finalists. - -5. **Statistical Analysis**: - - **Poisson Distribution for Rare Events**: Assume our event to see at least one player winning multiple medals is rare but feasible given past occurrences: - - Historical base rate production: Out of four referenced Olympic cycles, we observed notable near-misses or events leading close to dual medals (e.g., Williams' performances), approximating a rough likelihood of 25% based on limited occurrences. - -6. **Potential Novelties Influencing Probabilities**: - - **Technological/Training Improvements**: Players arriving in prime condition. - - **Political/Health Considerations**: Assuming no external disruptions impacting key athletes. - -### Final Calculation and Conclusion - -Combining historical base rates, present athlete preparations, and event structure impacts based on professional knowledge: - -- **Low-End Calculation**: Base rate occurrence at 25% without additional variables. -- **High-End Calculation**: Considered influx of current form top players in dual events like mixed doubles rewiring 5%-10% additional margin. - -Expected occurrence rate judiciously balances past data and fresh projections leading us to hold an optimistic forecast. - -### Final Probability Estimation -Probability: 40% - -This estimate is due to balancing historical precedence with the current top-field stack likelihoods, adjusting for expected variability, mid-tier performance convergence, and recounting potential outperformers across multiple events at Olympics." -26390,"Will the bubble in the Magnificent Seven pop before September 30, 2024? -","PERPLEXITY - -The question revolves around whether the bubble in the Magnificent Seven stocks will pop before September 30, 2024. Here is a concise rundown of the most relevant news and expert opinions: - -1. **Market Performance**: - - The Magnificent Seven, consisting of Apple, Alphabet, Amazon, Meta, Microsoft, NVIDIA, and Tesla, have significantly outperformed the broader market in 2023, with returns of over 100% compared to the S&P 500's 24% gain. - - In 2024, the performance has been mixed, with some stocks like NVIDIA seeing significant gains, while others like Tesla and Apple have declined. - -2. **Expert Opinions**: - - **Joseph A. Scarpo, CAPTRUST**: The Magnificent Seven's superior fundamentals, including revenue growth, earnings growth, and profit margin expansion, justify their outperformance. However, he expects a broadening of participation across market sectors and less dominance from the mega-cap tech names. - - **Jonathan Dane, Defiant Capital Group**: The pace of outperformance of these stocks is unlikely to persist, but they are expected to continue performing well due to their broad reach and importance in the economy. - - **Brian Tarquinio, The WTL Group, Morgan Stanley**: These stocks are essential global companies, and their prices, although high, are not stratospheric. Brief pullbacks are expected, but they will continue to perform well over the long term. - -3. **Valuation Concerns**: - - The high valuations of the Magnificent Seven have raised concerns about a potential bubble, reminiscent of the dot-com bubble. The Nasdaq 100's current valuation compared to small-cap stocks has surpassed levels seen during the dot-com bubble. - - The high-to-earn ratio (P ratio) of the Invesco QQQ Trust (NASDAQ:QQQ) ETF is 35.02, indicating that it would take an investor 35 years to recoup their initial investment if the tech sector's earnings remained constant over the past year. - -4. **Concentration Risk**: - - The Magnificent Seven account for more than 29% of the S&P 500 and more than 40% of the Nasdaq 100, raising concerns about concentration risk and the potential impact on the broader market if these stocks were to decline. - -5. **Prediction Markets**: - - A Metaculus prediction market is currently tracking this question, with users able to provide their predictions on whether the bubble in the Magnificent Seven will pop before September 30, 2024. - -Overall, while the Magnificent Seven have continued to dominate the market, there are growing concerns about their high valuations and the potential for a correction. Expert opinions are mixed, with some expecting continued outperformance and others predicting a broadening of participation across market sectors. - -######### - -GPT - -### Step-by-Step Analysis - -#### **1. Understanding the Resolution Criteria:** -The question resolves positively if at least four of the Magnificent Seven stocks close at least 50% below their all-time high before September 30, 2024. - -#### **2. Time Factor:** -From today (July 22, 2024) to September 30, 2024, there are approximately 10 weeks. This is a relatively short period in the context of financial markets, which generally show persistence and inertia. - -#### **3. Historical Context & Base Rates:** -Historically, stock bubbles and corrections occur, but they are relatively rare within short time frames, especially when considering blue-chip mega-cap stocks. Previous major corrections (e.g., the dot-com bubble of 2000, the financial crisis of 2008) have typically manifested over many months to years, not weeks. - -#### **4. Current Market Conditions:** -- **Performance:** The Magnificent Seven significantly outperformed in 2023, though mixed performance is noted in 2024. -- **Valuation Concerns:** High P/E ratios and comparisons to the dot-com bubble signal overvaluation. -- **Concentration Risk:** High representation in major indices increases systemic risk. -- **Market Sentiment:** There's apprehension about potential corrections, yet most expert opinions lean towards continued strength, albeit with potential pullbacks. - -#### **5. Expert Opinions:** -- While experts acknowledge high valuations, the consensus is mixed, with some predicting continued outperformance due to robust fundamentals and others foreseeing less dominance. -- This divergence in expert opinions suggests a moderate to high level of uncertainty around a sudden large-scale correction. - -#### **6. Statistical Models and Predictive Markets:** -- Prediction markets provide community insights but should be interpreted cautiously due to potential biases. -- It's significant to note that no major near-term catalysts (such as a financial crisis or abrupt policy changes) have been strongly signaled as of today. - -#### **7. Probabilistic Reasoning:** -- The probability of a single stock falling 50% is already relatively low for such significant companies. The combined probability of four or more of these stocks being down by 50% simultaneously is even lower. -- Historical analysis shows similar significant drops among blue-chip stocks concurrently is extremely rare in such a short period unless triggered by a significant external event. - -### Calculation and Deliberation - -#### **Worst-Case Probabilistic Outcome:** -Assume a moderate probability of a 50% drop of one significant stock individually is around 5% based on historical corrections and expert opinions. The correlated risk of four or more simultaneously: -- Following a binomial distribution \( P(X \geq 4) \) where \( X \) is the number of stocks dropping more than 50%, with stock drop probabilities fairly independent but recognizing some correlation: - -Using rough correlation adjustments and binomial approximations: -\[ P(X \geq 4) \approx C(7,4) \times (0.05)^4 \times (0.95)^3 \] -\[ = 35 \times (0.00000625) \times 0.857375 \approx 0.000187 \text{ or } 0.0187\% \] - -Recognizing possible underestimation and adding risk due to market concentration: -\[ Adjusted Probability: \approx 1-2\% \] - -Given the expert opinions, macroeconomic stability, and taken into broader context: - -### Final Answer: -**Probability: 5%** - -The forecast considers the inherent low likelihood of all four stocks experiencing such a drastic decline within this short time frame, even with elevated risks and mixed market sentiments." -26387,Will Tesla increase its deliveries in Q3 2024 compared with Q2 2024? ,"PERPLEXITY - -Here is a detailed rundown of the news relevant to the question: - -**Tesla's Q2 2024 Performance:** - -- Tesla's Q2 2024 deliveries were 443,956, slightly exceeding expectations but still a year-over-year decline of 3.7%. -- Production in Q2 was 410,831, a 14% decrease compared to the same period last year. -- The company delivered more cars than it produced in Q2, reducing the inventory gap. -- Energy storage deployments were a bright spot, with a record 9.4 GWh in Q2, more than doubling the previous quarter's number. - -**Challenges and Outlook:** - -- Tesla faces stiff competition in China, where deliveries have declined year-over-year for three consecutive months. -- The company has been slow to introduce new models, and its best-selling models have not seen significant design changes in years. -- Tesla has been dealing with production issues, including difficulties at the Model 3 Fremont plant and setbacks in Model Y production in China. -- Despite a positive Q2, analysts expect a decline in production and deliveries by 2024. -- Elon Musk has forecasted that Tesla will increase sales this year, but the company has warned of a notable slowdown in vehicle volume growth for 2024. - -**Expert Opinions and Forecasts:** - -- Barclays analyst Dan Levy predicts an 11% drop in Q2 deliveries, Tesla's biggest ever. -- Some analysts expect Tesla to post its first annual sales drop this year. -- Despite the challenges, analysts anticipate a return to year-over-year growth for Tesla in Q3, with a positive outlook for revenue and adjusted earnings per share. - -**Current Market and Stock Performance:** - -- Tesla's stock has lost a quarter of its value this year, making it one of the worst performers on the S&P 500. -- The stock has recently surged 42% in a month-long rally, driven by the positive Q2 delivery announcement and anticipation of the robotaxi unveiling. - -**Relevant Prediction Markets:** - -- No relevant prediction markets were found on Metaculus for this specific question. - -Overall, while Tesla's Q2 performance was not as bad as expected, the company still faces significant challenges in production, delivery, and competition. The outlook for Q3 is uncertain, with some analysts predicting a return to year-over-year growth, while others expect continued declines. - -######### - -GPT - -To predict whether Tesla will increase its deliveries in Q3 2024 compared to Q2 2024, we need to consider several factors, including historical delivery data, recent performance, competitive and market conditions, production capabilities, and expert forecasts. - -### Step-by-Step Evaluation - -**Historical Data Analysis:** -- **Deliveries:** - - Q1 2023: 422,875 - - Q2 2023: 466,140 - - Q3 2023: 435,059 - - Q4 2023: 484,507 - - Q1 2024: 386,810 - - Q2 2024: 443,956 - -- **Observations:** - - Q2 typically sees an increase from Q1. - - Q3 2023 saw a decline compared to Q2 2023. - - Q4 generally has the highest deliveries, possibly due to end-of-year demand surges. - - Q1 2024 was significantly lower than Q4 2023, but Q2 2024 rebounded. - -**Recent Performance and Challenges:** -- **Q2 2024 Performance:** - - Deliveries: 443,956 - - Production: 410,831 (down year-over-year) - - The gap between delivered and produced vehicles reduced. - - Energy storage deployments were strong, which, while positive, doesn't directly impact vehicle deliveries. - -- **Challenges:** - - Competition in China and declining deliveries there. - - Production issues at Fremont and delays in China for Model Y. - - Predicted decline in production and deliveries by some analysts for the year. - - Lack of new model introductions and design updates in existing models. - -**Expert Opinions and Forecasts:** -- Mixed predictions about Tesla's performance in the upcoming quarters. -- Positive sentiments towards revenue and adjusted EPS, despite anticipated production challenges. - -**Market Conditions and Stock Performance:** -- Stock volatility and recent surge due to positive Q2 results and speculative announcements like the robotaxi. - -**Base Rates and Historical Trends:** -- Historically, Tesla's Q3 deliveries aren't consistently higher than Q2 deliveries. Given the varying nature of deliveries across quarters and the noted challenges, a base rate might suggest more instability. - -**Production and Delivery Dynamics:** -- Deliveries outpacing production in Q2 might signal inventory rebalancing. -- Production issues and market dynamics in China specifically could influence Q3 negatively. - -### Probability Calculation -Considering the data above: - -1. **Historical Base Rate:** - - Inconsistent Q3 vs Q2 trends historically: Suggesting a ~50% probability on its own. - -2. **Production and Market Challenges:** - - Production issues, market competition, and expert predictions of declining figures reduce the likelihood of increased deliveries. - - Weight: ~30% chance of overcoming these challenges because of leadership optimism and some capacity for operational improvements. - -3. **Positive Developments:** - - Strong Q2 performance in deliveries (with respecting inventory adjustments). - - Rebounding market stock, potential unduly boosting predictions. - - Weight: ~20% optimism that Q3 can adhere to Musk’s forecasts. - -### Synthesis and Final Probability -Combining these weighted assessments: - -- Base Rate: 50% -- Challenges and Negative Factors: -20% -- Positive Market Sentiment: +10% - -Total Estimated Probability: -While the exact numbers may vary according to more sophisticated statistical modeling or additional granular data points, deriving a comprehensive probability from the rationale and calculations above suggests a more pessimistic outlook, factoring in uncertainties, producer and market dynamics, and the lower possibility of unexpected positive changes over a relatively short term. - -**Final Answer:** - -**Probability: 40%**" -26391,"Will a Israel-Hamas conflict ceasefire lasting at least 30 days be agreed to before August 31, 2024?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **Current Negotiations**: Intensive talks are underway between Israel and Hamas, mediated by Qatar, the United States, and Egypt, focusing on a phased release of Israeli hostages in exchange for a cessation of hostilities, the release of Palestinian prisoners, and additional aid to Gaza. -- **Key Demands**: Hamas is seeking a ""package deal"" that includes a permanent ceasefire before hostages are released, while Israel wants a phased approach, focusing on one stage at a time. Israel has offered to end the war if Hamas removes six senior leaders from Gaza, but this proposal has been rejected. -- **Recent Developments**: President Joe Biden detailed a three-phase deal proposed by Israel to Hamas, which includes a six-week period of comprehensive ceasefire, the withdrawal of Israeli forces from densely populated Gaza areas, and the release of hostages and Palestinian prisoners. The plan aims to transition into a permanent ceasefire after the initial phase. -- **Challenges**: The main obstacle is Hamas' insistence on a permanent ceasefire before releasing hostages, while Israel wants to focus on one stage at a time. Hamas is also seeking guarantees that Israel will not resume fighting once Palestinian civilians are freed, even if Israeli soldiers remain captive. -- **Expert Opinions**: A senior official from the Biden administration emphasized the need to agree on the ratio of prisoners to be released in each phase. The Wilson Center notes that the current agreement has the best chance of ending the conflict and deterring Iran, but Hamas must accept the proposal. -- **Recent Updates**: As of July 7, 2024, Prime Minister Benjamin Netanyahu stated that any Gaza ceasefire deal must allow Israel to resume fighting until its objectives are met. -- **Current Predictions**: There are no recent updates indicating significant movement towards a particular resolution. However, the ongoing negotiations aim to address the core demands of both sides while navigating the complexities of achieving a lasting peace in the region. - -No recent prediction markets or forecasting sites like Metaculus were found to be directly relevant to this specific question. - -######### - -GPT - -To determine the probability of a ceasefire lasting at least 30 days being agreed to before August 31, 2024, between Israel and Hamas, we need to systematically analyze the data and inputs provided. Here's a detailed breakdown of the thought process: - -### Step 1: Understanding the Context - -1. **Historical Context**: Historically, ceasefires between Israel and Hamas have been fragile, frequently breaking down even when brokered by international mediaries. Notable instances include the 2008 truce broken by an Israeli raid and recent short-term pauses that did not hold. - -2. **Current Negotiation Dynamics**: - - **Intensive Negotiations**: Current talks involve high-level mediators (Qatar, US, Egypt), signaling strong international support for a ceasefire. - - **Key Demands**: Critical issues are phased releases and guarantees of non-resumption of hostilities. Israel prefers a phased approach, while Hamas seeks a more comprehensive agreement upfront. - - **Recent Attempts**: There are consistent efforts and proposals, including a three-phase deal that implies a structured attempt towards a long-term solution. - -3. **Recent Developments**: As of mid-July 2024, there are signals from various entities (US officials, news outlets) indicating optimism and active negotiations. However, the situation remains volatile with recent conflicts such as the Hamas school strike suggesting setbacks may occur. - -### Step 2: Base Rates and Historical Precedent - -- **Historical Fragility**: Ceasefires have often been short-lived. Over the last decade, data shows that ceasefires between these entities typically break down frequently due to escalations or failures to meet demands. -- **Current Timeline**: With about 40 days remaining until August 31, the time frame is quite short for achieving a stable situation given the historical difficulty in sustaining long-term ceasefires. - -### Step 3: Analysis of Current Information and Trends - -1. **Recent News Analysis**: - - Articles from early July 2024 are hopeful but still highlight significant hurdles, such as disagreements on phrasing release sequences and conditions, inconsistent messages from leadership, and external sabotage claims. - - Statements from Hamas and Israel reflect deeply entrenched positions, indicating that significant compromises and guarantees would be required. - -2. **Mediator Dynamics**: - - High involvement of significant international players might increase pressure and probability of an agreement but guarantees of adherence remain uncertain (notably given the Palestinians’ mistrust of Israel's intentions). - -### Step 4: Consider the Probability Distribution - -1. **Immediate Ceasefire Tendencies**: Given there have been intensive talks and mediation, there may appear to be a reasonable chance that a ceasefire might be agreed upon. However, verifying this will last 30 days entails seeing at least until early September. -2. **Factors for Variance**: High mediation activity, global political dynamics (e.g., US election dynamics), and potential regional incentives or disincentives may fluctuate the stance of parties involved. - -### Step 5: Comprehensive Probability Assignment - -Given the importance and difficulty of attaining a stable ceasefire in historically conflict-ridden environments, the probability assessment must balance optimistic talks and entrenched skepticism. - -### Final Probability Estimation - -After considering the analysis, historical precedents, mediation activity, recent positive trends coupled with the persisting volatile nature of relations, the likelihood of an Israel-Hamas ceasefire which lasts at least 30 days before August 31, 2024: - -**Probability: 34%** - -This probability reflects a moderately low likelihood due to historical precedents of failure, current negotiation hurdles, and preliminary optimism balanced by geopolitical realism." -26392,"Will OpenAI announce GPT-5 before September 30, 2024? -","PERPLEXITY - -Here is a concise rundown of the news most relevant to the question: - -**Release Date Expectations:** -- **Late 2024 or Early 2025**: The latest available information suggests that GPT-5 might be released sometime in late 2024 or early 2025. -- **Before 2025**: A prediction market on Manifold Markets indicates a 52% chance of GPT-5 being released before January 1, 2025. -- **This Year**: Sam Altman mentioned in an interview that OpenAI plans to release an ""amazing new model this year,"" which could be GPT-5. - -**Development Status:** -- **Training Phase**: Reports indicate that GPT-5 is still in the training phase and will undergo extensive safety testing after completion. -- **Safety Testing**: The safety testing process, including ""red teaming,"" could take months and might expose issues that delay the release. - -**Competitive Landscape:** -- **Competition from Other AI Models**: OpenAI faces competition from other AI models like Claude Sonnet, which is already better at coding tasks and might influence the release timeline of GPT-5. - -**Expert Opinions:** -- **Bill Gates**: Bill Gates mentioned that progress on metacognition might be made next year, which could be related to GPT-5. -- **Sam Altman**: Altman has confirmed that GPT-5 is being actively developed and might be released soon, but no official release date has been announced. - -**Current Predictions:** -- **Manifold Markets**: The prediction market on Manifold Markets indicates a 52% chance of GPT-5 being released before January 1, 2025. -- **Metaculus**: A question on Metaculus asks when GPT-5 will be publicly available, but it is still awaiting approval. - -Overall, while there is no definitive release date, the majority of sources suggest that GPT-5 might be released in late 2024 or early 2025, with some predictions leaning towards a release this year. - -######### - -GPT - -To determine the probability that OpenAI will announce GPT-5 before September 30, 2024, we need to analyze the given information step-by-step, consider historical precedents, and evaluate the reliability of sources. - -### Step-by-Step Analysis - -#### 1. **Release Date Expectations** -- **Late 2024 or Early 2025**: This aligns with a typical timeframe for developing and testing highly complex models. -- **Before 2025**: With a 52% chance on Manifold Markets for a release before January 1, 2025, the market sentiment slightly favors a late 2024 release but shows significant uncertainty. -- **This Year**: Sam Altman's statement hints at a potential release in 2024, but without specifying GPT-5, it adds ambiguity. - -#### 2. **Development Status** -- **Training Phase**: The model is currently in training, suggesting a level of uncertainty regarding the immediate timeline. -- **Safety Testing**: The process could introduce delays, adding a buffer time that likely extends close to or beyond our deadline. - -#### 3. **Competitive Landscape** -- Competition may accelerate the desire to announce GPT-5 if other models like Claude Sonnet pressure OpenAI to keep up. - -#### 4. **Expert Opinions** -- **Bill Gates**: His comment on metacognition may relate to future AI capabilities, but it's tangential to our specific timeline. -- **Sam Altman**: Confirms active development but no exact release, adding uncertainty. - -#### 5. **Current Predictions** -- **Manifold Markets**: Show a 52% chance for release by January 1, 2025, indicating nearly equal odds. -- **Metaculus**: Awaiting a clearer consensus on their platform, so not too informative at this moment. - -### Historical Precedents -Historically, OpenAI has spaced substantial updates to their models approximately 1-2 years apart, reflecting the complexity and caution in their development cycle. The progression from GPT-3 to GPT-4, for instance, took around 1.5 years, and similar timelines can be expected given the increasing complexity. - -### Technological and Industrial Trends -- The rapid growth in AI capabilities and competitive pressures could lead to shorter intervals between announcements. -- However, given the rigorous safety protocols and thorough testing phases, significant delays could also occur. - -### Evaluation of Sources -- **Recency**: Recent statements and market sentiments are more reliable. -- **Expertise**: Higher weight on statements from OpenAI officials like Sam Altman. -- **Public Sentiment**: Manifold Markets and other predictive platforms offer a collective intuition but are not definitive. - -### Default Behavior and Base Rates -If OpenAI follows its previous pattern of prudent development and cautious announcements, there is a baseline expectation that significant milestones (like GPT-5) are not rushed and are thoroughly vetted. - -### Final Probability Calculation -Combining the above: -- The timeline suggests cautious optimism for a late 2024 announcement, supported but not guaranteed by market predictions and statements from OpenAI leadership. -- Safety testing and training phases might introduce delays, making a pre-September 30, 2024, announcement less certain. -- Given the 52% chance for pre-2025 release, adjusting for our more imminent deadline, and factoring in the nuances of rigorous testing: - -### Final Answer -Considering all the information, the probability that OpenAI will announce GPT-5 before September 30, 2024, is: -**Probability: 45%**" -26405,"Will at least 5 major hurricanes occur in the 2024 Atlantic hurricane season before Sept 30, 2024?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **NOAA Prediction**: NOAA predicts an 85% chance of an above-normal Atlantic hurricane season, with 17-25 named storms, 8-13 hurricanes, and 4-7 major hurricanes. -- **Current Conditions**: The Atlantic Basin is currently experiencing a lull in tropical activity due to an outbreak of Saharan dust, but this is expected to change in early August, leading to a more active hurricane season. -- **Expert Opinions**: Colorado State University (CSU) experts predict 25 named storms, 12 hurricanes, and 6 major hurricanes, with an above-normal probability of major hurricane landfalls along the continental United States coastline and in the Caribbean. -- **Sea Surface Temperatures**: Sea surface temperatures in the hurricane Main Development Region are near record warm levels, providing a conducive environment for hurricane formation and intensification. -- **ENSO Conditions**: The transition from El Niño to La Niña conditions, expected to occur between August and October, is likely to contribute to a more active Atlantic hurricane season. -- **Current Numbers**: As of July 11, there have been three named storms, including one major hurricane and two tropical storms. - -Overall, the predictions and current conditions suggest a high likelihood of an active hurricane season, with multiple factors favoring the formation of major hurricanes. - -######### - -GPT - -Sure, I'll walk through this methodically to arrive at a well-reasoned probability estimate for the question. - -### Step-by-Step Forecasting Process - -#### 1. **Historical Data and Base Rates:** - - The average number of major hurricanes in a typical Atlantic hurricane season is around 2-3. However, we know that certain years, especially those with favorable conditions, can far exceed this average. - - Statistical data from NOAA and Colorado State University suggest that in active seasons (those with expectations of more than 4 major hurricanes), the probability of hitting 5 major hurricanes by September 30 is substantial. - -#### 2. **Current Conditions (as of July 23, 2024):** - - **Sea Surface Temperatures (SSTs):** Near record-high SSTs significantly contribute to hurricane formation and intensification, providing a strong foundation for more major hurricanes. - - **ENSO Conditions:** The transition from El Niño to La Niña could catalyze more intense and frequent hurricanes, typical in favorable La Niña conditions. - - **Current Major Hurricane Count:** We already have one major hurricane (Beryl) as of early July, and the season’s activity is generally ramping up in August-September. - -#### 3. **Expert Forecasts:** - - **NOAA** predicts 4-7 major hurricanes. - - **Colorado State University** forecasts 6 major hurricanes, which is strongly indicative of a very active season. - -#### 4. **Temporal Considerations:** - - The peak of the hurricane season is generally from mid-August to October, with September being the most active month historically. Between now (late July) and September 30, the most active part of the season lies ahead. - - Other relevant factors like current lull due to Saharan dust are short-lived, and the forecast suggests activity will pick up significantly in early August. - -#### 5. **Recency and Reliability of Data:** - - The information provided, especially preexisting predictions from Colorado State University and the NOAA, are up-to-date and based on the latest climatological data. These sources are typically reliable and credible. - -### Analysis of the Potential Outcomes - -Given all this information: - -- We are at one major hurricane already, and predictions firmly suggest there's a strong possibility for five or more before September 30. -- Multiple conditions are highly favorable (high SSTs, transitioning ENSO) and point towards increased activity. -- Historical precedents also support the likelihood of seeing more major hurricanes given the time period we're considering is the most active part of the hurricane season. - -### Balancing the Uncertainty - -While the conditions are favorable and predictions are strong, we have to balance the inherent variability in hurricane formation. Meteorology is a field with notable uncertainty, even with sound models and favorable conditions. Although the conditions favor high hurricane activity, the exact number remains uncertain due to natural variability. - -### Conclusion - -Taking all factors into account, including the past data, current environmental conditions, expert predictions, and inherent uncertainties in weather forecasting, I will estimate the likelihood as follows: - -- There is a solid base rate and strong present indications favoring at least five major hurricanes. -- Given the favorable conditions and reliable predictions, the probability of hitting this number is high but not absolute. - -### Final Probability Estimate - -After weighting all the data points, historical precedents, and expert forecasts: - -**Probability: 80%** - -This estimate highlights a high likelihood while accounting for a 20% margin of uncertainty due to the inherent unpredictability in weather systems." -26404,"Will cannabis be removed from Schedule I of the Controlled Substance Act before September 30, 2024?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question of whether cannabis will be removed from Schedule I of the Controlled Substances Act before September 30, 2024: - -- **Recent Developments**: The U.S. Department of Justice announced in May 2024 that it was initiating rulemaking to reschedule cannabis from Schedule I to Schedule III of the Controlled Substances Act. -- **Background**: The rescheduling process began in 2022 when President Joe Biden instructed the Attorney General to review the classification of cannabis. The Department of Health and Human Services (HHS) subsequently recommended moving cannabis to Schedule III in August 2023. -- **Current Status**: The DEA has received the HHS recommendation and is preparing for a public consultation on the initiative. The proposal is awaiting review by the White House Office of Management and Budget (OMB). -- **Expert Opinions**: NORML Deputy Director Paul Armentano noted that while rescheduling cannabis to Schedule III is a significant step, it does not fully address the conflict between federal and state laws on cannabis use. -- **Timeline**: The rescheduling process involves public comments and possible judicial review before the DEA issues its final determination. Changes to marijuana's federal status will take effect 30 days after publication of the DEA's final rule in the Federal Register. - -Given the current progress and the fact that the DEA has already initiated the rulemaking process, it is likely that cannabis will be removed from Schedule I of the Controlled Substances Act before September 30, 2024. However, the exact timeline depends on the speed of the review and consultation process. - -######### - -GPT - -To determine the probability of cannabis being removed from Schedule I of the Controlled Substances Act (CSA) before September 30, 2024, let’s walk through the key factors methodically: - -### Key Background Information -**1. Recent Developments:** -- DEA initiated rulemaking to reschedule cannabis, published Notice of Proposed Rulemaking (NPRM) on May 21, 2024. -- The NPRM triggers a 62-day comment period ending July 22, 2024. - -**2. Process and Timeline:** -- After the public comment period, DEA will review comments and possibly modify the rule. -- The rule then requires approval by the White House Office of Management and Budget (OMB). -- Final rule publication would occur, followed by a 30-day period after publication for the rule to take effect. - -**3. Expert Opinions:** -- Experts, like NORML’s deputy director, suggest this is a significant step, though it doesn’t solve all federal-state law conflicts. - -**4. Historical Precedents:** -- Rescheduling efforts take significant time due to rigorous legal, scientific, and bureaucratic processes. -- Given the information that a rules change will take effect 30 days post-publication, a swift review and response period would be critical. - -### Key Dates: - -1. **July 22, 2024**: End of the public comment period. -2. **September 30, 2024**: Deadline for the cannabis rescheduling to be effective. - -### Factors Weighing Probability: -**1. Procedural Complexity and Time Requirements:** - - The process involves multiple stages: comment review, OMB approval, potential revisions, and final publication. - - Reviewing and incorporating public comments can be a time-consuming process. - -**2. Political Will and Prioritization:** - - The current Administration appears invested in rescheduling, having initiated the process with public statements and formal actions. - - However, there is still significant work between end of the comment period and publishing the final rule. - -**3. Urgency and Public Perception:** - - With an approaching election, there might be political motivation to expedite the process to appeal to certain voter demographics. - -**4. Base Rates and Historical Context:** - - Historically, changes in drug scheduling have taken months to years, not weeks, though there is a more recent trend towards speeding up administrative processes under pressure. - -### Estimation Process: -Given today’s date of July 23, 2024: -- **End of Comment Period**: July 22, 2024. -- **Potential Timeline for OMB and Finalization**: - - OMB review could be expedited but typically takes weeks. - - Additional internal review (if required) may add further delay. - - Time for actual publication and 30-day enactment post-publication. - -Approximately 69 days remain until September 30, 2024: -- Fallout from comment period review and OMB process's uncertain timeline together with publication need to fit within this period. -- Estimations suggest that rapid action would be needed to meet this stringent timeline. - -**Overall Judgment:** -There is administrative and political will coupled with a compressed and highly procedural timeline. -While favorable conditions and recent historical trends suggest an increased probability, procedural rigor historically slows down such processes. - -### Probability Distribution: -- **Best Case (Rushed timeline)**: ~40% chance of achieving this within the deadline. -- **Base Case (Moderate delays)**: ~30% chance. -- **Worst Case (Extended delays)**: ~30% chance. - -### Final Probability: -**Probability: 55%** - -This reflects an optimistic estimation that, while challenges exist, the focused political will and recent administrative efforts weigh up toward crossing the threshold just in time." -26408,"Will the minimum wage in Türkiye (Turkey) increase again before September 30, 2024? -","PERPLEXITY - -To assist the superforecaster in predicting whether the minimum wage in Türkiye (Turkey) will increase again before September 30, 2024, I have compiled a detailed rundown of the most relevant news and information: - -### Current Minimum Wage and Recent Changes -- The current minimum wage in Turkey is 17,002 Turkish liras (approximately $515 USD). -- The minimum wage was increased by 49% to 17,002 Turkish liras in December 2023, effective January 1, 2024. - -### Government Stance on Interim Increases -- Turkish Labor Minister Vedat Işıkhan ruled out a second hike in the minimum wage in 2024, aligning with President Recep Tayyip Erdoğan's statement in December 2023 that the minimum wage increase is made once a year. -- Despite high inflation and a cost-of-living crisis, the government has maintained its stance against an interim increase in the minimum wage. - -### Labor Union Recommendations -- The Confederation of Progressive Trade Unions (DİSK-AR) recommends increasing the minimum wage four times a year during periods of high inflation to tackle the cost-of-living crisis. -- The DİSK-AR report suggests that the minimum wage remains below the hunger threshold and is expected to be significantly lower by the end of the year if not adjusted. - -### Economic Context -- Turkey has been battling high inflation, with a rate of 75.4% in May 2024. -- The government has implemented tight monetary policies, including raising interest rates from 8.5% to 50%, to control inflation. - -### Expert Opinions -- Labor Minister Vedat Işıkhan and President Recep Tayyip Erdoğan have reiterated that there will be no second hike in the minimum wage in 2024. -- Finance Minister Mehmet Şimşek has stated that the monthly minimum wage is not low and is the highest among developing countries. - -### Prediction Markets -- There are no relevant prediction markets available on this specific topic. - -Given the current stance of the Turkish government and the lack of any recent updates indicating a change in policy, it appears unlikely that the minimum wage in Turkey will increase again before September 30, 2024. However, the ongoing high inflation and cost-of-living crisis, as well as the recommendations from labor unions, may continue to apply pressure on the government to reconsider its stance. - -######### - -GPT - -### Detailed Analysis - -#### Current Background Context -1. **Current Minimum Wage and Recent Changes**: - - The most recent increase took effect on January 1, 2024. - - Historically, Turkey has increased minimum wage several times due to inflation, with twice yearly adjustments in recent years (2022 and 2023). - -2. **Government Stance on Interim Increases**: - - Currently, the government has ruled out a second hike in 2024. - - Both prominent government figures (President Recep Tayyip Erdoğan and Labor Minister Vedat Işıkhan) have stated there will be no additional increases this year. - -3. **Labor Union Recommendations**: - - Labor unions advocate for multiple increases per year to keep up with inflation. - - The hunger threshold report suggests an urgent need for wage adjustments. - -4. **Economic Context**: - - Inflation rate remains very high at 68% year-on-year (75.4% in May 2024). - - The government's efforts to fight inflation include significant monetary policies, such as raising interest rates to 50%. - -5. **Expert Opinions**: - - The current stance from government officials is quite firm against a second wage increase. - -6. **Recent Elections**: - - The ruling party lost local elections recently, possibly prompting populist policies, yet no direct indication of an upcoming wage increase has emerged. - -#### Base Rate and Historical Precedent -- Historically, during periods of high inflation, Turkey has shown willingness to increase minimum wage multiple times within a year. -- However, current and historical statements from key officials suggest a strong resistance to additional wage increases in 2024. - -### Analysis of Likelihood -1. **Probability of Policy Shift**: - - Given the history of inflation prompting wage hikes, there is a non-zero chance that policy may shift under emergency economic pressures. - - However, with the firm stance of the current administration, the likelihood of such a shift seems lower without new compelling reasons. - -2. **External Pressure and Public Sentiment**: - - High inflation and public demand could create pressure for an increase. - - Despite this, strong indications from current government officials signal no planned increase. - -3. **Timeframe and Resolution Criteria**: - - There are approximately 2 months left until the resolution date. - - Historical behavior would have shown any emergent indications of policy softening by now. - -### Statistical Probability -- **Historical Increase Variable**: Past behavior suggests policy change due to inflation. -- **Current Indicators**: Strong government stance against a new increase. -- **Economic Pressure**: High inflation continues, but monetary policies aiming to stabilize. - -Given the above, we weigh the firmness of government declarations slightly higher than the external pressures from economic conditions and public opinion. - -### Expected Output -Taking all these factors into consideration, the probability of another minimum wage increase before September 30, 2024, should be evaluated carefully with a degree of conservative forecasting. - -### Final Assessment -Considering the weighted importance of the data sources and the time available for a decision, while accounting for both governmental stance and economic adaptation necessitated by inflation: - -#### Final Answer: -Probability: 30%" -26405,"Will at least 5 major hurricanes occur in the 2024 Atlantic hurricane season before Sept 30, 2024?","PERPLEXITY - -The 2024 Atlantic hurricane season is expected to be well above-normal due to a combination of factors, including near-record warm ocean temperatures, the development of La Niña conditions, and reduced Atlantic trade winds and wind shear. Here is a summary of the relevant news and forecasts: - -1. **NOAA's Outlook**: NOAA predicts an 85% chance of an above-normal season, with 17-25 named storms, 8-13 hurricanes, and 4-7 major hurricanes. -2. **Colorado State University (CSU) Forecast**: CSU estimates 25 named storms, 12 hurricanes, and 6 major hurricanes. They also predict a well above-average probability for major hurricane landfalls along the continental United States coastline and in the Caribbean. -3. **Other Forecasts**: Arizona State University forecasts 21 named storms, 11 hurricanes, and 5 major hurricanes. The Weather Company and Atmospheric G2 predict 24 named storms, 11 hurricanes, and 6 major hurricanes. -4. **Current Conditions**: As of July 11, there have been three named storms, including one major hurricane and two tropical storms. The waters from the coast of Africa to the Gulf of Mexico are unseasonably warm, which could contribute to a more active season. -5. **La Niña and Ocean Temperatures**: The transition from El Niño to La Niña is expected to occur between August and October, which could further enhance the likelihood of an active season. Sea surface temperatures in the hurricane Main Development Region are near record warm levels, providing ample fuel for tropical development. -6. **Saharan Dust**: A recent outbreak of Saharan dust has kept tropical activity in the Atlantic Basin at bay, but this lull is expected to end in early August, leading to a potential uptick in tropical activity. -7. **Peak Season**: The busiest time of hurricane season typically occurs in August and September, coinciding with the warmest ocean temperatures and lowest wind shear. - -Given these forecasts and conditions, it is likely that at least 5 major hurricanes will occur in the 2024 Atlantic hurricane season before September 30, 2024. - -######### - -GPT - -To accurately assess the likelihood of at least 5 major hurricanes occurring in the 2024 Atlantic hurricane season before September 30, 2024, we'll follow a structured, step-by-step approach based on available data, historical trends, current conditions, and expert forecasts. - -### 1. Understanding Background Information - -#### Forecasts and Current Conditions: -- **NOAA's Outlook**: Predicts 4-7 major hurricanes, with an 85% chance of an above-normal season. -- **Colorado State University (CSU) Forecast**: Estimates 6 major hurricanes. -- **Other Forecasts**: Range from 5-6 major hurricanes. -- **Current Conditions**: One major hurricane (Beryl) as of July 23, with very warm ocean temperatures and the persistence of La Niña conditions. -- **Peak Season**: August and September are the most active months for hurricanes. We are just entering this peak period. - -### 2. Historical Context and Base Rates - -#### Historical Data on Major Hurricanes: -- **Recent High-Activity Seasons**: The 2020 season had 7 major hurricanes, 2017 had 6, and 2005 had 7. -- **Base Rate**: Analyzing data from NOAA, the average number of major hurricanes per season over the last decade is around 3 to 6. - -### 3. Evaluating Contributing Factors - -#### Environmental Conditions: -- **Ocean Temperatures**: Unseasonably warm, which enhances the potential for hurricane development. -- **La Niña**: Typically associated with higher hurricane activity in the Atlantic. -- **Wind Shear**: Reduced wind shear further supports more intense storms. -- **Saharan Dust**: Its current impact is expected to diminish, which could increase storm activity. -- **Lead Time**: There are approximately 2.5 months remaining until September 30, which includes the peak season for hurricane activity. - -### 4. Statistical and Probabilistic Analysis - -#### Probability Calculations: -Given the provided forecasts and the base rate, let's construct a simple probabilistic model: -1. **CSU Forecast**: Predicts 6 major hurricanes for the full season. This implies high confidence in at least 3-5 more major hurricanes over the next two months. -2. **NOAA's Upper Bound**: 7 major hurricanes, suggesting 4-6 more major hurricanes. -3. **Weighted Average Prediction**: If we average the forecasts and factor in historical data, the expectation centers around 5-6 major hurricanes for the season. With 1 already formed, we need at least 4 more by September 30. - -Using Bayesian updating, let's assume that the likelihood of forming a major hurricane given the current favorable conditions (La Niña, warm SST, reduced wind shear) is consistent with higher-end forecasts. - -### 5. Synthesis and Conclusion - -#### Factors in Favor: -- Favorable environmental conditions. -- Historical precedence of high-activity seasons in similar conditions. -- High confidence from expert forecasts. -- Entering the peak hurricane season. - -#### Factors Against: -- Natural variability and unpredictability. -- Some forecasts being on the lower end (e.g., 5 major hurricanes). - -Given these considerations, I would weight the forecasters' expertise and current environmental conditions heavily, slightly offset by the unpredictability inherent to hurricane development. - -### Final Probability Assessment - -Given the convergence of expert opinions forecasting an active season, the persistence of favorable conditions, and historical trends indicating higher activity in such years, it is reasonable to assess a high probability for the formation of at least 5 major hurricanes by September 30, 2024. - -**Probability: 80%**" -26406,"Will the maximum weekly rate of COVID-19 hospitalizations occurring in the United States between June 1 and August 31, 2024 exceed 3.5 per 100,000 people?","PERPLEXITY - -To provide a concise and detailed rundown of the news relevant to the question, I have searched various sources, including official health organizations and data dashboards. Here is a summary of the most recent and relevant information: - -### Current COVID-19 Hospitalization Rates - -- The CDC reports a hospitalization rate of 2.5 per 100,000 population for the week ending June 29, 2024, which is an increase from the previous week's rate of 2.1. -- The COVID-NET interactive dashboard, updated weekly, provides detailed data on COVID-19-associated hospitalizations. The overall rate of COVID-19-associated hospitalizations for the 2023-24 season was 144.5 per 100,000 people. -- The WHO COVID-19 dashboard presents weekly data on reported cases. While the data may be incomplete for the latest week, it provides an overview of trends in COVID-19 cases. - -### Recent Trends and Updates - -- The CDC's COVID-NET data indicates that hospitalization rates are subject to reporting delays, which might increase around holidays or during periods of increased hospital utilization. -- The WHO has shifted its reporting from daily to weekly statistics to mitigate against visual misinterpretation of data due to differences in reporting practices among countries. -- The LA County Daily COVID-19 Data dashboard shows a rolling 7-day average of hospitalizations, which can help track recent trends. - -### Expert Opinions and Forecasts - -- There are no specific expert opinions or forecasts directly addressing the question of whether the maximum weekly rate of COVID-19 hospitalizations will exceed 3.5 per 100,000 people between June 1 and August 31, 2024. However, the CDC and WHO continue to monitor and report on COVID-19 trends, providing valuable insights into the ongoing pandemic. - -### Current Values and Prediction Markets - -- The current hospitalization rate per 100,000 population is 2.5, as reported by the CDC. -- There are no relevant prediction markets or forecasting sites like Metaculus that provide specific predictions for this exact question. - -In summary, the current hospitalization rate is 2.5 per 100,000 population, and there are no direct forecasts or expert opinions on whether it will exceed 3.5 per 100,000 people between June 1 and August 31, 2024. However, the CDC and WHO continue to monitor and report on COVID-19 trends, providing valuable insights into the ongoing pandemic. - -######### - -GPT - -Given the question about whether the maximum weekly rate of COVID-19 hospitalizations in the United States between June 1 and August 31, 2024, will exceed 3.5 per 100,000 people, here’s how I would approach the analysis step by step: - -### Step 1: Understand the Situation - -1. **Current Rate**: The most recent data shows a weekly rate of 2.5 per 100,000 as of June 29, 2024. -2. **Trend**: There has been a recent increase from 2.1 to 2.5, indicating a rising trend. -3. **New Variant**: The KP.2 variant, part of the FLiRT set, is rising in prevalence. It suggests an increased likelihood of infection spread, potentially influencing hospitalization rates. - -### Step 2: Historical Context - -1. **Base Rates**: During the winter surges, hospitalization rates often exceeded the 3.5 per 100,000 threshold. However, summer trends generally show lower rates. -2. **Previous Surges**: Significant surges usually occur with either a new highly transmissible variant or in colder months when people are indoors. - -### Step 3: Evaluate Current and Updated Factors - -1. **Reporting Delays**: The CDC has noted that reporting delays can cause temporary underreporting of data. -2. **Vaccine and Treatment**: We must assess the vaccination coverage, the effectiveness of current vaccines against KP.2, and the availability and utilization of treatments. -3. **Public Health Measures**: Consider whether any new public health measures or changes in behavior could impact the hospitalization rates. - -### Step 4: Statistical Analysis and Model Forecasts - -1. **Projected Trends**: I use available data to model projected trends considering variables like infection rates, hospitalization rates, R-values, and transmission potential of the new variant. -2. **Variance Analysis**: Look at the rate of change in hospitalizations from week to week and any exponential growth patterns. A small consistent increase can indicate potential spikes. - -### Step 5: Expert Opinions and Similar Cases - -1. **Expert Opinions**: Although no direct expert opinions were available, similar past surges and their characteristics are crucial. -2. **Analogous Situations**: Identifying situations where new variants appeared and their impact on hospitalization rates. - -### Step 6: Integrate All Information - -1. **Historical and Current Rates**: Summer rates are typically lower, but with a new variant, there is an increased risk. Recent weeks have shown a small uptick. -2. **Time Frame**: There are roughly 6 weeks left until August 31, 2024, allowing for the possibility of further increases. - -### Final Calculation - -Based on the above, here is the structured reasoning that leads to my probability estimate: -- **Current Rate**: 2.5 per 100,000. -- **Trend**: Rising, new variant could exacerbate but it's usually slower in summer. -- **Historical Context**: Summer trends are generally milder. -- **New Variant Impact**: KP.2 variant is relatively new and could push rates higher. -- **Expert Insight**: Lack of direct forecasts but inferred from analogous situations implies a moderate risk. -- **Reporting Delays**: Could affect real-time understanding but might reveal an actual higher rate post-facto. - -Considering all these factors, I will assign a probability given the current trend, potential impact of the new variant, and historical data, yet also weighing in against summer's traditionally lower rates. - -### Final Answer -**Probability: 62%** - -This reflects a moderate to high likelihood of the maximum weekly rate of COVID-19 hospitalizations exceeding 3.5 per 100,000 people between June 1 and August 31, 2024, considering the current trajectory and potential spike due to KP.2." -26407,"Will Russia start a second mobilization wave before September 30, 2024, according to the Institute for the Study of War?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question about Russia starting a second mobilization wave before September 30, 2024, I have searched various sources, including the Institute for the Study of War (ISW) and other news outlets. - -### Key Points - -1. **Mobilization Plans**: The Vyorstka Telegram channel reported on March 22 that Russia plans to announce a new wave of military mobilization, aiming to enlist up to 300,000 people to bolster its troops involved in the ongoing invasion of Ukraine. -2. **Current Mobilization Efforts**: The Russian government continues to improve its bureaucratic mobilization and conscription systems. The UK Ministry of Defense reported that Russia is enacting an ""information exchange system"" between the Russian Ministry of Defense and the Federal Security Service (FSB) to prevent potential draftees from fleeing the country. -3. **Recruitment Incentives**: Social media users amplified Russian recruitment posts offering one-time enlistment bonuses ranging from 705,000 rubles ($8,000) to 1.7 million rubles ($19,200) for signing military contracts in various regions. -4. **Belarusian Involvement**: Belarusian forces continued joint military exercises with the People's Liberation Army (PLA) and Kazakh forces, while Belarusian President Alexander Lukashenko reiterated that Belarus does not intend to join the war in Ukraine. -5. **Long-term Implications**: The Kremlin is reportedly concerned about the long-term social and political implications of Russian veterans returning from the war in Ukraine, including potential increased crime and discontent among Russian citizens. - -### Recent Updates - -- **July 16, 2024**: The UK Ministry of Defense reported on Russia's efforts to improve its conscription systems, including the ""information exchange system"" to prevent draftees from fleeing. -- **July 17, 2024**: Russian state news outlets editorialized comments by Russian Security Council Deputy Chairperson Dmitry Medvedev, suggesting that Ukraine will cease to exist by 2034, which sets expectations for a long war. -- **July 19, 2024**: The Kremlin expressed concerns about the long-term social and political implications of Russian veterans returning from the war in Ukraine, including potential increased crime and discontent among Russian citizens. -- **July 22, 2024**: No specific updates on mobilization plans were reported. - -### Expert Opinions - -- **Mark Denisov, Ombudsman in Russia's Krasnoyarsk Krai**: Stated that several penitentiaries in the Siberian region will be shut down this year due to the ongoing recruitment of inmates for the war. -- **Alexander Khodakovsky, Russian ""Vostok"" Battalion Commander**: Claimed that the Russian military is integrating elements of Donetsk and Luhansk People’s Republics’ militias, with some setbacks, and that veterans are subject to conscription under new laws. - -### Current Predictions - -- **Metaculus**: No relevant prediction markets were found on Metaculus regarding a second mobilization wave before September 30, 2024. - -### Conclusion - -While there are indications of Russia's ongoing efforts to improve its mobilization and conscription systems, as well as reports of potential new mobilization plans, there is no definitive confirmation of a second mobilization wave before September 30, 2024, according to the Institute for the Study of War. The situation remains fluid, with ongoing recruitment efforts and concerns about the long-term implications of the war. - -######### - -GPT - -To create an accurate probability forecast for the question, ""Will Russia start a second mobilization wave before September 30, 2024, according to the Institute for the Study of War?"", we need to consider several key factors: - -### Key Factors to Consider - -1. **Historical Context and Precedent**: - - In September 2022, Russia executed a partial mobilization of military reservists. Since then, this precedent suggests that partial mobilizations are within the realm of possibility. - - Historically, mobilizations are significant decisions rooted in both military necessity and political calculations. - -2. **Current Military Situation**: - - Russia has suffered substantial troop losses, and replenishing these troops is essential for sustained military efforts in Ukraine. - - Ukraine's planned mobilization of 500,000 soldiers indicates an intensification of the conflict, possibly forcing Russia to respond with increased recruitment. - -3. **Official Statements and Reports**: - - Recent reports indicate Russia planning a second mobilization wave with an aim to enlist up to 300,000 people. - - Improvements in bureaucratic systems to prevent drafts dodgers and high recruitment incentives show an ongoing effort to bolster troop numbers. - -4. **Political Considerations**: - - ISW assesses that Putin is aware of the unpopularity of a second mobilization wave and concerns about widespread discontent. This makes it a politically sensitive action. - - Current reports indicate the Kremlin's awareness of long-term social and political risks of continued mobilization, including issues with veterans and internal stability. - -5. **Recency and Reliability of Information**: - - The most recent updates (July 2024) highlight ongoing steps to enhance conscription systems and integrate veterans, which suggest preparatory actions for potential future mobilizations but do not confirm immediate plans for a second wave. - -### Analysis - -1. **Recency and Trends Conditions**: - - Recent measures to improve conscription and recruitment incentives are indicative of preparatory actions. - - However, no definitive announcement of a second wave has been made as of July 22, 2024. - -2. **Time Frame**: - - With approximately 2.5 months remaining until the September 30, 2024, deadline, there is sufficient time for the situation to develop. Given the fluid nature of military conflicts and political decisions, changes can occur relatively rapidly. - -3. **Default Resolution**: - - If nothing changes in the default pattern, the absence of a new mobilization wave will lead to a ""No"" resolution. However, recent preparatory actions suggest a departure from the status quo is possible. - -4. **Base Rates**: - - Mobilizations of this scale are rare and politically significant, indicating a low base rate. However, given the war context and replenishment need, the base rate is higher than during peacetime. - -5. **Uncertainty and Distribution**: - - Substantial uncertainty remains given the fluidity of the situation and the tension between military necessity and political risk. - -### Conclusion - -Considering all factors, we acknowledge the preparatory steps taken by Russia and the associated risks. While exact confirmation of a second wave is pending, the likelihood is non-negligible given the current circumstances and efforts. - -Thus, balancing the ongoing preparations and political concerns, we can estimate the probability of Russia starting a second mobilization wave before September 30, 2024, as follows: - -**Probability: 55%** - -This reflects an almost equal chances given the available information but slightly tipping towards ""Yes"" due to ongoing mobilization-related activities and military needs." -26555,Will the USA win more Gold than Silver medals at the Paris 2024 Olympics?,"PERPLEXITY - -To assist the superforecaster in making an informed prediction, I have compiled a detailed rundown of the most relevant news related to the question of whether the USA will win more gold than silver medals at the Paris 2024 Olympics. - -### Key Projections and Forecasts - -- **Nielsen's Gracenote Forecast**: The United States is projected to win 112 medals overall, comprising 39 gold, 32 silver, and 41 bronze medals. -- **Gold Medal Projections**: The USA is expected to win 39 gold medals, while China is forecast to win 36 gold medals, which could challenge the USA's position as the winner of most golds. - -### Recent Updates and Trends - -- **Host Nation Advantage**: France, as the host nation, is expected to significantly increase its medal haul, with a forecast of 56 medals, including 29 gold medals. -- **Russian and Belarusian Athletes**: The participation of Russian and Belarusian athletes is uncertain due to their absence from international competitions since February 2022. Any medals they win will not be included in the official medal table. - -### Expert Opinions and Context - -- **Geopolitical Influence**: The Olympics are seen as a surrogate for national pride and geopolitical influence, making the medal count a significant indicator of a nation's standing. -- **Historical Performance**: The United States has consistently topped the overall medal table in the Summer Games, with the last time it did not win the most gold medals being in 2008 in Beijing. - -### Current Values and Predictions - -- **Metaculus Prediction**: There is no current prediction on Metaculus specifically addressing the question of whether the USA will win more gold than silver medals at the Paris 2024 Olympics. - -### Additional Context - -- **US Hosting Experience**: The United States has hosted the Olympics eight times, with Los Angeles set to host the 2028 Summer Olympics and Salt Lake City the 2034 Winter Olympics. - -By considering these sources and projections, the superforecaster can make a more informed prediction about whether the USA will win more gold than silver medals at the Paris 2024 Olympics. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Determine the Type of Question** -This is an ""Other Binary Outcome Event"" as it pertains to whether a specific binary condition (winning more gold medals than silver) will occur or not. - -#### 2. **Analyze Key Projections and Forecasts** -- **Nielsen's Gracenote Forecast**: This forecast is a critical piece of data. They project 39 gold and 32 silver medals for the USA. - - **Rating**: Highly important due to its specificity and comes from a well-recognized entity. - - **Recency**: As recent as it can be (with the Olympics just around the corner). - -#### 3. **Examine Historical Performance** -- **Historical Trend**: The USA has a strong history of winning the most gold medals in recent Summer Olympics except for Beijing 2008. - - **Rating**: Important, as the USA's historical performance indicates a stable and high gold medal count. - - **Recency**: Relevant as historical data likely indicates future performance under typical circumstances. - -#### 4. **Geopolitical and Environmental Factors** -- **Russian and Belarusian Absence**: Their possible absence could remove strong competition in some events. - - **Rating**: Moderately important; affects competition but is not the primary driver for USA's medal counts. - - **Recency**: Relevant to this iteration of the Olympics, based on recent geopolitical developments. - -#### 5. **Other Contextual Data** -- **Host Nation Advantage**: France may see increased medals, but this does not directly affect the USA’s gold-to-silver ratio. - - **Rating**: Slightly important; relevant to overall medals but less so for the USA’s specific counts. - - **Recency**: Relevant for Paris 2024 but secondary to primary data. - -#### 6. **Expert Opinions & Metaculus Predictions** -- **Expert Opinions**: Suggest geopolitical importance but do not adequately cover the specific gold vs. silver dynamic. - - **Rating**: Moderately important as general context, but lacks specificity on the gold vs. silver count. - - **Recency**: Insightful but not directly impact-driven for this specific forecast. - -#### 7. **Time Frame and Variance** -- **Time Left for Change**: With the Olympics only a few days away, the variance in predictions from key sources like Nielsen is expected to be minimal unless unprecedented events occur. - - **Rating**: Highly important to bear in mind; unlikely significant changes from earlier projections within such a short span. - -### Synthesis and Final Probability - -#### 1. **Base Rate** -- Historically, the USA has a higher base rate for winning more gold than silver medals in the Summer Games. - -#### 2. **Current Forecasts** -- With Nielsen predicting 39 gold and 32 silver medals, the forecast from a reliable source strongly suggests a higher gold count. - -#### 3. **Temporal Considerations** -- Given the proximity to the event and the consistency of such predictions over recent years, deviation from forecasted counts is plausible but unlikely to be substantial. - -### Conclusion - -- **Probabilistic Calculation**: Given that Nielsen’s forecasted distribution shows a convincing lead in gold medals over silver, and considering all other factors, the probability should account for the slight uncertainty inherent in forecasts but should still be high. - -### Final Answer -**Probability: 85%** - -This high probability reflects high confidence based on historical precedent, detailed forecasts, and the low likelihood of significant deviations in the short time leading up to the Olympics." -26554,"Will the next interstellar object be discovered before September 30, 2024? -","PERPLEXITY - -To provide a concise rundown of the news relevant to the question of whether the next interstellar object will be discovered before September 30, 2024, I have summarized the following sources: - -1. **CNN Article**: The article discusses the discovery of 'Oumuamua, the first interstellar comet, and how it accelerated as it moved through our solar system. It highlights the upcoming Vera C. Rubin Observatory in Chile, which will begin operations in 2025 and aim to detect a few interstellar comets like 'Oumuamua each year. - -2. **Nature Article**: This article examines the detective work required to confirm the discovery of interstellar debris. It does not provide specific information about the discovery of the next interstellar object but emphasizes the importance of confirming such discoveries. - -3. **Wikipedia**: The Wikipedia page on interstellar objects provides background information on these objects, including their definition, nomenclature, and notable examples like 'Oumuamua and 2I/Borisov. It mentions that astronomers reported the possible capture of other interstellar objects in Near Earth Orbit (NEO) over the years. - -4. **Space.com**: This article provides a guide to 'Oumuamua, including an interview with David Trilling, a professor of astronomy. Trilling predicts that we will find an average of one interstellar object in our solar system every five years. He also estimates that there are between 1,000 and 10,000 interstellar objects in the solar system at any time. - -5. **Phys.org Article**: This article discusses the potential for capturing and studying future interstellar objects. It mentions that multiple proposals have been made for interceptor spacecraft to study ISOs and that the Vera C. Rubin Observatory will significantly increase the rate of ISO discoveries in the late 2020s and 2030s. - -**Key Points**: - -- The Vera C. Rubin Observatory, set to begin operations in 2025, is expected to detect a few interstellar comets like 'Oumuamua each year. -- Astronomers predict that we will find an average of one interstellar object in our solar system every five years. -- There are estimates that between 1,000 and 10,000 interstellar objects are in the solar system at any time. -- Next-generation instruments, including the Vera C. Rubin Observatory, will significantly increase the rate of ISO discoveries in the late 2020s and 2030s. - -**Recent Updates**: - -- There are no recent updates that specifically indicate movement towards the discovery of the next interstellar object before September 30, 2024. -- However, the upcoming Vera C. Rubin Observatory and other next-generation instruments are expected to increase the rate of ISO discoveries in the near future. - -**Expert Opinions**: - -- David Trilling predicts that we will find an average of one interstellar object in our solar system every five years. -- Astronomers are keen to study ISOs to learn about the formation of other stars and planets and the interstellar medium. - -**Current Values**: - -- There are no specific current values relevant to this question. - -**Forecasting Sites**: - -- There are no relevant prediction markets or forecasting sites that provide specific predictions about the discovery of the next interstellar object before September 30, 2024. - -######### - -GPT - -To determine the probability that the next interstellar object (ISO) will be discovered before September 30, 2024, we need to carefully evaluate the available evidence and base rates, considering the context, timing, and expert predictions. - - -### Key Pieces of Information: - -1. **Historical Discoveries**: - - 1I/'Oumuamua was discovered in October 2017. - - 2I/Borisov was discovered in August 2019. - - These two discoveries occurred within a span of about two years. - -2. **Vera C. Rubin Observatory**: - - Expected to begin operations in 2025, after the resolution date. Therefore, its impact on increasing the detection rate of ISOs will not affect our prediction timeframe. - -3. **Expert Predictions**: - - David Trilling predicts an average detection rate of one ISO every five years. This base rate helps us understand the typical frequency of ISO detections given the current technology and survey capabilities. - -4. **Current Sky Surveys**: - - Existing surveys like Pan-STARRS and others continue to monitor the sky, with the possibility of detecting new interstellar objects. - -### Base Rate Calculation: - -Given the expert prediction of detecting one ISO every five years, we have: -- \( 1 \text{ ISO} / 5 \text{ years} = 0.2 \text{ ISOs/year} \) - -From the time between 'Oumuamua's discovery (Oct 2017) and today's date (Jul 24, 2024), we have approximately 6.75 years. Using the base rate: -- Expected detections in 6.75 years = \( 0.2 \text{ ISOs/year} * 6.75 \text{ years} = 1.35 \text{ ISOs}\) - -Given two confirmed ISOs within this timeframe, we are slightly ahead of the base rate. This indicates we're within reasonable bounds for a detection. - -### Time Remaining Until Resolution: -- From July 24, 2024, to September 30, 2024, there are approximately 2.25 months (0.1875 years). - -Using the base rate of 0.2 ISOs/year, the expected detections in 0.1875 years: -- Expected additional detections = \( 0.2 \text{ ISOs/year} * 0.1875 \text{ years} = 0.0375 \text{ ISOs}\) - -### Conclusion: - -While the base rate and expert opinion suggest a relatively rare occurrence of ISO detections, the fact that we've seen two detected within a short period in the past suggests that it's not impossible to discover another ISO within the short remaining timeframe. However, the statistical likelihood based on historical data and predictions remains relatively low. - -Considering: -- Historical detection rates, -- Expert predictions, -- Upcoming technological advancements (post prediction window), -- Time remaining until the resolution date, - -**Final Probability**: -Taking into account the probability distribution and the inherent uncertainty, I project a modest chance for detecting the next ISO before the cutoff date. - -""Probability: 25%""" -26554,"Will the next interstellar object be discovered before September 30, 2024? -","PERPLEXITY - -To assist the superforecaster in predicting whether the next interstellar object will be discovered before September 30, 2024, I have compiled a summary of relevant news and research: - -1. **Recent Discoveries and Trends**: - - Astronomers have spotted two interstellar objects in the last two years, 'Oumuamua and 2I/Borisov, and expect to see several large objects passing through our solar system in the future. - - Researchers believe that hundreds of smaller interstellar objects could be passing through each year. - - The discovery of these objects can help astronomers understand where they came from and provide insights into the formation of other solar systems. - -2. **Detection Methods and Upcoming Surveys**: - - The Vera C. Rubin Observatory, currently under construction in Chile, is expected to gather its first light in January 2025 and significantly increase the rate of interstellar object discoveries. - - Astronomers are proposing methods such as stellar occultations, optical signatures from impacts with the moon or the Earth's atmosphere, and radio flares from collisions with neutron stars to increase the discovery rate. - - The Pan-STARRS survey has been instrumental in detecting interstellar objects like 'Oumuamua. - -3. **Expert Opinions and Predictions**: - - David Trilling, a professor in the Department of Astronomy and Planetary Science at Northern Arizona University, predicted that we would find an average of one interstellar object in our solar system every five years, which is consistent with the current rate of discovery. - - There are estimates that there are between 1,000 and 10,000 interstellar objects in the solar system at any one time, with most being in the distant solar system and undetectable. - -4. **Future Missions and Exploration**: - - Researchers are proposing interceptor spacecraft that could catch up with future interstellar objects, study them, and even conduct sample returns. - - The Interstellar Object Explorer (IOE) mission concept has been proposed to study interstellar objects up close and learn about the formation of other stars and planets. - -Given the increasing capabilities of detection methods and the upcoming surveys, it is likely that the next interstellar object will be discovered before September 30, 2024. The Vera C. Rubin Observatory, in particular, is expected to significantly enhance the discovery rate of interstellar objects. - -######### - -GPT - -### Analysis and Chain of Thought - -#### 1. **Relevant Information and Base Rates** -- **Historical Discoveries**: - - 'Oumuamua (October 2017) - - 2I/Borisov (August 2019) - - Approximate interval: 2 years between the first and second, then about 5 years without another definitive discovery. - - The discovery rate, based on historical data, is low, with a new interstellar object identified about every 1-3 years on average. - -- **Recent Data and Events**: - - No confirmed new interstellar objects in nearly 5 years. - - Researchers believe hundreds of interstellar objects pass through the Solar System yearly, but detection remains challenging. - -#### 2. **Instrumental Capabilities and Upcoming Surveys** -- **Vera C. Rubin Observatory**: - - Expected to significantly increase the detection rate of celestial bodies, including interstellar objects. - - First light expected in January 2025 (after our evaluation period). - - Since Rubin Observatory will not be operational until after the deadline, it should not greatly influence our probability. - -- **Pan-STARRS and Other Existing Surveys**: - - Pan-STARRS detected 'Oumuamua. - - Current capabilities are less advanced compared to what Rubin will offer but still have some success. - -#### 3. **Expert Opinions and Detection Probability** -- **David Trilling's Prediction**: - - Predicts one interstellar object every five years. - - As this is consistent with the historical rate of discoveries but implies we are due for another, given the last one was in 2019. - -#### 4. **Novel Factors and Future Missions** -- **Future Missions (IOE, etc.)**: - - Still in proposal stages and will not affect the discovery rate within the short timeframe until September 2024. - -#### 5. **Recency and Source Weighting**: -- **Recent Research and Trends**: - High potential for discoveries but no immediate enhancement expected before September 2024. -- **Technological and Survey Advancements**: - Improvements are expected but mainly post-2024 deadline. - -#### 6. **Conditional Probability and Rare Events** -- **Base Rate (Historical Discoveries)**: - ~ 1 interstellar discovery every 3 years effectively. - - Since it has been 5 years since the last, base probabilities indicate an overdue discovery, but not hugely overdue. - -#### Final Rationalization: -- The overall discovery rate remains low and skewed towards occasional detections rather than frequent. -- Despite a few promising trends, the significant technological boosts (i.e., Vera Rubin Observatory) are still months away. -- If undetected interstellar objects are currently traversing the Solar System, existing methods have had limited success. - -### Conclusion & Probability -Given all parameters: - -- Historical rate: ~1 every 3 years. -- Technological improvements marginally affecting the current cycle. -- Considering we are ~5 years from the last discovery, there is a moderate yet not high probability for discovery before mentioned date. - -**Probability: 40%** - -This respects historical data and accounts for slightly increased odds due to ongoing minor improvements in detection technology." -26553,"Will Hawaiian Electric Company file for bankruptcy before September 30, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether Hawaiian Electric Company (HECO) will file for bankruptcy before September 30, 2024, I have summarized the following sources: - -### Background and Current Situation - -- HECO is facing significant financial challenges due to the 2023 Maui wildfires, which led to over 200 lawsuits and a downgrade of its credit ratings to ""junk"" status. -- The company is seeking a $250 million credit line by selling utility assets to improve its financial stability. -- HECO's current credit rating prevents it from accessing capital markets, making it difficult to secure financing for operations and investments. - -### Regulatory Approvals and Proposals - -- HECO has requested expedited regulatory approval from the Hawaii Public Utilities Commission (PUC) to secure the credit line. -- The company plans to use accounts receivable as collateral to establish lines of credit, which would be managed through a ""bankruptcy remote"" special purpose entity to protect lenders. - -### Expert Opinions and Market Analysis - -- Ed Neiger, a bankruptcy lawyer, views HECO's proposal as an ""end run"" to protect lenders and avoid creditors. -- A Reddit user, familiar with HECO, notes that while the company has a monopoly on the islands, it can still file for bankruptcy and continue operations, but its value will likely be lower than before the fires. -- Another Reddit user expresses mixed feelings about HECO as an investment, citing the uncertainty of a settlement and potential dilution of equity and dividends. - -### Management and Restructuring - -- HECO hired Scott DeGhetto, a utility dealmaker with experience in mergers, restructuring, and bankruptcy, as its CFO to address legal, financial, and political issues related to the wildfires. -- DeGhetto's experience and the company's corporate structure have raised hopes that HECO could limit liability to its island subsidiary Maui Electric. - -### Recent Developments - -- A proposed $4 billion settlement, which includes HECO, Charter Communications, and government agencies, could help the company avoid bankruptcy. - -### Current Values and Predictions - -- HECO's shares are currently trading at around $13.30, compared to a 52-week high of $43.71 before the Maui fires. -- There are no recent updates on the current number of cases or predicted R0 and rate of spread, as this is not a disease-related event. - -### Forecasting Sites - -- There are no relevant prediction markets on Metaculus for this specific question. - -In summary, HECO is facing significant financial challenges due to the 2023 Maui wildfires and is seeking regulatory approval for a $250 million credit line. The company's management has been restructured with the hiring of a utility dealmaker experienced in mergers, restructuring, and bankruptcy. A proposed $4 billion settlement could help HECO avoid bankruptcy. However, the uncertainty of the settlement and potential dilution of equity and dividends remain concerns for investors. - -######### - -GPT - -To determine the probability that Hawaiian Electric Company (HECO) will file for bankruptcy before September 30, 2024, it is essential to systematically evaluate the provided background information, current developments, historical precedents, and related professional insights. Let’s break down the factors step by step: - -### Step 1: Assess the Current Financial and Legal Situations - -- **Financial Challenges**: HECO faces significant financial burdens due to the 2023 Maui wildfires, leading to over 200 lawsuits and a downgrade of its credit rating to ""junk"" status. -- **Credit Line and Asset Sales**: HECO is seeking a $250 million credit line to stabilize financially and is selling utility assets to support this. However, the downgraded credit rating prevents HECO from easily accessing capital markets, complicating its financial situation. - -### Step 2: Evaluate Recent Strategic Moves and Management Actions - -- **Special Purpose Entity**: HECO plans to establish a ""bankruptcy remote"" special purpose entity using accounts receivable as collateral for new credit lines. This indicates a proactive strategy to avoid insolvency. -- **Expert Hiring**: The appointment of Scott DeGhetto, a CFO with extensive experience in mergers, restructuring, and bankruptcy, suggests that the company is preparing for possible outcomes. - -### Step 3: Regulatory and Settlement Developments - -- **Regulatory Approval**: HECO has requested expedited regulatory approval from the Hawaii Public Utilities Commission to secure the credit line, which, if granted, could avert an immediate crisis. -- **Proposed Settlement**: A proposed $4 billion settlement inclusive of HECO and other entities could substantially relieve financial pressure if it comes to fruition. - -### Step 4: Historical Precedents - -- **Pacific Gas and Electric (PG&E)**: Filed for bankruptcy in 2019 after the Camp Fire of 2018. -- **Brazos Electric Power Cooperative**: Filed for bankruptcy in 2021 following the Texas icestorm. -- **Comparative Assessment**: Both cases show a trend wherein major financial hits from disasters led to eventual bankruptcies, providing a pertinent base rate. - -### Step 5: Expert Opinions and Market Sentiments - -- **Bankruptcy Lawyer's View**: Ed Neiger’s view that HECO’s proposal is an ""end run"" to protect lenders suggests skepticism about the company's long-term solvency. -- **Mixed Investor Sentiment**: Varying views among investors on HECO’s future stability reflect uncertainty in the market. - -### Step 6: Assess Time Remaining and Variability - -- **Two-Month Horizon**: There's just over two months remaining until the September 30, 2024 deadline. This period is relatively short, reducing the likelihood of radical changes in the company’s financial status and regulatory decisions, which usually take substantial time to process. -- **Inherent Variability**: Given financial instability and ongoing legal issues, variability remains, but the time constraint limits drastic shifts. - -### Step 7: Synthesis and Probability Estimation - -Given the mixture of current severe financial strain counterbalanced by proactive measures and strategic financial moves, along with a short impending timeline, we will now combine insights: - -- **Proactive Measures**: 40% chance they'll succeed in avoiding bankruptcy with new credit lines or settlements. -- **Financial and Legal Pressure**: 60% chance of failing these measures due to prolonged lawsuits and financial constraints. - -Historical base rates of utility companies filing for bankruptcy post-disasters (like PG&E and Brazos) bolster the possibility but considering HECO’s unique strategies and the imminent timeline: - -Combining these factors, I estimate the probability of HECO filing for bankruptcy before September 30, 2024, as: - -### Final Probability Calculation -**Probability: 35%** - -This estimation balances the severity of the situation with the company's strategic efforts and the short remaining timeline to the resolution date." -26554,"Will the next interstellar object be discovered before September 30, 2024? -","PERPLEXITY - -To assist the superforecaster in predicting whether the next interstellar object will be discovered before September 30, 2024, I have compiled a concise rundown of the most relevant news and information. - -### Background and Context - -- **Interstellar Objects (ISOs)**: Astronomical objects not gravitationally bound to a star, including asteroids, comets, and rogue planets. They can be detected if they pass through the Solar System, exhibiting hyperbolic trajectories and excess velocities. -- **Previous Discoveries**: The first ISO, 1I/'Oumuamua, was discovered in 2017, followed by 2I/Borisov in 2019. Both showed significant hyperbolic excess velocity, indicating they did not originate in the Solar System. -- **Potential for Capture**: Astronomers reported the possible capture of other interstellar objects in Near Earth Orbit (NEO) over the years, suggesting that some ISOs might be periodically captured and still present in the Solar System. - -### Recent Developments and Predictions - -- **Increased Detection Rate**: Next-generation instruments, such as the Vera C. Rubin Observatory, are expected to significantly increase the rate of ISO discoveries in the late 2020s and 2030s. -- **Population Studies**: Research suggests that about seven ISOs pass through the Solar System every year, with some being periodically captured and still present. -- **Expert Opinions**: David Trilling, a professor of astronomy, predicts that we might find an average of one interstellar object in our solar system every five years, which is consistent with the current rate of two objects in six years. - -### Current State and Future Prospects - -- **Upcoming Observatories**: The Vera C. Rubin Observatory, expected to gather its first light in January 2025, will likely enhance the detection rate of ISOs. -- **Mission Concepts**: Proposals for interceptor spacecraft, such as the Interstellar Object Explorer (IOE), aim to study ISOs up close and potentially conduct sample returns. - -### Key Dates and Milestones - -- **May 2023**: Astronomers reported the possible capture of other interstellar objects in Near Earth Orbit (NEO) over the years. -- **January 2025**: The Vera C. Rubin Observatory is expected to gather its first light, enhancing the detection rate of ISOs. - -### Current Predictions and Odds - -- **Metaculus Prediction**: There is no current Metaculus prediction market for this specific question. However, the general interest in ISOs and the expected increase in detection rates with new observatories suggest that the likelihood of discovering the next ISO before September 30, 2024, is higher than in previous years. - -In summary, while there is no definitive answer to whether the next interstellar object will be discovered before September 30, 2024, the recent developments and predictions suggest that the likelihood of such a discovery is increasing. The upcoming Vera C. Rubin Observatory and proposed mission concepts like the Interstellar Object Explorer will likely enhance our ability to detect and study ISOs. - -######### - -GPT - -### Evaluating the Probability of Discovering the Next Interstellar Object Before September 30, 2024 - -**1. Establishing Base Rates and Trends:** - - **Historical Data:** There's a historical discovery rate of two ISOs over approximately six years (2017-2023), roughly equating to an ISO discovery every three years. The absolute lack of discovery of any new ISO since 2019 could be attributed to the limited capability of current telescopes and sky surveys. - - **Current Developments:** The launch of the Vera C. Rubin Observatory is anticipated to significantly enhance ISO detection rates. However, it is set to gather its first light in January 2025, post the prediction resolution date. - -**2. Timeline Consideration:** - - **Period Between Today (July 24, 2024) and Deadline (September 30, 2024):** The critical window is approximately 68 days. Considering the discovery rate derived from historical data, new breakthroughs leading to monthly ISO discoveries are unlikely given the constraints on current capabilities. - -**3. Technological and Observational Capabilities:** - - **Current Sky Surveys:** Major telescopes and surveys like Pan-STARRS, which discovered 1I/'Oumuamua, and other existing projects have a relatively limited efficiency for detecting ISOs due to their transient and often unpredictable nature. - - **Vera C. Rubin Observatory Impact:** Given that it will start operations in January 2025, it will not affect the probability directly within our timeframe. - -**4. Occurrence Frequency and Population Estimates:** - - **Estimated Number of ISOs Passing Annually:** Research suggests that approximately seven ISOs pass through our solar system each year, yet detection is contingent on advanced observational tools and strategies which are limited till the Rubin Observatory becomes operational. - -**5. Expert Predictions and Judgements:** - - **Expert Insights:** David Trilling's estimate of discovering an ISO every five years once advanced observatories like the Vera C. Rubin become operational aligns with the expected rate rather than accelerated discovery within the imminent 68-day window. - - **General Consensus:** Despite an understanding of increasing ISO discovery rates in the future, experts do not expect a significant surge pre-advanced observatory commencement in early 2025. - -**6. Statistical Consideration:** - - **Poisson Distribution Analysis:** The historical rate (2 discoveries in 6 years, or about 0.33 ISOs per year) implies a low probability for frequent ISO discovery in the short term. - - **Noise Factors and True Anomalies:** Considering the probabilistic nature of astronomical discoveries, it is essential to account for variability, though this is best approximated by historically observed patterns. - -### Summary and Probability Estimation: - -**Historical Data** and **Current Technological Constraints** suggest a low likelihood of an ISO being discovered before the deadline given that no significant operational enhancements to survey capabilities are scheduled before January 2025. - -**Calculated Phenomena:** The probability is predominantly driven by infrequent discovery rates and current tool limitations, rather than definitive hyperbolic object trajectories detected on a frequent and predictable base. - -**Conclusion:** Thus, after evaluating the confluence of factors including base rates, recent trends, technological constraints, and expert insights, the probability of discovering the next interstellar object before September 30, 2024, is estimated to be relatively low. - -**Final Probability: 10%**" -26550,Will SpaceX's Super Heavy booster make contact with the catch tower during the landing phase of Starship integrated flight test 5?,"PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -**Recent Developments and Testing:** - -- SpaceX has been actively testing the tower chopsticks at Starbase, with a recent video showing the arms closing around a stationary Super Heavy booster. -- The Starship program is preparing for a possible tower catch attempt on Flight 5, with Elon Musk expressing optimism about the goal. -- Booster 14.1, a simulated small methane tank, was rolled out to the orbital launch pad and tested with the new crusher cap and lift points. -- The Super Heavy booster has been test-fired ahead of the fifth integrated flight test (IFT-5), which is expected to occur in early August. - -**Catch Tower Preparations:** - -- The launch tower's ""chopstick"" arms are designed to catch the Super Heavy booster by its grid fins, allowing for rapid refurbishment and relaunch. -- SpaceX has been testing the system's response and timing, including a recent test where the north arm quickly closed and hit the test tank. - -**Expert Opinions and Predictions:** - -- Elon Musk has stated that he thinks a catch attempt should be made on Flight 5, and the company is taking steps to make it happen. -- The fifth flight test is expected to occur in early August, with Musk's estimates often being optimistically hopeful. - -**Current Status:** - -- The Starship program has made significant progress, with each test flight achieving more than its predecessor. -- The Super Heavy booster has been successfully test-fired, and the launch tower's chopstick arms are being prepared for a potential catch attempt. - -There are no recent updates that would indicate movement towards a particular resolution, but the ongoing testing and preparations suggest that SpaceX is actively working towards a successful catch attempt on Flight 5. - -######### - -GPT - -### Analysis and Evaluation - -**Type of Question:** Other Binary Outcome Event - -**Time Factor:** -Today's date is July 24, 2024, and the expected date of IFT-5 is in early August 2024, a few weeks away. Considering Elon Musk’s timeline estimates often being optimistic, actual delays could push this timeframe further into August but likely before the cutoff of October 1, 2024. - -### Key Evaluation Points - -1. **Recent Developments and Testing:** - - Active tests of the tower's chopsticks. - - Super Heavy booster has been test-fired successfully. - - Positive rhetoric from Elon Musk about attempting to catch the booster on Flight 5. - -2. **Technical Feasibility:** - - SpaceX’s recent preparations seem robust, with a focus on the timing and response of the chopsticks. - - The system has been tested with stationary objects, though not with an actual landing booster yet. - -3. **Historical Performance and Trends:** - - As of the date, SpaceX has not yet attempted a tower catch with a live Super Heavy booster. - - SpaceX’s record with Starship test flights has been progressive, experiencing both successes and failures but learning iteratively. - -4. **Expert Opinions:** - - Optimism from Elon Musk about making a catch attempt. - -5. **Possible Scenarios Leading to Yes:** - - Successful launch and maneuver to catch the booster at the tower, resulting in any contact. - - The initial attempt to catch may be unsuccessful but still result in contact due to alignment issues. - -6. **Possible Scenarios Leading to No:** - - Decision to postpone the catch attempt due to a technical or safety concern. - - Booster explodes or is destroyed before it attempts the landing. - - The landing attempt misses the tower entirely without any contact. - -### Probability Estimation - -**Base Rate Consideration:** -Considering SpaceX’s fundamental goal to master rapid reusability, the likelihood of pursuing an ambitious goal such as catching the booster is high. However, given their iterative approach to testing and cautious escalation of new technologies, there’s uncertainty around achieving this on the very first attempt. - -**Adjustments for Novelty:** -The novelty of catching a descending booster with a tower introduces significant complexity and risk. Historically speaking, new aerospace technologies often require multiple attempts to perfect. - -**Weighing Recent Developments:** -- Recent preparedness and tests majorly indicate a strong possibility SpaceX will go ahead with the catch attempt. -- Musk’s optimistic timeframes call for a cautious adjustment but don't completely negate the commitment shown. - -**Uncertainty and Adjustments:** -We'll factor in the uncertain nature of logistics, safety concerns, and SpaceX’s historically iterative approach. - -### Final Probability - -Considering all elements - the positive developments and testing, the best-case scenarios, the equally possible failure modes, and broader historical context, the most comprehensive probability of the Super Heavy booster making contact with the catch tower during the landing phase of Starship integrated flight test 5 is about 60%. - -**Rationale:** -- High likelihood of an attempt given current intentions and preparations. -- Significant uncertainty owing to technical challenges and historical performance of complex new technology. - -**Final Answer:** -**Probability: 60%**" -26551,"Will Revolut obtain a UK banking license before September 30, 2024? -","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether Revolut will obtain a UK banking license before September 30, 2024, I have summarized the following sources: - -1. **Revolut's Confidence in UK Banking License Approval**: - - Revolut's CEO, Nikolay Storonsky, expressed optimism about obtaining a UK banking license, citing the company's successful navigation of significant obstacles and the lack of major concerns from regulators. - - Despite delays, Revolut has made progress in aligning its share structure with the Prudential Regulation Authority's guidelines, which involved an agreement with SoftBank to unify its share classes. - -2. **Financial Performance and Growth**: - - Revolut reported a pre-tax profit of $545 million for 2023, driven by a surge in user numbers and revenue diversification. - - The company's revenue for 2023 totaled £1.8 billion, representing a 94.9% increase from 2022. - - Revolut added 11.8 million new customers globally in 2023, bringing the total to 38 million. - -3. **Regulatory Challenges and Delays**: - - Revolut has faced various challenges and delays in its regulatory journey, including scrutiny over its size, financial reporting, and the departure of senior executives. - - Regulators have expressed concerns about Revolut's financial reporting and have requested the company to expand its board to include directors with more technology and banking expertise. - -4. **Importance of a UK Banking License**: - - A UK banking license would allow Revolut to offer more loans and expand its services, which is currently limited as an Electronic Money Institution (EMI). - - Revolut's lending capabilities are restricted due to capital requirements as an EMI, and a banking license would provide more flexibility and trust in the institution. - -5. **Recent Updates and Progress**: - - Revolut has already secured over 70 licenses across various financial services and was granted an EU banking license by the Bank of Lithuania in 2021. - - The company remains committed to progressing its UK banking license application and is working closely with the Prudential Regulation Authority (PRA). - -Given the recent updates and progress, there are indications that Revolut is moving towards obtaining a UK banking license. However, the regulatory process can be unpredictable, and the company still needs to address the concerns raised by regulators. - -######### - -GPT - -### Step-by-Step Analysis to Determine the Probability - -#### 1. **Evaluate the Background and Context**: - - - **Revolut's Growth and Performance**: - - Revolut has shown substantial growth, both in user base and revenue, which indicates robust market positioning and potential. - - Financial performance is strong, with pre-tax profits demonstrating financial stability. - - - **Regulatory and Structural Challenges**: - - The main hurdles revolved around AML compliance and share structure complexity. - - Progress has been made in resolving share structure issues by unifying share classes. - - There were recent mentions in the press about unresolved AML issues, which can be a significant regulatory barrier. - - - **Current Status of License Application**: - - Revolut's CEO shows optimism, suggesting internal confidence, but external regulatory validation is key. - - Regulatory processes are inherently uncertain and can experience delays. - - - **Recency and Completeness of Information**: - - Information up to July 2024 suggests ongoing efforts, with some issues persisting. - -#### 2. **Base Rate Analysis**: - - - Obtaining banking licenses typically involves lengthy and thorough regulatory reviews. - - Given the timeline, we are about two months away from the resolution date on September 30, 2024, which is a relatively short period for resolving complex regulatory issues if they are substantial. - - Companies with resolved major issues but with pending minor issues still have relatively high success rates if efforts are consistent and well-supported. - -#### 3. **Risk and Variability Assessment**: - - - **Remaining Time**: - - Approximately two months remain before the resolution date, a tight window but not unrealistic for final regulatory approval if conditions are favorable. - - - **AML Concerns**: - - Given recent press mentions of AML concerns, these need to be addressed adequately for the license to be granted. - - - **Management and Regulatory Interaction**: - - Revolut’s engagement with the PRA and FCA needs to be productive and responsive to expedite the process. - -#### 4. **Recent Updates and Momentum**: - - - Recent changes like unifying share classes indicate positive steps toward alignment with regulatory expectations. - - Ongoing communication with regulators suggests actions being taken to resolve any outstanding issues. - -#### 5. **Possible Scenarios and Their Probabilities**: - - - **Optimistic Scenario**: - - Revolut resolves AML concerns, and the response from regulators is positive leading to the granting of a full license. - - Probability: ~40% - - - **Pessimistic Scenario**: - - AML concerns or other unaddressed issues cause delays, and regulatory approval is not granted before the deadline. - - Probability: ~60% - -### Final Probability Assessment - -After weighing all factors, the higher likelihood revolves around the practical challenges and the tight timeline for resolving AML issues, despite substantial strides made by Revolut. - -Given this analysis, the logical probability for Revolut obtaining a UK banking license by September 30, 2024, is: - -**Probability: 40%** - -This accounts for the inherent complexity and unpredictability of the regulatory process, tempered by Revolut's significant efforts and recent positive developments." -26552,"Will a senolytic therapy be approved for commercial sale by the US FDA before September 30, 2024?","PERPLEXITY - -Here is a concise but detailed rundown of the news relevant to the question about senolytic therapy approval by the FDA: - -### FDA Updates and Announcements - -- The FDA has been actively updating its safety and availability communications for various biological products, including autologous chimeric antigen receptor (CAR) T cell immunotherapies and other cell and tissue-based products. -- In April 2024, the FDA announced several updates, including the authorization of the Eversense AP CGM System for diabetes treatment and the approval of tisotumab vedotin-tftv (Tivdak) for recurrent or metastatic cervical cancer. - -### Senolytic Therapy Research and Development - -- Senolytic compounds, such as dasatinib and quercetin, have shown promise in preclinical trials for treating age-related diseases, including Alzheimer's disease. -- Dasatinib is currently FDA-approved for chronic myeloid leukemia and acute lymphoblastic leukemia, but its potential for senolytic use is being explored. -- Unity Biotechnology is conducting clinical trials for senolytic therapy, specifically UBX1325, for diabetic macular edema and age-related macular degeneration. - -### Expert Opinions and Predictions - -- Dr. Nir Barzilai believes that FDA approval for senolytic therapy could happen within the next 5 to 10 years. -- Metaculus, a forecasting platform, currently predicts a 54% chance of FDA approval for senolytic therapy by 2030. - -### Recent Updates and Developments - -- There have been no recent updates specifically indicating movement towards FDA approval for senolytic therapy by September 30, 2024. However, ongoing research and clinical trials suggest progress in the field. - -### Current Predictions and Values - -- Metaculus prediction: 54% chance of FDA approval for senolytic therapy by 2030. -- No current values or specific predictions for approval by September 30, 2024. - -######### - -GPT - -To predict whether a senolytic therapy will be approved for commercial sale by the US FDA before September 30, 2024, we need to systematically consider the relevant factors, data, and timelines involved. Here’s a detailed analysis: - -### Step-by-Step Analysis - -**1. Base Rates and Historical Context:** - - Typically, drug development from Phase 1 clinical trials to FDA approval takes approximately 10-15 years, with significant variability depending on the therapy and disease. - - For innovative treatments in emerging fields (e.g., gene therapy, CAR T-cell therapy), although fast-tracked, approval often still spans several years. - - Senolytic therapy as a relatively new field means there’s limited historical precedent directly related to its approval timelines. - -**2. Current State of Senolytic Drug Development:** - - **Unity Biotechnology:** Their lead compound UBX0101 recently entered Phase 1 trials. Phase 1 is primarily for safety evaluation and sometimes efficacy in a small cohort of human subjects. - - **UBX1325:** Focus on diabetic macular edema and age-related macular degeneration is in trial phases, but specifics on its current phase progress can influence approval likelihood. - - Other senolytic compounds (e.g., dasatinib, quercetin): Investigational use but not explicitly designed or marketed as senolytics for broad consumer health. - -**3. Timeline Constraints:** - - From July 24, 2024, we are just over two months from the September 30, 2024 deadline. - - Given that UBX0101 recently entered Phase 1, we must consider typical timelines: - - Phase 1 duration varies (months to a few years). - - Phase 2 trials assess efficacy and side effects in larger patient groups over a longer duration (can last 1-2 years). - - Phase 3 trials involve large-scale testing for definitive evidence of efficacy and safety (lasting several years). - - Post-trial FDA review and approval process takes several months to over a year. - -**4. Expert Opinions and Predictions:** - - Dr. Nir Barzilai's conservative estimate (5-10 years). - - Metaculus forecasts a 54% chance of senolytic approval by 2030, indicating moderate optimism but not immediacy. - -**5. Current FDA Activities:** - - Recent FDA updates include approvals for major treatments in high-need areas (e.g., Tivdak). - - Active review of cellular and tissue-based therapies indicates infrastructure competent for evaluating emerging therapies, but no specific acceleration noted for senolytics. - -### Combining Insights - -1. **Historical Approval Timelines:** - - Given the typical drug approval process and current pipeline status, the base rate for approval within such a short timeframe (by September 2024) is very low. - - Approvals within a 2-month window, with the therapy still in early clinical stages, is highly improbable unless extraordinary fast-tracking is applied. - -2. **Pipeline and Progress:** - - Current senolytic compounds under evaluation are still in early or mid-clinical phases, with UBX0101 only in Phase 1. - - The step from early Phase 1 to complete FDA approval in approximately two months is virtually unprecedented. - -3. **Predictions and Opinions:** - - Combining expert opinions and predictive analysis (e.g., Metaculus projections) supports a longer-term outlook extending beyond September 2024. - -4. **Regulatory Environment:** - - No indications of imminent fast-track or breakthrough designations specific to senolytics that would radically compress typical timelines. - -### Final Probability Judgement - -Given the comprehensive analysis of current statuses, historical base rates, typical drug approval timelines, and expert opinions suggesting a longer horizon, the probability of a senolytic therapy being approved by the FDA by September 30, 2024, is exceedingly low. - -**Probability:** 1%" -26569,"Will the Atlantic Ocean's daily mean sea surface temperature surpass 2023's record peak temperature before October 1, 2024?","PERPLEXITY - -Here is a concise but detailed rundown of the news relevant to the question: - -**Context:** -- 2023 was the warmest year on record for global surface temperatures, exceeding the previous record set in 2016 by 0.14°C to 0.17°C. -- The North Atlantic Ocean has been experiencing record-warm sea surface temperatures since March 2023, with temperatures 2-5°C (1-3°C) warmer than normal across much of the central North Atlantic, Caribbean, and Gulf of Mexico. -- The year 2023 saw a record-high sea surface temperature globally, with daily SSTs averaged over the extrapolar ocean remaining at record levels from April onward. - -**Current Situation:** -- Sea surface temperatures in the North Atlantic are still tracking along a trajectory similar to 2023’s record highs, with temperatures across the entirety of the Atlantic expected to continue at high levels. -- The current marine heatwave in the North Atlantic has brought devastating heat stress to nearly all of the Atlantic’s tropical reef areas and contributed to a very active 2023 Atlantic hurricane season. - -**Expert Opinions and Predictions:** -- NOAA predicts an above-normal 2024 Atlantic hurricane season, driven by La Niña and warmer-than-average ocean temperatures. -- Carbon Brief predicts that global average surface temperatures in 2024 are likely to be slightly warmer than 2023 and set a new all-time record, although large uncertainties remain due to the exceptional warmth of 2023. - -**Relevant Prediction Markets:** -- Metaculus has a question asking if the Atlantic Ocean's daily mean sea surface temperature will surpass 2023's record peak temperature before October 1, 2024, but it is still awaiting approval by a Metaculus Moderator. - -**Recent Updates:** -- There have been no recent updates indicating a significant shift away from the current trend of high sea surface temperatures in the North Atlantic. - -######### - -GPT - -### Step-by-Step Analysis: - -1. **Evaluation of Background Information:** - - **Context:** - - - **2023 Temperature Records:** 2023 was an exceptionally warm year, with global surface temperatures hitting record highs. The referenced peak temperature for North Atlantic on August 31, 2023, was 25.4°C. - - - **Historical patterns:** Record-high sea surface temperatures have been persistent since March 2023, indicating a trend of increased ocean warming. - - - **Current Anomalies:** The most recent North Atlantic temperatures for June 24, 2024, demonstrate parity with 2023's record highs and remain significantly above historical averages (1982–2011). - - **Current Situation:** - - - **Temperature Trajectory:** Given the current projections and observed data, the North Atlantic sea surface temperatures continue to mirror those of the previous year, suggesting a sustained pattern of warmth. - - - **Climate Impact:** The 2023/2024 marine heatwave has caused significant stress to ecosystems and contributed to an active hurricane season, further supporting sustained high temperatures. - - **Expert Opinions and Predictions:** - - - **NOAA:** Predicts an above-normal hurricane season for 2024 due to La Niña and high ocean temperatures, which generally correlate with warmer sea surface temperatures. - - - **Carbon Brief:** Predicts 2024 could surpass 2023’s global temperature records, which further supports the potential for high North Atlantic sea surface temperatures. - -2. **Assessment of Variability and Trends:** - - - **Recency of Sources:** The provided sources and data points are recent, reflecting ongoing high temperature levels as of June 24, 2024. - - - **Seasonality Effects:** Assessing the typical seasonal variations, the peak sea surface temperatures in the Atlantic generally occur in late August to early September. This is critical, as we are currently near the July end-point and moving towards the historically warmest months. - -3. **Base Rates and Historical Data:** - - - **Historical Parallels:** Historically, following years of significant warmth often see similar or slightly higher temperatures, especially under similar environmental conditions (e.g., La Niña). - -4. **Statistical Analysis:** - - - **Daily and Weekly Variance:** Acknowledging daily and weekly fluctuations, the overall trend is more critical. The data shows a smooth upward trajectory with no abrupt downwards deviation, suggesting that temperatures are likely to continue on this path. - - - **Time Remaining:** From July 25, 2024, to September 30, 2024, there are over two months (circa 66 days), encompassing the period where maximum temperatures generally occur. This increases the likelihood of surpassing past records within this timeframe, given current trends. - -5. **Assessment Against Resolution Criteria:** - - - **Specific Resolution Guide:** For the outcome to resolve as ""Yes,"" daily mean sea surface temperature needs to exceed 25.4°C any day before October 1, 2024. - -### Final Probability Estimation: - -- **Trend and Current Level Justification:** Current trajectory and historical parallels indicate a sustained or increasing trend, making surpassing last year's peak likely. - -- **Expert Predictions and Impact:** NOAA and Carbon Brief predictions bolster confidence in warmer-than-average temperatures persisting. - -- **Timeframe Consideration:** With the prime warm season (July-September) well within the analysis window, there’s sufficient time for temperatures to potentially exceed the benchmark. - -**Probability estimation combines trend analysis, expert opinions, recognized variability, and historical parallels:** - -**Final Answer:** -**Probability: 75%**" -26568,"Will Russia have control of Chasiv Yar on October 1, 2024?","PERPLEXITY - -Here is a detailed rundown of the news relevant to the question: - -**Russian Military Operations** - -- Russian forces continued offensive operations near Chasiv Yar on July 23, but there were no confirmed advances. Russian milbloggers claimed marginal advances north of Kalynivka and near Hryhorivka, although these claims have not been visually confirmed. -- The Ukrainian General Staff reported a higher number of Russian attacks in the Chasiv Yar direction, particularly north of Chasiv Yar near Vasyukivka, Minkivka, Orikhovo-Vasylivka, Hryhorivka, Kalynivka, and Bohdanivka. -- Fighting also continued north of Chasiv Yar near Novomarkove and southwest of Chasiv Yar near Ivanivske, Klishchiivka, Andriivka, and Bila Hora on July 22 and 23. -- Elements of the Russian 132nd Motorized Rifle Brigade are operating near Chasiv Yar, and Russian forces plan to strengthen units operating in the Chasiv Yar direction with elements of two additional unspecified brigades. - -**Russian Control and Infrastructure** - -- Russia claims to have taken control of a district in Chasiv Yar, Ukraine, although Ukraine reports heavy fighting in the area. -- Russia continues to pursue infrastructure projects in occupied Ukraine aimed at increasing connectivity and economic integration between Russia and occupied Ukraine. This includes the construction of two parallel pipelines running from Taganrog, Rostov Oblast through occupied Mariupol, Berdyansk, and Melitopol. - -**Metaculus Prediction Market** - -- The Metaculus prediction market is currently assessing whether Russia will control Chasiv Yar on October 1, 2024. The question is awaiting approval by Community Moderators. - -**Recent Updates and Expert Opinions** - -- There have been no significant changes to the frontline in recent days, with Russian forces continuing to make minor advances but not establishing enduring positions. -- Ukrainian officials report that the tempo of Russian operations in the area is decreasing, and they are stabilizing the situation. - -Overall, while there have been ongoing military operations and claims of advances, there is no clear indication of significant changes in control over Chasiv Yar. The situation remains fluid, and the Metaculus prediction market is awaiting approval to assess the likelihood of Russian control by October 1, 2024. - -######### - -GPT - -Given the complex and fluid nature of the conflict in Ukraine, predicting the control of Chasiv Yar by Russia on October 1, 2024, requires a comprehensive assessment of various factors, data from multiple sources, and historical context regarding conflicts. - -### Step-by-Step Analysis: - -1. **Current Military Situation:** - - As of the last reports dated July 23, 2024, Russian forces are engaging in offensive operations near Chasiv Yar but have not confirmed significant advances. - - Frequent skirmishes and increased attacks suggest that Russia is investing considerable effort to gain control. However, no major breakthrough has been reported. - -2. **Strategic Importance:** - - Chasiv Yar’s elevation and strategic location in the Donetsk Oblast make it a critical point for both Russian and Ukrainian forces. - - The inability of Russia to capture the city by their previously intended deadline (Victory Day on May 9) indicates potential challenges in achieving their military objectives in this area. - -3. **External Support:** - - Ukrainian forces are set to receive new Western military aid, which historically has bolstered their defensive capabilities and potentially countered Russian advances. - - Western support can affect the tempo and success of Russian operations negatively. - -4. **Historical Context:** - - The conflict has seen fluctuating control over various regions, often hinging on supply lines, troop morale, and strategic reinforcements. - - Past Russian advances have been met with significant resistance and counter-offensives by Ukraine, making prolonged control uncertain. - -5. **Current Trends and Stability:** - - Reports indicate that the tempo of Russian operations is decreasing and Ukrainian officials claim stabilization. - - Russian control appears to be marginal in some northern districts of Chasiv Yar, which implies partial and contested control rather than complete dominance. - -6. **Prediction Markets:** - - The Metaculus prediction market question is still pending approval, so no aggregated predictions are available for integrating direct probabilistic insights from forecasting communities. - -### Consideration of Base Rates and Novel Developments: -- Control over any region in Donetsk has been highly contested, with territorial changes occurring frequently. -- The introduction of new Western military aid to Ukraine presents a novel variable that could offset Russian advantages. -- Recent shifts have seen slight fluctuations rather than major territorial shifts in similar contexts. - -### Time Frame and Possible Events: -- The forecast period from July 25, 2024, to October 1, 2024, includes approximately 68 days. Given the military dynamics and past cases, this period is enough for potential significant changes but leans more towards minor incremental shifts unless there is a major offensive or counter-offensive. - -### Final Probability Estimation: -Weighing all these factors, the probability distribution tilts towards a low likelihood of substantial Russian control over Chasiv Yar by October 1, 2024. However, given the volatile nature of military outcomes and the credible ongoing efforts by Russian forces, the likelihood isn't negligible either. - -**Probability: 30%** - -The defining reasoning centers on the combined force of ongoing Ukrainian resistance, the expected arrival of Western aid, and the current status of military operations which show contested control but no decisive dominance by Russian forces." -26577,"Will the CDC report more than 100 total human cases of H5 in the United States on October 1, 2024?","PERPLEXITY - -To provide context for forecasting the number of human cases of H5 in the United States, here is a summary of the current situation and recent updates: - -- **Current Situation**: As of July 25, 2024, there have been 13 human cases of H5 bird flu reported in the United States, with 11 of these cases reported since April 2024. The cases are associated with exposure to infected dairy cows and poultry. -- **Recent Updates**: On July 25, 2024, the CDC confirmed three additional human cases of H5 bird flu in Colorado, bringing the total number of human cases associated with the current poultry outbreaks in Colorado to nine. The CDC continues to monitor the situation closely and has not identified any unusual trends in reported clinical laboratory data or emergency department visits associated with influenza or potentially related symptoms. -- **Monitoring and Surveillance**: The CDC is actively monitoring multiple flu indicators, including surveillance systems that track key flu indicators. These systems show no indicators of unusual flu activity in people, including avian influenza A(H5N1) viruses, as of July 19, 2024. -- **Public Health Risk**: The CDC currently assesses the risk to the general public from H5N1 as low, but the agency continues to monitor the situation and provide updates. -- **Historical Context**: Prior to 2024, the only previous human case of H5N1 bird flu in the United States was reported among a poultry worker in Colorado in April 2022. - -Given the recent updates and the ongoing monitoring by the CDC, it is essential to consider the current trends and the agency's assessment of the public health risk when forecasting the number of human cases of H5 in the United States on October 1, 2024. - -######### - -GPT - -To forecast the likelihood of the CDC reporting more than 100 total human cases of H5 in the United States by October 1, 2024, I will follow a systematic approach. This involves examining the current situation, considering historical data, evaluating potential trends, and estimating probabilities with a clear rationale. - -### 1. Understanding the Current Situation - -Today’s Date: 2024-07-25 -Due Date: 2024-10-01 -Time Remaining: Approximately 2.25 months (68 days). - -**Current Cumulative Cases: 13** - -Human cases of H5 since April 2024: 11 -Human cases since July 22, 2024: 3 -Human cases prior to 2024: 1 (reported in April 2022) - -### 2. Historical Context and Analysis - -From 2022 to 2023, there was only 1 reported human case of H5. However, from April 2024 to late July 2024, there was a significant increase in cases (11 cases in just under 4 months). Now, as of July 25, an additional 3 cases have been reported, bringing the total to 13. - -### 3. Base Rate Calculation - -#### Calculating the Recent Rate of Cases -From April 1, 2024, to July 25, 2024 (approximately 3.75 months): -- Initial number of cases: 2 -- Final number of cases: 13 -- New cases in this period = 13 - 2 = 11 - -Incidence rate = 11 cases / 3.75 months ≈ 2.93 cases per month - -#### Projecting Cases to October 1, 2024 -From July 25, 2024, to October 1, 2024, there are approximately 2.25 months remaining. - -Expected new cases ≈ 2.93 cases/month * 2.25 months ≈ 6.59 ≈ 7 new cases (rounding up) - -Expected cumulative number of cases by October 1, 2024: -Current cases (13) + Expected new cases (7) = 20 - -### 4. Considering Uncertainties and Variance -The recent surge from April to July 2024 highlights significant potential for future cases. However, the base rate projection only yields 20 cases, considerably lower than the threshold of 100. - -We must also consider the following factors: -- Improved surveillance and containment measures by the CDC might slow or stop the trend. -- No significant historical precedent for a sudden and abrupt increase in human H5 cases. -- The likely continued low risk to the general population, as assessed by the CDC. -- Potential unknown factors that could alter the current trajectory, such as mutation of the virus or changes in exposure patterns among humans. - -### 5. Assessment of Novel Elements -While the virus causing infections in dairy cows and poultry is slightly new, the fundamental risk to humans seems consistent with historical patterns in zoonotic diseases. The clustering of cases among agricultural workers might not extend widely to the general population. - -### 6. Conclusion and Probability Distribution -Given the factors above, it is highly improbable for the total number of human H5 cases to exceed 100 in the given timeframe. The most plausible scenario is that the case count will remain well below the 100 mark by October 1, 2024. - -- Base rate projection indicates ~20, which is far from 100. -- 100 cases in ~68 days would require ~1.28 cases per day on average, an extreme jump in incidence compared to past rates. -- Historical trends and effectiveness of surveillance/mitigation measures. - -### Final Probability Estimate -Considering both the base rate analysis and the variance, the probability of the CDC reporting more than 100 total human cases of H5 in the United States by October 1, 2024, is extremely low. - -**Probability: 1%**" -26576,"Will the CDC report 21 to 100 total human cases of H5 in the United States on October 1, 2024?","PERPLEXITY - -To assist the superforecaster in answering the question, ""Will the CDC report 21 to 100 total human cases of H5 in the United States on October 1, 2024?"", I have compiled the following relevant information: - -### Current Situation - -- As of July 19, 2024, there have been 11 reported human cases of A(H5) in the United States, with 5 of these cases confirmed as A(H5N1). -- The cases are associated with exposure to either dairy cows or poultry. -- The CDC continues to monitor the situation closely and works with states to monitor people with animal exposures. - -### Surveillance and Monitoring - -- CDC's flu surveillance systems show no indicators of unusual flu activity in people, including avian influenza A(H5N1) viruses. -- The CDC monitors people exposed to infected animals for 10 days after exposure and has tested at least 375 people for novel influenza A. -- Since February 25, 2024, 34,091 specimens have been tested using a protocol that would have detected A(H5) and other novel viruses, with no positive results reported. - -### Public Health Risk Assessment - -- The current public health risk assessment for the U.S. general public remains low. - -### Prediction Markets - -- Metaculus has a question on whether the CDC will report 21 to 100 total human cases of H5 in the United States on October 1, 2024, but it is still awaiting approval by a Metaculus Moderator. - -### Recent Updates - -- The CDC confirmed four human cases of A(H5) in Colorado on July 14, 2024, and additional cases were reported in Colorado on July 19, 2024. -- The CDC continues to update its situation summary webpage daily, Monday through Friday, after 4 p.m. to reflect any new data. - -Based on the current information, there are no recent updates that would indicate a significant movement towards a particular resolution. The CDC's surveillance systems have not detected any unusual flu activity, and the public health risk assessment remains low. However, the ongoing monitoring and updates from the CDC will be crucial in determining the future trajectory of H5 cases. - -######### - -GPT - -### Evaluating the Situation - -**Current Data:** -- 11 human cases reported since 2022. -- 10 of these cases occurred between April 1, 2024, and July 22, 2024. -- Recent increase in cases, with 4 cases confirmed on July 14, 2024, and more on July 19, 2024. - -**CDC Surveillance and Monitoring:** -- No unusual activity detected in flu surveillance systems. -- Active monitoring and daily updates by the CDC. - -**Public Health Risk Assessment:** -- The risk to the general public remains low. - -**Time Frame:** -- The prediction concerns the situation as of October 1, 2024. We have a little over 2 months (67 days) from today's date. - -### Key Considerations - -1. **Current Trend:** - - The increase in cases between April and July 2024 suggests a potential upward trend. - - 10 cases in a span of about 3.5 months (approx. 100 days). - -2. **Base Rate Analysis:** - - Historical data on previous outbreaks and their scaling can help adjust expectations. - - Comparisons to other zoonotic disease spread patterns. - -3. **Uncertainty Factors:** - - Unknown variables such as further outbreaks in poultry or dairy farms. - - Effectiveness of public health interventions and controls. - -4. **Potential Influencing Factors:** - - Seasonal patterns (summer vs. fall) and whether these may affect the spread. - - New measures or changes in farm management practices to mitigate risk. - -### Statistical Analysis - -Given the past growth and current trajectory (10 new cases in 100 days), if growth remained linear (though unlikely given real-world complexities), we might expect roughly: -- \( (10 \text{ cases}/100 \text{ days}) \approx 0.1 \text{ cases/day} \) -- Over 67 days, we could estimate \( 0.1 \times 67 = 6.7 \) new cases, bringing the total to about 17-18 cases. - -However, because of potential non-linear dynamics (e.g., small clusters of outbreaks due to specific farm conditions), a simple linear projection might underestimate the likelihood of further spikes occurring. - -### Assessing Probabilities - -We need to balance the potential for continued spread with control measures in place. A few breakout clusters could quickly push within the range specified (21 to 100 cases). - -**Scenarios:** -1. **Optimistic (Low likelihood of spread):** - - Linear growth, adequate control measures. - - Few to no significant new clusters. - - Probable range: 15-20 cases. - -2. **Moderate (Current trend continues):** - - Sporadic new cases, semi-linear growth with occasional clusters. - - Probable range: 21-35 cases. - -3. **Pessimistic (High likelihood of spread):** - - Several new clusters due to potential lapses in control measures. - - Probability of exponential growth if clusters are not contained promptly. - - Probable range: 35-100 cases. - -### Final Probability Calculation - -To determine the probability of reaching or exceeding 21 cases but staying under 100, we consider: -- Current rapid trend, though not exponential yet. -- Possibility for containment measures to somewhat limit the growth. - -Reasonable assumptions based on data and patterns observed suggest a moderate likelihood of reaching within the range specified (21 to 100 cases). - -### Final Answer - -Combining these factors, here is our probability estimate: - -**Probability: 65%** - -This estimate reflects a moderate likelihood based on current trends, active CDC involvement, and the assumption that while the spread may continue, public health interventions will prevent exponential growth from pushing counts far past the 100-case mark by October 1, 2024." -26574,"Will the CDC report 11 total human cases of H5 in the United States on October 1, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question about the CDC reporting 11 total human cases of H5 in the United States on October 1, 2024, I have searched through various sources from the Centers for Disease Control and Prevention (CDC). - -### Current Situation - -- **Total Reported Human Cases of H5 in the United States**: As of July 19, 2024, there have been 11 human cases of avian influenza A(H5) reported in the United States. -- **Recent Cases**: Four of these cases were associated with exposure to sick dairy cows, and seven were associated with exposure to H5N1-infected poultry. -- **Current Public Health Risk**: The CDC considers the public health risk from H5N1 bird flu to be low for the general public, but people with close or prolonged exposure to infected birds or other animals are at greater risk of infection. - -### Monitoring and Surveillance - -- **CDC Surveillance Systems**: The CDC is actively monitoring influenza data among people to detect any unusual activity, including avian influenza A(H5N1). As of July 12, 2024, these systems show no indicators of unusual flu activity in people. -- **Case Reporting**: Human cases of novel influenza, including avian influenza A(H5N1), are nationally notifiable and are investigated and reported to the CDC. -- **Monitoring of Exposed Persons**: The CDC and state health departments monitor people exposed to infected animals for 10 days after exposure. Between February 2022 and July 2024, at least 10,600 people were monitored, and at least 375 were tested for novel influenza A. - -### Updates and Response - -- **CDC Response**: The CDC continues to respond to the public health challenge posed by the multistate outbreak of avian influenza A(H5N1) virus in dairy cows, poultry, and other animals. The agency is working with the U.S. Department of Agriculture (USDA), the Food and Drug Administration (FDA), and state public health and animal health officials using a One Health approach. -- **Recent Highlights**: The CDC's response includes monitoring for novel influenza A virus infections among people, including influenza A(H5), and providing guidance for clinicians, public health professionals, and the general public. - -### Prediction Markets and Expert Opinions - -- **Metaculus Prediction**: There is no current prediction market on Metaculus related to this specific question. -- **Expert Opinions**: The CDC continues to monitor the situation closely and update its guidance and recommendations as new information becomes available. The agency's current assessment is that the public health risk from H5N1 bird flu remains low for the general public. - -In summary, as of July 19, 2024, there have been 11 reported human cases of avian influenza A(H5) in the United States, and the CDC continues to monitor the situation closely. The public health risk from H5N1 bird flu is currently considered low for the general public. - -######### - -GPT - -### Rationale - -Given the complexity and high stakes of this forecast, my approach will be methodical, considering multiple factors including historical data, trends, base rates, and the mechanisms of the CDC's surveillance systems. This forecast can be categorized as a **Cumulative Count Event**, where the objective is to determine if the total number of reported human cases of H5 will be exactly 11 as of October 1, 2024. - -### Current Data and Status - -1. **Current Cases**: As of July 19, 2024, there are 11 reported human cases in the United States. -2. **Recent Trends**: 10 of these 11 cases occurred between April 1, 2024, and July 22, 2024. This significant spike could indicate higher transmission rates or better detection methods. -3. **Context**: The majority of these cases are linked to direct exposure to sick animals (dairy cows and poultry), indicating that occupational exposure is a significant risk factor. - -### Surveillance and Monitoring - -- **CDC Surveillance**: The CDC's systems are actively monitoring for unusual flu activity and known cases. -- **Exposure Monitoring**: Around 10,600 people have been monitored for exposure since 2022, indicating a robust system for detection and reporting. - -### Public Health Risk - -- **Perceived Risk**: Although the CDC assesses the general public health risk as low, the concentration on specific groups (dairy and poultry workers) indicates localized risk. - -### Foreseeing Future Trends - -Given the past rapid increase in reported cases, particularly recently: - -1. **Base Rates and Historical Trends**: Historically, the reporting of human cases of H5 is rare, but current conditions (outbreaks among animals and subsequent exposure to workers) have altered this trend. -2. **Novel Factors**: The recent outbreaks and the increased human exposure to infected animals are notable novel factors, potentially leading to more new cases being reported in short order if not adequately controlled. - -### Statistical Perspective - -To forecast the likelihood that by October 1, 2024, the number of total cases stays exactly at 11: - -1. **Current Knowledge**: As of now, the reported cases tally up to exactly 11. -2. **Time Remaining**: From July 25, 2024, to October 1, 2024, there are 68 days remaining. -3. **Rate of New Cases**: Given the rapid rise (approximately 10 cases within 3.5 months or about one case every 11 days), the probability of `no new cases` being reported in the next 68 days seems lower compared to the probability of at least one new case being reported. - -### Probability Assessment - -Using Poisson distribution for rare event approximation: -- **λ = rate of new cases per day**: - - Roughly 10 cases over about 107 days from April 1, 2024, to July 19, 2024, gives: - - λ ≈ 10/107 = 0.0934 cases/day - -### Poisson Probability Calculation - -- **Expected number of new cases (λ*T)** over the next 68 days: - - E(x) = λ * T = 0.0934 * 68 ≈ 6.35 -- **Probability of 0 new cases (P(X=0)):** - - P(X=0) = exp(-6.35) ≈ 0.0017, or **0.17%** - -### Conclusion - -Given the above calculations and the rapid increase in cases coinciding with increased surveillance and ongoing outbreaks in animals, it is highly unlikely that the number will remain exactly at 11 cases. The most likely scenario is the occurrence of at least one new case by October 1, 2024. - -**Probability: 0.17%**" -26574,"Will the CDC report 11 total human cases of H5 in the United States on October 1, 2024?","PERPLEXITY - -To provide a concise and detailed rundown of the news relevant to the question about the CDC reporting 11 total human cases of H5 in the United States on October 1, 2024, I have summarized the key points from the provided sources: - -1. **Current Situation**: As of July 26, 2024, the CDC has reported a total of 11 human cases of H5 in the United States since 2022, with 4 cases associated with exposure to dairy cows and 7 cases associated with exposure to poultry. -2. **Recent Updates**: On July 19, 2024, the CDC confirmed four human cases of H5 in Colorado, and on July 25, 2024, three additional human cases were confirmed among poultry workers in Colorado. -3. **Monitoring and Surveillance**: The CDC continues to monitor people exposed to infected animals, including dairy cattle and poultry, and has not identified any unusual trends in reported clinical laboratory data or emergency department visits associated with influenza or potentially related symptoms. -4. **Risk Assessment**: The CDC's current risk assessment for the general public remains low, but the agency continues to watch the situation closely and work with states to monitor people with animal exposures. -5. **Public Health Response**: The CDC is actively responding to the outbreak, collaborating with the USDA, FDA, and state health departments, and providing guidance to clinicians, public health professionals, and the general public on protective actions and prevention measures. - -Given the recent updates and the ongoing monitoring and surveillance efforts, it is likely that the CDC will continue to report new cases of H5 in the United States. However, whether the total number of cases will remain at 11 on October 1, 2024, is uncertain and will depend on the progression of the outbreak and the effectiveness of public health measures. - -No relevant prediction markets or forecasting sites were found for this specific question. - -######### - -GPT - -### Step-by-Step Analysis - -**1. Understanding the Question and Resolution Criteria:** -- The question revolves around whether the CDC will report 11 total human cases of H5 in the U.S. on October 1, 2024. -- The cumulative count of reported cases since 2022 needs to be considered. -- The question specifies that both confirmed and reported cases are counted, irrespective of confirmation as H5N1. - -**2. Current Situation Overview:** -- As of July 26, 2024, 11 total human cases have been reported in the U.S. since 2022. -- The breakdown includes 4 cases among dairy workers and 7 among poultry workers. - -**3. Recent Updates (High Importance):** -- Recent updates suggest a rising trend in reported cases: - - On July 19, 2024, four new cases were reported in Colorado. - - On July 25, 2024, three additional cases were reported among poultry workers in Colorado. - -**4. Monitoring and Surveillance:** -- The CDC is closely monitoring exposures and human infections. -- No unusual clinical trends or emergency department visit spikes have been noted, implying no sudden outbreak within the general population. - -**5. Risk Assessment and Public Health Response:** -- The risk to the general public remains low. -- However, the CDC is actively working with state and federal agencies to monitor and mitigate the spread. - -### Statistical and Historical Analysis - -**Base Rate and Historical Precedent:** -- Historical data shows variability in the spread and reporting of H5 cases. -- The sudden appearance of seven new cases within a week indicates potential for ongoing exposure risks, especially among poultry and dairy workers. - -**Future Predictions:** -- Extrapolating from recent trends and recognizing that there are approximately two months remaining until October 1, 2024: - - The steady reporting of cases in July hints that new cases could continue being reported. - - Assuming an average of a few cases per week, the number is likely to increase beyond 11. - -### Probability Distribution and Uncertainty: -- **Low-End Estimate**: No new cases are reported, keeping the number at 11. This is highly unlikely given the recent trend of new case reports. -- **High-End Estimate**: A steady rise continues with a few new cases added each week, potentially rising significantly beyond 11. -- **Central Estimate**: Additional cases are reported sporadically but do not form a large outbreak. - -### Final Probability Assessment: -- Given recent trends, ongoing monitoring, and the historical precedent of similar outbreaks, there is a high probability that new cases will be reported within the next two months. -- At this cadence, it is very likely that the total will exceed 11 by October 1, 2024. - -**Final Answer:** -- **Probability that the CDC will report exactly 11 total human cases of H5 in the United States on October 1, 2024**: - -**Probability: 10%**" -26573,"Will a country other than Poland, Germany, USA, Sweden, England, or France win the 2024 Warhammer 40,000 World Team Championship?","PERPLEXITY - -To assist the superforecaster in predicting whether a country other than Poland, Germany, USA, Sweden, England, or France will win the 2024 Warhammer 40,000 World Team Championship, I have compiled the following information: - -1. **Recent Tournament Results**: - - The first results of 2024 Warhammer 40k tournaments have been posted, showcasing the performance of various armies in different events. Notable results include: - - Glass City GT, USA: Aeldari 5-0, Necrons 5-0, Tyranids 4-1, World Eaters 4-1. - - Denver 40K Fight Club, USA: Adepta Sororitas 5-1, Necrons 5-1, Dark Angels 5-1, Space Marines 5-1, Death Guard 5-1. - - A Grimdark New Years 40k GT, USA: Space Marines 5-0, Chaos Space Marines 5-0, Aeldari 4-0-1, Adeptus Mechanicus 4-0-1. - - Rise of the Noodle, Australia: Custodes 5-0, Adeptus Mechanicus 4-0-1, Leagues of Votann 4-0-1, Tyranids 4-1. - - Vienna Tabletop Championship, Austria: Orks 5-0, Aeldari 4-0-1, Chaos Space Marines 4-0-1. - -2. **Metaculus Prediction Market**: - - A Metaculus question is currently under review, asking if a country other than Poland, Germany, USA, Sweden, England, or France will win the 2024 Warhammer 40,000 World Team Championship. The question is awaiting approval from a Metaculus Moderator. - -3. **Historical Context**: - - The 2019 ETC (European Team Championship) saw the USA team perform well, with a win against Sweden and a sixth-place standing at the time. - -4. **World Team Championship Information**: - - The World Team Championship is scheduled to take place from August 6th to 11th and will feature premium wargaming events. - -5. **Qualification Paths**: - - The Warhammer 40,000 Grand Narrative is one of the ways to qualify for a place in the World Team Championship. This event offers a more narrative experience for players. - -These sources provide a mix of recent tournament results, ongoing prediction markets, historical context, and information about the upcoming World Team Championship. This information can help the superforecaster make a more informed prediction about the likelihood of a country other than Poland, Germany, USA, Sweden, England, or France winning the 2024 Warhammer 40,000 World Team Championship. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Base Rates and Historical Context** - - The dominant countries in the Warhammer 40,000 WTC have traditionally been Poland, Germany, USA, Sweden, England, and France. Looking at historical performance, these countries have consistently placed highest in the tournament. - - Given the historical dominance of these countries, it is essential to understand the base rate of how often a country outside these top six has won the tournament. Historically, victories by countries outside of these six have been rare or non-existent. - -#### 2. **Current Context and Trends** - - **Recent Tournament Performance**: The information provided shows strong individual performances from a variety of countries. However, individual player results do not necessarily translate to team performance in a structured tournament such as the WTC. - - **Novel Competitors**: The inclusion of first-time competitors (e.g., Andorra, China, Mexico, Singapore/Thailand, South Korea) adds some unpredictability. However, their lack of historical performance data makes it difficult to gauge their chances accurately. - -#### 3. **Seeding Groups and Competitive Structure** - - **Seeding Group 1**: Includes Poland, Germany, USA, Sweden, England, and France. - - **Seeding Group 2**: The countries in this group (e.g., Canada, Belgium, New Zealand, etc.) are still strong but historically have not shown the same level of dominance as those in Seeding Group 1. - - **Seeding Group 3 and 4**: Generally, teams in these groups are less experienced or have lower historical performance levels. - - The structure of the WTC with its initial group stages and subsequent Swiss format naturally favors consistently strong teams over the period of multiple games, giving an advantage to historically dominant teams. - -#### 4. **Tournaments and Probabilities** - - Given the recency and importance of each data point: - - **Historical Performance**: Very high weight. Teams that have historically done well are likely to continue doing so. - - **Recent Tournament Results**: Moderate weight. Provides a sense of who's currently in form but less impactful in a team-based competition. - - **First-Time Entrants**: Low to moderate weight. Adds uncertainty but history favors established teams. - -#### 5. **Consideration of Trends and Bias** - - As an expert forecaster, it is vital to avoid over-indexing on recent results and recognize the significance of historical dominance. Trends in player performance are important but must be contextualized within team dynamics and historical precedence. - -#### 6. **Probability Assessment** - - Considering all the factors, there's a strong base rate favoring the top six nations. Given the dominance of these countries in the past, combined with the competitive advantages, I would expect that they will have an overwhelming probability of winning. - -### Final Probability Assessment - -- **Historical Dominance**: High likelihood that one of the traditionally strong countries will win. -- **First-time Competitors and Dark Horses**: Small chance but not likely given the structure and historical data. - -After weighing all data points and considerations, I conclude the following probability: - -**Probability: 15%** - -This means there is an 85% chance that one of Poland, Germany, USA, Sweden, England, or France will win the 2024 Warhammer 40,000 World Team Championship, and a 15% chance for all other countries combined." -26575,"Will the CDC report 12 to 20 total human cases of H5 in the United States on October 1, 2024?","PERPLEXITY - -To provide context for forecasting the number of human cases of H5 bird flu in the United States, here is a summary of the relevant news and data: - -1. **Current Situation**: As of July 25, 2024, the Centers for Disease Control and Prevention (CDC) has confirmed 13 human cases of H5 bird flu in the United States since April 2024. These cases are associated with outbreaks in dairy cows and poultry farms, primarily in Colorado. - -2. **Recent Updates**: The CDC has reported multiple cases in recent weeks, including four cases in Colorado on July 14, 2024, and three additional cases on July 25, 2024, all linked to poultry farm outbreaks. - -3. **Monitoring and Surveillance**: The CDC is actively monitoring people exposed to infected animals and has not identified any unusual trends in reported clinical laboratory data or emergency department visits associated with influenza or related symptoms. - -4. **Risk Assessment**: The CDC currently assesses the risk to the general public from H5N1 as low, but continues to monitor the situation closely. - -5. **Historical Context**: Prior to 2024, there was only one reported human case of H5N1 bird flu in the United States, which occurred in April 2022 among a poultry worker in Colorado. - -6. **Ongoing Response**: The CDC is working with state and local health officials, as well as other federal agencies, to respond to the outbreak using a One Health approach. - -Given the recent surge in cases and the ongoing monitoring and response efforts, it is essential to consider the latest updates and trends when forecasting the number of human cases of H5 bird flu in the United States. - -For more accurate forecasting, it is recommended to regularly check the CDC's situation summary and updates on H5 bird flu, as well as monitoring data from public health laboratories and emergency departments. - -######### - -GPT - -To forecast the likelihood that the CDC will report 12 to 20 total human cases of H5 bird flu in the United States by October 1, 2024, we'll approach this systematically, considering the available data, trends, and relevant factors. - -### Step-by-Step Analysis - -**1. Current Situation Assessment:** - - As of July 25, 2024, there have been 13 confirmed human cases of H5 bird flu in the United States since April 2024, plus 1 historical case in 2022, totaling 14 cases. - - Recent surges in cases with 4 cases reported on July 14, 2024, and 3 additional cases on July 25, 2024, indicate active transmission among poultry and dairy workers. - -**2. Surveillance and Monitoring:** - - The CDC is closely monitoring the situation and response efforts are ongoing. Active surveillance increases the likelihood that new cases will be detected and reported promptly. - - There are no unusual trends in broader population data, indicating limited spread beyond high-risk groups. - -**3. Historical Context and Trend Analysis:** - - Before 2024, only one case was reported in 2022. Within a more recent timeframe (April to July 2024), there have been significant increases. - - The recent reporting rate is approximately 10 cases over roughly 3.5 months, which equates to approximately 3 cases/month. - -**4. Temporal Considerations:** - - It’s currently July 26, and the forecasting date is October 1. Therefore, we have approximately 2 months and 5 days, or about 9 weeks, remaining until the resolution date. - - Based on the recent reporting trend (3 cases per month estimate), it’s reasonable to expect an additional ~5 cases by October 1, bringing the total to around \( 14 \text{ current cases} + 5 \text{ projected cases} = 19 \). This lies within our target range of 12 to 20. - -**5. Assessing Uncertainties and Outliers:** - - Consider the potential for upticks or reductions: given the current active surveillance and case emergence in specific occupational groups, it's plausible that the reported rate may continue or slightly vary. - - Mitigation efforts and seasonality (impact on exposure or reporting cycles) could somewhat modify this estimate, but significant deviations from trend are less likely given the ongoing monitoring. - -**6. Base Rates and Comparable Situations:** - - H5N1 typically has lower human case numbers than other influenzas, suggesting our recent uptick could stabilize rather than spike drastically. - - Ongoing localized outbreaks (like those in poultry farms or dairy) support a sustained, though not escalating, case reporting rate. - -### Probabilistic Forecasting - -**Combining all these insights:** - - **Base rate (historical context):** Stable but increasing within a high-exposure cohort. - - **Recent trends (short-term increase):** Likely continuation or mild fluctuation. - - **Time frame to resolution (remaining weeks):** Sufficiently long to allow several more cases but unlikely to explode given existing trends and mitigations. - -Considering all these factors, the most probable outcome is that cases will continue to rise modestly, ending up well within the 12 to 20 range. - -### Final Probability Distribution - -After synthesizing these insights, it is reasonable to estimate: - -- **Probability the count remains in the 12 to 20 range:** 90% -- There is always a residual probability for unexpected deviations: - - ≤ 11 cases: 5% - - ≥ 21 cases: 5% - -**Final Answer:** -""Probability: 90%""" -26570,"Will Poland win the 2024 Warhammer 40,000 World Team Championship?","PERPLEXITY - -To assist in forecasting whether Poland will win the 2024 Warhammer 40,000 World Team Championship, here is a summary of relevant news and context: - -1. **Team Poland Announcement**: A YouTube video from April 29, 2024, announced the roster for Team Poland for the 2024 World Team Championship (WTC). The team selection process involved a large number of candidates, with a focus on organizing international TT screams to evaluate participants. - -2. **2023 WTC Results**: Poland won the 2023 World Team Championships, going undefeated with six wins and a tie. The USA finished second, and Australia finished third. - -3. **WTC Event Details**: The World Team Championship is scheduled to take place from August 6th to 11th, 2024, and is managed by Tom ""Tomhammer"" Adriany, who has been involved with the ETC/WTC since 2009. - -4. **Metaculus Prediction Market**: A Metaculus question is currently under review, asking whether Poland will win the 2024 Warhammer 40,000 World Team Championship. Once approved, users can participate in the prediction market to forecast the outcome. - -Recent updates indicate that Poland has a strong team and has performed well in previous championships. The current prediction market on Metaculus will provide additional insights into the likelihood of Poland winning the 2024 championship once it is approved. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Identify the Type of Event** - -This is a *Competition Event*. - -#### 2. **Review Past Performance and Base Rates** - -1. **Poland's past performance:** - - Poland won the 2023 WTC, indicating a strong team. - - Historical performance is crucial for such competitions, and Poland has proven success at the top level. - -2. **Seeding and group stage performance:** - - Poland is a top seed based on its previous victory. - - This seeding provides an advantage in matchups, potentially facing slightly weaker teams in the initial rounds. - -3. **Base rates of repeat champions:** - - Reviewing the history of the WTC, repeat champions are somewhat common but not guaranteed. - - Team dynamics and player roster changes can significantly impact performance from year to year. - -#### 3. **Current Team Composition and Sources** - -1. **Poland's team composition:** - - The April 29, 2024, announcement confirms Poland’s roster was selected via a rigorous process, indicating serious preparation. - - The strong selection process likely means high-caliber players, bolstering Poland's chances. - -2. **Consistent leadership and organization:** - - Tom Adriany's continued involvement suggests a stable and efficiently organized tournament, reducing the likelihood of external organizational issues impacting the competition. - -#### 4. **External Factors and Novel Influences** - -1. **New Competitors:** - - New teams like Andorra, China, Mexico, Singapore/Thailand, and South Korea may bring unknown variables. However, they are in the lowest seeding groups, indicating they are not considered immediate threats. - - Historical data suggests first-time teams rarely win championships upon initial entries. - -2. **Meta and Game Balance:** - - Changes in game rules or dominant strategies (the ""meta"") could influence outcomes, but top teams generally adapt quickly. No significant meta shifts disadvantaging Poland are noted in the provided information. - -3. **Prediction Markets:** - - While Metaculus hasn’t finalized its prediction, the emergence of prediction markets could reflect public sentiment and insider knowledge, assuming they're accessible in time. - -#### 5. **General Probability Distribution** - -Taking all factors into account: -- **Strong Previous Showing:** Winning the previous year and having a robust selection strategy boosts confidence in Poland performing well again. -- **Base Rate for Repeat Champions:** While some teams do repeat wins, not all do, reflecting a moderately high, but not overwhelming, probability. -- **Context:** The event is organized and stable, and the competitive structure remains consistent year-to-year, suggesting low variance compared to sports with irregular events or high variance environments. - -### Final Probability Calculation -Given these elements, Poland stands a strong chance of winning, but unforeseen factors always exist in sports competitions. - -#### Aggregating Factors: -- Previous success and strong team: ~40% -- Competitive stability and organization: ~10% -- New or unknown competitive dynamics: -5% -- Base rate adjustment for repeat wins: ~5% - -### Final Probability Estimate -**Probability: 50%** - -This estimate places a balanced acknowledgement of Poland's strong past performance and the inherent unpredictability of competitive events, especially in the evolving landscape of a game like Warhammer 40,000." -26575,"Will the CDC report 12 to 20 total human cases of H5 in the United States on October 1, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question about the CDC reporting 12 to 20 total human cases of H5 in the United States on October 1, 2024, I have summarized the following key points from the provided sources: - -1. **Current Situation**: As of July 19, 2024, there have been 11 reported human cases of H5 in the United States, with 4 cases associated with exposure to dairy cows and 7 cases associated with exposure to poultry. -2. **CDC Response**: The CDC is actively monitoring the situation, collaborating with state and local health departments, and using multiple surveillance systems to detect any unusual flu activity, including avian influenza A(H5N1) viruses. -3. **Recent Updates**: On July 14, 2024, the CDC confirmed four human cases of H5 in Colorado, all of which were mild and associated with exposure to infected poultry. -4. **Ongoing Monitoring**: The CDC continues to monitor people exposed to infected animals, including dairy cattle and poultry, and has tested numerous specimens for novel influenza A viruses, including H5. -5. **Public Health Risk**: The current public health risk from H5 bird flu is considered low, but the CDC remains vigilant and is working closely with states to monitor the situation. - -Given the ongoing monitoring and response efforts, it is difficult to predict exactly how many cases will be reported by October 1, 2024. However, the CDC's continued surveillance and testing may lead to additional cases being identified. - -For a more accurate forecast, it would be helpful to consult prediction markets or expert opinions. - -######### - -GPT - -To forecast whether the CDC will report 12 to 20 total human cases of H5 in the United States on October 1, 2024, we need to analyze the current situation, trends, and historical data. Here's a step-by-step approach to elucidate an informed prediction: - -### Current Data and Situation -1. **Current Cases**: As of July 19, 2024, there are 11 reported human cases of H5 in the U.S. -2. **Recent Trends**: The significant rise in human cases has been noted recently, with ten cases reported between April 1, 2024, and July 22, 2024. -3. **Exposure Factors**: Cases are linked mainly to dairy cattle and poultry, which are being monitored closely by the CDC. - -### Analytical Approach -#### Base Rates and Trends -1. **Past Cases**: Only 1 case was reported in 2022, indicating that significant recent escalations are anomalies compared to past years. -2. **Recent Surge**: The surge to 11 cases indicates an increased rate of infection recently due to exposure from dairy animals and poultry. - -#### Time Remaining and Default Behavior -1. **Time Frame**: As of July 26, 2024, there are approximately 66 days until October 1, 2024. -2. **Weekly Reporting Rate**: Based on the recent trend, with 10 cases over approximately 113 days (April 1, 2024, to July 22, 2024), the rate of new cases is about 0.088 cases per day, or roughly 0.62 cases per week. - -#### Uncertainty Factors -1. **Ongoing Surveillance and Monitoring**: CDC is actively monitoring, but the cases generally remain linked to animals, implying exposure may vary or be contained. -2. **Comparing with Other Outbreaks**: Historically, H5N1 and other zoonotic diseases have shown variable human case numbers, often dependent on containment success and infection rates among animals. - -With three months of a relatively high infection rate and exposure among animal handlers, we can project potential cases: - -- **Projection of New Cases**: Using the recent rate, the estimate for future cases can be computed as follows: - - Assume another surge rate akin to the recent trend: ~0.62 cases per week for 9.5 weeks (from July 26 to October 1), ≈ 6 additional cases. - -### Probabilistic Distribution Analysis -Given the number of cases: -1. **Minimal New Cases**: Suppose no major outbreaks occur, 11 cases currently hold; even a slower-than-predicted rise in cases until October 1 would still likely meet the threshold. -2. **Major Containment Efforts**: If containment efforts drastically reduce the transmission rate, we're still within the orbit of adding minimal cases (i.e., at least one or two additional cases). - -### Final Probabilistic Outcome -- A most likely scenario of approximately 4-6 new cases in 66 days appears reasonable, suggesting reaching at least 15-17 (or higher) by October 1. - -So, the likelihood of the CDC reporting between 12 and 20 total human H5 cases on October 1, 2024, stands fairly high with minor variability possible due to intervention or unforeseen changes. - -### Conclusion -The probability of the CDC reporting 12 to 20 human cases by October 1, 2024, is highly probable given trends and current outbreak containment status. - -**Probability: 85%**" -26571,"Will Germany win the 2024 Warhammer 40,000 World Team Championship?","PERPLEXITY - -To assist in forecasting whether Germany will win the 2024 Warhammer 40,000 World Team Championship, here is a summary of relevant news and information: - -1. **Qualifying Events**: The World Championships of Warhammer have announced various qualifying events, including the Wet Coast GT 2024 and the Grand Clash, which will determine the participants for the championship. - -2. **Metaculus Prediction Market**: A question on Metaculus is currently under review, asking if Germany will win the 2024 Warhammer 40,000 World Team Championship. The question is awaiting approval by a Metaculus Moderator. - -3. **Recent Updates and Expert Opinions**: A recent podcast from Risky Rollers discussed the upcoming World Teams Championship 2024, providing updates on Warhammer 40k events around Australia and the world. The podcast did not specifically mention Germany's chances but highlighted the importance of the event. - -4. **US Events**: Games Workshop has announced various tournaments in the US for 2024, including events for Kill Team, Warhammer 40k, and Age of Sigmar. While this does not directly impact Germany's chances, it indicates a strong competitive scene for Warhammer games. - -5. **Reddit Discussion**: A Reddit post predicted Germany would win Euro 2024, citing their strong performance in previous matches. Although this is unrelated to the Warhammer championship, it shows a general confidence in Germany's competitive abilities. - -Currently, there are no specific updates or expert opinions directly indicating Germany's chances of winning the 2024 Warhammer 40,000 World Team Championship. The Metaculus prediction market is still awaiting approval, and recent updates focus on the general competitive scene rather than Germany's specific prospects. - -######### - -GPT - -Sure, let's walk through the reasoning process step by step to estimate the probability that Germany will win the 2024 Warhammer 40,000 World Team Championship. - -### **1. Understanding the Tournament Structure** -The World Team Championship's format involves: -- Initial group stage with 4 teams per group. -- Teams are seeded into 4 groups based on past performance. -- 40 national teams are competing. -- 5 first-time competitors (who likely have lower chances due to inexperience). - -Germany is in Seeding Group 1, suggesting they are among the strongest teams based on past performance. - -### **2. Base Rates and Historical Performance** -- According to the provided history, Germany has placed well in previous WTC events. -- Reviewing historical performance data for similar events is crucial. Assuming Germany has had consistent top-tier results should give them a reasonable base rate. -- We should weigh this base rate relative to the number of competing teams: - - If each of the 40 teams had an equal chance, base probability would be 2.5% (1/40). - - However, teams are not equal. Strongly seeded teams like Germany have higher odds. - -### **3. Adjustment for Recent Data and Trends** -Without specific data pinpointing individual player strength, team synergy updates, and recent tournament wins leading up to WTC: -- Germany's seeding in Group 1 is informative but should not be overemphasized alone. -- The podcast and media updates suggest a vibrant Warhammer competitive scene but do not provide precise details on the German team. -- Metaculus prediction market, although awaiting approval, can aggregate a valuable expert consensus. Once it is active, it should be monitored. - -### **4. Competition Landscape** -- Other strong teams, especially those in Seeding Group 1, pose significant competition. These teams likely include past winners and high performers. -- New entrants, particularly those less familiar with top-level play, present somewhat lower barriers, marginally increasing Germany's relative chances. - -### **5. Additional Considerations** -- No significant political, economic, technological changes affecting the championship directly have been noted. -- The time between now (July 26, 2024) and the event start (August 8, 2024) is relatively short, leaving limited room for sudden disruptive changes. -- Skill, strategy, and luck play interplaying roles in outcomes, with skill being predominant among top-seeded teams. - -### **Statistical Analysis** -We can roughly estimate Germany's winning chances by initially considering their base rate among Seeding Group 1 and factoring in limited perturbations: - -\[ -\text{Adjusted Probability for Seeding Group 1 Teams} = \frac{1}{10} = 10\% -\] -- Given Germany's standing and smaller factors (team dynamics, competitive edge), we might assume a modest advantage: - - Adding a small adjustment recognizing Germany's possibly superior edge within strong teams: approximately \(10\% + 5\%\). - -### **Final Probability Conclusion** -Integrating the analyzed evidence into a coherent prediction: - -- **Base Rate**: Approximately 10%. -- **Adjustment**: Increased to reflect competitive strength. -- **Final Estimate**: Adding 5% contribution for historical advantage and competitive edge. - -Given these factors, the final probability can be summarized as: - -**Probability: 15%** - -This value reflects the interplay of analytical evidence, historical performance, and competitive landscape insights, ensuring a balanced estimate of Germany's chances." -26572,"Will USA, Sweden, England, or France win the 2024 Warhammer 40,000 World Team Championship?","PERPLEXITY - -To assist with forecasting the 2024 Warhammer 40,000 World Team Championship, I have gathered relevant information from various sources: - -1. **Metaculus Prediction Markets**: There are two questions on Metaculus related to the championship. The first question asks if the USA, Sweden, England, or France will win the championship, while the second question asks if a country other than Poland, Germany, USA, Sweden, England, or France will win. Both questions are currently in review and awaiting approval from Metaculus moderators. - -2. **Live Updates and Analysis**: A YouTube video provides live updates and analysis of the scores from the 2023 Warhammer 40,000 World Team Championship. The video discusses the current standings and the possibilities of different teams advancing, including the USA, England, and France. - -3. **Qualification Process**: The Warhammer Community website outlines the various ways teams can qualify for the championship, including larger tournaments like the London GT, America Team Championships, and the NOVA Open. - -4. **US Events**: Spikeybits reports on the announcement of US events for the Warhammer World Tournaments, which will include tournaments for Warhammer 40,000, Kill Team, and Age of Sigmar. - -There are no recent updates that would indicate a clear favorite among the mentioned teams. The live updates and analysis from the 2023 championship provide some insight into the teams' performance, but the current standings and predictions are not definitive. The qualification process and upcoming US events will likely impact the final lineup and chances of each team. - -######### - -GPT - -To forecast whether the USA, Sweden, England, or France will win the 2024 Warhammer 40,000 World Team Championship (WTC), I'll utilize a structured approach considering both historical data and current information. The key categories of events I'll evaluate are Competition Event, as this scenario fits that type. - -### Step-by-Step Analysis: - -#### 1. **Historical Performance and Base Rates** - First, let's analyze the historical performance of the teams in question. According to the historical data provided: - - - **USA**: Has been consistently strong, with victories in the past and consistently high finishes. - - **Sweden**: Also a strong contender, with a history of good performances. - - **England**: One of the top teams historically, regularly finishing in top positions. - - **France**: While competitive, typically not the strongest of the four but still a capable team. - -Given that these teams are from seeding group 1, they are among the top 10 based on past performance. - -#### 2. **Recent Performance Insights** - The YouTube video and other sources suggest that USA, England, and France performed well in 2023. The analysis of standings and chances to advance gives a positive indication of their form going into the 2024 competition. - -#### 3. **Competitor Analysis** - Other top competitors include Poland and Germany from seeding group 1, and a number of potentially strong teams from groups 2 and 3 (e.g., Canada, Belgium, Netherlands). Additionally, teams from group 4, consisting of first-time participants, are less likely to win, reducing uncertainty from potential unknowns. - -#### 4. **Format and Competition Structure** - The Swiss format and group stages add some level of unpredictability. However, teams from seeding group 1 benefit from likely easier matches initially, helping them secure a better position for the Swiss rounds. - -#### 5. **Novel Factors** - No significant changes in rules, economic events, or other major influences are noted that would significantly alter the competitive balance. - -### Weighting and Probability Consideration: - -- **Seeding Significance**: The teams in question have historical seeding advantages and are more likely to perform well initially. -- **Competitive Landscape**: While teams like Poland and Germany are strong, the addition of weaker new teams marginally increases the chance for the historically strong teams. -- **Recent Performances**: Strong recent form for several of the teams in question increases their likelihood of success. - -### Calculation of Probabilities: -Based on historical data and recent performance, let's think in terms of base rates: - -- Close competition among top 10 teams. Assume roughly equal win chances among top-tier teams if not for added variables. - - Theoretical split among 10 seeding group 1 teams = 10% base rate per team. - - Adjust for USA, Sweden, England, and France having slightly higher probabilities due to consistent past performance and recency of strong showings. - -Fudge Factor Calculations: -- Given the strengths, the share among USA, Sweden, England, France slightly adjusted to higher than base 10% (shared likelihood proportional to maximum combined adjusted upwards to represent shared increased odds): - - Pure top 10 rated computed into shares (say 4 higher, yielding about 12-15% chances individually for each). - - Pool of prospective group 1 for primary leading four computes a normalized slight majority optimalized into about unified-quad combined above 50-60%. - -Assumption: -- If no disruptions, default resolution calculates towards higher likelihood remaining within the pool of baseline dominant historical spots. - -Final Prediction Based on Calculated Weighted Expectations: -- The unified estimate could be centralized onto: -``` -Probability: 65% -``` -This encompasses expected sensible distribution across key highly likely dominant persistent strain teams tipped slightly internally shard adjustments above to cover slack shifted expectations evenly profiled combining USA, Sweden, England, France." -26572,"Will USA, Sweden, England, or France win the 2024 Warhammer 40,000 World Team Championship?","PERPLEXITY - -Here is a detailed rundown of the news relevant to the question: - -**Recent Updates and Analysis:** - -- The 2024 Warhammer 40,000 World Team Championship is ongoing, with several countries competing, including the USA, Sweden, England, and France. -- Live updates from the tournament show that the USA is currently performing well, with 65 points and needing 15 more points to reach 80, which would put them in a strong position. -- England has won a recent match against France, but the result is still pending confirmation. -- Sweden is also in contention, with a close match against Germany that is still ongoing. - -**Qualification and Tournament Structure:** - -- The World Championships of Warhammer 40,000 have various qualifying events, including larger tournaments like the London GT, America Team Championships, and the NOVA Open. -- These events provide opportunities for teams to qualify for a place in the World Team Championship. - -**Prediction Markets and Expert Opinions:** - -- There are two prediction markets on Metaculus related to the 2024 Warhammer 40,000 World Team Championship. One market asks if the USA, Sweden, England, or France will win, while the other asks if a country other than these five will win. -- These markets are currently in review and have not yet been approved by Metaculus moderators. - -**Current Standings and Scores:** - -- The current standings and scores are not explicitly stated in the sources, but the live updates from the tournament provide some insight into the performance of the top teams. - -Overall, the tournament is ongoing, and the USA, Sweden, England, and France are all in contention. The live updates suggest that the USA is performing well, but the final results are still uncertain. The prediction markets on Metaculus will likely provide more insight into the probabilities of each team winning once they are approved. - -######### - -GPT - -To predict whether the USA, Sweden, England, or France will win the 2024 Warhammer 40,000 World Team Championship, let's break down the information step-by-step. - -### Base Rates and Historical Data -First, examining past performances of these teams: -- **USA**: Historically, USA has been a strong contender in Warhammer 40,000 championships. They have consistently placed high in recent years. -- **Sweden**: Sweden has also shown strong performance historically, frequently making it to the top tiers. -- **England**: England has had several strong performances in recent championships. -- **France**: France has been competitive but slightly less dominant than the other three. - -### Current Standings and Recent Performances -Based on the recent updates: -- The USA is currently performing well, with 65 points and needing 15 more to reach 80, suggesting they are in a strong position. -- England has a recent win against France which is pending confirmation. This would make England a strong contender. -- Sweden is in the middle of a close match against Germany, indicating a highly competitive standing. - -### Prediction Markets and Expert Opinions -Metaculus, a well-known platform for aggregating expert opinions, is reviewing two markets related to this event. Historical accuracy of prediction markets in similar contexts should not be ignored, even if not finalized. Generally, such markets synthesize a lot of expert insight which can be valuable. - -### Current Tournament Dynamics and Swiss Format -- **Swiss format**: This is crucial because it allows teams to accumulate points (based on several matches), hence a single loss or win does not drastically tip the balance but consistency is key. -- The ongoing performance suggests a tight competition but mentions that first-time competitors and less historically strong teams (like new entrants from South Korea, Mexico, etc.) are unlikely to emerge as sudden champions due to lack of historical data and immediate impact. - -### Statistical Analysis -Given the ongoing nature of the tournament and if we consider the probabilities loosely based on historical strength and current performance trends: - -1. **USA**: Given their current performance and historical dominance, let’s assign them a probability of winning around 30%. -2. **England**: With recent wins and general historical performance, let’s assign them a 25% probability. -3. **Sweden**: Close behind and historically consistent, let's give them a 20% probability. -4. **France**: Slightly less dominant recently compared to the other three but still competitive, let’s assign them a 15% probability. -5. **Other countries**: Low probability, since the top four teams have a combined high chance of winning. Let's give a remaining combined probability of 10% to cover unexpected outcomes. - -Given this spread, the base rate and historical performance, alongside current ongoing dynamics: - -### Conclusion -Combining historical performance, current metrics, and statistical spread adjusted for current progression and the Swiss format (highly competitive nature, but few drastic upsets): - -**Probability: 90%** - -This conclusion means there is a high likelihood that one of the teams from USA, Sweden, England, or France will indeed win the 2024 Warhammer 40,000 World Team Championship, based on the weighted probabilities derived from historical performance, current standings, and expert predictions." -26572,"Will USA, Sweden, England, or France win the 2024 Warhammer 40,000 World Team Championship?","PERPLEXITY - -The 2024 Warhammer 40,000 World Team Championship is an upcoming event that will bring together top teams from around the world to compete in the popular tabletop game. Here is a summary of the most relevant news and updates related to the question of which team will win the championship: - -- **Qualification Process**: The qualification process for the World Championships includes larger tournaments like the London GT, America Team Championships, and the NOVA Open, which are all qualifying events. -- **Metaculus Predictions**: There are two questions on Metaculus related to the championship. One question asks if a country other than Poland, Germany, USA, Sweden, England, or France will win the championship, while the other question asks if USA, Sweden, England, or France will win the championship. Both questions are currently in review and awaiting approval from Metaculus moderators. -- **US Events Announced**: Games Workshop has announced a series of tournaments and events in the US for 2024, which will cater to various Games Workshop games, including Warhammer 40k. -- **Previous Championship Updates**: A YouTube video from the 2023 World Team Championship provides live updates and analysis of match-ups, including interviews with players and captains. The video highlights the performance of teams like Poland, USA, England, and France, giving insight into their strengths and weaknesses. - -There are no recent updates that would indicate a clear favorite among the mentioned teams. The Metaculus questions are still in review, and no current predictions are available. The qualification process is ongoing, and the performance of teams in the announced US events will likely impact their chances in the World Championship. - -######### - -GPT - -Let's break down the problem step by step to estimate the probability accurately. - -### Step 1: Define the Question Category -The question at hand pertains to a **Competition Event**: ""Will USA, Sweden, England, or France win the 2024 Warhammer 40,000 World Team Championship?"" - -### Step 2: Analyze Historical Data -1. **Historical Performance**: Past performance can be pivotal in predicting future success. Let's consider the historical data: - - **Poland**: Dominant in past years as evident from the historical rankings. - - **Germany**: Also strong with consistent high placements. - - **USA, Sweden, England, France**: All have strong records but not as consistent as Poland and Germany. - -### Step 3: Evaluate Current Context -1. **Teams and Seeding Groups**: The seeding is based on historical performance, which preserves the competitive balance: - - Seeding Group 1 includes historically strong teams like Poland, Germany, and the four teams in question (USA, Sweden, England, France). - - The addition of new teams might introduce an element of unpredictability but is unlikely to significantly alter the balance of power. - -2. **Recency and News Updates**: There's no recent game-changing update suggesting any dramatic shift in the competitive landscape. The qualification process and recent tournaments remain consistent with past trends. - -### Step 4: Calculate Base Rates -1. **Base Rates of Winning**: Given that USA, Sweden, England, and France are in the top seeding group and each historically has a strong team, let's consider: - - Past top finishers: Poland, Germany, and the four teams in question (USA, Sweden, England, France) typically comprise the top contenders. - - If top teams from Seeding Group 1 historically account for around 70% of championships, evenly spreading their success over teams, we can assume each team has a roughly equal probability of winning (ignoring minor variations). - -### Step 5: Adjust for Peripheral Factors -1. **New Competitors**: The introduction of new teams typically has less impact on the final outcomes at the top tiers. Historically, newcomers rarely win in their first appearances. -2. **Swiss Format**: This system favors consistently strong teams, which may contrast with knockout formats that could heighten the unpredictability. - -### Step 6: Develop Probability Distribution -1. **Weigh Comparable Factors**: Assume equal chances among top contenders' percentage from group 1: - - Dominant contenders like Poland and Germany might each be assigned 20% probability each considering their form. - - Remaining 60% spread among USA, Sweden, England, and France, making 15% each. This is rounded to account for edge cases and historic data mix but significantly higher than lower-seeding groups. - -### Step 7: Compensate for Uncertainty -Given all known factors, there’s reasonable justification to focus more on strong historical contenders while accounting for a slightly lower average impact due to the structured format and consistency in team performance. - -### Final Calculated Probability -For the USA, Sweden, England, or France to win: -- Assumed balanced user-weight of 4x teams at 15% each: - - **Cumulative probability** = sum of individual probabilities = \(15% \times 4 = 60\%\) - -Thus, the adjusted estimation accounting for inherent minor uncertainties and aberrant outcomes: - -""Probability: 60%""" -26643,"Will the Spanish Wikipedia first exceed 2 million articles before Seotember 15, 2024?","PERPLEXITY - -To assist the superforecaster in forecasting whether the Spanish Wikipedia will first exceed 2 million articles before September 15, 2024, I have compiled a concise rundown of relevant news and data: - -1. **Current Status**: As of July 2024, the Spanish Wikipedia has approximately 1.5 million articles. - -2. **Growth Rate**: Wikipedia continues to grow, with the number of articles increasing by about 14,000 a month (as of January 2024). The Spanish Wikipedia has been growing steadily, with significant milestones such as exceeding 1 million articles in 2018 and 1.5 million articles in 2020. - -3. **Historical Context**: The Spanish Wikipedia has been one of the most active and rapidly growing editions, with significant milestones such as exceeding 1 million articles in 2018 and 1.5 million articles in 2020. - -4. **Comparison to Other Editions**: The Spanish Wikipedia is currently the fifth-largest Wikipedia edition by the number of articles, after the English, Cebuano, Swedish, and German editions. - -5. **Expert Opinions**: There is no specific expert opinion directly addressing this question. However, the general trend of steady growth in Wikipedia articles suggests that the Spanish Wikipedia is likely to continue growing towards the 2 million mark. - -6. **Current Values**: As of July 2024, the Spanish Wikipedia has approximately 1.5 million articles. - -7. **Prediction Markets**: There are no specific prediction markets available for this question on platforms like Metaculus. - -8. **Recent Updates**: There are no recent updates that would indicate a significant change in the growth rate of the Spanish Wikipedia. - -Based on this information, the superforecaster can assess the likelihood of the Spanish Wikipedia exceeding 2 million articles before September 15, 2024. - -######### - -GPT - -Sure, let's methodically analyze this prediction step by step. - -### Step-by-Step Analysis: - -1. **Current Data (July 2024):** - - Articles as of June 24, 2024: 1,968,552. - - If the current date is July 26, 2024, it is slightly more than a month later. - -2. **Recent Growth Rate:** - - We have the data point from June 2023: 1,866,921 articles. - - Another from May 2022: 1,774,468 articles. - - - The increase from May 2022 to June 2023 is: - \[ - 1,866,921 - 1,774,468 = 92,453 \text{ articles over 13 months} \rightarrow \approx 7,111 \text{ articles/month} - \] - - The increase from June 2023 to June 2024 is: - \[ - 1,968,552 - 1,866,921 = 101,631 \text{ articles over 12 months} \rightarrow \approx 8,469 \text{ articles/month} - \] - -3. **Estimation to Current Date (July 2024):** - - Using the recent growth rate (8,469 articles/month): - \[ - 1,968,552 + (8,469 \times 1) \approx 1,977,021 \text{ articles as of July 26, 2024.} - \] - -4. **Time Remaining Until September 15, 2024:** - - From July 26, 2024, to September 15, 2024, is approximately 51 days (\(\approx\) 1.7 months). - - So, the potential growth: - \[ - 8,469 \text{ articles/month} \times 1.7 \text{months} \approx 14,397 articles. - \] - - Predicting the future count: - \[ - 1,977,021 + 14,397 \approx 1,991,418 \text{ articles by mid-September 2024}. - \] - -5. **Error in Data Collection by Assistant:** - - The research assistant's note seems to be inaccurate by giving a figure of 1.5 million articles for July 2024, which is vastly different from our calculated growth. - -6. **Consideration of Anomalies:** - - Anomalies or unexpected spikes in article creation are not predicted. Looking at historical data does not reveal such sudden spikes, just a steady increase. - -7. **Base Rate and Default Behavior Analysis:** - - Given a steady growth rate and no evident disruptive events causing significant changes in trends, the projected number of articles by September 15 falls short of the 2 million mark. - -### Conclusion: - -Given approximately 1.98 million articles existed by late July, it seems plausible that the growth rate will fall short of reaching 2 million articles by mid-September, 2024, despite continued steady growth. - -### Final Probability: -Considering the calculated projection, the current growth rates, and the absence of significant events likely to cause a sudden massive increase in articles: - -**Probability: 20%**" -26640,"Will Donald Trump debate the Democratic nominee for president before October 1, 2024?","PERPLEXITY - -To assist the superforecaster in predicting whether Donald Trump will debate the Democratic nominee for president before October 1, 2024, I have compiled the following relevant information: - -1. **Recent Developments:** - - Donald Trump has chosen Sen. J.D. Vance (Ohio) as his running mate. - - Trump plans to meet with Israeli Prime Minister Benjamin Netanyahu in Palm Beach, Florida, and deliver an address at a nearby summit hosted by Turning Point Action, a conservative group. - - Vice President Kamala Harris met with Netanyahu on Thursday and picked up endorsements from former President Barack Obama and former First Lady Michelle Obama. - -2. **Debate Schedule:** - - The first presidential debate took place on June 27, 2024, hosted by CNN. - - A second debate is scheduled for September 10, 2024, hosted by ABC News. - - Trump has expressed willingness to debate Harris, but has hinted at potentially retracting his commitment to the ABC debate, suggesting Fox News as an alternative host. - -3. **Candidate Positions:** - - Trump has stated that he would ""absolutely"" debate Harris and is open to multiple debates. - - Harris has indicated that she is ""ready"" to debate Trump. - -4. **Polls and Predictions:** - - The race between Harris and Trump remains tight, with polls showing them locked in a neck-and-neck contest. - - Experts believe that the debates could be crucial in swaying undecided voters, particularly in key swing states. - -5. **Additional Context:** - - The Commission on Presidential Debates (CPD) typically sponsors and organizes presidential debates, but both Biden and Trump have opted out of participating in CPD-hosted debates. - - The debate format includes podiums, muted microphones, and no audience to ensure candidates maximize their time. - -Given these updates, it appears likely that Donald Trump will debate the Democratic nominee, Kamala Harris, before October 1, 2024. Trump has expressed his willingness to debate Harris and has already participated in one debate with Biden. Harris has also indicated her readiness to debate Trump. The scheduled debate on September 10, 2024, hosted by ABC News, is a strong indication that a debate will take place before October 1, 2024. However, Trump's hesitation about the ABC debate and his preference for Fox News as an alternative host may introduce some uncertainty. - -######### - -GPT - -To assess the probability that Donald Trump will debate the Democratic nominee for president before October 1, 2024, we'll employ a structured approach, considering the provided data, relevant background knowledge, and statistical analysis. - -### Key Information Analysis - -1. **Scheduled Debate and Recent Developments:** - - The second debate was scheduled for September 10, 2024, hosted by ABC. - - Donald Trump’s recent statements suggest he prefers FoxNews to ABC, potentially causing a disruption in the established debate schedule. - -2. **Candidate Willingness:** - - Both Trump and Harris have expressed a willingness to participate in debates. Trump explicitly mentioned he is open to multiple debates with Harris. - -3. **Historical Context:** - - Historically, most debates scheduled by major parties do occur, albeit occasionally facing last-minute changes in format or logistics. - -### Analysis of Factors - -1. **Time to Resolution**: - - Approximately 2 months until the resolution date (October 1, 2024). This allows a reasonable window for debates to occur, reschedules, or potential cancellations. - -2. **Candidate Motivation**: - - Debates are critical for voter engagement, particularly in a tight race as the one described between Trump and Harris. - - Both candidates would likely benefit from debating to gain visibility and potentially sway undecided voters. - -3. **Base Rates**: - - Past election cycles have a high rate of debates occurring when scheduled. Skipping debates generally carries significant reputational risk and campaign disadvantages. - -4. **Current Uncertainties**: - - Trump's insistence on switching the debate from ABC to FoxNews introduces uncertainty. However, there’s motivation on both sides to find a workable compromise. - - Historical precedent suggests that campaign teams typically resolve such issues to ensure debates go forward. - -### Risks and Potential Roadblocks - -1. **Negotiation Breakdown**: - - There is a non-negligible chance that negotiations between Trump's team and debate organizers (e.g., ABC vs. FoxNews) might fail, leading to a breakdown in debate plans. However, given the public pressure and historical norm, this is relatively low risk. - -2. **Health or Other Emergencies**: - - Unpredictable factors, such as health issues, could prevent either Trump or Harris from participating. While possible, these factors are less likely to disrupt all planned debates. - -### Probability Estimation - -1. **Default Resolution**: - - Given there is one scheduled debate on September 10, 2024, we consider it the default resolution and predict based on it happening. - -2. **Adjusting for Uncertainties**: - - The likelihood of Trump completely pulling out is mitigated by both public and political pressures. - - Multiplicative factor for moving the debate network is also low given historical solutions are usually found. - -### Conclusion - -With these factors in mind, evaluating the probability carefully: - -- Default positive resolution from the scheduled debate. -- Slight reduction for network switch and negotiation uncertainties. -- Additional minor reduction for health or other unprecedented cancellations. - -Hence, given all weighed factors, the probability that Donald Trump will debate the Democratic nominee for president before October 1, 2024, stands at: - -**Probability: 85%**" -26646,"At the Paris Summer Olympics in 2024, will the men's 100m dash winning time break the Olympic record of 9.63s?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question about the men's 100m dash at the Paris Summer Olympics in 2024, I have compiled the following information: - -1. **Historical Context**: - - The men's 100m dash has been a staple event at the Olympic Games since 1896, with US athletes winning the event 17 out of 28 times. - - The current Olympic record is held by Usain Bolt, who set a time of 9.63 seconds at the 2012 London Olympics. - -2. **Recent Performances**: - - Yohan Blake, the second-fastest man in history, has been active in recent years. In 2021, he ran 100m in 10.29 seconds, then improved to 10.27 seconds, and eventually reached a sub-ten performance with 9.95 seconds (+0.1 m/s) at the American Track League. - - At the 2021 Tokyo Olympics, Blake failed to advance to the final, placing sixth in his semifinal heat with a time of 10.14 seconds. - -3. **Paris 2024 Olympics**: - - The Paris 2024 Olympics will feature a new generation of athletes, with some rising stars to watch, including Nevin Harrison (USA) in canoeing, Quan Hongchan (China) in diving, and Scottie Scheffler (USA) in golf. - -4. **Current Records**: - - The men's 100m Olympic record remains at 9.63 seconds, set by Usain Bolt in 2012. - - The women's 100m Olympic record is held by Elaine Thompson-Herah (Jamaica) with a time of 10.61 seconds, set at the 2020 Tokyo Olympics. - -5. **Prediction Markets**: - - There are no specific prediction markets available for this question on Metaculus or other forecasting platforms. - -In summary, the men's 100m dash at the Paris Summer Olympics in 2024 will likely feature a new generation of athletes. While recent performances by Yohan Blake indicate that sub-ten second times are possible, breaking the Olympic record of 9.63 seconds set by Usain Bolt will require exceptional performances. - -######### - -GPT - -To forecast whether the men's 100m dash winning time at the Paris Summer Olympics in 2024 will break the current Olympic record of 9.63s, we need to critically evaluate multiple factors. - -### Step-by-Step Evaluation: - -#### 1. Historical Performance and Trends: -- **Olympic Record**: Usain Bolt’s 9.63s set in 2012 has not been broken in the subsequent 12 years. -- **Base Rate**: Historical data show that extraordinary performances in the 100m dash are rare. Since 1996, the Olympic record has only been broken once (Usain Bolt in 2008 and 2012). -- **Men’s World Record**: The world record stands at 9.58s, also by Usain Bolt from 2009. This gives us an upper bound of human performance but sets high benchmarks that have not been frequently matched or beat. - -#### 2. Recent Performances: -- **Yohan Blake**: As the second-fastest man historically, recent performances around 9.95s suggest he is still competitive but not at top form to surpass 9.63s. -- **Other Contenders**: Rising stars and current top sprinters like Trayvon Bromell and Andre De Grasse have shown capabilities near the 9.7s mark, but not sub-9.63s consistently in recent competitions. - -#### 3. Environmental and Technological Factors: -- **Track Technology**: Advances in track surfaces and sneaker technology can marginally improve performances but not drastically alter the probability of breaking such an extreme record. -- **Weather and Conditions**: Weather in Paris during the Olympics might impact performances, but there’s high variability, and it can be a mixed bag. - -#### 4. Current Year Performances: -- **Seasonal Bests**: Checking the seasonal bests for top sprinters in July 2024, we don’t have any sub-9.7s performances. -- **Competition and Form**: Athletes typically peak during championship events, but no recent data suggests an athlete is consistently dropping times that approach or surpass 9.63s. - -### Statistical Approach: -Using the binomial distribution model to estimate the probability, consider: -- Historical record-breaking rate (base rate). -- Current form and seasonal performances. -- Variability adjusted for peak performance during the Olympic games. - -Assuming: -- Base rate of record-breaking in 100m dash at Olympics: ~1/7 Olympics. -- Adjustment for contemporary near forms. - -### Probability Estimate: -Given: -- Historically, only 2 record-breaking performances since 1988. -- No current performance trends strongly indicate exceeding 9.63s. -- All environmental and incidental factors align only to a marginal favorability. - -#### Calculation: -- Base rate: 1/7 ≈ 14%. -- Adjust for current lack of sub-9.7s: Reduce by ~33% (due to fewer low 9.6s times observed). - -\[ \text{Adjusted Probability} = 14\% \times (1 - 0.33) \approx 9.4\% \] - -### Conclusion: -After evaluating the historical data, current athletic performances, and slight improvements via technological advancements, the likelihood of breaking the Olympic record appears moderate but improbable. - -Final Answer: -**Probability: 10%** (rounded appropriately for practical foresight)." -26645,"Will Saudi Arabia establish diplomatic relations with Israel before September 30, 2024? -","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question of whether Saudi Arabia will establish diplomatic relations with Israel before September 30, 2024, I have summarized the following sources: - -1. **Vox Article (May 3, 2024)**: - - The Biden administration is working on a deal between Israel and Saudi Arabia that would involve Saudi Arabia recognizing Israel and establishing diplomatic relations, with Israel taking steps towards a Palestinian state and the US providing security assurances to Saudi Arabia. - - Negotiators believe an agreement could be imminent, with the Saudi government reportedly deciding to pursue an accord with Israel as part of its rapprochement with the US. - -2. **Foreign Policy Article (May 8, 2024)**: - - The US is brokering a diplomatic deal that could include a defense pact with Saudi Arabia, which might be tied to normalization of relations between Israel and Saudi Arabia. - - The deal faces obstacles, particularly due to the ongoing war in Gaza, and the price Saudi Arabia is asking for normalization might be too high for Israel. - -3. **Brookings Institution Article (July 10, 2024)**: - - A proposed US defense pact with Saudi Arabia could be linked to a Saudi-Israeli agreement to normalize relations, which would involve US security guarantees and support for a civilian nuclear program in Saudi Arabia. - - The deal is associated with efforts to end the fighting in Gaza, and Saudi Arabia has called for a cease-fire and emphasized Palestinian statehood as a precondition for normalization with Israel. - -4. **The Guardian Article (September 21, 2023)**: - - Crown Prince Mohammed bin Salman stated that Saudi Arabia is getting closer to normalizing relations with Israel, but emphasized the need for major progress in creating a Palestinian state. - - The potential deal involves a formal defense pact with the US and assistance in developing a civilian nuclear program in exchange for normalization with Israel. - -5. **Reuters Article (February 7, 2024)**: - - Saudi Arabia has informed the US that it will not establish diplomatic relations with Israel unless an independent Palestinian state is recognized. - -Recent updates indicate that the deal is still being negotiated, but significant hurdles remain, particularly due to the ongoing conflict in Gaza and the requirement for a Palestinian state. Expert opinions suggest that the deal is contingent on progress in the Palestinian issue and that Saudi Arabia is unlikely to normalize relations without significant concessions from Israel. - -######### - -GPT - -### Step-by-Step Analysis - -1. **Context Evaluation**: - - **The Abraham Accords**: Significant step in Middle Eastern diplomacy, first signed in 2020 between Israel, UAE, and Bahrain, later joined by Morocco and Sudan. - - **Current Date**: 2024-07-26. - - **Deadline for Prediction**: September 30, 2024. - - **Efforts for Saudi Inclusion**: Ongoing diplomatic efforts by the Biden Administration. - -2. **Summary of Relevant Articles and Sources**: - - **Vox (May 3, 2024)**: High-level negotiations with positive signs, Saudi government leaning towards normalization. - - **Foreign Policy (May 8, 2024)**: Potential obstacles due to Gaza conflict, high demands from Saudi Arabia. - - **Brookings Institution (July 10, 2024)**: US defense pact and support for Saudi's nuclear program linked to normalization, cease-fire in Gaza vital. - - **The Guardian (September 21, 2023)**: Saudi Arabia getting closer to normalization, but Palestinian state remains a key condition. - - **Reuters (February 7, 2024)**: Saudi Arabia's stance on Palestinian statehood as a precondition. - -3. **Assessment of Key Points**: - - **Importance of Palestinian Statehood**: Consistently referenced as a major hurdle. - - **US Mediation Role**: Active, with significant political investment. - - **Conflict in Gaza**: A significant complicating factor for any immediate resolution. - - **Timing**: Approximately 2 months remain until the resolution date. - -4. **Evaluation of Likelihood**: - - **Historical Precedent**: The Abraham Accords and subsequent recognitions show that normalization agreements can be struck relatively quickly once political agreements are in place. - - **Negotiation Dynamics**: - - **Positive Indicators**: Reports from May 2024 suggest imminent deals. - - **Negative Indicators**: Conditionalities such as Palestinian statehood and Gaza conflict present significant barriers. - - **Geopolitical Context**: The Biden administration's prioritization and Saudi signals of willingness are strong motivating factors, but they must overcome substantial barriers. - -5. **Base Rates and Default Scenario**: - - **Base Rates**: Diplomatic normalization deals in the Middle East are relatively rare but possible given the correct conditions. Considering the recent history (~4 years), 5 countries have normalized relations with Israel. - - **Default Without Change**: No diplomatic relations will be established if the conditions regarding the Palestinian state and Gaza conflict remain unmet. - -6. **Quantitative Analysis and Probabilistic Thinking**: - - **Probability of Quick Diplomatic Movements**: Historically, normalization can be swift once political wills align, yet these typically take months of behind-the-scenes diplomacy. - - **Time Remaining**: Roughly 2 months, not a lot, but enough for high-level breakthroughs under concerted effort. - -7. **Final Synthesis**: - - **Positive Factors**: - - High-level negotiations ongoing. - - US diplomatic push significant. - - **Negative Factors**: - - Palestinian statehood key hurdle. - - Gaza conflict ongoing and complicates negotiations. - - Limited timeframe (2 months). - -### Conclusion -Given the above considerations and balancing factors, it seems less than likely but not impossible that diplomatic relations will be established by the given date. The hurdles are substantial, notably requiring significant progress on the Palestinian issue and the resolution of the Gaza conflict. - -**Probability**: Analyzing all elements, a measured probability estimate considering both historical base rates and current dynamics: - -**Probability: 35%**" -26644,"Will the Spanish Wikipedia first exceed 2 million articles betweeen September 15, 2024 and October 1, 2024?","PERPLEXITY - -To provide context for forecasting whether the Spanish Wikipedia will first exceed 2 million articles between September 15, 2024, and October 1, 2024, here is a summary of relevant information: - -1. **Current Status**: As of July 2024, the Spanish Wikipedia has not yet exceeded 2 million articles. The latest available data indicates that the English Wikipedia has approximately 6.86 million articles, with the Spanish Wikipedia likely to be significantly smaller. - -2. **Growth Rate**: The growth rate of Wikipedia articles has been steadily declining since 2006, with a peak of over 50,000 new articles per month. However, the amount of text added to Wikipedia articles every year has remained constant since 2006, indicating that existing articles are being expanded rather than new ones being created at the same rate. - -3. **Language-Specific Growth**: The Spanish Wikipedia, being a primarily human-written edition, grows at a slower rate compared to bot-generated editions like the Cebuano and Swedish Wikipedias. This implies that the Spanish Wikipedia's growth is more dependent on human contributions. - -4. **Historical Milestones**: The Spanish Wikipedia has been steadily growing, with significant milestones such as exceeding 100,000,000 page edits in 2017 and 1,500,000 articles in 2019. - -Given this information, it is crucial to consider the current growth rate and the historical milestones of the Spanish Wikipedia to make an informed forecast about whether it will exceed 2 million articles between September 15, 2024, and October 1, 2024. - -######### - -GPT - -To assess the probability that the Spanish Wikipedia will first exceed 2 million articles between September 15, 2024, and October 1, 2024, let's break down the information and perform an analysis step by step. - -### Step 1: Gather and Understand the Data - -1. **Current Article Count**: - - As of June 24, 2024: 1,968,552 articles. - -2. **Historical Data**: - - June 2023: 1,866,921 articles. - - May 2022: 1,774,468 articles. - -### Step 2: Calculate Growth Rates - -Using the historical data: - -**From May 2022 to June 2023**: -\[ -\text{Growth} = 1,866,921 - 1,774,468 = 92,453 \text{ articles} -\] -\[ -\text{Duration} = 13 \text{ months} -\] -\[ -\text{Monthly Growth Rate} = \frac{92,453}{13} \approx 7,112 \text{ articles per month} -\] - -**From June 2023 to June 2024**: -\[ -\text{Growth} = 1,968,552 - 1,866,921 = 101,631 \text{ articles} -\] -\[ -\text{Duration} = 12 \text{ months} -\] -\[ -\text{Monthly Growth Rate} = \frac{101,631}{12} \approx 8,469 \text{ articles per month} -\] - -### Step 3: Forecast Future Growth - -**As of July 26, 2024:** -\[ -\text{Time until September 15, 2024} \approx 1.5 \text{ months} -\] - -Using the most recent growth rate (8,469 articles per month): -\[ -\text{Expected Article Count by September 15, 2024} = 1,968,552 + 1.5 \times 8,469 \approx 1,981,256 -\] - -**Expected Article Count by October 1, 2024:** -\[ -\text{Time from now until October 1, 2024} = 2.2 \text{ months} -\] -\[ -\text{Expected Article Count by October 1, 2024} = 1,968,552 + 2.2 \times 8,469 \approx 1,987,451 -\] - -### Step 4: Analysis of Likelihood - -To exceed 2 million articles by October 1, 2024: -\[ -2,000,000 - 1,968,552 = 31,448 \text{ articles needed} -\] -\[ -31,448 \div 8,469 \approx 3.71 \text{ months} -\] - -It is clear that from now until the target date (about 2.2 months), the Spanish Wikipedia is expected to reach around 1,987,451 articles, which is still below 2 million. The expected monthly growth rate indicates that hitting the 2 million mark within the given date range is unlikely unless there is a significant unexpected increase in article creation. - -### Step 5: Include Variance and Uncertainty - -While not absolutely impossible, a significant spike in new articles would be required to bridge the difference of approximately 12,549 articles within the remaining 2.2 months. Historically, no drastic changes in growth patterns have been observed recently. Thus, high variance is unlikely. - -### Conclusion - -Given all the information and analyzing the historical growth trends, the probability of the Spanish Wikipedia first exceeding 2 million articles between September 15, 2024, and October 1, 2024, is relatively low. - -**Final Answer**: -\[ -\text{Probability: 10%} -\]" -26642,"Will the Spanish Wikipedia first exceed 2 million articles before October 1, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question about the Spanish Wikipedia exceeding 2 million articles before October 1, 2024, I have searched various sources, including Wikipedia articles and other relevant online resources. - -### Current Status and Growth Rate - -- As of July 2024, the Spanish Wikipedia has approximately 1.5 million articles. -- The Spanish Wikipedia has been steadily growing, with significant milestones reached in recent years, such as exceeding 1.5 million articles in 2019. -- The overall growth rate of Wikipedia articles has slowed down since 2006, but the number of articles added to Wikipedia every month has remained relatively consistent, with about 14,000 new articles added monthly as of January 2024. - -### Historical Milestones - -- The Spanish Wikipedia exceeded 1 million articles in 2013 and 1.5 million articles in 2019. -- Other language editions, such as the Cebuano and Swedish Wikipedias, have also reached significant milestones, with the Cebuano Wikipedia exceeding 6 million articles in 2021. - -### Prediction Markets and Expert Opinions - -- There are no specific prediction markets or expert opinions directly addressing the question of whether the Spanish Wikipedia will exceed 2 million articles before October 1, 2024. However, the consistent growth rate and historical milestones suggest that the Spanish Wikipedia is on track to continue expanding. - -### Relevant Statistics - -- As of April 2024, the English Wikipedia has almost 6.9 million articles, with the total number of articles across all language editions exceeding 63 million. -- The number of unique device visits to Wikipedia is over 1.5 billion per month, with the English Wikipedia receiving 10 billion page views monthly. - -### Conclusion - -Based on the current growth rate and historical milestones, it is likely that the Spanish Wikipedia will continue to expand and potentially exceed 2 million articles before October 1, 2024. However, without specific prediction markets or expert opinions directly addressing this question, a definitive forecast cannot be made. - -######### - -GPT - -To carefully evaluate the likelihood of the Spanish Wikipedia first exceeding 2 million articles before October 1, 2024, I will use a systematic approach, considering the available evidence, trends, and statistical analysis. - -### Step-by-Step Analysis - -#### 1. Current Data and Trends -- **Current Articles Count**: As of June 24, 2024, there are 1,968,552 articles. -- **Growth Rate**: Over the past year, June 2023 (1,866,921) to June 2024 (1,968,552), the Spanish Wikipedia added approximately 101,631 articles. This averages 8,469 articles per month. - - **Monthly Average Analysis**: - - **June 2023 to June 2024**: Total articles added = 101,631 - - **Monthly average increase**: 101,631 / 12 ≈ 8,469 articles/month - -#### 2. Projection to October 1, 2024 -Today's date is July 26, 2024. We have just over 2 months until October 1, 2024, or approximately 2.18 months. - -Using an average monthly growth rate of 8,469 articles, we can project the total articles by October 1: - -- **Projected Articles Growth**: 8,469 articles/month * 2.18 months ≈ 18,462 articles -- **Projected Articles Count by October 1, 2024**: - - Existing articles: 1,968,552 - - Projected growth: 18,462 - - **Total projected articles by October 1**: 1,968,552 + 18,462 ≈ 1,987,014 articles - -This projection indicates that the Spanish Wikipedia will likely fall short of the 2 million article mark by October 1, 2024. - -#### 3. Uncertainties and Variability -While projections are straightforward, they must account for potential variability: -- **Recent Increase in Growth Rate**: If the growth rate picks up closer to 18,000 articles/month instead of the last year's average: - - **Adjusted Growth Projection**: 18,000 articles/month * 2.18 months ≈ 39,240 articles - - **Total with Accelerated Growth**: 1,968,552 + 39,240 ≈ 2,007,792 articles - -However, this would be an unusually high rate, given that the past year did not see such growth rates consistently. - -#### 4. External Factors and Base Rates -- **Base Rate Stability**: From historical data, the Spanish Wikipedia has shown relatively stable growth. Without any significant increased efforts, campaigns, or technological changes announced, it's reasonable to assume the historical growth rate continues. -- **Potential Factors**: A sudden increase in concerted effort to add articles, a significant event stimulating Wikipedia contributions, or a change in how articles are counted could alter these projections. - -#### 5. Conclusion and Probability Assignment -Given the analysis, the central estimate points to falling just short of the 2 million article mark by the end of September 2024 unless there's an unexpected surge in article creation. - -Considering the potential variability and a slight chance for unforeseen surges, I account for this probability but keep a weighted focus on the historical trend. - -Final probability estimate: - -- Projected articles by October 1, 2024: Approximately 1,987,014 -- Required additional articles to hit 2,000,000: ≈ 12,986 - -Although not impossible, it seems statistically unlikely for a surge of nearly 13,000 additional articles over the current trend projection in the next 2.18 months. - -#### Final Probability -I assign a 15% probability that the Spanish Wikipedia will exceed 2 million articles by October 1, 2024. This accounts for both the likely base rate of growth and the low-probability high-impact events that could alter this trajectory. - -**Probability: 15%**" -26641,"Will Donald Trump debate the Democratic nominee for president less than twice before October 1, 2024?","PERPLEXITY - -To provide context for the query about whether Donald Trump will debate the Democratic nominee for president less than twice before October 1, 2024, here are the key points from recent news sources: - -1. **Debate Schedule**: There are two presidential debates agreed upon by Donald Trump and President Joe Biden, with the second debate scheduled for September 10, hosted by ABC News. -2. **Current Nominees**: Following President Biden's withdrawal from the race, Vice President Kamala Harris has become the presumptive Democratic nominee, with Trump expressing his willingness to debate her. -3. **Trump's Debate Commitment**: Trump has committed to debating Harris, stating he would ""absolutely"" debate her and is open to more than one debate. -4. **Potential Debate Changes**: Trump has hinted at potentially retracting his commitment to the ABC debate, suggesting Fox News as an alternative host. -5. **No Additional Debates Scheduled**: As of now, there are no additional presidential debates scheduled before the election, with both candidates having only committed to the two debates mentioned. - -Given these points, it appears that Trump is committed to debating the Democratic nominee at least twice before October 1, 2024, unless he retracts his commitment to the ABC debate. However, there is no indication of additional debates being scheduled beyond the two already agreed upon. - -######### - -GPT - -### Step-by-Step Analysis: - -#### 1. **Current Status and Default Resolution:** -- Today's date is July 26, 2024. -- Trump and Biden had agreed to two debates, with the first already concluded. -- Biden withdrew from the race on July 21, 2024. -- Kamala Harris is the presumptive Democratic nominee. -- The second debate scheduled for September 10 is now in question, as Trump has expressed a preference for Fox News over ABC News. - -#### 2. **Trump's Statements and Behavior:** -- Trump has publicly committed to debating the Democratic nominee and has shown willingness for multiple debates. -- His specific preference for Fox News as a debate host introduces a potential logistical delay or disruption to the already scheduled debate. - -#### 3. **Historical and Contextual Trends:** -- Historically, once presidential debates have been scheduled and agreed upon, they tend to happen, barring extraordinary circumstances. -- Debate commitments have at times been tenuous and subject to last-minute changes, particularly in unpredictable political climates. - -#### 4. **Timeframe Considerations:** -- There are 67 days between now (July 26) and October 1, 2024. -- Within this timeframe, candidates can potentially organize, cancel, or reschedule debates. - -#### 5. **Base Rates and Precedents:** -- It is rare for presidential candidates to skip agreed-upon debates unless there are significant changes or disruptions. -- Changing the debate host from ABC to Fox News could either accelerate or delay the scheduled debate by a significant margin. - -#### 6. **Evaluation of Key Points from Recent News:** -- The commitment to two debates with specifics thrown into uncertainty after Biden's withdrawal. -- Trump’s willingness to debate Kamala Harris. -- No additional debates formally scheduled. -- Trump’s preference for another debate host introduces a potential but not guaranteed disruption. - -#### 7. **Narrowing Down Outcomes:** -- Holding the second debate on Fox News requires organization and agreement, suggesting potential delays or cancellations. -- Without formal additional debates on the schedule, any new debate needs quick and substantial organization, which is unlikely given typical logistical challenges. - -### Probability Assessment: - -#### Negative Indicators: -- The already scheduled debate on September 10 might get canceled or rescheduled due to Trump’s demand for a host change. -- Historically, organizing new debates with new rules and hosts within two months can be logistically challenging. - -#### Positive Indicators: -- Trump’s strong interest in debating suggests he will push for at least one debate. -- The pathways to at least one debate happening are straightforward if organized promptly. - -#### Probability Distribution: -- **0 Debates:** Unlikely given Trump’s interest and public statements, but possible if logistics fall apart completely. -- **1 Debate:** Fairly plausible given the potential delay or dispute resolution concerning the host. -- **2 Debates or More:** Less likely, as there is no concrete plan or precedent at this moment for more than two debates, and further scheduling nuances exist. - -### Final Prediction: - -Based on the analysis: -- There is a high likelihood (around 70%) that Trump will debate less than twice before October 1, 2024, mainly due to the current uncertainties and logistical hurdles surrounding the debate organization. - -**Probability: 70%**" -26639,"Will Donald Trump debate the Democratic nominee for president more than once before October 1, 2024?","PERPLEXITY - -To assist the superforecaster in forecasting whether Donald Trump will debate the Democratic nominee for president more than once before October 1, 2024, I have compiled the following relevant information: - -1. **Scheduled Debates:** - - The second presidential debate between President Biden and Donald Trump is set for September 10, hosted by ABC News. - - There are no additional presidential debates scheduled before the election, with both candidates committing only to the two debates mentioned. - -2. **Potential Debate with Kamala Harris:** - - Kamala Harris has expressed her readiness to debate Donald Trump, and Trump has also indicated his willingness to debate her. - - Fox News has proposed a debate between Trump and Harris on September 17 in Pennsylvania. - -3. **Trump's Commitment to Debates:** - - Trump has expressed his openness to participating in multiple debates with Harris, suggesting that he might retract his commitment to the ABC debate due to his dissatisfaction with the network. - - Jason Miller, a Trump ally, believes that a Trump-Harris debate ""will happen"" and that there ""should be multiple debates"" between the two. - -4. **Current Political Climate:** - - A Gallup poll in June found that Donald Trump is viewed more positively than President Joe Biden across various measures, though neither candidate has a favorable image overall. - - Republicans are more united in their support for Trump than Democrats are for Biden, which might influence the debate dynamics. - -5. **Prediction Markets:** - - There are no specific prediction markets available on Metaculus for this exact question. However, it is essential to monitor updates on debate schedules and candidate commitments to accurately forecast the number of debates before October 1, 2024. - -In summary, while there are currently only two scheduled debates between Trump and Biden, there is a possibility of additional debates with Kamala Harris. Trump's willingness to debate Harris and his dissatisfaction with ABC News might lead to changes in the debate schedule. The superforecaster should closely follow updates on debate commitments and schedules to make an informed forecast. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Baseline Probability and Historical Context** -- Historically, major US presidential candidates typically debate three times before the general election. This forms a basis for comparison even though this year there is a departure from the norm with only two debates initially agreed upon. -- Given past elections, the fluctuation and changes to debate schedules are relatively rare, but possible. - -#### 2. **Evaluating Current Context** -- **Agreement on Debates:** Initially, Trump and Biden agreed to two debates, which sets a baseline expectation. However, Biden’s exit introduces significant uncertainty. -- **Trump's Stance:** Trump's statement on Truth Social indicates willingness but also conditions for participating in debates based on the hosting network. -- **Kamala Harris:** Was identified as a potential debater, and Trump has shown willingness to debate her. A debate is proposed by Fox News for September 17. - -#### 3. **Commitments and Plans** -- **Current Scheduled Debate:** The second debate (previously Trump and Biden) for September 10, which might now involve a different Democratic nominee. -- **Proposed Debate:** September 17 on Fox News with Harris. -- Given that the electoral context can lead to renegotiations, Trump's indicated willingness signals potential for flexibility. - -#### 4. **Political Dynamics and Influences** -- **Biden's Withdrawal Effect:** Biden's lackluster performance and subsequent withdrawal shake up the debate expectations, making the Democratic nomination process more fluid. -- **Republican Strategy:** Trump’s team may see debates as an opportunity to capitalize on the Democratic transition. -- **Media Strategy:** Trump’s preference for Fox News could lead to further negotiations and potential debates hosted by more favorable networks. - -#### 5. **Remaining Timeframe** -- There are about two months until October 1, 2024, providing ample opportunity for debate rescheduling or additional debates given political strategies and negotiations. - -#### 6. **Validation with External Signals** -- **Polls and Public Sentiment:** Trump's favorability could press his team to push for more exposure via debates. -- **Jason Miller’s Comment:** As a close ally, his belief in multiple debates gives weight to the probability of more than one debate happening. - -### Probabilistic Assessment -Based on the above analysis, let's quantify: - -1. **Base Rate of Two Debates in Normal Years:** Generally, presidential candidates engage in three debates. - - Given that only one future debate is definitively scheduled, there’s room for at least one more debate (usual rate of 67%). - -2. **Trump's Willingness and Statements:** - - Willingness to debate on Fox News and with Harris indicates high probability of at least one more debate (added weight of 15%). - -3. **Uncertainty Due to Biden’s Withdraw:** - - The transition might lead to logistical complications reducing probabilities slightly (-5%). - -4. **Jason Miller’s Comments:** - - Insider's perspective supporting multiple debates (added weight of 8%). - -5. **Historical Fluidity and Political Strategies:** - - Constant renegotiations and campaign strategies (net neutral but error margin adjustment of ±5%). - -### Final Statistical Synthesis -Through weighted synthesis: - -\[ P(\text{More than one debate}) = 67\% + 15\% - 5\% + 8\% ± 5\% \] -\[ \approx 85\% \text{ with an error margin of ± 5%} \] - -### Conclusion -Careful weighing of the factors leads to a confident prediction that the probability of Donald Trump debating the Democratic nominee for president more than once before October 1, 2024, is relatively high. - -### Final Answer -**Probability: 85%**" -26639,"Will Donald Trump debate the Democratic nominee for president more than once before October 1, 2024?","PERPLEXITY - -To provide context for the query about whether Donald Trump will debate the Democratic nominee for president more than once before October 1, 2024, here are the key points from the provided sources: - -1. **Current Debate Schedule:** - - Two debates are scheduled between President Biden and Donald Trump, with the second debate set for September 10, hosted by ABC News. - - There are no additional presidential debates scheduled before the election, as both candidates have only committed to these two debates. - -2. **Potential Debate with Kamala Harris:** - - Kamala Harris has expressed her readiness to debate Trump, and Trump has stated that he ""absolutely"" wants to debate Harris. - - Fox News has proposed a debate between Trump and Harris on September 17 in Pennsylvania. - - Trump's advisor, Jason Miller, has indicated that a Trump-Harris debate ""will happen"" and that there ""should be multiple debates"" between them. - -3. **Uncertainty Around ABC Debate:** - - Trump has expressed dissatisfaction with ABC, calling it ""fake news,"" and has proposed that Fox News host the debate instead. - -4. **Running Mates:** - - Trump has chosen Ohio GOP Senator JD Vance as his running mate. - - Harris is still in the process of selecting her running mate, with the goal to announce the pick before August 7. - -5. **Recent Developments:** - - Former President Barack Obama has endorsed Kamala Harris in the 2024 race. - - Trump is scheduled to meet with Israeli Prime Minister Benjamin Netanyahu and address a conservative group in Florida. - -Given these points, it appears that there are ongoing discussions and proposals for additional debates between Trump and Harris, but no concrete commitments have been made. The uncertainty around the ABC debate and Trump's dissatisfaction with the network may also impact the final debate schedule. - -######### - -GPT - -To address the question of whether Donald Trump will debate the Democratic nominee for president more than once before October 1, 2024, let's analyze the situation step-by-step. - -### Step-by-Step Analysis - -**1. Current State of Debates:** - - **Two initial debates were scheduled**: One on June 27, 2024 (which has already occurred with Biden), and another on September 10, 2024, hosted by ABC News. - - **Potential Changes**: With Biden dropping out of the race, there is potential for changes to the debate schedule. - -**2. Democratic Nominee Dynamics:** - - **Kamala Harris as the presumptive nominee**: Given that Biden has dropped out, Kamala Harris, being his Vice President, is the likely nominee. She has expressed her readiness to debate Trump. - -**3. Uncertainty Around ABC Debate:** - - **Trump's dissatisfaction with ABC**: Trump prefers Fox News over ABC and has been vocal about this preference. - -**4. Alternative Arrangements:** - - **Fox News Proposal**: Fox News proposed a debate between Trump and Harris on September 17, 2024. Trump's advisor has suggested multiple debates might occur. - - **Kamala Harris Selection of Running Mate**: Harris is expected to announce her running mate by August 7, 2024, which could solidify her campaign structure and debate agreements. - -### Evaluations - -**Existing Commitments and Likelihood of Execution:** - - **Scheduled Debate on September 10**: This is relatively close (45 days away) and could be rescheduled or replaced rather than entirely dismissed, especially significant for media outlets. - - **Potential for Multiple Debates**: Historical precedent shows that once a nominee is settled, the nominee usually participates in the scheduled debates. - -**Base Rates:** - - In previous elections, multiple presidential debates usually occur. However, changes in the debate schedule can occur due to candidate actions and preferences. - -**Current Negotiation Environment:** - - High public interest and media pressure often push candidates to appear in debates. - - Trump's strategy often involves media engagement, so foregoing opportunities to debate may contradict his historical behavior. - -### Probability Assessment - -**No Changes Scenario:** -- If no changes occur from the current situation, Trump debates the Democratic nominee once on September 10. Probability: Would resolve as **No**. - -**Possible Changes:** -- If Trump and Harris agree to the Fox News debate on September 17, that adds a second debate. -- The possibility of initial ABC debate being moved to Fox or additional debates being scheduled is plausible. - -With all factors considered, the likelihood of holding more than one debate (and hence resolving ""Yes"") revolves around: -1. Trump and Harris engaging in rigorous campaigning and use of debates as tools. -2. Media pressure and public interest pushing for multiple debates. -3. Historical campaign behaviors and patterns reinforcing the likelihood of adhering to established but flexible debate practices. - -Given the remaining time and political maneuvering potential, I estimate: - -### Final Probability Estimate: -""Probability: 70%"" - -This allows for the strong possibility that at least one additional debate between Trump and Harris might occur, considering dynamic political campaigns and media influence." -26638,"Before October 1, 2024, will a federal court rule in favor of a challenge to a major Clean Air Act provision, citing Chevron's overturning as a key factor?","PERPLEXITY - -The recent Supreme Court decisions in *Loper Bright Enterprises v. Raimondo* and *Corner Post* have significantly impacted the regulatory power of federal agencies. Here are the key points relevant to the query: - -1. **Overturning of Chevron Deference**: The Supreme Court has ended the 40-year precedent of Chevron deference, which allowed federal courts to defer to agency interpretations of ambiguous statutes. This change will lead to more challenges to agency actions and potentially more frequent rejections of agency interpretations. - -2. **Impact on Environmental Regulations**: The ruling is expected to affect efforts to address climate change and protect endangered species. Environmental groups had previously relied on Chevron deference to support agency regulations, but now courts will exercise independent judgment in interpreting statutes. - -3. **Clean Air Act Provisions**: The Clean Air Act, which was central to the original Chevron case, may be particularly susceptible to challenges. The EPA's ability to regulate greenhouse gases has been strengthened by President Biden's climate law, but other provisions of the Clean Air Act could be challenged in light of the new ruling. - -4. **Potential for Increased Lawsuits**: The decision is likely to lead to an increase in lawsuits against federal agencies, potentially extending beyond environmental organizations to other departments like Education, Labor, and Health and Human Services. - -5. **Metaculus Prediction Market**: A Metaculus prediction market is currently tracking the likelihood of a federal court ruling in favor of a challenge to a major Clean Air Act provision before October 1, 2024, citing Chevron's overturning as a key factor. - -In summary, the recent Supreme Court decisions have significantly altered the regulatory landscape, and the Clean Air Act is likely to be impacted. The potential for increased lawsuits and challenges to agency actions is high, and the Metaculus prediction market is tracking the likelihood of a significant ruling before October 1, 2024. - -######### - -GPT - -To begin forecasting whether a federal court will rule in favor of a challenge to a major Clean Air Act provision before October 1, 2024, citing Chevron's overturning as a key factor, I must break down the question into relevant components and consider them step-by-step: - -### Assessment of Key Elements: - -1. **Overturning of Chevron Deference**: - - This was a significant change in U.S. administrative law, expected to affect how courts handle cases regarding agency interpretations. - - Significant because courts no longer automatically defer to agencies’ interpretations of ambiguous statutes. - -2. **Court Cases and Legal Precedents**: - - Numerous pending lawsuits challenging various EPA regulations. - - Increased likelihood of courts ruling against EPA regulations due to the elimination of Chevron deference. - -3. **Timing**: - - Today's date is July 26, 2024, and the question asks about a ruling by October 1, 2024. - - This gives slightly over two months for a ruling to occur. - -4. **Recent Supreme Court Decisions**: - - In *Loper Bright Enterprises v. Raimondo* and *Corner Post*, major regulatory changes were discussed. - - Recent Supreme Court activity may expedite lower court cases, especially those influenced by prominent decisions. - -### Base Rates and Historical Context: - -- It generally takes time for legal challenges to proceed through the courts, even when motivated by significant judicial changes. -- Federal courts tend to have congested dockets, often delaying the timing of rulings. -- Legal procedures, from the filing of lawsuits to ruling, often extend beyond a couple of months, especially for major provisions. - -### Specific Case Factors: - -- Since the Supreme Court's Chevron decision was only in June 2024, the expedited trajectory of relevant cases may still be emerging. -- The sheer number of challenges against the EPA’s regulations suggests that at least one high-profile case might see accelerated handling. - -### Probability Assessment: - -1. **Importance of Recent Changes**: - - The precedence of Chevron deference being overturned is substantial. - - This change, being fresh, is still working its way through the judiciary but has already stirred significant litigation activity. - -2. **Trends and Likelihood**: - - Given that recent Supreme Court decisions highly influence the chances of lower court actions, the probability is non-trivial. - - However, the short timeframe of just over two months limits the probability materially. - -3. **Expert Insights and Analysis**: - - The recent Metaculus predictions and expert suggestions highlight a heightened chance but do not guarantee quick rulings given usual adjudicatory timelines. - -### Conclusion: - -Considering the above points—especially the time constraints and legal procedural norms—I'm inclined to assign a moderate yet cautious probability to a federal court issuing a final ruling by October 1, 2024. - -**Final Probability: 30%** - -While the context increases the likelihood, the narrow timeframe between now and October 1, 2024, limits the extent to which these legal changes would translate into a decisive ruling within the next two months." -26666,"Will Tether collapse before September 30, 2024?","PERPLEXITY - -To provide a comprehensive overview of the current situation and relevant news regarding Tether, I have summarized the key points from the provided sources: - -### Tether's Market Dominance and Lending Activities - -- Tether, the leading stablecoin issuer, has experienced a surge in stablecoin lending activities despite announcing plans to phase out such loans by 2024. -- As of June 2023, Tether reported $5.5 billion in loans, an increase from the previous quarter. -- The company has a substantial $3.3 billion in surplus reserves, a significant increase from 2022. -- Tether has minted approximately $16 billion in USDT since the beginning of 2023, solidifying its position as the leading USD-pegged stablecoin by market capitalization. - -### Regulatory Uncertainty in the EU - -- The European Union's Markets in Crypto Assets (MiCA) legislation will take effect on June 30, 2024, and may impact Tether's operations in the EU. -- Tether has not yet applied for a MiCA or e-money license in the EU, which could lead to delisting by CASPs (Crypto Asset Service Providers) if not obtained by the end of 2024. -- The EU regulations ban stablecoins with over 1 million daily transactions that pay for goods or services settled off- and on-chain. -- Circle's USD Coin (USDC) has obtained an e-money license in France and is considered compliant with EU regulations. - -### Expert Opinions on Tether's Stability - -- Crypto entrepreneur Travis Bott believes a Tether collapse is possible but unlikely due to its fiat-backed nature and dollar reserves. -- Crypto and tokenomics expert Eloisa Marchesoni warns that Tether could experience a run on the bank, which could impact the whole industry, due to concerns over its reserves and transparency. - -### Current Market Situation - -- Tether's market capitalization is currently $113 billion, making it the third-largest cryptocurrency behind Bitcoin and Ethereum. -- The stablecoin is indispensable as a dollar replacement, especially for crypto traders, and is one of the most traded cryptocurrencies daily. - -### Relevant Prediction Markets - -- There are no specific prediction markets mentioned in the provided sources that directly address the question of Tether's collapse before September 30, 2024. However, it is essential to monitor relevant prediction markets and expert opinions for any updates that may indicate movement towards a particular resolution. - -In summary, Tether's market dominance and lending activities are significant, but regulatory uncertainty in the EU and concerns over its reserves and transparency pose potential risks. Expert opinions are divided on the likelihood of a Tether collapse, and the current market situation highlights its importance as a dollar replacement. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. Contextual Framework - -This question falls under the **Other Binary Outcome Event** category because it is about whether Tether (USDT) will collapse according to specific criteria, and it does not pertain to market prices, cumulative count, or rate of spread of a disease. - -#### 2. Evaluating Important Information -- **Tether’s Market Dominance & Lending**: - - Tether's stablecoin lending activities have increased, indicating robust activity. - - Surplus reserves have increased significantly from 2022. - - Tether has minted $16 billion USDT since January 2023. - - Market capitalization is $113 billion, making it the third-largest cryptocurrency. - -- **Regulatory Uncertainty**: - - MiCA legislation in the EU takes effect on June 30, 2024, affecting operations. - - Tether has not yet applied for required licenses, which may lead to regulatory risks. - -- **Expert Opinions**: - - Mixed views on Tether’s stability. Concerns over transparency and reserves are ongoing. - -- **Market Situation**: - - Tether is vital as a dollar replacement in cryptocurrency trading, supporting high liquidity. - -#### 3. Historical Context & Base Rates -- Historically, no major stablecoin with Tether's profile has collapsed to below 0.50 USD for an extended period. Even during crypto market crashes, Tether has held its value relatively stable. - -- Regulatory challenges have historically had mixed impacts on cryptocurrencies; however, they generally induce short-term volatility rather than long-term collapses. - -#### 4. Current Timeline & Conditions -- There are two months left until September 30, 2024. This is a short time frame given Tether's current stability and market dominance. -- The provided sources suggest Tether remains widely used and traded, with significant reserves and surpluses reported. - -#### 5. Statistical Considerations -- Evaluating the criteria (USDT below 0.50 USD for 7 days on two exchanges, or trading suspension for 7 days): - - Given its current market capitalization and trading volume, a collapse equaling this magnitude within two months is statistically unlikely unless driven by an unprecedented event (e.g., significant regulatory action or a substantial financial mishap). - -#### 6. Conclusion -**Downside Risks**: -- **Regulatory Actions**: Potential for EU regulatory issues to pose significant risks. -- **Market Confidence**: Any news undermining confidence in Tether's reserves could trigger withdrawals akin to a ""run on the bank."" - -**Upside Strengths**: -- **Market Dominance**: Strong historical performance and market reliance. -- **Surplus Reserves**: Significant surplus reported, which provides a buffer against a sudden collapse. -- **Trading Volume and Use**: Continuous high trading volume and wide acceptance in the cryptocurrency market. - -### Prediction - -**Probability of Tether collapsing before September 30, 2024, according to defined criteria**: - -- Since the timeframe is short and Tether has maintained substantial market dominance and increased reserves, combined with no immediate evidence of a financial or regulatory crisis about to trigger downfall within two months, the probability remains low. - -**Final Answer**: -""Probability: 5%""" -26665,"Will Apple announce a portless iPhone before October 1, 2024? -","PERPLEXITY - -Here is a detailed rundown of the news relevant to the question: - -**Portless iPhone and USB-C Transition** - -- Apple has been considering a portless iPhone design, but significant hurdles remain, including the need for faster wireless data transfer and a reliable wireless charging solution. -- The European Union has mandated a universal charging port, which will require Apple to switch to USB-C by late 2024. -- Apple has confirmed it will comply with the EU ruling, but has expressed concerns about e-waste and the loss of revenue from Lightning sales and licensing. -- The company has already transitioned to USB-C in its iPad lineup and is expected to do so for the iPhone 15. - -**Current Status and Predictions** - -- The iPhone 15 is likely to feature a USB-C port, with testing beginning as early as January 2022. -- There is no clear indication of a portless iPhone being announced before October 1, 2024, as significant technical challenges need to be overcome. -- Apple has been pushing MagSafe wireless charging, which could potentially lead to a portless design in the future, but no concrete timeline has been given. - -**Expert Opinions and Prediction Markets** - -- Analyst Ming-Chi Kuo predicts Apple will retain the Lightning connector for the ""foreseeable future"". -- Metaculus has a prediction market on whether Apple will announce a portless iPhone before October 1, 2024, but no current predictions are available. - -**Recent Updates and Movement** - -- There have been no recent updates indicating significant movement towards a portless iPhone announcement before October 1, 2024. -- The focus remains on Apple's transition to USB-C, driven by EU regulations, with the iPhone 15 expected to be the first model to adopt this change. - -######### - -GPT - -To forecast whether Apple will announce a portless iPhone before October 1, 2024, we must perform a detailed analysis of the available information, base rates, and expert opinions, and evaluate these factors against the timeline. - -### Key Points from Background Information - -1. **Technical Challenges**: - - Significant hurdles remain for a portless design such as faster wireless data transfer and reliable wireless charging. - -2. **EU USB-C Mandate**: - - The EU has mandated that electronic devices, including smartphones, must adopt a universal charging port (USB-C) by late 2024. - - Apple is planning to comply with this regulation, potentially indicating a transition phase where USB-C will be adopted before any move to a portless design. - -3. **Current Developments**: - - iPhone 15 expected to feature a USB-C port. - - Apple has been promoting MagSafe wireless charging which could be a precursor to a portless design. - -4. **Expert Opinions**: - - Analysts like Ming-Chi Kuo have forecasted Apple will retain traditional port designs (inclusive of any USB-C transition). - - No recent updates or leaks indicate imminent movement towards a portless announcement. - -5. **Timeline**: - - Today’s date: July 29, 2024. - - Deadline for resolution: October 1, 2024. - - About two months are left within which Apple would need to make the announcement. - -### Base Rate and Historical Context - -- Historically, Apple has made significant hardware announcements in September during their annual product launch event. With early leaks and previews typically circulating a few months beforehand, there has been limited to no speculation or leaks about a portless iPhone specifically being unveiled this year. -- Considering the technological and regulatory transitioning phases from Lightning to USB-C, it’s prudent to expect this would dominate Apple's recent design focus. - -### Analysis of Factors - -1. **Technological Readiness**: - - Current wireless technology does not substantially support a fully portless design with acceptable performance (data transfer speed, charging efficiency). - - Current trends have shown incremental overhauls rather than radical shifts (e.g., removal of headphone jack was not followed soon after by removal of charging ports). - -2. **Regulatory and Market Forces**: - - Strong focus on transitioning to USB-C in compliance with EU regulations seems more immediate. - - Apple must ensure smooth transition with minimal consumer inconvenience before pushing groundbreaking changes. - -3. **Practicality and Strategic Moves**: - - Apple being a market leader could introduce a portless iPhone to revolutionize but requires an optimal innovation ecosystem ready for it. Current indicators (technological, expert analysis) suggest the ecosystem isn’t in place yet. - - The lack of substantial leaks or official proof hints at low probability for such an announcement in the immediate months ahead. - -### Quantitative Probability Estimate - -1. **Base Rate Analysis**: - - Given the unprecedented nature of a portless design paired with a narrow timeline, historical precedent of Apple's cautious and stepwise innovation approach places the probability towards the lower spectrum. - -2. **Time Constraint**: - - With barely two months until the deadline, the probability of announcing a portless design abruptly with no preceding substantial leaks or developer updates is considerably low. - -Given all these factors, the constrained timeline, and based on historical trends and current expert consensus, the probability of Apple announcing a portless iPhone before October 1, 2024 is very low. - -**Final Probability Estimate:** - -Probability: 5%" -26670,"Will the Prime Minister of France on August 31, 2024 belong to the New Popular Front but not LFI?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -- **Recent Election Results**: The New Popular Front (NPF), a left-wing alliance of four parties (Socialists, Greens, Communists, and Jean-Luc Mélenchon’s France Unbowed), won the most seats in the French parliament, but without an absolute majority. -- **Prime Minister Nomination**: The NPF has nominated Lucie Castets, a finance director at Paris City Hall, as its candidate for prime minister. -- **President Macron's Stance**: President Emmanuel Macron has stated that he will not name a new government until after the Paris Olympics, which end in mid-August. He emphasized the need for parties to work together to build a broad coalition. -- **Current Government**: The current centrist caretaker government, led by Prime Minister Gabriel Attal, will remain in place until a new government is formed. -- **Metaculus Prediction**: There is a Metaculus prediction market open for this question, but it is still in draft form and has not been submitted for review. - -Recent updates indicate that the situation remains uncertain, with Macron pushing for a broad coalition and the NPF advocating for its candidate, Lucie Castets. Expert opinions highlight the need for compromise among parties to form a stable government. - -######### - -GPT - -To forecast whether the Prime Minister of France on August 31, 2024, will belong to the New Popular Front (NPF) but not France Unbowed (LFI), we need to evaluate various factors carefully. Let's proceed step-by-step: - -### 1. Analyze Baseline Information -- **Recent Election Results**: The NPF won the most seats, indicating a strong position in the National Assembly but without an absolute majority. This implies that coalition-building will be necessary. -- **Prime Minister Nomination**: NPF has Lucie Castets as their candidate. She is a member of the NPF but not of LFI. -- **President Macron’s Stance**: Macron won’t appoint a new government until mid-August, preferring a broad coalition. This adds uncertainty since a broad coalition might necessitate compromises or alter plans. -- **Current Government**: The caretaker government led by centrist Gabriel Attal remains until a new government is formed. This status quo situation could maintain itself temporarily if a new coalition is not formed quickly. - -### 2. Consider Political Context and Players’ Incentives -- **Macron's Leverage**: As the President, Macron holds significant influence over the timing and formation of the new government. His push for a broad coalition might delay the installation of Lucie Castets if parties cannot quickly agree. -- **Coalition Dynamics**: The need for broad coalition implies other centrist or potentially right-leaning parties might influence the formation of the government, possibly favoring a candidate other than Castets or someone more centrist within NPF. -- **Olympics Timing**: The Paris Olympics end in mid-August, right when Macron plans to finalize government formation. This tight timeline could either rush decisions or allow further delays. - -### 3. Base Rates and Historical Precedents -- **Formation of Governments**: Historically, forming a government post-elections in France often takes time but typically concludes within a few months unless there is severe deadlock. -- **Coalition Governments**: Coalition governments in France, especially when no party holds a majority, often experience shifts and require compromise candidates who can appeal broadly beyond their party base. - -### 4. External Factors -- **Economic/Technological/Environmental States**: No significant novel factors appear in this domain to sway general trends. - -### 5. Risk and Uncertainty Assessment -- **Default Status Quo**: If no new government forms by then, the caretaker government led by Gabriel Attal will remain. Absent extraordinary changes, this caretaker set-up seems plausible in face of deadlock, but not likely to persist beyond mid-August given Macron's determination. -- **Variance and Volatility**: Given periodic routine political volatility in government formation but absence of extraordinary crises, the formation leans towards typical procedural resolution rather than extreme delay or overhaul. - -### Calculation and Final Consideration -To arrive at a probability, we weigh: -- Lucie Castets’ nomination and her fit within NPF but not LFI -- Macron’s broad coalition preference might delay or alter the process, endorsing a compromise candidate. -- Historical prompt resolutions post-major events, and majority's behavior. - -Given these factors, while Lucie Castets' probability of becoming PM seems high as the leading candidate of a winning coalition, Macron’s preferences and the nature of coalition dynamics introduce notable risks. In terms of chances, her prioritization is high but contingent on coalition agreements. - -### Final Probability Estimation -- I assess the likelihood of Lucie Castets (NPF but not LFI) retaining her nomination successfully but with an allowance for delays and potential coalition-required deviations. - -**Probability**: 72% - -This incorporates a balanced view of assertive trends favoring her position, weighed against moderate coalitionary volatility and Macron's considerable influence." -26683,"At the 2024 Olympics, will at least 5 countries win their debut gold medal?","PERPLEXITY - -The 2024 Paris Olympics are underway, and several countries are vying for their first-ever gold medals. Here are some key updates and insights that might help forecast the likelihood of at least 5 countries winning their debut gold medals: - -1. **Current Medal Count**: As of Sunday night, 30 countries have secured at least one medal. The United States leads with 12 medals, followed closely by China and France with 9 medals each. - -2. **Debut Gold Medal Winners**: So far, there have been some notable debut gold medal winners. For example, **Lian Junjie and Yang Hao (CHN)** won gold in the men's synchronized 10m platform diving event on their Olympic debut. Similarly, **Sheng Lihao (CHN)** won gold in the men's 10m air rifle event, setting an Olympic record. - -3. **Forecast and Predictions**: Analytics firm Gracenote predicted that several countries would win medals in various events. For instance, **Great Britain** is expected to win medals in team eventing, and **China** is forecast to win medals in multiple events, including diving and gymnastics. - -4. **Upcoming Events**: There are several events scheduled for the coming days that could see new countries winning their debut gold medals. For example, the men's street skateboarding final, rescheduled from Saturday due to rain, will take place today, with the Japanese team expected to perform well. Additionally, the men's team gymnastics final will feature a strong competition between China and Japan. - -5. **Expert Opinions**: While there are no specific expert opinions on the exact number of countries winning their debut gold medals, the general sentiment is that the 2024 Olympics will see a mix of established powerhouses and emerging nations vying for medals. - -Given the current medal count, the performance of debut gold medal winners, and the upcoming events, it is possible that at least 5 countries might win their debut gold medals. However, a more detailed analysis of the specific events and competitors would be necessary to make a more accurate prediction. - -**Current Medal Count**: -- United States: 12 medals -- China: 9 medals -- France: 9 medals -- Japan: 8 medals -- South Korea: 7 medals -- Australia, Great Britain, and Italy: 6 medals each - -**Forecast and Predictions**: -- United States: 112 medals (Gracenote forecast) -- China: 86 medals (Gracenote forecast) -- Great Britain: 63 medals (Gracenote forecast) -- France: 54 medals (Gracenote forecast) - -**Upcoming Events**: -- Men's street skateboarding final -- Men's team gymnastics final -- Swimming events (e.g., 100m breaststroke, 200m freestyle) -- Fencing events (e.g., men's foil) - -######### - -GPT - -### Step-by-Step Analysis: - -#### 1. **Understanding the Question:** -- We need to forecast whether at least 5 countries will win their first-ever gold medal at the 2024 Paris Olympics. -- Identifying past trends and current conditions will be pivotal in making an informed forecast. - -#### 2. **Historical Data and Base Rates:** -- **London 2012:** 3 first-time gold medal-winning countries -- **Rio 2016:** 9 first-time gold medal-winning countries -- **Tokyo 2020:** 3 first-time gold medal-winning countries - -These numbers give us a base rate of (3+9+3)/3 = 5 first-time gold medal-winning countries. This might indicate a central tendency, but given the high variance (ranging from 3 to 9), uncertainty remains. - -#### 3. **Current Status and Progress:** -- As of today, the Olympic Games are underway. -- No specific count of debut gold medals yet, but there are notable achievements and upcoming potential debut wins. -- Reviewing individual and team performances along with scheduled events is crucial. - -#### 4. **Distribution of Medals:** -- Dominant countries with historically high gold medal wins (e.g., USA, China, etc.) will likely take a significant proportion of gold medals. Therefore, fewer opportunities exist for new countries. -- Despite this, several countries remain competitive, indicating potential for first-time wins in less competitive events. - -#### 5. **Factors Influencing Gold Medal Wins:** -- **Economic Support:** - - Countries with emerging economies tend to invest more in sports infrastructure and training, potentially increasing their chances. -- **Technological Advances:** - - Improved training and sports science can level the playing field for emerging countries. -- **Global Participation:** - - The inclusion of new sports and events can provide opportunities for countries historically absent in traditional sports. - -#### 6. **Upcoming Events and Opportunities:** -- Assessing specific sports with potential for new winners. -- For example, less dominant sports historically or new Olympic events may open the door for underdog nations. - -#### 7. **Statistical and Probability Analysis:** -- Historical performance shows that having 3 to 9 new gold medal-winning countries is within the realm of possibility. -- Given current information about the specific events and nations showing promise, we can anticipate multiple potential debuts. - -### Probability Estimation: -- **Base Rate Establishment:** Typically, around 3-9 countries annually win their first gold medal, with an average closer to 5 during recent games. -- **Current and Upcoming Event Analysis:** Considering the events where new countries are competitive and any unexpected results so far. -- **Variance and Uncertainty:** Factoring in the inherent unpredictability in sports competitions. - -#### Quantifying: -Given historical data, ongoing trends, and considering current medal distribution alongside notable performances of potential debut gold medal contenders: - -- Assuming at least 3 countries win their debut gold medals (appears highly probable based on historical data and progress so far). -- Considering the likelihood of at least 2 additional countries emerging as debut gold medalists through upcoming events and less dominant sports. - -Historical data and the breadth of the competition give support to the claim that at least 5 countries could achieve their first-ever gold medal at the 2024 Olympics by the end. - -### Conclusion: -After weighing the factors and considering the evidence and analysis: - -Probability: **65%**" -26669,"Will the Prime Minister of France on August 31, 2024 belong to the France Unbowed (LFI) party?","PERPLEXITY - -Here is a detailed rundown of the news relevant to the question about the next Prime Minister of France: - -- **Election Results**: The recent French parliamentary elections resulted in a hung parliament, with no single party securing a clear majority. The left-wing New Popular Front (NFP) alliance, comprising the Communist Party, France Unbowed, the Greens, and the Socialist Party, won the most seats but fell short of a majority. -- **Potential Prime Minister Candidates**: The NFP alliance has not yet named a prime ministerial candidate. Key figures include Jean-Luc Mélenchon from France Unbowed, Marine Tondelier from the Greens, Laurent Berger from the CFDT trade union, and Fabien Roussel from the French Communist Party. -- **Uncertainty and Coalition Building**: The absence of a clear majority has led to uncertainty about the formation of a government. President Emmanuel Macron's centrist coalition ranked second, and the far-right National Rally came third. Macron may need to form alliances with the moderate left to establish a collaborative government, which could face resistance due to France's lack of precedent for such arrangements. -- **Expert Opinions**: Analysts believe Macron's presidency faces challenges in implementing his policy agenda effectively, but his credibility remains intact. A strong government in Paris is crucial for EU stability, and a left-leaning government sharing power with Macron could be a potential outcome. -- **Metaculus Prediction**: The Metaculus prediction market currently has a question open about whether the Prime Minister of France on August 31, 2024, will belong to the France Unbowed (LFI) party. - -Overall, the situation remains fluid, with various political blocs and alliances still in the process of forming and negotiating. - -######### - -GPT - -To determine the probability that the Prime Minister of France on August 31, 2024, will belong to the France Unbowed (LFI) party, a systematic approach needs to be followed. - -### Step-by-Step Analysis: - -1. **Current Political Landscape**: - - The recent snap elections resulted in a hung parliament with the New Popular Front (NFP) holding the most seats, but not a majority. - - The NFP includes France Unbowed (LFI), the Greens, the Communist Party, and the Socialist Party. - - No prime ministerial candidate has yet been announced by the NFP. - -2. **Key Candidates**: - - Jean-Luc Mélenchon (LFI) - - Marine Tondelier (Greens) - - Laurent Berger (CFDT Trade Union) - - Fabien Roussel (Communist Party) - -3. **Coalition and Negotiation Dynamics**: - - President Macron's centrist coalition has ranked second and might seek alliances with moderate components of the left or the right. - - Given President Macron’s requirement to establish a stable government, coalition building will be essential, and consensus candidates from various factions are more likely to be favored. - -4. **Historical Context and Base Rates**: - - France has rarely seen far-left candidates hold high executive office positions, especially in coalition governments. - - In previous scenarios where the left has achieved significant legislative success, power-sharing arrangements often led to more moderate or centrally acceptable figures being nominated. - -5. **Expert Opinions and Market Sentiment**: - - Analysts suggest difficulties for Macron’s presidency in implementing policies but don't discount him forming a moderate or left-leaning coalition government. - - The Metaculus prediction market, which aggregates expert forecasts, can provide insight through its current sentiment on the likelihood of an LFI Prime Minister. - -6. **Importance of Timeline**: - - With only a little over a month left until August 31, rapid political developments can occur, but the shorter the timeframe, the less likely drastic political changes are. - - Given the need for quick and stable government formation post-elections, negotiations might favor more universally acceptable candidates than those from the far-left. - -### Weighing the Factors: - -1. **Coalition Negotiations**: - - Highly crucial as the formation of the government hinges on coalition agreements. - - Given Macron's centrist coalition and potential moderate left alliances, LFI’s chances might reduce. - -2. **Historical Precedents**: - - Rare for far-left candidates to be appointed in key executive roles, particularly in coalition scenarios. - -3. **Time Before Resolution Date**: - - One month typically allows for coalition agreements, but drastic shifts in party control are less likely. - -4. **Expert Opinions and Predictions**: - - Should be considered but not weighted excessively due to variability in interpretation. - -### Probability Calculation: - -Based on historical precedent, political dynamics, expert opinions, and the unique factors within the current scenario, a holistic view suggests that while there's a tangible chance for an LFI candidate, other factions within the NFP coalition or a more moderate candidate is more likely. - -**Final Answer**: -- Considering all the factors and weighing them appropriately: - - **Chance of LFI candidate becoming Prime Minister**: - - **Probability: 20%** - -This reflects a reasonable likelihood given the left's dominance but also accounts for the coalition complexities and the possibility of a moderate candidate being chosen." -26668,"On September 30, 2024, will JD Vance be the Republican nominee for vice president in the 2024 election?","PERPLEXITY - -To provide context for forecasting whether JD Vance will be the Republican nominee for vice president in the 2024 election on September 30, 2024, here is a summary of recent news and developments: - -1. **JD Vance's Selection**: Donald Trump named JD Vance as his running mate on July 15, 2024, during the Republican National Convention. Vance, a venture capitalist and author of ""Hillbilly Elegy,"" was elected to the Senate in 2022. - -2. **Background and Qualifications**: Vance has a working-class background, having grown up in a poor Rust Belt town in Ohio. He is a former Marine and a Yale Law School graduate. His supporters argue that he can appeal to working-class voters and minority voters, given his wife's background as the child of Indian immigrants. - -3. **Popularity and Criticism**: Vance has been criticized for his views on women's issues, particularly his call to ban abortion. His popularity has declined since his nomination, with a negative approval rating of minus six percent, making him the least liked vice-presidential nominee since 1980. - -4. **Potential Replacement**: There are calls to replace Vance due to his low popularity, but logistical and political challenges make it difficult to do so at this stage. The Republicans would need to call a national convention and hold a voting process, which could be complicated given the upcoming election and already printed ballot papers. - -5. **Speech and Platform**: Vance has emphasized his working-class background and criticized President Joe Biden's policies, particularly the North American Free Trade Agreement. He has pledged to prioritize American workers and address issues like foreign policy, the southern border, and national security. - -6. **Trump's Support**: Despite Vance's low popularity, Donald Trump continues to support him. Trump has emphasized the need for a qualified individual who could step into the role of president if necessary. - -Given these developments, the key factors to consider for forecasting whether JD Vance will remain the Republican nominee for vice president on September 30, 2024, are: - -- The ongoing popularity and approval ratings of JD Vance -- The potential for the Republicans to replace him, considering the logistical and political challenges -- Donald Trump's continued support for Vance -- The impact of Vance's views and platform on the election - -For a more accurate forecast, it would be helpful to monitor updates on Vance's popularity, any potential moves to replace him, and the overall dynamics within the Republican Party. - -######### - -GPT - -In forecasting whether JD Vance will remain the Republican vice-presidential nominee on September 30, 2024, we need to evaluate several factors systematically, integrating historical precedents, current trends, and the political landscape. - -### Analysis Steps - -1. **Historical Precedents** - - Historically, vice-presidential nominees rarely get replaced after being officially nominated. The notable exception is Thomas Eagleton in 1972, who was replaced after it was revealed he had undergone electroshock therapy for depression. The rarity of such replacements provides a strong base rate against a change. - - The political cost and logistical challenges of replacing a vice-presidential nominee late in the campaign cycle are significant. The party would need to reconvene at least part of its convention or a similar mechanism to officially nominate a new candidate, and this could create disruption and uncertainty. - -2. **Current Popularity and Criticism** - - JD Vance’s current unpopularity, both nationally and in his home region, is a negative factor. Negative net favorability ratings can impact campaign dynamics, public perception, and lead to internal party discussions about electability. - - The negative publicity stemming from Vance’s past comments about Kamala Harris and other polarizing views could exacerbate his unpopularity. - -3. **Trump's Support** - - Donald Trump’s steadfast support for Vance provides a strong buffer against potential replacement. Trump's influence in the Republican Party is substantial, and his endorsement was likely central to Vance’s nomination. - - Trump has historically shown loyalty to his chosen associates, often prioritizing personal loyalty over broader strategic considerations. - -4. **Logistical Feasibility of Replacement** - - Considering the election is only about two months away, the logistics of replacing Vance are complex. Ballots may already be printed in some states, and re-nomination processes could introduce legal and administrative hurdles. - - The impending election creates urgency and may deter extensive changes to avoid disarray and maintain a strong campaign image. - -### Weighing the Factors - -- **Base Rate of Replacement (Low):** Given the historical precedent and significant challenges, the base rate for VP nominee replacement post-convention is very low. -- **Current Trends (Moderate Uncertainty):** Vance's unpopularity adds some uncertainty but does not suggest an immediate or imminent threat that outweighs the inertia of the current situation. -- **Trump’s Support (High Stability Factor):** Trump’s continued support strongly argues against a replacement, mitigating risks from Vance’s low popularity. -- **Logistics (High Stability Factor):** With only two months until the election, logistical hurdles are substantial. - -### Conclusion -Combining these factors, the most likely outcome is that JD Vance will remain the Republican vice-presidential candidate by September 30, 2024. While his low popularity introduces some risk, this risk is heavily mitigated by the logistical difficulties of replacement, historical rarity, and Donald Trump’s continued support. - -### Probability Distribution -- **Base Rate Adjustment:** Start with a base rate of about 95% (from historical precedence). -- **Popularity and Criticism Adjustment:** Downward adjustment to account for his unfavorable ratings (-5%). -- **Trump’s Support:** Upward adjustment due to Trump's strong influence (+5%). -- **Logistical Considerations:** Reinforcement of current stability (remains stable). - -Final assessed probability: **Probability: 90%** - -Therefore, the probability that JD Vance remains the nominee for vice president on September 30, 2024, is 90%." -26667,"Will Planet Nine be discovered before September 30, 2024?","PERPLEXITY - -Here is a concise rundown of the most relevant news related to the question ""Will Planet Nine be discovered before September 30, 2024?"": - -**Background and Theories** - -- Planet Nine is a hypothetical super-Earth-sized planet believed to have a mass of five to ten times that of the Earth and an elongated orbit 400 to 800 times farther from the Sun than the Earth. -- The planet's existence was first theorized in 2015 by Konstantin Batygin and Michael Brown to explain the unique orbits of several smaller celestial objects in the Kuiper Belt. -- Researchers suggest that Planet Nine could be the core of a giant planet ejected from its original orbit by Jupiter during the Solar System's genesis or captured from another star. - -**Recent Developments and Evidence** - -- In April 2024, researchers at Caltech found new evidence supporting the existence of Planet Nine, including simulations that show the planet's gravitational influence is required to explain the observed behavior of long-period objects crossing Neptune's orbit. -- The Vera Rubin Observatory in Chile is expected to begin operations in about a year, which could accelerate the detection of distant objects in the Solar System, including Planet Nine. -- Citizen science projects, such as the Zooniverse Backyard Worlds project, are using archival data to search for Planet Nine. - -**Detection Challenges and Possibilities** - -- Due to its extreme distance from the Sun, Planet Nine would reflect little sunlight, making it difficult to detect. However, its large mass would still emit heat, potentially detectable by submillimeter telescopes like ALMA. -- The planet's apparent magnitude is estimated to be fainter than 22, making it at least 600 times fainter than Pluto. If it exists and is close to perihelion, it could be identified based on existing images. - -**Current Predictions and Expectations** - -- The Metaculus prediction market currently resolves as ""Yes"" if a new planet in the Solar System is detected by direct optical observation before September 30, 2024. -- Researchers believe that the breakthrough in discovering Planet Nine could be close, with some expecting it to be discovered within the next few years. - -**Expert Opinions** - -- Konstantin Batygin, lead author of the recent study, emphasizes that the next significant step would be to find Planet Nine. -- Philip Plait, an astronomer, notes that there is still a lot of sky left to search, including spots that are much tougher to sift through, but he hopes that Planet Nine exists because it would advance our understanding of the Solar System's formation and evolution. - -Overall, while there is growing evidence supporting the existence of Planet Nine, its detection remains a challenging task. The upcoming operations of the Vera Rubin Observatory and ongoing citizen science projects may bring us closer to resolving this question. - -######### - -GPT - -### Step-by-Step Analysis and Rationale - -#### 1. **Understanding the Question and Resolution Criteria** - -The question asks whether Planet Nine will be discovered through direct optical observation by September 30, 2024. - -Direct optical observation means that credible sources must verify the light from Planet Nine itself, not just its inferred effects. The planet needs to be larger than Earth in radius and have an orbital period exceeding 5,000 years. - -#### 2. **Key Background Information** - -- **Origin of Hypothesis**: Batygin and Brown proposed Planet Nine based on gravitational effects on trans-Neptunian objects in 2016. -- **Subsequent Papers**: Various follow-up studies have supported, detailed, and critiqued the hypothesis. -- **Observation Techniques**: Potential breakthroughs could come from ongoing and future telescopic surveys and data analysis, including the Vera Rubin Observatory and submillimeter telescopes like ALMA. - -#### 3. **Recent Developments** - -- **April 2024**: New evidence from Caltech supports the existence of Planet Nine through improved simulations. -- **Upcoming Observation Tech**: Vera Rubin Observatory is a promising tool for such detections but won't be operational before the end of the forecast period. -- **Citizen Science Projects**: These projects may help but have so far not reported a discovery. - -#### 4. **Challenges in Detection** - -- Planet Nine is extremely faint and distant, making it difficult to spot directly. -- High confusion areas along the Galactic Plane add complexity to direct optical observations. - -#### 5. **Current Predictions and Expert Opinions** - -- Metaculus prediction market favors a ""No"" resolution, indicating skepticism. -- Experts like Batygin are optimistic but acknowledge the significant difficulty in detection. -- Timeframe: Less than two months left until the resolution date adds a constraint to the probability. - -#### 6. **Base Rates and Historical Precedent** - -Historically, new planetary discoveries in the outer solar system are rare and complex, usually taking years of observation and analysis. - -#### 7. **Evaluation of Evidence and Trends** - -- While indirect evidence and technological advancements are promising, they have not yet yielded direct optical confirmation. -- The upcoming Vera Rubin Observatory and ongoing projects hold potential but are timelines misaligned with our forecast period. - -### Chain of Thought and Final Conclusion - -1. **Immediate Evidence**: No current direct optical observation of Planet Nine has been confirmed. -2. **Constraints**: Limited time remaining before the resolution date significantly reduces the probability of a breakthrough. -3. **Technological and Scientific Processes**: Major observatories crucial for discovering such a faint object are not yet operational or have not reported new findings. - -### Conclusion - -Given the extremely short timeframe and the significant barriers to detection highlighted by the experts, current data and historical discovery rates strongly suggest that Planet Nine will not be discovered by the September 30, 2024 deadline. - -**Probability: 10%**" -26671,"Will the Prime Minister of France on August 31, 2024 belong to Macron's ""Together"" coalition?","PERPLEXITY - -To provide context for the query about the Prime Minister of France on August 31, 2024 belonging to Macron's ""Together"" coalition, here is a summary of the relevant news: - -- **Legislative Elections**: France recently held legislative elections, resulting in a split parliament with no single party or alliance holding a majority. The New Popular Front (NFP) leftist coalition secured the most seats, followed by President Emmanuel Macron's centrist ""Together"" (ENS) coalition, and then the National Rally (RN) far-right alliance. -- **Government Formation**: Macron has asked the incumbent Prime Minister, Gabriel Attal, to stay on as a caretaker while the parties negotiate to form a government. The NFP has proposed a candidate for prime minister, but Macron has not yet made a decision. -- **Coalition Uncertainty**: The election outcome has created a complex situation, with no clear majority and the need for coalition-building. Macron's centrist agenda might face challenges in implementation due to the lack of a clear majority. -- **Current Government**: The caretaker government, led by Attal, will continue until a new prime minister is appointed. Macron is set to attend a NATO summit in Washington, which may delay any significant decisions on the prime ministerial position. - -Given the ongoing negotiations and the lack of a clear majority, it is uncertain whether the Prime Minister of France on August 31, 2024, will belong to Macron's ""Together"" coalition. The situation remains fluid, and any updates will depend on the progress of coalition talks and Macron's decisions in the coming weeks. - -######### - -GPT - -To determine the probability that the Prime Minister of France on August 31, 2024, will belong to Macron's ""Together"" coalition, let's carefully analyze the available information and consider the relevant variables. - -### Step-by-Step Analysis: - -1. **Current Context:** - - **Legislative Election Results:** The National Assembly is split into three major factions without a clear majority. - - **Caretaker Government:** Gabriel Attal, the current caretaker Prime Minister, is from Macron's ""Together"" coalition. - - **Need for Coalition Building:** Macron's ""Together"" does not have a majority, meaning that forming a stable government will require coalition negotiations. - -2. **Political Dynamics and Stability:** - - Forming a coalition in such a fragmented parliament is complicated and time-consuming. - - Macron's coalition is centrist, which might make it a potential partner for both the leftist NFP and moderates, depending on the negotiation dynamics and policy agreements. - - **Past Precedents:** In previous fragmented parliaments, coalitions have taken varied lengths of time to form, but caretaker governments often remain in power for several months in the absence of swift agreement. - -3. **Role of Macron:** - - As the sitting President, Macron has significant influence over the appointment of the Prime Minister. - - Macron is attending a NATO summit soon, which could delay negotiations. This indicates potential stability in the status quo for the near term. - -4. **Historical Tendencies:** - - Historically, fragmented parliaments can lead to prolonged negotiations, often extending beyond one month. - - In France, caretaker governments can stay in place for several months when no immediate majority is found. - -5. **Immediate Next Steps and Default:** - - If no new coalition is formed soon, Gabriel Attal will continue as Prime Minister. - - Macron's alignment and preferences will weigh heavily on choosing continuity (Attal or another “Together” member) versus compromise with another major faction. - -6. **Factors of Change:** - - Only a significant breakthrough in coalition talks would change the current status. However, such breakthroughs usually manifest longer than a single month unless there’s a pressing urgency or political deal-makings happen swiftly. - -### Conclusion and Probability: - -Based on the detailed analysis and historical context: -- The current caretaker, Gabriel Attal, from Macron's ""Together"" coalition, is likely to continue at least in the short term due to ongoing negotiations and Macron's strategic preferences. -- The lack of a clear majority suggests that rapid changes are less likely within the short timeframe until August 31, 2024. -- There's a reasonable probability of stability resulting in the continuity of the caretaker government, or the appointment of another ""Together"" coalition member if negotiations lead to an early exit for Attal. - -By weighing these aspects, I estimate the probability that the Prime Minister of France on August 31, 2024, will belong to Macron's ""Together"" coalition as follows: - -**Probability: 70%**" -26700,"Will the total market cap of all cryptocurrencies be more than $1.5 trillion on September 29, 2024?","PERPLEXITY - -Here is a concise rundown of the news most relevant to the question: - -**Market Cap and Current Trends:** - -- The total cryptocurrency market capitalization has slipped more than 2% to hover around $2.5 trillion as of July 30, 2024. -- Bitcoin's market cap is currently at $1.31 trillion, representing a dominance of 52.36% in the market. -- The overall cryptocurrency market capitalization has been tracked weekly from July 2010 to July 2024, with recent data available up to July 19, 2024. - -**Recent Updates and Predictions:** - -- There are predictions that the crypto market will be affected if Donald Trump wins the US election, with some expecting a significant impact on the market. -- A Metaculus question is currently under review, asking if the total market cap of all cryptocurrencies will be more than $15 trillion and less than or equal to $2 trillion on September 29, 2024. - -**Other Relevant Statistics:** - -- The daily 24-hour volume of all crypto combined is available up to July 19, 2024, along with the 100 most traded cryptocurrencies in the last 24 hours on that date. -- Monthly transaction history of crypto with the highest market cap is available up to May 2024, and the maximum/current supply of 100 cryptocurrencies worldwide is available as of July 19, 2024. -- Data on top crypto exchanges, Bitcoin ATMs, and energy consumption of Bitcoin and Ethereum are also available. - -**Expert Opinions:** - -- There are no specific expert opinions directly addressing the question of whether the total market cap will be more than $1.5 trillion on September 29, 2024. However, the ongoing market trends and predictions related to the US election may influence the market cap in the coming months. - -**Current Values:** - -- The current market cap of Bitcoin is $1.31 trillion, and the overall cryptocurrency market capitalization is around $2.5 trillion. - -**Prediction Markets:** - -- The Metaculus question mentioned above is currently under review and does not have a prediction market available yet. - -######### - -GPT - -Given the information and context, this question falls under the ""Non-Market Threshold Value Event"" category, as we are concerned with whether the total cryptocurrency market cap will be above or below $1.5 trillion on a specific date. - -### Step 1: Current State Analysis -- **Current Market Cap**: The market cap is recorded at around $2.5 trillion as of July 30, 2024. -- **Bitcoin's Market Cap**: Bitcoin alone has a market cap of $1.31 trillion, representing roughly 52.36% market dominance. - -### Step 2: Historical Context and Base Rates -- Historically, the cryptocurrency market has shown high volatility, affected by various factors such as regulatory news, economic policies, technological advancements, and market sentiment. -- Since July 2010, the market has seen both substantial bull and bear runs, with daily, weekly, and monthly volatility levels often being significant. - -### Step 3: Political and Economic Influences -- Political events such as the US elections can have significant but typically short-term impacts on the market. While there are predictions about potential impacts if Donald Trump wins the election, these scenarios suggest uncertainty but not necessarily a prolonged downturn of this magnitude. - -### Step 4: Time to Resolution -- From July 30, 2024, to September 29, 2024, there are two months remaining. The level of volatility in the cryptocurrency market makes rapid swings possible. However, a massive drop from $2.5 trillion to below $1.5 trillion would require a market contraction of around 40%. - -### Step 5: Recent Trends and Predictions -- The general trend of the cryptocurrency market has been growth, with fluctuations. -- No specific predictions or expert opinions suggest a drop of this magnitude in the coming months. - -### Step 6: Analysis of Volatility and Past Drops -- To estimate the likelihood of a 40% drop in two months requires looking at past similar events. Historical data shows similar declines have occurred in the past but were usually tied to major regulatory actions, widespread economic downturns, or significant technological failures. - -### Step 7: Default Behavior if Nothing Changes -- Given the market's current state and trends, if there are no sudden negative shocks, the market cap should remain above $1.5 trillion. - -### Step 8: Novel Developments -- While political uncertainty could create some volatility, there is no current evidence to suggest a sufficient crisis that would shrink the market cap by 40% in two months. - -### Conclusion: -Using the provided data, base rates, and considering the potential impacts of known variables, the probability of the total cryptocurrency market cap being above $1.5 trillion on September 29, 2024, is quite high. - -**Probability**: 90%" -26704,"Will the median person on Bloomberg's Billionaires Index come from the Energy industry on September 16, 2024?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -### Current Status of Billionaires and the Energy Industry - -- As of July 29, 2024, the Bloomberg Billionaires Index ranks the world's richest people daily, with net worth figures updated after the close of trading in New York. -- The index includes the 10 richest people in the world, nine of whom are Americans and founders or CEOs of major corporations. -- The median person on the index is not currently from the energy industry, as the top billionaires are primarily associated with companies like Tesla, Amazon, and Microsoft. - -### Energy Industry Outlook - -- BloombergNEF's New Energy Outlook 2024 presents long-term energy and climate scenarios for a low-carbon economy, highlighting the need for accelerated spending on low-carbon energy sources. -- The report suggests that a fully decarbonized global energy system by 2050 could require a $215 trillion investment, which is only 19% more than an economics-driven transition where the Paris Agreement goals are missed. - -### Prediction Markets - -- Metaculus has a prediction market for this question, but it is currently in draft and awaiting review. - -### Expert Opinions - -- There are no recent updates indicating a shift towards the median person on the Bloomberg Billionaires Index coming from the energy industry by September 16, 2024. - -### Current Values - -- The current net worth figures of the top billionaires can be found on the Bloomberg Billionaires Index, updated daily. - -### Recent Updates - -- There are no recent updates that would indicate a significant movement towards the median person on the Bloomberg Billionaires Index coming from the energy industry. - -######### - -GPT - -### Analyzing the Question - -#### Key Points to Consider: - -1. **Current Status**: As of the latest update (July 29, 2024), the median person (ranked 250th) is not currently from the Energy industry. - -2. **Recent Rankings**: - - 248th to 251st: Energy sector billionaires - - 252nd: Finance - - 253rd: Energy - - 254th to 256th: Finance - -3. **Energy Industry Outlook**: Although there are significant investments mentioned for the future, there's no direct indication of rapid near-term growth affecting net worth rankings in a dramatic way before September 16, 2024. - -4. **Prediction Markets and Expert Opinions**: No strong recent updates or shifts towards the Energy sector. - -5. **Resolution Criteria**: The 250th-ranked person must be from the Energy sector exactly. Any change in Bloomberg's classification system or the unavailability of information would resolve the question negatively. - -### Assessing the Data - -#### Base Rates and Historical Context: - -1. **Stability in Rankings**: The Bloomberg Billionaires Index tends to be quite stable in the short term. Large changes in rankings usually result from significant market events or individual company performance. - -2. **Energy Sector Volatility**: The Energy sector is subject to market fluctuations (e.g., oil prices). However, substantial shifts in wealth within just over a month (from late July to mid-September) are uncommon without major geopolitical or economic events impacting market dynamics. - -#### Short-Term Variability: - -1. **Daily and Weekly Variability**: Given the nature of daily updates, small shifts can occur, but moving from non-Energy to Energy industry for the exact 250th rank is significant. - -2. **Recent Ranking Proximity**: With Energy billionaires close to the median, there is some potential for a marginal rank change placing a person from the Energy industry in the 250th spot. - -3. **Trends and Momentum**: Current trends do not show an overwhelming shift in favor of the Energy sector that would suggest a high probability of an Energy billionaire landing exactly in the 250th spot. - -### Calculating Probability - -Considering the factors: -- **Current Rankings**: Energy billionaires are close but not leading. -- **Short-Term Shifts**: Historical stability indicates low short-term shift likelihood. -- **Industry Volatility and Influence**: No immediate indicators suggest a rapid rise placing new Energy billionaires in the 250th position. -- **Resolution Criteria**: Any misclassification or changes reduce chances. - -#### Applying Probabilistic Framework: - -1. **Base Rate of Unchanged Outcome**: Assuming the status quo, the base probability is relatively low for a significant disruption favoring Energy. -2. **Marginal Movements**: Considering minor market fluctuations, there is some chance but remains statistically low. - -### Statistical Consideration and Final Probability - -Given the historical stability and lack of current trends suggesting a clear increase in Energy sector dominance over the specific timeframe: - -**Final Answer:** -**Probability: 10%**" -26703,"Will Venezuela invade Guyana before September 30, 2024?","PERPLEXITY - -To provide a comprehensive rundown of the news relevant to the question of whether Venezuela will invade Guyana before September 30, 2024, I have summarized the key points from the provided sources: - -1. **Territorial Dispute and Recent Developments**: - - Venezuela has created a new state in the disputed territory with Guyana, with Tumeremo as the capital until a mutually acceptable solution is found. - - The dispute centers around the Essequibo region, which is rich in oil and gas and has been a point of contention for over a century. - -2. **Maduro's Motivations and Threats**: - - Maduro's threats to invade Guyana are seen by some as an effort to consolidate power ahead of potential presidential elections next year, rather than a genuine intention to invade. - - The U.S. is concerned that Maduro might use the threat of invasion to impose martial law and gain an advantage in the elections. - - Maduro has been under U.S. sanctions for undermining democracy and is seeking leverage against the U.S. at the negotiating table. - -3. **International Response and Concerns**: - - The U.N. Secretary-General has called on Venezuela to avoid the use of force and respect the International Court of Justice's (ICJ) ruling, which ordered Venezuela to refrain from taking any action that would change Guyana's control over the Essequibo region. - - The ICJ's ruling is seen as a victory for Guyana, as it prohibits Venezuela from annexing or trespassing on Guyanese territory by force or referendum. - - Regional organizations like CARICOM (a 15-member organization of Caribbean nations) have expressed concerns over the potential for a shooting war and have called for Venezuela to uphold the ICJ's order and respect international law. - -4. **Potential for Invasion and U.S. Involvement**: - - While analysts speculate that Maduro might not invade Guyana, there are strong reasons for him to do so, including the desire to distract from Venezuela's economic woes and gain control over the oil-rich region. - - The U.S. is not expected to deploy troops to defend Guyana, but might rally a coalition of nations to condemn Venezuela and put added pressure on Maduro. - - Some experts argue that the U.S. should send troops to Guyana as a deterrent to prevent an invasion, citing historical precedents like the invasion of Kuwait by Iraq and the Falklands War. - -5. **Current Situation and Predictions**: - - There are no imminent signs of an invasion, but the situation remains tense, with Venezuela proceeding with a referendum to grant Maduro special powers to invade Guyana and create a new state. - - The outcome of the referendum and the subsequent actions of Maduro's government will be crucial in determining whether an invasion occurs before September 30, 2024. - -In summary, the situation is complex, with multiple factors influencing Maduro's decisions. While there are no immediate signs of an invasion, the threat remains, and the international community is closely monitoring the situation. - -######### - -GPT - -To forecast whether Venezuela will invade Guyana before September 30, 2024, I'll follow a structured approach: - -1. **Assess Background Information**: - - The long-standing territorial dispute between Venezuela and Guyana primarily centers on the oil-rich Essequibo region. - - In 2023, Venezuela drafted a public referendum to assert its claims over the disputed region. - - Current political and strategic landscape: Venezuela's President Maduro is under pressure both internally and externally, and might use the threat of invasion to consolidate power and deflect from domestic issues. - - International response, especially from the U.N., has been to deescalate the situation. The ICJ ruling favoring Guyana puts legal constraints on Venezuela's actions. - - U.S. involvement is notable but likely insufficient to involve direct military support, focusing instead on diplomatic and economic pressures. - -2. **Evaluate Recent Developments**: - - Venezuela's referendum and internal political maneuvering are key factors, but as of now, no direct military action has been taken. - - Regional organizations like CARICOM have called for de-escalation, likely exerting additional diplomatic pressure. - - The ICJ's ruling is a significant legal victory for Guyana, limiting Venezuela's actions without causing international backlash. - - Maduro's motivations might push towards brinkmanship rather than actual military conflict. - -3. **Time Factor and Default Resolution**: - - With roughly two months before the resolution date, consider the timeframe for an escalation from current status to a full invasion involving over 100 ground troops. - - Historical precedence suggests that major territorial invasions are rare and typically have build-up periods longer than a few months. - - Current tensions and monitoring by the international community further reduce the time window for unilateral aggressive actions. - -4. **Statistical and Probabilistic Analysis**: - - Base rates: Considering the rarity of interstate conflicts, especially involving direct territorial seizures in recent decades. - - Influencing factors: International legal constraints (ICJ ruling), geopolitical deterrents (U.S. and CARICOM stances), internal political dynamics (Maduro’s potential use of an invasion threat as political leverage). - - Logistic feasibility: Organizing, executing, and maintaining a substantial invasion force within two months is logistically challenging, especially under significant international scrutiny. - -5. **Formulate Probabilistic Estimate and Distribution**: - - Default probability of no significant change = high - - Potential for sudden escalation due to miscalculation or unforeseen events = low but non-zero - - Influence of U.N. and regional diplomacy to prevent conflict = high - - Historical and recency bias towards non-invasion given the political benefits without direct conflict - -6. **Uncertainty and Confidence Level**: - - There is some uncertainty due to Maduro's potential unpredictable actions. - - However, the weight of international diplomatic and legal frameworks provides confidence against imminent invasion. - -**Final Probabilistic Estimate**: - -Given the current geopolitical landscape, internal and external pressures, historical precedent, and the limited timeframe, the likelihood of Venezuela invading Guyana by the specified date appears low. Factoring in all the information provided and the structured analysis, I arrive at the following estimate: - -“Probability: 10%”" -26701,"Will the total market cap of all cryptocurrencies be more than $2 trillion on September 29, 2024?","PERPLEXITY - -To provide a detailed rundown of the news relevant to the question about the total market cap of all cryptocurrencies, I have searched various sources, including business-oriented websites and statistical databases. - -### Current Market Situation - -- **Market Capitalization**: As of July 30, 2024, the total cryptocurrency market capitalization has slipped more than 2% to hover around $2.5 trillion. -- **Bitcoin Price**: The Bitcoin price has dropped nearly 5% recently, indicating market volatility. - -### Expert Predictions and Trends - -- **Crypto Crash or Climb**: There are mixed predictions about the future of cryptocurrency prices. Some experts, like Carol Alexander, expect Bitcoin to plummet to $10,000, while others believe it could reach $100,000 by the end of 2024. -- **Adoption and Growth**: Industry professionals are optimistic about the continued growth of the crypto market in 2024, driven by increasing adoption and confidence in the technology. -- **Bitcoin Dominance**: Bitcoin currently dominates the market with a market cap of $1.31 trillion, representing 52.36% of the total market. - -### Historical Data and Statistics - -- **Market Cap History**: The overall cryptocurrency market capitalization has shown significant growth since 2010, with a current market cap of over $2.5 trillion. -- **Daily Volume and Transactions**: The daily 24-hour volume of all cryptocurrencies combined has been tracked up to July 19, 2024, and the monthly transaction history of the highest market cap cryptocurrencies is available up to May 2024. - -### Prediction Markets - -- **Metaculus**: No relevant prediction markets were found on Metaculus for this specific question. - -### Recent Updates and Movement - -- **Market Volatility**: The recent 2% drop in market capitalization and 5% drop in Bitcoin price indicate ongoing market volatility. -- **Expert Opinions**: The mixed predictions from experts suggest that the market is uncertain and could move in either direction. - -### Key Statistics - -- **Current Market Cap**: $2.5 trillion (as of July 30, 2024). -- **Bitcoin Dominance**: 52.36% (as of the current date). -- **Daily Volume**: Available up to July 19, 2024. - -These sources provide a comprehensive overview of the current market situation, expert predictions, and historical data relevant to the question about the total market cap of all cryptocurrencies on September 29, 2024. - -######### - -GPT - -### Step-by-Step Reasoning - -1. **Understanding the Resolution Criteria:** - - The total market capitalization of all cryptocurrencies needs to be more than $2 trillion on September 29, 2024, according to CoinMarketCap. - - The current market capitalization is $2.5 trillion as of July 30, 2024. - -2. **Historical Context and Recent Trends:** - - Cryptocurrency market has a history of volatility and rapid changes in value. - - Market capitalization peaked and reached $2.5 trillion after a period of growth and some recent corrections. - -3. **Market Sentiment and Expert Predictions:** - - Mixed predictions: Some experts predict substantial declines (e.g., Bitcoin plummeting to $10,000), while others are more optimistic (up to $100,000). - - General positive sentiment towards adoption and growth, but also ongoing volatility as seen with the recent 2% drop in market capitalization and 5% drop in Bitcoin price. - - Bitcoin's dominance suggests it's a key driver of the market. - -4. **Time Frame and Volatility Considerations:** - - About 60 days until September 29, 2024. - - Significant market movements within this timeframe are possible given historical precedent. - -5. **Statistical and Probabilistic Analysis:** - - Base rate analysis of major drops in market cap within a 60-day period: such events are relatively rare but not unprecedented in cryptocurrency markets. - - Given current market cap of $2.5 trillion, a drop below $2 trillion would require a decline greater than 20%. - - Historical performance shows that such declines, while possible, are not highly probable within a short timeframe without significant market disruptions. - -6. **Current Market Conditions:** - - Market capitalization at $2.5 trillion is comfortably above the $2 trillion threshold. - - A drop below $2 trillion implies a substantial market downturn within two months. - -### Analysis and Forecast - -- **Positive Factors:** - - Market adoption and growth trends. - - Current market capitalization at a strong position well above the threshold. - - General market confidence despite recent volatility. - -- **Negative Factors:** - - Potential significant negative events (regulatory crackdowns, security breaches, major market corrections). - - High volatility within the cryptocurrency market. - - Historical instances of rapid declines. - -### Final Calculation - -Given the current data, expert analysis, and historical trends: - -- There is a strong probability that the market cap will remain above $2 trillion unless significant unprecedented negative events occur. -- Historical precedent and current market sentiment both suggest stability or moderate fluctuations, rather than a massive crash. -- Considerations of Bitcoin's relative stability and dominance within the overall market are also weighed positively. - -Therefore, after calculating both potential positive and negative influences, the probability that the total market cap of all cryptocurrencies will be more than $2 trillion on September 29, 2024, is determined as: - -**Probability: 85%**" -26702,"Will the total market cap of all cryptocurrencies be more than $1.5 trillion and less than or equal to $2 trillion on September 29, 2024?","PERPLEXITY - -The total market cap of all cryptocurrencies is currently around $1.32 trillion. Bitcoin dominates the market with a market cap of over $1.4 trillion, representing approximately 52.36% of the total market cap. There are currently around 13,217 cryptocurrencies in circulation, although many are inactive or worthless. - -Recent updates indicate that the market cap has been volatile, with a 2% drop to around $2.5 trillion on July 30, 2024, accompanied by a 5% drop in Bitcoin's price. The market cap has fluctuated significantly over the years, with a high of over $2 trillion in the past. - -Metaculus, a forecasting platform, has a question pending approval that asks if the total market cap of all cryptocurrencies will be more than $1.5 trillion and less than or equal to $2 trillion on September 29, 2024. This question is currently under review and has not received any predictions yet. - -In terms of expert opinions, there are no recent updates that would indicate a clear movement towards a particular resolution. However, the market's volatility and the large number of cryptocurrencies in circulation suggest that the market cap could continue to fluctuate in the coming months. - -Key statistics: - -- Total market cap: $1.32 trillion -- Bitcoin market cap: over $1.4 trillion -- Bitcoin dominance: 52.36% -- Number of cryptocurrencies: 13,217 -- Recent market cap drop: 2% to $2.5 trillion on July 30, 2024 -- Recent Bitcoin price drop: 5% on July 30, 2024 - -These statistics provide context for the question about the total market cap of all cryptocurrencies on September 29, 2024. - -######### - -GPT - -### Step-by-step Thought Process: - -**1. Context and Importance of Background Sources** -- **Current Market Cap:** $1.32 trillion -- **Recent Market Cap Drop:** 2% drop to around $2.5 trillion on July 30, 2024 -- **Bitcoin Dominance:** ~52.36% -- **High Market Fluctuation:** Historical high over $2 trillion -- **Number of Cryptocurrencies:** 13,217 -- **Recent Bitcoin Price Drop:** 5% on July 30, 2024 - -Given the volatility and fluctuations in the current market, it's essential to focus on both the historical volatility patterns and any novel influencing factors that could impact the market in the coming months. - -**2. Evaluation of Recency and Importance of Different Sources:** -- **Recent Data:** The market clearly showed significant fluctuation, from 2% on July 30, 2024, even though the current market cap is $1.32 trillion. -- **Historical Data:** Historical highs over $2 trillion indicate the capability of the market to reach and cross this threshold, but the key is the sustainability of such levels. -- **Bitcoin’s Market Influence:** Bitcoin's dominance and its impact on overall market cap due to its significant share (52.36%) mean that significant changes in Bitcoin's price would considerably influence the total market cap. - -**3. Time and Granularity:** -There are approximately two months until September 29, 2024. This span of time is moderate enough to observe potential significant changes, but also relatively short, emphasizing the need to consider weekly and daily volatilities. - -**4. Base Rates and Novel Influences:** -- **Base Rate Analysis:** Crypto markets have seen substantial upswings and crashes. The $1.5T-$2T range was a notable zone in the past, making it a plausible range to reoccur. -- **Novel Factors:** High inflation, potential adoption trends (e.g., institutional investments, usage in more countries), and regulatory changes could significantly impact. - -**5. Default Behavior and Trends:** -- **Default Case:** If the market remains at its current trend, it is currently below $1.5 trillion, implying moderate rises would need to happen for the range to be met. -- **Market Volatility:** Given the recent fluctuations and the high volatility (2% and 5% drops respectively for the market and Bitcoin), sudden shifts either upwards (due to positive events like institutional adoption or technological advancements) or downwards (due to regulatory crackdowns or major cybersecurity issues) could swing the market. - -### Analyzing Likely Outcome: -Considering: -- **Short Time Span:** Short term variations in cryptocurrency prices can be extremely volatile. -- **Historical Trends:** The market cap can realistically range between $1.5 trillion and $2 trillion. -- **Recent Data:** While the market cap is at $1.32 trillion now, recent fluctuations and historical highs make reaching the $1.5 trillion–$2 trillion range probable if positive trends hold. - -### Statistical and Probabilistic Analysis: -Given the historical volatility, the data suggests a 2-3% fluctuation range typically, it implies there’s a substantial probability for the market to hit the desired range. - -### Final Answer: -Considering all these factors, there is a **high probability** that the market cap will be within the inclusive range of $1.5 trillion to $2 trillion on September 29, 2024. - -**Probability: 75%** - -This accounts for the likelihood that the market will trend upwards slightly from its current value and stabilize within this range despite potential volatility." -26706,"Before October 1, 2024, will Stripe announce on the news section of its website that it is planning an IPO?","PERPLEXITY - -Here is a concise rundown of the news relevant to the question: - -**Stripe IPO Rumors and Updates:** - -- Stripe, a fintech company, has been valued at $65 billion and is expected to go public, but not until 2025 at the earliest. -- The company raised $6.5 billion from existing and new investors in March 2023, valuing it at $50 billion. -- Stripe's valuation has fluctuated, peaking at $95 billion in March 2021 and currently standing at $53.65 billion. -- The company's payment processing platform and financial infrastructure have made it a significant player in the digital payments sector. -- Stripe's IPO is highly anticipated, with some investors bullish on its prospects despite the valuation reduction. -- The company's services cater to businesses globally, supporting over 135 currencies, and its platform is designed for customization and integration. -- Stripe's IPO is expected to be a landmark event in the fintech sector, but no official announcement has been made. - -**Recent Market Context:** - -- The S&P 500 has hit all-time highs multiple times this year, confirming a bull market in stocks that began in October 2022. -- The Renaissance IPO ETF, an index of the largest IPO stocks in recent years, is up 63% since the start of last year. - -**Prediction Markets:** - -- Metaculus has a question asking if Stripe will announce an IPO on its website before October 1, 2024. - -**Expert Opinions:** - -- No specific expert opinions are mentioned in the sources, but investors remain optimistic about Stripe's IPO prospects despite the valuation reduction. - -**Current Values:** - -- Stripe's current valuation is $53.65 billion. -- The S&P 500 has hit all-time highs multiple times this year. - -**Recent Updates:** - -- There are no recent updates indicating movement towards a particular resolution, but the anticipation around Stripe's IPO remains high. - -######### - -GPT - -### Evaluating the Probability of Stripe Announcing an IPO - -#### Step-by-Step Analysis: - -1. **Current Date and Time Frame**: - - Today’s date: 2024-07-30 - - Remaining time until October 1, 2024: 2 months (approximately 60 days). - -2. **Nature of the Question**: - - This is an **Other Binary Outcome Event** type question: Will Stripe announce an IPO on its website before the specified date? - -3. **Historical and Baseline Data**: - - Stripe has been a significant player in the fintech sector, with a history of rapid growth and significant funding rounds. - - The company is valued at $53.65 billion, down from its peak but still highly valuable. - - Past IPO announcements usually come after strong financial performance and preparatory steps for the public market; Stripe is currently allowing employees to cash out shares, which typically precedes an IPO. - -4. **Market Conditions**: - - Favorable market conditions: The S&P 500 is at an all-time high, indicating a robust stock market which is generally a conducive environment for IPOs. - - The Renaissance IPO ETF up 63% suggests a strong market appetite for new public offerings. - -5. **Recent News and Developments**: - - While IPO rumors persist, recent commentary has suggested that Stripe is eyeing a 2025 IPO, which implies it might be slightly out of the window in question. - - No recent specific announcements or clear indicators point towards an imminent IPO within the next two months. - -6. **Base Rates and Timing**: - - Historically, large tech companies tend to carefully plan and announce IPOs well in advance, especially when considering the regulatory and preparatory requirements. - - The act of allowing employees to cash out shares is often a precursor to an IPO, but it doesn't necessarily mean an announcement must follow immediately within a tight timeframe. - -7. **Prediction Markets**: - - Metaculus' forecast and commentary on the IPO aligns with industry expectations and market sentiment but doesn't provide a decisive direction. - -#### Synthesis and Calculation: - -- **Probability Based on Historical Trends**: - - Assuming a conservative estimate from historical IPO announcement timing, large tech firm behaviors, and preparatory steps like employee cashouts, we might assume a baseline probability of around 25% for large, well-anticipated companies within a short timeframe. - -- **Impacts of Market Conditions**: - - Favorable market conditions might slightly increase the probability but not significantly change the fundamental readiness and indicative timing suggested by recent news. Adjusting upward slightly for market-driven incentives, we might add 5%. - -- **Adjusted Probability Based on Recent News**: - - Given explicit speculation of a 2025 IPO and no strong indicators pushing the timeline forward, the probability that Stripe would suddenly move forward with an announcement might only marginally increase. - -Incorporating the weighting, adjustments, and considering uncertainties: - -**Final Estimate**: -- **Prior Knowledge (Base Rate + IPO Conditions)**: 25% -- **Market Context and Incentives**: +5% -- **Recent News Speculative but Non-committal Towards 2024**: Net negligible. - -**Summarized Probability**: 30% - -### Conclusion: -Given the mixture of strong market conditions but explicit recent speculation pointing to a 2025 IPO, the overall weighted probability of Stripe announcing an IPO by October 1, 2024, considering both evidence and timing, seems: - -**Probability: 30%**" -26705,"Will Apple announce an iPhone with a graphene thermal system before October 1, 2024? -","PERPLEXITY - -To assist in forecasting whether Apple will announce an iPhone with a graphene thermal system before October 1, 2024, here is a summary of the relevant news and updates: - -- **Metaculus Prediction**: The question is currently under review on Metaculus, awaiting approval by a moderator. - -- **iPhone 16 Release Date**: The iPhone 16 is expected to be released in September 2024, following the typical Apple release pattern. - -- **Graphene Thermal System**: There are strong rumors that Apple is working on a graphene thermal system to combat overheating issues, which were prevalent in some iPhone 15 models. This new thermal design is expected to be featured in the iPhone 16 lineup, with graphene having higher thermal conductivity than the currently used copper. - -- **Recent Updates**: The latest rumors suggest that Apple is actively developing a foldable iPhone, which may be released in 2026, and is also working on improved AI features for iOS 18. - -- **Expert Opinions**: Analyst Jeff Pu believes that Apple will use A18 and A18 Pro chip names for the new iPhone 16 models, and there are expectations of improved camera features and a possible price increase due to rising material costs. - -- **Current Status**: There are no recent updates that would indicate a significant shift away from the expected September release date or the inclusion of a graphene thermal system in the iPhone 16. - -- **Prediction Markets**: No relevant prediction markets are currently available on Metaculus. - -Overall, the available information suggests that Apple is likely to announce an iPhone with a graphene thermal system before October 1, 2024, as part of the iPhone 16 lineup expected to be released in September 2024. - -######### - -GPT - -To forecast whether Apple will announce an iPhone with a graphene thermal system before October 1, 2024, let's break down the available information and follow a systematic approach. - -### 1. **Establish the Base Rate for Apple's Announcements:** -- Historically, Apple has announced new iPhone models in September. Previous announcements have included significant technological upgrades. -- Let's consider the base rate of technological innovation in Apple's iPhone announcements. Given they often introduce new features annually, there is a high base rate (roughly 70-80%) for announcing significant new technology in any given year. - -### 2. **Assess Information Specific to Graphene Thermal Systems:** -- According to the **MacRumors** summary, there are strong rumors about Apple working on a graphene thermal system for the iPhone 16, and earlier models faced overheating issues. This increases the likelihood of Apple pushing this technology forward. -- Notably, such rumors feed into speculation that Apple wants to mitigate overheating and improve performance. Given the stringent heat dissipation requirements for high-performance devices, adopting graphene aligns with this need. - -### 3. **Evaluate Expert Opinions and Predictions:** -- Analysts like Jeff Pu believe in the addition of the A18 chips, which might require better thermal management due to increased processing power. The anticipated rise in material costs might also prompt a need for more efficient heat management solutions, like graphene. -- We find it reasonable to weigh Jeff Pu's insights substantially, given his track record and closeness to the industry. - -### 4. **Time Frame for Announcement:** -- The expected iPhone 16 announcement date in September 2024 gives Apple about two months until the deadline of October 1, 2024. Given their regular pattern of star-studded events and the critical nature of addressing overheating complaints, the timing aligns favorably. -- If we consider the time frame, Apple has enough runway to develop and market-test such a feature adequately. - -### 5. **Market and Technological Trends:** -- Technologically, graphene's adoption in consumer electronics has been slow due to manufacturing challenges, but recent advances have made it more feasible for commercial use. This context favors our question, as cutting-edge tech tends to trickle into consumer devices. -- Apple faces performance and overheating criticisms, particularly with recent models. Thus, the incentive to innovate further bolsters the probability. - -### 6. **Default Hypothesis and Resolution Criteria:** -- Default behavior implies holding an announcement event in September. Given the specificity of the resolution criteria (official announcement), Apple's expected behavior under scrutiny corroborates readiness for major announcements only during keynotes. - -### 7. **Probability and Distribution Assessment:** -- The question fits under an ""Other Binary Outcome Event"" category; hence, we should consider the likelihood of binary outcomes (announcement vs. no announcement). - -### Final Probability Calculation: - -1. Base rate for significant innovation in iPhones annually: 70-80% -2. Specific rumors and expectations about graphene in the iPhone 16: 60-70% -3. Analyst insights and potential penalties for overheating issues being addressed: 60-70% -4. Time frame until announcement and historical pattern: 80-90% - -We adjust for overlaps and distribution, yielding an aggregated probability measure. Given our factors and methodological scrutiny: - -### Final Answer: -**Probability: 85%** - -This incorporates the integrated likelihood of Apple's alignment with addressing overheating, industry trends, base rates in innovation, and the timing congruent with its announcement pattern." -26816,"Before October 1, 2024, will Donald Trump and Kamala Harris shake hands in any of their debates, if any happen?","PERPLEXITY - -To determine the likelihood of Donald Trump and Kamala Harris shaking hands in any debates before October 1, 2024, we need to consider several factors, including their current campaign dynamics, past behavior in debates, and any recent updates that might indicate their willingness to engage in such a gesture. - -### Current Campaign Dynamics - -1. **Kamala Harris's Momentum**: - - Harris has been gaining momentum, especially among young voters and communities of color, as evidenced by her recent rally in Atlanta. - - She has been endorsed by former President Barack Obama, which has further solidified her position as the presumptive Democratic nominee. - -2. **Trump's Campaign Strategy**: - - Trump has been trying to regain prominence in the race, particularly after Harris's rise in popularity. - - He has been critical of Harris, suggesting she would be unable to hold her own with world leaders. - -### Past Behavior in Debates - -1. **Trump's Debate History**: - - Trump has a history of being unpredictable and confrontational in debates. In the 2020 presidential debates, he frequently interrupted and clashed with his opponents. - - He has recently expressed reluctance to commit to debating Harris, suggesting that debates should be held after voting starts in some states. - -2. **Harris's Debate History**: - - Harris has shown a willingness to engage in debates and has challenged Trump to debate her. - - Her campaign has accused Trump of ""backpedaling"" on his earlier commitment to debate her. - -### Recent Updates - -1. **Debate Commitment**: - - Trump has indicated that he ""probably"" will debate Harris, but his commitment remains uncertain. - - Harris is ready to debate Trump on the scheduled September 10 date, but Trump's campaign has not committed to this date. - -2. **Public Gestures**: - - There have been no recent updates indicating a willingness from either candidate to engage in a handshake or any other form of public reconciliation during debates. - -### Conclusion - -Given the current dynamics and past behavior of both candidates, it is unlikely that Donald Trump and Kamala Harris will shake hands in any debates before October 1, 2024. Trump's unpredictability and reluctance to commit to debates, combined with his confrontational history in such settings, suggest that a handshake is not a likely outcome. Additionally, Harris's campaign has been critical of Trump's behavior, which further reduces the likelihood of a friendly gesture like a handshake. - -######### - -GPT - -Given the information provided, let's analyze the factors and produce a calculated probability for the likelihood of Donald Trump and Kamala Harris shaking hands in a debate before October 1, 2024. Here's a step-by-step breakdown: - -### 1. Current Campaign Dynamics -- **Kamala Harris's Momentum**: - - Gaining support among young voters and specific communities. - - Endorsed by Barack Obama, solidifying her position. -- **Trump's Campaign Strategy**: - - Focused on regaining prominence. - - Critical of Harris's ability to handle herself with world leaders. - -### 2. Past Behavior in Debates -- **Trump's Debate History**: - - Unpredictable and confrontational. - - Did not shake hands with Biden in the 2020 debates, but did shake hands with Hillary Clinton in 2 of the 3 debates in 2016. - - Recent reluctance to commit to debates, casting doubt on procedural norms like handshakes. -- **Harris's Debate History**: - - Willingness to engage in debates. - - Challenged Trump publicly and accused him of avoiding debates. - -### 3. Recent Updates -- **Debate Commitment**: - - Trump ""probably"" will debate Harris, but no firm commitment indicating a handshake will happen. - - Harris is ready to debate Trump, but no handshake is assured. -- **Public Gestures**: - - No recent evidence suggesting willingness to handshake. - -### 4. Resolution Criteria and Definitions -- Requires a clear, verifiable handshake during the live debate period. -- Must be a traditional handshake as defined, without substitutes like fist bumps. - -### 5. Statistical and Historical Analysis -- **Base Rates**: - - Historically, presidential candidates often shook hands pre-COVID. The norm was disrupted during the pandemic. - - Political dynamics and heightened tensions make any friendly gesture less likely. -- **Current Political Climate**: - - High animosity and divisiveness in current US politics, suggesting adherence to traditional norms is unlikely. - - Polarization indicates less likelihood of friendly gestures. - -### 6. Timeframe -- We're looking at events supposed to transpire in approximately 2 months. -- Significant behavior change in such a short period is unlikely without substantial external motivations. - -### 7. Evaluation Under Default Conditions -- Default assumption (if nothing changes): Trump and Harris do not engage in a handshake. - -### Conclusion -Combining Trump's confrontational history, his reluctance to commit to debates, recent trends where traditional gestures have been omitted, and the current polarized political environment, it is reasonable to project a low probability of a handshake occurring. - -**Adjusted probability** for the handshake happening given all factors, base rates, and considering the historical precedent disrupted by COVID-19: - -**Probability: 15%** - -The relatively low probability reflects the inherent uncertainties of potential behavior changes for political optics, but the predominant expectation based on current trends and historical behavior strongly suggests minimal likelihood of the handshake." -26844,"Will the opposition candidate, Edmundo González, concede the 2024 Venezuelan election to Maduro before September 2024?","PERPLEXITY - -The 2024 Venezuelan presidential election has been marked by significant controversy and political tension. Here are the key points relevant to the question of whether opposition candidate Edmundo González will concede the election to Nicolás Maduro before September 2024: - -1. **Election Results and Dispute**: - - The National Electoral Council declared Maduro the winner with 51% of the vote, while González received 44%. - - González's campaign has contested the results, citing irregularities and lack of transparency in the voting process. - -2. **Opposition's Stance**: - - González and his team have not indicated any intention to concede. Instead, they are preparing to challenge the vote counts from the voting machines. - - Opposition leader María Corina Machado has claimed González's victory was ""overwhelming"" based on data from campaign representatives at around 40% of ballot boxes. - -3. **Political Climate**: - - The election has been marred by allegations of fraud and voter suppression by the Maduro government. - - The opposition has expressed a desire for change, driven by economic hardships and political repression under Maduro's rule. - -4. **International Pressure**: - - The U.S. and European Union are unlikely to recognize the election results if they are deemed fraudulent, which could lead to increased sanctions against Venezuelan officials. - - The opposition is relying on international support to pressure Maduro to step down if González is declared the winner. - -5. **Potential for Protests**: - - There have been protests against Maduro's claim of victory, and the opposition has urged supporters to remain calm but vigilant. - - The possibility of further protests and civil unrest remains high if the opposition feels the election was not fair. - -Given these points, it is unlikely that González will concede the election to Maduro before September 2024. The opposition's strong stance against the election results, combined with international pressure and the potential for continued protests, suggests that the political situation in Venezuela will remain contentious in the coming months. - -######### - -GPT - -### Step-by-Step Analysis - -#### Default Behavior and the Current Situation -- **Default behavior**: - The default behavior without any changes would be that Edmundo González does not concede. He is currently contesting the election results, and neither he nor his team have indicated any intention to concede. - -- **Current Situation**: - Multiple credible sources report election irregularities, claims of fraud, and international condemnation of the process. The opposition is steadfast in contesting the results, and María Corina Machado's claims of González's victory further reinforce their stance. - -#### Base Rates and Historical Precedent -1. **Base rates of opposition candidates conceding in disputed elections historically**: - - In highly contested elections, especially where allegations of fraud and voter suppression are rampant, opposition candidates often do not concede quickly, if at all. Historical instances like Juan Guaidó in Venezuela (2019), Raila Odinga in Kenya (2017), and others can be referenced where opposition leaders did not concede. - -2. **Relevant Novel Factors**: - - Increased international scrutiny and the active role of international observers make it less likely that González will concede without significant pressure to do so. - - The political climate under Maduro's regime is characterized by repression and a lack of democratic norms, further decreasing the likelihood of a concession. - -#### Context Analysis -- **Election Results and Dispute**: - The narrow margin of the result and the immediate contestation by González add layers of complexity to the likelihood of a concession. - -- **Opposition's Stance**: - The opposition’s continued push against the election results is a significant factor. González appears firmly dedicated to contesting the results. - -- **International Pressure**: - Historical patterns suggest that international pressure (i.e., economic sanctions, diplomatic isolation) would reinforce González's stance rather than push him towards a concession unless overwhelming pragmatic considerations arise. - -- **Potential for Protests**: - The potential for protests adds another layer of complexity. If civil unrest gains significant momentum, it might either compel González to double down on his non-concession strategy or, conversely, force a strategic concession to avoid further conflict. - -#### Timing and Event Probability Distribution -- **Timeframe**: - From July 31 to September 1, there is basically a month for the situation to evolve. Given the steadfastness and recentness of González's stance, one month is a tight window for a significant shift to occur towards concession. - -- **Likely Variance**: - The political environment in Venezuela is characterized by low variance when it comes to rapid changes in stance by opposition leaders given recent history and the entrenched position of the Maduro regime. - -### Statistical Analysis -- **Probability Calibration**: - Based on the analysis above and the factors considered: - - **Base rate of no-concession in disputed elections**: High (approximately 80-90% historically where strong contestation exists). - - **Impact of International Pressure**: Minor shift down by 5-10%, as it tends to solidify stances rather than prompt concessions. - - **Time Constraint**: Reduces the probability further as addressing systemic issues within a month is unlikely. - -With a confident margin for error and an evidence-backed assertion aligned with recent historical trends, the probability distribution would skew heavily towards non-concession. - -### Final Prediction -After evaluating all factors, historical precedents, and the situational specifics, the final predicted probability that Edmundo González will concede the election to Nicolás Maduro before September 2024 is: - -**Probability: 10%**" -26821,"Will there be a debate between the Republican vice presidential nominee and the Democratic vice presidential nominee before October 1, 2024?","PERPLEXITY - -Based on the sources provided, there are indications that a debate between the Republican vice presidential nominee and the Democratic vice presidential nominee could occur before October 1, 2024. Here are the key points: - -1. **Historical Context**: Vice presidential debates typically occur in early October. For example, in 2020, the debate between Mike Pence and Kamala Harris took place on October 7, and in 2016, the debate between Tim Kaine and Mike Pence was on October 4. - -2. **Current Plans**: The Commission on Presidential Debates has proposed a vice presidential debate for September 25, 2024. However, it is not yet clear if the Biden and Trump campaigns will agree to participate in this debate. - -3. **Recent Developments**: JD Vance, Donald Trump's pick for vice president, has previously refused to debate Kamala Harris. However, with Kamala Harris now the presumptive Democratic presidential nominee, Vance is waiting to see who the new VP nominee will be. Pennsylvania Gov. Josh Shapiro is widely considered one of Harris' top choices for vice president, which could potentially set up a debate between Shapiro and Vance in the fall. - -Given these points, while there is no definitive confirmation of a vice presidential debate before October 1, 2024, the historical pattern and current proposals suggest it is likely. The exact date and participation are still to be confirmed. - -######### - -GPT - -Let's carefully analyze the situation and weigh the various factors influencing the probability of a debate occurring between the Republican and Democratic vice presidential nominees before October 1, 2024. - -### Historical Context - -1. **Historical Trends**: Historically, vice presidential debates have typically occurred in early October: - - In 2020, Mike Pence and Kamala Harris debated on October 7. - - In 2016, Tim Kaine and Mike Pence debated on October 4. - -2. **Base Rate**: Given the consistency in the timing of these debates, there is a strong historical precedent for scheduling such events around early October. This pattern can influence current planning unless disrupted by unprecedented factors. - -### Current Plans - -1. **Proposed Date**: The Commission on Presidential Debates has proposed a vice presidential debate for September 25, 2024. This is significant as it is before October 1 and aligns with the resolution criteria. - -2. **Campaign Agreement**: At present, it is unclear if the Biden and Trump campaigns will agree to participate in the proposed debate. This uncertainty is a crucial factor to consider, as non-participation by either side would nullify the occurrence of a debate. - -### Recent Developments - -1. **Candidates' Stance**: - - JD Vance, the Republican nominee, has shown reluctance to debate Kamala Harris, the presumptive Democratic presidential nominee. - - Kamala Harris's vice presidential pick remains unannounced, which adds an element of uncertainty. However, potential candidates like Pennsylvania Gov. Josh Shapiro could influence the decision to debate. - -2. **Transition Dynamics**: With President Joe Biden dropping out of the election, there might be dynamics and negotiations taking place behind the scenes that could delay or alter traditional debate schedules. - -### Evaluation of Factors - -1. **Time Until Event**: There are two months from today's date (July 31, 2024) until the proposed debate date of September 25, 2024. This time frame allows for developments that could either lead to the confirmation or cancellation of the debate. - -2. **Political Climate and Campaign Strategies**: The unpredictable nature of the current political climate, given Biden's withdrawal and the evolving campaign strategies of both parties, adds an element of risk and uncertainty. - -3. **Resolution Criteria**: According to the resolution criteria, the criteria will be met if a formal, live debate occurs before October 1, 2024. - -### Probability Distribution and Analysis - -Given the factors: -- Historical precedent strongly supports the occurrence and timing of such debates. -- The Commission on Presidential Debates has already proposed a date within the specified time frame. -- Uncertainty remains due to non-confirmation of participation by both campaigns. -- Candidate dynamics and the current political climate introduce additional variability. - -With these considerations, we can estimate the probability of a vice presidential debate occurring before October 1, 2024: - -#### Probability Calculation -- **Historical Context and Base Rates**: 60% weight based on historical consistency. -- **Proposed Date by the Commission**: 20% weight as a strong indicator. -- **Uncertainty in Campaign Participation**: Negative weight but significant, approximately -10%. -- **Political Dynamics**: Approximately -10% weight due to Biden's withdrawal and unsettled conditions. - -### Final Probability Estimate -Combining these weighted factors, we derive an estimated overall probability: - -\[ \text{Total Probability} = 60\% + 20\% - 10\% - 10\% = 60\% \] - -Therefore, considering the historical trends, proposed plans, and current uncertainties: - -**Probability: 60%**" -26817,"Will the UK Abolish the Two-Child Benefit Cap before October 1, 2024?","PERPLEXITY - -The UK's two-child benefit cap, introduced by the Conservative government in 2017, has been a contentious issue, particularly among Labour MPs and anti-poverty campaigners. The cap restricts state benefits, including Universal Credit and child tax credits, to two children per family, significantly impacting low-income families and contributing to rising child poverty levels. - -### Current Developments and Pressure - -1. **Labour Party Stance**: - - **Keir Starmer's Position**: Prime Minister Keir Starmer has faced criticism for not abolishing the cap in Labour's manifesto. He has acknowledged the concerns but deemed the abolishment financially unsustainable. - - **Labour MPs' Rebellion**: Several Labour MPs, including John McDonnell, have been suspended for voting against the government on the issue. McDonnell believes the rebellion will pressure the government to act on the cap. - -2. **Child Poverty Taskforce**: - - **Launch and Scope**: The government has launched a child poverty taskforce to address the root causes of poverty. While seen as a positive step, many MPs feel it does not go far enough and lacks a clear timeline for addressing the two-child cap. - - **MPs' Concerns**: MPs like McDonnell and Ian Byrne have expressed concerns about the taskforce's effectiveness and the lack of a defined role for charities like Save the Children and Barnardo's. - -3. **Financial Implications**: - - **Cost of Abolition**: Estimates suggest that abolishing the cap would cost between £2.5 billion and £3.6 billion annually. - - **Impact on Families**: The cap results in an average loss of £4,300 annually for low-income families, significantly contributing to child poverty. - -4. **Public and Expert Opinion**: - - **Charities and Advocates**: Organizations like the Child Poverty Action Group and Barnardo's have strongly advocated for the cap's abolition, citing its harmful effects on families and children. - - **Political Support**: The SNP, Lib Dems, Greens, and Reform UK also oppose the cap, indicating broad political support for its abolition. - -### Recent Updates - -- **Labour's Internal Conflict**: The suspension of Labour MPs for their dissent has highlighted internal party tensions and the urgency of the issue. -- **Taskforce Timeline**: The government has clarified that there is no set timeline for the taskforce to provide its findings before the anticipated October budget, which may delay any potential action on the cap. - -### Conclusion - -Given the current political climate, the pressure from within Labour and from other political parties, and the ongoing debate, there is a strong likelihood that the UK will abolish the two-child benefit cap before October 1, 2024. The recent formation of the child poverty taskforce and the vocal opposition from various political figures and advocacy groups suggest that the government is under significant pressure to address this issue. - -######### - -GPT - -### Step-by-Step Evaluation - -#### 1. **Establishing the Base Rate** -Historically, the repeal of major welfare policies in the UK has been relatively rare and often contentious. With the current government led by Keir Starmer and the Labour Party, which is showing hesitation to remove the cap due to financial constraints, the base rate for such a repeal happening within a short time frame is low. - -#### 2. **Political Context** -- **Current Prime Minister's Position**: Keir Starmer's administration has explicitly mentioned financial constraints as a reason for not abolishing the cap. This indicates a significant barrier to policy change without a corresponding change in the financial outlook or public opinion. - -- **Labour Party Unity**: The suspension of MPs who defied the party line shows internal conflict. However, this indicates that the Labour Party leadership is willing to enforce discipline to maintain the status quo, highlighting the existing reluctance rather than a move toward change. - -#### 3. **Public Opinion** -- The YouGov poll indicates that 60% of the general public, and 50% of Labour supporters, are against lifting the cap. Governments are typically responsive to public opinion, especially on fiscally sensitive issues. The fact that a majority opposes lifting the cap adds weight against an imminent policy change. - -#### 4. **Political Support from Other Parties** -- There is broad political support from the SNP, Lib Dems, Greens, and Reform UK for abolishing the cap. However, the Labour Party, as the ruling party, holds the most influence over whether this change will occur within the given timeframe. - -#### 5. **Taskforce and Timeline** -- The child poverty taskforce does not have an established timeline for presenting its findings. The delay before the October budget suggests that any major recommendations are unlikely to come into effect swiftly. - -#### 6. **Financial Constraints** -- The estimated cost of abolishing the two-child benefit cap is significant (£2.5 billion to £3.6 billion annually). Given the current economic environment and no substantial increase in government revenue or cut in other spending to offset this cost, such a policy change is less probable. - -#### 7. **Possible Pathways for Change** -- **Standalone Bill**: Time before October 1, 2024, is limited, and passing a bill quickly in Parliament is a complex and lengthy process. -- **Finance Bill**: Requires alignment with budget cycles and broader fiscal policies. Given that the next budget will likely need time for implementation, this quick change seems improbable. - -#### 8. **Novel Elements and Recent Trends** -- Recent rebellions within the Labour Party reveal discontent but aren't significant enough movements to override the leadership’s established position. - -#### Conclusion: -Given the multiple factors aligning against a rapid policy change, including the internal firmness within Labour, the financial constraints, negative public opinion, and procedural challenges, the likelihood of abolishing the two-child benefit cap before October 1, 2024, is low. However, a non-zero probability persists due to ongoing political pressure and the ever-present potential for unforeseen shifts in policy. - -### Final Probability -Considering all the outlined factors, especially the strong indicators against an imminent policy change and the limited time frame, I estimate the probability of the UK abolishing the two-child benefit cap before October 1, 2024, to be: - -**Probability: 15%**" -26819,"Will Kamala Harris have more than 15 million TikTok followers on Sept 30, 2024?","PERPLEXITY - -To determine whether Kamala Harris will have more than 15 million TikTok followers by September 30, 2024, we need to analyze her current follower growth and the factors influencing her popularity on the platform. - -### Current Follower Growth -- **Initial Surge**: Vice President Kamala Harris joined TikTok on July 26, 2024, and quickly amassed over 1.1 million followers within six hours of her first video. -- **Recent Numbers**: As of July 26, 2024, her follower count had risen to 1.3 million just twelve hours after her initial video. - -### Factors Influencing Popularity -- **Political Context**: President Biden's withdrawal from the race and his endorsement of Harris have significantly boosted her visibility and positive sentiment on TikTok. -- **Engagement and Sentiment**: Zelf's report indicates that over 85,000 TikTok videos were posted about Harris after Biden's announcement, generating over 1.8 billion views and showing a 49% higher engagement with positive content compared to negative content. -- **Meme Culture**: Harris's campaign has effectively leveraged meme culture, particularly among younger voters, which has contributed to her increased popularity on the platform. -- **Campaign Strategy**: Harris's TikTok account, Kamala HQ, has been actively posting content in a style appealing to Gen-Z users, further enhancing her reach and engagement. - -### Comparative Analysis -- **Donald Trump's Followers**: Despite Harris's rapid growth, she still trails behind Donald Trump, who has 9.2 million followers on TikTok as of July 26, 2024. - -### Expert Opinions and Predictions -- **Social Media Analysts**: Experts like Annie Wu Henry and Chi Ossé highlight Harris's relatability and memeability as key factors in her growing popularity among younger voters. -- **Prediction Markets**: There are no specific prediction markets mentioned in the sources provided, but the general sentiment suggests a strong likelihood of continued growth for Harris's TikTok presence. - -### Conclusion -Given the rapid growth in followers and the positive sentiment around Harris on TikTok, it is plausible that she could reach more than 15 million followers by September 30, 2024. The combination of her political prominence, effective use of meme culture, and the platform's younger demographic aligns well with the potential for sustained growth. However, this conclusion is based on current trends and may be influenced by future events or changes in user engagement. - -To accurately forecast this, it would be beneficial to monitor her follower count regularly and consider any significant political or social events that might impact her popularity on TikTok. - -######### - -GPT - -Alright, let's break this down step by step to arrive at a probability estimate for Kamala Harris having more than 15 million TikTok followers on September 30, 2024. - -### Analysis of Current Data and Trends -1. **Initial Growth Rates**: - - Kamala Harris gained 2.1 million followers within the first 24 hours of her TikTok launch. - - As of July 28, 2024, she has 2.8 million followers, indicating a 0.7 million follower increase over two days. - - If we extrapolate her growth in the first two days as an extreme best-case scenario, she would gain: - - **0.7 million/day for 60 days**: 42 million. This rate is unsustainable in the medium-long term but highlights the potential for rapid early growth. - -2. **Slowing Growth**: - - Growth typically slows after an initial surge as early excitement wanes. This rate needs recalibration considering slower periods. - - Assuming an optimistic follower addition rate of 0.1 million/day (10% of the initial surge rate): - - **0.1 million/day for 60 days**: 6 million. - - Adding this to her current 2.8 million: she would have 8.8 million by end of September 2024. - -3. **Realistic Growth Rate**: - - Using a midpoint between initial burst and slower growth phases, assuming she gains 0.05 million (50,000) followers/day: - - **50,000/day for the next 60 days**: 3 million. - - This gives us 5.8 million followers by end of September 2024. - -### Comparative Context -1. **Donald Trump's Follower Growth**: - - Trump joined on June 2 and has 9.3 million followers by July 28, giving him about 7.2 million followers in 57 days. - - This is approximately 126,000 followers per day. Harris's maximum growth rate will likely not reach this pacing but rather scale back in line with similarly high-profile public figures. - -2. **Exponential vs. Linear Trends**: - - Growth trajectories often show initial exponential spikes followed by moderated linear climbs. - - If Harris mirrors Trump’s deceleration: - - Trump grew by ~ 126,000/day initially and may slow down as he approaches a saturation point. - -3. **Estimating Variance**: - - Given Harris' growth dynamics and significant short-term factors: - - Sustained daily growth: 0.05 - 0.07 million/day range. - - Influences from virals, campaigns, debates: potential but uncertain spikes. - -### External Influencing Factors -1. **Campaign Dynamics**: - - Being a VP candidate with frequent mentions across media and campaign strategies. - - Her engagement rate and relatability could affect continued interest. - -2. **Political Landscape Shocks**: - - Any dramatic event (scandal, PR stunts, endorsements) can shift this rapidly. - -### Probability Calculation -1. **Total Expected Growth**: - - Baseline: Current 2.8 million + linear growth (best guess) 0.06 million/day for 60 days. - - Total best guess growth: 2.8 million + 3.6 million = 6.4 million. - - Higher spikes (Campaign Period Intensification): - - On higher intensity multiples could range from 6.4 million + 4-6 million (viral & engagement). - -2. **Broadest Calculated Estimate**: - - **6.4 million** (Conservative linear best guess) to **12.4 million** (High Multiply scenario). - -### Statistical Probability Distribution -- Weighing data consolidation into bias-free measures: - - Median High Conflict Range: Harris can lie between 6 to 13 million. - - Modal shift scenarios toward event surges (0.05-0.07 million average/day + virals). - -### Final Analysis Conclusion -- Given extrapolated trends but skepticism of maximum potential repeatability and general decay in social interest over time: - - Concluding potential Harris-follow trajectory would land her in plausible growth. - - For reaching 15 million, scrutiny on outliers (>15 M = Tail cases). - -### Resolution: Aggregate probability: -**Probability: 35%** - -Harris achieving >15m by Sept 30, 2024 presents moderate-high outreach, prone to virals, but bounds within conservative electoral follow metrics. The likelihood surfaces beneath `high` margin given variations." -26818,"Will Donald Trump have more than 15 million TikTok followers on Sept 30, 2024?","PERPLEXITY - -To assess whether Donald Trump will have more than 15 million TikTok followers by September 30, 2024, we need to consider his current follower count, the rate of growth, and any factors that might influence his popularity on the platform. - -### Current Follower Count -As of July 30, 2024, Donald Trump has more than 9 million followers on TikTok. This number has been steadily increasing since he joined the platform in early June 2024. - -### Rate of Growth -Trump's TikTok account gained 3 million followers within a day of joining the platform. By the end of the first week, he had amassed over 1.1 million followers and his initial video had garnered more than 1 million likes and 24 million views. This rapid growth indicates a strong initial interest in his presence on TikTok. - -### Factors Influencing Popularity -1. **Political Campaign**: Trump's use of TikTok is part of his broader political campaign strategy to engage with younger voters. His campaign believes that TikTok offers a valuable platform to connect with this demographic. - -2. **Content Engagement**: Trump's videos have been highly engaging, with his first video receiving over 166 million views. This high engagement rate suggests that his content is resonating with users. - -3. **Political Events**: Significant political events, such as debates and rallies, can influence follower growth. For example, posts concerning Trump surged nearly eightfold following an assassination attempt against him. - -4. **Competitive Landscape**: While Trump is one of the few major Republican figures on TikTok, his presence is crucial for maintaining engagement with his base and encouraging voter turnout. Democratic candidates, including Vice President Kamala Harris, are also leveraging TikTok to reach younger voters. - -### Expert Opinions -- **Steven Cheung**, Trump's campaign spokesman, emphasized the importance of using TikTok to reach a younger audience and maintain a strong online presence. -- **Katie Harbath**, a Republican digital strategist, noted that while TikTok may not sway undecided voters, it is crucial for maintaining engagement with the base and assisting with fundraising. - -### Prediction Markets -As of the current date, there are no specific prediction markets available on Metaculus regarding Donald Trump's TikTok follower count by September 30, 2024. - -### Conclusion -Given Trump's current follower count of over 9 million and his rapid growth rate, it is plausible that he could reach more than 15 million followers by September 30, 2024. The ongoing political campaign, high engagement rates, and strategic use of TikTok to connect with younger voters all support this possibility. However, the actual outcome will depend on various factors, including the effectiveness of his campaign's social media strategy and any unforeseen events that might impact his popularity on the platform. - -######### - -GPT - -### Step-by-Step Analysis to Determine the Probability: - -#### 1. **Current Follower Count**: -- Trump has 9.3 million followers as of July 28, 2024. - -#### 2. **Rate of Growth**: -- Trump's follower count grew from 3.2 million in the first 24 hours to 9.3 million over approximately 58 days (June 2 to July 30). This suggests a follower growth rate of approximately 0.104 million followers per day. - -#### 3. **Remaining Time**: -- From July 31, 2024, to September 30, 2024, there are 61 days left. - -#### 4. **Future Projection**: -- Assuming the growth rate of approximately 0.104 million followers per day continues: - - Future followers count = Current followers + (Growth rate per day * Remaining days) - - = 9.3 million + (0.104 million * 61 days) - - = 9.3 million + 6.344 million - - = 15.644 million - -This simplistic projection suggests that under the assumption of a constant growth rate, Trump will exceed 15 million followers. - -#### 5. **Adjusting for Factors**: -To adjust this initial estimate, we'll account for various influencers: - -**Political Campaign**: -- Trump's ongoing presidential campaign will continue to boost his visibility on TikTok as he participates in more rallies, debates, and media coverage. -- The competitiveness of the election will likely increase attention on his TikTok presence. - -**Content Engagement**: -- High engagement rates (e.g., video views and likes) indicate that the content resonates well with the audience. -- Assuming he maintains or enhances the quality and virality of the content, sustained or even increased growth is plausible. - -**Comparison with Other Candidates**: -- Kamala Harris and Robert F. Kennedy Jr. joining the platform could both lead to greater overall engagement in political content on the platform, but could also mean that Trump has competition for TikTok attention. -- However, Trump's current lead in followers suggests he has a head start. - -**Unforeseen Events**: -- There could be events like major political developments, viral trends, or scandals that could either spike or drop his follower growth. -- For instance, major political events like debates could cause spikes in follower counts. - -**Platform Environment**: -- TikTok's user demographics (younger, more politically engaged in social media) might favor rapid growth in Trump's follower base. -- No major regulatory or platform changes are anticipated that would impede his access or engagement. - -#### 6. **Base Rates and Historical Context**: -- Comparing political figures' social media growth rates, rapid increase tends to occur around major campaign events. The sustained visibility often leads to continued steady growth. -- Historical precedents of rapid follower accumulation for political figures around election periods affirm that an upward trend is likely. - -#### 7. **Risk Factors**: -- The potential for TikTok bans, changes in user preferences, or major scandal could impact growth negatively. -- Given the historical stability of TikTok usage patterns, these risks, while possible, are not highly likely over the next two months. - -#### Conclusion: -Given the initial calculation of reaching around 15.644 million followers by September 30, 2024, and factoring in the political and social environment's potential continued boost to his follower numbers, we conclude there is a strong probability that Trump will achieve more than 15 million followers. - -Therefore, the probability that Donald Trump will have more than 15 million TikTok followers by September 30, 2024, is: - -### ""Probability: 85%""" -26820,"Will Robert F. Kennedy Jr. have more than 15 million TikTok followers on Sept 30, 2024?","PERPLEXITY - -To determine whether Robert F. Kennedy Jr. will have more than 15 million TikTok followers by September 30, 2024, we need to analyze his current social media presence, recent trends, and the dynamics of TikTok usage. - -### Current Social Media Presence -- **Robert F. Kennedy Jr.**: As of March 2024, Kennedy has around 1.1 million followers on TikTok. By June 2024, this number had increased to 1.3 million. -- **Donald Trump**: Trump joined TikTok in June 2024 and quickly amassed 5.6 million followers. -- **Joe Biden**: Biden's campaign account has around 360,100 followers on TikTok. - -### Recent Trends -- **Growth Rate**: Kennedy's follower count has been steadily increasing, with a significant boost following his participation in the presidential debate and his strategic use of TikTok and X (formerly Twitter) to engage with his audience. -- **Engagement**: Kennedy's videos on TikTok have garnered significant attention, with one video accumulating 12.1 million views. His campaign has also seen increased engagement through interactive town halls and content creator collaborations on TikTok Live. - -### Dynamics of TikTok Usage -- **Young Voters**: TikTok is particularly popular among young voters, with 62% of 18- to 29-year-olds using the app. Kennedy has been performing well with this demographic, with 26% of his supporters using TikTok often. -- **Algorithmic Impact**: The algorithmic nature of TikTok can significantly impact follower growth and engagement. Kennedy's campaign has been leveraging this by creating engaging content and participating in interactive sessions. - -### Expert Opinions -- **Lindsay Gorman**: Gorman notes that unconventional candidates like Kennedy can gain traction on TikTok, but distinguishing between algorithmic impact and user preferences is challenging. -- **Amaryllis Kennedy**: The campaign director emphasizes the importance of X and TikTok in facilitating open dialogue and increasing engagement. - -### Prediction Markets -- **Metaculus**: As of the current date, there are no specific prediction markets on Metaculus regarding Robert F. Kennedy Jr.'s TikTok follower count by September 30, 2024. - -### Conclusion -Given Kennedy's current follower count of 1.3 million and his recent growth trends, it is uncertain whether he will reach 15 million followers by September 30, 2024. However, his strategic use of TikTok and the platform's popularity among young voters suggest a potential for significant growth. The campaign's focus on engaging content and interactive sessions could further boost his follower count. - -######### - -GPT - -To forecast whether Robert F. Kennedy Jr. will have more than 15 million TikTok followers by September 30, 2024, we need to thoroughly assess current data, trends, base rates, and influencing factors. Here’s a step-by-step evaluation: - -### Step 1: Current Data and Growth Rates - -#### Current Follower Counts and Growth -- **As of July 28, 2024**: - - **Donald Trump**: 9.3 million followers - - **Kamala Harris**: 2.8 million followers - - **Robert F. Kennedy Jr.**: 1.7 million followers -- Historical data shows that: - - From March 2024 to June 2024, Kennedy grew from 1.1 million to 1.3 million followers, a growth of approximately \( \frac{0.2 \text{ million}}{3 \text{ months}} = 0.067 \text{ million/month} \). - - From June 2024 to July 28, 2024, Kennedy grew from 1.3 million to 1.7 million followers, a growth of 0.4 million in 1.93 months or approximately \( 0.21 \text{ million/month} \). - -### Step 2: Projecting Future Growth - -#### Linear Growth Scenario -- Average growth in the past few months: \( (0.067 + 0.21) / 2 \approx 0.138 \text{ million/month} \). -- Time remaining until September 30, 2024: about 2 months. -- Linear growth estimate: \(1.7 \text{ million} + (0.138 \times 2) = 1.976 \text{ million}\). - -#### Exponential Growth Scenario -- Social media growth often follows an S-curve with initial exponential growth which then saturates. -- Assuming an optimistic higher monthly growth of 0.25 million followers (considering recent higher engagement and activities): - - Optimistic growth estimate: \(1.7 \text{ million} + (0.25 \times 2) = 2.2 \text{ million}\). - -### Step 3: Analyze Influencing Factors - -#### Platform Popularity and Engagement -- TikTok thrives on virality; significant events, viral content, and trending topics could greatly accelerate follower growth. -- Robert F. Kennedy Jr. has generated more engagement recently through debates and interactive sessions. - -#### Comparisons with Other Political Candidates -- Donald Trump amassed 9.3 million followers in about two months. -- Trump and Harris grow significantly faster initially, but their growth might plateau. -- Kennedy must leverage significant continuous viral content to match or surpass this growth trajectory. - -### Step 4: Base Rates and Historical Precedents - -#### Political Accounts Growth -- Historically, only very few political figures amassed over 15 million followers on TikTok within a short span without pre-existing massive popularity or controversial presence. -- Growth dynamics for political figures and celebrities can diverge due to differing engagement strategies and audience demographics. - -### Step 5: External Factors - -#### Electoral Campaign Influence -- Upcoming significant political events, debates, or campaign maneuvers may spike follower counts. -- Social media algorithm changes might affect organic reach and recommendations. - -#### Technological Factors -- No significant foreseen changes in TikTok's algorithm or political climate that could drastically alter baseline growth expectations within two months. - -### Step 6: Final Probabilistic Estimation - -Based on the current growth trajectory, external influencing factors, and historical data: - -- **Current growth rate** (conservative and optimistic projections) shows significant additional followers are unlikely to be achieved within such a short time frame to more than 15 million followers by September 30, 2024. -- **Exponential growth** assumption in a short window without a significant viral event (and saturating growth dynamics) still shows low probability. - -### Conclusion - -Given the analysis, the probability that Robert F. Kennedy Jr. will have more than 15 million TikTok followers on September 30, 2024, is low. Taking into account the multiple factors and trends, a reasonable probability estimation would be: - -**Probability: 5%**" -26777,Will the Seattle-Tacoma-Bellevue WA metro area experience 2 to 5 days with an Air Quality Index value above 150 in the 3rd quarter of 2024?,"PERPLEXITY - -To forecast whether the Seattle-Tacoma-Bellevue WA metro area will experience 2 to 5 days with an Air Quality Index (AQI) value above 150 in the 3rd quarter of 2024, we need to consider several factors and sources: - -### Historical Air Quality Data -- **Seattle's Air Quality Trends**: Seattle experiences an average of 14.2 unhealthy pollution days per year, with 7 days due to high ozone and 7.2 days due to high PM2.5 levels. This exceeds the EPA's recommended maximum of 3.2 unhealthy days per year for each pollutant. -- **Recent Decline**: Since 2016, key measures for PM2.5 and ozone pollution have been increasing, driven by population growth, new industry, construction, and the increased frequency and severity of wildfires. - -### Current Air Quality Monitoring -- **AQI Categories**: The AQI measures air pollutants and categorizes them based on health risks. The categories include Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, and Hazardous. -- **2023 Data Summary**: In 2023, King County had nearly 40% of its days in the Moderate category, with some days reaching Unhealthy levels due to wildfire smoke. The EPA's health-based air quality index changed in 2024, potentially leading to more Moderate days. - -### Predictive Models and Forecasts -- **Metaculus Predictions**: Metaculus has a question open for forecasting the number of days with an AQI above 150 in the 3rd quarter of 2024. This platform aggregates predictions from a community of forecasters, providing a collective estimate of the likelihood of such events. - -### Expert Opinions and Mitigation Efforts -- **Mitigation Strategies**: Efforts to improve air quality include reducing emissions from mobile sources, promoting electric vehicles, and managing forest underbrush with prescribed fires. These strategies aim to bring Seattle's air quality within federal attainment levels. -- **Climate Change Impact**: Climate change is expected to increase ozone formation and extend the ozone season, potentially counteracting some mitigation efforts. - -### Recent Updates -- **Wildfire Impact**: Wildfires significantly impact air quality, especially in late summer and fall. August was the most polluted month in 2017 and 2018 due to wildfires, and similar patterns are expected in 2024. - -### Conclusion -Given the historical data, current monitoring, and predictive models, it is likely that the Seattle-Tacoma-Bellevue WA metro area will experience 2 to 5 days with an AQI value above 150 in the 3rd quarter of 2024. The combination of increasing pollution levels, the impact of wildfires, and the potential effects of climate change support this likelihood. - -######### - -GPT - -To forecast the likelihood of the Seattle-Tacoma-Bellevue WA metro area experiencing 2 to 5 days with an Air Quality Index (AQI) value above 150 in the 3rd quarter of 2024, I will consider a combination of historical data, trends, the impact of external factors such as wildfires, and any mitigation efforts. - -### Step 1: Analyze Historical Data -First, let's establish a baseline with historical data on the number of days with AQI above 150 for the specific timeframe: -- 2014: 0 days -- 2015: 3 days -- 2016: 1 day -- 2017: 11 days -- 2018: 7 days -- 2019: 0 days -- 2020: 9 days -- 2021: 2 days -- 2022: 11 days -- 2023: 3 days - -From this data, we see that the average number of days with AQI above 150 over the past 10 years is about 4.7 days. The range of days with high AQI has varied significantly from year to year, largely due to the variability of wildfire patterns and other atmospheric conditions. - -### Step 2: Evaluate Current and Recent Trends -#### Wildfires -Wildfires have been a significant contributor to poor air quality in recent years. Key months are typically August and September when wildfire activity peaks, and Seattle often experiences smoke from regional fires. - -#### Current Monitoring and Predictive Models -As of July 29, 2024, there has already been one day in the third quarter of 2024 with an AQI above 150. Wildfire activity is expected to increase in late summer, and August is historically a critical month for poor air quality. This aligns with predictions and current patterns. - -### Step 3: Consider Expert Opinions and Mitigation Efforts -#### Climate Change and Pollution Trends -- Climate change has extended the fire season and potential for ozone formation. -- Efforts to reduce pollution and mitigate air contamination, such as promoting electric vehicles and forest management, may have long-term benefits but might not be sufficient to offset immediate threats from wildfires this season. - -### Step 4: Calculate Probabilities -Considering the base rate (average of 4.7 days) and recent years' trends: -- **Baseline**: 4.7 days with AQI > 150 -- **Recent Highs**: Recent years like 2017, 2020, and 2022 have shown more variability with higher peaks. -- **Current Year 2024**: Already 1 day in July with predictions of more wildfire activity. - -Given this, the historical data and current trends support a moderate likelihood of hitting 2 to 5 days with an AQI above 150 in the third quarter of 2024. - -### Base Rate Adjustment -Adjusting the base rate to consider: -- 2022, 2020, 2018, and 2017 had big peaks that might skew the average upwards. -- Additional climate impact models suggest a moderate increase in likelihood for poor air quality due to persistent wildfires. - -Given current trends, mitigation efforts not outweighing wildfire activity, and statistical patterns, my estimation falls within the range forecasted by historical data with a slight increase in likelihood due to climate change exacerbation. - -#### Probability Estimation: -1. **Highly Likely** (40%) - I consider it highly likely due to consistent past patterns, wildfires this summer, and current trends. -2. **Moderately Likely** (30%) - Weighing the variance in data over the past decade. -3. **Less Likely** (20%) - Mitigation efforts could potentially reduce the number of high AQI days. -4. **Unlikely** (10%) - Significantly improved mitigation and favorable weather. - -Combining these estimations, accounting for all factors and base rate impacts, the probability of Seattle-Tacoma-Bellevue experiencing 2 to 5 days with AQI above 150 in the 3rd quarter of 2024 seems solid. - -### Conclusion: -Given all the information, my final probability for the requested scenario is: -""Probability: 70%""" -26776,Will the Seattle-Tacoma-Bellevue WA metro area experience exactly 1 day with an Air Quality Index value above 150 in the 3rd quarter of 2024?,"PERPLEXITY - -To forecast whether the Seattle-Tacoma-Bellevue WA metro area will experience exactly 1 day with an Air Quality Index (AQI) value above 150 in the 3rd quarter of 2024, we need to consider several factors: - -1. **Historical Air Quality Data**: - - In 2023, King County, where Seattle is located, had nearly 40% of its days in the MODERATE air quality category, with some days reaching UNHEALTHY FOR SENSITIVE GROUPS and UNHEALTHY levels due to wildfire smoke. - - Seattle experiences an average of 14.2 unhealthy pollution days per year, with 7 days due to high ozone and 7.2 days due to high PM2.5 levels. - -2. **Current Air Quality**: - - As of June 26, 2024, Seattle's AQI was categorized as ""Good"". - - The Puget Sound Clean Air Agency reported that the EPA health-based air quality index changed in 2024, potentially leading to more days in the MODERATE category. - -3. **Forecasting and Prediction Markets**: - - Metaculus, a forecasting platform, has a question open for whether the Seattle-Tacoma-Bellevue WA metro area will experience exactly 1 day with an AQI value above 150 in the 3rd quarter of 2024. This question is awaiting approval by community moderators. - - Another Metaculus question asks how many days the area will experience an AQI value above 150 in Q3 2024, which is also awaiting approval. - -4. **Expert Opinions and Trends**: - - Experts highlight that Seattle's air quality is influenced by population growth, new industry, construction, and the increased frequency and severity of wildfires. These factors contribute to rising PM2.5 and ozone levels. - - Efforts to improve air quality include shifting to cleaner energy, promoting electric vehicles, and managing emissions from mobile sources. - -5. **Seasonal Factors**: - - Wildfires in the late summer and fall, as well as wood burning in the winter, contribute to Seattle's highest PM2.5 levels. August was the most polluted month in 2017 and 2018 due to wildfires. - -Given these factors, the likelihood of the Seattle-Tacoma-Bellevue WA metro area experiencing exactly 1 day with an AQI value above 150 in the 3rd quarter of 2024 seems plausible but uncertain. The area's historical data shows a mix of good and moderate air quality days, with occasional spikes due to wildfires and other pollution sources. The recent changes in the EPA's health-based air quality index and ongoing efforts to improve air quality add complexity to the forecast. - -######### - -GPT - -Given the nature of the question and to answer it with the most accuracy, we need to analyze several key factors: - -1. **Historical Data Analysis**: - - The Seattle-Tacoma-Bellevue WA area has a history of experiencing days with an AQI above 150 due to various reasons such as wildfires. Let's examine the historical AQI data in the third quarter in detail: - - 2023: 3 days - - 2022: 11 days - - 2021: 2 days - - 2020: 9 days - - 2019: 0 days - - 2018: 7 days - - 2017: 11 days - - 2016: 1 day - - 2015: 3 days - - 2014: 0 days - - From this data, we can say that bad air days show a highly variable pattern, influenced primarily by wildfires. - -2. **Recent Data and Trends**: - - As of July 29, 2024, there has already been one day in the third quarter with an AQI above 150. - - We need to account for the likelihood of additional AQI events given that the peak season for wildfires (late summer to early fall) is mostly ahead of us. - -3. **Air Quality Influencing Factors**: - - Wildfire activity in the region, population growth, industrial activities, and seasonal wood burning are significant influencers of air quality. - - The ongoing trends and expert opinions also suggest increasing incidents of wildfires and PM2.5-related pollution. - -4. **Forecasting and Prediction Markets**: - - While it would be advantageous to have insights from prediction markets like Metaculus, currently, we do not have a complete consensus there. - -5. **Statistical Approach**: - - Given the past data, we need to generate a base rate. Let's calculate it step by step using historical probabilities: - - **P(>0 Q3 AQI 150 days)**: Historically, this happens almost every year except 2014 and 2019, giving us an empirical probability of \(\approx 0.8\). - - **P(exactly 1 Q3 AQI 150 day)** occurrence frequency: Only 2016 shows exactly 1 day, suggesting it happens \(\approx 0.1\). - - **P(more than 1 day)**: Given most years with days >150 feature more than 1, this probability is \(\approx 0.7\). - - Hence, the likelihood of having more than 1 day is significantly higher when wildfires are considered. - -6. **Upcoming and Existing Conditions**: - - Given wildfires typically exacerbate in August and September, the probability of additional AQI spikes beyond what's already experienced (i.e., moving from exactly 1 to >1) is non-trivial. - -### Probability Calculation: -Given that there is already exactly 1 day with AQI above 150, we need to evaluate the probability of no further such days versus additional days occurring. - -#### Reasoning Process: -1. **Base Rate Calculation**: - - It is somewhat likely (~80%) there would be at least one day > 150 AQI in Seattle in Q3. - - The probability of exactly 1 day (~10%) having happened already means there would need extraordinary conditions to stop there from being a further increase. - -2. **Evaluate Wildfire Season Inference**: - - Wildfire patterns tend to follow through given the history, influencing multiple days. - -Thus, with both historical context and current trends, the most likely continuation is the region experiencing more than the singular day. Balancing these models: - - If the median forecast considers general wildfire progression, the probability for exactly 1 AQI day remains significantly less. - -### Conclusion: -Thus, the evaluation leads succinctly to estimating with lower probability on ""exactly 1 day"": - -**Final Answer**: **Probability: 20%**" -26778,Will the Seattle-Tacoma-Bellevue WA metro area experience 6 to 10 days with an Air Quality Index value above 150 in the 3rd quarter of 2024?,"PERPLEXITY - -### Context for Forecasting Air Quality in Seattle-Tacoma-Bellevue WA Metro Area - -#### Current Air Quality Index (AQI) and Historical Data - -- **Current AQI**: The Seattle-Tacoma-Bellevue WA metro area currently has a ""Good"" AQI rating, indicating minimal health risks from air pollution. -- **Historical Data**: In 2023, King County experienced nearly 40% of its days in the MODERATE air quality category, with some days reaching UNHEALTHY levels due to wildfires and other pollution sources. - -#### Factors Influencing Air Quality - -- **Wildfires**: Wildfires significantly impact air quality, especially during late summer and fall. In August 2023, wildfires caused air quality levels to reach unhealthy categories in King and Snohomish counties. -- **Seasonal Trends**: Elevated PM2.5 levels are common during winter months due to wood burning, and summer months can see spikes due to wildfires. -- **Population Growth and Emissions**: Increased population and industrial activities contribute to higher emissions, which can worsen air quality. - -#### Prediction Markets and Expert Opinions - -- **Metaculus Predictions**: Questions on Metaculus are currently under review and awaiting approval, indicating ongoing interest in forecasting air quality in the region. -- **Expert Opinions**: Experts emphasize the need for multifaceted air quality policies, including reducing emissions from mobile sources and managing controllable factors to improve air quality. - -#### Recent Updates and Trends - -- **EPA Standards**: The EPA health-based air quality index changed in 2024, which may result in more days being classified as MODERATE even if overall air quality hasn't worsened. -- **Mitigation Efforts**: Seattle aims to increase the adoption of electric vehicles and reduce emissions from mobile sources, which could improve air quality over time. - -### Conclusion - -Given the historical data and ongoing factors influencing air quality, the Seattle-Tacoma-Bellevue WA metro area is likely to experience some days with an AQI value above 150 in the 3rd quarter of 2024, especially considering the impact of wildfires and seasonal trends. However, the exact number of days remains uncertain and will depend on various environmental and policy factors. - -######### - -GPT - -### Step-by-Step Analysis - -#### Historical Data Analysis -Let's start by examining historical data to determine the base rate for the number of days with AQI values above 150 in the third quarter for the Seattle-Tacoma-Bellevue area. - -From the data provided: -- 2023: 3 days -- 2022: 11 days -- 2021: 2 days -- 2020: 9 days -- 2019: 0 days -- 2018: 7 days -- 2017: 11 days -- 2016: 1 day -- 2015: 3 days -- 2014: 0 days - -#### Mean and Standard Deviation Calculation -Calculate the average (mean) number of days with AQI values above 150: -\[ \text{mean} = \frac{3 + 11 + 2 + 9 + 0 + 7 + 11 + 1 + 3 + 0}{10} = 47/10 = 4.7 \] - -Calculate the standard deviation (σ) to understand the variability: -\[ \sigma = \sqrt{\frac{(3-4.7)^2 + (11-4.7)^2 + (2-4.7)^2 + (9-4.7)^2 + (0-4.7)^2 + (7-4.7)^2 + (11-4.7)^2 + (1-4.7)^2 + (3-4.7)^2 + (0-4.7)^2}{10}} \] -\[ \sigma ≈ 3.98 \] - -#### Base Rate Probability -Considering the standard deviation, we look at a typical range of outcomes: -\[ \text{Mean} \pm \text{Standard Deviation} = 4.7 \pm 3.98 \] -This spans a range from around 0.72 to 8.68 days. - -#### Critical Factor: Wildfires -The critical factor influencing high AQI days is wildfires: -- Wildfires around the region are most frequent in late summer and early fall. -- Events such as the Bolt Creek Fire have previously significantly impacted AQI levels in the Seattle-Tacoma-Bellevue metro area. - -Currently, there is no major wildfire significantly impacting the air quality, but this could change quickly with seasonal dynamics. - -#### Current Context and Forecasts -- **Current AQI Levels**: As of now, the area has good AQI levels. -- **Existing Data**: Only 1 day above 150 AQI so far in Q3 2024 (as of July 29, 2024). -- **Seasonal Trends**: Historically, August and September are critical months for wildfire activity. - -#### Expert Opinions and Recent Trends -- Policy changes, such as electric vehicle adoption, take time to affect air quality. -- Increased population and industrial activities add a consistent upward pressure on AQI. - -#### Statistically Informed Decision -With data showing significant variability, the historical average of 4.7 days being below the 6-10 days range but the standard deviation indicating it's not uncommon for multiple high AQI days in some years, we must weigh the risks. - -#### Probability Estimation -Given the historical volatility and considering that we are still early into the third quarter, it’s reasonable to make a probability estimate that accounts for moderate risks and the impacts of unpredictable factors like wildfires. - -#### Conclusion -- **Historical Base Rate**: Mean of 4.7 days with significant variability. -- **Current Observations**: 1 day above 150 AQI so far, with critical wildfire-prone months upcoming. -- **Impact of Wildfires**: A major wildfire can quickly elevate AQI above 150 for multiple days. -- **Policy and Mitigation Factors**: Insufficient immediate impact to strongly skew probabilities in either direction. - -Given these factors, estimating the probability that the metro area will experience 6 to 10 days with an AQI above 150 in this range seems moderate but still with room for caution due to the unpredictable nature of recent wildfire seasons. - -**Final Probability: 55%** -This reflects a balance between the historical data, current conditions, and seasonally elevated risk factors." -26779,Will the Seattle-Tacoma-Bellevue WA metro area experience 11 or more days with an Air Quality Index value above 150 in the 3rd quarter of 2024?,"PERPLEXITY - -### Context for Forecasting Air Quality in Seattle-Tacoma-Bellevue WA Metro Area - -#### Historical Air Quality Data -- **2023 Summary**: King County experienced nearly 40% of its days in the MODERATE air quality category, with some days reaching UNHEALTHY FOR SENSITIVE GROUPS and UNHEALTHY due to wildfire smoke. Fine particles, primarily from wood smoke, vehicles, wildfires, and industry, were a significant concern. -- **Recent Trends**: Seattle has seen a decline in air quality since 2016, with an increase in PM2.5 and ozone pollution levels. Despite some improvements, the city still exceeds federal targets for unhealthy pollution days. - -#### Current Air Quality -- **Current AQI**: As of June 26, 2024, Seattle's Air Quality Index (AQI) is categorized as ""Good"". -- **Unhealthy Days**: Seattle averages 14.2 unhealthy pollution days per year, exceeding the EPA's recommended limit of 3.2 unhealthy days for each pollutant. - -#### Predictions and Forecasts -- **2024 Projections**: IQAir predicts that Seattle will experience poor air quality for three weeks in 2024, with a trend expected to worsen in the coming decades due to climate change and increased wildfires. -- **Metaculus Predictions**: The Metaculus community is currently discussing and forecasting the number of days with an Air Quality Index value above 150 in the 3rd quarter of 2024. The question is pending approval by community moderators. - -#### Expert Opinions -- **IQAir CEO**: Glory Dolphin Hammes highlighted that fossil fuels are the primary contributor to air pollution in Seattle, and that the city experiences 10% more air pollution than similar American cities due to industry and transportation. -- **Mitigation Strategies**: Experts suggest that shifting to cleaner energy, increasing the use of electric vehicles, and implementing prescribed fires to clear forest underbrush could help reduce air pollution levels. - -#### Recent Updates -- **Wildfire Impact**: Wildfires are becoming more common and longer-lasting, significantly impacting air quality. The West Coast experienced a less severe wildfire season in 2023, but this trend is expected to worsen. -- **Seasonal Variations**: Elevated PM2.5 levels are common in late summer and fall due to wildfires, and in winter due to wood burning. - -### Conclusion -The Seattle-Tacoma-Bellevue WA metro area faces significant challenges with air quality, driven by a combination of local emissions, wildfires, and broader climate trends. While there have been some improvements, the area continues to exceed federal health standards for air quality. Predictions for 2024 indicate a worsening trend, with a focus on mitigating factors such as fossil fuel use and wildfire management. - -######### - -GPT - -### Step-by-Step Reasoning: - -1. **Historical Context and Base Rates**: - - In the past ten years, Seattle has had the following number of days above AQI 150 during the periods considered: - - 2023: 3 days - - 2022: 11 days - - 2021: 2 days - - 2020: 9 days - - 2019: 0 days - - 2018: 7 days - - 2017: 11 days - - 2016: 1 day - - 2015: 3 days - - 2014: 0 days - - This gives a base rate of \(\frac{5}{10}\) or 50% for years reaching 11 or more days with AQI over 150. - -2. **Recent Trends and Data**: - - We have noted an increasing trend in both PM2.5 and ozone pollution levels. - - As of July 29, 2024, there has been one day in the third quarter with an AQI over 150. - - Predictions indicate three weeks of poor air quality at some point in 2024. - -3. **Seasonal and Environmental Factors**: - - Wildfire season peaks in late summer, which aligns with an increased number of high AQI days. - - Historical data shows that the majority of high AQI days fall within the third quarter due to wildfire smoke. - -4. **Mitigating and Aggravating Factors**: - - Mitigating factors include improved air quality measures and predictability of wildfire management. - - Aggravating factors include climate change contributing to longer and more intense wildfire seasons, and data showing a decline in air quality over recent years. - -5. **Short-Term Reports and Predictions**: - - As of the current date, local air quality is considered ""Good"" but this can change rapidly during wildfire season. - - Expert opinions suggest worsening trends due to climate change and increased industry emissions. - -6. **Probability Calculation**: - - Considering that the probability is already enhanced by recent trends and climate change effects: - - Current guess for the base rate of exceeding 11 AQI > 150 days in the 3rd quarter alone could be closer to recent trend rates. Since this year seems typical or slightly worse in terms of wildfire effects, let's lean towards recent trend data. - - Given 5/10 years monitored exceed 11 days above 150 AQI, applying reasonable increase: - -7. **Updated Probability Estimation**: - Through incorporation of trends (including the recent 1 day out of one month reported above 150, tendency for late summer fires, and worsening projections): - - Expected counted days so far: likely underestimated compared to base rate due to peak AQI in later summer. - - Based on worsening trends through 2024, suggest improved aggressive base rate: - -### Conclusion: - -Considering the above factors and the base rates affected by trends and realistic projections for 2024, I come to a refined estimation: - -**Probability: 55%-65%** for 11 or more AQI >150 days - -Settling within range with balanced weight on variance: - -**Final Probability: 58%** - -Given this is conservative multiple factors eligibility grant more nuanced probability, final capture represented: ""Probability: 58%,"" balacing to practical insights and projections analyzed." -26775,"Will someone other than Fabiano Caruana, Alireza Firouzja, Praggnanandhaa Rameshbabu, or Gukesh Dommaraju win the 2024 Grand Chess Tour?","PERPLEXITY - -**Question:** Will someone other than Fabiano Caruana, Alireza Firouzja, Praggnanandhaa Rameshbabu, or Gukesh Dommaraju win the 2024 Grand Chess Tour? - -**Context:** - -1. **Current Standings:** As of July 30, 2024, Fabiano Caruana leads the Grand Chess Tour with 22.25 points, followed by Alireza Firouzja with 17.58 points, and Praggnanandhaa Rameshbabu with 16.25 points. - -2. **Recent Tournament Results:** The Superbet Chess Classic Romania, part of the Grand Chess Tour, saw Firouzja bounce back with a win against Abdusattorov after a poor first day. Caruana drew his game with So, missing an opportunity to reclaim the world number-two position in live ratings. - -3. **Candidates Tournament:** Gukesh Dommaraju won the Candidates Tournament, becoming the youngest player to do so. Despite his victory, Fabiano Caruana and Ian Nepomniachtchi had higher accuracy rates during the tournament. - -4. **Field and Snubs:** The 2024 Grand Chess Tour field includes notable players like Caruana, Firouzja, Praggnanandhaa, and Gukesh. There are discussions about potential snubs, with some players like Arjun and Vidit feeling they were equally deserving of a spot. - -5. **Prediction Markets:** Metaculus users are discussing the likelihood of other players winning the tour, with questions focusing on whether someone outside the top contenders will emerge victorious. - -**Recent Updates:** -- Firouzja's recovery in the Superbet Chess Classic Romania indicates his potential to catch up in the standings. -- Caruana's draw with So, despite a strong start, suggests he might face challenges maintaining his lead. -- Gukesh's recent win in the Candidates Tournament boosts his chances in the Grand Chess Tour. - -**Expert Opinions:** -- The field is highly competitive, with several top players having strong performances in recent tournaments. -- The tour's format and the players' recent form suggest that while Caruana, Firouzja, Praggnanandhaa, and Gukesh are favorites, other players could still make a strong push. - -**Current Predictions:** -- Metaculus users are actively discussing and predicting the outcomes, with a focus on the top contenders but also considering the potential for other players to win. - -This context provides a comprehensive overview of the current standings, recent tournament results, and expert opinions, which are crucial for forecasting the winner of the 2024 Grand Chess Tour. - -######### - -GPT - -To determine the probability that someone other than Fabiano Caruana, Alireza Firouzja, Praggnanandhaa Rameshbabu, or Gukesh Dommaraju will win the 2024 Grand Chess Tour, we need to take a systematic approach, considering both the data provided and general knowledge about the structure and dynamics of similar tournaments. - -### Analysis Structure - -#### 1. **Current Standings and Points Distribution** - - **Caruana**: 22.25 points - - **Firouzja**: 17.58 points - - **Praggnanandhaa**: 16.25 points - -The three are leading significantly; typically, leading by notable points at this stage means high probabilities of maintaining leading positions through remaining tournaments. - -#### 2. **Remaining Tournaments** - - **2024 SAINT LOUIS RAPID & BLITZ** - - **2024 SINQUEFIELD CUP** - -The remaining two tournaments offer opportunities for other players to accumulate points, but significant variances in outcomes are less probable given the leading positions of the top contenders. - -#### 3. **Historical Performance and Variability** - - The top players tend to be consistent in their performance. - - Upsets and big swings in points are less common in high-level chess tournaments. - -#### 4. **Competitiveness and Recent Updates** - - Firouzja's recent recovery and Caruana's consistency indicate both are in strong form. - - Gukesh's win in the Candidates Tournament shows his capacity to perform but he is currently not in the top 3. - -#### 5. **Expert Opinions and Prediction Markets** - - Experts and prediction market discussions suggest the top contenders have the highest likelihood of winning. - - Metaculus and similar forums offer insights but considering the strong positions of the leading players makes upsets less likely. - -#### 6. **Remaining Potential Competitors** - - To foresee another player emerging from behind and winning the whole tour would require extraordinary performances and multiple favorable outcomes, which statistically have a lower probability. - -### Summary of Findings: - -- **Top Three leading with a significant margin**: Caruana, Firouzja, and Praggnanandhaa. -- **Remaining Tournaments**: Potential for shifts but top players' consistency reduces likelihood of major swings. -- **Consistency and High-level Play**: Top 3 displaying consistent strong performances. - -### Probability Calculation: -Considering the high probability of the current leaders maintaining their positions based on historical data and the structure of chess tournaments, the probability of someone outside this group winning becomes quite low. We need to adjust for plausible variance but keep in mind the practical constraints: - -- Base rate estimation: If there is a 90% chance one of the top leaders will hold their position based on existing data, there’s approximately a < 10% chance for an upset. -- Adjusting for two significant tournaments left, maintain conservative estimates in line with historical patterns and expert discussions. - -### Final Probability: -Combining our considerations, including the limited number of games left and the strength of the current leaders: - -**Probability: 15%** - -Thus, there’s a 15% chance (near one in seven) that someone other than Fabiano Caruana, Alireza Firouzja, Praggnanandhaa Rameshbabu, or Gukesh Dommaraju will win the 2024 Grand Chess Tour." -26772,Will Alireza Firouzja win the 2024 Grand Chess Tour?,"PERPLEXITY - -To forecast whether Alireza Firouzja will win the 2024 Grand Chess Tour, we need to consider recent performances, tournament standings, and expert opinions. Here is a concise rundown of the most relevant news: - -### Recent Performances and Standings -1. **Chess.com Classic 2024**: - - Alireza Firouzja defeated Magnus Carlsen twice in the Grand Final, winning the tournament and earning $30,000 and 100 tour points. - - This victory marked Firouzja's first win in a Champions Chess Tour event and secured his spot in the in-person live Finals. - -2. **CrunchLabs Masters 2024**: - - Firouzja set up an all-French Grand Final against Maxime Vachier-Lagrave by defeating Ian Nepomniachtchi in the Losers Final. - - This performance indicates Firouzja's strong form and ability to handle high-pressure situations. - -### Grand Chess Tour Standings -- Firouzja has accumulated significant points and prize money in the 2024 Grand Chess Tour, including wins in the 2021 FIDE Grand Swiss and the 2022 Sinquefield Cup and Saint Louis Rapid & Blitz. -- His current standings and recent wins position him as a strong contender for the overall tour victory. - -### Expert Opinions and Predictions -- **Metaculus Prediction Market**: - - The Metaculus prediction market is currently awaiting approval for a question on who will win the 2024 Grand Chess Tour. This platform often reflects expert and community opinions, which can be a valuable indicator of likely outcomes. - -### Recent Updates and Movement -- Firouzja's recent victories and consistent performance in high-level tournaments suggest he is in strong form and well-positioned to win the 2024 Grand Chess Tour. -- His ability to defeat top players like Magnus Carlsen and Ian Nepomniachtchi in critical matches underscores his potential to succeed in the tour's final events. - -### Conclusion -Alireza Firouzja's recent wins, strong tournament standings, and expert opinions collectively indicate that he is a strong candidate to win the 2024 Grand Chess Tour. His performance in the Chess.com Classic and the CrunchLabs Masters, along with his historical successes in major tournaments, support this forecast. - -######### - -GPT - -To arrive at a well-founded probability for Alireza Firouzja winning the 2024 Grand Chess Tour, let's systematically parse through the relevant data and consider multiple perspectives. We'll start with a close examination of the context provided, taking into account Firouzja's recent performance, current standings, and remaining tournaments. - -### Current Standings -- Caruana: 22.25 points -- Firouzja: 17.58 points -- Praggnanandhaa: 16.25 points - -### Upcoming Tournaments -1. **2024 SAINT LOUIS RAPID & BLITZ (August 12-16, 2024)**: Rapid and Blitz events are generally more volatile than classical chess, which gives more room for upsets and less predictable outcomes. -2. **2024 SINQUEFIELD CUP (August 17-23, 2024)**: This is a classical tournament, which tends to favor players with strong, consistent performance in classical chess. - -### Firouzja's Recent Performances and Form -- Victory in the Chess.com Classic 2024, including a notable double win against Magnus Carlsen. -- Strong performance in the CrunchLabs Masters 2024, defeating high-caliber opponents like Ian Nepomniachtchi. -- Historical success in similar formats, enhancing his credibility and skill level. - -### Expert Opinions and Prediction Markets -- While we don't have concrete data from Metaculus yet, the prediction markets are often reliable indicators of community sentiment and expert analysis. - -### Calculation of Points Needed -Firouzja needs to surpass Caruana, implying he needs at least 4.68 points plus one more to tie or win outright, assuming Caruana doesn't score any more points (an unrealistic but simplifying assumption). - -### Competition Dynamics -- **Volatility in Rapid & Blitz**: While Firouzja is a strong Rapid and Blitz player, these formats are inherently more unpredictable. If Firouzja can win this tournament or place high, his chances will increase significantly. -- **Classical Performance**: Caruana is renowned for consistency in classical chess, but Firouzja’s recent form indicates he can challenge and outperform even top classical players. - -### Historical Context -- In recent Grand Chess Tours and similar chess competitions, it's uncommon but not unprecedented for players to make significant point gains in the final tournaments. - -### Quantitative Analysis -Assuming: -- Firouzja needs to secure first or very high placements in the remaining events. -- A win or second place in Rapid & Blitz can give significant but not overwhelming points. -- Strong finish in Sinquefield Cup will be crucial. - -### Bayesian Update -Given Firouzja's current skill, recent performance, and the volatility of upcoming formats, let's quantify: - -1. **Base Rate Analysis**: Historically, the leader after three events has a strong chance but not a guarantee of winning (~60%). -2. **Performance Update**: Considering Firouzja’s recent performance and his ability to handle high-pressure matches, I'd adjust his likelihood upwards (~30%). -3. **Competition and Event Volatility**: Give a cautious adjustment because of the unpredictability (balance out at 10%-15%). - -**Initial Priors**: -- Firouzja’s chance assuming linear progression: 17.58 points vs needing a lead (~20%). - -### Considering All Factors -Considering Firouzja's form, current standings, remaining tournaments, historical patterns, his recent victories, and the potential for high-scoring in volatile formats: - -- **Lower Bound**: 30% (accounting lower probability due to volatility). -- **Upper Bound**: 50% (incorporating higher historical performance rates and lower variance). - -### Final Probability -Balancing all the data points: ""Probability: 40%""" -26773,Will Praggnanandhaa Rameshbabu win the 2024 Grand Chess Tour?,"PERPLEXITY - -To forecast whether Praggnanandhaa Rameshbabu will win the 2024 Grand Chess Tour, we need to consider recent performances, current standings, and expert opinions. Here is a concise rundown of the most relevant news: - -### Recent Performances and Standings -- **Superbet Classic 2024**: Praggnanandhaa Rameshbabu had a significant opportunity to win against Wesley So but missed it, ending in a draw. Despite this, he remains competitive, with Fabiano Caruana leading the standings with 3.5/5 points. -- **SuperUnited Rapid & Blitz Croatia**: Maxime Vachier-Lagrave took the lead after day one, with Praggnanandhaa and Nodirbek Abdusattorov missing from the event. This tournament is crucial for the overall tour standings. -- **Superbet Chess Classic Romania**: Praggnanandhaa and Maxime Vachier-Lagrave both missed winning positions, leading to a day of draws. This event is part of the Grand Chess Tour and contributes to the overall standings. - -### Expert Opinions and Predictions -- **Magnus Carlsen's Withdrawal**: The withdrawal of Magnus Carlsen due to family reasons has significantly impacted the tour, as he was a strong contender. His replacement, Levon Aronian, has been performing well. -- **Current Standings**: The Grand Chess Tour standings before the SuperUnited Rapid & Blitz Croatia event show Praggnanandhaa Rameshbabu and Nodirbek Abdusattorov as leaders, but their absence from this event could affect their overall position. - -### Prediction Markets -- **Metaculus**: The question on Metaculus about the 2024 Grand Chess Tour winner is awaiting approval. Once approved, it will provide a community-driven prediction based on user inputs. - -### Conclusion -Praggnanandhaa Rameshbabu's chances of winning the 2024 Grand Chess Tour depend on his performance in the remaining events, particularly the Sinquefield Cup and the St. Louis Rapid & Blitz. His recent draws and missed opportunities suggest he is competitive but not dominant. The absence of Magnus Carlsen and the strong performances of other players like Maxime Vachier-Lagrave and Fabiano Caruana add complexity to the forecast. - -######### - -GPT - -Let's break down the problem into relevant parts and consider each one carefully. - -### Current Standings and Points - -As of today, the standings show: -- **Fabiano Caruana** leading with **22.25 points**. -- **Alireza Firouzja** in second place with **17.58 points**. -- **Praggnanandhaa Rameshbabu** in third place with **16.25 points**. - -### Tournaments Remaining -- **Saint Louis Rapid & Blitz** (Quicker time controls) -- **Sinquefield Cup** (Classical time control) - -In chess, faster formats like Rapid and Blitz tend to have more variance compared to Classical formats. Players like Praggnanandhaa, who are strong in faster time controls, can take advantage of this variance to potentially gain substantial points. - -### Performance Context -Let's briefly analyze the potential performance of Praggnanandhaa across the remaining events. - -1. **Saint Louis Rapid & Blitz**: - - This format could favor Praggnanandhaa, who has shown prowess in quicker time controls, thus potentially gaining significant points. -2. **Sinquefield Cup**: - - Being a Classical format tournament, it tends to be more predictable and align more closely with the standard rating hierarchy. - -### Competitor Strength -Consider the primary competitors: -- **Fabiano Caruana**: - - Currently leading and known for consistent performances, especially in Classical formats. -- **Alireza Firouzja**: - - Another strong competitor. Capable of high performances, especially if in good form. - -### Expert Opinions and Past Results -Expert insights have shown: -- Magnus Carlsen's withdrawal impacts the predictability. -- Other players who missed winning chances or key events could influence standings variance. - -### Historical Data and Base Rate Analysis -- Historically, leaders at this point in the Grand Chess Tour have a higher probability of maintaining their lead. -- Turnarounds are less common but possible due to the high variance in Rapid & Blitz formats. - -### Statistical Considerations -To quantify Praggnanandhaa’s chances, let's estimate: -1. **Saint Louis Rapid & Blitz**: - - Suppose Praggnanandhaa has a 30% chance to achieve a top position (substantial gain of ~5 points). -2. **Sinquefield Cup**: - - Assume a more moderate probability where he gains another ~3-4 points. - -If he scores optimally in both, he may gather another 8-9 points, putting him close to 25 points overall. However, Caruana only needs to finish moderately well to stay ahead, given his lead. - -### Final Probability Assessment -Considering all factors: -- Praggnanandhaa’s skill in Rapid & Blitz. -- The different formats involved. -- Caruana's strong current standing and likelihood to maintain it. -- Historical outcomes where leaders tend to hold their positions. - -Putting this all together: - -**Final Probability**: -- **Praggnanandhaa Rameshbabu wins the 2024 Grand Chess Tour**: - -**Probability: 20%**" -26774,Will Gukesh Dommaraju win the 2024 Grand Chess Tour?,"PERPLEXITY - -To forecast whether Gukesh Dommaraju will win the 2024 Grand Chess Tour, we need to consider his recent performance, the structure of the Grand Chess Tour, and expert opinions. Here is a concise rundown of the most relevant information: - -### Recent Performance -- **Superbet Chess Classic Romania**: Gukesh won his first classical game since winning the Candidates, defeating wildcard GM Bogdan-Daniel Deac. This tournament is part of the Grand Chess Tour and features a $350,000 prize fund. -- **SuperUnited Rapid & Blitz Croatia**: Gukesh had a mixed day, initially struggling against Maxime Vachier-Lagrave but eventually winning due to a blunder by his opponent. He also drew against Vidit Gujrathi and won against Ian Nepomniachtchi, putting him two points behind the leader, Fabiano Caruana. - -### Grand Chess Tour Structure -- The 2024 Grand Chess Tour consists of multiple events, including rapid and blitz tournaments, with the overall winner determined by cumulative points across all events. -- The tour includes five main stops: Superbet Rapid & Blitz Poland, Superbet Chess Classic Romania, SuperUnited Rapid & Blitz Croatia, Saint Louis Rapid & Blitz, and the Sinquefield Cup. - -### Expert Opinions and Prediction Markets -- **Metaculus Prediction Market**: As of the latest update, the question of who will win the 2024 Grand Chess Tour is pending approval. However, it indicates that users are actively discussing and predicting the outcome, which can provide insights into the community's expectations. - -### Current Standings and Future Events -- **Current Standings**: Gukesh has shown resilience and skill in recent events, particularly in the SuperUnited Rapid & Blitz Croatia, where he rebounded from a tough start to climb back into contention. -- **Future Events**: The remaining events on the Grand Chess Tour will be crucial in determining the overall winner. Gukesh's performance in these events will be closely watched, especially given his recent form and the competitive field. - -### Conclusion -Gukesh Dommaraju has demonstrated strong performance in recent Grand Chess Tour events, particularly in the Superbet Chess Classic Romania and the SuperUnited Rapid & Blitz Croatia. His ability to rebound from difficult positions and his strategic play make him a strong contender for the overall title. However, the Grand Chess Tour is highly competitive, and other top players like Fabiano Caruana and Maxime Vachier-Lagrave are also in strong contention. The outcome will depend on how well Gukesh performs in the remaining events. - -######### - -GPT - -### Chain of Thought - -1. **Current Standings**: Gukesh Dommaraju is trailing behind Caruana, Firouzja, and Praggnanandhaa. Caruana's lead is significant with 22.25 points. Gukesh would need substantial performances in the remaining tournaments to catch up. - -2. **Structure and Remaining Tournaments**: The remaining tournaments are the Saint Louis Rapid & Blitz and the Sinquefield Cup. Both events offer opportunities for players to gain points, but the rapid and blitz formats could offer more volatile and unpredictable outcomes compared to the classical format. - -3. **Recent Performance**: Gukesh has shown resilience and capability in recent tournaments, indicating strong current form. However, he has had mixed results and is often trailing top competitors like Caruana. - -4. **Competitor Analysis**: - - **Caruana**: Strong lead, experienced, and consistently high performer. Likely to maintain a significant portion of his lead if he performs to his historical level. - - **Firouzja and Praggnanandhaa**: Both have performed well and are ahead of Gukesh in the standings. They also have strong recent forms and are capable of strong showings in remaining events. - -5. **Statistical Analysis and Base Rates**: - - Past Grand Chess Tour winners have often been the ones leading at the midway point due to the structure of point accumulation. - - Rapid and blitz tournaments can cause more variability, but classical tournaments often favor the most consistently strong players. - -6. **Time to Event and Variance Considerations**: With two events remaining, there's room for significant movement in standings—especially in a shorter time control format like blitz and rapid. However, given the current point distribution, making up the gap with the leading players will be difficult. - -7. **Expert Opinions/Evaluation**: While there is no explicit expert data given, it is inferred from the standings and typical competitive dynamics that current leaders likely maintain their edge in cumulative point systems. - -### Rationale for Probability - -- **Default Outcome**: If trends hold and Caruana continues his performance, his lead would be difficult for Gukesh to overcome. -- **Potential for Change**: Due to the format and remaining events, there's still the opportunity for volatility, but it heavily depends on Gukesh performing extremely well and others faltering. -- **Base Rates**: Historically, leaders mid-tour often maintain their lead with consistent performances. - -Given the above factors, Gukesh winning the Grand Chess Tour remains possible but unlikely given the current point discrepancy and his need for exceptional performance in both remaining formats and also requiring missteps from his top competitors. - -### Final Probability Estimate - -Taking all the factors into account and adjusting for the uncertainty and the potential for variance: - -**Probability: 10%**" -26781,"Will William Ruto cease to be President of Kenya before October 1, 2024?","PERPLEXITY - -To forecast whether William Ruto will cease to be President of Kenya before October 1, 2024, we need to consider recent political developments, public sentiment, and legal challenges. Here is a concise rundown of the most relevant news: - -### Recent Political Developments -1. **Protests and Public Sentiment**: - - **Protests**: There have been significant antigovernment protests in Kenya, with demonstrators calling for President Ruto's resignation. These protests have been ongoing since mid-June, prompted by government-planned tax hikes and exacerbated by issues of stagnating wages and corruption. - - **Government Response**: President Ruto has attempted to quell the unrest by scrapping the planned tax increases and dismissing his cabinet. He has also invited opposition leaders to join his government, appointing key allies of Raila Odinga to his cabinet. - -2. **Legal Challenges**: - - **Court Ruling**: The Kenyan Court of Appeal recently declared the government's 2023 finance law unconstitutional, which is a significant legal blow to President Ruto's administration. - -### Expert Opinions -- **Security Analyst**: Stella Agara, a Nairobi-based security analyst, believes that the protests will intensify and become more frequent unless President Ruto addresses the demonstrators' grievances directly. - -### Prediction Markets -- **Metaculus**: The prediction market on Metaculus indicates that there is a significant probability that William Ruto will cease to be President of Kenya before October 1, 2024, reflecting the ongoing political instability and legal challenges. - -### Current Values and Updates -- **Protest Casualties**: Over 50 people have been killed during the protests, with many more injured or missing. -- **Cabinet Changes**: President Ruto has expanded his cabinet to include opposition members, a move aimed at stabilizing his government. - -### Conclusion -Given the ongoing protests, legal challenges, and the recent cabinet reshuffle, there is a heightened sense of political instability in Kenya. While President Ruto has taken steps to address the unrest, the situation remains volatile. The prediction market and expert opinions suggest a possibility that he might cease to be President before October 1, 2024, but the outcome is uncertain and dependent on how these factors evolve in the coming weeks. - -######### - -GPT - -Sure, let's break down the situation methodically and apply careful analysis to arrive at a probability estimate for whether William Ruto will cease to be President of Kenya before October 1, 2024. - -### Analysis - -#### Context and Recent Developments - -1. **Protests and Public Sentiment**: - - Anti-government protests have been significant and widespread, sparked by proposed tax hikes and exacerbated by issues such as corruption and stagnant wages. - - At least 39-50+ people have been killed in these protests, indicating a high level of unrest and public dissatisfaction. - - These protests have evolved into demands for Ruto's resignation, indicating a serious challenge to his authority. - -2. **Government Response**: - - Ruto's actions such as firing his cabinet and subsequently inviting opposition members to join it are clear attempts to mitigate the situation. These steps indicate he is taking the protests seriously and trying to stabilize his administration. - - The effectiveness of these measures is uncertain. While inclusivity may appease some factions, it might not be enough given the depth of the grievances. - -3. **Legal Challenges**: - - The recent court ruling against the government's 2023 finance law adds another layer of complication, possibly weakening Ruto's position further. - - Legal instability can reflect or contribute to political instability, but alone it may not be enough to cause a resignation or removal from office. - -4. **Security Apparatus and Key Resignations**: - - The resignation of the police chief amid accusations of excessive force could be indicative of instability within the security apparatus, which is critical for maintaining order and authority. - - This resignation might embolden protestors and opposition forces. - -5. **External and Internal Political Pressures**: - - The inclusion of opposition members in Ruto's cabinet suggests significant internal political pressure. How these dynamics play out will be crucial in the coming weeks. - -### Base Rates and Historical Context - -1. **Historical Precedents in Kenya and Similar Political Environments**: - - Historically, political crises in Kenya have seen a range of outcomes from negotiated settlements to periods of prolonged unrest without a change in leadership. - - Across the globe, high-intensity protests leading to a leader's ouster are relatively rare but not unprecedented. The context in Kenya seems particularly volatile. - -2. **Default Scenario and Time Frame**: - - In most scenarios, political leaders survive short term crises unless there is a cohesive, strong opposition with sufficient power to enforce change, or the leader is internally weakened enough to consider resignation or face forced removal. - - With only two months until October 1, 2024, the time frame for such a significant political change is relatively short. This compresses the likelihood of a complete transition despite current volatility. - -### Expert Opinions and Prediction Markets - -1. **Expert Opinions**: - - Security analysts suggest that protests are likely to intensify unless grievances are addressed more directly. - -2. **Prediction Markets**: - - Platforms like Metaculus show significant probability, reflecting general sentiment of instability, but exact numerical probabilities should be viewed with slight caution against overreacting to short-term trends. - -### Final Considerations - -Given these factors, the current scenario portrays a highly unstable political situation, but a dramatic shift such as the cessation of Ruto's presidency within the next two months is a considerable leap, even in the volatile climate. Historical precedents, the time frame, and typical political inertia factor heavily. - -### Probability Estimation - -Taking into account the protests' intensity, government response, recent judicial rulings, and internal political maneuvers: -- **Imminent, drastic changes** (such as resignation or removal): Moderately possible but not the highest probability due to time constraints and inherent political resilience. -- **Maintaining status quo with ongoing instability**: More likely, given typical political dynamics. - -### Conclusion - -After a thorough analysis balancing the many factors in play, the probability that William Ruto will cease to be President of Kenya before October 1, 2024 is estimated at: - -**Probability: 35%** - -This acknowledges significant instability and potential for change, but also the resilience and shorter time frame working in favor of status quo." -26780,"Will Bitcoin reach a new all-time high before October 1, 2024?","PERPLEXITY - -To determine whether Bitcoin will reach a new all-time high before October 1, 2024, we need to consider recent trends, expert opinions, and significant events that could influence its price. - -### Recent Trends and Events - -1. **Historical Highs**: Bitcoin reached an all-time high of $73,000 in March 2024, driven by the SEC's approval of spot Bitcoin ETFs and the halving event in April 2024. - -2. **Price Fluctuations**: After reaching the all-time high, Bitcoin's price dropped to around $63,000 by May 2024. This volatility is typical for Bitcoin, with prices often fluctuating significantly. - -3. **Institutional Adoption**: The approval of spot Bitcoin ETFs by the SEC in January 2024 has been a significant catalyst for Bitcoin's price. This approval has increased institutional interest and legitimacy for cryptocurrency investments. - -4. **Halving Event**: The recent halving event in April 2024 reduced the block reward from 6.25 BTC to 3.125 BTC. Historically, halving events have been followed by significant price increases, although the impact of this event is still being assessed. - -### Expert Opinions and Predictions - -1. **Bullish Sentiment**: Many experts are optimistic about Bitcoin's future, predicting a potential bull run that could push prices to new highs. For example, Cathie Wood of Ark Invest has predicted that Bitcoin could reach $1.48 million by 2030. - -2. **Short-Term Predictions**: Some analysts predict that Bitcoin could reach $100,000 by the end of 2024 or early 2025, driven by increased adoption and the halving event. - -3. **Technical Indicators**: Technical indicators suggest that Bitcoin's price could increase by 13.15% to reach $72,839 by August 3, 2024, indicating a positive short-term outlook. - -### Market Predictions and Volatility - -1. **Prediction Markets**: While there are no specific prediction markets mentioned in the sources, the overall sentiment from analysts and investors is bullish, suggesting a high likelihood of reaching new highs before October 1, 2024. - -2. **Volatility**: Bitcoin's price is inherently volatile, and while it has the potential to reach new highs, it also faces the risk of significant drops. The recent price drop from $73,000 to $63,000 highlights this volatility. - -### Conclusion - -Given the recent trends, expert opinions, and significant events, there is a strong likelihood that Bitcoin could reach a new all-time high before October 1, 2024. The bullish sentiment, institutional adoption, and the potential impact of the halving event all contribute to this positive outlook. However, the inherent volatility of Bitcoin's price means that there is always a risk of significant fluctuations. - -######### - -GPT - -To accurately forecast whether Bitcoin will reach a new all-time high before October 1, 2024, we need to systematically consider a variety of factors, trends, historical data, and expert opinions. Let's break down the analysis into several steps: - -### 1. Historical Trends and Recency -- **All-Time Highs and Volatility**: Bitcoin's all-time high (ATH) occurred on March 14, 2024, at $73,750.07. Bitcoin has a history of extreme volatility and frequent significant price corrections, as evidenced by the subsequent drop to around $63,000 in May 2024 and a local minimum of $53,905.56 on July 5, 2024. -- **Recent Price Recovery**: Bitcoin has shown recovery in late July, reaching over $69,000 on July 29. This indicates a resilient bullish sentiment. - -### 2. Key Catalysts and Events -- **Halving Event**: The recent halving in April 2024 historically drives price increases due to reduced new supply. The ongoing impact of this event could still be influential as the market digests the reduced miner rewards. -- **Institutional Adoption**: Approval of spot Bitcoin ETFs by the SEC in January 2024 has bolstered institutional interest, which tends to bring in significant capital and supports higher prices. -- **Macroeconomic Factors**: Factors such as global economic conditions and regulatory developments can have substantial impacts on Bitcoin’s price. Currently, cryptocurrency seems to have positive regulatory momentum. - -### 3. Expert Opinions and Technical Analysis -- **Bullish Predictions**: Many experts, including prominent investment voices like Cathie Wood, provide bullish long-term predictions, though these are often seen as optimistic projections rather than short-term certainties. -- **Technical Indicators**: Short-term technical indicators showed the potential for an increase to approximately $72,839 by August 3, 2024. This demonstrates a strong upward trend but just shy of reaching a new ATH. -- **Prediction Markets**: Although not explicitly detailed, sentiment suggests a bullish outlook, which aligns with the technical analyses and expert opinions. - -### 4. Statistical Analysis and Volatility Considerations -- **Volatility Analysis**: Bitcoin's historical volatility must be factored in. The price has seen frequent swings and has shown the capability to move several thousand dollars in days. -- **Daily and Weekly Movement**: Analyzing daily and weekly historical movement, Bitcoin commonly sees 5-10% daily fluctuations under high volatility periods. Within the next two months, significant upward or downward movement is possible. - -### 5. Time Frame Consideration -- **Time Span**: We have approximately two months until October 1, 2024. While short, this is enough for a significant price movement considering Bitcoin’s historical behavior. -- **Current Price vs. Target**: As of August 1, 2024, Bitcoin's price is above $69,000, needing to exceed $73,750.07 to set a new ATH. This represents an increase of about 7%—a feasible movement within the given period. - -### 6. Base Rate Examination -- **Historical ATH Frequency**: Historically, Bitcoin has set new ATHs periodically, often within months following significant bullish catalysts like halving events. -- **Market Psychology**: Positive sentiment coupled with institutional backing and halving events historically leads to new highs. - -### Conclusion -Given the above analysis, significant factors lean towards Bitcoin potentially reaching a new ATH in the specified timeframe. Although inherent volatility is a risk, the convergence of historical catalysts, bullish sentiment, and strong recent price action suggests a favorable outcome. - -Thus, integrating all these considerations, here is the final forecast: - -**Probability: 75%**" -26780,"Will Bitcoin reach a new all-time high before October 1, 2024?","PERPLEXITY - -To determine whether Bitcoin will reach a new all-time high before October 1, 2024, we need to consider recent trends, expert opinions, and historical patterns. - -### Recent Trends and Expert Opinions - -1. **Historical Highs and Current Price**: - - Bitcoin reached a new all-time high of over $73,000 in March 2024, driven by the approval of spot Bitcoin ETFs by the SEC. - - As of July 2024, Bitcoin's price has fluctuated, dropping below $63,000 in May but showing resilience. - -2. **Halving Event**: - - The recent halving event in April 2024 reduced the block reward from 6.25 BTC to 3.125 BTC, which historically has led to price increases. - - Experts like Nicholas Sciberras from Collective Shift note that the halving event is a significant factor in Bitcoin's price trajectory, though its impact is still being evaluated. - -3. **Institutional Adoption**: - - The approval of spot Bitcoin ETFs by the SEC in January 2024 has been a major catalyst for Bitcoin's price, indicating strong institutional interest. - - This institutional adoption is seen as a positive factor for Bitcoin's future price movements. - -4. **Market Cycles**: - - Historical data suggests that Bitcoin's price cycles tend to peak 12 to 18 months after a halving event, which would place the next peak in late 2024 or early 2025. - - CCData's analysis indicates that Bitcoin has not yet reached the top of its current appreciation cycle and is likely to surpass its all-time high this year. - -### Current Predictions and Forecasts - -1. **Technical Indicators**: - - Changelly's technical indicators suggest that Bitcoin's value will increase by 13.15% and reach $72,839 by August 3, 2024. - -2. **Expert Forecasts**: - - Thomas Perfumo from Kraken cryptocurrency exchange believes that historical trends indicate further potential for Bitcoin's price growth, suggesting that the market cycles typically peak 12 to 18 months post-halving. - - Lucas Lu, CEO of Bytetrade Lab, is optimistic about Bitcoin's future, citing increased adoption and institutional interest. - -### Conclusion - -Given the recent trends, expert opinions, and historical patterns, there is a strong likelihood that Bitcoin will reach a new all-time high before October 1, 2024. The combination of the halving event, increased institutional adoption, and historical market cycles all point towards a bullish momentum for Bitcoin in the near future. - -######### - -GPT - -To accurately forecast whether Bitcoin will reach a new all-time high (ATH) before October 1, 2024, I need to evaluate the available information systematically and perform a thorough analysis considering multiple factors. - -### Review of Given Information - -1. **Current ATH and Historical Data**: - - The current ATH is $73,750.07, set on March 14, 2024. - - Price recovery trends suggest resilience after reaching a low of $53,905.56 on July 5, with a recent high of over $69,000 on July 29. - -2. **Halving Event**: - - Historically, Bitcoin's price tends to increase following halving events, typically peaking 12 to 18 months afterwards. - - The most recent halving occurred in April 2024, which is still within the likely upward trend period. - -3. **Institutional Adoption**: - - Approval of spot Bitcoin ETFs by the SEC and growing acceptance in the financial sector indicate strong institutional interest, which is a catalyst for price increases. - - Growing use cases, such as donations for US presidential campaigns, contribute to wider adoption. - -4. **Market Cycles**: - - Historical cycles indicate that Bitcoin hasn’t yet peaked in its current cycle, hinting at upward potential. - -5. **Expert Opinions and Technical Indicators**: - - Predictions suggest Bitcoin could reach $72,839 by August 3, 2024. - - Experts provide a positive outlook based on historical trends and current adoption. - -### Analytical Breakdown - -1. **Short-term Trends**: - - The recent rise to $69,000 suggests a potential bullish trend. Bitcoin prices have fluctuated substantially but tend to exhibit strong rebounds after dips, aligning with typical market behavior. - -2. **Historical Context**: - - Post-halving behaviors historically lead to sustained upward trends within a specific timeframe (12-18 months). - - Bitcoin’s cyclical nature and previous recoveries from significant drops provide a model for potential future performance. - -3. **Institutional Impact**: - - Institutional investors can drive demand significantly, pushing prices higher. The January 2024 approval of Bitcoin ETFs is a pivotal factor indicating increased facilitation of investment. - -4. **Technical Analysis**: - - Predictions for early August suggest a rise, indicating that there’s enough room and momentum to likely surpass the previous ATH in the coming few months. - -### Possible Countervailing Forces - -- **Market Volatility**: Bitcoin’s historical volatility means that prices can swing dramatically due to investor sentiment, regulatory changes, geopolitical events, or shifts in broader financial markets. -- **Regulatory Risks**: Potential regulatory crackdowns or negative news can adversely affect prices. -- **Macroeconomic Conditions**: Economic downturns or a decline in risk-appetite among investors might limit significant price increases. - -### Statistical Consideration - -Given historical precedents and current market dynamics: - -1. **Base Rate Calculation**: - - Analyzing past halving events, Bitcoin has consistently reached new highs within the subsequent 12-18 months. - -2. **Trend Analysis**: - - Current trends imply an increase from $69,000 to beyond $73,750.07 within the next two months is possible based on current momentum, institutional interest, and broader adoption. - -3. **Standard Deviation Implications**: - - Daily and weekly price variations must be considered; Bitcoin’s high volatility means large swings can occur in short periods, providing opportunities for new highs. - -### Conclusion - -Considering the historical patterns, current trends, and expert predictions, the likelihood of Bitcoin reaching a new ATH before October 1, 2024, seems reasonably high. There’s bullish momentum, and the halving event’s historical effects, combined with institutional interest, provide rational grounds for optimism. - -Final Probability Assessment: - -**Probability: 70%** - -This reflects a strong, but not certain, likelihood that Bitcoin will achieve a new ATH within the specified period, accounting for both supportive trends and potential risks." -26771,Will Fabiano Caruana win the 2024 Grand Chess Tour?,"PERPLEXITY - -Fabiano Caruana is currently leading the 2024 Grand Chess Tour with 22.25 points, ahead of Alireza Firouzja with 17.58 points and Rameshbabu Praggnanandhaa with 16.25 points. Caruana recently won the 2024 Superbet Chess Classic Romania, securing the title for the second year in a row after a dramatic final day. He also dominated the 2024 SuperUnited Croatia Rapid & Blitz, winning the event with five rounds to spare and matching Magnus Carlsen's record of 27 points in a Grand Chess Tour rapid and blitz event. - -Caruana's performance has been consistent and strong, with significant leads in both the Superbet Chess Classic Romania and the SuperUnited Croatia Rapid & Blitz. His ability to perform under pressure and his strategic play have been highlighted by his victories in these events. - -The Grand Chess Tour will conclude with the Saint Louis Rapid & Blitz and the Sinquefield Cup, both taking place in August 2024. Given his current lead and recent performances, Caruana is in a strong position to win the 2024 Grand Chess Tour. However, the final events could still see significant shifts in standings, especially if other top players like Firouzja or Praggnanandhaa have strong performances in the remaining tournaments. - -######### - -GPT - -Alright, let's take a step-by-step approach to determine the probability of Fabiano Caruana winning the 2024 Grand Chess Tour. The relevant information we have is as follows: - -1. **Current Standings and Lead**: - - Fabiano Caruana: 22.25 points - - Alireza Firouzja: 17.58 points - - Rameshbabu Praggnanandhaa: 16.25 points - -2. **Remaining Tournaments**: - - Saint Louis Rapid & Blitz - - Sinquefield Cup - -3. **Caruana's Past Performance**: - - Won the 2024 Superbet Chess Classic Romania. - - Dominated the 2024 SuperUnited Croatia Rapid & Blitz. - -4. **Timeframe**: - - Today’s date is 2024-08-01. The remaining tournaments are scheduled in August 2024. - -### Key Factors - -1. **Current Lead**: - - Caruana's lead of 4.67 points over Firouzja and 6 points over Praggnanandhaa is significant, but not insurmountable. - - Each tournament awards a significant number of points, and two tournaments remain. - -2. **Historical Performance and Consistency**: - - Caruana has shown consistent and strong performances in recent events. - - His winning record demonstrates his ability to perform under pressure, especially in both rapid/blitz and classical formats. - -3. **Competitors' Potential**: - - Firouzja and Praggnanandhaa are also strong players. Significant performances from either can lead to a shift in standings. - - The variability in chess (similar to tennis or golf) means that a player having a good or bad tournament can significantly change the outcome. However, top-tier players like Caruana generally maintain a high performance level. - -4. **Remaining Tournament Formats**: - - Saint Louis Rapid & Blitz will have quicker time controls. - - Sinquefield Cup will have classical time controls. - - Caruana has already proven adept in both rapid/blitz and classical formats this year. - -5. **Historical Precedents**: - - Reviewing past GCT winners, it’s common for leading players mid-season to continue strong performances. - - The structure of the tour, distributing points over several tournaments, provides a buffer that rewards consistency. - -### Uncertainty Factors - -1. **Injuries/Health**: - - Unexpected health issues or withdrawals can impact results but are typically rare for professional chess players. - -2. **Competition Performance Variability**: - - The recent tour history shows variance in final standings based on performance in the last legs. - - Both Firouzja and Praggnanandhaa have the skill to close the gap if they perform exceptionally well. - -### Statistical Analysis - -Given the remaining points to be awarded, we need to estimate the probability of Caruana maintaining his lead: -- Let’s assume each tournament awards around 13 points for first place (consistent with previous GCT tournaments). - -If Caruana's competitors were to win both tournaments with maximum points, Caruana would need to perform decently (top 3 finishes) to secure the overall GCT title. Given his lead and consistency, the probability of him not securing adequate points is relatively low. - -### Final Estimation: - -Considering Caruana’s significant lead, strong historical performance, and need for competitors to have exceptional outcomes in both remaining tournaments, I would estimate the probability based on Bayesian updating from the current points and recent results: - -- **Subjective probability of Caruana maintaining top 2 finishes**: Around 70-75% given his recent form and consistency. -- **Competitors' top performances**: Around 25-30% chance they will outperform him significantly over both remaining tournaments. - -Combining these factors, we get: -\[ \text{Probability} = (0.70 + 0.25 \times (1 - 0.70)) = 77.5\% \] - -**Final Answer**: - -**Probability: 78%**." -26840,Will Tesla increase its production in Q3 2024 compared with Q2 2024? ,"PERPLEXITY - -### Summary of Relevant News - -#### Production and Deliveries -- **Q2 2024 Performance**: Tesla produced 410,831 vehicles and delivered 443,956 vehicles, marking a year-over-year decline in production and deliveries. The Model 3/Y production and deliveries decreased by 16% and 5%, respectively, while ""other models"" saw a 24% increase in production and a 12% increase in deliveries. -- **Inventory Management**: Tesla has been focusing on reducing inventory, which led to a significant drawdown in Q2. The difference between production and deliveries was 33,125 units, indicating a strategic effort to manage inventory levels. - -#### Market and Financial Performance -- **Revenue and Profitability**: Despite record quarterly revenue of $25.5 billion in Q2, the automotive business saw a 7% year-over-year decline in revenue. Energy storage and generation business drove the revenue growth, with a notable increase in deployments. -- **Stock Performance**: Tesla's stock has experienced a significant surge, climbing 42% in a month-long rally, partly due to positive Q2 delivery numbers and energy storage growth. However, the stock price has fluctuated, influenced by consumer spending trends and interest rates. - -#### Future Outlook -- **Q3 Expectations**: Analysts anticipate a return to year-over-year growth in Q3, driven by positive revenue and adjusted earnings per share outlooks. However, the company has warned of slower vehicle volume growth for the rest of 2024. -- **Future Products and Initiatives**: Tesla is preparing for the launch of new products, including a more affordable model in 2025 and advancements in autonomy. The robotaxi unveiling has been postponed to October, and the company is also working on its autonomous humanoid robot, Optimus. - -#### Challenges and Competition -- **Competition in China**: Tesla faces significant competition in China, particularly from BYD, which has been outselling Tesla in the region. This has contributed to Tesla's declining deliveries in China. -- **Regulatory Environment**: There are concerns about potential regulatory changes, including comments from former President Donald Trump about ending electric vehicle mandates and tax credits, which could impact Tesla's growth. - -### Recent Updates and Expert Opinions -- **Production Issues**: Tesla has faced production issues, including difficulties in ramping up Model 3 production in Fremont and Model Y production in China, as well as production halts in Germany. -- **Energy Storage Growth**: The energy storage segment has been a bright spot, with record deployments in Q2. This growth is expected to continue and contribute significantly to Tesla's revenue and gross profit. - -### Current Values and Prediction Markets -- **Current Stock Price**: As of the latest update, Tesla's stock price sits at $221.71 per share, with a market capitalization of $785 billion. -- **Prediction Markets**: There are no specific prediction markets available for Tesla's Q3 production. However, the general sentiment among analysts is cautiously optimistic, with expectations of a return to growth in Q3. - -### Conclusion -While Tesla's Q2 deliveries were strong, the company faces challenges in maintaining production and delivery growth. The energy storage segment has shown significant growth, but the automotive segment remains the key driver of financial performance. The outlook for Q3 is positive, but the company must navigate production issues and competitive pressures to achieve sustained growth. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Examine Historical Production Data** -First, let's take a look at the historical production numbers: - -- Q1 2023: 440,808 -- Q2 2023: 479,700 -- Q3 2023: 430,488 -- Q4 2023: 494,989 -- Q1 2024: 433,371 -- Q2 2024: 410,831 - -From the data, a few key observations can be made: -- There's been considerable fluctuation in production numbers across quarters. -- Q2 2024 saw a significant decline in production compared to both Q1 2024 and Q4 2023. -- Production peaked in Q4 2023 and then went into a downward trend. - -#### 2. **Contextual News and Trends** -Using the provided summary, several important points influence the forecast: -- **Inventory Management**: There's been a deliberate effort by Tesla to manage inventory, which might imply a controlled slowdown rather than an inherent inability to produce. -- **Production Issues**: There are clear challenges with ramping up production, particularly with Model 3 and Model Y, and production halts in Germany. If these issues persist, they could negatively impact future quarters. -- **Competitor Pressures**: Tesla faces strong competition, notably from BYD in China, which is likely affecting their production decisions and market strategies. - -#### 3. **Market and Financial Performance** -Despite strong revenue from the energy business, the automotive side showed a decline. The forecast for Q3 shows cautious optimism but also a realization that significant challenges remain, particularly in vehicle volume growth. - -#### 4. **Future Outlook and Expert Opinions** -The expert outlooks suggest some optimism for a return to year-over-year growth in Q3. However, given the recent production issues and the company's own cautionary stance on volume growth for 2024, the overall expectations may need tempering. - -#### 5. **Evaluation of Catalysts for Change** -Considering the current date is August 2, 2024, there are approximately two months left in Q3 for potential changes in production: -- **Time for Recovery**: It's relatively short for addressing deep production issues. -- **Unlikely Events**: No significant novel technology or political shifts have been indicated that would drastically change production capabilities in this short period. - -#### 6. **Base Rates and Historical Precedent** -Looking at the data, Tesla historically has had some quarters of rapid growth followed by plateaus or declines. Given recent performance trends, a further reduction or slight improvement might be more base rate aligned than a significant increase. - -#### 7. **Statistical Approach** -Producing a simple linear regression or calculating expected trend lines might point to a continuous decline or a leveling off. The standard deviation in Tesla's quarterly production figures suggests there's a chance of variance but within a moderate range. - -### Final Analytical Thoughts -Compiling all factors, Tesla has demonstrated periods of volatility, but the current indicators point towards challenges that might be too significant to overcome within the next quarter. The cautious outlook given by analysts and Tesla itself about vehicle volume growth also suggests a lower probability for a significant uptick in Q3 2024. - -Considering historical data, recent production difficulties, competitive pressures, and time constraints, the probability that Tesla will increase its production in Q3 2024 compared with Q2 2024 is moderately low but not impossible. - -### Final Probability Estimate -""Probability: 35%""" -26839,"Will Apple announce an iPhone with stacked battery technology before October 1, 2024? -","PERPLEXITY - -Apple is expected to announce the iPhone 16 lineup in September 2024, which includes models that will use stacked battery technology. This technology is anticipated to result in higher battery capacity and longer battery life for the iPhone 16 Pro models. The release date for the iPhone 16 is likely to be in the first or second week of September, with pre-orders starting on the Friday of the same week and general availability the following Friday. Therefore, it is highly likely that Apple will announce an iPhone with stacked battery technology before October 1, 2024. - -######### - -GPT - -### Understanding the Question - -The question seeks to determine the likelihood that Apple will announce an iPhone model featuring stacked battery technology before October 1, 2024. - -### Step-by-Step Analysis - -1. **Background Evaluation and Source Reliability**: - - [MacRumors](https://www.macrumors.com/roundup/iphone-16/) is generally considered a reliable source for Apple product rumors and leaks. - - The information from MacRumors suggests high confidence that stacked battery technology is indeed part of the iPhone 16 Pro models. - - The rumors align with Apple's historical trends of incremental hardware improvements in successive models. - -2. **Timing of Announcement**: - - Apple typically announces new iPhones in September. - - If the iPhone 16 lineup is expected in September 2024, it fits within the timeframe before October 1, 2024. - -3. **Technical Feasibility and Readiness**: - - Stacked battery technology is already in use in other fields, suggesting technical feasibility for smartphones. - - Apple’s history of adopting and announcing new technologies at their annual events enhances the credibility of this specific technology being announced soon. - -4. **EU Law Implications**: - - The EU law requiring easily replaceable batteries by 2025 might push Apple to start introducing new battery technologies ahead of time, which supports the rumor of stacked batteries. - -5. **Base Rate and Historical Precedent**: - - Apple's track record shows consistency in announcing the next generation of iPhones every September. - - Recent introductions of new technologies in Pro models further align with this trend. - -6. **Factoring Uncertainty**: - - While most signals point toward a September announcement, the probability isn't absolute. Unexpected delays or strategic decisions could shift the timeline. - - Given that unofficial leaks don’t count and the technology just has to be announced (not necessarily available for purchase), the uncertainty is somewhat mitigated. - -### Final Probability Estimation - -Given the convergence of several credible sources, historical trends, and proactive adaptations to impending regulatory changes, the likelihood is extremely high that Apple will make this announcement in September 2024. - -Therefore, based on the evaluation: - -**Probability: 90%** - -This high probability accounts for strong indicators from multiple respected sources, Apple's predictable announcement patterns, and the pressing timeline regarding regulatory compliance and product upgrades." -26838,"Will the median net worth on Bloomberg's Billionaires Index be above $10.2 billion on September 16, 2024?","PERPLEXITY - -To forecast whether the median net worth on Bloomberg's Billionaires Index will be above $10.2 billion on September 16, 2024, we need to consider several factors, including the current net worth of the top 500 billionaires, recent trends in their wealth, and any significant economic or market changes that could impact their net worth. - -### Current Net Worth and Rankings -As of July 24, 2024, the top 10 billionaires on the Bloomberg Billionaires Index have the following net worths: -- Elon Musk: $240.5 billion -- Jeff Bezos: $203.6 billion -- Bernard Arnault: $187.4 billion -- Mark Zuckerberg: $164.5 billion -- Bill Gates: $156.8 billion -- Larry Page: $154.5 billion -- Larry Ellison: $152.9 billion -- Steve Ballmer: $148.4 billion -- Sergey Brin: $145.5 billion -- Warren Buffett: $136.6 billion. - -### Recent Trends -The net worth of these billionaires is heavily influenced by the performance of their respective companies. For instance, Elon Musk's net worth is closely tied to Tesla's stock performance, while Jeff Bezos's net worth is influenced by Amazon's stock price. Recent market trends and company performance will be crucial in determining their net worth on September 16, 2024. - -### Economic and Market Factors -Economic indicators, such as stock market performance, GDP growth, and inflation rates, can significantly impact the net worth of billionaires. Any major economic shifts or market volatility between now and September 16, 2024, could alter the net worth of these individuals. - -### Methodology -The Bloomberg Billionaires Index calculates net worth based on the most recent closing prices of publicly traded companies and converts valuations to U.S. dollars at current exchange rates. This methodology ensures that the net worth figures are updated daily to reflect market changes. - -### Expert Opinions -Economic experts and financial analysts often provide insights into market trends and potential future movements. Their opinions can be valuable in predicting whether the median net worth will exceed $10.2 billion. For example, if experts predict a strong market performance for tech stocks, this could positively impact the net worth of tech billionaires like Elon Musk and Mark Zuckerberg. - -### Prediction Markets -Prediction markets, such as those found on betting sites, can also provide insights into the likelihood of the median net worth exceeding $10.2 billion. These markets aggregate the opinions of many participants, which can sometimes be more accurate than individual expert opinions. - -### Conclusion -To accurately forecast whether the median net worth on Bloomberg's Billionaires Index will be above $10.2 billion on September 16, 2024, it is essential to monitor the current net worth of the top 500 billionaires, recent market trends, economic indicators, and expert opinions. Additionally, tracking prediction markets can provide further insights into the likelihood of this event. - -######### - -GPT - -To forecast the probability that the median net worth on Bloomberg's Billionaires Index will be above $10.2 billion on September 16, 2024, we need to carefully analyze several factors mentioned in the background information and address the uncertainty inherent in forecasting economic and market trends. Here's a systematic approach to make an informed prediction: - -### Step-by-Step Analysis - -1. **Current Status and Recent Trends:** - - As of April 30, 2024, the median net worth was $9.535 billion. - - As of July 10, 2024, the median net worth was $10.0 billion. - - This suggests a positive trend, with a notable increase in the median net worth over a rough period of 2 months. - -2. **Time Horizon:** - - Today's date is August 2, 2024, and the target date is September 16, 2024, leaving approximately 1.5 months. - -3. **Market Dynamics and Economic Indicators:** - - The net worth of billionaires is heavily influenced by stock market performance, GDP growth, inflation, and other macroeconomic factors. - - Recent market trends, particularly in the technology sector where many of the top billionaires hold significant stakes, have been positive. - -4. **External Economic Factors:** - - Given the global economic landscape, factors such as interest rate decisions by central banks, geopolitical events, and international trade dynamics can affect market performance. - -5. **Baseline Performance:** - - If we consider past performance as indicative, a continuation of the current trend suggests that the median net worth could exceed $10.2 billion by the target date. - -6. **Volatility and Uncertainty:** - - Stock markets are inherently volatile, and significant events between now and September 16 could disrupt trends. - - Recent trends have shown an upward trajectory, but external shocks like political instability, natural disasters, or sudden economic downturns can alter this. - -7. **Estimation and Statistical Analysis:** - - To understand the likelihood of crossing the $10.2 billion threshold, we observed an increment from $9.535 billion to $10.0 billion over approximately two months. - - Assuming a similar growth rate, over the next 1.5 months, we can expect a proportionately smaller increase. - - If the growth continues at the same pace, the median might increase by ≈$0.465 billion or slightly more, placing it above $10.2 billion. - -### Final Considerations - -- The historical trend is positive, but market volatility introduces uncertainty. -- Given expert opinions and market predictor analysis as stated, the tech sector's growth seems promising. -- The probability distribution should be weighted towards maintaining the recent trend without overestimating sudden variances. - -### Conclusion - -Given the available data, analyzing historical trends, and considering the market dynamics, it seems moderately likely that the median net worth on Bloomberg's Billionaires Index will be above $10.2 billion on September 16, 2024. As a superforecaster, maintaining a balance between optimism grounded in recent trends and caution due to inherent market uncertainties leads me to the following probability estimation: - -**Probability: 65%** - -This percentage reflects a cautious optimism based on observed trends and the remaining time to the target date." -26837,"Will ""Woman's World"" by Katy Perry achieve a ranking higher than 15th on the Billboard Hot 100 before October 1, 2024?","PERPLEXITY - -To forecast whether ""Woman's World"" by Katy Perry will achieve a ranking higher than 15th on the Billboard Hot 100 before October 1, 2024, here is a concise rundown of the most relevant news and context: - -1. **Release and Reception**: - - ""Woman's World"" was released on July 11, 2024, as the first single from Katy Perry's upcoming seventh album, *143*. - - The song has received mixed reactions from fans and critics. Some have expressed disappointment, noting that the song did not meet expectations despite a big promotional push and an expensive music video. - -2. **Current Performance**: - - As of the latest available data, ""Woman's World"" has been performing moderately well. It has more than a billion streams and is still generating significant daily streams, though its chart performance has been less impressive. - -3. **Chart Rankings**: - - The song has not yet broken into the top 15 on the Billboard Hot 100. Current rankings indicate it is still outside this range, though it remains popular in terms of streaming metrics. - -4. **Future Prospects**: - - Katy Perry has announced another single, ""Lifetimes,"" set to release on August 9, 2024. This could potentially draw attention away from ""Woman's World"" or boost overall interest in her upcoming album. - - Perry's exit from ""American Idol"" and her focus on her music career might also influence public interest and support for her new releases. - -5. **Expert Opinions and Predictions**: - - The Metaculus prediction market indicates a mixed outlook, with some users predicting the song will achieve a higher ranking while others are less optimistic. - - Fan reactions on platforms like Reddit suggest a divided opinion, with some fans expressing disappointment and others hoping for better performance from future releases. - -Given the current performance and mixed reactions, it is uncertain whether ""Woman's World"" will achieve a ranking higher than 15th on the Billboard Hot 100 before October 1, 2024. The release of ""Lifetimes"" and overall public interest in Perry's upcoming album could significantly impact this outcome. - -######### - -GPT - -### Initial Assessment - -#### Current Performance and Metrics: -- **Current Ranking**: ""Woman's World"" by Katy Perry is ranked 63rd on the Billboard Hot 100 as of July 26, 2024. -- **Mixed Reception**: The song has received lukewarm to negative reviews from both critics and fans. However, it is significant that the track has achieved over a billion streams, suggesting a substantial but not overwhelming popularity. -- **Upcoming Single**: Another single, ""Lifetimes,"" is set to release on August 9, 2024. This could either cannibalize interest in ""Woman's World"" or boost overall attention to Katy Perry's music. - -#### Industry and Historical Context: -- **Popular Trends**: Songs that start with mixed receptions often struggle to improve their Billboard rankings unless they capture the broader public's imagination. -- **Historical Precedents**: Historically, most songs that debut outside the top 50 and receive mixed reviews find it challenging to break into the top 15. -- **Competition**: The Billboard Hot 100 is highly competitive, with new songs and albums being released continuously. - -### Timeframe for Forecasting -- **Time Remaining**: Roughly two months remain until October 1, 2024. This is a relatively short period for a song to jump from 63rd to a top 15 position, especially given its current trajectory. -- **Weekly Variance**: While there could be some weekly variance, significant upward movement is unlikely given the song's initial performance and reception barriers. - -### Statistical and Analytical Support - -#### Probability Calculations -1. **Base Rate Analysis**: - - According to historical Billboard data, fewer than 5% of songs that debut outside the top 50 manage to break into the top 15 after receiving lukewarm reviews. - -2. **Trend Analysis**: - - Reviewing similar cases, songs that did break into the top 15 despite low debuts often had viral moments, significant social media campaigns, or strong public sentiment shifts, none of which are currently observable for ""Woman's World."" - -3. **Billboard Chart Dynamics**: - - The Billboard Hot 100 is influenced heavily by streaming, sales, and radio play. Given the mixed reviews, organic growth through these channels seems unlikely without transformative external factors. - -4. **Potential Catalysts**: - - The upcoming single ""Lifetimes"" could act as a catalyst, but it's unclear if this will boost ""Woman's World"" or overshadow it. - -### Reducing Bias and Checking Assumptions -- Given the competition and mixed reception, we will not over-index on the isolated data of one billion streams or the political association with Kamala Harris's campaign. While these factors offer some support, they are not strong enough to outweigh the overall negative sentiment. - -### Final Probability Assessment - -Combining the mixed reception, current chart position, short timeframe, and historical base rates: -- Likelihood of achieving a ranking higher than 15th before October 1, 2024, appears low based on available data. -- A significant upward momentum would require notable and sustained attention, likely absent based on current information. - -**Final Answer**: -**Probability**: 15% - -This forecast heavily considers the song's current position, historical precedent, the competitive nature of the Billboard Hot 100, and the relative lack of strong catalysts to drive the song into a higher ranking significantly within the remaining timeframe." -26842,"Before October 1, 2024, will OpenAI announce on the news section of its website that it is planning an IPO?","PERPLEXITY - -OpenAI, currently a capped-profit company, is contemplating a significant restructuring to become a traditional for-profit entity. This potential shift could pave the way for a future IPO. CEO Sam Altman has discussed the possibility of transitioning the company, which has been a non-profit since its inception in 2015. - -### Key Points: - -1. **Restructuring Plans**: - - OpenAI is considering changing from a capped-profit to a for-profit model, which could lead to an IPO. - - This move is seen as a strategic shift to capitalize on the company's growing valuation and revenue. - -2. **Equity Concerns**: - - OpenAI has a valuation exceeding $80 billion, making it an expensive acquisition target. Secondary transactions are the primary means for shareholders to realize value from their equity. - - The company has held several tender rounds for employees to sell their shares, but ex-employees have raised concerns about unequal treatment and potential clawbacks. - -3. **Recent Developments**: - - OpenAI has doubled its annualized revenue to $3.4 billion as of June 2024. - - The company has been involved in various AI-related developments, including the unveiling of SearchGPT and potential antitrust inquiries. - -4. **IPO Speculation**: - - Despite previous denials, there is ongoing speculation about an IPO. Venture capital firms and individual investors are keen on investing in OpenAI, anticipating future IPO opportunities. - - Online brokers like TradeStation and Robinhood are preparing for potential IPO access for retail investors. - -5. **Prediction Markets**: - - Metaculus, a prediction market, has a question open regarding whether OpenAI will announce an IPO plan before October 1, 2024. The current prediction is around 60% likelihood. - -### Expert Opinions: -- Legal experts have raised concerns about OpenAI's treatment of ex-employees, particularly those who now work at competitors, suggesting potential legal issues in the future. -- Financial experts like Larry Albukerk have noted that creating an adversarial relationship with former employees can negatively impact morale and the company's overall performance. - -### Current Values: -- OpenAI's valuation: Over $80 billion. -- Annualized revenue: $3.4 billion as of June 2024. - -### Recent Updates: -- OpenAI has been actively involved in AI safety initiatives, including a pledge to give the U.S. AI Safety Institute early access to its next AI models. -- The company has been expanding its product offerings, such as the introduction of SearchGPT. - -Overall, while there is no definitive announcement from OpenAI regarding an IPO, the company's strategic moves and financial performance suggest a strong possibility of such an event in the near future. - -######### - -GPT - -### Step 1: Understanding the Context and Critical Factors -- **Current Date:** August 2, 2024 -- **Resolution Deadline:** October 1, 2024 (approximately two months from now) -- **Key Event:** Whether OpenAI will announce an IPO before the deadline on its website. - -### Step 2: Key Information Analysis -1. **Valuation and Growth** - - OpenAI valued at $80 billion after the latest funding round. - - Annual revenue has doubled to $3.4 billion as of June 2024. - -2. **Corporate Restructuring Considerations** - - OpenAI is contemplating shifting from a capped-profit to a traditional for-profit model. - - Transition discussions have been publicly noted by CEO Sam Altman. - -3. **Employee and Shareholder Dynamics** - - Shareholders are engaging in secondary transactions. - - Ex-employees have raised legal concerns regarding equity treatment, affecting internal perceptions. - -4. **Market and Investor Sentiment** - - High interest from venture capital firms and individual investors in potential future IPO opportunities. - - Online brokers preparing for the potential IPO participation by retail investors. - -5. **Prediction Markets and Expert Opinion** - - Metaculus predicts a 60% likelihood for an IPO announcement by the given deadline. - - Legal and financial experts discussing both risks and preparatory steps for an IPO. - -### Step 3: Historical Precedent and Current Environment -- **Base Rate of IPO Announcements:** - - Historically, larger tech firms that consider restructuring for profit often lead to IPO announcements, especially given the interest from secondary markets. - - However, the process of transitioning from a capped-profit to a traditional for-profit model might introduce delays and complexities. - -- **Economic & Regulatory Climate:** - - Regulatory scrutiny on AI companies has been increasing, but no major roadblocks have been noted specifically affecting an IPO announcement. - - Antitrust inquiries could add uncertainty but not necessarily halt an IPO discussion. - -### Step 4: Possibility Assessment -- **POSITIVE INDICATORS:** - - Significant valuation and annual revenue increase make a compelling case for an IPO announcement. - - Market and investor readiness indicate substantial external pressure and support for an IPO. - -- **NEGATIVE INDICATORS:** - - Internal mild discord and legal disputes with ex-employees may slow the processes. - - The transition from a capped-profit to a for-profit model might introduce internal delays needing extensive board and legal consultations. - -- **TIMING CONSIDERATION:** - - Roughly eight weeks until the deadline, the timeframe is narrow but feasible for announcing an IPO given the preparatory steps observed since June. - -### Step 5: Adjusting for Uncertainty and Default Behavior -- **Default Behavior:** Without a change, there's no IPO announcement by default. -- **Trends and Variance:** Given recent developments and public discussions regarding possible restructuring, the trend leans towards imminent decisions. - -### Step 6: Statistical Consideration and Conclusion -- Generally, IPO announcements are more likely closer to the end of fiscal quarters. The period in question coincides with the end of Q3, which might be a strategic time. - -### Final Probability Estimation: -- Given all factors, balancing positive trends and potential hurdles, and the specific timeframe: - -**Probability: 75%**" -26842,"Before October 1, 2024, will OpenAI announce on the news section of its website that it is planning an IPO?","PERPLEXITY - -To forecast whether OpenAI will announce an IPO before October 1, 2024, several key points and recent developments are relevant: - -1. **OpenAI's Current Structure and Valuation**: - - OpenAI is currently a capped-profit organization, but there are discussions about transitioning it to a traditional for-profit entity. This change could pave the way for an IPO. - - The company's valuation exceeds $80 billion, making it a significant player in the tech industry. - -2. **Equity Sales and Employee Concerns**: - - OpenAI has been allowing employees to sell a portion of their equity annually, but ex-employees have raised concerns about unequal treatment and potential clawbacks of vested equity. - - This internal equity market could be a precursor to an IPO, as it allows employees to realize value from their shares without going public. - -3. **Recent Financial Performance**: - - OpenAI has doubled its annualized revenue to $3.4 billion, indicating strong financial health and potential readiness for an IPO. - - The company conducted a private funding round in January 2023, securing sufficient funding for operations, which could support an IPO preparation phase. - -4. **Market and Investor Sentiment**: - - AI has become the hottest tech industry, and OpenAI is at the forefront, attracting significant investor interest. This could drive demand for an IPO. - - Venture capital firms and individual investors have invested heavily in OpenAI, and they may seek to liquidate their ownership in the coming years, potentially through an IPO. - -5. **Regulatory and Legal Considerations**: - - There are ongoing antitrust inquiries into OpenAI, which could impact the timing of an IPO. However, these inquiries do not necessarily preclude an IPO announcement. - - Legal experts have raised concerns about OpenAI's treatment of ex-employees, particularly in California, which could influence the company's decision-making regarding an IPO. - -6. **Prediction Markets and Expert Opinions**: - - Metaculus, a prediction platform, has a question open regarding whether OpenAI will announce an IPO before October 1, 2024. The community's feedback and voting will help gauge the likelihood of such an announcement. - - Sam Altman, OpenAI's CEO, has discussed the possibility of transitioning the company to a for-profit model, which could be a step towards an IPO. - -In summary, while there are no definitive announcements from OpenAI about an IPO, the company's financial performance, market position, and ongoing discussions about restructuring suggest that an IPO announcement before October 1, 2024, is plausible. The concerns and legal issues related to equity sales and employee treatment may influence the timing but do not rule out the possibility of an IPO announcement. - -######### - -GPT - -For this forecast, the evaluation will be carried out step-by-step, considering all relevant available information and applying statistical thinking to derive a probability. - -### Step-by-Step Analysis - -1. **Current Organizational and Financial Structure** - - **Valuation and IPO Readiness**: OpenAI's valuation at $80 billion, combined with strong financial performance (doubling annualized revenue to $3.4 billion), puts it in a favorable position for an IPO. High valuations often lead to IPO considerations to provide liquidity to investors and employees. - -2. **Internal Equity Dynamics** - - **Equity Sales**: Allowing employees to sell portions of their equity annually suggests preparedness for an IPO. However, internal issues around equity treatment of ex-employees might generate risk averseness in the management regarding IPO timing. - -3. **Market Sentiments** - - **Investor Interest**: With AI being the hottest tech sector and OpenAI at the forefront, investor demand for an IPO is high. Venture capital firms, having invested heavily, might push for an IPO to realize returns. This creates external pressure towards announcing an IPO. - -4. **Regulatory Considerations** - - **Antitrust Inquiries and Legal Issues**: Active antitrust inquiries and legal concerns might delay the timing of an IPO; however, they do not necessarily prevent an announcement of intent. If OpenAI sees a clear path to resolving these issues, they might announce the IPO plans soon. - -5. **External Forecast and Opinions** - - **Metaculus and Expert Opinions**: Public prediction platforms and expert opinions gauge a higher probability due to the recent discussions around restructuring and IPO possibilities. - -6. **Timing and Remaining Window** - - **Time-to-Event**: There are approximately two months until the resolution date (October 1, 2024). Based on historical IPO announcements for companies of similar scale, IPO announcements typically come several months before the actual IPO. Hence, the window period is adequately fitting for an announcement if preparations started earlier this year. - -### Base Rate and Historical Context -Understanding the probability of large tech companies announcing IPO plans within a given timeframe helps frame default likelihood: -- **Historical Base Rate**: For tech giants considering IPOs, the incubation period between internal discussions and public announcements varies, but given high valuations and market readiness, there tends to be a significant push within 6-12 months prior to the financial year-end. - -### Probability Distribution and Uncertainty -Given the default position (no IPO announcement), each piece of evidence contributes to our probability adjustment: -- Base rate (neutral, nothing changing) + Internal dynamics (moderate strength) + Market sentiment (high strength) - Regulatory/legal resistance (moderate) + timing (suitable window). - -### Integrative Probability Calculation -Accounting for the accelerative market factors and balancing them with regulatory drag and readiness, an adjusted probability calculation can be undertaken: - -- **Base Rate Assumption**: Assuming a generic readiness of highly valued tech companies to IPO within 6 months: ~30% -- **Boost from Financial Performance and Valuation**: +15% (valuation and performance well-suited for IPO) -- **Moderate Resistance from Internal/Legal Issues**: -10% (internal disputes and legal concerns subtracting the readiness) -- **Market Pressure and Time Window**: +10% (market trends and acceptable announcement window boosting probability). - -### Final Probability Estimation -Given the balances drawn and factors appropriately weighed: - -**Probability: 45%**" -26841,"Will Spirit Airlines file for bankruptcy before October 1, 2024?","PERPLEXITY - -### Summary of Relevant News - -#### Financial Struggles and Debt -- **Debt Burden**: Spirit Airlines is facing significant financial challenges, including a $1.11 billion debt maturity in September 2025. The airline has been burning cash, with a loss of $63.9 million in the first nine months of 2023 and $89 million in 2022. -- **Credit Concerns**: The airline's credit profile is under scrutiny, with Fitch lowering the rating on its 8% secured notes to ""B"" with a negative outlook. These notes traded as low as 43 and closed at about 62, indicating serious concerns about full payment recovery. - -#### Merger Injunction and Legal Actions -- **Failed Merger**: The merger between Spirit Airlines and JetBlue Airways was blocked by a federal judge due to antitrust concerns. Both airlines have filed an appeal, which could take a long time to resolve. -- **Legal Uncertainty**: The failed merger has left Spirit Airlines in a precarious financial position, with analysts speculating about the likelihood of bankruptcy. The judge's ruling did not impose a permanent injunction, leaving room for future legal actions. - -#### Operational Challenges -- **Grounded Aircraft**: Spirit Airlines has faced operational challenges, including grounding several planes due to engine issues. This has further strained its financial situation. -- **Leadership Changes**: The airline recently announced the departure of its CFO, Scott Haralson, and the appointment of Brian McMenamy as interim CFO, highlighting internal adjustments amidst financial struggles. - -#### Market and Analyst Views -- **Bankruptcy Speculation**: Some Wall Street analysts believe that Spirit Airlines might file for Chapter 11 bankruptcy, while others are uncertain about its future profitability. The airline's CEO, Ted Christie, has reassured that bankruptcy is not being considered, despite ongoing financial difficulties. -- **Prediction Markets**: Metaculus has a question open on whether Spirit Airlines will file for bankruptcy before October 1, 2024, indicating ongoing speculation and uncertainty. - -#### Recent Updates -- **Current Financial Status**: Spirit Airlines has not been profitable since 2019 and reported significant losses in Q4 2023 and across 2023. The airline has deferred the delivery of Airbus A320 aircraft until 2030 to relieve pressure on its balance sheet. -- **Late Payments**: Creditsafe reported an increase in late payments by Spirit Airlines, further indicating financial distress. - -### Expert Opinions -- **Analyst Views**: Some analysts foresee a Chapter 11 restructuring followed by liquidation, while others express uncertainty about Spirit's path to profitability. The airline's financial resilience is under scrutiny, and there are concerns about its ability to enhance liquidity and profitability. -- **CEO's Stance**: Ted Christie has expressed optimism about the company's strategy and has reassured that bankruptcy is not being considered, despite the financial challenges. - -### Current Values -- **Stock Performance**: Spirit Airlines' stock has plummeted over 90% in the last five years, reflecting the ongoing financial struggles. -- **Debt Maturity**: The airline's $1.11 billion 8% secured notes mature on September 20, 2025, which is a significant financial challenge. - -### Conclusion -Spirit Airlines is facing severe financial difficulties, including a significant debt burden, operational challenges, and a failed merger. While the CEO has reassured that bankruptcy is not being considered, analysts and market speculation suggest that bankruptcy remains a possibility. The airline's financial situation continues to deteriorate, with ongoing losses and late payments, making the future uncertain. - -######### - -GPT - -To forecast whether Spirit Airlines will file for bankruptcy before October 1, 2024, we need to carefully weigh all available information, considering both qualitative and quantitative aspects. Here's how to approach this systematically: - -### Step-by-Step Forecasting Approach - -#### 1. **Evaluate Key Factors:** - a. **Financial Struggles and Debt:** - - Significant financial losses in recent quarters and years. - - A substantial debt burden, particularly the $1.11 billion debt maturity in September 2025. - - Negative credit conditions signaled by a low Fitch rating and poor performance of secured notes. - - b. **Operational and Legal Challenges:** - - The grounding of aircraft due to engine problems stifles revenue generation. - - High turnover in leadership positions, which may affect financial and strategic stability. - - The blocked JetBlue merger adds to financial uncertainty with no quick resolution in sight. - - c. **Market Sentiment and Analyst Views:** - - Mixed opinions: pessimistic views towards bankruptcy and restructuring versus the CEO's claims of avoiding bankruptcy. - - Negative stock performance, indicating lack of investor confidence. - -#### 2. **Observe Trends and Probabilities:** - - Unprofitable since 2019. - - Continuous decline in stock value and increase in late payments. - -#### 3. **Contextualize Against Historical Data:** - - Airlines in financial distress often proceed to Chapter 11 to reorganize their debt and attempt to return to profitability. - - Spirit faces a severe cash burn rate and a limited time window to correct its course. - -#### 4. **Evaluate Time Frame and Binary Resolution Nature:** - - Given the cut-off date (October 1, 2024), Spirit Airlines has about 60 days until the predictive resolution date. - - Turnaround time for major restructuring decisions (like bankruptcy) can vary but significant distress makes such events more probable within weeks/months. - -### Probability Estimation Process: -1. **Base Rate Analysis**: According to historical data of distressed airlines, the base rate for filing for bankruptcy within short periods of distress is notable, though it is critical to adjust for incoming short-term factors (e.g., major debt maturities). - -2. **Assessment of Influences and Context**: - - CEO’s assurances against bankruptcy might delay such a decision but not prevent it. - - Analyst and market outlook predominantly negative imply higher risk. - - Usually, the closer to debt maturity and operational crises, the higher the probability. - -3. **Time Considerations**: - - Immediate financial pressures include upcoming repayments and operational expenditure. - - August and September are limited, and bankruptcy decisions often come with some pre-warning patterns already visible, supporting the increased likelihood in the immediate short-term. - -### Conclusion & Probability Distribution: - -Weighing all factors, there's a high probability Spirit won’t withstand financial strain without drastic measures, including bankruptcy within the two months timeframe. - -Thus, I assess the probability of Spirit Airlines filing for bankruptcy before October 1, 2024, as follows: -""Probability: 68%"" - -This probability aligns with the severe financial signals, continued operational and market challenges, and short timeline until the resolution date." -26920,"If Israel does not invade Lebanon before October 1, 2024, will there be 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024?","PERPLEXITY - -The question is: ""If Israel does not invade Lebanon before October 1, 2024, will there be 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024?"" - -### Context and Recent Developments - -1. **Escalating Tensions**: - - The conflict between Israel and Hezbollah has been ongoing for over nine months, with both sides engaging in cross-border attacks. Hezbollah has been launching rocket attacks into northern Israel, while Israel has been conducting airstrikes against Hezbollah positions in southern Lebanon. - - The situation is highly volatile, with recent escalations in hostilities and significant humanitarian concerns, particularly regarding the use of white phosphorus by Israel in populated areas. - -2. **Military Preparations**: - - Israel has approved operational plans for a full-scale invasion of Lebanon, which could lead to a two-front war for Israel, given its ongoing conflict with Hamas in Gaza. - - Hezbollah is well-prepared for a potential war, with a substantial arsenal of rockets and missiles that could reach deep into Israel. - -3. **Humanitarian Impact**: - - The conflict has already resulted in significant civilian casualties and displacements. Over 1,000 Lebanese civilians have been killed, and many more have been displaced or affected by the ongoing violence. - - The use of phosphorus munitions has caused extensive damage to agricultural areas and civilian infrastructure in southern Lebanon. - -4. **Political Context**: - - The Israeli government is under pressure to address the security concerns along the northern border. The Netanyahu government faces internal divisions and public dissatisfaction, which could influence its decision-making regarding military action. - - Hezbollah has stated that it will continue its attacks until there is a permanent ceasefire in Gaza, while Israel refuses to end its war in Gaza until Hamas is defeated and all Israeli hostages are freed. - -5. **International Concerns**: - - The United Nations has issued warnings about the potential for a catastrophic escalation of the conflict, which could involve other regional actors and have far-reaching consequences for the Middle East. - - Genocide Watch has warned of the potential for significant civilian casualties and war crimes if a full-scale war erupts. - -### Prediction Markets and Expert Opinions - -- **Metaculus Predictions**: - - The Metaculus platform has questions related to the conflict, including one that asks if there will be 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024. This question is pending approval by Community Moderators. - - Another question on Metaculus asks if there will be 400 or more deaths in the Israel-Hezbollah conflict by January 1, 2024, which is still awaiting approval. - -- **Expert Opinions**: - - Israeli analysts believe that Hezbollah is well-prepared for a potential war and that the conflict could be highly destructive for both sides. There is a concern that the situation could escalate unintentionally due to heightened military readiness and the potential for preemptive strikes. - - The international community is urging caution and diplomatic resolution, with the US and UN advocating for peace plans to end the conflict in Gaza and prevent further escalation. - -### Conclusion - -The situation between Israel and Hezbollah remains highly volatile, with ongoing hostilities and significant military preparations on both sides. The potential for a full-scale war is high, and the humanitarian and political implications are severe. The international community is urging caution and diplomatic resolution to prevent further escalation. - -######### - -GPT - -### Key Factors to Consider - -1. **Historical Context and Recent Developments** - - **Historical Conflicts**: Past conflicts between Israel and Hezbollah, such as the 2006 Lebanon War, resulted in significant casualties but not necessarily reaching the threshold of 1000 deaths in a single year. However, the intensity and scale of conflicts can vary. - - **Recent Escalations**: The recent escalations post-October 7, 2023, have seen increased hostilities. The situation is already volatile, with heavy exchanges of rocket and artillery fire. - -2. **Current State of Affairs** - - **Casualties so far**: Reports indicate over 1,000 Lebanese civilians have already been killed due to the conflict. However, it is important to determine the specific cause, which should be attributed to armed conflict with Hezbollah. - - **Operational Readiness**: Both sides are heavily armed and prepared for further conflict. Hezbollah's substantial arsenal can cause significant casualties if used on a large scale. - -3. **Political Pressures and International Involvement** - - **Internal Pressures in Israel**: The Israeli government is under significant pressure to act decisively against threats, which might lead to increased military actions. - - **International Concerns**: International bodies are urging diplomacy, but the situation on the ground remains tense and can escalate rapidly. - -4. **Potential for Future Escalation** - - **Military Actions**: Israel's readiness for a full-scale invasion signals a high potential for increased conflict. Even if an invasion does not occur, escalated airstrikes and missile attacks could continue. - - **Duration and Intensity**: With nearly two months left until the resolution date, each side has ample time to carry out sustained military operations. - -### Statistical Analysis and Base Rates - -- **Past Casualties**: In the 2006 Lebanon War, approximately 1200 Lebanese and 165 Israelis were killed. This gives a base rate indicating high casualty potential, albeit with variations. -- **Current Casualty Counts**: Given recent reports and assuming continuation of hostilities, daily and weekly casualty rates could spike with major military escalation, especially considering current elevated tensions and military preparations. - -### Probability Calculation - -1. **Assessing Current Data**: - - Already reported 1,000 Lebanese civilian casualties show a high potential, but need confirmation they are due to conflict with Hezbollah directly. - - Vital attention should be given to the trend of casualties, whether civilian or combatant, and cross-verifying them with authenticated sources. - -2. **Upcoming Duration**: - - August 5, 2024, to October 1, 2024, is approximately 57 days. The period allows substantial time for hostilities to result in cumulative casualties reaching or exceeding 1000 if hostilities continue or intensify. - - Variance and daily rates can fluctuate, but as of recent escalations, the daily toll can be significant nearing the final month. - -3. **Probability Distribution**: - - **Conservative Estimation**: Considering non-invasion while maintaining high hostilities keeps a substantial chance of cumulative death tolls reaching specified criteria. - - **Historical Comparison and Current Escalations**: Base rates adjusted for current intensity give a mid-to-high probability range. - -### Final Probability Statement - -After considering all factors—current hostilities, preparedness for war, international pressures, and historical precedents—the probability of 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024, considering no invasion occurs, is: - -**Probability: 75%** - -This reflects the substantial likelihood of continued military operations resulting in significant casualties, while also considering the risks of potential de-escalation or cessation of current intensities." -26918,"Will there be 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024?","PERPLEXITY - -To forecast whether there will be 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024, we need to consider recent developments and trends in the conflict. - -### Current Conflict Overview -- **Recent Attacks**: Since October 7, 2023, there have been over 4,400 attacks by Israel and Hezbollah combined, with a significant escalation in violence. -- **Casualties**: As of July 5, 2024, there have been approximately 366 deaths in Lebanon and 33 in Israel due to cross-border attacks. -- **Displacement**: Over 150,000 individuals have been displaced on both sides of the border, with extensive damage to structures and land. - -### Key Factors Influencing the Conflict -- **Hezbollah's Capabilities**: Hezbollah has a stockpile of 120,000 to 200,000 short-range guided ballistic missiles and other weapons, posing a significant threat to Israel. -- **Israeli Response**: Israel has conducted more attacks than Hezbollah, with a focus on targeting Hezbollah's military capabilities and infrastructure. -- **Diplomatic Efforts**: The United States has been involved in diplomatic efforts to prevent a wider war, but the situation remains volatile. - -### Recent Developments -- **Escalation**: The conflict has not yet escalated to full-scale warfare, but the ongoing near-daily assaults have left communities severely impacted. -- **Destruction**: Satellite imagery and military activity logs show extensive damage to neighborhoods and agricultural land on both sides of the border. - -### Expert Opinions -- **Risk of War**: Experts warn of a high risk of war between Israel and Hezbollah, with the potential for a much wider and more violent conflict in the Middle East. -- **Strategic Considerations**: Israel faces a dilemma between risking war now or waiting, which could lead to a more serious conflict in the future with a better-armed Hezbollah. - -### Prediction Markets -- **Metaculus**: Questions on Metaculus about the conflict, such as whether there will be 400 deaths by January 1, 2024, or 1000 deaths before January 1, 2025, are awaiting approval and feedback from the community. - -### Conclusion -Given the current rate of violence and the significant number of attacks and casualties, there is a high likelihood of reaching 1000 or more deaths due to the armed conflict between Israel and Hezbollah before October 1, 2024. The ongoing hostilities and the strategic considerations of both sides suggest a continued escalation of the conflict. - -######### - -GPT - -To forecast whether there will be 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024, we'll take a methodical, multi-step approach. We'll consider historical data, current conflict trends, strategic factors, and future projections to determine the likelihood. Let's break it down step-by-step: - -### Step 1: Historical Context and Base Rate - -**Historical Conflicts**: Historically, conflicts between Israel and Hezbollah have seen large casualties but generally in more peaked periods of conflict, such as during the 2006 Lebanon War. That conflict resulted in over 1000 deaths, but shorter skirmishes generally have lower death tolls. - -### Step 2: Current Data - -**Current Casualty Numbers**: As of July 5, 2024, there have been approximately 366 deaths in Lebanon and 33 in Israel, totaling 399 deaths from the conflict up to this point. - -**Time Remaining**: We have approximately two months remaining until the forecast date of October 1, 2024. - -### Step 3: Recent Development and Escalation Potential - -**Escalation Factors**: -- **Hezbollah's Capabilities**: Hezbollah has a significant stockpile of weapons which can cause high casualties, especially in population centers. -- **Israeli Military Action**: Israel's military doctrine often includes preemptive and high-intensity strikes which could increase casualty rates if conflict intensifies. -- **Current Pace**: At the current pace of daily or near-daily hostilities, additional casualties are occurring, but significant escalation would be necessary to achieve a total of 1000 deaths. - -**Strategic Factors**: -- **Risk of Broader Conflict**: Although experts warn of a high risk for a more violent and broader conflict, historical precedents suggest that such escalations are uncommon without a significant triggering event. - -### Step 4: Expert and Diplomatic Opinions - -**Expert Opinions**: Generally, there is a high risk of an escalated conflict, but the actual move to full-scale warfare requires certain political and strategic triggers which aren't currently present. - -**Diplomatic Efforts**: The US and other international actors are deeply involved in preventing a wider war, though these efforts are not always successful. - -### Step 5: Projection and Statistical Analysis - -Given the historical context and the current data, we will project casualty rates linearly and for potential escalation scenarios: - -#### **Linear Projection**: -- To achieve 1000 deaths, an additional 601 deaths are needed. -- With 57 days left, an average of approximately 10.54 deaths per day would be necessary. -- Current daily casualty rates are significantly lower, although specific high-casualty events could spike these numbers. - -#### **Escalation Scenario**: -- An escalated conflict scenario leading to a spike in deaths within short periods (e.g., an intense week of fighting could induce dozens or hundreds of deaths). However, sustained conflict at this level is relatively rare and would still need a significant incident to trigger. - -### Step 6: Assessing Current vs. Trigger Events - -**Default Behavior**: -- Without a significant change in current behavior, the conflict is likely to continue at its present pace. -- The default behavior suggests continued daily casualties but not at the volume needed to reach 1000 deaths without escalation. - -**Triggers**: -- Current situation indicates a volatile but non-escalated conflict. The probability of trigger events (e.g., a significant cross-border incident or assassination) is present but not highly probable. - -### Conclusion - -Weighing the data and trends with statistical analysis and expert inputs: -- The current pace of conflict does not suggest we will reach 1000 deaths without significant change. -- The probability of a sudden escalation leading to above-normal casualties exists but remains lower based on historical incidences and lack of current triggers. - -**Final Probability Estimate**: -Accounting for the linear trends, possible escalation events, and strategic/diplomatic efforts aimed at preventing wider war, the probability of reaching 1000 or more deaths by October 1, 2024, is being calculated conservatively but realistically on the higher end of near-term historical base rates. - -**Probability: 40%**" -26917,"Will Israel invade Lebanon before October 1, 2024?","PERPLEXITY - -### Context for Forecasting: Will Israel Invade Lebanon Before October 1, 2024? - -#### Current Tensions and Recent Developments - -- **Escalating Tensions**: The situation in the Middle East has significantly deteriorated following the assassinations of high-profile figures. The US embassy in Beirut has urged its citizens to leave Lebanon, and the UK has also issued similar warnings, indicating a high likelihood of imminent conflict. -- **Military Preparations**: Israel has approved plans for an offensive targeting Hezbollah, with the Israeli military stating that operational plans for a ground assault across the northern border have been validated. -- **Hezbollah's Capabilities**: Hezbollah possesses a range of missiles with varying ranges, including the FATEH-110 with a range of 186 miles, which could penetrate deep into Israel. -- **US Involvement**: Despite US efforts to prevent an escalation, the situation remains volatile. The US has deployed additional warships and fighter jets to the region to support Israel. - -#### Historical Context and Expert Opinions - -- **Ongoing Conflict**: The conflict between Israel and Hezbollah has been ongoing since October 2023, with both sides engaging in regular attacks. Hezbollah has launched over 2,100 military operations against Israel since October 8, 2023. -- **Genocide Warning**: Genocide Watch has issued a warning that an invasion could lead to significant civilian casualties and potential war crimes. -- **Expert Views**: Analysts believe that while an Israeli invasion is possible, it is not imminent due to internal and regional challenges and potential international condemnation. - -#### Recent Updates and Movements - -- **Recent Attacks**: There have been recent rocket strikes and airstrikes, including an Israeli strike on a school in Gaza, which has heightened tensions. -- **Political Statements**: Israeli leaders have made statements indicating that an invasion is imminent if Hezbollah does not cease its attacks. Hezbollah has vowed to continue its attacks until Israel halts its invasion of Gaza. - -### Conclusion - -Given the escalating tensions, military preparations, and recent attacks, there is a high likelihood that Israel may invade Lebanon before October 1, 2024. The situation is volatile, with both sides ready for an all-out war. The US and other international actors are trying to prevent an escalation, but the conflict's trajectory suggests a significant possibility of an invasion. - -######### - -GPT - -To predict whether Israel will invade Lebanon before October 1, 2024, I will systematically break down and analyze the given background data, base rates, trends, and other relevant factors. Here is the step-by-step approach I will take: - -### Step 1: Understand the Historical Context and Base Rates -Historically, Israel and Hezbollah have engaged in significant conflicts, with the most notable being the 2006 Lebanon War. Historically, large-scale invasions are relatively rare due to the substantial diplomatic and military costs involved. However, the current context is unique with severe escalations and numerous recent altercations. - -### Step 2: Analyze Current Tensions and Diplomatic Efforts -- **Escalating Tensions**: The recent launch of over 200 rockets by Hezbollah and the subsequent Israeli strikes indicate a severe escalation. However, severe tensions do not always lead to full-scale invasions. -- **US and France Involvement**: The active involvement of the US and France in preventing an all-out war adds a layer of deterrence against a full-scale invasion. Diplomatic efforts often intensify as conflicts approach critical points. -- **Internal and External Pressure**: Israel is under both national security pressures to act against Hezbollah and international pressures to avoid an escalation. This might create conflicting motivations. - -### Step 3: Evaluate Military Preparedness and Statements -- **Military Preparations**: Israel's military has validated operational plans for a ground assault and has stated publicly that it may invade if diplomatic solutions fail. -- **Statements by Israeli Leaders**: Threats of an imminent invasion have been made, indicating potential military resolve, but such statements can also serve as strategic bluffs for leverage in negotiations. - -### Step 4: Recent Trends and Ongoing Conflicts -- **Hezbollah's Arsenal**: The group's substantial missile arsenal and frequent attacks on Israel represent a constant threat that could push Israel towards a significant military response. -- **Recent Attacks**: Intensified military activities and specific recent events, like the attack on a school in Gaza and numerous Hezbollah operations, signal a potentially escalating trajectory. - -### Step 5: Analyze Timeframe and Possibility of Further Escalation -- **Current Date: 2024-08-05**: The forecast window is slightly under 2 months (56 days). -- **Historical Trends**: Significant military invasions typically have a build-up period and are not spur-of-the-moment decisions, unless triggered by a major provocation. -- **Volatility and Unpredictability**: Given the situation’s volatility, there is a non-negligible chance that events could spiral quickly out of control, leading to an invasion. - -### Step 6: Evaluate the Resolution Criteria and Fine Print -- **Specific Criteria**: To resolve as ""Yes"", credible sources must confirm that over 100 Israeli ground troops have entered Lebanon on deliberate orders from the Israeli government, without Lebanon’s permission or UN's assent. -- **Historical Base Rate**: Full-scale invasions over similar timeframes have been relatively low, especially considering international dynamics and deterrence efforts. - -### Step 7: Combine All Factors and Adjust for Uncertainty -- **Diplomatic Efforts and Constraints**: Significant international efforts are being made to prevent an all-out war, adding a constraining factor. -- **Military Capabilities and Threat Perceptions**: High military preparedness and public statements by Israel increase the risk but are balanced by deterrents. -- **Uncertainty and Volatility**: Given the volatile nature of the situation, uncertainty remains high, necessitating a non-trivial probability. - -After analyzing all the above factors: - -- There is a meaningful chance that the situation could escalate rapidly, leading to an invasion. -- There are also significant diplomatic and strategic deterrents that could avert a full invasion. -- Given the tight time frame, historical precedence, current conditions, and unpredictable volatility, I assess the probability of such an event. - -### Final Probability Estimate -Considering all the factors, the probability of Israel invading Lebanon before October 1, 2024, is: -**Probability: 40%** - -This estimate reflects the combined impact of escalating tensions, military readiness, diplomatic efforts, historical context, and the specified timeframe." -26915,"Will Kamala Harris's net favorability rating be higher than -8 as reported by 538 on September 1, 2024?","PERPLEXITY - -To forecast whether Kamala Harris's net favorability rating will be higher than -8 as reported by FiveThirtyEight on September 1, 2024, we need to consider recent trends and factors influencing her popularity. Here is a concise rundown of the most relevant news: - -1. **Current Polls and Trends**: - - **Net Favorability**: As of the latest polls, Harris's net favorability has surged to +1 from -11, indicating a significant improvement in her popularity following Biden's endorsement. - - **Recent Polls**: Trump leads Harris by one or two points in head-to-head matchups, but Harris performs better when third-party candidates are included. - -2. **Political Landscape**: - - **Biden's Endorsement**: President Biden's withdrawal from the race and endorsement of Harris has given her campaign a boost, with some Democratic donors celebrating the move as a ""shot of adrenaline"" for the party. - - **Trump's Response**: Trump has criticized Biden and Harris, calling Biden the ""worst"" president of his lifetime and suggesting that Harris has been complicit in his policies. - -3. **Upcoming Events**: - - **Democratic National Convention**: The DNC, scheduled for August 19-22, will be crucial for Harris to differentiate herself from Biden and appeal directly to voters. This event could significantly impact her favorability ratings. - -4. **Expert Opinions**: - - **FiveThirtyEight Analysts**: Analysts at FiveThirtyEight are cautious about predicting long-term trends based on current polls, emphasizing that the political environment and electorate can change significantly between now and the election. - -Given these factors, Harris's recent surge in popularity and the upcoming DNC could further improve her net favorability. However, the volatile nature of political polls and the potential for unforeseen events mean that her rating could fluctuate. As of now, there are indications that her favorability could remain positive or at least improve, potentially exceeding the -8 threshold by September 1, 2024. - -######### - -GPT - -To forecast whether Kamala Harris's net favorability rating will be higher than -8 as reported by FiveThirtyEight on September 1, 2024, we need to consider the available data, contextual factors, and prior trends. Here's a step-by-step rationale: - -### 1. Current Situation Analysis -As of July 30, 2024, Kamala Harris has seen a significant improvement in her net favorability, skyrocketing from -11 to +1. This positive swing in net favorability indicates favorable momentum primarily driven by: - -- **Biden's Endorsement**: Biden's endorsement has created a significant boost, with Democratic unity fostering increased support. -- **Contrasts with Trump**: The head-to-head comparison and the polarized political environment ensure that Harris's negative ratings don't significantly diverge from her opponent Trump, who maintains a similar -8.7 rating. -- **DNC Event**: The upcoming Democratic National Convention offers a crucial platform for Harris to further improve her ratings by outlining her policy agenda and differentiating herself from Biden. - -### 2. Upcoming Key Events -**Democratic National Convention (August 19-22)**: -- Party conventions typically give candidates a ""bounce"" in favorability. -- Harris’s projected performance and her speech could have a major positive effect on her ratings. - -### 3. Historical Context and Poll Trends -- **Volatility**: Favorability ratings tend to fluctuate significantly before elections. Campaign activities, debates, news cycles, and emerging events could all influence public opinion. -- **Base Rates and Historical Trends**: Historically, incumbent administrations face challenging polling when transitioning to another potential candidate within the same party, especially if they are closely associated with the prior administration's policies. -- **Survey Margins of Error**: Remember that polls have margins of error of roughly ±3-4%, which means even marginal reductions in approval could significantly impact net favorability ratings. - -### 4. Potential Negative Factors -- **Republican Counter-Messaging**: Trump and other Republican campaigns will actively criticize Harris, aiming to suppress her favorability. -- **Unforeseen Events**: Scandals, economic downturns, or significant national events (e.g., terrorism, economic crises) could quickly sway public opinion. -- **Policy Backlash**: Continued scrutiny on policies associated with the current administration might negatively affect Harris. - -### 5. Evaluation Based on Available Data -- **Recent High**: Harris's current +1 rating suggests a swing of 9 points from her previous standing of -8, which indicates a significant trend reversal. -- **Historical Conventions Bounce**: Typically, candidates experience a post-convention bounce. Given the DNC's timing (late August), the impact might sustain until early September. - -### Statistical Consideration -Given a baseline favorability of -11 and the recent surge to +1 (net improvement by 12 points): -- There's strong momentum and rationale for a continued positive trend, albeit subject to natural poll variations and external factors. - -### Conclusion -Assessing both the positive momentum and the potential for volatility, the likelihood of Harris maintaining a net favorability higher than -8 by September 1, 2024, appears favorable but not without risk. We should consider a balanced approach, combining historical trends, recent data, and a range of potential scenarios. - -Given this analysis: - -### Final Probability Estimation -Assuming normal fluctuation and potential for periodic negative messaging balanced with potential positive convention bounce, there is a high likelihood she will sustain net favorability better than -8. - -**Probability: 75%**" -26914,"Will Donald Trump's net favorability rating be higher than -8 as reported by 538 on October 1, 2024?","PERPLEXITY - -To determine whether Donald Trump's net favorability rating will be higher than -8 as reported by FiveThirtyEight on October 1, 2024, we need to analyze recent trends and data from various sources. - -### Favorability Ratings -- **FiveThirtyEight Polls**: As of recent polls, Trump's favorability ratings among Republicans have been consistently high. For example, a poll conducted from March 2-4, 2024, showed a net favorability of +60 among likely voters, with 79% favorable and 19% unfavorable. Another poll from February 25-28, 2024, indicated a net favorability of +66 among likely voters, with 83% favorable and 17% unfavorable. -- **ABC News/Ipsos Poll**: Following an assassination attempt and the Republican National Convention, Trump's favorability rating among Americans rose to 40%, marking his highest favorability rating in four years. However, 51% still viewed him unfavorably. - -### Recent Developments -- **Super Tuesday Primaries**: Trump dominated the Super Tuesday primaries, winning 14 out of 15 GOP presidential nominating contests. This strong performance likely boosted his favorability among Republicans. -- **Biden's Campaign End**: President Joe Biden's decision to drop out of the race after a poor debate performance against Trump has shifted the political landscape. Trump's favorability ratings have increased in the aftermath, with some polls showing him leading potential matchups against Vice President Kamala Harris. - -### Expert Opinions -- **FiveThirtyEight Analysis**: Trump's popularity and influence among Republican voters remain significant. He has maintained a substantial lead in early polling of the Republican primary, often polling in the high 40s or low 50s, 20-30 points ahead of his closest competitor, Ron DeSantis. -- **Political Analysts**: The ongoing investigations against Trump could potentially affect his electability in a general election, but so far, Republican voters have shown little concern over these investigations. - -### Prediction Markets -- **Current Predictions**: As of the latest updates, prediction markets and polling averages suggest that Trump's favorability ratings are likely to remain high among Republicans. His strong performance in the primaries and recent political events have contributed to this trend. - -### Conclusion -Given the recent data and trends, it is likely that Donald Trump's net favorability rating will be higher than -8 as reported by FiveThirtyEight on October 1, 2024. The consistent high favorability ratings among Republicans, coupled with his strong performance in the primaries and recent political developments, support this conclusion. - -######### - -GPT - -To address this forecasting question, we need to systematically evaluate the available data, trends, and influential factors before arriving at a probability estimation. Let's break this down step by step. - -### Step 1: Current Situation Analysis -As of July 30, 2024, Donald Trump's net favorability rating is -8.7. We have two months until October 1, 2024, and several variables to consider: - -#### a. Recent Trends: -- **Trump's favorability among Republicans remains high**, with net ratings of +60 to +66 in early 2024. This suggests a strong base of support that could positively influence his overall ratings. -- **Recent increase in favorability following key events** (e.g., assassination attempt, Republican National Convention) suggests momentum could be in his favor. - -#### b. Comparisons with Opponents: -- Trump's net favorability rating (-8.7) is similar to Kamala Harris's (-8.5), indicating a competitive environment. -- Biden's much lower favorability rating (-17.6) could mean voters are more positive about other candidates by comparison. - -### Step 2: Analyzing Potential Influences and Trends -#### a. Positive Influences: -- **Post-Convention Bounce**: Political conventions often give candidates a temporary boost in favorability, and Trump’s favorability did see an increase post-convention. -- **Sympathy and Rally Effects**: The assassination attempt and public response could rally further support around Trump. -- **Performance in Primaries**: Strong performance in the Super Tuesday primaries signifies robust backing, possibly increasing his overall favorability among the electorate. - -#### b. Negative Influences: -- **Ongoing Investigations**: Legal troubles could adversely affect his favorability, though past trends show limited impact among his core supporters. -- **Public Debates and Campaign Dynamics**: How Trump performs in public debates and campaign events could impact his favorability—for better or for worse. - -### Step 3: Historical Data Context -Looking back at historical favorability trends for presidential candidates during election years can provide some insights: -- Typically, favorability ratings fluctuate significantly due to debates, campaign strategies, and unforeseen events. - -### Step 4: Statistical Analysis -Considering the factors, a statistical approach would model the likelihood of Trump's favorability improving from -8.7 to higher than -8. We require an improvement of about +0.7 points or better. - -### Step 5: Putting It All Together -Let's weigh the recent trends and potential impacts: - -- **Current Favorability and Momentum**: Positive (+0.2 points) -- **Republican Base Loyalty**: Consistently strong, minimal change. -- **Recent Positive Influences**: +0.1 to +0.4 points -- **Potential Negative Impacts**: Monitoring cautious expectation, likely neutralize some gains but not significantly until actual events occur. -- **Historical Fluctuations**: Minor upward drift during mid-campaign season in presidential election years can be about ~1-2 points given positive events. - -Given the time frame (55 days) until October 1, 2024, the likelihood of major shifts becomes less as the date approaches, but incremental positive movements are plausible considering the momentum and current favorability among core voters, alongside some rally effects. - -### Concluding Probability Estimate -Factoring in the trends, influences, and historical variability: - -**Probability: 75%** - -This estimate reflects a moderate to high confidence that Donald Trump’s net favorability rating will improve from -8.7 to higher than -8 by October 1, 2024, given recent positive trends and sustained support among his base." -26913,"Will Donald Trump's net favorability rating be higher than -8 as reported by 538 on September 1, 2024?","PERPLEXITY - -To forecast whether Donald Trump's net favorability rating will be higher than -8 on September 1, 2024, we need to consider recent trends and expert opinions. Here is a concise rundown of the most relevant news: - -1. **Current Favorability Ratings**: - - As of recent polls, Trump's net favorability rating is around -8, according to FiveThirtyEight. This indicates a slight negative perception among the general public. - -2. **Recent Polls and Trends**: - - A recent Wall Street Journal poll shows that the presidential race between Kamala Harris and Donald Trump is essentially tied, with Trump leading 49% to 47%. This suggests that Trump's favorability might be improving slightly, especially among certain demographics. - - Another poll from Monmouth University indicates that Trump's favorable rating is 50%, with a net favorability of +9 percentage points in South Carolina. This is a positive sign for Trump in a key state. - -3. **Expert Opinions**: - - Nate Silver and other analysts at FiveThirtyEight have discussed Trump's high floor but potentially low ceiling in terms of voter support. This means that while Trump has a dedicated base, he may struggle to expand his support beyond that base. - - There is also a discussion about whether Trump's momentum will continue as the race narrows down to fewer candidates. Some analysts believe that Trump benefits from a divided opposition and might lose some support in one-on-one matchups. - -4. **Forecasting and Betting Markets**: - - Prediction markets and betting odds have not been explicitly mentioned in the sources, but they typically reflect current polling data and expert analysis. Given the recent polls showing a close race between Trump and Harris, these markets might indicate a slight improvement in Trump's favorability. - -5. **Potential Movement**: - - The recent WSJ poll showing a tied race between Trump and Harris suggests that Trump's favorability might be improving, especially among nonwhite voters and Democrats who are becoming more enthusiastic about the campaign. - - However, the consistency of Trump's negative net favorability rating across multiple polls indicates that any improvement might be marginal. - -In summary, while there are signs that Trump's favorability might be improving slightly, especially in key states and among certain demographics, his overall net favorability rating remains negative. Given the current trends and expert opinions, it is uncertain whether his net favorability rating will exceed -8 by September 1, 2024. - -######### - -GPT - -To forecast whether Donald Trump's net favorability rating will be higher than -8 as reported by 538 on September 1, 2024, let's systematically evaluate the information and apply forecasting methodologies. - -### Step-by-Step Analytical Approach: - -1. **Current Favorability Ratings (as of July 30, 2024)**: - - Donald Trump's net favorability rating is at approximately -8.7. - - Kamala Harris has a net favorability of -8.5. - - Joe Biden's net favorability is significantly lower at -17.6. - -2. **Recent Polls and Trends**: - - The Wall Street Journal poll indicates a very close race between Trump and Harris (49% to 47% favoring Trump). This suggests that Trump's position may be improving as the election approaches. - - The Monmouth University poll, showing Trump with a favorable rating of 50% and a net +9 percentage points in South Carolina, indicates localized strength. - - General trends will be observed over the weeks leading to September 1, including changes in perceptions due to events, media coverage, and campaign activities. - -3. **Expert Opinions**: - - Analysts at FiveThirtyEight emphasize Trump’s high support floor but potentially constrained ceiling. Despite having a dedicated base, broadening his support to positively impact net favorability is challenging. - - Given the analysis, there is a notable resistance to substantive, long-term improvements in net favorability. - -4. **Forecasting and Prediction Markets**: - - Prediction markets reflect aggregated views based on probabilities and current information. If they lean towards close outcomes and reflect slight improvements in Trump’s base, there's an indication of positive momentum that we need to account for. - -5. **Time to Resolution**: - - From August 5, 2024, to September 1, 2024, is a span of just under a month. Within this window, there is sufficient time for minor fluctuations but limited for significant changes unless catalyzed by major events or revelations. - - Past trends show that dramatic changes in favorability ratings are rare unless driven by significant incidents (scandals, major campaign shifts). - -6. **Historical Context and Base Rates**: - - Historically, incumbents or prominent political figures experience stable, though potentially volatile, favorability ratings in the lead-up to elections. Sudden positive or negative swings beyond a few percentage points are less common. - - Base rate analysis suggests it's challenging for Trump to acquire positive net favorability given his historical polling patterns, polarizing factors, and the entrenched opinions of voters. - -### Weighing Factors: - -1. **Current Rating and Trends**: Trump's current net favorability is close to -8, showing minor positive adjustments but essentially stable. -2. **Potential Catalysts**: The possibility for significant perception change exists but is mitigated by recent historical consistency. -3. **Expert Opinion and Analysis**: High floor, low ceiling; improvements are likely marginal unless driven by unforeseen major events. -4. **Prediction Markets**: Align with a closely contested race, reflecting potential tightening but not strong evidence of a major uptrend. - -### Probability Distribution: - -Based on the analysis, while there is a potential for Trump's net favorability to slightly improve, it remains highly uncertain whether it will surpass the -8 threshold given the time frame and current dynamics. - -### Conclusion: - -Given all factors considered, including recent trends, expert opinions, historical context, and statistical analytics, I estimate the likelihood of Donald Trump’s net favorability rating being higher than -8 on September 1, 2024, as follows: - -**Probability: 35%** - -This considers the potential for marginal improvement but acknowledges the inherent difficulty in overcoming entrenched negative perceptions within the given timeframe." -26916,"Will Kamala Harris's net favorability rating be higher than -8 as reported by 538 on October 1, 2024?","PERPLEXITY - -To forecast whether Kamala Harris's net favorability rating will be higher than -8 as reported by 538 on October 1, 2024, we need to consider recent polling data, trends, and expert opinions. - -### Current Polling Data -- **Recent Polls**: As of August 2024, Harris's net favorability rating has shown some improvement. For example, an Ipsos poll for US ABC News conducted in late July showed her net favorability surged to +1 from -11 the previous week. -- **Historical Context**: Harris's approval ratings have been consistently low. In December 2023, her net disapproval rating was -17.4, with an approval rating of 36.3% and a disapproval rating of 53.7%. - -### Trends and Expert Opinions -- **Improvement Post-Biden Withdrawal**: Since President Joe Biden withdrew from the presidential race and endorsed Harris, her polling numbers have seen a slight improvement. This suggests that some Democrats who were dissatisfied with Biden might be returning to support Harris. -- **Expert Views**: Thomas Whalen, an associate professor at Boston University, suggests that while Harris's current ratings are poor, it is too early to write her off. He points out that vice presidents are often unpopular during their first term but can still perform well in elections. - -### Prediction Markets and Betting Odds -- **Prediction Markets**: As of August 2024, prediction markets do not yet have comprehensive data on Harris's favorability ratings. However, they do indicate that Trump is marginally ahead of Harris in national polls, but Harris performs better when third-party candidates are included. - -### Conclusion -Given the recent improvement in Harris's net favorability rating and the historical context of her low approval ratings, it is uncertain whether her net favorability rating will be higher than -8 by October 1, 2024. The trend suggests a possible improvement, but the margin is still narrow and subject to various factors, including the upcoming Democratic convention and further campaign developments. - -### Key Points: -1. **Recent Improvement**: Harris's net favorability rating has improved slightly since Biden's withdrawal. -2. **Historical Context**: Harris's approval ratings have been consistently low. -3. **Expert Opinions**: Some experts believe it is too early to write Harris off despite her current ratings. -4. **Prediction Markets**: Trump is marginally ahead of Harris in national polls, but Harris performs better with third-party candidates included. - -These factors indicate a complex and dynamic situation, making it challenging to predict with certainty whether Harris's net favorability rating will exceed -8 by October 1, 2024. - -######### - -GPT - -**Step-by-Step Thought Process** - -### 1. **Understand the Current Situation** -- **Current Favorability Ratings**: As of July 30, 2024, Kamala Harris’s net favorability is -8.5, and the target is a net favorability higher than -8 by October 1, 2024. -- **Recent Improvement**: There has been a recent improvement, with a notable increase from an Ipsos poll showing a move from -11 to +1. -- **Competition Context**: She will be up against Donald Trump, who has a net favorability rating of -8.7. - -### 2. **Evaluating Historical Context & Trends** -- **Consistently Low Ratings**: Harris's approval ratings historically have been low (e.g., -17.4 in December 2023). -- **Impact of Biden’s Withdrawal**: Since Biden’s withdrawal, her ratings have shown some improvement. This could be due to re-engagement of Democratic supporters who were dissatisfied with Biden. -- **Presidential Campaign Effects**: The upcoming campaign activities, Democratic convention, and other political events could potentially boost her ratings further. - -### 3. **Base Rates & Historical Precedent** -- Vice presidents often face low approval ratings during their initial terms, but this can change during election campaigns. -- Campaign fluctuations are common, and significant changes in favorability can occur within a two-month frame, especially as election activities ramp up. - -### 4. **Expert Opinions & Prediction Markets** -- **Expert Views**: Some experts believe Harris could see further improvements, especially as she garners more support during her campaign. -- **Prediction Markets**: Trump is marginally ahead, but Harris performs better when third-party candidates are considered, indicating she has room for improvement. - -### 5. **Assessment of Factors Influencing Change** -- **Time Available**: Approximately two months are remaining until October 1, 2024. -- **Variance in Ratings**: Favorability ratings can be volatile during election campaigns. -- **Events to Come**: Democratic convention, debates, and key campaign events that could swing ratings. -- **Default Resolution**: If no major changes occur, Harris’s favorability might hover around current levels or see slight improvement due to increased campaign visibility. - -### 6. **Assigning Probabilities** -Considering all these factors, we need to weigh the probabilities based on: -1. **Recent Improvement**: Shows positive trend (weight: 30% importance) -2. **Historical Context of Low Ratings**: Generally low favorability (weight: 30% importance) -3. **Impact Events**: Significant campaign events low yet potential high impact (weight: 40% importance) - -### 7. **Final Probability Distribution** -- **50% Probability**: Net favorability significantly increases due to effective campaign efforts, positive media coverage, and successful Democratic convention, resulting in > -8. -- **30% Probability**: Net favorability remains relatively stable with minor fluctuations, leading to a range close to -8. -- **20% Probability**: Net favorability worsens due to negative campaign events, opposition effectiveness, or significant missteps by Harris. - -Given that the current favorability trend shows improvement, and considering the volatile nature of approval ratings during campaign seasons, I would assign a slightly optimistic yet cautious probability. - -**Final Prediction:** -""Probability: 65%"" - -This suggests that there is a 65% chance that Kamala Harris’s net favorability rating will be higher than -8 as reported by 538 on October 1, 2024." -26919,"If Israel invades Lebanon before October 1, 2024, will there be 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024?","PERPLEXITY - -### Context for Forecasting the Likelihood of 1000 or More Deaths Due to Armed Conflict Between Israel and Hezbollah Before October 1, 2024 - -#### Current Situation and Recent Developments - -1. **Escalating Tensions and Attacks**: - - Since October 7, 2023, there have been over 4,400 rocket, missile, and other stand-off attacks by Israel and Hezbollah combined. - - Recent strikes by Israel have targeted Hezbollah sites deep inside Lebanese territory, including central Lebanon, marking a deeper-than-usual incursion. - - Hezbollah has retaliated with rocket attacks on Israeli military bases, leading to civilian casualties on both sides. - -2. **Casualties and Displacement**: - - At least 543 people have been killed in Israeli strikes on southern Lebanon since October 2023, while attacks from Hezbollah and other armed groups have killed at least 21 people in Israel. - - Tens of thousands of Israeli and Lebanese civilians have been evacuated from border towns due to the hostilities. - -3. **Military Capabilities and Strategic Considerations**: - - Hezbollah possesses a significant arsenal, including 120,000 to 200,000 short-range guided ballistic missiles and other weapons, posing a substantial threat to Israel. - - Israeli intelligence estimates suggest that Hezbollah could launch up to 3,000 rockets and missiles per day in a full-scale conflict, potentially overwhelming Israel's missile defenses. - -4. **Political and Diplomatic Context**: - - The United States has been involved in diplomatic efforts to prevent an escalation, with President Joe Biden pushing back against Israeli plans for preventive strikes against Hezbollah in October 2023. - - The conflict has sparked increased regional tensions, with other Iran-backed groups launching attacks on U.S. military positions in Iraq and Syria. - -5. **Expert Opinions and Predictions**: - - Analysts believe that while Hezbollah's leader, Hassan Nasrallah, hopes to avoid a full-scale war, further escalation is entirely possible due to the high alert status of both sides. - - The Israeli government faces a dilemma between risking a broader war with Hezbollah now or waiting and potentially facing a more dangerous conflict in the future. - -### Recent Updates and Movements - -- **Increased Hostilities**: The recent strikes and retaliations indicate a heightened state of conflict, which could lead to a significant increase in casualties. -- **Diplomatic Efforts**: Despite ongoing diplomatic efforts, the situation remains volatile, with no clear resolution in sight. -- **Military Preparations**: Both sides are on high alert, with Israel approving plans for a full-scale attack in southern Lebanon and Hezbollah preparing to broaden its rocket attacks. - -### Forecasting Considerations - -Given the escalating tensions, ongoing hostilities, and significant military capabilities on both sides, the likelihood of 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024, appears high. The current situation is marked by frequent exchanges of fire, significant casualties, and a lack of a clear diplomatic resolution, all of which contribute to the potential for further escalation and increased fatalities. - -######### - -GPT - -To determine the probability that there will be 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024, given an Israeli invasion of Lebanon, we need to consider several key factors methodically: - -### Step 1: Evaluate the Current Situation -The data provided indicates: -- **High Current Tensions:** Over 4,400 rocket and missile attacks between Israel and Hezbollah. -- **Existing Casualties:** At least 543 deaths in Lebanon and 21 deaths in Israel due to recent attacks. -- **Military Escalation:** Continued and escalating hostilities with deeper Israeli strikes into Lebanon and significant Hezbollah retaliation. - -### Step 2: Assessing Potential for Continued Escalation -Given the current military capabilities: -- **Hezbollah's Arsenal:** Significant, with up to 3,000 rockets potentially launched per day in a full-scale conflict. -- **Israel’s Preparedness:** High alert status with plans for full-scale attacks in southern Lebanon. - -### Step 3: Historical Base Rates and Trends -- Past conflicts (e.g., the 2006 Lebanon War) resulted in significant casualties (over 1,100 Lebanese and around 150 Israelis killed). This provides a relevant historical context that similar escalations have previously led to high fatalities. - -### Step 4: Diplomatic Efforts and Political Considerations -- Continuous diplomatic efforts are ongoing but have not yet resolved the conflict. -- Political pressures both internally within Israel and from international actors (e.g., the U.S.) might both limit and provoke further military actions. - -### Step 5: Timeframe and Variability -- There is a nearly two-month period until October 1, 2024, which, given current high levels of conflict and hostilities, leaves ample time for escalations leading to substantial casualties. - -### Step 6: Quantitative Assessment -Given: - 1. **Current death tolls:** around 564 already. - 2. **Potential for rapid escalation:** evidenced by significant military capabilities and high tensions. - 3. **Historical precedence:** past conflicts have crossed the threshold of 1000 deaths within a similar period. - -This trend and historical context, combined with the ongoing high-alert status and frequency of attacks, suggest a significant risk of surpassing 1000 deaths should an invasion occur. - -### Conclusion -Considering the aforementioned factors: -- The high casualties so far. -- The potential for rapid escalation. -- Historical examples where similar conflicts have resulted in substantial casualties. - -### Summary of Steps: -1. Current data shows significant hostilities with over 564 deaths already reported. -2. Both sides have substantial military capabilities that could lead to rapid escalation. -3. Diplomatic efforts are ongoing but with limited effect. -4. Historical conflicts indicate that such escalations can easily surpass 1000 deaths, especially within similar timeframes. - -Given these points, the chain of reasoning strongly supports a high probability of 1000 or more deaths due to armed conflict between Israel and Hezbollah before October 1, 2024, if Israel invades Lebanon. - -Therefore, the most probable outcome, based on the integration of historical data, current trends, and potential for further escalation, is: - -**Probability: 85%**" -27020,"Before October 1, 2024, will Anthropic announce on the news section of its website that it is planning an IPO?","PERPLEXITY - -To forecast whether Anthropic will announce an IPO before October 1, 2024, we need to consider recent funding, valuations, and any statements from the company or its investors. Here is a concise rundown of the relevant news: - -### Funding and Valuations -- **Recent Funding**: Anthropic has received significant funding, including a $450 million Series C round in May 2023 and a $2 billion investment from Google in October 2023. Additionally, Amazon committed to a $4 billion investment in September 2023, which was completed in March 2024. -- **Valuation**: The company's valuation has been reported to be around $18.4 billion as of recent funding rounds, with estimates as high as $25 billion in October 2023. - -### IPO Potential -- **IPO Plans**: Despite the substantial funding and high valuations, Anthropic has not publicly announced any plans for an IPO. The company remains privately held and has not filed any confidential S-1 filings with the SEC. -- **Investor Interest**: Major investors like Google, Amazon, Salesforce, and Zoom are involved, which could influence future decisions regarding an IPO. However, there are no recent updates indicating a move towards an IPO. - -### Expert Opinions -- **Market Conditions**: The decision to go public often depends on broader market conditions. As of now, there are no indications that Anthropic is preparing for an IPO, and the company's ability to raise capital privately reduces the urgency to go public. -- **Company Focus**: Anthropic is focused on developing its AI technology and has not shown any immediate need to access public markets for funding. The company's growth and investments suggest it may remain private for the foreseeable future. - -### Recent Updates -- **No Recent Announcements**: There have been no recent announcements or updates from Anthropic or its investors indicating a move towards an IPO. The company's website does not currently mention any IPO plans. - -### Conclusion -Given the current information, there is no strong indication that Anthropic will announce an IPO before October 1, 2024. The company's ability to secure significant private funding and its focus on developing its AI technology suggest it may remain private for the time being. - -######### - -GPT - -To forecast whether Anthropic will announce an IPO before October 1, 2024, we need to methodically evaluate all the information provided, and consider additional relevant factors. Let's break down this analysis step-by-step. - -### Step 1: Assess the Current Situation -- **Funding and Valuations**: Anthropic has been successful in raising significant private funding. The company's valuation is currently around $18.4 billion, with higher estimates reaching $25 billion in October 2023. This buoyancy in securing private funds reduces the urgency for an IPO. -- **Recent Funding Rounds**: Major recent investments from Google, Amazon, Salesforce, and Zoom indicate robust financial backing, suggesting that Anthropic does not need public funds immediately. - -### Step 2: Analyze What Influences an IPO Decision -- **Investor Expectations**: Investors' expectations could influence an IPO, but given the profile of Anthropic's investors (Google, Amazon, etc.), it's only necessary if those investors push for liquidity or public valuation metrics. -- **Market Conditions**: Broader market conditions and appetite for tech IPOs play a crucial role. Current market conditions (from what we can ascertain by August 2024) don't show exceptional favorability toward tech IPOs, but there’s no dramatic downturn either. -- **Confidential Filings**: No confidential S-1 filings have been made, which would typically precede a public announcement by several months. - -### Step 3: Consider the Timing -- **Time until October 1, 2024**: We have less than two months until the cutoff date. Announcement preparations (especially public ones) would typically begin some months prior, suggesting tight time constraints. -- **Past Trends**: Historically, companies with substantial private funding rounds tend to prolong their private status unless under significant investor or market pressure to go public. - -### Step 4: Base Rates and Comparisons -- **Historical Base Rates**: Tech companies with large and recent funding rounds usually delay IPOs compared to those urgently needing capital influx. -- **Novel Factors**: The presence of big-name backers like Google and Amazon might delay the need for an IPO as these companies can sustain growth privately. - -### Step 5: Probability Calculation -- **Default Scenario**: No IPO announcement (the status remains unchanged). -- **Recent Events Probability Modification**: Given no tangible recent moves towards an IPO, the probability of an announcement drops further considering only a short timeframe remaining. - -### Step-by-Step Calculation: -- Original Base Rate: Tech startup announcing IPO within any given year: approximately 10-15% given optimal conditions. -- Adjusted for Anthropic's Funding Success: Decreases probability due to strong private funding. -- Time Constraint Adjustment: Less time to make preparations reduces likelihood. - -### Conclusion -We have less than two months until October 1, 2024, and no significant indicators suggesting an upcoming IPO announcement exist. High levels of private funding and the absence of confidentiality filings further reduce probability. - -Therefore, after thorough consideration, I conclude: - -**Probability: 5%**" -27021,Will the USA win more Gold than Silver medals at the Paris 2024 Olympics?,"PERPLEXITY - -To determine whether the USA will win more gold than silver medals at the Paris 2024 Olympics, we need to analyze the current medal count and trends. - -### Current Medal Count and Trends - -1. **Medal Count Breakdown**: - - **Team USA**: As of the latest updates, Team USA has won a total of 28 medals in swimming alone, including 8 gold, 13 silver, and 7 bronze medals. - - **Overall Medal Count**: The United States is currently leading in the overall medal count, but China is maintaining a lead in gold medals. - -2. **Recent Performance**: - - **Swimming**: The U.S. Swimming team had a strong finish, winning a total of 28 medals, including a world record in the women's 4x100 medley relay. - - **Gymnastics**: Simone Biles and Jordan Chiles earned medals in their final events, with Biles tying for the second-most medals by a female gymnast in Olympic history. - - **Track and Field**: Valarie Allman won gold in the women’s discus throw final. - -3. **Historical Context**: - - **Past Olympics**: In the first week of the Tokyo Olympics, Team USA won 14 gold medals. In Rio 2016, they won 20 gold medals in the first week. This year, they have won fewer golds in the initial week but have picked up golds in events that were scheduled later in previous Olympics. - -### Expert Opinions and Predictions - -- **Athlete Confidence**: Athletes like Caeleb Dressel express confidence in the team's performance, despite a slow start, and believe the U.S. will remain competitive. -- **Medal Projections**: The U.S. is expected to win more medals overall but faces stiff competition from China in the gold medal count. - -### Recent Updates - -- **Medal Table**: As of August 5, the U.S. is leading in the overall medal count, but China has more gold medals. -- **Upcoming Events**: The schedule for the remaining events could influence the final medal count, with several key events still to be decided. - -### Prediction Markets and Polls - -- **Prediction Markets**: There are no specific prediction markets available for the exact question of whether the USA will win more gold than silver medals. However, general predictions favor the U.S. to top the overall medal count but not necessarily the gold medal count. - -### Conclusion - -While the USA is currently leading in the overall medal count, the competition for gold medals is intense, particularly with China maintaining a lead in golds. The U.S. has shown strong performances in various events, but the final outcome will depend on the results of the remaining events. The trend suggests that the U.S. might win more overall medals but may not necessarily win more golds than silvers. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. Historical Context -The USA has consistently performed well in the Olympics, often topping the overall medal count. Historically, the USA tends to have a strong showing in many sports, especially swimming, gymnastics, and track & field. Reviewing past Olympics: - -- **Tokyo 2020**: USA had 113 total medals (39 Gold, 41 Silver, 33 Bronze). -- **Rio 2016**: USA had 121 total medals (46 Gold, 37 Silver, 38 Bronze). -- **London 2012**: USA had 104 total medals (46 Gold, 28 Silver, 30 Bronze). - -From these numbers, the USA has more frequently won gold medals compared to silver in recent Olympics, although sometimes by a small margin. - -#### 2. Current Performance and Trends -As of August 6, 2024: -- The USA has 28 medals in swimming alone, with a ratio of 8 Gold to 13 Silver. -- China has been leading in gold medals. -- Key sports like gymnastics and track & field have also started yielding golds, but the swim results show a higher silver count. - -#### 3. Upcoming Events -- **Track and Field**: Historically a strong suit for the USA, with potential for more golds. -- **Basketball**: Another gold medal opportunity. -- **Swimming and Gymnastics**: Events already concluded with a significant number of silvers. - -#### 4. Athlete Confidence and Expert Predictions -- Positive indications from athletes like Caeleb Dressel. -- Expert predictions favor the USA for topping the overall medal count but are less clear on the gold-versus-silver ratio. -- This mixed sentiment indicates variability in final outcomes. - -#### 5. Recent Data -- Current leading position for USA in overall medals. -- China is leading in golds which adds pressure on future events for the US to secure more golds. - -#### 6. Statistical Base Rates -- Historically, USA secures more golds than silvers in the Olympics, albeit with close margins. -- Data suggests potential deviations based on competitive peer teams and event-specific performances. - -#### Calculation and Probability -Assuming historical trends and current partial results, the statistical likelihood of the USA pulling ahead in gold medals relative to silver is significant but not a certainty. The following points are critical: - -- There's natural regression to the mean in medal counts. -- Remaining high-yield events (track & field, basketball) where the USA traditionally excels might tilt the balance toward golds. -- The historical data shows the USA often ends with more golds than silvers, albeit narrowly. - -### Conclusion -Given historical precedence, current standings, and remaining events, an informed probability estimate that the USA will win more gold than silver medals is warranted. However, given current trends showing a high silver count in already completed events, the confidence level should reflect this balance. - -### Final Answer -**Probability: 65%**" -27019,"Will there be a debate between Kamala Harris and Donald Trump on September 4, 2024?","PERPLEXITY - -**Competition Event: Debate between Kamala Harris and Donald Trump on September 4, 2024** - -### Summary of Relevant News - -- **Donald Trump's Proposal**: Trump has announced that he will skip the scheduled September 10 debate with Kamala Harris on ABC and instead wants to debate on Fox News on September 4. He claims this is due to his litigation against ABC News and the fact that Joe Biden, his original debate opponent, has ended his campaign. - -- **Harris Campaign's Response**: The Harris campaign has stated that they will stick to the original plan for the September 10 debate on ABC. They accuse Trump of ""running scared"" and trying to back out of the debate he already agreed to. - -- **Fox News Debate Details**: Trump has agreed to a debate on Fox News on September 4, which would be moderated by Bret Baier and Martha MacCallum. The debate would be held in Pennsylvania with a full audience, unlike the previous debate with Biden which had a mostly empty studio. - -- **Legal Conflict**: Trump cited his ongoing litigation against ABC News as a conflict of interest for participating in the ABC debate. He filed a lawsuit against ABC in March following a claim by anchor George Stephanopoulos that Trump was found liable for rape, though the jury did not find him liable for rape. - -- **Previous Debates**: Trump has a history of skipping debates, including all the 2024 Republican presidential primary debates. His previous debate with Biden on CNN contributed to Biden's decision to end his campaign. - -### Recent Updates - -- **Trump's Social Media Posts**: Trump has been vocal on Truth Social about his decision, stating that if Harris does not agree to the new date and network, he will do a ""major Town Hall"" with Fox News instead. - -- **Harris's Commitment**: Harris is committed to the September 10 time slot and will appear ""one way or the other"" to address a prime-time national audience. - -### Expert Opinions - -- **Michael Tyler, Harris Campaign**: Tyler believes Trump is ""running scared"" and trying to back out of the debate. He emphasizes that Harris will participate in the September 10 event regardless of Trump's decision. - -### Prediction Markets - -- **Current Predictions**: As of the latest updates, there are no clear predictions from forecasting or betting sites regarding the likelihood of a debate between Trump and Harris on September 4. The situation remains fluid, with both campaigns holding firm to their respective positions. - -### Conclusion - -Given the current stance of both campaigns, it appears unlikely that there will be a debate between Kamala Harris and Donald Trump on September 4, 2024. Trump's insistence on a Fox News debate and Harris's commitment to the ABC debate on September 10 create a significant impasse. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Current Status and Default Behavior** -- **Trump's Position:** - - Trump has expressed intent to cancel the September 10 debate with Harris on ABC. - - He wishes to debate Harris on September 4, hosted by Fox News. - - Trump's history of skipping debates is notable and suggests a pattern of non-compliance with regular debate formats. - -- **Harris's Position:** - - Harris is committed to the original debate scheduled for September 10th on ABC. - - Her campaign has clearly stated that there has been no agreement to debate on September 4th on Fox News. - -#### 2. **Historical Base Rate Analysis** -- **Historical Pattern:** - - Trump has a history of avoiding debates not on his terms, evidenced during the 2024 Republican presidential primary debates as well as other occasions. - - Last-minute changes to debate schedules are rare in presidential campaigns, given the extensive planning and logistics involved. - -- **Legal and Media Dynamics:** - - Trump's ongoing legal conflicts with ABC may influence his likelihood to avoid the ABC-hosted debate. - - If Trump follows through with litigation threats, this further reduces the chance he participates in an ABC event but may also complicate the logistics of any sudden new debate. - -#### 3. **Recent Developments and Trends** -- **Political Statements:** - - Trump has been vocal on social media about his plans which suggests he might be seeking to leverage media attention rather than commit to the new debate date. - - Harris’s campaign confirms commitment to the original date and dismisses the alternative proposal. - -- **Media Responses:** - - Fox News has prepared for a potential debate on September 4, showing some readiness, but no confirmation from Harris. - -#### 4. **Analysis of Uncertainty** -- **Time Frame:** - - There are approximately 4 weeks until September 4, which provides limited time for negotiations and changes to the agreement. - - However, political campaigns can act quickly if strategically necessary. - -- **Probability of Variance:** - - Given the firm stances currently, a debate is unlikely unless significant negotiations or shifts occur. - - Logistical preparation for a formal debate, venue prep, travel plans, and security often require more than a month of lead time. - -#### 5. **Novel Circumstances** -- **Present Climate:** - - 2024 is an election year with heightened unpredictability due to legal conflicts and dynamic political strategies. - - Both campaigns are strategically maneuvering based on perceived advantages, which introduces further complexity. - -### Conclusion -With the current stance of both campaigns, the entrenched positions of each, the short time frame for resolution, and historical patterns, it is highly improbable that both sides will shift significantly to agree on a new debate date and location. The default behavior, in this case, strongly leans towards the original date being adhered to by Harris and Trump's potential non-participation if his demands are not met. - -Thus, assessing the likelihood of a debate occurring on September 4, 2024, between Kamala Harris and Donald Trump: - -**Probability: 10%** - -This reflects a highly unlikely but not impossible scenario given the factors analyzed." -27022,"Will astronauts Suni Williams and Butch Wilmore be on Earth on September 15, 2024?","PERPLEXITY - -**Context for Forecasting: Will Astronauts Suni Williams and Butch Wilmore be on Earth on September 15, 2024?** - -### Key Points: - -1. **Current Status**: - - Astronauts Suni Williams and Butch Wilmore have been on the International Space Station (ISS) since June 6, 2024, after the first crewed flight of Boeing's Starliner spacecraft. - - Their mission has been extended due to technical issues with the Starliner, including helium leaks and thruster problems. - -2. **Technical Issues**: - - The Starliner has faced significant technical challenges, including helium leaks and thruster malfunctions, which have delayed their return to Earth. - - NASA and Boeing are conducting extensive tests to ensure the spacecraft's propulsion system is reliable for the return journey. - -3. **Return Planning**: - - The return date has been rescheduled multiple times due to ongoing issues. A tentative return date of July 6 was previously suggested but has been extended. - - NASA and Boeing are currently analyzing data from recent tests to finalize the flight rationale and confirm system reliability. - -4. **Alternative Options**: - - There is a possibility that the astronauts might return on a SpaceX Crew Dragon if the Starliner's issues cannot be resolved in time. - - A Crew-9 mission on a SpaceX Dragon is scheduled to dock at the ISS on August 18, which may necessitate the Starliner's departure. - -5. **Expert Opinions**: - - NASA and Boeing remain confident in the Starliner's ability to safely return the astronauts, despite the delays and technical issues. - - Astronauts Williams and Wilmore have expressed confidence in the Starliner's capabilities and are supporting daily duties on the ISS while awaiting further instructions. - -6. **Recent Updates**: - - As of August 5, 2024, there is no firm date set for the return journey, with ongoing tests and evaluations continuing to determine the best course of action. - - NASA has emphasized that astronaut safety is the top priority and that more information on the return planning is expected to be shared soon. - -### Conclusion: -Given the ongoing technical issues and the lack of a firm return date, it is uncertain whether Suni Williams and Butch Wilmore will be on Earth by September 15, 2024. The situation remains fluid, with NASA and Boeing working diligently to resolve the problems and ensure a safe return. The possibility of alternative transportation methods, such as using a SpaceX Crew Dragon, adds another layer of complexity to the forecast. - -######### - -GPT - -To forecast whether astronauts Suni Williams and Butch Wilmore will be on Earth on September 15, 2024, it's essential to analyze the provided information and consider relevant historical data, base rates, technological standards, and expert opinions. Let's systematically go through the key points and apply forecasting methodologies recognized by Tetlock and other superforecasters. - -### Step-by-Step Analysis - -#### Current Situation: -- **Status and Mission Duration**: - - They have been on the ISS since June 6, 2024. The mission has already been extended from the original return date in mid-June due to unforeseen technical issues with the Starliner. - -#### Technical Issues: -- **Starliner Problems**: - - **Helium Leaks and Thruster Issues**: Significant enough to delay the mission ""indefinitely"". - - **NASA and Boeing Response**: Ongoing extensive tests to ensure the propulsion system's reliability. - -#### Alternative Options: -- **SpaceX Crew Dragon**: - - **Possible Usage**: If the Starliner issues remain unresolved, Williams and Wilmore might return on the SpaceX Crew Dragon. - - **Crew-9 Mission**: Scheduled to dock on August 18, 2024, might facilitate their return on an alternative spacecraft. - -#### Historical and Technological Context: -- **Historical Base Rates**: - - Space missions have experienced delays due to technical issues before, but alternative spacecraft like SpaceX Crew Dragon have shown reliability and flexibility in past returns. - - Based on past collaborations, NASA has utilized private sector alternatives effectively when primary plans faced issues. - -#### Timeline and Feasibility: -- **Time Window**: - - With the current date being August 6, 2024, there are 40 days until September 15, 2024. - - The historical base rate for similar issues (e.g., spacecraft technical problems) being resolved within approximately 40 days is moderate; NASA’s focus on safety ensures that delays are cautious but also highly prioritized. - -#### Expert Opinions and Recent Developments: -- **Internal Confidence**: - - Both NASA and Boeing remain confident in the Starliner’s eventual return, but they have not set a firm return date. - - Astronauts' confidence and readiness indicate preparation and flexibility for potential return plans. - -### Probability Assessment: -Given the above factors, I will break down the analysis into probabilities of technical resolution: - -1. **Technical Resolution on Starliner**: - - Given active problem-solving but ongoing indefinite delays, I assess a 45% probability (moderate confidence due to unpredictable technical diagnostics and repairs). - -2. **Alternative Return via SpaceX Crew Dragon**: - - NASA's past behavior shows adaptability and redundancy plans. There is a high likelihood this backup will be activated if needed. I assign a 45% probability (assuming they would not remain indefinitely; there are established protocols). - -3. **Unresolved Issues or Additional Delays**: - - There's a minority probability due to persistent technical issues extending the timeline further or causing mission reevaluation. I assign a 10% probability (unlikely due to strong stakeholder pressures). - -Adding up the probabilities of a positive resolution through either primary (Starliner) or alternative (SpaceX) methods within the remaining time gives us: -\[ 45\% (Starliner Resolution) + 45\% (SpaceX Alternative) = 90\%\] - -### Conclusion -Considering the collected data, existing safety and operational protocols, historical precedence, and the strategic alternatives at play, the likelihood of astronauts Suni Williams and Butch Wilmore being back on Earth by September 15, 2024, is relatively high. - -**Probability: 90%**" -26957,"Will Iran carry out a deadly attack within Israel before September 1, 2024?","PERPLEXITY - -### Context for Forecasting: Will Iran Carry Out a Deadly Attack Within Israel Before September 1, 2024? - -#### Recent Developments and Tensions - -1. **US Intelligence and Official Statements**: - - The United States believes Iran may retaliate against Israel in the coming days, potentially using drones and missiles, in response to Israel's recent airstrike in Damascus, Syria, which killed a top Iranian commander and six other personnel. - - President Biden has reaffirmed the U.S. commitment to Israel's security, stating that their commitment is ""ironclad"" and that they will do all they can to protect Israel. - -2. **Israeli Preparations and Threats**: - - Israel's Defense Minister Yoav Gallant has warned that if Iran attacks from its territory, Israel will respond by attacking Iran. - - Israel has been preparing for potential attacks, evacuating embassies and jamming GPS signals in anticipation of aerial bombings. - -3. **Escalation and Proxy Conflicts**: - - The conflict has involved multiple actors, including Hezbollah, Houthis, and other Iranian proxies, which have launched attacks on Israel. - - The U.S., UK, and Jordan have intercepted Iranian drones to defend Israel, while Syria has shot down some Israeli interceptors. - -4. **High-Profile Assassinations**: - - Israel has carried out targeted assassinations of prominent leaders in Hamas and Hezbollah, including Ismail Haniyeh and Fuad Shukr, which has significantly escalated tensions. - - These assassinations have been seen as a new Israeli strategy to enhance deterrence and send clear messages to Tehran and its allies. - -5. **Iranian Threats and Potential Responses**: - - Iranian officials have vowed to retaliate, with Foreign Minister Hossein Amirabdollahian stating that the response will be ""crushing"". - - Analysts predict that Iran's response could be similar to the broad attack in April, featuring a barrage of ballistic missiles and drones, though it may be calibrated to avoid triggering a wider war. - -6. **Geopolitical Context**: - - The region is experiencing heightened tensions, with ongoing conflicts in Gaza and Lebanon, and the U.S. maintaining a significant military presence in Iraq. - - The U.S. is expected to begin withdrawing troops from Iraq in September, which could influence the dynamics of the conflict. - -#### Expert Opinions and Predictions - -- **Michael Mulroy**, former deputy assistant secretary of defense for the Middle East, believes that Hezbollah and Iran will likely retaliate, pushing the region closer to an all-out conflict. -- **Bilal Saab**, an expert on U.S.-Middle East security issues, notes that while neither side seeks a full-scale war, provocative acts could lead to an escalation. -- **Kenneth McKenzie**, a retired U.S. Marine general, suggests that Iran will have to think carefully about its next steps to avoid triggering a wider war. - -#### Current Predictions and Betting Markets - -- There are no specific prediction markets available for this exact event. However, the general consensus among analysts and officials is that the likelihood of a significant attack by Iran on Israel before September 1, 2024, is high due to the recent escalations and threats. - -### Conclusion - -Given the recent high-profile assassinations, ongoing proxy conflicts, and explicit threats from both Iran and Israel, the likelihood of a deadly attack by Iran within Israel before September 1, 2024, appears to be significant. The geopolitical context, including the U.S. military presence and withdrawal plans, adds to the complexity and potential for escalation. - -######### - -GPT - -### Step-by-Step Forecasting Approach - -#### 1. **Understanding the Context and Criteria** -- **Resolution Criteria**: - - Must result in at least five fatalities. - - Must occur within the borders of Israel as defined. - - Must be attributed to Iran or explicitly acknowledged by Iran. - - Must be a kinetic (non-cyber) attack. - - Must occur before September 1, 2024. - -#### 2. **Identifying Historical and Recent Patterns** -- **Base Rates**: Historically, serious escalations between Iran and Israel have resulted in periodic attacks but not always of high fatality within a specific short timeframe. -- **Recent Events**: - - Assassinations of prominent leaders of Hamas and Hezbollah are significant provocations. - - Iran has explicitly vowed revenge. - - April’s drone and missile attack resulted in injuries, highlighting that capability exists but wasn’t fatal at that time. - -#### 3. **Assessment of Current Dynamics** -- **Statements and Warnings**: - - Both sides have issued very stark warnings and threats. This raises the stakes and likelihood of retaliatory action. - - Explicit statements from the Iranian leadership about the need for a response are notable. -- **Military Movements and Preparations**: - - Israel is on high alert, suggesting anticipation of an attack. - - US involvement and the readiness to support Israel indicate an understanding that an attack is expected. - -#### 4. **Expert Opinions and Geopolitical Analysis** -- **Expert Predictions**: Consensus among Middle East security experts leans towards an increased likelihood of escalation. -- **Proxy Conflicts**: Iran’s history of using proxies for attacks adds complexity but also within the criteria accusations could be attributed to Iran specially if it leads to fatalities. - -#### 5. **Time Frame and Variability Considerations** -- **Time Until Deadline**: Approximately 26 days remaining (from August 6 to September 1). The short timeframe implies the need for rapid escalation which can be less probable but recent events suggest urgency. -- **Volatility**: Recent regional geopolitical volatility increases the uncertainty and potential for sudden conflict changes. - -#### 6. **Evaluating Probabilities** -- **Base Rate Modification**: - - **Historical Base Rate**: Severe attacks happen but only occasionally result in high casualties promptly. Adjusting for urgency and recent provocations. - - **Conditional Likelihood**: Given recent escalations and very direct threats, the historical base probability could incrementally increase. -- **Adjust for Explicit Threats and Preparations**: - - Direct leadership threats, explicit retaliations indicate higher potential. - - Military preparations hint readiness which can negate or mitigate attacks thus lower probability. - -#### 7. **Balancing Factors** -- **Increased Readiness**: Israel’s high alert state tends to intercept/mitigate attacks, reducing effective fatality. -- **Explicit Threats**: These are significant but how much they translate into actual actions before the deadline. -- **Expert Consensus**: Agrees on retaliation but may not result in over-5 fatal scenarios within set borders. - -### Final Probability Calculation -1. **Historical Base Adjustment**: ~10% to 20% initially due to the often nature but not always high fatality. -2. **Escalation Impact**: Increase due to threats, still considering Israeli defenses ~20-30%. -3. **Current Context Weightage**: - - Readiness and defenses reduce likelihood. - - Statements/commitments of retaliation increase slightly ~40% upper bound from potential direct-threat scenarios. - -Thus composite rationale fine tunes around **30%**: - -**Probability: 30%**" -27017,"Will the United Nations have more than 193 member states before October 1, 2024?","PERPLEXITY - -The United Nations currently has 193 member states. The process for admitting new member states involves a recommendation from the Security Council and approval by a two-thirds majority vote in the General Assembly. - -### Recent Developments: -- **Palestine's Bid**: Palestine's application for full member state status continues to face challenges despite broad international support. The UN General Assembly recently adopted a resolution granting Palestinians additional rights and privileges starting from September 2024, but this does not equate to full membership. -- **Other Potential Candidates**: The Cook Islands and Niue, associated states of New Zealand, have expressed a desire to become UN member states but face opposition from New Zealand regarding their constitutional relationship. -- **Western Sahara and Kosovo**: Both territories have disputed sovereignty and are not UN members. Western Sahara is listed as a non-self-governing territory, and Kosovo, despite having relations with many UN member states, is not a member due to ongoing disputes with Serbia. - -### Expert Opinions: -- The Lieber Institute notes that Palestine's bid for full membership will continue to face significant roadblocks, indicating that full membership is unlikely before October 1, 2024. -- The UN's own guidelines and procedures suggest that any new membership would require extensive diplomatic efforts and consensus among member states, which is a time-consuming process. - -### Prediction Markets: -- There are no recent updates from prediction markets indicating a high likelihood of new member states joining the UN before October 1, 2024. - -Given the current status and the complexities involved in the admission process, it is unlikely that the United Nations will have more than 193 member states before October 1, 2024. - -######### - -GPT - -To forecast whether the United Nations will have more than 193 member states before October 1, 2024, we must assess the likelihood of a new member state being admitted. Let's break this down systematically: - -1. **Historical Context and Base Rate**: - - The United Nations admitted its last new member, South Sudan, in 2011. This indicates that new admissions are infrequent. - - Reviewing the pattern of new memberships, we witness periods of dormancy followed by geopolitical shifts leading to new state formations or recognitions. - -2. **Admission Process Complexity**: - - The process involves securing a Security Council recommendation, where any permanent member can exercise a veto. This then requires a two-thirds majority in the General Assembly. - - This inherently makes the process lengthy and susceptible to geopolitical influences, especially from permanent Security Council members. - -3. **Current Geopolitical Landscape**: - - **Palestine's Bid**: Despite international support, its progress towards full membership is complicated by geopolitical tensions, particularly with the United States' historical veto. - - **Kosovo**: Although many recognize it, Serbian opposition and another potential veto from Russia remain obstacles. - - **Taiwan**: China's stance prevents its admission likelihood. - - **Other Candidates** (e.g., Western Sahara, Northern Cyprus) face more regional recognition issues. - -4. **Time Frame**: - - With less than two months remaining until the deadline, there is little time for the lengthy negotiation and decision-making process required for a new member state to gain admission. - -5. **Recent Developments**: - - The latest updates regarding applications and efforts for statehood or recognition do not suggest any breakthrough that would accelerate the admittance process within the next two months. - - No visible momentum or news regarding any successful or potential candidate, suggesting an unchanged status quo. - -6. **Prediction Markets and Expert Opinions**: - - Recent predictions and expert opinions do not reflect an expected increase in the membership of the UN by the target date. Markets often react to underlying shifts and announcements, and a lack of activity here is significant. - -7. **Considering Novel Factors**: - - While geopolitical landscapes can shift rapidly, the complex procedural and political barriers mean that such shifts very rarely materialize quickly enough to result in membership changes within a short time frame. - -In conclusion, given the historical trend, geopolitical complications, procedural hurdles, and the short time frame, the likelihood of the United Nations admitting a new member before October 1, 2024, is exceedingly low. With all gathered evidence, this prediction can be framed as a cumulative count event where the probability of a new member state appearing within a two-month period, especially without any significant or groundbreaking changes, remains negligible. - -Probability: 2%" -26959,"Will a US Vice Presidential debate be held before October 1, 2024?","PERPLEXITY - -The question of whether a US Vice Presidential debate will be held before October 1, 2024, hinges on the participation of the candidates and the scheduling by the Commission on Presidential Debates (CPD). Here is a summary of the relevant information: - -1. **Scheduled Dates**: - - The CPD has scheduled a vice presidential debate for September 25, 2024, at Lafayette College in Easton, Pennsylvania. - - This date is before October 1, 2024. - -2. **Participation Uncertainty**: - - The participation of the vice presidential candidates in the debate is not yet confirmed. The Biden and Trump campaigns have not publicly committed to participating in the debates organized by the CPD. - - The Republican National Committee (RNC) has withdrawn from participating in the CPD debates, citing bias and a lack of reforms. - -3. **Historical Context**: - - Presidential debates have been a significant part of the campaign season since 1976, and the CPD has been responsible for organizing these debates since 1988. - - The early debate on June 27, 2024, was organized by CNN, and a second debate is scheduled for September 10, 2024, organized by ABC News, bypassing the CPD. - -4. **Prediction Markets**: - - Metaculus, a prediction platform, has a question regarding whether a US Vice Presidential debate will be held before October 1, 2024. The platform indicates that the debate is scheduled but does not confirm participation. - -### Conclusion -Given the scheduled date of September 25, 2024, for the vice presidential debate, it is likely that a US Vice Presidential debate will be held before October 1, 2024, if the candidates agree to participate. However, the uncertainty surrounding the participation of the candidates and the RNC's withdrawal from the CPD debates introduces some doubt. - -######### - -GPT - -To assess the probability of a U.S. Vice Presidential debate being held before October 1, 2024, let's carefully consider all the factors presented, using a structured approach: - -### Key Points Analysis: - -1. **Historical Context & Base Rates**: - - Vice presidential debates have historically been a part of the U.S. election cycle since 1976, with the CPD organizing since 1988. - - However, 2024 is atypical due to the CPD's complete cancellation of all debates, suggesting a break from historical norms. - -2. **Current Situation**: - - The originally planned vice presidential debate on September 25, 2024, by the CPD was cancelled. - - There is significant uncertainty around candidate participation, compounded by the fact that the Democratic nominee is yet to be finalized. - -3. **Political Dynamics**: - - The Republican National Committee has stepped away from CPD debates, further complicating traditional debate processes. - - Both major presidential campaign teams, Harris and Trump, are at an impasse with debates. - -4. **Time Constraint**: - - Less than two months remain before the deadline of October 1, 2024, a short timeline for organizing such a high-profile event amidst political disputes. - -5. **Media and Alternative Hosting**: - - There were negotiations for debates outside the traditional CPD, such as CBS hosting, although past agreements are now moot. - - Media networks have historically stepped in as alternative hosts, but there's no current agreement and no explicit plans mentioned for a VP debate post-CBS cancellation. - -6. **Recent Trends**: - - There is a shift towards debates hosted by TV networks without CPD involvement, which could pivot but currently applies to presidential debates only. - -7. **Resolution Criteria**: - - A formal live debate must occur between the Democratic and Republican nominees to resolve as ""Yes,"" and nominees must be finalized for debate participation. - -### Probability Assessment: - -- **Default Behavior & Trends**: Given the cancellation of CPD debates and ongoing political impasse, the default behavior seems to skew towards no debate occurring without significant developments. -- **Historical Precedent**: While historically debates occur, the CPD's cancellation this year signals a break from tradition. -- **Uncertainty & Time Frame**: In less than two months, resolving political impasses, finalizing nominees, and organizing the debate infrastructure presents challenges, especially without current movement towards a resolution. - -### Conclusion: - -In weighing these factors, the likelihood of a formal, live vice presidential debate occurring before October 1, 2024, given the outlined resolution criteria and current circumstances, appears low. The key determinants, such as candidate agreement and logistics, face significant hurdles within the limited timeframe. - -Therefore, after examining the likelihood based on current factors and historical considerations, I estimate the probability as: - -""Probability: 20%""" -27018,"Will a publicly-available LLM achieve gold on the International Math Olympiad before October 1, 2024?","PERPLEXITY - -### Query: Will a publicly-available LLM achieve gold on the International Math Olympiad before October 1, 2024? - -#### Context and Recent Developments: - -1. **DeepMind's AI Performance**: - - Google DeepMind's AI, AlphaProof, recently achieved a silver medal at the International Mathematical Olympiad (IMO) 2024, scoring 28 out of 42 points, just one point shy of the gold medal threshold. - - AlphaProof demonstrated the ability to solve a wide range of mathematical problems, including geometry and other disciplines, using reinforcement learning. - -2. **Public Availability and Eligibility**: - - The AI Mathematical Olympiad (AIMO) Prize, offered by XTX Markets, requires the AI to be publicly available to be eligible for the $5 million prize. AlphaProof is not eligible because it is not publicly available. - -3. **Prediction Markets**: - - Metaculus predicts a 24% chance that an AI will get gold on any International Math Olympiad by 2025. - - Manifold Markets indicates a 24% chance that an AI will get gold on any International Math Olympiad by the end of 2025. - -4. **Expert Opinions**: - - Gregor Dolinar, the IMO president, noted that AI's improvement rate is ""breathtaking"" and that missing the gold medal by just one point is impressive. - - Timothy Gowers from the University of Cambridge highlighted that AlphaProof's performance was surprising and that it found ""magic keys"" to solve problems similarly to humans. - -5. **Technical Challenges**: - - Formalizing combinatorics and other mathematical disciplines remains a significant challenge for AI systems, which could impact their ability to achieve gold in the near future. - -#### Conclusion: -While DeepMind's AI has shown remarkable performance, achieving a silver medal at the IMO 2024, the technical challenges and the requirement for public availability pose significant hurdles for a publicly-available LLM to achieve gold before October 1, 2024. The prediction markets and expert opinions suggest a low to moderate probability of this happening within the specified timeframe. - -######### - -GPT - -To forecast the probability of a publicly-available LLM achieving a gold medal at the International Mathematical Olympiad (IMO) by October 1, 2024, we must consider several factors: recent technological advancements, the specific criteria set forth for this competition, historical trends, and the likelihood of rapid developments in AI capabilities. - -### Technological Context and Historical Trends - -1. **Recent Achievements and Progress**: - - DeepMind's AlphaProof narrowly missing gold by one point at the IMO 2024 demonstrates significant advancements in AI's mathematical problem-solving capabilities. However, it is a domain-specific, closed-source AI and thus doesn't meet the requirements for public accessibility. - -2. **Publicly-Available LLMs in 2024**: - - Publicly-available LLMs like ChatGPT or open models have traditionally not been as specialized in mathematical problem-solving as closed systems like AlphaProof. - - While general LLMs have improved their reasoning and calculation capabilities, they historically excel in natural language tasks rather than highly specialized domains like the IMO. - -3. **Expert Opinions and Market Predicitions**: - - The opinions from experts like Gregor Dolinar and Timothy Gowers highlight impressive progress but also acknowledge the current gaps. - - Prediction markets such as Metaculus and Manifold Markets estimate about a 24% chance of an AI achieving gold on the IMO by 2025, which implies an even lower chance within just a few months, considering our current date is August 2024. - -4. **Timeframe and Rate of Progress**: - - From the current date of August 7, 2024, to the resolution date, there is less than two months for any significant technological breakthrough or deployment of a publicly-accessible LLM capable of achieving such a feat. - - Progress in AI is rapid, but the development of domain-specific capabilities in an existing general LLM, while possible, seems unlikely in such a short span. - -5. **Base Rate Consideration**: - - Historically, the base rate of publicly-available AI achieving gold in such competitive academic environments is non-existent to very low, given the novelty and complexity of the tasks involved. - -### Probability Assessment - -Given the insights: - -- The distinction between domain-specific closed systems (like AlphaProof) and the broader applicability required of publicly-available LLMs suggests a major gap in current capabilities. -- The requirement that the AI must be publicly accessible and match the gold medal level under competition constraints further complicates the scenario. -- The limited timeframe until October 1, 2024, constrains the feasibility of such a breakthrough occurring, especially when factoring in development, testing, and deployment processes. - -Considering these factors, it is rational to conclude a relatively low probability for the event in question: - -**Probability: 5%**" -26958,"Will YouTube be banned in Russia before October 1, 2024?","PERPLEXITY - -**Summary of Relevant News:** - -1. **YouTube Speed Throttling and Potential Ban:** - - Russia has started throttling YouTube speeds, citing ""technical problems"" with Google's equipment. This slowdown is expected to continue and may lead to a full ban in September. - - The Kremlin has denied any involvement in slowing down YouTube, blaming Google for the issues. - -2. **Government Statements:** - - Russian Minister of Digital Development, Communications and Mass Media, Maksut Shadayev, stated that there are no plans to ban YouTube, emphasizing the need for a competitive alternative before any such action. - - Despite this, there are ongoing threats and actions against YouTube, including the removal of pro-Kremlin channels and artists from the platform. - -3. **Impact on Users and Opposition:** - - The slowdowns have already affected YouTube viewership in Russia, with many users resorting to VPNs to access content. Independent journalists and opposition voices are preparing for a potential total blackout. - - Efforts are being made to develop alternative means for Russians to bypass restrictions, such as VPN generators. - -4. **Legal and Regulatory Context:** - - Russia has tightened its control over the internet, passing laws that require foreign social media companies to comply with local regulations. Non-compliance can result in fines or even the arrest of local employees. - - The country has blocked several other major social media platforms, including Facebook and Instagram, and has restricted independent media outlets. - -5. **Recent Developments:** - - As of July 2024, YouTube remains accessible in Russia, albeit with significantly reduced speeds. The situation is fluid, with ongoing discussions and actions that could lead to a ban. - -**Current Predictions and Expert Opinions:** -- Despite Minister Shadayev's statement against a ban, the ongoing throttling and threats suggest a high likelihood of a ban before October 1, 2024. -- Experts and opposition voices are preparing for the worst-case scenario, indicating a belief that the ban could indeed happen. - -**Conclusion:** -The current situation indicates a strong possibility of YouTube being banned in Russia before October 1, 2024, despite official denials. The ongoing throttling and legal pressures suggest that the Russian government is moving towards restricting access to the platform. - -######### - -GPT - -To determine the likelihood of YouTube being banned in Russia before October 1, 2024, we should consider a number of factors, applying a structured approach to forecasting. Here's a breakdown of how I would weigh the available information and apply historical context: - -1. **Default Status and Current Indications:** - - As of August 7, 2024, YouTube is still accessible in Russia, albeit with degraded performance. The default resolution, without further action, is that YouTube remains unblocked. - - Recent actions such as speed throttling and the ongoing rhetoric suggest a potential escalation towards a full ban, despite official denials of such plans. These technical constraints and threats could potentially precipitate a change. - -2. **Official Statements vs. Covert Actions:** - - The official statements from Russian officials, such as Minister Shadayev, assert that there are no plans to ban YouTube until a viable alternative exists. However, Russian authorities have a history of implementing sudden internet restrictions despite public reassurances. - - Russia has already blocked other major platforms like Facebook and Instagram, showing a pattern of increasing internet control, especially targeting platforms with significant foreign influence. - -3. **Timing and Legal Context:** - - The increasing regulatory measures on internet freedom and the legal framework facilitating restrictions set a conducive environment for further actions against global platforms like YouTube. - - The fact that several social media platforms have already been banned suggests a relatively high base rate for such actions, though YouTube has been more critical to Russian users, possibly delaying its ban. - -4. **Geopolitical Environment:** - - The geopolitical tensions post-Ukraine invasion have driven a wedge between Russia and Western technology firms. This context creates a backdrop that can instigate more aggressive actions against these firms to assert digital sovereignty. - - The Kremlin's strategic needs might view blocking YouTube as reducing the spread of what it perceives as anti-government sentiments or foreign influence. - -5. **Technological Circumstances and User Adaptation:** - - Users trying to bypass throttling using VPNs indicates the government could decide to shut down access to make control tighter, but significant public backlash could delay or prevent a full ban. - -6. **Past Predictions and Outcomes:** - - There were past expectations for YouTube to have been banned earlier; these did not materialize, emphasizing the platform's perceived value or strategic importance to Russian users. - - Given the experience with past predictions, it indicates the difficulty in precisely predicting such regulatory moves. - -7. **Current Trends and Recency of Developments:** - - Current trends indicate heightened control over digital platforms, progressing from mere threats to action like throttling. The recency and rapid escalation suggest that a ban could be imminent. - -With less than two months to the resolution date, actions can still unfold quickly. However, the precise likelihood needs to account for both the high precedent of action against social media and the current ambiguity. - -**Calculation of Probability:** -- Base rate adjustment: Social media bans have a high precedent, suggestive of at least a 50%-60% base probability. -- Weighting of official statements and contrarian technical actions suggest a push-pull dynamic, moderating the base rate to incorporate recent denials and strategic considerations. -- Approximation of user adaptability and potential hesitance to disrupt widely-used services impacts slightly towards a non-ban outcome. - -Given the factors considered, I assign the likelihood of YouTube being officially banned in Russia before October 1, 2024, as follows: - -**Probability: 70%**" -26975,"Will the US Federal Reserve make an emergency rate cut before September 17, 2024?","PERPLEXITY - -The likelihood of the US Federal Reserve making an emergency rate cut before September 17, 2024, is a topic of significant debate among economists and financial analysts. Here is a summary of the relevant news and expert opinions: - -### Economic Context -- **Recent Economic Data**: The July jobs report was weaker than expected, with only 114,000 jobs added and an unemployment rate rising to 4.3% from 4.1% in June. This has sparked concerns about the economy's health and the potential need for rate cuts. -- **Market Volatility**: The stock market has experienced a significant downturn, with the S&P 500 shedding 6% of its value since July 31. This volatility has led some to call for an emergency rate cut to stabilize the markets. - -### Expert Opinions -- **Jerome Powell**: Fed Chair Jerome Powell indicated that the Fed is watching the labor market closely but wants to see more evidence that inflation is cooling before cutting rates. This suggests a cautious approach to rate cuts. -- **Austan Goolsbee**: Chicago Federal Reserve President Austan Goolsbee mentioned that while the jobs numbers were weaker than expected, he does not believe the U.S. is in a recession. He also hinted that the Fed would take action if economic conditions deteriorate further. -- **Tyler Schipper**: An associate professor of economics at the University of St. Thomas, Schipper believes that an emergency rate cut would be a bad idea. He argues that the Fed's mandate is to focus on the broader economy, not just the stock market's volatility. -- **Tarek Hassan**: An economics professor at Boston University, Hassan views the recent market losses as a correction rather than a sign of a deeper economic issue. He suggests that the Fed should make a rate cut soon but does not need to rush it. - -### Market Predictions -- **Bloomberg News**: Traders are signaling a roughly 60% likelihood of an emergency 0.25 percentage point rate cut within one week. -- **Pantheon Macroeconomics**: Overnight index swap rates imply a roughly 30% chance of an emergency rate cut in the next week. - -### Historical Context -- **Emergency Rate Cuts**: The Fed has cut rates at nine emergency meetings in the last 30 years, but these cuts have typically been in response to significant financial crises, such as the 2007-08 financial crisis and the COVID-19 pandemic. - -### Conclusion -While there are strong arguments for and against an emergency rate cut, the consensus among many economists is that the Fed is likely to wait until its September meeting to make any changes. The recent economic data and market volatility have not yet reached the level of severity that typically prompts emergency actions from the Fed. - -######### - -GPT - -To estimate the likelihood of the US Federal Reserve making an emergency rate cut before September 17, 2024, I need to carefully analyze the provided information, consider historical and economic contexts, and weigh the arguments for and against such a decision. - -### Key Factors: - -1. **Current Economic Context**: - - **Jobs Market**: The recent jobs data showed a weaker-than-expected performance, with rising unemployment. This indicates some economic cooling but doesn't yet suggest a full-blown recession. - - **Market Volatility**: The significant downturn in the stock market is a point of concern, but market volatility alone isn't usually a catalyst for emergency rate cuts. - -2. **Statements from Fed Officials**: - - **Jerome Powell's Caution**: Powell seems focused on inflation and has suggested a cautious approach toward rate cuts, reinforcing stability and confidence before making decisions. - - **Austan Goolsbee's Perspective**: Goolsbee's statements suggest a readiness to act if necessary but not an immediate push for emergency action. - -3. **Expert Opinions**: - - Some experts, like Tyler Schipper and Tarek Hassan, argue against an immediate emergency rate cut, considering broader economic stability and long-term strategy. - -4. **Market Predictions and Statistics**: - - Bloomberg indicates a 60% probability for an emergency rate cut, which shows trader expectations of action but is not definitive. - - Historical context shows that emergency cuts are reserved for severe financial crises, which are not currently evident. - -5. **Recency and Trends**: - - The most recent information points to increased concern but not immediate financial crisis levels that historically trigger emergency cuts. - -6. **Timeframe**: - - With roughly one month remaining until the resolution date, there's some time for economic conditions to potentially worsen to crisis levels, but barring drastic changes, the current situation doesn't seem to meet the threshold for an emergency cut. - -### Historical Context and Base Rates: -Historically, the Fed has been cautious about emergency interventions unless faced with crises akin to 2008 or the COVID-19 pandemic. None of the current conditions appear to meet such extreme levels. - -### Final Assessment: -Given the analysis of these factors—particularly historical conduct, statements from Fed officials, expert opinions, and current economic signals—the likelihood of an emergency rate cut before September 17, 2024, appears low unless economic conditions sharply deteriorate. The Fed is more likely to wait for its scheduled September meeting absent a significant crisis. - -**Probability: 25%**" -27039,"Will Sheikh Hasina get asylum in the United Kingdom before October 1, 2024?","PERPLEXITY - -Sheikh Hasina, the former Prime Minister of Bangladesh, is currently in India awaiting approval for political asylum in the United Kingdom. Her stay in India may extend until she receives the approval from the UK. - -### Key Points: -- **Current Status**: Sheikh Hasina is in India, awaiting asylum approval from the UK. -- **Asylum Request**: Her asylum request in the UK remains unconfirmed, with no formal comment from the UK government. -- **UK Government Stance**: The UK Foreign Secretary, David Lammy, has emphasized the need for a peaceful and democratic future in Bangladesh but did not address the asylum rumors directly. -- **Protests in Bangladesh**: The protests that led to her resignation were initially against a controversial job quota system and escalated into broader anti-government unrest. -- **Interim Government**: An interim government has been announced in Bangladesh, with the army chief calling for citizens to maintain order. -- **Prediction Markets**: Metaculus has a question open on whether Sheikh Hasina will get asylum in the UK before October 1, 2024, indicating ongoing uncertainty. - -### Recent Updates: -- **Flight Postponement**: Reports indicate that her flight to London has been postponed due to certain circumstances. -- **UK Envoy's Stance**: Bangladesh's UK envoy has expressed allegiance to the upcoming interim government, suggesting a shift in diplomatic relations. -- **Alternative Asylum Considerations**: There are indications that Sheikh Hasina might consider other countries for asylum if the UK does not approve her request. - -### Expert Opinions: -- **David Lammy**: Emphasized the need for a peaceful and democratic future in Bangladesh, highlighting the deep people-to-people links and shared Commonwealth values between the UK and Bangladesh. -- **UK Government**: Expressed deep sadness over the violence in Bangladesh and hopes for swift action to ensure democracy prevails. - -### Prediction Markets: -- **Metaculus**: The question on Metaculus regarding whether Sheikh Hasina will get asylum in the UK before October 1, 2024, reflects ongoing uncertainty and speculation. - -Given the current situation and the lack of a formal response from the UK government, the outcome remains uncertain. - -######### - -GPT - -Given the situation involving Sheikh Hasina's potential asylum in the UK, I will evaluate the relevant factors to assess the likelihood of her gaining asylum by October 1, 2024. - -**Current Context and Status:** - -1. **Political Climate in Bangladesh and Hasina's Resignation:** - - She has been ousted from power due to protests and unrest, leading to her seeking asylum. - - The interim government and the army have stepped in to quell unrest, a factor which might impact her asylum situation depending on the new administration's stance towards her return. - -2. **Hasina's Position and Desire for Asylum:** - - She is seeking refuge due to mounting pressure and accusations in Bangladesh. - - Her trajectory from India to the UK represents serious intent regarding asylum. - -3. **UK's Historical Asylum Approval Rates:** - - The refusal rate has historically been high, but recent years show improved approval rates (76% granted when not immediately refused). - - High-profile political asylum cases may be handled differently than general cases, but there's no direct statement from the UK government. - -4. **UK's Political and Diplomatic Considerations:** - - The UK has a vested interest in a stable and democratic Bangladesh, which could influence asylum decisions to avoid diplomatic instability. - - A refusal could have diplomatic implications but equally, an approval might strain relations with Bangladesh's current interim government. - -5. **Timeframe Considerations:** - - There is a span of almost two months for a decision to be made, which might be sufficient given the high-profile nature of this case. - - Political asylum decisions can take a long time, and if initial rejections happen, they can result in appeals which extend the timeline even further. - -6. **Alternative Options and Uncertainty:** - - Reports suggest that if the UK does not grant asylum, Hasina could consider other countries, such as India, where she currently resides. - - This demonstrates options but also reflects uncertainty over UK's decision. - -**Prediction Methodology:** - -- **Base Rates Analysis:** General asylum approval rate over recent years in the UK shows improved approval conditions, particularly if the case gathers political/diplomatic attention. - -- **Trends and Variance Analysis:** The current scenario is not entirely static; the UK government, in its diplomatic interactions, has not yet committed to any decision openly. External pressures may arise from both the interim government and international parties. - -- **Novelty Consideration:** The political dynamics in Bangladesh and Hasina's standing as a former prime minister make this a less typical asylum case, which could lean towards exceptions in typical approval ratios. - -**Final Probability Assessment:** -Considering the above factors and given that the situation has not dramatically shifted towards a resolution that guarantees an approval or denial yet, it appears that the uncertainty remains significant. Therefore, while there is a chance of acceptance due to her high-profile status and the UK's historical stance supporting democratic interests, the lack of explicit action from the UK government, potential diplomatic concerns, and long decision-making processes weigh against a straightforward conclusion. - -""Probability: 40%"" that Sheikh Hasina will be granted asylum in the UK before October 1, 2024." -26976,"Will California Senate Bill 1047, the ""Safe and Secure Innovation for Frontier Artificial Intelligence Models Act,"" be enacted before October 1, 2024?","PERPLEXITY - -California Senate Bill 1047, the ""Safe and Secure Innovation for Frontier Artificial Intelligence Models Act,"" has been a subject of intense debate and scrutiny. The bill, which aims to regulate the development and use of advanced AI models to ensure safety and security, has already passed the California State Senate and is set for a final vote in August 2024. - -### Key Points: - -- **Passage in Senate**: The bill passed the California State Senate with a vote of 32-1, indicating strong support from the Senate. -- **Assembly Judiciary Committee**: The California State Assembly Judiciary Committee also passed the bill, further advancing its legislative journey. -- **Regulatory Scope**: The bill targets AI models trained with over $100 million in computing power, aiming to mitigate risks such as bias, privacy breaches, and potential misuse. -- **Support and Opposition**: The bill has garnered support from various AI safety advocates and organizations, while facing opposition from major tech companies and venture capitalists who argue it could stifle innovation and favor large tech giants. -- **Forecasting and Betting Sites**: Metaculus, a forecasting platform, has a question open regarding the enactment of SB-1047 before October 1, 2024, awaiting approval from Community Moderators. - -### Recent Updates: - -- **Amendments and Analysis**: Recent amendments have been made to the bill, including the creation of a Division of Frontier Models and whistleblower protections. Despite these changes, concerns about regulatory uncertainty and compliance costs persist. -- **Public and Expert Opinions**: The debate continues, with proponents emphasizing the need for safety measures and opponents warning about potential negative impacts on innovation and competition. - -Given the current legislative progress and the scheduled final vote in August, there is a strong likelihood that California Senate Bill 1047 will be enacted before October 1, 2024. However, the final outcome will depend on the Assembly's vote and any further amendments or challenges that may arise. - -######### - -GPT - -### Step-by-Step Analysis - -1. **Understanding the Context** - - **Bill Overview**: SB 1047, aimed at regulating advanced AI models for safety and security, must pass through the California State Assembly and be signed by the Governor to become law. - - **Current Status**: The bill has passed the Senate overwhelmingly and the Assembly Judiciary Committee, indicating legislative progress. - -2. **Eligibility for Enactment** - - A ""Yes"" resolution criteria requires full passage by the California legislature and the Governor’s signature by October 1, 2024. - -3. **Stakeholders** - - **Support**: AI safety advocates and organizations suggest strong support due to safety concerns. - - **Opposition**: Significant pushback from large tech companies such as Anthropic. - -4. **Time Consideration** - - With less than two months until the resolution date, the timing is critical for the Assembly's final vote and subsequent gubernatorial action. - -5. **Legislative Dynamics** - - **Assembly’s Final Vote**: A scheduled vote in August provides a consolidated timeline for determining the bill’s fate in the Assembly. - - **Governor's Decision**: Regardless of Assembly passage, the Governor's approval is still needed. Historically, the Governor’s decisions can align with legislative actions, especially if there's overwhelming political backing. - -6. **Recent History and Trends** - - Bills with high-profile discussions often see rapid legislative movements but might face delays in amendments or last-minute lobbying influences. - -7. **Base Rates and Novelty** - - Historically, bills of significant public and political discourse have variable paths to enactment. However, given this bill's apparent backing, there's a favorable comparison to legislation designed for the tech industry in California. - - Recent tech industry involvement and ethical debates on AI might skew the base rates upward for regulation initiatives. - -8. **Default Behavior and Changes Over Time** - - If current trends continue without unexpected legislative hurdles or gubernatorial hesitations, the default trajectory supports enactment. - -9. **Public and Legislative Pressure** - - Growing advocacy for AI safety legislation could create favorable pressure towards enactment, despite tech industry resistance. - -10. **Official Predictions** - - With platforms like Metaculus monitoring the bill, the community's sentiment may provide a collective forecast may further align with assessments pointing towards enactment. - -### Probabilistic Assessment - -While considering all the variables: - -- **Legislative Progress**: Strong, with scheduled votes. -- **Opposition Influence**: Present but may be outweighed by legislative support and public safety arguments. -- **Time Window**: Adequate for completion, though tight. -- **Executive Factor**: The Governor’s role is crucial but typically in alignment with strong bipartisan legislative actions. - -Given these considerations, there is a reasonable to high probability of SB 1047 being enacted by the resolution deadline. - -**Final Probability**: 75%" -26980,"Will the Accumulated Cyclone Energy of the 2024 Atlantic hurricane season be greater than 120 on October 7, 2024?","PERPLEXITY - -The 2024 Atlantic hurricane season is forecasted to be extremely active, with several key indicators pointing to above-average activity: - -1. **Forecasted Activity**: - - **Named Storms**: 23 (average is 14.4). - - **Named Storm Days**: 120 (average is 69.4). - - **Hurricanes**: 12 (average is 7.2). - - **Major Hurricanes**: 6 (average is 3.2). - - **Accumulated Cyclone Energy (ACE)**: 230 (average is 123). - - **Net Tropical Cyclone Activity (NTC)**: 240 (average is 135%). - -2. **Sea Surface Temperatures**: - - Sea surface temperatures in the hurricane Main Development Region of the tropical Atlantic and Caribbean are near record warm levels, creating a conducive environment for hurricane formation and intensification. - -3. **ENSO Conditions**: - - Cool neutral ENSO or La Niña conditions are anticipated during the peak of the Atlantic hurricane season, which will reduce tropical Atlantic vertical wind shear, further supporting hurricane activity. - -4. **Landfall Probabilities**: - - The probability of at least one major hurricane landfall on the entire continental U.S. coastline is estimated at 56% (full-season average from 1880–2020 is 43%). - - The probability for the U.S. East Coast, including the Florida Peninsula, is 30% (full-season average from 1880–2020 is 21%). - - The probability for the Gulf Coast from the Florida Panhandle westward to Brownsville is 38% (full-season average from 1880–2020 is 27%). - -5. **Current Activity**: - - As of August 5, 2024, the season has already seen named storms Alberto, Beryl, Chris, and Debby. - -6. **Prediction Markets**: - - Metaculus predicts a 55% chance that the Accumulated Cyclone Energy of the 2024 Atlantic hurricane season will be greater than 120 on October 7, 2024. - -Overall, the consensus among forecasters is that the 2024 Atlantic hurricane season will be significantly more active than average, with a high likelihood of major hurricane landfalls. - -######### - -GPT - -To assess the likelihood of whether the Accumulated Cyclone Energy (ACE) for the 2024 Atlantic hurricane season will exceed 120 by October 7, 2024, we will consider several key factors from the prompt and apply a structured forecasting approach. - -### Key Factors and Analysis: - -1. **Base Rates and Historical Data:** - - The average ACE over the period 1951-2020 is approximately 123 for a full season, based on NOAA data. - - The historical median suggests that reaching an ACE of over 120 is possible, but whether it occurs by October 7 requires analysis of season timing and intensity. - -2. **Current Forecasted Activity:** - - The forecasts are highly active, predicting 230 ACE for the entire season. This forecast significantly surpasses 120, implying a strong likelihood of surpassing 120 by early October. - - Comparatively, a typical full-season target is noted, yet reaching this number two months before the season conclusion emphasizes current strength indicators. - -3. **Seasonal Timing:** - - Peak hurricane season occurs from mid-August to late October, suggesting a high-activity period is forthcoming. Given today's date is August 8, hurricanes over the next two months will be pivotal. - - Historical data shows that a significant portion of ACE is accumulated between mid-August and mid-October. - -4. **Sea Surface Temperatures and ENSO:** - - Record warm sea surface temperatures and favorable ENSO conditions (cool neutral or La Niña) are conducive to frequent and possibly intense hurricane activity. - - Reduced vertical wind shear due to these conditions supports storm development, indicating potential high ACE contributions. - -5. **Current Storm Activity:** - - Already, four named storms have been recorded as of early August, hinting at an active start. - - This stage in the season often has intermediate ACE contributions that are vital for surpassing thresholds like 120 before October. - -6. **Prediction Markets:** - - Metaculus provides a conservative 55% prediction for ACE exceeding 120 by October 7. This suggests some expertise-driven anticipation of strong but not absolute certainty. - -7. **Potential Variance:** - - Storm intensity and frequency vary significantly annually. - - The indicators point to above-average processes known for producing energy to dissipate. These measures should be respected or varied contextually for a forecast this year. - -### Conclusion: - -Combining the forecasted seasonal activity level, favorable environmental conditions, and the timing within the season, the probability appears strong that the ACE will surpass 120 by October 7. The main uncertainties lie in potential underperformance in storm intensities or surprising changes in meteorological conditions. However, the background statistics and strong predictive factors build a comprehensive argument. - -Therefore, consolidating all these factors and weighing the likelihood of less significant variance challenges, the most likely outcome for this Non-Market Threshold Value Event is as follows: - -**Probability: 75%**" -26978,"Will the Accumulated Cyclone Energy of the 2024 Atlantic hurricane season be greater than 80 on October 7, 2024?","PERPLEXITY - -The Accumulated Cyclone Energy (ACE) index for the 2024 Atlantic hurricane season is projected to be significantly higher than the historical average. Multiple meteorological agencies and research institutions have issued forecasts indicating an above-average to hyperactive season. Here are the key points: - -1. **Forecast Predictions**: - - **NOAA**: Predicts 17 to 25 named storms, with eight to 13 expected to become hurricanes, and four to seven becoming major hurricanes. The ACE index is projected to range between 145 and 237, which is above the 1991–2020 median ACE index value of 96.7. - - **Colorado State University**: Forecasts 25 named storms, 12 hurricanes, and six major hurricanes. The ACE index is expected to be around 230. - - **Moody’s**: Highlights a strong consensus for an above-average season, with some forecasters not ruling out a hyperactive season. - -2. **Current Activity**: - - Hurricane Beryl, a Category 5 hurricane, has already strengthened the ACE index, indicating a potentially very active season. - - Extremely warm sea surface temperatures in the main development region of the tropical North Atlantic are near record highs, providing favorable conditions for hurricane formation and intensification. - -3. **Environmental Factors**: - - La Niña conditions are expected to prevail during the peak hurricane months, which typically reduce vertical wind shear and increase atmospheric instability, favoring hurricane formation and intensification. - - Warmer-than-average ocean temperatures and the Atlantic Multidecadal Oscillation (AMO) in a positive phase are also contributing to the favorable conditions for hurricanes. - -4. **Prediction Markets**: - - Metaculus, a forecasting platform, has a question regarding whether the ACE index will be greater than 80 on October 7, 2024. This question is currently in review and awaiting submission for community moderation. - -Given the consensus among meteorological agencies and the current environmental conditions, it is highly likely that the Accumulated Cyclone Energy of the 2024 Atlantic hurricane season will be greater than 80 on October 7, 2024. - -######### - -GPT - -To forecast whether the Accumulated Cyclone Energy (ACE) on October 7, 2024, will be greater than 80, I'll consider several elements including historical data, current forecasts, environmental conditions, and probability of upcoming events. - -### Analyzing Historical Trends: - -1. **Seasonal ACE Averages:** - - Historically, Atlantic hurricane seasons with especially high levels of activity (those predicted with ACE indices upwards of 145, as NOAA suggests for 2024) reliably surpass an ACE level of 80 by early October. - - The historical median ACE Index is 96.7, and the season lasts usually from June through November, with peak activity typically occurring between August and October. - -### Current and Forecast Conditions: - -1. **NOAA and CSU Forecasts:** - - NOAA predicts an ACE between 145 and 237, while CSU forecasts an ACE of approximately 230. Both forecasts indicate a season well above the average ACE. - - These forecasts suggest a level of activity conducive to reaching an ACE of greater than 80 by October 7. - -2. **Current Storm Activity:** - - With active high-energy storms like Hurricane Beryl contributing early in the season, the ACE is trending upwards. - - August is already indicating strong hurricane activity, which aligns with the predicted high-end totals for named storms and major hurricanes. - -3. **Environmental Indicators:** - - La Niña conditions typically correlate with increased Atlantic hurricane activity due to reduced shear. - - Warmer than average sea surface temperatures also contribute to hurricane intensity and longevity, boosting ACE metrics. - -4. **Metaculus and Prediction Markets:** - - Although currently in review, the consensus on forecasting platforms leaning towards a high-activity season further supports this conclusion. - -### Base Rate Comparison and Uncertainty: - -1. **History of Reaching ACE above 80:** - - In high-activity seasons, ACE values often exceed 80 by October. This reduces the uncertainty gradient leading up to October 7. - -2. **Potential Deviators:** - - Unpredictable environmental changes, such as unexpected wind shear episodes or quiet tropical waves, could alter the predicted path. - - However, given current trends and alignments, these potential deviations' influence is minimal. - -### Default Resolution and Adjustments: - -1. **Statistically Modeled Predictions:** - - Given the statistical alignment of forecasts and historical context, the modeled predictions heavily favor an outcome where ACE exceeds 80. - -Taking into account all these factors, the probability of the ACE being greater than 80 by October 7, 2024, is extremely high. The consensus of expert forecasts, active environmental conditions supportive of high hurricane activity, and the progress already made in accumulating ACE toward the target date all suggest this outcome. - -Final Answer: **Probability: 95%**" -26979,"Will the Accumulated Cyclone Energy of the 2024 Atlantic hurricane season be greater than 100 on October 7, 2024?","PERPLEXITY - -The Accumulated Cyclone Energy (ACE) of the 2024 Atlantic hurricane season is expected to be significantly higher than average, with multiple forecasts indicating a highly active season. Here are the key points from various sources: - -1. **NOAA Prediction**: - - NOAA predicts an 85% chance of an above-normal 2024 Atlantic hurricane season, driven by La Niña and warmer-than-average ocean temperatures. - -2. **Colorado State University (CSU) Forecast**: - - CSU forecasts 23 named storms, 12 hurricanes, and 6 major hurricanes, with an ACE of 230, which is significantly above the 1991-2020 average of 123. - - The forecast has been updated to reflect a higher number of named storms and major hurricanes due to favorable environmental conditions. - -3. **Other Forecasts**: - - AccuWeather predicts 20 to 25 named storms and 8 to 12 hurricanes, indicating a potentially ""explosive"" season. - - The UK Met Office and other institutions also predict an above-average season, with 15 to 28 tropical storms and 8 to 16 hurricanes. - -4. **Current Activity**: - - As of early August, the season has already seen significant activity, with storms like Hurricane Beryl contributing to the accumulated cyclone energy. - - The season has been marked by extremely warm sea surface temperatures, which are conducive to hurricane formation and intensification. - -5. **Prediction Markets**: - - Metaculus, a prediction platform, has a question pending approval regarding whether the ACE will be greater than 100 on October 7, 2024. This indicates ongoing interest and uncertainty about the season's activity. - -Given the consensus among forecasts and the current activity, it is highly likely that the Accumulated Cyclone Energy of the 2024 Atlantic hurricane season will be greater than 100 on October 7, 2024. - -######### - -GPT - -To predict the likelihood that the Accumulated Cyclone Energy (ACE) of the 2024 Atlantic hurricane season will exceed 100 by October 7, 2024, we need to carefully analyze the information provided and other relevant considerations. - -### Key Points and Analysis: - -1. **Historical Context and Base Rates:** - - ACE values from past Atlantic hurricane seasons show a broad range of activity levels. A threshold of 100 ACE by early October generally signifies an active season. - - Looking at historical data from Our World In Data, it is common for average or above-average seasons to surpass 100 ACE by October 7. - -2. **Current Year Forecasts and Predictions:** - - **NOAA Forecast:** The 85% chance of an above-normal season with influences such as La Niña and warmer seas points towards a high ACE. - - **CSU Forecast:** The predicted ACE of 230 significantly surpasses the threshold, suggesting a strong likelihood of exceeding 100 by the target date. - - **Other Forecasts:** High numbers of predicted storms and hurricanes reinforce the anticipation of a highly energetic season which aligns with high ACE. - -3. **Recent and Current Activity:** - - As of early August, there are indications of significant cyclone activity and favorable environmental conditions (e.g., warm sea surface temperatures). Already active storms have begun contributing to the ACE. - -4. **Timeframe Consideration:** - - From August until October 7, there is ample time for additional storms to develop and contribute to the ACE. Historically, this period often sees high activity as it coincides with the peak of the hurricane season. - -5. **Environmental and Novel Factors:** - - The presence of warmer-than-average sea temperatures, often associated with more potential for cyclone formation and strengthening, could skew historical base rates slightly higher. - -6. **Prediction Market Data:** - - While not yet complete, prediction market activity suggests ongoing consideration and analysis, reflecting the forecasters' engagement with the season's pace. - -### Balancing Research Assistant's Analysis and Historical Data: - -- The forecasts and current data strongly suggest conditions conducive to a higher-than-average hurricane season. -- Historical patterns demonstrate that in seasons projected to be very active, surpassing 100 ACE by early October is typical. -- Despite uncertainty and variance inherent in meteorological forecasts, the consensus and current conditions strongly support an above-normal season. - -### Final Probability Assessment: - -Combining historical data, current season forecasts, and the specific resolution criteria, the probability that the ACE of the 2024 Atlantic hurricane season will exceed 100 by October 7 is high. - -**Final Probability: 90%**" -26981,"Will more than 10 Chinese aircraft violate Taiwan's air defense identification zone (ADIZ) in a single day between August 15 and September 15, 2024?","PERPLEXITY - -### Context for Forecasting Question: Will more than 60 Chinese aircraft violate Taiwan's air defense identification zone (ADIZ) in a single day between August 15 and September 15, 2024? - -#### Recent Incursions and Trends -- **July 2024**: The People's Liberation Army (PLA) significantly increased its daily incursions into Taiwan's ADIZ, with 232 violations in the first ten days of July. This includes a record-tying 56 violations on July 10, similar to the highest daily total on October 4, 2021. -- **June and May 2024**: There were at least 325 and 289 PLA violations of Taiwan’s ADIZ, respectively, the second and third-highest monthly totals on record. -- **Historical Context**: The highest monthly total was 446 violations in August 2022, during large-scale PLA exercises in response to Nancy Pelosi’s visit to Taiwan. - -#### Current Military Activities -- **July 2024**: PLA aircraft entered Taiwan's ADIZ every day except for July 24-27, when Typhoon Gaemi disrupted operations. -- **July 2, 2024**: A PLA drone approached Matsu’s Nangan Airport, disrupting two flights. -- **July 5, 2024**: A Chinese Coast Guard (CCG) ship attempted to expel Taiwanese fishing boats near the Penghu Islands. - -#### Political and Diplomatic Developments -- **July 2024**: The PRC attempted to prevent legislators from at least six countries from attending an international parliamentary forum. -- **July 27, 2024**: US Secretary of State Antony Blinken and PRC Foreign Minister Wang Yi met on the sidelines of the ASEAN Regional Forum, with Wang calling on ASEAN to resist US- and NATO-led ""external interference"". - -#### Expert Opinions and Predictions -- **Metaculus Predictions**: The question on Metaculus about whether more than 60 Chinese aircraft will violate Taiwan's ADIZ between August 15 and September 15, 2024, is pending approval. -- **General Trend**: The heightened number of incursions reflects an intensified PRC pressure campaign against Taiwan under the new administration of Lai Ching-te, whom the PRC considers a dangerous separatist. - -### Conclusion -Given the recent surge in PLA incursions into Taiwan's ADIZ and the ongoing political tensions, there is a significant likelihood of continued high levels of military activity in the region. The historical context and current trends suggest that the possibility of more than 60 Chinese aircraft violating Taiwan's ADIZ in a single day between August 15 and September 15, 2024, is substantial. - -######### - -GPT - -To forecast whether more than 10 Chinese aircraft will violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024, I will analyze the information provided with several factors in mind: - -1. **Historical Context and Base Rates**: - - There have been significant fluctuations in the number of aircraft violating Taiwan's ADIZ over the past few years. The record high of 56 violations occurred in October 2021, and there have been multiple instances of over 10 violations in a single day, particularly in the months following significant political events. - - Historical data shows that incursions have intensified during periods of heightened political tension. The recent inauguration of Lai Ching-te marks such a period, with elevated activity levels in May and June 2024, further supporting the possibility of increased incursions during August-September 2024. - -2. **Recent Trends**: - - An increase to a 30-day average of 10 violations per day following the May 2024 inauguration, along with spikes such as 56 violations on July 10, 2024, indicate heightened tension and a greater likelihood of surpassing 10 incursions on some days in the forthcoming period. - - Monthly totals remain high, indicating sustained tensions and military pressure, which likely continues into August and September. - -3. **Current Events and Political Climate**: - - China's tough stance against the newly elected DPP administration and their classification as separatists leads to expectations of continued or increased military activity as a form of intimidation. - - The geopolitical dynamics, with recent diplomatic efforts by the U.S. and China, may not lead to an immediate de-escalation in cross-strait tensions in the near term. - -4. **Variation and Daily Granularity**: - - The incursion data indicates enough daily and weekly variance to support the probability of high-incursion days amidst otherwise average ones. This variance suggests that on some days, the number of violations could easily exceed the threshold of 10. - -5. **Forecasting the Likelihood**: - - Given the upward trend in violations, the increased political tension, and recent significant daily incursions, the base rate of exceeding 10 violations is non-zero. Historically, major events or political changes have spurred increased violations, which suits the current political climate post-inauguration. - -With these factors in mind, it seems reasonably probable that given the high tensions, current average rates, and historical patterns, more than 10 Chinese aircraft will enter Taiwan's ADIZ on at least one day in the timeframe considered. - -Final assessment of the likelihood of this event occurring between August 15 and September 15, 2024: - -Probability: 85%" -26983,"Will more than 30 Chinese aircraft violate Taiwan's air defense identification zone (ADIZ) in a single day between August 15 and September 15, 2024?","PERPLEXITY - -### Context for Forecasting: Chinese Aircraft Violations of Taiwan's ADIZ - -#### Recent Incursions and Trends -- **July 2024**: The People's Liberation Army (PLA) conducted at least 439 military incursions into Taiwan's Air Defense Identification Zone (ADIZ), surpassing all previous months except August 2022. PLA aircraft entered Taiwan's ADIZ every day of July except for the four days of July 24-27, when Typhoon Gaemi disrupted military operations. -- **Historical Context**: The record for most ADIZ violations in one month was 446 in August 2022, in response to then-US Speaker of the House Nancy Pelosi’s visit to Taiwan. - -#### Current Tensions and Military Activities -- **Increased Pressure Campaign**: The heightened number of incursions reflects an intensified PRC pressure campaign against Taiwan under the new administration of President Lai Ching-te, whom the PRC considers a dangerous separatist. -- **Military Exercises and Drone Activities**: The PLA has conducted large-scale military exercises that encircled Taiwan and announced legal guidelines that threaten severe punishment for Taiwanese “separatists.” A PLA drone flew near an airport on Taiwan’s Matsu Islands, disrupting two flights. - -#### Expert Opinions and Predictions -- **Forecasting Sites**: Prediction markets and forecasting sites have not provided specific predictions for the exact number of violations between August 15 and September 15, 2024. However, the trend of increased incursions suggests a high likelihood of continued violations. -- **Military Analysts**: Analysts from the Missile Defense Advocacy Alliance and the Institute for the Study of War (ISW) highlight the strategic importance of these incursions as part of a broader campaign to exert pressure on Taiwan. - -#### Legal and Diplomatic Developments -- **Detentions and Negotiations**: The PRC has detained several ROC nationals, including a fishing boat and its crew, and an ROC soldier from Kinmen. Taiwan is lobbying for their release, but the PRC has halted official contact with the Democratic Progressive Party (DPP) government in Taiwan since 2016. - -### Conclusion -Given the recent surge in ADIZ violations and the ongoing pressure campaign by the PRC, it is highly likely that more than 30 Chinese aircraft will violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024. The historical context and current military activities support this likelihood. - -######### - -GPT - -To determine the probability that more than 30 Chinese aircraft will violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024, let's follow a structured analysis approach. - -### Step 1: Base Rates and Historical Context -- **Historical Peak:** The record for ADIZ violations in one month was 446 in August 2022, which is suggestive of heightened activity during periods of increased tension. -- **Recent Activity:** July 2024 saw 439 incursions, the second highest monthly total on record. - -### Step 2: Current Trends and Political Context -- **Political Tensions:** The PRC sees the new Taiwanese administration as hostile and has already ramped up military pressure since the new president’s inauguration. This suggests potential for more significant events. -- **Military Exercises:** Chinese military exercises and activities have been more frequent, reflecting a potential for continued or increased incursions. - -### Step 3: Recent Maximum Violations -- **Frequency of High Count Days:** There are notable peaks in daily violations, such as the 66 aircraft over two days in June 2024. This demonstrates the potential for high single-day counts. - -### Step 4: Variance and Default Behavior -- There's a higher variance in incursions, with the possibility of one or a few days having significantly more violations than the daily average. This variance is potentially increased by political motives or reactions to specific events. - -### Step 5: Novel Developments -- **Escalated Rhetoric and Detentions:** Diplomatic strife such as detentions and halted communications suggests a lack of de-escalation channels and, potentially, an inadvertent escalation in military posturing. - -### Step 6: Weighing the Probability -- **High Tensions and Historical Peaks:** Given that historical peaks have coincided with high tension and political events and that current tensions are substantial, there is a higher probability for occurrences of more than 30 violations in a single day. -- **Statistical Likelihood:** Based on historical data of peaks, a quick calculation suggests that during periods of increased tension, several days with high incursion counts can be expected. - -### Conclusion -Given the historical patterns of high tension leading to spikes in incursions, the current political climate, and the recent increases in daily violations, it is reasonable to estimate a high likelihood for at least one day having more than 30 violations within the given time frame. We must also consider the default behavior if there is no significant change, which still indicates a high average and notable peaks in incursions. - -Therefore, synthesizing all these factors, the probability of more than 30 Chinese aircraft violating Taiwan's ADIZ in a single day between August 15 and September 15, 2024, is estimated to be quite high. - -**Probability: 80%**" -26984,"Will more than 40 Chinese aircraft violate Taiwan's air defense identification zone (ADIZ) in a single day between August 15 and September 15, 2024?","PERPLEXITY - -To forecast whether more than 40 Chinese aircraft will violate Taiwan's Air Defense Identification Zone (ADIZ) in a single day between August 15 and September 15, 2024, we need to consider recent trends and historical data on ADIZ incursions. - -### Recent Trends and Historical Data - -1. **Increased Incursions Post-Election**: - - Since the election of President Lai Ching-te on May 20, 2024, the People's Liberation Army (PLA) has significantly increased its daily incursions into Taiwan's ADIZ. This is part of a broader campaign to exert pressure on Taiwan, which the PRC considers a dangerous separatist under Lai's administration. - -2. **July 2024 Incursions**: - - In July 2024, the PLA conducted at least 439 military incursions into Taiwan's ADIZ, with 280 of these occurring in the first two weeks of the month. This is the third consecutive month of significantly higher and rising numbers of ADIZ incursions. - - The record for most ADIZ violations in one month was 446 in August 2022, in response to then-US House Speaker Nancy Pelosi’s visit to Taiwan. - -3. **Daily Incursion Records**: - - On July 10, 2024, there were 56 ADIZ violations, tied with October 4, 2021, as the highest daily total on record. - - The PLA has been conducting near-daily air and naval violations of Taiwan’s ADIZ, which drains Taiwan’s resources, exhausts military personnel, and degrades Taiwan’s threat awareness. - -4. **Expert Opinions and Predictions**: - - The heightened number of ADIZ violations reflects an intensified PRC pressure campaign against Taiwan. Experts suggest that this trend is likely to continue as the PRC seeks to wear down Taiwan’s threat awareness and resources. - - Prediction markets and forecasting sites like Metaculus have questions related to ADIZ violations, indicating ongoing interest and concern about the frequency and intensity of these incursions. - -### Conclusion - -Given the recent escalation of ADIZ incursions and the historical context of significant increases in response to political events, it is plausible that more than 40 Chinese aircraft could violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024. The ongoing pressure campaign by the PRC and the precedent set by previous high-incursion periods support this possibility. - -######### - -GPT - -To approach this prediction task, I will carefully analyze the available data, trends, and contexts surrounding Chinese aircraft incursions into Taiwan's ADIZ, while considering the specific resolution criteria provided. This particular question fits under the ""Non-Market Threshold Value Event,"" as it focuses on whether a quantifiable measure (the number of incursions) will exceed a value (40) in a specific time period. - -### 1. Historical Base Rates -- Historically, Chinese aircraft incursions into Taiwan's ADIZ fluctuate in response to political tensions and significant events. -- The record number of incursions in a single day tied for 56 (July 10, 2024, and October 4, 2021). - -### 2. Recent Trends Post-Inauguration -- There has been a noticeable increase in PLA incursions following significant political events, particularly post-May 2024 inauguration of Taiwan's new president. -- The average number of daily incursions spiked after this event, with July 2024 registering at least 439 incursions, marking heightened activity. - -### 3. Recent Highs and Contextual Analysis -- A recent single-day high of 56 aircraft on July 10, 2024, demonstrates China's capability and willingness to exceed the 40-aircraft threshold under current political tensions. -- Historical response patterns to significant Taiwan-related events suggest a continuity of intimidation tactics, possibly peaking during noteworthy political anniversaries or decisions. - -### 4. Timeframe Considerations -- The forecast period between August 15 and September 15 corresponds with a moderate duration where international observances or further developments could provoke additional displays of military power from China. -- The recent increase in daily incursions makes it plausible for another peak beyond or matching July 2024 to occur again within this timeframe. - -### 5. Geopolitical Environment -- China's assertion of air power as a political signal remains a crucial tool in its strategy to pressure Taiwan. -- The interpretation of Taiwan's new leadership by the PRC as separatists may incite more aggressive military posturing. - -### Probability Assessment -Given all the considerations: -- **Historical Max**: There is precedent for days exceeding 40 incursions. -- **Trends**: Current patterns show increased incursions, indicating a possible repeat. -- **Political Context**: Continued Chinese military pressure in response to perceived political provocations. -- **Timeframe**: Sufficiently short that the current rise in incursions is likely to persist. - -Accordingly, the probability that more than 40 Chinese aircraft will breach Taiwan's ADIZ in a single day between August 15 and September 15, 2024, is assessed as relatively high. Integrating all the factors, a reasonable estimate is: - -**Probability: 70%**" -26982,"Will more than 20 Chinese aircraft violate Taiwan's air defense identification zone (ADIZ) in a single day between August 15 and September 15, 2024?","PERPLEXITY - -### Context for Forecasting: Chinese Aircraft Violations of Taiwan's ADIZ - -#### Historical Incursions -- **June 21, 2022**: 29 Chinese aircraft, including 17 fighter jets and six bombers, crossed into Taiwan’s ADIZ. -- **May 30, 2022**: 30 jets entered the area, including more than 20 fighters. -- **May 6, 2022**: 18 warplanes, including fighters and bombers, entered Taiwan’s ADIZ. -- **March 14, 2022**: 13 aircraft, including Y-8 and J-16 aircraft, entered Taiwan’s ADIZ. -- **February 24, 2022**: 8 J-16 fighters and 1 Y-8 reconnaissance aircraft entered the ADIZ. -- **January 23, 2022**: 39 advanced air assets crossed Taiwan’s ADIZ. -- **October 4, 2021**: A record 56 Chinese warplanes, including 12 nuclear-capable bombers, entered Taiwan’s ADIZ. -- **October 3, 2021**: 16 warplanes, including J-16 and Su-30 fighter jets, entered the ADIZ. -- **October 2, 2021**: 38 warplanes entered the ADIZ, the highest number in a single day at the time. -- **July 16, 2021**: 28 Chinese military planes flew into Taiwan’s ADIZ, including J-16 and J-11 fighters, and H-6 bombers. - -#### Recent Incursions -- **July 2024**: The PLA conducted at least 439 military incursions into Taiwan’s ADIZ, surpassing all previous months except August 2022. 280 of these incursions occurred in the first two weeks of July. -- **July 10, 2024**: 56 violations, tied with October 4, 2021, as the highest daily total on record. -- **July 2, 2024**: A PLA drone flew near an airport on Taiwan’s Matsu Islands, disrupting two flights. - -#### Expert Opinions and Trends -- The increased incursions are part of a broader campaign to exert pressure on Taiwan following the election of President Lai Ching-te, whom the PRC considers a dangerous separatist. -- The high frequency of incursions drains Taiwan’s resources, exhausts military personnel, and degrades Taiwan’s threat awareness. -- Taiwan does not scramble aircraft in response to all PRC ADIZ incursions but keeps military personnel on standby to respond quickly if needed. - -#### Prediction Markets -- Metaculus has a question on whether more than 60 Chinese aircraft will violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024. This question is pending approval by Community Moderators. - -### Conclusion -Given the recent surge in ADIZ violations and the historical context of significant incursions, it is plausible that more than 20 Chinese aircraft could violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024. The PRC's intensified pressure campaign against Taiwan under President Lai Ching-te's administration supports this likelihood. - -######### - -GPT - -To accurately forecast whether more than 20 Chinese aircraft will violate Taiwan’s ADIZ in a single day between August 15 and September 15, 2024, we need to consider several aspects carefully. This includes analyzing historical data, recent trends, political context, and the likelihood of changes over the forecast period. - -### Historical Context and Base Rate: -1. **Past Incidents**: Historically, we have seen significant incursions that surpass 20 aircraft on a single day. For instance, the incidents on July 10, 2024, and previous years demonstrate that such occurrences are not rare, but not regular either. -2. **Incursion Events with Over 20 Aircraft**: Over the past few years, there have been several instances where over 20 aircraft crossed the ADIZ. The occurrence is more likely during periods of heightened geopolitical tension. - -### Recent Trends: -1. **July 2024 Data**: 439 incursions occurred in July 2024, highlighting intensified activity, with July 10 showing a high of 56 on a single day. -2. **Increase Post-Inauguration of President Lai**: The number of incursions increased to an average of 10 per day post-inauguration, demonstrating sustained elevated activity since May 2024. - -### Political Context: -1. **PRC's Stance on Taiwan**: China considers the new Taiwanese administration as separatist, which suggests sustained or potentially increased military pressure. -2. **Ongoing Military Exercises**: Historical and recent data suggest an upswing in activity correlating with political events such as elections or inaugurations. - -### Probability Assessment: -1. **Base Rate**: Several past instances exceeded 20 incursions in a day, though not regularly. Given the frequency increase since May 2024, the base rate of high-incursion days appears higher during politically sensitive periods. -2. **Current Trend**: There is a visible trend of increased incursions which might continue given the tense political climate. -3. **Variance in Daily Incursions**: While daily averages give an overview, the variance and spikes suggest possibilities of high-incursion events. - -### Conclusion and Probability: -- Given the historical data, current trends, and political climate, it is plausible that more than 20 aircraft could enter the ADIZ in a single day during the forecast period. Yet, the event's regularity is not guaranteed, and while plausible, it remains significantly uncertain. - -Based on these considerations, it is fair to assess the probability of more than 20 Chinese aircraft violating Taiwan's ADIZ in a single day between August 15 and September 15, 2024, as: - -**Probability: 65%**" -26977,"Will the Accumulated Cyclone Energy of the 2024 Atlantic hurricane season be greater than 60 on October 7, 2024?","PERPLEXITY - -### Rundown of Relevant News for the 2024 Atlantic Hurricane Season - -#### Forecasts and Predictions: -- **NOAA Prediction**: NOAA has predicted an 85% chance of an above-normal hurricane activity during the 2024 Atlantic hurricane season, driven by La Niña and warmer-than-average ocean temperatures. -- **Colorado State University Forecast**: The forecast from Colorado State University indicates an extremely active season with 23 named storms, 12 hurricanes, and 6 major hurricanes. The Accumulated Cyclone Energy (ACE) is predicted to be around 230, significantly above the 1991-2020 average of 123. - -#### Current Season Activity: -- **Historic Start**: The season began with Hurricane Beryl, the strongest July Atlantic hurricane on record as a Category 5 storm, which made landfall in Texas as a Category 1 hurricane. -- **Recent Activity**: As of August 6, 2024, the season has seen named storms Alberto, Beryl, Chris, and Debby, with a slight reduction in the forecast number of named storms due to a recent lull in storm formations. - -#### Expert Opinions: -- **Philip J. Klotzbach**: The Colorado State University team, led by Philip J. Klotzbach, maintains a high confidence level in their forecast, emphasizing the importance of thorough preparations for coastal residents. -- **NOAA**: NOAA's forecast highlights the potential for a record-breaking season, similar to the 2005 and 2020 seasons, due to favorable climatic conditions. - -#### Market Predictions: -- **Metaculus**: The Metaculus prediction market indicates a high likelihood of the Accumulated Cyclone Energy exceeding 60 by October 7, 2024, reflecting the consensus on an above-normal season. - -### Conclusion: -Given the current trends and forecasts, it is highly likely that the Accumulated Cyclone Energy of the 2024 Atlantic hurricane season will be greater than 60 by October 7, 2024. The combination of favorable climatic conditions, historic storm activity, and expert predictions all point towards an extremely active season. - -######### - -GPT - -To assess the probability of the 2024 Atlantic hurricane season's ACE being greater than 60 by October 7, 2024, let's evaluate the information and context provided: - -### Context and Historical Context: -1. **Definition and Measurement of ACE**: - - ACE is a measure of the energy released by tropical cyclones. An ACE of 60 is relatively modest compared to historic full-season totals, especially in active years. - -2. **Historical Base Rates**: - - Typical ACE values for full seasons in active years (above 60 by October 7) are common, especially in years with strong climate signals like La Niña or warm ocean temperatures. - - The 1991-2020 median ACE value for a full season is around 123, indicating that reaching 60 by early October is not unusual for active years. - -### 2024 Season Forecasts and Observations: -1. **NOAA and Colorado State University Forecast**: - - both have predicted an active or extremely active 2024 season. - - CSU's specific ACE forecast is 230 for the season, implying significant cyclone activity. - -2. **Current Season Activity**: - - So far, four named storms, including a powerful Hurricane Beryl, have already occurred by early August. - - This indicates a strong start to the season, which supports the likelihood of achieving at least ACE 60 by early October. - -3. **Climatic Conditions**: - - La Niña and warmer ocean temperatures both favor higher cyclone activity, consistent with higher ACE. - -4. **Expert Opinions**: - - Expert meteorologists, including Philip J. Klotzbach, express confidence in their forecasts for active season activity. - -5. **Market Prediction Signals (e.g., Metaculus)**: - - The consensus in market predictions suggests a high probability of the threshold being exceeded. - -### Uncertainty and Variability: -1. **Time till the Resolution Date**: - - From August 8 till October 7, there are nearly two months for additional storm development, which is ample time during the peak of hurricane season. - - September is historically the peak month for hurricane activity in the Atlantic, further supporting the expectation of significant ACE accumulation. - -2. **Potential for Lull**: - - While a recent lull in storm formation was noted, the overall climatic conditions still favor elevated activity levels in the coming weeks. - -### Conclusion: -Considering the historical base rates, current season status, climatic conditions indicating significant cyclone activity, expert forecasts/products, and prediction market expectations, the likelihood of the ACE exceeding 60 by October 7 is extremely high. Historical patterns and current indicators strongly support this outcome. - -Given these considerations, my final prediction is: - -**Probability: 95%**" -26986,"Will more than 60 Chinese aircraft violate Taiwan's air defense identification zone (ADIZ) in a single day between August 15 and September 15, 2024?","PERPLEXITY - -To forecast whether more than 60 Chinese aircraft will violate Taiwan's air defense identification zone (ADIZ) in a single day between August 15 and September 15, 2024, we need to consider recent trends and historical data on Chinese military activities around Taiwan. - -### Recent Trends and Historical Data - -1. **Recent Activity**: - - **July 2024**: Taiwan reported a single-day record of 66 Chinese military aircraft operating around the island, with 56 crossing the median line of the Taiwan Strait and entering Taiwan's ADIZ. - - **May 2024**: During the inauguration of Taiwan's President William Lai, China conducted joint military exercises around Taiwan, including activities against the main island and its outlying islands. This exercise, named Joint Sword-2024A, involved significant maritime and air assets. - -2. **Historical Context**: - - **August 2022**: China conducted large-scale exercises around Taiwan, focusing on the main island and issuing specific coordinates for seven exercise zones. - - **April 2023**: China conducted exercises named ""Joint Sword,"" which did not announce specific zones but still involved significant military activity. - -### Expert Opinions and Predictions - -- **Military Exercises**: China has been routinizing large-scale PLA exercises, indicating a pattern of increased military activity around Taiwan. The recent exercises have included both the main island and its outlying islands, suggesting a broader scope of operations. -- **Increased Activity**: The surge in Chinese military aircraft detected around Taiwan in July 2024, including a record high of 66 aircraft in a single day, suggests an escalation in military activity. - -### Prediction Markets and Forecasts - -- **Metaculus**: The question on Metaculus regarding whether more than 60 Chinese aircraft will violate Taiwan's ADIZ between August 15 and September 15, 2024, is awaiting approval. However, given the recent trends, it is likely that the community will predict a high likelihood of such an event occurring. - -### Conclusion - -Given the recent surge in Chinese military activity around Taiwan, including a record number of aircraft violations in July 2024, and the pattern of increased military exercises, it is reasonable to expect that more than 60 Chinese aircraft could violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024. The routinization of large-scale PLA exercises and the broader geographic scope of these operations support this expectation. - -######### - -GPT - -To address the question of whether more than 60 Chinese aircraft will violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024, we'll proceed through a structured analysis: - -### Step 1: Understanding the Background and Context -- **Recent Trends**: Chinese military aircraft activity around Taiwan has been increasing, with a notable peak in July 2024 where 66 aircraft were observed in one day, though 56 were reported as crossing the median line. This is crucial because it shows a capacity for large-scale sorties. -- **Historical Patterns**: There have been other periods of escalated activity, notably in August 2022. These occurrences often correlate with political events or Chinese military exercises. -- **Political Climate**: Following Taiwan's President Lai’s inauguration, China has intensified its military pressure, portraying the new administration as separatist and thereby rationalizing increased military activity. - -### Step 2: Synthesizing Information from Data and Expert Opinions -- **Base Rate Analysis**: Historically, while increased activity is common, records like the one in July 2024 are relatively infrequent. The usual daily numbers have been lower but have trended upwards recently. -- **Expert Outlook**: Analysts suggest a continuity of this trend, potentially reaching or surpassing the July peak during the prescribed dates. - -### Step 3: Evaluating the Timing and Potential Catalysts -- **Potential Catalysts**: Any new developments in cross-strait relations, high-profile political events, or military exercises may trigger another peak. The period between August 15 and September 15, 2024, provides a month-long window, making it significant for potential variances. -- **Natural Variation**: There's a natural ebb and flow to these activities; external factors can push the numbers upwards suddenly. - -### Step 4: Considering Defaults and Variances -- **Default Pattern**: If no extraordinary event occurs, the default pattern of increased but not record-breaking activity seems likely. The possibility of surpassing 60 is significant but depends heavily on specific triggers. -- **Probability of High-Scale Event**: The recent occurrence of a peak surpassing 60 aircraft during a tense political climate indicates that conditions are ripe for another such event, particularly as exercises become more routine. - -### Conclusion and Probability Estimate: -Given: -- **Recency and Historical Rates**: July 2024's peak suggests an upward trend in capability and willingness for high aircraft numbers. -- **Political and Military Context**: The current period remains politically charged, with potential for further provocations or exercises. -- **Statistical Odds**: The previous peak of 66, albeit across two days, proves a significant numbers surge is within reach. - -I assign a moderately high probability that more than 60 Chinese aircraft could enter Taiwan’s ADIZ in a single day within the specified timeframe. - -Probability: 65%" -26985,"Will more than 50 Chinese aircraft violate Taiwan's air defense identification zone (ADIZ) in a single day between August 15 and September 15, 2024?","PERPLEXITY - -To forecast whether more than 50 Chinese aircraft will violate Taiwan's Air Defense Identification Zone (ADIZ) in a single day between August 15 and September 15, 2024, we need to consider recent trends and historical data. - -### Historical Context and Recent Trends - -1. **Historical Data**: - - **2022**: The year saw a significant increase in ADIZ violations, with a total of 1,737 aircraft tracked, a 79% increase from 2021. - - **2021**: Notable incursions included 56 Chinese warplanes on October 4, 2021, and 38 warplanes on October 1, 2021. - -2. **Recent Trends**: - - **July 2024**: The PRC conducted at least 439 military incursions into Taiwan’s ADIZ, with 280 of these occurring in the first two weeks. This is part of a trend of increased ADIZ violations since President Lai Ching-te took office in May 2024. - - **July 11, 2024**: Taiwan reported a single-day record of 66 Chinese planes around the island, with 56 crossing the median line of the Taiwan Strait and entering Taiwan's ADIZ. - -### Current Situation - -- **Increased Pressure**: The PRC has been increasing military pressure on Taiwan, particularly since President Lai Ching-te took office. This includes large-scale military exercises and near-daily ADIZ incursions. -- **Recent Incursions**: The frequency and number of incursions have been high, with July 2024 seeing significantly higher numbers than previous months. - -### Expert Opinions and Predictions - -- **Metaculus Prediction Market**: The question on Metaculus about the maximum number of aircraft violating Taiwan's ADIZ in a single day in Fall 2024 is pending approval, indicating ongoing interest and uncertainty about future violations. -- **Military Analysts**: Analysts have noted that the PRC's actions are part of a broader campaign to exert pressure on Taiwan, which could lead to further escalations. - -### Conclusion - -Given the recent trends and historical context, it is likely that more than 50 Chinese aircraft will violate Taiwan's ADIZ in a single day between August 15 and September 15, 2024. The PRC's increased military activities and the recent record of 66 aircraft in a single day suggest a high probability of such an event occurring. - -######### - -GPT - -To forecast the likelihood of more than 50 Chinese aircraft violating Taiwan's ADIZ in a single day between August 15 and September 15, 2024, I will analyze historical data, recent trends, political context, and the base rate of similar occurrences. - -### Historical Context and Recent Data - -1. **Historical Peaks**: Notable events include a peak of 56 aircraft in a single day on October 4, 2021, and 66 aircraft around Taiwan on July 11, 2024. -2. **Trend Data**: The average number of daily violations post-inauguration of President Lai Ching-te rose to 10 from previous averages of 6 and 2 in earlier periods of 2024. -3. **Recent Violations**: July 2024 saw a heightened number of violations with 439 incursions and a single-day high of 66. - -### Political Context - -PRC's escalated military activities are reflective of tensions with Taiwan's new administration. Since the inauguration of President Lai, the frequency and magnitude of military maneuvers have increased as a pressure tactic. - -### Base Rates and Novel Developments - -- The base rate of exceeding 50 aircraft incursions in a single day is low, given occurrences like the one on October 4, 2021, and July 11, 2024, are exceptions rather than the norm. -- Given the current geopolitical climate with heightened tension post-inauguration, the likelihood is higher but should be weighed against historical rarity. - -### Probability Analysis - -1. **Default Behavior**: If current trends continue without change, there is a relatively low overall base rate but with increased recent incidents, especially with July 11 as a precedent, it signals an environment conducive to potential repetition. -2. **Timeframe Assessment**: The period between August 15 and September 15, 2024, is 31 days, providing substantial time for such an event to occur again, especially amidst ongoing tensions. -3. **Variance**: The variance in daily incursions suggests room for singular high-count days, influenced by political and military intentions of the PRC. - -### Conclusion - -Considering the increased military activity since May 2024, the peak observed in July, and the general backdrop of tensions, the probability of exceeding 50 aircraft incursions in a single day between August 15 and September 15, 2024, is significantly higher than the historical base rate would suggest but should be tempered by the uncertainty of it occurring within a short one-month window. - -Given the heightened tensions and previous instances in similar contexts, I assess the probability as follows: - -Probability: 65%" -27089,"Will any new iPhone model be announced supporting Wi-Fi 7 before October 1, 2024? -","PERPLEXITY - -Yes, new iPhone models supporting Wi-Fi 7 are expected to be announced before October 1, 2024. Here is a summary of the relevant information: - -- **Release Date**: The iPhone 16 lineup, including the iPhone 16 Pro and iPhone 16 Pro Max, is expected to be announced in September 2024, with a probable announcement date on September 10, 2024, and a market release date on September 20, 2024. -- **Wi-Fi 7 Support**: The iPhone 16 Pro and iPhone 16 Pro Max are widely expected to support Wi-Fi 7, which offers faster Wi-Fi speeds, lower latency, and more reliable connectivity compared to Wi-Fi 6E. -- **Features and Upgrades**: The new models will also feature improved 5G connectivity with Qualcomm's Snapdragon X75 modem, enhanced AI capabilities with iOS 18, and other upgrades such as improved microphones and a revamped Photos app. - -Given this information, it is clear that the new iPhone models supporting Wi-Fi 7 will be announced and released before October 1, 2024. - -######### - -GPT - -To address the question, I will carefully evaluate the information provided, consider relevant historical trends, and estimate the probability of the event occurring. Let's proceed step by step: - -### Analysis - -1. **Source Credibility and Information:** - - Ming-Chi Kuo is a well-respected Apple analyst with a history of accurate predictions. His belief that the iPhone 16 Pro models could adopt Wi-Fi 7 is a strong indicator. - - The information from MacRumors, a reputable source for Apple news, further supports Kuo’s analysis, suggesting the announcement date will likely be in September 2024. - - The research assistant mentions that the iPhone 16 Pro and Pro Max models are expected to support Wi-Fi 7. This aligns with historical timelines of Apple's gradual incorporation of new technologies. - -2. **Historical Trends:** - - Historically, Apple has a pattern of announcing new iPhone models in September, with releases shortly thereafter. - - Apple tends to incorporate the latest technology once it has matured and been optimized. Wi-Fi 7 is the next technological advancement likely to be embraced. - -3. **Current Technological and Market Environment:** - - Wi-Fi 7 presents significant advancements over Wi-Fi 6E, and it's logical for Apple to include this in their flagship models to maintain competitive edge. - - Apple continues to push for improved connectivity and speed in their devices, consistent with the adoption of newer Wi-Fi standards. - - The timeline allows Apple sufficient time to integrate this technology before the projected September 2024 announcement. - -4. **Resolution Criteria:** - - As per the criteria, official announcements are the key factor. Leaks or rumors, thus far, indicate a high likelihood of an official announcement. - - The integration of Wi-Fi 7 in the iPhone 16 Pro series fulfills the criteria for a ""Yes"" resolution if announced before October 1, 2024. - -5. **Timeframe:** - - With the current date of August 9, 2024, and typical Apple announcement patterns, there is a reasonable time window for an announcement before the deadline. - -6. **Uncertainty and Risk Factors:** - - While tech development timelines can face unforeseen disruptions, Apple has consistently adhered to its September release cycle. - - Any last-minute technological or production issues could potentially delay the announcement, but such risks are minimal based on current information. - -### Conclusion - -Considering the credible sources, consistent historical patterns of product releases, and the technological incentives for Apple to adopt Wi-Fi 7, the probability of Apple announcing a new iPhone model supporting Wi-Fi 7 before October 1, 2024, is high. Given these considerations: - -**Probability: 90%**" -27088,"Will Tim Walz cease to be Kamala Harriss's running mate before October 1, 2024?","PERPLEXITY - -To assess whether Tim Walz will cease to be Kamala Harris's running mate before October 1, 2024, we need to consider recent developments and expert opinions. Here is a detailed rundown of the relevant news: - -### Recent Developments -1. **Selection Announcement**: Vice President Kamala Harris announced Minnesota Governor Tim Walz as her running mate on August 6, 2024. This decision was made after President Joe Biden ended his reelection campaign, leading to a rapid selection process for Harris's running mate. - -2. **Campaign Kickoff**: Harris and Walz kicked off their campaign in Philadelphia, emphasizing unity and a positive message. They plan to tour critical battleground states, including Wisconsin, Michigan, Arizona, and Nevada. - -3. **Fundraising Success**: The campaign raised over $10 million from grassroots donations following Walz's selection, indicating strong initial support. - -4. **Public Reception**: Walz is described as a down-to-earth, affable politician who can connect with rural constituents. His selection has been seen as a safe choice to maintain party unity. - -5. **Legal Challenges**: There are ongoing legal challenges to Harris's candidacy, but these do not specifically target Walz's position as running mate. - -6. **Trump Campaign Response**: The Trump campaign has criticized Walz, labeling him a ""West Coast wannabe"" and a far-left liberal, but this does not indicate any likelihood of Walz stepping down. - -### Expert Opinions -1. **Political Analysts**: Analysts believe that Walz's selection was strategic to reassure the Democratic Party's left faction and maintain unity. His background as a social studies teacher, football coach, and military veteran is seen as beneficial in connecting with a broader audience. - -2. **Campaign Spokespersons**: Harris's campaign team has expressed confidence in Walz's ability to resonate with voters, particularly in the upper Midwest. They view his selection as a positive step in unifying the party. - -### Prediction Markets -As of August 9, 2024, there are no significant prediction markets indicating that Tim Walz will cease to be Kamala Harris's running mate before October 1, 2024. The focus remains on the upcoming election and the campaign's efforts to secure battleground states. - -### Conclusion -Given the recent developments and expert opinions, there is no substantial evidence to suggest that Tim Walz will cease to be Kamala Harris's running mate before October 1, 2024. The campaign's initial success and the strategic nature of Walz's selection indicate a stable partnership moving forward. - -######### - -GPT - -To assess the probability that Tim Walz will cease to be Kamala Harris's running mate before October 1, 2024, let's consider the relevant information and analyze it step by step. - -### Evaluation of the Current Situation: -1. **Recent Developments**: - - Walz was selected just a few days ago, on August 6, 2024, and his selection appears strategic. - - There are no signs of discontent or significant issues within the Democratic Party that would suggest instability in the Harris-Walz ticket. - - The campaign has started on a positive note with strong initial fundraising and good public reception. - - Legal challenges to Harris’s candidacy do not directly target or involve Walz, reducing the likelihood of immediate disruption from this angle. - -2. **Historical Precedents and Base Rates**: - - Historically, vice-presidential candidates dropping out from a ticket is rare. The most notable case is Thomas Eagleton in 1972, which involved specific and severe personal circumstances. - - Other notable instances typically involve significant personal, legal, or political scandals—none of which seem to be on the horizon for Walz. - -3. **Time until Resolution Date**: - - There are less than two months between the current date (August 9, 2024) and the resolution date (October 1, 2024). The short timeframe reduces the window for unexpected events or rapid shifts. - -4. **Expert Opinions and Prediction Markets**: - - Analysts see Walz as a stabilizing choice, aimed at reinforcing party unity and broadening appeal. - - Prediction markets and expert opinion do not highlight any significant risks to Walz's position on the ticket. - -5. **Political Dynamics**: - - Within a political campaign, unexpected resignations or changes in the ticket can typically be triggered by scandals, major strategic shifts, or intense pressure from within the party. None of these seem probable given the current information. - -### Risk Factors: -- **Unknown Unknowns**: As highlighted by the Eagleton example, unforeseen personal issues or significant political events could arise. However, the likelihood of such events occurring within a short time span is statistically low. -- **Political Pressure or Strategy Changes**: Although possible, any major strategic shake-up tends to have longer lead times and more obvious precursors, none of which are currently evident. - -### Final Assessment: -Considering all these factors, the base rate of vice-presidential candidates being dropped from tickets, especially in such a short time frame and absent any overt crisis, is extremely low. The campaign appears stable, and any significant unknowns would lack sufficient time to manifest before October 1, 2024. - -### Final Probability: -Based on the analysis of the situation, historical precedents, time remaining, and expert opinions, the probability that Tim Walz will cease to be Kamala Harris's running mate before October 1, 2024, is low. - -**Probability: 5%**" -27085,"Before Sept 30, 2024, will the International Criminal Court issue an arrest warrant for Benjamin Netanyahu?","PERPLEXITY - -The International Criminal Court (ICC) has initiated the process to seek arrest warrants for Israeli Prime Minister Benjamin Netanyahu and other officials over alleged war crimes and crimes against humanity committed during the Israel-Hamas conflict. However, no arrest warrants have been issued yet. The ICC's Chief Prosecutor, Karim Khan, announced the intention to request these warrants, and the decision now rests with a panel of three judges, who typically take about two months to deliberate. - -### Key Points: -- **Current Status**: The ICC has requested but not issued arrest warrants for Netanyahu and other officials. -- **Deliberation Period**: The judges usually take around two months to decide on issuing warrants. -- **Historical Context**: Previous examples, such as the warrant for Russian President Vladimir Putin, took a month, while the first warrant for Omar al-Bashir took eight months. -- **Political Reactions**: Netanyahu has labeled the move a 'disgrace' and accused the prosecution of antisemitism, while Hamas has denounced the request as equating victims with executioners. -- **Legal Implications**: Even if warrants are issued, Israel's non-membership in the ICC means Netanyahu and others would not face immediate prosecution, but their international travel could be complicated. - -### Recent Updates: -- **May 20, 2024**: The ICC prosecutor formally requested arrest warrants for Netanyahu and Hamas leaders. -- **May 21, 2024**: Reuters confirmed that the ICC has requested but not issued arrest warrants. - -### Expert Opinions: -- **Karim Khan**: The ICC's Chief Prosecutor has emphasized the importance of impartiality and warned against threats to retaliate against the court. -- **Netanyahu**: He has vehemently opposed the move, calling it a ""historic outrage"". - -### Prediction Markets: -As of the current date, there are no specific prediction markets available for this event. However, the general sentiment in legal and political circles suggests that the issuance of arrest warrants is a possibility given the ICC's history of pursuing high-profile cases. - -### Conclusion: -Given the typical deliberation period and the recent formal request for arrest warrants, it is plausible that the ICC could issue an arrest warrant for Benjamin Netanyahu before September 30, 2024. However, the final decision rests with the judges, and political reactions and legal complexities may influence the outcome. - -######### - -GPT - -To assess the likelihood of the International Criminal Court (ICC) issuing an arrest warrant for Benjamin Netanyahu before September 30, 2024, I'll break this down into critical elements, considering various factors such as historical context, legal proceedings, political dynamics, and recent updates. - -### Historical Context and Base Rate -- Since its inception in 2002, the ICC has issued a total of 42 arrest warrants, averaging roughly 2-3 warrants per year. Notably, they have pursued high-profile figures like Vladimir Putin. -- The expected timeline for these warrants varies significantly; for instance, it took a month for Vladimir Putin's warrant and eight months for Omar al-Bashir. This suggests considerable variation based on case specifics and external pressures. - -### Current Status and Timeline -- The ICC's Chief Prosecutor announced intentions to seek arrest warrants on May 20, 2024. Given that judges typically deliberate for about two months, a decision could reasonably be expected between July and September. -- As of August 9, 2024, it appears that the ICC is within the typical deliberation window. - -### Political and Diplomatic Considerations -- Netanyahu has actively sought intervention from influential nations, such as the United States, to prevent the issuance of warrants. Additionally, the ICC faces political pressures from major powers like the G7, known to dissuade actions that may jeopardize peace deals, such as the one between Israel and Hamas. -- The geopolitical stakes are high, particularly considering Israel's non-membership in the ICC and the complexities around prosecuting non-member state officials, which could lead to diplomatic reluctance. - -### Legal and Procedural Factors -- Issuing an arrest warrant would require sufficient legal grounds, and while the ICC can make bold decisions, such as with Putin, the court's decisions can be influenced by broader political contexts, constraints, and diplomatic considerations. -- Despite the ICC's established processes and emphasis on legal impartiality, external political pressures and strategic diplomatic relationships can sway timelines and decisions. - -### Uncertainty and Variability -- The major sources of uncertainty hinge on the ICC judges' decision-making process and external diplomatic interventions. Historical precedent offers some guidance but with significant variance. -- Given no recent prediction market data or other metrics available for this exact scenario, estimating this probability demands a nuanced analysis of the current geopolitical landscape and ICC's past behavior. - -### Conclusion -Drawing from these elements, the probability is derived by integrating historical averaging of warrant issuances, the specific political and legal context of this case, and the potential for altering the timeline via high-stakes diplomatic engagements. - -**Probability Calculation:** -- Base Rates and Historical Averages: 5-10% -- Current Status and Judicial Process Likelihood: 10-20% -- Political/Diplomatic Pressure Reduction: -10% -- Remaining variables and potential positive indications (e.g., recent shifts or ICC's assertive actions in similar cases): +10% - -Balancing all these factors leads to a considered estimation. - -**Final Answer: Probability: 25%** - -This estimation accounts for a realistic appraisal of dynamic and static factors, with the primary constraints being the ICC's processes and the aforementioned international political dynamics." -27086,"Will Elon Musk be the richest person in the world on September 30, 2024, according to Forbes?","PERPLEXITY - -To determine whether Elon Musk will be the richest person in the world on September 30, 2024, according to Forbes, we need to consider several factors, including his current net worth, recent trends in his wealth, and the performance of his major assets such as Tesla and SpaceX. - -### Current Net Worth and Trends -- **Elon Musk's Current Net Worth**: As of August 1, 2024, Elon Musk's net worth is $221.4 billion, making him the richest person in the world. -- **Recent Wealth Trends**: Musk's fortune has seen significant fluctuations. He became the world's richest person in September 2021 and held the title for most of 2022. He regained the top spot in June 2023 and lost it briefly in January 2024. He reclaimed the title in late May 2024 after his startup xAI raised $6 billion from private investors at a $24 billion valuation. - -### Performance of Major Assets -- **Tesla**: Tesla's stock performance is a significant factor in Musk's net worth. The company's market capitalization soared during 2020 and 2021, contributing to Musk's wealth. However, Tesla's stock price can be volatile, and any significant changes could impact Musk's net worth. -- **SpaceX**: SpaceX's valuation and funding rounds also contribute to Musk's wealth. The company has been successful in securing significant funding, which has bolstered Musk's net worth. - -### Competitors' Net Worth -- **Bernard Arnault**: As of July 1, 2024, Bernard Arnault's net worth is $233 billion, making him the second richest person. Arnault's wealth is primarily tied to LVMH, the luxury goods conglomerate. -- **Jeff Bezos**: Jeff Bezos' net worth is $194 billion, placing him third. His wealth is largely tied to Amazon's stock performance. - -### Expert Opinions and Predictions -- **Forbes Real-Time Rankings**: Forbes tracks the daily changes in net worths of billionaires. This real-time tracking can provide insights into how wealth is shifting among the top billionaires. -- **Market Predictions**: Market analysts and financial experts often provide predictions on stock performance and economic trends that could impact the net worth of billionaires. For example, if Tesla's stock is predicted to rise, Musk's net worth is likely to increase. - -### Recent Updates -- **Tesla Cybertruck in Fortnite**: A recent update includes the introduction of Tesla's Cybertruck in Fortnite, which could potentially boost Tesla's brand visibility and stock performance. -- **Legal Issues**: Musk is involved in legal disputes regarding his stock options at Tesla, which could impact his net worth if the outcome is unfavorable. - -### Conclusion -Given the current trends and the volatile nature of stock markets, it is challenging to predict with certainty whether Elon Musk will remain the richest person in the world on September 30, 2024. However, as of August 1, 2024, Musk holds the top spot with a net worth of $221.4 billion. The performance of Tesla and SpaceX, as well as any significant legal or market developments, will be crucial factors in determining his position by the end of September. - -######### - -GPT - -To assess the probability of Elon Musk being the richest person in the world on September 30, 2024, according to Forbes, we need to consider several factors: - -1. **Current Position and Wealth Comparison**: - - As of August 1, 2024, Elon Musk's net worth is $221.4 billion, making him the richest person in the world. - - Jeff Bezos and Bernard Arnault are his nearest competitors, with their net worths being significantly lower ($194 billion for Bezos and $233 billion for Arnault). However, it's important to note that there seems to be a discrepancy regarding Arnault's net worth; it should be lower than $221.4 billion if Musk holds the current top position. - -2. **Net Worth Dynamics**: - - Musk's wealth primarily depends on Tesla's and SpaceX's performance and valuations. Musk’s wealth has experienced significant fluctuations in the past due to the volatile nature of the stock markets, especially Tesla's stock. - - His fortune dipped earlier in the year but has recently been augmented by a substantial investment in xAI. - -3. **Asset Performance**: - - Tesla is a major component of Musk's wealth, and any volatility in its stock price could have a substantial impact on his net worth. The introduction of Tesla’s Cybertruck in Fortnite might potentially increase brand visibility, which could positively influence Tesla’s stock price. - - Legal issues regarding his stock options could pose a risk, though the outcome and timing are uncertain. - -4. **Market Trends and Competitors**: - - Bernard Arnault's wealth largely depends on LVMH's performance. Factors affecting the luxury goods market, such as global economic conditions, could influence Arnault’s rank. However, the gap between Musk and Arnault still provides Musk a buffer. - - Jeff Bezos’s wealth is closely linked to Amazon. Significant changes in Amazon’s financial health could alter Bezos’s wealth ranking. - -5. **Historical Precedents and Base Rates**: - - Considering the past fluctuations in wealth rankings, it is not uncommon for the top position to change due to stock price variations and business developments. However, given the gap between Musk and his competitors and the slight period (approximately 1.5 months) until September 30, 2024, significant changes would need substantial developments in the financial performance of Tesla, SpaceX, Amazon, and LVMH. - -6. **Timeframe and Variance**: - - Given the proximity to the resolution date, there's limited time for drastic changes unless unforeseen events or major market shifts occur. - -Based on the current state, Musk is in the lead, and there's a minimal period for any substantial change to occur to dethrone him, acknowledging the volatility element. However, the presence of some legal uncertainty regarding his stock options and the inherent volatility in assets like Tesla means there's non-negligible risk. Nevertheless, foreseen conditions, the buffer Musk holds and absence of significant imminent threats suggest he is likely to remain at the top. - -**Probability: 70%**" -27087,"Before October 1, 2024, will Tech Crunch report new layoffs at Microsoft?","PERPLEXITY - -To forecast whether TechCrunch will report new layoffs at Microsoft before October 1, 2024, we need to consider recent trends and announcements related to Microsoft's workforce adjustments. Here is a concise rundown of the most relevant information: - -### Recent Layoffs and Announcements -- **Microsoft Layoffs in 2024**: Microsoft has already conducted significant layoffs in 2024. In January, the company laid off 1,900 employees across its gaming divisions following the acquisition of Activision Blizzard. Additionally, in June, Microsoft announced layoffs affecting its Azure cloud business, impacting around 1,000 employees. -- **Historical Context**: Microsoft has a history of making workforce adjustments around the end of its fiscal year, which is June 30. This pattern suggests that the company might continue to make minor cuts as it transitions to a new fiscal year. - -### Current Market and Economic Conditions -- **Economic Uncertainty**: Many business leaders are concerned about a potential recession, which could lead to further layoffs across various industries, including tech. -- **AI and Automation**: The increasing use of AI and automation is another factor driving layoffs, as companies seek to replace certain roles with technology. - -### Prediction Markets and Expert Opinions -- **Prediction Markets**: Metaculus, a prediction platform, has a question open regarding whether TechCrunch will report new layoffs at Microsoft before October 1, 2024. While the current prediction is not available, such platforms often reflect the collective wisdom of forecasters and can provide a probabilistic outlook. - -### Conclusion -Given the recent layoffs and the historical pattern of Microsoft making workforce adjustments around the end of its fiscal year, it is plausible that TechCrunch could report new layoffs at Microsoft before October 1, 2024. The economic uncertainty and the trend of tech companies cutting jobs to adapt to AI and automation further support this likelihood. - -######### - -GPT - -To forecast accurately whether TechCrunch will report new layoffs at Microsoft before October 1, 2024, several factors and analytical steps must be considered: - -### Step 1: Historical Context and Base Rate -- Microsoft has conducted multiple layoffs in 2024, notably in January and June. Historically, Microsoft has shown a tendency to announce layoffs around the end or beginning of its fiscal year (June 30), which aligns with their fiscal reporting adjustments. -- The base rate for Microsoft layoffs in recent years, particularly within the current economic climate and with previous fiscal year patterns, suggests there is a precedent for multiple rounds of layoffs within a year. - -### Step 2: Economic and Market Conditions -- Current market conditions hint at potential economic downturns with discussions of recession, causing businesses across industries, including tech, to be conservative about their workforce sizes. -- Additionally, the tech industry is experiencing a transformative phase with increased AI and automation implementation, which could lead management to reassess staffing needs and potentially result in layoffs. - -### Step 3: Time Frame and TechCrunch's Role -- There are 52 days (less than two months) until the resolution date (October 1, 2024). This is a short period, but given Microsoft's past behavior and unpredictable macroeconomic conditions, the potential for announcements is plausible. -- TechCrunch is a reputable source for tech industry news, and it's likely they would report on any significant layoffs by a major company like Microsoft, particularly if it aligns with broader tech industry layoffs. - -### Step 4: Recent Layoffs -- With recent layoffs in June 2024, it's necessary to evaluate whether Microsoft typically follows up with subsequent layoffs shortly after an initial round. While historically they've had layoffs in concentrated periods, whether this pattern continues remains uncertain. - -### Step 5: Additional Analysis and Considerations -- Consideration of the timing (post-fiscal year) and external economic pressures increases the probability slightly. -- As the resolution will depend solely on TechCrunch's published list, attention must be paid to whether smaller, less publicized cuts are included or if Microsoft's subsidiary layoffs impact the decision (bearing in mind the clarity of 'Microsoft' being explicitly mentioned on the list). - -### Conclusion -Given the combination of past patterns, current economic conditions, the time frame, and the necessity for any announcement to be captured by TechCrunch, the situation presents a reasonable likelihood of layoffs being reported within the specified period. However, the short time frame and potentially stabilizing market might mitigate the likeliness somewhat. - -After carefully weighing all the factors, I estimate the probability that TechCrunch will report new Microsoft layoffs before October 1, 2024 as: **Probability: 65%**." -27136,"Will Robert F. Kennedy Jr. suspend his 2024 presidential campaign before September 24, 2024?","PERPLEXITY - -### Context for Forecasting: Will Robert F. Kennedy Jr. Suspend His 2024 Presidential Campaign Before September 24, 2024? - -#### Current Campaign Status -- **Visibility and Polling**: Robert F. Kennedy Jr.'s independent presidential campaign has seen a significant decline in visibility and polling numbers. His support has dropped from around 9% to approximately half that figure since President Biden's exit from the race. -- **Financial Struggles**: The campaign is in debt, with more than half of the $0.6 million in its bank account being owed. Kennedy has claimed that both Democratic and Republican parties have conspired to make it difficult for him to secure ballot access across all 50 states. - -#### Legal Challenges -- **New York Ballot Access**: A New York judge ruled that Kennedy falsely claimed a New York address as his residence, leading to his exclusion from the state's November ballot. This decision could pave the way for similar challenges in other states. -- **Residency Claim**: Kennedy testified that he has only slept in the New York home once, and a neighbor confirmed that he has never seen Kennedy at the address. This has raised questions about the legitimacy of his residency claim. - -#### Public Perception and Support -- **Controversies**: Kennedy has been involved in several controversies, including allegations of sexual misconduct and a peculiar incident involving a staged bicycle accident scene in Central Park. These have led to some supporters distancing themselves from him. -- **Endorsements**: Despite losing some supporters, Kennedy has gained endorsements from figures like Russell Brand and Joe Rogan. - -#### Campaign Activities -- **Virtual Engagements**: Kennedy has primarily participated in virtual discussions and attended conferences, such as the Bitcoin Conference, rather than actively campaigning on the ground. -- **Security Concerns**: Following the attempted assassination of Donald Trump, Kennedy received Secret Service protection. He had previously expressed concerns about his personal security and the potential for threats. - -#### Expert Opinions -- **Democratic Party View**: The Democratic National Committee has criticized Kennedy, labeling him a ""spoiler candidate"" who could help Donald Trump. They argue that he is a ""deeply troubled, reckless, and dangerous man"" who refuses to consider the consequences of his actions. - -### Recent Updates -- **August 13, 2024**: A New York judge ruled that Kennedy cannot appear on the state's November ballot due to a false residency claim. -- **August 9, 2024**: NBC News reported on the decline of Kennedy's campaign, highlighting his reduced visibility and polling numbers. - -### Prediction Markets -- **Metaculus**: The question on Metaculus regarding whether Kennedy will suspend his campaign before September 24, 2024, is pending approval by Community Moderators. Users are encouraged to provide feedback and upvote the question if they find it interesting or well-written. - -### Conclusion -Given the current state of Kennedy's campaign, including his legal challenges, financial struggles, and declining public support, there are significant indicators suggesting that he might suspend his campaign before September 24, 2024. The recent ruling in New York and ongoing legal battles over ballot access further complicate his campaign's viability. - -######### - -GPT - -This question falls into the category of ""Competition Event,"" as it pertains to a political election. The key task is to assess the likelihood of Robert F. Kennedy Jr. suspending his 2024 presidential campaign before September 24, 2024. We will consider various factors that could influence this outcome. - -### Current Situation: -1. **Polling and Visibility**: Kennedy's support has dropped significantly from around 9% to half that figure. This indicates declining momentum, which can demoralize a campaign and potentially lead to suspension. - -2. **Financial Struggles**: The campaign is in debt, with more liabilities than available funds. Financial difficulties can pressure candidates to suspend campaigns, especially if they cannot maintain sufficient operational funding. - -3. **Legal and Ballot Access Challenges**: Kennedy's exclusion from the New York ballot due to a false residency claim suggests potential issues in other states, jeopardizing his chances of being a viable candidate. Difficulty in securing state-by-state ballot access is a serious blow to any campaign. - -4. **Public Perception and Controversies**: Kennedy faces several controversies, including allegations of misconduct and bizarre incidents that may damage his public image. Such issues can further erode public and financial support. - -5. **Campaign Activities**: Limited personal campaigning and mostly participating in virtual events may indicate waning energy or resources for the campaign. - -6. **Security Concerns**: While receiving Secret Service protection does highlight his campaign, it also underscores personal safety concerns that could detract from campaigning efforts. - -### Historical Precedences and Base Rates: -- Independent or third-party candidates often struggle due to lack of infrastructure and financial support compared to major party candidates. Historically, such campaigns face uphill battles, but the suspension isn't always guaranteed unless the situation becomes untenably challenging. - -### Time Consideration: -- The date is August 13, 2024, leaving just over a month until the resolution date. Although changes in campaign status can occur rapidly, a mere few weeks may be a short window for a radical departure from current trends without a critical decisive incident or pressure to suspend. - -### Evaluation: -- Though Kennedy's campaign is faltering, he has persisted through considerable challenges thus far. The decision to suspend a campaign, especially with controversies and ballot access battles ongoing, isn't always immediate. -- Substantial financial backing earlier in the campaign, though now reduced, might still buffer somewhat against pressure to suspend. -- The recent legal ruling in New York, while critical, might not immediately translate into other states taking similar actions within the next few weeks. - -### Conclusion: -Given all the factors at play, there is a moderate probability but no overwhelming certainty that Kennedy will suspend his campaign before September 24, 2024. The combination of legal challenges, financial difficulties, and waning support suggest a higher likelihood of suspension than otherwise, but his persistence up to this point indicates that a suspension is not predetermined. - -**Probability: 55%**" -27134,Will the S&P 500 index go up in September 2024?,"PERPLEXITY - -To determine whether the S&P 500 index will go up in September 2024, we need to consider various factors including economic forecasts, market trends, and expert opinions. Here is a concise but detailed rundown of the relevant news: - -### Economic Forecasts and Market Trends - -1. **J.P. Morgan Research**: - - **Mid-Year Outlook**: J.P. Morgan Research anticipates a mild recession in the U.S. near the end of 2023 due to the Federal Reserve's restrictive policy stance. This could impact the stock market in the following months, including September 2024. - - **Global Economic Outlook**: The research suggests that the global economy might face a synchronized recession before the end of 2024, which could affect stock market performance. - -2. **U.S. Bank**: - - **Market Correction Concerns**: As of August 5, 2024, the S&P 500 was down 8.5% from its July 10, 2024, high, nearing correction territory. This indicates recent volatility and potential for further correction. - -3. **Investors.com**: - - **Yardeni Research Forecasts**: Yardeni Research predicts S&P 500 operating earnings to be $250 in 2024, up 12% from 2023, and $270 in 2025, up 8%. This suggests positive earnings growth, which could support the stock market. - -4. **Forbes**: - - **Stock Market Forecast for 2024**: Despite concerns about inflation and interest rates, investors are optimistic about the stock market's performance in 2024. The consensus analyst price target for the S&P 500 is 5,090, indicating potential upside. - - **AI-Led Bull Market**: The AI-fueled bull market is expected to continue, with tech stocks leading the way. This could provide a boost to the overall market. - -5. **CNBC**: - - **Mid-Year Outlook**: The S&P 500 is up 16% in 2024, and market strategists believe there is still potential for further growth. However, there are concerns about valuation and the impact of political unrest. - - **Sector Performance**: Technology sectors have performed exceptionally well, and there is potential for broader market segments to catch up, leading to more widespread gains. - -### Expert Opinions - -1. **Bruce Kasman, J.P. Morgan**: - - Kasman expects a synchronized global recession before the end of 2024, which could impact the stock market negatively. - -2. **Dubravko Lakos-Bujas, J.P. Morgan**: - - Lakos-Bujas sees unattractive risk-reward for equities and increasing investor complacency ahead of an expected business cycle deceleration in the second half of 2024. - -3. **James Demmert, Main Street Research**: - - Demmert believes the AI-fueled bull market could last a decade, indicating long-term optimism for the tech sector and the broader market. - -4. **Jeffrey Buchbinder, LPL Financial**: - - Buchbinder recommends a neutral tactical allocation to equities and a modest overweight to fixed income, suggesting caution but not outright pessimism. - -### Recent Updates and Current Values - -- **Current S&P 500 Performance**: The S&P 500 has seen significant gains in 2024, up 16% year-to-date as of July 2024. -- **Valuation Concerns**: The S&P 500 is trading at a premium compared to its historical averages, which could lead to a market correction if investor enthusiasm wanes. - -### Prediction Markets - -- **Current Predictions**: As of the latest data, prediction markets and analysts generally remain optimistic about the stock market's performance, though there are concerns about potential corrections and the impact of economic and political factors. - -### Conclusion - -While there are positive forecasts and strong performance in certain sectors, there are also concerns about valuation, potential recession, and political unrest. The S&P 500's performance in September 2024 will depend on how these factors interplay. - -######### - -GPT - -To predict whether the S&P 500 index will increase in September 2024, we need to analyze the gathered information within the context of historical trends, current economic indicators, and market sentiment. Let's go through a structured analysis: - -### Analysis of Factors - -1. **Economic Forecasts**: - - J.P. Morgan's forecast of a mild recession near the end of 2023 and a possible synchronized global recession before the end of 2024 suggests significant downside risk. A recession would typically exert downward pressure on the equity markets, although the timing and severity are uncertain. - - The U.S. Bank's observation of the S&P 500 nearing correction territory indicates volatility and a potential reversal of the strong gains seen earlier in 2024. - -2. **Market and Earnings Forecasts**: - - Yardeni Research's projection of earnings growth suggests underlying corporate strength, which is a critical driver of the stock market. - - Forbes notes optimism regarding an AI-led bull market and significant upside in the consensus analyst price target, although concerns about inflation and interest rates persist. - -3. **Market Performance and Valuation**: - - The S&P 500's robust performance in 2024, up 16%, indicates current market strength; however, valuation concerns suggest the possibility of overheating, which could prompt corrections. - - Gains in the technology sector could continue to bolster the index if matched by broader market strength. - -4. **Expert Opinions**: - - There are diverging views, ranging from cautious optimism with neutral or modest equity allocation recommendations to concerns about unattractive risk-reward ratios and a business cycle deceleration. - -5. **Current Context and Recent Developments**: - - Political unrest and other macroeconomic factors can contribute to uncertainty and potentially dampen market sentiment. - - Prediction markets reflect optimism yet acknowledge risks that could realize in the near term. - -### Historical Context and Base Rates - -Historically, the S&P 500 shows monthly fluctuations, and historical September performances can oscillate based on macroeconomic factors. September is often seen as a volatile month due to the end of the earnings season. However, it's crucial to recognize that while historical base rates can guide expectations, current conditions (economic forecasts, sectoral strength, etc.) must weigh heavily in predictions. - -### Probabilistic Evaluation - -Given the mix of positive earnings expectations and the AI-led market optimism against the backdrop of recessionary forecasts, market corrections, and potential geopolitical or economic disruptions, it's plausible to expect continued volatility. Nevertheless, the strong year-to-date performance and potentially supportive stock-specific news could contribute to potential gains in September. - -**Uncertainties**: -- Impact of recessionary pressures versus growth in corporate earnings. -- Sector performance variability, particularly in technology. -- External shocks, including geopolitical factors and policy changes. - -### Conclusion - -On balance, the mixed indicators and the strength from certain sectors suggest a cautious optimism with a moderate probability of the index closing higher at the end of September 2024 than at the start. Given the volatile environment but considering the robust earnings growth prospects and historical trends, there is a reasonable but moderate chance that the index will indeed increase. - -**Final Probability**: 55% - -The edge above 50% reflects the potential positive drivers counterbalancing the risks." -27133,"Will Donald Trump debate the Democratic nominee for president more than once before October 1, 2024?","PERPLEXITY - -Donald Trump has agreed to debate the Democratic nominee, Vice President Kamala Harris, on September 10, 2024, hosted by ABC News. Additionally, Trump has expressed a desire to participate in two more debates on September 4 and September 25. However, it is unclear if these additional debates will materialize as they have not been formally agreed upon by both parties. - -### Key Points: -- **Confirmed Debate**: Trump and Harris will debate on September 10, 2024. -- **Proposed Debates**: Trump has suggested additional debates on September 4 and September 25, but these have not been confirmed. -- **Historical Context**: The Commission on Presidential Debates (CPD) typically sponsors debates, but this year, the major party nominees have chosen to bypass the CPD and negotiate debate terms directly. - -### Expert Opinions: -- **Kamala Harris' Debate History**: Harris has a mixed record in debates, with both memorable moments and testy exchanges. -- **Trump's Debate Performance**: Trump's past debates have been marked by controversial statements and unaddressed falsehoods. - -### Recent Updates: -- **Biden's Withdrawal**: President Joe Biden withdrew from the race on July 21, 2024, leading to Harris becoming the Democratic nominee. -- **Debate Schedule**: The first debate between Biden and Trump took place on June 27, 2024, and the second is scheduled for September 10, 2024. - -### Prediction Markets: -- **Current Predictions**: As of August 13, 2024, there are no specific prediction markets available for the number of debates Trump will participate in before October 1, 2024. However, the consensus is that the September 10 debate is confirmed, and the additional debates proposed by Trump are uncertain. - -In summary, while there is a confirmed debate on September 10, 2024, the likelihood of additional debates before October 1, 2024, remains uncertain and dependent on further agreements between Trump and Harris. - -######### - -GPT - -To assess the probability that Donald Trump will debate the Democratic nominee for president more than once before October 1, 2024, let's break down the information provided. - -### Key Points: -1. **Confirmed Debate:** There is one confirmed debate between Donald Trump and Kamala Harris on September 10, 2024. -2. **Proposed Debates:** Trump proposed additional debates on September 4 and September 25; however, these debates have not been formally agreed upon by both parties. -3. **Biden's Withdrawal and Implications:** With Biden withdrawing, Kamala Harris is the Democratic nominee. This withdrawal changes the debate dynamics, with Harris now stepping in. -4. **Trump's Debate Strategy:** Trump expressed a desire for more debates, indicating a willingness to participate in more than one debate. -5. **Historical Context:** Presidential debates are traditionally scheduled and moderated events, but the norm is being bypassed this cycle with direct negotiations. - -### Analysis: -- **Base Rates:** Historically in U.S. presidential elections, candidates typically engage in multiple debates. However, this cycle diverges from precedence as the candidates have bypassed the Commission on Presidential Debates and are instead negotiating terms directly. -- **Political Strategy:** Biden's withdrawal might change the campaign strategy for both parties. Trump has expressed willingness to debate on more than one occasion, and it's plausible that the political environment could lead to further debates if both parties see strategic advantages. -- **Logistical and Time Constraints:** As of August 13, there is limited time to organize additional debates before October 1, 2024. Also, the nominations and campaign efforts will be unfolding rapidly, so there might be limited bandwidth for setting up and conducting more debates. -- **Uncertainty and Flexibility:** While the confirmed debate on September 10 seems set, the other additional debates are uncertain. However, the spontaneous and fluid nature of campaigns means last-minute decisions are possible. - -### Probability Estimation: -- **Debate 1 Probability (September 10):** 100%, as this debate is confirmed. -- **Probability of At Least One More Debate (Either on September 4 or September 25):** - - Given Trump's desire and the potential strategic benefit, and considering there’s still time especially for the September 25 date, there’s a moderate chance these debates will materialize. - - However, without formal agreements or strong indications from Harris, there's uncertainty. - -My conclusion is that the chances are slightly tilted towards another debate possibly occurring, but uncertainty remains due to the absence of solid commitments for any debates beyond September 10. - -Final Probability: -Given these factors, I estimate the probability that Donald Trump will engage in more than one debate with the Democratic nominee before October 1, 2024 is around **40%**. - -**Probability: 40%**" -27135,"Before October 1, 2024, will Tech Crunch report new layoffs at Google or Alphabet?","PERPLEXITY - -To forecast whether TechCrunch will report new layoffs at Google or Alphabet before October 1, 2024, we need to consider recent trends and announcements in the tech industry, particularly those involving Google and Alphabet. Here is a concise rundown of the most relevant news: - -### Recent Layoffs and Trends - -1. **Google Layoffs**: - - **January 2024**: Google laid off hundreds of workers in its central engineering division and hardware teams, including those working on its voice-activated assistant. - - **April 2024**: Google laid off staff across key teams like Flutter, Dart, and Python, with the exact number of impacted employees not specified. - - **May 2024**: Google laid off 100 employees across several Cloud “Go To Market” teams in the Asia-Pacific region. - -2. **Alphabet Layoffs**: - - There have been no specific reports of layoffs at Alphabet itself, but the parent company's subsidiaries, including Google, have been actively reducing their workforce. - -3. **Industry-Wide Layoffs**: - - The tech industry has seen significant layoffs in 2024, with over 60,000 job cuts across 254 companies, including major players like Tesla, Amazon, and Microsoft. - - Companies are restructuring and cutting costs due to economic concerns and the integration of AI and automation. - -### Expert Opinions and Predictions - -- **Economic Concerns**: Many business leaders are concerned about a potential recession, which is driving the layoffs. -- **AI and Automation**: The increasing use of AI and automation is also a significant factor, with companies like Dropbox, Google, and IBM conducting layoffs to replace workers with AI. - -### Prediction Markets and Forecasts - -- **Metaculus Question**: A question on Metaculus asks whether TechCrunch will report new layoffs at Google or Alphabet before October 1, 2024. The question is currently under review and awaiting approval by Community Moderators. - -### Conclusion - -Given the recent layoffs at Google and the ongoing trend of job cuts in the tech industry, it is likely that TechCrunch will report new layoffs at Google or Alphabet before October 1, 2024. The economic climate and the integration of AI and automation suggest that companies will continue to restructure and reduce their workforce. - -### Key Points for Forecasting - -- **Recent Layoffs**: Google has already conducted several rounds of layoffs in 2024. -- **Industry Trends**: The tech industry is experiencing a wave of layoffs due to economic concerns and the adoption of AI. -- **Prediction Markets**: The Metaculus question indicates ongoing interest in this topic, suggesting that there is a belief that further layoffs are possible. - -These factors collectively suggest a high likelihood of new layoffs being reported by TechCrunch before October 1, 2024. - -######### - -GPT - -To forecast whether TechCrunch will report new layoffs at Google or Alphabet before October 1, 2024, let's systematically analyze this using a structured, step-by-step approach: - -1. **Recent Layoff History at Google and Alphabet**: - - Google has conducted layoffs in January, April, May, and July of 2024. This indicates a trend of workforce reduction through the year, suggesting that they are continually reevaluating and possibly trimming their workforce. - -2. **Industry-Wide Trends**: - - The tech industry is experiencing significant layoffs in 2024, with a pattern of restructuring and cost-cutting amidst economic concerns and the integration of AI and automation. This macro-level environment influences individual companies like Google. - -3. **Economic and Technological Factors**: - - There's a prevailing concern about a potential economic downturn. Coupled with companies adopting more AI and automation, these factors provide incentives for continued layoffs. - -4. **Prediction Markets and Forecasting Context**: - - Metaculus is tracking this question, suggesting there is interest and, likely, some uncertainty or expectation of future layoffs. While it's not an opinion, its existence indicates community attention on this topic. - -5. **Time Consideration**: - - It is currently August 13, 2024, leaving a little more than six weeks until the resolution date. Given the current frequency of layoffs and industry conditions, this is a significant window for additional layoffs to occur. - -6. **Base Rates and Novelty**: - - The base rate, considering Google's behavior earlier in 2024, supports a hypothesis of continued downsizing. The novelty might come from the specific teams or departments affected in future layoffs, but the overarching trend seems consistent. - -7. **Resolution Criteria**: - - The question will resolve as ""Yes"" if a layoff is reported by TechCrunch under its comprehensive list by October 1, 2024. We've observed that Google has been mentioned multiple times, increasing chances of them being specifically listed if new layoffs occur. - -Based on these factors: - -- The current patterns of layoffs at Google and the tech industry at large suggest a higher likelihood of continued layoffs. -- The time between now and October 1, 2024, is sufficient for new developments, given Google's current trajectory and the industry's adaptive behavior to economic pressures. -- The specificity of the resolution hinges on TechCrunch's official reporting, but given the frequent past updates, it's reasonable to expect consistency. - -**Conclusion**: -Given the combination of historical layoffs, industry trends, and economic pressures, it seems likely that additional layoffs could be reported within the remaining timeframe. - -Probability: 70%" -27132,"Will Iran carry out a deadly attack within Israel before October 1, 2024?","PERPLEXITY - -### Context for Forecasting: Will Iran Carry Out a Deadly Attack Within Israel Before October 1, 2024? - -#### Current Tensions and Threats -- **Escalating Tensions**: The assassination of Hamas political figure Ismail Haniyeh on July 31 has significantly heightened tensions between Iran and Israel. Iranian officials have vowed to retaliate, with the Iranian Revolutionary Guards indicating preparations for a large-scale counterattack. -- **Military Preparations**: Israel has taken defensive measures, including prohibiting career officers from traveling abroad and ordering soldiers stationed in Georgia and Azerbaijan to return to Israel. The U.S. has deployed a nuclear-guided missile submarine and additional fighter jets and naval vessels to the region to bolster Israeli defenses. - -#### International Reactions -- **Diplomatic Appeals**: Leaders from France, Germany, and Britain have urged Iran to abstain from retaliatory actions, calling for a ceasefire in Gaza and the release of hostages held by Hamas. They emphasized the urgent need for unrestricted humanitarian assistance in Gaza. -- **Flight Cancellations**: Several international airlines have canceled flights to the region due to the escalating fears of an Iranian assault, indicating the growing concern over potential military action. - -#### Recent Developments -- **Iran's Stance**: Iranian officials have asserted their right to an ""appropriate and deterrent response"" against Israel. The deputy commander of the Iranian Revolutionary Guards has stated that Iran is set to carry out an order from Supreme Leader Ayatollah Ali Khamenei to ""harshly punish"" Israel. -- **US Involvement**: The U.S. has increased its regional force posture and is prepared for significant attacks by Iran or its proxies. The deployment of the USS Georgia and additional military assets underscores the U.S.'s commitment to safeguarding Israel. - -#### Prediction Markets -- **Metaculus Prediction**: The Metaculus prediction market currently indicates a high likelihood of Iran carrying out a deadly attack within Israel before October 1, 2024. The question is awaiting approval by Community Moderators, but user feedback and upvotes suggest it is considered relevant and well-written. - -### Conclusion -Given the heightened tensions, military preparations, and diplomatic appeals, the likelihood of Iran carrying out a deadly attack within Israel before October 1, 2024, appears significant. The recent developments and international reactions highlight the escalating risk of a broader conflict in the Middle East. - -######### - -GPT - -To forecast the likelihood of Iran carrying out a deadly attack within Israel before October 1, 2024, let's consider various factors methodically: - -### Historical Context and Base Rates -1. **Historical Conflict Dynamics**: Historically, tensions between Israel and Iran have been significant, particularly via proxy conflicts such as through Hezbollah or Hamas. Direct attacks from Iran itself have been less frequent but have occurred nonetheless through drones and missiles. -2. **Recent Escalations**: The assassination of high-profile figures like Ismail Haniyeh and Fuad Shukr, coupled with Iran's previous response to attacks (e.g., April 2024’s drone and missile barrage), indicate a pattern of retaliation, although usually less than the threshold for the deadly criteria specified. - -### Current Situation Analysis -1. **Tensions and Threats**: The current geopolitical situation is exceptionally tense. The supreme leader of Iran has vowed retaliation following the assassination on Iranian soil, which escalates potential for a high-profile retaliatory attack. However, such pronouncements are typical in these contexts and do not always lead to significant military action. -2. **Military and Diplomatic Posture**: Both Israel and international allies like the U.S. have increased their military readiness. The U.S.'s deployment of military assets in the region is both a deterrent and a signal of potential wartime readiness. -3. **Risk Mitigation by Israel**: Israel’s defensive measures indicate serious threat perception but also readiness to neutralize potential threats. - -### Factors Influencing Likelihood -1. **Retaliatory Intentions**: Iran's statements and military preparations suggest a genuine threat of some form of retaliation, which increases the probability in the short term. However, executing a significant attack that results in five or more deaths within Israel is more challenging. -2. **Operational Feasibility for Iran**: Conducting a successful and deadly operation within Israel's heavily surveilled and defended territories is complex, particularly with heightened alertness and international military presence. -3. **International and Regional Diplomacy**: Diplomatic pressures from the EU and potential repercussions could deter Iran from escalating conflicts to a deadly attack threshold. -4. **Time Frame**: The timeframe till October 1, 2024, is relatively short considering the usual pace of overt direct military retaliations, which are often calculated and not impulsive. - -### General Considerations -1. **Base Rate of Similar Events**: Historically, major cross-border attacks directly attributed to Iranian forces resulting in significant casualties within Israel's borders are rare. -2. **Variables in Play**: Although the rhetoric and recent incidents heighten the risk, several variables including Iran's potential risk calculations about escalation and international implications influence actions. - -### Conclusion -- **Overall Assessment**: While the chance of an aggressive retaliatory stance by Iran exists, achieving the specific criteria for a deadly attack within Israel's borders remains complex and challenging. -- Given time constraints, historical base rates, defensive postures, and high international stakes, it tampers the likelihood of such a severe escalation occurring by the deadline specified. - -Final Calculation: -- Accounting for all these factors, a slightly elevated risk from baseline but moderated by the geopolitical and military contexts suggests: - -**Probability: 30%**" -27138,"On September 17, 2024, will Nvidia's market capitalization be larger than Apple's?","PERPLEXITY - -To forecast whether Nvidia's market capitalization will be larger than Apple's on September 17, 2024, we need to consider recent trends, market dynamics, and expert opinions. - -### Recent Market Trends and Dynamics - -1. **Nvidia's Market Capitalization Surge**: - - As of June 2024, Nvidia's market capitalization surpassed Apple's, reaching $3.02 trillion, making it the second-largest company behind Microsoft. - - Nvidia's shares have seen significant growth, with a 32% month-on-month increase and a 214% increase over the past year, driven by high demand for its AI-focused hardware. - -2. **Apple's Market Performance**: - - Apple's market capitalization has also been strong, but it trails Nvidia's recent surge. Apple's shares have advanced, but at a slower pace compared to Nvidia. - - Apple faces challenges in its smartphone business and consumer spending pressures, which could impact its market capitalization growth. - -3. **AI-Driven Growth**: - - Nvidia's dominance in AI chip manufacturing, with an 80% market share, positions it favorably for continued growth. Major tech companies like Microsoft, Amazon, and Meta Platforms are significant clients, contributing to Nvidia's revenue. - - The AI boom is expected to continue driving Nvidia's valuation, with many analysts predicting it will be the first to reach a $4 trillion market capitalization. - -### Expert Opinions and Predictions - -1. **Analyst Predictions**: - - Experts believe Nvidia will lead the race to a $4 trillion market capitalization due to its strong AI-focused hardware demand and strategic diversification into various sectors. - - Some analysts predict Nvidia could reach a $10 trillion market capitalization by 2030, driven by its rapid product roadmap and impenetrable moat in the CUDA software platform. - -2. **Market Outlook**: - - Despite concerns about high valuations and potential economic slowdowns, the prevailing optimism among experts suggests a positive outlook for Nvidia's growth trajectory. - - The tech giants' remarkable performance this year, with Nvidia, Microsoft, and Apple all exceeding the $3 trillion mark, indicates a robust economy and profitable AI boom. - -### Current Market Capitalization - -- **Nvidia**: $3.02 trillion as of June 2024. -- **Apple**: $3.00 trillion as of June 2024. - -### Prediction Markets - -- **Metaculus Prediction**: The question on Metaculus about Nvidia's market capitalization being larger than Apple's on September 17, 2024, is still in review and does not provide current prediction data. - -### Conclusion - -Given Nvidia's recent market capitalization surge, its dominance in AI chip manufacturing, and the bullish outlook from analysts, it is likely that Nvidia's market capitalization will remain larger than Apple's on September 17, 2024. However, market dynamics can be unpredictable, and any significant changes in the tech landscape could impact this forecast. - -######### - -GPT - -To forecast whether Nvidia's market capitalization will be larger than Apple's on September 17, 2024, we need to carefully evaluate the information provided, historical data, potential market dynamics, and the likelihood of significant unforeseen events. - -### Analysis: - -1. **Current Market Capitalizations**: - - As of June 2024, Nvidia's market cap was $3.02 trillion, while Apple's was $3.00 trillion. This is a close margin, but Nvidia currently leads. - -2. **Recent Growth Trends**: - - Nvidia has experienced explosive growth, with a 32% increase month-on-month and 214% year-on-year driven by their AI chip dominance. Apple's growth, while positive, has been slower. - - Demand for Nvidia's AI-focused GPUs remains strong, positioning the company to continue its upward trajectory. - -3. **Broader Market Dynamics**: - - Nvidia has a strong position in the AI boom, partially due to its ""moat"" in the CUDA software platform. - - The tech sector, in general, has seen strong valuation increases. Despite market volatility, Nvidia's growth story is compelling, particularly underpinned by AI demand. - - Apple's challenges, notably in their core business and consumer spending concerns, might restrain its growth relative to Nvidia. - -4. **Analyst Intuition**: - - Analysts are bullish on Nvidia, citing its innovative product lineup and growth potential. Nvidia is deemed a frontrunner to reach new market cap milestones. - - Apple's prospects rely on more traditional tech products which aren't experiencing the same growth trajectory as Nvidia's AI offerings. - -5. **Timeframe and Historical Precedent**: - - With roughly five weeks until the resolution date, major valuation changes are less likely unless driven by significant events such as earnings releases, major geopolitical changes, or a market-wide correction. - - The default situation if nothing dramatic occurs favors Nvidia, given its current lead and strong growth story. - -6. **Potential Unforeseen Events**: - - Potential risks include macroeconomic factors affecting the broader market, such as interest rate changes or economic downturns. - - Specific risks to Nvidia could involve competition, supply chain issues, regulatory hurdles, or a slowdown in AI demand. Apple's strong brand and market presence may mitigate some downside risks, but it also faces its own industry-specific challenges. - -### Conclusion: - -Given the available data, Nvidia's stronger growth performance, the bullish outlook from analysts, and its tech sector position, it is more likely that Nvidia's market cap will exceed Apple's as of the resolution date. While risks exist, they seem less potent relative to the factors driving Nvidia's current valuation. - -**Probability Estimate**: Based on the analysis, the probability that Nvidia's market capitalization will be larger than Apple's on September 17, 2024, is 75%. - -**Final Answer**: ""Probability: 75%""" -27137,"Will at least 200 Benin Bronzes go from the British Museum to Nigeria before October 1, 2024?","PERPLEXITY - -### Context for Forecasting: Will at least 200 Benin Bronzes go from the British Museum to Nigeria before October 1, 2024? - -#### Recent Developments and Current Status -- **Stanley Museum of Art's Repatriation**: The Stanley Museum of Art at the University of Iowa has returned its collection of Benin Bronzes to the Oba of Benin, marking a significant step in the repatriation of these artifacts. This move underscores the growing momentum for museums to address the legacy of colonial-era acquisitions and return cultural property obtained through illegitimate means. -- **British Museum's Position**: The British Museum has received formal requests for the return of the Benin Bronzes from the Nigerian government and the Benin Royal Palace. Despite these requests, the museum has not yet returned any of the over 900 Benin Bronzes in its collection. - -#### Controversies and Challenges -- **German Government's Stalled Return**: The German government has faced criticism for stalling the return of 22 Benin Bronzes due to concerns about the risk of these heritage items entering private collections. -- **British Museum's Concerns**: The British Museum has expressed concerns about the security and preservation of the artifacts in Nigeria, despite facing its own issues with missing, stolen, and damaged artifacts. - -#### Expert Opinions and Predictions -- **Metaculus Prediction Market**: The Metaculus prediction market is currently tracking the likelihood of at least 200 Benin Bronzes being returned from the British Museum to Nigeria before October 1, 2024. This platform allows users to provide feedback and vote on the likelihood of such events, which can influence the prediction's accuracy. -- **Nigerian Authorities' Urgency**: Nigerian officials have renewed calls for the British Museum to return its Benin Bronzes, emphasizing the urgency and importance of repatriating these cultural artifacts to their rightful owners. - -#### Historical Context -- **Origin and Loot**: The Benin Bronzes were looted by British soldiers from the Kingdom of Benin in 1897. These artifacts have been housed in European state museums, with the British Museum holding one of the largest collections. -- **Previous Repatriations**: Other museums, such as the Horniman Museum in London, have already returned some Benin Bronzes to Nigeria, setting a precedent for future repatriations. - -### Conclusion -The recent repatriation by the Stanley Museum of Art and ongoing discussions between the British Museum and Nigerian authorities suggest a growing momentum for the return of the Benin Bronzes. However, the British Museum's concerns and the complexities surrounding the repatriation process indicate that the return of at least 200 Benin Bronzes before October 1, 2024, is not guaranteed. The Metaculus prediction market and expert opinions will continue to evolve as new developments emerge. - -######### - -GPT - -To assess the likelihood that at least 200 Benin Bronzes will be returned to Nigeria from the British Museum by October 1, 2024, I will consider several factors, including historical precedents, current events, and institutional behaviors. - -### Historical Context and Precedents -1. **Past Repatriations**: While there have been instances of other museums returning Benin Bronzes to Nigeria, the British Museum is under tighter constraints. These constraints are due to its legal reasons and the sheer number of artifacts involved. -2. **Institutional Behavior**: Historically, large institutions such as the British Museum are slow to enact changes, especially when legal and logistical hurdles are involved. These repatriations typically take a long time to negotiate and process. - -### Current Context and Events -1. **Recent Developments**: The Stanley Museum's repatriation provides a potential catalyst for action but doesn't necessarily compel the British Museum to act within such a short timeframe. -2. **British Museum's Position**: The British Museum maintains a legal stance that it cannot simply transfer ownership of its collections, making any significant and rapid shift unlikely without legislative support. -3. **Security Concerns**: Ongoing security and preservation concerns at both ends (in the UK and Nigeria) further complicate the logistics. - -4. **Metaculus and Expert Opinions**: If prediction markets and experts suggest low probabilities, it's likely there hasn't been a pivotal shift in circumstances. Although they can sometimes be off, these platforms are useful for gauging broader sentiment. - -5. **Political and Public Pressure**: While there is rising pressure for repatriation globally, translating this into action with a timeline as tight as October 2024, for such a large number of objects, may be over-optimistic. - -### Timeframe Considerations -- **Time for Behavioral Change**: With just over 1.5 months until the deadline, the timeframe is quite short for major bureaucratic changes, logistics, negotiations, and actual transfers. -- **Base Rate Expectations**: Generally, cultural artifact repatriations—especially involving a large quantity—tend to have a long lead time. - -### Conclusion -Given the constraints, the British Museum's historical resistance, and limited time for resolution, the probability that at least 200 Benin Bronzes will return to Nigeria from the British Museum by the specified date is relatively low. - -My final assessed probability is: - -**Probability: 5%**" -27141,"Will the Crimean Bridge be hit with an attack before Sept 30, 2024?","PERPLEXITY - -The question of whether the Crimean Bridge will be hit with an attack before September 30, 2024, is a complex one, involving geopolitical tensions, military capabilities, and historical context. Here is a concise rundown of the most relevant news and information: - -### Historical Context and Previous Attacks -- **2022 Crimean Bridge Explosion**: On October 8, 2022, a bomb loaded onto a truck caused extensive damage to the Crimean Bridge, resulting in the collapse of two vehicular spans and the death of five people. The attack was later confirmed to have been carried out by Ukraine's Security Service. -- **Previous Damage and Repairs**: The bridge has been hit and repaired twice before. In July 2023, Ukrainian sea drones caused extensive damage to the road section, and in October 2022, an explosion damaged the bridge, which was subsequently repaired. - -### Current Military and Political Situation -- **Ukrainian Intentions**: Ukrainian officials have repeatedly stated their intention to destroy the Crimean Bridge, considering it a legitimate military target. Commander of the Navy of the Armed Forces of Ukraine, Oleksii Neizhpapa, recently stated that the bridge would be destroyed in the first half of 2024. -- **Russian Defenses**: Russia has strengthened its defenses around the bridge, deploying S-300 air-defense missile systems and Pantsir-S1 anti-aircraft artillery systems. Additionally, a ""target barge"" has been deployed as a decoy for incoming guided missiles. - -### Recent Developments -- **Explosions in Kerch**: On March 5, 2024, explosions were heard in the Crimean city of Kerch, and the bridge was closed. Unconfirmed reports suggested the Kerch region was being attacked by aerial and seaborne surface drones. -- **Metaculus Predictions**: On the prediction platform Metaculus, users are actively discussing and forecasting the likelihood of an attack on the Crimean Bridge before September 30, 2024. The platform allows users to submit predictions and provides a community-driven forecast based on user inputs. - -### Expert Opinions and Predictions -- **Military Analysts**: Military analysts and experts have expressed doubts about Ukraine's capability to successfully attack the well-defended bridge. However, Ukrainian officials remain confident in their ability to disable the bridge soon. -- **Political Implications**: The destruction of the Crimean Bridge would have significant symbolic and strategic implications for both Ukraine and Russia. It would disrupt Russia's supply lines to Crimea and be a major propaganda victory for Ukraine. - -### Conclusion -Given the historical context, current military preparations, and recent developments, there is a heightened likelihood of an attack on the Crimean Bridge before September 30, 2024. The ongoing conflict and the strategic importance of the bridge make it a prime target for Ukrainian forces. However, the success of such an attack remains uncertain due to Russia's extensive defensive measures. - -######### - -GPT - -As an expert superforecaster, I will carefully analyze the information provided, using a rigorous step-by-step approach to determine the probability of an attack on the Crimean Bridge before September 30, 2024. - -### Step 1: Understanding the Context -The Crimean Bridge is a strategically significant infrastructure, providing a crucial link between the Crimean peninsula and the Russian mainland. Several recent incidents highlight its vulnerability to attack, and past successful attempts by Ukraine to damage the bridge indicate a continued willingness to target it. - -### Step 2: Historical Analysis -The bridge has been attacked multiple times: -- **October 2022**: A truck bomb caused extensive damage. -- **July 2023**: Ukrainian sea drones struck again. -- **August 2023**: An unsuccessful attack occurred. -These incidents suggest a pattern of recurring attacks approximately every 9-12 months, and especially during periods of heightened military or political significance. The most recent attack being in August 2023 might suggest another attempt before the end of September 2024. - -### Step 3: Current Military and Political Dynamics -Ukraine’s officials have expressed a clear intention to target the bridge as part of their military strategy. Intelligence reports suggest the capability to attack exists, and the symbolic importance of attacking the bridge adds pressure to follow through on these threats. Russia's significant defensive measures complicate but do not eliminate the prospect of a successful attack. - -### Step 4: Probability Estimation Based on Base Rate and Trends -- **Base Rate**: Considering two successful attacks within a period of two years, the base rate of an attack within the next few months is not negligible. -- **Trend Analysis**: The ongoing conflict and the symbolic importance of the bridge imply that the prospects for an attack remain high, especially given the recent reports suggesting Ukraine’s continued focus on the bridge as a target. - -### Step 5: Evaluation of Resolution Criteria -Given the requirement that the attack must credibly threaten the bridge's integrity, it’s crucial to consider the types of attacks that could meet this threshold. Previous successful attacks with sea drones and explosives provide plausible means for another attempt. - -### Step 6: Time Factor -With only a few weeks remaining until September 30, the window for such an attack is small. However, given the historical context of approximately annual attacks and recent military escalations, there exists sufficient time for a well-timed and well-executed strike. - -### Step 7: Current Developments and Expert Predictions -Recent reports and expert opinions suggest a continuous strategic interest by Ukraine to disable the bridge before year's end, notwithstanding Russia’s defensive posture. - -### Synthesis of Information -Considering all factors, including the strategic motivations for Ukraine, Russia's defensive measures, the historical frequency of attacks, and the time available, the probability of an attack on the Crimean Bridge before September 30, 2024, is significant but not overwhelming. - -**Final Probability Estimate**: Considering all aspects and balancing the uncertainties, I estimate the probability of the Crimean Bridge being hit with an attack that meets the criteria described before September 30, 2024, to be approximately 65%. - -**Probability: 65%**" -27140,"Will the CBOE S&P 500 3-Month Volatility Index (VXVCLS) - be higher Year-over-Year on September 20, 2024?","PERPLEXITY - -To determine whether the CBOE S&P 500 3-Month Volatility Index (VXVCLS) will be higher year-over-year on September 20, 2024, we need to consider recent market trends, historical data, and expert opinions. - -### Recent Market Trends -- **July 2024 Market Shift**: The month of July 2024 saw a significant shift mid-month, transitioning from stable price appreciation and low volatility to increased volatility. The S&P 500 Index finished up 1.13%, while the Russell 2000 Index rose more than 10%. The Cboe Volatility Index (VIX) increased by more than 30% late in the month, indicating a rise in expected volatility. -- **Current Volatility Levels**: The VIX Index, which reflects 30-day expected volatility, has seen notable increases in recent weeks. This suggests that market participants are anticipating higher volatility in the near term. - -### Historical Data -- **Historical Volatility**: The VXVCLS, which measures 3-month implied volatility, has historically been influenced by significant market events and economic data releases. The index has shown periods of high volatility during times of economic uncertainty and market stress. -- **Year-over-Year Comparison**: To assess whether the VXVCLS will be higher year-over-year, we need to compare current levels with those from September 2023. Historical data from the Federal Reserve Economic Data (FRED) can provide insights into past trends and fluctuations in the VXVCLS. - -### Expert Opinions -- **Market Analysts**: Market analysts often use term structure data to gauge future volatility expectations. The term structure of the VIX Index can provide insights into how market expectations of volatility have changed over time and how they might evolve in the future. -- **Trading Strategies**: Traders and investors use VIX futures and options to manage risk and generate alpha. The recent increase in volatility has likely influenced trading strategies, with some participants positioning for higher volatility in the coming months. - -### Prediction Markets -- **Metaculus**: The Metaculus platform allows users to forecast and discuss various questions, including financial market indices. While the question about the VXVCLS being higher year-over-year on September 20, 2024, is pending approval, user feedback and upvotes can indicate interest and potential trends in forecasts. - -### Conclusion -Given the recent increase in volatility and the historical context of the VXVCLS, there is a strong indication that the index could be higher year-over-year on September 20, 2024. Market participants' increased expectations of volatility, as reflected in the VIX Index, support this likelihood. However, final confirmation would require a detailed comparison of current and historical data, as well as ongoing market developments. - -######### - -GPT - -To predict whether the CBOE S&P 500 3-Month Volatility Index (VXVCLS) will be higher Year-over-Year on September 20, 2024, let's break down the evaluation into several key areas, ensuring a thorough assessment. - -### 1. **Understanding the Current Context** - -- **Recent Volatility Trends:** There has been a noted increase in market volatility starting in July 2024. The significant rise in the VIX, which closely correlates with the VXVCLS for shorter time frames, is an important indicator. The market has shifted from a stable period to one of increased uncertainty. -- **Historical Volatility Levels:** It's crucial to compare the VXVCLS from both September 2023 and the current period leading into September 2024. The reported volatility in September 2023 serves as the baseline for this comparison. - -### 2. **Historical and Trend Analysis** - -- **Historical Context:** Generally, periods of economic unrest or market stress lead to higher volatility levels, which reflects investor sentiment and risk perception. If September 2023 was a time of relative calm, the base rate might skew towards a year-over-year increase for 2024 given the current unrest is persistent. -- **Volatility Fluctuations:** Volatility indices like VXVCLS can experience significant day-to-day variability, but they generally respond to underlying market sentiments and geopolitical or economic conditions. - -### 3. **Market Participant Expectations and Expert Opinions** - -- **Market Perception:** The elevated VIX and a potential change in VIX futures term structure might reflect that traders expect sustained or increasing volatility leading into September 2024. -- **Expert Analysis:** The suggestion is that there's an anticipation of a more volatile period in the future. With expert analysis seemingly in alignment with recent data trends, this supports a prediction for a higher index value year-over-year. - -### 4. **Assessment of Potential Novelties or Extremes** - -- **Unexpected Events:** Consideration must be given to potential catalysts, whether economic, geopolitical, or pandemic-related, which could dramatically alter volatility levels. -- **Default Behavior and Base Rates:** Historically, absent new crises or significant shifts, there might be a regression to typical volatility with any peaks smoothing out over time. - -### 5. **Temporal Factors and Resolution Criteria** - -- The current date is August 13, 2024. This allows over a month for significant changes in market conditions. However, the time may be insufficient for a major reduction in heightened volatility levels, assuming no immediate resolution to the current drivers of market stress. - -### Probabilistic Evaluation - -Drawing on these aspects, there's a more pronounced probability for a higher VXVCLS in 2024 compared to the previous year, given the strong trend in increased volatility and limited time for a revert to less volatile norms. However, the inherent unpredictability surrounding market events requires weighting this carefully. - -**Final Probability Judgment:** Taking into account all of the above factors, I would estimate the probability that the VXVCLS will be higher year-over-year on September 20, 2024 as **70%**. This accounts for continued market volatility against historical tendencies for fluctuations in periods without overarching stress." -27222,"On October 1, 2024, will Ukraine have no offensives reaching at least five miles into Russian territory?","PERPLEXITY - -### Context for Forecasting Ukraine's Military Operations on October 1, 2024 - -#### Recent Developments and Current Situation - -1. **Ukrainian Incursion into Kursk Oblast**: - - **August 6, 2024**: Ukrainian forces launched a significant offensive into Russia's Kursk Oblast, marking the largest incursion into Russian territory since World War II. Ukrainian troops advanced several miles beyond the border, seizing settlements and capturing Russian prisoners. - - **Operational Surprise**: The incursion caught Russian forces off guard, highlighting vulnerabilities in Russia's border defenses and leadership structures. - - **Current Status**: Ukrainian forces continue to advance, with reports indicating they are as far as 35 kilometers from the international border with Sumy Oblast. - -2. **Russian Response**: - - **Initial Chaos**: The incursion caused significant shock and chaos within the Kremlin and Russian military leadership. - - **Countermeasures**: Russia has launched counterattacks using missiles, drones, and aerial bombardments to impede Ukrainian progress. - - **Strategic Considerations**: The Kremlin is likely to prioritize retaking the seized territory to maintain Putin's legacy of Russian stability and security. - -3. **Western Support and Ukrainian Capabilities**: - - **Western Aid**: Ukraine has received significant Western military aid, including artillery, advanced rocket systems, and long-range missile systems like ATACMS, which have bolstered Ukraine's defensive capabilities and allowed for counteroffensives within Russia. - - **Mobilization Efforts**: Ukraine's mobilization and coordination of military aid are crucial for sustaining operations and resisting Russian advances. - -4. **Historical Context**: - - **Russian Offensive Campaign**: Since October 2023, Russian forces have been conducting offensive operations in Ukraine, aiming to demoralize Ukraine and the West. Delays in Western aid have given Russia the initiative, allowing them to make tactical gains and expand their operational objectives. - - **Ukrainian Defensive Strategy**: Ukrainian forces have been on the strategic defense since early 2024, with limited ability to launch large-scale counteroffensives until late 2024 or 2025, depending on Western support. - -### Expert Opinions and Predictions - -- **Operational Security**: Ukrainian officials have not commented directly on the ongoing operation in Kursk Oblast, maintaining operational security. -- **Potential Outcomes**: Analysts believe the incursion aims to divert Russian combat power away from Donbas and/or Kharkiv, and to draw attention to less fortified parts of Russia’s border. -- **Russian Vulnerabilities**: The incursion has highlighted Russia’s unclear leadership structures and lack of coordination, which hinder their ability to respond effectively to sudden changes on the battlefield. - -### Current Values and Recent Updates - -- **Ukrainian Advances**: As of August 13, 2024, Ukrainian forces have advanced up to 30 kilometers into Russian territory. -- **Russian Counterattacks**: Russia has admitted that Ukrainian units have driven deep into the country and has launched counterattacks to impede their progress. - -### Forecasting Considerations - -- **Continued Ukrainian Operations**: Given the current momentum and operational surprise achieved by Ukrainian forces, it is likely that Ukraine will continue to maintain some level of offensive operations into Russian territory. -- **Russian Countermeasures**: The effectiveness of Russian counterattacks and the redeployment of Russian forces will be crucial in determining the extent of Ukrainian advances. -- **Western Support**: The ongoing provision of Western military aid will continue to influence Ukraine's ability to sustain and expand its offensive operations. - -### Conclusion - -Based on the recent incursion into Kursk Oblast and the ongoing military dynamics, it is unlikely that Ukraine will have no offensives reaching at least five miles into Russian territory by October 1, 2024. The current momentum and operational surprise achieved by Ukrainian forces, combined with the strategic importance of maintaining pressure on Russian defenses, suggest that Ukraine will continue to engage in offensive operations within Russian territory. - -######### - -GPT - -### Let's Break Down the Analysis - -#### 1. **Understanding the Question and Resolution Criteria** - -The question asks whether Ukraine will have no offensives reaching at least five miles into Russian territory by October 1, 2024. The criteria for resolution involve the presence of Ukrainian forces in Russian territory as reflected in the Institute for the Study of War (ISW) Ukraine Interactive War Map or other credible sources. For Ukraine to have ""no offensives,"" all Ukrainian-held areas that are at least five miles inside Russian borders as of October 1, 2024, must be vacated. - -#### 2. **Current Situation and Initial Conditions** - -- **Recent Offensive**: As of the most recent reports (August 13, 2024), Ukrainian forces have advanced as much as 18 miles (or around 30 km) into Russia's Kursk Oblast, capturing territory and establishing control over several settlements. -- **Operational Momentum**: Ukraine has achieved operational surprise, exploiting weaknesses in Russian border defenses. -- **Strategic Importance**: The incursion appears aimed at drawing Russian resources away from other fronts and pressuring Russian defenses, indicating a strategic goal beyond mere territory acquisition. - -#### 3. **Evaluating Probability Based on Current Trends and Historical Context** - -- **Historical Precedent of Military Campaigns**: Maintaining momentum in an ongoing operation is challenging, particularly when moving into enemy territory. However, when momentum is achieved, as it has been in this case, militaries often strive to maintain positions to leverage them for strategic or tactical gains. - -- **Russian Countermeasures**: The chaotic initial response from Russian forces has given Ukraine time to consolidate gains. However, Russia is likely to mount a significant effort to reclaim this territory given its political and symbolic importance. The history of this conflict, however, shows mixed effectiveness of Russian counteroffensives. While Russian forces could make significant attempts to push back, they might struggle with coordination, logistics, and morale issues, especially due to the surprise and scale of this Ukrainian offensive. - -- **Western Support**: Continued Western support in the form of weapons, intelligence, and training will be crucial for Ukraine. This support likely gives Ukraine a strong chance of sustaining its current positions and potentially even expanding. - -#### 4. **Considering the Time Frame** - -- **Seven Weeks Horizon**: The period from August 14 to October 1 allows for considerable change on the ground. The most volatile periods of warfare are often early in an offensive or during decisive counterattacks. That said, the Ukrainian forces have the upper hand currently and have entrenched themselves to some degree. - -- **Rate of Change**: While territories can change hands rapidly, entrenched positions, especially provided by surprise advances and backed by consistent support (such as from Western allies), are less likely to be vacated as quickly. Notably, the August 2024 incursion saw a notably well-coordinated and significant advance, complicating the possibility of a full Russian retake within the narrow 7-week window. - -#### 5. **Base Rate and Novelty** - -- **Base Rate for Holding Territory**: Historically, once an armed force seizes and fortifies enemy territory, especially significant distances beyond borders, it can take weeks or months for the situation to change fundamentally unless an overwhelming counterattack occurs. - -- **Novelty Factors**: The unprecedented scale of this cross-border incursion into Russian territory, particularly in the context of this current conflict, skews the expectation towards the maintenance of some degree of presence, given the broader strategic implications for Ukraine. - -#### 6. **Default Outcome If Conditions Remain Static** - -Given the current conditions and trajectory, a default assumption would expect Ukrainian forces to hold at least some territory five miles into Russian territory by October 1, 2024. This is due to the entrenched nature of the forces, the recentness of the gains, and the implied strategic elements involved. - -### Final Probability Assessment - -Given the above factors—current Ukrainian advance, historical precedent, the importance of maintaining territorial gains for strategic purposes, the timeframe until October 1, and potential Russian and Ukrainian operations—I estimate that the probability that Ukraine will maintain offensive operations reaching at least five miles into Russian territory by October 1, 2024, is high. - -**Probability that Ukraine will continue to have offensives reaching at least five miles into Russian territory on October 1, 2024**: **85%** - -Thus, the probability of the question resolving to ""Yes"" (indicating Ukraine will *not* have such offensives) is approximately: - -**Probability: 15%**" -27221,"On October 1, 2024, will Ukraine’s offensives reaching at least five miles into Russian territory not include Kursk Oblast, but include at least 1 other oblast?","PERPLEXITY - -To forecast whether Ukraine’s offensives reaching at least five miles into Russian territory will not include Kursk Oblast but include at least one other oblast by October 1, 2024, we need to consider recent military activities, strategic objectives, and the current state of the conflict. - -### Recent Developments -1. **Ukrainian Incursion into Kursk**: - - Ukraine has made significant advances into Russia's Kursk region, capturing 74 settlements and approximately 1,000 square kilometers (386 square miles) of territory. - - This incursion has been the most substantial attack on Russian soil since World War II, causing significant concern and prompting evacuations. - -2. **Russian Response**: - - Russia has ramped up its assaults in eastern Ukraine, including a twofold increase in daily attacks near Pokrovsk in the Donetsk region. - - Russian forces have attempted to counter the Ukrainian incursion, with reports of thwarting an assault near Maryinka. - -3. **Strategic Objectives**: - - Ukraine's offensive is aimed at compelling Russia to consider peace negotiations and to alleviate pressure on Ukrainian front lines. - - Analysts suggest that Ukraine's initiative may have been to force Kremlin forces to defend Kursk and other border territories, thereby diverting troops from Ukraine. - -4. **Military Dynamics**: - - Ukrainian forces have demonstrated the ability to conduct successful localized counterattacks, increasing the costs for Russian forces. - - The Institute for the Study of War noted that Ukrainian advances into Russian territory are challenging the operational and strategic assumptions of Kremlin forces. - -### Expert Opinions -- **Pasi Paroinen**, an analyst at the Finland-based Black Bird Group, suggested that the most challenging phase of Ukraine's incursion is likely to commence as Russian reinforcements join the fray. -- **Ara Massicot** from the Center for a New American Security noted that the Ukrainian advance capitalized on the disorganization among various Russian commands in Kursk. - -### Current Situation -- **Control and Advances**: Ukraine currently controls significant territory in Kursk Oblast, with ongoing efforts to maintain and expand this control. -- **Russian Reinforcements**: Russia has sent reinforcements to the region, claiming to have halted the Ukrainian advance and repelled attacks near villages. - -### Forecasting Considerations -- **Ukrainian Capabilities**: Ukraine's ability to launch powerful and organized counterattacks is contingent on sustained Western military support. -- **Russian Defenses**: The effectiveness of Russian defenses and their ability to respond to future incursions will be crucial in determining the extent of Ukrainian advances. - -### Conclusion -Given the current dynamics and strategic objectives, it is plausible that Ukraine could extend its offensive operations into other Russian oblasts by October 1, 2024. The success of such operations would depend on Ukraine's ability to maintain military pressure and exploit any weaknesses in Russian defenses. The recent incursion into Kursk Oblast has already demonstrated Ukraine's capability to penetrate Russian territory, and future operations could target other regions to further pressure Russia. - -######### - -GPT - -### Step 1: Clearly Understand the Question and Resolution Criteria -The question is asking whether, on October 1, 2024, Ukraine will be holding territory at least five miles into Russian territory that **does not** include Kursk Oblast but **does** include at least one other oblast. - -#### Key Points for Resolution: -- The situation is measured on October 1, 2024. -- Ukraine needs to hold territory that’s 5 miles or more into some Russian oblast other than Kursk, and **not** Kursk itself at that point. -- The assessment is based on the ISW map or similar credible sources. - -### Step 2: Current Situation and Strategic Context -#### Ukrainian Incursion into Kursk: -- Ukraine has made substantial advances into Russia's Kursk Oblast, with 28 settlements under control. -- The advance is a major strategic offensive, indicating Ukraine has the capacity for cross-border operations. - -#### Military Dynamics: -- Ukraine's goals include applying pressure on Russian forces, forcing them onto the defensive. -- The incursions serve to distract Russian troops from frontlines in Ukraine, potentially providing operational advantages. - -#### Russia’s Response: -- Russia has reportedly sent reinforcements and is fighting back to reclaim the lost territory in Kursk Oblast. - -### Step 3: Assess the Likelihood of Continued Ukrainian Operations into Other Oblasts -#### Possible Targets: -- Neighboring regions like Belgorod, Bryansk, and Voronezh could potentially be next for Ukrainian operations. These regions are geographically close to the current area of operation. -- Ukraine could be incentivized to repeat its success in Kursk by launching similar operations in these bordering oblasts to maintain pressure on Russia. - -#### Timeline & Variability: -- We have about a month and a half until the resolution date. Within this time, significant changes could occur on the battlefield. -- Recent military operations have shown a high degree of mobility and success on the Ukrainian side, but this trend could stall if Russia consolidates its defenses or if Ukraine redirects its efforts. -- For Ukraine to abandon Kursk, they would likely have to face increasing Russian defense pressure or deem it strategically less valuable. - -### Step 4: Evaluate Base Rates and Historical Precedent -- Cross-border incursions are historically rare but have occurred in this conflict (e.g., Belgorod raids). -- Ukraine’s strategy seems to involve making aggressive (but possibly temporary) thrusts into Russian territory to create political and military pressure. -- Abandoning territory after an advance, moving to another region, and establishing another incursion is a repeated tactical pattern seen in some conflicts. - -### Step 5: Consider Novel Factors -- **Western Support:** Sustaining or increasing Western military support may enable Ukraine to continue or expand operations in other oblasts. -- **Russian Defensive Agility:** Russia’s ability to quickly deploy reinforcements and regain lost territory, especially in these border regions, could be a significant factor. - -### Step 6: Weighing the Defaults and Scenario Outcomes -1. **Base Scenario (0%):** - - Ukraine retains its hold in Kursk but does not advance into any other Russian oblast at least 5 miles. - -2. **Low-Probability Scenario (10%-25%):** - - Ukraine holds territory in Kursk and another oblast by October 1, 2024, but Kursk is still included. This scenario does not satisfy the condition. - -3. **Medium-Probability Scenario (25%-50%):** - - Ukraine abandons Kursk in favor of a continued offensive in another oblast, holding ground by the resolution date. This could result from either military overstretch by Russia or a strategic repositioning by Ukraine. - -4. **High-Probability Scenario (50%-75%):** - - Ukraine decides to both maintain the Kursk initiative and extend incursions into other oblasts, which then get abandoned prior to Oct 1. This is less likely due to the challenge of concurrently managing deep forays into multiple regions. - -### Final Probability Assessment -After carefully considering the timeline, battlefield dynamics, and the possible scenarios, it seems moderately likely that Ukraine could shift its focus from Kursk to another oblast and secure territory there by October 1, 2024. - -Given the rapid and dynamic nature of frontline changes and the time left until October 1st, the probability aligns more significantly with the possibility that Ukraine will advance into another oblast and potentially abandon Kursk within this timeframe due to strategic choices or battlefield pressures. - -**Probability: 40%**" -27220,"On October 1, 2024, will Ukraine’s offensives reaching at least five miles into Russian territory include Kursk Oblast and at least 1 other oblast?","PERPLEXITY - -To forecast whether Ukraine's offensives will reach at least five miles into Russian territory, including Kursk Oblast and at least one other oblast by October 1, 2024, we need to consider the current military situation, recent developments, and expert opinions. - -### Current Military Situation -- **Ukrainian Advances**: Ukrainian forces have made significant advances into Russia's Kursk region, capturing about 1,000 square kilometers of territory and controlling 74 settlements. They have also taken more than 100 Russian soldiers prisoner and destroyed a Russian Su-34 jet bomber. -- **Russian Response**: Russia has declared emergencies in both Kursk and Belgorod regions due to heavy Ukrainian shelling. Russian forces are attempting to react to the unexpected offensive, but their response has been criticized for being inadequate. -- **Troop Shortage**: Russia is facing a deepening shortage of soldiers, prompting increased recruitment bonuses. However, these efforts have shown little success, and regional officials are failing to meet recruitment quotas. - -### Recent Developments -- **Ukrainian Strategy**: Ukraine's offensive is aimed at stopping Russia from firing missiles into Ukraine from Kursk. There is no intention to occupy Russian territory permanently. -- **Civilian Impact**: The attacks have caused significant civilian casualties and displacement, with over 76,000 individuals evacuating their homes in Kursk. The situation remains tense, with children being moved to safety. -- **International Reaction**: The U.S. President Joe Biden has noted the dilemma this situation poses for Russian President Vladimir Putin, while the Institute for the Study of War suggests that Russia will likely continue deploying limited forces to Kursk due to higher priority operations in Donetsk. - -### Expert Opinions -- **Military Analysts**: The Institute for the Study of War believes the incursion is unlikely to shift the dynamics of the war significantly but will force Russia to allocate additional resources along the border. -- **Pasi Paroinen**: An analyst from the Black Bird Group suggests that the most challenging phase of Ukraine's incursion is likely to commence as Russian reinforcements join the fray, making further advances difficult. - -### Prediction Markets -- **Current Predictions**: As of the latest updates, there are no specific prediction markets available that forecast the exact extent of Ukraine's advances by October 1, 2024. However, the ongoing offensive and Russia's response suggest a high level of uncertainty and potential for further escalation. - -### Conclusion -Given the current pace of Ukrainian advances and the challenges Russia faces in responding effectively, it is plausible that Ukraine could extend its offensive to at least five miles into Russian territory, including Kursk Oblast and potentially another oblast by October 1, 2024. However, the success of such an offensive would depend on various factors, including the effectiveness of Russian reinforcements and the strategic decisions made by both sides in the coming weeks. - -######### - -GPT - -### Step 1: Understanding the Question and Resolution Criteria - -The question is asking if, by October 1, 2024, Ukraine's offensives will have reached at least five miles into Russian territory, including Kursk Oblast and at least one other Russian oblast, with the situation verified using the ISW (Institute for the Study of War) map. - -### Step 2: Structural Breakdown - -To answer this question, we need to evaluate: -1. **Current territorial control:** Acknowledge Ukraine's current successes in Kursk Oblast. -2. **Potential for further advances:** Evaluate the likelihood of Ukraine extending its operations to other Russian oblasts. -3. **Russian response and logistics:** Assess Russia's ability to reinforce border regions and counter or limit Ukrainian advances. -4. **Timeframe:** The deadline is set for October 1, 2024, giving us a window of approximately six weeks. -5. **Historical precedents:** Examine previous instances of cross-border wars and campaigns that experienced rapid territorial changes within a similar timeframe. - -### Step 3: Evaluating the Current Situation (August 14, 2024) - -- **Ukrainian Offensive Successes:** - - Ukraine has made significant advances into Kursk, controlling a substantial portion of territory (~1,000 square kilometers), including important settlements, and has managed to push around 18 miles into Kursk Oblast. Troops have also entrenched in those positions. - - The goal, per current reports, is not outright occupation but to prevent missile and artillery attacks originating from Russian territory. - -- **Russian Response:** - - Russia's military is under strain, dealing with personnel shortages and facing multiple fronts, which may diminish their ability to retake lost ground or effectively reinforce the Kursk region despite the emergency declaration. - - That said, Russia has significant reserves and may prioritize defending its territory within its borders more aggressively. - -- **Strategic Implications of Advances:** - - If Ukraine seeks to suppress missile attacks from Russian territory, it could naturally extend operations into Belgorod Oblast, bordering Kursk, which has already seen significant skirmishes. - - Ukraine might also strike further into other oblasts if they become launching points for Russian attacks or to take critical military or logistical targets. - -### Step 4: Timeline and Variance Analysis - -- **Timeframe (August 14 to October 1, 2024):** - - Approximately 6-7 weeks remain for this scenario to play out. - - Daily or weekly variance in war operations can be high, especially in cross-border incursions where fluid frontlines and rapid advances are common. - - However, Ukraine also needs time to solidify and hold new positions before extending further. Aggressive advances can be constrained by logistics, resources, and the need to maintain momentum. - -- **Historical Base Rates:** - - In the 20th and 21st centuries, successful cross-border incursions meeting the territorial milestones in a similar timeframe have varied depending on military capabilities, surprise factors, and geopolitical pressure. - - In Ukraine's case, each mile could be significantly harder to gain due to stiffening Russian resistance as they mobilize local defenses. - -### Step 5: Synthesizing Data and Arriving at Key Probabilities - -**Territorial Possession in Kursk Oblast:** -- Current Ukrainian success suggests that holding Kursk territory at least five miles within the Russian border by October 1 is highly plausible. Russia's response has been muted and defensive, suggesting Ukraine could consolidate its gains, keeping them into October. -- Probability: ***~85%*** (Ukraine will hold territory at least five miles within Kursk Oblast by October 1, 2024). - -**Expansion into Another Oblast:** -- Belgorod Oblast adjacent to Kursk is the most probable candidate for further incursion due to its proximity and ongoing shelling. Given the focused Ukrainian strategy on disrupting missile and artillery launches, a significant advance into Belgorod or another Russian oblast by at least five miles by October 1 is feasible but depends on the distribution of resources, Russian responses, and overall war dynamics. -- Probability: ***~60%*** (Ukraine will hold territory at least five miles within another oblast by October 1, 2024). - -**Overall Assessment:** -- Integrating both elements (Kursk and another neighboring oblast), the combined probability that Ukraine will hold at least five miles of territory in Kursk and one other oblast by October 1, 2024, is calculated as: - -\[ P(Kursk and Another Obast) = P(Kursk) \times P(Another Oblast) \] - -Using the above probabilities, the final estimate becomes: - -\[ P(overall) = 0.85 \times 0.60 = 0.51 \] - -Given the uncertainties and the inherent volatility in the scenario, I would round this to about: - -**Probability: 55%** - -This accounts for the likelihood of advances being challenged or slowed, as well as the broader strategic environment. Hence, Ukraine advancing this far into Russian territory across two oblasts is slightly more likely than not, but by a narrow margin." -27219,"On October 1, 2024, will Ukraine’s offensives reaching at least five miles into Russian territory include Kursk Oblast and no other oblasts?","PERPLEXITY - -### Context for Forecasting Ukraine's Offensives into Russian Territory - -#### Current Situation: -- **Ukrainian Incursion into Kursk Oblast**: Ukraine launched a significant cross-border incursion into Russia's Kursk Oblast on August 6, 2024. This marks the largest foreign incursion into Russian territory since World War II. -- **Control and Advances**: Ukrainian forces now control approximately 1,000 square kilometers (386 square miles) of Russian territory, with some sources indicating they have advanced up to 40 kilometers (25 miles) into Kursk Oblast. -- **Russian Response**: Russia has relocated troops from southern Ukraine to Kursk to combat the incursion. Moscow has also claimed to have halted the Ukrainian advance and repelled attacks near villages in the region. -- **Ukrainian Objectives**: Ukrainian President Volodymyr Zelensky stated that the offensive aims to force Russia into peace negotiations, not to annex Russian land. - -#### Recent Updates: -- **Emergency Declarations**: The governor of Belgorod has declared a regional emergency due to the ongoing bombardment by Ukrainian forces, with plans to appeal for a nationwide emergency declaration. -- **US Support**: The Biden Administration has expressed support for Ukraine's defensive actions, though they were not informed prior to the operation. -- **Military Dynamics**: Analysts suggest that the most challenging phase of Ukraine's incursion is likely to commence as Russian reinforcements join the fray. - -#### Expert Opinions: -- **Russian Military Preparedness**: Retired General Andrei Gurulev criticized Russia's military for not effectively safeguarding the border, highlighting insufficient troop deployment and intelligence capabilities. -- **Strategic Implications**: The Institute for the Study of War noted that Ukraine's advances into Russian territory are challenging the operational and strategic assumptions of Kremlin forces, potentially forcing Russia to allocate additional military resources along the border. - -#### Prediction Markets and Betting Odds: -- **Current Predictions**: As of August 14, 2024, there are no specific prediction markets or betting odds available that directly address the question of whether Ukraine's offensives will reach at least five miles into Russian territory and include Kursk Oblast exclusively. - -### Conclusion: -The current situation indicates that Ukraine's offensive into Kursk Oblast is ongoing, with significant advances and control over Russian territory. The Russian response has been to relocate troops and declare emergency protocols. The objectives of the Ukrainian offensive are to compel Russia into peace negotiations rather than to annex land. Expert opinions highlight the strategic challenges faced by Russian forces and the potential for increased military resource allocation along the border. Given the dynamic nature of the conflict, the likelihood of Ukraine's offensives reaching at least five miles into Russian territory and including Kursk Oblast exclusively by October 1, 2024, will depend on the effectiveness of both Ukrainian and Russian military strategies and responses in the coming weeks. - -######### - -GPT - -### Step 1: Understanding the Question and Resolution Criteria - -The question asks if, by October 1, 2024, Ukraine's offensives reaching at least five miles into Russian territory will include Kursk Oblast and no other oblasts. - -This is a binary outcome (""yes"" or ""no"") where the resolution is dependent on: -1. **Ukrainian forces holding territory at least five miles within Russian borders:** in particular, Kursk Oblast. -2. **The absence of any other Russian oblast involving Ukrainian incursions that reach 5+ miles into Russian territory.** - -### Step 2: Evaluate Current Information and Trends - -- **State of Ukrainian Incursion in Kursk Oblast (as of August 14, 2024):** - - Ukrainian advances up to 30 km (around 18.6 miles) into Kursk with control over ~1,000 square kilometers. - - Russia has diverted resources to resist the incursion, though reports indicate Ukraine is still holding significant territory. - - Strong public statements from Ukraine's leadership suggest that their goal isn't to annex Russian land but to build bargaining leverage. - -- **Potential for Other Offensives:** - - There’s no current evidence or significant indications of Ukrainian forces making equivalent incursions into other Russian oblasts, though neighboring Belgorod is seeing bombardment. - - Ukrainian strategies might still be unpredictable, but launching similar large-scale incursions into other oblasts would stretch their resources even more, which might not be feasible given the scale of current military activities. - -### Step 3: Historical Context and Base-Rate Analysis - -- **Base Rates**: It’s unprecedented in modern Russian-Ukrainian conflict history for Ukraine to engage in full-scale offensives into Russian territory, especially into multiple oblasts simultaneously. This novelty reduces the base rate of predicting such events based on historical patterns. - -- **Strategic Implications**: Given the significance of the current Kursk incursion, it is reasonable to assume that Ukraine might focus more on consolidating gains there rather than opening new fronts significant enough to cross the 5-mile incursion threshold in other oblasts. - -### Step 4: Future Projections and Probabilistic Reasoning - -- **Time Frame Considerations**: There are about six weeks until October 1, 2024, leaving sufficient time for changes in the dynamics on the ground. - - However, given that major offensives require planning, logistics, and resources (which are currently focused on Kursk), it is less likely that Ukraine will have the capability to conduct another significant, deep incursion into additional Russian territories within this timeframe. - -- **Unpredictability Factor**: Conflicts can change rapidly, and although current trends suggest focus on Kursk, surprising shifts could occur (e.g., a sudden Ukrainian offensive in Belgorod). However, the limited time available reduces the probability of major new cross-border operations of equivalent magnitude. - -### Step 5: Conclusion - -Given the factors evaluated: -- Current trends support the notion that Ukraine will focus its resources and strategic aims in Kursk Oblast. The absence of evidence suggesting another deep incursion elsewhere by October 1, 2024, underpins a significant bias towards the ""yes"" outcome. -- Though unforeseen events could introduce surprises, the probability of additional incursions crossing the 5-mile mark elsewhere is relatively low. - -Final Decision: -* **Probability: 85%** that Ukraine’s offensives reaching at least five miles into Russian territory will include Kursk Oblast and no other oblasts by October 1, 2024.*" -27223,"Will Ukrainian forces capture Rylsk, Russia before October 1, 2024?","PERPLEXITY - -To assess whether Ukrainian forces will capture Rylsk, Russia before October 1, 2024, we need to consider recent military activities, strategic assessments, and expert opinions. - -### Recent Military Activities -- **Ukrainian Incursion into Kursk**: Ukrainian forces recently conducted a significant incursion into Russia's Kursk region, capturing approximately 1,000 square kilometers of territory and several settlements. This operation has been described as the largest attack on Russian soil since the invasion began in 2022. -- **Russian Response**: Russia has responded by deploying additional military assets, including Grad multiple-launch rocket systems, artillery, and tanks, to the Kursk region. This indicates a heightened level of military engagement in the area. - -### Strategic Assessments -- **Ukrainian Counterattacks**: Ukrainian forces have been conducting localized counterattacks, particularly in northern Kharkiv Oblast and towards Kreminna. These counterattacks have forced Russian troops to redeploy, indicating that the Ukrainian military is actively contesting the battlefield initiative. -- **Western Aid**: The arrival of Western-provided military aid has allowed Ukrainian forces to stabilize critical areas of the front and conduct successful localized counterattacks. However, the scale and regularity of these counterattacks are expected to increase as more aid arrives. - -### Expert Opinions -- **General Sir Richard Barrons**: The former commander of the UK's Joint Forces Command has warned that Ukraine could face defeat in 2024 due to critical shortages in ammunition, troops, and air defenses. He anticipates a major Russian offensive this summer, which could break through Ukrainian lines. -- **Mick Ryan**: A retired Australian general, Mick Ryan, suggests that Ukrainian forces will have three options following their incursion into Kursk: maintaining control, withdrawing, or expanding the offensive. The choice will depend on the deployment of more capable Russian forces and the sustainability of Ukrainian supply chains. - -### Conclusion -Given the recent incursion into Kursk and the ongoing localized counterattacks by Ukrainian forces, there is a possibility that Ukrainian forces could attempt to capture Rylsk, Russia. However, the success of such an operation would depend on the sustainability of their offensive, the effectiveness of Russian countermeasures, and the continued support from Western military aid. The current momentum suggests that Ukrainian forces are capable of making significant gains, but the long-term strategic advantage remains uncertain. - -### Key Points: -- **Current Control**: Ukrainian forces have captured significant territory in the Kursk region. -- **Russian Response**: Russia is deploying additional military assets to counter the incursion. -- **Western Aid**: Continued support from Western military aid is crucial for Ukrainian operations. -- **Expert Opinions**: There are mixed assessments regarding the likelihood of Ukrainian success, with some experts warning of potential Russian breakthroughs. - -### Prediction Markets -As of the current date, there are no specific prediction markets available for the capture of Rylsk, Russia. However, general forecasts on the conflict suggest a high level of uncertainty and ongoing military engagements. - -### Recent Updates -- **August 10, 2024**: Russia dispatched military reinforcements to confront Ukrainian forces in the Kursk region. -- **August 12, 2024**: General Oleksandr Syrsky, the commander-in-chief of Ukraine, asserted that Ukrainian troops had gained control over approximately 1,000 square kilometers of Russian land. - -These updates indicate a dynamic and evolving situation on the battlefield, with both sides engaged in significant military activities. The capture of Rylsk, Russia, before October 1, 2024, remains a possibility but is contingent on various factors, including the effectiveness of Ukrainian operations and the Russian response. - -######### - -GPT - -### Step 1: Establish the Baseline Probability -To begin with, it's essential to understand the base rate of similar events—the capture of towns or small cities in wartime—in this particular conflict or historically. Considering the Ukrainian-Russian conflict, urban captures (like Lyman or Izyum in Ukraine) by either side have taken several weeks to a few months, dependent on several factors including the size of the town, defensive preparations, and logistical capacities. - -Urban environments complicate engagements, increasing the difficulty of rapid advances. And since Rylsk is relatively small, with a population of around 15,000, it’s on the scale of a small town rather than a fortified city. This would generally suggest a base rate where—with optimal conditions—it could be captured by a determined attacking force within a couple of weeks if opposition is weak. - -Given the current date (August 14, 2024), there are approximately 1.5 months (around 47 days) until October 1, 2024. This interval provides adequate time for strategic shifts. - -### Step 2: Evaluate Key Factors -#### 1. **Ukrainian Offense and Momentum** -Ukrainian forces are currently on the offensive with rapid advances into Russian territory, capturing 1,000 square kilometers in about a week. The rapidity of this advance thus far indicates good momentum. However, recent history also shows that initial rapid advances into enemy territory often slow down as the offensive extends and supply lines are stretched. Russia has already started reinforcing the Kursk region, which points to a likely counter-offensive or a stronger defensive posture. - -#### 2. **Russian Reinforcements and Strategic Response** -Russia’s deployment of additional military assets (including Grads, artillery, and tanks) into the Kursk region is a significant consideration. Historically, Russian military doctrine emphasizes heavy use of artillery and defensive depth, and the deployment of additional assets is a concerning factor that could blunt Ukrainian advances. Additionally, the ongoing evacuation of civilians from Rylsk suggests that Russia is preparing for potential combat in or near the town. - -Considering these factors, the Russian military response is likely to slow and possibly halt the Ukrainian advance before it could reach Rylsk, even if Ukrainian forces maintain some momentum. - -#### 3. **Western Support** -The Ukrainian ability to sustain and advance their current offensive is heavily dependent on continued Western support, particularly in terms of artillery ammunition, air defenses, and other critical supplies. While this support has been reliable, there may be logistical bottlenecks, and success in sustaining current operations at the same pace for the next 1.5 months is uncertain. - -#### 4. **Expert Opinions** -The expert opinions highlight the uncertainty: While Ukrainian forces have been capable of localized counteroffensives and launched an impressive incursion, they face significant risks, including stretched supply lines and Russian redeployments. This aligns with the possibility of diminishing Ukrainian momentum as they encounter reinforced Russian defenses. - -### Step 3: Consider the Specific Context of Rylsk -Rylsk, as an administrative center, would be an important capture for Ukrainian forces. Still, it is positioned somewhat deeper behind the current Ukrainian front line. The assumption that Ukraine would pursue its advance in that direction is reasonable but not guaranteed. The attack's goal and whether advancing further into Russian territory or consolidating and holding the current gains is preferred would significantly affect this scenario. - -Given that Rylsk is being evacuated by Russian authorities, the town’s potential capture is recognized as a possibility, but the defensive preparations being made also indicate that Russia would focus efforts to defend it. - -### Step 4: Assessing the Likelihood -Given the short timeframe (1.5 months) and the involvement of heavily reinforced Russian defensive efforts, the likelihood of Ukrainian forces managing to maintain an offensive sufficient to face Russian countermeasures and capture Rylsk before October 1, 2024, seems low but not entirely out of the question. The momentum is on the Ukrainian side, but they face significant operational challenges ahead. - -### Step 5: Adjusting for Uncertainties and Unknowns -There are significant uncertainties, especially considering factors like Ukrainian strategy changes, unforeseen breakdowns, or Russian counteroffensives failing catastrophically. These factors, while unlikely to lead to quick changes in control over Rylsk, could influence the situation. - -### Conclusion -Considering the factors above—the momentum of Ukrainian forces, the defensive response from Russia, the logistical challenges, and the time available for these dynamics to evolve—there is indeed a non-zero chance that Rylsk could fall to Ukrainian forces by October 1, 2024. However, the various challenges and the increased Russian defensive posture lower the probability significantly. - -**Probability: 20%**" -27200,"Will the CDC confirm a case of Clade I mpox in a US resident before August 23, 2024?","PERPLEXITY - -### Context and Relevant Information - -#### Disease Spread and Current Situation -- **Clade I Mpox Outbreak**: There is an ongoing outbreak of clade I mpox in the Democratic Republic of the Congo (DRC), with 7,281 confirmed or suspected cases reported as of June 2, 2024. The virus is more transmissible and causes more severe disease compared to clade II mpox. -- **Spread to Neighboring Countries**: Clade I mpox cases have been confirmed in neighboring countries, including the Central African Republic (CAR), Republic of the Congo (ROC), Rwanda, Uganda, and Burundi. -- **Risk Assessment in the U.S.**: The CDC assesses the likelihood of clade I mpox infection in the U.S. as low to moderate, particularly among men who have sex with men (MSM). There are no known cases of clade I mpox in the U.S., but the virus could potentially spread through infected travelers. - -#### Public Health Measures -- **Vaccination Efforts**: A vaccine effective against both clade I and clade II mpox is available, but it is not yet widely available in the DRC. The country is actively working on a vaccination plan. -- **Testing and Surveillance**: The CDC has robust testing capacity, including clade-specific testing, and is monitoring wastewater samples for early detection of mpox activity. - -#### Expert Opinions and Predictions -- **WHO Considerations**: The World Health Organization (WHO) is considering declaring a public health emergency of international concern due to the increased spread of mpox in Africa. -- **Prediction Markets**: On Metaculus, a forecasting platform, the question of whether the CDC will confirm a case of clade I mpox in a U.S. resident before August 23, 2024, is being tracked. The current prediction is based on user inputs and may reflect the perceived likelihood of such an event occurring. - -#### Recent Updates -- **CDC Alerts**: The CDC has issued health alerts and advisories to maintain a heightened index of suspicion for mpox in patients who have recently traveled to the DRC or its neighboring countries. -- **Travel Notices**: The CDC has issued travel health notices for several countries, including those with ongoing outbreaks of other diseases like chikungunya, polio, and dengue. - -### Summary -The likelihood of the CDC confirming a case of clade I mpox in a U.S. resident before August 23, 2024, is considered low to moderate. The ongoing outbreak in the DRC and neighboring countries, combined with the potential for spread through travelers, suggests a risk, albeit low. Public health measures, including vaccination efforts and enhanced surveillance, are in place to mitigate this risk. - -######### - -GPT - -### Context and Analysis - -#### 1. **Current Situation Overview:** - - **Outbreak Status:** There is an ongoing outbreak of Clade I mpox in the Democratic Republic of the Congo (DRC) with 7,281 cases as of June 2, 2024. This strain is more severe than Clade II with a higher fatality rate (4%). - - **Geographic Spread:** Clade I cases have spread to neighboring countries, including CAR, ROC, Rwanda, Uganda, and Burundi. However, no cases have been confirmed outside Central and Eastern Africa. - - **U.S. Risk Assessment:** The CDC assesses the likelihood of Clade I reaching the U.S. as low to moderate, particularly within the MSM community. Despite ongoing close surveillance, no cases have been detected in the U.S. yet. - -#### 2. **Public Health Measures:** - - **Preparedness and Testing:** The CDC has robust systems in place for detecting Clade I mpox, including wastewater surveillance and clade-specific testing. Alerts have been issued to healthcare providers to remain vigilant, particularly for travelers from affected regions. - - **Preventative Actions:** A vaccine effective against Clade I is available, although distribution in affected African regions is still ramping up. This could help mitigate the spread, but it may not prevent an initial case from reaching the U.S. via travel. - -#### 3. **Expert Opinions and Predictive Indicators:** - - **WHO's Potential Declaration:** The WHO is considering declaring a public health emergency of international concern (PHEIC), which underscores the seriousness of the situation but does not directly indicate imminent spread to the U.S. - - **Prediction Market (Metaculus):** The market's current assessment provides some insight but is not a decisive factor by itself. It reflects a range of informed and uninformed opinions. - -#### 4. **Timeframe for Resolution:** - - **Critical Evaluation:** The window from August 14, 2024, to August 23, 2024, is very narrow. For a case to be confirmed in the U.S., several events must occur: - 1. **Infection**: A traveler would need to contract the virus from an affected region. - 2. **Travel**: The individual must travel to the U.S. - 3. **Detection**: Symptoms must develop, leading to healthcare engagement. - 4. **Confirmation**: Testing and confirmation by the CDC must be completed and publicly announced by August 23, 11:59 PM EDT. - - Given that no cases have been reported in regions commonly associated with high travel volume to the U.S., the default expectation is no cases will be detected in this short timeframe. - -#### 5. **Base Rate Consideration:** - - The base rate of an infectious disease spreading from endemic areas in Central/Eastern Africa to the U.S. within an approximately 9-day period (from August 14 to August 23) should typically be low, especially if no significant cases or transmission chains are reported in regions with strong travel links to the U.S. - - Historically, diseases with similar vectors and transmission dynamics (e.g., Ebola, previous Clade II mpox) have shown limited short-term spread to the U.S., especially with vigilant public health monitoring. - -#### 6. **Default Resolution Likelihood:** - - The current state, without drastic changes, would lead to a ""No"" resolution. A positive outcome (""Yes"") requires a significant deviation from current trends (e.g., sudden influx of travelers from affected regions without strict screening). - -### Conclusion: - -Considering the present information: -- The outbreak is geographically contained. -- The Clade I strain has not yet penetrated regions with significant travel to the U.S. -- There are only a few days left until the resolution date, limiting the likelihood of detection and confirmation of a case within the U.S. by August 23, 2024. -- Public health surveillance and vaccine efforts further reduce the probability, though they do not eliminate it entirely. - -### Final Probability Estimate: - -**Probability: 10%** - -This low probability accounts for the remote chance that a traveler could bring Clade I mpox to the U.S., develop symptoms, and be confirmed within the remaining timeframe. However, the base scenario highly favors no confirmed cases occurring by the specified date." -27160,"Will diplomatic expulsions or recalls take place between the Philippines and China before September 30, 2024?","PERPLEXITY - -### Context for Forecasting Diplomatic Expulsions or Recalls Between the Philippines and China - -#### Recent Developments: -- **Diplomatic Tensions**: The Philippines' national security adviser has called for the expulsion of Chinese diplomats over an alleged leak of a phone conversation with a Filipino military official, which has significantly escalated tensions in the South China Sea dispute. -- **Incidents at Sea**: China has harassed and shot water cannons at a Filipino humanitarian mission headed to Scarborough Shoal, a key flashpoint in the region. -- **Talks and Agreements**: Despite these tensions, Chinese and Philippine diplomats have agreed to ease South China Sea tensions but remain firm on their respective claims during recent talks. - -#### Historical Context: -- **US-China Relations**: The Biden administration has seen escalating trade tensions and sanctions on Chinese technology companies. Recent meetings between US and Chinese defense ministers have reaffirmed commitments to maintain open lines of military communication and address global security issues, including stability across the Taiwan Strait. -- **Philippines' Dilemma**: The Philippines faces strategic competition with China in the South China Sea, with ongoing maritime disputes and diplomatic challenges. - -#### Expert Opinions: -- **Philippines' National Security Adviser**: Advocates for the expulsion of Chinese diplomats to address alleged disinformation and violations of Philippine laws. -- **Chinese Foreign Ministry**: Views the Philippine adviser's comments as provocative and emphasizes the importance of allowing Chinese diplomats to carry out their duties without interference. - -#### Prediction Markets: -- **Current Predictions**: There are no specific prediction markets available for this event. However, the ongoing tensions and recent incidents suggest a high likelihood of diplomatic actions such as expulsions or recalls. - -### Conclusion: -Given the recent escalation of tensions, including the alleged leak of a phone conversation and incidents at sea, there is a strong likelihood of diplomatic expulsions or recalls between the Philippines and China before September 30, 2024. The Philippines' call for expelling Chinese diplomats and China's response indicate a heightened level of conflict that may lead to such actions. - -######### - -GPT - -### Step 1: Understand the Background and Context - -**Diplomatic Tensions**: The key driver for the possibility of diplomatic expulsions or recalls between the Philippines and China is the escalating tension in the South China Sea, particularly surrounding Scarborough Shoal. The conflict has involved multiple physical confrontations at sea, including China’s aggressive actions (water cannons, ramming tactics) and alleged disinformation. - -**Recent Developments**: The leak of a sensitive phone conversation involving a Philippine admiral has worsened relations, leading to the Philippine National Security Adviser publicly advocating for the expulsion of Chinese diplomats. China, unsurprisingly, views this as a provocation and has taken the stance that the calls are baseless. - -**Past Trends**: Diplomatic expulsions or recalls are relatively rare but can occur during periods of heightened tension, especially when national dignity or sovereignty is perceived to be at stake. The Philippines has had a history of diplomatic tensions with China, especially over maritime disputes, yet these have rarely escalated to diplomatic expulsions. - -### Step 2: Assess Base Rates - -**Base Rate of Diplomatic Expulsions**: Historically, diplomatic expulsions or recalls between the Philippines and China have been very rare. Even during periods of significant maritime disputes, both nations have generally avoided such drastic diplomatic actions, opting instead for less confrontational measures like protests, diplomatic notes, or non-traditional warfare at sea. - -**Current Events**: However, the current situation has elements of novelty—specifically, the leak of a sensitive conversation and the public advocacy for expulsions by a high-ranking official in the Philippines. While such statements might be intended to signal national resolve without necessarily leading to action, the combination of naval confrontations and disinformation may result in a departure from the historical norm. - -### Step 3: Timeframe and Recurrence - -**Time Remaining**: As of the date of this evaluation (August 14, 2024), there is just over a month and a half until September 30, 2024. While time is short, it is not uncommon for diplomatic expulsions to be triggered by recent confrontational events, especially in the context of ongoing disputes and public calls for action. However, avoiding escalation is also a common diplomatic outcome. - -### Step 4: Weighing the Factors - -**Likelihood of Escalation**: -- **In Favor**: The incidents of the phone leak and increasingly aggressive encounters at sea are inflaming tensions. Public calls for expulsion by a high-ranking Filipino official add to this pressure. -- **Against**: Both countries have strategic interests in avoiding further escalation. China often prefers to keep diplomatic avenues open to avoid wider international backlash, and the Philippines, despite public rhetoric, may hesitate to take a step as drastic as expelling diplomats, knowing this could severely damage bilateral relations. - -**Other Considerations**: -- **Asian and US Diplomacy**: The United States is a key ally of the Philippines and is currently in a complex relationship with China. A Philippine diplomatic expulsion could complicate US-China-Philippines relations further. However, the US may support or even encourage such a move if it sees it as aligning with its strategic interests in containing China's influence in the region. - -### Step 5: Integrate All Data - -Given the above considerations: - -1. Diplomatic expulsions and recalls have been rare between the Philippines and China. -2. However, current tensions are particularly high, with public and governmental advocacy for such actions. -3. The short timeframe left before the resolution date limits the time window but the situation seems to be on a trajectory that could lead to escalated diplomatic actions. -4. Both nations have strong incentives to manage tensions carefully, yet the incident-driven nature of such expulsions means a sudden escalation cannot be ruled out. - -### Final Probability Estimate - -While the historical base rate would suggest a low likelihood of diplomatic expulsions or recalls, the current geopolitical environment, the time-sensitive nature of recent escalations, and public statements promoting diplomatic action make such an event more probable than in a typical scenario. - -**Probability: 55%** - -This reflects a slightly better-than-even chance of diplomatic expulsions or recalls occurring before the deadline but also acknowledges the significant possibility that both sides will opt for restraint to avoid further escalation." -27161,Will Bo Nix be the starting quarterback for the Denver Broncos in their Week 4 game against the New York Jets?,"PERPLEXITY - -Bo Nix is increasingly likely to become the starting quarterback for the Denver Broncos, especially after his impressive performance in the preseason opener against the Indianapolis Colts. Here are the key points that support this likelihood: - -1. **Preseason Performance**: - - **Bo Nix**: Nix had a standout performance, leading the Broncos to a 34-30 victory. He shared reps with Jarrett Stidham and Zach Wilson but outshone them, particularly after Stidham's early struggles. Nix completed 15-of-21 passes for 131 yards and a touchdown, also showcasing his mobility with three carries for 16 yards. - - **Jarrett Stidham**: Stidham started the game but had a difficult outing, ending his first drive with a punt and his second with an interception. - - **Zach Wilson**: Wilson performed well in the third quarter, completing 10 of his 13 throws for 117 yards, but Nix's overall performance was more impactful. - -2. **Expert Opinions**: - - **Dan Orlovsky**: The former NFL star and current pundit praised Nix's command of the field, endorsing the comment ""Obvious QB1"" on a post highlighting Nix's game figures. - - **Sean Payton**: The Broncos' head coach described the quarterback room as 'orphaned dogs,' indicating a competitive environment where Nix has made a strong case for himself. - -3. **Comparative Analysis with Other Rookie QBs**: - - Nix's performance was favorably compared to other first-round quarterbacks in the 2024 NFL Draft. While Caleb Williams and Jayden Daniels also had notable debuts, Nix's ability to lead scoring drives and his overall stats were particularly impressive. - -4. **Future Outlook**: - - Nix's strong debut has positioned him favorably for the starting role. The Broncos' upcoming preseason game against the Green Bay Packers on August 18 will be another opportunity for Nix to solidify his position. - -Given these points, it appears that Bo Nix has a strong chance of becoming the starting quarterback for the Denver Broncos in their Week 4 game against the New York Jets. - -######### - -GPT - -### Analysis and Thought Process - -#### 1. **Understanding the Scenario** -This is a **Competition Event** type question about whether Bo Nix will be the Denver Broncos' starting quarterback for their Week 4 game against the New York Jets on September 29, 2024. Today's date is August 14, 2024, which means we are forecasting an event that will occur roughly 6 weeks from now. Given the resolution criteria, the question hinges on whether Nix is the starting quarterback at the very beginning of the game. - -#### 2. **Key Points from the Background Information** - -- **Preseason Performance**: Bo Nix exhibited an impressive performance in the preseason opener against the Indianapolis Colts, outperforming his competitors, Jarrett Stidham and Zach Wilson. This suggests he has momentum and potential to win the starting job. - -- **Expert Opinions**: Notable endorsements from Dan Orlovsky and positive comments from head coach Sean Payton signal that Nix is being seriously considered for the starting role. - -- **Comparative Analysis**: Nix has been compared favorably to other rookie quarterbacks in his class, which is a positive sign but doesn't directly indicate he is a lock for the starting job. - -#### 3. **Base Rates and Historical Precedents** - -- **Historical Precedents on Rookie QB Starts**: Historically, a sizeable portion of first-round rookie quarterbacks do not start right away. Even when they do, it often takes a few games for the coaching staff to make the transition unless the rookie is exceptionally gifted or the other quarterbacks are particularly underwhelming. - -- **Injuries and Other Unexpected Events**: Injuries are common in football, so while this factor introduces uncertainty about who starts, it also opens the door for the more mobile Nix if other quarterbacks face injury or continued poor performance. - -#### 4. **Time to Event and Variance** -- **Time to Week 4**: We have about 6 weeks until the resolution date. While Nix is trending toward being the starting quarterback, 6 weeks is a significant amount of time in a sports season. There could be shifts in performance, health, or coaching strategy that alter the decision. Still, Nix's main competition, Jarrett Stidham, did not perform well in the preseason opener, increasing Nix's odds. - -- **Preseason Context**: Preseason play is important but not definitive. Often, quarterbacks like Nix taking reps and starting in early games doesn't guarantee they'll start in the regular season since teams sometimes give rookies experience before deferring to a veteran once the games count. - -#### 5. **Conclusion and Final Probability** -- **Weight of Current Evidence**: Given that Bo Nix has momentum and support from both analysts and his coach, and considering the performance and positional fallout from the preseason opener, he is reasonably likely to continue on the trajectory of becoming the starting quarterback by Week 4. However, given the timeframe and potential for variance (such as unpredictable competition from Zach Wilson or a significant performance from Stidham in upcoming games), the probability can't be too high. - -- **Impact of Uncertainty**: The uncertainty stemming from the possibility that something drastic could change in the next few weeks (shift in performance, injuries, coaching decisions) tempers the probability. - -### Final Answer -""Probability: 68%"" - -This percentage reflects a leaning towards Bo Nix being the starting quarterback in Week 4 based on current information but accounting for the uncertainty inherent in the situation over the remaining weeks." -27159,"Will there be any reported human-to-human transmission of highly pathogenic avian influenza H5N1 globally before Sept 30, 2024?","PERPLEXITY - -### Summary of Relevant News and Data - -#### Current Situation and Historical Context -- **Human Cases and Transmission**: As of August 2024, there have been no reported cases of sustained human-to-human transmission of the H5N1 virus globally. Limited, non-sustained human-to-human transmission has not been reported since 2007. -- **Global Cases**: From January 2003 to May 2024, 889 human cases of H5N1 infection have been reported from 23 countries, with 463 fatalities (case fatality rate of 52%). -- **Recent Cases**: The most recent human case in the Western Pacific Region was reported in China with an onset date of March 26, 2024. No new cases were reported between June 28 and July 4, 2024, or between July 26 and August 1, 2024. - -#### Risk Assessment and Public Health Concerns -- **WHO Risk Assessment**: The World Health Organization (WHO) assesses the public health risk posed by H5N1 to the general population as low. The virus has not acquired mutations that facilitate human-to-human transmission. -- **CDC Recommendations**: The Centers for Disease Control and Prevention (CDC) emphasizes the importance of precautions for individuals with exposure to infected animals or environments. The risk to the U.S. general public is considered low, but continued surveillance is critical. - -#### Expert Opinions and Surveillance -- **AMA Update**: The American Medical Association (AMA) notes that highly pathogenic avian influenza cases are likely being underreported. The AMA emphasizes the need for continued surveillance and preparedness efforts. -- **CDC Technical Report**: The CDC's June 2024 report highlights that nearly all human cases since 2022 have been associated with poultry exposures. No human-to-human transmission has been identified, and the risk to the public remains low. - -### Conclusion -Given the current data and expert opinions, there is no evidence to suggest that sustained human-to-human transmission of H5N1 will be reported before September 30, 2024. The risk to the general public remains low, and surveillance efforts continue to monitor for any changes in the virus's transmissibility and virulence. - -######### - -GPT - -To tackle this problem, let’s break down the situation systematically, using principles of forecasting and probabilistic reasoning. - -### Step 1: Understand the Question and Resolution Criteria -The question asks if there will be any reported human-to-human transmission of highly pathogenic avian influenza H5N1 globally before September 30, 2024. The CDC is the key authority for determining this, and the event would resolve as ""Yes"" if the CDC reports at least one specific case of H5N1 being spread from human to human, or if it expresses high confidence in such a transmission. - -### Step 2: Assess the Current Status (Base Rate and Historical Context) -- **Historical Base Rate**: H5N1, while highly pathogenic and lethal when it infects humans, has not frequently achieved sustained human-to-human transmission. The last reported instance where limited human-to-human transmission may have occurred was in 2007, and there have been very few subsequent human cases. Recovery data up to 2024 reflects that the pattern of infections is sporadic and largely associated with animal exposure, not human-to-human spread. -- **Recent Cases**: The most recent relevant case is that of an infection in Texas in April 2024, traced to exposure from infected cattle, with no subsequent human-to-human transmission reported. - -### Step 3: Analyze the Risk of Change During the Forecast Period -There are approximately 6.5 weeks (or around 46 days) until the target date of September 30, 2024. Here, we must consider: -- **Current Trends**: No evidence of human-to-human transmission has been identified in recent months, despite global surveillance. This indicates an absence of viral mutations that would make human-to-human transmission more likely. -- **Expert Assessment**: The CDC and WHO continue to assess the human-to-human transmission risk as ""low"". Additionally, despite the presence of H5N1 in global avian populations, no credible sources suggest it has evolved into a form more likely to spread between humans. -- **Surveillance Systems**: Surveillance by global health authorities is robust, meaning that if human-to-human transmission were occurring, it would likely be detected and reported. However, as of now, no such cases have been reported, and the risk continues to be monitored carefully. - -### Step 4: Consider the Time Frame and Potential Variations -Given that no transmission has been reported in over a decade and that the chance for any change in the virus's transmissibility (based on current observations) is low over the next 46 days, the probability of a sudden change is limited. Although the theoretical possibility of such transmission occurring exists (through a mutation or other factors), the historical base rate and recent trends suggest it remains very unlikely. - -### Step 5: Calculate the Probability Distribution -- **Base Rate of H5N1 Human-to-Human Transmission**: Near zero in the context of the past few years, especially considering that trends have shown no increased transmissibility. -- **Probability Adjustment for Mutation or Other Factors**: Less than 1% of cases might develop to show sustained human-to-human transmission, especially over a short period. -- **Default Expectation if Trends Hold**: If no significant change is observed, it’s almost certain that we will not see a reported case of human-to-human transmission. - -Factoring these considerations, I estimate the probability as low: - -### Probability Estimate - -Given the low base rate, the absence of recent cases, the surveillance in place, and the historical context, I assess the likelihood of reported human-to-human transmission of H5N1 occurring before Sept 30, 2024 as approximately **2%**. - -Final Answer: **Probability: 2%**" -27158,Will Democrats have a brokered convention in 2024?,"PERPLEXITY - -The possibility of a brokered convention for the Democratic Party in 2024 has been a topic of significant discussion and speculation. Here is a concise rundown of the relevant news: - -### Background on Brokered Conventions -- **Historical Context**: Brokered conventions were more common before the modern primary system. The last brokered convention occurred in 1952 at the Democratic National Convention, where Adlai Stevenson was nominated after multiple rounds of voting. -- **Modern Rules**: Since 1968, rules have been established to bind delegates to the results of their state's primary or caucus, significantly reducing the likelihood of brokered conventions. - -### Current Situation -- **Biden's Status**: President Joe Biden has faced intense scrutiny, particularly after his debate performance on June 27, 2024. Despite this, he has maintained that he will not drop out of the race. -- **Potential for Brokered Convention**: If Biden were to drop out, the Democratic Party could face a brokered convention. This scenario is considered unlikely but possible given the current political climate. - -### Expert Opinions -- **Elaine Kamarck**: A prominent figure in Democratic Party procedures, Kamarck has highlighted the process of replacing a candidate at the convention. She emphasizes the importance of maintaining democratic principles and the potential for a smooth transition if Biden were to endorse a successor like Vice President Kamala Harris. -- **Barack Obama**: Former President Obama has suggested an open convention as a solution to select a new nominee, though this lacks a clearly defined procedure at this stage. - -### Recent Developments -- **Biden's Withdrawal**: On July 21, 2024, President Biden officially withdrew from the race, triggering a potentially chaotic process for the Democratic Party to select a new nominee. Vice President Kamala Harris is currently favored to succeed him due to his endorsement and her existing position. -- **Mini-Primary Proposal**: There have been proposals for a 'mini-primary' to introduce potential replacements to voters and delegates on an expedited timeline. This could involve town halls, public forums, speeches, and debates. - -### Conclusion -While the possibility of a brokered convention exists, it remains uncertain and dependent on various factors, including Biden's continued participation in the race and the party's ability to navigate the nomination process smoothly. The Democratic National Convention is scheduled for August 19-22, 2024, in Chicago, where the party will officially nominate their candidate. - -### Prediction Markets -As of the current date, there are no specific prediction markets available that directly forecast the likelihood of a brokered convention. However, political analysts and pundits continue to speculate on the potential outcomes based on recent developments and historical precedents. - -In summary, while a brokered convention is a possibility, it is not the most likely scenario given the current political landscape and the party's efforts to maintain a smooth nomination process. - -######### - -GPT - -### Step 1: Clarifying the Question - -The question asks whether the Democratic Party will have a brokered convention in 2024. Specifically, the question will resolve as ""Yes"" if more than one round of voting is required to formally nominate the Democratic candidate for the 2024 U.S. presidential election. - -### Step 2: Analyzing the Status Quo - -1. **Historical Context and Base Rates**: - - In the 20th and 21st Century, brokered conventions have become rare. The last Democratic brokered convention was in 1952. This suggests that under normal circumstances, the base rate for a brokered convention is quite low. - -2. **Current Political Situation**: - - The Democratic Party's path towards nominating Joe Biden initially appeared smooth because he secured the vast majority of pledged delegates. - - However, Biden's debate performance on June 28, 2024, triggered a significant amount of concern and unrest within the party. - - On July 21, 2024, he officially withdrew from the race, which dramatically changes the expected outcome from a straightforward nomination to one involving greater uncertainty. - -3. **Probable Outcome Given Biden’s Withdrawal**: - - With Biden’s withdrawal, and Kamala Harris as Biden’s endorsed successor, there is still a strong likelihood that she takes control of the delegate count. - - However, given the endorsement isn't foolproof, and the presence of intra-party turmoil suggests possible opposition and other candidates vying for the nomination at the convention. - -### Step 3: Timing and Procedural Considerations - -1. **Timing**: - - There's still a small window before the Democratic National Convention (August 19-22), but most of the major states have already held their primaries or caucuses, which previously focused on Biden. - - The transition and the process to name a new candidate could be rushed, potentially leading to conflicts. - -2. **Democratic Rules and Virtual Roll Call**: - - The Democratic Party's rules, which greatly limit the circumstances by which a brokered convention can occur, are important to consider. Delegates are largely bound to the winner unless they are released. - - The virtual roll call process is designed to wrap up the voting earlier, making it less likely that any uncertainty will remain by the time the convention happens in-person. - -### Step 4: Assessing Mechanisms for Conflict and their Likelihood - -1. **Potential Competitors**: - - If a viable challenger to Harris emerges and manages to consolidate enough delegates or causes division among pledged delegates, this could lead to a brokered convention. - - The possibility of rising figures causing splits—say, Gavin Newsom, Bernie Sanders, Elizabeth Warren, or others—depends on the effectiveness of any campaign they might run in the limited time available. - -2. **Party Unification Efforts**: - - The Democratic Party tends to avoid the divisiveness of a brokered convention to present a unified front against Republicans. - - Key influential figures like Barack Obama and other party leaders are likely to prioritize resolving disputes earlier rather than risking a brokered convention scenario. - -### Step 5: Synthesizing the Information - -Given all the factors, the default likelihood of a brokered convention is very low. Even with Biden’s withdrawal from the race, the Democratic Party (with guidance from Barack Obama and other senior figures) would likely seek to avoid a chaotic situation by consolidating support behind Harris or another consensus candidate. - -However, the absence of Biden introduces a non-trivial risk of disunity or delegate negotiations extending beyond the first ballot. Given historical precedents, the strategic preparation of the party, and the mere weeks left to solidify a nomination, the probability of a brokered convention seems low, but not zero. - -### Step 6: Final Probability Estimation - -Taking all factors into account: -- Base rate of brokered conventions: historically low. -- The strong likelihood that party leaders will coalesce quickly behind a single candidate. -- The short window of time available to influence pledged delegate votes. -- The structured approach of Democrats, particularly considering the virtual roll call. - -**Final Prediction:** - -**Probability: 20%** - -This low probability reflects the unlikely but still plausible scenario where intra-party dynamics force more than one ballot to nominate the candidate." -27157,"Before October 1, 2024, will there be an armed forces death in a conflict opposing China to Taiwan, the United States, the Philippines, or Japan?","PERPLEXITY - -**Context for Forecasting: Armed Forces Death in Conflict Opposing China to Taiwan, the United States, the Philippines, or Japan Before October 1, 2024** - -### Overview -The question pertains to the likelihood of armed forces deaths in a conflict involving China and any of the mentioned countries (Taiwan, the United States, the Philippines, or Japan) before October 1, 2024. This context summary will provide relevant news, expert opinions, and prediction markets data to assist in forecasting. - -### Recent Developments -1. **PRC Military Preparedness**: - - President Xi Jinping has emphasized the readiness of China's People's Liberation Army (PLA) to take Taiwan by force by 2027, indicating a heightened state of military preparedness. - - The PLA Eastern Theater Command released a video on its 97th anniversary depicting an assault on Taiwan, which is seen as cognitive warfare aimed at intimidating Taiwan and deterring it from supporting independence. - -2. **Cross-Strait Tensions**: - - The PRC has listed Taiwanese political figures on a new website section aimed at punishing ""Taiwan independence diehards,"" part of a broader pressure campaign against Taiwan's new administration. - - A PRC state-sponsored hacker group, APT41, was involved in stealing passwords and documents from a Taiwanese government-affiliated research center in July 2023. - -3. **International Relations**: - - The United States has imposed sanctions on individuals and entities in Iran and the PRC for facilitating the procurement of components for Iran’s ballistic missile and UAV programs. - - The PRC has been engaging in diplomatic efforts to generate support for its peace mediation efforts in various regions, including Brazil, South Africa, and Indonesia. - -### Prediction Markets and Expert Opinions -- **Metaculus Prediction Market**: The current prediction on Metaculus indicates a 1% likelihood of an armed forces death in a conflict opposing China to Taiwan, the United States, the Philippines, or Japan before October 1, 2024. -- **Expert Opinions**: Analysts from the Institute for the Study of War and the American Enterprise Institute highlight the PRC’s aggressive military posturing and cyber operations as significant indicators of potential conflict escalation. - -### Conclusion -While the current prediction markets suggest a low likelihood of armed forces deaths in the specified conflict before October 1, 2024, recent military posturing and diplomatic tensions between the involved parties indicate a volatile situation. The PRC's aggressive actions, including military exercises and cyber attacks, suggest a heightened risk of conflict. However, the absence of recent major incidents and the ongoing diplomatic efforts may also contribute to a stable situation. These factors should be considered when making a forecast. - -######### - -GPT - -### Chain of Thought - -#### 1. **Understanding the Context** - - **Current Date:** August 14, 2024 - - **Time Until Resolution Date:** ~1.5 months (6-7 weeks) - - **Situation Dynamics:** - - Increasing tensions and military activity involving China, Taiwan, the United States, the Philippines, and Japan. - - Notable events include recent collusion between Chinese and Philippine ships, PRC military drills near Taiwan, and the U.S. hosting the RIMPAC exercises which involved simulating an attack on a Chinese warship. - -#### 2. **Historical Base Rates** - - Historical base rates of direct military conflict resulting in deaths between China and these countries since the late 20th century are low. Even during periods of high tension, outright conflict has been generally avoided. - - For example, despite frequent incidents in the South China Sea and around Taiwan in the past, these have not led to direct, sustained armed conflicts resulting in fatalities. - - However, the base rate is biased by the fact that global powers, especially the U.S. and China, have generally exercised caution to avoid escalation. - -#### 3. **Assessment of Risk Factors** - - **Sino-Philippine Incident:** The incident involving colliding vessels could have intensifying knock-on effects, though historically these have led to diplomatic rows rather than significant military exchanges. - - **Taiwan Strait Tensions:** The large-scale Chinese military drills around Taiwan are concerning. Nevertheless, such exercises have occurred before and haven’t escalated into war. The timing of the exercises shortly after Taiwan’s new president took office indicates a message of deterrence rather than a prelude to immediate conflict. - - **U.S. Involvement:** While the U.S. conducting drills to simulate sinking a Chinese vessel could be provocative, the exercises have passed, and there is no indication that this transitioned to direct conflict. Historical precedent suggests considerable reluctance from both sides to escalate to direct military conflict. - -#### 4. **Recent Developments and Expert Opinion** - - Prediction markets (e.g., Metaculus) indicate a 1% likelihood of an armed forces death, reflecting the current market sentiment that immediate conflict remains unlikely. - - Expert opinions indicate that while tensions are high, they haven’t yet crossed the threshold where armed conflicts become likely. - -#### 5. **Potential Factors Leading to an Armed Conflict and Deaths** - - **Miscalculations or Accidents:** These are non-zero probabilities, but the environment is heavily managed to prevent such cases from escalating. - - **Uncontrolled Escalation:** Despite the volatile environment, mechanisms exist (diplomatic channels, global pressure) to de-escalate before hitting the point of armed conflict with fatalities. - -#### 6. **Weighing the Information** - - **Base Rate Analysis:** Low likelihood of direct military conflict involving fatalities between China and any of the involved countries in the recent past. - - **Short Timeframe:** Less than two months to the resolution date. Significant geopolitical events tend to build over time, and the timeframe may not be sufficient for a full-on escalation to occur. - - **Current Behavior:** The pattern of provocations largely remains in the domain of military drills, diplomatic posturing, or skirmishes without transitioning to major armed conflict. - -### Conclusion and Probability Calculation -Taking into account: -1. **Current high-tension situation** but still adhering to historical patterns of avoiding direct confrontation. -2. **Short timeframe** limiting the possibility of a large-scale event developing fully before October 1, 2024. -3. **Market sentiments and expert analyses** favoring a very low possibility of conflict escalating to a point of causing armed forces deaths. - -**Final Probability:** 5% - -This estimate accommodates a small uptick in probability given the heightened tensions but recognizes that an escalation to the level of fatalities remains quite unlikely. - -**Probability: 5%**" -27249,"Will the 7-day average confirmed mpox cases in 2024 exceed the peak 7-day average confirmed mpox cases from the 2022-2023 outbreak by September 30, 2024?","PERPLEXITY - -To provide context for the query about whether the 7-day average confirmed mpox cases in 2024 will exceed the peak 7-day average confirmed mpox cases from the 2022-2023 outbreak by September 30, 2024, we need to examine recent trends, expert opinions, and current data on mpox cases. - -### Current Trends and Data - -1. **CDC Update**: According to the CDC, during October 1, 2023–April 30, 2024, a total of 1,802 probable and confirmed mpox cases were reported in the United States, with an average of 59 cases per week. This represents a significant decline from the peak of approximately 3,000 cases per week in late July–August 2022. - -2. **WHO Situation Report**: As of June 30, 2024, WHO reported a total of 934 new laboratory-confirmed cases of mpox and four deaths from 26 countries in June 2024. The African Region, particularly the Democratic Republic of the Congo, accounted for the majority of these cases. The global trend shows a fluctuation between 700 and 1,000 cases per month from January to June 2024. - -3. **WHO Declaration**: WHO has declared the 2024 mpox surge a public health emergency of international concern due to a notable increase in cases, especially in the Democratic Republic of Congo and neighboring countries. The variant circulating now is recognized to be more lethal than the strain from 2022. - -4. **Metaculus Prediction**: The Metaculus question regarding whether the 7-day average confirmed mpox cases in 2024 will exceed the peak from the 2022-2023 outbreak by September 30, 2024, is currently open for predictions. - -### Expert Opinions - -- **WHO Officials**: Experts like Dr. Dimie Ogoina and WHO Director-Generalros Adom Gbrey emphasize the global implications of the current mpox surge, noting that reported cases are likely an underestimate due to surveillance limitations. -- **Africa CDC**: Dr. Jean Kaseya, director general of Africa CDC, highlighted the need for proactive and aggressive measures against mpox, noting that the situation in Africa, particularly among children, is alarming. - -### Recent Developments - -- The Democratic Republic of Congo has reported 14,000 cases and 511 deaths in 2024, with a significant proportion affecting children. -- The U.S. has committed nearly $424 million to assist with the mpox response in the DRC, along with an additional $10 million for vaccine donations. - -### Conclusion - -Given the current trends and expert opinions, there is a growing concern about the increase in mpox cases, especially in the African Region. However, whether the 7-day average confirmed mpox cases in 2024 will exceed the peak from the 2022-2023 outbreak by September 30, 2024, remains uncertain. The situation is dynamic, with efforts to enhance surveillance, vaccination, and international collaboration ongoing. - -######### - -GPT - -### Step 1: Clarify the Event Type and Resolution Criteria - -This question concerns a **Disease Spread Rate Event**. Specifically, it's asking whether the 7-day average confirmed mpox cases in 2024 will exceed the peak 7-day average confirmed mpox cases from the 2022-2023 outbreak by **September 30, 2024**. - -The resolution is based on publicly reported data from [Our World in Data](https://ourworldindata.org/mpox). The key metric is the comparison between the 7-day average case count at any point before September 30, 2024, and the peak 7-day average during the 2022-2023 outbreak. - -### Step 2: Analyze Historical Data and Base Rates - -1. **2022-2023 Mpox Outbreak Peak**: - - The 2022-2023 outbreak reached a peak of approximately **3,000 cases per week** in July-August 2022. - - The outbreak had a total of 66,387 confirmed cases globally. - -2. **2024 Mid-Year Data**: - - Between October 1, 2023, and April 30, 2024, the CDC reported an average of **59 cases per week** in the U.S., significantly lower than the 3,000/week peak from 2022. - - WHO reported a total of 934 new cases worldwide for June 2024, with fluctuations between 700-1,000 cases per month globally from January to June 2024. - -### Step 3: Consider Current Situation and Expert Opinions - -1. **New Outbreak Dynamics**: - - This outbreak involves the **more severe Clade I** variant, which could contribute to an increase in both transmission and mortality rates. - - The WHO has recently declared a **Public Health Emergency of International Concern (PHEIC)**, indicating a severe escalation. - -2. **Trends**: - - A significant portion of cases is being reported in the Democratic Republic of Congo (DRC), which has seen **14,000 cases and 511 deaths** in 2024, with many cases among vulnerable populations like children. - - The global spread indicates potential underreporting, especially in regions with limited surveillance infrastructure. - -3. **Recency and Momentum**: - - The declaration of a global emergency and the presence of a more severe variant points to a potential uptick in cases. - - However, from June to August 2024, we haven't yet seen numbers anywhere close to the 2022 peak, nor have we seen evidence of exponential growth akin to what might be needed to eclipse the previous peak in just over a month. - -### Step 4: Weigh the Impact of Time and Possible Changes - -1. **Limited Timeframe**: - - There is only **~6 weeks** remaining until September 30, 2024. - - Historically, while outbreaks can escalate quickly, achieving a 7-day average exceeding 3,000 cases is particularly challenging within such a short period, especially given that cases were fluctuating between 700 and 1,000 per month globally up until recent months. - -2. **Uncommon But Possible Escalation**: - - Consider the possibility of a disruptive event, such as a sudden surge due to superspreading events, changes in reporting, or mass exposure in a high-density region. - - Nonetheless, the base rate for a sudden exponential surge that surpasses the 2022 numbers within such a short timeframe remains low. - -### Step 5: Draw a Probabilistic Conclusion - -Given the data and analysis above: - -- **Low Current Numbers**: The current case counts are significantly below the 2022 peak. -- **Short Time Left**: There is limited time for the numbers to escalate to the necessary level, notwithstanding the more severe Clade I variant. -- **Potential for Sudden Changes**: While a sudden surge cannot be entirely ruled out, the base rate for an outbreak to reach such heights in such a short time frame is historically low. - -### Final Answer - -Taking all factors into consideration, I estimate a low probability of the 7-day average mpox cases in 2024 exceeding the peak 7-day average from the 2022-2023 outbreak by September 30, 2024, but it's not negligible given the uncertainty in the data and the potential for rapid escalation. - -**Probability: 15%**" -27248,"Will the Secretary of the US Department of Health and Human Services declare a public health emergency for mpox (monkeypox) before October 1, 2024?","PERPLEXITY - -To address the query of whether the Secretary of the US Department of Health and Human Services will declare a public health emergency for mpox (monkeypox) before October 1, 2024, we need to consider recent developments and the current context of the mpox outbreak. - -1. **Current Public Health Emergency Declarations**: - - The US Department of Health and Human Services (HHS) has previously declared a public health emergency for mpox. The declaration was made on August 2, 2022. This indicates that the HHS has recognized the severity of mpox outbreaks in the past. - -2. **Global Response**: - - The World Health Organization (WHO) has declared mpox a global public health emergency for the second time, following an outbreak in the Democratic Republic of Congo that has spread to neighboring countries. This global declaration underscores the seriousness of the current situation. - -3. **US Response and Preparations**: - - The Centers for Disease Control and Prevention (CDC) has alerted healthcare providers to be vigilant for mpox in individuals who have recently traveled to the Democratic Republic of Congo or its neighboring countries. This suggests that the US is taking proactive measures to monitor and potentially mitigate the spread of mpox. - -4. **Vaccine Availability and Funding**: - - The US has committed significant funding to assist with the mpox response in the Democratic Republic of Congo, including the provision of vaccine doses. This commitment indicates a level of concern and engagement from the US government. - -5. **Expert Opinions and Concerns**: - - Experts and officials from the WHO and the Africa Centers for Disease Control and Prevention have emphasized the need for a unified international response to halt the outbreaks and save lives. The high incidence of cases and fatalities among children in the affected regions has been particularly concerning. - -Given these points, the declaration of a public health emergency by the HHS Secretary before October 1, 2024, seems plausible, especially considering the recent global declarations and the proactive measures being taken by health authorities. However, the decision would depend on various factors, including the evolution of the outbreak in the US and the effectiveness of current response efforts. - -######### - -GPT - -Given the task, I'll carefully walk through the key considerations step-by-step to determine the probability of the US Secretary of the Department of Health and Human Services (HHS) declaring a public health emergency (PHE) for mpox (formerly known as monkeypox) before October 1, 2024. - -### Step 1: Understanding the Criteria for Declaring a PHE - -The Secretary of HHS can declare a PHE if a disease or disorder presents a threat, or if significant outbreaks of infectious diseases occur. Mpox has previously met these criteria, and the authority to declare a PHE is a discretionary function based on the perceived need for additional federal resources to manage a health crisis. - -### Step 2: Review of Recent Developments - -1. **Clade I Mpox Outbreak in Africa (July 2024)**: The outbreak in the Democratic Republic of Congo (DRC) is significant and severe, involving the Clade I strain which is more virulent than the Clade II strain from the previous 2022-2023 mpox outbreak. The outbreak has led to discussions of a continent-wide health emergency declaration by Africa CDC, indicating a high level of concern. - -2. **Global Response**: The World Health Organization (WHO) has already declared the outbreak a global public health emergency. This declaration places pressure on national health authorities, including the US, to respond to the outbreak with necessary precautions and resources. - -3. **US Preparations**: The Centers for Disease Control and Prevention (CDC) has issued alerts related to mpox for travelers from the affected regions, and the US has committed significant resources to help control the outbreak in the DRC. This signifies a proactive stance from US authorities. - -### Step 3: Base Rate Analysis and Historical Precedents - -1. **Past Mpox Incidents**: The previous mpox outbreak in 2022 led to a PHE declaration within a few months after the first case was confirmed. The promptness of past PHE declarations, when there appeared to be a considerable threat, indicates a pattern of responsive action by HHS. - -2. **Public Health Emergencies from Infectious Diseases**: The base rate for infectious disease-related PHE declarations is weighted by the perceived threat level to the population. If outbreaks show signs of spreading within the US, a PHE declaration becomes more likely. - -### Step 4: Timing Considerations - -We are considering a time window between August 12, 2024, and October 1, 2024. There's roughly a month and a half for a possible PHE declaration: - -1. **Time Frame of Potential Spread to the US**: If there is a significant delay in mpox cases entering or spreading within the US, the probability decreases. If cases are already detected in the US and are showing signs of a spike, the likelihood of a declaration increases. - -2. **Decision Making Lag**: There may be a lag from the detection of initial cases in the US to an official PHE declaration, as HHS waits for more data or signals indicating that federal coordination is needed. - -### Step 5: Other Considerations - -1. **Political Landscape**: There is currently no significant opposition to declaring PHEs if necessary, as most public health crises are recognized across both political aisles, especially given pre-established PHE declarations for mpox in the past. - -2. **Default Behaviors**: In the absence of a significant increase in cases within the US, it's worth noting that the default stance of HHS would likely be to continue monitoring rather than immediately issuing a new PHE. - -### Final Probability Estimation - -Given all these factors: - -- **Strength of the Current Outbreak in Africa**: High concern due to severity and spread. -- **Global and US Response**: Vigilant, but no PHE in the US currently. -- **Timing Constraints**: A relatively short window for declaring a PHE exists between now and October 1st. If an outbreak were to spread rapidly in the US, a declaration would be more likely, but this assumes the situation worsens by mid-September. -- **Base Rate of PHE Declarations**: Typically progressive and responsive as situations develop. - -Given the current situation, I estimate that there's a notable chance, but not overwhelming probability, that the HHS Secretary will declare a PHE for mpox before October 1, 2024. - -**Final Probability: 32%**" -27241,"Will Keir Starmer's approval rating as Prime Minister of the United Kingdom be less than 60 on September 18, 2024?","PERPLEXITY - -To address the question of whether Keir Starmer's approval rating as Prime Minister of the United Kingdom will be less than 60 on September 18, 2024, we need to consider recent trends and data available up to August 16, 2024. - -### Recent Approval Ratings - -As of early August 2024, Keir Starmer's net favourability rating has seen a decline. According to YouGov, his net favourability rating dropped from ±0 in mid-July to -9 by early August. This indicates a decrease in his popularity following a brief honeymoon period after the 2024 general election. - -### Historical Context and Comparisons - -Historically, prime ministers often experience a honeymoon period after elections, but this can vary significantly. For instance, Tony Blair enjoyed an extended period of high approval ratings after his 1997 election win, with his satisfaction ratings jumping from 51% before the election to 65% afterwards and remaining high for several years. - -### Current Public Perception - -Public perception of Starmer is mixed, with many Britons unsure of what he stands for. A YouGov poll found that 49% of Britons say they don’t know what Keir Starmer stands for, including 21% of those intending to vote Labour. This lack of clarity could impact his approval ratings as the public becomes more familiar with his policies and leadership style. - -### Expert Opinions and Predictions - -Experts and commentators have noted that Starmer's lead in the polls and his eventual election win might be more about the public's dissatisfaction with the Conservative Party rather than a strong endorsement of Starmer himself. This suggests that his approval ratings could be volatile and dependent on how he manages expectations and delivers on his promises. - -### Prediction Markets and Betting Odds - -As of the current date, specific betting odds or prediction market data on Keir Starmer's approval rating by September 18, 2024, are not provided in the sources. However, given the recent decline in his net favourability rating and the historical context of prime ministerial approval ratings, it's plausible that his approval rating could be less than 60 by that date. - -### Conclusion - -While there is no direct data predicting Keir Starmer's approval rating on September 18, 2024, the recent decline in his favourability ratings, mixed public perception, and the challenges of managing high expectations suggest that his approval rating could indeed be less than 60 by that date. However, this is speculative and would depend on various factors, including his policy implementations and public reactions in the coming weeks. - -######### - -GPT - -### Step 1: Understanding the Context and Type of Event -The question at hand is asking to predict whether Keir Starmer's approval rating will be less than 60 on September 18, 2024. This is a **Non-Market Threshold Value Event** because it deals with a quantifiable measure (approval rating) that either will or will not meet a certain threshold by a specific point in time. - -### Step 2: Analyzing the Background Information -#### Political Context -- **Keir Starmer's Recent Election Victory**: Starmer has just been elected Prime Minister with a significant majority, although the overall voter turnout was low, and the vote was fragmented. This suggests a mandate that may not be as broad or strong as the parliamentary numbers alone might suggest. - -- **Short Honeymoon Period**: Historically, leaders often benefit from a ""honeymoon"" period after an election, during which their approval ratings tend to be higher. However, according to the provided data, Starmer’s approval rating has already begun declining—moving from a net favourability of ±0 in mid-July to -9 by early August. - -- **Public Perception**: Almost half of the public does not have a clear understanding of what Starmer stands for, which poses a risk to his approval ratings as his government starts making more decisions and taking visible actions. - -#### Comparative Historical Data -- In past instances, prime ministers who gained power under similarly complex or unenthusiastic mandates have seen their approval ratings fluctuate more significantly. The mention of Tony Blair’s initial approval ratings is an outlier, as he rode a wave of optimism that doesn’t seem to parallel Starmer's situation. Instead, Starmer's approval trajectory might more closely match leaders like Gordon Brown, who experienced a sharp decline after his initial honeymoon. - -### Step 3: Reviewing Quantitative and Qualitative Data -- **Approval Ratings History**: Given that Starmer's approval rating has already shown a decline soon after taking office, it's critical to evaluate how much further it might fall. The decline to a net rating of -9 so quickly could indicate underlying dissatisfaction or high uncertainty among the electorate. - -- **Current Events Influence**: The upcoming months from mid-August to mid-September 2024 will be an essential period in determining Starmer's approval. Issues such as economic performance, management of ongoing domestic challenges, and external pressures could all potentially impact his approval rating. - -- **Volatility of Polls**: Polls are known to exhibit volatility over short periods. A key point here is that significant changes in approval ratings are not uncommon, particularly for leaders who face uncertainty or opposition to their policies. - -### Step 4: Assess Default Behavior and Predict Future Trends -- **Base Rate**: Based on general trends, approval ratings tend to decline after the initial honeymoon period unless there is a particularly strong and effective governance period or a series of popular decisions. - -- **Trend Extrapolation**: Given the current downward trend in Starmer’s net approval, it is plausible that this will continue, especially given the public’s unclear understanding of his positions and the emerging political challenges. - -### Step 5: Calculating the Probability -Taking all the factors into account—recent approval rating declines, public uncertainty around Starmer’s leadership, the short honeymoon period, and historical comparisons—a downward trajectory is more likely. - -- The notable decline to a net favourability of -9 within just a short period suggests a softening of support that could be exacerbated by further challenges and unclear policy directions. - -- Given the political climate, it seems more likely than not that his approval rating could be less than 60 on September 18, 2024. - -### Conclusion -After weighing all the elements carefully, the probability that Keir Starmer's approval rating will be **less than 60** on September 18, 2024, is calculated to be relatively high. However, some uncertainties remain (e.g., potential positive outcomes from key policy implementations), so a slight margin should be built into the prediction. - -**Probability: 72%**" -27239,"Will Keir Starmer's approval rating as Prime Minister of the United Kingdom be less than 40 on September 18, 2024?","PERPLEXITY - -To provide context for the question of whether Keir Starmer's approval rating as Prime Minister of the United Kingdom will be less than 40 on September 18, 2024, we need to examine recent polls, public perceptions, and any significant events that might influence his ratings. - -1. **Public Perception and Polls**: - - A poll by Public First shared with POLITICO suggests that while Starmer remains somewhat of a mystery to the British public, there is cautious optimism that he can bring positive change. - - YouGov polls indicate that Starmer's net favorability rating has seen fluctuations. Immediately after the election, his favorability increased, but recent polls show a decline. As of early August 2024, his net favorability rating dropped to -9, with 40% of Britons having a favorable view and 49% an unfavorable view. - - Another YouGov article highlights that half of Britons (49%) don't know what Starmer stands for, and only 9% feel they have a clear understanding of his positions. - -2. **Recent Events and Challenges**: - - Starmer has faced several challenges, including public disorder in Stockport and Leeds, and making additional spending cuts to address a £22bn hole in public finances. These events have likely contributed to the decline in his favorability ratings. - - The new chancellor, Rachel Reeves, and other key Labour figures have also seen their popularity decline, particularly among non-Labour voters, following announcements of cuts and other policy decisions. - -3. **Historical Context and Comparisons**: - - Starmer's leadership style and potential as a prime minister are often compared to previous Labour leaders like Tony Blair and Gordon Brown. The public sees him as more conventional and similar to these predecessors. - - Foreign Policy magazine suggests that Starmer's record as Labour leader is mixed, and he has moved the party towards the right. His stance on Brexit, among other issues, has been a subject of scrutiny. - -4. **Prediction Markets and Betting Odds**: - - Unfortunately, specific prediction markets or betting odds on Keir Starmer's approval rating as of September 18, 2024, are not provided in the search results. However, these platforms could offer valuable insights into the public's and experts' expectations. - -Given the recent decline in Starmer's favorability ratings and the challenges he has faced, it seems plausible that his approval rating could be less than 40 by September 18, 2024. However, public opinion can be volatile, and significant events or policy announcements could influence his ratings in either direction. - -######### - -GPT - -### Step 1: Understanding the Question - -This question is a **Non-Market Threshold Value Event** as it concerns whether a quantifiable measure (Keir Starmer's approval rating) will be below a specific threshold (40%) on a particular date (September 18, 2024). - -### Step 2: Establishing the Base Rate - -To create an effective forecast, it's crucial to understand Starmer's current situation, historical context, and similar past events. The base rate here includes: -- **Recent Approval Ratings**: Starmer's recent approval rating has seen a decline, with the most recent data showing 40% favorable, 49% unfavorable (net -9). -- **Historical Comparison**: Previous Labour Prime Ministers such as Tony Blair and Gordon Brown had fluctuating ratings largely correlated to their government’s performance, public opinion, and economic conditions. Starmer’s initial phase as Prime Minister reflects a critical period where both honeymoon effect and early disillusionment can occur. - -**Base rates for someone in Starmer's position post-election with some public discontent but a larger majority could see declining ratings in the 30s or 40s.** - -### Step 3: Key Factors to Consider -1. **Recent and Upcoming Events**: - - **Public Challenges**: Starmer’s government has already faced significant public challenges like public disorder and budgetary issues. - - **Political Dynamics**: The emergence of a third party (Reform U.K.), low voter turnout, and public dissatisfaction suggest vulnerabilities in Starmer’s support. - - **Economic Conditions**: Policy moves to address a £22bn deficit are almost certainly unpopular but necessary. The impact of these policies on public perception will be critical. - -2. **Volatility and Timeframe**: - - Starmer’s approval rating has shown some volatility, and approval ratings often react strongly to political, economic, or even international events. - - There is still a month (August 16 to September 18), leaving ample time for swing factors like unexpected positive/negative news that could influence public opinion. - -3. **Poll Data Uncertainty**: - - It's necessary to be wary of the reliability and recency of polling data. Polls reflect past events and attitudes, they lag the current situation and are an imperfect prediction tool. - -### Step 4: Weighing the Likelihood - -- **Default Resolution Rate**: Based on the current trajectory, if no significant positive event occurs to boost Starmer’s rating, it seems plausible that his approval could dip under 40, particularly if public dissatisfaction grows along with the challenges faced by his administration. - -- **Historical Precedent**: Prime Ministers often see their ratings drop under 40% after a few months due to erosion of the honeymoon period post-election, especially when grappling with significant national issues or perceived policy blunders. - -- **Short-Term Fluctuations**: Large abrupt changes in approval (both positive and negative) near elections and significant policy events have historically driven substantial approval variance. - -### Step 5: Calculation of Probability Distribution - -- **Most Recent Data Analysis**: With his approval already hovering near 40% and signs pointing toward continued discontent (economic austerity, social challenges), the probability of an approval rating below 40 seems higher than average, but should not be considered a certainty. - -- **Consideration for Tail Risk**: There is some chance of a rebound if positive events occur, but the momentum currently suggests continued or increasing dissatisfaction, leading to a moderately high likelihood of the approval dropping below 40%. - -### Final Probability Assessment -- Given Starmer’s current approval trends, coupled with the likely impact of ongoing political and economic challenges, the setup heavily leans towards Starmer’s approval being below 40 on September 18, 2024. -- Abrupt changes to ratings are possible, but from present data and trends, a low to moderate probability (not exceedingly high or low) seems appropriate. - -**Probability: 65%** - -This forecast leaves a considerable margin for other outcomes, reflecting the inherent unpredictability and volatility of public opinion, while acknowledging the current downward trend in Starmer's approval rating." -27238,"Will the USD be worth 0.935 EUR or more at the close of trading on any day before October 1, 2024?","PERPLEXITY - -To address the query of whether the USD will be worth 0.935 EUR or more at the close of trading on any day before October 1, 2024, we need to examine the current exchange rate trends and forecasts for the EUR/USD pair. - -### Current Exchange Rate and Forecasts - -1. **Current Exchange Rate**: As of the latest data available, the EUR/USD exchange rate is approximately 1.0916. This translates to about 0.916 EUR per USD, which is below the threshold of 0.935 EUR. - -2. **Forecasts**: - - **J.P. Morgan**: Predicts EUR/USD to reach 1.09 in December 2024, which is about 0.917 EUR per USD. - - **Long Forecast**: Estimates EUR/USD to be around 1.06 by the end of 2024, which is about 0.943 EUR per USD. - - **Lite Finance**: Suggests a range for EUR/USD from 1.0400 to 1.2400 in the next 12 months, with a potential strong correction in August-September 2024. - - **CAPEX.com**: Forecasts EUR/USD to remain trapped around 1.08 by the end of 2024, which is about 0.926 EUR per USD. - - **Trading Economics**: Expects EUR/USD to trade at 1.09 by the end of this quarter, which is about 0.917 EUR per USD. - -### Analysis - -Given the current exchange rate and the forecasts from various sources, there is a possibility that the USD could reach or exceed 0.935 EUR before October 1, 2024, especially considering the range and volatility predicted by some forecasts. However, most predictions suggest that the EUR/USD rate will remain below the threshold that translates to 0.935 EUR per USD. - -### Recent Updates and Expert Opinions - -Recent economic data and monetary policy expectations have influenced the EUR/USD exchange rate. Weak US economic data and expectations of interest rate cuts by the Fed could potentially weaken the USD against the EUR. However, the ECB's actions and the overall economic outlook also play significant roles in determining the exchange rate. - -### Prediction Markets and Betting Sites - -While specific prediction markets and betting sites' data are not provided in the search results, incorporating such data could offer additional insights into market expectations. - -### Conclusion - -Based on the available forecasts and current trends, while there is some potential for the USD to reach or exceed 0.935 EUR before October 1, 2024, the majority of predictions suggest that the exchange rate will remain below this threshold. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Current Situation and Short-Term Forecasts** - - **Current USD/EUR exchange rate**: As of August 13, 2024, 1 USD ≈ 0.9128 EUR. - - **Threshold**: We need the USD to be worth 0.935 EUR or more on at least one day before October 1, 2024. This corresponds to a EUR to USD exchange rate of approximately \( \frac{1}{0.935} \approx 1.069\). - - **Predictions**: The forecasts suggest a variation in EUR/USD ranging from about 1.04 to 1.24 over the next several months, with some expectations of possible volatility. However, most forecasts do not suggest a sharp movement toward a EUR/USD rate that would translate to 0.935 EUR per USD. - -#### 2. **Macroeconomic Indicators** - - **Interest Rates**: Both the Federal Reserve and the European Central Bank are key players. The Fed's policies leaning towards rate cuts could weaken the USD, whereas the ECB's interest policies are less aggressively easing. - - **Economic Indicators**: Economic indicators are mixed, and there isn't a strong indication that the US will have a significant strengthening relative to the Eurozone's economy in the next two months. - -#### 3. **Volatility and Market Sentiment** - - In the short term, currency markets can experience significant volatility. Short-term predictions can often underestimate the possibility of major shifts due to unforeseen events. - - **August-September volatility**: Lite Finance hinted at a strong potential for correction during this period, which could lead to unusual price movements and possibly cause the USD value to spike temporarily to or above the 0.935 EUR threshold. - -#### 4. **Historical Base Rates and Recency** - - Historically, USD/EUR rates have fluctuated significantly, but achieving a sudden sharp appreciation in a short period (like advancing from about 0.913 to 0.935 EUR/USD) is relatively uncommon without a significant financial event. - - However, sustained deviations from baseline expectations historically occur, on average, more frequently during periods of known volatility or market uncertainty. - -#### 5. **Resolution Criteria and Timing** - - The key metric is the ""Close"" price, meaning short-living spikes in intraday trading will not suffice. The timeframe from now until the end of September is about 1.5 months, a relatively short period. - - The forecasts and economic data don't strongly suggest that USD will significantly break through recent trends. Still, given potential volatility, one cannot fully discount the probability of hitting the threshold. - -### Conclusion and Probability Estimate - -Given the available information, the most likely outcome is that the USD will *not* reach the threshold of 0.935 EUR. Even though minor volatility and short-term fluctuations could drive the exchange rate higher, current predictions and economic indicators do not provide a strong basis for believing that such a spike is imminent. - -However, the potential for unexpected geopolitical events, sudden shifts in market sentiment, or economic data releases could push the exchange rate up temporarily. Considering these possibilities, the probability of the USD reaching or exceeding 0.935 EUR at least once before October 1, 2024, remains non-negligible. - -Final estimation: - -**Probability: 25%**" -27240,"Will Keir Starmer's approval rating as Prime Minister of the United Kingdom be less than 50 on September 18, 2024?","PERPLEXITY - -To provide context for the question of whether Keir Starmer's approval rating as Prime Minister of the United Kingdom will be less than 50 on September 18, 2024, we need to examine recent polls, public perceptions, and the political landscape following the 2024 general election. - -### Recent Polls and Public Perception - -- **Popularity Post-Election**: As of July 2024, shortly after the general election, Keir Starmer enjoyed a significant level of popularity. A poll by Ipsos found that 36% of the public thought Starmer was doing a good job as Prime Minister, which is the highest rating for a premier since February 2021 when 37% said the same about Boris Johnson. -- **Favorability Ratings**: Despite being seen as the best man for the job, Starmer's net favorability rating was -18, according to a YouGov poll. This is significantly better than his Conservative opponent, Rishi Sunak, who had a net favorability rating of -53. -- **Public Expectations**: There is a mixed view on how well Starmer will perform as Prime Minister. Only 23% of Britons expected him to be ""good"" or ""great,"" while 33% expected him to be ""poor"" or ""terrible"". - -### Political Landscape - -- **Economic Challenges**: The UK has been facing economic challenges, including high inflation and slow GDP growth. However, recent data showed some positive signs, with GDP growth in the first quarter of 2024 being 0.6%, the fastest since the end of 2021, and CPI inflation at 2.3%, the lowest rate in three years. -- **Labour's Election Win**: Labour's victory in the 2024 general election, winning a parliamentary election by a landslide, marks a significant shift in the political landscape. This is the first time Labour has won a general election since 2005. - -### Prediction Markets and Betting Odds - -As of the current date (August 16, 2024), specific prediction market data or live betting odds on Keir Starmer's approval rating by September 18, 2024, are not provided in the search results. However, given the recent polls and the traditional honeymoon period for new leaders, it would be reasonable to expect that Starmer's approval ratings might remain relatively stable or even improve slightly in the short term following his election win. - -### Conclusion - -Given the recent polls and the political context, while there are mixed views on Starmer's performance and significant economic challenges ahead, the immediate post-election period often sees a honeymoon effect for new leaders. This could potentially keep his approval ratings from dipping below 50 by September 18, 2024. However, the long-term sustainability of his approval ratings will depend on his government's ability to address the economic challenges and meet public expectations. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Understanding the Context and Question** -- **Current Date**: August 16, 2024. -- **Resolution Date**: September 18, 2024. -- **Prime Minister**: Keir Starmer. -- **Task**: Estimate the probability that Starmer's approval rating will be **less than 50%** on the specified date. - -#### 2. **Identifying Key Factors** -- **Recent Polls**: - - Post-election popularity: 36% approval (highest since 2021). - - Net favorability: -18%. - - Public expectations are mixed but cautious. - -- **Political Environment**: - - Labour wins a landslide victory but with low voter turnout and fragmented votes. - - Emergence of Reform U.K. as a significant force. - - First Labour government since 2005, suggesting potentially high public expectations but also heightened scrutiny. - -- **Economic Context**: - - UK is currently facing economic challenges, but there are slight improvements as indicated by recent GDP growth and lower inflation. - - Economic performance is a crucial factor for any Prime Minister's approval rating. - -- **“Honeymoon Period”**: - - New leaders often enjoy a brief ""honeymoon period"" where approval ratings are usually higher due to optimism and benefit of the doubt from the public. - - This could shield Starmer from immediate dips in approval, but the effect diminishes over time with looming challenges. - -- **Base Rates for Similar Events**: - - Analyses of past leaders show that honeymoon periods do exist, but significant drops in approval ratings can occur within months if major challenges (economic, political crises) are not navigated well. - -#### 3. **Statistical Considerations and Timeline** -- **Historical Data**: Majority of new Prime Ministers tend to have approval ratings above 50% during the initial months unless they face significant crises. - -- **Current Performance Indicators**: - - Starmer’s initial approval is below 50% (36% approval as of July 2024). This suggests he does not enjoy overwhelming public support and will have to work hard to maintain it. - -- **Remaining Time Before Resolution**: - - Roughly 1 month is left before the resolution date. Although not a long time, significant events (e.g., economic, political scandals, major governmental decisions) could still shift public opinion. - - However, barring substantial crises, major shifts in public opinion tend to take time. - -- **Potential Catalysts for Change**: - - Negative: Poor handling of economic challenges, significant policy failures, scandals, or leadership missteps. - - Positive: Successful policy implementations, further economic recovery, or major positive international engagements. - -#### 4. **Synthesizing the Information** -- **Current Default Behavior**: Starmer’s approval rating is relatively low, and public perception is mixed. However, he hasn't yet encountered a significant crisis that could drastically drop it below 50%. - -- **Historical Precedents vs. Novel Situations**: The situation has some novelty, particularly with a long Conservative dominance ending, and Starmer’s performance will be scrutinized. However, novelty doesn't strongly suggest an imminent drastic drop unless new major problems emerge. - -- **Base Rate**: Most new Prime Ministers retain approval above 50% in the absence of significant negative developments within the first few months. Starmer’s base rate suggests he should *just* manage to stay afloat unless something substantial changes. - -#### 5. **Final Probability Assessment** -- Given that Starmer's approval is already rather low (36%), the potential for it to increase significantly without major positive developments is limited, while the chance for it to decrease further could be higher if he mismanages the early months. - -- However, the slight honeymoon effect coupling with the absence of any immediate crises might mean that approvals don't necessarily dip drastically that soon. - -- **Main Assumptions**: - - No extraordinary negative external events occur. - - The polling methodology remains consistent and unbiased. - -#### 6. **Prediction Distribution** -- **Base Case**: Starmer’s approval remains fairly stable around where it is, with a gradual, slight decline as optimistic expectations fade. - -- **Extreme Cases**: - - **High-end**: His approval could improve slightly if early policies hit the right notes and the economic situation slightly improves. - - **Low-end**: Enduring or emerging scandals, poor economic handling, or mishaps could push his approval lower, possibly below 50%, but this drop is less likely to happen rapidly. - -#### 7. **Final Probability** -With all the factors considered: - -- **Margin**: Likelihood of approval falling below 50% is plausible but not overwhelmingly likely within the short remaining timeframe, presuming no crises. - -**Final Probability: 40%** - -This reflects the relatively balanced risk of a dip in approval vs. the likelihood of stability in the near term." -27240,"Will Keir Starmer's approval rating as Prime Minister of the United Kingdom be less than 50 on September 18, 2024?","PERPLEXITY - -To provide context for the question of whether Keir Starmer's approval rating as Prime Minister of the United Kingdom will be less than 50 on September 18, 2024, we need to examine recent polls, public perceptions, and expert opinions. - -### Recent Polls and Public Perceptions - -- **Public Perception**: As of early July 2024, half of Britons (49%) say they don’t know what Keir Starmer stands for, and his net favorability score is -18, with 36% having a favorable view and 54% an unfavorable one. -- **Comparative Approval**: Starmer is seen as more capable than his predecessor, Rishi Sunak, with a net favorability rating of -17% compared to Sunak's -51% in May 2024. -- **Initial Approval Ratings**: After his first week as Prime Minister, 36% of the public thought Starmer was doing a good job, the highest rating for a premier since February 2021, and only 14% thought he was doing a bad job. - -### Expert Opinions and Analysis - -- **Leadership Style**: Starmer is described as a social liberal and fiscal moderate, promising stability and competency, which contrasts with the perceived missteps of the Conservative Party. -- **Challenges Ahead**: Experts note that Starmer faces high expectations and a country hobbled by years of austerity and economic instability. His ability to deliver against public priorities will dictate his political fortunes. -- **Historical Context**: Labour's victory marks a significant shift after 14 years of Conservative rule, akin to Tony Blair's landslide victory in 1997. - -### Prediction Markets and Betting Odds - -While specific betting odds or prediction market data on Keir Starmer's approval rating by September 18, 2024, are not provided in the sources, it's crucial to monitor these platforms for up-to-date forecasts. - -### Conclusion - -Given the recent polls and expert opinions, Keir Starmer enjoys a honeymoon period with higher approval ratings than his predecessor. However, the long-term challenge lies in managing high public expectations and addressing the country's economic and social issues. Whether his approval rating will be less than 50 by September 18, 2024, depends on his government's performance and ability to deliver on promises. - -######### - -GPT - -### **Step 1**: Define the Problem and Context - -- **Question**: Will Keir Starmer's approval rating as Prime Minister be less than 50 on September 18, 2024? -- **Current Date**: August 16, 2024, so the prediction involves a time horizon of just over a month. -- **Base Context**: Starmer recently became Prime Minister after a landslide victory and is currently in a honeymoon period. His approval rating is higher compared to his predecessor, but still faces significant challenges due to the fragmented vote and low turnout. - -### **Step 2**: Analyze Recent Polling Data & Trends - -- **Polling Data**: - - Polls indicate that as of early July 2024, 36% of the public viewed Starmer favorably, and 49% weren't sure what he stands for, with a net favorability score of -18. - - After his first week as PM, 36% thought Starmer was doing a good job, the best rating for a PM since 2021. - -- **Historical Approval Ratings**: - - Leaders often experience a dip in approval as the honeymoon phase ends and real governance challenges emerge. However, during the initial months, approval ratings tend to be inflated. - - The approval rating for UK Prime Ministers generally fluctuates based on their handling of crises, policy decisions, and the economy. - -### **Step 3**: Consider Political and Economic Factors - -- **Economic Conditions**: The UK has been in a turbulent economic phase, with prolonged austerity measures and recent shockwaves from Brexit and global inflation. Economic performance and public perception around managing critical issues, such as inflation and cost of living, play a significant role in approval ratings. - -- **Public Sentiment**: The fact that 49% of the public didn't know what Starmer stood for as of July suggests that his approval ratings could be volatile and subject to change based on high-profile decisions or events. Moreover, dissatisfaction stemming from low voter turnout could translate into future discontent with his leadership. - -- **Key Risks**: - - **Economic Missteps**: Any major economic mismanagement or worsening of the cost-of-living crisis could significantly lower Starmer’s approval. - - **Political Crises**: If Starmer fails to effectively manage party unity or if any scandals emerge, his approval could suffer. - - **Global Events**: Unforeseen global events, such as geopolitical tensions or international economic downturns, could impact domestic approval ratings. - -### **Step 4**: Base Rates and Historical Precedents - -- **Base Rate Data**: Historically, UK Prime Ministers see a drop in approval ratings after the first few months in office as the complexities of governance form public opinion. However, a plunge below 50 within the first few months is less common unless triggered by specific crises. - -- **Recent Precedents**: Recent UK Prime Ministers, like Boris Johnson during COVID-19 or Liz Truss during her short tenure, saw rapid changes in approval ratings. Nonetheless, moderate shifts within a few months without severe crises tend to stay within a 40-60% range. - -### **Step 5**: Evaluation of Forecast Biases - -- **Positive Bias (Honeymoon period)**: Traditional positive bias related to the initial months in office might overestimate Starmer’s approval ratings but should be weighed against growing public scrutiny. - -- **Negative Bias (Economic conditions)**: Economic difficulties may depress his ratings but need to be weighed against the public's expectations and preconceptions set by the previous government. - -### **Step 6**: Final Probability Estimation - -Given the analysis: - -1. **Current Indicators**: Polls suggest that starting approval ratings are relatively high but not outstanding. With a potential dip after the honeymoon period or any political/economic crises, Starmer’s approval rating falling below 50 is plausible. - -2. **Timeframe Consideration**: A month remains before the resolution date; barring major crises, a drastic decline is unlikely but possible. - -3. **Historical Comparisons**: Most leaders' approval ratings fall over time if no significant positive or negative events occur, but a drop below 50 within the first few months might still require a triggering event. - -Given the data and contextual factors: - -- There is **moderate likelihood** that Starmer’s approval might drop below 50. - -**Final Answer**: ""Probability: 60%."" - -This reflects a moderate belief that Starmer's approval rating will likely be <50, but the possibility remains of it maintaining above that threshold due to current favorable perceptions and the absence of a decisive negative event within the remaining time window." -27259,"Before October 1, 2024, will Tech Crunch report new layoffs at Netflix?","PERPLEXITY - -To answer this question, we need to examine the recent layoff trends and reports involving Netflix and the broader tech industry. Here are the key points from the provided sources: - -1. **Netflix Layoffs in 2024**: According to the sources, Netflix has already conducted layoffs in 2024. For instance, in June 2024, Netflix laid off 300 staff due to slowed revenue growth. - -2. **Tech Industry Layoff Trends**: The tech industry has seen significant layoffs in 2024, with over 60,000 job cuts across 254 companies as of August 2024. This trend suggests that many tech companies, including Netflix, are adjusting their workforces in response to economic conditions and over-hiring during the pandemic. - -3. **Recent Layoff Reports**: As of the latest reports in August 2024, there are no new layoffs announced by Netflix. However, given the ongoing trend of layoffs in the tech industry and Netflix's previous actions, it's plausible that Netflix could announce further layoffs before October 1, 2024. - -4. **Prediction Markets and Expert Opinions**: While there are no specific prediction markets or expert opinions provided in the sources regarding new layoffs at Netflix before October 1, 2024, the overall context suggests that the tech industry is still in a period of adjustment, which could lead to additional layoffs. - -In summary, while there are no recent announcements of new layoffs at Netflix as of August 2024, the ongoing trend of layoffs in the tech industry and Netflix's past actions suggest that further layoffs could be possible before October 1, 2024. However, without specific prediction markets or expert opinions on this matter, the likelihood remains speculative. - -######### - -GPT - -### Step 1: Analyze the Baseline - -- **Historical Data**: - - Netflix has conducted sporadic layoffs in recent years, including in 2023 and 2022. - - In 2024, it already conducted layoffs in June, affecting 300 staff due to slowed revenue growth. This suggests a precedent for layoffs within the same year, which is not uncommon for the company. - -- **Current Trends in the Tech Industry**: - - Broader industry trends also indicate significant layoffs in the tech sector during 2024. This is likely due to a combination of economic factors, including slowdown after aggressive hiring during the pandemic, inflationary pressures, and possibly underperformance of some segments in the market. - -### Step 2: Account for the Remaining Timeframe - -- **Time Until October 1, 2024**: - - There are approximately 6 weeks remaining before the resolution date. - - Historically, layoffs at major tech companies are often announced unexpectedly with little lead time (a few days to a couple of weeks at most). Hence, the time remaining is sufficient for another round of layoffs to occur. - -- **Industry Context**: - - Given the context of widespread layoffs across the industry, Netflix may be under pressure to continue streamlining its operations or reducing costs if revenue growth remains sluggish. Normally, broader industry pressures can serve as a lead indicator of potential actions across big players such as Netflix. - -### Step 3: Assess the Likelihood Based on Base Rates - -- **Previous Layoff Frequency**: - - The historical precedent shows Netflix has not shied away from multiple rounds of layoffs in a year if needed. They have already conducted layoffs in June 2024, implying they might again if circumstances compel them to do so. - -- **Economic and Industry Trends**: - - As economic pressures continue, especially with a high pace of layoffs in the tech industry, it’s plausible Netflix might enact another round of layoffs in line with or in response to ongoing economic stressors. - -### Step 4: Weigh the Evidence and Final Probability Estimation - -- **Critical Factors**: - - The cumulative evidence from multiple partial layoffs at Netflix in previous years. - - Broader tech industry layoffs in 2024, which indicate ongoing economic stress within the sector. - - A new round of layoffs being reasonably probable within the next 6 weeks, especially given Netflix’s sporadic track record and other tech firms' behavior this year. - -- **Potential Contra-indicators**: - - If Netflix has already completed a significant round of layoffs in June, they may have optimized their workforce sufficiently, reducing the likelihood of additional layoffs unless new negative economic information emerges. - -### Conclusion: Final Probability Estimate - -Based on the above analysis: - -- **Historical Precedent (Base Rate)**: Netflix completes multiple layoffs in a year during economically challenging periods. -- **Remaining Timeframe**: There is ample time for new layoffs to be announced and reported by Tech Crunch. -- **Industry Conditions and Pressures**: Broader industry conditions might compel Netflix to act further, in line with many of its peers. - -Given the context, a new round of layoffs at Netflix is plausible but not inherently certain. - -**Probability: 65%**" -27262,"Between August 21 and Sept 11, 2024, will the strongest geomagnetic storm have a K-index (""Kp"") greater than 7 and less than or equal to 8?","PERPLEXITY - -To answer the query about whether the strongest geomagnetic storm between August 21 and September 11, 2024, will have a K-index (""Kp"") greater than 7 and less than or equal to 8, we need to consider the current state of solar activity, forecasts, and historical data on geomagnetic storms. - -1. **Current Solar Activity and Forecasts**: - - The current solar cycle is nearing its peak, expected around 2025. This period is associated with increased solar activity, including sunspots, solar flares, and coronal mass ejections (CMEs), which can cause geomagnetic storms. - - Forecasts from the Geophysical Institute indicate varying levels of geomagnetic activity, with Kp indices ranging from 2 to 4 in the near term. However, these forecasts are subject to change based on real-time solar activity. - -2. **Historical Data on Geomagnetic Storms**: - - Historical data show that geomagnetic storms with Kp indices greater than 7 are significant events. For example, the 7–8 September 2017 G4 storm had Kp values exceeding 7 during several periods. - - The study on auroral oval boundary dynamics during geomagnetic storms highlights the variability and intensity of such events, with some storms reaching Kp values of 7 or higher. - -3. **Prediction Markets and Expert Opinions**: - - While specific prediction markets for geomagnetic storm intensities are not mentioned in the provided sources, expert opinions suggest that the current solar cycle's peak could lead to increased geomagnetic activity. However, predicting the exact intensity of future storms is challenging. - -4. **Recent Updates and Movement Towards Resolution**: - - Recent updates, such as the May 10-13 geomagnetic storm, demonstrate the potential for significant geomagnetic activity. This event reached Kp6 levels and was expected to possibly reach G5 conditions, indicating the unpredictability and potential severity of such storms. - -Given the information available, there is an increased likelihood of significant geomagnetic storms due to the approaching solar maximum. However, predicting a storm with a Kp index greater than 7 and less than or equal to 8 between August 21 and September 11, 2024, specifically, requires real-time monitoring of solar activity and space weather forecasts. - -######### - -GPT - -### Background Analysis - -We are tasked with predicting whether the strongest geomagnetic storm between August 21 and September 11, 2024, will have a K-index (Kp) greater than 7 and less than or equal to 8. This forecasting challenge is a **Non-Market Threshold Value Event**, where we have to determine the likelihood of a specific Kp index range falling within predefined parameters during a specified period. - -### Key Considerations - -#### 1. **Solar Activity and Solar Cycle Context** -- We are currently near the peak of Solar Cycle 25, with the peak expected in 2025. Solar maximums are associated with high solar activity, including more frequent and intense sunspots, solar flares, and CMEs. -- The active sunspot region, AR3697, has been creating significant solar disturbances, including M- and X-class flares in May and potentially June of 2024. These flares are capable of producing CMEs, which can drive geomagnetic storms on Earth. -- Given that AR3697 continues to be a focus of attention, and its history suggests volatility, it is plausible that it could still produce further significant solar events. - -#### 2. **Kp Index Dynamics** -- The Kp index is a measure of geomagnetic activity, with values ranging from 0 (very quiet) to 9 (extremely disturbed). For this question, we are interested in the probability that the Kp index will be between 7.1 and 8.0 during the period in question. -- **Historical Occurrences:** Historically, Kp values between 7 and 8 are uncommon but not rare during solar maximums. Geomagnetic storms reaching this level tend to correlate with strong CMEs and related solar events. -- **Recent Trends and Projections:** Recent geomagnetic activity during this solar cycle has seen Kp values spike into the 5-7 range on multiple occasions, particularly in May 2024. Given the nature of solar cycles, as we move closer to the solar maximum, the chances of experiencing strong geomagnetic storms typically increase. - -#### 3. **Environmental & Base Rate Analysis** -- **Base Rates:** According to historical records, during a solar maximum, the probability of a geomagnetic storm exceeding a Kp of 7 but not surpassing 8 over a three-week period is relatively low. In solar cycles, such strong storms occur a few times per year but can cluster around active periods. -- **Environmental Factors:** There are no additional significant skew factors such as unusual cosmic events (e.g., galactic cosmic ray bursts) or significant geomagnetic precursors that might either elevate or reduce the general probability distribution. - -#### 4. **Forecasts and Volatility** -- **Space Weather Forecasts:** Forecasts can change rapidly depending on real-time solar observations. The forecast for Kp values in the immediate days around the target period shows moderate activity (around 2-4) with no immediate indicators pointing towards significant flares or CMEs. However, forecasts alone might be unreliable beyond a few days. - -#### 5. **Default Expectation** -- **Default Behavior:** If no solar event occurs, the default expectation is that the Kp values would remain within the low to moderate range (0-5). The occurrence of a Kp index in the 7.1-8.0 window necessitates a significant solar event like a CME or a series of intense flares. - -### Probability Estimate - -Given the confluence of factors: - -#### **Likelihood of Active AR3697 Producing a Strong Event**: -- AR3697 is still active and facing Earth again, meaning there's a decent probability it could produce another significant solar flare or CME that could result in a geomagnetic storm exceeding a Kp of 7 but staying under 8. - -#### **Historical Precedents & Base Rates**: -- The historical likelihood of a Kp index between 7 and 8 over a short period is rare but not negligible, particularly when solar maximum is near. - -#### **Uncertainty Considerations**: -- Rapid changes in solar activity could occur that are not currently predictable. -- The exact timing and strength of potential flares can create a wide variance in the Kp index values. - -By weighing these factors, I estimate the probability for the strongest geomagnetic storm during the specified period having a Kp index greater than 7 but less than or equal to 8 as **Probability: 35%**. - -This reflects the scenarios where the significant solar activity predicted from AR3697 coincides with Earth-directed CMEs inducing strong but not extreme geomagnetic storms, balanced against the likelihood that no such events will occur within the specific time window." -27261,"Between August 21 and Sept 11, 2024, will the strongest geomagnetic storm have a K-index (""Kp"") greater than 6 and less than or equal to 8?","PERPLEXITY - -To address the query regarding the strongest geomagnetic storm between August 21 and September 11, 2024, having a K-index (""Kp"") greater than 6 and less than or equal to 8, we need to consider various sources that provide insights into geomagnetic activity and the forecasting of such events. - -1. **Geomagnetic Storms and Kp Index**: - - The Kp index is a measure of geomagnetic activity, ranging from 0 (very quiet) to 9 (very disturbed) in 28 discrete steps. A Kp index greater than 6 indicates a significant geomagnetic storm. - -2. **Forecasting and Historical Data**: - - The Geophysical Institute provides daily forecasts of geomagnetic activity, which can indicate periods of heightened Kp levels. However, the forecast available only goes up to a certain date and does not cover the entire period of interest (August 21 to September 11, 2024). - - Historical data and research on geomagnetic storms suggest that periods of high solar activity, such as solar flares and coronal mass ejections (CMEs), can lead to significant geomagnetic storms. The impact of these events on Earth's magnetic field depends on the orientation and strength of the magnetic field within the CME. - -3. **Solar Activity and Geomagnetic Storms**: - - Solar flares and CMEs are key drivers of geomagnetic storms. An X5.89 long-duration solar flare was reported to have produced a CME, but preliminary analysis suggested it was heading northward and would not have a significant Earth-directed component. However, a glancing blow could not be ruled out. - - The strength and orientation of the magnetic field within a CME are crucial in determining the severity of the geomagnetic storm it can cause. High solar wind speeds and densities also contribute to increased geomagnetic activity. - -4. **Prediction and Monitoring**: - - NOAA/SWPC estimates the Kp and Ap indices based on real-time data from magnetometer stations around the world. These estimates are crucial for predicting geomagnetic storms. - - Research on geomagnetic storms and their effects on electrical power grids has shown that intense storms can cause significant disruptions. Indicators such as sudden storm commencement, Kp ≥ 5, and fast halo CMEs are used to assess the impact of these storms. - -Given the information available, predicting the exact Kp index of the strongest geomagnetic storm between August 21 and September 11, 2024, is challenging without real-time data or specific forecasts covering that period. However, understanding the factors that contribute to geomagnetic storms, such as solar flares, CMEs, and the orientation of the magnetic field within these events, is crucial for making informed predictions. Monitoring real-time data and forecasts from reliable sources like NOAA/SWPC and the Geophysical Institute will be essential for accurate predictions closer to the date. - -######### - -GPT - -### Rationale and Chain of Thought - -#### 1. **Understanding the Problem** -- The question is asking whether the highest Planetary K-index (Kp) will be greater than 6 and less than or equal to 8 between August 21 and September 11, 2024. -- The Kp index ranges from 0 to 9 and is used to quantify geomagnetic activity. -- A Kp index greater than 6 indicates a significant geomagnetic storm, and a storm with a Kp value between 6.33 and 8.0 would be categorized as either a strong (G3, Kp=7) or moderate geomagnetic storm (G2, Kp=6). - -#### 2. **Base Rates from Historical Data** -- According to historical data, significant geomagnetic storms (Kp > 6) are relatively rare but more likely during times of increased solar activity. -- Solar cycles typically last around 11 years, and solar maximum (a period of increased solar activity) is correlated with a higher frequency of geomagnetic storms. The current solar cycle (Solar Cycle 25) is predicted to reach its peak around mid-2025, with increased activity already noted throughout 2024. - -- **Strong Geomagnetic Storms (Kp > 6):** Typically, during a solar maximum, there could be several storms with a Kp > 6 in a given month. -- **Moderate Geomagnetic Storms (6 < Kp ≤ 8):** Based on NOAA archives and historical data, these occur during active phases approximately once every 1-2 months, more often during a solar maximum. - -#### 3. **Recent and Current Solar Activity** -- AR3697 has been a source of intense solar flares and CMEs in the recent past. Given that these events have already produced significant geomagnetic storms earlier in 2024, the likelihood of additional storms when the region comes into Earth-facing view again is a factor. -- X-class and M-class solar flares, particularly with Earth-directed CMEs, could easily generate geomagnetic storms with Kp indices in the range of interest (6.33 to 8). - -#### 4. **Time Frame and Forecasting** -- The window between August 21 and September 11 represents a 22-day period. Given the fluctuation in solar activity, there is ample time for solar events to occur that could influence geomagnetic activity. -- In typical forecasting models for geomagnetic activity, periods of active geomagnetic storms are often clustered with quiet periods, suggesting that if an active storm period begins, several storms could happen in quick succession. - -#### 5. **Uncertainty and Probability Distribution** -- **Solar Activity Uncertainty:** Despite predictive models, solar weather remains inherently probabilistic, with precise bidding on storm severity and timing being difficult until imminent events are observable. -- **Likelihood Given Current Data:** Given the context, the likelihood of AR3697 producing additional flares and CMEs that impact Earth is moderate to high. The uncertainty comes primarily from the orientation and velocity of the CMEs and how they interact with Earth's magnetosphere. - -### Conclusion & Probability Estimate - -- **Baseline Historical Context:** Given past data, the Kp going above 6 is likely in this period, especially with a region like AR3697 in play. The probability of one or more severe geomagnetic storms (Kp > 6) occurring in a 22-day window around solar maximum is fairly high. - -- **Pattern and Anomaly Consideration:** - - The question focusing on a Kp between 6 and 8 covers a relatively common range for storms during heightened solar activity phases. - - **Proximity to Solar Maximum** amplifies the likelihood of geomagnetic storms falling in the 6 to 8 Kp range, making this threshold realistic if such a storm occurs. - -- Given this information, accounting for both the historical frequency of such events and the ongoing intense solar activity: - -**Final Probability: 70%** - -This reflects a strong likelihood, though not certainty, for the Kp index to hit the specified range within the given timeframe." -27260,"Will Big Lots file for bankruptcy before September 30, 2024?","PERPLEXITY - -To address the question of whether Big Lots will file for bankruptcy before September 30, 2024, we need to consider several key points from the provided sources: - -1. **Financial Health and Bankruptcy Probability**: - - **Macroaxis** estimates Big Lots' probability of bankruptcy to be under 35%, indicating a slight risk of financial hardship in the near future. - -2. **Store Closures and Financial Performance**: - - Big Lots announced plans to close up to 40 stores initially, citing financial difficulties and ""substantial doubt"" about its ability to continue as a going concern. - - This number was later increased to 315 stores, roughly a quarter of its total locations, as part of an effort to secure its finances and operate more efficiently. - -3. **Economic Challenges**: - - The company faces challenges such as elevated inflation and decreased consumer spending, which have impacted its sales and profitability. - - Big Lots reported a 10% decrease in sales and a $205 million loss for the quarter, further straining its liquidity. - -4. **Transformation Plan and Liquidity Measures**: - - Despite financial struggles, Big Lots has implemented a transformation plan, dubbed Project Springboard, aimed at improving its value proposition and securing additional liquidity. - - The company has taken steps to improve its financial position, including securing a new loan and considering the sale or leaseback of assets. - -5. **Expert Opinions and Market Performance**: - - Analysts note that while Big Lots has secured liquidity, it remains in a precarious financial position, with significant losses and cash burn. - - Shares of Big Lots (BIG) are down nearly 90% for the year, reflecting market concerns about its financial health. - -Given these points, while Big Lots is taking measures to address its financial challenges, the company's current situation and the broader economic context suggest a significant risk of bankruptcy. However, the specific timing of such an event, including whether it would occur before September 30, 2024, remains uncertain and would depend on various factors, including the success of its transformation plan and the company's ability to manage its financial obligations. - -######### - -GPT - -### **Step 1: Analyze the Background and Current Context** - -#### **1. Financial Health and Bankruptcy Probability** -- **Macroaxis** estimates a <35% chance of bankruptcy, which indicates that while the probability is non-negligible, it's not extremely high. However, their formula may not fully incorporate the latest financial data and market conditions. -- **Key Points**: - - A percentage below 35% does provide early warning signs, but doesn't strongly suggest imminent bankruptcy in the next month. - -#### **2. Store Closures and Financial Performance** -- Big Lots has announced closing 315 stores (~25% of its total), which indicates significant distress. -- **Key Points**: - - Downsizing so drastically reflects severe liquidity issues and cost-cutting measures due to underperformance in numerous locations. - - This could be a defensive strategy to avoid imminent collapse, or it might be insufficient to stem a worsening situation. - -#### **3. Economic Challenges** -- The company is facing macroeconomic headwinds like inflation and decreased consumer spending, further exacerbating its internal problems. -- **Key Points**: - - The $205 million loss in the last quarter is quite significant, suggesting a bleak short-term outlook and limited maneuverability. - -#### **4. Transformation Plan and Liquidity Measures** -- Big Lots has implemented a ""Project Springboard"" and is looking into asset sales and loan agreements to stabilize its financial health. -- **Key Points**: - - Securing liquidity and undergoing a transformation plan suggests an awareness of financial strife and leaders actively working on stabilization. - - However, if these efforts are deemed insufficient by creditors and stakeholders, bankruptcy could still be inevitable, despite this last-ditch effort. - -#### **5. Expert Opinions and Market Performance** -- Share prices have collapsed by nearly 90% this year, affirming that market confidence is extremely low. -- **Key Points**: - - Share price collapses are a red flag, often signaling diminished confidence from investors and lenders alike. - - This sharp decline increases the likelihood that Big Lots could lose access to critical capital. - -### **Step 2: Base Rates and Timing** - -- **Base Rate of Retail Bankruptcies**: Retail bankruptcies for companies with distressed financials are somewhat high due to the nature of the retail industry and rapid burning through cash reserves. -- **With 40 days remaining**: Companies with roughly 40 days to go before a specific endpoint often have reductionist chances of major events like bankruptcy filing unless an immediate catalyst is present. Conversely, they may use last-ditch strategies to delay or avoid a collapse, even if temporarily. - -### **Step 3: Weigh Each Factor** - -- **Liquidity Measures and Transformation Strategy**: While efforts are being made towards a turnaround, these do not guarantee success and might only serve to defer an inevitable bankruptcy. -- **Store Closures and Recent Performance**: Signs point to severe distress, yet Big Lots might still drag out the process to avoid a rapidly approaching bankruptcy filing. -- **Market Sentiment**: The 90% stock devaluation suggests a crisis of confidence, often a precursor to bankruptcy when compounded by operating losses and structural challenges. - -### **Step 4: Compare to Historical Precedent** - -- **Retail Chains**: - - Retail chains often file for bankruptcy when desperate before they completely run out of liquidity. However, turnaround attempts can push the timeline by a few months or contingent upon an unsuccessful last effort. - -### **Step 5: Conclusion and Probability Determination** - -Everything considered—financial distress, store closures, liquidity measures, broader economic environment, and the short timeframe remaining—it's essential to acknowledge that while Big Lots appears highly distressed, they are actively engaged in measures to stave off filing. Given that there's only about 40 days till September 30, conceivably, these efforts could be sufficient to delay filing until beyond that date. - -However, total financial collapse within that timeframe is possible, but less probable than non-bankruptcy scenarios in the very short term. With closing stores, protecting whatever liquidity remains, and minimal runway left, the risk is significant but not yet overwhelmingly imminent with only a month and a half to go. The probability of serious financial problems leading to a judicial bankruptcy filing within this time frame is notable but doesn't yet outweigh the countermeasures available. - -### **Final Probability**: **Probability: 45%** -This is based on the assumption that while there is significant risk, ongoing efforts to stave off bankruptcy might prove narrowly successful. The relatively short time window makes an immediate bankruptcy less probable, but still possible." -27258,"Will Jeff Bezos be the 1st or 2nd richest person in the world on September 30, 2024, according to Forbes?","PERPLEXITY - -To answer whether Jeff Bezos will be the 1st or 2nd richest person in the world on September 30, 2024, according to Forbes, we need to examine the current net worths of the top billionaires and any recent trends or fluctuations in their wealth. - -As of August 2024, Elon Musk is the richest person in the world with a net worth of approximately $222 billion, followed by Jeff Bezos with a net worth of around $186 billion. Another source from August 1, 2024, places Musk's net worth at $240.7 billion and Bezos' at $204.8 billion. These figures indicate that Bezos' position as the second richest person is consistent across recent reports. - -However, the rankings can change rapidly due to stock market fluctuations. For instance, Bezos briefly surpassed Musk as the richest person in the U.S. in early March 2024, but Musk reclaimed the title shortly thereafter. This volatility suggests that while Bezos is currently the second richest, his position could change by September 30, 2024. - -Given the information available up to August 20, 2024, and without specific predictions or market analysis beyond this date, it's reasonable to infer that Bezos could remain the second richest person by September 30, 2024, based on recent trends. However, the dynamic nature of stock market values means that any significant changes in the fortunes of Musk or Bezos could alter their rankings. - -**Current Rankings as of August 2024:** -1. Elon Musk - $222 billion (or $240.7 billion as of August 1, 2024) -2. Jeff Bezos - $186 billion (or $204.8 billion as of August 1, 2024) - -**Prediction Markets and Expert Opinions:** -No specific prediction markets or expert opinions are provided in the sources regarding the exact ranking of Jeff Bezos by September 30, 2024. However, the fluctuating nature of their net worths suggests that any predictions would need to account for potential market volatility. - -**Recent Updates and Movements:** -The most recent update shows Musk reclaiming the top spot after briefly losing it to Bezos in early March 2024. This indicates that while Bezos could potentially surpass Musk again, Musk's diverse investments, including his recent $6 billion fundraising for xAI, contribute to his ability to maintain the top position. - -In conclusion, while Jeff Bezos is currently the second richest person in the world, the dynamic nature of the stock market means that his position could change by September 30, 2024. Without specific predictions or further market analysis, it's challenging to definitively state his ranking at that future date. - -######### - -GPT - -**Step 1: Analyze Current State and Historical Data** - -As of August 20, 2024, Jeff Bezos is currently the second wealthiest person in the world, according to Forbes, with a net worth close to approximately $204.8 billion (according to the most recent and higher of the August values). Elon Musk leads with a substantial gap at $240.7 billion. The ranking has been relatively consistent, although there have been periods of volatility, with Bezos occasionally surpassing Musk. - -**Step 2: Consider Timeframe and Market Volatility** - -The question concerns Bezos's ranking on September 30, 2024, which is approximately 6 weeks from the current date. Given the short timeframe, it’s crucial to assess how likely significant shifts in the rankings are within this period. Both Musk's and Bezos's wealth are heavily tied to the stock performance of the companies they are most associated with—Tesla (for Musk) and Amazon (for Bezos). Market volatility can dramatically alter their net worths. However, it's essential to contextualize this volatility: - -- **Stock Market Behavior**: - - The broader market conditions between now and the end of September could affect Tesla and Amazon stock prices, but there is limited time for drastic shifts. While the U.S. economy has shown increased volatility in recent months, large changes that would significantly impact Bezos or Musk's positions are statistically less likely over just six weeks. - -- **Company-Specific News or Events**: - - Musk's recent activities, such as fundraising for xAI, indicate ongoing business expansion ventures, which could contribute to or detract from his wealth depending on investor sentiment. Any significant news related to Tesla or SpaceX could cause fluctuations in his net worth. - - Bezos is comparatively less active day-to-day in the media spotlight, having stepped back from the daily leadership of Amazon. While the retail and cloud computing sectors remain strong, any significant shifts would likely require more time to materially impact Bezos’s wealth relative to Musk. - -**Step 3: Consider Base Rates of Sudden Wealth Changes** - -In the past, mega-billionaires like Bezos and Musk have seen fluctuations in their wealth, but significant and sustained shifts are relatively rare over such short periods. Being second in wealth, Bezos would need significant positive performance in Amazon stock (or a substantial decrease in Tesla stock) to challenge Musk’s position directly for first. Meanwhile, if any major decline occurred in Bezos's wealth, the most immediate challenge would be whether Bernard Arnault or anyone else could surpass him by September 30, 2024. - -Given that Arnault trails by a substantial margin, it would require an extraordinary event for him to leapfrog Bezos into second, but such events are not unprecedented. - -**Step 4: Resolution Criteria and Final Consideration** - -According to the resolution criteria, Bezos must be *either* first *or* second on the list. This means that as long as Bezos remains second or even moves up to first, the resolution is ""Yes."" - -Given the current wealth disparities and the limited time for change, Musk will likely remain first, and Bezos likely second. The most considerable threat would be a combination of negative events for Amazon and positive ones for Bernard Arnault’s business ventures, substantially improving Arnault's relative position. - -However, an analysis of base rates and the consideration of typical market volatility over short periods suggest that Bezos is more likely than not to maintain his second-place standing. - -**Conclusion:** -- Bezos remains favored to be first or second, most likely remaining in second. -- While shifts in fortune can happen, the timeframe limits the probability of dramatic change. - -**Final Answer:** -**Probability: 80%** that Jeff Bezos will be either the 1st or 2nd richest person in the world on September 30, 2024, according to Forbes." -27263,"Between August 21 and Sept 11, 2024, will the strongest geomagnetic storm have a K-index (""Kp"") greater than 6 and less than or equal to 7?","PERPLEXITY - -To answer the query about whether the strongest geomagnetic storm between August 21 and September 11, 2024, will have a K-index (""Kp"") greater than 6 and less than or equal to 7, we need to consider the current solar activity, the solar cycle phase, and any recent or predicted geomagnetic storms within this timeframe. - -1. **Solar Cycle Phase**: The solar cycle, which affects the frequency and intensity of geomagnetic storms, peaks approximately every 11 years. The last solar maximum was in 2014, and the next is expected around 2025. This means that in 2024, we are approaching the peak of the solar cycle, a period when stronger geomagnetic storms are more likely. - -2. **Geomagnetic Storms and Kp Index**: The Kp index is a measure of geomagnetic activity, ranging from 0 (very quiet) to 9 (very disturbed). A Kp index greater than 6 indicates a significant geomagnetic storm. Such storms can cause auroras at lower latitudes and potentially disrupt electrical grids and communication systems. - -3. **Recent and Predicted Activity**: As of the latest available data in May 2024, there was a significant geomagnetic storm with a Kp index reaching 7, indicating a G3-level storm. This event demonstrates that the current solar activity is capable of producing storms within the Kp range of interest (greater than 6 and less than or equal to 7). - -4. **Forecasting**: Predicting geomagnetic storms involves monitoring solar flares, coronal mass ejections (CMEs), and the solar wind's speed and magnetic field orientation. While specific forecasts for the period between August 21 and September 11, 2024, are not provided in the available data, the increasing solar activity as we approach the solar maximum in 2025 suggests an elevated likelihood of significant geomagnetic storms. - -Given the approaching solar maximum and the recent occurrence of a G3-level storm, there is a reasonable likelihood that between August 21 and September 11, 2024, a geomagnetic storm could occur with a Kp index greater than 6 and less than or equal to 7. However, precise forecasting of such events is challenging and depends on real-time monitoring of solar activity and space weather conditions. - -######### - -GPT - -### Analyzing the Problem Step by Step: - -#### 1. **Understanding the Question and Resolution Criterion:** - - The task at hand is to predict whether the highest K-index (""Kp"") value between August 21, 2024, and September 11, 2024, will be greater than 6 but less than or equal to 7. - - The resolution criterion requires this event to be captured in the data product from the German Research Center for Geosciences, where the K-index is tracked with fine granularity (rounded to the nearest thousandth). - -#### 2. **Contextual Information Assessment:** - - **Solar Activity and Solar Cycle:** - - Solar activity follows an approximately 11-year cycle, with periods of maximum activity (referred to as the solar maximum) and minimum activity. During the solar maximum, the likelihood of significant solar phenomena like solar flares and coronal mass ejections (CMEs) rises, which directly impacts geomagnetic activity on Earth. - - We are currently near the peak of Solar Cycle 25, expected around 2025, suggesting an increased probability of geomagnetic storms in 2024. - - - **Sunspot Activity:** - - The return of sunspot region AR3697, which previously produced intense geomagnetic storms, is a significant consideration. This region has demonstrated the capability to trigger geomagnetic storms with Kp values in the range of interest. - - - **Recent Activity and forecasts:** - - Notably, a G3-level storm (Kp = 7) occurred in May 2024. Given that AR3697 returns to face Earth, there’s concern that similarly strong activity could occur. - - Space weather forecasters have been closely monitoring this sunspot region, implying an elevated likelihood of significant geomagnetic activity in the near future. - -#### 3. **Base Rate and Past Data Analysis:** - - **Historical Kp Index Distribution in Solar Maximum Years:** - - Historically, during years around the solar maximum, Kp values fluctuate widely. However, values greater than 6 but less than or equal to 7 are relatively uncommon but not rare during active periods. - - Historical analysis shows that geomagnetic storms with Kp values of 7 are more frequent than those that reach values of 8 or 9, especially during periods of heightened solar activity. - -#### 4. **Statistical and Probabilistic Assessment:** - - **Period Length Consideration:** - - The prediction window is narrow: 21 days. - - With the currently active solar conditions and the history of AR3697's activity, it’s plausible that a number of geomagnetic disturbances could occur within this period. - - - **Conditional Probability Analysis:** - - Conditioned on the known active solar state and the fact that we are nearing a solar maximum, it’s reasonable to assign a higher probability to stronger geomagnetic storms (Kp > 6). Given the observable trends and the history of AR3697, a Kp of 6.6̅ to 7 is well within the expected range during this period. - -#### 5. **Considering Uncertainty:** - - Events with such a narrowly defined range (Kp > 6 but ≤ 7) face a combination of factors: while solar activity enhances the likelihood, the specific Kp range will always include some uncertainty due to the chaotic nature of solar weather and our current forecasting capabilities. - - It is also important to note that if the storm conditions are stronger or weaker than expected, the Kp value might fall outside the target range of 6 < Kp ≤ 7. - -### Conclusion: -- **Baseline Probability Estimation:** - Given the current active phase of the solar cycle, AR3697’s past behavior, and the time of year (approaching solar maximum), it is likely (but not overwhelmingly so) that a storm within the Kp range of interest could occur. - -- **Probability Calculation:** - Considering everything, I would estimate the probability that the Kp value between August 21 and September 11, 2024, will be greater than 6 and less than or equal to 7 is approximately **65%**. This estimation accounts for the high solar activity, the return of AR3697, and the proximity to the expected solar maximum, while also factoring in the unpredictability and the narrow range of the required Kp index. - -Final Answer: **Probability: 65%**." -27282,"Will the US see a large-scale riot between July 17, 2024 and Sept 30, 2024?","PERPLEXITY - -To address the question of whether the US will see a large-scale riot between July 17, 2024, and September 30, 2024, it's essential to consider various factors and sources that might indicate potential unrest. Here's a concise rundown of relevant information: - -1. **Political Tensions and Election Violence**: - - The period leading up to the 2024 US presidential election is expected to be highly contentious, with potential for violence from both far-right and far-left extremists. - - The risk of election-related violence is heightened due to ongoing political polarization and the presence of extremist groups. - -2. **Recent Protests and Unrest**: - - Recent public protests in support of Hamas have shown that left-wing rioting tactics used in 2020 can be repurposed for different causes, indicating a potential for future unrest. - - Conservative leaders are advised to prepare for possible violence by establishing interagency task forces to monitor and disrupt violent activist networks. - -3. **Homeland Security Threat Assessment**: - - The 2024 Homeland Threat Assessment indicates that the threat of violence from domestic extremists will remain high but largely unchanged, with a focus on lone offenders or small group attacks. - - The assessment also highlights the risk of foreign influence targeting election infrastructure and processes during the 2024 election cycle. - -4. **Expert Opinions and Recommendations**: - - Experts recommend that political leaders take proactive steps to shore up civic trust and prevent violence ahead of the 2024 election. - - Strategies include developing plans for state and local deterrence, riot control, and prosecution of those responsible for violence, as well as creating persuasive rhetorical strategies to manage public perceptions. - -5. **Current Events and Predictions**: - - As of the current date (August 20, 2024), there have been no large-scale riots reported within the specified timeframe. However, the potential for unrest remains due to the factors mentioned above. - -In summary, while there have been no large-scale riots reported between July 17, 2024, and the current date, the risk of such events occurring before September 30, 2024, cannot be ruled out due to heightened political tensions, the potential for election-related violence, and ongoing concerns about domestic extremism. - -######### - -GPT - -### Analytical Approach: - -#### Step 1: Consider the Base Rate - -Large-scale riots in the United States are rare events. When considering the base rate for a given period of a few months: - -- The 1992 Los Angeles riots meet the criteria for a large-scale riot but were an exception rather than the norm. -- The January 6th, 2021 Capitol Riot was much smaller in scale but was still a significant event; however, even this event would not meet the criteria set forth in the question. - -Base rates suggest that such large-scale events are extremely rare. Given that the question pertains to a roughly 2.5-month window, the default base rate probability is *very* low. - -#### Step 2: Consider the Current Context and Trends - -- The U.S. is in the lead-up to the 2024 Presidential Election, a period historically marked by heightened tensions, especially given the polarization in recent years. -- Recent public protests and incidents, like protests in support of Hamas, indicate that societal tensions can flare into violence. However, ongoing protests have not yet escalated into large-scale riots. -- The Department of Homeland Security’s 2024 Threat Assessment reports a high risk of domestic extremist activity, which could lead to scattered violence but not necessarily large-scale rioting. -- Political tensions are notably high, but the significant level of planning and monitoring by law enforcement and government agencies could mitigate or prevent the escalation of small incidents into large-scale riots. - -#### Step 3: Time Remaining Until Resolution - -The window for this forecast is closing, with only about six weeks remaining until September 30th. Historical precedent suggests that the sudden escalation of protests to the level meeting the stated criteria within six weeks is unlikely, barring an extraordinary trigger. - -#### Step 4: Evaluate the Specific Criteria - -Given the stringent criteria for what constitutes a ""large-scale riot"": -- **Deaths**: A riot resulting in 50 or more deaths in a single event is hard to envision unless there’s an extreme trigger, which seems unlikely given current events. -- **Arrests**: 10,000 arrests would require vastly scaled-up, sustained unrest over several days in a concentrated area, which is again improbable within the time remaining. -- **Property Damage**: Property damages reaching $1 billion in a single incident would require extreme, rapid escalation, likely in a densely populated urban area. - -Given recent history, even severe protests or violent incidents have not met these stringent criteria. - -#### Step 5: Account for Potential Changes - -While it's important to remain open to the possibility of events escalating unexpectedly, the relative quiet in recent weeks and the protective measures in place by authorities significantly reduce the probability of a large-scale riot within the provided timeframe. - -#### Final Probability Estimation: - -Taking all factors into consideration—the very low base rate for large-scale riots, the current situational assessment, the relative calm in the weeks after July 17, 2024, and the limited time remaining until September 30, 2024—the likelihood of a large-scale riot meeting the specified criteria seems very low, but non-zero given the heightened political tensions. - -**Probability: 5%** - -This estimate reflects both the rarity of such events and the potential, albeit low, for political or social triggers to unexpectedly shift existing trends." -27278,"On Sept 30, 2024 will more than 300 measles cases have been reported in the United States in 2024?","PERPLEXITY - -To address the query of whether more than 300 measles cases will have been reported in the United States by September 30, 2024, we need to examine the current trend and data available up to August 20, 2024. - -### Current Data and Trends - -1. **CDC Data as of August 15, 2024**: As of August 15, 2024, a total of 219 measles cases were reported by 27 jurisdictions in the United States. This includes 13 outbreaks, with 68% of cases (150 of 219) being outbreak-associated. - -2. **Comparison with Previous Years**: The number of measles cases in 2024 has already surpassed the total for 2023, which was 59 cases. This indicates a significant increase in measles cases this year. - -3. **Vaccination Rates**: The increase in measles cases is partly attributed to falling vaccination rates among children. In the 2022-23 school year, only 93.1% of kindergarteners in the US had completed their MMR vaccine series, which is below the CDC's target of 95%. - -4. **Global Context**: Globally, there has been an increase in measles cases, particularly in regions with lower vaccination rates. This global trend could contribute to more imported cases in the US, especially with increased travel. - -### Expert Opinions and Predictions - -- **CDC's Concerns**: The CDC has expressed concern over the rising number of measles cases and the potential for losing the measles elimination status if outbreaks continue for over a year. -- **Importance of Vaccination**: Experts emphasize the need for high vaccination coverage to prevent outbreaks. The current vaccination rate among kindergarteners is below the target, indicating a need for increased vaccination efforts. - -### Prediction Markets and Forecasts - -No specific prediction market data is provided in the sources. However, based on the trend of increasing cases and the factors contributing to this increase (such as lower vaccination rates and global measles incidence), it is plausible that the number of cases could continue to rise. - -### Conclusion - -Given the current data and trends, it is possible that more than 300 measles cases could be reported in the United States by September 30, 2024. The ongoing outbreaks, lower vaccination rates, and global increase in measles cases all contribute to this potential scenario. However, the actual outcome will depend on various factors, including public health responses and changes in vaccination rates. - -######### - -GPT - -### Step-by-Step Reasoning - -#### Step 1: Understand the Question Type and Resolution Criteria -- **Question Type**: **Cumulative Count Event** - We are asked to determine if more than 300 measles cases will be reported in the U.S. by September 30, 2024. The count is cumulative for the year 2024, so it includes all cases from January 1 to September 30, 2024. -- **Resolution Criteria**: The data should match what is reported by the CDC on their official page on September 30, 2024. - -#### Step 2: Current Data and Base Rate Calculation -- **Latest Data (August 15, 2024)**: 219 measles cases have been reported. -- **Trend Analysis**: 167 cases as of July 15, 2024, to 219 cases as of August 15, 2024. This indicates an increase of 52 cases in a month. - -#### Step 3: Project the Trend Forward -- Going by the recent trend: If the number increased by 52 cases from July 15 to August 15, this suggests a rough rate of approximately 1.73 cases per day. -- **Time Until September 30, 2024**: - - From August 15 to September 30 is approximately 1.5 months. - - 45 days at a rate of 1.73 would lead to approximately 77.85 additional cases. - -#### Step 4: Predicted Total by September 30, 2024 -- **219 + 77.85 ≈ 297 cases** as the projected number by September 30. - -#### Step 5: Consider Other Factors -- **Vaccination Rates**: Vaccination rates are currently below targets, which could lead to more cases. -- **CDC Warnings and Outbreaks**: Public health responses might be ramped up, particularly if the CDC issues stronger warnings or takes more aggressive measures, which could impact the number of new cases. -- **Travel and Importation**: The global context indicates the potential for more imported cases, especially if travel remains high. - -#### Step 6: Assess Variance and Uncertainty -- **Variability in Reports**: Future fluctuation of daily rates is possible, especially if there are sudden outbreaks. If a new significant outbreak occurs, the rate could spike significantly. -- **Public Health Responses**: Potential changes in behavior due to increased awareness, vaccines, or other interventions might skew the numbers downwards or limit the increase. - -#### Step 7: Synthesize the Analysis and Estimate Probability -- The projection based on current trends gives us approximately 297 cases by September 30, 2024. However, with even minor variance or a single outbreak, this number could easily pass 300. -- Given the base rate, trend analysis, and external factors, there is a reasonable, though not exceedingly high, probability that more than 300 cases will be reported. - -### Final Assessment and Probability -- After weighing all factors, such as the gradual rate of case increase, the potential for outbreaks, as well as the fact that we are very close to the 300-case threshold, a probability estimate can be made. - -**Probability**: **65%** that there will be more than 300 measles cases reported in the United States by September 30, 2024." -27277,"On Sept 30, 2024 will more than 400 measles cases have been reported in the United States in 2024?","PERPLEXITY - -To address the query of whether more than 400 measles cases will have been reported in the United States by September 30, 2024, we need to consider the current trend and data available up to August 20, 2024. - -### Current Data and Trends - -1. **CDC Data**: As of August 15, 2024, a total of 219 measles cases were reported by 27 jurisdictions in the United States. This already surpasses the total number of cases reported in all of 2023, which was 59. - -2. **Outbreaks**: There have been 13 outbreaks reported in 2024, with 68% of cases (150 of 219) being outbreak-associated. This is a significant increase from 2023, which saw 4 outbreaks. - -3. **Vaccination Rates**: Vaccination coverage has dropped in 41 states since the 2019-20 school year, with only 13 states meeting the CDC’s 95% coverage target in the 2022-23 school year. - -4. **Global Context**: Global estimates suggest that first-dose measles vaccination coverage had declined from 86% in 2019 to 83% in 2022, leaving almost 22 million children aged <1 year susceptible to measles. - -### Expert Opinions and Concerns - -- **CDC Warning**: The CDC has warned that the rapid increase in cases ""represents a renewed threat to elimination"". -- **Public Health Concerns**: Experts have expressed concern about the early rise in cases, particularly given the contagious nature of measles and the potential for severe complications. - -### Prediction Markets and Forecasts - -No specific prediction market data is provided in the search results. However, based on the trend and the concerns raised by public health officials, there is a clear indication that the number of measles cases is on the rise. - -### Conclusion - -Given the current data and trends, it is plausible that the number of measles cases could exceed 400 by September 30, 2024, especially considering the increased number of outbreaks and the decline in vaccination rates. However, without specific forecast data, this conclusion is based on the analysis of current trends and expert concerns. - -######### - -GPT - -### Analyzing the Situation - -1. **Current Data Overview** - - **219 Cases as of August 15, 2024**: The CDC reported 219 measles cases in the United States by August 15, 2024, over a period of approximately 7.5 months (229 days). - - **13 Outbreaks**: With 68% (150 out of 219 cases) being outbreak-associated, this indicates a heightened transmission risk that could lead to further cases. - - **Vaccination Rates**: Vaccination coverage has dropped in many states, increasing susceptibility to measles, particularly in under-vaccinated populations. - -2. **Historical Context and Base Rates** - - **Annual Case Counts**: The annual number of measles cases in recent years has varied but typically ranges from low to mid-triple digits, with some exceptions (e.g., 2019 had over 1,200 cases due to major outbreaks). - - **Comparison to Previous Years**: By mid-August 2023, there were only 59 cases reported, while in 2024, there are already 219 cases reported -- a substantial increase. - -3. **Upcoming Timeframe (Aug 20 - Sept 30, 2024)** - - **Time Until September 30, 2024**: There are around 40 days between August 20 and September 30. This is a relatively short period, but with elevated outbreak activity and the potential for underreporting or delayed case identification, additional cases may emerge. - -4. **Potential Growth Factors** - - **Continuation of Outbreaks**: The sustained or increased number of active outbreaks could significantly contribute to the case count. The counting of 13 outbreaks up until August indicates persistent transmission in certain regions. - - **Seasonal Factors**: It is late summer and heading into early fall, a time when schools resume, increasing the likelihood of transmission among unvaccinated children. - - **Vaccination Shortfall**: Lower vaccination rates increase the likelihood of additional outbreaks and transmission chains, particularly within clusters of unvaccinated individuals. - -5. **Recent Historical Precedents** - - In 2019, the U.S. observed more than 1,200 cases despite relatively high vaccination coverage overall because of outbreaks in unvaccinated communities. - -### Statistical Analysis and Projection - -Assuming a linear rate of increase in line with the cases reported up to August 15, 2024: - -- **Daily Case Rate**: From 167 cases as of July 15, 2024, to 219 cases as of August 15, 2024, this follows an increase of 52 cases over 31 days, resulting in an average of approximately 1.68 cases per day. -- **Extrapolation Over the Next 40 Days**: If the current rate continues with 1.68 cases per day, an additional ~67 cases could be expected by September 30, 2024. This would yield a total of approximately \(219 + 67 = 286 \) cases by that time, still below the 400-case threshold. - -However, this assumes a constant rate of new cases, which might be overly conservative given the outbreak-related potential for exponential spread, particularly in clusters with low vaccination coverage. - -### Consideration of Variability and Uncertainty -- **Potential for Exponential Outbreaks**: Measles' R0 (basic reproduction number) is one of the highest of all infectious diseases, and outbreaks can lead to rapid case increases. If even one significant outbreak occurs in a densely populated, under-vaccinated area, the case count could spike significantly. -- **Expert Concerns**: Public health experts have expressed worries about an increasing trend, which might not yet be reflected fully in the daily case rate. - -### Assessment of Resolution Criteria -- The threshold for resolution is 400 cases, and the most pertinent data available is that we're currently at 219 cases. Given the trend, a linear assumption might suggest it's unlikely we'll reach 400 by September 30, but the potential exists for faster, outbreak-driven growth. - -### Final Probability Calculation - -Given the statistical analysis, base rates, and the potential for significant, sudden changes due to new outbreaks: - -- **Lower Bound (Conservative Estimate)**: If no significant outbreaks occur, the linear estimate projecting 286 cases would indicate a low probability (~25%) of crossing 400 cases. -- **Upper Bound (Outbreak-Driven Scenario)**: If one or more significant new outbreaks occur, rapid growth and surpassing 400 cases is plausible, driving the forecast probability higher (possibly around 60-70%). - -Taking the midpoint with an allowance for potential sudden spread: - -**Final Probability: 45%** - -This reflects moderate uncertainty with a slightly higher chance of staying below the 400-case threshold but acknowledges the real risks if more outbreaks occur." -27279,"On Sept 30, 2024 will more than 300 and less than or equal to 400 measles cases have been reported in the United States in 2024?","PERPLEXITY - -To address the query about whether more than 300 and less than or equal to 400 measles cases will have been reported in the United States in 2024 by September 30, 2024, we need to examine the current data and trends. - -1. **Current Data as of August 15, 2024**: - - As of August 15, 2024, a total of 219 measles cases were reported by 27 jurisdictions in the United States. - - There have been 13 outbreaks (defined as 3 or more related cases) reported in 2024, with 68% of cases (150 of 219) being outbreak-associated. - -2. **Trend Analysis**: - - The first quarter of 2024 saw a significant increase in measles cases, with 113 cases reported as of April 5, which is 17 times higher than the average number seen during the same period in the previous three years. - - The rapid rise in measles cases in the first months of 2024 threatens the United States' measles elimination status. - -3. **Vaccination Rates and Importations**: - - Most measles importations were cases among persons traveling to and from countries in the Eastern Mediterranean and African WHO regions, which experienced the highest reported measles incidence among all WHO regions during 2021–2022. - - Global estimates suggest that first-dose measles vaccination coverage had declined from 86% in 2019 to 83% in 2022, leaving almost 22 million children aged <1 year susceptible to measles. - -4. **Expert Opinions and Recommendations**: - - The CDC emphasizes the need for increased vaccination efforts, especially in close-knit and undervaccinated communities, and encourages vaccination before international travel to maintain measles elimination. - - Experts recommend that children get the measles, mumps, and rubella (MMR) vaccine in two doses, with the first dose between 12 months and 15 months of age, and the second dose between 4 and 6 years old. - -Given the current trend and the significant increase in cases in the first quarter of 2024, it is plausible that the number of measles cases could exceed 300 by September 30, 2024. However, predicting whether the total will be less than or equal to 400 requires careful consideration of vaccination efforts, travel patterns, and the effectiveness of public health measures to control outbreaks. - -**Prediction Markets and Forecasts**: -As of the current date, there are no specific prediction markets or forecasts mentioned in the provided sources that directly address the query. However, the trend and expert opinions suggest a continued risk of increased measles cases, which could impact the total count by September 30, 2024. - -In conclusion, while the data suggests an upward trend in measles cases, the exact number by September 30, 2024, is uncertain and will depend on various factors including vaccination rates, importations, and public health response. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Classifying the Event Type:** - - The question at hand is most appropriately classified as a **Cumulative Count Event**, as it involves predicting whether a specific count of measles cases falls within a defined range by a certain date. - -#### 2. **Understanding the Resolution Criteria:** - - The question will resolve ""Yes"" if on **September 30, 2024**, the count of measles cases reported by the CDC is **more than 300 and less than or equal to 400**. Importantly, this range needs to be exactly between 301 and 400 (inclusive). - -#### 3. **Current Data Overview:** - - **Current Count**: As of August 15, 2024, **219 cases** have been reported. - - **Time Remaining**: About **6 weeks** remain until September 30, 2024. - - **Reporting Intervals**: The CDC updates this information biweekly, which means there will likely be only about three more reporting points before September 30. - -#### 4. **Trend Analysis:** - - **Past Case Accumulation Rate**: - - From July 15 to August 15: 167 to 219 cases -> 52 new cases in one month (roughly 13 new cases per week). - - From April 5 to July 15: 113 to 167 cases -> 54 new cases in about three months (~4.5 new cases per week). - - The case rate appears to have increased more rapidly in the past month. - - Given the pattern of recent increases, it's clear that the cumulative cases are still rising (due to both ongoing outbreaks and potential spread from importations). - -#### 5. **Probability Distribution:** - - **Required Cases for Resolution**: For a ""Yes"" outcome, at least **82 additional cases** (to reach 301) but no more than **181 additional cases** (to fall within the threshold of 400) need to occur between August 15 and September 30. - - **Weekly Case Accumulation Scenarios**: - - If cases continue at the recent rate (13 per week), about **78 additional cases** (13 cases/week * 6 weeks) would be reported by September 30, bringing the total to **297 cases**. - - However, if the spread rate increases or there are new outbreak clusters, this number could easily go beyond 300. - -#### 6. **Influences and Potential Deviations:** - - **Factors that could cause significant deviations**: - - **Outbreak Management**: If current public health interventions are effective, the transmission rate could be controlled, limiting additional cases. - - **Seasonality**: Measles is typically less prevalent in the US during the late summer. - - **Travel and Importations**: Given higher cases globally, any increase in related travel due to holidays or unplanned events might still introduce new cases. - - **Underreporting or Delays**: Unexpected changes in reporting rates could skew the numbers. - -#### 7. **Historical Context & Base Rates:** - - **Previous Years' Data**: In the recent past, annual US measles cases have varied significantly due to various factors (from a few dozen to over a thousand in severe outbreak years, such as 2019). The present year shows early signs of resembling a higher-outbreak year (although not as extreme as 2019). - - However, base rates indicate a high likelihood of stabilizing around the middle hundreds, given strong public health response and varying rates in 2024. - -### Conclusion and Probability Estimation -- **Considering the factors**: - 1. **Current cases**: 219 as of the last report. - 2. **Trend and growth rates**: With the recent rate at around 13 cases per week, this supports an increase to ~297 by September 30, just shy of the 300 mark. - 3. **Potential for spikes** in cases would push the tally into the 300-400 range, but not so high as to surpass 400. - 4. **The probability** of the case count being in the relevant range seems reasonably high, given the potential for a modest continuation in current upward trends, but with some uncertainty about the exact number reaching between 301-400. - -**Probability: 70%** - -This estimate accounts for variability in the case growth rate, the possibility of interventions lowering the spread rate, and the narrow target range for a ""Yes"" outcome. While there’s a significant chance the tally could exceed 300 by September 30, the confidence is tempered by the inherent uncertainty in disease spread predictions." -27283,"Before October 1, 2024, will Tech Crunch report new layoffs at Rivian?","PERPLEXITY - -To address the question of whether TechCrunch will report new layoffs at Rivian before October 1, 2024, we need to examine the current layoff trends in the tech industry, specifically focusing on Rivian's recent activities and the overall context of tech layoffs in 2024. - -1. **Recent Layoffs at Rivian**: As of the latest information available up to August 20, 2024, Rivian has already conducted several rounds of layoffs. In February 2024, Rivian laid off 10% of its salaried workforce to cut costs in a tough market for electric vehicles. This was the third round of layoffs for the company since July 2022. Additionally, in May 2024, Rivian announced another layoff, affecting 120 employees. - -2. **Tech Industry Layoff Trends**: The tech industry has seen significant layoffs throughout 2024, with over 60,000 job cuts across 254 companies as of August 2024. This trend indicates a broader restructuring and cost-cutting effort within the tech sector, potentially suggesting that further layoffs could be anticipated. - -3. **Economic and Market Pressures**: Rivian, like many tech companies, faces economic and market pressures, including high interest rates and geopolitical uncertainty. These factors have led companies to prioritize cost-cutting and efficiency measures, which often include workforce reductions. - -Given these points, while there is no direct information available as of August 20, 2024, that confirms new layoffs at Rivian to be reported by TechCrunch before October 1, 2024, the ongoing trend of layoffs within the tech industry and Rivian's recent history of workforce reductions suggest that further layoffs could be possible. However, without specific information or announcements from Rivian or TechCrunch, this remains speculative. - -######### - -GPT - -### Step by Step Analysis: - -#### Step 1: Understanding the Question and Background -The question is whether Tech Crunch will report new layoffs at Rivian before October 1, 2024. The resolution criteria are very specific, requiring the mention of Rivian in Tech Crunch's tracker for tech layoffs in August or September 2024. - -#### Step 2: Analyzing Relevant Background Information -Let’s break down the background information provided by the research assistant: - -1. **Rivian’s Recent Layoffs:** - - In February 2024, Rivian laid off 10% of its workforce, which was followed by another smaller round in May 2024. This illustrates a recent history of cost-cutting at Rivian. - - Rivian has had multiple rounds of layoffs since mid-2022. - -2. **Tech Industry Layoff Trends:** - - The tech industry overall has seen significant layoffs in 2024, with over 60,000 jobs cut across 254 firms by August 2024. - - This suggests a broader trend of layoffs, potentially increasing the likelihood that Rivian, as a part of this industry, may experience further layoffs. - -3. **Economic and Market Pressures:** - - The EV market is challenging, with high interest rates and broader economic uncertainties. - - Companies like Rivian may need to continue cost-cutting measures, which could include further layoffs. - -#### Step 3: Assessing Time Frame and Trend Considerations -It is currently August 20, 2024, leaving a window of about 1.5 months (41 days) for potential layoff announcements at Rivian that would be reported by TechCrunch. - -- **Historical Layoff Timing:** There might be organizational timing considerations. The February layoff was a major cut (10%), while May had a follow-up (smaller scale). - -- **Trend Extrapolation:** Given Rivian's repeated layoffs in the past, coupled with the overall industry trend, there is a reasonable possibility that similar actions may recur in the short term (before October 1). - -#### Step 4: Base Rate Analysis and Novel Factors -- **Base Rate:** How often do companies in similar distress situations as Rivian conduct layoffs more frequently within a short span? Rivian has already shown a pattern of workforce reduction within a few months. - -- **Novel Factors:** Higher interest rates and economic uncertainties could further exacerbate the situation, making additional rounds of layoffs possible as Rivian seeks to stay afloat. - -#### Step 5: Potential Counterarguments -- **Counter:** Rivian has already laid off a significant portion of its workforce in 2024, and another round may be postponed beyond October 1, to avoid strategic disruption in preparing for year-end financial reviews. - -- **Counter 2:** If financial restructuring strategies or new investments are being pursued, Rivian may hold off additional layoffs to present a more stable front. - -#### Step 6: Weighing the Evidence -- **Risk factors point towards a possibility of further layoffs,** given the company’s track record and current economic pressures. -- **No evidence to suggest significant improvement in Rivian’s financial situation** since the earlier layoffs – if anything, further challenges seem expected. -- **TechCrunch is likely to report such layoffs:** Given their role in tracking industry-wide developments, confirming layoff announcements by prominent companies like Rivian seems assured should they occur. - -### Final Estimation and Probability Distribution -Balancing historical data, current trends, base rate information, and economic pressures: - -- **Likelihood of New Layoffs:** Moderately high based on ongoing pressures. -- **Likelihood of Reporting by TechCrunch:** High if layoffs occur. - -### Final Answer: -Given the critical analysis, the chance that TechCrunch reports new layoffs at Rivian before October 1, 2024, appears to be plausible, but with a degree of uncertainty considering potential delays in such decisions. - -**Probability: 65%**" -27280,"Will Israel invade Lebanon before October 1, 2024?","PERPLEXITY - -To provide a comprehensive rundown of the news relevant to the question of whether Israel will invade Lebanon before October 1, 2024, we need to consider several key points from recent developments and expert opinions. - -### Current Situation and Tensions - -- **Escalation of Conflict**: The conflict between Israel and Hezbollah has been escalating since October 2023, with regular rocket attacks by Hezbollah into northern Israel and retaliatory strikes by Israel against Hezbollah positions in southern Lebanon. -- **Military Preparations**: Israel has approved operational plans for an invasion of Lebanon, indicating a readiness for an ""all-out war"". The Israeli military has been launching airstrikes on Lebanon, targeting Hezbollah's military infrastructure and personnel. -- **Hezbollah's Stance**: Hezbollah has warned of an unrestrained conflict if provoked and has been expanding its attacks, including drone strikes and rocket launches against Israeli positions. - -### International Involvement and Diplomatic Efforts - -- **US Warning**: The Biden administration has warned Israel that a ground invasion of Lebanon could draw Iran into the conflict, leading to a broader and more devastating war. -- **Diplomatic Efforts**: The US and France have been involved in diplomatic efforts to prevent further escalation, emphasizing the need for a ceasefire in Gaza to restore stability along the Israeli-Lebanese border. - -### Humanitarian Concerns - -- **Civilian Impact**: The ongoing conflict has resulted in significant civilian casualties and displacement on both sides. In Lebanon, over 400 people have been killed, and 90,000 displaced, while in Israel, 25 deaths have been reported, and over 60,000 people have been forced from their homes. - -### Prediction Markets - -- **Manifold Markets**: The prediction market on Manifold indicates a 49% chance that Israel and Lebanon/Hezbollah will go to war in 2024. Participants discuss various scenarios that could lead to a full-scale war, including targeted strikes and ground operations. - -### Expert Opinions and Forecasts - -- **Genocide Watch**: Genocide Watch has issued a warning stating that Israel's planned invasion of Lebanon indicates preparation for war and potential crimes against humanity. They recommend a peaceful resolution to the conflict in Gaza and warn against the invasion of Lebanon. -- **Israeli Officials**: Israeli officials, including Foreign Minister Israel Katz, have stated that Israel is ready for an ""all-out war"" in Lebanon, aiming to destroy Hezbollah. - -### Recent Updates and Movement Towards Resolution - -- **Escalation**: Recent updates suggest an escalation of hostilities, with both sides showing readiness for a potential full-scale war. The situation remains volatile, with diplomatic efforts ongoing to prevent further escalation. - -In summary, the situation between Israel and Lebanon/Hezbollah is highly tense, with both sides prepared for a potential full-scale war. International diplomatic efforts are underway to prevent escalation, but the prediction market and expert opinions suggest a significant likelihood of conflict before October 1, 2024. - -######### - -GPT - -### Step 1: Understand the Context and Resolution Criteria - -**Resolution Criteria Recap:** -The question will resolve as ""Yes"" if, between July 15, 2024, and before October 1, 2024, the Government of Israel or any two Permanent Members of the UNSC announce or acknowledge that Israeli ground troops have entered Lebanese territory, and credible sources indicate that more than 100 troops have entered. These troops must be acting deliberately on orders of the Israeli government without Lebanese government permission or UN assent. - -**Current Situation Recap:** -- **Tensions**: Increased skirmishes and threats between Israel and Hezbollah. -- **Military Movements**: Israel's prep for an ""all-out war,"" including possible plans for land invasion. -- **Diplomacy**: Active international efforts (U.S., France) to de-escalate the situation. -- **Base rates**: Israel has historically conducted relatively short and targeted incursions instead of extended ground invasions. - -### Step 2: Current Situation and Recent Developments Analysis - -1. **Escalation of Conflict:** - - The conflict intensity has ramped up significantly, with high levels of rocket exchanges and airstrikes. This could suggest that both sides are inching closer to a larger conflict. - -2. **Military Preparations:** - - Israel's operational plans for an invasion and their indications of readiness for ""all-out war"" are noteworthy. These need to be balanced against the historical tendency to avoid prolonged ground operations in Lebanon due to high risks and international backlash. - -3. **Hezbollah's Stance:** - - Hezbollah's threat of ""unrestrained conflict"" on provocation could either deter Israel from full invasion or, conversely, lead to a pre-emptive strike if Israel feels the situation necessitates it. - -4. **International Involvement:** - - U.S. warning against a ground invasion carries significant weight. Israel depends on U.S. support, and the U.S. is likely advising restraint to avoid a larger regional conflict involving Iran, which could deter immediate ground invasion plans. - -5. **Diplomatic Efforts:** - - Diplomatic efforts by the U.S. and France can be crucial in de-escalation. Ongoing negotiations, if successful, could reduce the likelihood of an invasion. - -6. **Humanitarian Concerns:** - - High civilian impact and displacement on both sides increase international pressure for a ceasefire. This would incentivize Israel to pursue non-military options to resolve the issue with Hezbollah. - -### Step 3: Base Rates and Historical Context - -**Historical Precedents:** -- **2006 Lebanon War**: The last significant full-scale conflict involving Israel and Lebanon featured a limited ground invasion which was strategically focused and with considerable international pushback. -- **Patterns of Escalation**: Typically, Israel has shown restraint in full-scale ground invasions, preferring air strikes, limited incursions, or special operations over extended ground warfare due to the high cost, both militarily and diplomatically. - -### Step 4: Analysis of Time Sensitivity and Resolution Window - -**Time Remaining**: -- There's approximately 40 days left until the resolution date of October 1, 2024. Given the complexities and incremental nature of military escalation, a sudden and deliberate decision to invade might be constrained by time, especially considering ongoing diplomatic efforts. - -**Daily Variance and Trend Analysis**: -- **Weekly Granularity**: So far, the trend is towards incremental escalation rather than an immediate large-scale invasion. The most significant recent activities have been airstrikes and increased mobilization—but not ground troop invasion. - -### Step 5: Probability Distribution & Consideration of Uncertainties - -**Likelihood of Status Quo Holding**: -- Without major shifts or incidents, the current state of limited but significant military confrontation could continue without escalating to a full-scale invasion. International pressure, internal political dynamics, and strategic considerations make a ground invasion a high-bar decision. - -**Potential Variables**: -- **Black Swan Events**: Surprises, such as a dramatic Hezbollah military action or a significant Israeli government policy shift, could quickly change the picture. -- **Domestic Politics in Israel**: Ongoing political considerations, such as rising or falling public support for military action, could influence the Israeli government's decision-making, especially given Netanyahu's historically cautious approach to large-scale invasions. - -### Conclusion - -Given the detailed analysis above: -- **International resistance** (especially from the U.S. and France) and **diplomacy** are likely to disincentivize a large-scale invasion. -- **Time constraints** paired with current trends suggest that while the threat remains significant, an actual ground invasion within this small window is less probable. -- **Default Resolution**: The absence of an invasion before the deadline could reflect historical base rates where conflicts escalate to a point but do not necessarily lead to full ground operations. - -**Final Probability Assessment:** -Given all these factors, the probability of Israel invading Lebanon with more than 100 troops before October 1, 2024, under the criteria specified, is likely on the lower side but not negligible. - -**Final Answer**: **Probability: 35%**" -27281,"Will the Warren Buffett Indicator exceed 200% before September 17, 2024? -","PERPLEXITY - -To address the question of whether the Warren Buffett Indicator will exceed 200% before September 17, 2024, we need to examine the current state of the US stock market valuation relative to the GDP, recent trends, and expert opinions. - -### Current State of the Buffett Indicator - -As of June 30, 2024, the Buffett Indicator stands at 202%, indicating that the total US stock market valuation is significantly higher than the annualized GDP. This ratio suggests that the stock market is strongly overvalued relative to the economy's current output. - -### Recent Trends and Expert Opinions - -1. **Historical Context**: The Buffett Indicator has surpassed 200% in the first quarter of 2024, reaching a level that is considered a warning sign for potential overvaluation in the stock market. - -2. **Warren Buffett's Cash Position**: Berkshire Hathaway's record cash pile of $276.9 billion as of June 2024 indicates Warren Buffett's cautious stance on current stock valuations. This significant cash position, coupled with Berkshire's persistent net selling of stocks, suggests that Buffett views the market as overvalued. - -3. **Shiller P/E Ratio**: The Shiller P/E ratio, another valuation metric, was nearly 34.5 as of August 9, 2024, which is roughly double its historical average. This further supports the notion that the stock market is overvalued. - -4. **Expert Opinions**: Fund manager Chris Bloomstran has noted that while the Buffett Indicator has its flaws, it is flashing a warning signal for stock market investors. He believes that the cap-weighted stock market is ""incredibly dangerous"" at current levels. - -### Prediction Markets and Forecasts - -While specific prediction markets or forecasts directly addressing whether the Buffett Indicator will exceed 200% before September 17, 2024, are not provided in the sources, the current data and expert opinions suggest that the indicator is already at a critical level. Given the recent trends and the fact that the indicator has already surpassed 200%, it seems plausible that it could remain above this threshold or even increase further before September 17, 2024. - -### Conclusion - -Based on the current state of the Buffett Indicator, recent trends in stock market valuation, and expert opinions, there is a strong indication that the US stock market is overvalued. While the sources do not provide a direct forecast for the period leading up to September 17, 2024, the existing data and analyses suggest that the market conditions are ripe for the Buffett Indicator to remain elevated or potentially exceed its current level before that date. - -######### - -GPT - -### Step 1: Understand the Question and Resolution Criteria - -The question at hand is whether the Warren Buffett Indicator (as defined by the Wilshire 5000 to GDP ratio) will exceed 200% before September 17, 2024. The most recent figure you provided was 196.20375% as of July 8, 2024. The question resolves as ""Yes"" if the indicator reaches even a fraction above 200% (i.e., 200.00000000001), and as ""No"" if it stays below that threshold. - -### Step 2: Analyze the Current Indicator Level - -- **Current Trend**: The most recent value is just slightly below the 200% threshold at 196.20375%. Historically, once markets have approached a certain valuation level, they have shown volatility. -- **Historical Context**: The Buffett Indicator has exceeded 200% in the past, but such levels are often followed by either periods of high volatility or corrections, making it a precarious zone. However, some expansions can continue for a while, pushing the ratio above that level for an extended period. - -### Step 3: Weighing Key Factors - -#### 3.1 Macroeconomic Conditions -- **Economic Growth**: If the GDP grows faster than expected, it could pull the ratio down, but given the limited time frame (barely less than a month), it's unlikely that GDP growth would be significant enough to bring the ratio below 196%. -- **Inflation or Deflation**: Significant inflation could potentially boost nominal GDP figures, lowering the ratio. Conversely, deflation would hurt GDP relative to market caps, increasing the ratio. - -#### 3.2 Stock Market Volatility -- **Equity Market Behavior**: The stock market could either increase, leading to a rise in the Wilshire 5000 index and thereby the ratio, or suffer a correction/drop which would lower the ratio. However, any large correction in such a short period is difficult to predict. -- **Earnings & Dividends**: A major change in corporate earnings, dividends, or profit outlook could impact market sentiment, causing deviations from current levels. -- **Seasonality**: Markets can sometimes experience corrections or surges around certain times (August is often a volatile month), but such influences are not broadly correlated to pushing indicators above or below such precise thresholds. - -#### 3.3 Market Sentiment and Behavior -- **Investors' Expectations**: Given that the indicator is reaching historically significant levels, market participants may adjust their portfolios leading to either stabilization or a short-term bubble, potentially driving the indicator over the 200% mark. -- **Buffett and Other Institutions**: While Buffett's analysis suggests caution, the actual actions of large institutional investors, such as significant buying or selling, will play a significant role. - -### Step 4: Consider Base Rates and Variance - -Given that the indicator is at 196.20375% and it has shown a trend of nearing 200%, there is a high likelihood it could reach or exceed 200%: -- The base rate of the Buffett Indicator staying above 200% after it has approached such high levels is relatively rare, but it has occurred in recent history (e.g., just before the 2022 correction). -- The latest number was close to but below 200%, which means the most likely ""default"" outcome with no major news/events would be for the indicator to oscillate slightly, potentially nudging above 200%. - -### Step 5: Final Probability - -Given the above steps, the factors most heavily weighed include: -- **The proximity to the 200% threshold**. -- **The market's tendency to be volatile around such levels**. -- **The short time frame before the question's resolution on September 17, 2024**. - -### Conclusion - -While there is always the possibility of a market correction or other factors that could push the Buffett Indicator down, the current trend and proximity to 200% imply that the indicator could easily exceed the threshold with any minor continuation of current upward trends in the market. The existing momentum and typical volatility chapter makes this a situation where a slight move upward could happen. - -Thus, the final answer is: - -**Probability: 75%**" -27524,"Will Ukraine withdraw from Kursk before October 1, 2024?","PERPLEXITY - -To address the question of whether Ukraine will withdraw from Kursk before October 1, 2024, it's essential to consider the current military situation, strategic objectives, and statements from key leaders. - -### Current Military Situation - -- **Ukrainian Advances**: Ukrainian forces have made significant advances into the Kursk region, controlling over 1,250 square kilometers of Russian territory and 92 settlements as of August 19, 2024. -- **Russian Response**: Despite these advances, Russian forces are under orders from President Vladimir Putin to push Ukrainian forces out of the Kursk region by October 1, 2024, without withdrawing troops from key areas in Donbas. - -### Strategic Objectives - -- **Ukrainian Objectives**: Ukraine aims to create a ""buffer zone"" in Kursk to prevent cross-border attacks by Russian forces. This operation also seeks to stretch Russia's military resources and potentially gain leverage in negotiations for the release of prisoners of war. -- **Russian Objectives**: Russia is focused on defending its territory and maintaining its offensive in eastern Ukraine, particularly in the Donbas region. The order to liberate Kursk without withdrawing from Donbas indicates the strategic importance of both fronts for Russia. - -### Expert Opinions and Analysis - -- **Feasibility of Russian Deadline**: Many experts and commentators doubt the feasibility of Russia meeting the October 1 deadline, citing Russia's lack of manpower, logistics, and resources. -- **Impact on Negotiations**: The Kursk offensive could provide Ukraine with bargaining chips in negotiations, including the potential for a large-scale prisoner exchange. - -### Prediction Markets and Betting Odds - -No specific prediction markets or betting odds are mentioned in the provided sources. However, the general sentiment among experts and commentators suggests skepticism towards Russia's ability to meet the October 1 deadline. - -### Conclusion - -Given the current military situation, strategic objectives, and expert opinions, it seems unlikely that Ukraine will withdraw from Kursk before October 1, 2024. The ongoing conflict dynamics and the strategic importance of the Kursk region for both sides suggest a prolonged engagement rather than a swift withdrawal. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. Understanding the Question Type: -This is an ""Other Binary Outcome Event"" because it asks whether a specific event—a Ukrainian withdrawal from Kursk—will occur by a given date. - -#### 2. Background Review: -- **Current Situation**: As of August 19, 2024, Ukraine controls over 1,250 square kilometers in Kursk Oblast. The Ukrainian military advances are significant, and the frontline appears to have stabilized. -- **Russian Response**: Putin has ordered Russian forces to push Ukrainian troops out by October 1, 2024, but without making troop withdrawals from other key conflict areas (like the Donbas). -- **Strategic Importance**: For Ukraine, maintaining a position within Kursk Oblast has both symbolic and strategic value. For Russia, regaining control of Kursk is crucial and is tied to national security. - -#### 3. Timeframe Consideration: -- Today's date is August 22, 2024, with 39 days until October 1, 2024. -- Military movements and campaigns, especially involving frontlines of this scale, often move slowly, especially when both sides are entrenched and have limited resources. -- Any significant change on the ground would require a substantial event (e.g., a major Russian offensive or diplomatic development like a ceasefire). - -#### 4. Base Rates and Historical Context: -- **Historical Precedents**: Historically, when territory is occupied during conflicts, the occupiers do not typically withdraw quickly unless facing overwhelming force or severe logistical challenges. -- **Other Conflicts**: When examining conflicts similar in scale and importance, even smaller incursions tended to drag out over months or years (e.g., Crimea, the Donbas). Swift reversals tend to happen under extraordinary circumstances. - -#### 5. Recent Developments: -- **Logistics and Manpower**: Analysts and experts have cited Russia's logistical and manpower constraints. This is crucial because if Russia cannot mount a powerful counter-offensive, Ukraine is less likely to be pushed out. -- **External Support**: Ukraine receives significant military and material support from Western nations. With continued support, Ukraine’s ability to hold its ground is bolstered. - -#### 6. Scenario Analysis: -- **Scenario 1 - Stalemate (60% Probability)**: Ukraine holds its ground or marginally expands its control, while Russia fails to mount significant counter-attacks but manages to contain further expansion. -- **Scenario 2 - Partial Withdrawal Under Pressure (20% Probability)**: Under heavy Russian pressure or strategic recalculations, Ukraine partially withdraws. However, complete withdrawal is unlikely. -- **Scenario 3 - Total Ukrainian Withdrawal (10% Probability)**: A successful Russian offensive, substantially aided by mobilization or diplomatic pressure (e.g., pressure from Western allies on Ukraine), leads to a full retreat. -- **Scenario 4 - Ceasefire/Negotiated Settlement (10% Probability)**: Diplomatic negotiations lead to a withdrawal agreement, possibly part of a broader peace settlement or ceasefire, but not before October 1. - -#### 7. Weighing the Default: -Given the default and the stable frontlines, the most likely outcome is that Ukraine remains in control of some or all of its captured territory in Kursk Oblast by October 1, 2024. The likelihood of significant changes seems low, considering the time available and the challenges to Russia's attempted counter-offensive. - -#### 8. Cross-Referencing Sources: -- **DeepStateMap and ISW**: Since these are the data sources for resolution, any sudden change in their reporting could indicate a shift. However, given current expertise and without contradictory signals, the probability leans heavily towards no sudden changes. - -### Conclusion and Final Probability: - -**Probability: 75%** chance that Ukraine will *not* withdraw from Kursk before October 1, 2024." -27524,"Will Ukraine withdraw from Kursk before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether Ukraine will withdraw from Kursk before October 1, 2024, we need to consider recent developments and expert opinions on the situation. - -### Recent Developments - -1. **Ukrainian Advance**: Ukraine has made significant advances into Russia's Kursk Oblast, claiming more Russian land in two weeks than Russia has seized in Ukraine since the start of 2024. This unexpected turn of events has put pressure on Russian forces and exposed the Russian military's overstretched position. - -2. **Putin's Orders**: Vladimir Putin has ordered his military to push Ukrainian forces out of the Kursk region by October 1 without withdrawing troops from key areas in Donbas. This indicates that Russia is trying to manage multiple fronts simultaneously, which could strain its military resources. - -3. **Kremlin's Propaganda Efforts**: The Kremlin is using propaganda to downplay the significance of the Ukrainian incursion into Kursk Oblast, portraying it as a minor issue that will be resolved after achieving offensive objectives in eastern Ukraine. This suggests that the Kremlin is not prioritizing an immediate response to the incursion. - -### Expert Opinions and Analysis - -- **Military Challenges**: Experts point out that Russia faces significant logistical and manpower challenges in attempting to expel Ukrainian forces from Kursk by October 1. The lack of ""free"" forces and reserves, combined with the ongoing offensive in Donbas, complicates Russia's ability to respond effectively. - -- **Public Perception**: The Kremlin's efforts to condition Russian society to accept the situation in Kursk Oblast as a ""new normal"" indicate a strategy to buy time and manage public expectations. - -- **Ukrainian Control**: As of August 22, Ukrainian forces control over 1,250 square kilometers of enemy territory and 92 settlements in the Kursk region, according to President Volodymyr Zelenskyy. - -### Prediction Markets and Betting Odds - -No specific prediction markets or betting odds are mentioned in the provided sources. However, the general sentiment from experts and analysts suggests skepticism about Russia's ability to meet the October 1 deadline due to military and logistical constraints. - -### Conclusion - -Given the current situation and expert opinions, it seems unlikely that Ukraine will withdraw from Kursk before October 1, 2024. Russia's military challenges, combined with the Kremlin's strategy to downplay the incursion and focus on eastern Ukraine, suggest that a quick resolution is not expected. - -######### - -GPT - -### Step 1: Analyzing the Background Information - -#### **1. Military Situation** -- **Ukrainian Advance**: Ukraine has captured over 100 square kilometers in approximately two weeks. This was an unexpected development, and it shows a significant capability on Ukraine’s part to launch successful offensive operations into Russian territory. Given that the frontline has -- after five days -- stabilized, it indicates that Ukraine is likely consolidating its gains and setting up defenses to hold onto the captured territory. -- **Russian Military Challenges**: Russia is reportedly attempting to expel Ukrainian forces from Kursk without withdrawing troops from Donbas. This dual-front pressure means that Russia will find it challenging to muster the necessary resources, both in terms of logistics and manpower. This suggests that a quick and decisive response from Russia is unlikely. - -#### **2. Political Situation** -- **Putin's Orders**: Putin has dictated an October 1 deadline for reclaiming Kursk but this could be more of a face-saving declaration than a practical strategic move, especially given the existing military constraints. It should be noted as an important marker of political intent, but the challenge of execution remains. -- **Kremlin Propaganda**: Russia’s downplaying of the situation in its public messaging is a tactic to manage domestic expectations. It shows that the Kremlin does not believe it can achieve results imminently in Kursk, at least not before bolstering public opinion first. - -#### **3. Expert Opinions and Trends** -- The expert analysis suggests that Russian forces are overstretched and facing logistical constraints. This is important because without the necessary resources, it's hard to see Russia accomplishing its goals by October 1. Additionally, the public perception management strategy hints at a scenario where Russia is preparing for a longer campaign rather than a rapid counterstrike. - -### Step 2: Evaluating Historical and Base Rates -- **Historical Precedents**: Historically, offensive military campaigns that have resulted in territorial gains are not easily reversible without significant counter-efforts. For example, when one side manages to hold captured territory, even if it is smaller, it usually holds until conditions change substantially (such as the arrival of reinforcements or a shift in the logistics situation). -- **Similar Recent Events**: Similar incursions or captures of hard-ground in conflicts (e.g., certain areas in Syria and Donbas conflicts) often see prolonged hold times even in the face of counter-attacks, particularly when those counter-attacks are launched hastily or without full preparation. - -### Step 3: Considering the Timing and Resolution Criteria -- **Time Factor**: We are about 5 weeks away from the October 1 deadline. This is a short time frame in which Ukraine would presumably need to either be militarily defeated or decide to withdraw for other strategic reasons. Given the ground situation, it's unclear what could suddenly precipitate a full-scale withdrawal by Ukraine. - -- **Resolution Criteria**: The criteria is very stringent; the question resolves positively only if Ukraine holds **no** territory in Kursk at **any point** before October 1. If Ukraine continues to hold even a fraction of the initial gains, the question will resolve negatively. - -### Step 4: Estimating the Chances of Different Outcomes -- **Existing Situation Persists**: Most likely, Ukraine maintains its current position and continues fortifications. Given the inability of the Russian forces to massively divert resources due to other priorities (like Donbas), Ukraine holding the territory is the default scenario. -- **Possible Russian Counter-Attack**: Even with a counter-attack, the likelihood of Ukraine losing all of the territory without holding any is low. A rolling back of gains to some extent could happen, but it would typically take a significant amount of Russian force to displace troops entirely. -- **Unilateral Withdrawal**: Ukraine might withdraw if they decide the tactical disadvantage is too significant or if external pressure (international or otherwise) mounts. Nevertheless, given the strategic implications of holding territory within Russian borders, such a move would require strong external factors, which are not apparent at this time. - -### Final Probability Estimation -- Given the above considerations, it seems highly probable that Ukraine will **not** withdraw from the Kursk before the October 1 deadline, and they will maintain their control of at least part of the region. The constraints on Russian forces and the time required to organize a full-scale counter-offensive suggest that they are unlikely to meet their objectives within the given timeframe. - -**Probability: 75%** -Ukraine will **not** withdraw from Kursk before October 1, 2024." -27524,"Will Ukraine withdraw from Kursk before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether Ukraine will withdraw from Kursk before October 1, 2024, several key points and recent developments need to be considered: - -1. **Putin's Orders**: Vladimir Putin has ordered his military to push Ukrainian forces out of the Kursk region by October 1, 2024, without withdrawing troops from key areas in Donbas. This indicates a strategic decision to prioritize offensive operations in eastern Ukraine over immediately retaking Kursk. - -2. **Current Situation in Kursk**: Ukrainian forces currently control over 1,250 square kilometers of enemy territory and 92 settlements in the Kursk region, as stated by President Volodymyr Zelenskyy. This significant presence suggests that any attempt to expel Ukrainian forces would require substantial military resources. - -3. **Russian Military Challenges**: The Russian military is facing challenges in terms of manpower, logistics, and equipment. They are pulling troops from other frontlines and even using conscripts drafted in the spring of 2024 to reinforce the Kursk region. This reshuffling indicates a lack of ""free"" forces and reserves. - -4. **Kremlin's Propaganda Efforts**: The Kremlin is engaging in a messaging campaign to justify focusing on eastern Ukraine over Kursk, aiming to condition the Russian public to accept the situation in Kursk as a ""new normal"". This suggests a recognition that retaking Kursk immediately might not be feasible. - -5. **Expert Opinions and Public Sentiment**: Many experts and commentators doubt Russia's ability to meet the October 1 deadline, citing logistical issues, manpower shortages, and the strategic prioritization of Donbas. Public sentiment, as seen on platforms like Reddit, largely mocks the feasibility of Putin's order, highlighting the difficulties Russia faces. - -6. **Political Implications**: The situation in Kursk is seen as a significant humiliation for Putin, marking the third major military embarrassment since the invasion of Ukraine began. This political context underscores the challenges Russia faces in achieving its objectives in Kursk. - -Given these points, the likelihood of Ukraine withdrawing from Kursk before October 1, 2024, seems low. The Russian military faces significant logistical and strategic challenges, and the Kremlin's efforts to manage public expectations suggest an acknowledgment of these difficulties. - -######### - -GPT - -### Step-by-Step Evaluation - -#### **1. Categorization of the Event** -This is an ""Other Binary Outcome Event,"" where we are asked to forecast whether Ukraine will withdraw from the Kursk Oblast by October 1, 2024. - -#### **2. Understanding the Key Factors** -We need to consider both the likelihood of Ukraine staying in Kursk and the likelihood of a successful Russian push to force a Ukrainian withdrawal. The key factors include: - -- **Putin's Orders**: The order to retake Kursk by October 1 suggests a high level of intent, but intent does not equal capability. We need to assess Russia's actual capacity. - -- **Ukrainian Control**: Ukraine currently controls a significant area within Kursk, more than just a small incursion. The depth and breadth of their control are non-trivial, suggesting they are well-entrenched. - -- **Russian Military Challenges**: There are significant issues within the Russian military, including logistical challenges and manpower shortages. This puts constraints on how effectively Russia might be able to push back Ukraine. - -- **Kremlin’s Propaganda**: The Kremlin's attempt to normalize the situation and minimize the political impact implies that even they doubt their ability to resolve this issue in the short term. - -- **Expert Opinions/Public Sentiment**: Sentiment leans heavily towards skepticism about Russia's ability to meet the deadline, further supported by historical precedent where the Russian military has struggled to quickly reverse gains from Ukraine. - -- **Political Pressure**: The political implications for both Russia and Ukraine are significant. Russia will be under immense pressure to achieve results, while Ukraine might use its position in Kursk as leverage in broader negotiations. - -#### **3. Base Rate Analysis** -The base rate for military withdrawals under this kind of pressure, coupled with significant territorial control and the given time frame, tends to be low unless overwhelming force or strategic loss occurs. Historically, when one side has entrenched itself in captured territory, it generally requires major operational changes, external negotiations, or significant battlefield reversals to prompt withdrawal. - -In addition, considering that we are close to the resolution date (September 30 is just over a month away), the window of time for Russia to mount a substantive counter-offensive is narrowing. Historically, such operations are complex and time-consuming. - -#### **4. Time Sensitivity** -The time remaining is approximately 5 weeks. Given the current military situation and the need for not just a successful counter-offensive but also verification (via independent sources like DeepStateMap or ISW), the likelihood diminishes with each passing day. - -Ukraine withdrawing voluntarily is unlikely unless strategic conditions significantly deteriorate or there is an overwhelming Russian attack, neither of which seems imminent based on current data. - -#### **5. Default Behavior** -If nothing changes, the default assumption is that Ukraine will remain in control of the territory. Given the background data, the default behavior of Ukraine digging in is the most likely scenario. - -#### **6. Novel Influences** -There are no extraordinary factors such as new technologies or entirely novel tactics that could dramatically alter the likelihood of a Ukrainian withdrawal in the very near future. The battle lines are more or less stabilized, and Ukraine has demonstrated a capacity to hold ground. - -#### **7. Final Probability Assessment** -Given all the factors above, the probability of Ukraine withdrawing from Kursk before October 1, 2024, is evaluated to be low. Nonetheless, it’s essential to leave a small margin for unexpected developments (e.g., a sudden and successful Russian strategic operation or a diplomatic agreement leading to withdrawal). - -### **Conclusion** -Given the available information, the most logical forecast is: - -**Probability: 15%** - -This reflects a low but not negligible chance that unforeseen events could force or lead Ukraine to withdraw from Kursk before October 1, 2024. The overwhelming likelihood is that Ukraine will continue to hold its positions and the status quo will remain until the deadline." -27446,"Will Kamala Harris say ""artificial intelligence"" or ""AI"" in her 2024 Democratic National Convention keynote speech?","PERPLEXITY - -To address the question of whether Kamala Harris will mention ""artificial intelligence"" or ""AI"" in her 2024 Democratic National Convention keynote speech, we need to consider several factors and recent news articles. - -1. **Kamala Harris's Role in AI Policy**: Harris has been significantly involved in AI policy during her tenure in the Biden administration. She has led efforts to ensure the safe, secure, and trustworthy development and use of AI, including framing and following up on an executive order on AI in October 2023. This background suggests that AI could be a relevant topic for her to discuss, especially given its growing importance in various sectors. - -2. **Recent Disinformation Campaigns**: There have been recent instances where AI has been used to spread disinformation, including against Harris herself. For example, Trump falsely claimed that Harris's campaign used AI to fabricate crowds at a rally. Addressing such issues could be a way for Harris to highlight the challenges and ethical considerations surrounding AI. - -3. **Political Context and Campaign Themes**: Harris's campaign has focused on looking forward and not going back, emphasizing hope and optimism. Discussing AI could fit into this narrative by highlighting its potential for positive change while also addressing the need for responsible regulation and ethical use. - -4. **Expert Opinions and Analysis**: Analysts have noted that AI, despite not being a central campaign issue, is crucial for the future and affects jobs, income equality, national security, and citizen rights. Highlighting these aspects could help Harris connect with a broader audience and demonstrate her vision for the future. - -Given these points, it seems plausible that Kamala Harris could mention ""artificial intelligence"" or ""AI"" in her keynote speech as a way to discuss her policy initiatives, address recent controversies, and align with her campaign's forward-looking theme. However, without direct confirmation from her campaign, this remains speculative. - -######### - -GPT - -### Introduction: -The task is to estimate the probability that Kamala Harris will say ""artificial intelligence"" or ""AI"" in her 2024 Democratic National Convention keynote speech. This question falls under the ""Other Binary Outcome Event"" type, where we are predicting whether a specific event (mentioning certain terms) will occur during a future speech. - -### Step-by-Step Reasoning: - -1. **Base Rate & Historical Context:** - - **AI in Political Speeches:** AI has increasingly been a topic of interest in political discourse, but it still isn't as common as certain broader topics like healthcare or the economy. Generally, the terms ""artificial intelligence"" or ""AI"" may be mentioned occasionally by leading political figures, particularly when discussing technology, the future, or specific policies. - - **Kamala Harris's Past Speeches:** Harris has a strong history of addressing technological advancements and their implications. Given her role as Vice President and her involvement in AI policy, it's not unusual for her to incorporate topics like these in her public addresses. However, it's important to remember that convention keynote speeches often focus more on vision and broader themes like unity, hope, or democracy, rather than deep dives into policy details. - -2. **Contextual Factors & Recent Trends:** - - **AI Involvement:** Kamala Harris has been involved in AI policy, and there’s been recent controversy regarding the use of AI in disinformation, notably targeting her. This might incline her to address AI to defend herself and discuss its implications for democracy. - - **Campaign Themes:** Her campaign’s themes of looking forward, not backward, and focusing on hope and optimism could encompass a discussion about AI as a future-shaping technology that must be responsibly managed. - - **Recent Attacks Using AI:** Awareness of AI's growing role in political discourse and potential threats makes it more plausible that Harris might mention AI during her speech to underscore her readiness to handle modern challenges. - -3. **Speech Dynamics:** - - **Keynote Style:** Keynote speeches at party conventions are typically grand, thematic, and meant to excite the base and reassure undecided voters. While there may be focus on broad themes, specific terms like ""artificial intelligence"" or ""AI"" could be included, particularly in relation to future-looking statements or as a way to contrast the speaker’s perspective with opponents. - -4. **Recency and Importance Analysis:** - - **Recency of AI Discussions:** The involvement in AI policy is recent (within the past year), and AI itself remains a hot topic, increasingly penetrating public discourse. - - **Importance:** Considering AI is a critical aspect of modern technology with vast implications, and Harris is positioned to be influential in this arena, it makes sense that she might touch upon it in some form, especially if it aligns with broader speech goals. - -5. **Default Behavior Consideration:** - - **Default Consideration:** Historically, such keynote speeches might prioritize unity, hope, and broader campaign rhetoric over specific technologies. However, given the increasing significance of AI and Harris’s direct involvement in related policies, the likelihood of her mentioning AI in some context cannot be dismissed. - -### Probability Analysis: - -- **High Relevance:** Given Harris's involvement in AI policy, the recency of AI-related controversies, and the thematic relevance to her campaign, the probability increases. -- **Historical Context:** Given that while not all political figures discuss AI explicitly in such speeches, the overall trend is for impactful or topical issues to be addressed, especially when they align with the candidate's strengths and recent initiatives. -- **Event Dynamics and Nature of Keynote Speeches:** Taking into consideration the grandstanding nature of keynote speeches, one could argue that Harris is more likely to mention broad, impactful technologies than specific policy details. However, recent controversies might push her to include a brief mention of AI. - -### Final Answer: -After assessing the base rates, trends, recency of events, and the thematic focus of convention speeches, I estimate the probability that Kamala Harris will say ""artificial intelligence"" or ""AI"" in her 2024 Democratic National Convention keynote speech is: - -**Probability: 70%** - -This considers a significant likelihood based on her recent associations with AI, but also takes into account the possibility that AI might not make it into the final, perhaps broader-focused, keynote speech." -27446,"Will Kamala Harris say ""artificial intelligence"" or ""AI"" in her 2024 Democratic National Convention keynote speech?","PERPLEXITY - -To provide context for the question of whether Kamala Harris will mention ""artificial intelligence"" or ""AI"" in her 2024 Democratic National Convention keynote speech, we need to consider several factors: - -1. **Harris's Record on AI**: Kamala Harris has a significant record on artificial intelligence (AI) policy. She has been instrumental in framing and following up on the White House's executive order on AI, designed to ensure the safe, secure, and trustworthy development and use of AI. Her involvement in AI policy and her commitment to establishing rules and norms for AI that reflect democratic values suggest that AI could be a relevant topic for her speech. - -2. **Political Context**: The 2024 Democratic National Convention is a platform for Harris to reintroduce herself to the American public and outline her vision for the future. Given the growing importance of AI in various aspects of life, including jobs, national security, and consumer protections, mentioning AI could help her connect with a broader electorate and demonstrate her forward-looking approach. - -3. **Recent Disinformation Campaigns**: The use of AI in disinformation campaigns, particularly those supported by foreign nations like Iran and Russia, has been a recent concern. Highlighting the need to combat such campaigns could be a way for Harris to address current issues and emphasize her commitment to protecting democracy. - -4. **Campaign Strategy**: Harris's campaign has focused on portraying her as a champion of everyday Americans, with a message of hope and optimism. Discussing AI in the context of creating a better future for all Americans could align with this strategy. - -5. **Prediction Markets and Betting Odds**: As of the current date (August 22, 2024), there are no specific prediction markets or live betting odds directly related to whether Kamala Harris will mention ""artificial intelligence"" or ""AI"" in her keynote speech. However, her past actions and the political context suggest it is plausible. - -In summary, while there is no definitive answer to whether Kamala Harris will mention ""artificial intelligence"" or ""AI"" in her keynote speech, her track record on AI policy, the political context, and her campaign strategy suggest it is a possibility. - -######### - -GPT - -### Step-by-Step Analysis - -#### Step 1: Evaluate the Importance of the Question - -This is an **""Other Binary Outcome Event""**: A yes/no question about whether Kamala Harris will say ""artificial intelligence"" or ""AI"" in her 2024 Democratic National Convention keynote speech. - -Given the stakes of this competition and the formality of a keynote speech at the Democratic National Convention, it’s critical to be meticulous in estimating the probability. The speech is likely to be well-crafted by a team of advisors who will weigh each word carefully to maximize political impact. - -#### Step 2: Examine Harris's Record on AI - -Harris has been significantly involved in AI policy, which makes it highly plausible that AI could be a topic in her speech. The technology is currently a hot-button issue in multiple domains—economic, national security, and social. This is a strong piece of evidence suggesting that she may mention AI during her speech, given: - -1. **Economic Impact**: AI’s impact on jobs and the economy is a pressing concern. -2. **National Security**: AI’s use in foreign disinformation campaigns is relevant, especially given the proximity to the 2024 election. -3. **Social Impact**: Issues like privacy, misinformation, and ethical AI development play into broader societal concerns. - -#### Step 3: Analyze the Political Context - -The 2024 Democratic National Convention is a critical platform for Harris to lay out her policy vision. The convention speeches typically cover major points that will define the election's policy agenda. Given how prominently AI is featured in many discussions about the future, it would be reasonable to assume Harris would mention it to emphasize her forward-looking perspective. - -AI is not just a tech trend; it’s a topic with bipartisan interest (and concern), which makes it likely she’ll discuss it to resonate with voters who care about tech policy, job security, national security, and privacy. - -#### Step 4: Recent Political Developments - -Recent use of AI in disinformation by foreign actors (e.g., Russia, Iran) could offer a timely reason for Harris to address AI. Given the upcoming 2024 election, discussing AI in this context would not only be pertinent but could serve to frame Harris as vigilant about national security and election integrity. - -#### Step 5: Campaign Strategy Analysis - -Her campaign has focused on presenting her as a champion of everyday Americans. Given that AI significantly affects everyday issues such as jobs, privacy, and security, it aligns well with her broader messaging. Speaking about AI would allow her to appear both technologically savvy and forward-thinking, which could benefit her campaign. - -#### Step 6: Base Rates & Similar Events - -To establish a base rate, we can look at previous DNC speeches and high-profile policy speeches by Democratic frontrunners during election years. In recent years, emerging technologies like AI have made appearances in such speeches, but they have not been ubiquitous except in contexts directly relevant to the broader narrative the speaker is trying to convey. - -- In 2020, topics like healthcare, the economy, racism, and climate change were more prominent, but tech issues were also mentioned. -- AI specifically might only appear in about a third of analogous speeches (i.e., speeches by candidates with a record of tech policy interest). - -However, 2024 is a unique year due to AI's much larger role in the public consciousness compared to 2020. - -#### Step 7: Consideration of Default Outcome - -If nothing changes between now and the date of the speech, Harris would likely address key policy points central to her campaign and the party's platform. There is a good chance AI will be discussed given her established interest and the current political climate, but it's far from certain. - -Given AI's relevance, there might also be a greater probability she would use terminology like ""AI"" rather than ""artificial intelligence"" for brevity or effective communication. Both are plausible, so we should consider them as an aggregate probability. - -#### Final Assessment - -**Uncertainty Factors**: -- While all signs point to AI being included, unpredictability in speech writing or last-minute changes could affect this outcome. The presence of other pressing issues might lead to the omission of AI-specific mentions in favor of broader themes. -- However, given her history and current events, it's very likely that ""artificial intelligence"" or ""AI"" will be mentioned. - -### Conclusion - -The probability that Kamala Harris will say ""artificial intelligence"" or ""AI"" in her 2024 Democratic National Convention keynote speech seems high due to her policy history, the current political context, and the likelihood that AI will remain a relevant issue in the months leading up to the Convention. - -**Final Probability: 80%**" -27523,"Will Yahya Sinwar cease to be the acting Chairman of the Hamas Political Bureau before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether Yahya Sinwar will cease to be the acting Chairman of the Hamas Political Bureau before October 1, 2024, we need to consider recent developments and expert opinions. - -**Recent Developments:** - -- **Appointment as Chairman:** Yahya Sinwar was appointed as the new Chairman of the Hamas Political Bureau on August 6, 2024, following the assassination of Ismail Haniyeh. -- **Background and Influence:** Sinwar has been a key figure in Hamas, known for his militant stance and his role in the October 7, 2023, attacks on Israel. He has been instrumental in building Hamas's military capabilities and has strong connections with Iran. -- **Current Conflict:** The ongoing conflict in Gaza has resulted in significant casualties and destruction, with nearly 40,000 Palestinians killed and large parts of Gaza's infrastructure destroyed. - -**Expert Opinions and Reactions:** - -- **Israeli Reaction:** Israel has vowed to eliminate Sinwar, with officials stating that his appointment is another reason to target him. -- **US Perspective:** US Secretary of State Antony Blinken noted that Sinwar is the key decision-maker regarding a ceasefire, indicating that his leadership could impact negotiations. -- **Analyst Views:** Experts believe that Sinwar's appointment signals a more hardline direction for Hamas, potentially complicating ceasefire negotiations. However, some suggest that his leadership may not significantly alter the dynamics of negotiations since Israel does not engage directly with Hamas and prefers third-party mediation. - -**Prediction Markets and Betting Odds:** - -- No specific prediction markets or betting odds were found in the provided sources. However, given the volatile nature of the conflict and the targeted assassinations of Hamas leaders, the situation remains highly unpredictable. - -**Conclusion:** - -Given the recent appointment of Yahya Sinwar as the Chairman of the Hamas Political Bureau and the ongoing conflict, it is challenging to predict with certainty whether he will cease to be the acting Chairman before October 1, 2024. The situation is highly volatile, with both internal dynamics within Hamas and external pressures from Israel and other international actors potentially influencing outcomes. Expert opinions suggest a hardening stance within Hamas under Sinwar's leadership, which could impact negotiations and the conflict's trajectory. - -######### - -GPT - -### Overview - -To predict whether Yahya Sinwar will cease to be the acting Chairman of the Hamas Political Bureau before October 1, 2024, we need to carefully examine several factors: - -1. **Context and Timeline**: - - **Date of Appointment**: Sinwar was appointed on August 6, 2024. Today is August 22, 2024, which means there are 40 days until the resolution date of October 1, 2024. - - **Hamas Leadership Tenure**: Previous leaders Khaled Mashal and Ismail Haniyeh held their positions for many years, indicating that significant changes in leadership during a short period like 40 days are uncommon. However, given the ongoing conflict, the situation may differ this time. - -2. **Current Conflict Dynamics**: - - **Volatility**: The ongoing Israel-Hamas conflict is extremely volatile, making it difficult to apply standard base rates to this case. Sinwar’s leadership places him at the center of both operational command and Israeli targeting. The assassination of his predecessor, Haniyeh, is a reminder of the potential dangers for any Hamas leader, especially during active conflict. - - **Israeli Intent**: Israel has expressed clear intent to target Sinwar, particularly given his involvement in the October 2023 attacks. - - **Military Tactics**: Israel has shown increasing sophistication in targeting Hamas leadership, as evidenced by several near-misses, including a close encounter where the IDF was reportedly ""minutes away"" from capturing Sinwar. This suggests a heightened level of risk for him. - -3. **Risk Analysis**: - - **Capture or Death**: The most likely events that could cause Sinwar to cease his position include either his death or capture by Israeli forces. The likelihood of either event is elevated by the ongoing conflict and Israel's resolve. - - **Internal Hamas Factors**: - - **Potential Replacement**: If Sinwar were unable to fulfill his duties due to injury, capture, or perceived failure, Hamas could appoint a new leader. Given Sinwar's relatively fresh appointment and strong influence, an internal replacement is unlikely unless he is incapacitated. - - **No Likely Step Down**: It’s improbable that Sinwar would step down voluntarily within this short period unless his leadership brings about significant internal or external pressure leading to a leadership crisis. - -4. **Historical and Recent Precedents**: - - **Previous Assassinations**: The precedent of Haniyeh’s assassination shows that Israeli forces are capable of targeting top Hamas leaders during active conflict. - - **Sinwar's Survival Strategy**: Sinwar has been a high-profile target before and has managed to evade capture multiple times, indicating experience and caution in avoiding such threats. However, he has only been in this new role for a few weeks, and the targeting on him will be extraordinarily intense. - -### Weighing Probabilities - -**Base Rate (Lead Time for Leadership Changes)**: -- Normally, extreme volatility or external intervention (like an assassination) is required for a leader to cease their role within such a brief duration of 40 days. The base rate for this in a standard political scenario would be low (under 20%). - -**Ongoing Conflict and Targeting**: -- Given the current intensity of the Israel-Hamas conflict, the likelihood of Sinwar being targeted is much higher than in peaceful times. Considering the recent assassinations, the probability of a similar event in just over a month (40 days) is elevated to a moderate-to-high level. - -**Other Considerations**: -- All-out warfare and the chaos that typically follows mean the probability of a significant shakeup is higher than in usual circumstances, but the amount of time remaining (40 days) dampens the probability moderately. - -**Conclusion**: -Given the volatile environment, Israeli intent, recent precedents, and heightened operational risk due to the ongoing conflict in Gaza, the probability that Yahya Sinwar will cease to be the acting Chairman of the Hamas Political Bureau before October 1, 2024, is significant, but not overwhelmingly certain. - -### Final Probability Estimate: -**Probability: 65%**. - -This estimate reflects the real possibility that something might happen to Sinwar, causing him to stop acting as Chairman before October 1, 2024, due to the extraordinary circumstances. However, the uncertainty remains high given his experience and ability to evade capture or assassination so far." -27521,"Will Claude 3.5 Opus be released before October 1, 2024?","PERPLEXITY - -To provide context for the query about whether Claude 3.5 Opus will be released before October 1, 2024, we need to examine the available information and expert opinions from various sources. - -1. **Anthropic's Official Statements**: - - Anthropic has officially announced that Claude 3.5 Opus will be released ""later this year"" as part of the Claude 3.5 model family, which includes Sonnet and Haiku. - - The release of Claude 3.5 Sonnet on June 20, 2024, was accompanied by the statement that Claude 3.5 Haiku and Opus would follow later in the year. - -2. **Bug Bounty Program**: - - Anthropic is accepting applications for a bug bounty program until August 16, 2024, with follow-ups planned for the fall. This suggests that the testing phase for new models, potentially including Claude 3.5 Opus, is ongoing. - -3. **Community Speculations and Predictions**: - - Speculations on Reddit suggest a possible release in late August or early September, aligning with the end of the bug bounty application period and the follow-up timeline. - - Some users on Manifold Markets predict a release in August or September, while others speculate about a release in October or November. - -4. **Prediction Markets**: - - Manifold Markets show varying predictions for the release of Claude 3.5 Opus, with some bets placed on August or September and others on October or November. - -Given these points, while there is no definitive answer to whether Claude 3.5 Opus will be released before October 1, 2024, the information suggests that a release in late August or September is plausible based on Anthropic's development timeline and community speculations. However, without an official release date from Anthropic, these remain speculative predictions. - -######### - -GPT - -### Step 1: Understand the Question and Resolution Criteria - -The goal is to predict the likelihood that **Claude 3.5 Opus** will be released and accessible to the general public before **October 1, 2024**. Resolution criteria stipulate that the model must be available without requiring users to join a waitlist, participate in early access, or be part of a testing phase. - -### Step 2: Establish Base Rate for AI Model Releases - -#### Historical Pattern: -- Previous releases by Anthropic were spaced apart based on progress and readiness. -- Claude 3.5 Sonnet was released on June 20, 2024. Based on the general industry pattern and past behavior of companies like Anthropic, several months between major releases is a common practice. -- Companies like OpenAI and DeepMind tend to release model updates on a predictable cadence, usually spaced around 3-6 months apart. - -Given that ""every few months"" is the stated aim, the next ""few-month"" target from June would be September or October. - -### Step 3: Analyze the Evidence Specific to Claude 3.5 Opus - -**1. **Anthropic's Statements:** - - The clear statement that ""**later this year**"" implies that both Claude 3.5 Haiku and Opus are expected within the 2024 calendar year. - - ""Later this year"" in relation to an already-released model (Sonnet in June) usually indicates a fall timeline, but certainly within the year. - -**2. **Bug Bounty Program Insight:** - - The bug bounty program ending in mid-August suggests that Anthropic was confident enough in the model version being stable for external evaluation. - - Follow-ups in fall indicate that they’re preparing or launching products soon, possibly including Claude 3.5 Opus. - -**3. **Community and Market Speculations:** - - Speculation on Reddit and Manifold Markets tends to believe that the release could be as soon as late August or early September. While community speculations are notoriously hit-or-miss, they often reflect educated guesses based on past behavior and partial insider knowledge. - - Average community expectation aligns with an August/September release. - -**4. **Prediction Markets:** - - Prediction markets are leaning toward a late August/September release. While speculative, these markets tend to aggregate wisdom from a broad range of participants, including those with technical knowledge or access to insider information. - -### Step 4: Consider the Default Scenario and Time Sensitivity - -The default scenario for a major tech release planned for ""later this year"" would likely see a rollout occurring no earlier than the end of August (soon after the bug bounty program), but no later than mid-September to hit the target before October while allowing time for initial public feedback. - -Given how close the current date (August 22, 2024) is to October 1, unless there are significant quality issues or changes in strategy, delays beyond September seem unlikely. - -### Step 5: Account for Uncertainty and Probability Distribution - -A holistic view should account for: -- **High Probability:** Everything goes as per plan, model releases in September. -- **Medium Probability:** Slight delays into late September. -- **Low Probability:** Unexpected delays push the release to October or later. - -Factoring in these distributions: -- **80% chance** of release in August/September. -- **15% chance** of release during the last days of September. -- **5% chance** of significant delays pushes release beyond October 1, 2024. - -### Step 6: Final Probability Estimate - -Given all the information, accounting for both the recency and reliability of the data, the timelines and statements by Anthropic, as well as market expectations: - -""**Probability: 85%**"" - -This reflects a strong likelihood that Claude 3.5 Opus will be accessible to the general public before October 1, 2024." -27522,"Will the decision to reallocate the bronze medal in the women's floor exercise at the 2024 Paris Olympics from the US to Romania be overturned before October 1, 2024?","PERPLEXITY - -The decision to reallocate the bronze medal in the women's floor exercise at the 2024 Paris Olympics from the US to Romania has been a subject of controversy. Here is a concise rundown of the relevant news: - -- **Background**: The bronze medal was initially awarded to American gymnast Jordan Chiles after her coach, Cecile Landi, successfully appealed for a review of Chiles' score. However, the Court of Arbitration for Sport (CAS) later ruled that the appeal was submitted four seconds after the one-minute deadline, leading to the reallocation of the medal to Romanian gymnast Ana Barbosu. - -- **Current Status**: The International Olympic Committee (IOC) has respected the CAS ruling and reallocated the bronze medal to Ana Barbosu. Barbosu has been presented with the medal in a ceremony in Bucharest, Romania. - -- **US Response**: USA Gymnastics has announced that it will continue efforts to allow Chiles to retain her medal. The governing body contests the CAS ruling, arguing that they have video evidence showing Landi initiated her appeal within the time limit. - -- **Expert Opinions and Reactions**: Chiles has expressed her disappointment and frustration with the decision, calling it ""unfair"" and a ""major setback."" She has also faced racially motivated attacks on social media, which she described as ""wrong and extremely hurtful."" Barbosu, on the other hand, has expressed happiness at receiving the medal and hopes to represent Romania at the highest level. - -- **Prediction Markets and Betting Odds**: There is no specific information available on prediction markets or betting odds regarding the overturning of the decision before October 1, 2024. - -Given the current information, there are no recent updates that would indicate a movement towards overturning the decision before October 1, 2024. However, USA Gymnastics' continued efforts to contest the ruling suggest that the situation remains fluid. - -######### - -GPT - -### Introduction -The question pertains to whether the decision by the Court of Arbitration for Sport (CAS) to reallocate the bronze medal from Jordan Chiles (USA) to Ana Bǎrbosu (Romania) at the 2024 Paris Olympics will be overturned before October 1, 2024. As an expert superforecaster, I'll approach this using structured analysis based on available information, base rates, historical precedent, and context, while keeping in mind the criteria established in the resolution. - -### Step 1: Define the Key Variables and Context -- **Current Date**: August 22, 2024 -- **Deadline for the Forecast**: October 1, 2024 -- **Domain**: International sports arbitration and potential court appeals. -- **Relevant Entities**: Court of Arbitration for Sport (CAS), USA Gymnastics, Jordan Chiles, Ana Bǎrbosu, and Swiss Federal Tribunal (SFT). - -### Step 2: Analyze the Status Quo -- **CAS Decision**: The CAS has already ruled in favor of Romania, reallocating the bronze medal to Ana Bǎrbosu due to the violation of the filing deadline. The International Olympic Committee (IOC) has complied with this ruling, and Bǎrbosu received the medal. -- **Appeal Mechanism**: USA Gymnastics can appeal this decision directly to the Swiss Federal Tribunal (SFT). However, historical data shows that the success rate of appeals against CAS rulings is exceedingly low (around 1%-0.3%). -- **Time Constraint**: There are about 40 days remaining until the October 1 deadline. Legal proceedings usually take time, and it would require an expedited process if any reversal were to occur. - -### Step 3: Establish the Base Rate -- **Low Success Rate of Overturning CAS Decisions**: Empirical data from CAS's track record suggests that successful appeals are extremely rare. -- **Time Sensitivity**: There is limited time available for any appeal to be filed, reviewed, and resolved. The chance of a comprehensive review and reversal happening in less than 40 days is low, especially considering that legal systems are often slow. - -### Step 4: Adjust the Base Rate Based on Current Context -- **Potential Evidence and Public Pressure**: USA Gymnastics is attempting to introduce new evidence, and there may be significant media and public attention, providing some slight upward pressure on the likelihood of a review. -- **No Jurisdictional Oversight**: The CAS generally operates with significant autonomy, and the SFT rarely overturns CAS decisions unless there is a gross procedural error or a violation of fundamental principles of law. - -### Step 5: Uncertainty Assessment and Final Estimation -- **Base Rate**: Establishing from historical context, the base rate remains exceedingly low, around 1%-2%. -- **Adjustment for Context**: Accounting for new evidence and possible public pressure, the probability may increase marginally but not significantly. - -### Conclusion -Given the overall situation, including the short timeframe remaining, the methodical slowness of legal appeals, and the historically low success rate of overturning CAS rulings, the probability of this decision being overturned before October 1, 2024, remains very low. - -**Probability: 3%**" -27487,Will the Fed cut rates by more than 50 bps at their September meeting?,"PERPLEXITY - -Based on the latest news and expert opinions, here is a concise rundown of the context relevant to the question of whether the Fed will cut rates by more than 50 basis points (bps) at their September meeting: - -### Current Interest Rates -- The federal funds rate is currently at 5.25% to 5.5%, a level it has maintained since July 2023. - -### Expectations for September Meeting -- The majority of economists surveyed by FactSet predict a 0.5 percentage point cut in September, bringing the rate to 4.75% to 5%. -- CME Group's FedWatch Tool shows a split between expectations for a 25 bps and a 50 bps cut, with the latter slightly more probable. -- Some experts, like Jeremey Siegel, suggest the need for an even larger cut, such as 75 bps, but this is less common. - -### Reasons for Rate Cuts -- The weakening job market and inflation retreating towards the Fed's 2% target are cited as reasons for potential rate cuts. -- The unemployment rate has increased to 4.3% from a historic low of 3.5% a year ago, fueling recession fears. - -### Expert Opinions -- Amanda Agati, chief investment officer of PNC Financial Services Group, believes a 50 bps cut in September is very possible. -- Solita Marcelli, chief investment officer at UBS Global Wealth Management, forecasts rates to be 1 percentage point lower by year-end, suggesting multiple cuts. - -### Prediction Markets -- As of the latest updates, prediction markets and futures indicate a high likelihood of a rate cut in September, but the size of the cut remains debated. - -### Recent Updates -- The release of the July FOMC minutes and recent economic data have strengthened the case for a rate cut in September, but most experts lean towards a cautious approach, favoring a 25 bps cut unless further economic weakness is demonstrated. - -In summary, while there is a strong expectation for a rate cut in September, the majority of experts and prediction markets lean towards a cut of 50 bps or less. Suggestions for cuts larger than 50 bps are less common and would likely require significant further economic deterioration. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Understanding the Question and Resolution Criteria** - -The question is asking whether the Federal Reserve will cut interest rates by more than 50 basis points (bps) at their September 2024 FOMC meeting. The current federal funds rate target is 5.25%-5.5%, implying that a cut of more than 50 bps would bring the target down to 4.75%-5% or lower. - -The resolution of the question depends solely on the official policy announcement made by the Fed at the conclusion of the September FOMC meeting, regardless of any emergency actions taken before that meeting. - -#### 2. **General Economic Context** - -- **August 2024 Market Sentiment**: - - The shift in market sentiment has been driven by weaker-than-expected economic data and recession fears. - - Bond markets have been rallying, which indicates that investors are expecting a significant economic slowdown and anticipate that the Fed will respond by lowering rates. - -- **Current Expectations**: - - Market participants and economists are largely predicting a rate cut of 50 bps in September. - - Some analyses suggest that smaller cuts of 25 bps may be more likely unless more significant economic deterioration occurs. - - A smaller subset of experts is considering the possibility of a larger cut (75 bps or more). - -#### 3. **Factors to Consider** - -- **Historical Precedent**: - - The Fed typically cuts rates gradually rather than in large steps. However, there have been instances, particularly during economic crises, where larger cuts were implemented (e.g., during the 2008 financial crisis). - -- **Current Data**: - - The recent weak jobs report, which showed only 114,000 jobs added and rising unemployment, is already suggestive of economic slowing. - - Inflation is retreating towards the Fed's 2% target, reducing the urgency to maintain high rates to combat inflation. - -- **Market Pricing**: - - Futures markets are pricing in substantial rate cuts by the end of the year, with some predicting more aggressive action, including the possibility of a larger cut in September. - -- **Time Sensitivity**: - - As of August 2024, there is a bit less than a month before the September meeting, which leaves room for more data to influence the Fed's decision. - - If economic data continues to worsen, the case for a more aggressive rate cut would strengthen. Conversely, if data stabilizes or improves, the Fed might choose a more cautious approach. - -#### 4. **Interpreting Expert Opinions and Current Sentiment** - -- **Baseline Scenario**: - - The majority of experts and models seem to point towards a 50 bps cut rather than a more daring move over 50 bps. - -- **Potential for Over-50 bps Cut**: - - If we assume further economic weakening (e.g., worse jobs data or a faster drop in inflation), the Fed might take more decisive action. - - The threshold for a decision to cut over 50 bps would likely include either a sharp deterioration in key economic indicators or an external shock that significantly disrupts the economy. - -- **Counterfactual Considerations**: - - Would the Fed implement more than a 50 bps cut as a proactive move to stabilize market expectations and prevent further deterioration? Or would they prefer smaller, incremental cuts? Historical behaviors suggest the latter, but extraordinary situations could prompt more aggressive action. - -#### 5. **Final Probability Estimation** - -- **Base Rates and Past Behavior**: - - Large rate cuts (>50 bps) are relatively rare and usually tied to significant economic crises. - -- **Current Economic Climate**: - - Current data suggests weakening but not a full-blown crisis yet. - -- **Expert Consensus and Market Sentiment**: - - Leaning towards a 50 bps cut with some possibility of a smaller step. The scenario for a cut over 50 bps requires further weakening, which we can’t definitively forecast, although there is some evidence in that direction. - -#### **Final Probability** - -Given all the discussed factors, there is a plausible scenario for a greater-than-50-bps cut, but it would require further significant economic deterioration—a situation that is currently uncertain but not impossible. - -Thus, the probability of a rate cut of more than 50 bps at the September 2024 meeting is: - -**Probability: 25%** - -This reflects a moderate chance of further weakening in economic conditions that could push the Fed to take more aggressive action but acknowledges the Fed’s general tendency to be cautious and incremental in their rate adjustments." -27538,"Will Russia detonate a nuclear weapon in Ukraine or Ukrainian-held territory before October 1, 2024?","PERPLEXITY - -To address the question of whether Russia will detonate a nuclear weapon in Ukraine or Ukrainian-held territory before October 1, 2024, it's essential to consider recent developments, expert opinions, and historical context. - -### Recent Developments and Context - -1. **Historical Concerns**: The Biden administration had been preparing for the possibility of Russia using a nuclear weapon in Ukraine since late 2022, especially as Russian forces faced significant losses. This concern was heightened by Russia's propaganda about a Ukrainian ""dirty bomb,"" which U.S. officials feared could be used as a pretext for a Russian nuclear strike. - -2. **Nuclear Capabilities and Threats**: Russia possesses the world's largest nuclear arsenal, with nearly 6,000 nuclear weapons, including tactical nuclear weapons that could be used in a battlefield scenario. Russian officials have made veiled threats about using nuclear weapons if they perceive a threat to Russian sovereignty or territorial integrity. - -3. **International Response**: The international community, including NATO, has strongly condemned any potential use of nuclear weapons. NATO has communicated to Russia that using a nuclear weapon in Ukraine would result in severe consequences, including military action against Russian forces in Ukraine and the Black Sea Fleet. - -4. **Current Stance**: As of July 2024, President Putin has stated that he does not need nuclear weapons to achieve his goals in Ukraine but also emphasized that it's wrong for the West to assume Russia will never use them. - -### Expert Opinions and Predictions - -- **Military Response**: Retired U.S. General David Petraeus has suggested that a Russian nuclear strike in Ukraine would prompt NATO to destroy Russian troops and equipment in Ukraine and sink the Black Sea Fleet. -- **International Condemnation**: The use of a nuclear weapon would lead to widespread international condemnation, potentially isolating Russia further on the global stage. -- **Predictions and Betting Markets**: As of the current date, there are no specific prediction markets or betting odds provided in the sources. However, the general consensus among experts is that while the threat is taken seriously, the use of a nuclear weapon would have catastrophic consequences for Russia, making it a less likely option. - -### Conclusion - -While there have been significant concerns and threats regarding the use of nuclear weapons by Russia in Ukraine, the latest statements from President Putin suggest a cautious approach, emphasizing that nuclear weapons are not necessary for achieving his current objectives. However, the situation remains volatile, and the international community continues to monitor developments closely. As of the last update in July 2024, there are no recent indicators that would suggest an imminent nuclear strike, but the situation could change rapidly. - -######### - -GPT - -### Step 1: Understand the Context - -Let's first consider the context and the significance of recent developments. Russia, under President Putin, has repeatedly made nuclear threats during the Ukraine conflict, although none of these threats have materialized so far. The situation is fluid, with the international community placing strong pressure on Russia to avoid the use of nuclear weapons, while Russia itself seems to maintain the option as a form of deterrence or brinkmanship. - -### Step 2: Identify the Baseline Probability - -Given that nuclear weapons have not been used in warfare since 1945, the baseline probability for their use is low. The decision to use a nuclear weapon would be exceptionally consequential, leading to significant international ramifications including potential global conflict or massive sanctions against Russia. Historically, nuclear powers have avoided the use of such weapons even during critical phases of conflicts (e.g., during the Cold War and various proxy wars). Based on this historical context and expert consensus, the baseline probability of a nuclear weapon being used in any given year is generally very low—likely less than 1%. - -### Step 3: Assess the Specific Situation - -Now, we'll update this baseline with the specific current situation between Russia and Ukraine: - -- **Escalation Risks**: The relationship between Russia and the West has deteriorated, with Russia facing setbacks on the battlefield. The fact that state TV has floated the idea, and high-level officials, including Putin, reiterate that nuclear weapons are an available option, increases the slight possibility. However, there's a difference between using nuclear rhetoric as a deterrent and actually deploying nuclear weapons. - -- **Expert Opinions**: Military and geopolitical analysts generally agree that a nuclear strike by Russia is unlikely, but not impossible. Most point out that using nuclear weapons would drastically change Russia's strategic situation for the worse by isolating it further and inviting massive retaliation, potentially even from NATO. - -- **Putin's Doctrine**: The latest statements by Putin indicate that he reserves the right to use nuclear weapons if Russia's sovereignty is threatened. There hasn't been a direct indication that he views the current state of the conflict as severe enough to warrant such an escalation. However, the fact that he does not rule out changes to Russia’s nuclear doctrine adds a layer of uncertainty. - -### Step 4: How Much Time Is There? - -Today is August 22, 2024, and the question resolves on October 1, 2024. This gives us roughly 40 days. The time window is narrow, which is key in influencing the probability. While the situation is volatile, significant changes in strategic posture (like the decision to use nuclear weapons) typically do not happen overnight unless there is a major triggering event. - -### Step 5: Weigh Against Default Behavior - -The default outcome here, not using nuclear weapons, has been the consistent behavior of Russia and other nuclear powers for many decades. We should heavily weigh this default behavior. - -### Step 6: Base Rates and Novel Factors - -Base rates for use of nuclear weapons are extremely low. What is novel is Russia’s ongoing failures in Ukraine, significant losses, and the use of nuclear rhetoric. However, even with these novel factors, crossing the nuclear threshold would mark a substantial shift from any historical precedent since 1945. - -### Step 7: Consider Potential Outcomes - -The most likely scenarios moving forward include continued conventional warfare, economic leveraging, and diplomatic maneuvering. Significant escalation leading to a nuclear strike remains less likely, even with the uncertainty. Diplomatic and military consequences might be enough to deter Russia from such a drastic action in the short and medium term. - -### Conclusion: Probabilistic Estimation - -- **Base rate of nuclear use historically**: <1% -- **Factors increasing risk**: Ongoing conflict, nuclear rhetoric, Russian setbacks. -- **Factors decreasing risk**: Enormous international consequences, deterrence, time frame. -- **Time until the forecast resolves**: ~40 days. - -Given these considerations, I will adjust the baseline probability upward slightly due to the unique and dire situation, yet still maintain a low overall probability due to historical precedent, the severe consequences of such action, and the short time frame available. - -### Probability: 2%" -27539,"Will a non-test nuclear weapon be detonated by October 1, 2024?","PERPLEXITY - -To address the question of whether a non-test nuclear weapon will be detonated by October 1, 2024, we need to consider various factors and recent developments in nuclear weapons and international relations. - -### Nuclear Testing and Development - -1. **Historical Context**: Since the first nuclear test in 1945, over 2,000 nuclear tests have been conducted by at least eight nations. Most early tests were atmospheric, spreading radioactive materials, while later tests were mostly underground, some of which still vented radioactive material into the atmosphere. - -2. **Comprehensive Test Ban Treaty (CTBT)**: Negotiations for a global CTBT began in 1994 and concluded in 1996. The treaty, which prohibits all nuclear explosions, has not yet entered into force due to lack of ratification by key states, including the United States. Russia withdrew its ratification in 2023, citing an imbalance with the U.S.. - -### Current Nuclear Weapon Development - -3. **United States Nuclear Weapons**: The U.S. is modernizing its nuclear arsenal, including the development of the B61-12 and B61-13 bombs. The B61-12 is a tactical bomb with improved accuracy and lower yield options, while the B61-13 is a higher-yield gravity bomb. Production of these weapons is ongoing, with no indication of plans for nuclear testing. - -4. **North Korea’s Nuclear Program**: North Korea has not conducted a nuclear test since 2017, but there are concerns it might conduct a test before the U.S. presidential election in 2024. North Korea seeks to develop more powerful nuclear weapons, possibly with yields of around 100 kilotons, but it faces opposition from China. - -### International Relations and Policies - -5. **Nuclear Disarmament and Non-Proliferation**: The Treaty on the Non-Proliferation of Nuclear Weapons (NPT) aims at nuclear disarmament, non-proliferation, and peaceful use of nuclear energy. However, progress has been slow, and nuclear-armed states have not signed the UN Treaty on the Prohibition of Nuclear Weapons. There is a growing emphasis on “no first use” policies as a realistic approach to reduce nuclear threats. - -6. **Recent Developments**: NATO is modernizing its nuclear posture in Europe, and there are talks about deploying more nuclear weapons. China’s nuclear arsenal is growing, and it is expected to increase to 1,500 warheads by 2035. The U.S. and Russia have suspended participation in the New START treaty, and Russia has withdrawn its ratification of the CTBT. - -### Prediction Markets and Expert Opinions - -- **Prediction Markets**: There is no direct information from prediction markets regarding the detonation of a non-test nuclear weapon by October 1, 2024. However, the geopolitical tensions and the race for nuclear superiority suggest a heightened risk. - -- **Expert Opinions**: Experts like Bruce W. Bennett from RAND highlight the potential for North Korea to conduct a nuclear test, while others emphasize the importance of “no first use” policies to reduce nuclear threats. - -### Conclusion - -Given the current geopolitical tensions, ongoing nuclear weapon modernization, and the lack of progress in disarmament and non-proliferation efforts, the risk of a non-test nuclear weapon being detonated by October 1, 2024, cannot be ruled out. However, there is no concrete evidence to predict such an event with certainty. The situation remains fluid and highly dependent on the actions of nuclear-armed states and their adherence to or violation of international treaties and policies. - -######### - -GPT - -### Step 1: Define the question and contextualize the scenario - -The question asks about the likelihood of a non-test nuclear weapon detonation by October 1, 2024. The potential scenarios for such a detonation include: - -- **Deliberate nuclear attack**: A state or non-state actor using nuclear weapons intentionally. -- **Accidental nuclear attack or detonation**: Nuclear weapons being detonated unintentionally, either through a mechanical/electronic failure, human error, or false intelligence. -- **Nuclear terrorism**: A non-state actor (e.g., terrorist group) acquiring and detonating a nuclear device. - -### Step 2: Gather and assess relevant factors and base rates - -#### *Base Rates and Historical Context* -1. **Historical Base Rate**: There have been two confirmed instances of a non-test nuclear weapon being used in combat (Hiroshima and Nagasaki in 1945). Since then, nuclear weapons have not been used outside of testing environments despite numerous near-misses and heightened tensions (e.g., Cuban Missile Crisis). - - - **Relevant Base Rate**: Given that no non-test nuclear weapon has been detonated in about 79 years since WWII (0 out of tens of thousands of days), the base rate is extremely low. This translates to an exceedingly low frequency of occurrence. - -#### *Current Geopolitical Tensions and Developments* -2. **U.S.-Russia Relations**: The suspension of the New START treaty and Russia's withdrawal from the CTBT exacerbate tensions. However, both nations have strong incentives to avoid mutual destruction through nuclear launch. The absence of ongoing active conflict where a nuclear exchange is imminent decreases the probability of a nuclear detonation. - -3. **North Korea**: North Korea has the capability and the unpredictability factor. While they could potentially conduct a test, the step from testing to an actual offensive nuclear strike is enormous and would involve significant retaliation from other nations that North Korea would likely want to avoid. This limits but does not eliminate the risk. - -4. **China’s Expansion of Nuclear Capabilities**: China is expanding its nuclear arsenal, but there is no concrete evidence to suggest China plans to initiate conflict by October 1, 2024. Although nuclear modernization increases longer-term risks, it does not necessarily translate into immediate heightened danger. - -5. **Recent Incidents and Deterrence Policies**: While there have been nuclear close calls (e.g., Cuban Missile Crisis, accidental launches avoided), these close calls have historically resulted in increased safety measures and protocols rather than actual detonations. Most nuclear nations have “no first use” or similar deterrence policies in place, which lower the chance of accidental or intentional nuclear engagement drastically in the short term. - -#### *Short-term Factors & Variability* -6. **Time-Shortened Horizon**: The time frame considered is narrow — just over a month between now (August 22, 2024) and October 1, 2024. Extremely high-stakes events like the detonation of a nuclear weapon typically involve prolonged buildup, signaling, and global diplomacy or strategic maneuvers before they occur, reducing the likelihood of a sudden event within the final time window. - -7. **Prediction Markets and Expert Opinions**: Although there’s heightened tension, the absence of signals such as specific prediction market activity pointing to imminent risk and the curated opinions of experts who have not declared a high likelihood of an imminent strike indicate a lessened immediate risk. - -### Step 3: Statistical Reasoning and Base Rate Adjustment -Given the very low base rate of past occurrences, and considering the fact that while tensions are heightened, no situation has escalated to the point of active nuclear conflict, the likelihood remains low. The risk might be slightly elevated due to geopolitical factors, but still remains within a very low probability range for the specified period. - -### Step 4: Final Probability Estimate - -Given all of the information: -- **Base Rate Context**: Extremely low historical base rate. -- **Recent Developments**: Heightened but not critically acute tensions. -- **Short Time Frame**: The short window suggests low likelihood of a sudden event. - -Final Answer: ""Probability: 3%"" - -This prediction considers the small but non-zero chance due to the complexity of international relations, while heavily weighting the overall historically low likelihood of such an event." -27484,Will the Fed hold rates steady at their September meeting?,"PERPLEXITY - -Based on recent news and expert opinions, here is a concise rundown of the context relevant to the question of whether the Fed will hold rates steady at their September meeting: - -- **Current Interest Rate**: The Federal Reserve has maintained its benchmark interest rate at 5.25% to 5.5% since July 2023. -- **Expectations for September Meeting**: There is a strong indication from Fed officials and market participants that a rate cut is likely in September. Fed Chair Jerome Powell hinted that the time for a rate cut is approaching, contingent on forthcoming economic data, particularly inflation rates. -- **Inflation and Labor Market**: Recent data shows inflation easing, with the Personal Consumption Expenditures index slowing to 2% for the year ending in June. However, the labor market is showing signs of softening, with job growth slowing to an average of 177,000 a month for the past three months. -- **Expert Opinions**: Many economists and analysts expect a rate cut in September, with some speculating about the possibility of a 50 basis point cut, though a 25 basis point reduction is more commonly anticipated. -- **Prediction Markets**: Trading in fed funds futures indicates a high probability of rate cuts starting in September. - -Recent updates suggest movement towards a rate cut in September, driven by easing inflation and concerns about the labor market. However, the extent of the cut remains uncertain and will depend on economic data leading up to the September meeting. - -######### - -GPT - -### Step 1: Contextual Analysis - -**Background Overview:** - -The question revolves around whether the U.S. Federal Reserve will hold interest rates steady during their September FOMC meeting. As of late August 2024, the economic environment indicates a potential weakening, driven by slowing job growth, an increasing unemployment rate, and easing inflation. The current federal funds rate target is between 5.25% to 5.5%, a relatively high rate maintained since July 2023. - -**Weakening Economic Data:** - -1. **Job Market:** The latest jobs report showed that only 114,000 jobs were added in July, which is significantly lower than forecast, and unemployment has risen. Historically, the Fed considers the labor market when determining rate adjustments, as a weakening job market often signals broader economic problems. - -2. **Inflation:** Inflation has eased, falling closer to the Fed's target of 2%. Softer inflation likely reduces the pressure to maintain high rates, as the aggressive rate hikes over the last year were an effort to curb inflation. - -3. **Bond Market Rally:** The bond market rally suggests that investors expect economic weakness. This kind of rally typically signals expectations of rate cuts due to economic downturn concerns. - -**Market Expectations and Fed Behavior:** - -1. **Fed Chair Jerome Powell’s Comments:** Powell indicated that a rate cut might be needed, subject to inflation and other economic data. This setup creates an expectation that the high rates might be unsustainable if the economic outlook continues to darken. - -2. **Futures Markets:** Futures markets indicate expectations of up to 100 basis points of cuts by the end of 2024. - -**Baseline Assumptions:** - -- **Historical Precedent:** Typically, the Fed is cautious and prefers to hold rates steady unless there's a clear and sustained shift in economic indicators. However, the Fed also moves to cut rates more quickly in response to deteriorating economic conditions. - -- **Policy Independence:** The Fed tends to avoid being swayed solely by market sentiment and requires strong supporting data before making rate changes. - -### Step 2: Consideration of Time Frame and Variability - -The time between late August and mid-September allows for the release of further economic data (e.g., August jobs report, various inflation data) that might either reinforce or undermine market sentiment regarding the need for a rate cut. Given this situation: - -- **August Jobs Report:** If weaker than expected, it could prompt the Fed to consider cutting rates, increasing the likelihood that they won’t hold rates steady. - -- **Inflation Data:** Should inflation continue to ease, it could reinforce or even amplify current market sentiments. Alternatively, if inflation unexpectedly ticks up, the Fed could feel compelled to hold rates steady. - -### Step 3: Statistical Analysis and Base Rate Consideration - -Historically, the Fed has been more reactive in cases where there is a clear and present economic downturn, particularly with rising unemployment and slowing job growth. However, they typically need to see a pattern in economic data before moving to lower rates, especially if rates are already high. - -Considering the indicators: - -- **Fed Reactions to Past Data:** The Fed has historically reduced rates only after confirming sustained economic weakness, particularly focusing on inflation and GDP data. Market expectations align with the historical pattern of the Fed cutting rates in response to deteriorating economic indicators. - -### Step 4: Final Probability Estimate - -Given the data: - -- **Indicators point towards market and expert forecasts favoring a rate cut in September.** -- **The Fed has shown a willingness recently to respond to changing economic conditions, evidenced by Powell's acknowledgment of the potential need for cuts.** -- **Historical behavior of the Fed in similar situations supports the idea of rate cuts rather than holding steady if the data continue to weaken.** - -**Counterweights**: - -- **Potential for Surprising Data:** Economic data releases between now and the meeting could shift the Fed's decision. If, for instance, the August jobs report shows unexpected resilience, then the Fed might hold rates steady. - -- **Fed’s Caution:** While market indicators suggest a cut, the Fed is typically more conservative and may wait for more conclusive evidence. - -**Uncertainty Analysis:** -- Consider the economic data variability, the inherent lag in economic data interpretation, and the possibility that the Fed could still avoid an aggressive rate cut to retain flexibility. - -### Final Answer: - -Considering all of these factors, while there is a strong leaning towards a rate cut at the September meeting, there remains a non-negligible chance that the Fed might wait for more concrete data before reducing rates, especially given their cautious nature historically. - -**Probability: 30%** that the Fed holds rates steady at their September meeting." -27485,Will the Fed cut rates by 25 bps at their September meeting?,"PERPLEXITY - -**Fed Rate Cut in September: A Review of Expert Predictions and Market Indicators** - -The Federal Reserve is widely expected to cut interest rates at its next meeting in September, with the majority of experts predicting a 25 basis point (bps) reduction. Here's a summary of the latest news and expert opinions: - -- **Current Interest Rate**: The federal funds rate is currently at a range of 5.25% to 5.5%, the highest level in two decades. - -- **Expert Predictions**: - - Nearly 80% of economists surveyed by Bloomberg predict a 25 bps cut to a range of 5% to 5.25% in September. - - Capital Economics' Stephen Brown expects a 25 bps cut, citing the need for caution after retail sales rebounded in July. - - Jamie Cox, managing partner at Harris Financial Group, believes the Fed's easing cycle is coming, with the September rate cut being a certainty. - -- **Market Indicators**: - - CME Group’s FedWatch Tool shows a split between the possibility of a 50 bps cut and a 25 bps cut, with the probability of the larger cut slightly higher. - - Bond traders are pricing in a 36.5% chance of a 50 bps rate cut in September, up from 32.5% before the release of the FOMC minutes. - -- **Economic Context**: - - The job market has weakened, with the unemployment rate increasing to 4.3% from a historic low of 3.5% a year ago. - - Inflation is retreating back to the Fed's 2% target, making a rate cut more likely. - -- **Potential Downsides**: - - Cutting rates too soon could lead to increased inflation and the risk of a bubble forming in the markets. - - Delaying rate cuts could cause the economy to slow down further, leading to higher unemployment. - -In summary, while there is a strong consensus among experts and market indicators that the Fed will cut rates in September, the debate over the size of the cut continues. The majority lean towards a cautious 25 bps reduction, but some argue for a larger cut to address the weakening job market and cooling inflation. - -######### - -GPT - -### Step 1: Understanding the Context - -The question at hand is whether the Federal Reserve will cut interest rates by 25 basis points (bps) at their September 2024 meeting. Analyzing the background and current conditions is crucial: - -- **Current Situation**: As of August 2024, the US economy is showing signs of weakening with a slowdown in job creation and rising unemployment. The bond market has been rallying as traders increasingly anticipate rate cuts due to these conditions. The Federal funds rate is currently at a range of 5.25% to 5.5%. - -- **Expectations in the Market**: The CME Group's FedWatch Tool indicates a high probability of rate cuts by the end of 2024, with the market anticipating around 100 bps in total cuts. The consensus among economists and market participants leans towards a 25 bps cut at the upcoming September meeting, though there is some speculation about the possibility of a larger 50 bps cut. - -- **Signals from the Fed**: The Federal Reserve is likely aware of the potential for a recession and would prefer to manage this risk cautiously. Cutting rates too aggressively could lead to inflationary pressures, while not acting sufficiently could exacerbate an economic downturn. - -### Step 2: Consideration of Historical Base Rates - -Historically, the Fed tends to move cautiously, especially when economic conditions are uncertain. Given that the Fed has already raised rates significantly up to this point, they may prefer to ease into rate cuts rather than making larger or more abrupt changes, which might explain why most experts lean towards a 25 bps cut. - -### Step 3: Evaluating the Potential Novel Factors - -Several novel factors could potentially influence the Fed's decision: -- **Weakening Labor Market**: The job market has weakened significantly as evidenced by the July report, and if this trend continues or worsens, the Fed may feel pressure to implement rate cuts. -- **Inflation Retreating to Target**: With inflation showing signs of cooling and getting closer to the Fed’s 2% target, the Fed might feel that inflationary pressures are under control, allowing them to focus more on economic growth. - -### Step 4: Considering the Probabilities - -Given the current situation: -- The Fed is more likely to cut rates, but given their historical caution, a modest 25 bps reduction seems more probable than a more aggressive 50 bps cut. -- The fact that inflation is cooling down and job growth is slowing down means that the Fed will likely want to act, but not so aggressively as to reignite inflation concerns or appear panicked. - -### Step 5: Decomposition of Possible Outcomes - -1. **No Rate Cut**: - - This would occur if there's a major surprise with strong economic data or significant geopolitical events that encourage the Fed to maintain the current rate. Historical caution also speaks to the Fed leaning towards no change when there is major uncertainty. - - Probability: ~20% - -2. **25 bps Rate Cut**: - - Reflects a cautious approach. This is the majority expectation among analysts and market participants. Also, historically, such a cut size is typical when transitioning from a tightening to an easing cycle, especially during periods of economic uncertainty. - - Probability: **~60%** - -3. **50 bps Rate Cut**: - - Would be seen as a more aggressive move. This would likely occur if the economic data in the first half of September shows a more rapid deterioration than expected or if external factors (like a significant financial market dislocation) warrant drastic action. - - Probability: ~20% - -### Final Probability Estimate - -Given that we're operating on August 22, with multiple factors lining up towards a rate cut but also balancing caution against an aggressive policy response, the chances of a 25 bps rate cut at the September meeting appear most likely based on current information. - -### Conclusion - -**Probability: 60%** for a **25 bps rate cut** at the September 2024 Fed meeting." -27486,Will the Fed cut rates by 50 bps at their September meeting?,"PERPLEXITY - -**Summary of Relevant News** - -The Federal Reserve is widely expected to cut interest rates in September, but the size of the cut is uncertain. Here are key points from recent news sources: - -- **Interest Rate Cut Expectations**: Most experts believe that a rate cut in September is almost guaranteed, with the debate focusing on whether it will be a 25 basis point (bps) or 50 bps cut. -- **Fed Meeting Minutes**: The July Federal Open Market Committee (FOMC) meeting minutes indicated that a ""vast majority"" of Fed officials see a rate cut as ""likely"" in September, contingent on economic data coming in as expected. -- **Economic Data**: Recent economic data, including a weak July jobs report and revisions showing the labor market is weaker than previously estimated, support the case for a rate cut. -- **Market Predictions**: Bond traders are pricing in a 36.5% chance of a 50 bps rate cut in September, up from previous estimates, according to CME Group's FedWatch tool. -- **Expert Opinions**: Some experts, like Jamie Cox, managing partner at Harris Financial Group, are confident in a September rate cut, while others, such as Stephen Brown of Capital Economics, believe a 25 bps cut is more likely unless there is further economic deterioration. -- **Future Rate Cuts**: There are predictions of further rate cuts beyond September, with some economists forecasting rates could be as low as 4% to 4.25% by year-end. - -**Current Values and Predictions** - -- **Current Interest Rate**: The federal funds rate is currently at 5.25% to 5.5%. -- **Predicted Rate Cut**: The market is split between a 25 bps and 50 bps cut in September, with some experts leaning towards a larger cut if economic data worsens. -- **Year-End Rate Predictions**: Some forecasts suggest rates could be lowered to 4% to 4.25% by the end of 2024. - -**Recent Updates and Movement** - -- The recent stock market selloff and weak economic data have increased the likelihood of a more aggressive rate cut in September. -- The August labor report is seen as crucial in determining the size of the September rate cut. - -**Expert Opinions and Forecasts** - -- Amanda Agati, chief investment officer of PNC Financial Services Group's asset management unit, believes a 50 bps rate cut in September is possible. -- Solita Marcelli, chief investment officer Americas at UBS Global Wealth Management, forecasts rates could be 1 percentage point lower by year-end. - -Overall, while a rate cut in September is widely anticipated, the size of the cut remains uncertain and will likely depend on upcoming economic data. - -######### - -GPT - -### Analysis and Rationale - -#### 1. **Current Economic Conditions and Market Sentiment:** - - The US economy appears to be deteriorating, with the notable weak July jobs report where only 114,000 jobs were created and unemployment increased. - - Bond markets have been rallying in anticipation that the Federal Reserve will have to pivot towards rate cuts, moving away from the tightening phase that had been prompted by earlier inflation concerns. - - Market expectations point towards not just a rate cut, but several rate cuts through the end of 2024 with a 36.5% probability of a 50 bps cut at the September meeting. - -#### 2. **Fed Communication and Historical Precedent:** - - The Fed's July FOMC meeting minutes suggest a broad consensus amongst Fed officials that a rate cut is ""likely"" in September, but it's conditioned on the continuation of current economic data trends. - - Historically, the Fed has been conservative in the size of rate cuts. A typical move is 25 bps, and it has often required severe economic deterioration to result in a cut larger than that. - - The probability of a 50 bps cut is growing, as reflected in both market sentiment and expert opinion, but it is still not the baseline scenario. - -#### 3. **Key Events Before the September Meeting:** - - Several pivotal economic data releases are expected before the September meeting, particularly the August job report. If the August report shows further signs of weakness or worsening of the labor market, the likelihood of a 50 bps cut could increase significantly. - - If other economic data (e.g., GDP, inflation, consumer spending) also shows substantial weakness, the Fed might opt for a more aggressive cut to preempt further economic trouble. - -#### 4. **Weighing Likelihood of the Outcomes:** - - Base Rate: Generally, after a period of tightening, the Fed tends to move cautiously with easing, typically beginning with smaller cuts (25 bps). - - Recent historical events: The probability of a 50 bps cut typically ramped up significantly during severe crises (e.g., during the COVID-19 pandemic, the 2008 financial crisis). - - Feedback from Markets and Economists: The fact that the probability of a 50 bps cut is at 36.5% in odds markets suggests that a significant minority of informed participants believe the Fed is facing enough pressure to bypass a more conservative 25 bps cut. - -#### 5. **Time Factor and Variance:** - - With nearly four weeks until the September FOMC meeting, there’s still substantial time for new data to emerge that could shift the Fed’s decision-making logic. However, if economic conditions hold steady or worsen slightly, a 25 bps cut remains a safer bet based on historical Fed behavior. - - Unexpectedly weak data in the August report or severe financial instability could raise the likelihood of a bolder 50 bps move. - -### Final Probability Assessment: - -Given the balance of all factors: -- The base case suggests a more conservative 25 bps cut. -- However, the current environment does have the Fed in a more flexible or reactive stance, especially since substantial economic weakness has been observed. -- Considering the historical precedent, market predictions, and importance placed on upcoming data, I lean towards a conclusion that considers both likelihoods but weighs the probability of a 50bps cut a bit lower than 50%. - -**Final Answer:** - -**Probability: 40%** - -This reflects a reasonable chance given current trends, while still acknowledging that a 25 bps cut is the more historically favored option by the Fed in similar past scenarios." -27490,Will the month over month percentage increase in core CPI inflation in the US in August 2024 be 0.2%?,"PERPLEXITY - -To address the question of whether the month-over-month percentage increase in core CPI inflation in the US in August 2024 will be 0.2%, we need to examine recent trends and expert opinions in the field of economics and inflation. - -### Recent Trends and Data - -1. **July 2024 CPI Report**: The Consumer Price Index (CPI) rose by 0.2% in July, with the annual inflation rate decreasing to 2.9%, the lowest since March 2021. Core CPI, which excludes food and energy, also increased by 0.2% monthly and 3.2% annually, meeting expectations. - -2. **Shelter Costs**: A significant contributor to the inflation rise was a 0.4% increase in shelter costs, which accounted for 90% of the total inflation hike. This indicates that housing expenses continue to be a driving force behind inflationary pressures. - -3. **Federal Reserve's Stance**: The Federal Reserve has expressed readiness to consider easing monetary policy, with futures market indicators suggesting a slightly increased likelihood of a quarter percentage point reduction during the Fed's next meeting in September. - -4. **Expert Opinions**: Experts like Seema Shah, chief global strategist at Principal Asset Management, and Liz Sonders, chief investment strategist at Charles Schwab, have noted that while inflation is trending downwards, there are persistent areas of concern. They emphasize the need to monitor both inflation and employment data closely. - -5. **Forecasting and Betting Sites**: Trading Economics projects the core inflation rate to trend around 2.60% in 2025, indicating a gradual decrease in inflationary pressures. - -### Analysis - -Given the recent data and expert opinions, there is a trend towards decreasing inflationary pressures, with core CPI showing a consistent monthly increase of 0.2%. However, the persistence of high shelter costs and mixed signals in other categories (such as automotive prices and insurance premiums) suggest that while inflation is easing, it does so at a gradual pace. - -### Prediction Markets and Forecasts - -- **Trading Economics**: Projects a core inflation rate of 2.50% by the end of this quarter and 2.60% in 2025. -- **Bloomberg**: Reports that underlying US inflation eased for a fourth month, keeping the Federal Reserve on track to lower interest rates. - -### Conclusion - -While there is a clear trend towards easing inflation, with the core CPI consistently showing a 0.2% monthly increase, the persistence of certain inflationary pressures (notably in shelter costs) and the gradual nature of this decrease suggest caution. The Federal Reserve's potential to cut interest rates in September could further influence inflation dynamics. Therefore, while recent data supports the possibility of a 0.2% month-over-month increase in core CPI in August 2024, it is essential to consider the broader economic context and ongoing trends. - -######### - -GPT - -### Step 1: Understand the Question and Resolution Criteria - -The task is to predict whether the month-over-month (MoM) percentage increase in core CPI inflation in the US for August 2024 will be **0.2%**. The core CPI excludes volatile food and energy prices, and the result will be based on the data reported by the Bureau of Labor Statistics (BLS). The answer will resolve as “Yes” if the BLS reports a MoM core CPI increase of 0.2%. - -### Step 2: Examine the Historical Data and Recent Trends - -#### **Historical Core CPI Data:** -- **Recent Data**: For the past few months, core CPI has shown a consistent pattern of a 0.2% MoM increase. This pattern is reflective of a period where inflation has been moderating. -- **July 2024**: Reported a 0.2% increase for core CPI. -- **General Base Rate**: Over the past year, the core CPI has frequently landed at 0.2%, with periodic variations one notch above or below this level. - -Given this stability, the **base rate** for a 0.2% MoM increase is already established as relatively high. - -### Step 3: Assess the Economic Context and Outlook - -#### **Current Economic Influences:** -- **Shelter Costs**: Continue to be a significant factor in driving core CPI inflation. With shelter costs accounting for a large part of recent inflation figures, this is key to monitoring. Shelter costs increased by 0.4% in July. -- **Interest Rate Environment**: The Federal Reserve has signaled possible easing through rate reductions. This could either serve to stabilize or potentially increase core CPI by influencing economic activity. -- **Expert Analysis**: Analysts have largely forecasted a continued moderate inflation environment, which supports a recurring 0.2% MoM pattern. However, areas like shelter could cause small deviations. - -### Step 4: Likely Scenarios and Probability Distribution - -Given that core CPI has displayed strong tendencies toward hitting that 0.2% mark, and considering minor variations in economic indicators and forecasts for ongoing moderation, there are several scenarios to consider: - -1. **Scenario 1 (65% Probability)**: Core CPI continues to increase at a stable rate of 0.2% MoM in August 2024. This is supported by the trend over recent months. - -2. **Scenario 2 (20% Probability)**: Core CPI rises slightly higher, at 0.3% MoM, due to the persistent inflationary pressures in certain sectors such as shelter, or potential upward surprises in other core categories. - -3. **Scenario 3 (15% Probability)**: Core CPI increases lower than 0.2% (e.g., 0.1%) due to faster-than-expected disinflation or easing in core inflation components due to Fed policies or stabilizing prices in shelter. - -### Step 5: Historical and Contextual Weighting - -Given the weight and recency of the data, recent behaviors suggest that a 0.2% MoM increase remains the most likely outcome. Add to this the Federal Reserve's ongoing adjustments to monetary policy, and it's plausible they aim to nudge metrics like core CPI into stability before considering further steps. - -Given that the rate has consistently hit 0.2% over recent months, and there’s minimal evidence to suggest dramatic change, the **default prediction**—continuing the trend—is the highest probability outcome. - -### Final Step: Conclusion - -After weighing all the evidence: - -**Probability:** **65%** - -This prediction aligns with the frequent MoM increments seen historically, the recent 0.2% results, and the moderate, predictable inflation environment. The 0.3% or 0.1% scenarios are less likely but still possible, reflected here in the lower combined probabilities." -27447,"Will either of Donald Trump's campaign managers get fired, resign, or otherwise leave their positions before October 1, 2024?","PERPLEXITY - -To address the question of whether either of Donald Trump's campaign managers will get fired, resign, or otherwise leave their positions before October 1, 2024, we need to consider recent news and developments related to Trump's campaign staff. - -1. **Staff Transitions and Speculations**: - - **Speculation on Firing**: There has been speculation about Trump possibly firing his campaign managers, Chris LaCivita and Susie Wiles, due to dissatisfaction with the selection of JD Vance as his running mate. This speculation was fueled by comments from Anthony Scaramucci, a former White House communications director. - - **Staff Departures**: Trump's administration and campaign have seen significant staff turnover. This includes high-profile departures from his administration, such as David Shulkin, H.R. McMaster, and Gary Cohn, among others. - -2. **Campaign Dynamics**: - - **Campaign Staff Importance**: The makeup of a candidate's staff can signal the strength of their support from influential party activists and whether they are aligning with a particular faction or group within the party. - - **Trump's Leadership Style**: Trump has indicated a more assertive approach in his second term, stating he would be more willing to fire officials who do not align with his vision. - -3. **Prediction Markets and Polls**: - - **Recent Polls**: There is no direct information from the provided sources on current prediction markets or polls specifically addressing the potential departure of Trump's campaign managers. However, the dissatisfaction with JD Vance as the running mate could potentially impact staff dynamics. - -4. **Expert Opinions**: - - **Scaramucci's Comments**: Anthony Scaramucci's comments suggest internal dissatisfaction with the campaign's direction, which could lead to staff changes. - - **Trump's Assertiveness**: Trump's own statements about being more assertive in his second term could indicate a higher likelihood of staff changes if he perceives underperformance or disloyalty. - -In conclusion, while there is speculation and some indication of internal dissatisfaction that could lead to staff changes, there is no definitive evidence from the provided sources that either of Donald Trump's campaign managers will certainly leave their positions before October 1, 2024. The situation remains fluid, with potential for changes based on campaign dynamics and Trump's leadership style. - -######### - -GPT - -### Evaluation of the Situation - -1. **Historical Trends and Base Rates**: - - Donald Trump has a history of high turnover rates among his campaign and White House staff. In both the 2016 and 2020 campaigns, Trump replaced his campaign managers during the course of the election cycle. - - **2016**: Corey Lewandowski was fired in June, and Paul Manafort resigned in August. - - **2020**: Brad Parscale was demoted in July, and Bill Stepien took over. - - Given the historical precedent, there is a strong base rate for Trump making significant changes to his senior campaign staff leading up to the election. - -2. **Recent Developments**: - - August 2024 has seen a considerable uptick in rumors and speculation about potential dissatisfaction within Trump's campaign. The Daily Mail and Newsweek articles suggest that Trump has been contemplating changes, possibly involving his current campaign managers, LaCivita and Wiles. - - Although the Trump campaign spokesperson has dismissed these rumors, it's worth noting that such dismissals do not always correspond to the actual outcome. Additionally, Trump’s decision to bring back Corey Lewandowski indicates that he's willing to make changes, and perhaps feels the need to shake up his campaign team as the election nears. - -3. **Trump’s Leadership Style**: - - Trump is known for his unpredictable and assertive leadership style. He values loyalty but is also quick to replace individuals he perceives as underperforming or not aligned with his strategic vision. This volatile management style increases the likelihood of last-minute changes, especially when he perceives a need for a tactical shift. - - The addition of new senior staff members on August 16, 2024, and the ongoing speculation about dissatisfaction with the campaign’s direction, suggest that Trump might consider further changes to the structure of his campaign, particularly if internal polls do not show favorable trends. - -4. **Timeframe Consideration**: - - Today is August 22, 2024, and the question is asking about an event that could occur within the next 5-6 weeks. The next 6 weeks are a critical period for any presidential campaign as they prepare for the final stretch. Trump's historical behavior indicates that such a time period is within the range where he might make significant adjustments to his campaign team. - -5. **Certainty and Uncertainty Factors**: - - **Certainty**: The history of staff turnover in Trump’s campaigns combined with recent rumors and structural changes in his 2024 campaign support the possibility of another shake-up. - - **Uncertainty**: Official denials from Trump’s campaign team and the fact that both LaCivita and Wiles expressed confidence about ""adding to the impressive campaign team"" could indicate stability within the campaign, at least for the short term. - -### Weighing All Factors and Assigning Probability - -- **Historical Precedent**: High turnover of campaign management staff: Strong indicator (~60% likelihood increase) -- **Recent News and Speculation**: Significant, but conflicting reports: Moderate indicator in the direction of staff changes (~20% likelihood increase) -- **Leadership Style**: Trump’s unpredictable and decision-making style: Strong indicator (~60% likelihood increase) -- **Time Until October 1**: The event window is short but critical; volatility is expected in this timeframe: Neutral-to-positive (~10% likelihood increase). - -### Potential Scenarios - -- **Scenario 1** (Firing/Resignation Likely): Trump follows past behavior and fires or demotes one of the campaign managers as part of a shakeup to address perceived campaign issues. This scenario is supported by recent speculation and the historical precedent. - -- **Scenario 2** (Status Quo): Trump retains the current campaign structure, despite the rumors, perhaps due to a belief that consistency is needed heading into the final stretch. Official denials and confidence by the campaign provide support for this scenario. - -Given the combination of historical trends, recent news, and Trump’s leadership style, it is more likely than not that at least one of Trump’s campaign managers will either resign, be fired, or be reassigned to another role before October 1, 2024. However, this is not a certainty, as the official stance of the campaign and the potential desire for stability could act as countervailing forces. - -### Final Probability Estimate - -Taking into account the points outlined above: - -**Probability: 70%** - -This reflects a greater likelihood that one of Trump's campaign managers will leave their position before October 1, 2024, but also acknowledges the uncertainty and the counter-signals from the campaign's official communications." -27492,Will the month over month percentage increase in core CPI inflation in the US in August 2024 be 0.4%?,"PERPLEXITY - -To address the question of whether the month-over-month percentage increase in core CPI inflation in the US in August 2024 will be 0.4%, we need to examine the latest trends and data releases related to US inflation. - -1. **Recent Inflation Trends**: - - The Consumer Price Index (CPI) for All Urban Consumers (CPI-U) increased by 0.2% in July 2024 on a seasonally adjusted basis, following a 0.1% decrease in June. - - The core CPI, which excludes food and energy, rose by 0.2% in July, slightly up from the 0.1% increase in June. - - The annual inflation rate slowed to 2.9% in July, the lowest since March 2021. - -2. **Core CPI Components**: - - Shelter costs were a significant contributor to the inflation increase, rising by 0.4% in July and accounting for nearly 90% of the monthly increase in the all items index. - - Other components like motor vehicle insurance also saw increases, while categories such as airline fares, used cars and trucks, and communication experienced decreases. - -3. **Expert Opinions and Forecasts**: - - Analysts and strategists expect core inflation to moderate further as the economy slows and the labor market becomes more balanced. - - The Federal Reserve is anticipated to begin easing monetary policy, potentially starting with a rate cut in September, given the easing inflationary pressures. - -4. **Prediction Markets and Forecasts**: - - Trading Economics forecasts the US Core Inflation Rate MoM to be around 0.10% by the end of this quarter, with long-term projections trending around 0.30% in 2025. - -Given these points, while there is an expectation for continued moderation in inflation, the specific prediction of a 0.4% month-over-month increase in core CPI for August 2024 seems slightly above the current trends and forecasts. Most recent data and forecasts suggest a stabilization or slight decrease in core inflation rates rather than an increase to 0.4%. However, the shelter component remains a critical factor that could influence the core CPI, and any unexpected changes in this or other sectors could impact the overall rate. - -######### - -GPT - -### Step 1: Understand the Question and Resolution Criteria -The question asks whether the month-over-month (MoM) percentage increase in the core Consumer Price Index (CPI), excluding food and energy, for August 2024 will be exactly 0.4%. The answer will be based on the first reported data from the Bureau of Labor Statistics (BLS), and it will resolve as ""Yes"" if the MoM percentage increase is exactly 0.4%. - -### Step 2: Compile Relevant Information -#### **Recent Inflation Trends** -- Core CPI increased by 0.2% in July 2024 and 0.1% in June 2024, indicating a relatively low inflationary pressure in recent months. -- The annual inflation rate slowed to 2.9% in July 2024, the lowest since March 2021, signaling a general downward trend in inflationary pressures. - -#### **Components Impacting Core CPI** -- Shelter costs, a key component, rose by 0.4% in July and accounted for a significant portion of the overall increase. -- Other components like vehicle insurance are also increasing whereas some components, such as airline fares and used cars, are decreasing in price. - -#### **Expert Opinions and Forecasts** -- Analysts are predicting a continued moderation in core inflation. -- Some forecasts are projecting a 0.1%-0.3% MoM increase in core CPI for upcoming months, with a trend towards lower inflation. - -#### **Monetary Policy** -- The Federal Reserve is anticipated to ease monetary policy, possibly starting with a rate cut in September 2024, given the easing inflationary pressures. - -### Step 3: Evaluate Historical Data and Base Rates -#### **Historical CPI Data Analysis** -- Historically, the US core CPI has rarely shifted by exactly 0.4% MoM, except during periods of extreme inflation or deflation. The more common monthly changes are within the 0.1% to 0.3% range. - -### Step 4: Consider Potential Exogenous Factors -- No novel, immediate, or significant shocks (e.g., geopolitical, monetary policy, natural disasters) are anticipated at this stage based on the available literature and predictions. - -### Step 5: Synthesize Information and Weigh Probabilities -Given the following points: -1. **Recent Trends:** Inflation is moderating, with core CPI relatively low and stable. -2. **Expert Predictions:** Economists are forecasting a continuation of this moderation, suggesting smaller monthly changes around 0.1%-0.3%. -3. **Component Volatility:** While shelter costs have been rising, the overall trend suggests components other than shelter are not inflating sharply. -4. **Base Rates/Classical Data:** Historically, significant monthly spikes of 0.4% in core CPI are uncommon in a stabilized economic environment, although a rise spurred by the shelter component could bring it closer to that range. - -Putting all these together, a 0.4% MoM increase seems slightly higher than recent trends but is within the realm of possibility given the volatility in specific components like shelter. However, the probability is still low given the moderation trend and expert opinions that support a more moderate increase. - -### Final Answer -**Probability: 20%** - -This probability represents the likelihood of the August 2024 core CPI coming in at 0.4% MoM based on the current trends, historical data, and economic forecasts. The prediction accounts for the small chance of upward surprises (especially due to shelter prices) but weighs more heavily on the moderation trend and expert forecasts." -27493,Will the month over month percentage increase in core CPI inflation in the US in August 2024 be 0.5% or more?,"PERPLEXITY - -To address the query regarding whether the month-over-month percentage increase in core CPI inflation in the US in August 2024 will be 0.5% or more, we need to analyze the recent trends and data related to core CPI inflation. - -1. **Recent Core CPI Trends**: - - The core CPI, which excludes food and energy prices, increased by 0.2% in July 2024, following a 0.1% rise in June. - - The annual core CPI rate stood at 3.2% in July, the lowest since April 2021. - -2. **Shelter Costs**: - - Shelter costs have been a significant contributor to inflation, with a 0.4% increase in July, accounting for 90% of the total inflation hike. - - The rent price indicator rose by 0.4%, reflecting a 5.3% annual increase. - -3. **Expert Opinions and Forecasts**: - - Seema Shah, chief global strategist at Principal Asset Management, noted that while the CPI report eliminates inflation barriers for the Fed to initiate rate cuts, it doesn't indicate an urgent need for a 50 basis point reduction. - - Liz Sonders, chief investment strategist at Charles Schwab, highlighted the importance of monitoring both inflation and employment data closely due to persistent problematic areas. - - J.P. Morgan strategists expect core inflation to moderate further as the economy slows and the labor market becomes more balanced. - -4. **Prediction Markets and Forecasts**: - - Trading Economics' global macro models and analysts' expectations suggest the core inflation rate MoM will be around 0.10% by the end of this quarter and trend around 0.30% in 2025. - -Given these insights, the recent data does not strongly suggest a month-over-month increase of 0.5% or more in core CPI inflation for August 2024. The trends and forecasts indicate a moderation in inflation rates, with shelter costs being a key factor but not likely to drive the core CPI increase to 0.5% or higher in the next month. - -######### - -GPT - -### Step 1: Understanding the Question -The question at hand is whether the month-over-month percentage increase in core CPI inflation in the US for the month of August 2024 will be 0.5% or more. The resolution is binary: either the increase is 0.5% or more (resulting in a ""Yes""), or it is less than 0.5% (resulting in a ""No""). - -### Step 2: Examination of Recent Data and Trends -#### **Recent Core CPI Trends** -- **July 2024 Data:** The core CPI for July 2024 was 0.2% MoM. This is relatively low and follows similar low readings in previous months (0.1% MoM in June 2024). -- **Yearly Core CPI Trends:** The annual core CPI stood at 3.2% in July, showing moderation from earlier periods, which aligns with the trend of decreasing inflation pressure. - -#### **Shelter Costs** -- Shelter costs have been a significant contributor to inflation, with a 0.4% increase in July, accounting for 90% of the inflation rate increase. Shelter cost increases seem to be moderating, contributing less to drastic month-over-month changes. - -### Step 3: Analysis of Expert Opinions and Economic Indicators -- **Expert Opinions:** Analysts like Seema Shah and Liz Sonders have noted a moderation in inflation, which aligns with the general expectation of a gradual reduction in inflation pressure. No immediate signs suggest a spike in inflation. -- **Market Forecasts:** Prediction markets and macro-economic models point towards a sustained moderation, with the expectation of core CPI increases trending below 0.5%. - -### Step 4: Consideration of External Factors -- **Macroeconomic Environment:** While inflation has been a key concern, the Federal Reserve's series of rate hikes seems to be having the desired cooling effect on inflation. -- **Recession Fears:** If recession fears materialize, demand could cool further, putting downward pressure on inflation. However, any sudden spikes in inflation could be attributed to several variables like geopolitical developments or unexpected changes in commodity prices; none of these extreme scenarios appear imminent based on current information. - -### Step 5: Statistical Consideration and Base Rates -- **Base Rate Analysis:** Historically, extreme month-over-month increases in core CPI (0.5% or more) are relatively rare in a low-inflation environment, especially when inflation is moderating after a period of elevated pressures. -- **Likelihood of 0.5% or More:** Given the recent months' data, a jump from 0.2% to 0.5% within one month would require a significant, unexpected upward shock, something which does not seem to be supported by the current trends. - -### Step 6: Making the Forecast -Given the above considerations: - -1. **Default Scenario:** If nothing changes, the trends indicate that core CPI will likely remain around its current level (0.2%-0.3% MoM). The likelihood of a sudden spike above 0.5% seems low without a triggering event. - -2. **Upper Bound Analysis:** The possibility of inflation spiking to 0.5% or more would require an unexpected economic shock—a scenario that cannot be entirely ruled out but remains unlikely. - -3. **Probability Distribution:** The most probable outcome is that the core CPI for August 2024 will be below 0.5%, but not zero risk, hence a small but non-negligible chance needs to be included for an unexpected change. - -### Step 7: Final Probability Assignment - -After evaluating all factors, the probability that the month-over-month percentage increase in core CPI inflation in the US for August 2024 will be 0.5% or more is: - -**Probability: 10%**" -27491,Will the month over month percentage increase in core CPI inflation in the US in August 2024 be 0.3%?,"PERPLEXITY - -To address the question of whether the month-over-month percentage increase in core CPI inflation in the US in August 2024 will be 0.3%, we need to examine recent trends and forecasts related to core inflation rates. - -1. **Recent Trends**: - - The core inflation rate in the US, which excludes volatile items such as food and energy, increased by 0.2% in July 2024, up from a 0.1% increase in June. This indicates a slight acceleration in core inflation. - - The annual core inflation rate eased to 3.2% in July 2024, down from 3.3% in June, marking the lowest level since April 2021. - -2. **Forecasts and Expectations**: - - Trading Economics' global macro models and analysts expect the core inflation rate to be around 0.10% by the end of this quarter, with long-term projections trending around 0.30% in 2025. - - The consensus among economists for the August core inflation rate is not explicitly stated in the provided sources, but given the recent trend of a 0.2% increase in July, a forecast of 0.3% for August might be on the higher end of expectations. - -3. **Expert Opinions and Market Indicators**: - - Experts like Seema Shah, chief global strategist at Principal Asset Management, suggest that the recent CPI report eliminates any remaining inflation barriers for the Fed to initiate rate cuts in September, indicating that inflation pressures are easing. - - Liz Sonders, chief investment strategist at Charles Schwab, notes that while there is a downward trend in inflation, persistent areas remain problematic, emphasizing the need to closely monitor both inflation and employment data. - -4. **Prediction Markets and Current Predictions**: - - The sources provided do not include specific prediction market data for the August core CPI inflation rate. However, based on the trends and forecasts mentioned, a 0.3% increase might be slightly above the expected range but within the realm of possibility given the recent acceleration in July. - -In summary, while there is an indication of a slight increase in core inflation rates, the forecast of a 0.3% month-over-month increase in August 2024 seems to be on the higher end of expectations based on recent trends and expert opinions. The actual outcome will depend on various factors, including housing costs, which have been a significant contributor to inflation, and the Federal Reserve's actions on interest rates. - -######### - -GPT - -### Step-by-Step Analysis: - -#### Step 1: Context and Background -- The **core CPI inflation** measures the change in prices for goods and services, excluding the volatile components of food and energy. It's a key indicator that the Federal Reserve monitors when making monetary policy decisions, especially in the current economic climate where inflation has been a persistent concern. -- As of the most recent data in **July 2024**, the core CPI increased by **0.2%** month-over-month, which was an acceleration compared to **June's 0.1%**. The annual core inflation rate has eased to **3.2%**. -- There are ongoing concerns about a potential recession in 2024, and expectations for monetary policy changes (like potential rate cuts by the Fed) hinge heavily on how inflation evolves in the near term. - -#### Step 2: Base Rates and Historical Data -- Looking at historical base rates for core CPI, month-over-month changes of **0.3%** or close to that range are not uncommon, especially during periods of fluctuating inflation. However, based on recent trends (particularly in **2023 and early 2024**), monthly increases have been more subdued, often ranging between **0.1% to 0.4%**. -- **2024 trend**: The general pattern has shown sporadic increases, suggesting a **moderating trend** overall in core inflation compared to the earlier, more aggressively increasing period of inflation. - -#### Step 3: Evaluating Forecasts and Expert Opinions -- **Trading Economics** and other macroeconomic models seem to forecast a general moderation to **around 0.10%** by the end of the quarter, with potential for rates around **0.30%** at some points in 2025. This suggests that a **0.3%** increase in **August 2024** is on the higher end of immediate expectations but within a possible range. -- **Expert commentary**: There's recognition that while inflation has eased, some stickiness in certain sectors remains problematic (e.g., service sector costs, housing). The Fed's influence and potential rate cuts suggest an anticipated continued abatement of inflation, but any lag effects or persistent inflationary pressures could still push certain readings higher temporarily. - -#### Step 4: Assessing Current Data and Trends -- **July's 0.2% increase** might indicate a temporary upward bump in inflationary pressures, but it’s close to the average range experienced recently. If inflation pressures remain close to this level, then a **0.3%** reading in August represents a plausible slight acceleration but would necessitate some external or internal economic shock, like continued high service-sector inflation, unexpected wage increases, or supply chain issues. - -#### Step 5: Prediction Market and External Factors -- **Prediction markets** data is lacking, so we're relying on existing expert forecasts, which suggest that although the acceleration in August toward **0.3%** is possible, it's not the baseline scenario that most are forecasting right now. -- **External factors** might play a role: geopolitical tensions, further shocks to energy markets (even if energy is excluded, it has ripple effects), or unanticipated strength in wage growth could push core CPI higher than expected. - -### Final Probability Calculation: - -- **Baseline probability**: Given historical base rates, if core CPI readings have been averaging around **0.2%-0.3%** in past months. -- **Adjusting for current estimates and economic forecasts**: Recognize that most predictive models and experts are projecting moderation towards **0.1%-0.2%**, with **0.3%** being a higher edge scenario. -- **Accounting for variance/risk factors**: Slightly increase the odds given the variability observed in inflation data and occasional surprises. - -### Conclusion: -After considering all the inputs, the probability that the core CPI for August 2024 will **increase by 0.3%** month-over-month is slightly below the upper bound of the expectations, but still within a plausible range given recent data and the potential for unforeseen inflationary pressures. - -**Probability: 35%**" -27489,Will the month over month percentage increase in core CPI inflation in the US in August 2024 be 0.1%?,"PERPLEXITY - -To address the question of whether the month-over-month percentage increase in core CPI inflation in the US in August 2024 will be 0.1%, we need to consider recent trends and forecasts related to core inflation. - -1. **Recent Trends**: - - The annual core inflation rate for the 12 months ending in July was 3.2%, slightly down from 3.3% in June. - - Core CPI inflation rose by 0.2% month-over-month in July, following a 0.1% increase in June. - - The shelter index, a significant component of core inflation, rose by 0.4% in July, contributing to the majority of the monthly headline CPI increase. - -2. **Forecasts and Expectations**: - - Trading Economics global macro models and analysts expect the core inflation rate to be 2.50% by the end of this quarter. - - J.P. Morgan strategists anticipate slowing inflation to continue enhancing consumer purchasing power, which could influence future inflation rates. - -3. **Expert Opinions and Analysis**: - - The White House Council of Economic Advisers (CEA) notes that inflation cooled in 2024Q2, with core CPI inflation up 0.1% in June, below expectations. They also highlight that housing costs eased in June, which could indicate a continuation of the disinflation trend. - - J.P. Morgan analysts suggest that the easing in headline inflation, coupled with weaker-than-expected employment reports, could lead the Federal Reserve to begin easing policy, possibly starting at the September Federal Open Market Committee meeting. - -Given these points, while there is a general expectation of continued disinflationary pressures, the specific forecast for August 2024's month-over-month core CPI inflation increase is not directly provided in the sources. However, the trend of slowing inflation and the easing of housing costs could support a modest increase, potentially aligning with or slightly above the 0.1% figure, considering the recent monthly increases have been in this range. - -######### - -GPT - -**Step 1: Understand the Question** - -The question is whether the month-over-month percentage increase in core CPI inflation in the US in August 2024 will be 0.1%. The answer will resolve as ""Yes"" only if the seasonally adjusted Core CPI for August 2024, as reported by the Bureau of Labor Statistics (BLS) in September, is exactly 0.1%. It will resolve as ""No"" if it is any other value. - -**Step 2: Identify the Type of Event** - -This falls under the ""Non-Market Threshold Value Event"" category, where the focus is on whether a specific quantitative measure will meet an exact threshold value. - -**Step 3: Review Background Information and Data** - -1. **Recent Months’ Data**: - - June 2024: Core CPI increased by 0.1%. - - July 2024: Core CPI increased by 0.2%. - -2. **Economic Context**: - - Inflation slowed in 2024Q2, with core inflation showing a general easing trend. - - The Residential Shelter Index, a significant component of core CPI, has been volatile but showed signs of stabilizing. - - Despite disinflation trends, the labor market seems to have weakened, potentially impacting inflationary pressures. - -3. **Forecasts and Predictions**: - - J.P. Morgan and other analysts anticipate another disinflationary trend, suggesting a possible 2.5% year-over-year core CPI by end of Q3 2024. - - White House economists observed that June’s reading was below expectations and noted a cooling trend. - -4. **Economic Indicators**: - - It is implicit that other factors (like energy prices, though excluded from core CPI, and broader inflation measures) could sway expectations regarding how core inflation behaves. - -**Step 4: Base Rates and Historical Data** - -Historical base rates indicate that month-over-month changes in core CPI tend to fluctuate primarily between 0.1% and 0.3% under normal economic conditions. An increase of exactly 0.1% is relatively low on the spectrum but is not exceptional, especially in periods of low and stable inflation. - -**Step 5: Consideration of Variance and Uncertainty** - -Given the timing (August 2024), there is very little time before the data for August is set in stone, implying that current economic trends, particularly from July and early August, are likely to strongly influence the final result. There is also an inherent uncertainty in predicting exact values because of the multiple factors influencing inflation. - -- Given the only recent deviations have been relatively small (0.1-0.2% increases), no extreme change is expected. -- The expected cooling in inflationary pressures—based on weakening labor market, modest wage growth, and ongoing disinflation processes—would lean towards smaller increases, aligning closer to a month-over-month change near 0.1%. - -**Step 6: Assign Probability** - -Given the recent data points, it is reasonable to project August’s core CPI could show similar behavior to the immediate prior months. However, predicting the exact value to be 0.1% vs. 0.2% involves some non-trivial uncertainty given macroeconomic factors, despite the cooling forecast. - -- Base rate of 0.1% in recent months, disinflationary signals, and aligning expert forecasts support a moderately high probability of this outcome. -- Potential variability in shelter costs or other core components keeps clear ""Yes"" from being overwhelmingly likely. - -**Final Consideration**: - -Given the data and trends pointing towards cooling but stable inflation, and given the historical range typically yielding no more than minor month-on-month variations: - -**Probability: 40%** - -This accounts for a roughly equal chance of a small increase that might slightly exceed 0.1%, but the cooling trend and stable signals keep the probability of 0.1% quite plausible." -27488,Will the month over month percentage increase in core CPI inflation in the US in August 2024 be 0.0% or less?,"PERPLEXITY - -To provide context for the question of whether the month-over-month percentage increase in core CPI inflation in the US in August 2024 will be 0.0% or less, we need to examine recent trends and data related to core CPI inflation. - -1. **Recent Core CPI Inflation Trends**: - - The core CPI, which excludes food and energy prices, rose by 0.2% month-over-month (MoM) in July 2024, following a 0.1% increase in June. - - Over the past year, core CPI increased by 3.2%, which is the smallest annualized increase since April 2021. - -2. **Shelter Component Influence**: - - The shelter index, a significant component of core CPI, increased by 0.4% MoM in July, contributing substantially to the core CPI increase. Shelter prices rose by 5.1% over the past year, driving over 70% of the annual gain in core CPI. - -3. **Expert Opinions and Forecasts**: - - Analysts at J.P. Morgan expect core inflation to further moderate this year as the economy slows and the labor market becomes more balanced, partly due to high interest rates. - - The Federal Reserve is closely monitoring inflation trends, and the recent cooldown in inflation, coupled with a weaker-than-expected jobs report, may lead to a cut in interest rates in September, which could influence inflationary pressures. - -4. **Prediction Markets and Forecasts**: - - As of the latest data available, prediction markets and economic forecasts generally anticipate a continued moderation in inflation rates, but specific predictions for August 2024 core CPI inflation are not provided in the sources. - -Given these points, while there is a trend towards moderation in core CPI inflation, the shelter component remains a significant factor that could influence the month-over-month changes. The expectation is for inflation to continue cooling, but whether the core CPI increase in August 2024 will be 0.0% or less depends on various factors, including the behavior of the shelter index and the overall economic conditions. - -######### - -GPT - -### Step 1: Clarify the Question and Resolution Criteria - -The task is to predict whether the **month-over-month percentage increase** in **core CPI** inflation in the U.S. for August 2024 will be **0.0% or less**. This means we are trying to estimate the likelihood that the core CPI will either not increase or decrease for the month of August. - -### Step 2: Identify the Relevant Data & Context - -1. **Recent Core CPI Inflation Trends**: - - July 2024: Core CPI rose by 0.2% MoM. - - June 2024: Core CPI rose by 0.1% MoM. - - Annual Core CPI increase: 3.2% as of July 2024, which is moderating from the recent past. - -2. **Shelter Component**: - - The shelter index has been a significant contributor to core CPI, particularly because shelter prices have shown a strong upward trend (0.4% MoM in July). It exerts a strong upward bias on the core CPI. - -3. **Expert Opinions and Predictions**: - - Economists expect moderation in core CPI due to a slowing economy and a softening labor market. However, the shelter index might sustain some upward pressure. - -4. **Base Rates and Historical Context**: - - Historically, even during periods of disinflation, achieving a 0.0% or less month-over-month change in core CPI is rare. For example, during 2023-2024, core CPI has consistently remained positive on a monthly basis. - -5. **Forecasting Risks**: - - There is some uncertainty related to economic events, such as potential GDP contraction or unexpected shocks that might reduce inflation more aggressively. - -### Step 3: Consider Potential Scenarios Affecting Core CPI - -- **Scenario 1: Continued Disinflation** — The moderation of price pressures continues. However, even with easing inflation, certain elements like rent/shelter may persist in pushing CPI upwards. -- **Scenario 2: Temporary Economic Weakness or Shock** — There might be a more dramatic slowdown or unexpected deflationary pressures due to weaker demand. -- **Scenario 3: Stable or Negligible Price Fluctuations** — Prices remain stable, resulting in a 0.0% change for the month. -- **Scenario 4: Shelter Price Influence Remains Strong** — Shelter prices continue to rise, making a 0.0% or negative core CPI highly unlikely. - -### Step 4: Probability Estimation - -#### **Base Rate Consideration:** -- From historical data, months where Core CPI is exactly 0.0% or less are uncommon, particularly in recent periods when inflation has been high, and even disinflation has not led to such low monthly levels. - -#### **Current Economic Context:** -- Even though disinflation is occurring, the key metric—the shelter index—remains strong. Given its weighting in core CPI, it's tough to see a sharp enough drop in other categories to offset any gains here. - -### Step 5: Reassessing Forecasting Factors - -1. **Strength of Shelter Influence**: Shelter has consistently been a large contributor to core CPI. Unless there's a significant drop in the shelter index (which is unlikely according to trends from July), the core CPI will likely remain above 0.0%. - -2. **Economist Predictions Moderation**: Even though inflation is cooling, widespread deflation is not expected at this time. - -3. **Macroeconomic Considerations**: The U.S. economy is showing some signs of weakening, but this is mostly a gradual progression; a major shock or rapid change in CPI isn't highly probable by August 2024. - -### Step 6: Conclusion & Probability - -Considering the historical trends, the influence of the shelter component on CPI, and predictions for continuing but moderate inflation: - -- The probability of core CPI for August 2024 being **0.0% or less** is low. - -**Final Answer:** -- **Probability: 20%**" -27584,"Will Angola confirm Clade I mpox infections before October 1, 2024?","PERPLEXITY - -To address the query of whether Angola will confirm Clade I mpox infections before October 1, 2024, we need to consider the current situation and recent developments regarding the spread of mpox, particularly Clade I, in the region. - -### Current Situation and Recent Developments - -1. **Global and Regional Spread**: As of the latest reports in August 2024, there has been a significant outbreak of Clade I mpox in the Democratic Republic of the Congo (DRC) and neighboring countries. The DRC has reported the largest number of yearly suspected Clade I mpox cases on record, with more than 22,000 suspect cases and over 1,200 suspected deaths since January 1, 2023. - -2. **Neighboring Countries Affected**: Countries bordering the DRC, such as the Republic of the Congo (ROC), Central African Republic (CAR), Burundi, Rwanda, and Uganda, have also reported confirmed cases of Clade I mpox. These cases often have travel links to the DRC, indicating cross-border transmission. - -3. **Risk to Angola**: Angola shares borders with the DRC and is considered at risk for the spread of Clade I mpox. However, as of the latest reports in August 2024, there have been no confirmed cases of Clade I mpox in Angola. - -4. **Preparedness and Surveillance**: The U.S. government and global health organizations like the WHO and CDC are closely monitoring the situation and have issued guidelines for heightened surveillance and preparedness in at-risk countries, including Angola. - -### Expert Opinions and Predictions - -- **WHO Alert**: The WHO Director-General has declared the upsurge of mpox in the DRC and neighboring countries as a public health emergency of international concern, indicating a high level of alertness and the need for immediate action. -- **CDC Recommendations**: The CDC recommends that clinicians and jurisdictions maintain a heightened index of suspicion for mpox in travelers from affected areas, including Angola, and advises on the importance of robust testing capacity. - -### Prediction Markets and Forecasts - -As of the current date, specific prediction market data on whether Angola will confirm Clade I mpox infections before October 1, 2024, is not provided in the search results. However, given the regional spread and the proximity of Angola to affected areas, there is a plausible risk of Angola confirming Clade I mpox cases within the specified timeframe. - -### Conclusion - -Given the current spread of Clade I mpox in the DRC and neighboring countries, the risk to Angola, and the global health alerts issued, there is a reasonable likelihood that Angola could confirm Clade I mpox infections before October 1, 2024. However, this assessment is based on the available data up to August 2024 and does not account for future developments or interventions that could alter the course of the outbreak. - -######### - -GPT - -### Step 1: Clarify the Nature of the Problem - -The question at hand is a **Disease Spread Rate Event**, as it focuses on predicting whether a specific disease (Clade I mpox) will spread and be confirmed in a new geographic area (Angola) before a specified date (October 1, 2024). My goal is to estimate the probability that Angola will confirm Clade I mpox infections by this date. - -### Step 2: Establish the Base Rate - -Understanding the historical and current spread of Clade I mpox in and around Angola is crucial for forming a reliable base rate: - -1. **Past Spread**: Historically, Clade I mpox has only been reported in five Central African countries, with DRC being the main reservoir. This suggests that while the virus has been geographically contained within Central Africa, it is not unheard of for it to cross borders, especially into neighboring countries. - -2. **Current Spread**: As of late August 2024, there is a significant outbreak of Clade I mpox in the DRC and several of its neighboring countries (ROC, CAR, Burundi, Rwanda, Uganda). Cases in these countries are often linked to travel or cross-border transmission from the DRC. - - - Notably, over 100 laboratory-confirmed cases of Clade 1b mpox have been reported in four neighboring countries that had not reported mpox before. The WHO and CDC are on high alert, which implies an existing infrastructure for monitoring and confirming new cases. - -3. **Geographic Proximity and Travel Links**: Angola shares a border with the DRC, although the region just south of the border is less populated and more remote. However, travel between DRC and Angola exists for commerce and labor, particularly through cross-border trade routes. Any cases in border towns or among travelers could lead to spread into Angola. - -### Step 3: Assess the Likelihood of Spread to Angola - -Considering the geographic proximity to DRC and the spread of Clade I mpox in several neighboring countries, the probability that Angola will confirm cases by October 1, 2024, depends on several factors: - -1. **Cross-Border Transmission**: Since Clade I mpox has already spread to other neighboring countries of the DRC, the likelihood of it reaching Angola through similar routes is elevated. Angola is at risk primarily due to people crossing the border from affected areas. - -2. **Surveillance and Healthcare Capacity**: Both Angola’s healthcare infrastructure and its capacity for disease surveillance will affect whether cases are detected and confirmed. While there is WHO guidance and heightened alertness, the effectiveness of Angola's response could influence whether cases are documented and officially reported in time. - -3. **Timeframe**: There are approximately 5 weeks remaining until the resolution date (October 1, 2024). Given the rapid spread in nearby countries around similar timelines, and the size of the outbreak, 5 weeks could be sufficient for a case to emerge and be confirmed in Angola. - -### Step 4: Analyze Novelty and Current Circumstances - -There are a few novel aspects worth noting: - -1. **New Clade and Transmission Patterns**: The Clade I mpox (specifically clade 1b) has demonstrated the ability to spread rapidly, particularly within sexual networks and close contacts, a different dynamic compared to traditional spread mechanisms seen with other clades. This novel behavior increases risk of spread. - -2. **WHO’s PHEIC Declaration**: The international community is more proactive now in terms of detection and reporting, which means any cases that do emerge and spread to Angola are more likely to be confirmed formally. - -### Step 5: Combine the Elements into a Probability Estimate - -Given the base rate of spread, Angola’s geographic proximity to DRC, the amount of time left until the resolution date, and the current vigilance levels monitored by WHO and local health authorities, the overall risk of Clade I mpox crossing into and being confirmed in Angola seems plausible but not certain. - -Given the important signs: - -- Outbreaks have already been documented in countries around DRC with confirmed cases. -- Health organizations are vigilant and are likely to catch any emerging cases. -- The time frame allows for spread and subsequent confirmation. - -**Assumption**: If this pattern of spread continues without significant interruption from public health interventions (e.g., border closures, mass vaccination), it seems more likely than not that Angola could confirm a case within the next 5 weeks. - -### Final Probability Estimate - -After assessing the situation with available data and typical behavior for epidemic events: - -**Probability: 75%** - -This high probability reflects the advanced progression of Clade I mpox in neighboring countries, the short remaining timeline, and the heightened global health response likely to identify and report a case if it does occur." -27576,"Will any two countries out of the US, UK, Germany, Canada, France, or Australia curtail diplomatic ties with Israel before 2025, citing Israel’s military actions in Gaza?","PERPLEXITY - -To address the query of whether any two countries out of the US, UK, Germany, Canada, France, or Australia will curtail diplomatic ties with Israel before 2025, citing Israel's military actions in Gaza, it's essential to examine the current stance and actions of these countries regarding the conflict. - -1. **United States**: The US has traditionally been a strong ally of Israel. While there have been calls for moderation and concerns over civilian casualties, there has been no indication that the US is considering cutting diplomatic ties with Israel. - -2. **United Kingdom**: The UK has condemned the attacks by Hamas and expressed support for Israel's right to self-defense. However, it has also called for restraint and supported UN resolutions for a ceasefire. There has been no announcement about severing diplomatic relations. - -3. **Germany**: Germany has also condemned the attacks by Hamas and supported Israel's right to self-defense. Like the UK, it has called for restraint and supported efforts for a ceasefire but has not indicated any intention to cut diplomatic ties. - -4. **Canada**: Canada has strongly condemned the attacks by Hamas and supported Israel's right to defend itself. However, Canadian Prime Minister Justin Trudeau has also urged Israel to stop the killing of civilians in Gaza. Despite criticisms and calls for moderation, there has been no indication of severing diplomatic ties. - -5. **France**: France has condemned the attacks by Hamas and expressed solidarity with Israel. President Emmanuel Macron has called for a ceasefire and de-escalation but has not indicated any intention to cut diplomatic ties with Israel. - -6. **Australia**: While not explicitly mentioned in the provided sources, Australia has generally aligned with its Western allies in condemning Hamas's attacks and supporting Israel's right to self-defense. There has been no indication from Australia about cutting diplomatic ties with Israel. - -Given the current positions of these countries, there is no indication that any two of them plan to curtail diplomatic ties with Israel before 2025 over its military actions in Gaza. The countries that have taken such steps, like Colombia, Bolivia, and Belize, have done so in response to what they perceive as excessive and unjustified actions by Israel in Gaza. The Western countries listed in the query have maintained their support for Israel's right to self-defense while calling for moderation and a ceasefire. - -######### - -GPT - -### Step 1: Understand the Question and Resolution Criteria - -The primary question asks whether at least **two** of the following countries (US, UK, Germany, Canada, France, Australia) will curtail diplomatic relations with Israel before October 1, 2024, explicitly citing Israel's military actions in Gaza as the reason. - -#### What counts as curtailing diplomatic relations: -1. **Recalling an ambassador** with a public announcement that it is in protest of Israel's actions. -2. **Downgrading diplomatic relations** (reducing the chief of mission to below the level of ambassador). -3. **Severing diplomatic relations** (withdrawing diplomatic staff entirely). -4. **Recognizing the state of Palestine** specifically citing Israel's actions in Gaza. - -Additionally, the US can trigger a ""Yes"" condition by: -1. **Voting in favor** of a UN resolution against Israel regarding Gaza. -2. **Moving the embassy** back to Tel Aviv (or elsewhere). -3. **Implementing an arms embargo** on Israel. - -### Step 2: Analyzing the Current Environment and Historical Context - -#### Base Rate Analysis: -Historically, the US and its close allies (UK, Germany, France, Canada, Australia) have **consistently supported Israel** diplomatically, even during significant military conflicts between Israel and Palestinian territories. Curtailing diplomatic relations has generally been unprecedented among these countries, especially in a time frame as short as a year. - -**Historical Precedent:** -In previous situations of conflict (e.g., the Gaza wars in 2008-09, 2014), despite international criticism, no major Western country severely downgraded or severed diplomatic ties with Israel. - -#### Current Environment: -- **United States:** Despite abstaining at the UN in March 2024, the US's longstanding alliance with Israel makes it unlikely that the US would sever relations or impose a full embargo, especially after having already limited military support. -- **Germany:** While supportive of Israel, Germany has been vocal about humanitarian concerns. However, as Israel is a significant security partner, drastic steps like severing or downgrading ties seem unlikely. -- **UK and France:** Both have taken a balanced approach in the past, criticizing excessive force while maintaining diplomatic ties. The likelihood of either severing or downgrading relations is low based on historical behavior. -- **Canada:** Canada has a similar stance to other Western nations—a close relationship with Israel combined with concern over civilian casualties. Curtailing ties seems improbable without very significant provocation. -- **Australia:** Generally aligns with the US and the UK in foreign policy. Unlikely to break ranks for a significant change in relations with Israel. - -#### Trends and Shifts: -Though there is increasing international pressure on Israel, particularly from non-Western nations, the specified countries in the question have deeply entrenched ties with Israel. Even as international sentiment grows more critical of Israel’s actions, breaking diplomatic ties is a substantial political move that typically occurs in only the most extreme cases. - -### Step 3: Timeframe and Probability Assessment - -#### Time Remaining: -The time left until the resolution date (Oct 1, 2024) is approximately 1.5 months, which is a relatively short period for such significant diplomatic shifts to occur, particularly involving multiple countries. - -#### Likelihood of Two Countries Curtailing Relations: -- **United States:** A change significant enough to force a diplomatic curtailment by the US is extremely unlikely in the next 1.5 months. -- **Germany, UK, France, Canada, Australia:** None of these countries even had serious conversations about these moves to date, meaning a sudden shift is improbable. - -Given the consistency in past behavior, default diplomatic ties would remain intact unless a new, exceptionally dramatic development occurs in the conflict. - -### Final Calculation: - -**Base Probability of one country** breaking diplomatic ties or downgrading in the next 1.5 months based on history and current signals: **<10%**. -For two such countries to act similarly within the specified timeframe (by October 1, 2024), the combined probability drops considerably. - -### Final Decision: - -**Probability: 10%** - -This represents a very low likelihood, given the historical context, time constraints, current diplomatic stances, and the necessity of at least two countries making such significant moves, which have shown no indications so far." -27583,"Will Zambia confirm Clade I mpox infections before October 1, 2024?","PERPLEXITY - -To address the query of whether Zambia will confirm Clade I mpox infections before October 1, 2024, we need to consider the current situation and trends in the region. - -**Current Situation:** - -- The Democratic Republic of the Congo (DRC) has reported the largest number of yearly suspected Clade I mpox cases on record since January 2023, with over 22,000 suspected cases and more than 1,200 suspected deaths. -- Clade I mpox cases have been confirmed in neighboring countries such as the Central African Republic (CAR), the Republic of the Congo (ROC), Burundi, Rwanda, and Uganda, indicating a spread beyond DRC's borders. -- The World Health Organization (WHO) has noted the rapid spread of Clade I mpox among adults through close physical contact, including sexual contact, and within households. -- No cases of Clade I mpox have been reported outside central and eastern Africa until recently, when Sweden reported its first case on August 15, 2024. - -**Relevant Context:** - -- Zambia shares borders with DRC and other affected countries, increasing the risk of transmission. -- The Centers for Disease Control and Prevention (CDC) recommends maintaining a heightened index of suspicion for mpox in patients who have recently been in DRC or any country sharing a border with DRC, including Zambia. -- The spread of Clade I mpox to new countries in the region, such as Burundi, Rwanda, and Uganda, suggests a potential for further expansion. - -**Prediction Markets and Expert Opinions:** - -- There is no specific data from prediction markets or betting sites provided in the search results. However, the rapid spread of Clade I mpox in the region and the geographical proximity of Zambia to affected areas suggest a heightened risk. - -**Conclusion:** - -Given the current spread of Clade I mpox in central and eastern Africa, the geographical proximity of Zambia to affected countries, and the recommendations from health organizations to maintain vigilance, there is a reasonable likelihood that Zambia could confirm Clade I mpox infections before October 1, 2024. However, without direct access to prediction markets or specific expert forecasts on this query, we cannot provide a quantified probability. - -######### - -GPT - -### Step 1: Define the Event Type -This question falls under a **Disease Spread Rate Event** as it pertains to the likelihood of Zambia confirming an infection of a specific clade (Clade I) of mpox by a specified date, October 1, 2024. - -### Step 2: Establish the Base Rate -To estimate the base rate, we'll consider the historical occurrence of diseases spreading across borders in sub-Saharan Africa, particularly the previous spread of mpox (formerly known as monkeypox). - -- Clade I mpox has been historically endemic in a small cluster of countries within Central Africa, making its spread to new territories less common without some precipitating event. -- However, in the past year, there has been significant spread into neighboring countries, indicating a potential shift in transmission dynamics. - -### Step 3: Assess the Current Context -#### **Geographical Proximity and Spread** -- Zambia shares a border with the DRC, where Clade I mpox is actively spreading, which increases the risk of importation due to cross-border human activity. -- Recent cases in Burundi, Rwanda, and Uganda further support the idea that the virus is capable of spreading beyond the DRC. This implies that Zambia is at heightened risk. - -#### **Time Horizon** -- The timeline is relatively short: just about 5 weeks until October 1, 2024. This requires an assessment of how quickly mpox can spread, be detected, and confirmed in Zambia. -- Given the PHEIC declared on August 14, 2024, global health systems—including in Zambia—are likely on high alert. This could facilitate faster detection and reporting if an infection were to occur. - -#### **Recent Developments** -- The detection of Clade I mpox in Sweden, the first case outside Africa, suggests that international spread is now a genuine possibility, further indicating that countries neighboring the DRC could see cases soon. -- Without confirmed cases in Zambia yet, the main question is whether Zambia’s health infrastructure, border controls, and surveillance will detect and confirm a Clade I mpox case within the next 5 weeks, especially given the current heightened state of alert. - -### Step 4: Consideration of Spread Dynamics and Health Infrastructure -- **Transportation and Human Movement**: Zambia shares major trade routes with DRC, and informal border crossings are common. This increases the probability of an undetected importation. -- **Health Infrastructure**: The efficiency of Zambia's health infrastructure in detecting and confirming mpox could influence the likelihood of a confirmed case. If comparable to neighboring countries like Rwanda or Uganda, the chances of a detection and confirmation would be reasonably high. - -### Step 5: Weigh the Probability -#### **Default Assumption - No Change** -- If no Clade I infections are present or imported, the assumption would be that Zambia will not confirm a case. However, this assumption starts to weaken as neighboring countries experience outbreaks. - -#### **Trends and Surrounding Events** -- The pattern of spread to adjacent countries and heightened vigilance increases the likelihood that Zambia will confirm a case, especially with about 5 weeks remaining in the forecast window. - -#### **Uncertainties** -- The exact pathway for Clade I mpox into Zambia is uncertain, but the multiple vectors of human movement (formal travel, refugees, trade) contribute to potential exposure. - -### Step 6: Final Probability Calculation -Given: -- The rapid spread in neighboring countries. -- Zambia's geographic proximity to primary outbreak zones. -- The 5-week timeframe, in which trends favor detecting increased cases. -- Active global attention which could lead to swift detection and reporting. - -**Judgment**: -- This situation balances on a relatively high likelihood that Zambia might confirm a case before October 1, given the circumstances described. -- However, some uncertainty remains as no specific cases have been reported in Zambia yet. - -**Estimated Probability**: There seems to be a **60%** chance that Zambia will confirm Clade I mpox infections before October 1, 2024. - -Final Answer: -**Probability: 60%**" -27547,"Will the CDC's assessment of the risk posed by mpox to the US general public exceed ""Moderate"" before October 1, 2024?","PERPLEXITY - -The current assessment of the risk posed by mpox to the US general public is ""Moderate"" according to the CDC. Here is a concise rundown of the most relevant news and information: - -- **Current Situation**: As of the latest updates in August 2024, the global mpox outbreak continues, with a notable increase in cases in the Democratic Republic of Congo (DRC) and neighboring countries. The World Health Organization (WHO) has declared mpox a health emergency of international concern due to the rising cases of clade 1b mpox in these regions. - -- **US Context**: In the United States, after a peak in cases in late July and early August 2022, the number of mpox cases decreased significantly but has remained stable at a low level since October 2023. The CDC reports an average of 58 cases per week nationally, which is 2% of the peak case counts. New York City has seen slightly higher but still relatively low numbers. - -- **Risk Assessment**: The CDC's current risk assessment for mpox in the US is ""Moderate."" This assessment is based on the ongoing but stable low-level transmission within the country. The CDC emphasizes the importance of continued vigilance, testing, and vaccination efforts, especially among high-risk groups. - -- **Expert Opinions and Recommendations**: The WHO advises countries to maintain epidemiological surveillance, strengthen laboratory diagnostic capacities, and ensure diagnostic capabilities for detecting both clades of the mpox virus. The CDC encourages clinicians to test for mpox and other sexually transmitted infections (STIs) in patients with relevant symptoms. - -- **Prediction Markets and Forecasts**: There is no specific data available from prediction markets or forecasts regarding the CDC's risk assessment exceeding ""Moderate"" before October 1, 2024. However, given the current stable low-level transmission and the absence of significant increases in case numbers in the US, there is no immediate indication that the risk level will increase beyond ""Moderate"" in the near future. - -In summary, while the global situation with mpox is concerning, particularly in the DRC and neighboring countries, the current risk assessment in the US remains ""Moderate."" There are no recent updates that would indicate a movement towards a higher risk level before October 1, 2024. - -######### - -GPT - -### Step-by-Step Analysis: - -1. **Understand the Question**: - - We need to predict whether the CDC's risk assessment for mpox to the US general public will exceed ""Moderate"" before October 1, 2024. - - The current assessment (as of August 2024) is ""Moderate."" - - The decision must occur based on a written CDC document, and not based on statements or sub-categories (likelihood, impact). - -2. **Current Status**: - - The number of mpox cases in the US is low (58 cases per week), accounting for only 2% of the peak case counts. - - The CDC has established that the situation remains stable, thus the risk level is Moderate. - - Globally, there are concerns around the outbreak in Sub-Saharan Africa, but the impact on the US continues to be contained as of the last assessment. - -3. **Evaluate Recency and Relevance**: - - The recency of data (from just over a month ago) is critical if deciding on changes before October 1, 2024. - - The CDC is currently re-evaluating the July 2024 risk assessment. However, barring significant changes in mpox trends or new data from the CDC itself, it’s reasonable to assume the updated assessment may stay similar, especially given stable domestic rates. - -4. **Base Rates and Historical Precedents**: - - In many cases, without dramatic changes or new emerging data, health organizations are unlikely to overstate the risk level, especially when data indicates controlled outbreaks. - - Mpox, though concerning in other countries, hasn’t shown signs of escalation in the US to date. - -5. **Likelihood of Risk Increase**: - - The CDC might adjust its assessment if there’s a sudden spike in domestic cases, or if new, highly transmissible strains emerge and spread in the US. - - The CDC could also revise if there are unforeseen outbreaks in local populations with low immunity or high susceptibility. - -6. **Consideration of Time Frame**: - - We have around 5 weeks before October 1, 2024. - - In a stable outbreak scenario like the current one, significant changes in risk classification typically take time to develop and confirm. - -7. **Impact of Existing Measures**: - - The CDC remains vigilant, emphasizing testing and vaccination within the US. With continued public health messaging and response capacity, escalation in risk might likely be prevented or delayed irrespective of global trends. - -8. **Uncertainty and Variability**: - - Although unlikely, the possibility of a change exists, given the mention of a CDC review and the inherent unpredictability of disease outbreaks. - -9. **Conclusion & Rationale**: - - Given the data, an assessment exceeding ""Moderate"" requires a significant increase in infections or impact in the remaining weeks, for which there is currently no indication. - - The existing national effort should potentially contain or keep the risk level stable unless confronted by unpredictable mutations, sudden high transmission, or other severe developments. - -### Final Answer: -Based on the above analysis, I believe the likelihood of the CDC’s assessment of mpox risk exceeding ""Moderate"" by October 1, 2024, remains low. Therefore, the probability of a ""Yes"" outcome, in this case, should be modest. - -**Probability: 15%**" -27548,"Will the CDC's assessment of the risk posed by mpox to the US general public exceed ""High"" before October 1, 2024?","PERPLEXITY - -To address the query of whether the CDC's assessment of the risk posed by mpox to the US general public will exceed ""High"" before October 1, 2024, we need to consider several factors and recent developments related to mpox. - -1. **Current Risk Assessment**: As of the latest information available, the CDC and other health organizations have already categorized mpox as a significant public health concern. The World Health Organization (WHO) has declared the mpox outbreaks in Africa a global emergency, indicating a high level of concern internationally. - -2. **Transmission and Spread**: Mpox primarily spreads through close contact with infected individuals, which includes sexual contact. The virus has shown the potential for rapid spread in high-risk networks and in areas with high population density. - -3. **Vaccine Availability and Access**: While vaccines are available, their distribution and access are limited in many parts of the world, particularly in Africa where the outbreak is most severe. This limitation increases the risk of further spread. - -4. **CDC Guidelines and Precautions**: The CDC has provided detailed guidelines for infection control in healthcare settings, including recommendations for monitoring exposed healthcare personnel and patients. These guidelines indicate a proactive approach to managing the risk but also suggest that the current measures might be insufficient to prevent the risk level from increasing. - -5. **Recent Developments and Expert Opinions**: The declaration of mpox as a global emergency by the WHO and the increasing number of cases and deaths in Africa suggest that the situation is worsening. Experts, including the WHO Director-General, have expressed concern about the potential for further spread within Africa and beyond. - -Given these factors, it is plausible that the CDC's assessment of the risk posed by mpox could exceed ""High"" before October 1, 2024, especially if the outbreak in Africa continues to worsen and if there is evidence of increased transmission within the US or among US citizens abroad. However, this assessment would depend on various factors, including the effectiveness of current public health measures, the availability and distribution of vaccines, and the evolution of the virus itself. - -**Prediction Markets and Forecasts**: As of the current date, specific prediction market data or forecasts directly addressing the CDC's risk assessment level for mpox are not provided in the search results. However, the overall trend in the information available suggests an increasing concern and potential for a higher risk assessment. - -In conclusion, while there is no direct forecast or prediction market data provided, the context suggests that there is a significant possibility the CDC's risk assessment could increase beyond ""High"" before October 1, 2024, based on the current trajectory of the outbreak and the concerns expressed by global health authorities. - -######### - -GPT - -### **Step 1: Analyze the Current State** -- **Current Risk Assessment by the CDC**: As of July 8, 2024, the CDC assessed the risk posed by mpox to the U.S. general public as ""VERY LOW."" -- **Recent Developments**: The CDC is updating its July risk assessment due to the spread of clade I mpox in Sub-Saharan Africa. This signals a potential change, but not necessarily an immediate or drastic increase in the risk level. -- **Global Context**: Mpox has seen spread in Sub-Saharan Africa, leading the WHO to declare it a global emergency. However, the spread is most concentrated in a specific geographical region with different public health circumstances compared to the U.S. - -### **Step 2: Consider the Timeline and Likelihood of Change** -- **Timeline Oct 1, 2024**: There are approximately 5-6 weeks left before the resolution date of October 1, 2024. This timeframe is moderately tight. -- **Nature of a Pandemic**: Significant changes in public health risk assessments typically occur when there is a drastic change in the conditions, such as a surge in cases, new information about transmissibility, or mutation of the virus. - -### **Step 3: Historical Base Rates and Precedents** -- **Mpox’s Historical Impact**: Mpox, while concerning, has not had a history of large-scale, rapid transmission in industrialized countries with strong health infrastructure. Previous outbreaks have been relatively contained and manageable. -- **CDC Risk Reassessment Behavior**: The CDC tends to be cautious in upgrading or downgrading their risk levels significantly. They require compelling evidence of the need to change risk levels, particularly if it would mean a deviation from their initial very low assessment. - -### **Step 4: Factors Against a Meaningful Increase** -- **U.S. Public Health Infrastructure**: The U.S. has strong public health measures, surveillance, and vaccination accessibility. Even if some U.S. cases arise, it is likely these cases will be isolated and quickly managed. -- **Vaccine Availability**: Vaccines are available and can be mobilized if a threat increases. This acts as a mitigating factor against a large, uncontrollable outbreak in the U.S. -- **Lack of Domestic Spread**: To date, there has been no noted significant domestic spread within the U.S. that would warrant an immediate escalation in the risk level to ""High"" or beyond. - -### **Step 5: Consider Potential for Escalation** -However, we also have to consider: -- **Worsening Global Conditions**: Should the situation in Africa deteriorate further with a higher number of cases coupled with reports of international spread, this could precipitate an escalation in the U.S. risk level, though to jump directly to ""High"" or ""Very High"" seems unlikely without serious domestic developments. -- **Possibility of Mutation or Change in Transmission Dynamics**: Unpredictable changes in the virus could alter the risk assessment, though there is no current evidence to support an imminent threat of this nature. - -### **Step 6: Assess Probabilities** -Given the above factors, let’s estimate the probability that the CDC will increase the risk assessment from ""Very Low"" to a level that exceeds ""High"" (which would either be ""High"" or ""Very High"") before October 1, 2024. - -- **Baseline Probability for Sudden Risk Escalations**: Based on past CDC adjustments during pandemics like COVID-19, significant risk level changes over a short period (like 5-6 weeks) happen, but typically not to the highest levels unless there's a compelling and drastic reason. - -- **Consideration of Current Trends**: Given the current stability in the U.S. and the relatively localized nature of the outbreaks in other parts of the world, it seems that a significant transmission event inside the U.S. would be needed to escalate the risk dramatically. - -Based on these assessments, while there is a possibility for an escalation in risk to ""High,"" the chance that it exceeds ""High"" to reach levels of ""Very High"" in the given timeframe is particularly low. - -### **Final Probability Estimate** -Taking everything into account, I would estimate the probability as follows: - -- **Probability that CDC’s assessment exceeds ""High"" by October 1, 2024**: **3%** - -Given the current situation, while there is a small risk that the situation deteriorates and justifies an increase beyond ""High,"" the probability is still low." -27546,"Will the CDC's assessment of the risk posed by mpox to the US general public exceed ""Low"" before October 1, 2024?","PERPLEXITY - -To address the query of whether the CDC's assessment of the risk posed by mpox to the US general public will exceed ""Low"" before October 1, 2024, we need to consider several factors including the current spread of the disease, global health emergency declarations, and the measures in place to control the outbreak. - -1. **Current Spread and Global Health Emergency**: - - The World Health Organization (WHO) has declared the mpox outbreaks in Congo and elsewhere in Africa a global emergency, with cases confirmed in more than a dozen countries and resulting in over 500 deaths in 2024. - - The Africa Centers for Disease Control and Prevention (CDC) has also announced that the outbreaks of mpox are a public health emergency, calling for international help to stop the virus' spread. - -2. **Risk Assessment in the US**: - - As of the current date, the Southern Nevada Health District states that mpox is rare and does not spread easily between people without close contact, and the threat of mpox to the general population in the United States is considered low. - - The CDC has guidelines in place for infection control in healthcare settings, including recommendations for monitoring healthcare personnel (HCP) exposed to mpox and when to apply work restrictions. - -3. **Vaccination and Treatment**: - - There are vaccines available for mpox, such as JYNNEOS and ACAM2000, and antiviral medications developed to protect against smallpox may be used to treat and prevent mpox infections. - - The CDC recommends vaccination for individuals exposed to mpox within four days after exposure to prevent the onset of the disease. - -4. **Recent Updates and Expert Opinions**: - - The WHO has expressed concern about the potential for further spread within Africa and beyond, indicating a need for continued vigilance and international support. - - Local health departments in the US, such as the San Francisco Department of Public Health, are monitoring the situation closely and have confirmed that the risk of the new strain remains relatively low in the Bay Area. - -Given the current information, the CDC's assessment of the risk posed by mpox to the US general public is low, and there are no immediate indicators that this assessment will change to exceed ""Low"" before October 1, 2024. However, the situation is dynamic, and any significant increase in cases or changes in the virus's transmission characteristics could lead to a reevaluation of the risk level. - -######### - -GPT - -### Step-by-Step Analysis: - -#### 1. **Understanding the Question and Criteria** - - The question asks whether the CDC's assessment of mpox risk to the U.S. general public will exceed ""Low"" before October 1, 2024. - - The resolution depends on a formal written document from the CDC that indicates an overall risk level higher than ""Low"" (i.e., ""Moderate,"" ""High,"" or ""Very High""). - -#### 2. **Current Status and Available Information** - - As of the latest update (July 8, 2024), the CDC has classified the risk to the general public as ""Very Low."" - - There is a note of the CDC’s intention to update their assessment due to recent developments in the spread of clade I mpox in Sub-Saharan Africa. - - Globally, the WHO has declared the mpox outbreaks in parts of Africa a public health emergency, but within the U.S., public health officials still consider the risk relatively low. - -#### 3. **Analysis of Risk Factors** - - **Global Situation:** The situation in Africa is concerning with a significant outbreak, especially with the clade I strain, which might have slightly different characteristics compared to earlier strains circulating outside Africa. This has led to concerns at the WHO level. - - **Transmission Dynamics:** In the U.S., the primary mode of transmission remains close contact, limiting the likelihood of widespread transmission in the general population. Given cultural, behavioral, and public health factors, widespread transmission in the U.S. is currently an unlikely event. - - **Vaccination and Treatment:** The availability of vaccines (like JYNNEOS) and antiviral treatments serves as a mitigator, reducing both the likelihood of infection and the impact on the population. - - **Recent Trends:** The situation remains dynamic. The CDC is updating its risk assessment, which indicates ongoing monitoring. However, only extreme changes in infection rates or new evidence suggesting easier transmission or more severe disease could push the risk level above ""Low."" - -#### 4. **Potential for Change Before the Deadline** - - From August 23, 2024, to October 1, 2024, there’s approximately one month for a potential change in the risk assessment. - - Historical precedent shows that large shifts in CDC risk levels typically occur over more extended periods or in response to sudden, dramatic changes in the epidemiological situation. - - For the risk level to surpass ""Low,"" either the likelihood of infection or the impact of the disease would need to escalate considerably. - -#### 5. **Base Rates and Similar Events** - - In past infectious disease outbreaks (e.g., Ebola, Zika), CDC's risk assessment for the U.S. general public typically only moved to ""Moderate"" or higher when there was a clear indication that transmission was likely or that severe outcomes were becoming more widespread. - - Given public health infrastructure, surveillance systems, and the ability to control local outbreaks, such movements up the risk scale tend to be rarer and more deliberate. - -#### 6. **Assessing Overall Probability** - - **Default Case:** If no significant outbreak occurs in the U.S. within the next month, the likelihood of the CDC revising its risk level above ""Low"" is minimal. - - **Potential Variability:** A significant outbreak, especially in vulnerable subpopulations or dense urban centers, could prompt a revision of the risk assessment. However, given the current situation, the short timeline, and ongoing mitigation measures, the probability of such an event before October 1, 2024, is low. - -### Conclusion: -Given the detailed analysis, the most likely outcome is that the CDC's assessment will remain at or below ""Low."" The probability of the risk level exceeding ""Low"" is low, but not negligible due to the dynamic nature of disease outbreaks. - -**Probability: 15%**" -27581,"Will Greg Brockman return to OpenAI following his leave of absence before October 1, 2024?","PERPLEXITY - -To address the question of whether Greg Brockman will return to OpenAI following his leave of absence before October 1, 2024, we need to consider the available information and context. - -1. **Brockman's Announcement**: Greg Brockman, the president of OpenAI, announced that he is taking a sabbatical through the end of the year to ""relax and recharge"" after nine years at the company. This indicates that his leave is intended to last until the end of 2024. - -2. **Company Context**: OpenAI has been experiencing a series of high-level departures, including John Schulman, another co-founder, who left to join rival AI startup Anthropic, and Peter Deng, the vice president of consumer product. These departures suggest a period of significant change and potentially instability within the company. - -3. **Leadership Dynamics**: The dynamics within OpenAI's leadership have been tumultuous, with CEO Sam Altman being ousted and then reinstated last year. This has led to speculation about the reasons behind the departures and the future of the company. - -Given this context, there are no explicit indications that Brockman plans to return before October 1, 2024. His statement about taking a sabbatical through the end of the year suggests that he does not intend to return before 2025. - -**Prediction Markets and Expert Opinions**: There are no specific prediction markets or betting odds mentioned in the provided sources regarding Brockman's return. Expert opinions, such as those from Sam Altman and other industry leaders, do not provide a clear indication of Brockman's intentions beyond his stated sabbatical. - -**Conclusion**: Based on the available information, it appears unlikely that Greg Brockman will return to OpenAI before October 1, 2024, given his announced sabbatical through the end of the year. - -######### - -GPT - -### Step 1: Define the Problem Clearly -The task is to forecast whether Greg Brockman will return to work at OpenAI following his leave of absence before October 1, 2024. The resolution criteria specify that Brockman must return to work as reported by credible sources. If he does not return, or if he exits OpenAI without returning, the answer would resolve as ""No"". The context suggests that Brockman himself stated that his sabbatical would last ""through the end of the year"", implying into 2025. - -### Step 2: Evaluate the Baseline Scenario & Base Rates -1. **Brockman’s Statement**: - - Brockman has stated his intention to take a sabbatical for the remainder of the year, implying a return in 2025. Generally, when high-level executives take extended breaks for personal reasons (especially following intense periods of work), the likelihood of early returns is low unless driven by extraordinary circumstances. - -2. **Leadership Turbulence & Departures**: - - OpenAI has undergone significant leadership upheaval. Such instability often leads to more unpredictability in management behavior, but also might delay a return rather than accelerate it, particularly if there is no urgent reason for someone on sabbatical to return early. - -3. **Base Rate Considerations**: - - Historical base rates for high-level executives taking extended time off show a low likelihood of early returns unless there's an unforeseen critical event requiring their involvement. Furthermore, with Brockman already having taken a definitive public stance on the length of his sabbatical, changing his mind within a short period would be uncharacteristic unless prompted by significant events. - -### Step 3: Incorporate Contextual & Recent Information -1. **Time Remaining Before October 1, 2024**: - - There is a little over a month before the October 1, 2024 cutoff. Close proximity to the deadline reduces the likelihood of a last-minute change in plans. - -2. **Potential Catalysts for a Change**: - - While leadership issues or urgent company needs could theoretically prompt Brockman to return early, there’s no indication of such a likely catalyst. The uncertainty due to the exodus of other founders raises some potential for unexpected developments, but without clear trends that point toward an urgent requirement for his return, the base scenario remains intact. - -3. **Broader Industry & Organizational Trends**: - - Industry trends towards the shuffling of executives and talent, especially in AI, suggest that another move (e.g., Brockman leaving permanently or joining a rival) is possible. However, those typically take time and coordination, particularly when moving or returning from a sabbatical. - -### Step 4: Weigh the Evidence and Estimate Probabilities -- **Default Scenario** (Brockman does not return): This seems very likely given his public, reasoned announcement and the short time remaining until October 1, 2024. - - **Probability Estimate**: ~80-85% - -- **Alternative Scenario** (Brockman returns early due to unforeseen circumstances): Less likely, but not entirely unimaginable. - - **Probability Estimate**: ~15-20% - -This alternative scenario could involve an urgent need at OpenAI that only Brockman could address, but there is currently no strong evidence pointing toward such a situation. - -### Step 5: Conclusion and Final Probability Estimate -Given all the above considerations—Brockman's own statements, the history of executive sabbaticals, limited time before the resolution deadline, and a lack of strong catalysts for an early return—the likelihood of Greg Brockman returning to OpenAI before October 1, 2024, is low. - -**Final Answer:** - -**Probability: 15%** - -This reflects a strong leaning towards the ""No"" resolution, with a small allowance for unforeseen circumstances that could trigger an exception." -27582,"Will the US confirm Clade I mpox infections before October 1, 2024?","PERPLEXITY - -To address the question of whether the US will confirm Clade I mpox infections before October 1, 2024, we need to consider several key points from the provided sources: - -1. **Current Situation**: As of the latest reports, there have been no confirmed cases of Clade I mpox in the United States. The US has only seen cases of Clade II mpox, with a significant decrease in cases since the peak in 2022. - -2. **Global Context**: There is an ongoing outbreak of Clade I mpox in the Democratic Republic of the Congo (DRC), which has raised concerns about its potential spread to other countries, including the US. - -3. **Preparedness and Response**: The US has been preparing for the potential introduction of Clade I mpox, with the CDC issuing health alerts and updating guidelines for clinicians and public health departments. Vaccination efforts are also ongoing, with the JYNNEOS vaccine available commercially and recommended for at-risk populations. - -4. **Risk Factors**: While Clade I mpox is considered more severe than Clade II, several factors may mitigate its impact in the US, including existing immunity from vaccinations and prior infections, as well as the country's preparedness and response measures. - -5. **Expert Opinions**: Experts express cautious hope that Clade I mpox will not cause significant harm in the US, citing the country's preparedness and existing immunity. - -Given these points, while there is a risk of Clade I mpox being introduced into the US, the country's preparedness, existing immunity, and the fact that no cases have been reported as of the latest updates suggest that the likelihood of confirming Clade I mpox infections before October 1, 2024, might be low. However, the situation is dynamic, and continuous monitoring is necessary. - -######### - -GPT - -### **Question Recap:** - -We're being asked to evaluate the probability that the United States will confirm a Clade I mpox infection before October 1, 2024. The date today is August 23, 2024, so there are approximately 5.5 weeks or about 39 days until the resolution date. - -### **Key Points to Consider:** - -1. **Current Global and US Situation:** - - Mpox Clade I (historically called Congo Basin Clade) has been endemic in certain Central African countries but was not previously reported outside of Africa until recently (August 15, 2024, in Sweden). - - The US has not yet reported any Clade I mpox cases. - - The ongoing outbreak of Clade I mpox in the DRC and its spread to neighboring countries increase the risk of global transmission. - -2. **Transmission Pathways:** - - Clade I mpox has a history of spreading through different transmission vectors—contact with infected animals, human-to-human transmission via close contact, and potentially sexual networks as seen in Clade II. - - International travel, particularly from affected regions (e.g., Sub-Saharan Africa or now Sweden), creates a conduit for the virus to spread to the US. - -3. **US Preparedness and Response:** - - The CDC and US health agencies are on alert, equipped with vaccines (like the JYNNEOS vaccine), treatments, and contingency plans. - - The US has robust systems for detection and response, particularly for a known pathogen like mpox. - - There is existing ""cross-immunity"" due to past vaccination campaigns against similar orthopoxviruses (e.g., smallpox). - -4. **Base Rates and Historical Context:** - - When considering pandemics and infectious disease outbreaks, new and rapidly transmissible pathogens often spread internationally within weeks, particularly when there’s an ongoing public health emergency of international concern (PHEIC). - - However, the spread of mpox Clade I outside Africa is relatively recent, and its specific dynamics, especially in developed countries like the US, might differ from past outbreak patterns due to advanced detection and control measures. - -5. **Time Considerations:** - - There’s a short time frame (39 days), which limits the opportunity for significant changes in the current scenario unless there’s an undetected infection already in the US. - - The confirmation of new infections can be delayed because of various factors including time taken for international travel, incubation periods, testing, and reporting lag. - -### **Weighing Factors:** - -1. **Spread Dynamics:** - - Generally, once a pathogen appears in a new geographical area, global spread is highly probable but depends on the transmission dynamics and measures to contain it. - -2. **US Preparedness:** - - The US has substantial public health infrastructure, which could delay or prevent initial cases from escalating into widespread in-country transmission. - -3. **Ongoing Spread and International Context:** - - The fact that Sweden has already confirmed a Clade I case suggests an increasing likelihood of further cases in Europe and possible spread to other regions, including North America. - -4. **Default Outcome:** - - If the situation remains unchanged with no new international travel from infected areas, the default would be no confirmed case. However, the recent globalization and travel history show a trend of eventually identifying cases in the US after an international outbreak is well underway. - -5. **Rate of Change:** - - Although new cases can spread rapidly, it also depends on the ability of the US health system to quickly detect and contain such cases. - -### **Final Probability Estimation:** - -Given the above factors: - -- **High-Risk (10-20%)**: A sudden case may emerge due to international travel from affected regions, for example, healthcare workers, immigrants, or visitors from Europe or Africa. - -- **Low-Risk Default (80-90%)**: The US might not have any confirmed cases given the short time window and existing preventive measures. - -I will weigh these considerations and arrive at a probability. Given the recency of the first non-African Clade I case, the spread might be slower to the US due to containment efforts yet cannot be fully ruled out. - -``` -Probability: 20% -``` - -This reflects a modest likelihood considering current global dynamics, but it accounts for robust international travel oversight and containment by US health systems." -27545,"Will the CDC's assessment of the risk posed by mpox to the US general public exceed ""Very Low"" before October 1, 2024?","PERPLEXITY - -The current assessment by the CDC indicates that the risk of mpox spreading from the DRC outbreak to the U.S. general population is very low. This assessment is based on several factors, including the limited number of travelers and lack of direct commercial flights from the DRC and its neighboring countries, as well as the availability of vaccines and the nature of the virus, which requires close contact for transmission. - -Recent updates from the CDC and WHO have highlighted the resurgence of mpox in Central Africa, particularly the emergence of a new subtype of clade I mpox called clade Ib, which has spread rapidly in the DRC and is now being detected in other countries. However, experts agree that the risk to the general public in the United States remains very low due to the reasons mentioned above. - -As of the latest reports, there are no known cases of mpox clade I or Ib in the U.S.. The CDC has been working on mpox with partners in the DRC for more than a decade and is assisting the DRC and neighboring countries in their response to the outbreak. - -Given the current information and expert opinions, there are no recent updates that would indicate a movement towards a higher risk assessment by the CDC before October 1, 2024. The CDC's assessment of the risk as ""very low"" is consistent across recent reports and statements from health officials. - -**Current Data and Predictions:** -- **Current Cases in the U.S.:** As of the latest reports, there have been a total of 1,122 cases in 2024, with a stable national average of about 58 cases per week since October 2023. -- **Global Cases:** Over 91,000 cases were reported globally during the 2022/23 outbreak, with more than 4,700 in the UK, 7,500 in Spain, 11,000 in Brazil, and 31,000 in the U.S. (to October 2023). -- **WHO Declaration:** On August 14, 2024, WHO declared mpox a public health emergency of international concern due to the rising cases of clade 1b reported in the DRC and neighboring countries. - -**Expert Opinions:** -- Dr. Onyema Ogbuagu, an infectious diseases specialist at Yale Medicine, notes that clade II mpox, which caused the 2022 epidemic, is generally more transmissible but rarely life-threatening. -- Anne Rimoin, Ph.D., professor of epidemiology at the University of California, Los Angeles, Fielding School of Public Health, emphasizes that while an infection anywhere is potentially an infection everywhere, the likelihood of widespread cases in the U.S. is low. -- Dr. Kuritzkes agrees that the risk of a major outbreak in the U.S. is unlikely due to the nature of the virus and the availability of vaccines. - -**Prediction Markets and Forecasts:** -No specific prediction markets or forecasts were found that directly address the CDC's risk assessment exceeding ""very low"" before October 1, 2024. However, the consensus among health officials and experts is that the risk to the U.S. general public remains very low. - -######### - -GPT - -### Initial Assessment -The problem at hand involves predicting whether the CDC's risk assessment of mpox to the U.S. general public will exceed ""Very Low"" before October 1, 2024. - -### Context and Data Analysis - -**1. Current Status:** -- The latest CDC assessment (dated July 8, 2024) rates the risk to the U.S. general population as ""Very Low."" -- The CDC is updating their risk assessment due to the spread of a new clade (Ib) of mpox in Sub-Saharan Africa. -- To date, no cases of this clade Ib have been reported in the U.S. - -**2. Global Situation:** -- The WHO has declared mpox a public health emergency of international concern (PHEIC) due to rising cases in the DRC and neighboring regions. -- Historically, mpox has required close contact for transmission, limiting its spread in non-endemic regions. - -**3. Forecast and Expert Opinions:** -- Experts generally agree that the likelihood of mpox spreading substantially in the U.S. remains low. -- The availability of vaccines further mitigates the risk. -- The stable case numbers in the U.S. (about 58 cases per week) show no indication of a major outbreak. - -### Considerations - -**1. Likelihood of Transmission to the U.S.:** - - Mpox typically requires close contact for transmission, which reduces the likelihood of widespread infection in the general U.S. population. - - Although the spread of clade Ib in Sub-Saharan Africa is concerning, there is no evidence suggesting that this clade will reach the U.S. and cause significant infections before October 1, 2024. - - The lack of direct flights and limited traveler exposure from regions with clade Ib further reduces risk. - -**2. Impact of Infection:** - - Healthcare infrastructure and available vaccines in the U.S. provide a significant buffer against any potential outbreak. - - Effective containment and treatment protocols are already in place, which makes it unlikely that the impact of mpox would escalate to a level that would warrant a higher risk assessment. - -**3. Recency of Data:** - - The most recent data from July 8, 2024, still assesses the risk as ""Very Low,"" even with knowledge of the updated clade Ib developments. - - While a slight increase in concern is warranted as the CDC updates their assessment (mentioned in the background), it’s crucial to note that they continue to work with the current understanding and context, which has so far not shifted the needle beyond ""Very Low."" - -### Historical Base Rates and Trends -- In the past, U.S. risk assessments of similar diseases typically remain low unless there is direct evidence of substantial domestic transmission. This aligns with the fact that despite global threats, there has not been a significant historical precedent of mpox or similar diseases causing widespread issues in the U.S. under the current levels of alertness and preparedness. - -### Temporal Window -- With just five weeks left until the resolution date, there's limited time for a significant and rapid shift in the situation. Historical trends suggest that infectious disease assessments typically do not change dramatically over such a short period without accompanying major domestic developments—none of which appear to be on the horizon for mpox. - -### Most Likely Outcome -Given the current data, historical base rates, expert opinions, and contextual factors: -- The CDC is likely to maintain the ""Very Low"" risk level barring any significant and unexpected developments directly affecting the U.S. population. -- Recent updates concerning clade Ib, though alarming from a global perspective, have not yet materially impacted the CDC’s assessment of the U.S. risk. - -**Final Answer:** -Based on the available information, which points to continued low incidence rates within the U.S., along with limited exposure risk and containment measures, the likelihood of the CDC increasing their risk assessment beyond ""Very Low"" by October 1, 2024, appears minimal. - -**Probability: 10%**" -27515,Will Apple announce an iPhone with 40W fast charging by the end of its September 2024 event?,"PERPLEXITY - -To address the question of whether Apple will announce an iPhone with 40W fast charging by the end of its September 2024 event, we need to consider several key points from the available sources: - -1. **Release Date and Event**: Apple is expected to announce the iPhone 16 series at a dedicated keynote event on September 10, 2024, with the market release likely happening around September 20, 2024. - -2. **Fast Charging Upgrades**: Multiple sources indicate that the iPhone 16 Pro and iPhone 16 Pro Max will support 40W wired charging speeds, an upgrade from the 27W charging speeds of the iPhone 15 series. Additionally, there will be an upgrade in MagSafe wireless charging speeds from 15W to 20W. - -3. **Battery Capacity**: The new iPhones are expected to have slightly larger batteries, with the exception of the iPhone 16 Plus, which might have a smaller battery compared to its predecessor. The use of stacked battery technology could result in higher capacity and longer lifespan. - -4. **Apple Intelligence Delay**: While the iPhone 16 series will be released in September, the Apple Intelligence feature, a significant AI-powered upgrade, will be delayed until an iOS 18.1 update likely in October. - -Given these points, it is reasonable to conclude that Apple is indeed planning to announce iPhones with 40W fast charging capabilities during its September 2024 event. This upgrade, along with improvements in battery capacity and wireless charging speeds, aims to enhance the overall user experience, particularly in addressing the increasing charging time caused by larger batteries. - -######### - -GPT - -### Step 1: Analyzing the Background Information - -#### **1. Apple’s Past Behavior and Market Positioning:** -- **Charging Speed Increases**: Historically, Apple has been conservative in adopting extremely rapid charging, especially compared to some Android manufacturers. For example, Apple's iPhone 15 provides a peak charging speed of 27W, significantly less than the 45W of Samsung's Galaxy S24 or Xiaomi's 120W capacities. -- **Pattern of Incremental Updates**: Typically, Apple releases incremental updates to its technology rather than making large leaps. Moving from 27W to 40W falls into this incremental category. - -#### **2. Current Rumors and Leaks:** -- **Source Validity**: The major sources cited—Mac Rumors, Forbes, and Economic Times—are relatively established entities with a history of reasonable accuracy when it comes to Apple rumors. It's important to note, however, that rumors and predictions aren't always entirely accurate. -- **Strength of Rumor**: Multiple sources seem to be corroborating moderate advancements in charging technology for Apple’s September 2024 event, particularly with the iPhone 16 Pro and iPhone 16 Pro Max models. There's no mention of this feature being delayed or underdeveloped, which would otherwise be worrying. - -#### **3. Technological Feasibility:** -- **Battery Technology Development**: There’s nothing technologically prohibitive about Apple advancing to a 40W charging speed, especially given the progress Android manufacturers have made. -- **Stacked Battery Technology**: A new method for extending the battery's lifespan and performance by allowing faster charging. The fact that this is anticipated with the iPhone 16 supports the likelihood of the feature being announced. - -#### **4. Market Pressure:** -- **Competitive Landscape**: Apple has faced increasing competition regarding the specifications of its charging capacity. This could incentivize Apple to make the jump to 40W charging, aligning with consumer demand for faster-charging devices. - -### Step 2: Assessing the Timeframe and Likelihood of Change - -#### **Time Horizon:** -- We're just 18 days before the expected event date of September 10, 2024. The likelihood of any last-minute drastic changes in Apple's technological directions or decisions is extremely low. By this time, the event’s details are likely finalized. - -#### **Impact of Delay or External Influences:** -- No unusual economic, supply chain, or regulatory events have been reported that could delay the release of this particular feature. -- Apple has consistently held its major iPhone release events in September. A delay beyond September 30, 2024, is extremely unlikely. - -### Step 3: Evaluating Similar Historical Events (Base Rates) - -- **Charging Advancements**: Apple's shift from 18W (iPhone 11) to 20W (iPhone 12) and to 27W (iPhone 14/15) shows a trend of consistent, albeit modest, increases. Moving to 40W from 27W fits this pattern and is not unprecedented. Base rates suggest that Apple does introduce upgrades in a consistent fashion. -- **Announcement Success Rates**: Historically, a large majority of features strongly rumored before Apple's September events do materialize. Factoring in leaks and pre-event intelligence, Apple often delivers on expectations set by widely covered rumors. - -### Step 4: Default Resolution and Uncertainty Assessment - -#### **Default Expectation:** -- Given that the default scenario is that Apple will announce a new iPhone, and that given the consensus among industry insiders that the 40W charging capability is expected, the default expectation should lean toward this event occurring. - -#### **Identifying Potential Bias and Adjustments:** -- Current industry skepticism, if any, around Apple’s willingness to advance quickly in terms of hardware innovation could be a source of bias. Adjusting for too much conservatism or excessive optimism can help mitigate misjudged probabilities. - -- **Conservativism vs. Iteration Previously Seen**: Given Apple's pattern of cautious innovation (small but regular gains), it's realistic to rate the importance of this rumored technological leap as credible and probable for their new model without being too outlandish. - -### Final Consideration - -**Given the strong alignment of market expectations, technological capability, and corroborated rumors**, I consider the probability that Apple will announce an iPhone with 40W fast charging by the end of its September 2024 event to be high. - -### Final Probability Estimate - -**Probability: 85%**" -27603,"Will Japan confirm Clade I mpox infections before October 1, 2024?","PERPLEXITY - -To address the question of whether Japan will confirm Clade I mpox infections before October 1, 2024, we need to consider the current global situation of mpox, particularly the spread of Clade I, and any recent updates or cases that might indicate a potential for spread to Japan. - -1. **Global Situation of Mpox**: - - **Nature**: A recent study published in Nature highlights the emergence of a new MPXV clade I lineage in Central Africa, particularly in the Democratic Republic of Congo (DRC), with cases increasing in remote forest areas, likely due to zoonotic spillover followed by human-to-human transmission. - - **WHO Reports**: The World Health Organization (WHO) has reported a total of 466 new laboratory-confirmed cases of mpox and three deaths from 22 countries in March 2024. The WHO has also noted the spread of a novel strain of clade I MPXV that may evade diagnostic confirmation. - - **CDC Response**: The Centers for Disease Control and Prevention (CDC) has issued a fact sheet on the United States' response to the Clade I mpox outbreak, emphasizing the higher severity and mortality rate of Clade I compared to Clade IIb. The CDC has been preparing for potential domestic cases, enhancing diagnostic testing, and distributing the JYNNEOS vaccine. - -2. **Recent Cases and Spread**: - - **Wikipedia**: As of August 2024, more than 21,300 confirmed cases and 590 deaths have been reported, primarily in Central Africa. There have been reports of Clade II cases in various countries, including Pakistan, Portugal, Indonesia, and the Philippines, but these are considered unrelated to the Clade I outbreak. - - **WHO Situation Report**: Recent weeks have seen an unprecedented increase in countries in the WHO African Region reporting mpox cases and outbreaks, including Burundi, Kenya, Rwanda, and Uganda, with confirmed presence of Clade Ib MPXV. - -3. **Prediction Markets and Forecasts**: - - No specific prediction market data or forecasts regarding Japan confirming Clade I mpox infections before October 1, 2024, are provided in the search results. - -Given the information available: -- The global spread of mpox, particularly Clade I, is a concern with its higher virulence and potential for international spread. -- There is no direct information indicating that Japan has confirmed or is on the verge of confirming Clade I mpox infections. -- The absence of recent updates or cases specifically related to Japan makes it challenging to definitively say whether Japan will confirm Clade I mpox infections before October 1, 2024. - -However, considering the global trend and the fact that several countries have recently reported their first mpox cases, it's plausible that the risk of spread to other countries, including Japan, cannot be entirely ruled out. Urgent measures, including intensified surveillance, community engagement, and vaccination efforts, are recommended to prevent further spread. - -######### - -GPT - -### **Step 1: Understand the Question and Setup** - -The question asks whether Japan will confirm a Clade I Mpox infection before October 1, 2024. As of today, August 26, 2024, there are a little over five weeks remaining until the resolution date. - -### **Step 2: Review Background Information** - -1. **Current Global Situation**: - - Clade I (particularly Clade 1b) Mpox has been spreading in Central Africa and is of high concern due to its higher severity and mortality rates. - - **Geographic Spread**: Clade I cases have emerged outside Africa for the first time, with Sweden confirming a case on August 15, 2024. - - **WHO Declaration**: The WHO has already declared Mpox a public health emergency, indicating the seriousness and international engagement in containing the disease. - -2. **Japan Specific Context**: - - No immediate reports or evidence suggesting that Clade I Mpox has reached Japan. - - Mpox, as a disease, has shown the ability to spread through international travel, but clade I specifically has just started appearing outside Africa. - -### **Step 3: Analyzing and Weighing Each Factor** - -1. **Spread Likelihood**: - - The confirmation of a Clade I case in Sweden suggests that the virus has the potential to spread globally. Given that Japan is a highly connected country with significant international travel, there is a non-negligible risk that Clade I Mpox could reach Japan. - - However, the timeline is short (just over a month), limiting how many additional cases outside of Africa might appear and be confirmed in this period. The spread of Clade I cases outside of Africa is still in the early stages. - -2. **Public Health Measures**: - - Japan has strong public health infrastructure and is likely to be vigilant in screening and preventing outbreaks of internationally spreading diseases, particularly with a WHO-declared emergency. - - Enhanced global surveillance and containment measures following the WHO’s declaration might prevent rapid international spread, including to Japan. - -3. **Base Rate and Historical Precedent**: - - Japan has had experience with similar diseases (like COVID-19), and its response involves strict measures. Historically, Japan detects such cases through rigorous screening and quarantine protocols. - - Still, how quickly Clade I Mpox might enter Japan is uncertain, given this situation is only emerging, and is unprecedented. - -4. **Factors Limiting Immediate Spread to Japan**: - - Travel restrictions, or enhanced surveillance following the WHO declaration, could slow the spread to Japan. - - The window for a new disease to spread, be detected, and confirmed is relatively short with just over a month. - -### **Step 4: Evaluate Default and Changes in Behavior** - -The default scenario, without new developments, would be that Japan does *not* confirm a Clade I Mpox infection. However, given the recent trend of cases entering non-African countries (like Sweden) and the rapid international movement, there's a real chance this could change. - -### **Step 5: Apply Reasoning and Quantitative Analysis** - -The probability of Clade I Mpox moving from Sweden (or another future non-African country reporting cases) to Japan in the next month is low but non-negligible. When adjusting for the recent international spread (i.e., first cases emerging but still relatively isolated), the timeline until October 1 limits the likelihood of a confirmed case in Japan. - -### **Step 6: Assign Final Probability** - -Given the information: -- Clade I Mpox is spreading, but not yet rapidly across multiple countries. -- Japan has robust measures but is also vulnerable to international travel. -- No current cases or evidence of Clade I in Japan or nearby regions yet. - -Given that the risk is non-zero but still slim due to a combination of robust public health measures in Japan, stringent surveillance, and limited timeframe, the probability that Japan will confirm a Clade I Mpox infection before October 1, 2024, is estimated to be: - -**Probability: 18%** - -This takes into account the potential for international spread, the efficacy of containment measures, and the short timeline." -27606,"Will Ukrainian forces capture Tetkino (51°16'45.1""N 34°16'57.7""E) before October 1, 2024?","PERPLEXITY - -To address the question of whether Ukrainian forces will capture Tetkino (51°16'45.1""N 34°16'57.7""E) before October 1, 2024, we need to consider recent developments and assessments from various sources regarding the conflict in Ukraine. - -1. **Current Situation and Recent Advances**: - - Ukrainian forces have recently made significant advances into Russian territory, particularly in the Kursk region. This includes marginal advances in areas such as Ulanok and Kamyshnoye, southeast of Sudzha. - - The incursion into Kursk has led to the evacuation of thousands of civilians by Russian authorities, indicating a shift in the dynamics of the conflict. - - Ukrainian forces have also been conducting successful localized counterattacks in northern Kharkiv Oblast and towards Kreminna, which suggests an improvement in their capabilities, likely due to the arrival of Western-provided aid. - -2. **Assessments and Predictions**: - - Western and US officials believe that Ukrainian forces will remain on the defensive for the next six months and are unlikely to conduct large-scale counteroffensive operations until 2025. - - However, Ukrainian forces are already attempting to contest the battlefield initiative through limited and localized counterattacks, which could potentially scale up with further Western security assistance. - - The Institute for the Study of War (ISW) assesses that Russian forces are unlikely to achieve operationally significant gains in Ukraine, and their current strategy focuses on making gradual, creeping advances. - -3. **Expert Opinions and Challenges**: - - Experts like Yohann Michel have highlighted Russia's intelligence failure in anticipating the Ukrainian incursion into Kursk, which has raised questions about Russia's surveillance and border fortifications. - - Managing the territory gained by Ukraine in Kursk could present significant challenges for the Ukrainian military in the future. - -4. **Prediction Markets and Forecasts**: - - Unfortunately, specific prediction market data or forecasts regarding the capture of Tetkino by Ukrainian forces before October 1, 2024, are not provided in the sources. However, the overall trend suggests that while Ukrainian forces are making gains, their ability to capture specific locations like Tetkino within a short timeframe is uncertain and contingent on various factors, including the scale of Western aid and the effectiveness of Russian defenses. - -In conclusion, while Ukrainian forces have made notable advances and are contesting the initiative in certain areas, the capture of Tetkino before October 1, 2024, is uncertain. It depends on the dynamics of the conflict, the effectiveness of Ukrainian counterattacks, and the response of Russian forces. - -######### - -GPT - -### Step 1: Define the Situation and the Question - -**Question**: Will Ukrainian forces capture Tetkino (51°16'45.1""N 34°16'57.7""E) before October 1, 2024? - -**Context**: As of August 26, 2024, Ukrainian forces have made some territorial advances into Kursk Oblast, Russia, as part of a broader incursion that started in early August 2024. The proximity of Ukrainian forces to Tetkino, the current position of the front lines, and the trends of military engagement in the area over the next five weeks are of critical importance in answering this question. - -### Step 2: Evaluate the Recent Developments - -Ukrainian forces have notably advanced into Russian territory, and Russian milbloggers have reported Ukrainian activity near Tetkino: -- **Advances by Ukrainian Forces**: Ukrainian forces have advanced westward towards Korenevo, with some activities near Tetkino mentioned. -- **Russian Defensive Actions**: Russian forces have destroyed bridges over the Seim River near Tetkino to slow or prevent Ukrainian progress. -- **Significance of Tetkino**: Tetkino lies close to the Ukrainian-Russian border, and its capture by Ukrainian forces would be symbolically and strategically significant. However, the destruction of bridges and infrastructure could complicate Ukrainian advances. - -### Step 3: Consider Base Rates and Historical Context - -Given the historical context and the overall pace of the conflict: -- **Historical Pace of Territorial Capture**: In the Russo-Ukrainian war, particularly during 2023-2024, territorial gains, when achieved, have often been incremental and hard-fought. Both sides have entrenched defenses, and this results in slow territorial changes barring unforeseen major breakthroughs. -- **Previous Occupation of Towns/Villages**: It is often the case that once either side has prepared defenses, towns or key locations like Tetkino are not captured swiftly within a short time period. -- **Influence of Time**: Between August 26, 2024, and October 1, 2024, there are approximately 5 weeks. Historical precedent in this war suggests that such a short timeframe typically does not see significant changes barring sudden and overwhelming force or drastic failure from the defending side. - -### Step 4: Evaluate the Current Dynamics and Proximity - -- **Current Proximity and Capabilities**: Ukrainian forces are reported near Tetkino and have made some advances in nearby areas, which means they are in a position to attempt to take Tetkino. However, the destruction of infrastructure by Russian forces serves as a logistical and tactical barrier. -- **Russian Resistance**: Russian forces have shown a capacity to mount counterattacks and have been actively engaged in the defense of towns and strategic locations. The destruction of key bridges indicates Russia’s intent to obstruct and defend Tetkino by hindering logistical routes. - -### Step 5: Weigh Expert Assessments and Prediction Markets - -- **Expert Assessments**: While Western experts are somewhat pessimistic about the likelihood of large-scale Ukrainian counteroffensives in the near term, the ongoing Ukrainian incursions and the fluid nature of small unit tactics in conflict zones could allow smaller, localized advances. Given the timeframe (5 weeks) and the uncertainty regarding Ukrainian capabilities in this zone, expert assessments tend to offer a mixed view. -- **Prediction Markets**: While not specifically mentioned, broader market sentiment about Ukrainian territorial gains could play a role in general expectations. These markets often reflect the uncertainty in military operations, particularly when both military success and operational challenges exist simultaneously. - -### Step 6: Uncertainty and Probability Distribution - -Considering the variables: -- Ukrainian forces are near Tetkino, but there are significant obstacles. -- Historical pace of similar operations suggests uncertainty and slow gains. -- Russian defensive efforts and infrastructure denial are substantial impediments. - -### Final Probability Estimate - -Given the factors outlined: -- There is a tangible chance that Ukrainian forces may focus on encircling or pressuring Tetkino but may not fully secure it by the given deadline, especially considering Russian pre-emptive defensive measures and the relatively short timeframe. - -**Final Probability**: -Given the above elements of uncertainty and the historical tendency for slower territorial changes in these circumstances: - -**Probability: 25%** - -This reflects the uncertainty and the significant challenges posed by Russian defensive actions and the historical pace of territorial changes. While not impossible, a decisive capture of Tetkino by October 1, 2024, is less likely under current conditions." -27607,"Will Ukrainian forces capture Korenevo (51°24'37.7""N 34°54'02.2""E) before October 1, 2024?","PERPLEXITY - -To address the question of whether Ukrainian forces will capture Korenevo (51°24'37.7""N 34°54'02.2""E) before October 1, 2024, we need to examine recent developments and assessments from various sources. - -1. **Current Situation and Recent Developments**: - - **Ukrainian Advances**: As of August 20, 2024, Ukrainian forces have made significant advances in the Kursk Oblast. They have entered Korenevo and captured Martynovka and Vishnevka. This indicates a strong push by Ukrainian forces into the region. - - **Russian Resistance**: Despite Ukrainian advances, Russian forces are attempting to stabilize the frontline in Kursk Oblast. There are reports of increasing Russian resistance, particularly in the direction of Kursk City. - - **Ongoing Operations**: Ukrainian operations in Kursk Oblast are causing defensive, logistical, and security impacts within Russia. Satellite imagery shows new field fortifications, indicating ongoing efforts to secure positions. - -2. **Assessments and Predictions**: - - **Institute for the Study of War (ISW)**: The ISW assesses that Ukrainian forces are likely to continue contesting the initiative in limited counterattacks. However, they also note that Ukrainian forces are not yet conducting counterattacks at the scale necessary to seize the battlefield initiative. - - **Western and US Officials**: There are assessments that Ukrainian forces will remain on the defensive for the next six months and may not be able to conduct a large-scale counteroffensive operation until 2025. - -3. **Relevance to Korenevo**: - - Given the recent capture of Martynovka and Vishnevka, and the entry into Korenevo as of August 20, 2024, it seems plausible that Ukrainian forces could secure Korenevo before October 1, 2024, especially if they maintain their current momentum. - - However, the increasing Russian resistance and the overall defensive posture of Ukrainian forces, as assessed by Western officials, could slow down or complicate these advances. - -4. **Prediction Markets and Forecasts**: - - No specific prediction market data is provided in the sources. However, based on the military assessments and recent developments, there is a possibility that Ukrainian forces could capture Korenevo before October 1, 2024, but this would depend on maintaining their current pace of advance and overcoming increasing Russian resistance. - -In summary, while there are positive indicators of Ukrainian advances towards and into Korenevo, the outcome is contingent on various factors including the scale of Ukrainian counterattacks, the effectiveness of Russian resistance, and the arrival and impact of Western security assistance. - -######### - -GPT - -To address the question of whether Ukrainian forces will capture Korenevo (51°24'37.7""N 34°54'02.2""E) before October 1, 2024, we need to systematically analyze the situation. - -### Step 1: Analyze the Current Situation -- **Recent Ukrainian Advances**: As of August 21, 2024, Ukrainian forces have made significant progress into Kursk Oblast, capturing nearby villages like Martynovka and Vishnevka and pushing into Korenevo itself. This suggests strong offensive momentum. -- **Russian Resistance**: Despite Ukrainian gains, Russian forces are actively attempting to stabilize the front, using airstrikes, artillery, and local counterattacks. They've regained some positions in fields south of Safonovka, which is close to Korenevo. Ongoing resistance efforts might slow down or halt Ukrainian advances. - -### Step 2: Evaluate Recency and Reliability of Sources -- The information provided is recent (as of August 21, 2024), meaning it's highly relevant to the situation today. -- The sources (Institute for the Study of War, Wikipedia) are credible and known for regular and accurate updates, though it's essential to remember that Ukrainian military operations might not always be fully disclosed. - -### Step 3: Consider Historical Base Rates and Context -- **Base Rates in Military Conflicts**: Historically, urban areas or strategic villages like Korenevo can be difficult to capture, especially when defenders are deeply entrenched. However, this varies based on the broader strategic context. -- **Strategic Significance**: Korenevo seems to be a strategic point within this campaign given its proximity to the international border and the level of attention Ukrainian forces are paying to it. - -### Step 4: Consider Timeframe and Potential Changes -- **Time until October 1, 2024**: The timeframe is approximately five weeks from today's date (August 26, 2024). In warfare, this period can allow for significant developments but also variances depending on operational logistics, weather conditions, external reinforcements, and battle fatigue. - -- **Ukrainian Momentum vs. Russian Defenses**: Momentum is currently in favor of Ukrainian forces, but this could be impacted by factors like increased Russian reinforcements, defensive preparations, or a shift in Ukrainian focus given broader strategic considerations (e.g., operations in other regions or impact of loyalist forces). Maintain a watchful eye on the strategic importance of the ongoing operations in Kursk. - -### Step 5: Breakdown of Potential Scenarios -1. **Ukrainian Success in Capturing Korenevo**: - - **High Momentum Scenario**: Continued rapid advances with effective suppression or circumvention of Russian countermeasures. - - **Medium Momentum Scenario**: Steady advances with occasional setbacks but successful capture within the timeframe. - -2. **Ukrainian Failure to Capture Korenevo**: - - **Russian Resilience Scenario**: Russian forces mount a successful defense, halting the Ukrainian advance or launching effective counterattacks. - - **Strategic Shift Scenario**: Ukrainian forces deprioritize Korenevo in favor of a different strategic objective. - -### Step 6: Assess Probability Distribution -Given: -- **Steady Ukrainian Momentum**: Ukrainian forces have already made significant advances towards Korenevo. -- **Ongoing Russian Resistance**: Increasing Russian resistance might slow but not necessarily stop Ukraine’s advance. -- **Time Frame Adequacy**: Five weeks allows for further operations, barring significant disruptions. - -Overall, capturing Korenevo by October 1 is plausible; however, the level of uncertainty remains because of Russia’s response and the inherent unpredictability of warfare. - -### Final Chain of Thought: -- Current Ukrainian gains indicate a possible successful operation within the given timeframe. -- The main uncertainties lie in the potential effectiveness of Russian counteroffensives and strategic shifts by Ukrainian forces. -- Given historical data, the time left, and ongoing developments, the probability appears balanced but slightly in favor of a capture outcome. - -### Conclusion -Based on the analysis, the most likely scenario seems to be that Ukrainian forces will maintain enough operational momentum to capture Korenevo before October 1, 2024. However, recognizing the uncertainties involved in such dynamic environments, a conservative estimate is warranted. - -""Probability: 65%""" -27602,"Will Russia confirm Clade I mpox infections before October 1, 2024?","PERPLEXITY - -To address the query of whether Russia will confirm Clade I mpox infections before October 1, 2024, we need to consider several factors, including the current global spread of mpox, particularly the Clade I variant, and any recent updates or measures taken by Russian health authorities. - -### Global Spread of Mpox - -1. **Mpox Variants**: There are two main variants of the mpox virus: Clade I and Clade II. Clade I is considered more severe and has been associated with higher mortality rates compared to Clade II. - -2. **Recent Outbreaks**: A significant outbreak of Clade I mpox has been reported in Africa, with over 22,000 suspected cases and more than 1,200 suspected deaths in the Democratic Republic of the Congo (DRC) since January 2023. Cases have also been identified in other African countries, as well as in Sweden and Thailand, indicating international spread. - -3. **WHO Declaration**: The World Health Organization (WHO) declared the mpox outbreak a Public Health Emergency of International Concern (PHEIC) on August 14, 2024, highlighting the global risk posed by the spread of Clade I mpox. - -### European Response - -1. **ECDC Assessment**: The European Centre for Disease Prevention and Control (ECDC) has assessed the risk of Clade I mpox importation into Europe as ""highly likely"" due to frequent travel between Europe and Africa. However, the ECDC believes that sustained transmission in Europe can be kept low with effective surveillance, testing, and contact tracing. - -2. **Cases in Europe**: As of August 15, 2024, Sweden reported the first Clade I mpox case outside of Africa, in a person who had recently traveled to an area with an ongoing outbreak. - -### Russian Response - -1. **Preparedness**: While there is no direct information on Russia's specific preparations for Clade I mpox, the global alert and the spread of cases to Europe would likely prompt Russian health authorities to enhance surveillance and preparedness measures. - -2. **Detection and Reporting**: The confirmation of Clade I mpox infections in Russia before October 1, 2024, would depend on the country's ability to detect and report cases promptly. Given the global alert and the proximity of the outbreak to Europe, it is plausible that Russia could identify cases if they occur. - -### Prediction Markets and Expert Opinions - -1. **Prediction Markets**: As of the current date, there is no specific data from prediction markets regarding the confirmation of Clade I mpox infections in Russia before October 1, 2024. - -2. **Expert Opinions**: Experts emphasize the importance of preparedness, surveillance, and rapid response to imported cases to prevent sustained transmission. The declaration of a PHEIC by WHO underscores the need for global vigilance. - -### Conclusion - -Given the global spread of Clade I mpox, the declaration of a PHEIC by WHO, and the proximity of the outbreak to Europe, it is reasonable to anticipate that Russia could confirm Clade I mpox infections before October 1, 2024, especially if the country maintains high levels of surveillance and reporting. However, the actual occurrence would depend on various factors, including the effectiveness of Russian health measures and the extent of international travel and contact. - -######### - -GPT - -## Step 1: Understanding the Question Type and Resolution Criteria -This is an ""Other Binary Outcome Event,"" where we are predicting whether **Russia will confirm Clade I mpox infections before October 1, 2024**. The result is binary: either ""Yes"" or ""No."" The prediction hinges on whether a credible source confirms at least one Clade I mpox infection in Russia before the deadline. - -**Important Consideration:** -- **Confirmation vs. Occurrence:** The focus is on official confirmation, not just occurrence. The Russian authorities must detect, verify, and report the case within the specified timeframe. - -## Step 2: Establishing the Base Rate -### Historical Spread and Surveillance of Infectious Diseases: -1. **Global Spread:** Historically, pathogens like SARS-CoV-2 spread rapidly across borders once initial outbreaks occurred. Mpox-specific historical data also shows its international spread, albeit at a slower rate than airborne diseases. - -2. **Russia's Surveillance History:** Russia has a reasonably strong medical surveillance infrastructure, and in recent years, it has been quick to report notable infections when they occur. Russia also often responds seriously to health alerts from organizations like the WHO. - -3. **Travel and Clade Spread:** Russia is geographically large, with significant travel connections to both Africa and Europe. Considering recent mpox developments, particularly the spread to Sweden, the likelihood of virus introduction via international travelers is realistic. - -### Known Spread of Clade I Mpox: -- **Spread to Europe**: Clade I has been confirmed in Sweden as of mid-August 2024, and the ECDC assesses the virus as highly likely to spread across Europe. -- **Recent Confirmations:** The fact that Clade I has already reached Europe and spread outside Africa is a strong indicator of continuing geographical spread. - -### Historical Spread Timing: -- Once a disease has reached a new geographical area, it can take several weeks to months for new cases to be confirmed depending on local surveillance capabilities, travel frequency, and public health responses. - -### Russia’s Healthcare System: -- **Laboratory Capacity:** Russia has robust viral lab capabilities, capable of detecting and confirming Clade I mpox if it enters the country. -- **Historical Handling of Infectious Diseases:** Russia has handled other outbreaks (e.g., COVID-19) relatively swiftly, meaning they would likely report any Clade I mpox case promptly. - -## Step 3: Evaluate the Spread, Europe vs. Russia Proximity, and Global Alertness -### Global Alertness: -- The global declaration of a PHEIC by WHO increases international vigilance. -- Russia would likely activate extensive screening measures, particularly for travelers from affected areas, increasing the chances of detecting the virus. - -### Recent Outbreak Trends: -- Given that the virus has reached a non-African country (Sweden), it highlights its potential for international travel and spread comprehensively across Europe. -- With the confirmation date set within just over five weeks, and given how fast global spread can happen once a virus breaches a new geographic area, there's a solid chance of confirmation. - -### Russia’s Likelihood to Report (Non-Political Considerations): -- Russia’s recent geopolitical posture has shown a willingness to acknowledge outbreaks, particularly during global health emergencies. Therefore, a cover-up is unlikely if a case is detected. - -**Timeframe weigh-in:** Considering that it’s already August 26, 2024, Russia has roughly five weeks to confirm a case. This is a tight window, given the global progression but not outside the realm of possibility. - -## Step 4: Assigning Probabilities -1. **High Likelihood Short-Term Spread (Europe to Russia) | ~60%**: Given the proximity between Russia and Europe, regular travel, and virality of the disease under current conditions. - -2. **Government Confirm (if Infected Identified) | ~80%**: Russia generally reports viral outbreaks promptly due to its effective public health infrastructure. - -3. **Overall Likelihood | Strong Corridors and Timing Considerations | ~70%:** - - The timing is the critical factor here. The tight window reduces the odds slightly but the ongoing outbreak dynamics fuel the possibility. - -**Final Calculation**: -Given all these factors: -- There is moderate-to-strong evidence that Russia will detect and confirm Clade I mpox if it spreads to the country within the next few weeks. -- The key variable is time, but the global spread pattern, the PHEIC declaration, and Russia’s activation of its public health measures bolster the probability that confirmation will occur within the stipulated timeframe. - -## Final Answer: -**Probability: 70%**" -27605,"Will George R. R. Martin's “The Winds of Winter” be released before October 1, 2024?","PERPLEXITY - -To address the question of whether George R.R. Martin's ""The Winds of Winter"" will be released before October 1, 2024, we need to consider the latest updates and information available. - -1. **Current Status and Updates**: - - As of August 2024, there is no official release date for ""The Winds of Winter"". - - George R.R. Martin has been working on the book for several years, and despite periodic updates, a specific release date remains uncertain. - - Martin has assured fans that once the book is finished, there will be a major announcement. - -2. **Progress on the Book**: - - In October 2022, Martin stated he had completed approximately three-quarters of the novel, estimating he had written around 1,100 to 1,200 pages, with roughly 400 to 500 pages left. - - In November 2023, he gave a similar estimate, mentioning he was ""struggling"" with the manuscript. - -3. **Prediction Markets and Expert Opinions**: - - Prediction markets suggest a low probability of the book being released in 2024. For example, one market indicates a 12% chance of the book being published in 2024. - - Expert opinions and fan speculations vary widely, with some predicting a release in the latter half of 2024, while others believe it might not be published until 2025 or later. - -4. **Recent Developments**: - - There have been no recent updates that would indicate a movement towards a release before October 1, 2024. Martin's last update in July 2024 scolded impatient fans, suggesting that the book is still in progress. - -Given the information available, it seems unlikely that ""The Winds of Winter"" will be released before October 1, 2024. The lack of a confirmed release date, the ongoing work on the manuscript, and the low probabilities in prediction markets all point towards a release date beyond the specified timeframe. - -######### - -GPT - -### Step 1: Understand the Context and Available Information - -First, let's summarize the essential details we have: - -- **Current Date**: August 26, 2024 -- **Deadline for Question**: Before October 1, 2024 -- **Recent updates**: - - In August 2024, there is no official release date for ""The Winds of Winter."" - - Martin mentioned in October 2022 and November 2023 that he was struggling with the manuscript, with around 400-500 pages left. - - His last update in July 2024 indicated displeasure with impatient fans, subtly implying that significant progress might still be pending. -- **Prediction Markets**: Offer a low probability (~12%) for the book to be released in 2024. - -### Step 2: Base Rate and Historical Analysis - -Let's carefully think through the situation using both base rate thinking and understanding of George R.R. Martin's previous behavior: - -1. **Base Rate of Book Releases**: - - Historically, Martin's previous book ""A Dance with Dragons"" took nearly six years to write after ""A Feast for Crows,"" suggesting a significant delay is typical for Martin, particularly for ""A Song of Ice and Fire"" books. - - ""The Winds of Winter"" has been in progress for over a decade, with the amount of information and updates hinting at substantial ongoing work without a set completion date. - -2. **Past LA & Behavioral Patterns**: - - Martin has been historically known for underestimating the time frames for his writing. The lack of concrete updates, combined with reassurances rather than specifics, further points to the possibility that we're still some distance from publication. - - Martin has often been focused on various side projects, such as the TV series adaptations, which may take up time that could otherwise be dedicated to writing. - -3. **Timing of Announcement to Release**: - - Generally, the release of a major book is accompanied by an extensive marketing campaign. When a title as anticipated as ""The Winds of Winter"" is completed, the publisher would likely maximize the window for building up a marketing campaign. There's no indication yet that such a campaign is underway. - - Given that it's already near the end of August 2024, it seems highly unlikely that any such campaign could be rolled out in just a month without considerable prior signs. - -4. **State of Progress**: - - Even if Martin were close to finishing, the time required for final edits, formatting, printing, and distribution could easily exceed the remaining weeks until October 1. Further, if he's still working on the manuscript as of July 2024, then even the scenario of finished writing soon after August seems unlikely. - -### Step 3: Probabilistic Estimation - -Given all the above factors: - -- **Very Low Probability Base**: With already extensive delays, no immediate signals of completion, and short remaining time to October 1, 2024, the base probability should start low. -- **Historical Patterns**: Martin’s typical behavior, with consistent underestimation of deadlines, suggests remarkable improbability unless surprising developments have occurred with little to no public indication. -- **Market Tendencies**: While the prediction markets shouldn’t be entirely relied upon, a 12% estimated probability highlights collective skepticism about a release this year. - -The probability-to-date represents Martin's inertia, a high probability of continued delays, and the logistical impracticality of executing a complete book release from the current moment to the end of September 2024. - -### Final Conclusion - -Taking all this into account, I estimate the probability of **The Winds of Winter** being released before October 1, 2024, to be: - -**Probability: 5%**" -27608,"Will Ukrainian forces capture Glushkovo (51°20'43.9""N 34°38'54.9""E) before October 1, 2024?","PERPLEXITY - -To address the question of whether Ukrainian forces will capture Glushkovo before October 1, 2024, we need to consider recent developments and ongoing military operations in the region. - -1. **Current Military Operations**: As of August 20, 2024, Ukrainian forces have been actively attacking throughout the Ukrainian salient in Kursk Oblast and have made additional advances. Specifically, they have been targeting Russian pontoon bridges and engineering equipment west of the current Kursk Oblast salient over the Seim River in Glushkovo Raion. Satellite imagery and geolocated footage have confirmed that Ukrainian forces have destroyed at least one pontoon bridge across the Seim River and have advanced in areas near Glushkovo, including Vishnevka and along Sudzhanskiy Shlyakh Street in eastern Korenevo. - -2. **Ukrainian Advances**: Ukrainian forces have shown the capability to advance in the region, capturing territory and equipment. For instance, geolocated footage has shown Ukrainian forces towing a captured Russian T-90M tank along the Snagost-Liubimivka road, indicating their operational presence well within the claimed limits of Ukrainian advances in Kursk Oblast. - -3. **Russian Countermeasures**: Despite Ukrainian advances, Russian forces have been conducting artillery and airstrikes to contain these movements. Russian milbloggers have reported that Russian forces regained some lost positions and advanced in fields south of Safonovka, northeast of Korenevo. - -4. **Strategic Assessment**: The Institute for the Study of War (ISW) has assessed that Ukrainian forces are likely to continue efforts to strike Russian pontoon bridges and engineering equipment to hinder Russian logistical capabilities. However, the ISW also notes that Russian forces are attempting to maintain consistent offensive pressure on Ukraine to force Ukrainian forces to commit manpower and materiel to defensive operations instead of accumulating resources for counteroffensive operations. - -5. **Prediction Markets and Forecasts**: As of the current date, there are no specific prediction markets or forecasts directly addressing the capture of Glushkovo by Ukrainian forces before October 1, 2024. However, the ongoing dynamics suggest a fluid situation with both sides experiencing gains and setbacks. - -Given the recent advances by Ukrainian forces in the region and their active targeting of Russian logistical capabilities, there is a plausible scenario where Ukrainian forces could capture Glushkovo before October 1, 2024. However, the outcome is highly contingent on the continued support of Ukrainian forces with Western security assistance and their ability to counter Russian defensive and counteroffensive measures. - -######### - -GPT - -### Step 1: Define the Problem - -The question at hand is to determine the probability that Ukrainian forces will capture Glushkovo (51°20'43.9""N 34°38'54.9""E) before October 1, 2024. The key considerations involve analyzing the current military situation, historical trends, and future potential actions with a time horizon of roughly five weeks. - -### Step 2: Contextual Analysis -#### Current Situation (as of August 26, 2024): -1. **Ukrainian Advances:** - - Ukrainian forces have made significant incursions into Kursk Oblast, capturing territory and disrupting Russian supply lines. - - Recent reports suggest Ukrainian forces are actively targeting logistical routes like pontoon bridges in the Glushkovo Raion, indicating intent to complicate Russian supply and reinforcement efforts. - -2. **Russian Countermeasures:** - - Russian forces are actively conducting defensive operations, including artillery and airstrikes aimed at containing Ukrainian advancements. - - Russian forces have reportedly had some success in reclaiming lost positions, indicating they are still capable of effective local counterattacks. - -3. **Strategic Environment:** - - Both sides are under significant pressure, with Ukraine looking to capitalize on momentum and Russia focused on maintaining territorial integrity in crucial strategic areas. - - The Institute for the Study of War (ISW) has mentioned that Ukrainian forces are consolidating positions in the area but doesn't specify full control over Glushkovo yet. - -4. **Time Frame:** - - From now (August 26, 2024) to the predicted date (October 1, 2024), there are 36 days. This is a tight timeline, but not impossible for military operations, especially if momentum and supply chains are sustained or accelerated. - -### Step 3: Historical Context & Base Rates -- **Large-scale Regional Advances:** Historically, capturing a town like Glushkovo could take weeks to months depending on resistance, logistical challenges, and the effectiveness of preceding military operations. -- **Recent Ukrainian Progress:** Ukrainian forces have displayed adeptness at exploiting breakthroughs quickly, but sustaining and consolidating gains across several weeks without overextending has been challenging historically. - -### Step 4: Potential Scenarios: -1. **High Probability (60%-80%):** Ukrainians successfully leverage their momentum to capture Glushkovo within the available time. Factors favoring this scenario include disruptions to Russian supply chains, successful neutralization of pontoon bridges, and continuous Western support. - -2. **Medium Probability (30%-50%):** The Ukrainians face stiff resistance, Russian counterattacks, and logistical snags, resulting in stagnation of their advance before reaching Glushkovo. This could involve a stalemate where both sides dig in and wait for a new phase of operations. - -3. **Low Probability (10%-20%):** Ukrainian forces significantly break through Russian defenses, rapidly capture Glushkovo well ahead of October 1, and continue their advance deeper into Kursk Oblast. - -4. **Very Low Probability (1%-10%):** Ukrainian forces overextend, suffer significant setbacks, and fail to make notable advances, possibly resulting in a stalled or reversed campaign. In this case, Glushkovo remains securely in Russian hands. - -### Step 5: Weighing the Data and Uncertainties -- **Recent Gains by Ukraine:** Support the argument that a successful advance within the next 36 days is within the realm of possibility. -- **Russian Defenses and Counterattacks:** Introduce significant uncertainty; Russia's historical ability to reinforce and counteract Ukrainian advances, particularly in crucial, contested areas, cannot be ignored. -- **Logistical & Tactical Factors:** The destruction of logistical routes like pontoon bridges could ease Ukrainian advances by stretching Russian supply lines, but reliance on artillery and air superiority still gives Russia an advantage. - -### Step 6: Assign Probability -Balancing the ongoing Ukrainian momentum, the complexity of capturing a well-defended locality, and the limited time frame, I will assign a moderate probability to Ukraine capturing Glushkovo before October 1, 2024. The situation remains fluid, but the probability isn't excessively high due to the formidable Russian defenses and the short time window available. - -**Final Answer:** -**Probability: 45%** - -This score reflects the nuanced balance between Ukrainian offensive momentum and recognized Russian defensive capabilities, alongside the constraints of time." -27610,"Will Ukrainian forces capture Kursk Nuclear Plant 51°40'30.1""N 35°36'11.5""E before October 1, 2024?","PERPLEXITY - -To address the question of whether Ukrainian forces will capture the Kursk Nuclear Plant before October 1, 2024, we need to consider several key points from recent news and analyses: - -1. **Current Situation and Concerns**: - - The International Atomic Energy Agency (IAEA) Director General, Rafael Grossi, plans to visit the Kursk Nuclear Power Plant due to safety concerns amid the ongoing conflict. - - Russia has accused Ukraine of attempting to target the plant, a claim Ukraine denies. The IAEA has received information about drone remnants near the plant's spent fuel storage area. - -2. **Military Progress**: - - Ukrainian forces have made significant advances in the Kursk region, capturing over 1,250 square kilometers of Russian territory and 92 localities. - - A YouTube analysis suggests Ukrainian forces are halfway to the Kursk Nuclear Power Plant, indicating a feasible path to capturing it. - -3. **Strategic Importance**: - - The Kursk Nuclear Power Plant is a strategic target. Capturing it could allow Ukraine to cut power output to the Kursk region, potentially disrupting Russian operations and supply chains. - -4. **Expert Opinions and Predictions**: - - Analysts on Reddit discuss the strategic implications of seizing the plant, including disrupting Russian internal narratives and gaining leverage for future negotiations. - - The Institute for the Study of War (ISW) notes that Russian forces are redeploying units to defend against the Ukrainian incursion, indicating a significant response to the threat. - -5. **Prediction Markets and Forecasts**: - - No specific prediction market data is provided in the sources. However, the progression of Ukrainian forces and the strategic importance of the plant suggest a plausible scenario for its capture before October 1, 2024. - -In summary, while there is no definitive answer to whether Ukrainian forces will capture the Kursk Nuclear Plant before October 1, 2024, the current military advancements, strategic importance of the plant, and expert analyses suggest that it is a feasible and strategically valuable objective for Ukrainian forces. - -######### - -GPT - -### Initial Considerations: - -We are tasked with predicting the likelihood that Ukrainian forces will capture the Kursk Nuclear Power Plant (KNPP) at specified coordinates (51°40'30.1""N 35°36'11.5""E) before October 1, 2024. Today's date is August 26, 2024. - -Here are the key factors to consider: - -1. **Current Progress of Ukrainian Forces**: - - Ukrainian forces have reportedly been making significant advances into Kursk Oblast since their incursion began on August 8, 2024. They have captured over 1,250 square kilometers and 92 localities. The map provided shows Ukrainian forces pushing towards the Kursk region from multiple directions. - - The Kursk Nuclear Power Plant, located in the western part of Kursk Oblast, is some distance from the reported Ukrainian positions. - -2. **Time Frame**: - - We have approximately 36 days until October 1, 2024. - - Given the rapid pace of the advances so far, this time frame is critical. However, Ukraine's forces are still a significant distance away from the plant. - -3. **Strategic Importance**: - - The KNPP is a strategically vital asset. Capturing it would not only disrupt Russian nuclear power generation but also hold significant psychological and political value. - - The capture of this plant could cause substantial international concern, especially regarding potential nuclear accidents. - -4. **Russian Response**: - - Russia is unlikely to allow the plant to fall without a strong defense. Reports indicate that the Russian military, including the National Guard (Rosgvardiya), is focusing on defending this sensitive area. - - Additionally, Russia has prepared defensive lines near the plant and is likely to redeploy significant forces from other fronts if deemed necessary. - -5. **ISW Reporting and Mapping**: - - The question resolution relies on the mapping by the Institute for the Study of War (ISW). The criteria allow for claimed Ukrainian control rather than firmly established control. This means that even advances close to the plant could result in a ""Yes"" resolution if those advances are claimed by Ukrainian sources. - - Given ISW’s cautious approach to mapping areas under Ukrainian control, this implies that Ukraine must establish and sustain a credible presence close to or at the KNPP coordinates for it to be marked on the interactive map. - -### Evaluation of Likely Scenarios: - -1. **Rapid Advance and Capture**: - - Ukrainian forces may continue their current momentum, bypass Russian defensive positions quickly or exploit weaknesses, resulting in the capture or at least a credible claim of advance on the KNPP. This scenario requires sustained Ukrainian pressure and Russian defensive failures. - - **Probability**: Medium to Low. This would require an uninterrupted and successful Ukrainian offensive push, which might be challenging given the importance Russia places on the KNPP. - -2. **Stalemate or Slowed Progress**: - - Ukrainian advances may slow down as they encounter heavily fortified Russian defenses. Russian forces might use this time to regroup and launch counter-attacks. The close proximity to the October 1 deadline might mean that Ukraine won’t get close enough to claim control. - - **Probability**: High. Stalemates or slowed advances are common in war, especially when one side is defending a critical strategic asset. - -3. **Partial Capture or Strategic Reach Without Full Control**: - - Ukrainian forces might reach close proximity to the plant, possibly resulting in reported skirmishes near the plant but falling short of establishing clear or defensible control. The ISW map might show claimed advances, but without full control, it could still resolve as ""No."" - - **Probability**: Medium. This is a plausible scenario where Ukraine gets close but either does not fully capture the plant or the situation remains too ambiguous for a classification of ""under Ukrainian control."" - -### Summary and Final Probability Assessment: - -Given approximately one month until the resolution date, the most likely scenario is that Ukrainian forces will either be slowed down or get close to the KNPP without fully capturing it. The strategic value of the plant means Russian forces are likely to heavily defend it, and time constraints make a successful Ukrainian all-out capture less likely. - -**Potential Distribution**: -- **High-Certainty Capture (Yes)**: 15% - Requires sustained and overwhelming Ukrainian momentum, coupled with Russian defensive failures. -- **Stalemate or Insufficient Progress (No)**: 70% - Most likely that strategic fortifications and defenses will delay Ukrainian advances. -- **Unclear or Partial Control (Yes)**: 15% - Potential for Ukrainian advances that are perceived or reported as significant but fall short of undisputed control. - -### Final Answer: -**Probability: 30%** - -This suggests that while there is some non-trivial chance of Ukrainian forces capturing or claiming significant control near the KNPP, the overall likelihood remains low given the current date, available time frame, and strategic significance of the KNPP to Russia." -27609,"Will Ukrainian forces capture B. Soldatskoe (51°20'15.4""N 35°30'27.1""E) before October 1, 2024?","PERPLEXITY - -To address the question of whether Ukrainian forces will capture B. Soldatskoe (51°20'15.4""N 35°30'27.1""E) before October 1, 2024, we need to consider recent developments and assessments from various sources. - -1. **Current Military Situation and Advances**: - - **Ukrainian Advances in Kursk Oblast**: Ukrainian forces have been making advances in Kursk Oblast. Geolocated footage and maps shared by Ukrainian Commander-in-Chief Colonel General Oleksandr Syrskyi indicate that Ukrainian forces have advanced slightly beyond previously assessed positions, capturing several settlements. - - **Russian Offensive Campaigns**: Despite Ukrainian advances, Russian forces continue their offensive campaigns, particularly in Donetsk Oblast, aiming to capture the entirety of the region. - -2. **Assessments and Predictions**: - - **Western and US Officials' Assessment**: Western and US officials believe that Ukrainian forces will remain on the defensive for the next six months and won't be able to conduct a large-scale counteroffensive until 2025. However, Ukrainian forces are attempting limited counterattacks in select sectors. - - **Institute for the Study of War (ISW) Assessment**: ISW has noted Ukrainian advances in Kursk Oblast but also highlights ongoing Russian ground attacks along the Kupyansk-Svatove-Kreminna line. ISW's assessments suggest a complex and fluid situation on the ground. - -3. **Territorial Control and Recent Developments**: - - **Territorial Control Map**: The map of territorial control during the Russo-Ukrainian War shows various settlements changing hands, indicating a dynamic frontline. - - **Recent Updates**: Ukrainian forces have been active in Kursk Oblast, and there have been reports of them seizing over 20 settlements, including the town of Sudzha. However, Russian forces have also made advances, particularly in the Donetsk region. - -Given these points, the situation on the ground is highly dynamic, with both sides making advances and facing setbacks. The capture of B. Soldatskoe before October 1, 2024, would depend on various factors, including the pace of Ukrainian advances, the effectiveness of Russian defenses, and the impact of Western aid on Ukrainian military capabilities. - -**Prediction Markets and Expert Opinions**: -- Specific prediction markets or expert opinions directly addressing the capture of B. Soldatskoe before October 1, 2024, are not provided in the sources. However, the general consensus among Western officials and analysts is that Ukrainian forces are likely to remain on the defensive for the foreseeable future, with limited opportunities for significant counteroffensives. - -In conclusion, while Ukrainian forces have shown the ability to make advances in certain areas, the overall assessment suggests that capturing B. Soldatskoe before October 1, 2024, might be challenging given the current military dynamics and predictions of continued defensive posturing by Ukrainian forces. - -######### - -GPT - -### Step 1: Evaluate the Background Information - -1. **Current Military Progress**: - - Ukrainian forces have been making advances in various locations in the Kursk Oblast. The advances are incremental and have been met with significant resistance from Russian forces. The Ukrainian progress appears to be localized to a few key areas, particularly around Tetkino, Korenevo, and Glushkovo. However, figuring out how those advances translate to capturing B. Soldatskoe, a specific point located southwest of the currently contested areas, is crucial. - -2. **Assessment from Analysts**: - - **Institute for the Study of War (ISW)** and **Western Intelligence** believe that Ukrainian forces are showing some offensive potential but are predicted to mainly hold a defensive stance for the foreseeable months. This does not bode well for significant territorial gains unless circumstances change dramatically. - -3. **Territorial Control Map**: - - Based on ISW's map, current Ukrainian control isn't close to B. Soldatskoe. The nearest reported Ukrainian position is in Korenevo and Tetkino. B. Soldatskoe is around 35-40 km from the present areas of Ukrainian operation. Terrain and logistical barriers must be considered. - -### Step 2: Analyze Military Strategy & Tactics - -1. **Logistics and Terrain**: - - **Distance and Terrain**: Ukrainian forces would have to push through approximately 35-40 km of potentially hostile territory. Given the state of the conflict and likely defensive fortifications by Russian forces (noted defensive around key areas like the Kursk Nuclear Plant), this forward momentum could slow down significantly. - - - **Time Remaining**: There are about 5 weeks until October 1, 2024. Given current trends and military history, a 35-40km advance into enemy territory would be very aggressive and likely face significant obstacles. - -2. **Operational and Strategic Importance**: - - The capture of B. Soldatskoe, a rural locality, might not be strategically critical unless it serves as a stepping stone towards a larger strategic objective, such as bringing pressure on Kursk City or securing a flank for broader territorial advances. Its importance relative to Ukrainian forces' overall military objectives impacts the probability of it being prioritized. - -3. **Base Rates & Historical Precedent**: - - Historically, local advances of ~35 km in a span of weeks in a complex battlefield are rare without overwhelming superiority or a collapse of the opposing defenses. The probability of such a swift and focused movement towards a specific rural objective isn't high under a steady conflict scenario. - -### Step 3: Weighing the Default Behavior and Variability - -1. **Default Behavior**: - - The default scenario is a relatively static front with limited gains on either side. Ukrainian forces primarily solidify gains close to the border instead of achieving long-distance incursions deep into enemy territory. - -2. **Variability**: - - In a dynamic war environment, sudden breakthroughs or collapses in defense are possible but low probability in a five-week window given the trends and reports on current military assessments. - -3. **Uncertainty & Potential Game Changers**: - - Again, while great variability exists in war, no significant event or incoming aid (foreign materials or tactical surprise) has been noted that could dramatically alter the situation on the ground in favor of rapid territorial conquest in this timeframe. - -### Step 4: Concluding Probability Estimate - -Taking into account the operational challenges, the strategic importance (or lack thereof) of the target, the short time frame, and the slow-moving nature of recent advances: - -- **Operational Likelihood**: - - Low probability given the required penetration depth into contested territory. - -- **Strategic Focus**: - - Likely concentration of forces elsewhere, particularly near the internationally monitored Kursk nuclear plant or around more vital junctions. - -So, the final probability that Ukrainian forces will capture B. Soldatskoe before October 1, 2024, ending up marked as Ukrainian-controlled on the ISW interactive map: - -### ""Probability: 15%""" -27604,"Will Mexico confirm Clade I mpox infections before October 1, 2024?","PERPLEXITY - -To address the query of whether Mexico will confirm Clade I mpox infections before October 1, 2024, we need to consider several factors including the current global situation of mpox, the specific strains involved, and Mexico's preparedness and response to the outbreak. - -### Global Situation and Strains - -- **Clade I mpox**: This strain is considered more severe and has a higher mortality rate compared to Clade IIb. It has been identified in several African countries, as well as in parts of Asia and Europe, including Sweden and Thailand. -- **WHO Declaration**: On August 14, 2024, the World Health Organization (WHO) declared a Public Health Emergency of International Concern due to the upsurge of Clade I mpox cases. - -### Mexico's Situation - -- **Previous Outbreaks**: Mexico had 27 confirmed mpox infections as of July 4, 2022, which were part of the global outbreak of Clade IIb mpox. -- **Preparedness**: The United States has been preparing for potential Clade I mpox cases, including enhancing diagnostic testing capacity and vaccination efforts. Given the geographical proximity and potential travel between the U.S. and Mexico, it is likely that Mexico is also on high alert. - -### Expert Opinions and Predictions - -- **WHO and CDC**: Both organizations have emphasized the need for vigilance and preparedness for Clade I mpox, suggesting that countries, including those in the Americas, should be ready to detect and manage cases. -- **Prediction Markets**: As of the current date, there is no specific data from prediction markets regarding Mexico's confirmation of Clade I mpox infections before October 1, 2024. - -### Recent Updates and Movement Towards Resolution - -- **Global Spread**: The recent identification of Clade I mpox in non-endemic countries suggests a potential for wider spread, increasing the likelihood that Mexico could confirm cases. -- **Public Health Measures**: Enhanced public health measures, including surveillance, testing, and contact tracing, are crucial in detecting and containing outbreaks. Mexico's ability to implement these measures effectively will influence the likelihood of confirming Clade I mpox infections. - -In conclusion, while there is no direct evidence that Mexico will confirm Clade I mpox infections before October 1, 2024, the global spread of the virus, WHO's declaration of a Public Health Emergency of International Concern, and the emphasis on preparedness by health organizations suggest that the possibility cannot be ruled out. Continuous monitoring of the situation and adherence to public health guidelines are essential in addressing this potential threat. - -######### - -GPT - -**Analysis and Considerations** - -Let's break down the situation into key components for careful analysis: - -### 1. **Global Context and Spread of Clade I mpox** - - The emergence of Clade 1b mpox and its spread in Central Africa, particularly the Democratic Republic of the Congo (DRC), is the main driver behind the WHO's declaration of a Public Health Emergency of International Concern (PHEIC). - - **Noteworthy Events:** The recent identification of Clade I mpox outside Africa (e.g., in Sweden on August 15, and later in Thailand) suggests there is now some potential for international spread. - -### 2. **Current Status in Mexico and Nearby Regions** - - **Past Experience:** Mexico had previous experience with mpox during the Clade IIb outbreak, with health infrastructure likely bolstered due to past challenges. - - **Geographical Proximity:** Mexico is geographically close to the U.S., which has heightened mpox surveillance and preparedness, especially given concerns about international travel and cross-border movements. - -### 3. **Timeline Considerations** - - Today's date is August 26, 2024, and the question involves determining if Mexico will confirm Clade I mpox before October 1, 2024. This gives approximately five weeks (~35 days) for any such confirmation to occur. - - **Epidemiological Spread:** The limited number of international cases confirmed outside the African continent so far, with only about 1.5 weeks since the PHEIC declaration, limits the base rate at this early stage. - -### 4. **Impact of Surveillance and Testing** - - **Mexico's Detection Capability:** The likelihood of Mexico detecting a Clade I mpox case would largely depend on the country's level of preparedness, including its existing mpox surveillance, diagnostic capacity, and public health infrastructure. - - **Travel Risk:** With cases confirmed in Europe and Asia, the possibility of additional cases seeding in regions with frequent global travel connections (including Mexico) increases. - -### 5. **Base Rates and Prior Events** - - Historically, the initial appearance of a novel or particularly dangerous strain often shows a rapid emergence phase in multiple countries, followed by a steady rise in reported cases as case numbers build up and surveillance improves. - - - **Comparative Base Rate:** If we consider other emerging infectious disease explanations, the appearance of Clade I mpox within Mexico may be most likely if significant international travel introduces it and if neighboring countries report cases first. - -### 6. **Bayesian Updating with Prior Knowledge** - - Initial odds of Clade I mpox penetrating Mexico are relatively low, given the limited number of global cases outside of Africa. However, considering potential vectors (like international travelers or spillover from countries like the U.S., possibly reporting cases), we assign a modest prior probability. - -**Current Assessment:** - -Given five weeks to resolution, surveillance efforts, and global mpox spread, the likelihood of Mexico confirming Clade I mpox appears to hinge predominantly on these factors: - -- **Increasing global spread** (risk rises with time): Probability slightly increases as time progresses and more cases are potentially confirmed in more populous or travel-connected regions. -- **Previous public health response strength** in Mexico, USA, Central American countries. - -### Conclusion and Probability Estimate: -After weighing all factors with careful thought: -- The spread of Clade I mpox internationally is still in the early stages. -- Travel and trade routes add international spread risk. -- Strong public health infrastructure could detect a case more quickly. - -Thus, I estimate the likelihood that Mexico will confirm Clade I mpox infections by October 1, 2024 to be: - -**Probability: 29%** - -This probability reflects the balance between increasing international spread and the relatively short time window to the resolution date, with a moderate expectation that Mexico could detect a case within the given time frame." -27640,"Will YouTube be banned in Russia before October 1, 2024?","PERPLEXITY - -To address the question of whether YouTube will be banned in Russia before October 1, 2024, we need to consider recent developments and statements from Russian officials. - -### Recent Developments and Statements - -1. **Threats of Ban**: There have been consistent threats from Russian authorities to ban YouTube due to its refusal to comply with Russian laws and regulations. The government has accused YouTube of spreading ""fake news"" and ""anti-Russian"" content, particularly regarding the war in Ukraine. - -2. **Slowdown of YouTube**: Russia has already started throttling YouTube speeds. By the end of July, desktop YouTube download speeds were expected to drop by 40%, and by the end of the following week, by 70%. This move is seen as a pressure tactic to force YouTube to comply with Russian demands. - -3. **Alternative Platforms**: Russia is actively developing its own video-sharing platforms, such as Rutube and VK Video, as part of its effort to reduce dependence on Western internet services and to better control the narrative within the country. - -4. **Denials of Immediate Ban**: Despite these actions, there have been mixed signals from Russian officials. Minister of Digital Development, Communications and Mass Media, Maksut Shadayev, stated in May that Russia does not plan to ban YouTube, emphasizing the need for a competitive alternative before such a drastic measure. - -5. **Impact on Opposition Voices**: YouTube has become a critical platform for independent journalists and opposition voices in Russia. A ban would significantly impact their ability to reach audiences within the country, forcing them to find alternative means of communication. - -### Expert Opinions and Predictions - -- **Mikhail Klimarev**, head of the Internet Protection Society, believes that the slowdowns are a form of blackmail to force Google to negotiate and possibly restore pro-government channels. -- **Alexander Khinshtein**, a Russian lawmaker, has been vocal about the need for YouTube to comply with Russian laws, indicating that the platform's future in Russia depends on its actions. - -### Prediction Markets and Betting Odds - -As of the current date, there isn't specific information available on prediction markets or betting odds regarding the ban of YouTube in Russia before October 1, 2024. However, the general trend suggests increasing pressure from Russian authorities on Western internet platforms. - -### Conclusion - -While there are clear indications of Russia's intent to exert control over YouTube, including slowdowns and the development of alternative platforms, the outright ban before October 1, 2024, remains uncertain. The situation is fluid, with Russian officials sending mixed signals. The outcome may depend on YouTube's response to Russian demands and the development of viable domestic alternatives. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Background Context** - - **Past Precedents:** Russia has previously blocked major Western internet platforms like Twitter, Instagram, and Facebook following geopolitical tensions. These blocks have commonly followed a pattern of escalating restrictions: initially applying pressure through throttling or fines, followed by more extreme measures like outright blocking. - - **YouTube’s Status:** YouTube has maintained its presence in Russia despite these tensions, likely due to its significant user base and importance to Russian internet users. However, given Roskomnadzor's recent actions (e.g., throttling), the threat of a full ban is more tangible now than it was in previous years. - - **Current Developments:** The throttling of YouTube’s speeds, alongside reports and media speculation, suggests that Russia is edging closer to a potential ban. The rhetoric from officials and the ongoing crackdown on foreign media further underscores this point. - -#### 2. **Analyzing the Timeline** - - **Date of Assessment:** August 28, 2024. - - **Time Left Until October 1, 2024:** A little over a month (34 days). - - This is a crucial period because if a ban were to occur, it would have to transpire within the next month. The question is whether the existing pressure will escalate to a full ban within this narrow window. - -#### 3. **Evaluating the Likelihood of a Ban** - - **Stated Intentions from Russian Officials:** While some officials have denied that a ban is imminent, these statements are often strategic and meant to manage public sentiment. Given Russia’s history of sudden platform bans, these statements don’t necessarily indicate that a ban won’t happen. - - **Political Context:** The ongoing conflict in Ukraine and Russia's desire to control the narrative at home suggest that the Kremlin might see a YouTube ban as advantageous, particularly given its association with opposition voices and Western media. The slow speed reductions could take on an increasingly punitive stance leading to a full block. - - **Developments in Technology:** Russia is enhancing its own platforms like Rutube, providing a plausible alternative to YouTube if the ban does happen. The existence of alternative platforms gives the Russian government more freedom to block YouTube without significantly disrupting the flow of video content. - -#### 4. **Base Rate and Similar Events** - - **Prior Blocks:** The blocking of other major platforms like Twitter, Instagram, and Facebook occurred in the wake of heightened geopolitical tensions, typically within months following a critical incident or escalation. - - **Default Resolution if Nothing Changes:** If current trends persist without any escalation, YouTube would remain operational, albeit under severe restrictions. However, given the historical pattern where throttling often precedes outright blocking, the risk of this happening within the timeline seems elevated. - -#### 5. **Assigning Probability** - - **Certainties:** Roskomnadzor has initiated aggressive throttling, YouTube’s position within the Russian media landscape is threatening to Russian authorities, and the development of domestic alternatives reduces the political cost of a ban. - - **Uncertainties:** Mixed signals from officials, unclear timing of potential banning actions, and whether any behind-the-scenes negotiations may prevent a ban. - - **Trend Extrapolation:** Given the trajectory from initial threats, to throttling, to possible bannings of other platforms, it seems that a potential acceleration to a full ban is possible within the next 34 days. - -### Final Calculation -- Base rate considerations, recent developments (throttling), alternative platform preparations, and mixed government signals all feed into a moderately high probability. -- However, the critical factor is the uncertainty around the exact timing and whether this pressure will culminate in a ban before October 1, 2024. - -### Conclusion -Considering all the information: -- Historically when Russia begins aggressive throttling, it can lead to a ban of the service within a matter of months. -- With a little over a month left before the target date, there's no certain indication that Roskomnadzor will immediately escalate to a ban, but plenty of indicators suggest this is a real risk. - -Thus, the situation remains fluid, but the probability of a ban occurring before October 1, 2024, is moderately high. - -**Probability: 65%**" -27637,"Will ""Dilemma"" by Green Day win the award for Best Rock at the 2024 MTV VMAs?","PERPLEXITY - -To provide context for the question of whether ""Dilemma"" by Green Day will win the award for Best Rock at the 2024 MTV VMAs, we need to look at the current nominations, expert opinions, and any relevant prediction markets or betting odds. - -### Nominations and Competition - -- **Nominees for Best Rock**: The nominees include Green Day for ""Dilemma,"" Bon Jovi for ""Legendary,"" Coldplay for ""feelslikeimfallinginlove,"" Kings of Leon for ""Mustang,"" Lenny Kravitz for ""Human,"" and U2 for ""Atomic City"". -- **Expert Opinions and Fan Reactions**: There are mixed reactions from fans and experts. Some fans believe Green Day has a strong chance, while others criticize the list for being out of touch with current rock music trends. - -### Prediction Markets and Betting Odds - -- **Voting**: Voting is open until August 30, and the current vote counts are not publicly available. - -### Recent Updates and Context - -- **MTV VMAs Date**: The 2024 MTV VMAs will take place on September 10 at New York's UBS Arena. -- **Rock Representation**: There is a general sentiment that rock and metal are underrepresented in the nominations, with many categories dominated by pop artists. - -### Conclusion - -While there is support for Green Day's ""Dilemma,"" the outcome is uncertain due to the nature of public voting and the diverse list of nominees. The final result will depend on the votes cast before August 30. - -######### - -GPT - -### Analysis and Thought Process: - -#### **1. Understanding the Question and Criteria:** -The task is to predict the likelihood that Green Day's ""Dilemma"" will win the Best Rock award at the 2024 MTV Video Music Awards (VMAs). The resolution is binary: Green Day either wins (Yes) or does not win (No). We need to consider the relevant factors: the other nominees, the nature of the VMAs as a popularity-based event, past trends, and the specific timeframe in which the decision will be made. - -#### **2. Base Rates and Historical Data:** -- **Green Day's Past Performance:** Green Day is a well-established band with a successful history at the MTV VMAs, winning various awards in multiple categories over time. However, their success at recent VMAs, especially within the past decade, may have diminished as their music no longer dominates the rock genre in the way it did in the early 2000s. - -- **Best Rock Award Trends:** Historically, the VMAs have often favored newer or more innovative acts in the Best Rock category, but legacy acts still perform well occasionally. The VMAs can sometimes be unpredictable due to the influence of popular voting and the evolving music scene. - -#### **3. Competition Analysis:** -- **Nominees for 2024:** - - **U2 (""Atomic City"")**: A globally recognized band with a strong legacy and substantial fanbase. U2 has previously won similar awards and has remained relevant. - - **Coldplay (""Feelslikeimfallinginlove"")**: Another major act with a broad appeal and history of accolades at VMAs. Coldplay's music generally crosses genre boundaries, allowing them to attract not only rock fans but also pop audiences. - - **Kings of Leon (""Mustang"")**: Kings of Leon have had some success in the past but have been less dominant in recent years. - - **Lenny Kravitz (""Human"")**: Although Lenny Kravitz is a respected artist, he might be viewed as an outlier with less momentum than U2 or Coldplay. - - **Bon Jovi (""Legendary"")**: Also a legacy act, but one that might be seen as less relevant to newer audiences. - -- **Conclusion on Competition:** Coldplay and U2 are arguably the strongest contenders alongside Green Day, with Coldplay possibly having a slight edge should voting demographics favor a more mainstream audience. - -#### **4. External Factors:** -- **Fan Voting Dynamics:** As VMAs are determined primarily by fan votes, the reach and engagement of an artist's fan base are highly relevant. Social media presence, campaigns for voting, and recent tours or media appearances can sway outcomes. - -- **Public Sentiment**: Currently, there appears to be a mixed sentiment, with some criticism of the nominees reflecting potential fragmentation in the rock fanbase. This fragmentation could lead to a split vote, creating room for an upset or benefiting the artist with broader appeal. - -#### **5. Time Remaining Until the Awards:** -- As of August 28, 2024, just 13 days remain until the VMAs are held, and voting closes in just two days on August 30. This is a crucial timeframe, and any efforts by Green Day (or their competitors) to mobilize fans could still be impactful. However, no drastic changes are likely to occur within such a short time window. - -#### **6. Weighing All Factors:** -- **Legacy Factor:** Green Day has a historically significant status. -- **Competition Strength:** U2 and Coldplay are perceived as strong competitors, possibly with broader appeal. -- **Popularity Dynamics:** Green Day may not have the same broad appeal today as they once did. -- **Prediction Markets:** No clear data, but we must consider U2 and Coldplay as potentially leading contenders. - -#### **7. Assessing Uncertainty:** -Given all factors, the uncertainty remains, particularly because of the VMAs' reliance on public voting and fan engagement. Also, given that Coldplay and U2 are very strong contenders, the probability that Green Day could edge them out is not overwhelmingly high. - -### **Final Probability:** -Taking all of this into account, while Green Day has a non-negligible chance due to their legacy and dedicated fan base, other nominees—particularly Coldplay and U2—pose serious threats. I estimate the probability that Green Day's ""Dilemma"" will win the Best Rock award at the 2024 MTV VMAs as: - -**Probability: 30%**." -27639,"Will the median person on Bloomberg's Billionaires Index be from the United States on September 16, 2024?","PERPLEXITY - -To answer this question, we need to examine the current composition of Bloomberg's Billionaires Index and any recent trends that might influence the nationality distribution of the billionaires listed. - -1. **Current Composition of Bloomberg's Billionaires Index**: - - As of the latest update on August 27, 2024, the index ranks the world's 500 richest people based on their net worth. - - The index is dynamic and changes daily based on market fluctuations and economic news. - -2. **Methodology of the Index**: - - The index calculates net worth using a mixture of public and private data, tracking stock prices, publicly disclosed holdings, and calculated values for private companies. - - Valuations are converted to U.S. dollars at current exchange rates and do not include assumptions about personal debt. - -3. **Recent Trends and Updates**: - - As of August 23, 2024, Elon Musk is the wealthiest person on the list with a net worth of $243.7 billion. - - The index has uncovered more than 400 hidden billionaires since its founding, including individuals from various countries. - -4. **Historical Context**: - - In previous years, the United States has consistently had the highest number of billionaires on the list. For example, in 2022, the list included 2,668 billionaires with a significant portion from the United States. - -5. **Prediction Markets and Forecasts**: - - Unfortunately, specific prediction markets or forecasts regarding the nationality of the median person on the Bloomberg Billionaires Index by September 16, 2024, are not available in the provided sources. - -Given the historical dominance of U.S. billionaires on the list and the dynamic nature of the index, it is plausible that the median person could still be from the United States by September 16, 2024. However, without specific forecasts or recent data indicating shifts in the nationality distribution, this conclusion is based on historical trends rather than current predictions or market analysis. - -######### - -GPT - -### Step 1: Understand the Resolution Criteria and Problem Scope - -The task is to predict whether the 250th person on Bloomberg’s Billionaires Index on **September 16, 2024**, will be from the United States (U.S.). The question is binary: ""Yes"" if the person is from the U.S., and ""No"" if they are from any other country. - -### Step 2: Assess the Current Situation - -- **Current Data (as of August 27, 2024):** - - 189 out of 500 billionaires on the list are from the U.S. - - The highest concentration of billionaires from any country is from the U.S., with 189 individuals, which represents **37.8%** of the total list. - - Among ranks 246-255, 7 out of 10 people (70%) are from the U.S. - -### Step 3: Evaluate the Trend - -#### Historical Trend: -Historically, the United States has maintained a dominant presence on Bloomberg’s Billionaires Index. A significant share of the world’s wealth has been concentrated in the U.S., driven by large technology corporations, financial institutions, and other industries. There is no immediate indication that this trend has significantly deviated. - -#### Recent Trends: -- **Stock Market and Economic Factors:** - The U.S. stock market, particularly technology stocks, has continued to perform robustly, contributing to the wealth of U.S.-based billionaires. -- **Geopolitical Factors:** - While geopolitical tensions and trade issues could affect non-U.S. billionaires disproportionately, there hasn't been a dramatic disruptive event suggesting an immediate rise in non-U.S. billionaires relative to U.S. billionaires. - -### Step 4: Consider Market Dynamics - -- **List Composition and Dynamics:** - Bloomberg's Billionaires Index is dynamic and subject to daily changes based on asset prices, which are influenced by global market conditions, currency exchange rates, political events, and new billionaire discoveries. However, with just about three weeks until the resolution date, the median position on the list is not likely to change drastically unless there is a significant market event. - -- **Ranking Variance:** - The current density of U.S. billionaires around the median position suggests that even with minor fluctuations, it is still probable that the 250th-ranked person will be a U.S. national. - -### Step 5: Base Rates and Novel Factors - -- **Base Rates:** - Since the inception of global billionaires indices, U.S. individuals have dominated the list. Though certain regions like Asia are rising in billionaire representation, the growth isn’t rapid enough to entirely disrupt the current distribution in such a short period. - -- **Potential Novel Factors:** - While factors like AI advancements, market regulation changes, or economic crises could potentially shift the balance, no surprising drastic events have been reported that would suggest the emergence of a significant number of non-U.S. billionaires by September 2024. - -### Step 6: Estimate Probability Distribution - -Given the historic data, current conditions, and the proximity of the resolution date, it seems unlikely that the list composition will radically change in favor of non-U.S. billionaires between now and September 16, 2024. - -### Final Estimation - -- **Baseline Estimate:** The probability that the 250th-ranked person is from the U.S. centers around the current proportion of U.S. billionaires in the list, adjusted slightly for any standard fluctuations. -- **Risk Adjustment:** Considering market and global trends nearby the median, additional non-U.S. names could push the U.S. representation slightly down. However, a shift significant enough to move the median beyond a U.S. national would be relatively low. - -Thus, I estimate the probability that the person ranked 250th on Bloomberg's Billionaires Index on September 16, 2024, will be from the United States to be: - -**Probability: 75%**" -27638,"Before October 1, 2024, will Tech Crunch report new layoffs at Meta, Facebook or Instagram?","PERPLEXITY - -To provide context for the question of whether TechCrunch will report new layoffs at Meta, Facebook, or Instagram before October 1, 2024, we need to examine the current trends in tech layoffs, Meta's recent history of layoffs, and any relevant predictions or market sentiments. - -1. **Tech Layoff Trends in 2024**: - - The tech industry has seen significant layoffs in 2024, with over 60,000 job cuts across 254 companies as of August 15, 2024. This includes major companies like Tesla, Amazon, Google, TikTok, Snap, and Microsoft. - - The reasons behind these layoffs include economic uncertainty, the need to restructure and optimize business operations, and the impact of adopting AI and automation. - -2. **Meta's Recent Layoff History**: - - In 2022, Meta reported a decline in profits and a significant reduction in its market capitalization, partly due to increased competition and Apple's privacy changes. - - Meta laid off 11,000 employees in November 2022 and planned to lay off a further 10,000 employees starting in April 2023 as part of its ""Year of Efficiency"" to improve financial performance and reduce headcount. - - Instagram, owned by Meta, cut 60 technical program manager positions in January 2024, eliminating a layer of management and focusing on supporting creators who drive teen engagement. - -3. **Prediction Markets and Forecasts**: - - A question on Metaculus asks if TechCrunch will report new layoffs at Meta, Facebook, or Instagram before October 1, 2024, indicating ongoing speculation and interest in Meta's staffing decisions. - -Given the ongoing trend of layoffs in the tech industry and Meta's recent history of significant job cuts as part of its restructuring efforts, it is plausible that TechCrunch could report new layoffs at Meta, Facebook, or Instagram before October 1, 2024. The company's focus on efficiency and the challenging economic conditions suggest that further adjustments might be necessary. However, without a direct statement from Meta or a recent report from TechCrunch, we cannot confirm new layoffs definitively. - -######### - -GPT - -### Step 1: Clarify the type of question - -This question is a **""Binary Outcome Event""** — it asks whether a specific event (i.e., TechCrunch reporting new layoffs at Meta, Facebook, or Instagram) will occur before a specific date (October 1, 2024). - -### Step 2: Analyze Background Information - -**1. **Recent History of Layoffs at Meta:** - - Meta underwent significant layoffs in 2022 and 2023, with a total of over 21,000 employees laid off during this period. - - In 2023, layoffs were attributed to economic pressures and the company's “Year of Efficiency” initiative. - -**2. **Tech Industry Trends in 2024:** - - The tech industry saw over 60,000 job cuts by August 15, 2024, across 254 companies. - - Macroeconomic conditions, such as interest rate hikes, inflation, and possible recession fears, may be driving layoffs. - - There’s a trend in the tech sector to streamline operations and cut costs, with AI and automation influencing staffing needs. - -**3. **Meta-specific Context:** - - Meta is undergoing structural changes and efficiency improvements which previously led to layoffs. - - Instagram’s recent smaller layoff (60 employees) suggests that restructuring, while smaller in scale, is still ongoing. - -### Step 3: Consider the Importance and Recency of Information - -- **Importance of Meta’s recent layoff history:** Given Meta’s recent focus on reducing costs and improving efficiency, this background is crucial. Past layoffs were fairly recent and large in scale, which suggests Meta hasn’t hesitated to make big changes quickly. - -- **Tech industry layoff trends:** The ongoing wave of layoffs in the tech sector supports the idea that Meta, along with other tech companies, might also have more to come. - -- **Timeframe consideration:** The question spans just over 1 month (from August 28, 2024, until October 1, 2024). Given the short timeframe and recent trends, the likelihood of another layoff announcement, while possible, may be limited. - -### Step 4: Assess Base Rates and Prior Events - -- **Base Rate Analysis:** - - Meta conducted two major layoffs in the past 2 years. - - If the 2022 and 2023 layoffs were responses to major internal and external challenges, the primary disruptions might be largely addressed by now. - -- **Novel Factors:** - - Ongoing uncertainty in macroeconomics. - - The relatively rapid adoption of AI tools could either reduce or increase the need for layoffs depending on Meta’s adoption curve and business model adjustments. - -### Step 5: Weigh the Default Scenario - -The ""default"" scenario—assuming no changes—is that there are no further layoffs before October 1, 2024. Any layoffs would likely have already been made earlier in 2024 as part of the ongoing restructuring process. Instituting another round of layoffs within such a short time period might contradict the company’s messaging or might only occur in an unforeseen negative development. - -### Step 6: Account for Uncertainty and Variance - -Given that: -1. Meta has already gone through significant restructuring and layoffs. -2. The short window between today's date and October 1, 2024. -3. TechCrunch would need to report on any new layoffs for the resolution to be ""Yes."" -4. The uncertainty regarding macroeconomic trends that could trigger another round of layoffs at Meta. - -There is a small, yet non-insignificant, chance new layoffs might still occur due to sudden adverse economic conditions or an ongoing drive for efficiency. - -### Step 7: Estimate the Probability Distribution - -Taking all these factors into account, the probability of TechCrunch reporting new layoffs at Meta, Facebook, or Instagram before October 1, 2024, can be seen as modest but not entirely insignificant. The relatively short timeframe and the likelihood that major layoffs would generally be planned well in advance suggests lower probability. - -### Final Probability Estimate: - -**Probability: 25%** - -Given the balance of evidence, the forecast leans towards TechCrunch not reporting new layoffs, but the possibility remains non-trivial due to economic conditions and Meta's history." -27733,"Will there be a US presidential debate between Donald Trump and Kamala Harris held on September 10, 2024?","PERPLEXITY - -**US Presidential Debate between Donald Trump and Kamala Harris on September 10, 2024** - -The first presidential debate between Republican nominee Donald Trump and Democratic challenger Kamala Harris is scheduled for September 10, 2024, and will be hosted by ABC News in Philadelphia. Despite initial doubts expressed by Trump due to concerns over ABC News' perceived bias against Republicans, he confirmed on August 27 that he will participate in the debate. - -**Recent Developments and Controversies** - -- Trump had suggested he might back out of the debate due to what he saw as biased treatment of Senator Tom Cotton on ABC's ""This Week"". -- There has been a dispute over the debate rules, particularly regarding the use of live microphones. The Harris campaign wants both candidates' microphones to be live throughout the debate, while Trump's campaign prefers muted microphones during the opponent's speaking time, as agreed upon in the June CNN debate between Trump and President Joe Biden. -- Trump's campaign has insisted on no alterations to the agreed rules, which include a stand-up format without notes or ""cheat sheets"" and assurances from ABC that the debate will be ""fair and equitable"". - -**Prediction Markets and Betting Odds** - -As of the current date, August 28, 2024, specific prediction market odds on the debate happening as scheduled are not provided in the sources. However, the confirmation from Trump and the scheduled date suggest that the debate is likely to proceed as planned. - -**Expert Opinions** - -- Jason Miller, a spokesman for Trump's campaign, emphasized the importance of sticking to the agreed rules to ensure a fair debate. -- Brian Fallon, a spokesperson for Harris' campaign, argued for live microphones to allow for real-time responses to Trump's statements. - -**Conclusion** - -Given the recent confirmation from Donald Trump and the scheduled date, it appears that the US presidential debate between Trump and Kamala Harris on September 10, 2024, is set to proceed as planned, despite controversies over the debate rules and network bias. - -######### - -GPT - -### Step 1: Understand the Question and Resolution Criteria - -The task is to predict the probability that a formal, live presidential debate between Donald Trump and Kamala Harris will take place on September 10, 2024. The debate, as described, must meet the following criteria: -- It must be a live event held on September 10, 2024 (or partially overlapping September 10, 2024). -- Both Trump and Harris must participate as the Republican and Democratic nominees, respectively. -- It must involve real-time interactions between Trump and Harris, as a formal presidential debate. - -If any of these criteria are not met, the prediction resolves as ""No."" - -### Step 2: Analyze the Background Information - -#### Current Situation -- The debate is currently scheduled for September 10, 2024, and Donald Trump confirmed on August 27 that he will participate, despite concerns about ABC News' perceived bias. -- The Harris campaign remains in favor of the debate. -- There are disputes around debate conditions such as live microphones, but these issues are still under negotiation. - -#### Historical Context -- Trump has a history of proposing and confirming debates but later withdrawing (e.g., a presidential debate scheduled for October 15, 2020, was canceled, as was a primary debate scheduled for March 21, 2016). -- The shifting attitudes of Trump towards debates (expressed skepticism or posturing, followed by either participation or withdrawal) introduce volatility in the likelihood of the event happening. - -### Step 3: Consider the Base Rate - -**Base Rate for Political Debates:** -- During the election cycle, political debates generally occur as scheduled. -- However, Trump’s unpredictability and his prior cancellations of debates warrant consideration. - -**Factors That Could Cause the Debate to Not Occur:** -- **Trump's Withdrawal:** Trump has a demonstrated pattern of withdrawing from debates, particularly when he feels the network or format is unfair. -- **Last-Minute Negotiations/Disputes:** Ongoing disputes over the microphone rules or other conditions could escalate, leading to a potential breakdown in agreements. -- **Unforeseen Events:** Health issues, political developments, or major external events could lead to a postponement or cancellation. - -### Step 4: Weigh the Information -**Factors Supporting the Debate Occurring:** -- The debate is already scheduled, which sets a strong default expectation for it to happen. -- Trump’s recent confirmation via social media, albeit with criticism, indicates a current willingness to participate. -- The Harris campaign's consistent support for the debate suggests it is unlikely to contribute to any cancellation. - -**Factors Opposing the Debate Occurring:** -- Trump’s rhetoric and distrust of ABC News indicate a potential last-minute withdrawal. -- Historical precedent of Trump canceling debates close to the date. - -### Step 5: Calculate Probability Distribution - -Given the background, the base rate, and historical behavior: -- **Trump’s History of Withdrawals** adds uncertainty. Drawing from past behavior, one might estimate a roughly 20-30% probability that Trump cancels or withdraws from the debate. -- **Current Commitment:** Trump has confirmed participation, which supports the likelihood of the debate occurring as scheduled. - -After weighing these factors, let's tentatively place the likelihood of the debate being held as scheduled by examining similar events in past presidential cycles (adjusted for Trump’s past withdrawals): -- **70-80% probability** the debate occurs as planned due to Trump’s recent confirmation. -- **20-30% probability** the debate does not take place due to last-minute withdrawal or another factor. - -### Step 6: Final Prediction - -Considering all the elements and the slight unpredictability inherent in Trump’s decision-making, I would forecast the probability that the debate between Donald Trump and Kamala Harris will be held as scheduled on September 10, 2024, at: - -**Probability: 75%**" -27732,"Will another Chinese military aircraft violate Japanese territorial airspace before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether another Chinese military aircraft will violate Japanese territorial airspace before October 1, 2024, it's essential to analyze recent events and the broader geopolitical tensions between China and Japan. - -### Recent Incidents - -1. **First-Ever Violation**: On August 26, 2024, Japan reported the first-ever violation of its territorial airspace by a Chinese military aircraft. A Chinese Y-9 intelligence-gathering aircraft entered Japanese airspace near the Danjo Islands in the East China Sea for about two minutes. - -2. **Japanese Response**: Japan scrambled fighter jets in response and lodged a strong protest with China, demanding preventive measures. The incident was described as ""completely unacceptable"" by Japanese officials. - -3. **Chinese Reaction**: China stated it had no intention of violating any country's airspace and was verifying the details of the incident. However, China's military assertiveness in the Asia-Pacific region has been a growing concern for the United States and China's neighbors. - -### Broader Context - -- **Territorial Disputes**: The incident highlights the ongoing territorial disputes between Japan and China, particularly over the Senkaku/Diaoyu Islands. These disputes have led to increased military activities and tensions in the region. - -- **Regional Tensions**: The South China Sea is another flashpoint, with China claiming almost the entirety of this strategically important waterway. Recent confrontations, especially between China and the Philippines, have raised concerns about the potential for a broader conflict involving the United States. - -- **Diplomatic Efforts**: Despite these tensions, there are ongoing diplomatic efforts to improve relations. A bipartisan delegation of Japanese lawmakers visited Beijing, and U.S. National Security Adviser Jake Sullivan held talks with senior Chinese officials. - -### Prediction Markets and Expert Opinions - -- **Expert Opinions**: Analysts warn that any incident involving Japan and China could heighten the risk of a broader conflict, especially given Japan's mutual defense agreement with the United States. - -- **Prediction Markets**: As of the current date, there are no specific prediction markets or betting odds available on whether another Chinese military aircraft will violate Japanese airspace before October 1, 2024. However, the recent escalation and ongoing tensions suggest a heightened risk of future incidents. - -### Conclusion - -Given the recent violation of Japanese airspace by a Chinese military aircraft and the ongoing territorial disputes and military assertiveness in the region, there is a significant risk of another incident occurring before October 1, 2024. The diplomatic efforts and international pressure may influence China's actions, but the underlying tensions and China's expanding military operations in the Asia-Pacific suggest a continued risk of airspace violations. - -######### - -GPT - -### Step 1: Understanding the Question and Context - -The question asks for the probability that another Chinese military aircraft will violate Japanese territorial airspace before October 1, 2024, with today’s date being August 28, 2024. The specified event timeframe is slightly more than a month. - -- **Historical Context**: The key fact is that on August 26, 2024, a Chinese military Y-9 aircraft violated Japanese airspace for the first time. This incident occurred near the Danjo Islands, which are close to China and within a disputed area. -- **Geopolitical Tensions**: The event occurs against the backdrop of heightened tensions between China and Japan, primarily due to territorial disputes and other regional security matters. -- **Response and Reactions**: Japan responded diplomatically by protesting, and militarily by scrambling fighter jets. China asserted that it did not intentionally violate airspace and promised to investigate the matter. - -### Step 2: Base Rate Calculation - -Given that this was the **first-ever** confirmed airspace violation, statistical base rates for Chinese military aircraft violating Japanese airspace are almost non-existent. However, this is the start of an observable trend. - -- **Past Precedent**: Given the newness of the incident, the base rate of violations is low, but it should be adjusted upwards slightly because the ""first occurrence"" has set a new precedent in the relationship between both nations’ militaries. - -### Step 3: Assessing the Likelihood of Future Events - -**Factors Increasing Likelihood**: -1. **Recent Incident**: A recent significant event (like the violation on August 26) often increases the likelihood of similar incidents in the immediate future, especially if the underlying tensions remain unresolved. -2. **Ongoing Tensions**: The territorial disputes remain unresolved, increasing the likelihood of either intentional or unintentional territorial breaches. -3. **Military Assertiveness**: China’s growing military assertiveness and increasing the frequency of military operations and exercises in disputed areas like the South China Sea and East China Sea might suggest higher risks of future breaches. -4. **Action-Reaction Dynamics**: Japan’s interception and strong diplomatic protests may not necessarily deter further violations but could increase the chance of repeat provocations or unintentional incidents. - -**Factors Decreasing Likelihood**: -1. **Diplomatic Pressure and Efforts**: Diplomatic efforts to de-escalate tensions might reduce the likelihood of an intentional violation. China may also want to avoid further worsening relations, especially in an international environment where tensions with the U.S. and its allies are already high. -2. **International Scrutiny**: The global attention on this incident could make China more cautious in the short term, avoiding further breaches to prevent escalation. -3. **Self-Interest**: With diplomatic processes ongoing, China might avoid actions that could lead to serious escalations or disrupt delicate negotiations. - -### Step 4: Consideration of Time Frame - -Given that the timeframe is barely over a month (until October 1, 2024), there is limited time for significant changes in the status quo, either for better or worse. - -**Daily/Weekly Variance Consideration**: -- Daily or weekly variances in military maneuvering due to external factors like weather, political events, or crises elsewhere might dilute the probability of another violation. -- However, short-term forecasting should account for the possibility of cyclical or reactive incidents following the recent breach. - -### Step 5: Conclusion and Probability Determination - -Considering all factors—low statistical base rate, heightened but manageably predictable tensions, international pressure, and the limited time window—the probability of another violation occurring by October 1, 2024, is non-trivial but not overwhelmingly high, given the combination of escalation risks and countervailing diplomatic measures. - -Thus, weighing the probability distribution and uncertainty: -- The likelihood is significant yet not asymmetric enough to approach high-certainty levels. - -**Final Probability Estimate**: Based on the analysis above, my estimation for the event occurring is: - -**Probability: 40%**" -27723,"Will ""Feelslikeimfallinginlove"" by Coldplay win the award for Best Rock at the 2024 MTV VMAs?","PERPLEXITY - -To provide context for the query about whether ""Feelslikeimfallinginlove"" by Coldplay will win the award for Best Rock at the 2024 MTV VMAs, let's summarize the relevant information from the sources provided. - -### Nominees for Best Rock -- The nominees for the Best Rock category at the 2024 MTV VMAs include: - - Bon Jovi — “Legendary” - - Coldplay — “feelslikeimfallinginlove” - - Green Day — “Dilemma” - - Kings of Leon — “Mustang” - - Lenny Kravitz — “Human” - - U2 — “Atomic City” - -### Voting and Event Details -- Fans can vote for their favorites in 15 categories on MTV.com until August 30, with voting for Best New Artist extending until September 10. -- The VMAs will be broadcast live on MTV on Tuesday, September 10, from the UBS Arena in Elmont, New York. - -### Expert Opinions and Public Reaction -- There is a general sentiment among rock fans that the nominees for Best Rock are ""ridiculously weak"" and that MTV is ""completely out of touch"" with the current rock scene. -- Some fans have expressed disappointment with the list, noting that it feels like a throwback to the early 2000s rather than a representation of contemporary rock music. - -### Prediction Markets and Betting Odds -- Unfortunately, the provided sources do not include specific prediction markets or betting odds for the Best Rock category at the 2024 MTV VMAs. - -### Conclusion -While there is no direct information on prediction markets or betting odds for Coldplay winning the Best Rock award, the general sentiment among rock fans suggests dissatisfaction with the nominees. This dissatisfaction could potentially influence voting, but without specific odds or predictions, it's challenging to determine the likelihood of Coldplay winning. - -######### - -GPT - -### Step 1: Establishing the Nature of the Question - “Other Binary Outcome Event” -This question asks whether ""Feelslikeimfallinginlove"" by Coldplay will win the award for Best Rock at the 2024 MTV VMAs, which clearly falls into the ""Other Binary Outcome Event"" category. The event is singular and binary—either Coldplay wins, or they don’t. - -### Step 2: Establishing the Base Rate -Let’s consider the historical precedent (base rate) of Coldplay's success at music award shows like the MTV VMAs. Coldplay has been a successful and popular band for over two decades. They have won multiple awards at the MTV VMAs in the past. However, the ""Best Rock"" category might be more competitive depending on the cultural and musical context of the times. - -### Step 3: Assessing the Competitive Landscape -Coldplay is competing against several notable acts: -- **Bon Jovi — “Legendary”** -- **Green Day — “Dilemma”** -- **Kings of Leon — “Mustang”** -- **Lenny Kravitz — “Human”** -- **U2 — “Atomic City”** - -Each of these artists has considerable name recognition, and like Coldplay, they have a long-standing presence in rock music. However, it's essential to recognize that Coldplay has more crossover appeal and recent mainstream success compared to some of the other artists, who might be seen as aging rock acts with less relevance to today's audiences. - -### Step 4: Evaluating Current Sentiment -According to the research assistant, there's a general sentiment that the nominees for the Best Rock category are ""ridiculously weak"" and out of touch with contemporary rock music. If super fans feel disenchanted with the options, this could lead to a more randomized voting pattern where no one act stands out as a clear winner. Coldplay, with their bigger and more mainstream fanbase, might benefit from this attitude, as they can draw votes from a broader audience compared to some of the more niche or older acts. - -### Step 5: Time and Event Dynamics -There isn’t much time between now and when the event takes place (less than two weeks until the VMAs). Therefore, changes in public sentiment or major disruptions, like a sudden surge in popularity for one of the other nominees, are unlikely. - -### Step 6: Preparing a Probability Distribution -Let’s assign base probabilities, adjusting for these factors: - -- **Coldplay:** Given their mainstream appeal and previous success at award shows, Coldplay can be considered a strong contender. However, an environment of voter dissatisfaction might skew this slightly. -- **Green Day:** While iconic, recent output suggests their current relevance isn't as strong as in prior decades. -- **Kings of Leon:** Often seen as a second-tier act in comparison to Coldplay and U2. Still relevant but not at a peak. -- **U2:** Like Coldplay, U2 has massive name recognition and past success. Aging, but still formidable. -- **Others:** Bon Jovi and Lenny Kravitz are big names, but there’s not the same level of discussion or relevance around them. - -### Step 7: Consider Base Rate Adjustments and Assigning Probabilities -Given the strength of Coldplay’s brand, their recent success, and the lack of a clearly dominant alternative rock act, I would estimate the following rough probabilities for the possible outcomes: - -- **Coldplay:** ~40% -- **U2:** ~25% -- **Green Day:** ~15% -- **Kings of Leon:** ~10% -- **Bon Jovi:** ~5% -- **Lenny Kravitz:** ~5% - -### Step 8: Final Probability Estimate -Given the considerations above, Coldplay’s chances appear the most favorable, though not overwhelmingly so. - -**Final Answer: Probability: 40%**" -27722,"Before October 1, 2024, will Tech Crunch report new layoffs at Nvidia?","PERPLEXITY - -To address the question of whether TechCrunch will report new layoffs at Nvidia before October 1, 2024, we need to examine the current trends in tech layoffs and any specific information related to Nvidia. - -1. **Tech Layoff Trends in 2024:** - - The tech industry has seen significant layoffs in 2024, with over 60,000 job cuts across 254 companies as of August 15, 2024. - - Major tech companies such as Tesla, Amazon, Google, TikTok, Snap, and Microsoft have conducted sizable layoffs in the first months of 2024. - - The layoffs are part of a broader trend of companies adjusting to economic uncertainties and shifting focus towards areas like artificial intelligence (AI) and automation. - -2. **Nvidia's Current Situation:** - - There is no direct information in the provided sources about Nvidia planning or announcing layoffs. - - Nvidia has been a leader in AI, which has transformed it into one of the world's most valuable companies. - - Cisco's CEO, Chuck Robbins, highlighted Cisco's close relationship with Nvidia as a positive factor for Cisco's future in AI adoption. - -3. **Prediction Markets and Expert Opinions:** - - No specific prediction markets or expert opinions directly addressing Nvidia layoffs are mentioned in the provided sources. - -Given the information available, there is no direct indication that Nvidia plans to announce layoffs or that TechCrunch will report such layoffs before October 1, 2024. Nvidia's strong position in AI and its positive outlook based on partnerships (like the one with Cisco) suggest stability. However, the tech industry's overall trend of layoffs due to economic uncertainties and shifts towards AI and automation could potentially impact any company, including Nvidia. Without more specific information or recent updates directly related to Nvidia's employment plans, it's challenging to make a definitive statement. - -######### - -GPT - -### **Step 1: Understand the Question and Context** -The question asks whether Nvidia will experience layoffs that are reported by TechCrunch before October 1, 2024. The key details include: -- **Timeframe:** From now (August 28, 2024) to October 1, 2024. -- **Source:** TechCrunch's ""comprehensive list of 2024 tech layoffs."" -- **Nvidia's History:** Historically, Nvidia has had few layoffs and its CEO, Jensen Huang, has publicly expressed an aversion to layoffs. -- **Industry Trends:** The broader tech industry has seen significant layoffs due to economic uncertainties, but Nvidia is heavily involved in AI and has a strong market position. - -### **Step 2: Assess the Background Information** -1. **Nvidia's Standing:** Nvidia is a leading player in AI, which is currently a high-growth sector. Companies leading in AI are generally performing better than others in the tech sector facing economic headwinds. Nvidia’s financial health appears strong, reinforced by partnerships with companies like Cisco. - -2. **Industry Layoff Trends:** The tech industry has seen widespread layoffs in 2024, but it's important to recognize that Nvidia has specifics that may isolate it from this broader trend—namely its leadership in AI and less frequent history of layoffs. - -3. **CEO's Stance:** Jensen Huang's resistance to layoffs is a significant data point. It suggests that unless Nvidia faces severe financial stress (which there is currently no specific evidence for), layoffs may be less likely. - -4. **Current Situation:** There are no direct indications or rumors of Nvidia planning layoffs soon. The absence of such discussions further decreases the likelihood of a layoff event in the very near term. - -### **Step 3: Consider the Time Frame** -There are only about 33 days between now and October 1, 2024. Given that Nvidia has not already announced layoffs and there is no current pressure pushing towards such an announcement, the window is relatively narrow for a new development to emerge and be reported. - -### **Step 4: Evaluate the Base Rate and Adjust for Context** -- **Base Rate:** Analyze the frequency of Nvidia's layoffs historically. They have done layoffs before, but not frequently, and not in 2024 so far. This lends to a lower base rate of expectation for Nvidia conducting layoffs. -- **Industry Pressures:** While tech layoffs are high, Nvidia's specific market (AI) remains buoyant. Nvidia’s past behavior suggests a lower inclination to respond to industry trends by layoffs, unlike companies more broadly. - -### **Step 5: Account for Potential Variance** -- **Economic Shocks:** Barring any unforeseen economic events severely impacting Nvidia's bottom line in the next month, it is unlikely that Nvidia would suddenly perform layoffs. -- **Company Announcements:** The chance of an unanticipated Nvidia-specific announcement leading to layoffs is low in the immediate future given the lack of precursors. - -### **Step 6: Assign Probability** -Given the combination of Nvidia's strong market position, its CEO's aversion to layoffs, the absence of current pressure leading towards layoffs, and the relatively short time frame available for this to materialize, the likelihood of a layoff being reported by TechCrunch before October 1, 2024, seems low. - -Taking all this into account: - -**Probability: 10%** - -This suggests there is some non-zero chance of an unexpected event or decision occurring, but it is considered unlikely under the current circumstances, especially over the short term remaining before October 1, 2024." -27721,"Will Plug Power file for bankruptcy before October 1, 2024?","PERPLEXITY - -To address the question of whether Plug Power will file for bankruptcy before October 1, 2024, we need to consider several key points from recent news and financial analyses. - -### Financial Health and Business Model - -- **Financial Struggles**: Plug Power has been facing significant financial challenges, including negative gross margins and substantial operating losses. In 2023, the company reported operating cash flows of negative $1.1 billion and consumed $1.8 billion in total cash, including capital spending. -- **Business Model Issues**: The company's current business model, which involves selling hydrogen fuel at a loss, is unsustainable. This has led to a significant cash burn, with the company consuming $1.8 billion in cash in 2023. - -### Efforts to Improve Financial Health - -- **Green Hydrogen Production Facilities**: Plug Power is attempting to turn its business around by building its own green hydrogen production facilities. This could potentially allow the company to sell hydrogen fuel profitably. However, the cost of building these facilities is high, and the projects have faced delays. -- **Financing and Partnerships**: The company has secured some financing, including a $1 billion at-the-market security issuance agreement and nearly $76 million from the Department of Energy (DOE) for building its plants. It is also waiting on a $1.6 billion loan from the DOE. -- **Joint Ventures**: Plug Power has formed a joint venture with Olin Corporation to help fund its plant in Louisiana, which could alleviate some financial pressure. - -### Bankruptcy Concerns - -- **Going Concern Warning**: In November 2023, Plug Power issued a ""going concern"" warning, indicating that it might not have enough cash to fund its operations. However, this warning was removed in its 10-K filing in late February 2024. -- **Analyst Opinions**: Some analysts believe that while Plug Power is unlikely to go bankrupt in the near term due to its recent financing efforts, the company remains risky. Its ability to stabilize sales growth, diversify its customer base, and narrow its losses is crucial for its survival. - -### Recent Developments - -- **Operational Progress**: The company's Georgia plant is operational, and a plant in Tennessee is expected to come online soon. These facilities could help improve Plug Power's fuel margins. -- **Financing Efforts**: The securing of additional financing and the potential receipt of the DOE loan could significantly improve the company's liquidity and prospects for survival. - -### Conclusion - -While Plug Power faces significant financial and operational challenges, its recent efforts to secure financing and build its green hydrogen production facilities suggest that it may avoid bankruptcy before October 1, 2024. However, the company's long-term viability depends on its ability to execute its business plan, reduce its cash burn, and achieve profitability. - -######### - -GPT - -### Step 1: Clarify the Nature of the Event - -The question at hand is whether Plug Power will file for bankruptcy before October 1, 2024. This is a **binary outcome event** where the outcome can either be ""Yes"" (bankruptcy filing) or ""No"" (no bankruptcy filing by that date). - -### Step 2: Identify the Time Frame - -Today's date is August 28, 2024, which leaves approximately 34 days until October 1, 2024. This is a relatively short time frame, which limits the range of drastic changes that could happen between now and the resolution date. - -### Step 3: Evaluate the Importance and Credibility of Background Information - -#### Financial Health and Business Model - -- **Financial Struggles**: The company has had negative gross margins and significant operating losses. The stated cash burn of $1.8 billion in 2023 is a strong indicator of financial stress. -- **Going Concern Warnings**: The ""going concern"" warning was the most critical signal of potential bankruptcy. While the warning was removed in February 2024, the fact that it was issued at all is a red flag. -- **Recent Financing**: The company has secured substantial financing, which mitigates immediate liquidity concerns. However, reliance on external funding can be risky, especially if the underlying business remains unprofitable. - -#### Efforts to Improve Financial Health - -- **Green Hydrogen Production Facilities**: Building out these facilities to improve profitability is a longer-term strategy that may not yield immediate benefits. The operational progress at Georgia and the imminent opening in Tennessee are positives, but their short-term financial impact is uncertain. -- **Financing and Partnerships**: These are crucial for short-term survival. The $1 billion at-the-market offering, DOE loans, and joint ventures indicate significant efforts to stabilize operations. - -#### Bankruptcy Concerns - -- **Previous 'Going Concern' Warning**: This is a major red flag historically linked with eventual bankruptcy for some companies, though the exact timeline varies. -- **Operational Progress**: The commissioning of new plants shows forward momentum, reducing the immediate risk of bankruptcy. -- **Analyst Consensus**: Mixed, but some analysts suggest that bankruptcy is not likely in the near term, though the company remains high-risk. - -### Step 4: Base Rates and Comparable Scenarios - -- **Historical Base Rates**: For companies that issue a ""going concern"" warning, historical data indicates a significant percentage (around 30-50%) may go bankrupt within 18 months. However, the timeline for bankruptcy varies widely. -- **Recent Positive Adjustments**: The actions taken by Plug Power (e.g., new financing, operational progress) reduce the immediate probability of filing for bankruptcy. - -### Step 5: Consider the Default Scenario - -If nothing significant changes—in other words, if Plug Power's operations continue on the current trajectory—the likelihood of filing for bankruptcy in the next 34 days should be heavily influenced by recent financing efforts and operational progress. - -### Step 6: Statistical and Probabilistic Analysis - -Given that: -- The recent DOE loan and financings have likely extended the company's runway, potentially by several months or more. -- The operational improvements might help stabilize or even improve profitability, lowering short-term liquidity risks. -- The time period left until October 1, 2024, is too short for a dramatic financial collapse to necessitate immediate bankruptcy if we assume no catastrophic event occurs. - -The most tangible risk to a bankruptcy filing within this time frame would be if the financing solutions were insufficient to cover immediate obligations or if there were major unforeseen expenses or revenue shortfalls. However, given the data, it appears unlikely that such a scenario would materialize within the next 34 days. - -### Final Probability Estimate - -Balancing the substantial risks with the time frame and recent mitigations, I estimate that the probability of Plug Power filing for bankruptcy before October 1, 2024, is relatively low—but not negligible. - -#### Probability: **15%**" -27720,"Will Jensen Huang be in the top 10 of the Forbes Real-Time Billionaires List on September 30, 2024?","PERPLEXITY - -To answer whether Jensen Huang will be in the top 10 of the Forbes Real-Time Billionaires List on September 30, 2024, we need to consider his current ranking and net worth, as well as the dynamics of the list. - -1. **Current Ranking and Net Worth**: As of August 25, 2024, Jensen Huang is ranked with a net worth of $112 billion. This places him among the top billionaires globally. - -2. **Recent Trends**: Huang's net worth has seen significant growth, especially in 2024. His net worth increased from $21.1 billion in 2023 to $118.7 billion as of June 18, 2024, largely due to NVIDIA's success in the AI boom. This rapid increase indicates the potential for further growth, especially if NVIDIA continues to capitalize on AI technology advancements. - -3. **Forbes Real-Time Billionaires List Dynamics**: The list is highly dynamic, with rankings changing frequently due to market fluctuations. The top 10 positions are particularly competitive, with billionaires like Elon Musk, Jeff Bezos, and Bernard Arnault often vying for the top spots. - -4. **Market and Economic Factors**: The global economy and stock markets play a crucial role in determining the net worth of billionaires. Factors such as inflation, political unrest, and technological advancements can significantly impact the rankings. - -5. **Prediction Markets and Expert Opinions**: While specific predictions about Jensen Huang's ranking on September 30, 2024, are not available, experts note the increasing importance of AI and technology in driving wealth creation among billionaires. NVIDIA's strong position in the AI market could continue to boost Huang's net worth. - -Given these considerations, while Jensen Huang's current trajectory suggests he could remain among the top billionaires, the volatility of the market and the competitive nature of the top 10 rankings mean his position on September 30, 2024, is uncertain. Continuous monitoring of his net worth and market trends would be necessary to make a more informed assessment closer to the date. - -######### - -GPT - -### Step 1: Understand the Question & Resolution Criteria -The question asks whether Jensen Huang will be in the top 10 of the Forbes Real-Time Billionaires List on September 30, 2024. For this question to resolve as **Yes**, Huang must be ranked 10th or higher (not in a tie for 10th, but a tie for 9th or better is acceptable). Otherwise, it resolves as **No**. - -### Step 2: Establish the Current Situation -- **Current Net Worth**: As of August 27, 2024, Huang’s net worth is $112 billion. -- **Benchmark for 10th Place**: As of the same date, the 10th-ranked individual (Amancio Ortega) has a net worth of $123 billion. - -Jensen Huang is approximately $11 billion short of the 10th spot. - -### Step 3: Assess the Drivers -1. **NVIDIA's Impact**: - - **Past Performance**: NVIDIA has been a massive success in the tech and AI sectors, resulting in a significant and relatively fast increase in Huang's net worth. - - **Market Outlook**: The AI boom is expected to continue, potentially benefiting NVIDIA. Continued tech sector growth could further increase Huang’s wealth. - -2. **Market and Economic Volatility**: - - The global economic environment is uncertain and influenced by various factors like inflation, interest rates, geopolitical tensions, and technological changes. - - Stock market volatility could work both ways—driving NVIDIA's stock up or down, which would drastically affect Huang’s wealth. - -3. **Competition**: - - The competition for the top 10 on the Forbes list is fierce, involving market leaders from varied sectors such as technology, retail, and finance. - - The billionaire rankings can see significant, rapid rearrangements due to fluctuations in stock prices or major business decisions. - -### Step 4: Consider Historical Patterns -In recent history, the tech sector has been a consistent driver for major changes in the billionaire rankings. Entrepreneurs and CEOs in this sector can rapidly accumulate wealth, especially with the rapid technological advancements and market capitalization increases. - -However, with competition so tight around the top 10 range, even slight corrections or slower performance for a company like NVIDIA could see Jensen Huang fall short. - -### Step 5: Assess the Time Frame and Dynamics -There is roughly a month between today, August 28, 2024, and the potential September 30, 2024, resolution date: - -- **Market Sensitivity**: One month isn't much time for drastic changes unless there’s a significant event (e.g., a major product launch, geopolitical event, or stock market correction). Consider both positive events (boosting NVIDIA’s share price) and negative ones (resulting in a loss of value). - -- **Top 10 Territory**: For every individual in the top 10, fluctuations in billions are common, which means a few billion dollars swing is possible in either direction over a month. - -### Step 6: Probabilistic Analysis -Given the current status: -- **Huang’s Need**: Huang needs his net worth to increase by >10% over the next month or for some of those currently in the top 10 to see a decline. - -- **Likely Scenarios**: Market trends heavily favoring NVIDIA could indeed propel Huang into the top 10. Conversely, any market downturns or underperformance by NVIDIA could keep him in the same place or even cause him to slide further. - -### Step 7: Synthesize Everything into a Probability -Considering the closeness of recent rankings, the high volatility of billionaire net worths in the tech space, and the competitive nature of the top 10: - -- **Base Rate Historical Precedence**: Similar fluctuations in billionaires’ net worths have moved individuals up and down the rankings over a short period slightly less than half the time in similar situations. - -- **Default Stance**: With Huang already close to the top 10 but considering the difficulty of catching up to $123B, the base rate suggests that ""No"" is more likely. - -- **Overall Judgment**: Given the single month length and other considerations, a conservative estimate leans towards it being somewhat more probable that he will not break into the top 10 by September 30, 2024, but recognizing the upside potential as noteworthy. - -### Final Answer: -Probability: **No** at **70%** and **Yes** at **30%**. - -Thus, the final probabilistic forecast for the resolution being ""Yes"" is: - -**Probability: 30%**" -27918,Will Starliner Calypso undock from ISS before September 16?,"PERPLEXITY - -To answer whether Starliner Calypso will undock from the International Space Station (ISS) before September 16, we need to look at the latest information regarding its scheduled departure. - -According to NASA's announcements and updates: - -- **NASA's Decision**: On August 24, NASA decided that the Starliner would return to Earth without a crew due to uncertainties and risks associated with the performance of its reaction control system thrusters. -- **Scheduled Undocking**: The spacecraft is scheduled to autonomously undock from the ISS on September 6, 2024, at approximately 6:04 p.m. EDT, to begin its journey back to Earth. -- **Landing**: The targeted landing time is around 12:03 a.m. on September 7, 2024, at White Sands Space Harbor in New Mexico. - -Given this information, Starliner Calypso is indeed scheduled to undock from the ISS before September 16, specifically on September 6. - -######### - -GPT - -### Step 1: Understand the Question and Resolution Criteria -The task is to predict whether the Starliner Calypso will undock from the ISS before September 16, 2024. The resolution criteria state that if credible sources report that the Starliner has undocked before September 16, the situation resolves as a ""Yes"". - -### Step 2: Collect Relevant Information -- **Scheduled Undocking Date**: According to recent information, the Starliner Calypso is scheduled to undock autonomously from the ISS on September 6, 2024. This would involve the spacecraft starting its return journey after what was originally intended to be a short stay. -- **Duration Factors**: The time between today's date (September 4, 2024) and the undocking window is very short (just two days). This limits the possible changes and disruptions to this event. -- **Technical Confidence**: Boeing and NASA have expressed confidence that the spacecraft can execute the autonomous deorbit maneuver, despite earlier issues that made them reconsider whether it was safe to return the crew via this spacecraft. -- **No Past Catastrophes Before Undocking**: Historically, spacecraft undocking failures due to last-minute technical issues are uncommon, but not unprecedented. However, in most cases, delays due to operational or technical reasons might be the major consideration. - -### Step 3: Analyze Potential Influences -- **Technical Issues**: Although some uncertainties were related to Starliner’s thrusters, those concerned returning the crew, not the robotic return maneuver. Spacecraft like Starliner are designed with a margin of safety and multiple redundancies, plus they have now had extra time to prepare since the crew’s return was postponed. -- **Operational Considerations**: NASA, Boeing, and SpaceX all have interests in ensuring the return goes smoothly. NASA needs to assure the public of reliability, while Boeing needs to salvage its reputation after the publicized challenges. -- **External Factors**: No environmental or political issues appear to be in effect that could impede this planned operation in the immediate upcoming days. - -### Step 4: Assess Historical Precedent (Base Rates) -Looking at similar autonomous return operations with other vehicles like SpaceX Dragon, delays occur sometimes, but most missions continue as planned. Both NASA and Boeing have comparable operational standards historically, with most undocking events proceeding either on time or with a minor delay within a day or two. - -### Step 5: Probability of Delays or Other Outcomes -Given: -- **Very Short Time Window**: With only two days left before the scheduled undocking, the chances of a significant delay seem low. -- **Default Outcome**: If nothing changes, the scheduled undocking on September 6 will take place as planned. -- **Experience of the Operating Teams**: The teams are highly experienced, reducing the variability in performance. - -### Step 6: Final Probability Assessment -Given all the above factors, technical issues and other disruptors appear manageable. The likelihood that the undocking will occur as scheduled based on current evidence and historical moderators is high. - -### Final Answer: -“Probability: 95%” that Starliner Calypso will undock from ISS before September 16. - -This accounts for a small 5% uncertainty due to potential unforeseen last-minute technical or operational issues that might cause minor delays. However, with abundant preparation time and ample redundancy systems in place, these are unlikely to affect the September 6 scheduled undocking." -27918,Will Starliner Calypso undock from ISS before September 16?,"PERPLEXITY - -To answer whether Starliner Calypso will undock from the International Space Station (ISS) before September 16, we need to look at the most recent and relevant information provided in the sources. - -- **Source** from August 24, 2024, mentions that Starliner Calypso will undock from the ISS and return to Earth without crew, but it does not provide a specific date for the undocking. -- **Source** from August 14, 2024, indicates that NASA's tentative date for Starliner's undocking from the ISS is September 6, whether the spacecraft is crewed or not. -- **Source** from August 24, 2024, confirms that Starliner will return to Earth in early September without astronauts on board but does not specify the exact date of undocking. -- **Source** from September 4, 2024, provides the most up-to-date information, stating that the empty Starliner space capsule will undock from the ISS no earlier than Friday, September 6. - -Based on the information provided, especially the most recent update from September 4, 2024, it is clear that Starliner Calypso is scheduled to undock from the ISS no earlier than September 6, which is before September 16. Therefore, the answer to the query is yes, Starliner Calypso will undock from the ISS before September 16. - -######### - -GPT - -### Step 1: Understanding the Question and Context -The question asks whether Boeing's Starliner spacecraft (Calypso) will undock from the International Space Station (ISS) before September 16, 2024. Given the context and sources provided, Calypso was supposed to have undocked earlier, but due to technical issues and NASA’s decision to have astronauts return on a SpaceX capsule, the spacecraft is now going back to Earth without crew. - -### Step 2: Initial Assessment of Sources and Information -1. **August 24, 2024 Source**: Confirms that Starliner will return to Earth without crew in early September, but no exact undocking date is specified. - -2. **August 14, 2024 Source**: Mentions a tentative date for undocking around September 6. - -3. **September 4, 2024 Source**: The most recent mention that the Starliner will undock from the ISS no earlier than September 6. - -### Step 3: Analyzing the Probability Based on Trends and Base Rates -- **Base Rate**: Historically, spacecraft undocking from the ISS tend to follow the scheduled dates fairly closely, assuming there are no significant technical issues. However, given the issues that Starliner encountered (leaks, thruster malfunctions), any further delay cannot be ruled out entirely. - -- **Importance of Recent Information**: The most recent information from September 4 positions the undocking for September 6 or later, clearly putting it within the range before September 16. The recency of this information makes it highly reliable, implying that NASA and Boeing are quite certain of the timeframe. - -- **Probable Obstacles**: The malfunctioning thrusters and helium leak are known factors but there is no recent indication these issues would cause another delay serious enough to push the undocking past September 15. However, the fact that the thrusters underperformed introduces some risk of further technical challenges. - -### Step 4: Potential Variability in the Timeline -Given that NASA decided to return the astronauts using a different spacecraft because of Starliner's malfunctioning systems, there is an acknowledgment from NASA and Boeing that Starliner isn’t perfect. NASA’s caution with human life does not necessarily translate into malfunction severity for an uncrewed, automated mission. The timeline up to September 6 gives NASA a cushion that likely will absorb minor delays or recalibrations necessary for an autonomous return. - -### Step 5: Using Historical Delays and Potential for Slippage -Observing how frequently space missions experience slippage in timelines due to technical difficulties, especially when a new or unproven system (like Starliner) is involved, one might expect a slight possibility of delay. But given that September 6 is already a delayed target, the chances appear low for another 10-day pushing. - -### Step 6: Synthesizing All Information and Estimating Probability -- **Default Case**: If nothing were to change (software and hardware function as expected), the Starliner should undock on or around September 6, well before September 16. -- **Risk Factors**: Previous delays and technical malfunctions present a minor risk of pushing the undocking date. However, the absence of any reported show-stoppers makes this risk relatively low. - -### Step 7: Final Probability Estimate -Considering all factors—the reliability of the latest information, the historical adherence to planned undockings, and the minimal risk from prior technical malfunctions—it's reasonable to estimate a high probability that the undocking occurs before September 16. - -**Final Probability Estimate**: **90%** - -This would allow for a small degree of uncertainty related to unexpected malfunctions or other unforeseen technical challenges." -27919,"Will Edmundo González be deprived of liberty before October 1, 2024?","PERPLEXITY - -To address the question of whether Edmundo González will be deprived of liberty before October 1, 2024, we need to consider the current political situation in Venezuela, the international response to the recent presidential election, and the actions taken by the Venezuelan government against opposition figures. - -### Current Situation - -- **Arrest Warrant Issued**: An arrest warrant has been issued for Edmundo González, the opposition candidate who claims to have won the July presidential election. The warrant is based on allegations of ""serious crimes"" related to the opposition's claims of electoral victory. -- **International Condemnation**: The international community, including the United States, European Union, and several Latin American countries, has condemned the arrest warrant. These countries have expressed concerns over the authoritarian escalation in Venezuela and called for the respect of democratic processes. -- **González's Status**: As of the latest reports, González has not sought asylum and remains in hiding. His lawyer, Jose Vicente Haro, confirmed that no asylum request has been made. - -### Relevant Context - -- **Election Dispute**: The July presidential election in Venezuela was highly contested, with the National Electoral Council (CNE) declaring President Nicolas Maduro the winner without releasing detailed results. The opposition claims that González won the election based on their own polling station-level results. -- **Political Repression**: Since the election, there has been a significant crackdown on opposition figures, with numerous arrests and reports of violence. The human rights organization Foro Penal estimates that around 1,581 individuals have been detained since July 28. - -### Expert Opinions and Predictions - -- **International Pressure**: The international community's strong condemnation of the arrest warrant and the ongoing repression in Venezuela indicates a high level of scrutiny on Maduro's government. This pressure might influence the government's actions regarding González. -- **US Actions**: The United States has taken concrete actions, such as seizing a plane used by Maduro, citing sanctions violations. This suggests that the US is willing to take measures to support the opposition and could potentially influence the situation regarding González's liberty. - -### Prediction Markets and Betting Odds - -- **No specific data available**: The provided sources do not include current prediction markets or betting odds on whether Edmundo González will be deprived of liberty before October 1, 2024. - -### Conclusion - -Given the current political climate, international pressure, and the actions of the Venezuelan government, there is a significant risk that Edmundo González could be deprived of liberty. However, the strong international condemnation and the opposition's continued efforts to assert their claims of electoral victory might also influence the government's actions. Without specific prediction market data or betting odds, it is challenging to provide a quantitative assessment of this risk. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. **Identify the Key Factors:** - - **Arrest Warrant Status:** A court in Venezuela has issued an arrest warrant for Edmundo González. The warrant charges him with serious crimes like “usurpation,” conspiracy, and sabotage. - - **González's Current Status:** As of now, he is in hiding and has not sought asylum. - - **Venezuelan Political Climate:** The regime under Nicolás Maduro is known for authoritatively cracking down on opposition figures. This increases the likelihood of attempts to physically detain González. - - **International Dynamics:** The U.S. and other Western nations are exerting pressure, including sanctions. This could potentially deter, but by no means guarantee a restraint on Maduro's actions against González. - - **Age Factor and Venezuelan Law:** Venezuelan law prohibits incarceration of individuals over 70 in prison, mandating house arrest instead. Given González's age of 75, if captured, he’s likely to be placed under some form of house arrest rather than imprisonment. - -#### 2. **Base Rates:** - - **Repression in Venezuela:** Historically, the Maduro regime has taken severe steps against opposition leaders and dissenters, including illegal detentions and unlawful house arrest, especially around election disputes. - - **Opposition Leaders’ Fate:** In the past, opposition leaders who present a credible challenge to Maduro have often been detained. When the international community steps in, it typically results in house arrest or enforced silence rather than outright release. - -#### 3. **Short Term vs. Long Term Considerations:** - - **Time Frame:** We are examining a short window from September 4, 2024, to October 1, 2024 (27 days). The ""default behavior"" in the current climate is aggressive moves by Maduro against adversaries. Thus, while longer time frames might permit greater variance, the short period and immediate pressure increase the probability that the regime will act swiftly. - - **Variability:** Given the short time frame, it is harder for González to maintain hiding without external assistance. Government troops or loyalist groups searching for González are likely to intensify their efforts, especially under the pressure of the existing international scenario and the domestic crackdown. - -#### 4. **International Influence:** - - **Potential Mitigating Factors:** While the U.S. has been explicitly active and has taken actions like plane seizures and sanctions, this is unlikely to have a decisive deterrent effect on a government that has already historically weathered international criticism. - - **Sanctions:** Although the US and other countries may impose further sanctions, the immediate impact on Venezuela’s decisions—within the short period of one month—is probably limited. Maduro’s regime has historically been willing to face international backlash to secure control. - -#### 5. **Potential Outcomes:** - - **Capture Scenario:** Based on the issuing of an arrest warrant, if found, González could likely be subjected to detention or house arrest, qualifying as a deprivation of liberty. - - **Prolonged Hiding:** There is also the possibility that González might remain in hiding, avoiding arrest entirely before October 1, 2024. - -#### 6. **Summing Up Probabilities:** - - **High Risk Capture:** Given the authoritarian nature of the regime, the short time window, and the fact the warrant has already been issued, the likelihood of González being captured or deprived of his liberty is higher than if there was a longer time frame or less immediate pressure. - - **Mitigating Forces vs. Historical Precedent:** International pressure presents some deterrence, but given the documented history, it is not robust enough to entirely counter the government's determination to neutralize González. - -### Final Prediction: -After carefully weighing all factors, including the aggressiveness of the Maduro regime, the timeline, international actions, and the legal framework in Venezuela, I estimate the probability that Edmundo González will be deprived of liberty before October 1, 2024, as follows: - -- **Probability: 75%** - -This reflects a significant risk but allows for some uncertainty in the short timeline and the potential, albeit slim, for international actions to slow or prevent immediate governmental reprisals." -27914,Will The Bear win the 2024 Primetime Emmy Award for Outstanding Comedy Series?,"PERPLEXITY - -To provide context for the question of whether ""The Bear"" will win the 2024 Primetime Emmy Award for Outstanding Comedy Series, we need to examine the current landscape of the Emmy nominations and expert predictions. - -### Nominations and Predictions - -- **Nominations**: ""The Bear"" has broken the record for the most nominations for a comedy series with 23 nods, including Outstanding Comedy Series. This significant number of nominations across various categories indicates strong support from the voting members of the Academy. - -- **Competition**: Other notable nominees in the Outstanding Comedy Series category include ""Abbott Elementary,"" ""Curb Your Enthusiasm,"" ""Hacks,"" ""Only Murders in the Building,"" ""Palm Royale,"" ""Reservation Dogs,"" and ""What We Do in the Shadows"". Each of these series has its own strengths and fan base, but ""The Bear"" stands out with its record-breaking number of nominations. - -### Expert Predictions - -- **Variety Predictions**: According to Variety's predictions, ""The Bear"" is a top contender for the Outstanding Comedy Series award, alongside ""Abbott Elementary,"" ""Hacks,"" ""Curb Your Enthusiasm,"" and ""Only Murders in the Building"". These predictions are based on the current buzz and the evolving landscape of the competition. - -### Recent Updates - -- **Recent Performance**: The third season of ""The Bear"" premiered with 5.4 million views in its first four days, making it the most-viewed season premiere for any scripted series on FX via Hulu. This strong viewership could influence voter perception. - -### Conclusion - -While ""The Bear"" is a strong contender with a record-breaking number of nominations and positive expert predictions, the outcome of the Emmy Awards can be unpredictable. The competition is fierce, with several other highly acclaimed series vying for the award. The final decision will depend on the voting members of the Academy, and the winner will be announced at the 76th Primetime Emmy Awards on September 15, 2024. - -######### - -GPT - -### Step 1: Understand the Event and Its Context - -The question at hand concerns the likelihood of ""The Bear"" winning the 2024 Primetime Emmy Award for Outstanding Comedy Series. This falls under the ""Competition Event"" category as it involves evaluating the potential success of a competitor (a TV show in this case) in a contest (the Emmy Awards). - -### Step 2: Examine the Background Information - -1. **Nominations and Predictions**: - - ""The Bear"" has received 23 nominations, the most ever for a comedy series. This is a strong indicator of broad support from the Academy's voting body. - - Other major contenders include ""Abbott Elementary,"" ""Curb Your Enthusiasm,"" ""Hacks,"" ""Only Murders in the Building,"" ""Palm Royale,"" ""Reservation Dogs,"" and ""What We Do in the Shadows."" These shows all have their own strengths. - - It's worth noting the presence of ""Abbott Elementary"" as a formidable competitor, known for being a critical darling and having strong voter support in previous years. - -2. **Expert Predictions**: - - Variety, a reputable industry source, lists ""The Bear"" as one of the top contenders along with ""Abbott Elementary"" and others. While Variety’s predictions carry weight, it is still a prediction and not a certainty. - -3. **Recent Performance**: - - ""The Bear’s"" third season premiere set a viewership record for FX via Hulu, attracting 5.4 million views. High viewership figures signify strong public interest and relevance, potentially influencing voter decisions. - -### Step 3: Historical Precedents - -1. **Base Rates**: - - Historically, series that dominate the nominations and have a strong critical and commercial presence (viewership) often translate this momentum into winning major awards. Consider shows like ""The Marvelous Mrs. Maisel"" or ""Schitt's Creek,"" which had similar trajectories in the Emmy awards. - - However, it’s important to remember that even with the most nominations, a show is not guaranteed to win the top award. - -2. **Voter Behavior**: - - Emmy voters often value both critical acclaim and cultural impact. Critical acclaim can be deduced from the volume of nominations and the sentiment around the show, while cultural impact can be gauged by viewership and public conversations. - -3. **Competitor Analysis**: - - Shows like ""Abbott Elementary,"" which has also captured critical and popular acclaim, pose significant competition. Similarly, ""Hacks"" has previously received accolades and could split votes in the comedy category. - -### Step 4: Analyze Possible Outcomes - -1. **""The Bear"" Winning**: - - Pros: Record number of nominations, likely signaling strong industry support. - - Cons: Voter splits in a crowded field of strong contenders. - -2. **""The Bear"" Not Winning**: - - Pros: Strong competition from other popular series, particularly ""Abbott Elementary"" or ""Hacks."" - - Cons: Lesser number of voters being swayed by unexpected factors like voter fatigue or shifts in cultural discourse. - -### Step 5: Calculate the Probability - -**Baseline Calculation**: -- If we assume that each of the top five predicted contenders has roughly equal chances, each would have a base probability of 20%. However, given the dominance in nominations and recent positive indicators for ""The Bear,"" it would be reasonable to adjust upwards from this base. - -**Adjusting for Nominations and Momentum**: -- The record-breaking nominations and recent impressive viewership numbers suggest a significant momentum behind ""The Bear."" This could increase its probability by, conservatively, 10-15% over the baseline. - -**Adjusting for Competition**: -- Considering the strength of competitors like ""Abbott Elementary,"" some adjustment downward is warranted. Assuming ""Abbott Elementary"" might have a similar probability, we could lower ""The Bears"" chance slightly, by 5-10%. - -Final adjustments lead to the following: - -### Final Probability Estimation: - -Taking the strong indicators for ""The Bear"" and balancing them with its competition’s strengths: - -**Probability**: **55-65%**. - -Given uncertainties and the Emmy context, a final figure would be: - -**Probability: 60%** - -This places ""The Bear"" as a marginal favorite to win the 2024 Emmy for Outstanding Comedy Series, with a likelihood of 60%." -27912,"Before October 1, 2024, will Tech Crunch report new layoffs at Amazon?","PERPLEXITY - -To address the question of whether TechCrunch will report new layoffs at Amazon before October 1, 2024, we need to consider the current trend of layoffs in the tech industry and specifically at Amazon. - -1. **Recent Layoffs at Amazon**: - - Amazon has already announced several rounds of layoffs in 2024, affecting various divisions including Amazon Web Services (AWS), Prime Video, and Twitch. - - The reasons cited for these layoffs include overhiring during the pandemic, the need to reduce costs, and the shift towards AI and automation. - -2. **Industry-Wide Layoff Trends**: - - The tech industry has seen a significant number of layoffs in 2024, with over 153,000 employees let go as of August 2024, according to Crunchbase. - - Companies like Tesla, Google, Microsoft, and Salesforce have also conducted sizable layoffs, citing economic uncertainty and the adoption of AI as reasons. - -3. **Prediction Markets and Expert Opinions**: - - While there are no specific prediction markets mentioned for this query, the general sentiment in the tech industry suggests that layoffs may continue due to economic pressures and technological advancements. - - Experts and business leaders have expressed concerns about a recession and the impact of AI on job roles, which could lead to further layoffs. - -Given the ongoing trend of layoffs at Amazon and within the tech industry, along with the economic and technological factors driving these decisions, it is plausible that TechCrunch could report new layoffs at Amazon before October 1, 2024. However, without specific prediction market data or more recent updates, this assessment is based on the current layoff trends and industry factors. - -######### - -GPT - -### Step-by-Step Thought Process - -#### 1. **Understanding the Resolution Criteria** -This is a binary outcome event where the uncertainty lies in whether TechCrunch will report a new round of layoffs at Amazon between the date of the question (2024-09-04) and the resolution date (2024-10-01). To resolve as ""Yes,"" Amazon must be explicitly listed on TechCrunch's specific webpage tracking the layoff events for the period in question. - -#### 2. **Evaluating the Background Information** -- **Recent Layoffs at Amazon:** Amazon has already conducted multiple rounds of layoffs in 2024. The drivers for these layoffs include overhiring, cost-cutting, and shifting priorities (like AI and automation). -- **Industry-wide Layoffs:** The tech industry as a whole appears to be in a contraction phase due to economic pressures and technological shifts, leading to significant layoffs across major companies. Over 153,000 tech employees have already been laid off in 2024, and this follows industry trends seen in previous years. -- **TechCrunch’s Layoff Tracker:** TechCrunch is actively tracking layoffs, and if Amazon conducts more layoffs before October 1, 2024, it is very likely that TechCrunch will report it, providing a clear resolution for the question. - -#### 3. **General Knowledge and Base Rates** -- **Base Rates of Layoffs:** Historically, companies that have made layoffs earlier in the year are more likely to make additional layoffs, especially in response to ongoing economic pressures. Amazon's previous layoffs in January and February 2024 suggest a base rate where additional layoffs within the same year are plausible. -- **Trend Analysis:** Tech sector layoffs have been trending and show no significant signs of abating. Even giants like Amazon, which are relatively insulated, have shown themselves to be part of this broader trend. -- **Economic Indicators:** Economic factors like potential recession, inflation, and shifts in technology adoption (notably toward AI, which leads to automation and displacement of jobs) are still in play. These indicators suggest a non-trivial likelihood that additional layoffs could occur within the forecast period. - -#### 4. **Time and Variability Considerations** -- We have just under a month until the resolution date, which is a tight window. Layoff announcements are often clustered, and given Amazon's size and their previous announcement cadence, it's realistic to think they could announce more layoffs in the upcoming weeks. -- However, if Amazon does not have any immediate need to announce layoffs, the short window means that more extended forecasting factors may not have enough time to materialize. - -#### 5. **Adjusting for Uncertainty** -- While the ongoing economic and tech trends are supportive of the possibility of more layoffs at Amazon before October 1, 2024, we must also consider the possibility that Amazon's leadership may opt for strategic restructuring or already planned to halt further layoffs. The company's leadership might focus on rebuilding morale, operational efficiency, or maintaining their human capital without further reductions. - -Taking into account all these factors, there's a reasonable chance that TechCrunch will report new layoffs at Amazon, but it's not exceptionally high given the short timeframe. This is not a certainty because while the broader conditions suggest the possibility, internal corporate decisions might go another way. - -#### 6. **Final Probability Estimate** -Given all the considerations, I estimate the probability that TechCrunch will report new layoffs at Amazon before October 1, 2024, as **approximately 55%**. This reflects a more likely-than-not outcome but still accounts for significant uncertainty given the specific timeframe. - -**Final Answer: Probability: 55%**" -27913,"Will ""Rockstar"" by Lisa win the award for Best K-Pop at the 2024 MTV VMAs?","PERPLEXITY - -To provide context for the question of whether ""Rockstar"" by Lisa will win the award for Best K-Pop at the 2024 MTV VMAs, we need to consider several factors including the nominations, the nature of the songs nominated, and expert opinions. - -### Nominations and Competition - -- **Nominated Songs**: The nominees for the Best K-Pop category include ""Rockstar"" by Lisa, ""Seven"" by Jungkook featuring Latto, ""Smoothie"" by NCT Dream, ""SuperShy"" by NewJeans, ""Lalala"" by Stray Kids, and ""Deja Vu"" by Tomorrow X Together. -- **Competition**: The competition is strong, with all artists being prominent figures in the K-Pop scene. Jungkook's ""Seven"" is particularly notable as it has also been nominated for Best Collaboration, indicating its popularity and critical acclaim. - -### Nature of the Songs - -- **""Rockstar"" by Lisa**: ""Rockstar"" is a hip-hop track with hyperpop beats, entirely in English, which has received positive reviews and has been a commercial success, topping various charts globally. -- **""Seven"" by Jungkook**: ""Seven"" is a collaboration with rapper Latto, which has also gained significant attention and has been nominated in multiple categories. - -### Expert Opinions and Public Reaction - -- **Public Reaction**: There is some controversy among fans regarding the nominations, particularly with ""Rockstar"" and ""Seven"" being included in the Best K-Pop category despite being more pop-oriented and lacking Korean lyrics. -- **Expert Opinions**: While there are no direct expert opinions provided in the sources regarding the likelihood of ""Rockstar"" winning, the fact that it has received multiple nominations (including Best Editing, Best Art Direction, and Best Choreography) suggests that it is well-regarded by critics. - -### Prediction Markets and Betting Odds - -- **Current Predictions**: Unfortunately, the provided sources do not include current betting odds or predictions from prediction markets. However, considering the strong competition and the unique characteristics of ""Rockstar"" (being a solo English-language track), the outcome could be influenced by how voters perceive the song's fit within the K-Pop category. - -### Conclusion - -While ""Rockstar"" by Lisa has shown significant commercial success and has been well-received critically, the competition in the Best K-Pop category is fierce. The controversy surrounding the nominations, particularly the inclusion of songs without Korean lyrics, might also influence voting. Without specific prediction market data or betting odds, it's challenging to provide a definitive forecast. However, ""Rockstar"" certainly has a strong chance given its global impact and multiple nominations. - -######### - -GPT - -### Step 1: Define the Type of Question -This question falls under the **Competition Event** category. The goal is to determine the likelihood that ""Rockstar"" by Lisa will win the Best K-Pop award at the 2024 MTV Video Music Awards. - -### Step 2: Establish a Base Rate -To begin predicting, it's helpful to establish a base rate by examining past winners in the **Best K-Pop** category at the MTV VMAs and noting down any patterns: - -1. **2019**: BTS - ""Boy With Luv"" won. -2. **2020**: BTS - ""On"" won. -3. **2021**: BTS - ""Butter"" won. -4. **2022**: Lisa - ""Lalisa"" won. -5. **2023**: Stray Kids - ""S-Class"" won. - -### Observations: -- BTS has historically dominated this category. -- Lisa’s solo success with ""Lalisa"" in 2022 indicates that she has previous winning potential. -- The 2023 win by Stray Kids suggests that other groups also have significant chances. - -Given this history, BTS (despite not being nominated this year) or any BTS member would traditionally have a strong chance in a typical year. Since Jungkook, as a member of BTS, is nominated, we should consider him a strong competitor this year. However, ""Rockstar"" by Lisa also remains relevant given her past success. - -### Step 3: Analyze the Competition -Given the information provided: -- **""Seven"" by Jungkook** stands out due to its multiple nominations, which typically signals strong support from both the public and critics. -- **Stray Kids (""Lalala"")** and **TXT** are also formidable competitors, with recent successes and loyal fanbases. -- **General Public Perception**: The controversy over non-Korean songs in the K-Pop category might affect some voters' opinions on ""Rockstar"". If they prize traditional elements, this could harm Lisa's chances. - -### Step 4: Consider the Voting Base -MTV VMA awards are influenced by fan votes but also by a panel of industry professionals. Lisa’s global popularity helps her in fan voting, yet the industry panel might favor songs that they believe are better representative of the K-Pop genre, which could be a disadvantage for ""Rockstar"". - -### Step 5: Incorporate Novel Elements -- **Song Language**: ""Rockstar"" is entirely in English, which might be an obstacle given the category is Best K-Pop, although Lisa’s global fame could overcome this. -- **Global Trends**: Lisa's global status, especially in markets like North America and Europe, might pull in more votes than some of her rivals, especially from those who engage less with traditional K-Pop songs. - -### Step 6: Evaluate Recency -Considering the provided information, both ""Seven"" and ""Rockstar"" are recent and relevant. Specifics like ending peak chart positions, streaming counts, and nominations in other categories (which Lisa has) become crucial, as they represent edges in the competition. - -### Step 7: Assign a Probabilistic Estimate -Taking all these factors into account, let's assign probabilities to potential outcomes: -- **High Probability (35%)**: Jungkook's ""Seven"" - given BTS’s history and Jungkook’s current prominence. -- **Moderate Probability (30%)**: Lisa’s ""Rockstar"" - based on her winning last year and current critical acclaim. -- **Moderate Probability (20%)**: Stray Kids ""Lalala"" - strong fanbase and following their 2023 win. -- **Low Probability (15%)**: Other nominees like TXT or NewJeans - lower based on historical trends and less public visibility in this particular event. - -### Final Prediction -Given Jungkook’s strong historical precedent following BTS’s success, overall impact, and the favorable reception to ""Seven"", it may have the edge. However, Lisa remains a strong contender which carries a significant chance. - -**Probability**: 30% (that Lisa’s ""Rockstar"" will win the Best K-Pop category at the 2024 MTV VMAs)" -27911,"Will Catalent, Inc., file its 10-K annual report with the SEC before October 1, 2024?","PERPLEXITY - -To answer whether Catalent, Inc. will file its 10-K annual report with the SEC before October 1, 2024, we need to consider the company's recent announcements and historical filing patterns. - -1. **Recent Announcements and Filings**: - - As of August 30, 2024, Catalent, Inc. filed a Form NT 10-K, which is a notification that the annual report (10-K) will be submitted late. This indicates that the company has already missed the standard deadline for filing its annual report and has formally notified the SEC of the delay. - -2. **Historical Filing Patterns**: - - Catalent, Inc. has a history of filing its annual reports in a timely manner. However, the recent notification of a late filing (Form NT 10-K) suggests a deviation from this pattern for the fiscal year 2024. - -3. **Expert Opinions and Market Predictions**: - - There are no specific expert opinions or market predictions available in the provided sources regarding the exact filing date of the 10-K report. However, the formal notification of a delay (Form NT 10-K) is a significant indicator of the company's current status regarding its annual report. - -4. **Current Status and Predictions**: - - Given the filing of Form NT 10-K on August 30, 2024, it is clear that Catalent, Inc. has officially announced a delay in submitting its annual report. While this does not provide a definitive date for the filing, it suggests that the company is working towards completing the report as soon as possible. - -5. **Relevant Prediction Markets**: - - There are no specific prediction markets or betting odds mentioned in the provided sources that directly address the filing date of Catalent, Inc.'s 10-K report. - -**Conclusion**: -Based on the information available, Catalent, Inc. has notified the SEC that its annual report (10-K) will be filed late, but there is no specific date provided for when this will occur. Given this notification and the lack of further updates, it is uncertain whether the report will be filed before October 1, 2024. The company's recent governance enhancements and operational review initiatives might influence the timing, but without a direct statement from the company, the exact filing date remains speculative. - -######### - -GPT - -### Step 1: Clarify Question Type and Resolution Criteria -This question falls under the category of **Other Binary Outcome Event**, where we need to determine whether Catalent, Inc. will file its 10-K annual report with the SEC before October 1, 2024. The resolution will be determined solely based on whether the 10-K filing appears on the SEC Edgar website before the deadline. - -### Step 2: Establish Base Rates -1. **Base rate for filing after NT 10-K**: - - Historically, companies that file a Form NT 10-K typically submit their delayed annual report within a couple of weeks, though some go beyond that period. - - The SEC grants a 15-day extension upon filing an NT 10-K. Catalent submitted the NT 10-K on August 29, 2024, which directly implies a due date of September 13, 2024, for the company to file within the extended deadline. Failure to meet this extension increases uncertainty as it indicates possible deeper issues with financials or audits. - -2. **Default Pattern**: - - Catalent historically has filed its 10-Ks on time, indicating they normally have the internal capacity and systems in place for timely filings. But this pattern has already been broken by filing NT 10-K. - -### Step 3: Evaluate Recent Developments -1. **Filing of Form NT 10-K**: - - Catalent’s filing of NT 10-K on August 29 indicates they've anticipated a delay, but not necessarily an extreme or prolonged one. The NT 10-K is frequently used by companies experiencing temporary issues that still expect to file promptly after the extension period. - - If the delay is minor, they'd likely file by the end of the extension period (September 13). If issues are more severe—relating to financial restatements, or other complex matters—the delay could extend beyond that. - -2. **No Current Public Statements or Visible Catalysts**: - - No additional public or investor announcements hint at severe underlying issues or specific delays that would suggest catastrophic risk to filing before October 1. Lack of disturbing news may be treated as a neutral or slightly optimistic factor. - -### Step 4: Examine Timeframe and Default State -1. **Time Remaining**: - - With almost a month (26 days) remaining until the October 1 deadline, there appears to be ample time for Catalent to file, assuming they resolve the underlying issue that triggered the NT 10-K. - -2. **Variance**: - - If Catalent hits the September 13 extension deadline, it is highly probable (~95-99%) that the report would be filed within the next week. If not filed by September 13, the probability of filing before October 1 decreases but remains non-zero as companies can still resolve issues and file before the final date. - -### Step 5: Estimate Probability -1. **Scenario 1: Filing During Extension Period** - - This scenario has a high probability. Companies typically strive to file within the given extension to avoid further penalties and negative market reactions. - - A reasonable estimate here might be around 70-80% that Catalent will file before or on September 13. - -2. **Scenario 2: Filing After Extension but Before October 1** - - Should Catalent miss the extension, they still have an additional 17 days before October 1. - - Probability falls, but not dramatically since many companies resolve issues within a few weeks beyond their NT 10-K extension. - -Considering both scenarios and the company's historical reliability, I estimate a total aggregated probability. - -### Conclusion -- Examining the base rates, time remaining, Catalent’s history of compliance, and the typical behavior following an NT 10-K filing, the probability that Catalent will file its 10-K before October 1, 2024, seems relatively high but acknowledges the risk of delays. - -**Final Probability:** 85%" -27969,Will Fargo win the 2024 Primetime Emmy Award for Outstanding Limited or Anthology Series?,"PERPLEXITY - -To provide context for the question of whether Fargo will win the 2024 Primetime Emmy Award for Outstanding Limited or Anthology Series, we need to examine the current nominations, past wins, and expert opinions in the field. - -### Current Nominations - -- **Fargo** (FX) is nominated for Outstanding Limited or Anthology Series. -- Other nominees include ""Baby Reindeer"" (Netflix), ""Lessons in Chemistry"" (Apple TV+), ""Ripley"" (Netflix), and ""True Detective: Night Country"" (HBO). - -### Past Wins and Nominations - -- **Fargo** has a history of Emmy nominations and wins. It has been nominated multiple times in various categories, including Outstanding Limited or Anthology Series, and has won several awards in the past. -- The series has also received nominations and wins in other prestigious awards such as the Golden Globes and Satellite Awards. - -### Expert Opinions and Predictions - -- While specific expert predictions for the 2024 Emmy Awards are not provided in the sources, the nomination of Fargo in multiple categories, including Outstanding Limited or Anthology Series, indicates its strong standing in the competition. -- The series' past success in Emmy Awards and other prestigious ceremonies suggests it has a good chance of winning, but the outcome depends on the voting process and the competition from other nominees. - -### Prediction Markets and Betting Odds - -- Unfortunately, the provided sources do not include current betting odds or prediction market data. However, such information could offer additional insight into the perceived likelihood of Fargo winning the award. - -In summary, while Fargo has a strong history of nominations and wins, and is currently nominated for the 2024 Primetime Emmy Award for Outstanding Limited or Anthology Series, the outcome is uncertain and depends on various factors including the voting process and competition from other nominees. - -######### - -GPT - -### Step 1: Identify the Type of Question -This is a ""Competition Event"" question, specifically asking whether the TV series ""Fargo"" will win the 2024 Primetime Emmy Award for Outstanding Limited or Anthology Series. The competition includes other strong nominees, such as ""Baby Reindeer,"" ""Lessons in Chemistry,"" ""Ripley,"" and ""True Detective: Night Country."" - -### Step 2: Analyze the Base Rates -Historically, awards like the Primetime Emmy Awards are not entirely predictable, but trends do offer some guidance. The Past performance of nominees can be a valuable indicator. - -- **Fargo's Historical Performance:** The show has a strong track record at the Emmys, including multiple nominations and wins. This indicates that the show does appeal to Emmy voters and has resonated well in the past. - -- **Other Contenders’ Strengths:** - - **True Detective: Night Country:** True Detective has had significant acclaim in the past, with previous seasons earning multiple nominations and wins. However, the critical reception of each season varies, and the new season could either bolster or diminish its chances. - - **Baby Reindeer:** This is a Netflix production, and Netflix has been a dominant force in recent Emmy nominations. However, it’s crucial to check whether this particular show has received significant critical buzz or awards during the lead-up to the Emmys. - - **Lessons in Chemistry** and **Ripley:** Apple TV+ and Netflix productions respectively, and while both platforms have shown strength in awards, these shows may not have the established pedigree that Fargo or True Detective brings unless they have had extraordinary critical reception. - -### Step 3: Evaluate Expert Opinions -The research, though lacking specific 2024 predictions, shows Fargo has been a strong contender in previous years. There’s no clear frontrunner according to experts, given the provided data, but Fargo's multiple nominations and past wins suggest it’s a competing favorite. - -### Step 4: Consider Recent Developments and Trends -Consideration of trends in media consumption, viewing habits, and awards ceremonies should not be ignored: - -- **Streaming Platforms**: The increasing dominance of streaming platforms like Netflix and Apple TV+ has sometimes led to surprises in Emmy wins, with traditional television not always dominating as it once did. -- **Critical Reception**: The critical reception of each series up to the award date is key. While I don’t have specific data here, the buzz around these shows in the media, social buzz on platforms, and critic reviews will influence voter behavior. - -Given that there's still time until September 15, there might be changes in the perception of the individual shows. However, at this point, show quality and narrative strength largely outweigh other factors, though voter sentiment can still be variable up until ballots are cast. - -### Step 5: Weigh Factors and Predict -Weighing the available data: -- **Fargo’s strengths**: Strong past performance at Emmys, ongoing appeal, high-quality production, and critical acclaim. -- **Other nominees**: Some strong contenders, especially ""True Detective"" with past success, while Baby Reindeer and others could be dark horses. - -### Step 6: Conclusions and Probability -Considering all of the above information, ""Fargo"" has a reasonably strong chance, but there are formidable competitors that could sway the voters. The presence of other critically acclaimed limited series cannot be discounted. - -Given the historical context, expert opinions, and the competitive nature of the category: - -**Final Prediction:** -- Given the competitors and the trends, ""Fargo"" is a strong contender but not a clear-cut winner. -- The probability ""Fargo"" wins the 2024 Primetime Emmy Award for Outstanding Limited or Anthology Series is approximately: - -**Probability: 35%** - -This reflects a competitive landscape where Fargo stands out as a strong contender but not overwhelmingly so." -27967,"Before October 1, 2024, will Tech Crunch report new layoffs at Tesla?","PERPLEXITY - -To address the question of whether TechCrunch will report new layoffs at Tesla before October 1, 2024, we need to consider the current trends and news regarding layoffs in the tech industry, particularly focusing on Tesla. - -1. **Recent Layoffs at Tesla**: As of the latest information available up to September 5, 2024, Tesla has already conducted significant layoffs in 2024. For instance, in April 2024, Tesla laid off 500 employees, primarily affecting its Supercharging team. Additionally, there was a mention of Tesla cutting ""more than 10%"" of its global workforce, which could impact over 14,000 workers worldwide, as part of its restructuring efforts. - -2. **Industry Trends**: The tech industry has seen a wave of layoffs in 2024, with over 60,000 job cuts across 254 companies as of August 2024. Major companies like Amazon, Google, and Microsoft have also conducted sizable layoffs. The reasons behind these layoffs include economic uncertainty, overhiring during the COVID-19 pandemic, and the rise of AI. - -3. **Prediction Markets and Forecasts**: While specific prediction markets or forecasts regarding new layoffs at Tesla reported by TechCrunch before October 1, 2024, are not provided in the search results, the trend of layoffs in the tech industry, including at Tesla, suggests a possibility of further restructuring. - -4. **Expert Opinions**: The search results do not include direct expert opinions on whether TechCrunch will report new layoffs at Tesla before October 1, 2024. However, the ongoing trend of layoffs in the tech industry and the reasons behind them (economic uncertainty, overhiring, and the rise of AI) suggest that further layoffs could be anticipated. - -In summary, while there is no direct information or expert opinion in the provided search results that specifically addresses whether TechCrunch will report new layoffs at Tesla before October 1, 2024, the current trend of layoffs in the tech industry and at Tesla itself suggests a possibility of further layoffs. - -######### - -GPT - -### Analysis and Chain of Thought - -1. **Understanding the Question and Its Resolution**: - - **Question**: Will TechCrunch report new layoffs at Tesla before October 1, 2024? - - **Resolution Criteria**: This question resolves based on whether Tesla is listed in TechCrunch’s layoff tracker for August 2024 or September 2024. It will resolve as ""Yes"" if Tesla is listed for these months, otherwise, it will resolve as ""No."" - -2. **Context and Background**: - - **Recent Layoffs at Tesla**: In April 2024, Tesla laid off significant portions of its workforce, including 500 employees and a global round of layoffs affecting over 10% of the workforce. This is indicative of financial stress or operational restructuring at Tesla. - - **Industry Context**: In 2024, the tech industry has seen widespread layoffs due to economic uncertainty, overhiring during the pandemic, and the rise of AI. Tesla, being a tech-adjacent company with a heavy reliance on tech and engineering, is part of this broader trend. - - **Timeframe**: The question pertains to whether there will be a report of new layoffs within 25 days from today (September 5, 2024) until October 1, 2024. - -3. **Critical Factors to Consider**: - - **Tesla's Current Position**: - - Tesla has already undergone layoffs earlier in the year, which might reduce the immediate likelihood of more layoffs unless there are further negative shocks or strategic adjustments. - - However, the earlier large-scale layoffs suggest an ongoing or unresolved issue within the company, which might make additional layoffs relatively more probable. - - **Economic and Market Pressures**: - - Any sudden downturn in Tesla’s sales figures, regulatory issues, or market conditions could trigger additional restructuring or cost-cutting measures, including layoffs. - - We are currently in September, a period close to the end of Q3. Companies sometimes announce layoffs towards the end or start of fiscal quarters in response to financial results. - - **Industry-Wide Layoffs**: - - The tech industry, in general, has been facing a rough 2024. With over 60,000 job cuts across 254 companies by August, the environment is rife with layoffs. If Tesla faces similar pressures, it might indicate further job cuts. - -4. **Evaluating Probabilities**: - - **Base Rate Impact**: The recent layoffs at Tesla and the broader context of tech layoffs put the base rate at a non-negligible chance. - - **Recentness**: Given that large layoffs occurred just a few months ago, there's slightly less probability that Tesla will again lay off employees within this short time frame, unless they are actively restructuring or in significant financial trouble. - - **Timeframe Specificity**: There's approximately 25 days remaining, which is a narrow window. Layoff announcements can be somewhat unpredictable, but they often occur around quarterly financial disclosures or major news in the industry. - -5. **Conclusion**: - - **Risk of Further Layoffs**: While the likelihood of Tesla announcing another round of layoffs within such a short period is relatively lower given they’ve already had significant cuts, it's not entirely out of the question given the worst-case scenario in tough economic times, particularly if Q3 results are worse than expected. - - **Trend Analysis**: The broader industry trends and Tesla-specific factors suggest that the probability, though not extremely high, is not negligible either. - -Given all these factors, I estimate the likelihood of new Tesla layoffs being reported by TechCrunch before October 1, 2024, as follows: - -**Probability: 30%** - -This reflects a moderate chance that some new layoffs could occur within this specific time period given Tesla's recent history, noted pressures, and industry trends, but also recognizes that significant layoffs have already been made earlier in the year which could limit the likelihood of another round in the short term." -27966,"Will Greystone Logistics, Inc., file its 10-K annual report with the SEC before October 1, 2024?","PERPLEXITY - -To answer whether Greystone Logistics, Inc. will file its 10-K annual report with the SEC before October 1, 2024, we need to consider the company's filing history and any recent updates that might indicate their intentions or progress towards this deadline. - -1. **Filing History and Deadlines**: - - The company's fiscal year ends on May 31st, and they typically file their annual reports (Form 10-K) within a few months after the fiscal year-end. For instance, the 10-K for the fiscal year ended May 31, 2023, was filed on August 28, 2023. Given this pattern, the 10-K for the fiscal year ended May 31, 2024, would be expected around the same timeframe, which is before October 1, 2024. - -2. **Recent Filings and Updates**: - - A recent filing (Form NT 10-K) on August 30, 2024, indicates that Greystone Logistics has notified the SEC of their intention to file their 10-K annual report but needed additional time. This form is used to notify the SEC that the company cannot file their 10-K within the prescribed time period and are requesting an extension. - -3. **SEC Filings and Compliance**: - - The company has a history of complying with SEC filing requirements, as evidenced by their regular submissions of quarterly reports (10-Q) and annual reports (10-K). This suggests a commitment to transparency and regulatory compliance. - -4. **Market and Financial Performance**: - - The current stock price and market performance of Greystone Logistics (OTCMKTS:GLGI) might influence their filing timeline, but there is no direct indication that this would delay their 10-K filing beyond October 1, 2024. - -Given these points, it is reasonable to expect that Greystone Logistics, Inc. will file its 10-K annual report with the SEC before October 1, 2024, based on their historical filing patterns and recent notifications to the SEC. However, any unforeseen delays or complications could potentially impact this timeline. - -######### - -GPT - -To answer the question about whether Greystone Logistics, Inc., will file its 10-K annual report with the SEC before October 1, 2024, we need to break down the information and consider various factors: - -### **1. Filing History and Default Behavior** -- Greystone Logistics, Inc.'s fiscal year ends on May 31. -- Historically, they have filed their 10-K close to the end of August. For the fiscal year ending May 31, 2023, for example, they filed their 10-K on August 28, 2023. -- Their default behavior is on-time filing, so if nothing out of the ordinary occurs, they would likely file by the end of August or early September. - -### **2. Recent Developments** -- The most recent development is their filing of Form NT 10-K on August 30, 2024. This form notifies the SEC that they were unable to file their 10-K by the deadline and are requesting an extension. The typical extension is for 15 days, which puts the extended deadline around September 15, 2024. Filing the NT 10-K form suggests the company is facing some delay but is still likely to comply within the extended timeframe. -- Importantly, the NT 10-K form reveals that they were recently unable to meet the prescribed deadline, which raises concerns, but also indicates they are working actively on the report. - -### **3. Potential Reasons for Delay** -- Possible reasons for the delay could range from operational issues, auditor complications, or unanticipated technical or financial reporting challenges. Typically, companies facing more severe ongoing difficulties might delay their filings longer, but Greystone previously met filing deadlines consistently. -- The extension allows up to September 15, but the 10-K could be filed later in September if further delays occur. However, they would risk regulatory scrutiny, possible penalties, and loss of investor confidence if they miss the October 1 deadline. - -### **4. Market and Financial Performance** -- There is no major upheaval in market conditions or company performance flagged, which might suggest catastrophic delays. -- However, as Greystone trades on the OTC markets, the scrutiny is typically lower than in exchanges like NASDAQ or NYSE. That could lead to slightly less urgency, though still not likely enough to push beyond October 1, 2024. - -### **5. Similar Historical Events** -- From a base rate perspective, companies that file NT 10-K typically meet the extended deadline. The most common case scenario here would be filing within the 15-day extension, given that this extension is in line with their normal filing cadence. -- If we consider similar small-cap or micro-cap companies that file NT 10-K, the vast majority do eventually file within a short period if there's no systemic problem. - -### **6. Time Remaining (25 days)** -- There are approximately 25 days left before the October 1, 2024 deadline, giving ample time even for additional delays. Such a timeframe historically provides a high likelihood for resolution in favor of timely filing, especially given this is still within their typical filing period. - -### **7. Weighing Information & Uncertainty** -- The fact that an NT 10-K was filed introduces some level of uncertainty and delays, while historically the company has filed on time. -- The company’s history and lack of significant negative developments suggest they are highly likely to meet their extended filing deadline or shortly thereafter. -- The extended deadline being September 15, 2024, gives another 15 days leeway before October 1, so the base rate would likely suggest a high probability of filing by October 1. - -### **Final Probability Assessment** -Taking all of these factors into account, the probability that Greystone Logistics, Inc. will file its 10-K annual report before October 1, 2024, is high, but with some uncertainty due to the recent NT 10-K filing. Given the typical behavior for companies in similar situations, and their history of eventually meeting deadlines, I would estimate the probability to be: - -**Probability: 85%**" -27968,Will Taylor Swift win 5 or more awards at the 2024 MTV Video Music Awards?,"PERPLEXITY - -To provide context for the question of whether Taylor Swift will win 5 or more awards at the 2024 MTV Video Music Awards, here are the key points from the latest news and updates: - -1. **Nominations and Categories**: - - Taylor Swift leads the nominations with 10 nods, including 8 for her music video ""Fortnight"" and nominations for Artist of the Year and Best Pop categories. - - Other notable nominees for Artist of the Year include Ariana Grande, Bad Bunny, Eminem, Sabrina Carpenter, and SZA. - -2. **Competition and Potential Wins**: - - Swift has the opportunity to outdo her performance in 2023, where she won nine awards, and potentially match Peter Gabriel's record of 10 wins in a single night. - - If she secures half of her nominations, Swift could overtake Beyoncé, who currently holds 27 wins, with Swift having 23. - -3. **Event Details**: - - The 2024 MTV VMAs will be hosted by Megan Thee Stallion and will air live on September 11 at 8 p.m. Eastern Time from the UBS Arena on Long Island, New York. - - The event was rescheduled to avoid conflict with the U.S. presidential debate on September 10. - -4. **Performers and Special Awards**: - - Katy Perry will receive the Video Vanguard Award and perform at the event, marking her return to the VMA stage since 2017. - - Other performers include Benson Boone, Sabrina Carpenter, Shawn Mendes, Camila Cabello, GloRilla, Halsey, Lenny Kravitz, LISA, Rauw Alejandro, Anitta, Karol G, LL COOL J, and Halsey. - -5. **Voting Details**: - - Fan voting is open across 15 gender-neutral categories and ends on Friday before the event. - - Voting for the Best New Artist category will remain active throughout the show. - -Given these details, Taylor Swift is in a strong position to win multiple awards, but the outcome depends on the voting results and competition in each category. - -######### - -GPT - -### Step 1: Understanding the Question and Context -The question is asking whether Taylor Swift will win 5 or more awards at the 2024 MTV Video Music Awards (VMAs). Given today's date is September 5, 2024, the awards show is only a few days away on September 11, meaning that the opportunity for major changes in voting outcomes or other significant factors is limited. - -### Step 2: Background Data and Analysis -Let's break down the data we have: - -1. **Nominations:** - - Taylor Swift has received 10 nominations in total. - - The categories she is nominated in include high-profile awards like Video of the Year, Artist of the Year, and Song of the Year. - -2. **Competition:** - - Other prominent artists nominated in key categories include Ariana Grande, Bad Bunny, Eminem, Sabrina Carpenter, and SZA. - - Taylor Swift has a strong historical performance, having won nine awards at the 2023 VMAs. - -3. **Past Performance and Trends:** - - Taylor Swift is currently one of the most awarded artists in the history of the VMAs with 23 awards. - - The likelihood of her winning multiple awards is high given her popularity, fan base, and the momentum she has had over the past few years. - -4. **External Factors:** - - Fan voting, which can strongly influence award outcomes, is open until shortly before the event, meaning last-minute campaigns could sway the results in her favor. - - The event details and presenters don't significantly change the likelihood of her winning or losing. - -### Step 3: Base Rates and Historical Data -- **High-Profile Awards and Multiple Wins:** Historically, artists who are nominated in a large number of categories and are leading in nominations tend to win multiple awards, especially if they are well-established and popular in the current pop culture landscape. - -- **Swift's Performance in 2023:** In 2023, Taylor Swift won nine out of the 11 categories she was nominated in. This high conversion rate aligns with her likelihood of winning at least 5 awards with 10 nominations this year. - -### Step 4: Competition Evaluation -- **Competition from Other Artists:** While there are strong competitors like Ariana Grande and Bad Bunny, Taylor Swift's dominance in the VMAs has been significant. Her fan base is large and particularly engaged, especially in contexts like online voting where swift communication can mobilize large numbers. - -- **Likely Categories She Will Win:** Some categories like Video of the Year and Artist of the Year are almost guaranteed given her historical performance, recent album success, and overall popularity. Achieving 5 awards from 10 nominations is plausible given her historical win rate. - -### Step 5: Statistical Thinking and Final Consideration -- **Default Rate:** Given her historical win rate of over 80% in 2023, and considering some regression to the mean, a conservative estimate of about 50% success rate in each category would still suggest around 5 wins. -- **Confidence Interval:** Given the proximity to the event and the relative strength of the competition, predicting within a 10% margin of error seems reasonable. We should maintain some caution as unexpected outcomes (e.g., fan voting swing) are possible, though unlikely to drastically affect the overall result. - -### Final Judgment -Taking into account her 10 nominations, historical performance, strong fan base, and the level of competition, the probability of Taylor Swift winning 5 or more awards seems to be fairly high. While uncertainties exist, the likelihood that she will convert at least 50% of her nominations into wins is strong. - -**Final Answer:** -**Probability: 85%**" -27982,Will Starliner Calypso disintegrate in the atmosphere by September 30?,"PERPLEXITY - -To provide context for the question of whether Starliner Calypso will disintegrate in the atmosphere by September 30, here are the key points from recent news and updates: - -1. **Current Status and Schedule**: - - **Undocking and Landing**: The Starliner spacecraft is scheduled to autonomously undock from the International Space Station (ISS) on September 6, 2024, at approximately 6:04 p.m. EDT, with a targeted landing at White Sands Space Harbor in New Mexico at 12:03 a.m. on September 7, 2024. - - **Mission Background**: The spacecraft was launched on June 5 and was originally intended for a crewed test flight. However, due to technical issues including helium leaks and thruster malfunctions, the astronauts will return to Earth on a SpaceX Crew Dragon in February 2025 instead. - -2. **Risks and Safety Measures**: - - **Re-entry Risks**: There are risks associated with the spacecraft's re-entry into Earth's atmosphere, including the potential for a too-steep re-entry angle causing excessive friction and heat shield failure, or a too-shallow angle causing the spacecraft to bounce off the atmosphere and remain in orbit. - - **Thruster Redundancy**: NASA officials have emphasized the redundancy built into the spacecraft's systems, including the use of four healthy thrusters for maintaining the correct attitude and trajectory during re-entry, which contributes to a high level of confidence in the mission's success. - -3. **Expert Opinions and Confidence**: - - **NASA and Boeing Confidence**: Despite the risks, NASA and Boeing remain confident in the spacecraft's capabilities, with 27 of the 28 thrusters thoroughly tested and operational. - - **Post-Mission Evaluation**: A detailed evaluation process will be undertaken after the mission to assess the spacecraft's performance and identify areas for improvement, which will influence the scheduling and planning of future Starliner missions. - -4. **Future Implications**: - - **Schedule Adjustments**: The next crewed mission, initially planned for February 2025, has been adjusted to August 2025 to allow for a comprehensive review of the current mission’s data and any necessary modifications. - - **Operational Use**: The Starliner project is significantly over budget and behind schedule, which may impact its operational use and future missions. - -Given the current schedule and the emphasis on safety measures, it is unlikely that Starliner Calypso will disintegrate in the atmosphere by September 30, as the critical re-entry phase is planned to occur on September 6-7, 2024. However, the risks associated with re-entry cannot be entirely ruled out, and the outcome will depend on the successful execution of the planned safety measures. - -######### - -GPT - -### Step 1: Understand the Question and Resolution Criteria - -The task is to forecast whether the Boeing Starliner Calypso will disintegrate in the atmosphere by September 30, 2024. The resolution criteria are clear: - -- **""Yes""**: If the spacecraft burns up in the atmosphere or disintegrates to the point where it cannot be verified to have touched ground or splashed down. -- **""No""**: If it touches ground (at any location, whether planned or not) or splashes down in water. - -### Step 2: Analyze Relevant Background Information - -Let's break down the key factors provided and analyze them for their relevance and impact on the event: - -1. **Mission Plan and Schedule:** - - Starliner is scheduled to autonomously undock from the ISS on September 6, 2024, and is expected to land by September 7, 2024. - - Therefore, September 6-7 is the critical timeframe. - -2. **Historical Trends and Precedent:** - - Spacecraft re-entry and landing attempts are high-stakes but generally well-managed by organizations like NASA. - - The failure rate of spacecraft re-entries historically has been relatively low, particularly in recent history. Apart from accidents involving critical failures (e.g., Columbia in 2003), spacecraft are usually recovered in a mostly intact state either on land or in water. - -3. **Technological Concerns:** - - The Starliner suffered helium leaks and thruster malfunctions, which caused the astronauts to return on a SpaceX craft instead. - - Despite these issues, 27 of its 28 thrusters have been deemed operational after being tested, suggesting that the remaining technical issues are well known, and precautions are in place. - -4. **NASA and Boeing Confidence:** - - NASA and Boeing appear to have a high degree of confidence in the Starliner’s ability to land safely. This is based not only on the redundant systems in place but also on past experience managing similar spacecraft and the extensive precautions taken. - -### Step 3: Risk Assessment and Base Rates - -Given the information available, we can think about the outcome in two scenarios: - -#### Scenario A: Safe Landing or Splashdown (""No"") -- **Base Rates**: The vast majority of planned spacecraft landings succeed. Incidents where a spacecraft disintegrates upon re-entry tend to be rare, with the rate of total failure near re-entry being substantially below 1% given modern technology. Successful landings or splashdown are, by far, the most likely outcomes. -- **NASA and Boeing Protocols**: Both organizations have well-developed and rehearsed procedures for overseeing spacecraft re-entry to ensure it occurs as planned and safely. The presence of redundant systems increases the reliability. - -#### Scenario B: Disintegration/Burnup (""Yes"") -- **Failure Mode Possibility**: While technically possible, several redundant measures are in place to prevent catastrophic failure. The existing thrusters’ performance and system redundancies weigh heavily against the likelihood of total failure. -- **Unknowns & Novel Risks**: The possibility of software or hardware failure during re-entry is not zero but can be considered very low given the extensive checks and simulations that would have been performed. - -### Step 4: Timeframe and Remaining Uncertainty - -The landing is scheduled for September 6-7, 2024, meaning that the outcome will be known within this short timeframe. There is little time or room for major unexpected events to influence the outcome. - -### Step 5: Assign Probability and Conclusion - -Given the high confidence in the Starliner's systems and the structure of the resolution criteria, the probability of a successful landing (or splashdown) far outweighs the likelihood of a disintegration. - -**Final Conclusion:** -Taking into account the rarity of such failures, historical data, confidence from NASA and Boeing, and the upcoming landing schedule, the probability that the Starliner Calypso will disintegrate by September 30, 2024, is very low. - -**Probability: 2%**" -27983,Will Starliner Calypso be stuck in space on September 30?,"PERPLEXITY - -Based on the latest information available, the Starliner Calypso will not be stuck in space on September 30. Here are the key points from recent news sources: - -- **Return Plan**: NASA has announced that the empty Starliner spacecraft will undock from the International Space Station (ISS) no earlier than Friday, September 6, and will return to Earth in a near-six-hour journey, landing at White Sands Space Harbor in New Mexico. -- **Crew Situation**: The astronauts, Butch Wilmore and Suni Williams, who were part of the Starliner's crewed test flight, will remain on the ISS until February 2025 and will return on SpaceX's Crew Dragon with the Crew-9 members. -- **Technical Issues**: The Starliner encountered several technical issues, including helium leaks and thruster problems, which led to the decision to extend the astronauts' stay on the ISS and to use a SpaceX Crew Dragon for their return. - -Given this information, the Starliner Calypso itself will have returned to Earth by September 7, and the crew will remain on the ISS until February 2025, thus not being stuck in space on September 30. - -######### - -GPT - -### Analysis of the Situation - -1. **Recency and Importance of Sources:** - - The most relevant and recent sources include NASA's announcements and credible news outlets like The Economist and Ars Technica. These sources provide the latest updates about Starliner Calypso's condition and NASA's plans. - - The announcement that NASA plans to have Starliner Calypso undock no earlier than September 6 for an autonomous landing is crucial. This action would occur weeks before September 30, providing ample time to assess any further complications. - -2. **Technical Issues and Risks:** - - Starliner Calypso has had several technical issues, including helium leaks and thruster malfunctions. These are serious problems but do not automatically imply that the spacecraft will be ""stuck in space"" longer-term. - - NASA's and Boeing's engineers have likely spent significant time diagnosing these issues since the problems were first identified. As NASA is cautious in its decision-making, it is reasonable to assume that the decision to undock and attempt an autonomous return means they believe the spacecraft has at least a reasonable chance of success. - - However, if the problems persist or worsen when the spacecraft attempts to undock or re-enter, there could be complications that prevent a successful landing, leading to outcomes like burning up in the atmosphere or even remaining in orbit. - -3. **Time Availability and Mission Execution:** - - The six-hour journey from undocking to anticipated landing (September 6) allows ample time before September 30 to attempt other resolutions. If the first landing attempt fails, there could be backup attempts or contingency plans executed within the month. - - NASA has extensive experience with automated and remotely controlled landing procedures, enhancing the probability of success during this timeframe. - -4. **Base Rates and Historical Precedent:** - - Historically, NASA is adept at conducting safe landings, even in challenging circumstances. The only notable exceptions were catastrophic failures (e.g., Columbia). The Artemis program, SpaceX’s Dragon, and previous NASA missions have established a baseline in which most spacecraft re-entries proceed as planned, even after certain failures. - - The Starliner program has had its challenges; however, similar past anomalies were often corrected with design, operational, or contingency adaptations over sufficient time. - -5. **Default Resolution and Unlikely Events:** - - If nothing changes and the default plan is followed, the Starliner should land successfully. The base rate of failures leading to spacecraft being stuck in space, given how NASA approaches risk, is relatively low. - - Unlikely events (e.g., unexplained software or hardware failures impeding landing) must be considered, but given the extensive preparation, these should be evaluated as low probability. - -### Probability Consideration -1. **High Probability Scenario:** - - Given the recent announcements and assuming the plan proceeds without significant new malfunctions or events, the probability remains very high that the Starliner will successfully land, not remain stuck in space, and resolve to “No.” - -2. **Medium Probability Scenario:** - - Moderate issues during the undocking or re-entry process that may cause delays but still result in a landing outcome in time without being stuck on September 30. - -3. **Low Probability Scenario:** - - Significant technical failures, unforeseen complications delaying the mission, or errors leading to the spacecraft being effectively stranded or stuck in space through September 30. - -### Final Output: - -Given the information and weighing the likelihood: -- Historical precedent and NASA’s protocols heavily favor a successful resolution. -- The timing and execution of contingency procedures also allow ample scope for resolution. -- The probabilities of a complete failure resulting in the spacecraft being unable to land well before the September 30 deadline appear very low. - -**Probability: 95%** -That Starliner Calypso will **not** be stuck in space on September 30, 2024." -27983,Will Starliner Calypso be stuck in space on September 30?,"PERPLEXITY - -Based on the latest information available, it appears that Starliner Calypso will not be stuck in space on September 30. Here are the key points from the sources: - -- **Return Schedule**: NASA has planned for the uncrewed Starliner spacecraft to return to Earth as early as next week (from the current date of September 6, 2024), indicating that it will not be stuck in space by September 30. -- **Crew Return**: The astronauts who were on board Starliner, Butch Wilmore and Suni Williams, will remain on the International Space Station (ISS) until February 2025 and will return on a SpaceX Crew Dragon as part of the Crew-9 mission. -- **Current Status**: The Starliner has been undergoing tests and has successfully completed in-orbit tests, including thruster and helium leak assessments. However, due to the issues encountered, NASA has decided to use a SpaceX Crew Dragon for the next crew rotation instead of Starliner. - -Given these points, it is clear that Starliner Calypso itself will return to Earth before September 30, but the astronauts who were on board will stay on the ISS until 2025. - -**Prediction Markets and Expert Opinions**: -- There are no specific prediction markets or betting odds mentioned in the provided sources regarding the return date of Starliner Calypso. -- Expert opinions from NASA and Boeing officials emphasize the commitment to safety and the need for further testing and data collection before certifying Starliner for operational launches. - -**Current Values and Updates**: -- The current status is that Starliner is scheduled to return uncrewed, with the astronauts returning on a SpaceX Crew Dragon in February 2025. -- The latest updates indicate that the return of Starliner is planned for as early as next week, which would be before September 30. - -######### - -GPT - -### Step 1: Understand the Objective and Resolution Criteria - -The question at hand is whether the Boeing Starliner Calypso will be ""stuck in space"" on September 30, 2024. The resolution criteria are clear: - -- **No**: If the spacecraft has touched ground, splashed down in the ocean, or burned up in the atmosphere before September 30. -- **Yes**: If none of the above have occurred, meaning the spacecraft remains in orbit or otherwise unaccounted for by September 30. - -### Step 2: Evaluate the Background Information - -1. **Current Status and Planned Actions:** - - The Starliner is currently docked at the ISS, having experienced issues including a helium leak and thruster malfunctions. - - NASA has decided not to allow astronauts to return to Earth on Starliner, opting instead for SpaceX's Crew Dragon. This demonstrates a cautious approach by NASA. - - Boeing and NASA are planning for the uncrewed return of Starliner to Earth in early September (soon after the current date of September 6, 2024). - -2. **Technical Issues and Confidence:** - - The spacecraft faced several technical challenges earlier, including thruster malfunctions and a helium leak. This may decrease confidence in successful operations. - - The spacecraft has reportedly undergone tests and assessments, with completed in-orbit tests showing some level of operational capability, though issues are present. - -3. **Adaptation and Potential Contingencies:** - - There are multiple planned configurations for the spacecraft's return, including various landing sites and backup options such as splashing down or possible atmospheric re-entry/destruction. - -### Step 3: Base Rate Evaluation - -Looking at historical precedents for spacecraft challenges and resolution: - -- **Other spacecraft (e.g., Crew Dragon, Soyuz)** have encountered issues, but despite initial problems, agencies (like NASA and SpaceX) have typically managed to solve them and return the spacecraft successfully. -- **Test missions and uncrewed flights** often face delays or adjustments, but it is rare for a spacecraft to remain stuck in space indefinitely unless active measures weren't taken. - -Given the history of spacecraft recoveries with major space agencies like NASA, the likelihood of Boeing and NASA failing to retrieve or dispose of Starliner Calypso seems low. - -### Step 4: Time Frame Analysis - -Given that the planned return is in early to mid-September, before the target date of September 30, there will be enough time for Boeing and NASA to perform contingencies if the Starliner does not return at the first attempt—for example: - -- **Early to Mid-September** (most probable window for scheduled return or first attempt). - - If something goes wrong, multiple backup scenarios (e.g., re-entry burn, splashdown) could be initiated within days. - -- **Late September** might see only marginal uncertainty or additional unexpected complications, like delays. - -### Summary of Analyses and Final Prediction - -1. **Base Rate & Historical Precedent**: The high rate of resolving spacecraft issues in a timely manner (even in the presence of significant technical difficulties) implies that recovery or destruction of the spacecraft before September 30 is likely. - -2. **Time Window**: There's a fixed window of over three weeks after the planned return of Starliner for Boeing and NASA to handle any incidents. This favors a successful resolution, even if complications arise. - -3. **Resolution Criteria**: Given that ""No"" includes situations like atmospheric re-entry and splashdown, even if the spacecraft does not land as planned at the primary sites, it doesn't need to ""burn up"" to result in ""No""; the range of outcomes favoring a ""No"" resolution is broad. - -### Conclusion - -Given the available information, historical precedents, and the time window available for corrective actions, the probability that the Starliner Calypso will be stuck in space on September 30 is very low. - -**Probability: 10%**" -27979,Will Starliner Calypso touch ground at any of the planned sites by September 30?,"PERPLEXITY - -To address the question of whether Starliner Calypso will touch ground at any of the planned sites by September 30, here is a concise but detailed rundown of the relevant news: - -1. **Scheduled Return Date**: The Starliner spacecraft is scheduled to undock from the International Space Station (ISS) on September 6, 2024, and return to Earth on the same day, landing at White Sands Space Harbor in New Mexico. - -2. **Landing Criteria**: NASA and Boeing have outlined specific weather and operational criteria that must be met for the landing to proceed. These include wind speeds, ground temperatures, cloud ceilings, and visibility requirements. - -3. **Backup Landing Opportunities**: If weather or operational issues prevent the landing on September 6, there are backup landing opportunities every four days. - -4. **Planned Landing Sites**: The primary landing site is White Sands Space Harbor in New Mexico, with Willcox, Arizona, and Dugway Proving Ground, Utah, serving as alternative sites. Edwards Air Force Base in California is also available as a contingency landing site. - -Given the current schedule and the availability of backup landing opportunities, it is highly likely that Starliner Calypso will touch ground at one of the planned sites by September 30, 2024. - -**Key Points**: -- **Primary Landing Date**: September 6, 2024 -- **Primary Landing Site**: White Sands Space Harbor, New Mexico -- **Backup Landing Opportunities**: Every four days -- **Alternative Landing Sites**: Willcox, Arizona; Dugway Proving Ground, Utah; and Edwards Air Force Base, California - -**Recent Updates**: -- NASA and Boeing have confirmed the undocking and landing schedule for September 6, 2024. -- The decision to return Starliner uncrewed was made due to safety concerns related to thruster performance and helium leaks. -- The astronauts who were aboard Starliner will remain on the ISS and return to Earth in February 2025 aboard a SpaceX Dragon capsule. - -######### - -GPT - -### Step 1: Understanding the Resolution Criteria - -The question at hand is a binary outcome event: whether the Boeing Starliner Calypso will touch ground at any of the planned landing sites by September 30, 2024. The options are: - -- **Yes**: If Starliner Calypso lands at any of the planned locations. -- **No**: If Calypso touches ground at a non-planned location, splashes down in water, or burns up in the atmosphere before September 30, 2024. - -### Step 2: Assessing the Likelihood of a Planned Landing - -#### Base Rate: - -- **Successful Landings**: Starliner has had mixed results in the past, with one major failure during its first orbital flight test due to a clock anomaly that prevented docking with the ISS. The spacecraft has landed successfully before at White Sands during earlier tests. -- **Failures vs. Successes**: Despite issues, Boeing has generally managed to achieve the planned landings for uncrewed Starliner missions. However, the spacecraft’s overall operational reliability is lower when compared to other crewed spacecraft like SpaceX's Dragon Capsule. - -#### Specifics of the Current Mission: - -1. **Technical Failures**: The helium leak and malfunctioning thrusters are troubling signs. While these issues didn't prevent the spacecraft from docking with the ISS or maintaining its orbit, they do raise concerns about the spacecraft's re-entry and landing capabilities. -2. **Autonomous Landing**: The planned return to Earth is to be fully autonomous, which is further complicated by the software’s questionable reliability due to its past problems. -3. **Landing Site Choices**: The availability of multiple landing sites increases the chance of a successful touchdown if everything else works. Boeing’s strategy, having backups every four days, indicates a conservative and calculated approach. - -### Step 3: Backup Plans and External Factors - -1. **Backup Windows**: With backup landing opportunities every four days, there are several chances for Boeing to complete a successful landing. If weather or technical issues prevent a landing on one date, the spacecraft has ample opportunities to succeed later. -2. **External Factors**: Weather at all sites needs to be conducive; hurricanes, flash floods, or extreme weather could cause problems. But given the range of landing sites across the southern United States, it’s unlikely that all options would be unusable unless there's a severe, widespread weather event. -3. **NASA and Boeing's Approach**: NASA's conservative approach to not allowing astronauts to return aboard speaks to safety concerns, but not necessarily to the automatic failure of the unmanned landing. It’s likely that substantial resources and efforts will be deployed to ensure the Starliner's successful return to Earth safely in an ideal situation. - -### Step 4: Possible Negative Outcomes - -1. **Disintegration**: There is some probability that the spacecraft could encounter an issue upon re-entry, resulting in loss of control and disintegration. Given historical data on re-entry failures (most notably for non-Boeing spacecraft), the probability exists but seems relatively low. -2. **Splashdown**: An off-nominal re-entry could lead to the spacecraft attempting a controlled but unscheduled water landing, which doesn't align with the planned criteria mentioned. -3. **Touching Down at Non-Planned Locations**: This would indicate a severe deviation in trajectory or emergency landing scenario. While not impossible, this also shares a relatively low likelihood given the amount of prep, analysis, and the number of available planned sites. - -### Step 5: Comparison with Past Data and Final Assessment - -#### Recent Developments: -- Engagements from NASA and Boeing have primarily been focused on technical corrections and precautionary steps to ensure that the Starliner lands correctly. While risks exist (such as the software anomalies), they do not represent an overwhelming likelihood of complete mission failure given the information on hand. - -#### Statistical Analysis: -- **Historical Landing Success Rate**: Based on Boeing’s commercial track record and NASA’s strict regulations, the probability of landing at one of the planned sites is relatively high. -- **Multiple Attempts**: Backup landing opportunities further reduce the probability of a non-planned outcome. - -### Final Probability Calculation: - -Given the base rates, recent history of landings, multiple available sites, and backup landing attempts: - -- **Planned Site Landing**: Estimating the likelihood of successful autonomous re-entry and landing at a planned site by September 30 appears to be **high** but with a non-negligible risk due to the technical issues faced, possible re-entry anomalies, and typical mission-related risks. - -### Final Answer: - -**Probability: 85%** that Starliner Calypso will touch ground at any of the planned sites by September 30, 2024." -27981,Will Starliner Calypso splashdown in any body of water by September 30?,"PERPLEXITY - -To answer the question of whether Starliner Calypso will splashdown in any body of water by September 30, we need to look at the planned return and landing procedures for the spacecraft. - -1. **Landing Procedure**: Starliner is designed to land on land, not in water. It uses parachutes and airbags to soften the impact on landing. - -2. **Scheduled Return**: The spacecraft is scheduled to undock from the International Space Station (ISS) on September 6 and land at White Sands Space Harbor in New Mexico. The targeted landing time is approximately 12:03 a.m. on September 7. - -Given this information, it is clear that Starliner Calypso is not intended to splashdown in any body of water but is instead planned to land on land at White Sands Space Harbor in New Mexico. Therefore, the answer to the question is no, Starliner Calypso will not splashdown in any body of water by September 30. - -**Recent Updates**: There are no recent updates indicating any changes to the landing plan that would suggest a water splashdown. NASA and Boeing have confirmed the landing site and method, emphasizing the use of parachutes and airbags for a safe landing on land. - -**Expert Opinions**: The information provided by NASA and Boeing officials, such as Steve Stich and Mark Nappi, focuses on the safety and technical aspects of the landing procedure, which is designed for a land-based recovery. - -**Prediction Markets**: There is no specific information available from prediction markets regarding the landing method of Starliner Calypso. However, based on the official plans and technical specifications, it is clear that a land-based landing is planned. - -In summary, all available information confirms that Starliner Calypso is scheduled to land on land at White Sands Space Harbor in New Mexico, not in any body of water, by September 30. - -######### - -GPT - -### Step 1: Establishing the Base Rate - -First, let's establish the base rate for what typically happens to spacecraft in situations similar to the one facing Starliner Calypso. - -- **Planned Landings:** Starliner is designed to touch down on land, specifically using parachutes and airbags for a safe landing. The vast majority of planned landings are successful and occur as intended. Therefore, baseline, the probability of landing on land is quite high. - -- **Emergency Situations:** Emergency situations do occur, but spacecraft are generally designed with multiple redundancies to prevent significant deviations from planned procedures. In the rare cases where things do go awry, splashdowns or destructive re-entries (burn-ups) are more likely than landings at alternate, unplanned locations. Nevertheless, splashdowns are not typically planned for spacecraft like Starliner, as it is explicitly designed for land landings. - -### Step 2: Assessing Current Events and Specifics - -- **Helium Leak and Thruster Issues:** The helium leak and thruster malfunctions on Starliner Calypso represent significant technical issues, which could complicate the landing process. However, given that NASA and Boeing are still planning for a land landing at White Sands, this implies confidence that the identified issues won't result in a splashdown. - -- **Autonomous Landing Capability:** There is some uncertainty about whether the spacecraft's software is capable of a fully autonomous landing under these conditions. This is a potential concern because if the software malfunctions further during re-entry, a water landing might become necessary. However, given that NASA is planning to bring the spacecraft back via its standard land landing process, it suggests that the likelihood of an extreme deviation from that plan is still low. - -### Step 3: Timeline and Buffer for Changes - -- **Landing Timeline:** Starliner is scheduled to undock from the ISS on September 6 and land early on September 7. With only a few days between the undocking and intended landing, there is little time remaining to change any plans or to adapt to unexpected events in the interim. The narrow timeline reduces the window for significant deviations that could lead to a splashdown. - -- **Monitoring Developments:** Still, it's important to note that unexpected critical failures during re-entry could lead to an emergency procedure, including a potential for splashdown or burn-up. However, these remain low-probability scenarios given that the current issues, while serious, do not seem to demand such drastic alternative measures. - -### Step 4: Weighing the Evidence and Assigning Probability - -- **Official Plans and Confidence Level:** The most recent data indicates that NASA and Boeing still expect a land-based recovery. This is strong evidence that the probability of a land-based landing is high. - -- **Contingency Planning:** No explicit statements point to a high likelihood of a splashdown. The designed landing sites are all land-based, and prior experience with similar spacecraft would typically point toward a controlled landing being achieved even under some degree of malfunction. - -- **Uncertainty Buffer:** Given the technical difficulties faced by Starliner, however, it would be unwise to dismiss the potential for emergency procedures completely. We should consider a small but non-zero chance that additional unforeseen issues during re-entry could necessitate a splashdown. - -### Final Decision: - -Given all these factors, the most likely outcome is a land landing. However, due to the technical issues and the possibility, albeit small, of further complications during re-entry, the probability of a splashdown cannot be ruled out entirely. - -**Probability: 5%**. - -This means there's a 95% probability that Starliner Calypso will land on land and a 5% chance that it might splashdown in water by September 30." -27933,Will China's youth unemployment rate be greater than 16.0 and less than or equal to 17.0 for August 2024?,"PERPLEXITY - -To provide context for forecasting China's youth unemployment rate for August 2024, here are the key points from the latest available data and news: - -1. **Current Youth Unemployment Rate**: As of July 2024, China's youth unemployment rate stood at 17.1%, which is the highest since the introduction of the new record-keeping system in December. - -2. **Methodology Change**: The National Bureau of Statistics of China resumed publishing youth unemployment data in January 2024 after a six-month suspension, using a new methodology that excludes university students. This change resulted in a reported youth unemployment rate of 17.1% in July 2024. - -3. **Seasonal Fluctuations**: Youth unemployment in China typically peaks in July due to the large number of graduates entering the job market. - -4. **Overall Unemployment Rate**: The overall urban unemployment rate in China increased to 5.2% in July 2024 from 5% in June 2024, slightly above market forecasts of 5.1%. - -5. **Forecast**: According to Trading Economics, the youth unemployment rate in China is expected to be 15.5% by the end of this quarter, based on global macro models and analysts' expectations. However, this forecast does not specifically address the August 2024 rate. - -Given the seasonal peak in July and the new methodology excluding university students, it's crucial to consider whether the rate will decrease following the peak graduation season. The forecast models suggest a decrease, but the immediate trend from July to August is not explicitly predicted in the available data. - -**Recent Updates and Expert Opinions**: -- The spike in July is attributed to the graduation season and broader employment challenges, including structural issues where businesses struggle to find adequately skilled workers. -- The new methodology's impact on the reported rates should be considered when evaluating the trend from July to August. - -**Prediction Markets and Forecasts**: -- Specific prediction markets for China's youth unemployment rate in August 2024 are not mentioned in the provided sources. However, the general forecast trend suggests a potential decrease following the July peak. - -In summary, while the forecast models suggest a decrease in the youth unemployment rate by the end of the quarter, the immediate trend from July to August 2024 is not clearly indicated in the available data. The rate's behavior post-graduation season and under the new methodology will be critical in determining if it will fall within the specified range of greater than 16.0% and less than or equal to 17.0% for August 2024. - -######### - -GPT - -**Chain of Thought:** - -### Initial Overview and Context - -We are tasked with predicting whether China's youth unemployment rate (ages 16-24, excluding students), as recorded for August 2024, will fall between greater than 16.0% and less than or equal to 17.0%. - -### Relevant Data Points - -- **Current Rate**: As of July 2024, the youth unemployment rate was 17.1%. -- **Seasonal Trends**: Youth unemployment tends to peak in July due to a large number of graduates entering the labor market. This typically results in a spike in the unemployment rate. -- **Recent Forecasts**: Trading Economics has forecasted the youth unemployment rate to potentially decrease to 15.5% by the end of Q3 2024. -- **Economic and Structural Factors**: Economic challenges and the new methodology (excluding students from the unemployment calculation) create a backdrop where these fluctuations can be interpreted carefully. - -### Step-by-Step Reasoning - -1. **Base Rate and Historical Trends**: - - Historically, there is a seasonal spike in youth unemployment rates in July due to the surge in graduates entering the workforce. After July, the rate usually declines as the absorption of graduates into the job market stabilizes. - - Given July 2024's peak of 17.1%, and based on the seasonal norms, it is reasonable to expect a decrease in August 2024. Typically, this decline would fall into a lower bracket than the month before. - -2. **Methodology and Recent Changes**: - - The new methodology excludes students, focusing solely on young people who are actively searching for jobs. This change could result in a slightly steeper decline in unemployment numbers post-July, as students re-entering or continuing education will not be counted, and job absorptions will continue. - -3. **Expert Opinions and Forecasts**: - - Trading Economics projects the youth unemployment rate to be around 15.5% by the end of Q3 2024. This forecast signals a possible sharper decrease than in typical years, perhaps accelerated by seasonal and structural economic adjustments. - - Seeing a forecast trend toward 15.5% by end-Q3 2024, a decline from 17.1% in July should be expected for August, but the confidence interval needs to contemplate the upper range being slightly above 15.5%. - -4. **Considering the Boundaries**: - - The critical boundary for this prediction is between 16.0% and 17.0%. Given the significant seasonal peak in July and historical trends suggesting a downward move, it's plausible that the unemployment rate in August could drop just above 16%, but might also stay within our defined range, rather than plummeting below 16%. This decrease would align with the forecast while considering the inherent variability between monthly unemployment reports. - -### Uncertainty and Possible Variations - -- **Economic or Policy Shocks**: Any sudden economic downturn or government intervention (stimulus, job creation programs) could skew the rate up or down significantly. -- **Reporting Anomalies or Revisions**: We must account for adjustment history within the National Bureau of Statistics and how such revisions might impact final recorded figures. - -### Final Probability Determination -Considering all the data points: -- July rate (17.1%), -- Typical seasonal decline post-graduation, -- Forecasts pointing towards a further reduction by end-Q3 2024, - -It is reasonable to assume that the August 2024 unemployment rate will move down but still remain near the boundary. - -Given that the Youth Unemployment Rate is likely to see a small dip with high probability of staying within the range, the final prediction is that the unemployment rate will be **at the higher end of between 16.0% and 17.0%.** - -### Conclusion - -Final forecast probability: - -**Probability: 65%**" -27932,Will China's youth unemployment rate be less than or equal to 16.0 for August 2024?,"PERPLEXITY - -To provide context for the question of whether China's youth unemployment rate will be less than or equal to 16.0% for August 2024, here are the key points from the latest available data and news: - -1. **Current Youth Unemployment Rate**: As of July 2024, China's youth unemployment rate surged to 17.1%, marking the highest figure since the introduction of the new record-keeping system in December. - -2. **Trend**: Youth unemployment in China has been increasing over the years, with a record high of 21.3% in June 2023. The rate typically peaks in July due to the graduation season. - -3. **Methodology Change**: China resumed the release of youth unemployment data in January 2024 after a six-month suspension, adopting a new statistical methodology that excludes university students. This change resulted in a reported youth unemployment rate of 17.1% in July 2024. - -4. **Overall Unemployment**: The overall urban unemployment rate in China also increased to 5.2% in July from 5% in June 2024. - -5. **Forecasts**: According to Trading Economics, the youth unemployment rate is expected to be 15.50% by the end of this quarter, with long-term projections trending around 16.00% in 2025 and 18.00% in 2026. - -Given these points, the current youth unemployment rate is above 16.0%, and while there are forecasts suggesting a potential decrease by the end of the quarter, the immediate trend and the typical peak in July due to graduation season suggest that the rate for August 2024 might still be influenced by these factors. However, without specific August 2024 data, it's challenging to definitively say whether the rate will be less than or equal to 16.0% for that month. Expert opinions and forecasts suggest a potential for decrease but also highlight ongoing structural employment challenges. - -######### - -GPT - -### Analysis: - -#### **1. Context and Current Situation:** -- The youth unemployment rate in China as of **July 2024** is **17.1%**, the highest since the new methodology was introduced in December 2023. -- The rate has fluctuated significantly over time, peaking previously at **21.3%** in June 2023, under the old methodology. -- The ongoing trend indicates a rise in youth unemployment, though there could be some recent sign of easing if we look forward into the next few months. - -#### **2. Trend Considerations:** -- **Seasonality:** Youth unemployment tends to peak around graduation season in July, as a fresh influx of graduates enters the job market. This suggests that the rate may trend downward after July. -- **Previous Trends:** Historically, post-graduation months (August onwards) have shown a slight decline as some of the newly graduated youth find jobs, leading to a slight easing of the youth unemployment rate. However, the exact drop-off might not be considerable given the current high level. - -#### **3. Methodological Changes:** -- The unemployment rate currently excludes students who are still in school, which should technically reduce the unemployment rate compared to a scenario where students are included. This could lead to rates slightly underestimating true labor market distress. - -#### **4. Economic and Structural Factors:** -- **China's Economic Conditions:** China's economic challenges, such as slowed economic growth, real estate issues, and weak consumer demand, are likely to continue exerting upward pressure on the unemployment rate. The broader economic situation in China does not seem poised for a rapid recovery that would significantly pull down the unemployment rate in the short term. - -- **Government Policy Response:** There may be a possibility of government intervention to reduce this rate, whether through stimulus measures, job creation programs, or adjustments in statistical reporting. However, such interventions typically have a lag and may not immediately impact the August numbers strongly enough to bring unemployment below 16.0%. - -#### **5. Forecasts & Expert Predictions:** -- **Trading Economics Projections:** Trading Economics projects a potential reduction in youth unemployment potentially reaching around 15.5% towards the year's end. Still, the specific forecast for August remains unclear, complicating the assessment. - -- **Long Term Trends:** The long-term forecast shows a potential increase again in 2025, suggesting that the general trajectory remains concerning, with downward movement potentially being insufficient to meet the threshold of 16.0%. - -#### **6. Base Rates and Historical Analogues:** -- Historically, similar large economic and structural challenges in other countries didn’t resolve rapidly. Given China's current trajectory and economic headwinds, it seems challenging for the youth unemployment rate to experience a significant drop in such a short time frame. - -### Conclusion: - -Given the weight of evidence pointing towards sustained economic challenges in China and the historical stickiness of high unemployment numbers, coupled with the usual slight post-graduation drop-off in youth unemployment rates, several outcomes are possible. However, the likelihood of the August rate dropping to or below **16.0%** seems modest based on the data and trends discussed. While a small reduction from the July rate is conceivable, the drop might not be large enough to hit the threshold of **16.0%**. - -**Probability Distribution Consideration:** -- Downward adjustment: plausible but may not exceed ~0.5-1.0 percentage point decrease. -- Structural and economic pressures: very likely to keep it closer to current figures instead of a steep decline. - -### Final Probability Estimate: -Taking all factors into account, the likelihood that the youth unemployment rate for August 2024 will be **≤ 16.0%** seems moderate but not high. It is a non-market threshold value event, so we carefully consider both the trends and the base scenario. - -**Probability: 30%**" -27980,Will Starliner Calypso touch ground somewhere other than at one of the planned sites by September 30?,"PERPLEXITY - -To address the question of whether Starliner Calypso will touch ground somewhere other than at one of the planned sites by September 30, we need to consider the following points: - -1. **Planned Landing Sites**: The planned landing sites for Starliner missions include White Sands Missile Range in New Mexico, Willcox, Arizona, and Dugway Proving Ground, Utah, with Edwards Air Force Base in California as a contingency landing site. - -2. **Current Status**: Starliner Calypso is scheduled to land at White Sands Space Harbor in early September 2024, specifically on September 7, 2024, at 06:03 UTC (12:03 am MDT). - -3. **Landing Criteria**: The landing criteria include specific weather conditions such as winds of 6 mph or less when flying with crew (not applicable in this case since it's returning uncrewed), ground temperatures warmer than 15 degrees Fahrenheit, a cloud ceiling of at least 1,000 feet, one nautical mile of visibility, and no precipitation, thunderstorms, or lightning within a 22-mile radius. - -Given the current information, there is no indication that Starliner Calypso will deviate from the planned landing site at White Sands Missile Range by September 30. The specific landing date is set for September 7, well before the end of September. - -**Recent Updates and Expert Opinions**: -- The decision to return Starliner uncrewed was made due to concerns over the propulsion system's reliability and the risk it posed to the crew. -- NASA Administrator Bill Nelson emphasized safety as the core value guiding the decision to keep the astronauts aboard the ISS and return Starliner uncrewed. - -**Prediction Markets and Forecasts**: -No specific prediction markets or forecasts were found in the provided sources that address the landing site of Starliner Calypso deviating from the planned locations. However, the information available suggests that the landing is expected to proceed as planned at White Sands Missile Range. - -######### - -GPT - -### **Step 1: Understanding the Question** - -We're forecasting whether the Boeing Starliner Calypso will touch ground somewhere other than one of the planned or contingency landing sites by September 30, 2024. This is a **binary outcome event**, so our forecast must account for the probability of each possible outcome. - -### **Step 2: Evaluate Background Information** - -Let's break down the relevant information to assess likelihoods effectively: - -1. **Current Status**: - - Starliner Calypso is scheduled to land at White Sands Space Harbor on September 7, 2024. This is officially stated, giving us a precedent for how the mission is expected to proceed. - - The properties of this mission are slightly different from standard crewed missions given that its thruster and helium systems have technical issues. - -2. **Key Risks**: - - **Autonomous landing capability**: The software's ability to execute a fully autonomous landing is somewhat in question. While this introduces elements of risk, mission data, and control systems are typically scrutinized intensively by NASA, suggesting that the probability of significant deviation from planned behavior is low. - - **Contingency Plans**: The existence of multiple contingency landing sites (White Sands, Willcox Playa, Dugway Proving Ground, and Edwards Air Force Base) indicates a high level of preparation for adverse conditions. If slight deviations occur, they should fall within the acceptable parameters of these pre-designated locations. - - **Extreme Outcomes**: - - **Burn-up or Ocean Splashdown**: Should the autonomous system fail drastically, there might be a need for a destructive entry into the atmosphere, or potentially a splashdown in the ocean. However, these outcomes are generally engineered against and should be considered unlikely. - -3. **Historical Base Rates**: - - **NASA and Boeing Risk Management**: NASA and Boeing's record in managing re-entry missions typically favors successful outcomes over failure. While issues have occurred in space missions historically, catastrophic results often arise from brand new, untested systems or unforeseen external impacts. In this case, despite the noted anomalies, the system has undergone extensive review, and decisions have been made on a risk-first basis. - - **Past Incidents**: The most comparable issues (e.g., software glitches in autonomous systems, hardware malfunctions) have been mitigated through multiple redundancies and landing options. - -4. **Range of Dates**: - - **Date Consideration**: The Starliner is scheduled to land on September 7, which falls well before the deadline of September 30. Shortening the timeframe reduces the likelihood of ""drift"" towards a less controlled outcome (i.e., landing outside the designated areas). - -### **Step 3: Formulate the Probability** - -Let's synthesize the information into a coherent probability estimate: - -- **Default Scenario (Land at Planned Site)**: - - Historical base rates suggest a high likelihood of successful planned landings for similar spacecraft. - - The existence of contingency sites offers a margin of safety. - - The information provided by NASA indicates deliberate decisions have been made to favor mission safety and success. - -- **Deviations or Unexpected Outcomes**: - - Failure of autonomous systems and extreme weather are among the non-negligible but still low-probability events. - - Environmental conditions at the designated sites are continually monitored to allow for any redirection to other contingency sites if necessary. - - Drastic issues like total software failure leading to burn-up are extreme edge cases, possibly less than 5% on their own. - -Given that the data points toward a successful and controlled landing by September 7, with high safety margins built into the mission planning, the probability of the Starliner touching down somewhere other than a planned or contingency site is quite low. - -**Final Probability Estimate**: -- **Probability of Starliner Calypso Landing Outside a Planned Site by September 30**: Given the analysis above, the probability is notably small but not impossible due to the factors of spacecraft anomalies, weather, or miscalculations. - -**Probability: 5%**" -27920,"Before October 1, 2024, will Ethiopia and Somalia announce an agreement settling their dispute over the Somaliland port deal?","PERPLEXITY - -To provide context for forecasting whether Ethiopia and Somalia will announce an agreement settling their dispute over the Somaliland port deal before October 1, 2024, here are the key points from recent news sources: - -1. **Ethiopia-Somaliland Port Deal**: Ethiopia signed a memorandum of understanding with Somaliland on January 1, 2024, to lease a naval port in Berbera for 50 years. This deal grants Ethiopia access to the Red Sea in exchange for recognizing Somaliland's independence. - -2. **Somalia's Reaction**: Somalia has strongly opposed the deal, considering Somaliland a part of its territory. The Somali government has recalled its ambassador from Ethiopia, threatened legal action, and signed a law nullifying the agreement. - -3. **Regional Tensions**: The deal has strained Somali-Ethiopian relations and increased anti-Ethiopian sentiment in southern Somalia, potentially weakening regional counterterrorism cooperation and energizing al Shabaab. - -4. **Economic Motivations**: Ethiopia's move is driven by economic needs, including reducing dependence on Djibouti for port access and mitigating the financial strain from high port fees. - -5. **International Implications**: The deal has broader geopolitical implications, affecting the balance of power in the Horn of Africa and potentially provoking further tensions in the Red Sea, a critical global shipping route. - -Given these points, there are no recent updates indicating movement towards a resolution between Ethiopia and Somalia. The dispute remains contentious, with Somalia taking legal steps to nullify the deal and Ethiopia proceeding with its plans. Expert opinions suggest that the deal could lead to increased regional tensions rather than a swift resolution. - -**Prediction Markets**: There are no specific prediction markets mentioned in the provided sources that directly address the likelihood of an agreement between Ethiopia and Somalia before October 1, 2024. However, the current political and diplomatic landscape suggests that a resolution is unlikely in the near future. - -In summary, the current context does not indicate a likely agreement between Ethiopia and Somalia before October 1, 2024, given the strong opposition from Somalia and the strategic importance of the deal for Ethiopia. - -######### - -GPT - -### Step-by-Step Thought Process - -#### Overview: -The question asks about the likelihood of Ethiopia and Somalia announcing an agreement settling their dispute over the Somaliland port deal before October 1, 2024. Since today's date is September 6, 2024, we have just under a month to consider. - -#### Key Variables: -1. **Current Relations & Past Negotiations**: - - The second round of talks ended without an agreement on August 20, 2024. - - The third round is scheduled for September 17, 2024. - - A point of tension is that Somalia views the Ethiopia-Somaliland deal as impinging on its sovereignty. - -2. **Motivations and Stakes**: - - **Ethiopia**: Driven by economic need for access to the sea via Berbera, reducing its economic dependence on Djibouti. - - **Somalia**: Defending its territorial integrity and sovereignty claims over Somaliland. - - **Somaliland**: Seeking international recognition, starting with Ethiopia recognizing it. - - **Turkey**: Acting as a mediator, with strategic interests in the Horn of Africa, including trade lanes and regional influence. - -3. **International Context & Regional Dynamics**: - - The region holds significant geopolitical interest, especially with key players like Turkey involved. - - Ethiopia's recognition of Somaliland is a major potential shift for international norms—e.g., recognition of unrecognized territories. - - There's ongoing tension, risking military confrontation, perhaps reducing incentives for either party to quickly settle the dispute. - -4. **Time Constraints**: - - Given the timeframe (less than a month), there's limited time for negotiation post-September 17 talks. - - Typically, agreements that involve territorial disputes and sovereignty take longer to resolve, especially when multiple rounds of negotiations have already occurred without success. - -#### Base Rates & Historical Context: -- Historically, territorial disputes and conflicts over recognition often take significant time to resolve. -- Peaceful resolutions to similar disputes without third-party imposition or major crisis resolutions often span years, not weeks. - -#### Important Considerations: -- Turkey plays a potentially balancing role, but its influence remains limited by the fundamental issues at stake. -- Ethiopia's ongoing regional tensions with Somalia may create a strong disincentive for either side to make significant concessions rapidly. -- The announcement could be more probable if both sides seek to avoid further escalating tensions or conflict; however, the established positions suggest deep-seated issues. - -### Statistical/Tactical Analysis: -- **Base Rate**: Given negotiations, large geopolitical disputes with economic and territorial stakes typically have a low probability of resolution in a short timeframe. -- **Trend Fluidity**: The fluidity of diplomatic breakthroughs near deadlines can slightly raise the chances above extreme values (sub-5%). - -### Likely Scenarios: -1. **No Agreement (Default Outcome)**: - - Somalia remains firm on sovereignty. - - Ethiopia proceeds with the Somaliland deal, further escalating tensions. - -2. **Breakthrough with Condition** (Unlikely but possible): - - External mediation (Turkey) achieves a symbolic or temporary agreement, perhaps a framework for future negotiations, but with no finalized resolution, making the announcement more of a token. - -### Uncertainty Consideration: -Given the complexity and historical context, a wholly binary view would be imprudent. There’s a small window for an announcement—but it would be after a successful negotiation in mid-September, facing typical diplomatic slowdowns. The most likely outcome is that talks continue, no resolution is achieved quickly enough, and tensions persist. - -### Conclusion & Probability Assignment: -- **Default Behavior** (No Agreement): Strong evidence indicates a low likelihood due to entrenched positions, timing, and the nature of the dispute. -- **Breakthrough Chance** (If announced): Present, though small, considering prior failures to agree and the time required to hash out detailed terms. - -Given the dynamics, **the lack of any drastic changes** between now and October 1, 2024, seems the most likely scenario. **Probability of Agreement**: Given all these factors, the probability of Ethiopia and Somalia announcing an agreement settling their dispute before October 1, 2024, is: - -**Probability: 10%**" -27935,Will China's youth unemployment rate be greater than 18.0 for August 2024?,"PERPLEXITY - -To address the query about whether China's youth unemployment rate will be greater than 18.0% for August 2024, we need to consider the latest available data and trends: - -1. **Recent Data**: The most recent data available indicates that the youth unemployment rate in China was 17.1% in July 2024, using a new statistical methodology that excludes university students. - -2. **Trend Analysis**: The youth unemployment rate has shown a decline in recent months, with a decrease from 14.2% in May to 13.2% in June, before the methodology change. However, the new methodology resulted in a rate of 17.1% in July, which is higher than the previous months but lower than the record high of 21.3% in June 2023. - -3. **Seasonal Fluctuations**: Youth unemployment figures in China typically peak in July due to the large number of graduates entering the job market. Given this seasonal pattern, it is possible that the rate might decrease slightly in August. - -4. **Economic Context**: The Chinese economy has been experiencing weak momentum, and the service sector, which is crucial for youth employment, has been particularly affected. This suggests that while there might be a slight decrease in the unemployment rate due to seasonal factors, the overall economic conditions might not support a significant drop. - -5. **Expert Insights**: Analysts have pointed out that mismatches between the skills graduates acquire and those required by employers are contributing to high youth unemployment. This issue is not expected to be resolved in the short term. - -Given these considerations, while there is a possibility that the youth unemployment rate might decrease slightly from July's 17.1% due to seasonal factors, it is uncertain whether it will drop below 18.0% in August 2024. The economic context and structural issues in the labor market suggest that the rate might remain close to or slightly above 17.1%. - -**Prediction Markets and Forecasts**: There are no specific prediction markets or forecasts mentioned in the provided sources for the youth unemployment rate in August 2024. However, based on the trend and economic context, it seems plausible that the rate could be around or slightly above 17.1%, but whether it will exceed 18.0% is uncertain without more recent data. - -In summary, while there is a slight possibility that the rate might decrease due to seasonal factors, the overall economic and structural issues suggest that it might remain close to 17.1%, making it uncertain whether it will be greater than 18.0% in August 2024. - -######### - -GPT - -### Step 1: Classify the Question Type -This question is a **Non-Market Threshold Value Event**, specifically focusing on whether China's youth unemployment rate for August 2024 will be above 18.0%. The task is to forecast a quantifiable measure with historical precedents to provide guidance. - -### Step 2: Accounting for Current Data and Trends -- **Recent Data**: - - In July 2024, China's youth unemployment rate was 17.1%, a jump from the June 2024 revised figure using the new methodology. - - Previous figures, before the methodology change, showed a record high of 21.3% in June 2023. - -- **Trend Analysis**: - - There has been a general rise in the youth unemployment rate since the switch to the new calculation methodology. - - There was a seasonal spike in July, which is expected due to the large number of new graduates entering the labor market. - -- **Economic Context**: - - Current indicators suggest weak overall economic momentum. - - The service sector, crucial for youth employment, is underperforming, with many structural factors (e.g., skill mismatches) remaining unresolved. - -### Step 3: Evaluating the Likelihood of Change -- **Seasonal Fluctuations**: - - Typically, there is an uptick in unemployment rates in July following graduation season, which often sees a slight decrease in August. - -- **Structural and Economic Context**: - - While there might be a seasonal decrease, the underlying weak economic conditions might mitigate the extent of this reduction. - - The Chinese government might not have implemented sufficient measures to structurally improve job opportunities for young people between July and August 2024. - -### Step 4: Considering Base Rates and Historical Context -- The youth unemployment rate has seen large fluctuations in the past, partly attributed to changes in methodology. -- Before the recent falls, historical base rates using the old methodology were hovering around 20%+ during periods of instability. -- Under the new methodology, the recent shift from June (previous methodology) to July (new methodology) indicated a recalibration but still pointed to high unemployment figures. - -### Step 5: Considering Novel Factors -- Economic reforms or sudden changes in policy leading to an immediate improvement in employment rates seem unlikely given the data provided. -- Additionally, adverse factors such as economic slowdown or continued skill mismatches suggest structural issues that couldn't reasonably be resolved within a short time frame. - -### Step 6: Default Behavior Analysis -- If we follow the default behavior, which indicates a slight decrease in unemployment rates after the seasonal peak in July, it is probable that the rate might stay close to July’s figure. -- However, considering the economic context, it is more likely that the youth unemployment rate will hover around the same level or increase slightly rather than decrease significantly. - -### Step 7: Statistical Considerations -- The calculated rate increase in July, along with a weakening economy, suggests that the rate's trajectory could either stay stable or move slightly upwards rather than sharply downwards. -- Given that July had 17.1% unemployment, a small rise over 18.0% is within the range of reasonable scenarios, especially under pessimistic assumptions. - -### Step 8: Forecasting Probability -Given all the factors considered, the likelihood that the unemployment rate climbs to or exceeds 18.0% is significant but not overwhelmingly certain. There is a non-trivial chance that the rate could remain below 18.0% due to a small seasonal decrease. - -**Final Answer**: -- Considering the economic conditions, seasonal effects, and structural challenges: -- Probability: **60%** that China's youth unemployment rate will be greater than 18.0% for August 2024. - -This forecast leans slightly towards the rate exceeding 18.0%, with recognition of the current economic context and trends but acknowledges some inherent uncertainty, giving it a probability greater than 50% but not an overwhelming certainty." -27935,Will China's youth unemployment rate be greater than 18.0 for August 2024?,"PERPLEXITY - -To provide context for forecasting whether China's youth unemployment rate will be greater than 18.0% for August 2024, we need to consider recent trends and data adjustments. - -1. **Recent Trends and Data Adjustments**: - - **New Methodology**: China resumed releasing youth unemployment data in January 2024 after a six-month suspension, using a new methodology that excludes university students. This change resulted in a youth unemployment rate of 17.1% in July 2024. - - **Declining Rate**: The youth unemployment rate has shown a decline, with June 2024 recording a rate of 13.2%, down from 14.2% in May, marking the third consecutive monthly decline. - - **Historical Context**: The rate had previously hit a record high of 21.3% in June 2023 before the methodology change. - -2. **Expert Opinions and Structural Factors**: - - **Critique of Official Data**: Some analysts suggest that the actual youth unemployment rate could be higher due to issues like ""lying flat"" (young workers opting out of the labor market) and falsified employment records. - - **Structural Factors**: The expansion of vocational education, delays in employment due to the pandemic, and the influx of students returning from overseas are cited as contributing to the high youth unemployment figure. However, it is argued that these factors will eventually lead to absorption of unemployed youth into the workforce as the economy recovers. - -3. **Current Data and Predictions**: - - **July 2024 Data**: The most recent data available shows a youth unemployment rate of 17.1% in July 2024, using the new methodology. - - **Predictive Insights**: While there is a trend of decline, the proximity of the July rate to 18.0% suggests that August could potentially see a rate close to or slightly above this threshold, depending on various economic and labor market factors. - -Given these points, while there is a trend of decline in youth unemployment rates, the recent rate of 17.1% in July 2024 is close enough to 18.0% to suggest that August 2024 could potentially see a rate greater than 18.0%, especially considering the ongoing challenges in the labor market and the critique of official data by some analysts. However, without more current data or specific predictions, it remains uncertain. - -######### - -GPT - -### Question: Will China's youth unemployment rate be greater than 18.0% for August 2024? - -### Step 1: Identify the Type of Event -This falls under the **Non-Market Threshold Value Event** category as it measures whether a quantifiable value (youth unemployment rate) will exceed a specified threshold (18.0%) at a specific time (August 2024). - -### Step 2: Current Data and Trends Analysis -- **Recent Trends and Data**: The current youth unemployment rate for July 2024 is 17.1%, which is notably lower than the 21.3% reported in June 2023 using the previous methodology. The unemployment rate shows a declining trend, from 14.2% in May 2024 to 13.2% in June 2024 before increasing slightly to 17.1% in July 2024. - - **Trend Analysis**: The decline from May to June followed by a sudden rise in July could be due to temporary factors like graduation season, which typically increases the number of job seekers. If this July spike is seasonal, it might not indicate a long-term reversal in the general downward trend. -- **Methodology Change**: The newer methodology, excluding students, inherently lowers the unemployment rate because it removes individuals who are not seeking jobs (and hence not part of the labor force). Comparing rates before and after this change requires caution. - -### Step 3: Structural Factors and Critique -- **Structural Weaknesses**: The Chinese economy has seen significant structural changes, with sectors like tech, real estate, and education undergoing regulatory pressures, which could exacerbate youth unemployment. - - **Lying Flat and Falsified Records**: These phenomena suggest the official rate might understate the real situation, though this has likely been a persistent issue, meaning the official rate's trends still hold significance. - -### Step 4: Predictive Analysis -- **Seasonal Factors**: August often sees an influx of job seekers post-graduation, which can elevate unemployment rates temporarily, especially if economic absorption capacity is weak. -- **Economic Conditions**: Lingering effects of the COVID-19 pandemic, ongoing structural shifts, and regulatory pressures may continue inhibiting job growth, although the Chinese government often implements stimulative measures to counteract rising unemployment, usually toward the latter part of the year. - -### Step 5: Base Rate and Statistical Analysis -- The base rate under the new methodology is difficult to establish given the limited data. However, considering a recent rise to 17.1% and the likelihood of seasonal factors pushing the rate higher in August, the unemployment rate could potentially approach or exceed 18%. -- Also, given the proximity of July 2024's figure (17.1%) to the threshold (18.0%), a slight increase due to newly graduated job seekers entering the market makes it plausible the August rate could meet or exceed 18.0%. - -### Step 6: Final Considerations -- **Weight of Probabilities**: - - The downward trend seen earlier could continue post-July if July's rise was merely seasonal. However, the potential for further economic instability or a delayed impact from previous shocks (e.g., regulatory tightening) could drive rates up again. - - The critiques of official data suggest a slight risk that actual unemployment is higher than reported, though how this might reflect in the official release is uncertain. - -Considering the above factors, the likelihood of exceeding the 18.0% threshold is balanced but slightly favored due to seasonality and economic uncertainty. - -### Final Prediction -Given the trends, factors at play, and status of the economic and regulatory environment, I estimate the probability that China's youth unemployment rate will be greater than 18.0% for August 2024 to be: - -**Probability: 55%**" -27935,Will China's youth unemployment rate be greater than 18.0 for August 2024?,"PERPLEXITY - -To address the question of whether China's youth unemployment rate will be greater than 18.0% for August 2024, we need to consider recent trends and data adjustments: - -1. **Recent Trends and Data Adjustments**: - - **New Methodology**: China resumed the release of youth unemployment data in January 2024 after a six-month suspension, using a new statistical methodology that excludes university students. This adjustment aims to provide a more accurate reflection of the employment situation for young people actively seeking work. - - **Recent Rates**: The youth unemployment rate (excluding students) was 13.2% in June 2024, marking the third consecutive monthly decline. However, the rate increased to 17.1% in July 2024, which is typically a peak month due to the large number of graduates entering the job market. - -2. **Expert Opinions and Concerns**: - - **Data Accuracy**: Analysts have expressed concerns over the transparency and accuracy of China’s youth unemployment data, suggesting that the true rate could be higher due to factors like ""lying flat"" (young workers opting out of the labor market) and potential data manipulation by universities to inflate employment rates. - - **Economic Challenges**: China faces economic headwinds, including a sluggish property market, local government debt, and weak consumer spending, which could impact job creation and unemployment rates. - -3. **Historical Context**: - - **Record High**: The youth unemployment rate hit a record high of 21.3% in June 2023 before the methodology change. - - **Seasonal Fluctuations**: Youth unemployment figures typically fluctuate throughout the year, peaking in July when graduates enter the job market. - -Given these points, while the recent decline in June followed by an increase in July suggests volatility, the adjusted methodology and typical seasonal patterns indicate that the rate could remain high but potentially stabilize or slightly decrease following the July peak. However, considering the recent rate of 17.1% in July and the challenges in accurately assessing the true unemployment situation due to factors like ""lying flat"" and potential data manipulation, it is plausible that the rate could exceed 18.0% in August 2024, especially if economic recovery remains sluggish. - -**Prediction Markets and Forecasts**: There are no specific prediction markets or forecasts mentioned in the provided sources for August 2024. However, the general trend and expert opinions suggest caution and the possibility of a higher rate than officially reported. - -In conclusion, while there is a possibility that the youth unemployment rate could be greater than 18.0% in August 2024, especially considering the challenges in data accuracy and economic headwinds, the recent adjustments and seasonal patterns also suggest potential stabilization or slight decrease following the July peak. - -######### - -GPT - -### Step 1: Understand the Context and Background - -The question asks whether China's youth unemployment rate will be greater than 18.0% for August 2024. Current data indicates that the unemployment rate for July 2024 was 17.1%, a notable increase from the 13.2% in June 2024. This comes after China had revamped its methodology for calculating this rate, now excluding university students and focusing on youth actively seeking work. Given the historical context and current economic conditions, we need to consider factors that will either push this rate further up or lead to a stabilization or reduction. - -### Step 2: Assess Recent Trends and Statistical Biases - -- **New Methodology:** The new calculation method excludes university students, possibly underreporting the full extent of youth unemployment. This could mean the true rate is higher than reported, which introduces an element of uncertainty. While this bias exists, we must use the official numbers to resolve the question. - -- **Recent Trends:** July’s 17.1% marks an increase typical for that month due to a surge in graduates entering the job market. Despite the upward trend, this is not necessarily correlating to a continuing rise past August – some reabsorption into the workforce might eventually mitigate unemployment figures, at least slightly, in the subsequent month (August). - -- **Economic Variables:** China is currently facing economic hurdles including a lukewarm property market and weak consumer spending, which could stymie job creation. Additionally, traditional industries are not expanding rapidly, and sectors like tech might not fully absorb the workforce. These stress factors could incline the unemployment rate upwards slightly. - -### Step 3: Consider Seasonal Patterns and Historical Data - -- **Seasonality:** Historically, youth unemployment rates are highest in the months when graduates enter the job market, notably in June and July. Historically, rates may stabilize or slightly decrease going into August due to delayed employment or better economic conditions absorbing some of the unemployed graduates. - -- **Historical Peaks:** The previous high before the methodology change was 21.3% in June 2023. This is significantly above the current level, meaning that even in worse scenarios, the peak might not replicate to that level, especially given the methodological change which is more conservative. - -### Step 4: Evaluate the Likelihood of Major Economic or Political Changes - -- **Potential Economic Shocks:** An unexpected economic downturn could spike the unemployment rate above 18.0%, but current data doesn't indicate an impending sharp decline in economic conditions – the economic situation is challenging but not on the brink of rapid deterioration. - -- **Government Interventions:** The Chinese government has been signaling a commitment to stabilizing the economy, which might involve policy interventions to improve employment conditions, especially in a politically sensitive group like youth. - -### Step 5: Calculate the Probability - -Given the latest trend of 17.1% in July 2024, predicting the likelihood that the rate will increase beyond 18.0% entails evaluating the potential for either exacerbating or ameliorating economic conditions and the natural volatility of this metric. A rate increase of 0.9 percentage points is plausible but not guaranteed. - -- If the rate experiences a mild reduction or stabilization from July due to seasonal patterns (graduates finding jobs, temporary employment injections), it may remain below 18.0%. -- If the economic undercurrents are more severe than anticipated, and job market conditions worsen uniformly, we could see the rate surpass 18.0%. This scenario is plausible given China's economic stressors but not probable given recent trends of re-absorption post-July. - -\[ -\text{Probability: } \text{Estimate\_Stabilization\_Under\_18\%} = 55\% -\] -\[ -\text{Probability: } \text{Estimate\_Increase\_Over\_18\%} = 45\% -\] - -### Step 6: Final Probability Estimate - -Taking all factors into consideration, including seasonal adjustments, recent trends, and economic conditions, I would place the probability of the youth unemployment rate exceeding 18.0% in August 2024 at around: - -**Probability: 45%** - -This estimate weighs the likelihood of the current trend persisting into August with slight chances of either stabilization or slight increase given the economic landscape." -27935,Will China's youth unemployment rate be greater than 18.0 for August 2024?,"PERPLEXITY - -To provide context for forecasting China's youth unemployment rate for August 2024, we need to consider recent trends, statistical methodologies, and expert opinions. - -1. **Recent Trends and Methodology**: - - **Statistical Methodology**: China recently resumed the release of youth unemployment data in January 2024 after a six-month suspension, adopting a new methodology that excludes university students. This change resulted in a youth unemployment rate of 17.1% in July 2024. - - **Trend**: The youth unemployment rate has shown a decline in recent months, from 14.2% in May to 13.2% in June 2024, indicating a positive trend despite weak economic momentum. - -2. **Expert Opinions and Concerns**: - - **Data Accuracy**: There are concerns about the accuracy of the unemployment data, with some analysts suggesting that the actual rate could be higher due to factors like ""lying flat"" (young workers opting out of the labor market) and falsification of employment records by universities. - - **Economic Recovery**: China's economic recovery has been slow, with challenges such as ailing property markets, surging local government debt, and weak consumer spending, which could impact employment rates. - -3. **Historical Context**: - - **Peak Unemployment**: The youth unemployment rate hit a record high of 21.3% in June 2023 before the methodology change. - - **Seasonal Fluctuations**: Youth jobless figures typically peak in July in China due to the large number of graduates entering the job market. - -Given these points, while there has been a recent decline in the youth unemployment rate, concerns about data accuracy and the broader economic challenges suggest that the rate could fluctuate. However, the most recent data available indicates a rate of 17.1% in July 2024, which is below the 18.0% threshold for August 2024. Therefore, based on the current trend, it seems less likely that the rate will exceed 18.0% in August, but the situation remains uncertain due to the factors mentioned above. - -**Prediction Markets and Forecasts**: Specific prediction market data for this query is not provided in the search results. However, incorporating the recent trend and the broader economic context is crucial for any forecast. - -**Current Data**: As of July 2024, the youth unemployment rate in China, excluding students, was 17.1%. - -**Conclusion**: The forecast should consider the recent decline in the youth unemployment rate, the change in statistical methodology, and the ongoing economic challenges. While the rate has been below 18.0% in July, the broader context suggests that caution is needed in making predictions for August 2024. - -######### - -GPT - -To forecast whether China's youth unemployment rate will exceed 18.0% in August 2024, we'll need to carefully consider the relevant data points, trends, macroeconomic factors, and potential variables that could influence the outcome. - -### Step 1: Establish Baseline - -#### Current Data: -- The most recent youth unemployment rate recorded was **17.1% in July 2024**. This is below the 18.0% threshold. - -### Step 2: Determine Trend and Trajectory - -#### Recent Trends: -- Youth unemployment decreased from **14.2% in May** to **13.2% in June 2024**. -- Then it **increased to 17.1% in July 2024**. - -This increase from June to July is critical because it suggests a seasonal or structural uptick in unemployment among youth, most likely due to the influx of fresh graduates into the job market in July. - -#### Statistical Methodology: -- The new methodology excludes university students from the calculation, which generally results in a somewhat lower unemployment rate compared to the previous method. But this means the rate could still capture most immediate job-seekers, giving us confidence in the reported figures. - -### Step 3: Analyze the Key Factors - -#### Economic Conditions: -- China's broader economy is facing significant headwinds, including a sluggish recovery post-COVID, challenges in the property market, and high levels of debt. These could drag down economic growth, making it harder for young people to find jobs. -- Weak consumer spending and ongoing structural changes in the economy further contribute to a challenging job market. - -#### Data Integrity and External Factors: -- Concerns about data integrity (e.g., underreporting by universities, the ""lying flat"" phenomenon) could mean the actual youth unemployment situation is worse than officially reported. However, we must forecast based on the data that will be officially reported. - -### Step 4: Consideration of Seasonal and Structural Factors -- **Seasonality**: Youth unemployment typically peaks around the time of graduation (July and August). The July rise to 17.1% confirms this pattern. -- Fortunately, the seasonal spike should stabilize or potentially decrease slightly after August as some of these graduates find their footing in the labor market. - -### Step 5: Assess Potential for Significant Change -- A significant change would be needed to push the unemployment rate from **17.1% in July** to above **18.0% in August**. While economic factors have been poor, there haven't been new macroeconomic shocks or policy changes from late July into August that suggest the rate would spike considerably. - -### Step 6: Evaluate Base Rates and Potential Outliers -- Historically, even with the previous, higher-unemployment reporting methodology, the youth unemployment rate only just touched 21.3% during a severe economic downturn in June 2023. The new methodology suggests slightly lower figures. -- The default behavior, as seen in recent historical data, suggests rates in the **low to mid-17% range**. - -### Step 7: Calculate and Assess Probabilities -Using the above factors, my final assessment includes: -- **Recent Trend** (Moderate Increase from June to July): Suggests a possible stabilization or slight increase in August but unlikely to surpass 18.0%. -- **Economic Conditions**: Slow economic recovery and weak labor market might exert upward pressure but not to extremes. -- **Data Limitations and Methodology**: The current method reduces the reported rate by excluding large contingents of students, thus making a number above 18.0% less probable. - -**Final Rationale**: -Given the stabilizing seasonal spikes, ongoing economic conditions, and the recent minor upward trend, the probability of the youth unemployment rate exceeding 18.0% in August 2024 is moderate but unlikely. Taking into account all the factors, there is a low-to-moderate chance of a minor increase but not one sufficiently significant to breach the 18.0% mark. - -**Conclusion**: -- **Probability: 25%**" -27935,Will China's youth unemployment rate be greater than 18.0 for August 2024?,"PERPLEXITY - -To provide context for forecasting whether China's youth unemployment rate will be greater than 18.0% for August 2024, we need to examine recent trends and data adjustments. - -1. **Recent Data and Methodology Changes**: - - **New Methodology**: China resumed releasing youth unemployment data in January 2024 after a six-month suspension, using a new methodology that excludes university students. This change significantly lowered the reported rate. - - **Recent Rates**: The youth unemployment rate (excluding students) was 13.2% in June 2024, marking the third consecutive monthly decline. - - **July 2024 Rate**: The rate was 17.1% in July 2024, indicating a seasonal increase likely due to new graduates entering the job market. - -2. **Expert Opinions and Concerns**: - - **Underestimation**: Analysts and experts suggest that the new methodology might underestimate the true unemployment rate by excluding students who are not actively seeking work but might be unemployed if they were seeking jobs. - - **Historical Comparisons**: The lack of historical data using the new methodology makes it difficult to gauge the adjusted youth unemployment rate accurately. - -3. **Seasonal Trends**: - - **Peak in July**: Youth jobless figures typically peak in July in China when the largest number of graduates enter the job market. - -Given these points: -- The recent decline in youth unemployment rates before July suggests an improving trend. -- The July 2024 rate of 17.1% indicates a seasonal increase but is below the threshold of 18.0%. -- Expert concerns about underestimation and the lack of historical comparisons for the new methodology suggest caution in interpreting these figures. - -**Prediction Markets and Forecasts**: -No specific prediction market data is provided in the sources. However, considering the seasonal peak in July and the recent trends, it's crucial to monitor the latest data releases for August 2024 to make an informed forecast. - -**Conclusion**: -While the July 2024 rate is below 18.0%, and there was a declining trend before July, the seasonal increase in July and expert concerns about underestimation suggest that the August 2024 rate could potentially be influenced by these factors. The most recent data available does not directly indicate that the rate will exceed 18.0% in August, but caution is advised due to the complexities in the new methodology and seasonal variations. - -######### - -GPT - -### Context and Reasoning - -#### Recent Data and Trend Analysis: -- **July 2024 Youth Unemployment Rate**: The youth unemployment rate in July 2024 was 17.1%, up from 13.2% in June. This represents a sharp increase, which is consistent with seasonal patterns where the rate typically peaks in July due to new graduates entering the job market. -- **Three-Month Decline Before July**: Prior to this increase, the unemployment rate had been declining from higher levels in the previous months, suggesting some positive momentum in the job market. - -#### Methodological Considerations: -- **New Methodology**: The method for calculating the youth unemployment rate changed in January 2024 by excluding students. This tends to report lower unemployment rates than the previous methodology. However, given that this new methodology has been in place for several months now, any comparison must be based on these new figures. - -#### Expert Opinions: -- **Concerns Over Underestimation**: There are concerns that the new methodology underestimates unemployment because it excludes students who are technically not seeking jobs now but could be considered unemployed when they graduate. - -#### Historical Patterns: -- **Seasonality**: Historically, youth unemployment rates typically rise in July due to the influx of graduates into the job market. This is likely a major reason for the July spike to 17.1%. However, the seasonal factor should diminish in August as some graduates secure employment or stop actively looking for jobs. - -#### Economic Environment: -- **China's Economy**: China's economy has faced several challenges, including ongoing structural adjustments, regulatory crackdowns on certain industries, and lingering effects of the COVID-19 pandemic. These factors could jeopardize rapid improvements in youth employment despite the seasonal decline expected after July. - -### Probability Assessment - -Given the information: -1. The historical peak occurs in July with some decrease typically expected in August. -2. The rate spiked to 17.1%, well below the 18.0% threshold but close enough where minor variations or reporting adjustments could impact the final rate. -3. The new methodology could lead to later months presenting slightly distorted figures compared to past expectations, especially with underestimation concerns. -4. The larger economic context suggests structural issues impacting youth employment might limit significant drops in unemployment. - -**Statistical Note**: Given that July saw a spike from 13.2% to 17.1%, an increase of 3.9 percentage points, it would require an extraordinary, almost unprecedented, spike in unemployment from July to August for the rate to exceed 18.0%. While the possibility exists, the more likely outcome remains a figure below 18.0%, based on typical seasonal trends returning. - -### Conclusion: -Considering all the data, including seasonality, expert concerns, the new methodology, and recent economic trends, the likelihood of the unemployment rate in August 2024 being above 18.0% seems relatively low but remains non-zero, given the economic uncertainties and the close value in July. - -**Final Probability**: 20%" -27934,Will China's youth unemployment rate be greater than 17.0 and less than or equal to 18.0 for August 2024?,"PERPLEXITY - -To provide context for forecasting China's youth unemployment rate for August 2024, here are the key points from the latest available data and news: - -1. **Current Youth Unemployment Rate**: As of July 2024, China's youth unemployment rate (for individuals aged 16 to 24, excluding students) was 17.1%, marking the highest figure since the introduction of the new record-keeping system in December. - -2. **Trend**: The youth unemployment rate increased from 13.2% in June 2024 to 17.1% in July 2024, indicating a significant rise over the month. - -3. **Seasonal Factors**: The graduation season in July typically leads to a peak in youth unemployment rates in China, as a large number of graduates enter the job market. - -4. **Economic Indicators**: The overall urban unemployment rate also saw a slight rise, climbing to 5.2% in July from 5% in June, suggesting broader employment challenges. - -5. **Long-term Projections**: According to Trading Economics' global macro models and analysts' expectations, the China Youth Unemployment Rate is projected to trend around 16.00% in 2025 and 18.00% in 2026, indicating a potential for fluctuation in the coming years. - -Given these points, the recent spike in youth unemployment to 17.1% in July 2024 and the typical seasonal peak in July due to graduation, it is reasonable to consider that the rate might stabilize or slightly decrease in August 2024. However, the exact figure for August 2024 is not available in the provided sources, and any forecast would need to consider both the seasonal factors and the broader economic trends. - -**Prediction Markets and Forecasts**: There are no specific prediction markets or forecasts mentioned in the provided sources for the youth unemployment rate in August 2024. However, the long-term projections suggest a potential for the rate to fluctuate around 16% in the coming years, but this does not directly inform the short-term outlook for August 2024. - -In summary, while the recent data indicates a high youth unemployment rate in July 2024, the seasonal nature of this spike and broader economic trends suggest that the rate for August 2024 could potentially fall within the range of greater than 17.0% and less than or equal to 18.0%, but this is speculative without direct forecasts or more recent data. - -######### - -GPT - -### Chain of Thought: - -#### 1. **Understanding the Context and Data Recency**: - - **Current Youth Unemployment Rate**: The most recent data available is from July 2024, showing the youth unemployment rate at **17.1%**. - - **Seasonal Factors**: July typically sees a rise in youth unemployment because new graduates enter the job market. - - **Trend**: There was a significant jump from 13.2% in June 2024 to 17.1% in July 2024. This indicates high volatility, likely driven by seasonal factors, but it could suggest a temporary spike. - - **Economic Indicators**: The broader urban unemployment rate has also slightly increased, which might indicate challenges in the broader economy. - -#### 2. **Consideration of Economic and Structural Factors**: - - **China’s Economic Situation**: China has been facing multiple economic challenges, including effects from COVID-19, a slower recovery than expected, and structural changes in its economy, such as shifts towards automation and a weakened real estate sector. - - **Long-Term Trends**: Projections suggest that youth unemployment rates could trend around 16% in 2025 and 18% in 2026, suggesting that while fluctuations are expected, there may not be a large decrease or increase within a single month. - -#### 3. **Recent Revisions and New Calculation Method**: - - The new method of calculating the unemployment rate excludes students, which might result in more volatile but probably more accurate short-term readings. This also introduces some uncertainty regarding direct comparisons with previous years. - -#### 4. **Base Rate & Historical Data**: - - Historically, China's youth unemployment rates tend to fluctuate but do not show drastic short-term decreases without significant interventions. - - Given the seasonal nature, the unemployment rate might decrease slightly in August after the graduation surge in July stabilizes. - -#### 5. **Default Hypothesis**: - - If we presume that post-graduation effects slightly settle and the broader economic challenges remain, we can hypothesize that the unemployment rate might decline modestly in August from the July spike, bringing it potentially close to the range between 17.0% and 18.0%. - -#### 6. **Probability Assessment**: - - **For the Likelihood of 17.1%, Decaying Slightly from July**: - - Given the seasonality and the likely settling after the July spike, the youth unemployment rate has a good probability of being within or just above the current level but not drastically fluctuating beyond this range. - - The combination of seasonal peak effects, economic headwinds, and recent volatility suggests the unemployment rate could plausibly decrease slightly but remain elevated. - - - **For the Probability of It Falling Specifically Between 17.1% (Greater Than 17%) and ≤ 18.0%**: - - Since 17.1% is the starting point, a range estimate like this offers a plausible balance between a potential small improvement from the seasonal peak and the broader economic challenges. - - - **Final Probability**: - - Considering all factors—economic conditions, seasonality, recent trends, and historical patterns—the probability of the youth unemployment rate falling into the target range of >17.0% and ≤18.0% for August 2024 is reasonably high but not overwhelmingly certain. - -### Final Probability Estimate: -**Probability: 67%**. - -This estimate reflects the likelihood of modest stabilization in unemployment after the July peak, with ongoing economic challenges preventing a significant decrease but not resulting in a sharp increase either." -28006,"Will Kamala Harris say ""I'm speaking"" in the presidential debate with Donald Trump?","PERPLEXITY - -Given the context of Kamala Harris's past debate performances and her known strategies, it is highly likely that she will use phrases similar to ""I'm speaking"" during the presidential debate with Donald Trump. Here are key points from the sources: - -1. **Past Use of ""I'm Speaking""**: Harris has used the phrase ""I'm speaking"" effectively in previous debates, notably during the 2020 Vice Presidential Debate against Mike Pence. This line became iconic and symbolized her composure and confidence. - -2. **Debate Strategy**: Harris's debate strategy involves combining humor with substantive details and using nonverbal cues such as shaking her head or holding up her hand to express disapproval and assert control over the conversation. - -3. **Preparation for Trump**: Harris has been preparing extensively for the debate, including reviewing Trump's past debates and seeking guidance from Hillary Clinton and Joe Biden. Her team has been conducting mock debates to help her anticipate and respond to Trump's aggressive style. - -4. **Expectation of Interruptions**: Given Trump's history of interrupting his opponents, it is anticipated that Harris will need to assert herself during the debate. Her past experiences and preparations suggest she is likely to use phrases like ""I'm speaking"" to maintain control and focus. - -In summary, considering Harris's past debate performances and her preparation for the debate with Trump, it is probable that she will use phrases like ""I'm speaking"" to assert herself and maintain control during the debate. - -**Prediction Markets and Polls**: While specific prediction markets on this exact question are not mentioned in the provided sources, general polls and debate expectations suggest that Harris is well-prepared and likely to use her past strategies to handle Trump's aggressive style. - -**Recent Updates**: There are no recent updates that would indicate a shift away from Harris using her established debate strategies, including phrases like ""I'm speaking."" Expert opinions, such as those from James Carville and Julian Castro, emphasize Harris's growth and preparedness for the debate. - -######### - -GPT - -### **Step-by-Step Breakdown and Thought Process:** - ---- - -**1. Review of the Question**: -- **Event**: Whether Kamala Harris will say ""I'm speaking"" during the debate with Donald Trump scheduled for September 10, 2024. -- **Resolution criteria**: The forecast will resolve *Yes* only if Harris says the **exact** phrase ""I'm speaking"" or ""I am speaking"" during the debated, judged by live media documentation. Closely-related phrases or expressions do **not** count. - ---- - -**2. Translating Background into Variables**: - -- **Historical behavior**: Kamala Harris has used ""I'm speaking"" during past debates, most notably in her Vice Presidential Debate with Mike Pence in 2020. This phrase became iconic, signifying her firm yet composed demeanor. - -- **Debate Environment**: The Presidential debate scheduled for September 10, 2024 will feature Trump, a known aggressive debater who often interrupts his opponents, creating motivating factors for Harris to use assertive rebuffs like ""I'm speaking."" - -However, a potential disrupter to this is that **microphones will be muted** for the candidate when it’s not their turn. This is a new variable that didn’t feature prominently in her prior debates. If microphones are cut off for interruptions, the chances to use such phrases (logically in reaction to disruptions) may drop. - -- **Preparation**: Harris has been extensively coached for this debate. There's a history of preparation where the candidate works deeply with seasoned political teams to hone messaging and delivery. Her team is certainly aware of the public memory of her 2020 ""I'm speaking"" moment and likely understands the potential symbolic power of reprising that reference. - ---- - -**3. Base Rate of Similar Historical Events**: -The **precedent** includes not only Harris' 2020 debate against Mike Pence but also debates featuring Trump. In past debates, particularly against Hillary Clinton and Joe Biden, Trump interrupted frequently, provoking assertive responses from his counterparts. Given Trump’s aggressive debating style and history of interruptions, base rates would suggest a high likelihood of instances where Harris *could* use similar phrases. - -However, **microphone muting** is an unusual deterrent factor. This technology feature—designed specifically to reduce combative exchanges—could fundamentally lower the expected interruptions. Since the motive for using ""I'm speaking"" arises primarily from a need to reassert space during an interruption, this would logically reduce the opportunities for Harris to use it at all. - ---- - -**4. Adjusting for Specific Variables**: -- **Microphone Muting:** Given that moderators will have control over muting microphones, this mechanical process reduces the space for real-time interruptions where Harris might need to use ""I'm speaking."" If Trump is unable to vocally disrupt her while she speaks, this removes a common stimulus that would have triggered this phrase. - -- **Trump’s Aggressive Style**: Despite microphone muting, Trump may still employ non-verbal disruptions or flout norms aggressively, including speaking when his microphone is off or attempting visual distractions (e.g., gestures, body language). Harris might still feel compelled to look over and assert herself even if Trump is not *technically* interrupting her speech directly. However, without audio feedback from Trump, the key stimulus from his verbal interruptions is much diminished. - -- **Preparedness and Diligence**: Harris is known for her preparedness. Her team might coach her in ways that involve shifting her debate response strategies under these new conditions, possibly alleviating the need for ""I'm speaking"" given moderators’ control of interruptions. - ---- - -**5. Assessing Novel Elements (Variance)**: -- **Muted Mics**: This is a novel feature that overrides the base case assumption where a verbal rebuff would be necessary (in prior, non-muted debates). This radically changes the interplay dynamics, which serves as a **negative weight** on the likelihood of Harris using the phrase. - -- **Symbolism Considerations**: Despite this, Harris might intentionally repeat the phrase using ""I'm speaking"" as a callback to her well-received 2020 debate performance. Recalls like this do happen in debates, so there's a small upward likelihood that Harris could state this independently of interruptions—if she feels the need to anchor herself to 2020. - ---- - -**6. Scenario Mapping**: -- **High Likelihood Scenarios**: - - Trump begins interrupting frequently → Harris attempts to assert herself, repeating ""I'm speaking"" (but only feasibly if microphones are malfunctioning or rules are disregarded, both low-chance events). - -- **Moderate Likelihood Scenarios**: - - Trump tries to disorient via body language or visuals → Perhaps small chance Harris would adopt this phrase, but this scenario is relatively weaker without a direct microphone interruption. - -- **Low Likelihood Scenarios**: - - Harris recalls the 2020 moment intentionally in a calm context, repeating it merely for effect or as a symbolic gesture, even if not prompted by an explicit interruption from Trump. - ---- - -### **Final Probability Evaluation**: - -- **Muted microphones**: A major limiting factor, known to reduce the impetus for the phrase's use significantly. This places a large weight against the forecast. -- **Trump's debate style**: An interruption-prone debater, but compliance to new debate rules might reduce interruptions that we'd otherwise expect. -- **Symbolism or memorable callback**: There is some but limited likelihood Harris might consciously repeat the phrase as an intentional gesture akin to a soundbite. - -The possible outcome is that while the phrase was iconic in 2020, the muted microphone innovation and Harris' determination to remain stoic without necessarily repeating phrases from the past reduce its likelihood substantially. - ---- - -### **Conclusion**: - -Although there is a small chance Harris will intentionally repeat the phrase for rhetorical effect, particularly because of its symbolic visibility, this is outweighed heavily by the muting policy and the reduced need to assert control through an explicit ""I'm speaking"" line due to likely technical moderation. - -**Prediction**: ""Probability: 15%""" -28007,"Will the national price of gasoline exceed $3.25 per gallon when accessed by Metaculus Admins on October 1, 2024?","PERPLEXITY - -To address the question of whether the national price of gasoline will exceed $3.25 per gallon by October 1, 2024, we need to examine current trends and recent data. - -1. **Current Prices**: - - **AAA Gas Prices**: As of September 2, 2024, the national average gas price is $3.331 per gallon for regular gasoline. - - **Energy Information Administration (EIA)**: The latest data from the EIA, as of September 2, 2024, reports the U.S. regular all formulations retail gasoline price at $3.411 per gallon. - -2. **Trends**: - - **Recent Changes**: The price has seen a slight decrease over the past week, with a -0.64% change from the previous week and a -13.23% change from one year ago. - - **Historical Context**: The highest recorded average price for regular unleaded gasoline was $5.016 on June 14, 2022, indicating that current prices are significantly lower than the peak. - -3. **Predictions and Forecasts**: - - **Short-term Forecasts**: There are no specific short-term forecasts provided in the sources that predict prices exceeding $3.25 by October 1, 2024. However, given the current prices are already above $3.25, it seems likely that prices could remain above this threshold unless there is a significant decrease. - - **Long-term Trends**: The overall trend in gasoline prices can be influenced by various factors including global crude oil prices, OPEC negotiations, and domestic demand. - -4. **Expert Opinions**: - - **AAA Spokesperson**: Jeanette McGee mentioned in 2021 that robust gasoline demand and more expensive crude oil prices were pushing gas prices higher, but this does not directly address the current situation. - -Given the current prices are already above $3.25 per gallon and considering the recent trends, it appears likely that the national price of gasoline will exceed $3.25 per gallon by October 1, 2024, unless there is a significant downward shift in the market. However, specific predictions or forecasts for October 1, 2024, are not available in the provided sources. - -**Prediction Markets**: No specific data from prediction markets like Metaculus or betting sites are included in the search results to provide additional context on forecasted outcomes. - -In summary, based on current data and trends, it seems probable that gasoline prices will remain above $3.25 per gallon by October 1, 2024, but without specific forecasts or prediction market data, this assessment is based on recent price movements and historical context. - -######### - -GPT - -### **Step 1: Understand the Question** -We are tasked with forecasting whether the national price of gasoline, as listed by AAA, will exceed $3.25 per gallon when accessed by Metaculus Admins on **October 1, 2024**. The current price is $3.273 as of **September 8, 2024**, meaning it is already relatively close to the threshold. Our forecast window is approximately **three weeks from today's date, September 9, 2024**. - -According to the resolution criteria, the price displayed at the exact moment Metaculus Admins access AAA's site on October 1 will determine the result. - -### **Step 2: Analyze Current Situation** -- **Current National Average (AAA, Sept 8, 2024)**: $3.273 -- **EIA Data**: EIA shows prices marginally higher at $3.411 (Sept 2, 2024), indicating their broader data metrics have slightly different averages but point to a similar trend. -- **Price Movement**: We see a small **-0.64% decline over the last week**. This suggests prices are falling, but only slightly. -- **YTD Comparison**: Prices have fallen about **13.23% compared to a year ago**, reflecting a long-term downward trend since the 2022 peak of $5.016. -- **Recent High**: While prices reached a record high in June 2022, they’ve since decreased substantially. - -### **Step 3: Short-Term Price Movements** -Gasoline prices can be volatile and can fluctuate due to several factors: -- **Crude Oil Prices**: Gasoline prices are heavily influenced by crude oil prices, which in turn depend on global demand, supply constraints by OPEC, or geopolitical tensions. -- **Seasonal Factors**: September and early October generally see **decreasing gasoline consumption** in the U.S. post-summer driving season, which can reduce demand and lower prices. -- **Supply Chain Issues**: Any unforeseen disruptions in refining capacity, storms, or supply shortages could lead to price spikes. -- **Strategic Reserve Releases**: The U.S. government could intervene by releasing oil from the strategic petroleum reserve, though such action seems less likely in the current context unless an emergency arises. - -### **Step 4: Historical Data and Base Rates** -Looking at historical gasoline prices, we know that: -- The immediate post-summer period **typically does not see major spikes,** rather, demand tends to fall into the fall months. -- **Historical base rates** suggest that prices fluctuate narrowly in a window of a few cents up or down over the course of a few weeks. Calculating from a weekly drop of **~0.64%** in the current trend, we could estimate a decline over the next three weeks of around **1.8-2% cumulative drop**. - -### **Step 5: Key Drivers for October 2024** -For the period leading up to October 1, consider the following potential drivers: -1. **Predicted Oil Market Stability**: Global oil markets are relatively stable right now. There’s no major declared geopolitical crisis like an unexpected war in the Middle East, nor a significant supply shock on the horizon. Hence, crude oil prices should remain relatively stable to slightly declining. -2. **Refining Capacity and Major Storm Probability**: Gulf Coast hurricanes could hamper refining, but as of September 9, 2024, there are **no major storms forecasted**. While it's impossible to completely dismiss late-season hurricanes, it's more likely that no major external disruption will emerge within the next few weeks. -3. **Demand Trends**: Traditionally, after the Labor Day holiday and the end of summer driving season, demand decreases. This downward pressure on demand typically results in **modest price declines through September and October.** - -### **Step 6: Quantifying Likely Outcomes** -- **Base Rate of Decrease**: There is a reasonable precedent that gasoline prices, after the Labor Day holiday, continue to decline due to reduced demand. Using a **conservative estimate**, if the current trend of ~0.64% weekly drops continues steadily, we may see a reduction in gasoline prices approaching: - - (-2%) x $3.273 (comparable to recent weak trend) = approximately **$3.21-$3.22** by October 1. - -- **Volatility Consideration**: Gasoline prices are subject to volatility, but since no major shocks are anticipated (and hurricanes or geopolitical events don't have predictive indicators right now), we can forecast a period of stability or slight decline. This means the likelihood of prices **increasing above $3.25** is tied to a low-probability, high-impact event. - -- **Probability Model**: If we consider a stable-to-declining trend as the most likely outcome, the chances of prices staying **below $3.25** appear stronger than the likelihood that they will rise back above it. - -### **Conclusion** -- **Base Rate Default**: Gasoline is currently close to $3.25, but slight weekly declines indicate it could fall just below this threshold by October 1. -- **Volatility or Shocks**: While price spikes can't be ruled out entirely, no significant events appear on the horizon. -- **Trend Continuation**: Historically, late September and early October are associated with declining demand, which usually puts downward pressure on prices. - -Given these factors, I estimate a **higher likelihood** that prices drop below $3.25 by the resolution date: - -### **Final Probability Estimate**: -Given the confluence of trends suggesting a small but consistent decline in prices, balanced against low but non-negligible volatility potential, I estimate: - -**Probability: 35%** that gasoline will exceed $3.25 per gallon at resolution." -28008,"Will the US weekly 30-year fixed mortgage rate be above 6.35% on October 1, 2024, according to Freddie Mac?","PERPLEXITY - -To address the query about whether the US weekly 30-year fixed mortgage rate will be above 6.35% on October 1, 2024, according to Freddie Mac, here is a concise rundown of relevant information: - -1. **Current Rates**: As of the latest data available, the 30-year fixed mortgage rate has been trending downward. The most recent rate reported by Freddie Mac is 6.35% as of August 29, 2024. - -2. **Recent Trends**: The rates have been decreasing over the past few weeks, with a notable drop to 6.16% as of September 9, 2024, according to Investopedia. This indicates a continuing downward trend. - -3. **Economic Context**: The decrease in mortgage rates is aligned with the drop in long-dated Treasury yields, reflecting ongoing concerns about economic growth in the US. - -4. **Expert Opinions**: Sam Khater, Freddie Mac’s Chief Economist, suggests that softer incoming economic data indicate that rates will gently slope downward through the end of the year. He also mentions that rates would need to decline another percentage point to generate buyer demand. - -5. **Forecast**: The Mortgage Bankers Association (MBA) anticipates that 30-year mortgage rates will range between 6.1% and 6.8% in 2024, with expectations of rate cuts by the Federal Reserve later in the year. - -Given these points, the current downward trend and expert predictions suggest that the rate might continue to decrease or stabilize around the current levels. However, predicting the exact rate on October 1, 2024, involves uncertainty and depends on various economic factors and Federal Reserve actions. - -**Key Data Points**: -- **Current Rate**: 6.16% as of September 9, 2024. -- **Recent High**: 7.03% in December 2023. -- **Long-term Average**: 7.73%. -- **Forecast Range**: 6.1% to 6.8% in 2024. - -**Prediction Markets and Forecasts**: -- The MBA and various economists predict a range of 6.1% to 6.8% for 2024, with anticipated rate cuts by the Federal Reserve. -- Trading Economics forecasts the rate to be around 6.50% by the end of the quarter. - -This information provides context but does not make a definitive prediction about the rate on October 1, 2024. - -######### - -GPT - -### Step 1: Understanding the Context and Question Details -The key question is whether the 30-year fixed mortgage rate (according to Freddie Mac) will be above 6.35% on October 1, 2024. **Today's date is September 9, 2024**, providing three weeks to assess potential rate changes. - -#### **Current Situational Analysis:** -- As of **September 8, 2024**, the Freddie Mac rate was precisely **6.35%**. -- The most recent secondary data (from Investopedia) suggests that as of **September 9, 2024**, the rate fell to **6.16%**, continuing a **downward trend** over the last few weeks. - -##### **Important Factors:** -1. **Historical and Recent Trends**: - - The rate hit a high of over 7% in late 2023, aligning with the Fed's aggressive interest rate hikes. - - Recently, rates have been dropping, with reports indicating this is driven by declining long-term Treasury yields and economic growth concerns. - -2. **Economic Sentiment**: - - **Sam Khater (Freddie Mac Chief Economist)** and others have indicated that softer economic data is driving rates downward. - - Expectations of potential **rate cuts by the Federal Reserve** later in 2024 complicate the outlook, but at the moment, factors like inflation cooling and anticipation of less-aggressive Fed policy are pushing yields lower. - -3. **Forecasts from Credible Sources**: - - The **Mortgage Bankers Association (MBA)** projects a range of 6.1% to 6.8% for the 30-year fixed mortgage rate in **2024**. If these projections hold, there is uncertainty whether the 6.35% threshold will be exceeded, but the range allows room for upward movement. - - **Trading Economics** forecasts a rate of 6.50% by the end of Q3 2024, meaning there's a forecasted increase relative to the most recent rate (6.16%). - -4. **External Factors**: - - Economic data in the coming weeks, including inflation figures, GDP growth, or labor market statistics, could lead to volatility in longer-dated Treasury yields and mortgage rates. - - **Global economic risks** (a potential European downturn, stagnation in China) or domestic concerns (a looming government shutdown, political instability) could sway markets further. - -#### **Resolution Criteria Clarity**: -- The rate must be **above 6.35%** on October 1, 2024, when **Metaculus Admins** access the Freddie Mac tracker page. -- The distinction that matters is between 6.35% or **above** versus 6.35% or **below**, at a specific point in time on a specific date. - ---- - -### Step 2: Constructing a Base Rate from Historical Data - -Using a historical base rate for similar events can help inform expectations. However, it's hard to find exact direct analogs in a rapidly shifting macroeconomic environment such as in 2024. - -#### Key Observations: -- **Upside movements in mortgage rates** typically rely on increasing Treasury yields, which correlate strongly with inflation, GDP growth, and Federal Reserve tightening. -- Currently, macroeconomic indicators suggest a cooling economy with lower inflation, which points to a **stable to slightly declining mortgage rate** environment. -- Absent any shocking upward economic surprises (e.g., a resurgence in inflation or unexpected positive data in the labor market), the **current trend suggests sideways to slightly downward pressure** on rates (or at least no large upward movement). - -However, **mortgage rates do show volatility** even in short periods, and unforeseen events such as a sudden spike in inflation, an unexpected Fed interest rate change, or global financial instability could cause rapid changes in Treasuries and, thus, mortgage rates. - ---- - -### Step 3: Incorporating Expert Opinion and Economic Forecast Averages -The consensus view—based on predictions from sources like Freddie Mac, Trading Economics, and the MBA—suggests that rates for the remainder of 2024 will **hover around the upper 6% range**, but remain **below or slightly above 6.35%**. - -- **Predictions point to a range between 6.1% and 6.8%**, with a downward bias due to softer economic data. -- Multiple sources suggest low potential upward movement without strong external shocks. - -Additionally, even though some forecasts (e.g., Trading Economics) predict a modest **increase to 6.50%** by the end of September, there's uncertainty over whether this will translate to rates being above **6.35% specifically** on October 1, 2024. - ---- - -### Step 4: Synthesis of All Factors and Final Probability Estimate - -#### **Arguments for No**: -- Recent trends show a **downward move**, and today’s rate (6.16%) is already **below** the 6.35% threshold. -- The consensus from experts indicates that downward or stable trends are expected in the near term, given Treasury yields have been declining. -- Without a significant economic or financial market shock, it's more likely that rates will remain stable or slightly below 6.35%. - -#### **Arguments for Yes**: -- Volatility in rates may bring a **temporary spike** (as seen periodically in the past) due to unexpected economic reports, geopolitical events, or global market turbulence. -- The MBA forecast range (6.1% to 6.8%) does not completely rule out the possibility of rates exceeding 6.35%, and Trading Economics predicts a **rate of 6.50% by the end of Q3**. - -### Final Probability Judgment - -Given the combination of recent movements, expert forecasts, and macroeconomic conditions, the probability of mortgage rates being **above 6.35%** on **October 1, 2024** is **moderate but less likely** than remaining at or below that threshold. I will weigh the trends more heavily than rare potential shocks, as these are rare over short timeframes. - -**Final Probability Estimate:** - -- **Probability: 35%** that the rate will be above 6.35% on October 1, 2024." -28008,"Will the US weekly 30-year fixed mortgage rate be above 6.35% on October 1, 2024, according to Freddie Mac?","PERPLEXITY - -To provide context for forecasting whether the US weekly 30-year fixed mortgage rate will be above 6.35% on October 1, 2024, according to Freddie Mac, here are the key points from recent sources: - -1. **Current Rates**: - - As of August 29, 2024, the 30-year fixed mortgage rate is at 6.35%, which is lower than the long-term average of 7.73%. - - The rate has remained relatively stable, with slight fluctuations. For example, it was 6.44% in the week ending August 23, 2024, and 6.46% as of August 22, 2024. - -2. **Trend and Forecasts**: - - The Mortgage Bankers Association (MBA) anticipates that 30-year mortgage rates will range between 6.1% and 6.8% in 2024, suggesting a potential for slight increases or decreases. - - Freddie Mac’s Chief Economist, Sam Khater, notes that softer incoming economic data suggest rates will gently slope downward through the end of the year, indicating a possible decrease. - - The Federal Reserve’s signal of incoming rate cuts could lead to a drop in mortgage rates. Economists expect two rate cuts, likely starting in September, which could alleviate upward pressure on mortgage rates. - -3. **Recent Updates**: - - The recent stability and slight decreases in mortgage rates are attributed to the drop in long-term Treasury yields, as investors seek more secure fixed-income securities due to apprehensions about US economic growth. - - The rate has fallen significantly from its high of 7.23% in the same period last year, indicating a downward trend. - -Given these points, the current trend suggests that mortgage rates might remain stable or slightly decrease by October 1, 2024. However, the forecast is subject to changes in economic conditions and the Federal Reserve’s actions. The most recent data available does not strongly indicate an increase above 6.35% by October 1, but rather suggests a potential for rates to remain around or slightly below this level. - -**Prediction Markets and Forecasts**: -- Trading Economics forecasts the 30-year mortgage rate to be 6.50% by the end of the third quarter of 2024, which is slightly above the current rate but does not provide a specific forecast for October 1. - -**Expert Opinions**: -- Sam Khater, Freddie Mac’s Chief Economist, suggests that rates will gently slope downward through the end of the year, indicating a possible decrease rather than an increase above 6.35%. - -**Recent Values**: -- As of August 29, 2024, the 30-year fixed mortgage rate is at 6.35%. - -In summary, while there is always uncertainty in forecasting, the current trend and expert opinions suggest that the rate might not increase significantly above 6.35% by October 1, 2024, and could potentially remain stable or decrease slightly. - -######### - -GPT - -### Understanding the Question -The task is to predict whether the US 30-year fixed mortgage rate will be **above 6.35%** on **October 1, 2024**, based on information available as of September 10, 2024. - -The resolution criteria are clear: A ""Yes"" outcome will occur if the 30-year fixed mortgage rate is **greater than** 6.35% as displayed on the Freddie Mac page when retrieved on that specific date. If the rate is **equal to or less than 6.35%**, it resolves as ""No."" - -The current rate stands at **6.35%** as of **September 8, 2024**, making this a classic **Non-Market Threshold Value Event**. My task involves understanding mortgage rate trends and significant economic drivers over the next month to assess whether the rate is more likely to **increase, stay the same, or decrease**. - -### Chain of Thought - -#### Default Outcome: The Rate Stays the Same or Decreases Slightly -Since we are predicting future values of the 30-year mortgage rate, the **default assumption** based on momentum (if no strong factors push the rate up or down) is that the recent rate of **6.35%** will likely remain **close to** this threshold. The mortgage rate has remained **relatively stable** in recent weeks (fluctuating between 6.3-6.4%) despite historical averages being much higher than the current rate. This should be our **base case scenario**: no drastic deviations without significant shocks. - -**Strength of this Reason**: 7/10 -There isn't conclusive evidence suggesting drastic movements up or down in just 3 weeks. The rate might stay close to where it is if no major news (economic or geopolitical) shakes things up. - -#### Federal Reserve Actions: Potential for Rate Cuts -The research assistant notes **upcoming potential Federal Reserve rate cuts**. If the Fed implements these rate cuts, as economists expect, this could **reduce upward pressure** on mortgage rates. The Mortgage Bankers Association (MBA) has forecasted 30-year fixed mortgage rates for 2024 to be mainly **between 6.1% and 6.8%**, with **Freddie Mac’s** chief economist suggesting a **downward slope** for rates towards the year’s end. While these cuts might still be incremental, even a **small half-percent drop** in long-term rates would be enough to push the 30-year mortgage rate below 6.35%. - -**Strength of this Reason**: 8/10 -The Federal Reserve’s movement has historically been a significant driver of mortgage rates, and the prediction of upcoming cuts strongly supports a possible decrease, or at least stabilization, in the mortgage rate. This is a significant piece of information that weighs heavily on the forecast. - -#### Current Market Sentiment Favors Stability or Modest Decrease -Both **Sam Khater** (Freddie Mac’s Chief Economist) and the broader market expect rates to **gently decrease** due to softer economic data. A slowing US economy, softer inflation data, and job growth reductions all indicate a **moderating economy**, which could spur the Fed and other market forces to maintain supportive conditions for lower rates. Additionally, **Treasury yields**, which heavily influence mortgage rates, have been trending lower as investor risk appetite decreases amid economic uncertainty. - -**Strength of this Reason**: 7/10 -Given the macroeconomic environment, conditions are favorable for mortgage rates to remain stable or decline. However, it's worth noting that a **modest** rate increase could still appear, depending on incoming data. With **21 days** until the resolution date, this sentiment provides moderate confidence of stability or reduction. - -#### Prediction Markets & Forecasts Show Limited Upside -Forecasts tend to be clustered in the **6.1%-6.8%** range according to the **Mortgage Bankers Association** and other sources. While there is a slight chance the rate could clock in at **6.5%**, these forecast distributions suggest an increase, if any, would be **modest** and not enough to substantially clear the 6.35% threshold by October 1. Even more bullish forecasts don’t suggest rate hikes well into the 7% region, which happened earlier in 2023 during inflationary pressures. - -**Strength of this Reason**: 6/10 -While prediction markets are often good indicators, they are not perfect, especially when different agencies cluster around similar ranges without providing significant divergence. Additionally, the average forecast stretches over multiple months, not just by October 1st, so it still suggests a modest or slight increase, if any. - -### Reasons for an Increase Above 6.35% - -#### Economic Shock (Low Probability) -An unexpected economic shock could lead to upward pressures on rates. Should inflation reaccelerate or if some geopolitical event occurs (e.g., a surge in oil prices that shocks inflation expectations), mortgage rates could tick upwards in response to market fears, causing higher yields. However, nothing in the current data strongly indicates that a significant shock will emerge within the next three weeks. - -**Strength of this Reason**: 4/10 -While possible, the likelihood of such an event is low. Most indicators point to easing inflation and stabilizing growth rather than rising unpredictability. - -#### Slight Increase Given Historical Volatility -Given the volatility observed in mortgage rates in the past year, where rates fluctuated by whole percentage points over mere months, we cannot rule out short-term oscillations based on unexpected shifts in bond markets or liquidity events. However, even with that, these moves would need to push the rate **slightly higher** than expected for this short period. - -**Strength of this Reason**: 6/10 -While volatility could push the rates slightly upward, the momentum and expert forecasts currently lean towards stabilization or gentle decreases. A **small nudge above** 6.35% could occur, though it seems unlikely given the relatively stable recent trajectory. - ---- - -### Consideration of Base Rates and Historical Data -Historically, when the mortgage rate is near a stable number like **6.35%**, absent external shocks, it tends not to oscillate too far unless there are major drivers such as inflation surges or aggressive Fed policies. In the coming weeks, the base expectation should lean towards **persistence** within a narrow band barring shocks. The **uncertainty** comes in the **fine marginal difference** between a rounded 6.35% rate and where rates might drift toward (say, **6.3-6.5%**). - ---- - -### Conclusion -Given the balance of factors, it seems **more likely** the rate will either stay near **6.35%** or slightly **decrease**, with less strong signals for a significant upward shift. The downward pressure from potential Fed cuts, expert predictions, and softer economic conditions leads me to believe that the rate exceeding 6.35% is non-trivial but less probable than staying at or below it. - -### Probability: **40%**" -28022,"Will SpaceX make any new posts on its Updates page before October 1, 2024? ","PERPLEXITY - -To address the question of whether SpaceX will make any new posts on its Updates page before October 1, 2024, we need to consider the company's recent activities and posting patterns. - -1. **Recent Posts and Activities**: - - **Polaris Dawn Launch**: SpaceX recently launched the Polaris Dawn mission on September 10, 2024, which included the first commercial spacewalk and tested laser-based communications with Starlink. - - **Upcoming Missions**: NASA and SpaceX are targeting a launch period opening on October 10 for the Europa Clipper mission, indicating ongoing preparations and potential updates related to this mission. - - **General Posting Pattern**: SpaceX's Updates page typically includes announcements about upcoming launches, mission successes, and significant milestones in their projects. - -2. **Frequency of Updates**: - - The Updates page on SpaceX's website is used to share significant developments, such as launch schedules, mission outcomes, and partnerships. Given the frequency of SpaceX's launches and the variety of projects they are involved in, it is likely that there will be updates before October 1, 2024. - -3. **Specific Upcoming Events**: - - **Europa Clipper Mission**: With the Europa Clipper mission scheduled for October 10, it is reasonable to expect updates related to this mission before its launch date. - - **Other Launches and Missions**: SpaceX has a busy launch schedule, including commercial satellite launches and missions for the U.S. Space Force, which could prompt updates on their page. - -Considering these points, it is probable that SpaceX will make new posts on its Updates page before October 1, 2024, given the company's active launch schedule and the need to communicate about upcoming missions and recent achievements. - -**Recent Sources**: -- **NASA News Releases**: 2024 News Releases - NASA -- **SpaceX Updates**: Updates - SpaceX -- **Polaris Dawn Launch**: SpaceX launches Polaris Dawn crew to attempt 1st commercial spacewalk - ABC7 Chicago - -######### - -GPT - -### Analysis and Step-By-Step Forecasting: - -We are tasked with determining the likelihood that SpaceX will post on its Updates page before October 1, 2024. Let’s break down the problem piece by piece while considering the resolution criteria and relevant background information: - ---- - -#### **Step 1: Review of SpaceX Posting Patterns (Base Rates)** - -Examining SpaceX's historical posting behavior provides a vital base rate for predicting future posts. From my research and knowledge of SpaceX's updates page activity, the company consistently updates the page about notable milestones, successful missions, project announcements, and major partnerships. However, the frequency is not daily or even necessarily weekly, especially if there are no major new developments worth announcing to the public. For instance: - -- **August 2024**: A post was made on August 12, 2024, which was a month ago. -- There are month-long or longer gaps in updates unless there’s an event *prompting* a new post. This is crucial because while they do update with some regularity, the frequency is not high enough that a post within the next 19 days is guaranteed simply based on regular cadence. - -**Historical Base Rate Insight**: Gaps between updates are not uncommon. Therefore, the base rate of posting in a given month might be closer to around *1-2 updates*, meaning that posting within any given 19-day window – such as the one remaining here – is somewhere in the neighborhood of 50% based on historical precedent alone, *absent any specific upcoming events*. This gives us a decent prior unless there’s compelling evidence either way. - -**Strength of this reason**: 6/10 (significant but not a decisive signal for timing) - ---- - -#### **Step 2: Upcoming Notable Events** - -A critical component of the forecast involves what events might *trigger* an update: - -- **Polaris Dawn Mission (September 10, 2024)**: Given that the Polaris Dawn mission involved groundbreaking activities (first commercial spacewalk, laser communication via Starlink), it is plausible that SpaceX would post a detailed update about this mission in the coming days. However, the mission has already launched—so while there may be follow-up posts emphasizing results or milestones achieved from this mission, there’s no certainty that they will wait precisely until this post-launch period to post more information. - -- **Europa Clipper Mission (October 10, 2024)**: This is potentially a significant driver for a new post, but note the launch date is after October 1. Based on typical practices, mission updates might come a few days to a couple of weeks before launch, meaning there’s a window of about **10 days** left for this event to trigger content. If they have important preparations to announce, we could expect an update; however, they may wait until closer to the actual launch, reducing the likelihood of an imminent post. - -Both of these are strong triggers for an update, but neither appears to *demand* a post this September. The timing of these events is crucial to understanding the subtle probabilities of an update. **Upcoming potentially notable events** do have some probability-increasing factors, but the markets in focus may delay an update to ensure full-posted follow-through. - -**Strength of this reason**: 7/10 (positive, but not overwhelming) - ---- - -#### **Step 3: General Traffic and Activity within SpaceX** - -SpaceX is typically an active company, but as of today, **September 11, 2024**, no major unexpected trends or sudden developments have been reported in recent days that would compel an update besides the known event (Polaris Dawn). The company's general busy schedule is a positive indicator for updates, *but there isn’t an observable spike in activity*. Given SpaceX’s *operational inertia*—if things keep moving at current rates without unexpected demands for announcements—the likelihood may decrease unless posting trends pick up. That said, there’s always a chance that new partnerships, Starship progress, or other internal milestones, which often coincide with operational shocks, might lead to an unexpected post. - -**Strength of this reason**: 4/10 (possibly relevant but speculative unless something changes substantially) - ---- - -#### **Step 4: Other Considerations** - -1. **Website Downtime Risk**: The forecast assumes that if the website is down when a check is made, the answer automatically resolves as ""No."" Currently, there is no reason to anticipate SpaceX’s website will experience downtime before October 1, so I assign a negligible probability for this scenario. - -**Strength of this reason**: 1/10 (minimal chance, and I will discount it primarily) - -2. **Pressure to Communicate**: SpaceX's media and communications strategy are critical. The company is known for announcing key missions in advance (as seen with Starship tests, Falcon missions, Starlink launches). Still, given that Polaris Dawn’s launch happened only yesterday, and nothing urgently signals the need for regular updates, they may forego new posts unless there’s something major to communicate. - -**Strength of this reason**: 3/10 (decent, low impact since it's typical) - ---- - -### Summary & Probability Calculation: - -- **Base Rate Analysis**: The historical posting patterns favor irregular updates, often spaced with approximately 1-2 posts in a given month. This gives a moderate base likelihood of 50-55% depending on whether events trigger more frequent activity. - -- **Upcoming Events**: Polaris Dawn's success is upcoming and may trigger another post, especially given the significance of the commercial spacewalk. The Europa Clipper mission *could* prompt a post but may come a little too late (after October 1). The likelihood of a post related to one of these is not overwhelming but adds a secondary bolster to the probability, potentially raising it by 10-15%. - -- **Other Factors**: Website downtime seems negligible, and the general communication strategy likely sticks to major milestones, so extra unexpected triggers are unlikely. - ---- - -### Final Answer: -Considering all factors – base rates, upcoming events, and temporal dynamics – the probability that SpaceX will post a new update before October 1, 2024, is moderate but not certain. Given historical posting gaps, the closeness of upcoming missions, and the timing window available, my final probability estimate is: - -**Probability: 70%**" -28020,"Will the Boeing Starliner Commercial Crew program be canceled before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether the Boeing Starliner Commercial Crew program will be canceled before October 1, 2024, here are key points from recent news and developments: - -1. **Recent Flight Issues**: The Boeing Starliner faced significant issues during its crewed flight test, including thruster malfunctions and helium leaks, which led to the cancellation of its return journey with astronauts on board. Instead, the spacecraft returned to Earth uncrewed on September 6, 2024. - -2. **NASA's Decision**: NASA decided it was safer to bring the astronauts back on a SpaceX Crew Dragon, indicating concerns about the reliability of the Starliner. This decision was made after extensive ground testing and analysis of the technical issues encountered during the ascent. - -3. **Future Plans**: The next flight of the Starliner is scheduled for August 2025, but it is unclear if it will be crewed. NASA needs time to analyze the data gathered and assess what changes are required to both the design of the ship and the way it is flown. - -4. **Program Delays and Setbacks**: The Starliner program has faced numerous delays and setbacks, including a software glitch during its first uncrewed test flight in 2019 and the discovery of flammable tape in the cabin after its second test in 2022. - -5. **Contractual and Financial Context**: Boeing has faced substantial budget overruns for the Starliner program, exceeding $1.6 billion. The program's future is uncertain, especially considering the decommissioning of the ISS in 2030, which limits the time available for the Starliner to prove its worth. - -Given these points, while there is no direct statement indicating the program will be canceled before October 1, 2024, the ongoing issues and NASA's decision to use SpaceX for the return journey suggest significant challenges for the Starliner program. However, as of the current date (September 11, 2024), there is no official announcement of cancellation. - -**Prediction Markets and Expert Opinions**: There are no specific prediction markets or betting odds mentioned in the provided sources regarding the cancellation of the Boeing Starliner Commercial Crew program. Expert opinions, such as those from NASA's Commercial Crew Program chief Steve Stich, emphasize the need for further analysis and changes but do not indicate immediate cancellation plans. - -######### - -GPT - -### Context and Structure - -This forecasting question deals with a **""binary outcome event""**—whether the Boeing Starliner Commercial Crew program will be canceled before October 1, 2024. - -We’re tasked with weighing different factors: **technical issues**, **organizational responses**, **NASA's broader strategy**, and **Boeing’s business priorities**. With today’s date of **September 11, 2024**, there are only 20 days until the October 1, 2024 deadline. Given the length of time until the resolution date, we should lean heavily towards near-term factors that require immediate decision-making and execution. - ---- - -#### Framework for Analysis - -1. **Default Path: No Cancellation (Status Quo)** - - Boeing is still engaged in the Commercial Crew Program, albeit with significant delays and setbacks. - - There is no public or official announcement of Boeing’s withdrawal or NASA terminating Boeing as a provider. - - The 'Starliner-1' mission is scheduled for 2025, and Boeing, historically, has worked on fixing issues rather than abandoning programs in the face of cost overruns or delays. Furthermore, NASA has been relatively patient. - - **Strength of Reasoning: 9/10** – Organizational inertia and the status quo heavily suggest it is more probable that no significant change (like cancellation) will occur in a short time frame, particularly as canceling would be a major, highly visible reversal that would likely have accompanying formal announcements. - -2. **Recent Problems with Starliner Flights** - - The Crew Flight Test (CFT) in 2024 failed, especially the thruster system, which cast doubt over Boeing's handling of the program. - - NASA signaled a low level of immediate confidence in the Starliner spacecraft by bringing the astronauts back on a SpaceX Dragon capsule, effectively showcasing an operational ""loss of face"" for Boeing. - - Ongoing technical issues like this can be tipping points where repeated failures convert into program termination, especially given the cumulative $1.6 billion cost overruns Boeing has already seen. - - **Strength of Reasoning: 7/10** – The issues are serious, but they haven't triggered an official reaction yet. The fact NASA relied on SpaceX instead of Starliner subtly increases the risk of Boeing withdrawing, especially given Boeing's significant internal costs. - -3. **Time Constraints** - - Since we are less than a month from the deadline, the default expectation is inertia—Boeing is more likely to push forward through technical difficulty than take the extreme step of canceling the program on such short notice. - - Contracting arrangements often involve prolonged negotiations and formal announcements, neither of which have appeared publicly at this time. - - **Strength of Reasoning: 10/10** – Time constraints strongly favor a ""no cancellation"" outcome, as major program-level decisions like withdrawal or termination rarely happen on such a quick scale without years of internal gestation and external announcements. - -4. **NASA’s Primary Competitor: SpaceX** - - NASA already double-booked both the Starliner-1 and a SpaceX Crew-11 flight to ensure that ISS operations continue without hiccups. This raises the specter of redundancy that erodes the necessity of Starliner performing as planned or its indefinite delays becoming permissible. - - SpaceX's consistent success makes it easier for NASA to keep its options open with one credible provider, potentially diminishing Boeing’s long-term viability. - - **Strength of Reasoning: 6/10** – While this is a long-term challenge for Boeing, this doesn't immediately drive a specificity that would lead to program cancellation by October 2024. It does, however, impact the long-term calculation. - -5. **Base Rates of Space Program Cancellations** - - Historically, space programs that experience high-cost overruns are more likely to be delayed and reworked, rather than immediately canceled, especially when they have reached manned crew test phase. - - Starliner has now executed crew tests, albeit unsuccessfully. Given the critical failure took place in August-September 2024, time for mitigation and saving the program remains possible. - - Most space endeavors, once deep into execution, are politically and operationally challenging to stop without significant lead time. - - **Strength of Reasoning: 8/10** – The base rate of massive governmental programs being outright canceled at this stage (with millions spent and high political visibility) is low. Instead, delays and further test attempts are more common. The ISS decommissioning in 2030 provides some time for Starliner to adapt or pivot. - ---- - -### Likelihood of Factors Leading to Cancellation by October 1, 2024: - -##### Reasons it *may* be canceled: -1. **Recent crew safety concerns** have driven NASA to rely on SpaceX in early September 2024. If these issues prove unfixable or too costly, Boeing may be pressured to withdraw. - - **Strength: 7/10** -2. **Cumulative delays and cost overruns**, alongside competitive pressures from SpaceX, have stretched Boeing thin. - - **Strength: 6/10** -3. **NASA may lose patience** if Boeing cannot more rapidly demonstrate competence, especially following another failure or if investigation outcomes turn out negatively within the next couple of weeks. - - **Strength: 4/10** - -##### Reasons *not* canceled (default behavior): -1. **Boeing’s historic commitment** to fixing issues has been a defining characteristic, and the company has invested significant time and money that it will not abandon lightly. - - **Strength: 9/10** -2. **Absence of official announcements** of intent to cancel suggests continued effort towards recovery. Such decisions typically have a public window before they are finalized. - - **Strength: 9/10** -3. **NASA’s own delays and drawn-out processes** mean that agencies take longer to cancel programs, especially when contracted companies like Boeing are involved. - - **Strength: 10/10** -4. **Insufficient Time Window** of <30 days—there simply may not be enough time for Boeing or NASA to announce, finalize, and set into motion a full cancellation before the October 1 marker. - - **Strength: 10/10** - ---- - -### Final Thought Process: - -- The cancellation of **any large government-contracted aerospace project** has significant organizational and reputational costs, and it typically does not happen abruptly. Furthermore, decisions regarding termination or withdrawal usually have longer lead times. -- Boeing, despite technical struggles, has not been reported as signaling withdrawal or cancellation, and NASA's behavior points to difficulty, not abandonment. -- With only 20 days until the cutoff date, the likelihood of such a monumental decision in such a short timeframe is low. - ---- - -**Probability: 3%** -- There's a small chance an extremely destabilizing event could arise (e.g., an internal Boeing decision, a NASA mandate, or major political pressure); however, the overwhelming consistency of patterns and historical precedent pushes toward a ""No"" answer for cancellation in this short window." -28021,"Will the New York Yankees win more games than the Baltimore Orioles in the 2024 MLB season, before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether the New York Yankees will win more games than the Baltimore Orioles in the 2024 MLB season before October 1, 2024, we need to look at the current standings, recent performances, and head-to-head records. - -1. **Current Standings**: - - As of July 14, 2024, the New York Yankees are 58-40, and the Baltimore Orioles are 58-38. This indicates that the Orioles are currently leading the Yankees by 2 games. - -2. **Recent Performances**: - - The Orioles have shown resilience in recent games against the Yankees, including a 6-5 win on July 14, 2024, where Cedric Mullins hit a two-run double in the ninth inning. - - The Yankees have had strong performances as well, but the Orioles have managed to stay ahead in the standings. - -3. **Head-to-Head Record**: - - The Orioles are 6-4 against the Yankees this season, indicating a slight edge in head-to-head matchups. - -4. **Prediction Markets and Betting Odds**: - - Unfortunately, the provided sources do not include current betting odds or prediction markets for the overall season outcome. However, it's essential to consult these sources for the most recent and relevant data. - -Given the current information, the Orioles are slightly ahead in the standings and have a better head-to-head record against the Yankees this season. However, the season is ongoing, and many factors can influence the final standings, including injuries, trades, and team dynamics. - -For the most accurate and up-to-date information, it's crucial to consult recent news articles, current standings, and prediction markets. As of the last available data on July 14, 2024, the Orioles are in a better position, but there is still a significant portion of the season left to play. - -######### - -GPT - -To forecast whether the New York Yankees will win more games than the Baltimore Orioles by October 1, 2024, let's carefully evaluate the given data, trends, and key factors influencing the decision. I'll break this down into a step-by-step thought process, following a structured approach while also enhancing my forecast with base rates and historical comparisons. - ---- -### Information Summary and Resolution Clarifications: -- The current date is **September 11, 2024**. -- Both teams, the Yankees and Orioles each have **82 wins** **(as of September 9, 2024)**. - -- The question hinges on whether, at the cutoff **October 1, 2024**, the Yankees will end the season with more wins than the Orioles. -- If both teams have the same number of wins **(ties resolve as ""No"")**, or if the Yankees have fewer wins, this question resolves as ""No"". - -Since there are only **approximately three weeks left until October 1**, the further data points and variance that could come into play are limited to the remaining games left in the season. - ---- -### Step-by-step Breakdown: - -**1. Remaining Games and Schedule Analysis**: - - Both teams appear to be playing at relatively similar levels at this point of the season with the same number of **82 wins**. - - At this point, the Yankees and Orioles could have between 18 to 20 games left in the regular season (depending on actual games and specific schedules). - - Consulting the MLB standings schedule tracker, and analyzing the strength of schedule for the next few games should be a critical factor: - - - **Strength of schedule** for the Yankees: If, over the next few weeks, they face weaker teams (perhaps teams out of playoff contention), they could gain an advantage, while if they face stronger playoff-contending teams (such as Rays, Astros, Blue Jays), their probability of achieving more wins than the Orioles declines. If the Orioles, on the other hand, have a more favorable or easier schedule, it would strengthen their probability of winning more games. - - **Assumption:** Both teams are likely to face intense competition as the season winds down, especially since they are both likely competing for better playoff seeding or wild-card spots. This will inherently suppress the win variance normally experienced earlier in the season. - - **Importance**: 8/10. Remaining matchups and head-to-head clashes have direct bearing on the possible win-loss outcomes. - - **Strength of the Reason**: Careful analysis of the matchups and competition level can lead to better forecasts. The closer the strength of schedule, the less variance, which implies margin changes will be narrow. - ---- -**2. Key Player Performances and Injury Factors**: - - **Injuries and Roster Strength**: Injuries and player availability can heavily impact late-season success. By September, fatigue and injuries often play a greater role due to the long MLB season. - - - Yankees' **Aaron Judge** and **Giancarlo Stanton** are key batters; if they have been healthy and performing well recently, that could swing games in the Yankees' favor. On the other hand, if either team has any crucial injuries (e.g., to their pitching staff), this will hinder their ability to finish strong. - - Given no explicit or recent news of major injuries, it's reasonable to assume both teams will be fielding their strongest lineups unless more recent reports refute this. - - **Importance**: 7/10. Player health is a significant factor, but without immediate injury concerns for either team, both likely have comparable playing rosters. - - **Strength of the Reason**: Stable playing conditions, assuming no major late-season injuries, could mean relatively marginal advantages for either team. - ---- -**3. Historic Base Rates and Team Momentum**: - Looking historically, the Orioles and Yankees have both displayed highs and lows in past seasons. However, the Yankees have traditionally been seen as more dominant when nearing the postseason, though that trend has been less stark recently. - - - **Yankees Past Late-Season Performance**: Historically, August and September have tended to be the Yankees’ strongest months. - - - **Orioles 2024 Performance**: In 2024, by narrative and standings (e.g., a slight lead back in **July**), the Orioles seem to have been highly competitive and have consistently performed well. Their strong divisional placement and competitiveness with the Yankees suggest they will not easily slip. - - - **Momentum:** Both teams are tied right now, but slight differences could come from in-season momentum. The Orioles’ earlier momentum in the season indicates they might maintain that over the last few games. The Yankees, on the other hand, have a high-quality roster and may push hard to conclude the season on a high note. - - **Importance**: 6/10. The historical tendencies suggest the Yankees could have an edge, but this isn’t definitive for a single-season projection. - - **Strength of the Reason**: While past performance can give insights, baseball’s high season-to-season variability weakens reliance on historical trends alone. - ---- -**4. Brier Score Sensitivity and Probability Distribution**: - Given the short time period left (approximately three weeks), the distribution of possible outcomes varies less than if this were a projection at mid-season. Each game matters more, but with **both teams tied at 82 wins**, it implies a tight race. - - - **Default Scenario**: If both teams remain on their current trajectory, the question resolves **""No""** more often than it resolves ""Yes"", due to the tie-break criteria (zero-tie favoring No). - - - **Chance of Variance**: There's little room for a strong divergence in performance (e.g., one team winning significantly more than the other). This ultimately favors a **No** outcome due to the limited games left. - - **Importance**: 9/10. The default scenario of a ""No"" resolution is very strong unless there is significant movement in the remaining games. Therefore, the probability distribution is slightly skewed to a **No**. - - **Strength of the Reason**: The default tie favor and the limited time frame result in greater confidence in projecting similar outcomes rather than extreme divergence. - ---- -### Evaluation and Final Judgment: - -- **Likelihood Yankees outperform Orioles**: The tie at 82 wins and limited number of remaining games suggest the total number of possible changes is low. It's reasonable to expect both teams will finish close to each other in the standings, with a tie or Orioles slightly pulling ahead favored slightly by current conditions. - -- **Odds-based Conclusion**: A neutral weighting would suggest approximately **55-60%** likelihood of a **No** outcome, with a tie or Orioles very slightly ahead. A **Yes** outcome where the Yankees definitively outpace the Orioles is less likely but still possible, given historical strengths and talent levels, estimating this at around **40-45%**. - ---- -### Final Probability: - -**Probability: 40% Yankees will win more games than Orioles by October 1, 2024.** - -This reflects a relatively balanced but slightly ""No""-leaning view, accounting for base rates, current tie, and remaining variance." -28021,"Will the New York Yankees win more games than the Baltimore Orioles in the 2024 MLB season, before October 1, 2024?","PERPLEXITY - -To address the question of whether the New York Yankees will win more games than the Baltimore Orioles in the 2024 MLB season before October 1, 2024, we need to look at the current standings, recent performance, and any relevant statistical data. - -1. **Current Standings and Head-to-Head Record:** - - **Head-to-Head Record:** The Baltimore Orioles are 6-4 versus the Yankees this season as of the latest available data. - - **Recent Games:** Specific outcomes of recent games between the two teams include a 7-6 win for the Orioles on June 19, 2024, and a 4-2 win for the Orioles on April 30, 2024. - -2. **Recent Performance:** - - The videos and game recaps from July 12 and 14, 2024, indicate that the Yankees and Orioles have had close matchups, with the Orioles managing to secure a significant win on July 14, 2024. - -3. **Statistical Data:** - - **Team Statistics:** Detailed statistics on the performance of both teams in their head-to-head matchups are available, including batting averages, home runs, and pitching performances. - -However, to make a more informed assessment, we would need the current overall standings of both teams in the 2024 MLB season, which are not provided in the search results. The head-to-head record and specific game outcomes give us a snapshot but do not offer a complete picture of the season's progress. - -**Prediction Markets and Expert Opinions:** -- Unfortunately, the search results do not include current predictions from betting sites or expert opinions on the season's outcome. For a comprehensive forecast, it would be beneficial to consult the latest odds from sportsbooks and predictions from MLB analysts. - -**Conclusion:** -Given the information available, we cannot definitively say whether the New York Yankees will win more games than the Baltimore Orioles in the 2024 MLB season before October 1, 2024. The head-to-head record and recent game outcomes provide some insight but lack the broader context of the entire season's standings and performance trends. For a more accurate assessment, current standings, recent performance trends, and expert predictions would be necessary. - -######### - -GPT - -### Step-by-Step Approach - -#### 1. **Restating the Question** -The question asks: Will the New York Yankees win more games than the Baltimore Orioles in the 2024 MLB season before October 1, 2024? - -The Yankees and Orioles both currently had **82 wins** as of **September 9, 2024**. We have to make a forecast that accounts for the number of games left in the season (~20 days) and the probability that the Yankees will surpass the Orioles in number of wins—including the possibility of a tie, which resolves as a ""No."" - -#### 2. **Understand the Timeframe** -- Today is **September 11, 2024**, and the resolution date is **October 1, 2024**. -- This gives **20 days until resolution**, with approximately **20 games per team** remaining in the season (teams usually play ~162 total games in the regular season). - -#### 3. **Base Rate and Historical Precedent** -Looking at historical outcomes from previous MLB seasons, teams close in wins by September traditionally maintain a relatively small variance in their output over the course of the remaining games. It’s relatively rare for one team to suddenly ascend while another collapses barring exceptional circumstances like major injuries. - -- **Base rate of outcomes** (historical example analysis): In previous MLB seasons where two teams are tied in September, the difference in their final win totals tends to be 1-3 games. Extreme discrepancies are rare without something unusual happening. - -- **Average win rate down the stretch** (based on historical Sept. performances): Generally, down the stretch, teams aim for a winning percentage similar to what they've been posting over the season. Over the course of 20 games, this is likely to be somewhere between 11-9 and 9-11 win-loss records. - -#### 4. **Prior Performance and Head-to-Head** -The available head-to-head data reveals: - -- **Current Record:** Yankees and Orioles are tied at 82 wins. - -- **Head-to-Head Record**: Orioles lead 6-4 in their games against the Yankees. - - The Orioles seem to have a slight advantage head-to-head, but with only a few H2H games left, this record will be mostly irrelevant for our remaining forecast window (only overall wins matter). - -#### 5. **Key Factors Affecting Prediction (Strength Assessment)** - -##### **Why the Orioles might win more games** -1. **Head-to-Head Advantage for Orioles**: Orioles being 6-4 vs. the Yankees in the season offers a moderate advantage and indicates overall strength relative to the Yankees in direct matchups. - - - **Strength:** 5/10 (only limited games left) - -2. **Recent Performances in Crucial Games**: Orioles' recent wins in close matchups and overall strong form through the season suggest better momentum. Teams that display confidence in tough, late-season games have a historical tendency to finish stronger. - - - **Strength:** 6/10 - -3. **Baltimore Orioles (2024) Overall Performance**: The Orioles have been a consistently good team throughout recent seasons, and their younger, stronger outfield depth and breakout bullpen performance give them an edge. - - - **Strength:** 7/10 - -##### **Why the Yankees might win more games** -1. **Yankees’ Strong Roster & Payroll**: The Yankees have historically competed well in big-money games toward the season's end due to the depth of their roster and ability to rely on a few superstars. Despite a season that may not have reached historic expectations, players like Aaron Judge and Gerrit Cole have a track record of stepping up in key games. - - - **Strength of impact:** 5/10 - -2. **Streak Potential**: Late-season runs are not unheard of for the Yankees. Historically, their large payroll, veteran presence, and potential for a string of wins give them slightly more volatility potential than the Orioles. - - - **Strength**: 4/10 (streaks are pretty rare in MLB, but the Yankees are a slightly more volatile team than the Orioles) - -3. **Uncertainty in AL East**: Both teams face Division and Wild Card pressure, but the Yankees, facing the pressure of often being expected to do well, may step up with urgency in closer games. - - - **Strength of impact**: 4/10 (pressure can boost teams with poise) - -#### 6. **Betting Markets and Expert Opinions** -Lacking direct odds data, I cannot fully rely on a prediction from betting markets, which usually offer tighter forecasting as they reflect informed bettors’ opinions. However, as a subjective assumption: given the evenly-matched win records, most sportsbooks would likely offer near-even odds for both teams, with perhaps a slight edge to the Orioles due to their historical consistency this year and superior head-to-head record. - -#### 7. **Evaluating Trends** -Neither team has shown significantly wild swings recently. Trends from previous MLB seasons imply that teams with a 3-5 win difference avoid large, unexpected reversals without external shocks (like notable injuries). - -#### 8. **Default Resolution (if nothing substantially changes)** -The Orioles currently have a **6-4** head-to-head advantage, but it doesn't directly reflect the future. Given the tie state and uncertainty between these well-matched teams, the default expectation would be neither team significantly surging ahead but perhaps one winning 1-3 more games than the other. Given the Orioles' strengths and overall performance this season, a slight edge in final win numbers to the Orioles seems plausible. - -#### 9. **Final Probability Assessment** - -- **Weight of Base Rates** (Historical outcomes from Sept.): Orioles likely finish slightly ahead if trends persist (~55%). - -- **Strength of Yankees Superstars/Legacy:** Moderate lift for Yankees (~35% chance of them overtaking). Ties or small increments more likely. - -Given all evidence, the probability calculations suggest: - -### Final Answer: -**Probability: 58% chance that the Baltimore Orioles win more games than the Yankees." -28038,Will exactly 1 major Atlantic hurricane (Category 3 or higher) occur in September 2024?,"PERPLEXITY - -To address the question of whether exactly 1 major Atlantic hurricane (Category 3 or higher) will occur in September 2024, we need to consider the current state of the Atlantic hurricane season and the factors influencing hurricane development. - -1. **Current Season Activity**: - - The 2024 Atlantic hurricane season has been unusually quiet after a strong start. Despite predictions for an above-normal season, there has been a significant lull in activity since mid-August. - - As of September 12, 2024, there have been 6 named storms, 4 hurricanes, and 1 major hurricane (Beryl), which was the earliest Category 5 hurricane on record. - -2. **Factors Affecting Hurricane Development**: - - The recent quiet period is attributed to several factors, including a northward-shifted monsoon trough, extremely warm upper-level temperatures stabilizing the atmosphere, too much easterly shear in the eastern Atlantic, and unfavorable subseasonal variability associated with the Madden-Julian oscillation. - - Saharan dust has also played a role in suppressing storm development by blocking solar radiation and depriving the environment of moisture. - -3. **Outlook for September**: - - There are indications that the hostile environment suppressing storm development may ease up around the middle of September, potentially leading to better chances for strong hurricanes in late September. - - The transition to La Niña conditions, expected to start between September and January, could reduce wind shear and promote hurricane development. - -4. **Expert Forecasts**: - - Colorado State University and other forecasters still anticipate an above-normal season overall, despite the current lull, citing favorable large-scale conditions such as near-record warm ocean temperatures. - - The National Oceanic and Atmospheric Administration (NOAA) forecasts a range of 17 to 25 total named storms, with 4 to 7 major hurricanes. - -Given these factors, while there is a possibility for increased activity in late September, predicting exactly 1 major Atlantic hurricane in September 2024 is challenging. The season's dynamics are complex, and the transition to more favorable conditions could lead to multiple major hurricanes or none at all. - -**Recent Updates and Predictions**: -- As of September 12, 2024, there are no major hurricanes currently active or predicted in the immediate future. -- The current conditions and forecasts suggest that the potential for major hurricanes in September is not ruled out, but the exact number is uncertain. - -**Sources**: -- https://www.tallahassee.com/story/news/2024/09/05/atlantic-hurricane-season-strong-hurricane-september-possible/75075294007/ -- https://tropical.colostate.edu/Forecast/2024_0903_seasondiscussion.pdf -- https://www.newscentermaine.com/article/weather/weather-blog/hurricane-season-outlook-2024-2025-atlantic-hurricanes/97-6ef1c6b2-a006-4f7f-a825-8f83a2b97505 -- https://disasterphilanthropy.org/disasters/2024-atlantic-hurricane-season/ -- https://en.wikipedia.org/wiki/2024_Atlantic_hurricane_season - -######### - -GPT - -### Initial Assessment: - -The question at hand is whether exactly **one (1)** major Atlantic hurricane (Category 3 or higher) will occur in **September 2024**. - -Given today's date (September 12, 2024), we are already midway through the month and have a lot of data about season dynamics, conditions, and predictions for the remainder of September. There are multiple complex factors to consider, and we need to carefully weigh all the information and apply past data, trends, and forecasting methodology to derive a sound probability estimate. - -Let’s break down our thought process step by step. - ---- - -### Step 1: **Base Rates and Historical Data** - -Historically, September is the peak of the Atlantic hurricane season, and thus the month is commonly associated with a high frequency of major hurricanes. Based on historical data: - -- The average number of **total hurricanes** in September is around **3-4**, with typically **1-2 major hurricanes** (Category 3 or higher). -- In recent years (2010-2022), major hurricanes have occurred in September with a frequency of about **1 to 1.5** major hurricanes per September. -- According to data from NOAA and historical trends from Colorado State University, favorable atmospheric conditions in September usually increase the likelihood of major hurricanes. - -#### Reason for ""Yes"" — Historical Frequency: -- September is historically very active, and the typical chance of one or more major Atlantic hurricanes is fairly high. Based on the base rate, the occurrence of **at least one** major hurricane is quite likely. -- **Strength: 7/10** (Historical evidence supports the likelihood of at least one major hurricane in September.) - -#### Reason for ""No"" — No Guarantees for Major Storms: -- Despite September being a peak month, the base rate doesn’t guarantee exactly one hurricane. There is a significant chance the number could end up being **0, 2, or more**, given the variability of the season. -- **Strength: 6/10** (Uncertainty given variability on exactly one hurricane occurring.) - -### Step 2: **Current Season Trends and Updates** - -The 2024 hurricane season has shown early strong activity, highlighted by an unprecedented **Category 5 storm—Beryl**—early in the season (though outside September). However, there has been a surprising **lull** in activity since mid-August, largely due to **Saharan dust**, **high-level winds**, and **easterly wind shear**, which have been suppressing storm formation. - -However, key points from **September 12, 2024** observation: -1. **Warm sea surface temperatures** (SSTs) are still present, which remain **favorable** for hurricane development. -2. **La Niña** conditions are expected to develop, which typically reduce wind shear and enhance storm development. - -Overall, the quiet period is expected to ease towards the mid or late portions of September, allowing for **increased hurricane activity** in the final weeks of the month. - -#### Reason for ""Yes"" — Improving Conditions and Predictions: -- While the first half of September has been historically quiet, **expert forecasts** and recent models indicate a shift toward more **favorable environments for storm development** starting in the second half of the month. This suggests the conditions for potentially developing one (and only one) major hurricane remain plausible. -- **Strength: 6/10** (The general forecast points toward renewed activity, but it’s not guaranteed to give us exactly one major hurricane.) - -#### Reason for ""No"" — Factors Still Suppressing Strong Storms: -- There remain several challenges for hurricane development, particularly the continuing **Saharan dust layers**, which can hinder storm intensification into major hurricanes. Even if hurricanes develop, it’s possible many remain below the **Category 3 threshold** due to wind shear or other mitigating factors. -- **Strength: 5/10** (The conditions, while improving, may not ensure major intensification of storms to Category 3 levels.) - -### Step 3: **Time Window Analysis** - -With the current date being September 12, we are approximately halfway through September 2024. Given that there are **no active major hurricanes at the moment**, we are reliant on the conditions improving toward the **final two weeks** of September to produce at least one major hurricane. - -On average, hurricanes can develop and intensify fairly quickly in as little as a few days (as has been seen in past seasons). Therefore, there is **sufficient time** for one major hurricane to form and reach Category 3 before the month ends. - -#### Reason for ""Yes"" — Time Still Allows For One Big Storm: -- The next two weeks allow ample time for **one major hurricane** to develop, given the historical patterns and the improving environmental conditions later in September. Given the midpoint of the month, there's still ample room for one storm to appear. -- **Strength: 7/10** (Historical quick developments suggest that a major hurricane could still easily happen.) - -#### Reason for ""No"" — Shorter Window for Multiple Storms: -- While there is ample time for **one** storm to develop, the tighter time window remaining makes it less probable to have exactly one major hurricane. Either **none could form**, or, if conditions improve too much, **more than one** could feasibly develop by the end of the month. -- **Strength: 5/10** (Window for exactly one is shrinking with time and continued variability.) - ---- - -### Step 4: **Considering the Probability Distribution** - -Given the analysis, I must consider the likelihood for **exactly one**, **zero**, and **more than one** major hurricane. Knowing the historical base rate of about 1-2 major hurricanes in September (with exceptions), and the current trends indicating **improvements** in favorability for hurricane formation, we are balancing multiple outcomes. - -- **Zero hurricanes**: Given that no major hurricane has formed yet this month, I would estimate a non-trivial chance of **no major hurricanes** before the end of the month. The environment had previously been quite unfavorable, but it’s shifting toward more favorable conditions. - - Likelihood: ~25-30% - -- **Exactly one hurricane**: This is still quite possible due to the improving mid-late September conditions. It’s neatly within the base rate, though variability exists. - - Likelihood: ~40-45% - -- **More than one hurricane**: The persistence of highly favorable conditions (La Niña, record SSTs) combined with the typical peak of hurricane activity suggests there's a reasonable chance of more than one major hurricane. - - Likelihood: ~25-30% - -### Final Consideration: - -Given the high historical activity in September hurricane seasons, the improving conditions toward the latter half of the month, and considering the downward pressure from ongoing atmospheric suppression, my final probability estimate balances these factors. - -### **Final Answer**: -**Probability: 40%**" -28037,Will any major Atlantic hurricanes (Category 3 or higher) occur in September 2024?,"PERPLEXITY - -To address the question of whether any major Atlantic hurricanes (Category 3 or higher) will occur in September 2024, we need to consider recent forecasts and analyses from reputable sources. - -1. **Colorado State University (CSU) Forecast**: - - **Source**: - - **Summary**: CSU predicts an extremely active 2024 Atlantic hurricane season. Their forecast includes 23 named storms, 12 hurricanes, and 6 major hurricanes. The discussion issued on September 3, 2024, notes that despite a recent quiet period, large-scale conditions are expected to become more favorable around the middle of September, suggesting an increased likelihood of hurricane activity. - -2. **NOAA Predictions**: - - **Source**: - - **Summary**: NOAA forecasts 17 to 25 named storms, with 8 to 13 expected to become hurricanes, and 4 to 7 becoming major hurricanes. The predictions are influenced by La Niña conditions and warmer-than-average ocean temperatures, which are major drivers of tropical activity. - -3. **Moody’s Report**: - - **Source**: - - **Summary**: Moody’s highlights a strong consensus among meteorological agencies predicting an above-average 2024 North Atlantic hurricane season. The report mentions that La Niña conditions and near-record warm sea surface temperatures in the Atlantic are key factors enhancing tropical cyclone activity. - -4. **Center for Disaster Philanthropy**: - - **Source**: - - **Summary**: The center notes that the 2024 Atlantic Hurricane season is predicted to be well above-normal. The transition from El Niño to La Niña, combined with unseasonably warm waters in the Atlantic, suggests a more active hurricane season. - -**Recent Updates and Expert Opinions**: -- The recent quiet period in Atlantic hurricane activity is attributed to several factors, including a northward-shifted monsoon trough, extremely warm upper-level temperatures, too much easterly shear in the eastern Atlantic, and unfavorable subseasonal variability associated with the Madden-Julian oscillation. However, these conditions are expected to become more favorable around the middle of September. -- The consensus among meteorological agencies is that the 2024 Atlantic hurricane season will be above-average, with a high likelihood of major hurricanes occurring. - -**Current Predictions**: -- As of early September, there have been 5 named storms, including 3 major hurricanes, indicating that the season is already off to an active start. -- The forecast for an above-average season, combined with the expectation of more favorable conditions in September, suggests a high likelihood of major Atlantic hurricanes occurring in September 2024. - -In conclusion, based on the forecasts and analyses from CSU, NOAA, Moody’s, and the Center for Disaster Philanthropy, there is a strong indication that major Atlantic hurricanes (Category 3 or higher) could occur in September 2024, given the expected favorable conditions and the already active start to the hurricane season. - -######### - -GPT - -### Overview: -The question asks whether there will be any major Atlantic hurricanes (Category 3 or higher) that occur in September 2024. Notably, we're mid-month (September 12, 2024), so part of the time horizon has already passed, and no major hurricanes have yet been reported this month. However, hurricane forecasting involves many variables, and there’s significant room for activity in the remaining ~18 days of the month. - -We'll first outline the environment and key factors shaping hurricane activity, then analyze pros and cons for each possible scenario, before settling on an adjusted probability reflecting the uncertainty. - -### Key Factors: -1. **Current state of Atlantic hurricane season (2024)**: - - The season has already had **5 named storms** and **3 major hurricanes**, suggesting the overall environment this year supports the development of major hurricanes. - - This period falls in the **peak of the Atlantic hurricane season**, which typically spans late August through October, with September being the most active month historically. - -2. **Climate and atmospheric conditions**: - - **CSU, NOAA**, and other expert hubs are forecasting an **above-average hurricane season**, attributed to **La Niña** conditions and **warm sea surface temperatures** in the Atlantic. These two key features increase the likelihood of hurricane activity. - - Factors hindering development earlier in the month like **easterly shear** and the **Madden-Julian oscillation** are expected to **fade** in mid-September. Meaning conditions conducive for storms rising to Category 3+ will likely improve in the next few days. - -3. **Base rates**: - - Historical hurricane data show that **September** consistently accounts for one of the highest rates of major hurricane formation. For context: - - The **2020 season** (also an active one) saw 3 major hurricanes in September alone. - - From 2013 to 2022, the Atlantic basin averaged about **1-2 major hurricanes** forming in the month of September each year during active seasons. - - This historically active window correlates with the high probability outlined by the different expert sources. - -4. **Resolution criteria clarity**: - - The question will resolve based on whether a hurricane reaches **Category 3 or higher** at any point during September. Even if it forms on September 30 and quickly weakens, it would still count toward the result. - - This means we’re simply interested in **any hurricane** that crosses this threshold, rather than multiple hurricanes or sustained time at Category 3. - -### Reasons for ""Yes"" (A major hurricane will occur in September 2024): -1. **Peak of the season (strength: 9/10)**: - - September is climatologically the most active month for hurricanes. Storms often take time to form and intensify, and mid-to-late September typically shows increased hurricane development. - - The **remaining ~18 days of September** still offer ample time for storm development, transition to major hurricane status, and strengthening of existing systems. - -2. **Above-average season forecasts and conditions improving soon (strength: 8/10)**: - - Both **CSU and NOAA** agree that **2024 is likely to be above-average** in both the **number and intensity** of hurricanes due to conducive environmental conditions like **La Niña** and especially warm Atlantic waters. - - Current quietness is partly due to temporary transitory factors (like strong easterly shear, Madden-Julian oscillation), and these influences are expected to **weaken** soon, as specialists have suggested. This increases the likelihood of storms strengthening. - -3. **Past activity this year, including 3 major hurricanes already (strength: 7/10)**: - - The fact there have already been **3 major hurricanes** this year signals that underlying environmental factors favor major storm development. This is consistent with a high-activity season and increases the likelihood stats-wise that we could see another storm in the next few weeks. - -### Reasons for ""No"" (A major hurricane won’t occur in September 2024): -1. **12 days of the month have passed with no major hurricane (strength: 6/10)**: - - The first half of September has already passed without any major hurricanes forming. This could indicate an unpredictably slow year, or it might suggest that conditions aren’t as primed as forecasters expect (possibly some miscalibration in recent forecasts). So, time is running shorter, naturally limiting the available window. - -2. **Temporary unfavorable technical conditions (strength: 5/10)**: - - Experts mentioned factors like **easterly shear**, **monsoon trough shifts**, and the **current state of the Madden-Julian Oscillation**, which have suppressed storms so far. While these conditions are slated to improve, they could remain stronger than anticipated. - -3. **Hurricane intensity vs. formation (strength: 4/10)**: - - While storms could form, not all reach the level of a major hurricane (Category 3+). The baseline uncertainty is around whether a storm will not only form but escalate sufficiently in intensity. The Atlantic environment is prone to surprises where a promising disturbance might fail to strengthen due to unexpected atmospheric disruptions. - -### Weighing the Sources: -1. **Heaviest weight** to **CO, NOAA, and climate experts/forecasts** (recent, robust): - - **Forecasts predicting an active season and improving conditions** are consistent with base rates for September and reference valid influences like warm sea surface temperatures and La Niña that significantly affect storm formation. - - Given **seasonality and historical precedent**, their probability estimates are the most credible and highest-value source in shaping the forecast. - -2. **Moderate weight to recent data (first 12 days)** giving us reason for caution but not over-indexing on an initial quiet period, as much of the season’s peak still lies ahead. - -### Conclusion: -Given historical activity trends for September hurricanes, the scientific forecasts suggesting improving conditions, the fact we're in the peak of hurricane season, and based on base rates, the **most likely outcome** in the remaining portion of September is that at least one major Atlantic hurricane will occur. The lag in season activity to date shouldn't be overly concerning, given the likelihood of changes in atmospheric conditions soon. - -Thus, a reasonable probability for a **major hurricane occurring in September 2024** is high, though it doesn’t warrant extreme certainty due to variability of tropical weather systems. - -**Final Forecast:** -**Probability: 85%**." -28042,Will AfD's vote share be more than 32% in the 2024 Brandenburg state election?,"PERPLEXITY - -To provide context for forecasting the AfD's vote share in the 2024 Brandenburg state election, here are key points from recent sources: - -1. **Recent Election Trends**: - - The AfD has shown significant strength in recent state elections in eastern Germany. In Thuringia, it won 32.8% of the vote, and in Saxony, it secured 30.6% to 31.4%. - - The party's success in these elections indicates a growing trend of support for far-right parties among young voters in eastern Germany. - -2. **Polling Data**: - - Polls for Brandenburg suggest that the AfD is leading but with varying percentages. A poll by Insa institute puts the AfD at around 24%, ahead of the SPD and CDU. - - The AfD's lead in polls is consistent across different sources, but the exact percentage varies, indicating some uncertainty in the final outcome. - -3. **Political Context**: - - The AfD's popularity among young voters is driven by issues such as immigration, economic concerns, and dissatisfaction with mainstream politics. - - The party's anti-immigrant and anti-Islam platform resonates with voters in eastern Germany, who feel neglected by the federal government. - -4. **Election Dynamics**: - - The election in Brandenburg is scheduled for September 22, 2024, and will be a critical test for Chancellor Olaf Scholz’s center-left coalition. - - Other parties, including the newly formed left-wing populist party BSW, are expected to play a role in the election, potentially affecting coalition dynamics. - -Given these points, while the AfD is expected to perform well in the Brandenburg state election, the polling data does not consistently indicate a vote share above 32%. The most recent polls mentioned put the AfD at around 24% in Brandenburg, which is below the 32% threshold. However, the dynamic nature of political elections and the recent trends of increasing support for the AfD in eastern Germany suggest that there is still a possibility for the AfD to exceed this threshold, especially if it continues to capitalize on voter discontent with mainstream politics. - -**Prediction Markets and Betting Odds**: -No specific prediction market or betting odds are mentioned in the provided sources. For a more accurate forecast, it would be beneficial to consult current betting odds from reputable sources such as Vegas sportsbooks or prediction markets like PredictIt. - -**Recent Updates**: -The most recent updates suggest that the AfD is leading in polls for Brandenburg but with a percentage that does not consistently exceed 32%. The election's outcome will depend on various factors, including voter turnout and last-minute campaign efforts. - -**Expert Opinions**: -Experts note that the AfD's success is driven by its appeal to young voters and its ability to capitalize on issues such as immigration and economic discontent. However, no specific expert opinion is provided on whether the AfD will exceed 32% in the Brandenburg election. - -In summary, while the AfD is expected to perform well, the available polling data does not consistently indicate a vote share above 32% in the 2024 Brandenburg state election. The final outcome will depend on various factors, including last-minute campaign efforts and voter turnout. - -######### - -GPT - -### Step-by-Step Forecasting Process - -#### **1. Defining the Problem:** -The question at hand asks whether **AfD's vote share will be more than 32% in the 2024 Brandenburg state election**. The task is to forecast the likelihood that they will exceed this threshold based on given and publicly available data, historical information, expert opinions, and trends. - -We begin by considering the most directly relevant data: -- **Recent polling data** for Brandenburg. -- **AfD's historical performance** in previous elections, notably in similar regions like Thuringia, Saxony, and their prior Brandenburg election results (2019). -- **General trends in the public opinion** towards populist parties, and specific aspects of AfD's platform that might appeal to Brandenburg's electorate. - -We also keep in mind that the Brier score method rewards precise probability forecasts, so it's important to properly calibrate low or high probabilities. This method heavily punishes extreme probabilities if the event clusters around the middle. - -### **2. Important Factors and Their Relevance:** - -#### A. **Polling Data — Recent Trends** -- **Current polling in Brandenburg** places the AfD at **around 24%**, according to the research assistant's summary, and doesn't consistently hit 32%. However, 24% is still notable and indicates that AfD is the leading party. - -- **Relevance (9/10)**: Polling data is one of the most direct sources of information. Early September data likely reflects a significant amount of voter sentiment, though it's also important to account for undecideds, last-minute shifts, and polling error. - -#### B. **Past Performance in Similar Elections** -- AfD garnered **32.8% in Thuringia** and **30.6%-31.4% in Saxony** — two other eastern German states with generally similar socio-political landscapes to Brandenburg. - -- In 2019, AfD received **23.5%** in the Brandenburg elections, which is much lower, but over the past five years, their support has trended upward. - -- **Relevance (7.5/10)**: Historical election results help establish a base rate. Brandenburg's 2019 result provides a valuable baseline, but their increasing overall strength in the region points to significant potential for growth. - -#### C. **Current Political & Socio-Economic Climate** -- AfD’s support is driven by **anti-immigration sentiment**, **economic discontent**, and a sense that **eastern Germany is neglected** by national politics. These grievances are particularly strong post-2022, as the broader issues of energy prices (amid Russia’s invasion of Ukraine), inflation, and immigration have taken central roles. This could further solidify the AfD’s base and attract more voters. - -- **Relevance (8/10)**: These factors are longstanding contributors to AfD's rise, especially in eastern Germany. Timing matters here, too; large political and economic shifts can happen quickly in volatile periods, but they are spreading over months or years, and AfD is already seeing gains. - -#### D. **Voter Turnout and Uncertainty** -- Voter turnout could impact the AfD’s final numbers. Historically, **far-right parties tend to benefit** from lower voter turnout, while mainstream parties do better when voter turnout is higher. - -- There’s a potential **wild card of undecided voters or late shifts** in preference, particularly due to last-minute campaigning or external events. - -- **Relevance (7/10)**: Voter turnout is a known swing factor, though it's difficult to predict with precision. However, AfD’s core voters have a higher likelihood of turnout relative to some other parties, especially in an environment of economic or cultural dissatisfaction, so turnout may magnify their vote share. - -#### E. **Polling Error and Poll Dynamics** -- Polls can have significant **margin of errors**. A typical margin for error in this kind of polling could be 2-3 percentage points — which is substantial given the threshold (32%) and AfD's current polling (24%). - -- Trends in populist parties suggest **late swings can be in their favor**, especially if the established parties are struggling to mobilize enthusiasm. - -- **Relevance (6.5/10)**: Past elections globally have shown populist parties may exceed final polling expectations due to polling biases or potentially capturing last-minute opinion shifts. - -### **3. Statistical Analysis and Considering Probabilities**: - -The **default outcome** based on the poll would suggest AfD will get around 24%, below the 32% threshold. This suggests that, as of today, it’s more probable they will stay under 32%. However, given the trends in Thuringia and Saxony, along with the general political climate, I wouldn’t rule out an over-performance. - -#### **Possible Outcomes and Probability Thinking:** -- **Base Case (default)**: AfD is likely to achieve something around **25-28%** based on the current aggregate trends. Polling averages often end up being adjusted closer to Election Day, but polling hasn't yet captured evidence of a dramatic polling gap. -- **Scenario A (Overperformance)**: If AfD gains momentum during the campaign's final days or the polling underestimates their appeal (as seen in several populist elections elsewhere), they could surpass **32%,** particularly if other parties struggle to motivate voters. -- **Scenario B (Underperformance)**: If turnout surges for mainstream parties, disenfranchising AfD voters or fracturing the anti-mainstream vote block across smaller parties, AfD could very plausibly remain closer to **26-30%.** - -I estimate that ***momentum, discontent with the status quo, and regional dynamics make a last-minute surge possible, but unlikely***. 32% is a high bar considering current trends. - -### **4. Estimating the Probability:** -- **Base Rate of Exceeding 32% (similar elections)**: Around 25-30%. -- **Scenario A with strong momentum boost**: Adds potentially up to another 5-10%. -- **Scenario B where momentum falls short**: Drops the probability to around 10-20%. - -Given this, around **15%-20% seems reasonable** based on the data. - ---- - -### **Conclusion and Probability Estimate:** - -Given the current polling, historical performance, socio-political factors, and election dynamics: - -**Probability: 20%** - -This forecast reflects the relatively low likelihood that AfD will exceed 32%, tempered by the possibility of margin errors, polling inaccuracies, or last-minute sentiment shifts benefiting the AfD." -28041,Will 4 or more major Atlantic hurricane (Category 3 or higher) occur in September 2024?,"PERPLEXITY - -To address the question of whether 4 or more major Atlantic hurricanes (Category 3 or higher) will occur in September 2024, we need to consider the current state of the Atlantic hurricane season and the predictions from various forecasting entities. - -1. **Current Season Overview**: - - The 2024 Atlantic hurricane season has been predicted to be above-normal by NOAA, with an 85% chance of exceeding the average number of storms. - - As of September 5, 2024, the season has seen 6 named storms, 4 hurricanes, and 1 major hurricane, with a significant lull in activity in late August and early September. - -2. **Recent Activity and Predictions**: - - Despite the recent quiet period, forecasters believe that conditions will become more favorable for storm development around the middle of September. - - The second week of September is the climatological peak of the Atlantic hurricane season, and accumulated cyclone energy (ACE) typically hits its historical 50% mark around September 12. - - Indications suggest that the dry air intrusions that have suppressed storm development may ease up beyond September 15, potentially leading to better chances of strong hurricanes in late September. - -3. **Seasonal Forecasts**: - - Various forecasts predict a well above-normal season, with Colorado State University (CSU) forecasting 23 named storms, 12 hurricanes, and 6 major hurricanes, and Tropical Storm Risk (TSR) predicting 24 named storms, 12 hurricanes, and 6 major hurricanes. - - The warm ocean temperatures and the transition from El Niño to La Niña conditions are cited as factors that could lead to an active hurricane season. - -4. **Expert Opinions**: - - Dr. Ryan Truchelut, chief meteorologist at WeatherTiger, notes that while the season has been quiet recently, the strongest historical predictor, ocean temperatures, continues to signal that the hurricane season will likely become active at some point. - - Philip J. Klotzbach and his team at CSU discuss several reasons for the recent lull, including a northward-shifted monsoon trough and unfavorable subseasonal variability, but still anticipate an above-normal season. - -Given these points, while it is challenging to predict with certainty whether 4 or more major hurricanes will occur in September 2024, the consensus among forecasters is that the season will be above-normal, and conditions are expected to become more favorable for storm development in late September. However, the question specifically asks about the occurrence of 4 or more major hurricanes in September, which seems ambitious given the current state of the season and the typical distribution of major hurricanes throughout the season. - -**Key Points**: -- **Current Season**: 6 named storms, 4 hurricanes, 1 major hurricane as of September 5. -- **Predictions**: Above-normal season expected, with favorable conditions for storm development in late September. -- **Challenges**: Recent lull in activity, but ocean temperatures and La Niña conditions suggest increased activity. -- **Expert Opinions**: Anticipation of an above-normal season, but achieving 4 or more major hurricanes in September seems challenging. - -**Recent Updates**: -- As of September 5, there are no major hurricane threats, but conditions are expected to become more favorable around September 15. -- The ACE index as of September 10 is approximately 56.5 units, indicating a relatively quiet season so far but with potential for increase. - -**Prediction Markets**: -- No specific prediction market data is available in the provided sources, but the general consensus among forecasters is for an above-normal season. - -######### - -GPT - -### Step-by-Step Thought Process: - -#### 1. **Understanding the Question and Criteria:** - - The question asks whether **4 or more major Atlantic hurricanes (Category 3 or higher)** will occur in **September 2024**. - - The resolution criteria focus on whether a hurricane **reaches Category 3 status at any point in September**, regardless of how long it stays at that threshold. - - Given today's date is **September 12, 2024**, we are almost halfway through the month. Thus, we're not making predictions for the entire duration of the hurricane season, just for the period of September, particularly the remaining 18 days of the month. - -### 2. **Review of Current Season Observations:** - - **Current as of September 5th**: There have been 6 named storms, 4 hurricanes, and 1 major hurricane this season. - - **Lull in Activity**: There was a quiet period in late August/early September, with no major hurricane activity as of September 5. This is relevant because it represents the most recent historical activity. - - **Peak Activity Expected**: Historically, we are approaching peak hurricane activity, and forecasters expect that conditions will improve around **mid-September**, which could lead to more favorable storm development in the following couple of weeks. - -#### 3. **Expert Predictions and Models:** - - **NOAA and CSU** forecast an above-normal season with warm ocean temperatures, favorable atmospheric conditions, and a potential transition from **El Niño to La Niña** favoring more intense storm formation. - - **Recent Data**: According to experts, conditions may become more conducive to storm and hurricane formation starting **around September 15**. - - **Overall Seasonal Prediction**: Predictions from major forecasting agencies point to **12 hurricanes and 6 major hurricanes** for the season as a whole. - - However, the key here is that we are focusing on **September only** rather than the entire season. We need a high concentration of major hurricanes specifically within the month, which is less common. - -#### 4. **Historical Data and Base Rates:** - - Historically, the **average number of major hurricanes** for any **given September** is around 1 to 2 (based on research from NOAA). - - **Past High-Activity Seasons**: In rare cases (such as 2005, one of the most active seasons in history), more than 3 major hurricanes did occur in September, but this is unusual. - - For example, in 2005, there were 5 major hurricanes by the end of September, but this was an extreme outlier caused by abnormally favorable conditions. - -#### 5. **Key Points from Research Assistant:** - - **Current State**: As of September 5, only one major hurricane (Category 3 or higher) has occurred this season. - - **Forecasting Conditions**: Predictive models suggest that later September (post-September 15) could be conducive to storm development. - - **Challenges**: Even with favorable conditions in the second half of the month, getting 3 additional major hurricanes in less than 18 days is a tall order when considering historical patterns. - - Moreover, just because conditions are favorable, that doesn't guarantee the formation of **multiple major hurricanes** in such a short timeframe. - -#### 6. **Recency and Trends vs. Historical Base Rates:** - - The above-normal seasonal predictions (NOAA, CSU, etc.) certainly shouldn't be ignored, but **the base rate for 4+ major hurricanes in September remains quite low** historically. - - While warming ocean temperatures increase the likelihood of hurricanes overall, the leap from more hurricanes to more major hurricanes (Category 3+) in such a concentrated time period is substantial. - - Weighing the expert predictions alongside base rates tells us that **the occurrence of 4 major hurricanes in September 2024 is possible but highly unlikely**. - -### 7. **Analyzing Possible Outcomes:** - -#### Arguments for “Yes” (4 or more major hurricanes in September): - - **Above-Average Season Prediction**: Expert forecasts expect an above-normal season with favorable conditions developing from September 15 onwards (Strength: 8/10). - - **Sea Surface Temperatures**: Warmer sea temperatures typically lead to stronger storms, potentially bolstering the chances of more major hurricanes developing later in the month (Strength: 7/10). - - **Peak Hurricane Season**: Statistically, this is the peak period for hurricane activity, and models suggest that a surge in storm activity may occur after September 15 (Strength: 6/10). - -#### Arguments for “No” (Fewer than 4 major hurricanes in September): - - **Lull in Early September**: As of September 12, there have been no major hurricanes this month, and storm intensification takes time. Overlaying this with historical data, it’s difficult to see enough hurricanes developing, intensifying to Category 3+, and sustaining that intensity in the time remaining (Strength: 9/10). - - **Historical Base Rates**: In most years, September only yields 1-2 major hurricanes. Even in more intense seasons, exceeding 3 major hurricanes in one month is extremely rare (Strength: 9/10). - - **Time Constraints**: We are now midway through September with no major hurricanes yet reported this month. Developing 4 or more major hurricanes in less than two weeks would be a historic anomaly (Strength: 10/10). - -### 8. **Main Takeaways and Estimate:** - - **Time Left in September**: There are only 18 days left in the month, and no major hurricanes have formed so far. - - **Base Rates**: Historically, even in hyperactive seasons, 4 or more major hurricanes in a single September is extraordinarily rare. - - **Current Conditions**: While forecasters predict a more favorable environment mid-to-late month, this doesn’t guarantee a massive spike in major hurricanes. - -### Conclusion: -Given the historical base rates, current situation (mid-September lull), and the difficulty in achieving 4 or more major hurricanes in the few remaining weeks of the month, the likelihood of 4 or more major hurricanes forming in September seems **extremely low**. - -**Probability: 5%** (or slightly lower)" -28044,Will AfD's vote share be more than 28% and less than or equal to 30% in the 2024 Brandenburg state election?,"PERPLEXITY - -To provide context for the query about AfD's vote share in the 2024 Brandenburg state election, here are key points from recent sources: - -1. **Recent Election Trends**: The AfD has seen significant gains in recent state elections in Thuringia and Saxony, securing over 30% of the votes in both states. This indicates a strong performance in eastern Germany, which could potentially be mirrored in Brandenburg. - -2. **Brandenburg Election Context**: The upcoming election in Brandenburg on September 22, 2024, is crucial for understanding the political landscape ahead of the national elections in 2025. The AfD's performance in Thuringia and Saxony suggests it could also do well in Brandenburg. - -3. **Polling Data**: While specific recent polls for Brandenburg are not provided in the sources, the general trend in eastern Germany shows the AfD gaining traction. However, it's essential to note that polls can fluctuate, and the actual vote share could vary. - -4. **Political Landscape**: The AfD's success in Thuringia and Saxony has been attributed to its anti-immigration stance and capitalizing on dissatisfaction with the current national government. Similar sentiments in Brandenburg could influence the election outcome. - -5. **Coalition Challenges**: The AfD's rise complicates coalition formation in these states, as other parties are reluctant to partner with it. This dynamic could also play out in Brandenburg, affecting the political balance. - -Given these points, predicting AfD's vote share to be more than 28% and less than or equal to 30% in the 2024 Brandenburg state election is challenging without specific recent polling data for Brandenburg. However, the trend in eastern Germany suggests the AfD could perform strongly, potentially within this range, but the outcome remains uncertain. - -**Recent Updates and Expert Opinions**: -- The AfD's recent successes in Thuringia and Saxony indicate a strong performance in eastern Germany. -- Experts suggest that the AfD's anti-immigration stance and dissatisfaction with the national government could influence the election in Brandenburg. -- The challenge in forming coalitions without the AfD could impact the political landscape in Brandenburg and beyond. - -**Prediction Markets and Betting Sites**: -No specific data from prediction markets or betting sites is provided in the sources. However, incorporating such data could offer additional insights into the expected outcome. - -**Current Values**: -No current values or specific polls for the AfD's vote share in Brandenburg are provided in the sources. Recent polls and trends suggest a strong performance, but the exact vote share remains uncertain. - -######### - -GPT - -### Step-by-Step Process - -#### Key Question: -Will AfD's vote share be more than 28% and less than or equal to 30% in the 2024 Brandenburg state election? - -We should consider evidence pointing towards both the ""yes"" and ""no"" scenarios while keeping each factor weighted based on its importance to accurately judge probabilities. - ---- - -## Part 1: Assessing Base Rate & Historical Data - -### **Base Rate of AfD Vote Share in Brandenburg (Historical Performance)**: -- In the 2019 Brandenburg election, the AfD received **23.5%** of the vote. -- In East German areas, the AfD historically performs very well due to Euroscepticism, anti-immigration sentiments, and dissatisfaction with mainstream parties. Brandenburg fits this sociopolitical pattern. - -### **Recent AfD Voting Trends in Eastern Germany**: -**Thuringia (2024):** 32.8% -**Saxony (2024):** 30.6% - -- These results suggest the party is performing **above 30%** in nearby regions in 2024, indicating a **growing trend** for the AfD. - -### Similarities to 2019 Election: -Comparing the **2019 AfD vote share** in Brandenburg (23.5%) to neighboring regions, the AfD's starting point in a similar sociopolitical environment was closer to **23-24%**. With growth trends seen across other areas, this could push its numbers closer to the **28-30%** window as the question implies significant near-term growth for the AfD. - -### Importance of Base Rate: **9/10** -Historical voting data is slightly less predictive when the trend is showing upwards momentum across nearby states. Yet, it's the strongest indicator and should be combined with newer poll data (if available). Starting from a base rate of ~23-24% and growing from there in response to the same changes is key. - ---- - -## Part 2: Recent Polling Results and Political Climate - -### Poll Information: -- Polling can offer insights into short-term momentum and shifts that the party has made up until the week before the election. Unfortunately, polling data for Brandenburg specifically is not present in the provided research. However, the **growth trend across other Eastern states** like Thuringia and Saxony likely reflects similar dynamics in Brandenburg. -- Given the election is happening imminently (Sept 22, 2024), support patterns are unlikely to significantly change more than 1-2%, as major shifts this close are uncommon without a crisis or scandal. - -### Political Climate: -1. **Dissatisfaction with National Leadership**: The national government has faced criticism for immigration policies, energy policies, and handling of the economy. AfD has turned this dissatisfaction into a growing voter base, particularly in Eastern states like Brandenburg. -2. **Strong Opposition to Immigration**: Anti-immigration rhetoric continues to drive AfD's base, especially in areas of Eastern Germany with lower immigrant populations, creating a rallying point among voters feeling disconnected from national mainstream policies. -3. **Coalition Avoidance**: Concerns about coalition-building, given most parties avoid aligning with AfD due to extremism fears, have turned dedicated AfD voters away from the establishment. This dynamic boosts stable AfD support but also caps the ability for AfD to have drastic surges as mainstream media demonization limits potential mid-party swing voters shifting in. - -### Importance of Polling and Political Climate: **8/10** -The general dissatisfaction signals that AfD support is on the rise in the area. Polling might not pinpoint exact numbers, but the overall sentiment suggests it is entering a position of strength. - ---- - -## Part 3: Variance and Uncertainty - -### Potential for Variability: -Given the timeframe (just 10 days to go), there is **limited opportunity for major events** (such as an immigration crisis or economic upheaval) that could dramatically sway opinion. Hence, the forecast should not overemphasize sudden last-minute shocks. - -However, **margin of error in elections** is always a factor. Election polling in Germany, especially for populist parties like AfD, may **understate** their votes due to social desirability bias (voters may underreport support in polls). This introduces potential surprises that might increase AfD's likelihood of crossing into the **above 28% range**, although the jump from ~24% (2019) to more than 30% would still be a dramatically large growth. - -### Importance of Variance and Shocks: **5/10** -Polls in recent elections have often underestimated AfD by **1-2%**, making an AfD vote share slightly higher than expected plausible. However, it creates only modest uncertainty compared to long-term trends that favor slow movement. - ---- - -## Part 4: Likelihood Assessment via Scenario Building - -### Reasons Why the AfD Vote Share Could Be Between **28% - 30%**: -1. **Regional Trend Data (Thuringia and Saxony)**: The AfD has recently performed above 30% in similar cultural and political contexts. If we apply slight momentum from 2019 elections in Brandenburg to this trend, it's reasonable to expect the party to approach 28-30% territory in 2024. - Strength: **9/10** - -2. **Dissatisfaction with National Government**: General feelings of political dissatisfaction have precipitated AfD gains, particularly in former East Germany. This systemic dissatisfaction creates a stable base of AfD support, pushing their vote share upward. - Strength: **8/10** - -3. **Anti-Immigration Sentiments**: Growing backlash against immigration policies may consolidate more voters than anticipated behind AfD in these late stages, nudging their numbers into the high-20s. - Strength: **7/10** - -### Reasons Why AfD May Not Achieve 28-30%: -1. **AfD's Polarizing Nature**: Although AfD has strong support, it's also a polarizing choice, which could lead some moderate voters switching to other parties last-minute, preventing growth big enough to reach 28%. - Strength: **8/10** - -2. **Underestimation of Establishment Resurgence**: Voters unhappy with existing parties might still revert to them as election day nears, buoyed by the fear of empowering AfD further. Mainstream parties may organize more effectively and outperform polls slightly. - Strength: **6/10** - -3. **Poll Bias Feared, but Overestimated**: There's a temptation to view historical poll bias favoring AfD, but this election's deep scrutiny may more accurately reflect their current potency. - Strength: **5/10** - ---- - -## Conclusion: - -**Range Check**: -Based on the gradual increase in support for AfD in comparable states, rising dissatisfaction against mainstream parties, and polling difficulties underreporting populist votes, it’s reasonable to expect AfD to fall in the **higher part of the 20% range**, such as between **28% and 30%**. However, crossing the 30% mark is less probable, as the very strong base for AfD isn't growing fast enough. - -Thus, assigning **moderate confidence** for AfD achieving between 28% and 30% seems balanced. - ---- - -**Final Answer:** -Probability: **45%**" -28045,Will AfD's vote share be less than or equal to 28% in the 2024 Brandenburg state election?,"PERPLEXITY - -To provide context for the question of whether AfD's vote share will be less than or equal to 28% in the 2024 Brandenburg state election, we need to consider recent election trends and political dynamics in eastern Germany. - -1. **Recent Election Trends**: - - **AfD's Success in Eastern Germany**: The AfD has recently achieved significant victories in two eastern German state elections, Thuringia and Saxony, where they secured over 30% of the votes. This indicates a strong support base for the party in these regions. - - **Brandenburg Election Context**: The upcoming Brandenburg state election on September 22, 2024, is crucial for understanding the political landscape in eastern Germany. The AfD has been gaining traction in this region, and their performance in Brandenburg will be closely watched. - -2. **Political Dynamics**: - - **AfD's Popularity**: The AfD has capitalized on anti-immigration sentiments and dissatisfaction with the national government, particularly in less affluent eastern regions. This has contributed to their rising influence. - - **Coalition Challenges**: The AfD's success complicates the formation of new state governments, as most other parties are reluctant to form coalitions with them. This dynamic could impact the political stability in Brandenburg and other eastern states. - -3. **Polls and Predictions**: - - **AfD's Expected Performance**: While specific polls for the Brandenburg election are not provided in the sources, the general trend suggests that the AfD is likely to perform strongly, potentially exceeding 28% of the vote share based on their recent successes in neighboring states. - -4. **Expert Opinions**: - - **Political Analysts**: Experts note that the AfD's rising influence and the fragmented political landscape could lead to challenging coalition negotiations in Brandenburg, similar to those in Thuringia and Saxony. - -Given these considerations, it seems unlikely that AfD's vote share will be less than or equal to 28% in the 2024 Brandenburg state election, based on their recent performance in eastern Germany and the prevailing political dynamics. - -**Prediction Markets and Betting Odds**: -No specific prediction markets or betting odds are mentioned in the provided sources. However, considering the recent trends and political dynamics, it would be prudent to consult current betting odds and prediction markets for the most up-to-date forecasts. - -**Recent Updates**: -The most recent sources (dated September 2 and 3, 2024) indicate a strong performance by the AfD in eastern Germany, suggesting that their momentum could carry over into the Brandenburg election. - -######### - -GPT - -### Step 1: Define the question and its parameters - -The goal is to predict whether **Alternative for Germany (AfD)**'s vote share in the **2024 Brandenburg** state election will be **less than or equal to 28%**. The election is on September 22, 2024, and today's date is September 12, 2024, meaning we are just 10 days away from the election. We are interested in probabilities, and as the deadline approaches, this narrows down how much room there is for significant changes in polling or trends. - -The key piece of information here is the **vote share threshold** (28%), and we will need to assess whether the AfD's vote share is likely to exceed or stay below that level. - -#### Key considerations: -- AfD's recent performance in other eastern German states (Thuringia, Saxony). -- Polling trends leading up to the Brandenburg election. -- Political dynamics and broader context in Germany, especially in eastern regions. -- Impact of time remaining until the election (10 days) and the potential for substantial shifts in support during this period. - -### Step 2: Breakdown of reasons for each outcome - -#### Reasons why AfD's vote share **may be less than or equal to 28%**: - -1. **Historical ceiling of support in Brandenburg**: - - In the **2019 Brandenburg state election**, the AfD received **23.5%** of the vote. While this represented an impressive surge, passing **28%** would mean substantially increasing their vote share above that 2019 level. While AfD has grown in support in recent years, it is possible that their voter base in Brandenburg has a **natural ceiling** below or near 28%, making significant gains from their prior vote potentially harder to achieve. - - **Strength: 6/10** - -2. **Coalition dynamics affecting other party mobilization**: - - AfD’s rise could also increase the urgency of mobilization among more mainstream parties, such as the **SPD**, **CDU**, and **Greens**, driving a last-minute rally to oppose a far-right majority. **Anti-AfD sentiment** in Germany overall remains strong, and there is a precedent of other parties succeeding in **fending off AfD threats**, especially when awareness of their possible rise leads to strategic voting or tactical coalitions. - - **Strength: 5/10** - -3. **AfD’s branding as an extremist party**: - - As an officially **""suspected extremist""** party, significant factions of the voting public may remain reluctant to associate with the AfD, especially after witnessing the challenges they pose to governance across eastern Germany. AfD has faced concerns about authoritarianism and racism, which may restrict its capacity to expand further into the middle-class or moderate voting base. - - **Strength: 4/10** - -4. **Polling margins of error or underconfidence in polls**: - - Polling data typically have a **+/- margin of error** in the range of 2-3%, and polls may either **overestimate or underestimate** the AfD vote share. If current polls show the AfD at levels slightly above **28%**, there may still be **uncertainty** regarding whether they will finish within this range. - - **Strength: 4/10** - -#### Reasons why AfD's vote share **may exceed 28%**: - -1. **Recent polling and momentum in eastern Germany**: - - AfD has consistently gained support in **eastern German states**. In **Saxony** and **Thuringia**, they achieved significant vote shares of **30.6%** and **32.8%**, respectively. If these results are indicative of broader trends in the region, then it is likely that they will surpass 28% in Brandenburg, which shares many of the same demographic and political characteristics as these states. - - **Strength: 9/10** - -2. **Anti-immigration sentiments and dissatisfaction with federal policies**: - - The **AfD’s populist platform**—especially their anti-immigration stance—resonates particularly well in eastern Germany, where sentiment against immigration and dissatisfaction with federal government policies has been growing. **Eastern regions** such as Brandenburg are especially **fertile grounds for AfD's messaging**, and it is plausible that these trends continue to push them over 28%. - - **Strength: 8/10** - -3. **Weakening mainstream parties**: - - Traditional parties like the **SPD**, **CDU**, and **Greens** continue to experience internal divisions and challenges, potentially making it difficult for them to stem the tide of rising AfD support. **Disillusionment with mainstream politics** continues to benefit AfD, and these parties' chronic inability to form stable coalitions may drive further voter migration to the AfD. - - **Strength: 7/10** - -4. **Momentum and voter consistency**: - - AfD has seen **steady long-term growth** in eastern Germany. Since their vote share was **23.5%** in 2019, a rise of just over **4-5 percentage points** to exceed the 28% threshold does not seem like an **unrealistic escalation**, especially with polls indicating their support has continued to grow. - - **Strength: 8/10** - -### Step 3: Weighting of sources - -1. **Recent polling data** (high importance): - - Polls are generally the best indicator for short-term predictions just 10 days out. Near real-time polling provides clarity on the current status but must be considered with margins of error. - - **Importance: 9/10** - -2. **Historical election results** (moderate importance): - - Historical performance provides a base rate, especially if historical factors like regional dissatisfaction or demographic factors persist. However, parties can make large gains over time, or lose support, which limits the predictive power of historical results alone. - - **Importance: 7/10** - -3. **Polls from other eastern German states (Thuringia, Saxony)** (medium-high importance): - - The political climate in neighboring eastern states is a strong correlated indicator for Brandenburg, which shares demographic similarities with Thuringia and Saxony. While neighboring states provide clues, local dynamics can differ slightly. - - **Importance: 8/10** - -4. **Coalition dynamics and strategic voting** (moderate-low importance): - - While mobilization of mainstream voters to suppress the AfD's rise is possible, it has only worked sporadically in eastern Germany where AfD's presence is strong. The effectiveness of anti-AfD backlash is somewhat less reliable than one might expect in western Germany. - - **Importance: 6/10** - -### Step 4: Time sensitivity and volatility analysis - -With only 10 days to go until the election, **substantial changes** in polling are less likely to occur unless there is a surge in media coverage or a significant external event. We can expect some room for volatility in the final days (around 1-2 percentage points), but the larger factors driving AfD's popularity seem stable at this point. - -Given that **polls have been pointing to AfD exceeding 28%**, and with limited time remaining, it appears unlikely that AfD will experience the kind of drop necessary to push them below 28%. - -### Step 5: Final probability assessment - -Using the above factors, the strongest indicators are AfD's recent polling trends, their continued momentum in eastern Germany, and recent successes in neighboring states. While historical vote ceilings and coalition dynamics present moderate counterpoints, these are outweighed by the fact that AfD’s momentum seems robust. - -- **Probability that AfD exceeds 28%**: **85%** -- **Probability that AfD gets 28% or less**: **15%** - -### Final Answer: -**Probability: 85%**" -28040,Will exactly 3 major Atlantic hurricane (Category 3 or higher) occur in September 2024?,"PERPLEXITY - -To address the question of whether exactly 3 major Atlantic hurricanes (Category 3 or higher) will occur in September 2024, we need to consider the current state of the Atlantic hurricane season and forecasts from reputable sources. - -1. **Current Season Activity**: - - The 2024 Atlantic hurricane season has been predicted to be above-normal by various forecasters, including NOAA and Colorado State University (CSU). - - As of early September, the season has seen a lull in activity, but conditions are expected to become more favorable for storm development around the middle of September. - -2. **Seasonal Predictions**: - - NOAA predicts 17 to 25 named storms, with 8 to 13 becoming hurricanes, and 4 to 7 of those reaching major hurricane status. - - CSU forecasts 23 named storms, 12 hurricanes, and 6 major hurricanes. - -3. **Recent Trends and Conditions**: - - The Atlantic has experienced a quiet period since mid-August, attributed to factors like a northward-shifted monsoon trough, warm upper-level temperatures, and unfavorable subseasonal variability. - - However, sea surface temperatures are expected to remain warm, and upper-level temperatures are expected to cool, making conditions more favorable for storm development later in September. - -4. **September Outlook**: - - Dr. Ryan Truchelut of WeatherTiger notes that the hostile environment suppressing storm development is likely to ease up in late September, potentially leading to a strong hurricane or two. - - The climatological peak of the Atlantic hurricane season is around September 12, suggesting that activity could increase. - -Given these points, while there is a potential for increased activity in late September, predicting exactly 3 major hurricanes in September 2024 is challenging due to the inherent uncertainties in weather forecasting. The conditions are favorable for an active season, but the precise number of major hurricanes within a specific month is difficult to forecast accurately. - -**Recent Updates and Expert Opinions**: -- The latest updates from early September indicate a potential for increased activity later in the month, but no specific predictions for exactly 3 major hurricanes in September have been made by the sources reviewed. - -**Prediction Markets and Forecasts**: -- No specific prediction market data for exactly 3 major hurricanes in September 2024 was found in the reviewed sources. However, the overall seasonal forecasts suggest an above-normal season, which could imply a higher likelihood of major hurricanes occurring in September. - -In summary, while the conditions are favorable for an active season and there is a potential for increased activity in late September, predicting exactly 3 major hurricanes in September 2024 involves significant uncertainty. - -######### - -GPT - -### Step 1: **Review of the Key Information** - -#### **Question**: Will exactly 3 major Atlantic hurricanes (Category 3 or higher) occur in September 2024? - -Today's Date is **September 12, 2024**, so this assessment occurs 40% of the way through September. The rest of the month still holds significant potential for additional storm development. Let’s consider: -- **September Climatic Trends**: September is the peak month for hurricanes; conditions are usually most favorable during this month for the formation of major hurricanes (Category 3 or higher). -- **Background Forecast**: Numerous forecasts, including **NOAA** and **CSU**, predicted an ""above-normal"" hurricane season. These forecasts are often rooted in observations like **sea surface temperature (SST)** anomalies, wind shear, and mid- to long-term atmospheric conditions. -- **Current Activity**: So far, early September 2024 has seen **relatively calm weather conditions**, but a shift with increasingly conducive environments for developing strong hurricanes is expected as the month progresses. - - - **NOAA highlighted** that this lull could transition into more active phases soon, suggesting conditions support the possibility of Category 3 or greater hurricanes forming over the next two weeks. - -### Step 2: **Assessing Likelihood of the Event** - -To accurately predict whether **exactly** 3 major hurricanes will occur, we need to examine several key factors from different angles: -- **Base rates of past hurricane seasons** -- **Climate/Weather predictions** -- **Current trends** -- **Variance and uncertainty in hurricane forecasting** - - -#### 1. **Reason to Consider ""Yes"" (Exactly 3 Storms)** - -**Historical Data and Base Rates** -- **Historical Base Rate**: For September Atlantic hurricanes, historical data indicates peak hurricane activity typically happens between September 8–15, extending toward the end of the month. From 1950 to 2023, the mean number of major hurricanes forming in September ranges has been about 1–2 Category 3+ storms. In particularly active years, there can be just two, while others have seen up to 3 or 4 (e.g., in hyperactive years like 2004 and 2017). -**Strength: 8/10** -- **Climatic Outlook**: NOAA and CSU's predictions of an ""above-normal"" season could suggest more storms than the average historical base rate. More storms overall mean a higher possibility of 3 major hurricanes forming. While the mid-August lull slowed 2024's activity, periods of quiet frequently precede bursts in hurricane formation during this peak time. Current projections suggest the latter part of the month could witness an uptick in cyclogenesis or cyclone intensification. -**Strength: 7/10** - -**Current Favorable Conditions Ahead**: -- **Sea Surface Temperature (SST)** anomalies are still quite warm, particularly in the tropical Atlantic and Caribbean regions. Warm SST is a key driver of hurricane intensity because it adds energy to the system, increasing the likelihood that any developing storms could rapidly intensify. -- **Wind Shear Reduction Forecast** for later in the month further boosts the likelihood of increased storm formation and intensification. -**Strength: 8/10** - -In previous years, hurricanes have rapidly intensified in short timeframes, becoming Category 3+ in as little as 24–48 hours. This suggests that if a handful of developing storms form later in the month, there’s a good chance of having multiple major hurricanes by month-end. - -#### 2. **Reasons to Consider ""No"" (Not Exactly 3)** - -**High Uncertainty and Variability** -- **Difficulty Predicting Exact Numbers**: Weather systems are notoriously difficult to predict with such precision, particularly hurricane activity. While global climate factors may greatly influence the possibility of *some* major hurricanes occurring, predicting the specific count (exactly 3) is fraught with uncertainty. The chances of getting precisely three storms — rather than 2, 4, or another number — depends on aligning several volatile environmental factors simultaneously. - **Strength: 9/10** - -- **Recent Lull and Timing Constraints**: Although more favorable conditions are expected soon, as of September 12, 2024, the Atlantic has recently been relatively quiet. The time Left for 3 major hurricanes to form is only about 18 days. Hurricanes are more likely to form in the first half of September due to the higher sea-surface warming then. As the later part of September arrives, the chances of ""exactly three"" major storms diminish due to reduced overall calendar days. Simple likelihood tilt suggests the number could be 1, 2, or 4 just as easily as it could be 3. -**Strength: 7/10** - -**Operational Considerations** -- **Forecasting Range by Experts**: None of the major forecasters have specifically predicted **3** Category 3+ storms in September, though they forecast high activity on aggregate for the season. This indicates that statistically, having exactly 3 in one month isn't necessarily the modal outcome forecasted. Even with shifts, it's plausible to expect an increase in hurricanes without being able to pin down an exact count of three. -**Strength: 6/10** - -### Step 3: **Statistical Analysis and Probability Assignment** - -Now using statistical logic: The probability of having exactly 3 hurricanes is lower than having ""2"" or ""4+"" because the precision required is higher. This pattern generally follows Poisson-like distributions in climatological forecasts. - -- Base rate expectation is around **1-2 major hurricanes** (from historical data) while forecasts, combined with current trends, suggest **above normal activity**. Given this, we might expect between **2 to 4 major hurricanes**. -- The chance of seeing exactly **3** (as opposed to any of the other plausible values in this range) would naturally be smaller than more flexible predictions (like ""at least 3""). - -Even with a possible uptick in activity in late September, the sheer variability and sensitivity around storm number forecasting make the exact probability of 3 hurricanes resolving true significantly tricky (compared to, say, predicting ""at least 2""). - -Taken together, base rates of seasons past, current conditions, and forecasting limitations suggest the probability of *exactly 3* September hurricanes should fall somewhere in the **20-30%** range. - -### Step 4: **Final Probability Estimate** - -Based on: -- Base rates and historical data -- Favorable conditions for an uptick in activity -- The inherent difficulty in nailing an exact number of exactly 3 - -**Probability: 25%**" -28046,"Will the lithium carbonate (CNY/T) price fall below 70,000 before October 1, 2024?","PERPLEXITY - -To address the question of whether the lithium carbonate (CNY/T) price will fall below 70,000 before October 1, 2024, we need to consider recent market trends and forecasts: - -1. **Current Price and Trend**: - - As of September 3, 2024, lithium carbonate prices have steadied at the CNY 75,000 per tonne mark, which is the lowest in over three years, due to persistent concerns about oversupply and weak demand. - -2. **Forecast and Market Sentiment**: - - S&P Global projects average annual CIF North Asia lithium carbonate prices to stabilize near current levels in a range between $20,000/mt and $25,000/mt from 2024 to 2027, which translates to a higher range when converted to CNY but indicates a stabilization rather than a significant drop. - - However, there are predictions that lithium prices could remain weak in early Q3 2024 due to oversupply and new import tariffs on Chinese EVs, with prices in China projected to range between Yuan 80,000-90,000/mt. - - The global supply of lithium is expected to surge by 40% in 2024, leading to a predicted global lithium surplus of 12%, which could further depress prices. - -3. **Supply and Demand Dynamics**: - - The increase in global supply, particularly from top producers like Australia and Latin America, and a significant project by CATL in China, is expected to outstrip demand, potentially leading to lower prices. - - Despite better-than-expected EV sales in China in June, new US and EU tariffs on Chinese EVs may dampen future sales in these regions, further weakening demand. - -4. **Recent Updates and Expert Opinions**: - - Experts like Rahul Sen Sharma, president and co-CEO of Indxx, believe that long-term fundamentals remain strong for the lithium industry, but acknowledge that short-term market trends can be volatile. - - Jean-François Béland, vice president of Ressources Québec, emphasizes the cyclical nature of the lithium industry and the need for continued investment, especially with government support for the EV sector. - -Given these points, while there is a general expectation of weak lithium prices persisting into early Q3 2024, the forecasts do not strongly indicate a drop below 70,000 CNY/T before October 1, 2024. The current price is already at a three-year low, and while supply pressures and tariffs could further depress prices, the stabilization predicted by S&P Global and the cyclical nature of the industry suggest that a significant drop below 70,000 might not be imminent. However, market volatility and the impact of new tariffs could still lead to unexpected price movements. - -**Prediction Markets and Forecasts**: -- Trading Economics estimates lithium to trade at 73,837.50 CNY/T by the end of this quarter, which is above the 70,000 threshold. - -In summary, while the market conditions suggest weak prices, the forecasts and expert opinions do not strongly indicate a drop below 70,000 CNY/T before October 1, 2024, but acknowledge the potential for volatility. - -######### - -GPT - -### Step-by-step Framework for the Forecast - -#### 1. **Clarification of Question and Resolution Criteria** -The main question is **whether the lithium carbonate price (CNY/T) will fall below 70,000 between Sept 12, 2024, and Oct 1, 2024**. -- The current price is 72,500 CNY/T. -- The resolution of this forecast will occur based on the Trading Economics closing price for any single day in the given timeframe. -- You need to consider the likelihood of price dropping below 70,000 within the next ~19 days, and not just a long-term trend. - -#### 2. **Understanding Default Behavior and Base Rates** -- **Default behavior right now is that prices are at 72,500 CNY/T**, which is above the threshold (70,000 CNY/T). -- Prices have notably fallen over 80% since December of 2022, indicating already significant downward pressure. -- **Base rate analysis** of significant step-wise price declines over short timeframes in commodities markets could lend insight here. Commodity prices generally do not move in a smooth, predictable way — they tend to exhibit mean reversion in the absence of sustained, extraordinary shocks (e.g., an unexpected supply glut or demand collapse). - -Using historical pricing trends from other commodities, most price drops of over 3% (~3,000 CNY/T here) in a two-week period are rare outside of a major news event or significant destabilization (e.g., geopolitical, supply chain, or extreme oversupply surprise). - -- The historical *volatility* suggests that spot prices can be volatile over short time periods, but **daily and weekly price variance tend to be small** unless there’s a new external shock. - -#### 3. **Key Factors Favoring a ""Yes"" Outcome (Price Falls Below 70,000 CNY/T)** -##### 3.1 Current Weakness in Lithium Market (Strength: 8/10) -- Lithium prices have fallen dramatically over the last 18 months (80%) and market projections (especially from S&P Global) indicate **continued weakness due to oversupply and reduced demand**, possibly through the end of 2024. -- Further supply glut predictions (global supply expected to grow 40% in 2024) and weaker-than-expected demand due to EV tariff interruptions on Chinese markets could act as a short-term catalyst, pushing prices below 70,000. - -##### 3.2 Specific Market Pressures (Strength: 6/10) -- Weak EV sales due to tariffs on exports from China to the U.S. and the EU could further reduce demand for lithium carbonate, leading to additional downward pressure. -- These factors are near-term relevant and could create **short-term volatility** in the market, pushing prices down below the threshold by accident or knee-jerk reactions. - -##### 3.3 Potential for Negative Speculative Momentum (Strength: 5/10) -- Markets can sometimes experience overshooting in either direction during a negative trend. If the market perceives further bearish news (e.g., new, significant supply announcements or disappointing quarterly results from EV producers), **speculative trading could increase downward pressure on prices**, even briefly dropping them under 70,000. - -#### 4. **Key Factors Favoring a ""No"" Outcome (Price Remains Above 70,000 CNY/T)** -##### 4.1 Stabilization Forecast (Strength: 8/10) -- S&P Global and other key market players project **stabilization** at current price levels (~75,000 CNY/T) through the remainder of 2024, suggesting that while there may be some volatility, a further significant drop below 70,000 is less likely. -- McKinsey also points to continued long-term demand for lithium, particularly driven by the transition to renewable energy and EV adoption. This suggests the market could hold as excess supply may be temporary. - -##### 4.2 Insufficient Time for Large Price Movement (Strength: 9/10) -- With only **19 days** left before the forecast's resolution date, there is limited time for the necessary ~2,500 CNY/T drop (~3.5%). For commodities, this would be a notable decline over a short period. In the absence of a significant external shock (like major global lithium producer disruptions or sudden demand collapse), such a movement is **historically uncommon** in such a short window. - -##### 4.3 Historical Support Levels (Strength: 7.5/10) -- It's possible that **70,000 acts as a support price** for traders and suppliers. Market participants may view prices falling below this as a buying opportunity or simply resist selling at these levels, given expectations of longer-term lithium strengths (i.e., long-term upward pressure from EV and battery industries). -- This would **limit further downward movement** unless there is strong bearish momentum, which does not seem supported by current signals. - -#### 5. **Incorporating Prediction Markets and Recent Expert Guidance** -- **Trading Economics** forecasts lithium carbonate at 73,837.50 CNY/T by the end of the quarter. This is notably above the 70,000 threshold. -- **Expert opinions** suggest that while prices may stagnate or remain weak, a sharp drop of 3-5% below current prices seems unlikely in such a short timeframe given market stabilization expectations. - ---- - -### Final Analysis - -1. **Probability Distribution Analysis**: - - The **default scenario** of prices remaining above 70,000 is quite strong, based on historical price behavior, expert reports of stabilization, and the amount of time left (~19 days). - - **Volatility considerations** suggest that spot prices can move, but given current market conditions and forecasts, a move below 70,000 CNY/T is unlikely unless triggered by unexpected shocks. - -2. **Base Rate for Price Declines**: - - With a drop of ~2,500 CNY/T needed in just under 3 weeks, the base rate for such a movement in a stabilized market lies in the single-digit percentage range without a significant new catalyst. - -3. **Factors to Weigh Heavily**: - - Timing (19 days): This is critical in your evaluation — the odds diminish with limited time availability. - - Range of Market Predictions: Expert predictions (like S&P Global) suggest that stabilization is more likely than further drops. - - Support/Resistance Levels: Market actors may step in and halt a decline around the 70,000 level, acting as a floor. - -Taking all of the above into account: - -### Final Probability: **13%**" -28043,Will AfD's vote share be more than 30% and less than or equal to 32% in the 2024 Brandenburg state election?,"PERPLEXITY - -To address the query about whether AfD's vote share will be more than 30% and less than or equal to 32% in the 2024 Brandenburg state election, we need to consider recent trends and polls related to the AfD's performance in similar contexts. - -1. **Recent Performance in Neighboring States:** - - The AfD achieved significant victories in the recent state elections in Thuringia and Saxony, securing over 30% of the votes in both states. This indicates a strong support base for the AfD in eastern Germany. - -2. **Polls and Predictions:** - - While specific polls for the 2024 Brandenburg state election are not detailed in the provided sources, the general trend suggests that the AfD is expected to perform strongly. The Wikipedia article on the 2024 Brandenburg state election mentions that the AfD had 23.5% of the vote in the 2019 election, but recent polls are not specified. - -3. **Political Landscape:** - - The political landscape in Brandenburg and other eastern German states is characterized by a strong presence of the AfD and other parties like the Sahra Wagenknecht Alliance (BSW), which also opposes certain policies of the current federal government. This could indicate a continued shift towards these parties in the upcoming election. - -4. **Expert Opinions:** - - Experts and analysts have noted that the AfD's success in Thuringia and Saxony could be indicative of its potential performance in other eastern German states, including Brandenburg. However, specific predictions for the vote share in Brandenburg are not provided in the sources. - -Given the recent performance of the AfD in neighboring states and the general political trends in eastern Germany, it is plausible that the AfD could achieve a vote share of more than 30% in the 2024 Brandenburg state election. However, predicting whether it will be less than or equal to 32% specifically is challenging without more detailed and recent polling data. - -**Prediction Markets and Betting Sites:** -No specific information from prediction markets or betting sites is provided in the sources regarding the 2024 Brandenburg state election outcomes. - -**Recent Updates:** -The most recent updates are from the state elections in Thuringia and Saxony, which showed strong support for the AfD. However, these do not directly predict the outcome in Brandenburg but suggest a favorable trend for the AfD in eastern Germany. - -**Current Values:** -No current polling values specific to the 2024 Brandenburg state election are provided in the sources. The most recent data available is from the 2019 election and some older polls listed on Wikipedia. - -######### - -GPT - -### Step 1: Analyze Base Rates and Historical Data - -The key question asks about whether Alternative for Germany (AfD) will receive between 30% and 32% of the vote in the 2024 Brandenburg state election, which is coming up in roughly **10 days** (current date: 2024-09-12; election: 2024-09-22). - -We can begin by assessing AfD’s historical performance: - -- In the **2019 Brandenburg state election**, the AfD received **23.5%** of the vote. This is a critical baseline. -- Recently, in other eastern German states, AfD has received over 30%, specifically in **Saxony (30.6%)** and **Thuringia (32.8%)**. - -Thus, the broad trend shows the AfD has made gains, especially in eastern German states where the political landscape is increasingly moving toward right-wing populism. Given political, socioeconomic, and cultural similarities between **Brandenburg**, **Saxony**, and **Thuringia**, it's reasonable to assume Brandenburg may follow—or at least partially follow—a similar trend. - -#### Strength of this reason: 8/10 -- **Historical precedence** is important. The AfD’s previous results in Brandenburg (23.5%) and neighboring states signal growing momentum in the region, supporting a plausibility of the party crossing the 30% threshold. -- However, pushing to exactly the 30%-32% range adds uncertainty. - -### Step 2: Consider Polling Information - -There’s no concrete, up-to-date polling data presented in this scenario as the research assistant only vaguely refers to unspecified “recent polls” indicating AfD’s growth. Sparse or unclear polling data complicates making direct extrapolations about AfD’s upcoming vote share. - -However, **AfD exceeded 30% in Thuringia and Saxony very recently**, suggesting strong momentum in regions culturally aligned with Brandenburg. - -#### Strength of this reason: 6/10 -- Polling is crucial, but in this case, we lack specifics. Polls can often be stronger indicators in the final few weeks before elections; if recent polls were consistent with 30% support, that would increase our confidence greatly. -- In the absence of hard data, we must derive expectations largely from general trends and intuition from nearby elections. - -### Step 3: Analyze Political and Socioeconomic Context - -Support for AfD has escalated in eastern Germany due to opposition to immigration, dissatisfaction with the national government, and concerns about economic stagnation or inequality. Given: - -1. Eastern Germany’s increasing opposition to the national government’s policies. -2. AfD’s anti-immigration stance and nationalist appeal, particularly strong in rural and economically struggling areas. - -These factors suggest that voter sympathy will gravitate toward the AfD as the election nears. However, the presence of **other anti-establishment parties**, such as the Sahra Wagenknecht Alliance (BSW), could siphon some votes and curtail the AfD's growth. - -#### Strength of this reason: 7/10 -- The socioeconomic climate is conducive to AfD's popularity growing, but fragmentation in the anti-establishment vote (due to other populist parties) introduces uncertainty. - -### Step 4: Time until Election and Uncertainty Factors - -The election date is very close, less than two weeks away. This limits the possible variance leading up to the election, absent any major shifts in the political landscape during that time. Historically, late shifts in voting preferences can happen, but they are typically within small margins at this stage. - -#### Strength of this reason: 9/10 -- The short time horizon dramatically reduces uncertainty from today through the election date, though media coverage or political events could still shift public sentiment in the final days (though probably only by 1-2 percentage points). - -### Step 5: Base Rates and Novel Considerations - -In past elections, the AfD tended to grow stronger in eastern states over time, and this trend has been robust through 2023 and the early parts of 2024. Given the continued challenges facing Germany and the political dissatisfaction in the region, AfD’s chances of touching the 30%-32% range seem reasonable. - -However, **exceeding 30% but not crossing 32%** is a very narrow window. Even though AfD has already exceeded 30% in two other eastern states this year (Thuringia and Saxony), the specifics of Brandenburg's electorate, coupled with voter fragmentation, might lead to AfD slightly underperforming or overperforming relative to this narrow range. - -Historical base rates for political predictions show that while trends are informative, small deviations (within 1-2% margins) are common, adding heterogeneity to the ultimate outcomes. - -#### Strength of this reason: 7/10 -- While AfD is clearly gaining support, forecasting them landing precisely in the 30%-32% range is tricky, given normal election variability and the dynamic nature of voter sentiment. - - -### Step 6: Weighing All Factors - -- **Pros for AfD exceeding 30%**: Strong gains in neighboring states 10-20 days prior to this election, growing popular dissatisfaction with mainstream parties and federal policies, and a general shift towards right-wing populism in the region. -- **Con for precisely falling between 30% and 32%**: The vote share window is small, and fluctuations due to multi-party dynamics, other populist appeals, and election-day factors make such precision hard to predict. - -Given the time frame (10 days), the typical variability in elections, and historical performance, I expect a **moderate likelihood** that AfD exceeds 30% **but** either near the **lower or upper bounds** of this window. However, the likelihood of them landing in the narrow 30%-32% range specifically is far from guaranteed. - -### Final Probability Assessment: -1. **Pros for landing in 30%-32%**: Strong momentum, similar performances recently in eastern states, favorable socio-economic conditions (strength: 7.5/10). -2. **Cons for landing in 30%-32%**: Variability in outcomes, competing anti-establishment parties, lack of recent polling data or definitive forecasts (strength: 6/10). - -### **Final Probability: 36%** - -This accounts for legitimate chances they exceed 30%, but significant uncertainty remains for falling into such a narrow interval." -28039,Will exactly 2 major Atlantic hurricanes (Category 3 or higher) occur in September 2024?,"PERPLEXITY - -To provide context for the question of whether exactly 2 major Atlantic hurricanes (Category 3 or higher) will occur in September 2024, here are key points from recent sources: - -1. **Seasonal Predictions**: - - **NOAA**: Predicts an above-normal Atlantic hurricane season with 17-25 named storms, 8-13 hurricanes, and 4-7 major hurricanes. - - **Colorado State University (CSU)**: Forecasts 23 named storms, 12 hurricanes, and 6 major hurricanes. - - **Other forecasts**: Similar predictions include 24 named storms, 12 hurricanes, and 6 major hurricanes from Tropical Storm Risk (TSR) and The Weather Company. - -2. **Current Season Status**: - - The season has been quieter than expected, with a recent pause in activity until the formation of Hurricane Francine. - - The climatological peak of the Atlantic hurricane season is around September 10, when conditions are most favorable for tropical storms and hurricanes. - -3. **Factors Affecting Activity**: - - **Warm Ocean Temperatures**: Near-record warm ocean temperatures in the Atlantic favor storm formation. - - **La Niña Conditions**: The transition from El Niño to La Niña could signify a more active hurricane season. - - **Recent Lull**: The recent quiet period is attributed to a northward-shifted monsoon trough, warm upper-level temperatures stabilizing the atmosphere, easterly wind shear, and unfavorable subseasonal variability associated with the Madden-Julian oscillation. - -4. **Historical Context**: - - The Atlantic hurricane season officially runs from June 1 through November 30. - - September 10 marks the statistical peak of the Atlantic hurricane season, with conditions most optimal for tropical development. - -Given these points, while there are predictions for an above-normal season, the recent lull and various atmospheric conditions have contributed to fewer storms than expected. However, forecasters believe that activity will pick up, especially as we are now at the climatological peak of the season. There is no specific prediction for exactly 2 major hurricanes in September, but the overall forecasts suggest that several major hurricanes are expected throughout the season. - -**Recent Updates**: -- The formation of Hurricane Francine indicates a potential increase in activity. -- Forecasters still anticipate an above-normal season, with CSU predicting 6 major hurricanes overall. - -**Prediction Markets**: -- No specific prediction market data is available in the provided sources for the exact number of major hurricanes in September. - -In summary, while there are no direct predictions for exactly 2 major hurricanes in September, the overall forecasts indicate an above-normal season with several major hurricanes expected. The recent formation of Hurricane Francine and the climatological peak of the season suggest that activity may increase. - -######### - -GPT - -### Step 1: Understand the Question and Data -The task is to forecast the probability of **exactly 2 major Atlantic hurricanes (Category 3 or higher)** occurring in **September 2024**. - -- **Scope**: September 2024 -- **Unit of Measurement**: Major Atlantic hurricanes (Category 3 or higher) during September -- **Resolution Deadline**: October 1, 2024, based on data from the **National Hurricane Center (NHC)**. - -### Step 2: Analyze the Historical and Current Context - -#### Historical Patterns and Base Rates -- Historically, **September** is the most active month for Atlantic hurricanes, with peak hurricane days occurring around the middle of the month (September 10). -- Major hurricanes (Category 3 or higher) form more frequently in September than in any other month of the hurricane season. -- According to the National Hurricane Center data from 1991-2020, an *average of 2-3 major hurricanes* form throughout the full season, particularly concentrated in September. -- That said, hurricanes are stochastic (random) events, meaning there is a wide variance in the number formed in any given month. In past decades, September has recorded anywhere from **zero** to **4+ major hurricanes**. - -#### Current Season Predictions -- **NOAA** and **Colorado State University** predict an above-normal hurricane season, with general expectations of **6 major hurricanes** for the overall season. - - *Importance: 9/10*. This provides an estimate of the larger window of total hurricanes, but not necessarily for this specific month. - - Note: Based on the forecast, *up to half* of these major hurricanes could occur in September since it is the active peak of the season. - -#### Recent Data and Seasonal Status -- **Current activity**: There has been a quiet start to the season, with a pause in storms until the formation of **Hurricane Francine**. - - *Strength: 8/10*. The fact that a quiet phase just ended could signal resurgence, but it reduces the likelihood that multiple major hurricanes have already occurred by this point (September 12th). - - Given today's date, one hurricane (Francine) has already formed, and this might make it difficult to squeeze in two additional major hurricanes, as conditions are volatile and unpredictable. - -#### Climatic Conditions -- **Environmental drivers**: - - **Warm ocean temperatures** in the Atlantic favor the formation of major hurricanes. - - *Strength: 8/10*. Given no immediate temperature anomalies that reduce severity, this solidifies conditions for hurricane development. - - **Wind shear and unfavorable atmospheric conditions** were in place recently but are starting to dissipate. A transitioning **La Niña** could accelerate activity. - - *Strength: 7/10*. Transition periods aren’t perfectly predictable, but the fact activity started ramping up recently should weigh in. - -#### Current Timing and Room for Variance -- **Current time (September 12th)**: Half the month remains, which leaves ample time for additional storms. However, given that Hurricane Francine already formed, we’d need at least one more (and potentially a second) major hurricane to match the exact outcome. - - Recall the possibility that tropical storms, once formed, can rapidly intensify into **Category 3 or stronger** systems. - - Typical hurricanes can evolve and escalate in intensity over **days**, allowing for the possibility of another major storm developing before September ends. - - However, hurricanes, especially major ones, generally do not form *back-to-back* frequently, especially in the same region. This puts a natural brake on the likelihood of exactly two storms reaching Category 3 in the same month. - - - **Base-rate for exactly 2 major hurricanes**: In past decades, there is precedence for the Atlantic producing **exactly 2 major hurricanes** in September, though this outcome is not the most common result—three or more hurricanes, or 0 or 1, have occurred more frequently than exactly 2. - - *Importance: 8/10*. More hurricanes have frequently occurred than exactly 2, so this must temper extreme confidence. - -### Step 3: Reasons for 'Yes' (Why **Exactly 2 Major Atlantic Hurricanes** Might Occur) - -1. **Climatological Peak**: We are in the **peak month** for hurricane formation, and conditions (temperature, La Niña transition) are becoming increasingly favorable. - - **Strength Rating: 9/10**. - - The statistical likelihood of multiple hurricanes during this window is high. - -2. **Above-Normal Season Forecasts**: NOAA and other agencies have forecast an **above-average hurricane season**, which would imply a stronger likelihood of multiple major hurricanes being concentrated in the most active month. - - **Strength: 8/10**. - - This wider forecasting counts for more storms, but still leaves room for variability. - -3. **Presence of Hurricane Francine**: A major hurricane has already formed (Francine), so only one more hurricane needs to form and strengthen to reach Category 3 status in the next two weeks. - - **Strength: 7/10**. - - While a lull has stalled activity previously, the window is still open for another. - -### Step 4: Reasons for 'No' (Why **Exactly 2 Major Atlantic Hurricanes** Might Not Occur) - -1. **Quiet Early Season**: The season had a **slower than expected start**, and it is possible that not enough time remains for another two systems to intensify into major hurricanes. - - **Strength Rating: 8/10**. - - Although conditions have improved lately, much of September’s activity might have already been locked-up in Francine. - -2. **Statistical Distribution**: Historically, September often sees **1 or 3+** major hurricanes, but **exactly two** is less common. The distribution of hurricanes tends to skew towards more activity (3+ hurricanes) or very little (0-1), resulting in a lower average for the number exactly being 2. - - **Strength Rating: 9/10**. - - The base rate history of “exactly two” is relatively rare compared to other outcomes. - -### Step 5: Weighing Probabilities and Final Reasoning -- Taking all factors into account, the plausibility of exactly two major hurricanes this September is **moderate**. While major hurricane Francine provides an initial hurricane, the next few weeks are murky. Given historical variability and the rapid formation of September storms, exactly two is neither implausibly rare nor overly common. -- We are already past the 10th of September, limiting visibility on upcoming storms. - -### Final Probability Estimate: -Based on the historical context, current weather conditions, forecasts for La Niña, and base rates: -- The probability that **exactly 2 major Atlantic hurricanes occur in September 2024** is: - -**Probability: 35%**." -28155,"Will Comex Gold Spot prices exceed $2,700 per ounce before October 1, 2024 according to Yahoo Finance?","PERPLEXITY - -To provide context for the question of whether Comex Gold Spot prices will exceed $2,700 per ounce before October 1, 2024, we need to examine recent trends, expert opinions, and market drivers. - -1. **Current Price and Recent Trends**: - - The current price of gold has surpassed $2,500 per ounce, marking a historic high. - - Gold prices have risen more than 20% this year, driven by safe-haven demand, central bank purchases, and expectations of interest rate cuts by the Federal Reserve. - -2. **Expert Opinions and Forecasts**: - - Analysts at UBS and Goldman Sachs predict that gold prices could reach $2,700 per ounce by early 2025, citing impending U.S. interest rate cuts and ongoing central bank purchases. - - Bart Melek, global head of commodity strategy at TD Securities, also expects prices to rise to $2,700 in the coming quarters due to aggressive monetary accommodation by the Fed. - -3. **Market Drivers**: - - Central banks have been net buyers of gold, with purchases reaching 483 tons in the first half of 2024, supporting the price increase. - - The weakening dollar and growing investor confidence in the Fed’s likely rate cuts have also contributed to the price surge. - - Gold ETFs have seen significant gains, outperforming the precious metal itself, with some funds experiencing returns exceeding 37%. - -4. **Prediction Markets and Forecasts**: - - While there is no direct prediction market data provided in the sources, the consensus among analysts and the recent price trends suggest a strong possibility of gold prices exceeding $2,700 per ounce before October 1, 2024. - -In summary, the current upward trend in gold prices, supported by central bank purchases, expectations of interest rate cuts, and safe-haven demand, along with optimistic forecasts from analysts, suggests a positive outlook for gold prices reaching $2,700 per ounce before October 1, 2024. However, it is essential to note that market predictions are subject to change based on various economic and geopolitical factors. - -**Recent Updates and Movement**: -- The recent surge in gold prices above $2,500 per ounce and the increase in speculative net-bullish bets on Comex gold futures to a near four-year high indicate strong market momentum towards higher prices. -- The upcoming Federal Reserve rate cuts and ongoing central bank purchases are expected to drive more capital into the gold market, further supporting price increases. - -**Today’s Numbers**: -- As of the latest available data, the gold price stands at $2,518 per ounce, with recent highs above $2,500 per ounce. - -**Expert Opinions Highlighted**: -- Wayne Gordon, commodities strategist at UBS, and analysts at Goldman Sachs predict a target price of $2,700 per ounce by early 2025. -- Bart Melek, global head of commodity strategy at TD Securities, expects prices to rise to $2,700 in the coming quarters. - -######### - -GPT - -### Step 1: Identify the Question Type and Structure -This question asks whether **Comex gold spot prices** will **exceed $2,700 per ounce** before **October 1, 2024**, resolving based on data from Yahoo Finance. - -The question fits into the **Market Price Event** category, where we're concerned with whether a specific price threshold will be met for a financial asset (in this case, gold). - -### Step 2: Understand the Context and Important Drivers -We know that: -- **Today’s Date**: 2024-09-13 (near the mid-point of September). -- **Resolution Date**: October 1, 2024, which gives **a little more than 2 weeks** until the deadline. -- **Current Gold Price**: $2,591.30 (September 12, 2024). -- The gold price needs to rise about **$108.70** (4.2%) to surpass $2,700, which is not insignificant, but neither is it impossible in the given timeframe. - -### Step 3: Analyze Reasons for Either Outcome - -#### Reasons Gold Might Exceed $2,700 by October 1, 2024: -1. **Historical Momentum (Strength: 8/10)**: - - Gold prices have risen over 20% this year, largely due to safe-haven demand bolstered by economic uncertainty. The bullish momentum is strong, and prices recently broke through the $2,500 mark. - - Recent record closes of slightly over $2,590 imply *momentum* pushing prices closer to the threshold of $2,700 if trends continue. - -2. **Interest Rate Cuts Expectations (Strength: 7/10)**: - - The Federal Reserve’s projected rate cuts could further weaken the dollar, which typically correlates with a rise in gold prices. As the market anticipates monetary easing, the move to safe-haven assets like gold often accelerates. - -3. **Increased Demand (Strength: 6/10)**: - - The demand for gold as a hedge against inflation and recession risks remains high, further supported by central bank buying. This strong demand provides fundamental support for higher prices. - -4. **Positive Market Sentiment (Strength: 6/10)**: - - Analysts from major financial institutions like UBS and Goldman Sachs project gold will breach $2,700 over the next few quarters, suggesting broad confidence in the market’s upward pressure. - -#### Reasons Gold Might NOT Exceed $2,700 by October 1, 2024: -1. **Short Time Horizon (Strength: 9/10)**: - - There are only **17 days** left until the resolution date, giving relatively little time for the necessary price move (a 4.2% increase). Historical weekly changes in gold prices are often small, and while trends are bullish, extreme volatility isn’t typical in this timeframe. - -2. **Resistance Near Record Highs (Strength: 8/10)**: - - Psychological and technical resistance often occurs at round-number records (e.g. $2,600 or $2,700), where traders may take profit and dampen near-term gains. This makes a sharp move over $2,700 more difficult. - -3. **Lack of External Shocks (Strength: 7/10)**: - - While gold may benefit from safe-haven demand or economic shocks, no major geopolitical or financial crisis seems imminent within the next two weeks. If conditions remain relatively stable, gold may continue to hover near current record levels without a significant further spike. - -4. **US Dollar Stability (Strength: 6/10)**: - - If the U.S. dollar doesn’t weaken as much as anticipated, it could limit the extent of any short-term gold price rally. The Federal Reserve’s rate decisions can vary, and any unexpected hawkish commentary could dampen gold price appreciation. - -### Step 4: Establish Base Rates and Historical Data -- Historically, gold prices have shown moderate daily and weekly volatility, but rarely do they shoot up 4-5% within just two weeks unless propelled by a significant event like large-scale geopolitical tension or financial market shocks. - -- Using daily and weekly prices over the past few months shows that while gold has occasionally had big upward moves due to macroeconomic or geopolitical catalysts, these moves are the exception rather than the rule. - -### Step 5: Evaluate the Likelihood and Uncertainty -- **Default Path (Status Quo)**: If current behavior continues, and no large extraneous factors arise (no major geopolitical conflicts or surprising economic data), it’s entirely possible that gold will remain somewhere near the current level, with slight fluctuations below $2,600. The market's natural resistance trying to consolidate around $2,600 would be difficult to break in less than two weeks. - -- **Variance**: The $100+ needed to cross $2,700 is not minor in the gold markets, where daily moves of ±$20-$30 are more typical without a catalyzing event. - -- We acknowledge **positive momentum** from recent price highs, but the short time period, psychological resistance, and lack of pronounced catalysts weigh more heavily toward the prices *stalling just below* $2,700. - -### Step 6: Conclusion and Final Probability Forecast -We’re facing a situation where gold is highly likely to remain near current levels, barring an external shock or unexpected major macroeconomic shift. While it *is possible* for gold prices to exceed $2,700, it requires a fairly significant upward push in a relatively short timeframe. - -Taking all the factors into account, including historical trends, the limited time window, market drivers, and expert forecasts: - -- There’s a **non-negligible chance** of a small rally following recent pricing momentum and anticipation of upcoming rate cuts. -- However, **the more likely outcome** is that prices hover below $2,700 due to natural resistance, slower daily movement, and the lack of major catalysts. - -### Final Probability -Considering these uncertainties, I estimate that the probability of gold prices exceeding $2,700 by October 1, 2024, is: - -**Probability: 38%** - -This reflects a reasonable but sub-50% chance of the threshold being breached given the short timeframe but upward momentum in the market." -28154,"Will Kennedy Iyere win the September 21, 2024 Edo state gubernatorial election in Nigeria?","PERPLEXITY - -To provide context for forecasting the outcome of the September 21, 2024 Edo state gubernatorial election, focusing on Kennedy Iyere's candidacy, here are key points from recent sources: - -1. **Primary Election Outcome**: - - Kennedy Iyere has emerged as the governorship candidate of the Accord Party for the 2024 Edo gubernatorial election. He was returned unopposed after five other aspirants stepped down for him, indicating a strong consensus within the party. - -2. **Running Mate**: - - Iyere has chosen Bright Enabulele, a US-based African entrepreneur, philanthropist, software engineer, and blockchain specialist, as his running mate. This selection could potentially bring diverse expertise and international connections to the campaign. - -3. **Campaign Strategy**: - - Iyere plans to form a coalition with at least six other political parties to challenge the dominance of the APC and PDP in Edo State. This strategy aims to leverage a broader political platform to address issues like hunger, deprivation, unemployment, and extreme poverty. - -4. **Campaign Theme**: - - The campaign theme, ""FIX-EDO-2024 PROJECT,"" emphasizes the need for political and economic reform in Edo State, positioning Iyere's candidacy as a solution to the state's current challenges. - -5. **Expert Opinion and Public Perception**: - - There is a lack of recent polls or expert analyses in the provided sources to gauge Iyere's chances of winning. However, the consensus within the Accord Party and the planned coalition with other parties could indicate a strong grassroots support base. - -6. **Prediction Markets and Betting Odds**: - - No information on prediction markets or betting odds is available in the provided sources. For a more accurate forecast, it would be beneficial to consult recent polls, prediction markets, and analyses from political experts. - -Given the information available, while Kennedy Iyere has secured a strong position within the Accord Party and has a clear campaign strategy, the absence of recent polls and expert analyses makes it challenging to predict the outcome of the election with certainty. Further research into current polls, prediction markets, and expert opinions would be necessary to make a more informed forecast. - -######### - -GPT - -### Initial Step: Understanding the problem and estimating the base rates -This question centers on the likelihood that **Kennedy Iyere** will win the **September 21, 2024 gubernatorial election in Edo State, Nigeria**, given both a small party (Accord Party) context and a complex political environment dominated by larger parties such as the *All Progressives Congress (APC)* and the *People's Democratic Party (PDP)*. Historically, Nigerian gubernatorial elections in major states like Edo have been dominated by these larger parties. As of now (**September 13, 2024**), Iyere is one of 17 candidates competing, and this is clearly a **competition event** type question. - -The fundamental task here is to estimate the probability of Iyere winning, particularly factoring in past trends, current conditions, and the remaining time (8 days) until the election. Additionally, we need to understand both the **default base rate** (how likely it is typically for a smaller party candidate to win), and any **extraordinary factors** that might increase or decrease those odds. - -Let's begin with a SWOT (strengths, weaknesses, opportunities, threats) analysis for Iyere's candidacy. - ---- - -### Step 1: SWOT analysis for Kennedy Iyere - -#### **Reasons why Kennedy Iyere may win (Yes):** - -1. **Uncontested Primary within the Accord Party (Strength):** - - Iyere's candidacy enjoys a strong support base within the *Accord Party*, and his unanimous selection signals cohesion and unified backing by his own party members. This internal stability might provide better messaging discipline in his campaign. - - **Strength Rating: 3/10** (Accord Party is still small in Edo State, so cohesion isn't a game-changer by itself, but it prevents internal issues). - -2. **Coalition Strategy (Opportunity):** - - Iyere’s coalition plans with six other political parties signal potential collaboration that could help bridge the divide amongst smaller opposition factions, perhaps giving Iyere more visibility and credibility within opposition voting pools. Given that PDP and APC are contested as perennial rivals, strategy involving multiple minor parties could, theoretically, bring a broader alliance of disillusioned voters. - - **Strength Rating: 4/10** (Such coalitions do not always translate into voter shifting when pitted against well-established major parties with proven voter turnout mechanisms). - -3. **Focus on Developmental Issues (Strength):** - - Iyere’s focus on **""hunger, deprivation, unemployment, and extreme poverty""** aligns with themes of economic reform that resonate with poorer populations. If these messages gain traction, it could help rally support in regions suffering from poor governance and underdevelopment. - - **Strength Rating: 3/10** (While critical issues resonate with voters, stronger candidates from PDP or APC may co-opt similar platforms, especially in a heavily electoralized state like Edo). - -#### **Reasons Iyere is unlikely to win (No):** - -1. **Accord Party's Weakness at the National Level (Weakness):** - - Accord Party holds no seats in the National Assembly and has little established political machinery compared to the dominant parties (PDP and APC). Nigerian elections often depend on well-established political machines capable of delivering votes across regions. - - **Strength Rating: 8/10** (Historical trends show that smaller parties struggle heavily, especially in major states. Based on past gubernatorial election results, the likelihood of Accord breaking through in Edo State remains low). - -2. **APC and PDP Dominance (Threat):** - - **Edo State** alternates between *APC* and *PDP* in the last two election cycles, and these parties have significant clout, both financially and politically. Without a significant disruption, votes will likely polarize between these two frontrunners, leaving other candidates, like Iyere, with limited room to compete effectively. - - **Strength Rating: 9/10** (In most Nigerian gubernatorial elections, the overwhelming majority of votes shift toward the two dominant parties, making it very difficult for a third-party candidate to gain significant ground). - -3. **Lack of Polling Data, Media Attention, and Prediction Markets (Weakness):** - - Having no contemporary polling data or expert analysis available for Iyere’s candidacy is indicative of minimal nationwide media attention. In the absence of media exposure and voter outreach, elections remain a ground combat won by visibility, grassroots efforts, and established party dominance. - - **Strength Rating: 7/10** (Low media visibility correlates with low voter recall and name recognition. Iyere’s platform focus, though important, will not have enough reach or change voters' minds if they are not confronted with it repeatedly). - -#### **Importance of Time Remaining (8 Days):** - - There is very little time remaining for Iyere’s candidacy to significantly pick up traction. Nigerian campaigns live heavily in a short media cycle, but with 8 days left, any dramatic change to the campaign landscape is highly improbable. This compresses the potential for last-minute surges or coalitions to deliver more effective outcomes. - - **Strength Rating: 7/10** (Given the proximity to the election, it becomes statistically challenging for a candidate to reverse major polling dynamics dramatically). - ---- - -### Step 2: Considering the Base Rate -Historically, major elections in Nigeria are decided between the two dominant political parties (PDP and APC). Smaller party candidates have typically not managed to secure significant governorship positions in Nigeria’s larger states. Accord Party candidates, in particular, have never succeeded in Edo State gubernatorial elections. - -#### Key Base Rate Observations: -- Success rate of minor party candidates in Nigerian gubernatorial elections: Very low (likely under 5%) -- Specific odds for an Accord Party candidate in a major, competitive race like Edo: Likely under 3%, based on historical patterns of smaller party governorship wins. - ---- - -### Step 3: Synthesis and Conclusion -Given: -1. The **intrinsic dominance** of APC and PDP in Edo’s voting landscape. -2. The **minimal** media exposure and polling data for Kennedy Iyere, coupled with the weak position of Accord Party. -3. The **proximity of the election** (just 8 days away), which limits the likelihood of late momentum. -4. The historical **base rates** showing that smaller party candidates rarely, if ever, win major Nigerian gubernatorial elections. - -It is difficult to envision a significant deviation from the historical outcomes, which point overwhelmingly toward the larger parties like APC or PDP dominating this election. - -Thus, I estimate the probability of Kennedy Iyere winning the September 21, 2024, Edo gubernatorial election is quite low: - -### Final Prediction: -**Probability: 3%**" -28156,"Will the USDA's recall of Boar's Head deli meat be closed before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether the USDA's recall of Boar's Head deli meat will be closed before October 1, 2024, here are the key points from recent sources: - -1. **Recall Background**: - - **Initial Recall**: On July 26, 2024, Boar's Head Provisions Co. initiated a recall of ready-to-eat liverwurst and other deli meat products due to possible contamination with *Listeria monocytogenes*. - - **Expanded Recall**: The recall was expanded on July 30, 2024, to include approximately 7 million additional pounds of ready-to-eat meat and poultry products produced between May 10, 2024, and July 29, 2024, under the Boar’s Head and Old Country brand names. - -2. **Current Status**: - - **Facility Closure**: Boar's Head announced on September 13, 2024, that it will indefinitely close its Jarratt, Virginia facility, which was identified as the source of the listeria outbreak. - - **Investigation Findings**: The company has pinpointed the cause of the contamination to a specific production procedure unique to the Jarratt plant, used for liverwurst, and has decided to permanently cease the production of liverwurst. - -3. **Health Impact**: - - **Outbreak Severity**: The CDC reported that the outbreak has resulted in 9 fatalities and 57 hospitalizations as of August 28, 2024, making it the largest listeria outbreak in the U.S. since 2011. - - **Consumer Alert**: The CDC and FSIS are urging consumers to check their refrigerators for recalled products, some of which have sell-by dates extending into October. - -4. **Regulatory Actions**: - - **FSIS Mandate**: The FSIS mandated the suspension of operations at the Jarratt facility due to ongoing failures by the company to maintain hygienic conditions. - - **Inspection Findings**: FSIS records revealed numerous instances of noncompliance at the Jarratt facility, including mold, mildew, and insect infestations. - -Given the recent closure of the Jarratt facility and the ongoing efforts to address the contamination issues, it is likely that the recall process will continue beyond October 1, 2024, especially considering the need for thorough investigations and the implementation of enhanced food safety protocols. However, the exact timeline for closing the recall is not specified in the available sources. - -**Prediction Markets and Expert Opinions**: -- There are no direct references to prediction markets or specific expert opinions on the closure date of the recall in the provided sources. However, the severity of the outbreak and the comprehensive measures being taken by Boar's Head and regulatory agencies suggest a prolonged process. - -**Recent Updates**: -- The most recent update from September 13, 2024, indicates the indefinite closure of the Jarratt facility and the permanent cessation of liverwurst production, suggesting that the recall process is ongoing and focused on ensuring public safety. - -######### - -GPT - -### Step-by-Step Forecasting Process: - -#### Step 1: **Restating the Problem** -We are asked to predict whether the USDA's recall of Boar's Head deli meat will be closed before October 1, 2024. - -- **Current date**: 2024-09-13 (18 days before October 1, 2024) -- **Resolution Date**: On or before September 30, 2024, the recall must change from ""Active"" to ""Closed"" to resolve as **Yes**. -- **Background**: This is a large-scale recall of deli meats due to *Listeria* contamination, impacting both the Boar’s Head and Old Country brands. The Jarratt, Virginia, facility has been identified as the source of the contamination, and the plant’s operations have been indefinitely shut down. Multiple fatalities and hospitalizations have occurred, creating a significant public health crisis. - -#### Step 2: **Default Expectation** -The default behavior is that recalls of this magnitude, particularly those associated with fatalities and ongoing health risks (products with expiration dates into October), tend to take significant time to be classified as ""closed."" Based on historical precedent, regulatory actions like recalls typically have a slower resolution process, especially when related to foodborne illnesses with delayed symptom onset. -Therefore, the natural starting point would be inclined towards a **No** resolution unless there is clear evidence to expect a rapid resolution. - -#### Step 3: **Weighing Recency and Sources** -We have several data points, with the critical insights being: - -1. **Expanded scope of the recall (July 30, 2024)**: - - A recall of over 7 million pounds of deli meat suggests a large revisory and cleanup operation. - - Severe regulatory scrutiny is likely, given the life-threatening nature of the outbreak (9 deaths, dozens hospitalized). - -2. **Facility indefinitely closed** (as of September 13, 2024): - - The Jarratt facility has ceased operations indefinitely and permanently stopped liverwurst production. This action suggests that Boar’s Head is pivoting strongly to mitigate further risks. - - However, the indefinite closure at this late date implies that full regulatory or public health clearance might extend far past October 1, 2024. - -3. **Sell-by dates for recalled products extend into October**: - - This introduces a severe complication in closing the recall before all products can be removed from circulation. - - Regulatory authorities will be cautious about closing while there is potential for contamination to cause additional harm (especially given the link to fatalities). - -#### Step 4: **Assessing the Impact and Pace of Recalls Historically** -Historically, recalls related to major food contamination events (**Listeria-related or otherwise**) are frequently protracted processes. Common challenges include: -- Full removal of contaminated products from the supply chain and consumers’ homes – which is complicated by long product shelf-lives. -- Thorough resolution of health concerns involves giving ample time for those exposed to the contaminated product to present symptoms, particularly for *Listeria*, which has a long incubation period (up to 70 days in some cases). -- It’s exceedingly rare for such a recall to be closed quickly when fatalities are involved, specifically with mandated investigations still ongoing. - -#### Step 5: **Reasoning ""Yes"" Arguments (Probability that the recall closes by October 1, 2024)** -1. **Boar’s Head's premature closure of contamination source (Jarratt plant)**: - - **Strength: 3/10**. The permanent stoppage of liverwurst production and closing of the plant may allow Boar’s Head to expedite corrective measures. However, this has occurred late in the recall process (September 13), leaving insufficient time for full cleanup and regulatory clearance. - -2. **Focused investigation may speed up closure**: - - **Strength: 2/10.** The identification of the specific production procedure at fault might streamline the resolution process. If regulators think the risk is contained, this could (in theory) permit a faster recall closure timeline. Still, given the timeline of only 18 days until October 1 and lingering risks, this is unlikely to yield a swift closure. - -3. **Proactive consumer warnings and widespread media coverage**: - - **Strength: 4/10.** The FSIS and CDC have heavily publicized the outbreak and recall, meaning that public awareness could result in faster retrieval of contaminated products. However, distribution networks and individual consumer habits prolong the risk, especially for products not yet consumed, making a quick resolution improbable. - -#### Step 6: **Reasoning ""No"" Arguments (Probability that the recall **does not** close before October 1, 2024)** -1. **Sell-by dates extending into October**: - - **Strength: 9/10.** A major hurdle for closing the recall is the fact that some affected products are still within their sell-by dates. Until these dates pass and regulators verify that no further risks are posed, closure is unlikely. - -2. **Severity of the outbreak and fatalities**: - - **Strength: 8/10.** Given the significant public health impact, with 9 confirmed deaths, regulatory agencies typically err on the side of caution. Officials are likely to keep strict controls and retain the recall’s **Active** status until they are certain all risks have been mitigated. - -3. **Facility hygiene and compliance issues**: - - **Strength: 7/10.** The FSIS reports mention the Jarratt facility's serious non-compliance issues, including mold and insect infestations. Remediation of such issues often takes weeks or months of inspection and validation before regulators approve resuming normal operations or close related recalls. Given the Jarratt facility’s indefinite closure, fixing and validating compliance simply can't be done within the remaining 18-day window. - -4. **Incubation period of *Listeria***: - - **Strength: 7/10.** Since *Listeria* symptoms can take weeks to appear, regulators will be cautious about closing the recall while there’s any chance more cases could arise. The recall is likely to stay active until they pass the point where new infections are expected. - -#### Step 7: **Statistical Analysis** -While precise historical data on recall duration following large *Listeria* outbreaks may vary, the base rate for large-scale food contamination recalls is typically several weeks to months of active status after the initial event—especially with fatalities involved. The sell-by dates advancing into October further support this base rate observation. - -#### Conclusion: -Given the background, regulatory context, and historical precedent for similar recalls, the likelihood of the USDA classifying the Boar's Head deli meat recall as ""Closed"" before October 1, 2024, is low. The combination of unresolved contamination risks, long product sell-by dates, and ongoing investigations makes a quick resolution highly unlikely. - -**Final Answer**: **Probability: 10%** (with the highest likelihood of resolving as **No**)" -28210,Will a Llama model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024?,"PERPLEXITY - -To address the question of whether a Llama model will be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024, we need to consider recent data and insights from various sources. - -1. **Current Performance**: - - **Llama 3's Performance**: Llama 3-70B has shown remarkable performance, quickly rising to the top of the English Chatbot Arena leaderboard with over 50,000 battles. It excels in open-ended and creative tasks but struggles with close-ended technical tasks like math and coding. - - **Leaderboard Dynamics**: The LMSYS Chatbot Arena Leaderboard is a dynamic platform that ranks models based on crowdsourced human pairwise comparisons. It uses the Bradley-Terry model to determine Elo ratings, which reflect relative skill levels. - -2. **Recent Rankings**: - - As of August 13, 2024, the top models on the leaderboard were ChatGPT-4o-latest, Gemini-1.5-Pro-Exp-0801, and GPT-4o-2024-05-13, indicating that proprietary models from major tech companies are still leading. - - Llama 3 has been competitive, even tying with GPT-4 in some evaluations, but its overall ranking can fluctuate based on the types of prompts and user preferences. - -3. **Expert Opinions and Limitations**: - - **Benchmark Limitations**: The LMSYS benchmark has limitations, including the potential for an upper Elo bound determined by human capabilities rather than LLM capabilities. This means that as models improve, the benchmark's ability to differentiate between them may decrease. - - **User Preferences**: The evaluation is based on human judgments, which can be subjective and may not always capture the full capabilities of the models. Users tend to prefer models with more pleasing and direct answers, which might not necessarily be the most capable or intelligent. - -4. **Prediction Markets and Forecasts**: - - There are no direct prediction markets or forecasts mentioned in the provided sources that specifically address the ranking of Llama models at the end of the 3rd Quarter of 2024. - -Given the current data and the dynamic nature of the LMSYS Chatbot Arena Leaderboard, it is challenging to predict with certainty whether a Llama model will be ranked #1 overall by the end of the 3rd Quarter of 2024. The performance of Llama 3 is promising, especially in creative tasks, but the leaderboard's top spots are currently held by proprietary models from major tech companies. The limitations of the benchmark and the subjective nature of user preferences further complicate making a definitive forecast. - -######### - -GPT - -## Thought Process and Evaluation - -### Step 1: Assess the Background Sources and their Recency -- **Recent Performance Metrics:** - - Llama 3-70B's rise in performance and its tie for 6th place as of September 15, 2024, are crucial. This is a very recent data point. - - The leaderboard's dynamic nature means that rapid changes can happen within weeks, but significant shifts to the top could require sustained performance improvements or fundamental shifts. - -### Step 2: Reasons for ""Yes"" -1. **Rapid Improvements in Model Quality (Strength: 7/10)** - - Recent advancements within a few months show Llama 3-70B has improved significantly. Continued fine-tuning and updates could push it to the top. - -2. **Model's Unique Strengths (Strength: 6/10)** - - Llama 3’s strengths in open-ended and creative tasks could align better with user preferences over time, potentially skewing ratings positively for creative preference. - -3. **Competitive Landscape (Strength: 5/10)** - - While the top spots are currently held by proprietary models, increasing competition can sometimes spur rapid innovation. If Llama 3-70B or a new variant significantly outperforms current leaders in targeted areas, it could leapfrog to the top. - -### Step 3: Reasons for ""No"" -1. **Current Leaderboard Position and Trends (Strength: 8/10)** - - Being tied for 6th suggests it is behind several models from major companies. The gap in Elo ratings and current rankings make a rapid ascent to 1st place challenging within the short period until the end of Q3. - -2. **User Preference Variability (Strength: 7/10)** - - Reliance on crowdsourced evaluations adds subjectivity. Users’ biases towards models from well-known companies (OpenAI, Google) might persist, complicating a rise to the top for Llama models. - -3. **Benchmarking and Limits (Strength: 6/10)** - - The potential limits of the LMSYS benchmarks, driven by an upper Elo bound, may mean smaller improvements are harder to notice and rank well, especially in the crowded top spots. - -### Step 4: Evaluate Time Frame and Historical Base Rates -- The limited time between now (September 17, 2024) and the end of Q3 (September 30, 2024). -- Base rate: Historical data on leaderboard shifts and rapid performance improvements typically don’t show drastic changes, especially nearing top positions. - -### Step 5: Default Behavior and Fine Print -- **Default Resolution:** If the competition were to end today, Llama would not be ranked #1. -- **Fine Print Consideration:** A new Llama model release or update could quickly alter standings, but the probability remains low given the short timeframe. - -### Conclusion and Probability Determination -- After synthesizing all data points, weighing recent performances, leader dynamics, and inherent biases, a Llama model achieving #1 rank seems unfavorable given the close proximity to the end of Q3 and the leap it would need to make from its current position in 6th. - -### Final Answer -Probability: **12%**" -28209,Will a Grok model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024?,"PERPLEXITY - -To provide context for the question of whether a Grok model will be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024, here are the key points from recent sources: - -1. **Current Leaderboard Status**: - - As of the latest updates, GPT-4o models are leading the LMSYS Chatbot Arena Leaderboard. Specifically, GPT-4o-2024-08-08 holds the top spot with an ELO of 1316, followed closely by Gemini-1.5-Pro-Exp-0827 and Grok-2-08-13, both of which have ELOs of 1301 and 1295, respectively. - -2. **Grok-2 Performance**: - - Grok-2 has made a significant entry into the leaderboard, securing the second place and outperforming OpenAI's GPT-4o (May) in certain tasks. It excels particularly in mathematical tasks and has also secured second place in several other tasks, including complex prompts, programming, and following instructions. - -3. **Recent Updates and Market Predictions**: - - The LMSYS leaderboard has seen recent updates that included Grok-2, which initially appeared on the leaderboard on August 22nd, a day after the expected update on August 21st. This caused some confusion in prediction markets. - - Prediction markets have been active, with discussions around the potential for Grok-2 to climb or fall in rankings. However, as of mid-August, Google's Gemini was leading, though its score had dipped below 1300. The latest GPT-4o checkpoint released on August 12th took the top spot with a significant ELO margin over Gemini. - -4. **Style vs. Substance Analysis**: - - A recent analysis disentangled style and substance in the Chatbot Arena leaderboard, showing that style has a strong effect on models' performance. When controlling for length and style, noticeable shifts in the ranking were observed, with some models like Claude 3.5 Sonnet and Llama-3.1-405B rising substantially in certain categories. - -Given these points, as of the latest available data: -- **Grok-2's Performance**: It has secured a strong position but is not currently at #1. -- **Leaderboard Dynamics**: The leaderboard is subject to frequent updates and changes in rankings based on new model releases and improvements. -- **Prediction Markets**: There is ongoing speculation and betting on the potential rankings, with some expecting changes due to new model releases like GPT-5/Strawberry. - -**Conclusion**: As of the latest data available up to August 2024, a Grok model is not ranked #1 overall on the LMSYS Chatbot Arena Leaderboard. However, the dynamic nature of the leaderboard and ongoing improvements in models suggest that rankings could change by the end of the 3rd Quarter of 2024. - -######### - -GPT - -### Step-by-Step Evaluation: - -#### Analyzing Reasons For a ""Yes"" Outcome: - -1. **Recent Performance and Trend (Strength: 7/10):** - - Grok-2 has recently secured a strong position and demonstrated a significant performance, especially in mathematical and complex tasks. This indicates that it has the potential to advance. - -2. **Dynamic Nature of Leaderboard (Strength: 6/10):** - - The LMSYS Chatbot Arena Leaderboard is highly dynamic and subject to frequent updates. Given its recent performance, Grok-2 could potentially surpass its competitors if it shows improvement in the short remaining time. - -3. **Support from Prediction Markets (Strength: 5/10):** - - There is ongoing speculation and betting in prediction markets supporting Grok-2’s potential to climb. While not definitive, prediction markets can occasionally offer useful insights based on crowd wisdom. - -#### Analyzing Reasons For a ""No"" Outcome: - -1. **Current Standing and Competition (Strength: 8/10):** - - Grok-2 is currently tied for second but not first. The existing leader GPT-4o models have maintained a significant margin. Historically, significant shifts to unseat the top model can be challenging over a short period. - -2. **Market and Technological Uncertainty (Strength: 7/10):** - - The leaderboard can be impacted by new releases and updates from other competitors like GPT-5/Strawberry and Gemini. This introduces additional uncertainty, making a sudden rise for Grok-2 less predictable. - -3. **Base Rate Analysis (Strength: 6/10):** - - Historically, models that enter like Grok-2 often see initial quick climbs but maintaining and surpassing the leading spot shortly afterwards is less common due to intensifying competition and diminishing returns in improvements. - -#### Weighing the Evidence: - -- **Current Leaderboard Status:** - - GPT-4o is clearly leading with a significant ELO margin. Given the historical difficulty of surpassing models with such a lead and base rate of previous leaderboard changes, this is a critical indication weighing towards ""No"". - -- **Dynamic Nature of Leaderboard:** - - While dynamic, frequent updates can work both ways — they provide opportunities for Grok-2 to improve but also for competitors to defend or widen their lead. This introduces volatility and uncertainty. - -- **Time Until Resolution:** - - With only about two weeks remaining until the end of the 3rd quarter, substantial changes in such a short span are less probable. - -#### Final Analysis: - -Given the factors, especially the combination of current standing, the short time frame remaining, and the competitive landscape, the base scenario (Grok-2 not being #1) remains the most probable. - -### Probability Estimation: - -Combining historical base rates, recent performance, and weighing in the dynamic nature of leaderboards: -- **Base rate and historical precedent**: ~70% -- **Recent downward momentum of competitors**: ~10% -- **Prediction market speculation**: ~5% - -Upon adjusting the strength of each factor and compensating for variance: - -**Probability: 25%** - -This reflects a realistic but not overly optimistic chance that Grok-2 may achieve the #1 position given the current evidence and dynamics." -28209,Will a Grok model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024?,"PERPLEXITY - -To address the query of whether a Grok model will be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024, we need to consider recent developments and current standings on the leaderboard. - -1. **Current Leaderboard Standings:** - - As of the latest updates available, GPT-4o models and Gemini models are leading the LMSYS Chatbot Arena Leaderboard. Specifically, GPT-4o-2024-08-08 holds the top spot with an Elo rating of 1316, followed closely by Gemini-1.5-Pro-Exp-0827 with an Elo rating of 1301. - -2. **Grok Model Performance:** - - The Grok-2 model from xAI has made significant strides, securing the second place on the leaderboard and outperforming OpenAI's GPT-4o (May) in certain tasks, particularly in mathematical tasks where it holds the top spot. - - Despite its strong performance, Grok-2 is currently tied with Gemini-1.5-Pro-Exp-0827 and has not surpassed the leading GPT-4o-2024-08-08 model. - -3. **Recent Updates and Predictions:** - - The prediction market on Manifold indicates various forecasts related to the LMSYS Chatbot Arena Leaderboard, but none specifically predict Grok to overtake GPT-4o by the end of the 3rd Quarter of 2024. - - There have been concerns about the reliability of the LMSYS leaderboard due to potential manipulation with sockpuppet accounts or fraudulent voting, but these concerns are more about the future trustworthiness rather than current standings. - -4. **Conclusion:** - - Given the current standings and the performance of Grok models, it seems unlikely that a Grok model will be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024. The top spot is currently held by GPT-4o-2024-08-08, and while Grok-2 performs exceptionally well in certain tasks, it has not surpassed the leading model in overall Elo rating. - -**Recent Sources:** -- ** OpenLM.ai:** Provides the current leaderboard standings, showing GPT-4o-2024-08-08 in the top spot. -- ** AIbase:** Reports on Grok-2's performance, highlighting its strengths in mathematical tasks and its second-place ranking. -- ** Manifold:** Offers prediction markets related to the LMSYS Chatbot Arena Leaderboard, including questions about future rankings and potential manipulation. - -######### - -GPT - -### Chain of Thought and Rationale: - -#### Step-by-step Breakdown: - -1. **Understanding the Context and Timeline**: - - **Today's Date**: 2024-09-17. - - **Resolution Date**: End of the 3rd Quarter of 2024 (October 1). - - **Objective**: Determine if a Grok model will be ranked #1 on the LMSYS Chatbot Arena Leaderboard. - -2. **Current Standings and Trends**: - - **September 15, 2024 Standings**: GPT-4o-2024-08-08 is currently at the top with an Elo rating of 1316. - - **Grok-2 Model**: Tied for second place with Gemini-1.5-Pro-Exp-0827. - -3. **Assessing the Strength and Recency of Information**: - - **Sources of Information**: - - **Current Leaderboard from OpenLM.ai** (Very Recent): High relevance, Elo ratings update frequently. (Importance Rating: 9/10) - - **AIbase Reports on Grok-2** (Recent): Moderate relevance since it highlights Grok-2's performance specifics. (Importance Rating: 7/10) - - **Manifold Prediction Markets** (Up-to-date but non-specific): Provides sentiment and prediction but not always reliable for exact events. (Importance Rating: 6/10) - -4. **Time Window and Variability**: - - **Days Left Until Resolution**: 13 days. - - **Usual Variance**: Elo ratings can change based on performance metrics and user interactions but not drastically within short periods unless there are significant updates or model improvements. - -5. **Base Rates and Historical Precedents**: - - Historically, shifting from second to first place in such a short time period without a major update or breakthrough is low. - -#### Reasons Why the Answer May Be Yes: -1. **Improvement of Grok Models** (Strength: 6/10): - - If xAI deploys a notable update or enhancement to Grok-2 in the next couple of weeks, it could potentially boost its ranking. - -2. **Potential for GPT-4o Model Issues** (Strength: 5/10): - - Any unexpected issues or downgrades with GPT-4o-2024-08-08 could lower its rating, indirectly benefiting Grok-2. - -3. **Statistical Anomalies** (Strength: 4/10): - - Although rare, there's always a possibility that anomalous results or a surge in positive ratings for Grok-2 could place it at the top. - -#### Reasons Why the Answer May Be No: -1. **Current Dominance of GPT-4o** (Strength: 9/10): - - The Elo rating lead of 15 points for GPT-4o-2024-08-08 suggests significant user preference and performance stability. - -2. **Short Time Frame** (Strength: 8/10): - - Only 13 days remain; significant changes in leaderboard rankings typically require more time or substantial updates. - -3. **Absence of Specific Predictions for Grok-2 Overtaking GPT-4o** (Strength: 7/10): - - Manifold prediction markets and current expert opinions do not foresee Grok overtaking the top rank imminently. - -### Conclusion: -Taking into consideration the strength of current standings, time frame, and historical trends, it is highly unlikely that a Grok model will surpass GPT-4o-2024-08-08 and become the number 1 ranked model in the next 13 days. - -**Final Probability Distribution**: -- **Probability of Grok Model Ranked #1 by October 1, 2024**: `5%` -- **Probability of Grok Model NOT Ranked #1 by October 1, 2024**: `95%` - -**Final Answer**: -- **Probability**: 5%" -28206,Will a ChatGPT model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024?,"PERPLEXITY - -To address the question of whether a ChatGPT model will be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024, we need to consider recent developments and rankings on the leaderboard. - -1. **Current Rankings**: As of August 13, 2024, the top models on the LMSYS Chatbot Arena Leaderboard include ChatGPT-4o-latest (2024-08-08) with an Arena Score of 1314, closely followed by Gemini-1.5-Pro-Exp-0801 and GPT-4o-2024-05-13. - -2. **Recent Performance**: The leaderboard has seen fluctuations, with GPT-4 Turbo taking the first spot earlier in 2024, indicating that the rankings can change over time. - -3. **Competition**: The rise of other models, such as Anthropic’s Claude 3 Opus and Haiku, has posed a significant challenge to GPT-4’s dominance. Haiku, in particular, has been noted for its smaller size and lower cost compared to GPT-4, making it an attractive option. - -4. **Evaluation Method**: The LMSYS Chatbot Arena uses a crowdsourced platform with the Elo rating system, based on human pairwise comparisons. This method is valued for its ability to capture desirable AI traits beyond quantifiable skills, but it also has limitations, such as potential subjectivity and the challenge of differentiating between closely performing models. - -Given these points, while a ChatGPT model is currently at the top, the dynamic nature of the leaderboard and the emergence of competitive models like Claude 3 Opus and Haiku suggest that the ranking could change by the end of the 3rd Quarter of 2024. - -**Prediction Markets and Expert Opinions**: There are no direct references to prediction markets or recent expert opinions specifically addressing the end-of-3rd-Quarter ranking in the provided sources. However, experts like Jesse Dodge and Vanessa Parli emphasize the importance of robust evaluation methods and the challenges in comparing large language models, indicating that the competition is tight and can shift based on various factors. - -**Recent Updates**: The most recent update from August 13, 2024, shows ChatGPT-4o-latest at the top, but given the competitive landscape and the potential for new model releases, there could be movement towards a different resolution by the end of the 3rd Quarter. - -In summary, while a ChatGPT model is currently leading, the competitive dynamics and the evaluation method used by the LMSYS Chatbot Arena Leaderboard suggest that the ranking could change, making it uncertain whether a ChatGPT model will remain #1 by the end of the 3rd Quarter of 2024. - -######### - -GPT - -### Step-by-Step Thought Process - -#### Understanding the Question Type -The question is a **Competition Event**, focusing on whether a specific model (""chatgpt-4o-latest"") will remain at the top of a leaderboard by a particular date. - -#### Key Data Points and Background Information -1. **Current Leaderboard Status**: As of September 15, 2024, ""chatgpt-4o-latest"" is ranked first. -2. **Leaderboard Dynamics**: Fluctuations in rankings have occurred, with different models taking the top spot at various times. -3. **Competitors**: Strong competition from models like Gemini-1.5-Pro-Exp-0801, GPT-4o-2024-05-13, Claude 3 Opus, and Haiku. -4. **Evaluation Method**: Uses a crowdsourced Elo rating system, implying potential volatility due to human subjectivity. -5. **Timeline Considerations**: The resolution date is close (end of Q3 2024), providing limited time for substantial changes. - -#### Reasons for a ""Yes"" Resolution -1. **Current Leading Status** (Strength: 9/10) - - The fact that the model is currently ranked #1 implies it is performing very well competitively. -2. **Historical Dominance** (Strength: 7/10) - - ChatGPT models have had strong performance and established reputations. -3. **Limited Time Frame** (Strength: 6/10) - - With less than two weeks to the resolution date, substantial leaderboard changes are less likely. -4. **Frequent Model Updates** (Strength: 5/10) - - OpenAI regularly updates its models, potentially maintaining a competitive edge. - -#### Reasons for a ""No"" Resolution -1. **Recent Fluctuations** (Strength: 7/10) - - The leaderboard has experienced changes, showing a competitive landscape. -2. **Strong Competitors** (Strength: 8/10) - - Competitors like Gemini-1.5-Pro-Exp-0801 and Claude 3 Opus have shown strong performances. -3. **Evaluation Method Limitations** (Strength: 6/10) - - The Elo rating system’s subjectivity might induce volatility in short periods. -4. **Potential New Releases** (Strength: 5/10) - - New, improved models from competitors could emerge rapidly, potentially dethroning the current leader. - -#### Evaluating Background Sources -- **Current Rankings and Recent Performance**: Highly credible and recent, very relevant (Importance: 9/10). -- **Competition and Evaluation Methods**: Credible but slightly less immediate impact (Importance: 7/10). -- **Expert Opinions and Recent Updates**: Useful for context but lacking direct predictions (Importance: 5/10). - -#### Statistical Considerations -- **Base Rates**: Historically, dominant models tend to maintain their lead for some time unless a breakthrough competitor emerges rapidly. -- **Trend Analysis**: Short-term trends indicate some volatility, but less so over a two-week period. - -#### Final Analysis -Given the close proximity to the resolution date and the current leadership status, the probability of a significant shift is somewhat mitigated by the limited timeframe. However, the competitive landscape remains fierce. - -### Conclusion -- **Dominance and Current Lead**: The highest-weight factor supporting a 'Yes' is the current #1 status and historical dominance of ChatGPT models. -- **Volatility and Competition**: Acknowledging potential shifts due to strong competitive models and subjective evaluation methods. - -Considering all factors, the probability that a ChatGPT model will be ranked #1 overall at the end of Q3 2024 is: - -**Probability: 75%** - -This accounts for a strong likelihood of maintaining the lead given the short time frame, balanced against the real risk posed by close competitors." -28205,"Will Boeing's aircraft assembly factory workers continuously be on strike, according to the AP's Strikes Hub, through September 30, 2024?","PERPLEXITY - -To address the question of whether Boeing's aircraft assembly factory workers will continuously be on strike through September 30, 2024, here is a concise but detailed rundown of the relevant news: - -1. **Strike Initiation**: The strike began on September 13, 2024, after 33,000 Boeing machinists, mostly in Washington state, voted to reject a proposed contract that would have increased wages by 25% over four years. The strike affects the production of Boeing's 737 Max, 777, and 767 aircraft but not the 787 Dreamliners, which are manufactured by non-union workers in South Carolina. - -2. **Company Response**: Boeing has announced measures to conserve cash, including a hiring freeze, pausing pay increases for executives and managers, halting non-essential business travel, and reducing spending on advertising, marketing, and charitable contributions. The company is also considering temporary furloughs for many employees, managers, and executives. - -3. **Financial Impact**: Analysts warn that an extended strike could lead to a cash crunch for Boeing, potentially prompting credit downgrades. JP Morgan estimates the strike could cost Boeing $1.5 billion monthly, and Fitch Ratings notes that an extended strike could have a meaningful operational and financial impact, increasing the risk of a downgrade. - -4. **Negotiation Status**: Boeing has expressed its willingness to return to negotiations to establish a new agreement, but there is no indication of when the strike might end. The company's CEO, Kelly Ortberg, has emphasized the need to avoid a prolonged strike to prevent further erosion of trust with customers and to protect the company's recovery efforts. - -5. **Current Situation**: As of September 16, 2024, the strike continues, with Boeing taking steps to minimize financial and logistical challenges. However, there are no recent updates indicating a resolution or an end to the strike by September 30, 2024. - -Given the current information, there is no clear indication that the strike will end by September 30, 2024. The situation remains fluid, with ongoing negotiations and significant financial pressures on Boeing. - -**Prediction Markets and Forecasts**: There are no specific prediction markets or forecasts mentioned in the provided sources regarding the duration of the strike. However, financial analysts' estimates and warnings about the potential financial impact suggest that the strike's continuation could have significant implications for Boeing's financial health. - -**Recent Updates**: The most recent sources (dated September 16, 2024) do not indicate any movement towards a resolution that would end the strike by September 30, 2024. The emphasis is on the financial measures Boeing is taking to mitigate the strike's impact, rather than on a near-term resolution. - -**Expert Opinions**: Experts and analysts highlight the potential for an extended strike to cause financial challenges for Boeing, including the risk of credit downgrades. However, there are no expert opinions provided that specifically predict the strike's duration or its resolution by September 30, 2024. - -In summary, while there are significant financial and operational pressures on Boeing due to the strike, there is no clear indication from the provided sources that the strike will end by September 30, 2024. The situation remains uncertain, with ongoing negotiations and no recent updates indicating a near-term resolution. - -######### - -GPT - -Given the question’s complexity and the stakes involved, here is a detailed step-by-step reasoning for forming a probabilistic forecast on whether Boeing's aircraft assembly factory workers will continuously be on strike through September 30, 2024, as reported by the Associated Press's Strike Hub. - -### Step 1: Understanding the Current State -- **Strike Initiation and Context**: The strike began on September 13, 2024, involving 33,000 machinists who rejected a contract offer. -- **Company Response**: Boeing has taken measures to conserve cash and signaled willingness to negotiate. -- **Financial Impacts**: Analysts project a $1.5 billion monthly loss for Boeing. -- **Negotiation Status**: There are no updates indicating immediate resolution. - -### Step 2: Weighing the Reasons for Yes and No - -#### Reasons the Strike Could Continue Until September 30, 2024: -1. **Recent Initiation**: The strike has just begun and involves a significant number of workers. Large-scale strikes often take significant time to resolve. - - Strength: 8/10 -2. **Financial Impact and Negotiation Complexity**: The high financial stakes and ongoing loss suggest Boeing needs to be cautious, possibly prolonging negotiations. - - Strength: 7/10 -3. **No Immediate Resolution in Sight**: There is no current indication of progress towards an agreement. - - Strength: 6/10 -4. **Historical Precedents**: Similar strikes in the aerospace industry have historically taken weeks to resolve due to complex labor negotiations. - - Strength: 7/10 - -#### Reasons the Strike Could End Before September 30, 2024: -1. **Financial Pressures on Boeing and Union Workers**: Both Boeing and the workers have strong financial incentives to reach a resolution quickly to mitigate financial losses. - - Strength: 6/10 -2. **Potential for Mediators/Intervention**: The possibility of intervention by third-party mediators or the government, which can sometimes expedite negotiations. - - Strength: 5/10 -3. **Boeing's Willingness to Negotiate**: The CEO has already expressed a desire to avoid a prolonged strike, which might accelerate negotiations. - - Strength: 6/10 - -### Step 3: Analyzing Residual Time and Variance -- **Time Frame**: There are only 13 days between now (September 17) and the resolution date (September 30). -- **Daily/Weekly Variance**: Strikes can sometimes end abruptly if negotiations reach a breakthrough, but given the significant financial and operational issues at hand, rapid resolution within less than two weeks seems less likely. - -### Step 4: Base Rates and Historical Precedents -- **Historical Strike Durations**: Large-scale strikes in similar industries tend to last several weeks to months. This is a significant strike with high stakes for both parties. -- **Other Factors**: Current economic pressures and Boeing's existing financial troubles may skew the base rate towards a more prolonged strike. - -### Step 5: Default Behavior -- **Default Continuation**: If nothing changes, the strike continues. The probability of an immediate resolution within the next 13 days should be tempered by historical patterns and current context. - -### Conclusion and Probability Distribution -Considering all factors, including the recency and importance of current data, financial implications, the complexity of negotiations, and historical precedents, the likelihood of the strike continuing until September 30, 2024, seems considerably high. The risks and scenarios of a rapid resolution are present but appear less probable within the given time frame. - -**Final Probability Estimate:** -- **Probability of the strike continuing until September 30, 2024**: **Probability: 85%** - -This estimate balances the complex dynamics and acknowledges both the default state of continuation and the limited time frame for potential resolution." -28204,"Will the Chicago White Sox lose 124 games in the 2024 MLB season, before October 1, 2024?","PERPLEXITY - -To address the question of whether the Chicago White Sox will lose 124 games in the 2024 MLB season before October 1, 2024, we need to consider the current state of their season and relevant statistics: - -1. **Current Performance**: - - **Record**: As of September 3, 2024, the White Sox have a record of 31-108, indicating a severe struggle throughout the season. - - **Losing Streaks**: The team has experienced the three longest losing streaks in MLB this season, including streaks of 21, 14, and 11 games. - - **Historical Context**: The White Sox are on pace to have one of the worst seasons in MLB history, comparable to the 1962 New York Mets (40-120) and the 1899 Cleveland Spiders (20-134). - -2. **Season Projections**: - - **Projected Losses**: The team is projected to lose around 124 games, which would set a record for the most losses in a single season in the modern era (since 1901). - - **Win Total Odds**: At FanDuel Sportsbook, the Over/Under for White Sox wins is set at 42.5, indicating a low expectation for wins. - -3. **Recent Updates**: - - **Recent Performance**: The White Sox have shown no signs of improvement, with their latest losing streak extending to 11 games as of September 2, 2024. - - **Historical Milestones**: The team has already set several franchise records for consecutive losses and is on track to break more historical records for futility. - -4. **Expert Opinions and Fan Sentiment**: - - **Fan Engagement**: Fans and observers on platforms like Reddit are following the White Sox's dismal performance with a mix of fascination and despair, noting the team's historic ineptitude. - - **Management Criticism**: There is significant criticism towards the team's ownership and front office, with some suggesting that even random selections or a ""bag of potatoes"" could make better decisions. - -Given the current trajectory and historical context, it appears likely that the Chicago White Sox could approach or exceed 124 losses before October 1, 2024. However, the final outcome will depend on the team's performance in the remaining games of the season. - -**Prediction Markets and Betting Odds**: -- **FanDuel Sportsbook**: Offers an Over/Under for White Sox wins at 42.5, with the Under at -118, indicating a high likelihood of the team not exceeding this win total. - -**Conclusion**: -The Chicago White Sox are having a historically bad season, with a current record of 31-108 and on pace to lose around 124 games. The team's recent performance, historical context, and expert opinions all suggest a high likelihood of reaching or exceeding this loss total before October 1, 2024. - -######### - -GPT - -### Step-by-Step Analysis - -Considering the question at hand—whether the Chicago White Sox will lose 124 games in the 2024 MLB season before October 1, 2024—it falls into the category of **Non-Market Threshold Value Event**. - -### Current Team Performance - -**Team Record:** -- As of September 3, 2024: 31-108 -- Games remaining: 23 - -**Historical Losing Streaks:** -- 21-game losing streak -- 14-game losing streak -- 11-game losing streak (ongoing as of September 2, 2024) - -### Historical Context - -**Notable Poor Performances:** -- 1962 New York Mets: 40-120 (most losses in modern era) -- 2003 Detroit Tigers: 43-119 -- 1916 Philadelphia Athletics: worst winning percentage (23.5%) - -### Statistical Analysis - -1. **Base Rate Analysis:** - - Historical data indicates extremely poor seasons with losses >110 are rare. Only the 1962 Mets and 2003 Tigers have approached such poor performance in the modern era. - - Teams losing 124 games in a season hasn't occurred historically. - -2. **Current Pace:** - - The White Sox currently have a 23.8% winning percentage. - - Losses: 108 - - Need 124 - 108 = 16 more losses in the remaining 23 games. - - Required losing percentage: 100% of remaining games = 16/23 ≈ 69.6%. - -### Factors Affecting Predictions - -#### Reasons for ""Yes"" (Probability of losing 124 games): -1. **Current Losing Streaks** (Strength: 8/10) - - Current 11-game streak signifies severe ongoing issues. - - Recent form suggests no signs of improvement. - -2. **Injuries and Morale** (Strength: 7/10) - - Ongoing poor performance often leads to declining morale and potential injuries, contributing to continued losses. - -3. **Historical Comparisons** (Strength: 5/10) - - While rare, there's precedent for prolonged extreme underperformance. - -#### Reasons for ""No"" (Probability of not losing 124 games): -1. **Regression to the Mean** (Strength: 8/10) - - Historically, even the worst teams eventually win some games due to the law of averages. - -2. **Short-Term Variability** (Strength: 7/10) - - Variability in performance game-to-game can lead to unexpected wins. - -3. **Roster Changes** (Strength: 5/10) - - Potential late-season call-ups or roster changes might inject unforeseen competency. - -### Weighing Sources -- **Current Record** (31-108): Strong Indicator due to being the most recent data. -- **Historical Analysis**: Useful but must take into account rarity of extreme negative outliers. - -### Conclusion - -Combining the current trajectory with the historical difficulty of losing 124 games, extreme underperformance is likely but not certain. We account for variance in outcomes over the remaining games. - -### Probability Calculation - -Given: -- Current record: 31-108. -- Needed losses: 16 out of 23 remaining games. -- Likelihood to lose ≥ 16 games: Needs to lose ~70% of remaining games. -- Adjust historical rarity and potential variances. - -**Final Estimate:** -Balancing all factors: - -- Exaggerated recent poor form supports the likelihood of continuing poor performance. -- Regression to mean reduces the extremity of the prediction. - -Combining judgments leads to: ""Probability: 75%"" - -This takes into account current performance trajectory, historical context, and the inherent variance in sports outcomes." -28207,Will a Gemini model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024?,"PERPLEXITY - -To address the question of whether a Gemini model will be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024, we need to consider recent developments and current standings on the leaderboard. - -1. **Current Leaderboard Standings**: - - As of August 2024, OpenAI's ChatGPT-4o (20240808) has reclaimed the top spot with a score of 1314, 17 points ahead of Gemini-1.5-Pro-Exp. - -2. **Recent Developments**: - - The competition between OpenAI and Google Gemini has been intense, with both models frequently updating and improving their performance. - - The latest version of ChatGPT-4o has shown notable improvements in technical domains, particularly in coding and instruction-following tasks. - -3. **Upcoming Models**: - - There are mentions of upcoming models like Google Ultra 1.5 and xAI’s Grok 2, which could potentially shake up the rankings. - -4. **Historical Context**: - - Earlier in 2024, Anthropic’s Claude 3 Opus was at the top of the LMSYS Chatbot Arena Leaderboard, indicating that the rankings can change significantly over time. - -Given these points, while Gemini models have been strong contenders, the current top spot is held by OpenAI's ChatGPT-4o as of August 2024. The dynamic nature of the leaderboard, with frequent updates and new model releases, makes it challenging to predict the ranking at the end of the 3rd Quarter of 2024 with certainty. However, the recent performance of ChatGPT-4o and the anticipation of new models suggest that Gemini might face stiff competition to reclaim the top spot by the end of the quarter. - -**Prediction Markets and Expert Opinions**: -- There are no direct references to prediction markets or expert opinions specifically addressing the end-of-quarter rankings in the provided sources. However, the general sentiment among experts and users is that the competition is heating up, and the top spot could change hands with new model releases. - -**Recent Updates**: -- The most recent update from August 2024 shows OpenAI's ChatGPT-4o in the lead, but there are no updates from September 2024 that could indicate a shift towards Gemini reclaiming the top spot. - -In conclusion, while Gemini models are strong contenders, the current data and recent developments suggest that OpenAI's ChatGPT-4o is in a strong position. However, the dynamic nature of the leaderboard means that the ranking could change by the end of the 3rd Quarter of 2024, especially with the anticipated release of new models. - -######### - -GPT - -To evaluate the probability that a Gemini model will be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024 (October 1, 2024), let's consider various factors and analyze the situation step-by-step. - -### Reasons for a ""Yes"" Outcome: - -1. **Recent Close Position**: - - As of September 15, 2024, the Gemini-1.5-Pro-Exp model was in a tie for second place. - - **Strength of this reason**: 7/10. The model is already performing at a top level, indicating it has the capability to reach the number 1 spot with incremental improvements. - -2. **Frequent Updates and Improvements**: - - Both OpenAI and Google have a history of rapidly iterating and releasing improved versions of their models. With continual enhancements, it is plausible that a new version of the Gemini model could take the top spot. - - **Strength of this reason**: 5/10. While updates are common, they are unpredictable and must significantly outperform the current leader. - -3. **Competitive Advantage**: - - If Google releases a new version of the Gemini model before the end of September, it might address the shortcomings and potentially leapfrog to the top position. - - **Strength of this reason**: 4/10. This is contingent on the timing and efficacy of the updates, which are not guaranteed. - -### Reasons for a ""No"" Outcome: - -1. **Current Leader Performance**: - - As of August 2024, OpenAI's ChatGPT-4o is ahead by 17 points. Recent advancements in its capabilities, especially in coding and instruction-following tasks, strengthen its lead. - - **Strength of this reason**: 8/10. The substantial lead and specific improvements indicate a solid position that might be hard to overtake in a short time span. - -2. **Upcoming Competitors**: - - The potential release of new models like Google Ultra 1.5 and xAI’s Grok 2 could introduce additional competition, potentially displacing not just Gemini but also ChatGPT-4o. - - **Strength of this reason**: 6/10. While these models could disrupt the leaderboard, their exact impact is uncertain and they might focus on niches rather than overall ranking. - -3. **Historical Volatility**: - - The LMSYS Chatbot Arena Leaderboard has shown significant volatility with previous leaders like Claude 3 Opus being displaced. However, the closer we get to the evaluation date, the less time there is for major shifts. - - **Strength of this reason**: 6/10. Historical volatility suggests change is possible, but less likely in a constrained timeframe. - -### Base Rates and Historical Precedents: - -- Significant position changes on the leaderboard have occurred historically but often take more than a few weeks to materialize. With only two weeks until the end of the quarter, a shift from second to first place is less probable due to the required margin and pace of required improvements. -- Recent trends show leading models maintaining their positions for months, barring major new releases. - -### Time and Variability Consideration: - -- The timeframe until the end of the quarter (14 days) is short for an overhaul in leaderboard standing, especially given the 17-point lead of ChatGPT-4o. -- Daily and weekly variances in leaderboards do occur, but tend to be driven by major updates rather than gradual improvements. - -### Conclusion: - -Based on the analysis above, taking into account the short time frame, current lead of ChatGPT-4o, upcoming competition, historical trends, and the general performance dynamics, the probability of a Gemini model being ranked #1 overall on the LMSYS Chatbot Arena Leaderboard by the end of Q3 2024 appears low. However, since the Gemini model is already near the top, a moderate but non-negligible chance exists. - -### Final Probability Assessment: - -Given all considerations and weighed by their strengths and importances: - - - Current Leader Advantage (8/10) - - Short Timeframe for Significant Shifts (6/10) - - Rapid Iteration Potential of Gemini (7/10) - - Potential for New Competitors Changing Dynamics (6/10) - -The final calculated probability is: - -**Probability: 15%**" -28208,Will a Claude model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024?,"PERPLEXITY - -To address the query of whether a Claude model will be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 3rd Quarter of 2024, we need to consider recent developments and current standings: - -1. **Current Leaderboard**: As of the latest available data, GPT-4o holds the top spot, with Gemini models closely behind, followed by older GPT-4 models and Claude 3 Opus. - -2. **Claude 3.5 Sonnet Performance**: The newly released Claude 3.5 Sonnet has shown impressive performance, outperforming GPT-4o on LiveBench and demonstrating strong capabilities in the multimodal arena. - -3. **Multimodal Arena**: In the multimodal leaderboard, which includes image-based interactions, Claude 3.5 Sonnet ranks second, closely behind GPT-4o, indicating its robust performance across different modalities. - -4. **Style Control Analysis**: Recent analysis by LMSYS on style control in the Chatbot Arena suggests that when controlling for style factors like answer length and markdown usage, Claude 3.5 Sonnet and other Claude models see a significant rise in rankings, sometimes even tying for the top spot with GPT-4o in certain categories. - -5. **Market Predictions**: The prediction market on Manifold Markets indicates that there is a possibility of Claude 3.5 Sonnet achieving a high rank, with some traders betting on it reaching ranks #2-#5, and there is a mention of the potential for a tie for the first place due to LMSYS's method of declaring ties within margin of error. - -Given these points, while GPT-4o currently holds the top spot, the strong performance of Claude 3.5 Sonnet, especially in multimodal tasks and when style factors are controlled, suggests that it has a potential to reach the top rank by the end of the 3rd Quarter of 2024. However, the outcome is not certain and could be influenced by various factors, including updates to other models and changes in user preferences. - -**Recent Updates and Expert Opinions**: -- The introduction of the multimodal arena and the analysis on style control have provided new insights into the models' performance, highlighting the strengths of Claude 3.5 Sonnet. -- Expert opinions and user feedback suggest that Claude 3.5 Sonnet's distinctive style and robust performance make it a strong contender for the top spot. - -**Current Values**: -- As of the latest available data, GPT-4o is ranked #1, and Claude 3.5 Sonnet is ranked #2 in the multimodal leaderboard. -- The prediction market indicates a close competition, with the possibility of a tie for the first place. - -In conclusion, while there are indications that Claude 3.5 Sonnet could potentially reach the top rank, the outcome is uncertain and will depend on various factors, including future updates and user preferences. - -######### - -GPT - -### Step-by-Step Thinking and Analysis - -#### 1. Identifying the Type of Question -This is a **Competition Event** question since it involves predicting the ranking outcome in a model competition hosted by the LMSYS Chatbot Arena. - -#### 2. Reviewing the Current State and Trends -- **Current Leaderboard**: GPT-4o is currently ranked #1. Claude 3.5 Sonnet is tied for sixth but is closely trailing behind the top models. -- **Model Performance and Analysis**: - - Claude 3.5 Sonnet has shown strong recent performance, potentially tying or closely following GPT-4o when style factors are controlled. - - It performs robustly in multimodal tasks, ranking second. -- **Market Predictions**: - - Traders on prediction markets are betting on Claude 3.5 Sonnet reaching ranks #2-#5, with some indications of a tie for the first place being possible. - -#### 3. Reasons Why ""Yes"" Might Occur -1. **Strong Performance in Recent Tests**: - - Claude 3.5 Sonnet has shown it can potentially outperform GPT-4o in style-controlled analysis and multimodal tasks. - - Rate: 8/10 - -2. **Close Competition**: - - The gap in performance between the top models is not insurmountable, and slight improvements or updates could push Claude to #1. - - Rate: 7/10 - -3. **Market Sentiment**: - - Active betters are considering the possibility of Claude taking the top spot, indicating they see a credible chance based on market intelligence. - - Rate: 6/10 - -#### 4. Reasons Why ""No"" Might Occur -1. **Current Position and Time Remaining**: - - As of now, Claude 3.5 Sonnet is tied for sixth, and significant improvement in the next few weeks is unlikely given the lead by GPT-4o. - - Rate: 9/10 - -2. **Dominance of GPT-4o**: - - GPT-4o has a consistent leading position which suggests it is robust across a variety of tasks and evaluations. - - Rate: 8/10 - -3. **Historical Precedent**: - - Historically, once a model achieves dominance, especially in competitive AI spaces, it is difficult for another model to displace it quickly without a transformative update or shift. - - Rate: 8/10 - -#### 5. Assessing Recency and Importance of Sources -- **Current Leaderboard**: Important and highly recent. Assign importance score: 9/10. -- **Claude Performance**: Recent and relevant. Assign importance score: 8/10. -- **Market Predictions**: Recent but speculative. Assign importance score: 6/10. - -#### 6. Time Left and Variance -- Around two weeks left until the end of Q3 2024. -- Model rankings usually do not change dramatically over such a short period unless there is a revolutionary update. - -#### 7. Base Rates and Novel Influences -- Generally, competition models require more time to climb from the sixth position to first place. -- No novel influences substantial enough to sway the base rate significantly seen in tech, economic, or political environments. - -#### 8. Default Resolution and Uncertainty -- Default resolution without significant updates or changes: GPT-4o remains #1. -- Blend in the probabilities while accounting for minor updates possible but no drastic overhauls expected. - -### Final Probability Calculation -- Combining insights and thinking through possible outcomes, the dominant lead by GPT-4o, historical stability, and time constraints inform a higher likelihood of no significant positional change. - -**Probability: 14%** -- This assigns a reasonably low chance to Claude 3.5 Sonnet being ranked #1, mainly due to current standings, lead robustness by GPT-4o, and limited time for any significant leap." -28244,"Will the USDA-posted recall of Michael Foods Inc.'s Fair Meadow Foundations Liquid Egg Products issued June 30, 2024 be closed before October 1, 2024?","PERPLEXITY - -To provide context for forecasting whether the USDA-posted recall of Michael Foods Inc.'s Fair Meadow Foundations Liquid Egg Products issued June 30, 2024, will be closed before October 1, 2024, here are the key points from recent sources: - -1. **Recall Details**: - - **Date of Recall**: June 30, 2024. - - **Reason for Recall**: Misbranding and undeclared allergens (dairy/milk) in approximately 4,620 pounds of liquid egg products. - - **Affected Products**: 32-ounce cartons of Fair Meadow Foundations whole eggs with citric acid, bearing a lot code of 4162G and a use-by date of September 16, 2024. - -2. **Distribution**: - - The recalled products were shipped to restaurant consignees for institutional use in Alabama, Idaho, Louisiana, Minnesota, Nebraska, New Mexico, Ohio, South Carolina, and Utah. - -3. **Current Status**: - - As of the recall announcement, there have been no confirmed reports of adverse reactions due to consumption of these products. - - The USDA has advised that the products should be thrown away or returned to the place of purchase, and institutions are urged not to serve these products. - -4. **Relevance to Forecast**: - - Given that the use-by date of the recalled products is September 16, 2024, and considering the nature of the recall (misbranding and undeclared allergens), it is likely that the recall process will be completed or near completion by October 1, 2024, assuming compliance from the affected institutions and no further complications. - -5. **Expert Opinion and Updates**: - - There are no recent updates indicating any complications or extensions of the recall process. The lack of reported adverse reactions and the clear instructions from the USDA suggest a straightforward resolution. - -In summary, based on the information available, there are no indicators that would suggest the recall will not be closed before October 1, 2024. The recall process seems to be proceeding as expected, with clear instructions for the disposal or return of the affected products. However, any forecast should consider potential unforeseen complications, though none are currently reported. - -######### - -GPT - -**Context and Key Points Analysis:** - -1. **Recall Details**: - - Recall Date: June 30, 2024. - - Reason for Recall: Misbranding and undeclared allergens (dairy/milk) in approx. 4,620 pounds of liquid egg products. - - Affected Products: 32-ounce cartons with a lot code of 4162G and use-by date of September 16, 2024. - -2. **Distribution**: - - Shipped to multiple states for institutional use. - -3. **Current Status**: - - No confirmed adverse reactions reported. - - Product recall advisement clear: do not serve, return, or throw away. - -4. **Time until Resolution**: - - Today's date: September 18, 2024. - - Recall Assessment Deadline: October 1, 2024. - - Time remaining until resolution deadline: 13 days from today. - -### Reasons for ""Yes"" (Recall Closed by October 1, 2024) - -1. **Use-by date approaching**: - - The use-by date of September 16, 2024, means the implicated products are likely to have been disposed of by institutions, reducing the complexity of the recall process. - - **Strength: 8/10**. - -2. **No complications reported**: - - The lack of reported adverse reactions implies no need for an extended investigation or additional actions beyond the standard recall procedure. - - **Strength: 7/10**. - -3. **Clear instructions and compliance**: - - Institutions have clear instructions to return or dispose of the products, which increases the likelihood of an efficient recall. - - **Strength: 6/10**. - -4. **Historical precedent**: - - Typically, recalls due to labeling issues rather than food safety tend to be resolved relatively quickly, especially if the use-by date closely aligns with the closing period. - - **Strength: 6/10**. - -### Reasons for ""No"" (Recall Not Closed by October 1, 2024) - -1. **Potential Reporting or Compliance Delays**: - - Delays in reporting compliance by all institutions or administrative processing could push the closure date slightly past October 1. - - **Strength: 5/10**. - -2. **Unexpected Adverse Reports**: - - Though no adverse reactions have been reported, any emergence of health issues related to the recall could necessitate extended investigation and delay in closure. - - **Strength: 3/10**. - -3. **Logistical Challenges**: - - Given the wide geographical distribution (9 states), coordinating and confirming disposal or returns from all affected institutions might take longer. - - **Strength: 4/10**. - -### Base Rate and Historical Context - -For standard food recalls due to undeclared allergens without reported health issues: - - The average processing and closure time typically ranges from a few weeks to a few months. - - Given the recall date of June 30 and the clear steps outlined, a closure within approximately 3 months (around October 1) is reasonable. - - Historical data supports that recalls announced with sufficient lead time and clear disposal/use-by dates are usually closed promptly. - -### Weighing the Factors - -- **Time Remaining**: 13 days is a critical factor. The immediacy of the situation due to the use-by date of the product suggests rapid resolution to avoid continued risk. -- **Standard Resolution Timeline**: With no complicating factors and given the typically straightforward nature of allergen-based recalls with no health incidents reported, closure is highly probable within the timeframe. - -### Final Probability Distribution - -Considering the above points and the resolution criteria specifying the importance of the default behavior and timely closure based on historical data: - -**Probability**: **85%** - -This confident forecast is due to the imminence of the use-by date, clear instructions and compliance, and historically quick resolution for similar recalls with no adverse health reports." -28243,"Will Donald Trump sell any shares of Trump Media before October 1, 2024, as revealed by a Form 4 filing?","PERPLEXITY - -To address the question of whether Donald Trump will sell any shares of Trump Media before October 1, 2024, as revealed by a Form 4 filing, we need to consider recent news and expert opinions: - -1. **Lockup Period Ending**: The lockup period for Trump Media & Technology Group Corp. (TMTG) insiders, including Donald Trump, is set to end on September 19, 2024. This means that after this date, Trump and other insiders will be legally allowed to sell their shares. - -2. **Trump's Intentions**: Donald Trump has publicly stated that he has no intention of selling his shares in Trump Media. He emphasized this during an event on Friday, September 13, 2024, saying, “I have absolutely no intention of selling... I didn’t do it for the money. I did it because I really wanted to have a strong voice, and it’s a great voice for me”. - -3. **Market Pressure**: Despite Trump's assertions, investors are preparing for potential selling pressure once the lockup period ends. Experts like Paul Karger, co-founder and managing partner at TwinFocus, warn of a ""race to the bottom"" where everyone tries to get out at any price, potentially leading to a collapse in the stock price. - -4. **Regulatory Scrutiny**: Any sale by Trump, who holds nearly 60% of the company, would require a disclosure and would have to be done on a regularly scheduled program, according to Jack Ablin, chief investment officer at Cresset Capital. This means that any sale would be closely monitored by the Securities and Exchange Commission (SEC). - -Given these points, while Trump has stated he does not intend to sell his shares, the ending of the lockup period and potential market pressures could influence his decision. However, any sale would be subject to regulatory scrutiny and would need to be disclosed in a Form 4 filing. - -**Recent Stock Performance**: -- **Current Price**: The stock price of Trump Media & Technology Group Corp. (DJT) has been volatile, recently trading at $17.97, down from $40.58 on July 15. -- **Yearly High/Low**: Specific yearly high and low prices are not provided in the sources, but the stock has shed nearly $6 billion in value over the past four months. - -**Prediction Markets and Expert Opinions**: -- **Expert Opinions**: Experts like Paul Karger and Michael Klausner express skepticism about the company's valuation and warn of potential selling pressure. -- **Prediction Markets**: No specific prediction market data is provided in the sources regarding Trump selling his shares before October 1, 2024. - -In summary, while Trump has stated he will not sell his shares, market pressures and the end of the lockup period could potentially influence his decision. Any sale would be subject to regulatory disclosure and scrutiny. - -######### - -GPT - -### Step-by-Step Analysis - -#### Understanding the Context and Available Information - -1. **Lockup Period Ending**: - - Critical date: September 19, 2024 (tomorrow). - - After this date, Trump can legally sell his shares. - - Importance: 10/10 (allows the event to happen). - -2. **Trump's Public Statement**: - - On September 13, 2024, Trump said he doesn't intend to sell his shares. - - Importance: 8/10 (strong influencer but politicians sometimes deviate from public statements). - -3. **Market Situation**: - - Stock has been volatile and lost significant value. - - There is speculation about selling pressure after the lockup period ends. - - Importance: 7/10 (market pressures can incentivize sales, but Trump's decision might be insulated due to non-monetary reasons for holding). - -4. **Regulatory Scrutiny**: - - Any sale must be disclosed via a Form 4 filing. - - Importance: 9/10 (ensures transparency and affects timing). - -#### Reasons for ""Yes"" (Trump will sell shares): - -1. **Market Pressure** (Strength: 7/10): - - Volatile stock prices and potential for increased selling could incentivize Trump to cash out before a potential drop in stock value. - -2. **Lockup Period End** (Strength: 10/10): - - Enables the possibility of selling. - -3. **Business Strategy** (Strength: 6/10): - - Trump may decide it's a strategic move to sell some shares. - -#### Reasons for ""No"" (Trump will not sell shares): - -1. **Public Statement by Trump** (Strength: 8/10): - - He explicitly stated he has no intention of selling shares. - -2. **Political Considerations** (Strength: 7/10): - - Trump might consider the optics and political fallout of selling shares right after the lockup period, especially given his position and upcoming elections or public opinion. - -3. **Regulatory and Filing Delays** (Strength: 5/10): - - Even if he decides to sell, administrative delays might prevent a Form 4 filing from appearing before October 1, 2024. However, IR pages are typically timely updated for impactful events. - -#### Weighing and Aggregating the Importance of Sources - -- **Lockup Period Ending**: Critical enabler (10/10). -- **Trump's Statement**: Strong but not infallible (8/10). -- **Market Pressure**: Influential (7/10). -- **Regulatory Scrutiny**: Ensures any action is visible (9/10). - -Given this, the strongest reasons against a sale are Trump's public statement, potential political strategizing, and administrative delays. However, the strongest reason for a sale is the newly available opportunity post-lockup, countered by Trump's financial independence and stated intent. - -### Base Rates and Historical Patterns -- Trump generally adheres to public statements regarding high-visibility financial actions to maintain his image. -- High-profile individuals often exercise extreme caution with initial post-lockup sells due to potential market and media reactions. - -### Final Probability Distribution - -After considering all factors, the probability of Trump selling any shares before October 1, 2024, given his public statements and considering the short timeframe, appears low but not negligible given surprise factors. - -### Conclusion -- Probability Trump will sell shares: 20% - -""Probability: 20%""" -28245,"Will the yield curve be inverted on Friday September 27, 2024?","PERPLEXITY - -To address the question of whether the yield curve will be inverted on Friday, September 27, 2024, we need to consider recent developments and expert opinions on the yield curve's behavior and its implications for the economy. - -1. **Recent Yield Curve Developments**: - - The U.S. Treasury yield curve officially exited its prolonged inversion on September 6, 2024, marking the end of over two years when short-term yields were higher than those on long-term bonds. - - As of September 6, the 10-year Treasury yield stood at 3.72%, with the two-year at 3.65%, indicating a positive spread of 7 basis points. - - However, the yield curve remains far from “normal” and is still steeply inverted at the short end, with the 3-month yield at 5.13% compared to the 10-year yield at 3.72%, showing a significant inversion in this part of the curve. - -2. **Expert Opinions and Historical Context**: - - Historically, the yield curve inversion has preceded every U.S. recession since the 1970s, but there is typically a lag between the inversion and the onset of a recession. - - The recent un-inversion of the 2-year to 10-year part of the yield curve does not necessarily indicate that recession risks have diminished, as the overall yield curve remains distorted. - - Experts like Sahm and Berezin argue that the Fed needs to act decisively with rate cuts to avoid the worst economic outcomes, suggesting that the economic situation remains precarious. - -3. **Prediction Markets and Forecasts**: - - There are no direct prediction markets or forecasts mentioned in the provided sources that specifically address whether the yield curve will be inverted on September 27, 2024. However, the anticipation of rate cuts by the Fed, as indicated by the yield curve movements, suggests that market participants are expecting further adjustments in monetary policy. - -Given these points, while the 2-year to 10-year part of the yield curve has un-inverted, the overall yield curve remains in a state that indicates economic uncertainty. The question of whether the yield curve will be inverted on September 27, 2024, cannot be definitively answered based on the provided information, but the current state of the yield curve and expert opinions suggest that economic risks persist. - -**Current Values**: -- 10-year Treasury yield: 3.72% (as of September 6, 2024) -- 2-year Treasury yield: 3.65% (as of September 6, 2024) -- 3-month Treasury yield: 5.13% (as of September 6, 2024) - -**Recent Updates**: -- The yield curve's partial un-inversion indicates some movement towards normalization, but the overall shape of the curve remains far from typical, suggesting ongoing economic risks. -- Expert opinions emphasize the need for decisive action by the Fed to mitigate these risks, indicating that the economic situation is still volatile. - -######### - -GPT - -### Step-by-Step Analysis: - -#### Question Type: -- This is a **Non-Market Threshold Value Event**. The event concerns whether a quantifiable measure (the spread between 10-year and 2-year Treasury yields) will fall below a threshold (become negative) at a specific point in time (September 27, 2024). - -#### Default Behavior and Current State: -- **Current Spread (as of September 6, 2024)**: The 10-year yield is 0.07% higher than the 2-year yield. -- **Default Prediction**: If nothing changes, the yield curve spread remains at 0.07%, meaning it is not inverted. - -#### Reasons for Inversion: -1. **Economic Weakness and Fed Policy Response**: - - Possible further rate cuts by the Fed could lower short-term yields more aggressively compared to long-term yields. - - **Strength**: 7/10. Monetary policy changes in response to unexpected economic indicators, such as an impending recession, can quickly alter yield spreads. - -2. **Historical Precedent**: - - Yield curve inversions have been strongly correlated with economic downturns and can reflect market sentiment predicting further economic stress. - - **Strength**: 6/10. Given that yield curve inversions have historically preceded recessions, if the market perceives a recession as imminent, the curve could invert again. - -3. **Market Volatility**: - - Unforeseen geopolitical or financial market shocks could drive a flight to safety, lowering long-term yields more than short-term yields. - - **Strength**: 5/10. Market shocks can impact yields abruptly, though the probability of such a shock occurring within such a short timeframe is lower. - -#### Reasons Against Inversion: -1. **Recent Yield Curve Un-Inversion**: - - The yield curve recently exited inversion, suggesting some normalization. - - **Strength**: 8/10. The trend toward un-inversion indicates that current economic policies might be stabilizing the situation, making a re-inversion less likely in the short term. - -2. **Fed's Potential Stabilizing Actions**: - - The Fed has been and may continue to take corrective actions to ensure economic stability, including managing rates to avoid yield curve inversion. - - **Strength**: 7/10. Proactive policies by the Fed to stabilize the economy and support growth reduce the likelihood of a yield curve re-inversion. - -3. **Positivity Bias in Economic Data**: - - Recent economic data and analyst opinions do not overwhelmingly predict an immediate economic downturn. - - **Strength**: 6/10. If economic data remains stable or improves, further un-inversion is more likely. - -#### Assessment of Background Sources: -- **Recent Developments**: Highly relevant and recent, given the date proximity (within weeks). -- **Expert Opinions**: Provides context on the expectations and views of knowledgeable economists. -- **Historical Context**: Slightly less immediate but important for recognizing patterns and potential outcomes. - -#### Statistical and Base Rate Analysis: -- **Base Rates**: Historically, significant shifts in yield curve inversion status are not frequent within very short periods. -- **Daily and Weekly Variance**: Yield curve changes within such a short timeframe typically involve gradual movements unless there are extraordinary circumstances. - -#### Conclusion: -1. Based on the default current spread of 0.07% in positive territory, assuming no major shocks, the curve is unlikely to re-invert within 9 days. -2. However, accounting for economic volatilities and the potential for minor rate fluctuations, some degree of inversion risk remains. - -Combining these insights with historical patterns, expert opinions, and recency of the data: - -**Final Probability Assessment:** -- **Probability of Yield Curve Inversion on September 27, 2024**: Based on the preponderance of current data, expert commentary suggesting stabilization measures, and the improbability of significant swings in a short duration: - -**Probability: 20%** - -There is a 20% chance that the yield curve will be inverted on September 27, 2024, based on the combination of present economic indicators, the Fed's likely actions, and the transient nature of yield curve movements." -28292,"Will the closing value of Tesla's shares be at least $230 on September 30, 2024?","PERPLEXITY - -To address the question of whether the closing value of Tesla's shares will be at least $230 on September 30, 2024, we need to consider recent market trends, expert analyses, and current stock performance. - -1. **Current Stock Performance and Predictions**: - - **Recent Price**: As of the latest available data, Tesla's stock price was around $213.77, which is below the target of $230. - - **OpenAI's o1 Model Prediction**: OpenAI's o1 model predicts that Tesla shares will climb 8.68% from their latest closing price of $230.03 and reach $250 by the end of 2024, indicating a potential for the stock to exceed $230 by September 30, 2024. - - **Analyst Forecasts**: The median target price from 92 analysts is $225.93, with a high estimate of $350.00 and a low estimate of $115.00, suggesting mixed opinions but a general lean towards reaching or surpassing $230. - -2. **Market and Economic Factors**: - - **Interest Rate Cuts**: The Federal Reserve is expected to cut interest rates, which could make vehicle financing more affordable and potentially boost Tesla's sales. However, some analysts believe that Tesla's challenges run deeper than just the tight monetary environment. - - **Competition and Product Lineup**: Tesla faces increasing competition in the EV space, particularly from newer models offered by competitors in China. Additionally, Tesla's aging product lineup is seen as a challenge. - -3. **Expert Opinions**: - - **Peter DiCarlo**: A trading expert who believes Tesla shares could target the price range between $280 and $300, indicating a potential for the stock to be above $230 by September 30, 2024. - - **Jonalius**: Another analyst who identifies $240 as a crucial resistance level for Tesla to break to target price levels near $300. - - **Gordon Johnson**: A critic who believes Tesla will not regain its old highs due to broken promises, particularly with self-driving technology. - -4. **Recent Updates**: - - The recent anticipation of interest rate cuts and the upcoming 'Robotaxi' event are seen as potential catalysts for Tesla's stock price. - -In conclusion, while there are mixed opinions among analysts, recent predictions and market factors suggest that Tesla's shares could potentially reach or exceed $230 by September 30, 2024. However, the stock market is inherently volatile, and various factors could influence the actual outcome. - -**Key Points**: -- **Current Price**: Around $213.77. -- **Predicted Price by End of 2024**: $250 (OpenAI's o1 model). -- **Median Analyst Target**: $225.93. -- **Upcoming Events**: Interest rate cuts and Tesla's 'Robotaxi' event. -- **Challenges**: Increasing competition and an aging product lineup. - -######### - -GPT - -### Step-by-Step Analysis - -#### 1. Define the Event Type -This question is clearly a **Market Price Event** where we need to predict the stock price of Tesla on a specific future date. - -#### 2. Evaluate the Default Behavior -The current price of Tesla stock is $213.77. The historical volatility of Tesla’s stock shows that it can swing quite significantly within a short period. However, with the resolution date being very soon, only about 11 days away (from today's date, September 19, 2024), short-term stock price movements need to be considered with high granularity. - -#### 3. Sources and Recency -- **Current Stock Performance:** $213.77 as of the latest data – Most recent and factual data point. Importance: 10 -- **OpenAI's o1 Model Prediction by End of 2024:** $250 – Plausible AI prediction, slightly overshadowed by shorter timeframe. Importance: 8 -- **Median Analyst Target:** $225.93 – Slightly under target but closer to the current price. Importance: 8 -- **Peter DiCarlo's Potential Range ($280-$300):** Optimistic but a respected expert. Importance: 7 -- **Jonalius’ Resistance Level:** $240 – Critical technical point. Importance: 7 -- **Gordon Johnson’s Criticism:** Pessimistic viewpoint but adds perspective. Importance: 6 -- **Upcoming Catalysts:** Interest rate cuts, Robotaxi event – Potential for significant short-term movement. Importance: 9 - -#### 4. Reasons for the Answer being ""Yes"" -1. **Upcoming Catalysts:** The announcement of the Robotaxi event could create a positive market sentiment, leading to a potential surge in the stock price. Strength: 8 -2. **Interest Rate Cuts:** The potential for interest rate cuts could provide a macroeconomic boost to Tesla’s stock price. Strength: 7 -3. **OpenAI's o1 Model & Analysts' Predictions:** General positive sentiment toward a price increase by end of 2024. Sign of upward trend. Strength: 7 - -#### 5. Reasons for the Answer being ""No"" -1. **Current Price Below Target:** With $213.77 as the current stock price, a significant move of 7.6% upwards is required within 11 days. Strength: 9 -2. **Market Volatility:** Short-term stock movements can be highly unpredictable. Strength: 8 -3. **Mixed Analyst Opinions:** Despite some optimistic predictions, the median target is slightly below $230, and there are also pessimistic views. Strength: 7 - -#### 6. Assess Base Rates and Historical Patterns -In similar market conditions, a 7.6% increase in the share price over an 11-day period is within the realm of possibility but is relatively aggressive for an established stock like Tesla. Considering past instances of such movements, significant short-term increases often occur due to strong catalysts or market sentiment shifts. - -#### 7. Calculate Probability Distribution -Given these elements, we need to estimate the probability based on: -- The current undervaluation -- Potential strong short-term catalysts -- Historical volatility - -Given Tesla’s volatility and high-profile nature, stock movements of this magnitude are not extraordinary but given the short timeline, it is less probable yet possible. - -#### 8. Final Probability Estimation -Balancing the strong current default (negative factor) with potential positive catalysts (positive factors) and weighing historical patterns gives a balanced view. - -**Final Answer:** -- Given the stronger weight of the current undervaluation and the required short-term bullish movement vs. potential catalysts, -- **Probability: 30%** - -This estimation considers the inherent market volatility, the short window for significant price action, and both positive and negative expert predictions and market sentiments." -28290,"Will Virgin Galactic make any new posts on its News page before October 1, 2024? ","PERPLEXITY - -To address the question of whether Virgin Galactic will make any new posts on its News page before October 1, 2024, let's examine the available information and recent trends: - -1. **Recent Activity**: The most recent news update on Virgin Galactic's investor relations page is from September 16, 2024, which indicates that the company is actively posting updates. - -2. **Company Developments**: Virgin Galactic has been undergoing significant changes, including the halt of Unity suborbital flights by mid-2024 to focus on the Delta-class vehicles. This transition and the development of the Delta-class vehicles are critical milestones that might prompt further updates. - -3. **Earnings Calls and Financial Updates**: The company has used earnings calls to discuss financial models and the economics of its future spaceplanes, indicating a pattern of regular communication with investors and the public. - -4. **Future Plans**: With the Delta-class vehicles scheduled to enter commercial service in 2026, it is likely that Virgin Galactic will continue to provide updates on the progress of these vehicles, including manufacturing and testing phases. - -Given these points, it is reasonable to expect that Virgin Galactic will make new posts on its News page before October 1, 2024, as part of its ongoing communication strategy and to keep stakeholders informed about its progress, especially regarding the transition to the Delta-class vehicles and other significant company developments. - -**Prediction Markets and Expert Opinions**: There are no direct prediction markets or expert opinions available in the provided sources that specifically address whether Virgin Galactic will make new posts on its News page before October 1, 2024. However, the company's recent activity and the significance of its current projects suggest that updates are likely. - -**Recent Updates**: The latest news from September 16, 2024, and the company's pattern of using earnings calls to discuss future plans indicate movement towards providing regular updates, which supports the expectation of new posts before October 1, 2024. - -######### - -GPT - -Let's break this down step-by-step, integrating available information, base rates, and the probabilistic approach typical of a superforecaster. - -### Step 1: Define the Baseline -The baseline is to estimate the probability that a new post appears on Virgin Galactic's News page between today (September 19, 2024) and October 1, 2024. - -**Historical Post Frequency:** -- **Recent Activity**: The last post was on July 10, 2024, and there has been individual activity on their investor relations page as recently as September 16, 2024. This does suggest regular communication efforts. -- **Pattern**: Virgin Galactic may post updates irregularly, but major activities such as earnings calls and significant milestones often result in updates. - -### Step 2: Factors Increasing the Probability (Reasons for ""Yes"") -1. **Recent Activity Indicator** (Strength: 7/10): - - The company posted on their investor relations page on September 16, 2024, showing recent engagement. - -2. **Upcoming Important Milestones or Announcements** (Strength: 8/10): - - Virgin Galactic is transitioning to Delta-class vehicles. Key updates in manufacturing, testing, or any transitional themes are likely to be announced. - -3. **Regular Communication Pattern** (Strength: 6/10): - - Historical trend of posting updates surrounding earnings calls and major announcements. - -### Step 3: Factors Decreasing the Probability (Reasons for ""No"") -1. **Short Time Frame** (Strength: 7/10): - - The timeframe between today, September 19, 2024, and the deadline, October 1, 2024, is relatively short (12 days), which lowers the probability of new content emerging. - -2. **No Explicit Upcoming Events** (Strength: 6/10): - - While important milestones are indicated, there are no explicitly known events between now and the deadline compelling a new post. - -3. **Website Maintenance or Downtime** (Strength: 3/10): - - The possibility of the web page being down is non-negligible but relatively low given the significance of maintaining investor communication for a company like Virgin Galactic. - -### Step 4: Base Rate Analysis -- Historically, for high-stakes projects, companies do tend to keep stakeholders updated around specific milestones. Given recent activity aligns with financial reporting schedules and significant project updates, it’s reasonable to assume similar patterns going forward. - -### Step 5: Assess Weight of Information and Calculate Base Rate -**Relevant Factors and Weights:** -- **12-Day Remaining Interval**: Significant (Importance Weight: 30%) -- **Recent Post Activity (September 16)**: Moderately Significant (Importance Weight: 25%) -- **Milestone Communications, like Delta-class**: Very Significant (Importance Weight: 35%) -- **Potential Issues (e.g., downtime)**: Slightly Relevant (Importance Weight: 10%) - -Combining these weighted factors, we derive a blend of high likelihood from patterns and reasonable expectations that progress and related announcements will drive posts. - -### Step 6: Quantitative Probability Distribution -Given the above analysis: -- **Short interval** (negative weight): Slightly reduces likelihood. -- **Active Communication Strategy** (positive weight): Strong increase. -- **Base Rates of Similar Update Frequencies**: Holding steady, with recent multi-month activity in short intervals. - -Considering the short timeframe, yet also accounting for potential significant updates and recent activity, I blend these to set a strong but conservative probability: - -**Final Forecast:** -Probability: 70%" -28290,"Will Virgin Galactic make any new posts on its News page before October 1, 2024? ","PERPLEXITY - -To address the question of whether Virgin Galactic will make any new posts on its News page before October 1, 2024, let's examine the available information and recent activities of the company. - -1. **Recent News and Updates**: - - **Financial Results**: Virgin Galactic recently announced its second quarter 2024 financial results, indicating that the company is actively updating its stakeholders on financial performance and business developments. - - **Business Strategy**: The company has outlined the economics of its future spaceplanes, specifically the Delta-class vehicles, which are scheduled to enter commercial service in 2026. This shows a focus on future operations and financial projections. - - **Operational Changes**: Virgin Galactic has halted Unity suborbital flights by mid-2024 to concentrate resources on the Delta-class vehicles, indicating significant operational changes that might necessitate updates. - -2. **Communication Patterns**: - - **Investor Relations**: The company maintains an active investor relations page, suggesting a commitment to keeping investors informed about significant developments. - - **Press Center**: Virgin Galactic's press center is updated regularly, with the latest update being the second quarter 2024 financial results and business update. - -3. **Upcoming Milestones**: - - **Delta-Class Vehicles**: With the Delta-class vehicles on track for commercial service in 2026, and the company beginning to pivot from design to manufacturing, there might be updates related to this transition. - - **Operational Shifts**: The shift from Unity to Delta-class vehicles and the establishment of a new factory near Phoenix could lead to updates on operational progress. - -Given these points, it is reasonable to expect that Virgin Galactic might make new posts on its News page before October 1, 2024, especially considering the company's recent activities and upcoming milestones. However, without a specific schedule or announcement from the company, this cannot be confirmed. - -**Prediction Markets and Forecasts**: There are no specific prediction markets or forecasts mentioned in the provided sources that directly address whether Virgin Galactic will make new posts on its News page before October 1, 2024. - -**Recent Updates**: The most recent update from Virgin Galactic was on September 16, 2024, indicating that the company is actively posting news. - -In conclusion, while there is no definitive answer, the company's recent activities and communication patterns suggest that new posts on its News page before October 1, 2024, are plausible. - -######### - -GPT - -To determine the probability that Virgin Galactic will make a new post on its News page before October 1, 2024, I will evaluate the given information, consider base rates, recent patterns, and weigh various factors that could influence the outcome. - -### Initial Factors to Consider - -1. **Recent Patterns (Past Posts) Analysis**: - - Most recent post timestamped July 10, 2024. - - As of September 16, 2024, there was another update, suggesting recent activity continues. - -2. **Scheduled and Expected Announcements**: - - Virgin Galactic has various ongoing and scheduled activities related to their operational changes and future plans (e.g., Delta-class vehicles). - - Companies typically post about operational milestones, financial results, and major business news. - -3. **Historical Frequency of Posts**: - - If we evaluate how often Virgin Galactic posts news updates historically, especially during significant operational periods, it would help in establishing a base rate. - - Assume an average posting frequency of once every few months based on recent historical patterns. - -4. **Time Period Analysis**: - - The window for this forecast is relatively short (less than two weeks). - - Random, unexpected events contributing to posting frequency are less likely within such a short timeframe. - -### Reasons for ""Yes"" and Their Strength - -1. **Ongoing Major Projects and Announcements (Strength: 7/10)**: - - The ongoing shift to Delta-class vehicles and operational changes could warrant additional updates. - - The company may announce further specifics of their manufacturing progress or strategic changes. - -2. **Recent Posting Activity (Strength: 6/10)**: - - The company has been making posts relatively recently, indicating active communication with stakeholders. - - Recent announcements suggest continued activity. - -3. **Timing of Financial Disclosures (Strength: 5/10)**: - - End of the fiscal quarter often involves financial summaries and forward guidance posts. - - It is now nearing the end of the third quarter of 2024. - -### Reasons for ""No"" and Their Strength - -1. **Short Time Frame (Strength: 8/10)**: - - With less than two weeks left, the historical posting frequency might be key, and such updates do not occur every two weeks. - - Any significant business updates may not necessarily align perfectly within this short window. - -2. **No Specific Scheduled Events within Time Frame (Strength: 7/10)**: - - No specific upcoming milestone or event scheduled to take place by October 1, 2024, which would force a required news post. - - Companies often pool news for a bundle release unless highly time-sensitive. - -3. **Existing Commitments Accounted in Past Posts (Strength: 6/10)**: - - The recent updates may already encompass all the significant information Virgin Galactic intended to share for the month. - - If there were already significant updates around mid-September, it's plausible the next update isn't imminent. - -### General Distribution and Final Analysis - -Combining these analyses, the default scenario without any sudden significant events leans slightly towards ""No"" due to how infrequent and spaced out these updates typically are, coupled with the short time frame remaining. - -Base rate given recent historical data (~2 updates in 3 months): -Assume each post generally occurs once every 1.5 months. The probability of a post within any two-week period then is conditioned by these intervals and adjusted by the immediacy of the business need. - -### Final Probability Distribution: - -Given available data and the associated analysis: - -**Probability of Virgin Galactic making a new post on its News page before October 1, 2024:** - -**Probability: 35%** - -Thus, considering recency, ongoing projects, and communication patterns alongside the limited timeline, I estimate the probability at around 35%, indicating it is somewhat likely but not the most probable outcome within such a narrow window." -28291,"Will Laredo Oil, Inc., file its 10-K annual report with the SEC before October 1, 2024?","PERPLEXITY - -To address the question of whether Laredo Oil, Inc. will file its 10-K annual report with the SEC before October 1, 2024, we need to consider recent filings and any relevant updates from the company. - -1. **Recent Filings and Updates**: - - **Late Filing Notice**: Laredo Oil, Inc. filed a Notice of Late Filing (NT 10-K) on August 29, 2024, indicating that the company would not meet the deadline for filing its 10-K annual report for the fiscal year ended May 31, 2024. - - **SEC Filings**: The company's history of SEC filings shows that it has made several filings in 2024, including the NT 10-K notice, but the actual 10-K report for the fiscal year 2024 has not been filed yet. - -2. **Filing Deadlines and Extensions**: - - The NT 10-K filing on August 29, 2024, suggests that the company is taking additional time to prepare its annual report. The SEC allows for a 15-day extension beyond the original filing deadline with the submission of Form 12b-25, which Laredo Oil has done. - -3. **Company's Filing History**: - - Laredo Oil, Inc. has a history of filing its annual reports and other SEC documents, though sometimes with delays as indicated by the NT 10-K filings. - -4. **Expert Opinions and Predictions**: - - There are no specific expert opinions or predictions available in the provided sources regarding the exact filing date of the 10-K report. However, the NT 10-K filing indicates the company's intention to file the report as soon as possible but does not specify a date before October 1, 2024. - -Given the information available, it is uncertain whether Laredo Oil, Inc. will file its 10-K annual report before October 1, 2024. The company has filed a notice of late filing, indicating a delay, but has not provided a specific filing date. - -**Recent Updates and Movement Towards Resolution**: -- The NT 10-K filing on August 29, 2024, is the most recent update, indicating a delay in the filing of the 10-K report but does not provide a new expected filing date. - -**Current Predictions**: -- There are no current predictions or betting markets available in the provided sources that address the specific question of the filing date of Laredo Oil, Inc.'s 10-K annual report. - -In summary, while Laredo Oil, Inc. has indicated a delay in filing its 10-K annual report, there is no definitive information available to predict whether the report will be filed before October 1, 2024. - -######### - -GPT - -To address the question of whether Laredo Oil, Inc. will file its 10-K annual report with the SEC before October 1, 2024, let's go through a comprehensive step-by-step analysis. - -### Analyzing the Background Information -1. **Late Filing Notice (NT 10-K)**: - - Laredo Oil filed a Notice of Late Filing (NT 10-K) on August 29, 2024, indicating a delay in submitting the 10-K for the fiscal year ended May 31, 2024. - - Importance: High (8/10). This information establishes that the company is aware of the deadline and has taken procedural steps for a delayed filing, extending the deadline by 15 days. - -2. **Company’s Past Filing History**: - - The company has a history of filing its annual reports and other SEC documents, though sometimes with delays. - - Importance: Medium (6/10). Past behavior is indicative but not definitive for future actions. - -3. **No Specific Date Provided**: - - The NT 10-K filing shows intent to file but lacks an exact expected filing date. - - Importance: High (7/10). The absence of a specific date increases uncertainty. - -4. **Recent Filings**: - - Laredo Oil has made several filings in 2024, indicating an ongoing attempt to stay compliant. - - Importance: Medium (6/10). This shows some operational capacity to meet deadlines, though consistency in timing is uncertain. - -### Reasons for ""Yes"" -1. **Proactive Filing of NT 10-K**: - - The company filed the NT 10-K, indicating awareness and intent to comply. - - Strength: 7/10. This proactive step shows at least an attempt to stay within the regulatory framework even if delayed. - -2. **History of Eventual Compliance**: - - Although historically filing has been delayed, it eventually gets done. - - Strength: 6/10. Past behavior suggests a reasonable likelihood of eventual compliance, though timing is crucial here. - -3. **15-Day Extension Window**: - - Given the NT 10-K filed on August 29, the 15-day extension implies a new deadline around mid-September, providing some buffer period before October 1. - - Strength: 7/10. This gives them some leeway to get their filing in order. - -### Reasons for ""No"" -1. **No Specific Filing Date Indicated**: - - The recent NT 10-K filing does not specify a new anticipated filing date within the extension period. - - Strength: 8/10. This uncertainty makes it difficult to be confident about a timely filing. - -2. **Proximity to Deadline**: - - Today is September 19, 2024, making the window for filing the 10-K only 12 days. - - Strength: 9/10. The short timeframe significantly reduces the probability of changing the default behavior. - -3. **Administrative or Financial Troubles**: - - The need for an NT 10-K filing might indicate deeper issues such as financial discrepancies or administrative inefficiencies. - - Strength: 6/10. If the company is facing significant internal issues, timely filing is less likely. - -### Statistical and Base Rate Analysis -From a base rate perspective: -- Companies that file NT 10-K may eventually file within the extension period, but the rate of actual compliance close to the new deadline might drop considerably, say around 70% compliance within 15 days. -- Given only 12 days remain before October 1, even a default 70% chance (best case extension scenario) has to be adjusted downward for the shorter remaining timeframe. - -Given these assumptions and various strengths of reasons: - -### Final Consideration -Given the high importance and proximity to the deadline combined with the uncertainty about exact new filings: -- I place a significant weight on the short time remaining and typical filing behavior post NT 10-K. - -Balancing these factors: - -**Probability Calculation**: -1. Assume a base compliance rate of 70% within the 15-day extension. -2. Adjust down for only 12 days left and uncertainty from lack of specific updates: reducing by approximately 30% for short timeframe and uncertainty. - -Result: -70% base rate * (1 - 0.3) = 49% chance of filing before October 1, 2024. - -**Final Answer**: -Probability: 49%" -28335,"Will any of the Waltons be worth $100 billion or more on September 30, 2024, according to Forbes?","PERPLEXITY - -To address the query of whether any of the Waltons will be worth $100 billion or more on September 30, 2024, according to Forbes, we need to consider the recent trends and updates on their wealth. - -1. **Current Wealth Status**: - - **Jim Walton**: His net worth has already surpassed $100 billion, reaching $102.6 billion as of September 17, 2024. - - **Rob Walton**: He has also crossed the $100 billion mark with a net worth of $100.4 billion as of September 17, 2024. - - **Alice Walton**: She is close to the $100 billion mark with a net worth of $99.7 billion as of September 17, 2024. - -2. **Trend Analysis**: - - The Walton family's wealth has seen significant growth this year, driven by a 53% surge in Walmart's stock price. - - The company's strong e-commerce growth and investors' optimism about stabilizing prices and decreasing interest rates have contributed to this increase. - -3. **Forecast**: - - Given the current trend and the fact that two of the Walton siblings have already surpassed the $100 billion mark, it is likely that Alice Walton will also reach this milestone by September 30, 2024, if the stock continues to perform well. - -4. **Recent Updates**: - - As of September 17, 2024, Walmart's stock has hit an all-time high, which has directly benefited the Walton family's wealth. - -5. **Expert Opinions**: - - Financial analysts have noted that if the current trends persist, all three Walton siblings could soon be in the $100 billion club, which would be unprecedented. - -In conclusion, based on the recent trends and the current wealth status of the Walton family, it is highly likely that at least two of the Waltons (Jim and Rob) will be worth $100 billion or more on September 30, 2024, according to Forbes. Additionally, there is a strong possibility that Alice Walton will also reach this milestone by the end of September 2024, given the continuous growth in Walmart's stock price. - -######### - -GPT - -### Step 1: Define the Problem and Gather Information -The question asks whether any of the Waltons will be worth $100 billion or more on September 30, 2024, according to Forbes. - -### Step 2: Analyze the Background Information -1. **Current Wealth Status**: - - Jim Walton: $102.6 billion - - Rob Walton: $100.4 billion - - Alice Walton: $99.7 billion - -2. **Trend Analysis**: - - Significant growth in Walton family's wealth due to a 53% surge in Walmart's stock. - - Strong e-commerce growth and positive economic indicators (stabilizing prices, decreasing interest rates). - -3. **Forecast**: - - Jim and Rob Walton have already crossed the $100 billion mark. - - Alice Walton is very close to $100 billion and may reach the milestone soon if trends persist. - -4. **Recent Updates**: - - Walmart stock at an all-time high. - -5. **Expert Opinions**: - - Analysts project continued positive performance for Walmart. - -### Step 3: Identify Reasons for and Against the Outcome - -#### Reasons for ""Yes"": -1. **Current Wealth Status of Jim and Rob Walton**: Both are already above $100 billion. - - **Strength**: 10 -2. **Positive Economic Indicators and Walmart Performance**: Stock prices at an all-time high. - - **Strength**: 9 -3. **High Likelihood of Alice Walton Crossing $100 Billion**: Alice Walton is just short of the $100 billion mark and could reach it with a relatively small stock price increase. - - **Strength**: 8 - -#### Reasons for ""No"": -1. **Market Volatility**: Stock prices can fluctuate significantly in a short period due to market correction. - - **Strength**: 6 -2. **Event Risk**: Unforeseen events like geopolitical issues, sudden economic downturns, or corporate scandals. - - **Strength**: 5 -3. **Valuation Changes**: Potential risks from changes in Forbes' valuation methodology or sudden financial disclosures. - - **Strength**: 4 - -### Step 4: Evaluate Timing and Recency -The resolution date is just 10 days away, which heavily weighs towards the current status being stable unless a notable event occurs. - -### Step 5: Base Rates and Historical Precedent -Historically, once individuals cross significant wealth milestones, they tend to stay above those levels barring major economic crashes. Given the current high confidence in economic stability and Walmart's performance, this supports the ""yes"" outcome. - -### Step 6: Apply Bayesian Reasoning and Probability -Given the close proximity of the resolution date, the current baseline heavily leans towards a ""yes"": -- Jim and Rob are already above $100 billion, and it’s highly unlikely to dip below without extreme market changes. -- Alice is extremely close and likely to cross the mark unless there's a drastic price drop in Walmart stock rapidly. - -### Step 7: Check Assumptions and Probabilistic Forecast -- **Default Case**: If no significant changes occur, the answer will be ""yes"". -- **Magnitude of Change Needed**: Any sharp decline needed to bring Jim or Rob below the mark or prevent Alice is quite unlikely in a 10-day span given strong economic and company indicators. - -### Conclusion -Balancing all factors, assigning weights to each reason, and recognizing the short timeframe until resolution, the probability of at least one Walton being worth $100 billion or more on September 30, 2024, according to Forbes is very high. - -**Final Probability**: 95%" -28334,"Will there be a deadly attack within Israel causing at least five deaths before October 1, 2024?","PERPLEXITY - -Given the current escalation of tensions between Israel and Hezbollah, as well as the ongoing conflict with Hamas, there are several factors to consider when assessing the likelihood of a deadly attack within Israel causing at least five deaths before October 1, 2024. - -1. **Recent Escalation**: - - **Israel-Hezbollah Conflict**: Recent Israeli airstrikes targeted over 100 Hezbollah rocket launchers in Lebanon, following Hezbollah's vow to retaliate over deadly exploding device attacks blamed on Israel. This escalation indicates a heightened risk of further violence. - - **Hezbollah's Stance**: Hezbollah leader Hassan Nasrallah has characterized recent attacks as a declaration of war and promised retribution, suggesting that the conflict may intensify. - -2. **Historical Context**: - - **October 7, 2023 Attacks**: The coordinated attacks by Hamas on Israeli military bases and civilian areas resulted in significant casualties, demonstrating the capability and willingness of Palestinian armed groups to carry out deadly assaults. - - **Ongoing Conflict**: The conflict has seen numerous violations of international law, including indiscriminate attacks on civilians by both Israeli forces and Palestinian armed groups, contributing to a volatile environment. - -3. **Current Situation**: - - **Israeli Military Operations**: Israel has announced a ""new phase of the war"" with an intensified focus on northern operations, indicating that military actions are expected to continue. - - **International Calls for Restraint**: U.S. Secretary of State Antony Blinken has called for restraint from all parties involved, highlighting international concerns over the escalating crisis. - -4. **Prediction Markets and Expert Opinions**: - - While specific prediction markets on this exact question are not cited in the provided sources, the general trend of escalating violence and the statements from leaders on both sides suggest a heightened risk of further attacks. - -In summary, the recent escalation of the Israel-Hezbollah conflict, the historical context of deadly attacks by Palestinian armed groups, and the current military operations by Israel all contribute to a heightened risk of a deadly attack within Israel causing at least five deaths before October 1, 2024. However, without direct access to prediction markets or more specific expert forecasts on this exact question, it is challenging to quantify this risk precisely. - -######### - -GPT - -### Analytical Approach -Considering the complexity of the question, I will break down the analysis into several parts: - -1. **Event Context and Criteria** - - **Criteria**: The deadly attack must occur within Israel's defined borders (excluding Gaza, West Bank, and Golan Heights) and must result in at least five fatalities within a 24-hour period. Only kinetic attacks are considered. - - **Time Frame**: The event must occur before October 1, 2024, giving an 11-day time window. - -2. **Recent Background and Current Dynamics** - - **Israel-Hamas War**: Continuous rocket attacks and historical context suggest ongoing hostilities. - - **Israel-Hezbollah Conflict**: This conflict has recently intensified, raising the likelihood of escalations. - - **Iran and Houthi Attacks**: Previous but fewer fatalities, showcasing a mix of high threats but not necessarily deadly outcomes. - -3. **Base Rate and Historical Context** - - Review historical events to estimate base case probabilities. - - Consider current heightened tensions and compare with previous cycles of violence. - -### Reasons Analysis - -#### Reasons Why the Answer May Be Yes -1. **Recent Escalation with Hezbollah and Hamas (Strength: 8)** - - Ongoing hostilities and recent escalations make it likely that an aggressive action causing significant casualties could occur. -2. **Historical Precedent of Attacks (Strength: 7)** - - Historically, Israel has experienced attacks causing multiple fatalities, especially during periods of heightened tension. -3. **Ongoing Rocket Attacks (Strength: 6)** - - Continuous rocket attacks imply persistent attempts to inflict damage, increasing the probability of a successful deadly attack. - -#### Reasons Why the Answer May Be No -1. **Strong Israeli Defense Systems (Strength: 8)** - - Iron Dome and other defense measures have been generally effective at minimizing fatalities from incoming attacks. -2. **Short Time Frame (Strength: 9)** - - With only 11 days remaining, the window for a deadly attack causing at least five deaths is narrow. -3. **Diplomatic Efforts (Strength: 5)** - - International calls for restraint might temporarily reduce the propensity for large-scale attacks aiming at de-escalation. - -### Data and Recent Sources Importances -- Recent escalations and military actions are highly significant due to their immediate proximity to the deadline. -- Historical data is useful for setting a base rate but is secondary to current trends and immediate precedents. -- Defense capability is a constant, high-impact factor. - -### Base Rate Analysis -During periods of intense conflict, Israel has experienced deadly attacks that meet the criteria but not frequently within such narrow windows. - -For instance, the October 7, 2023, attacks resulted in numerous fatalities, but such large-scale incidents are relatively spaced out over time. Considering the base rate and current circumstances, there's some likelihood but not exceedingly high within 11 days. - -### Statistical Estimation -Given limited time (11 days), exceptional Israeli defense capabilities, but also a heightened threat level due to increased conflict: - -1. Assume a base rate of a deadly attack meeting the criteria for similar conflict periods could be around 5-10% annually. -2. Applying this in an 11-day window proportionally (approx. 3% probability purely from the base rate), adjusted by the current heightened conflict, suggests an elevated but still limited probability. - -### Final Prediction -Incorporating these elements: -- **Base Case**: 3% -- **Current Escalation Adjustment**: +5% due to heightened tensions and recent incidents -- **Defense Effect Mitigation**: -2% due to strong defense measures and capabilities - -I conclude with a balanced estimation based on the provided data and context. - -### Final Answer -**Probability: 6%** - -Please note that this assessment focuses on the specific narrow window and stringent criteria for the outcome, balancing historical base rates and recent high-tension factors." -28336,"Will Prince Tom Iseghohi win the September 21, 2024 Edo state gubernatorial election in Nigeria?","PERPLEXITY - -Given the recent developments, it is highly unlikely that Prince Tom Iseghohi will win the September 21, 2024 Edo state gubernatorial election in Nigeria. Here is a concise rundown of the relevant news: - -1. **Withdrawal from the Election**: Prince Tom Iseghohi-Okojie, the governorship candidate of Action Alliance (AA), and his running mate, Deaconess Mabel Onaiwu, have announced their withdrawal from the election. They have directed their supporters to vote for the All Progressives Congress (APC) candidate, Senator Monday Okpebholo. - -2. **Current Candidates**: Despite Iseghohi's name still being on the ballot, he has publicly endorsed another candidate, effectively removing himself from the race. The main contenders now include Asuerinme Ighodalo (Peoples Democratic Party, PDP), Monday Okpebholo (All Progressives Congress, APC), and several other candidates from various parties. - -3. **No Recent Polls or Predictions**: There are no recent polls or predictions available that suggest Iseghohi could win despite his withdrawal. The focus has shifted to the remaining candidates, particularly those from the PDP and APC. - -4. **Expert Opinions**: There are no expert opinions suggesting that Iseghohi has a chance of winning after his withdrawal. The emphasis is now on the strategies and chances of the remaining candidates. - -In conclusion, given Prince Tom Iseghohi's withdrawal from the election and his endorsement of another candidate, it is highly improbable that he will win the September 21, 2024 Edo state gubernatorial election. - -######### - -GPT - -### Analysis and Breakdown: - -#### Reasons the Answer May Be Yes: -1. **His Name Remains on the Ballot (Strength: 2/10)**: - Even though Iseghohi has withdrawn from the election, his name is still printed on the ballot. If there were a confluence of unexpected events—such as a significant number of voters not being aware of his withdrawal or strictly voting along party lines—there might be a non-zero chance of him receiving enough votes to win. - -2. **Potential Last-Minute Developments (Strength: 1/10)**: - In the final hours leading up to an election, unexpected events can occur. While highly unlikely, a major development causing a massive shift in voter sentiment towards Action Alliance and Iseghohi could theoretically change the outcome. - -3. **Voter Confusion (Strength: 1/10)**: - In some scenarios, voter confusion and fragmentation of votes between other candidates might lead to an unexpected outcome. However, it's not plausible to expect this would result in Iseghohi winning given he endorsed another candidate. - -#### Reasons the Answer May Be No: -1. **Withdrawal from the Race (Strength: 10/10)**: - He has publicly withdrawn from the election and endorsed another candidate, urging his supporters to vote for them. This action effectively removes him as an active competitor in the race. - -2. **Support Endorsement (Strength: 9/10)**: - Redirecting his supporters to vote for the APC candidate, Okpebholo, significantly diminishes his chances of winning. Given this endorsement, voter bases are likely to shift their support to the endorsed candidate rather than vote for a candidate no longer in the race. - -3. **Definitive Shift in Media Focus (Strength: 8/10)**: - The media, public opinion, and expert analyses have shifted their attention to the leading candidates from the PDP and APC. This suggests that stakeholders do not consider Iseghohi a viable candidate anymore. - -4. **Lack of Recent Polls Favoring Iseghohi (Strength: 7/10)**: - The absence of any recent polling data indicating support for Iseghohi post-withdrawal strongly suggests that he is not seen as a contender. - -### Base Rates and Historical Patterns: -- **Base Rates of Withdrawn Candidates Winning (Very Low)**: - Historical data on elections where candidates formally withdraw show that it is exceedingly rare for such candidates to win. If they withdraw, it typically signals to voters and the political ecosystem that they are not viable options. - -### Timelines and Variability: -- **Timing Constraints (Strength: 10/10)**: - Given the election is a day away, the probability of a significant, unexpected event shifting the election outcome is minimal. -- **Daily Variance and Consistency (Strength: 9/10)**: - In the last 24 hours leading up to an election, changes in voter sentiment and election dynamics are generally minor unless triggered by a major incident, which there is no indication of currently. - -### Weight of Different Sources: -- **Recent Developments**: Highly important given they provide the latest context and are the most indicative of the current political landscape. -- **Historical Precedent**: Useful for establishing the base rate and likelihood of candidates winning post-withdrawal. -- **Expert Opinions**: Essential for nuanced understanding but are firmly aligned against Iseghohi's chances. - -### Conclusion: -Considering the withdrawal from the race, the public endorsement of another candidate, the timing of the election, and historical precedent, the probability of Prince Tom Iseghohi winning the September 21, 2024, Edo state gubernatorial election is extremely low. - -### Final Probability: -Given the overwhelming evidence against him and the timing of the election tomorrow: - -**Probability: 0.1%**" -28304,"Will the US national average retail price of regular gasoline be greater than $3.30 on September 30, 2024, according to AAA?","PERPLEXITY - -To provide context for the question of whether the US national average retail price of regular gasoline will be greater than $3.30 on September 30, 2024, according to AAA, here are the key points from recent sources: - -1. **Current Price and Trend**: - - As of September 16, 2024, the US Retail Gas Price is at $3.307, down from $3.36 last week and $3.941 one year ago, indicating a downward trend. - - The national average price for a gallon of gas has dipped to $3.23 as of September 9, 2024, which is 5 cents less than the previous week and 18 cents less than a month ago. - -2. **Regional Prices**: - - Regional prices vary significantly, with the Gulf Coast having the lowest average at $2.80 and the West Coast having the highest at $4.10 as of September 9, 2024. - - Mississippi has the cheapest gas price at $2.77 as of September 9, 2024. - -3. **Forecast and Expert Opinion**: - - Gas prices are expected to continue dropping as Americans drive less with the onset of fall. The national average could fall below $3 per gallon as early as October for the first time since 2021, according to De Haan. - - The decline in gas prices is attributed to seasonal trends and the transition from summer to fall, with a continued drop expected unless there are major disruptions such as a hurricane in the Gulf or worsening situations in the Middle East. - -4. **Recent Updates**: - - The latest update from September 19, 2024, indicates a national average gas price of $3.23, further supporting the downward trend. - -Given these points, the current trend and expert opinions suggest that the US national average retail price of regular gasoline is likely to be below $3.30 on September 30, 2024, based on the ongoing decline in prices and the expected seasonal drop. However, this forecast is subject to change based on unforeseen events such as natural disasters or geopolitical issues. - -######### - -GPT - -To make an accurate forecast on whether the US national average retail price of regular gasoline will be greater than $3.30 on September 30, 2024, we will systematically evaluate the available information, weigh the importance of different factors, and explore the probability distribution. - -### Initial Data Summary - -1. **Current Price and Trend**: - - US Retail Gas Price on September 16, 2024: $3.307 (down from $3.36 a week ago, down from $3.941 a year ago). - - National average on September 9, 2024: $3.23. - -2. **Regional Prices**: - - Gulf Coast: $2.80. - - West Coast: $4.10. - - Mississippi: $2.77. - -3. **Expert Opinion**: - - Expected continued decrease due to less driving in fall. - - Potential drop below $3/gallon as early as October. - -4. **Recent Updates**: - - As of September 19, 2024, national average price: $3.23. - -### Reasons for ""Yes"" (Price > $3.30) - -1. **Unforeseen Events** (Strength: 6/10) - - Hurricanes, geopolitical instability, or economic disruptions can abruptly spike gasoline prices. - - Geopolitical situations such as the Israel-Hamas war could cause sudden increases. - -2. **Regional Volatility** (Strength: 5/10) - - Price pressures from high-cost regions like the West Coast might push the national average slightly higher. - -3. **Market Speculation and Supply Chain Disruptions** (Strength: 3/10) - - Commodity market speculation might cause temporary spikes. - - Supply chain issues or refinery problems might lead to price increases. - -### Reasons for ""No"" (Price ≤ $3.30) - -1. **Current Downward Trend** (Strength: 8/10) - - National average fell from $3.36 to $3.23 between early and mid-September. - - This continuous drop suggests stable downward pressure on prices. - -2. **Seasonal Trends** (Strength: 7/10) - - Historically, gas prices tend to decline in the fall as demand decreases. - - Expert opinions support a continued decrease. - -3. **Market Forecasts and Historical Base Rates** (Strength: 7/10) - - Experts suggest prices could fall below $3/gallon. - - Historical data shows significant price drops in fall rather than spikes unless disrupted by unforeseen events. - -4. **Current Price Proximity to Threshold** (Strength: 9/10) - - With the latest price data at $3.23, it would require a notable reversal to go above $3.30. - - The time frame (9 days) is quite short for a significant move above the $3.30 threshold. - -### Statistical and Trend Analysis - -1. **Historical Price Movement Analysis**: - - Average weekly change analysis shows recent decline trends of about 4-7 cents per week. - - With the price at $3.23 on September 19, 2024, and assuming a similar trend, the price is more likely to remain at or drop slightly from $3.23, rather than rise significantly. - -2. **Probability Calculation**: - - Assuming current trends remain and considering possible unexpected events, we assign higher weight to continued trend analysis backed by market expertise. - -Given the substantial evidence of a downward trend, the short time frame, and the expected seasonal behavior, the most likely scenario is that gas prices will not spike above $3.30 within the next 9 days. - -### Final Probability Distribution - -Based on the aggregation of all these considerations, the final weighted probability that the US national average retail price of regular gasoline will be greater than $3.30 on September 30, 2024: - -**Probability: 10%** - -This low probability accounts for the possibility of unexpected events but heavily weighs the strong downward trend and seasonal factors in favor of prices remaining below the $3.30 mark." -28305,"Will the US national average retail price of regular gasoline be greater than $3.40 on September 30, 2024, according to AAA?","PERPLEXITY - -To address the question of whether the US national average retail price of regular gasoline will be greater than $3.40 on September 30, 2024, according to AAA, we need to consider recent trends and data: - -1. **Current Trends**: - - **Declining Prices**: The national average gas price has been declining. As of September 16, 2024, the average price for regular gasoline in the United States stood at $3.21 per gallon, down from $3.36 the previous week and significantly lower than the $3.941 price a year ago. - - **Seasonal Factors**: The transition from summer to fall typically leads to lower gas prices due to decreased demand and the switch to cheaper winter gasoline blends. - -2. **Regional Prices**: - - **Variation Across Regions**: Prices vary significantly across different regions. For example, as of September 9, 2024, the Gulf Coast had the lowest average price at $2.80 per gallon, while the West Coast had the highest at $4.10 per gallon. - - **Weekly Changes**: Most regions experienced a decline in gas prices, with the East Coast seeing the greatest decrease, dropping 8 cents in one week. - -3. **Predictions and Expert Opinions**: - - **Forecast**: Experts like De Haan predict that gas prices will continue to drop, potentially falling below $3 per gallon by October, assuming no major disruptions such as hurricanes in the Gulf or worsening situations in the Middle East. - -4. **Historical Context**: - - **Yearly High and Low**: The highest weekly gas price in 2024 was $3.64 per gallon on May 2, 2024, and the lowest was $3.05 per gallon on January 11, 2024. - - **Long-term Trends**: Gas prices in 2024 are higher than the six-year national average of $3.05 per gallon but have been consistently above this average since early March 2021. - -Given these points, the current trend suggests that the national average retail price of regular gasoline is likely to remain below $3.40 on September 30, 2024, based on the ongoing decline in prices and the seasonal factors that typically lead to lower prices in the fall. However, this forecast can be influenced by unforeseen events such as natural disasters or geopolitical tensions. - -**Recent Data**: -- **September 16, 2024**: $3.21 per gallon. -- **Weekly Change**: Down 1.58% from the previous week. -- **Yearly Change**: Down 16.09% from the previous year. - -**Prediction Markets and Forecasts**: -No specific prediction market data is provided in the search results. However, expert opinions suggest a continued decline in prices. - -######### - -GPT - -### Step-by-Step Analysis - -#### Current Trends and Recent Data -1. **Current Price**: As of September 16, 2024, the national average price is $3.21 per gallon. -2. **Recent Decline**: There has been a noticeable decline from $3.36 the previous week to $3.21. -3. **Year-over-Year Decline**: Down from $3.94 a year ago, showing a significant decrease over the year. - -Strength: High (9/10). This represents the most current and relevant data. - -#### Seasonal Factors -1. **Transition from Summer to Fall**: Typically leads to lower prices due to reduced demand and cheaper winter gasoline blends. - -Strength: High (8/10). Historically consistent pattern seen in gasoline pricing. - -#### Regional Prices -1. **Variations**: Prices range from $2.80 in the Gulf Coast to $4.10 in the West Coast, but the national trend is downwards. -2. **Weekly Change**: Continued decreases across most regions, with notable drops on the East Coast. - -Strength: Medium (6/10). This shows regional fluctuations but does not negate the national downward trend. - -#### Predictions and Expert Opinions -1. **Expert Forecast**: Experts like De Haan predict further declines, potentially below $3 by October assuming no major disruptions. - -Strength: High (7/10). Expert opinions are valuable but contingent on no major disruptions. - -#### Historical Context -1. **Yearly Peaks and Valleys**: The highest price this year was $3.64 in May, and the lowest was $3.05 in January. -2. **Long-term Trends**: Prices since early March 2021 have been consistently above the six-year average of $3.05. - -Strength: Medium (5/10). While historical data provides context, gas prices tend to be volatile. - -### Considerations for Unforeseen Events -1. **Geopolitical Tensions**: Possible escalation in the Israel-Hamas conflict or other Middle Eastern tensions could disrupt supply. -2. **Natural Disasters**: Hurricanes or other natural events in key oil-producing regions could spike prices. - -Strength: Medium (5/10). These are potential disruptions but not guaranteed to occur within the short time frame. - -### BASELINE FORECASTING -Using the current trend ($3.21 per gallon and declining), it's unlikely prices will rise above $3.40 within 9 days. The average weekly change suggests a continued decline or stabilization below the threshold. - -### Bayesian Updating -- **Prior**: Given the recency and consistency of decline, the baseline assumption leans towards prices remaining below $3.40. -- **Evidence**: Recent declines support this, compounded by experts forecasting continued drops and seasonal trends. - -### EV Calculations (Hypothetical Brier Score Exercise) -- **Brier Score**: Minimizing error by assigning probabilities closer to the observed reality. -- **Likely Scenario**: Assume low probability (<10%) for a price spike above $3.40. - -### Final Rationalization -1. **Time Frame**: Only 9 days to significant change. -2. **Current Trend**: Strong evidence of decline. -3. **Disruption Likelihood**: Geopolitical or natural disruptions have low immediate probability within the next 9 days. - -### Conclusion -Given the current downward trend and lack of significant immediate disruptive events, it is highly probable that the national average retail price of regular gasoline will be below $3.40 on September 30, 2024. - -**Probability: 90%** that the price will be ≤ $3.40. -**Probability: 10%** that the price will be > $3.40. - -**Final Answer: Probability: 10%**" -28303,"Will the US national average retail price of regular gasoline be greater than $3.10 on September 30, 2024, according to AAA?","PERPLEXITY - -To provide context for the question of whether the US national average retail price of regular gasoline will be greater than $3.10 on September 30, 2024, according to AAA, here are some key points and recent data: - -1. **Current Price**: As of September 16, 2024, the US Retail Gas Price is at $3.307, down from $3.36 the previous week and down from $3.941 one year ago. This represents a -1.58% change from the last week and a -16.09% change from one year ago. - -2. **Recent Trends**: The national average gas price has been decreasing. For example, it fell from $3.36 per gallon on August 29, 2024, to $3.32 per gallon on September 5, 2024, a decrease of $0.04 per gallon or about -1%. - -3. **Weekly Data**: The Energy Information Administration (EIA) provides weekly data on US regular all formulations retail gasoline prices. The latest available data shows a price of $3.236 for the week of September 9, 2024. - -4. **General Outlook**: Gas prices have been trending above the six-year national average since early March 2021. However, recent weeks have seen a decline in prices alongside crude oil benchmarks. - -Given these points, the current price is already above $3.10 as of September 16, 2024. However, the trend over the past few weeks indicates a slight decline in prices. Predicting whether the price will remain above $3.10 by September 30, 2024, requires considering these trends and any potential changes in the energy market or global events that could impact oil prices. - -**Prediction Markets and Forecasts**: There are no specific prediction markets or forecasts mentioned in the provided sources that directly address the price on September 30, 2024. However, the general trend and recent data suggest that prices could remain close to or slightly above $3.10, barring significant changes in the market. - -**Expert Opinions**: The sources provided do not include specific expert opinions on the future price of gasoline as of September 30, 2024. Experts in the field would likely consider a variety of factors, including global oil supply, demand, geopolitical events, and economic conditions. - -In conclusion, while the current price is above $3.10, and recent trends show a slight decline, the future price as of September 30, 2024, could be influenced by a variety of factors. Continuous monitoring of weekly data and market trends is necessary for a more accurate forecast. - -######### - -GPT - -### Step-by-Step Analysis - -#### Current Data and Trends: -1. **Current Price:** $3.307 as of September 16, 2024. -2. **Weekly Trends:** Decreasing from $3.36 on August 29, 2024, to $3.32 on September 5, 2024, further declining to $3.307 on September 16, 2024. -3. **Recent Rates:** About -1% change from the previous week and -16.09% from a year ago. - -#### Key Considerations: -- **Trend Analysis:** Prices have been decreasing slightly but steadily over the past few weeks. The average weekly decrease appears to be around 1%. -- **Base Rates and Historical Volatility:** Historical data suggests volatility during election years and sensitive geopolitical or economic events. -- **Short Time Frame:** With only nine days left until the resolution date, significant changes in price are less likely unless triggered by sudden geopolitical or economic events. - -### Reasons for Price Staying Above $3.10: -1. **Current Price Above Threshold:** The current price is $3.307, which is safely above $3.10. - - *Strength:* 8/10 -2. **Short Time Frame:** Nine days is a short period for prices to drop significantly unless there is a major event. - - *Strength:* 7/10 -3. **Historical Stability:** Gas prices have some inertia and do not typically fluctuate wildly in a short time frame without cause. - - *Strength:* 6/10 - -### Reasons for Price Dropping Below $3.10: -1. **Recent Downward Trend:** The price has been trending downwards recently, albeit at a slow pace. - - *Strength:* 5/10 -2. **External Economic Factors:** Any sudden global economic changes or shifts in oil supply could rapidly affect gas prices. - - *Strength:* 5/10 -3. **Seasonal Demand Changes:** Post-summer demand decrease can lead to lower prices, though the end of September is generally not as impactful as the start of autumn. - - *Strength:* 4/10 - -### Statistical Analysis and Probability Distribution: -To quantify the rate of change, a simple linear model using the past few weeks' data can help estimate the likely price on September 30, 2024. - -- **Current Price:** $3.307 -- **Weekly decrease rate:** ≈ 1% - -Using this trend for the next 1+ week: -- **Estimated Next Week's Price:** $3.307 - ($3.307 * 0.01) ≈ $3.273 - -Probability Distribution: -- Assume a normal distribution of changes for the remaining week, around the observed rate. -- Based on historical volatility and short-term stability, the probability of a large swing down to below $3.10 in such a short time frame is relatively low. - -Given the current trends and short duration until the resolution date: -- **Probability of Prices Remaining Above $3.10:** High -- **Probability of Prices Dropping Below $3.10:** Low but not zero - -### Final Comprehensive Answer: -Considering all the factors, trends, and historical context, the most likely scenario is that the prices will remain above $3.10 on September 30, 2024. - -### Final Prediction: -**Probability: 85%** - -This accounts for the high likelihood based on current trends and the low but non-zero chance of unforeseen factors causing a significant drop." -28306,"Will the US national average retail price of regular gasoline be less than $3.00 on September 30, 2024, according to AAA?","PERPLEXITY - -To provide context for forecasting the US national average retail price of regular gasoline on September 30, 2024, here are the key points from recent sources: - -1. **Current Price Trend**: - - As of September 16, 2024, the average US retail price for regular gasoline is $3.307 per gallon, down from $3.36 the previous week and $3.941 one year ago. - - The price has been decreasing, with a change of -1.58% from the last week and -16.09% from one year ago. - -2. **Recent Price Movements**: - - Gas prices have been falling, with 43 out of 51 states seeing a decrease in the week leading up to September 5, 2024. The national average fell from $3.36 to $3.32 per gallon during this period. - - The most recent data available up to September 16, 2024, indicates a continued downward trend, with the price reaching $3.307. - -3. **Historical Context**: - - The year 2024 has seen higher-than-average gas prices, with the lowest weekly national average being $3.05 per gallon on January 11, 2024. - - The six-year national average is $3.05 per gallon, which is lower than the current prices but indicates that prices below $3.00 are within historical norms. - -4. **Expert and Market Predictions**: - - There are no direct predictions from AAA in the provided sources regarding the price on September 30, 2024. However, the trend suggests a possibility of reaching below $3.00 if the current rate of decrease continues. - -Given the current downward trend and the historical context, it seems plausible that the US national average retail price of regular gasoline could be less than $3.00 on September 30, 2024, but this would depend on various factors including global oil prices and supply conditions. - -**Recent Updates and Movement**: -- The most recent update from September 16, 2024, shows a price of $3.307, which is a decrease from the previous week. -- The trend over the past few weeks indicates a steady decline in prices, which could potentially lead to prices below $3.00 by the end of September if the trend continues. - -**Key Figures**: -- Current price as of September 16, 2024: $3.307 -- Change from last week: -1.58% -- Change from one year ago: -16.09% - -######### - -GPT - -### Chain of Thought - -**Current Situation and Trends** -- As of September 16, 2024, the average US retail price for regular gasoline is $3.307. -- Prices have been decreasing, with a weekly change of -1.58%. -- The one-year change is -16.09%, indicating a significant drop from $3.941 to $3.307 over the past year. -- Recent weeks have seen a continued decline, from $3.36 to $3.307. - -**Historical Context** -- The lowest price in 2024 was $3.05 per gallon on January 11, 2024. -- Over the past six years, the national average is $3.05 per gallon. -- Prices below $3.00 are within historical norms. - -**Economic and Market Influences** -- The current price trend and historical lows suggest a continuation of the decline might bring prices below $3.00. -- External factors, such as global oil prices and supply conditions, could quickly impact current trends. -- The volatility of gas prices, particularly in election years, adds an element of uncertainty. - -**Reasons for ""Yes"" (Price < $3.00)** -1. **Trend Continuation**: - - Recent trends show a consistent decrease (~consistent weekly decrease). - - Strength: 7/10 -2. **Historical Norms**: - - Prices below $3.00 have been observed and are within historical norms. - - Strength: 6/10 -3. **Economic Factors**: - - The better sense of balance in the market, as mentioned by experts, may lead to further decreases in price. - - Strength: 5/10 - -**Reasons for ""No"" (Price ≥ $3.00)** -1. **Current Price Level**: - - The current price is $3.307. With only a few weeks left, a decrease of more than 10 cents might be challenging. - - Strength: 8/10 -2. **Market Volatility**: - - Gas prices are highly volatile, especially during election years. Unexpected increases could occur. - - Strength: 7/10 -3. **Supply and Demand Shocks**: - - Any unexpected changes in supply (e.g., geopolitical events) could halt or reverse the trend of decreasing prices. - - Strength: 6/10 - -**Recency and Source Importance** -- Recent data showing a decline is highly relevant. -- Expert analysis and historical context help frame the range of potential outcomes. - -### Probability Calculation -- The recent trend shows a consistent decrease, but the margin needed is significant. -- Given the short timeframe remaining (just around 10 days), significant market shifts are less probable, but not impossible. -- The base rate of similar events suggests prices can dip below $3.00, but this seems historically infrequent in such a short span. - -Given these factors, I will weigh the recent consistent decline heavily but consider the time constraint and current price level as major hurdles. - -### Final Prediction -1. **Base Case** (If nothing changes drastically): Prices remain around $3.30 with minor fluctuations. -2. **Probability Distribution**: - - A sharp decrease to below $3.00 is relatively less probable due to time constraints. - - However, the trend keeps it somewhat plausible. - -**Final Probability**: -Given the current trends, base rates, historical volatility, and the time constraint, the probability of the US national average retail price of regular gasoline being less than $3.00 on September 30, 2024, is: - -**Probability: 30%**" -28306,"Will the US national average retail price of regular gasoline be less than $3.00 on September 30, 2024, according to AAA?","PERPLEXITY - -To address the query about whether the US national average retail price of regular gasoline will be less than $3.00 on September 30, 2024, according to AAA, we need to examine recent trends and data: - -1. **Current Trends**: - - **Recent Prices**: As of September 16, 2024, the average price for regular gasoline in the United States was $3.21 per gallon. - - **Weekly Changes**: The price has been decreasing, with a drop from $3.36 per gallon on September 9, 2024, to $3.21 per gallon on September 16, 2024, which is a decrease of $0.15 per gallon or about -4.4%. - - **Annual Comparison**: The current price is down from $3.941 one year ago, representing a -16.09% decrease. - -2. **Historical Context**: - - **Yearly High and Low**: The highest weekly gas price in 2024 was $3.64 per gallon on May 2, 2024, and the lowest was $3.05 per gallon on January 11, 2024. - - **Long-term Trends**: Gas prices have been above the six-year national average consistently since early March 2021. - -3. **Expert Insights and Forecasts**: - - **Short-term Predictions**: While there are no direct predictions from AAA in the provided sources, the recent downward trend suggests a possibility of further decreases. However, predicting prices below $3.00 by September 30, 2024, would require a continued decline. - - **Market Influences**: The decrease in gas prices is partly due to the decrease in crude oil benchmarks. - -4. **Prediction Markets and Forecasts**: - - **No specific prediction market data is provided in the sources**. However, based on the trend, a continued decrease could potentially bring prices close to or below $3.00, but this would depend on various factors including global oil prices and supply chain stability. - -Given the current trend and the information available, while it's possible for prices to drop below $3.00, it's uncertain without specific forecasts from AAA or detailed prediction market data. The recent decrease in prices and the yearly low of $3.05 suggest that reaching below $3.00 is plausible but not guaranteed by September 30, 2024. - -######### - -GPT - -### Analysis - -#### Trend Analysis and Current Data -1. **Current Price**: - - As of September 16, 2024, the average price for regular gasoline is $3.21 per gallon (AAA). - -2. **Price Trend**: - - Weekly trend shows a significant drop from $3.36 on September 9, 2024, to $3.21 on September 16, 2024. This is a decrease of $0.15 per gallon or approximately -4.4%. - - The price has consistently decreased over the past weeks. If this trend continues, the decrease could be significant approaching September 30, 2024. - -3. **Annual Comparison**: - - The current price ($3.21) is down from $3.941 one year ago, marking a -16.09% annual decrease. - -4. **2024 High and Low Points**: - - The lowest price in 2024 so far was $3.05 on January 11, 2024, and the highest was $3.64 on May 2, 2024. - -5. **Historical Context**: - - Gas prices have been above the six-year national average since early March 2021. The decline below $3.00 represents a significant departure from recent trends. - -#### Volatility and Election Influence -- **Election Year Volatility**: - - Historically, gas prices exhibit volatility in election years. External events, geopolitical issues, and domestic policies often impact gas prices significantly during this period. - -- **Influence of Global Events**: - - Recent global events like the Russia-Ukraine conflict have influenced gas prices. The reduction in prices could continue if global tensions ease or if there are significant changes in oil production and supply. - -#### Statistical Analysis and Forecasts -- **Sustaining the Trend**: - - If the weekly decrease rate of 4.4% is sustained, the price needs to fall below $3.00 within the next 9 days. Mathematically: - - Current price: $3.21 - - Percentage decrease needed: \(3.21 - 3.00 = 0.21\) - - Proportional decrease needed: \(0.21 / 3.21 ≈ 0.07\) or 7% - - Sustaining a 4.4% weekly decrease approximates a \( \frac{4.4}{7} \times 7 \) days need \<9 days. -- **Short Timeframe**: - - Forecasting precise economic and geopolitical changes within a short period is challenging. Minor disruptions or supply issues can dramatically alter the gas price trajectory. - -### Reason Assessment - -- **Reasons for Prices Falling Below $3.00**: - - **Continued Decrease in Crude Oil Prices**: - - If crude oil prices continue to decline, this strong driver could lead to gas prices dropping further. - - Strength: 8/10 - - **Decreased Demand**: - - Economic conditions or reduced demand due to seasonal factors may sustain or accelerate price drops. - - Strength: 6/10 - - **Political Influence**: - - Election-related policies or rhetoric may push for lowering fuel prices. - - Strength: 5/10 - -- **Reasons against Prices Falling Below $3.00**: - - **Price Volatility During Election Year**: - - Sudden spikes or drops may occur due to unpredictable geopolitical situations or domestic policies. - - Strength: 7/10 - - **Historical Context**: - - Given the historical trend of prices consistently above the six-year average, unless a significant event drives the price down sharply, it may not fall below $3.00. - - Strength: 8/10 - - **Short Time Horizon**: - - Achieving a drop from $3.21 to below $3.00 in 9 days needs a sustained and significant decrease, which is less probable in such a short timeframe. - - Strength: 9/10 - -### Final Probability - -Given the data analysis and consideration of the uncertainties and the tight timeframe, the trend would have to accelerate significantly to drop below $3.00. Historically, while volatility in election years is expected, the probability of such a significant drop within the next 9 days is relatively low. - -**Final Probability: 20%**" -28301,"Will any of these large tech companies announce layoffs before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether any large tech companies will announce layoffs before October 1, 2024, here are some key points and recent developments: - -1. **Ongoing Layoff Trend**: The tech industry has seen a significant wave of layoffs in 2024, with over 124,000 employees laid off as of August 20, 2024, according to Layoffs.fyi. This trend suggests that further layoffs are possible. - -2. **Recent Layoffs**: Major tech companies such as Microsoft, Google, Apple, Amazon, Tesla, Dell, Intel, IBM, Cisco, and SAP have already conducted layoffs in 2024. These layoffs have been attributed to various factors including cost-cutting measures, investments in AI, and global economic downturns. - -3. **AI-Driven Layoffs**: Companies like IBM and SAP have specifically mentioned AI-driven efficiencies as reasons for restructuring and layoffs. This indicates that the shift towards AI could lead to more job cuts. - -4. **Economic Pressures**: Intel, for example, laid off 15,000 employees due to weak financial results and the need to invest in AI to compete with other chipmakers. - -5. **No Indication of End**: There is no clear indication that the layoff trend will end before October 1, 2024. In fact, the ongoing economic pressures and the shift towards AI suggest that more layoffs could be announced. - -6. **Prediction Markets**: While specific prediction markets on this question are not mentioned in the provided sources, the general trend and recent layoffs suggest a high likelihood of further layoffs. - -In summary, given the ongoing trend of layoffs in the tech industry and the factors driving these layoffs, it is plausible that more large tech companies could announce layoffs before October 1, 2024. However, specific predictions or forecasts are not provided here, only context based on recent developments. - -######### - -GPT - -To determine the probability of any of the listed large tech companies announcing layoffs before October 1, 2024, we need to break down the information and consider various factors methodically. - -### Summary of Recent Trends and Events: - -**1. Ongoing Layoff Trend (Strength: 8/10):** - - Since the tech industry has seen ongoing layoffs, this trend suggests a non-negligible probability of further layoffs. Economic pressures and shifts such as AI adoption provide additional reasons for companies to restructure. - -**2. Specific Company Contexts:** - - **Amazon (Strength: 9/10):** Recently mandated return-to-office, which often correlates with subsequent layoffs to restructure workforce. The tone indicates pending difficulties and adjustments. - - **Apple (Strength: 6/10):** Multiple layoffs this year, but latest event was in August. High but diminishing recent activity. - - **Google/Alphabet (Strength: 6/10):** Several layoffs in the first half of 2024 but no recent signs in Q3. - - **IBM (Strength: 7/10):** Recent layoffs and heavily rumored, suggesting potential for more announcements. - - **Meta/Facebook (Strength: 3/10):** No layoffs this year, though significant events in previous years. - - **Nvidia (Strength: 2/10):** Traditionally avoids layoffs and has reasoned through delays rather than job cuts. - - **Oracle (Strength: 5/10):** Active rumors but no events yet in 2024. Forum activity suggests potential unrest. - - **Salesforce (Strength: 6/10):** Active layoff year but diminished recent activity. - - **Tesla (Strength: 6/10):** Major layoffs in April, though difficult to predict subsequent events closely following significant action. - -### Base Rate Perspective: -- **Historical Context** (Strength: 7/10): The historical frequency of layoffs in tech companies shows a significant probability. Given that every month in 2024 up to August has seen layoffs in various companies, the likelihood of September seeing none is low. - -### Consideration of Recent Updates and Economic Context: -- **Macro-Economic Pressures (Strength: 8/10):** Global economic downturns, shifts towards AI efficiencies, and ongoing cost-cuttings provide a layer of sustained risk across the tech sector. - -### Time Until Resolution: -- **Short-Term Variability (Strength: 9/10):** With only around 9 days left until the deadline, the probability for change diminishes. Any significant event would likely have signs or immediate precursors due to the short timeline. - -### Consolidation of Factors: -- Given the ongoing trend of layoffs, we should lean towards a higher probability. -- Amazon appears to be the most likely candidate due to recent significant operational changes. -- The close timeline limits the chance for substantial deviations from existing trends. - -### Final Probability Calculation: -Using a weighted approach: -- **Amazon: 9/10 chance for layoffs**, -- Other companies contribute smaller probabilities, but cumulatively significant. - -After aggregating these probability strengths and considering potential overlaps: - -### Final Answer: -**Probability: 80%**" -28301,"Will any of these large tech companies announce layoffs before October 1, 2024?","PERPLEXITY - -To provide context for the question of whether any large tech companies will announce layoffs before October 1, 2024, here are some key points and recent developments: - -1. **Ongoing Layoff Trend**: The tech industry has seen a significant wave of layoffs in 2024, with over 124,000 employees laid off as of August 20, 2024, adding to the total of 428,449 layoffs since 2022. - -2. **Recent Layoffs**: Major tech companies such as Intel, Microsoft, Google, Amazon, and Dell have already conducted sizable layoffs in the first months of 2024. For example, Intel announced 15,000 layoffs in August, and Microsoft had multiple rounds of layoffs affecting various departments. - -3. **AI and Cost-Cutting**: Many companies are citing the need to invest in AI and cut costs as reasons for layoffs. This includes IBM, which is replacing jobs with AI, and Dell, which is pivoting to AI and made cuts in sales and marketing divisions. - -4. **Industry Expectations**: A survey in late December 2023 indicated that nearly 40% of business leaders expected layoffs in 2024, with half citing concerns about a recession and around four in 10 mentioning the replacement of workers with AI. - -5. **Current Layoff Numbers**: As of September 17, 2024, 60,000 job cuts have been reported across 254 companies in the tech sector for 2024. - -Given these points, it is reasonable to expect that the trend of layoffs in the tech industry may continue, driven by factors such as the need to invest in AI, cost-cutting measures, and economic concerns. However, predicting specific announcements before October 1, 2024, requires considering the recent patterns and the strategic decisions of these companies. - -**Prediction Markets and Expert Opinions**: While there are no direct prediction markets or betting odds available for this specific question, the ongoing trend and industry expectations suggest a likelihood of further layoffs. Expert opinions, such as those from ResumeBuilder's survey, indicate a continued concern about layoffs in the tech sector. - -**Recent Updates**: The most recent sources (September 12 and 17, 2024) indicate that layoffs are still occurring, with no clear indication of a halt before October 1, 2024. - -In summary, while there is no definitive answer, the context suggests that it is plausible for large tech companies to announce further layoffs before October 1, 2024, given the ongoing trend and industry factors. - -######### - -GPT - -### Step-by-Step Analysis: - -**1. Understanding the Question Context:** -- **Question:** Will any of these large tech companies announce layoffs before October 1, 2024? -- **Resolution Criteria:** Based on TechCrunch's tracker, the question resolves as ""Yes"" if any of the listed companies announce layoffs in September 2024, otherwise, it resolves as ""No"". - -**2. Analyzing Past Data and Trends:** -- **Ongoing Layoff Trend:** The tech industry has seen significant layoffs from 2022 through 2024. -- **Data Source Recency:** The most recent update on layoffs is September 17, 2024, indicating ongoing layoffs in the tech sector. -- **Recent Layoff Events:** Major companies have already conducted layoffs in 2024. - -**3. Evaluating Company-Specific Information:** -- **Amazon:** Ordered back to office and warned of restructuring on September 16, 2024. -- **Apple:** Multiple layoff events in 2024, the latest being in August. -- **Google:** Several layoff events up to June 2024. -- **IBM:** Active rumors of layoffs, last slashed jobs in March 2024. -- **Meta:** No layoffs in 2024, but did so in 2022 and 2023. -- **Nvidia:** Generally avoids layoffs but faces shipment delays. -- **Oracle:** Active layoff board, no layoffs in 2024 yet. -- **Salesforce:** Layoff events in January and July 2024. -- **Tesla:** Last layoff event in April 2024. - -**4. Strength of Reasons:** -### **Reasons for ""Yes"":** -1. **Amazon's Restructuring Warning (High):** Given the recent warning and industry trends, Amazon might announce layoffs soon. -2. **Active Rumor Boards (Medium):** IBM and Oracle have active layoff rumor boards, indicating potential upcoming layoffs. -3. **Ongoing Layoff Trend (High):** The tech industry is still laying off workers, as shown by the 60,000 job cuts reported as of September 17, 2024. - -### **Reasons for ""No"":** -1. **Companies with No Recent Layoffs (Low-Medium):** Meta and Oracle haven't laid off employees recently, which might mean a pause in layoffs. -2. **Nvidia’s Policy (Medium):** Nvidia's policy against layoffs, despite economic pressures. -3. **Proximity to Date (High):** The forecast date is very close (within 10 days), reducing the likelihood of new, unanticipated announcements. - -### **Weighing the Importance of Background Information:** -- High relevance: Amazon warning, ongoing layoff trends. -- Medium relevance: Companies' layoff histories in 2024. -- Low relevance: Specific historical trends from less recent periods (e.g., 2022 and earlier). - -**5. Consideration of Base Rates and Novel Factors:** -- **Base Rates:** High frequency of layoffs in 2024 among major tech firms. -- **Novel Factors:** Economic pressures, AI cost-cutting, and tech industry dynamics. - -**6. Statistical Analysis and Forecast Distribution:** -Given that layoffs have been frequent and the economic and industry conditions have not drastically improved, the chances of no announcements decrease. However, given the short timeframe, we must consider statistical deviations typically observed in such forecasts. - -**7. Final Probability Assessment:** -After thorough evaluation, considering trends, company-specific behaviors, and the short timeframe, I estimate the probability as follows: - -**Probability: 65%** - -This reflects a frequent likelihood given the conditions, balanced by the low variance over the immediate short term." -28302,"Will the US national average retail price of regular gasoline be greater than $3.205 on September 30, 2024, according to AAA?","PERPLEXITY - -To provide context for the question of whether the US national average retail price of regular gasoline will be greater than $3.205 on September 30, 2024, according to AAA, here are some key points from recent sources: - -1. **Current Trends**: - - The current national average cost for gas is $3.23 as of September 19, 2024. - - Gas prices have been falling, with a decrease of $0.05 per gallon or about -1% from the previous week. - - The national average gas price fell from $3.36 per gallon on August 29, 2024, to $3.32 per gallon on September 5, 2024, and further to $3.23 as of September 19, 2024. - -2. **Regional Variations**: - - Regional prices vary significantly, with the Gulf Coast having the lowest average at $2.80 and the West Coast having the highest at $4.10 as of September 9, 2024. - - Mississippi has the cheapest gas at $2.77 per gallon, followed closely by Tennessee and Oklahoma at $2.80. - -3. **Future Projections**: - - Experts like De Haan suggest that gas prices will continue to drop as Americans drive less with the onset of fall, potentially leading to a national average below $3 per gallon as early as October for the first time since 2021. - - The changeover to cheaper winter gasoline, expected in a couple of weeks, could further contribute to the decline in gas prices. - -4. **Historical Context**: - - The annual average cost for gas in 2024 is $3.40, which is higher than the six-year national average of $3.05. - - Gas prices have been above the six-year national average consistently since early March 2021. - -Given these points, the current trend suggests that gas prices are likely to continue falling, which could result in the national average being below $3.205 by September 30, 2024. However, this is speculative and subject to various factors, including seasonal changes, geopolitical events, and supply chain dynamics. - -**Prediction Markets and Forecasts**: -- No specific prediction market data is provided in the sources. However, based on the trend and expert opinions, there seems to be a leaning towards prices continuing to decrease. - -**Recent Updates**: -- The most recent update from September 19, 2024, indicates a national average of $3.23, which is slightly above the $3.205 threshold but shows a continuing downward trend. - -######### - -GPT - -To determine the probability that the US national average retail price of regular gasoline will be greater than $3.205 on September 30, 2024, we'll need to carefully analyze the provided information. Let's break down the key points systematically: - -### Current Price Trends and Projections -1. **Curent national average**: $3.23 (as of September 19, 2024). -2. **Recent price trend**: Decrease from $3.36 on August 29, 2024, to $3.23 on September 19, 2024. This indicates a decrease of $0.13 over 21 days. -3. **Expert opinion**: GasBuddy's Patrick DeHaan predicts a continued drop in prices due to factors like decreased driving and switch to winter gasoline. - -### Regional Variations -- **Wide regional differences**: Prices as low as $2.80 (Gulf Coast) and as high as $4.10 (West Coast). The average of these extremes is around $3.45, which suggests that localized variations play a significant role. - -### Historical and Economic Factors -1. **Annual average**: For 2024 is $3.40, consistently above the $3.205 threshold. -2. **Seasonal trends**: Historically, gas prices do fall in the winter, contributing to the case for lower prices. - -### Geopolitical and Market Influences -1. **Political Turbulence**: This is an election year, which can cause significant fluctuations in fuel prices due to policy changes and market speculation. -2. **Global Events**: The Israel-Hamas war and its potential to disrupt global oil supply must be considered, although its impact is assumed to be currently under the influence threshold. - -### Recent Developments and Importance -1. **Recent updates**: The gas price recently fell from $3.36 to $3.23 in less than a month, which suggests a strong downward trend. -2. **Winter Gasoline Transition**: This imminent change usually causes a price drop. - -### Statistical Context and Analysis -- **Recent price drop rate**: $0.13 in 21 days translates to approximately $0.0062 per day decline. -- **Remaining days until resolution**: 9 days. -- **Projected price drop over next 9 days at current rate**: 9 * $0.0062 = $0.0558. - -Considering a straightforward linear trend, the average price might reach approximately: -\[ 3.23 - 0.0558 = 3.1742 \] - -Which we’ll round to approximately $3.17. This places the expected value below $3.205. - -### Base Rates and Historical Occurrences -Base rates for gas prices decreasing during fall align with current trends. Current data suggests prices historically dip rather than spike at this time of year under normal market conditions. - -### Reasons for Yes and No -#### Yes -1. **Geopolitical instability** (Medium): Unpredictable events could spike prices briefly. -2. **Supply Chain Disruptions** (Low): Sudden, unexpected events causing supply issues. -3. **Policy Interventions** (Low): Government measures might temporarily increase prices. - -#### No -1. **Current Downward Trend** (High): Strong downward pricing trend supported by multiple data points. -2. **Seasonal Trends** (Medium): Transition to winter gasoline reduces prices consistently. -3. **Expert Forecasts** (Medium): Industry projections supporting further declines. - -### Weighting of Information -- **Recent data**: High importance due to their direct evidence. -- **Historical trends**: Medium, relevant but subject to short-term volatility. -- **Expert opinion**: Medium, informative but not infallible. - -### Final Probability Distribution -Given the strong downward trend and only a brief period remaining until the resolution date, the probability of gas prices remaining above $3.205 seems low. - -### Conclusion -Based on statistical analysis, current trends, expert opinion, and historical context, the probability that the US national average retail price of regular gasoline will be greater than $3.205 on September 30, 2024, is assessed as follows: - -**Probability: 15%**" -28300,"Before October 1, 2024, will any of these prominent Democrats appear as guests on the Lex Fridman podcast?","PERPLEXITY - -Given the nature of the question, which pertains to a specific event involving the Lex Fridman podcast and potential Democratic guests, the focus should be on recent activities and statements related to the podcast and its guest selection. Here’s a concise rundown of relevant information: - -1. **Recent Episodes and Guest Selection**: - - The Lex Fridman podcast has featured a wide range of guests, including political figures, scientists, and celebrities. Recent episodes have included interviews with Donald Trump and Jared Kushner, indicating an openness to hosting political figures. - -2. **Political Neutrality and Guest Diversity**: - - Lex Fridman has been praised for his neutral and respectful approach to interviewing guests, regardless of their political affiliations. This suggests that he might be open to hosting Democrats as well. - -3. **Public Reception and Criticism**: - - The podcast has received both praise and criticism for its handling of political guests. Some viewers appreciate the long-form interviews and the opportunity to hear from politicians in a more in-depth manner, while others criticize the lack of rigorous questioning and follow-up on controversial statements. - -4. **Upcoming Episodes and Announcements**: - - There are no specific announcements or indications in the provided sources about upcoming episodes featuring prominent Democrats before October 1, 2024. - -5. **Prediction Markets and Forecasts**: - - No specific prediction markets or forecasts related to this question are mentioned in the provided sources. - -In summary, while there is no direct information indicating that any prominent Democrats will appear on the Lex Fridman podcast before October 1, 2024, the podcast’s history of hosting diverse guests and its neutral approach suggest that it is possible. However, without specific announcements or predictions, the outcome remains uncertain. - -######### - -GPT - -To forecast whether any of the specified prominent Democrats will appear on the Lex Fridman podcast before October 1, 2024, let's break down the task systematically: - -### Step-by-Step Analysis: - -1. **Background Context and Previous Guests**: - - Lex Fridman's podcast has a reputation for hosting high-profile guests from various fields, including political figures. Recent notable interviews include Donald Trump, Elon Musk, and Benjamin Netanyahu. - -2. **Public Statements and Invitations**: - - On September 4, 2024, Fridman publicly invited prominent Democrats such as Kamala Harris, Barack Obama, Hillary Clinton, Bernie Sanders, and Alexandria Ocasio-Cortez, signaling his openness to having them on his show. This is a crucial factor suggesting a reasonable probability that one of them might appear. - -3. **Time Frame**: - - Today's date is September 21, 2024, and the resolution date is October 1, 2024. There are only 10 days left for this event to occur. Given the narrow window, this significantly impacts the likelihood. - -4. **Logistics and Scheduling**: - - Coordinating interviews with high-profile political figures often involves significant logistical planning and scheduling. Even with a public invitation, securing a guest and having the episode recorded, edited, and released in the given timeframe (next 10 days) is challenging. - -5. **Analysis of Recent Trends**: - - The podcast has shown a trend of featuring politically significant figures. However, it takes time to organize such episodes, and there are no specific announcements about upcoming episodes featuring these prominent Democrats. - -6. **Base Rates and Historical Precedent**: - - Historical appearances by politicians on popular podcasts tend to be infrequent and often announced with some lead time. None of the invited Democrats have appeared on his podcast previously, adding to the uncertainty. - -### Reasons for a Yes Outcome: - -1. **Invitation and Openness** (Strength: Medium): - - Lex Fridman has publicly invited these Democrats, and his neutral approach may appeal to them. However, public invitations do not guarantee acceptance or appearances within a short timeframe. - -2. **Trend of High-Profile Guests** (Strength: High): - - The podcast's recent trend of featuring high-profile guests may increase the likelihood of securing a prominent Democrat. However, the extremely short timeline overrides this to an extent. - -### Reasons for a No Outcome: - -1. **Narrow Timeframe** (Strength: High): - - With only 10 days left, the probability of organizing, recording, and publishing such an episode decreases significantly. Coordinating schedules and the typical lead time for such high-profile interviews make it improbable. - -2. **Lack of Announcements** (Strength: High): - - There have been no announcements or indications that any of these Democrats will appear on the podcast imminently. Announcements typically precede such events, giving a clue to the likelihood. - -3. **Logistical Challenges** (Strength: Medium): - - Even if one of the invited Democrats were to agree, logistical issues and the current short timeline introduce significant challenges to making the appearance possible. - -### Importance and Recency of Background Sources: - -- **Public Invitation Tweet**: This is important because it openly invites Democrats and sets a premise. However, it was posted only recently (September 4), which is quite close to the current date for meaningful scheduling. -- **Podcast Trends**: The podcast's trend of featuring prominent guests is significant, but again, needs to be balanced against the narrow timeframe. -- **Historics of Political Interviews**: Relevant but often have longer lead times, reducing the recent significance. - -### Statistical Consideration: - -Given the short timeline and the lack of specific announcements, the base rate of such high-profile interviews appearing without prior notice within a 10-day window is low. Based on the reasoning and base rates: - -**Final Probability Assessment:** - -Given the likely reasons for a no outcome carrying high strengths and the extreme narrow timeframe coupled with logistical challenges, the calculated probability leans heavily toward the event not occurring within the next 10 days. - -**Probability: 5%**" -28905,"Will Tim Walz cease to be Kamala Harriss's running mate before November 1, 2024?","Let's approach this step-by-step: - -1. Timeline: - - Today's date: October 21, 2024 - - Resolution date: November 1, 2024 - - Time remaining: 11 days - -2. Historical context: - - The Thomas Eagleton case in 1972 is a rare example of a VP candidate being replaced. - - Such replacements are extremely uncommon in modern U.S. politics. - -3. Current situation: - - Tim Walz was selected as Harris's running mate on August 6, 2024. - - He has already participated in the VP debate on October 1, 2024. - - No recent indications of any issues or potential replacement. - -4. Base rate: - - In the last 50 years, there has been only one instance of a major party VP candidate being replaced (Eagleton in 1972). - - This gives a rough base rate of 1 in 25 election cycles, or about 4%. - -5. Time factor: - - With only 11 days remaining until the resolution date, the likelihood of a change decreases significantly. - - Most vetting and potential issues would likely have surfaced by now. - -6. Potential scenarios: - - Health emergency: Low probability given Walz's apparent good health. - - Scandal emergence: Reduced likelihood due to thorough vetting process. - - Strategic decision: Extremely unlikely this close to the election. - -7. Expert opinions: - - No expert opinions suggesting a potential change have been reported. - -8. Uncertainty factors: - - ""Unknown unknowns"" always exist, but their impact decreases as the election nears. - -9. Comparative timelines: - - Probability within 1 week: Even lower, around 0.5% - - Probability within 1 month: Slightly higher, around 2% - - Probability within 3 months: Higher, perhaps 5% - -Given these factors, I believe the probability of Tim Walz ceasing to be Kamala Harris's running mate before November 1, 2024, is very low. The base rate suggests a small but non-zero chance, but the extremely short timeframe until the resolution date significantly reduces this probability. - -Accounting for the possibility of unforeseen events but also recognizing the rarity of such late-stage changes, I would estimate the probability as follows: - -Probability: 1% - -This low probability reflects the extreme unlikelihood of such a change occurring in the final days before a presidential election, while still acknowledging that unexpected events can occur. The 1% probability is significantly lower than the base rate due to the very short time remaining and the lack of any current indications of potential changes." -29013,"Will Tesla deliver more than 450,000 vehicles in Q4 2024? ","Let's approach this question step-by-step: - -1. Historical Context: - - Tesla's quarterly deliveries have been fluctuating, with a range from 386,810 to 484,507 since Q1 2023. - - The most recent Q3 2024 delivery number (462,890) shows growth compared to Q2 2024 and Q3 2023. - -2. Trend Analysis: - - There's a general upward trend in deliveries, but it's not consistent quarter-over-quarter. - - Q4 has historically been strong (484,507 in Q4 2023), suggesting potential for higher deliveries. - -3. Company Goals: - - Tesla aims to deliver 2 million vehicles in 2024, requiring about 515,000 deliveries in Q4 2024. - - This goal sets a high bar and may push the company to maximize deliveries. - -4. Market Factors: - - Increased competition in the EV market could pressure Tesla's market share. - - Economic uncertainties may affect consumer spending on high-ticket items like cars. - - The Chinese market, crucial for Tesla, shows growth potential with new subsidies. - -5. Production Capacity: - - Q3 2024 production (469,796) slightly exceeded deliveries, indicating potential for growth. - - Tesla's ability to ramp up production in response to demand is a key factor. - -6. Timeframe Consideration: - - The resolution date is shortly after Q4 2024 ends, leaving little room for uncertainty in reporting. - -7. Statistical Analysis: - - Mean of last 6 quarters: 441,558 - - Median of last 6 quarters: 443,956 - - Standard deviation: 33,707 - - 450,000 is within one standard deviation of the mean, suggesting it's achievable. - -8. Probability Distribution: - - Given the recent upward trend and Q4 historically being strong, there's a higher likelihood of exceeding 450,000. - - However, market challenges and the ambitious nature of the target introduce uncertainty. - -9. Short-term vs. Long-term Outlook: - - Short-term (1 month): Probability of exceeding 450,000 monthly rate: 30% - - Long-term (1 year): Probability of consistently exceeding 450,000 quarterly: 65% - -10. Adjusting for Overconfidence: - - Given the historical tendency for overconfidence, we should lean towards a more conservative estimate. - -11. Base Rate Consideration: - - Out of the last 6 quarters, Tesla exceeded 450,000 deliveries twice (33% of the time). - -Considering all these factors, I believe there's a good chance Tesla will exceed 450,000 deliveries in Q4 2024, but it's not certain. The recent growth trend, historical Q4 performance, and company goals push towards a positive outcome. However, market challenges and the need to significantly outperform recent quarters introduce substantial uncertainty. - -Adjusting for potential overconfidence and considering the base rate, I'll provide a more conservative estimate than my initial inclination. - -Probability: 62% - -This probability reflects a cautiously optimistic view, acknowledging the positive factors while also considering the challenges and uncertainties in the market and Tesla's performance variability." -29012,"Will the Warren Buffett Indicator exceed 205% before November 15, 2024? -","To predict whether the Warren Buffett Indicator will exceed 205% before November 15, 2024, let's perform a detailed analysis: - -1. **Understanding the Indicator**: The Warren Buffett Indicator is a measure of the total market capitalization of all publicly traded stocks divided by the GDP. The October 17, 2024, value was 202.52%. - -2. **Recent Trends**: - - The S&P 500 has risen 21.5% so far in 2024, indicating a strong upward trend in market valuations. - - Specific sectors, particularly those related to AI, have seen substantial growth. However, growth in specific sectors must be balanced against the overall market cap. - -3. **Economic Context**: - - A strong jobs report and economic optimism could encourage market growth but may also lead to interest rate adjustments. - - Interest rate changes can affect market capitalization due to changes in investment and borrowing costs, which can have a cooling effect on stock valuations. - -4. **Historical Context and Base Rates**: - - Historically, rapid increases in the Buffett Indicator are relatively rare and typically associated with specific economic conditions. - - Notably, surpassing 205% would signal extreme market valuations akin to pre-dot-com bubble levels. - -5. **Timeframe to Resolution**: - - As of October 21, we have less than a month for the indicator to rise by 2.48 percentage points to surpass 205%. - - Weekly and daily fluctuations in market values and GDP calculations do occur, but this is a significant leap in a short time, indicating lower baseline likelihood. - -6. **Base Case and Default Likelihood**: - - If no significant market shifts or external shocks occur, the default probability would suggest that the indicator remains close to where it is now, around 202.52%. - - Given strong recent market performance, there might be an upward trend, but it would need to be quite consistent and robust to reach 205%. - -7. **Market Volatility and High Uncertainty**: - - Financial markets are inherently volatile, and unexpected political or economic events could drive rapid changes in market capitalization. - -8. **Probability Distribution and Uncertainty**: - - Factor in prior predictions' overconfidence bias by giving a more conservative estimate for ""Yes"". - - The probability of sudden drastic events impacting this metric should be considered relatively low in the absence of current signals toward such events. - -Combining all these factors, we account for a strong sense of uncertainty but also acknowledge the unlikely nature of achieving such a significant leap in under a month. Considering historical precedent, ongoing trends, and potential variability, a conservative estimate is warranted. - -**Probability Distribution**: -- If considering a shorter timeline (e.g., 1 week), the likelihood of reaching 205% is lower, perhaps around 10%. -- Extending to 6 months, the probability could increase but still remain moderate due to typical long-term mean reversion tendencies in valuations, possibly around 25%. - -Finally, based on the current data and historical context, the forecast for exceeding 205% before the resolution date is: - -**Probability: 15%**" -29011,"Will US airline passenger volume for the Veterans Day weekend of 2024 be up year over year, according to the TSA?","To forecast the likelihood of US airline passenger volume for the Veterans Day weekend of 2024 exceeding the volume of the previous year, we can break down the problem using a structured approach: - -1. **Contextual Understanding and Initial Assessment:** - - - **Veterans Day Timing:** In 2024, Veterans Day falls on a Monday. Compared to 2023, when it was on a Saturday, this can create a longer travel weekend for some people, which could slightly increase travel numbers. - - - **Travel Trends:** There's an overall trend in increasing air travel, as noted by the TSA’s record screenings in 2024 thus far. An increase of 6.3% over the same period in 2023 is significant. - -2. **Historical Data and Base Rates:** - - - **Historical Data (2023):** The passenger volume from Friday Nov 10, 2023, to Tuesday Nov 14, 2023, was 11,870,141. - - - **Comparative Data (2024):** To resolve positively, the 2024 volume needs to exceed the 11,870,141 figure. - -3. **Trend Analysis:** - - - **Base Rate:** Historical growth rates in such metrics can be slightly optimistic due to general growth trends in travel. - - - **Increasing Demand:** The steady increase in air travel and the holiday mode can help boost volumes. - -4. **Potential Variability and Risk:** - - - **Economic or External Factors:** Potential factors such as economic downturns, geopolitical disruptions, or major weather events could impact travel, though no imminent issues are currently evident to severely disrupt the travel plans. - -5. **Recent Data Insight:** - - - **2024 Passenger Growth:** The growth in passenger volume for the year so far (678 million screenings, a 6.3% increase) is a strong indicator that overall travel volume is likely to maintain or exceed previous benchmarks, barring unforeseen events. - -6. **Considering Uncertainty:** - - - **Statistical Uncertainty:** Variance in day-to-day TSA figures can be influenced by a myriad of micro factors. - - - **Compensate for Overconfidence:** Despite positive indicators, past overconfidence requires reserving a portion of doubt. - -7. **Probability Assessment Across Timelines:** - - - **Immediate Assessment:** Higher probability of a positive resolution comes from upcoming holiday travel dynamics and the mentioned growth trends. - - - **Month-Long Outlook:** Assuming current trends hold, the probability remains roughly constant over an extended period. - -8. **Final Probability Calculation:** - - - **Calculating Based on Factors:** Balancing growth trends, holiday timing, and surrounding external factors, a reasonable estimate can be formed. - -Taking all factors into account, including historical trends, current data, and the need to adjust for past overconfident forecasts, a comprehensive evaluation suggests that the probability of passenger volumes increasing year-over-year for Veterans Day weekend 2024 is: - -**Probability: 70%**" -29010,"On October 31, 2024, will Nvidia's market capitalization be larger than Apple's?","Let's approach this step-by-step: - -1. Current situation: - - Nvidia's market cap: $3.385 trillion (as of October 20, 2024) - - Apple's market cap: $3.6 trillion - - Gap: $215 billion (about 6% of Nvidia's current value) - -2. Time frame: - - Today's date: October 21, 2024 - - Resolution date: October 31, 2024 - - Only 10 days for change to occur - -3. Recent performance: - - Nvidia up 179% in 2024, 13.6% in October - - Apple's recent performance not specified, but still ahead - -4. Analyst projections: - - Positive for Nvidia, with price targets suggesting 18-28% growth potential - -5. Upcoming events: - - Major tech earnings reports at end of October could impact Nvidia - -6. Historical precedent: - - Nvidia has shown extraordinary growth, but overtaking the world's most valuable company in just 10 days is extremely rare - -7. Base rate consideration: - - Changes in market cap rankings among top companies typically occur over months or years, not days - -8. Uncertainty factors: - - Short timeframe increases uncertainty - - Tech earnings reports could cause significant market movements - -9. Probability distribution: - - Given the short timeframe, the most likely outcome is the status quo - - However, Nvidia's recent volatility and upcoming tech earnings introduce some uncertainty - -10. Alternative timeframes: - - 1 month: slightly higher probability, maybe 15% - - 3 months: higher probability, perhaps 25% - - 1 year: significantly higher, possibly 40% - -11. Overconfidence adjustment: - - Given past overconfidence, we should lean towards a more conservative estimate - -Rationale: -While Nvidia has shown incredible growth and is closing the gap with Apple, the timeframe of just 10 days is extremely short for such a significant market cap shift. The $215 billion gap represents about 6% of Nvidia's current value, which would require an extraordinary and rapid shift in investor sentiment or a significant drop in Apple's value. - -The upcoming tech earnings reports introduce an element of uncertainty, but it's unlikely they would cause such a dramatic shift in such a short time. Historical precedent suggests that changes in market cap rankings among top companies typically occur over longer periods. - -Given the short timeframe and the significant gap to overcome, the probability of Nvidia surpassing Apple in market cap by October 31, 2024, is low. However, considering Nvidia's recent performance and analyst projections, it's not impossible. - -Adjusting for potential overconfidence and the tendency for questions to resolve negatively more often than expected, I would estimate the probability as follows: - -Probability: 8% - -This low probability reflects the significant challenges Nvidia faces in overtaking Apple's market cap in just 10 days, while still acknowledging a small but real possibility of it occurring due to Nvidia's strong momentum and potential market-moving events." -29013,"Will Tesla deliver more than 450,000 vehicles in Q4 2024? ","To forecast whether Tesla will deliver more than 450,000 vehicles in Q4 2024, let's analyze the situation using the background and context provided. - -**1. Historical trends and base rates:** -- In Q4 2023, Tesla delivered 484,507 vehicles, which is crucial because it surpassed 450,000 vehicles in the same quarter last year. -- In 2024, Tesla's deliveries have been fluctuating: 386,810 in Q1, 443,956 in Q2, and 462,890 in Q3. There's a notable increase from Q2 to Q3 2024. -- Historically, Q4 tends to have higher deliveries for Tesla, possibly due to production optimizations and demand increases at year-end. - -**2. Current performance and targets:** -- Tesla reported a delivery of 462,890 vehicles in Q3 2024, which shows they're approaching the 450,000-delivery threshold again. -- However, Tesla aims to deliver 2 million vehicles in 2024, which means they need over 515,000 vehicles in Q4 to meet this goal. This is substantially higher than their Q3 deliveries and historically challenging. - -**3. Market conditions:** -- Tesla faces increased competition and economic uncertainty, impacting the broader market. This could curb demand and impact delivery numbers. -- Despite these factors, Tesla's year-over-year performance still shows growth, indicating resilience. - -**4. Other considerations:** -- Production capacity and logistical constraints need to be evaluated. If Tesla can maintain or increase its production pace from Q3, delivering over 450,000 in Q4 becomes more plausible. -- Wall Street expectations slightly exceeded Q3's actual results, which suggests that experts anticipate a challenging Q4. - -**5. Psychological bias and calibration:** -- Past competitions have shown a pattern of overconfidence in forecasts, suggesting the need for a conservative adjustment. -- Historically, about 35% of questions resolving positively means there’s a bias towards negative outcomes. - -**Conclusion:** -Taking all factors into account, the likelihood of Tesla delivering over 450,000 vehicles in Q4 2024 is stronger than for some periods in 2024, but not a certainty. Given Q4's historical strength, the recent upward delivery trend, and Tesla's surge strategies towards the end of the year, we should lean moderately positive without excessive overconfidence. - -**Prediction timeline evaluation:** -- Within one month: Limited additional data will be available, keeping likelihood modestly uncertain around high 30s%. -- Entire quarter (before resolution date): As production scales towards year-end, a positive outcome becomes increasingly likely, accounting for seasonal trends and production increases. - -Therefore, combining the historical data, production, market expectations, and past behavioral biases, we arrive at the final prediction: - -**Probability: 60%**" -29009,"Will Doug Pederson cease to be the Jacksonville Jaguars head coach before January 1, 2025?","To accurately forecast whether Doug Pederson will cease to be the Jacksonville Jaguars head coach before January 1, 2025, let's assess the situation and break down the contributing factors step-by-step: - -1. **Current Performance and Market Speculation:** - - The Jaguars' poor performance (1-5) this season contributes to speculation about a possible coaching change. Betting odds have Pederson as the favorite to be fired next. These indicators suggest a higher-than-normal risk for Pederson, but betting odds commonly reflect short-term sentiment, not long-term decision-making. - -2. **Owner's Support:** - - Shad Khan, owner of the Jaguars, has publicly supported Pederson. In prior coaching decisions, owners' public statements can give insight into their likelihood of making a mid-season coaching change. Public support, while often shifting, tends to weigh against a coach being fired in the immediate term since owners consider continuity important. - -3. **Expert Opinions and Precedents:** - - Expert opinions are mixed, without a strong consensus that Pederson is on the brink of being fired. Additionally, while there is precedent (as seen with the Jets) for mid-season firings, they are less common and usually follow sustained poor performance without improvement. - -4. **Timeframe and Base Rate Analysis:** - - The timeframe for this question is limited to just over two months. This shorter window means the likelihood of significant shifts in team performance or the owner's stance, which could induce a firing, is lowered. Historically, in-season firings do occur, but not as frequently as firings at the end of a season. - - The default scenario without change implies Pederson remains as coach, considering firings tend to happen at season's end. - -5. **Distribution Across Timeframes:** - - If Pederson will cease to be the head coach, it is more probable to occur at season's end. Therefore, the likelihood of this change occurring before January 1, 2025, should consider the variance across typical NFL firing windows, and it should factor in the rarity of December in-season firings compared to post-season firings. - -6. **Adjusting for Overconfidence:** - - Given past tendencies toward overconfidence, I will adjust my initially considered probabilities toward a more moderate estimate, recognizing the inherent uncertainty. - -After evaluating all the factors: - -- The Jaguars' current poor performance and market speculation increase the probability of a firing. -- Owner support and the historical tendency to maintain coaches through the end of a season, counterbalance this. -- The short resolution timeframe further reduces the probability of a mid-season firing. - -My final probability estimation is a balanced assessment of these considerations: - -Probability: 20%" -29008,"Will the CBOE S&P 500 3-Month Volatility Index (VXVCLS) be higher Year-over-Year on October 30, 2024?","To determine the probability that the CBOE S&P 500 3-Month Volatility Index (VXVCLS) will be higher year-over-year on October 30, 2024, let's break down the information and analysis step by step. - -1. **Current and Historical Values Analysis**: - - The current value of VXVCLS on October 20, 2024, is 21.35. - - On October 30, 2023, it was 19.12. - - The current value is already higher, suggesting a need for something substantial to push it back below the prior year's figure. - -2. **Market Trends and Indicators**: - - Recent volatility increases due to inflation fears and geopolitical tensions suggest a current upward bias in volatility. These factors push the VXVCLS higher. - - The S&P 500's increased volatility in the past month supports the likelihood of a sustained higher trend in volatility indices as these conditions persist. - -3. **Expert Opinions**: - - Analysts and market strategists predict short-term elevated volatility affected by upcoming U.S. elections and continued economic uncertainties. - - A potential further increase in volatility indexes like VXVCLS is linked to fears of additional Federal Reserve rate hikes. - -4. **Timeframe and Volatility of Index**: - - The timeframe until resolution is quite short—just 10 days. This limits the potential for significant market conditions to shift back towards a lower volatility environment. - - Volatility is inherently variable, but recent trends suggest continuation. - -5. **Base Rates and Historical Precedents**: - - While recognizing my tendency towards overconfidence, I need to account for the historical overestimation of positive outcomes (only 35% of questions resolved positively in past competitions). - - However, given the current VXVCLS index being higher than last year's and the sustained near-term uptick in market volatility, maintaining a probability above 50% seems justified. - -6. **Extreme Predictions Avoidance and Confidence Adjustment**: - - Although the present conditions are favorable for a positive resolution, acknowledging the potential for reversals or changes under a volatile market context is necessary. - - Aiming for a balanced forecast, integrating potential shifts in market fear levels or unexpected calming measures by central banks or geopolitical developments. - -**Conclusion**: Given the above analysis, emphasizing the limited time until the resolution and the current market dynamics, I will assign a probability midway between where pure data and cautious historical performance warrant confidence in predicting a positive resolution. - -**Probability: 67%**" -29007,"Will Nippon Steel acquire US Steel before January 1, 2025?","To forecast whether Nippon Steel will acquire US Steel before January 1, 2025, I'll systematically analyze the available information, account for similar events, and use historical precedents to establish a probability. - -### 1. **Understanding the Situation:** -From the background information, we have several key elements: -- **Arbitration Outcome:** Positive for the acquisition, which is generally indicative of a reduced barrier. -- **Regulatory Review:** Reapplication shows Nippon Steel is actively pursuing necessary approvals. This is typical in such deals but doesn't guarantee resolution in the short term. -- **Union Agreements:** Generally lowers labor-related barriers. -- **National Security Reviews:** This is a significant hurdle. The Committee on Foreign Investment in the United States (CFIUS) can indeed delay or even block a transaction if national security concerns are not adequately mitigated. The expressed opposition by President Biden and other political figures adds to potential obstacles. -- **Timeline and Confidence:** While US Steel's CEO expresses optimism, the complexity and stage of regulatory issues often introduce delays. - -### 2. **Historical and Base Rate Analysis:** -- **Base Rate of Acquisition Deals:** Historically, a significant portion of international acquisitions involving substantial national security reviews encounter delays, with some being outright blocked. However, many also manage to resolve these issues, albeit sometimes later than initially planned. -- **Timeframe Analysis:** With just over 2 months remaining until the resolution date, this adds a substantial time constraint for the transaction to clear regulatory hurdles, which are notoriously time-consuming. Regulatory and political factors complicate this further. - -### 3. **Probability Distribution and Uncertainty:** -Due to the high stakes involved with national security reviews, such reviews can often push final decisions past hopeful deadlines. This scenario echoes past acquisitions in similar contexts. My conservative prior, considering historical precedents, is that although many deals eventually go through, it is common for such complicated cases to stretch timelines. - -### 4. **Recent Trends and Developments:** -- No recent Specific updates indicate a breakthrough in the regulatory review process. -- The consistent move of Nippon Steel to reapply and conform with CFIUS's requests shows determination but not immediate resolution. - -### 5. **Probability Evaluation:** -Considering all elements: -- **Short-term Likelihood (by January 1, 2025):** The obstacles like CFIUS and political opposition seem substantial and unresolved. Given the limited timeframe, a probability of completion before the deadline is modest. I would estimate this at approximately 30%. -- **Extended Period Likelihood (by mid-2025):** The likelihood increases without tight deadlines. Extending beyond the stipulated deadline might see a higher probability as hurdles are systematically addressed. - -### Conclusion: -Given the intense scrutiny, regulatory concerns, and the complexity typical of such large-scale international deals, while there is potential for a positive outcome, the conclusion before the specified deadline is not excessive. Hence, an accounting of these factors and correcting for overconfidence: - -**Probability: 30%**" -28999,"Will a book written by a language model make the NY Times Best Seller list before 2025? - -","Let's approach this step-by-step: - -1. Current state: As of October 21, 2024, no book written by a language model has made the NY Times Best Seller list. - -2. Time frame: We have approximately 2 months and 10 days until the resolution date (January 1, 2025). - -3. Technological progress: Language models have been improving rapidly, but creating a bestselling book requires more than just coherent text generation. - -4. Publishing industry dynamics: Traditional publishing cycles often take months or years from manuscript completion to bestseller list appearance. - -5. Market acceptance: There might be resistance from readers or the publishing industry to AI-generated books. - -6. NY Times Best Seller list criteria: The list considers sales figures, which require significant marketing and distribution efforts. - -7. Precedent: No AI-generated book has made the list so far, setting a strong base rate against this happening. - -8. Novelty factor: If such a book were to be published, it might generate curiosity and boost sales. - -9. Ethical and disclosure considerations: There might be debates about whether AI authorship should be disclosed, potentially affecting sales or list eligibility. - -10. Short timeframe: The extremely short period until resolution (about 70 days) significantly reduces the likelihood of this occurring. - -Let's consider different timeframes: - -- Probability of happening in the next month (by November 21, 2024): 0.1% -- Probability of happening in the next 6 months (by April 21, 2025): 2% -- Probability of happening in the next year (by October 21, 2025): 5% - -Given the short timeframe for our question (by January 1, 2025), we need to adjust our probability downward from the 6-month estimate. - -Factors reducing probability: -- Very short timeframe -- No current indications of such a book in the pipeline -- Publishing industry's typical slow pace - -Factors increasing probability: -- Rapid AI advancement -- Potential novelty appeal - -Considering the base rate (no such occurrences so far), the short timeframe, and the multiple hurdles that would need to be overcome (writing, publishing, marketing, achieving bestseller status), I believe the probability is very low, but not impossible. - -Adjusting for my historical overconfidence and the fact that novel technological breakthroughs can sometimes happen unexpectedly, I will slightly increase my initial estimate. - -Probability: 1%" -28998,"Will a nuclear weapon be detonated as an act of war before January 1, 2025? -","Thank you for the comprehensive briefing. I'll approach this question step-by-step, considering various factors and timelines. - -1. Historical context: - - No nuclear weapons have been used in war since 1945, despite numerous conflicts and tensions. - - The base rate for nuclear weapon use in war is extremely low: 2 instances in ~79 years of nuclear weapon existence. - -2. Current global situation: - - Heightened tensions involving nuclear-armed states, particularly Russia-Ukraine conflict and North Korea's threats. - - Iran's potential nuclear capabilities development. - - However, deterrence mechanisms and international pressure against nuclear use remain strong. - -3. Time frame consideration: - - Only about 2.5 months until the resolution date. - - Short timeframe reduces the likelihood of significant escalation leading to nuclear use. - -4. Geopolitical factors: - - Russia-Ukraine conflict: Despite threats, actual use remains unlikely due to global consequences. - - North Korea: Historically prone to threats but has not followed through. - - Iran: Still some time away from weapon capability, unlikely to develop and use within 2.5 months. - -5. Deterrence and international response: - - Strong global opposition to nuclear weapon use. - - Potential for swift and severe consequences for any state using nuclear weapons. - -6. Technical and logistical considerations: - - Preparation and decision-making process for nuclear weapon use is complex and time-consuming. - - Likelihood of accidental or unauthorized use is extremely low due to safeguards. - -7. Alternative timelines: - - 1 month probability: 0.1% (even less likely in shorter term) - - 1 year probability: 1% (slightly higher due to more time for escalation) - - 5 year probability: 3% (allowing for more geopolitical changes and potential conflicts) - -8. Uncertainty factors: - - Unpredictable nature of global events and potential for rapid escalation. - - Limited public information on nuclear states' decision-making processes. - -9. Overconfidence adjustment: - - Given the extremely low base rate and short time frame, it's crucial to avoid overconfidence in a negative outcome. - -Rationale: -The historical precedent strongly suggests that nuclear weapon use in war is extremely unlikely. Despite current global tensions, the short timeframe until resolution (2.5 months) significantly reduces the probability of escalation to nuclear conflict. The strong deterrence factors, international opposition, and the complex nature of nuclear weapon deployment all work against the likelihood of use. - -However, we cannot completely discount the possibility due to the unpredictable nature of global events and the existence of nuclear-armed states in conflict zones. The slightly higher probabilities for longer time frames reflect the increased uncertainty over extended periods. - -Adjusting for potential overconfidence and considering the historically low resolution rate for positive outcomes in similar forecasts, I will err on the side of a slightly higher probability than my initial instinct might suggest. - -Probability: 1% - -This low probability reflects the extremely unlikely nature of the event within the given timeframe, while still acknowledging that it's not entirely impossible given the current global situation." -28997,"Will a senolytic therapy be approved for commercial sale by the US FDA before January 1, 2025?","Thank you for the detailed instructions. I'll approach this question step-by-step, considering various factors and timelines. - -1. Current state of development: - - Unity Biotechnology's most advanced senolytic therapy (UBX0101) is in Phase 1 clinical trials. - - Typical drug development timeline: - - Phase 1: 1-2 years - - Phase 2: 2-3 years - - Phase 3: 2-4 years - - FDA review: 6-10 months - -2. Time remaining until resolution date: - - Today's date: October 21, 2024 - - Resolution date: January 1, 2025 - - Time remaining: Approximately 2 months and 10 days - -3. Historical precedent: - - FDA approval process typically takes 10-15 years from discovery to approval. - - Fast-track designations can speed up this process, but still usually take several years. - -4. Novel factors: - - Increasing interest in longevity and anti-aging therapies. - - Potential pressure for faster approvals due to aging populations in developed countries. - - However, novel mechanism of action may require more scrutiny from FDA. - -5. Base rate consideration: - - Very few drugs make it through all clinical trial phases to approval. - - Approximately 10% of drugs that enter clinical trials eventually get approved. - -6. Alternative timelines: - - Probability of approval within 1 month: <0.1% - - Probability of approval within 6 months: ~0.5% - - Probability of approval within 1 year: ~1% - - Probability of approval within 3 years: ~5% - -7. Uncertainty factors: - - Unpublished data from ongoing trials. - - Potential for unexpected breakthroughs or setbacks. - - Changes in FDA leadership or policies. - -Given the extremely short timeframe until the resolution date (about 2 months), the early stage of current clinical trials, and the typical length of the FDA approval process, it is highly improbable that a senolytic therapy will be approved before January 1, 2025. - -Even considering potential fast-tracking or breakthrough designations, the remaining time is insufficient for completing necessary clinical trials and FDA review. The novelty of the therapy may actually increase scrutiny and potentially lengthen the approval process. - -The base rate of drug approvals and the typical timeline for bringing a drug to market both strongly suggest that approval within this timeframe is extremely unlikely. While there is growing interest in anti-aging therapies, this is unlikely to dramatically accelerate the approval process to the degree necessary for a positive resolution. - -Adjusting for my historical overconfidence and the general tendency for questions to resolve negatively, I will slightly increase the probability from what my initial analysis suggests. However, given the extremely short timeframe, this adjustment is minimal. - -Probability: 1% - -This very low probability reflects the extremely unlikely nature of FDA approval occurring within the next 2 months, given the current state of senolytic therapy development and the typical drug approval process." -29074,Will the Schwab Trading Activity Index be lower for October 2024 than it was for September 2024?,"To forecast whether the Schwab Trading Activity Index (STAX) will be lower for October 2024 than it was for September 2024, let's consider the factors at play: - -1. **Recent Market Trends**: - - The S&P 500 and Dow Jones Industrial Average reaching new highs suggests strong market performance. - - Robust economic indicators like retail sales and GDP growth point to a buoyant economy. - - Anticipation of a Federal Reserve rate cut could lead to increased investor optimism, potentially spurring trading activity. - -2. **Investor Behavior in September**: - - The STAX decreased in September, indicating reduced trading activity. This was attributed to risk reduction and profit-taking amid a surprise rate cut and economic news. - - A shift from equities to fixed income in September shows a cautious stance that could persist if uncertainties remain. - -3. **October Outlook**: - - Strong economic data and market performance might boost investor confidence, possibly increasing trading activity in October. - - However, the cautious behavior observed in September might continue, especially with anticipated economic data releases. - -Considering the above: - -- **Base Rate Consideration**: With the STAX at 47.10 in September, a decrease would require consistent cautious behavior from investors despite macro-economic indicators suggesting otherwise. - -- **Timing and Volatility Consideration**: With the resolution date on November 7, there is little time left for patterns to change significantly unless immediate economic shocks occur. Historically, extreme volatility in such indices is less common toward the year's end. - -- **Uncertainty Management and Overconfidence Check**: Historically, my predictions have tended towards overconfidence, and extreme predictions get penalized heavily under Brier scoring if incorrect. Therefore, I must hedge slightly more conservatively. - -**Consolidation of Information**: -- Given the strong economic performance and market highs, along with the anticipation of potential policy changes like a Fed rate cut, a significant decrease in STAX seems less likely, assuming investors regain confidence. -- However, the continuation of September's cautious sentiment into October, though partially offset by positive indicators, still presents a chance for lower trading activity. - -Based on the balance of these factors: - -- **Assessment**: While there are potential factors that could decrease the STAX, the likelihood suggests it may stabilize or increase slightly rather than decrease further. Thus, the probability of the STAX being lower for October compared to September leans towards a 'No' resolution. - -Final Probability Assessment: -- Probability that STAX will be lower in October than September: 40% - -Thus, my final answer is: ""Probability: 40%""" -29073,"Will Travis Kelce and Taylor Swift officially announce their engagement before January 1, 2025?","To forecast the likelihood of Travis Kelce and Taylor Swift officially announcing their engagement before January 1, 2025, we need to break down various factors into a coherent analysis. Here’s the step-by-step process: - -### 1. Analysis of Background Information -- **Current Relationship Status**: They've been dating for over a year, which indicates a level of seriousness, but such relationships do not always translate to imminent engagements. - -- **Engagement Speculations**: Although there’s fan speculation and media buzz, both Kelce and Swift have not indicated any immediate plans for an engagement. Kelce explicitly deflected engagement questions as early as February 2024, opting to focus on his professional commitments. - -- **Recent Developments**: Current sightings and reports suggest a strong, ongoing relationship. However, the lack of recent updates indicating an imminent engagement is notable. - -- **Fan and Media Speculation**: Often, fan speculations are not reliable indicators unless there are strong supporting signs, such as official statements or leaks from credible insiders. - -### 2. Temporal Factors -- **Timeframe**: About 2.5 months remain until the resolution date. While this is enough time for a couple to decide and announce an engagement, there is a limited precedent for quick engagements following a period of deflection, especially without recent rumors or leaks moving toward more certainty. - -### 3. Base Rates and Historical Precedent -- Relationships involving high-profile celebrities often move at a pace balanced between personal affection and public scrutiny. Swift and Kelce, both being very public figures, might avoid impulsive announcements. -- Historically, celebrity engagements after a year of dating aren’t uncommon but typically come with increasing speculation and leaks in the lead-up, which are absent here. - -### 4. Probability Distribution and Uncertainty Analysis -Considering the recency and weight of each piece of information, as well as comparing it with historical precedents, here's a structured breakdown: - -- **Base Probability of Engagement**: In the general population, a 1-year relationship might escalate to engagement within 2-3 months at perhaps 20-30%. High-profile celebrity relationships might have slightly higher volatility, but also more caution due to public and career implications. - -- **Impact of Limited Timeframe**: Given the limited time frame and absent signals, this probability should be considerably adjusted downward. - -- **Adjustments for Overconfidence**: Historically, only about 35% of similar ""will it happen"" questions resolve positively, suggesting a further downward adjustment. - -### 5. Conclusion and Probability Calculation - -Taking into account the lack of concrete evidence of an impending engagement, the past behavior of both individuals in similar circumstances, and my own tendency toward overconfidence, a cautious estimate is required. - -Thus, a reasonable probability for an engagement announcement by January 1, 2025, is quite modest: - -**Probability: 15%** - -This estimation reflects a balance between the plausible ongoing seriousness of the relationship and the existing lack of substantive current indicators of an engagement." -29068,Will RCP's polling bias for Trump based on national polls in the month before the 2024 election be greater than 1.0 percentage points?,"To accurately forecast the probability distribution for Trump's polling bias as described in the questions, I'll take the following steps: - -1. **Historical Context and Base Rates**: Looking at the previous election cycles, 2016 and 2020 both showed a significant bias against Trump in polls, whereas the 2022 midterms showed almost no bias. This context suggests that while biases have historically existed, they can be corrected over time, indicating a broad uncertainty about the magnitude of the bias this year. - -2. **Current Polling Trends**: The race for 2024 appears very close, with various polls indicating no strong lead for either candidate nationally. This closeness makes any potential bias significant but does not necessarily indicate in which direction it will trend. - -3. **Methodological Adjustments**: Polling firms have made substantial procedural adjustments to address past inaccuracies, especially efforts to better reach Republican voters and account for demographic shifts. Despite these efforts, skepticism remains about whether these adjustments might overcorrect or still fall short. - -4. **Shy Trump Voter Hypothesis**: The notion that some Trump supporters might not disclose their preference remains contested. While it affected past results, if pollsters have learned from previous elections, the impact could be less pronounced. - -5. **Recency and Timeliness**: With the current proximity of the election (less than a month away), there's little time for some of these polling biases to shift dramatically assuming current trends hold. Polls typically become more accurate as an election approaches. - -6. **Distribution of Probabilities**: Understanding the question structure, most potential bias values cluster around small differences due to historical underestimation corrections. Given this, the likelihood of a large bias exceeding 1 pp is reduced. - -Considering these factors, I develop a probability distribution that's aware of both historical patterns and current circumstances, without overconfidence in large shifts or extreme changes: -- Greater than 1 pp (Question 29068): Given historical bias, previous corrections, and the current close race, I place this at a lower probability, representing the chance of significant error despite corrections and adjustments. Probability: 10%. -- 0 to 1 pp (Question 29067): Represents a moderate clustering of results where polls could underestimate Trump's support, weighted against recent polls' tightening and historical correction efforts. Probability: 35%. -- Greater than -1 to 0 pp (Question 29066): This range includes minor underestimations or overestimations corrected noticed in recent elections, likely given increased poll accuracy. Probability: 40%. -- -2 to -1 pp (Question 29065): Reflects historically larger errors, though methodological advances and past forecasting improvements suggest fewer major misestimations today. Probability: 10%. -- Less than -2 pp (Question 29064): These unlikely large errors lack recent historical precedent, especially with noted improvements and better poll accuracy tactics. Probability: 5%. - -- Adjacent unlisted categories (5%): Accommodates possibilities outside these specific ranges, including potential large underestimations or overestimations. - -Overall, I provide an even distribution based on known information, correcting for prior overconfidence tendencies." -29067,Will RCP's polling bias for Trump based on national polls in the month before the 2024 election be greater than or equal to 0 percentage points and less than or equal to 1.0 percentage points?,"To accurately forecast the probability distribution for Trump's polling bias as described in the questions, I'll take the following steps: - -1. **Historical Context and Base Rates**: Looking at the previous election cycles, 2016 and 2020 both showed a significant bias against Trump in polls, whereas the 2022 midterms showed almost no bias. This context suggests that while biases have historically existed, they can be corrected over time, indicating a broad uncertainty about the magnitude of the bias this year. - -2. **Current Polling Trends**: The race for 2024 appears very close, with various polls indicating no strong lead for either candidate nationally. This closeness makes any potential bias significant but does not necessarily indicate in which direction it will trend. - -3. **Methodological Adjustments**: Polling firms have made substantial procedural adjustments to address past inaccuracies, especially efforts to better reach Republican voters and account for demographic shifts. Despite these efforts, skepticism remains about whether these adjustments might overcorrect or still fall short. - -4. **Shy Trump Voter Hypothesis**: The notion that some Trump supporters might not disclose their preference remains contested. While it affected past results, if pollsters have learned from previous elections, the impact could be less pronounced. - -5. **Recency and Timeliness**: With the current proximity of the election (less than a month away), there's little time for some of these polling biases to shift dramatically assuming current trends hold. Polls typically become more accurate as an election approaches. - -6. **Distribution of Probabilities**: Understanding the question structure, most potential bias values cluster around small differences due to historical underestimation corrections. Given this, the likelihood of a large bias exceeding 1 pp is reduced. - -Considering these factors, I develop a probability distribution that's aware of both historical patterns and current circumstances, without overconfidence in large shifts or extreme changes: -- Greater than 1 pp (Question 29068): Given historical bias, previous corrections, and the current close race, I place this at a lower probability, representing the chance of significant error despite corrections and adjustments. Probability: 10%. -- 0 to 1 pp (Question 29067): Represents a moderate clustering of results where polls could underestimate Trump's support, weighted against recent polls' tightening and historical correction efforts. Probability: 35%. -- Greater than -1 to 0 pp (Question 29066): This range includes minor underestimations or overestimations corrected noticed in recent elections, likely given increased poll accuracy. Probability: 40%. -- -2 to -1 pp (Question 29065): Reflects historically larger errors, though methodological advances and past forecasting improvements suggest fewer major misestimations today. Probability: 10%. -- Less than -2 pp (Question 29064): These unlikely large errors lack recent historical precedent, especially with noted improvements and better poll accuracy tactics. Probability: 5%. - -- Adjacent unlisted categories (5%): Accommodates possibilities outside these specific ranges, including potential large underestimations or overestimations. - -Overall, I provide an even distribution based on known information, correcting for prior overconfidence tendencies." -29066,Will RCP's polling bias for Trump based on national polls in the month before the 2024 election be greater than -1.0 and less than 0 percentage points?,"To accurately forecast the probability distribution for Trump's polling bias as described in the questions, I'll take the following steps: - -1. **Historical Context and Base Rates**: Looking at the previous election cycles, 2016 and 2020 both showed a significant bias against Trump in polls, whereas the 2022 midterms showed almost no bias. This context suggests that while biases have historically existed, they can be corrected over time, indicating a broad uncertainty about the magnitude of the bias this year. - -2. **Current Polling Trends**: The race for 2024 appears very close, with various polls indicating no strong lead for either candidate nationally. This closeness makes any potential bias significant but does not necessarily indicate in which direction it will trend. - -3. **Methodological Adjustments**: Polling firms have made substantial procedural adjustments to address past inaccuracies, especially efforts to better reach Republican voters and account for demographic shifts. Despite these efforts, skepticism remains about whether these adjustments might overcorrect or still fall short. - -4. **Shy Trump Voter Hypothesis**: The notion that some Trump supporters might not disclose their preference remains contested. While it affected past results, if pollsters have learned from previous elections, the impact could be less pronounced. - -5. **Recency and Timeliness**: With the current proximity of the election (less than a month away), there's little time for some of these polling biases to shift dramatically assuming current trends hold. Polls typically become more accurate as an election approaches. - -6. **Distribution of Probabilities**: Understanding the question structure, most potential bias values cluster around small differences due to historical underestimation corrections. Given this, the likelihood of a large bias exceeding 1 pp is reduced. - -Considering these factors, I develop a probability distribution that's aware of both historical patterns and current circumstances, without overconfidence in large shifts or extreme changes: -- Greater than 1 pp (Question 29068): Given historical bias, previous corrections, and the current close race, I place this at a lower probability, representing the chance of significant error despite corrections and adjustments. Probability: 10%. -- 0 to 1 pp (Question 29067): Represents a moderate clustering of results where polls could underestimate Trump's support, weighted against recent polls' tightening and historical correction efforts. Probability: 35%. -- Greater than -1 to 0 pp (Question 29066): This range includes minor underestimations or overestimations corrected noticed in recent elections, likely given increased poll accuracy. Probability: 40%. -- -2 to -1 pp (Question 29065): Reflects historically larger errors, though methodological advances and past forecasting improvements suggest fewer major misestimations today. Probability: 10%. -- Less than -2 pp (Question 29064): These unlikely large errors lack recent historical precedent, especially with noted improvements and better poll accuracy tactics. Probability: 5%. - -- Adjacent unlisted categories (5%): Accommodates possibilities outside these specific ranges, including potential large underestimations or overestimations. - -Overall, I provide an even distribution based on known information, correcting for prior overconfidence tendencies." -29065,Will RCP's polling bias for Trump based on national polls in the month before the 2024 election be greater than or equal to -2.0 and less than or equal to -1.0 percentage points?,"To accurately forecast the probability distribution for Trump's polling bias as described in the questions, I'll take the following steps: - -1. **Historical Context and Base Rates**: Looking at the previous election cycles, 2016 and 2020 both showed a significant bias against Trump in polls, whereas the 2022 midterms showed almost no bias. This context suggests that while biases have historically existed, they can be corrected over time, indicating a broad uncertainty about the magnitude of the bias this year. - -2. **Current Polling Trends**: The race for 2024 appears very close, with various polls indicating no strong lead for either candidate nationally. This closeness makes any potential bias significant but does not necessarily indicate in which direction it will trend. - -3. **Methodological Adjustments**: Polling firms have made substantial procedural adjustments to address past inaccuracies, especially efforts to better reach Republican voters and account for demographic shifts. Despite these efforts, skepticism remains about whether these adjustments might overcorrect or still fall short. - -4. **Shy Trump Voter Hypothesis**: The notion that some Trump supporters might not disclose their preference remains contested. While it affected past results, if pollsters have learned from previous elections, the impact could be less pronounced. - -5. **Recency and Timeliness**: With the current proximity of the election (less than a month away), there's little time for some of these polling biases to shift dramatically assuming current trends hold. Polls typically become more accurate as an election approaches. - -6. **Distribution of Probabilities**: Understanding the question structure, most potential bias values cluster around small differences due to historical underestimation corrections. Given this, the likelihood of a large bias exceeding 1 pp is reduced. - -Considering these factors, I develop a probability distribution that's aware of both historical patterns and current circumstances, without overconfidence in large shifts or extreme changes: -- Greater than 1 pp (Question 29068): Given historical bias, previous corrections, and the current close race, I place this at a lower probability, representing the chance of significant error despite corrections and adjustments. Probability: 10%. -- 0 to 1 pp (Question 29067): Represents a moderate clustering of results where polls could underestimate Trump's support, weighted against recent polls' tightening and historical correction efforts. Probability: 35%. -- Greater than -1 to 0 pp (Question 29066): This range includes minor underestimations or overestimations corrected noticed in recent elections, likely given increased poll accuracy. Probability: 40%. -- -2 to -1 pp (Question 29065): Reflects historically larger errors, though methodological advances and past forecasting improvements suggest fewer major misestimations today. Probability: 10%. -- Less than -2 pp (Question 29064): These unlikely large errors lack recent historical precedent, especially with noted improvements and better poll accuracy tactics. Probability: 5%. - -- Adjacent unlisted categories (5%): Accommodates possibilities outside these specific ranges, including potential large underestimations or overestimations. - -Overall, I provide an even distribution based on known information, correcting for prior overconfidence tendencies." -29064,Will RCP's polling bias for Trump based on national polls in the month before the 2024 election be less than -2.0 percentage points?,"To accurately forecast the probability distribution for Trump's polling bias as described in the questions, I'll take the following steps: - -1. **Historical Context and Base Rates**: Looking at the previous election cycles, 2016 and 2020 both showed a significant bias against Trump in polls, whereas the 2022 midterms showed almost no bias. This context suggests that while biases have historically existed, they can be corrected over time, indicating a broad uncertainty about the magnitude of the bias this year. - -2. **Current Polling Trends**: The race for 2024 appears very close, with various polls indicating no strong lead for either candidate nationally. This closeness makes any potential bias significant but does not necessarily indicate in which direction it will trend. - -3. **Methodological Adjustments**: Polling firms have made substantial procedural adjustments to address past inaccuracies, especially efforts to better reach Republican voters and account for demographic shifts. Despite these efforts, skepticism remains about whether these adjustments might overcorrect or still fall short. - -4. **Shy Trump Voter Hypothesis**: The notion that some Trump supporters might not disclose their preference remains contested. While it affected past results, if pollsters have learned from previous elections, the impact could be less pronounced. - -5. **Recency and Timeliness**: With the current proximity of the election (less than a month away), there's little time for some of these polling biases to shift dramatically assuming current trends hold. Polls typically become more accurate as an election approaches. - -6. **Distribution of Probabilities**: Understanding the question structure, most potential bias values cluster around small differences due to historical underestimation corrections. Given this, the likelihood of a large bias exceeding 1 pp is reduced. - -Considering these factors, I develop a probability distribution that's aware of both historical patterns and current circumstances, without overconfidence in large shifts or extreme changes: -- Greater than 1 pp (Question 29068): Given historical bias, previous corrections, and the current close race, I place this at a lower probability, representing the chance of significant error despite corrections and adjustments. Probability: 10%. -- 0 to 1 pp (Question 29067): Represents a moderate clustering of results where polls could underestimate Trump's support, weighted against recent polls' tightening and historical correction efforts. Probability: 35%. -- Greater than -1 to 0 pp (Question 29066): This range includes minor underestimations or overestimations corrected noticed in recent elections, likely given increased poll accuracy. Probability: 40%. -- -2 to -1 pp (Question 29065): Reflects historically larger errors, though methodological advances and past forecasting improvements suggest fewer major misestimations today. Probability: 10%. -- Less than -2 pp (Question 29064): These unlikely large errors lack recent historical precedent, especially with noted improvements and better poll accuracy tactics. Probability: 5%. - -- Adjacent unlisted categories (5%): Accommodates possibilities outside these specific ranges, including potential large underestimations or overestimations. - -Overall, I provide an even distribution based on known information, correcting for prior overconfidence tendencies." -29072,"Will North Korea test a nuclear weapon before January 1, 2025?","When assessing the likelihood of North Korea conducting a nuclear test before January 1, 2025, several factors must be considered, including historical trends, recent developments, and the geopolitical context. Let's break down each of these factors: - -1. **Historical Context**: North Korea has conducted six nuclear tests between 2006 and 2017. Historically, these tests have occurred approximately every 2-3 years when they were active. The last test was in 2017, which suggests there has been a relatively long pause of six years. This could indicate either a strategic change, challenges in carrying out tests, or a change in objectives. - -2. **Current Geopolitical Tensions**: Recent developments include North Korea's escalating rhetoric and changes in its constitutional language towards South Korea, coupled with increased military enhancements. These actions could indicate a heightened military posture. However, they do not directly imply an imminent nuclear test, as similar rhetoric has occurred without resulting in a nuclear test in the past. - -3. **U.S. Election Influence**: The upcoming U.S. presidential election could be a factor. Historically, North Korea has used provocative actions, including missile tests, to attract attention from new administrations. However, these actions more often tend to be missile launches rather than nuclear tests, which are seen as significant escalations. - -4. **Global Reactions and Monitoring**: The global community, particularly through the International Monitoring System and satellite intelligence, closely watches North Korea's nuclear sites. While North Korea may attempt a test, the risk of preemptive actions or sanctions in response could be a deterrent, depending on their strategic calculation. - -5. **Timeframe**: The resolution date provides a slightly more than two-month window until January 1, 2025. This is a relatively short time frame in which significant geopolitical events or policy changes need to occur for a nuclear test to be a sensible action from North Korea's perspective. - -6. **Default Behavior**: The base rate of North Korean nuclear tests historically, and particularly the gap since 2017, suggests that the default is no test in any given year. There’s been no recent confirmed significant preparatory activity reported on major news or monitoring networks that explicitly points to an imminent test. - -7. **Uncertainty and Overconfidence Compensation**: Given past overconfidence in forecasting, it's essential to mitigate this by slightly adjusting expectations downward if initial instincts suggest higher likelihoods. - -Based on a synthesis of these factors, this is a familiar scenario for forecasting: while tensions and provocations are normative, nuclear tests are relatively rare. The presence of international deterrents, along with North Korea having alternative means of provocation, suggests testing is not the primary or preferred strategy. Considering the evidence and default behavior, I estimate the probability of North Korea conducting a nuclear test before January 1, 2025, at around: - -Probability: 25%" -29071,"Will the USDA-posted recall of Pork Dynasty Inc.'s Ready-to-Eat Fried Pork Skin Products issued September 27, 2024 be closed before December 31, 2024?","Let's approach this step-by-step: - -1. Recall Timeline: - - Issued: September 27, 2024 - - Current Date: October 23, 2024 - - Resolution Date: December 31, 2024 - - Time from issue to resolution: 95 days - -2. Scope of Recall: - - Small scale: 469 pounds of product - - Limited distribution: Only Oklahoma and Texas - - Reason: Misbranding and undeclared allergen (soy) - -3. Severity: - - No confirmed reports of adverse reactions - - Allergen-related, which is serious but not as severe as bacterial contamination - -4. Historical Data: - - While specific data isn't provided, FSIS recalls for labeling issues typically resolve faster than those for contamination - - Small-scale recalls generally close more quickly than large-scale ones - -5. Process Considerations: - - FSIS conducts recall effectiveness checks - - Company must notify customers and remove/destroy products - - Given the small scale, this process could be relatively quick - -6. Time Factors: - - 26 days have already passed since the recall was issued - - 69 days remain until the resolution date - - This is a significant amount of time for a small-scale recall - -7. Base Rate: - - Without specific data, we can estimate that most small-scale, allergen-related recalls would likely resolve within 3-4 months - -8. Potential Delays: - - Holiday season (November-December) might slow down administrative processes - - No information on company's efficiency or FSIS workload - -9. Probability Distribution: - - 1 month (from now): 30% - - 2 months (resolution date): 65% - - 3 months: 85% - - 6 months: 95% - -10. Adjustments: - - Given the overconfidence warning, we should adjust our 2-month estimate downward - - The 35% historical positive resolution rate suggests caution, but this doesn't directly apply to recall closures - -11. Final Considerations: - - The small scale and limited distribution favor a quicker resolution - - The allergen issue is serious but manageable - - Ample time remains before the resolution date - - Need to account for potential administrative delays - -After careful consideration, I believe there's a good chance the recall will be closed before December 31, 2024, but I want to account for potential delays and the historical tendency towards overconfidence. - -Probability: 58% - -This probability reflects a cautiously optimistic view that the recall will be closed before the deadline, considering the small scale and ample time, but also accounting for potential delays and the need to avoid overconfidence." -29070,"Will Spirit Airlines file for bankruptcy before January 1, 2025?","Let's approach this step-by-step: - -1. Current Situation: - - Spirit Airlines is in significant financial distress. - - They're in talks with bondholders about a potential Chapter 11 bankruptcy filing. - - They have a $3.3 billion debt load and haven't been profitable since before the COVID-19 pandemic. - - Recent deadline extension for debt renegotiation suggests urgency in their financial situation. - -2. Time Frame: - - Today is October 23, 2024. - - The resolution date is January 1, 2025. - - This gives about 2 months and 1 week for the situation to develop. - -3. Historical Context: - - Airline bankruptcies are not uncommon, especially for struggling carriers. - - However, bankruptcy is typically a last resort after other options have been exhausted. - -4. Recent Developments: - - Failed merger with JetBlue has left Spirit in a vulnerable position. - - Operational cuts (20% capacity reduction) indicate severe financial strain. - - CEO previously rejected bankruptcy speculation, but recent talks suggest it's now being seriously considered. - -5. Mitigating Factors: - - Spirit is still operating and even announcing new cheap flights. - - There's still time for potential turnaround strategies or last-minute financial arrangements. - -6. Time Frame Analysis: - - 2 months is a relatively short period for a major corporate decision like bankruptcy. - - However, given the urgency of their financial situation, it's not implausible. - -7. Probability Distribution: - - Let's consider the probability over different time frames: - - 1 month: 25% - - 2 months (our question timeframe): 40% - - 6 months: 60% - - 1 year: 75% - -8. Base Rate Consideration: - - Historically, airline bankruptcies are more likely during economic downturns or industry-wide crises. - - The current situation is specific to Spirit, which increases the likelihood compared to the base rate. - -9. Uncertainty Factors: - - Potential last-minute deals or government intervention. - - Unpredictable market conditions or fuel prices. - - Possible new investors or strategic partnerships. - -10. Overconfidence Adjustment: - - Given the complexity of corporate decisions and the short timeframe, we should be cautious about overconfidence. - - The 35% historical positive resolution rate suggests we should lean slightly towards a ""No"" outcome. - -Considering all these factors, I believe there is a significant chance of Spirit Airlines filing for bankruptcy before January 1, 2025, but it's not a certainty. The short timeframe and the fact that they're still operating normally in some aspects slightly reduce the probability. - -After adjusting for potential overconfidence and the historical tendency towards negative resolutions, I would estimate the probability as follows: - -Probability: 38% - -This reflects the serious financial troubles Spirit is facing, balanced against the short timeframe and the possibility of last-minute alternatives to bankruptcy." -29069,"Will Spotify remove rapper Diddy's songs from Diddy's Spotify page before January 1, 2025?","### **Analyzing the Context and Background Information** - -1. **Legal Situation**: The charges against Diddy are serious (sex trafficking, racketeering), but he has pleaded not guilty. The legal process and its potential outcomes could influence public perception, but until concrete developments (e.g., conviction) occur, it is uncertain how this will affect his music on Spotify. - -2. **Impact on Music Streams**: The increase in music streams (18.3%) indicates that controversy can boost interest in an artist’s work temporarily. This isn’t a direct reason for removal but does highlight public interest in his music. - -3. **Industry Context**: Historically, music by artists facing legal or moral controversies hasn’t been removed from platforms like Spotify. The case of R. Kelly, whose music remains available despite serious allegations and even a conviction, sets a precedent that works against the likelihood of Spotify removing Diddy’s music solely due to charges. - -4. **Spotify's Policies**: Spotify generally does not remove content for legal accusations. Their removals are usually policies related to content violative of community guidelines, like hate speech and explicit content. These aspects of the policy don’t currently apply to Diddy's music. - -5. **Recent Discussions**: Dialogue in the industry about the impact of personal controversies on professional work suggests growing awareness and sensitivity but hasn’t translated into action like the removal of music content from platforms yet. - -### **Base Rates and Additional Considerations** - -- **Timeline**: With the resolution date of January 1, 2025, less than 3 months away, any sudden changes in Spotify policies or decisions concerning Diddy would need to be highly reactive. Historically, such sudden and reactive decisions are rare unless catalyzed by major external events (e.g., conviction). - -- **Base Rate of Removal**: The base rate for music removal due to personal conduct is extremely low. - -- **Potential Trigger Events**: A conviction or similarly substantial development in his legal case might influence Spotify’s actions more than just ongoing legal proceedings. However, as of today, such developments haven’t occurred. - -### **Thought Process and Probability Estimation** - -1. **Default Expectation**: Given Spotify's past behavior and policies, the default expectation is for Diddy's music to remain. - -2. **Possible Deviations**: Conviction or overwhelming public or organizational pressure might lead to a reconsideration, but this is speculative without tangible developments. - -3. **Historical Precedent and Policy**: With past artists facing similar or more severe accusations not having their music removed, the precedent strongly favors the status quo. - -4. **Recency and Short Timeframe Consideration**: Within less than three months, drastic policy changes or external legal outcome changes are uncertain. - -5. **Adjusting for Overconfidence**: In previous forecasting, I’ve been overconfident, typically predicting more drastic moves than occur in reality. Being mindful of this, I adjust my probability towards maintaining status quo. - -### **Final Conclusion** - -After carefully weighing the background, potential catalysts for change, historical precedents, and adjusting for forecasting bias, the likelihood of Spotify removing Diddy’s music by January 1, 2025, is low. - -**Probability: 5%**" -29039,Will the Botswana Democratic Party win the 2024 general election?,"To forecast whether the Botswana Democratic Party (BDP) will win the 2024 general election, I need to carefully consider the relevant data, historical context, and various influencing factors. - -**Historical Context and Base Rates:** -1. The BDP has been in power since 1966 and has consistently won elections. This establishes a strong base rate for the party's continued success. -2. Historically, ruled democracies with long-standing dominant parties have shown a pattern of eventual power shifts. However, such shifts have not been prevalent in Botswana's history yet. - -**Current Political Environment:** -1. **Opposition Dynamics:** The UDC and BPF pose significant challenges. The Afrobarometer poll from 2022 shows the UDC ahead significantly. However, given it's over two years old, it may not represent the current sentiment accurately. Opposition fragmentation can also weaken their collective efforts against the BDP. -2. **Tensions and Rivalries:** Tensions, especially involving figures like Ian Khama, may sway public opinion and voter turnout. -3. **International Interest:** International focus on democratic stability may encourage fair practices, but largely doesn't affect domestic voting patterns. - -**Short Timeframe Considerations:** -1. With the election date only a week away, significant shifts in voter preferences are less probable. -2. Usually, any notable campaign effects would have already become apparent by now. - -**resolving Bias & Overconfidence:** -Account for my historical tendency towards overconfidence by slightly reducing my initial confidence in any forecast direction. - -**Forecast Rationale:** -- **Base Case** heavily favors the BDP given historical dominance and a consistent track record of winning elections. -- **Opposition Strength** is notable based on outdated poll data, but internal divisions might dampen effectiveness. -- **Voting System:** First-past-the-post benefits incumbents, like the BDP, particularly in fragmented oppositions. - -Given the stability of the BDP's past electoral performance, alongside less recent poll data showing the opposition's lead, and considering factors like fragmented opposition and the impact of a short timeframe, the probability of BDP winning still remains strong albeit less certain than in previous years due to opposition efforts. - -Final Probability: -- Acknowledge historical precedence but slightly adjust for potential variability due to stronger opposition. -- Assign a moderate decrease from complete certainty to balance past tendencies and current information uncertainties. - -**Final Probability:** 65%" -29038,Will the Liberal Democratic Party win a majority of seats in the 2024 Japanese general election?,"Let's approach this step-by-step: - -1. Historical Context: - The LDP has dominated Japanese politics for most of the post-war period. They've maintained a majority or near-majority in most elections, which gives them a strong base rate. - -2. Current Situation: - - The LDP held 247 of 465 seats before dissolution, well above the 233 needed for a majority. - - Prime Minister Ishiba called for a snap election, which often favors the incumbent party. - - The LDP is polling at around 1/3 of the vote, which is strong but not overwhelming. - -3. Electoral System: - Japan's parallel voting system, with 5/8 of seats elected by first-past-the-post, tends to favor larger parties like the LDP. This could amplify their seat count relative to their vote share. - -4. Recent Developments: - - The corruption scandal that led to Kishida's resignation could hurt the LDP's chances. - - However, new leadership under Ishiba might mitigate some of this damage. - -5. Opposition Strategy: - The opposition's shift in strategy to focus on proportional representation blocks might reduce the LDP's seat advantage somewhat. - -6. Recent Polls: - The Sankei/FNN poll projecting 196 seats for the LDP is concerning for their majority prospects. However, this is just one poll and may not be fully representative. - -7. Time Factor: - With only 6 days until the election, there's limited time for major shifts in public opinion or campaign dynamics. - -8. Base Rate Consideration: - Historically, the LDP has often managed to maintain a majority or close to it, even in challenging times. - -9. Uncertainty Factors: - - The impact of the recent corruption scandal is hard to quantify precisely. - - Voter turnout and last-minute shifts in public opinion can affect results. - -10. Comparative Timelines: - - Probability of LDP majority in 1 month: N/A (election will be over) - - Probability of LDP majority in 1 year: 65% (allowing for potential coalition reshuffling or by-elections) - -Analysis: -The LDP has strong historical performance and structural advantages in the electoral system. However, the recent scandal and polling data suggest they're facing more challenges than usual. The short timeframe before the election limits the potential for major shifts, which could work in their favor given their incumbent status. - -The recent poll showing them well short of a majority is concerning, but it's important not to overweigh a single data point. Their starting position of 247 seats gives them some buffer, and the electoral system tends to favor them. - -Given the uncertainty and the tendency for forecasters to be overconfident, I'll adjust my estimate downward slightly from what the raw data might suggest. - -Probability: 58% - -This reflects a slight edge for the LDP to maintain their majority, acknowledging the significant challenges they face while also recognizing their structural advantages and historical performance." -29037,"Will IBM lower its Pay-as-you-go quantum computing prices before January 1, 2025?","Let's approach this step-by-step: - -1. Current situation: - - IBM's current Pay-as-you-go quantum computing price is $96 USD/minute. - - The resolution date is January 1, 2025, which is about 2 months from today. - -2. Recent developments: - - IBM has expanded its quantum data center in Poughkeepsie, NY, and opened a new one in Germany. - - Focus seems to be on expanding capabilities and services rather than changing pricing. - -3. Market trends: - - The quantum computing market is growing rapidly (32.7% CAGR projected from 2024 to 2029). - - This growth suggests increasing demand, which typically doesn't lead to price reductions in the short term. - -4. IBM's strategy: - - Recent announcements focus on technological advancements and expansion. - - No indications of plans to lower prices in the near future. - -5. Time frame consideration: - - With only about 2 months until the resolution date, the likelihood of a significant price change is lower than if we had a longer timeframe. - - Major pricing decisions for tech services often align with fiscal quarters or years, which might not coincide with this timeline. - -6. Historical precedent: - - Quantum computing is a relatively new field, so historical data on pricing changes might be limited. - - In general, tech services tend to become more affordable over time, but this usually happens gradually. - -7. Market competition: - - Increased competition could drive prices down, but in the short term, IBM's focus appears to be on capability rather than cost reduction. - -8. Economic factors: - - Global economic conditions and inflation trends could influence pricing decisions, but these typically don't lead to rapid price reductions in high-tech services. - -9. Probability distribution: - - Given the short timeframe and lack of indicators pointing to an imminent price reduction, the probability of a price decrease before January 1, 2025, seems low. - - However, we can't completely rule out the possibility, as strategic decisions can sometimes be made quickly in response to market forces. - -10. Alternative timelines: - - Probability of price reduction within 1 month: 2% - - Probability of price reduction within 6 months: 15% - - Probability of price reduction within 1 year: 30% - -Considering all these factors, especially the short timeframe and the lack of any indications from IBM about plans to reduce prices, I believe the probability of IBM lowering its Pay-as-you-go quantum computing prices before January 1, 2025, is relatively low. However, given the dynamic nature of the tech industry and the potential for unexpected strategic shifts, it's not impossible. - -Adjusting for my historical tendency towards overconfidence and the fact that positive resolutions are less common than expected, I'll err on the side of caution in my final estimate. - -Probability: 8%" -29118,Will the New York Yankees lose the 2024 World Series in baseball in exactly 7 games?,"To forecast the probability of each potential outcome for the New York Yankees in the 2024 World Series, we need to consider a number of factors, including historical probabilities of World Series outcomes, current strengths and weaknesses of the participating teams, and expert analyses. Historically, World Series outcomes vary, but it's not uncommon for a best-of-seven series to end in either 4, 5, 6, or 7 games. The variability in game outcomes in prior series combined with expert predictions suggests a relatively balanced probability distribution across potential resolutions. - -The New York Yankees and Los Angeles Dodgers are both highly competitive teams, and expert analyses suggest a fairly even match-up. This means that there is a non-trivial possibility for the series to go to a full seven games. A few experts have pointed out the series could potentially end in any of these outcomes. However, we must weigh more towards a middle ground of probabilities being most likely around 6 and 7 games, considering the teams' strengths. - -Additionally, we should consider that the base rate for any specific occurrence in a seven-game series (such as Yankees losing in exactly 7 games) is relatively low given the multitude of possible outcomes. Therefore, any single specific outcome like losing or winning in exactly 4, 5, 6, or 7 should not exceed 25% based on their individual probabilities and should collectively reflect the variability in this best-of-seven format. Furthermore, we cannot ignore the default state where events are less likely to dramatically deviate from the norm within a short timeframe. - -Ultimately, based on historical base rates, expert opinions, and the inherent uncertainty of a best-of-seven series, our assumptions are weighted slightly towards longer series lengths, while ensuring that no specific exact game outcome dominates or is treated as too high without clear evidence. We also recognize our tendency towards overconfidence and adjust by avoiding extremes in probability for any single series result." -29117,Will the New York Yankees lose the 2024 World Series in baseball in exactly 6 games?,"To forecast the probability of each potential outcome for the New York Yankees in the 2024 World Series, we need to consider a number of factors, including historical probabilities of World Series outcomes, current strengths and weaknesses of the participating teams, and expert analyses. Historically, World Series outcomes vary, but it's not uncommon for a best-of-seven series to end in either 4, 5, 6, or 7 games. The variability in game outcomes in prior series combined with expert predictions suggests a relatively balanced probability distribution across potential resolutions. - -The New York Yankees and Los Angeles Dodgers are both highly competitive teams, and expert analyses suggest a fairly even match-up. This means that there is a non-trivial possibility for the series to go to a full seven games. A few experts have pointed out the series could potentially end in any of these outcomes. However, we must weigh more towards a middle ground of probabilities being most likely around 6 and 7 games, considering the teams' strengths. - -Additionally, we should consider that the base rate for any specific occurrence in a seven-game series (such as Yankees losing in exactly 7 games) is relatively low given the multitude of possible outcomes. Therefore, any single specific outcome like losing or winning in exactly 4, 5, 6, or 7 should not exceed 25% based on their individual probabilities and should collectively reflect the variability in this best-of-seven format. Furthermore, we cannot ignore the default state where events are less likely to dramatically deviate from the norm within a short timeframe. - -Ultimately, based on historical base rates, expert opinions, and the inherent uncertainty of a best-of-seven series, our assumptions are weighted slightly towards longer series lengths, while ensuring that no specific exact game outcome dominates or is treated as too high without clear evidence. We also recognize our tendency towards overconfidence and adjust by avoiding extremes in probability for any single series result." -29116,Will the New York Yankees lose the 2024 World Series in baseball in exactly 5 games?,"To forecast the probability of each potential outcome for the New York Yankees in the 2024 World Series, we need to consider a number of factors, including historical probabilities of World Series outcomes, current strengths and weaknesses of the participating teams, and expert analyses. Historically, World Series outcomes vary, but it's not uncommon for a best-of-seven series to end in either 4, 5, 6, or 7 games. The variability in game outcomes in prior series combined with expert predictions suggests a relatively balanced probability distribution across potential resolutions. - -The New York Yankees and Los Angeles Dodgers are both highly competitive teams, and expert analyses suggest a fairly even match-up. This means that there is a non-trivial possibility for the series to go to a full seven games. A few experts have pointed out the series could potentially end in any of these outcomes. However, we must weigh more towards a middle ground of probabilities being most likely around 6 and 7 games, considering the teams' strengths. - -Additionally, we should consider that the base rate for any specific occurrence in a seven-game series (such as Yankees losing in exactly 7 games) is relatively low given the multitude of possible outcomes. Therefore, any single specific outcome like losing or winning in exactly 4, 5, 6, or 7 should not exceed 25% based on their individual probabilities and should collectively reflect the variability in this best-of-seven format. Furthermore, we cannot ignore the default state where events are less likely to dramatically deviate from the norm within a short timeframe. - -Ultimately, based on historical base rates, expert opinions, and the inherent uncertainty of a best-of-seven series, our assumptions are weighted slightly towards longer series lengths, while ensuring that no specific exact game outcome dominates or is treated as too high without clear evidence. We also recognize our tendency towards overconfidence and adjust by avoiding extremes in probability for any single series result." -29115,Will the New York Yankees lose the 2024 World Series in baseball in exactly 4 games?,"To forecast the probability of each potential outcome for the New York Yankees in the 2024 World Series, we need to consider a number of factors, including historical probabilities of World Series outcomes, current strengths and weaknesses of the participating teams, and expert analyses. Historically, World Series outcomes vary, but it's not uncommon for a best-of-seven series to end in either 4, 5, 6, or 7 games. The variability in game outcomes in prior series combined with expert predictions suggests a relatively balanced probability distribution across potential resolutions. - -The New York Yankees and Los Angeles Dodgers are both highly competitive teams, and expert analyses suggest a fairly even match-up. This means that there is a non-trivial possibility for the series to go to a full seven games. A few experts have pointed out the series could potentially end in any of these outcomes. However, we must weigh more towards a middle ground of probabilities being most likely around 6 and 7 games, considering the teams' strengths. - -Additionally, we should consider that the base rate for any specific occurrence in a seven-game series (such as Yankees losing in exactly 7 games) is relatively low given the multitude of possible outcomes. Therefore, any single specific outcome like losing or winning in exactly 4, 5, 6, or 7 should not exceed 25% based on their individual probabilities and should collectively reflect the variability in this best-of-seven format. Furthermore, we cannot ignore the default state where events are less likely to dramatically deviate from the norm within a short timeframe. - -Ultimately, based on historical base rates, expert opinions, and the inherent uncertainty of a best-of-seven series, our assumptions are weighted slightly towards longer series lengths, while ensuring that no specific exact game outcome dominates or is treated as too high without clear evidence. We also recognize our tendency towards overconfidence and adjust by avoiding extremes in probability for any single series result." -29114,Will the New York Yankees win the 2024 World Series in baseball in exactly 7 games?,"To forecast the probability of each potential outcome for the New York Yankees in the 2024 World Series, we need to consider a number of factors, including historical probabilities of World Series outcomes, current strengths and weaknesses of the participating teams, and expert analyses. Historically, World Series outcomes vary, but it's not uncommon for a best-of-seven series to end in either 4, 5, 6, or 7 games. The variability in game outcomes in prior series combined with expert predictions suggests a relatively balanced probability distribution across potential resolutions. - -The New York Yankees and Los Angeles Dodgers are both highly competitive teams, and expert analyses suggest a fairly even match-up. This means that there is a non-trivial possibility for the series to go to a full seven games. A few experts have pointed out the series could potentially end in any of these outcomes. However, we must weigh more towards a middle ground of probabilities being most likely around 6 and 7 games, considering the teams' strengths. - -Additionally, we should consider that the base rate for any specific occurrence in a seven-game series (such as Yankees losing in exactly 7 games) is relatively low given the multitude of possible outcomes. Therefore, any single specific outcome like losing or winning in exactly 4, 5, 6, or 7 should not exceed 25% based on their individual probabilities and should collectively reflect the variability in this best-of-seven format. Furthermore, we cannot ignore the default state where events are less likely to dramatically deviate from the norm within a short timeframe. - -Ultimately, based on historical base rates, expert opinions, and the inherent uncertainty of a best-of-seven series, our assumptions are weighted slightly towards longer series lengths, while ensuring that no specific exact game outcome dominates or is treated as too high without clear evidence. We also recognize our tendency towards overconfidence and adjust by avoiding extremes in probability for any single series result." -29113,Will the New York Yankees win the 2024 World Series in baseball in exactly 6 games?,"To forecast the probability of each potential outcome for the New York Yankees in the 2024 World Series, we need to consider a number of factors, including historical probabilities of World Series outcomes, current strengths and weaknesses of the participating teams, and expert analyses. Historically, World Series outcomes vary, but it's not uncommon for a best-of-seven series to end in either 4, 5, 6, or 7 games. The variability in game outcomes in prior series combined with expert predictions suggests a relatively balanced probability distribution across potential resolutions. - -The New York Yankees and Los Angeles Dodgers are both highly competitive teams, and expert analyses suggest a fairly even match-up. This means that there is a non-trivial possibility for the series to go to a full seven games. A few experts have pointed out the series could potentially end in any of these outcomes. However, we must weigh more towards a middle ground of probabilities being most likely around 6 and 7 games, considering the teams' strengths. - -Additionally, we should consider that the base rate for any specific occurrence in a seven-game series (such as Yankees losing in exactly 7 games) is relatively low given the multitude of possible outcomes. Therefore, any single specific outcome like losing or winning in exactly 4, 5, 6, or 7 should not exceed 25% based on their individual probabilities and should collectively reflect the variability in this best-of-seven format. Furthermore, we cannot ignore the default state where events are less likely to dramatically deviate from the norm within a short timeframe. - -Ultimately, based on historical base rates, expert opinions, and the inherent uncertainty of a best-of-seven series, our assumptions are weighted slightly towards longer series lengths, while ensuring that no specific exact game outcome dominates or is treated as too high without clear evidence. We also recognize our tendency towards overconfidence and adjust by avoiding extremes in probability for any single series result." -29112,Will the New York Yankees win the 2024 World Series in baseball in exactly 5 games?,"To forecast the probability of each potential outcome for the New York Yankees in the 2024 World Series, we need to consider a number of factors, including historical probabilities of World Series outcomes, current strengths and weaknesses of the participating teams, and expert analyses. Historically, World Series outcomes vary, but it's not uncommon for a best-of-seven series to end in either 4, 5, 6, or 7 games. The variability in game outcomes in prior series combined with expert predictions suggests a relatively balanced probability distribution across potential resolutions. - -The New York Yankees and Los Angeles Dodgers are both highly competitive teams, and expert analyses suggest a fairly even match-up. This means that there is a non-trivial possibility for the series to go to a full seven games. A few experts have pointed out the series could potentially end in any of these outcomes. However, we must weigh more towards a middle ground of probabilities being most likely around 6 and 7 games, considering the teams' strengths. - -Additionally, we should consider that the base rate for any specific occurrence in a seven-game series (such as Yankees losing in exactly 7 games) is relatively low given the multitude of possible outcomes. Therefore, any single specific outcome like losing or winning in exactly 4, 5, 6, or 7 should not exceed 25% based on their individual probabilities and should collectively reflect the variability in this best-of-seven format. Furthermore, we cannot ignore the default state where events are less likely to dramatically deviate from the norm within a short timeframe. - -Ultimately, based on historical base rates, expert opinions, and the inherent uncertainty of a best-of-seven series, our assumptions are weighted slightly towards longer series lengths, while ensuring that no specific exact game outcome dominates or is treated as too high without clear evidence. We also recognize our tendency towards overconfidence and adjust by avoiding extremes in probability for any single series result." -29111,Will the New York Yankees win the 2024 World Series in baseball in exactly 4 games?,"To forecast the probability of each potential outcome for the New York Yankees in the 2024 World Series, we need to consider a number of factors, including historical probabilities of World Series outcomes, current strengths and weaknesses of the participating teams, and expert analyses. Historically, World Series outcomes vary, but it's not uncommon for a best-of-seven series to end in either 4, 5, 6, or 7 games. The variability in game outcomes in prior series combined with expert predictions suggests a relatively balanced probability distribution across potential resolutions. - -The New York Yankees and Los Angeles Dodgers are both highly competitive teams, and expert analyses suggest a fairly even match-up. This means that there is a non-trivial possibility for the series to go to a full seven games. A few experts have pointed out the series could potentially end in any of these outcomes. However, we must weigh more towards a middle ground of probabilities being most likely around 6 and 7 games, considering the teams' strengths. - -Additionally, we should consider that the base rate for any specific occurrence in a seven-game series (such as Yankees losing in exactly 7 games) is relatively low given the multitude of possible outcomes. Therefore, any single specific outcome like losing or winning in exactly 4, 5, 6, or 7 should not exceed 25% based on their individual probabilities and should collectively reflect the variability in this best-of-seven format. Furthermore, we cannot ignore the default state where events are less likely to dramatically deviate from the norm within a short timeframe. - -Ultimately, based on historical base rates, expert opinions, and the inherent uncertainty of a best-of-seven series, our assumptions are weighted slightly towards longer series lengths, while ensuring that no specific exact game outcome dominates or is treated as too high without clear evidence. We also recognize our tendency towards overconfidence and adjust by avoiding extremes in probability for any single series result." -29125,"Will rapper 50 Cent endorse Donald Trump in the 2024 election before November 5, 2024?","To forecast the probability of 50 Cent endorsing Donald Trump in the 2024 election, let's step through the information systematically: - -1. **Historical Behavior**: 50 Cent has a history of fluctuating political endorsements. He endorsed Trump in 2020 due to concerns over Biden's tax plans, but soon retracted that endorsement and endorsed Biden. This indicates a willingness to change his public political stance swiftly. - -2. **Recent Statements**: As of recently, 50 Cent has expressed a desire to remain neutral, although he's voiced admiration for Trump’s resilience. However, he has not made definitive statements indicating an endorsement, and his desire to stay neutral suggests his endorsement is not currently probable. - -3. **Timeframe**: With only around two weeks left before the election, the opportunity for a dramatic change in stance diminishes. The closer the election, the less likely significant changes are unless prompted by major unforeseen events. - -4. **Current Context**: The celebrity endorsement landscape is highly polarized, which could pressure him to remain neutral or lean towards a safe stance. It is also important to consider that any endorsement would likely be met with significant public scrutiny given his previous back-and-forth endorsements. - -5. **Base Rates and Bias**: Given my historical tendency to overestimate probability, alongside the fact that only around 35% of similar questions resolve positively, I need to adjust accordingly. - -6. **Probability Over Time**: The likelihood of any statement being made decreases as time shortens. Given the high variability in celebrity endorsements and political backing close to elections, these changes typically occur earlier in campaigns rather than in the final days. - -Combining these factors: - -- The base probability is low, as the default resolution without significant change would be ""No."" -- 50 Cent has not shown a strong push or momentum toward a Trump endorsement. -- Given the recency of the neutral stance and the limited time before resolution, the probability of a sudden change reduces further. - -Taking into account these factors and adjusting for my historical overconfidence: - -Probability: 7% - -This reflects a slim chance of endorsement, acknowledging possible but unlikely minor shifts in the upcoming fortnight." -29124,"Will South Dakota voters approve Constitutional Amendment G, which would provide for a state constitutional right to an abortion?","To assess the likelihood of South Dakota voters approving Constitutional Amendment G, which aims to establish a constitutional right to an abortion, I will analyze historical precedents, current dynamics, and specific details of the situation. - -### Historical Context and Base Rates -Historically, South Dakota has had conservative stances on abortion rights. Various initiatives related to abortion have been proposed in the past, and many have faced significant opposition, resulting in a consistent trend toward conservative outcomes. This existing legislative and cultural baseline suggests a challenging environment for the amendment's passage. - -### Current Dynamics -1. **Constitutional Amendment G Overview**: - The amendment seeks to introduce abortion rights with a trimester framework, which can be considered a middle-ground solution. While some may see this as a reasoned approach, others on both ends of the political spectrum may find it insufficient or excessive. - -2. **Current Abortion Law**: - South Dakota's current near-total abortion ban suggests strong institutional resistance. This indicates that reversing this entrenched position might be difficult. - -3. **Support and Opposition**: - - **Support**: There is organized support with endorsements from faith leaders and active campaigning. However, it remains uncertain whether this support can overcome the conservative voter base. - - **Opposition**: Strong opposition from pro-life groups, and some critique from certain pro-choice factions, could dilute the initiative's support. - -4. **Public Engagement**: - While the collection of over 55,000 signatures to place the amendment on the ballot indicates significant mobilization, it is crucial to realize this doesn't equate to electoral support. - -5. **Recent Developments**: - Legal challenges surviving ballot inclusion suggest an intense political climate with vested interests on both sides. However, fundraising and endorsement patterns are not definitive indicators and do not yet reflect overwhelming momentum for passage. - -### Considerations -- **Time Frame to Vote**: With only about two weeks remaining, significant shifts in public opinion become less likely. Given the short timeline, last-minute efforts might not have sufficient impact. -- **Default Probability**: Assuming changes are minimal in the coming weeks, I lean toward a continuation of past trends, which has typically been against abortion rights initiatives. - -### Adjustment for Overconfidence -Acknowledging my past tendency toward overconfidence, I'll exercise restraint in my estimation by not overly favoring outcomes I perceive as possible but unlikely. - -### Probability Distribution Assessment -Given the conservative orientation of the state, coupled with historical precedents and the limited timeline for change, the odds of this amendment passing appear modest. Evaluating my uncertainties and potential biases, a balanced probabilistic assessment is required. - -### Final Assessment -Considering the factors discussed and the state's conservative tendency on reproductive rights issues, I propose an estimated probability: - -**Probability: 28%**" -29123,Will Republican Congressman Juan Ciscomani be re-elected in Arizona's 6th Congressional District in the 2024 election?,"Let's approach this step-by-step: - -1. Current Status: Juan Ciscomani is the incumbent Republican congressman, first elected in 2022 with a narrow margin of 50.7% to 49.2%. - -2. Historical Context: Incumbents generally have an advantage in US House elections. However, this advantage has been decreasing in recent years, especially in competitive districts. - -3. District Competitiveness: Arizona's 6th Congressional District is considered highly competitive. Multiple forecasters rate it as a toss-up or leaning slightly Republican. - -4. Campaign Finance: Both Ciscomani and his Democratic challenger, Kirsten Engel, have raised equal amounts ($4.4 million), indicating a well-funded race on both sides. - -5. National Political Climate: As of October 2024, the national political climate could play a significant role. However, we don't have specific information about national trends this close to the election. - -6. Local Issues: Border security, healthcare, and economic policies are key issues in the district. Ciscomani's emphasis on these aligns with typical Republican priorities. - -7. Time Factor: With the election just two weeks away, major shifts in voter sentiment are less likely, but still possible. - -8. Previous Election Results: The 2022 result was extremely close, suggesting a nearly evenly divided electorate in the district. - -9. Voter Turnout: Presidential election years typically see higher turnout, which could affect the results differently than in the 2022 midterms. - -10. Polling: While we don't have specific polling data, the ""toss-up"" designation from reputable forecasters suggests the race remains close. - -11. Base Rate: Historically, House incumbents win re-election about 90% of the time. However, this rate is lower in highly competitive districts. - -Weighing these factors: - -- The incumbent advantage slightly favors Ciscomani, but the district's competitiveness significantly reduces this advantage. -- The equal fundraising suggests neither candidate has a clear resource advantage. -- The close result in 2022 and the ""toss-up"" designations indicate high uncertainty. -- With only two weeks until the election, major shifts are less likely, giving more weight to the current situation. -- The base rate for incumbent re-election is high, but this is a particularly competitive district. - -Given these considerations, I would estimate Ciscomani's chances of re-election as slightly above 50%, but with significant uncertainty. The race is genuinely close, and while incumbency provides some advantage, it's not decisive in this highly competitive district. - -To avoid overconfidence, I'll adjust my estimate slightly downward from what might initially seem appropriate given the incumbent advantage. - -Probability: 53%" -29122,"Will the USDA-posted recall of Boar's Head deli meat be closed before December 1, 2024?","Let's approach this step-by-step: - -1. Type of question: This falls under the ""Other Binary Outcome Event"" category. - -2. Current status: The recall is active and ongoing, with no clear indication of when it might be closed. - -3. Time frame: We have approximately 5 weeks from today (2024-10-23) until the resolution date (2024-12-01). - -4. Severity of the issue: This is a significant food safety issue involving multiple deaths and hospitalizations, which suggests a thorough and potentially lengthy investigation. - -5. Ongoing investigations: Both the USDA's FSIS and the inspector general are conducting investigations, which are still in progress. - -6. Historical context: Food safety recalls, especially those involving fatalities, tend to remain open for extended periods to ensure all affected products are removed from circulation and all necessary improvements are implemented. - -7. Expert opinions: Public health experts expect the number of cases could still increase, indicating the situation is not yet fully contained. - -8. Recall scope: The recall includes products with sell-by dates extending into October 2024, which is beyond our resolution date. - -9. Production status: Boar's Head has indefinitely closed its Virginia production facility, suggesting a potentially lengthy process to address the issue. - -10. Base rate consideration: While I don't have specific data on the average duration of similar recalls, the severity and scope of this one suggest it's likely to be on the longer end of the spectrum. - -11. Potential for early closure: There's always a possibility that the investigations could conclude quickly and all necessary actions could be taken before December 1, 2024. However, given the complexity and severity of the situation, this seems unlikely. - -12. Timeline analysis: - - Probability of closure within 1 month (by November 23, 2024): 5% - - Probability of closure within 2 months (by December 23, 2024): 15% - - Probability of closure within 6 months (by April 23, 2025): 60% - -Given these considerations, I believe the probability of the recall being closed before December 1, 2024, is relatively low. The ongoing investigations, the severity of the outbreak, and the extent of the recall all suggest that it's more likely to remain open past this date. - -However, to account for potential overconfidence and the possibility of unexpected developments, I'll adjust my estimate slightly upward from what my initial analysis suggests. - -Probability: 12%" -29121,"Will BigBear.AI file for bankruptcy before January 1, 2025?","Thank you for the detailed instructions. I'll approach this question step-by-step, considering various factors and timelines. - -1. Base rate consideration: -Bankruptcy filings for public companies are relatively rare. According to a study by the UCLA-LoPucki Bankruptcy Research Database, in recent years, the annual rate of public company bankruptcy filings has been around 0.5-1% of all public companies. However, BigBear.AI's situation appears more precarious than the average public company. - -2. Financial health: -BigBear.AI has been operating at a loss since 2020, which is a significant red flag. However, many tech companies, especially in AI, operate at a loss for years while scaling up. The recent capital raise of $53.9 million in Q1 2024 suggests they still have investor confidence and access to capital markets. - -3. Industry trends: -The AI industry is growing rapidly, which could provide opportunities for BigBear.AI to secure contracts and improve its financial position. However, competition is fierce, and larger, more established companies may have advantages in securing government contracts. - -4. Economic factors: -The broader economic environment, including potential inflation concerns, could make it more challenging for companies operating at a loss to secure additional funding or maintain investor confidence. - -5. Time consideration: -The resolution date is just over two months away from today's date. This is a relatively short timeframe for a company to go from its current state to bankruptcy, especially given the recent capital raise. - -6. Alternative timelines: -Let's consider the probability of bankruptcy at different timeframes: -- Within 1 month: 1% -- Within 2 months (by resolution date): 3% -- Within 6 months: 8% -- Within 1 year: 15% - -These estimates suggest that the risk of bankruptcy increases over time, which is logical given the company's financial situation. - -7. Mitigating factors: -- The recent capital raise provides a financial buffer. -- As a government contractor, BigBear.AI may have more stable revenue streams than some other AI companies. -- The company may have options for cost-cutting or restructuring to avoid bankruptcy. - -8. Accelerating factors: -- Continued operating losses could erode investor confidence. -- Failure to secure new contracts or retain existing ones could rapidly worsen their financial situation. -- A broader economic downturn could make additional fundraising more difficult. - -9. Uncertainty considerations: -Given the short timeframe and the recent capital raise, there's significant uncertainty about whether bankruptcy would occur so quickly. The company likely has some runway from its recent funding. - -10. Overconfidence adjustment: -Considering the instruction to adjust for historical overconfidence, I'll lean towards a more conservative estimate. - -Rationale summary: -While BigBear.AI shows signs of financial stress, the recent capital raise and the short timeframe until the resolution date suggest that immediate bankruptcy is unlikely. However, the company's continued operating losses and the competitive AI landscape do present real risks. The probability increases significantly when looking at longer timeframes, indicating that while bankruptcy before January 1, 2025, is possible, it's not highly probable. - -Adjusting for potential overconfidence and the historically low rate of positive resolutions, I'll provide a conservative estimate that still reflects the real risks facing the company. - -Probability: 4% - -This estimate reflects the low likelihood of bankruptcy in such a short timeframe while acknowledging the non-zero risk given the company's financial challenges. It's higher than the base rate for public companies but still low, considering the recent funding and the typical timelines for companies moving from financial stress to bankruptcy filings." -29120,"Will the 500th richest person on Bloomberg's Billionaires Index have $6.5 billion or more on Monday December 30, 2024?","To forecast if the 500th richest person on Bloomberg's Billionaires Index will have $6.5 billion or more on December 30, 2024, we need to consider various factors outlined in the background information, as well as any relevant statistical, historical, and market-based assessments. Here's my step-by-step reasoning: - -1. **Current Data and Trends**: - - The rankings are updated daily and are influenced by stock market fluctuations and exchange rates. This makes the net worth of billionaires, particularly those ranked lower, susceptible to volatility. - - The methodology and data collection by Bloomberg are transparent and use the latest market data, which should indicate a high level of accuracy in real-time assessments. - -2. **Economic and Market Conditions**: - - We must consider the state of the global economy. As of late 2024, economic conditions can be affected by several unpredictable factors such as geopolitical events, inflation rates, interest rates, and general market sentiment. - - Past trends typically show high wealth at the top of the list, but it's not uncommon for billionaires ranked around the 500th position to experience significant changes in their net worth due to market volatility. - -3. **Past Trends and Base Rates**: - - Reviewing historical data of those ranked near the 500th position on similar indices can provide a reference point. If historically, the person ranked 500th often has around $6 billion at year’s end, then exceeding $6.5 billion might require favorable market conditions and economic growth. - -4. **Timeframe and Resolution Criteria**: - - We are currently around two months away from the resolution date. A significant market rally or other favorable economic factors in this time frame could increase billionaires' net worths. Conversely, market downturns could decrease net worths. - - Given the cutoff is $6.5 billion, even small percentage changes in the value of assets and currencies can swing the net worth estimates of those around the 500th position. - -5. **Quantitative Assessment and Adjustments**: - - Statistically, let's consider if the 500th person’s wealth estimates hover around $6 billion, then for the net worth to exceed $6.5 billion, an approximate increase of 8-10% is required. - - Assume there is a 50% chance of upward or downward shifts greater than this magnitude over two months due to market conditions, given the recent market trends. - -6. **Adjusting for Overconfidence**: - - Given my tendency to overestimate positive resolutions, I'll adjust my probability downward. Past performance indicates a slight bias towards expecting positive resolutions. - - Historically, only about 35% of forecasted questions resolve positively, providing a more conservative baseline. - -In conclusion, estimating the probability of the resolution considering the market volatility and economic uncertainty requires adjusting for both potential upside and downside. Given that smaller market cap individuals are highly sensitive to market changes, both significant gains and losses are possible, with a slightly more conservative outlook considering recent economic dynamics. - -Final assessment: - -**Probability: 35%** - -This reflects a modest likelihood given the dynamic market environment, weighed against historical trends and acknowledging personal overconfidence." -29119,"Will Brian Daboll cease to be the New York Giants head coach before January 1, 2025?","Let's approach this step-by-step: - -1. Current situation: - - Brian Daboll is currently the head coach of the New York Giants. - - The team is facing challenges and underperforming. - - There's speculation about Daboll's future with the team. - -2. Time frame: - - Today's date is October 23, 2024. - - The resolution date is January 1, 2025. - - This gives approximately 2 months for a change to occur. - -3. Historical context: - - NFL head coaches are typically not fired mid-season unless the team is performing extremely poorly or there are significant off-field issues. - - Most coaching changes occur immediately after the regular season ends (usually early January). - -4. Team performance: - - The Giants are struggling, but it's not clear if their performance is bad enough to warrant a mid-season firing. - - The team made significant coaching staff changes in the 2024 offseason, which might buy Daboll some time as management may want to see how these changes play out. - -5. Ownership and management tendencies: - - The Giants organization has historically been patient with coaches, preferring stability. - - However, if the team's performance continues to decline dramatically, this could change. - -6. Timeline consideration: - - The resolution date is just before the typical time when NFL teams make coaching changes. - - If Daboll were to be fired, it's more likely to happen after the season ends, which would be after the resolution date. - -7. Probability distribution: - - Let's consider the likelihood of Daboll being fired at different timeframes: - - Within 1 month (by November 23, 2024): 5% - - Within 2 months (by December 23, 2024): 15% - - Within 3 months (by January 23, 2025): 30% - - Within 6 months (by April 23, 2025): 40% - -8. Base rate consideration: - - Historically, only about 3-5 NFL head coaches (out of 32) are fired or resign during or immediately after a season. - - This suggests a base rate of around 10-15% for any given coach to be replaced in a year. - -9. Adjusting for overconfidence: - - Given the historical tendency for overconfidence, we should lean towards a more conservative estimate. - -10. Final analysis: - - While there are concerns about Daboll's performance, the short timeframe before the resolution date and the Giants' historical patience with coaches suggest a lower probability of him being fired before January 1, 2025. - - The most critical period would be the last few weeks of December, but even then, it's more likely that any decision would be made after the season ends. - -Considering all these factors, and adjusting for potential overconfidence, I would estimate the probability as follows: - -Probability: 18% - -This reflects the possibility of Daboll being fired before January 1, 2025, while acknowledging that it's more likely any change would occur after this date. The 18% probability accounts for the pressure Daboll is under, the team's performance issues, but also factors in the short timeframe and the historical tendencies of both the NFL and the Giants organization." -29110,"Will Intel get dropped from the Dow Jones Industrial Average before January 1, 2025?","To forecast the probability that Intel will be dropped from the Dow Jones Industrial Average (DJIA) before January 1, 2025, we need to carefully consider a variety of factors and contextual information: - -1. **Current Information and Background**: The primary piece of data is the recent Reuters article highlighting Intel's struggles in the stock market and the fact that its status in the DJIA is ""under threat."" Given this negative performance, there's a legitimate risk that Intel could be removed from the index. However, it's important to note that while Intel's status is being scrutinized, such scrutiny does not guarantee removal. - -2. **Criteria for Changes to the DJIA**: The Dow Jones Industrial Average is managed by S&P Dow Jones Indices and changes are made to ensure it remains representative of the overall U.S. economy. Replacements are influenced by a variety of factors, including a company's industry leadership, market capitalization, and recent stock performance. Intel's recent stock underperformance and the decline of its market capitalization raise the risk of removal, but such decisions are neither automatic nor frequent. - -3. **Base Rate of Companies Being Dropped from the DJIA**: Historically, the DJIA does not change its components often. Over the last few decades, there have typically been one or two changes every few years. The process is discretionary and the timeline is not fixed. - -4. **Time Frame**: With fewer than three months remaining until the resolution date, the time frame is fairly short for any significant change. The index does not have a set schedule for making changes, but decisions typically result from a comprehensive review of not just the company's immediate past performance, but also broader market conditions and the economic representation of the DJIA. - -5. **Market Conditions and Economic Context**: The broader market has been performing relatively well, achieving new highs. This positive performance can sometimes delay or reduce pressure to make immediate changes to the index components unless absolutely necessary. - -6. **Probability Assessment**: - - A quick change within a few months involves thorough deliberations and potential candidates for replacement, which need to reflect broader economic representation. - - Recent dramatic developments, press breakthroughs, or major findings against the company's interests could influence probabilities, but these were not evident in the contextual setup provided. - -7. **Confidence and Bias Check**: Given the competitive settings and past overconfidence, it's crucial to weigh heavily the potential for bias and reflect realistic probabilities. Under the assumption of threats to Intel's status in the DJIA, the probabilistic base should illustrate a substantial possibility of non-removal as indicated by historical baselines. - -8. **Conclusion**: Based on historical activity regarding DJIA changes and the considerable decline in Intel's economy-driving sector, it's definitely plausible that Intel could be on the radar for potential removal from the DJIA component list. Nonetheless, given the concrete approach these decisions undergo, there is a reasonably higher likelihood that Intel remains, short of major substantive shifts. - -After weighing these factors and reflecting on past overconfidence, my final probability estimate that Intel will be dropped from the DJIA by January 1, 2025, is: - -Probability: 20%" -29109,"At the end of 2024, will Wikipedia still list Brazil as ""currently"" blocking access to Twitter/X?","In considering whether Wikipedia will still list Brazil as ""currently"" blocking access to Twitter/X at the end of 2024, we need to evaluate several factors, including the recency and conclusiveness of events, Wikipedia's updating patterns, and the potential for unforeseen developments. - -### Key Factors to Consider: - -1. **Lifting of the Ban:** - - As of October 8, 2024, the Brazil Supreme Federal Court lifted the ban on Twitter/X after compliance from Twitter, indicating the resolution of the conflict. - - Since the court's demands have been met, and operations are resumed, the immediate situation suggests that Brazil is no longer blocking Twitter. - -2. **Wikipedia's Updating Patterns:** - - Wikipedia is a widely used platform, with many vigilant editors who update content rapidly in response to significant changes. - - It is reasonable to expect that, given the ban was lifted on October 8, editors have since updated any relevant pages to reflect the current status. - -3. **Potential for Reversal or New Developments:** - - Although the platform has complied, considering the political dynamics, new developments leading to a re-imposition of the ban cannot be entirely ruled out. - - However, given the recent compliance, it is less likely such a reversal would take place so quickly, as negotiations had reached resolution. - -4. **Base Rates for Similar Situations:** - - When bans like these are lifted due to compliance, it usually suggests an enduring resolution unless provoked by significant new developments from either party. - - Wikipedia generally reflects longer-term changes once a conflict like this resolves. - -5. **Timeframe and Stability:** - - It is currently late October, and the resolution date is January 1, 2025. This provides a couple of months for events to unfold. - - The base case is that once resolved and operated correctly within the jurisdiction's laws, a listing as ""currently blocking"" would generally not apply. - -6. **Adjusting for Overconfidence:** - - Considering the historical performance of forecaster's overconfidence and the fact only 35% of positive outcomes were observed in past instances, we should temper expectations. - -### Probability Estimation: - -Using these considerations, the likelihood that Wikipedia will continue to list Brazil as blocking Twitter by the end of 2024 seems low. The immediate issue appears resolved, Brazil unlikely to re-impose a ban shortly after lifting it due to compliance, and Wikipedia probably updated the page by then. - -**Probability Analysis:** -1. **Ban Being Re-imposed (Unexpected Political/Reversal Development):** - - An unlikely scenario given the resolution, maybe around 5-10% would represent any lapses or missteps leading to re-banning. - -2. **Wikipedia Not Updated:** - - An unlikely situation as the Wikipedia community often maintains accuracy, perhaps even lower, around 3-5%. - -Adjusting for potential overconfidence bias, combined probability would be notably low: - -Final estimation: **Probability: 8%**" -29109,"At the end of 2024, will Wikipedia still list Brazil as ""currently"" blocking access to Twitter/X?","Let's approach this question step-by-step: - -1. Current status: As of October 23, 2024, the ban on Twitter/X in Brazil has been lifted (since October 8, 2024). This is a crucial piece of information that significantly impacts our forecast. - -2. Time until resolution: There are approximately 2 months and 1 week between the current date and the resolution date. This is a relatively short period, which reduces the likelihood of major changes. - -3. Wikipedia updating practices: Wikipedia typically updates its information quite quickly, especially for high-profile events like a country lifting a ban on a major social media platform. It's reasonable to expect that Wikipedia would have already updated this information. - -4. Stability of the decision: The lifting of the ban came after Twitter/X complied with court orders and paid significant fines. This suggests a resolution to the underlying issues, making it less likely for the ban to be reimposed in the short term. - -5. Past behavior: The ban was initially imposed due to non-compliance with Brazilian laws. Now that Twitter/X has complied, there's less reason for Brazil to reinstate the ban unless new issues arise. - -6. Political climate: The Brazilian government, including President Lula da Silva, supported the ban. However, now that Twitter/X has complied with the demands, there's likely less political will to maintain or reinstate the ban. - -7. Technical measures: Twitter/X's use of Cloudflare to circumvent the ban shows their commitment to maintaining access in Brazil, which further reduces the likelihood of an effective ban being in place. - -8. Wikipedia's current listing: Given that the ban was lifted over two weeks ago, it's highly likely that Wikipedia has already updated its listing to remove Brazil from the ""currently"" blocking section. - -9. Potential for change: While it's possible for new issues to arise between Twitter/X and the Brazilian government, the short timeframe until resolution makes this less likely. - -10. Base rate: Historically, once social media bans are lifted, they tend to stay lifted unless there are significant new developments. - -Considering these factors, I believe there's a very low probability that Wikipedia will still list Brazil as ""currently"" blocking access to Twitter/X at the end of 2024. The ban has been lifted, Twitter/X has complied with demands, and Wikipedia likely has already updated this information. - -However, we must account for some uncertainty. There's a small chance of new issues arising, delays in Wikipedia updates, or potential edit wars on the Wikipedia page. - -Given the short timeframe and the clear lifting of the ban, I would estimate the probability as follows: - -Probability: 3% - -This low probability reflects the fact that the ban has been officially lifted, but allows for a small chance of unexpected developments or issues with Wikipedia updating. It's important to note that this is still significantly higher than a 0.5% probability, accounting for the inherent uncertainty in predicting future events and potential unforeseen circumstances." -29109,"At the end of 2024, will Wikipedia still list Brazil as ""currently"" blocking access to Twitter/X?","To determine the probability that Wikipedia will list Brazil as ""currently"" blocking access to Twitter/X at the end of 2024, we'll evaluate several factors, including recent developments in Brazil, the nature of Wikipedia's updates, and the time between now and the resolution date. - -1. **Recent Developments in Brazil**: - - The most significant event is that the ban on Twitter/X was reportedly lifted on October 8, 2024, following compliance by Twitter/X with Brazilian legal demands. This included paying a substantial fine and appointing a legal representative in Brazil. This information is from multiple reputable sources such as The Guardian and BBC, suggesting its reliability. - - As it stands, Twitter/X appears to be operating normally in Brazil, suggesting no ongoing blockade from the Brazilian ISPs. - -2. **Wikipedia's Update Practices**: - - Wikipedia tends to update articles quite promptly, especially on topics with global interest like social media censorship in a large country such as Brazil. Once the ban was lifted, there was likely an update reflecting this change. - - Given the decentralized nature of Wikipedia, any sustaining mention of a ban would likely be corrected by editors contributing to the platform given the available evidence. - -3. **Time Between the Current Date and the Resolution Date**: - - As of October 23, 2024, there are over two months until the resolution date on January 1, 2025. During this period, geopolitical changes are possible but likely not dramatic enough to reverse the lifting of such a ban considering the settled compliance issues with Twitter/X. - - Furthermore, major alterations in the situation would likely begin to see hints or movements before the resolution date, and none is currently evident. - -4. **Probability and Uncertainty Analysis**: - - Considering the sources indicate the ban has been lifted, and access restored, should weigh heavily towards a resolution of ""No"" (i.e., Twitter is not currently blocked). There is no immediate evidence suggesting a reversal. - - Despite potential geopolitical uncertainties, the most likely base case is the status quo remains unchanged due to the recent significant resolution effort by Twitter/X. - - Historical overconfidence in predictions should remind us to leave room for unforeseen events, albeit with low probability in this case. - -Thus, after evaluating the current status, updates on Wikipedia, and potential changes until the resolution date, and considering statistical nuances such as historical overconfidence, the probability that Brazil will still be listed as “currently” blocking access to Twitter/X on Wikipedia by January 1, 2025, is relatively low. - -Final assessment: - -Probability: 10%" -29109,"At the end of 2024, will Wikipedia still list Brazil as ""currently"" blocking access to Twitter/X?","To answer this question, we need to carefully evaluate the probability that Wikipedia will still list Brazil as ""currently"" blocking access to Twitter/X at the end of 2024. - -### Key Considerations: - -1. **Lifting of the Ban**: - - The ban on Twitter/X in Brazil was lifted on October 8, 2024, after compliance with legal demands by Elon Musk. This is a clear indication that the direct reason for Wikipedia listing Brazil as ""currently"" blocking Twitter/X has been resolved. - -2. **Recent Developments**: - - X's resumption of operations in Brazil reduces the likelihood of Wikipedia maintaining outdated information. Wikipedia tends to be updated quickly following significant changes, especially in high-profile cases. - -3. **Wikipedia's Update Behavior**: - - Wikipedia is known for being a dynamic platform where articles are frequently updated by contributors. The recent removal of the Twitter/X block should trigger an update in Wikipedia's content regarding Brazil's status. - -4. **Timeframe**: - - Given today's date, October 23, 2024, there are over two months until January 1, 2025. This is a significant amount of time for Wikipedia contributors to update the page to reflect the lifting of the ban. - -5. **Potential for Reversal or New Developments**: - - Consider whether there might be new developments or political decisions that could lead to a reinstatement of the ban. While it's possible, there appears to be no indication of imminent danger of the ban being reinstated given the conflict's resolution and the compliance by Twitter/X. - -6. **Base Rates and Historical Precedent**: - - Generally, once bans on social media platforms are lifted after compliance, they are not frequently reinstated in the short term unless new violations occur. This forms a reasonable base case where the page would likely be updated to reflect the current state. - -7. **Bias and Uncertainty Reduction**: - - Historical overconfidence in predicting outcomes suggests I should be cautious in providing extreme probabilities. Empirical evidence suggests only about 35% of similar binary outcome questions tend to resolve positively. - -Considering all these factors, the most likely outcome is that Wikipedia will not list Brazil as ""currently"" blocking Twitter/X by the end of 2024. Given the combination of resolved legal issues, operational resumption, and a significant timeframe for updates, I assess a low probability that Wikipedia will erroneously continue to list Brazil as blocking Twitter/X. - -### Final Probability Calculation: - -- Considering the resolution of key issues and the prompt update nature of Wikipedia: - - **Probability of Wikipedia Listing Brazil as Blocking Twitter/X**: Low. -- Adjusting for possible overconfidence and newfound historical context without extreme skewing: - -Probability: 10%" -29109,"At the end of 2024, will Wikipedia still list Brazil as ""currently"" blocking access to Twitter/X?","To accurately predict whether Wikipedia will list Brazil as ""currently"" blocking access to Twitter/X at the end of 2024, we'll carefully analyze the situation using the information provided, considering prior trends, base rates, and potential future developments up to the resolution date. - -### Analysis - -1. **Current Situation and Events**: - - As of October 2024, Brazil has lifted its ban on Twitter/X following compliance by the company, including the payment of a fine and appointment of a local representative. - - The lifting of the ban suggests that Twitter is now accessible in Brazil. - -2. **Recent Developments**: - - The conflicts between Twitter/X and the Brazilian government seems to have been resolved amicably, suggesting no immediate political or legal tension. - - Historical precedent indicates that once bans of such nature are lifted, they are seldom reinstated quickly unless new major developments arise. - -3. **Likelihood of Reimposition**: - - Given the resolution of the issues that caused the ban initially, it seems unlikely that these same issues will quickly resurface to cause another blockage. - - Lula da Silva, the Brazilian President, originally supported the ban, but the resolution suggests cooperation between Twitter/X and the Brazilian government, at least temporarily. - -4. **Wikipedia Update Dynamics**: - - Wikipedia is typically quick to update following major policy changes, particularly when significant news gets widespread coverage, as was the case with this ban and its lifting. - -5. **Timeframe & Base Rates**: - - Historical base rates show that once barriers like this are removed, they tend to remain lifted unless significant political upheavals occur. - - Given there are about two months until the resolution date, the window for dramatic developments is relatively short in political terms. - -6. **Resolution Criteria**: - - The particular way the question resolves emphasizes the status as per Wikipedia's listing, which, considering the lifted ban, would not include Brazil blocking Twitter/X unless reinstated. - -### Prediction Probability - -- With the lifting of the ban and Twitter/X's compliance, there seems a strong base case that Brazil will continue allowing access to Twitter/X. -- There's no current indication of any pending issue or likelihood of the ban being reintroduced. -- I recognize my tendency for overconfidence, so I'll adjust by acknowledging potential unforeseen political developments, albeit low-probability. -- Estimating on these factors, a reasonable balanced forecast needs to remain moderate to account for unforeseen circumstances but lean towards the recent status quo being maintained. - -**Probability Assessment**: Based on the analysis, I conclude there is a low probability that Wikipedia will list Brazil as blocking Twitter/X by the end of 2024. - -Final Prediction: **Probability: 10%**" -29165,"Will Russia have control of Chasiv Yar on December 31, 2024?","To forecast whether Russia will have control of Chasiv Yar on December 31, 2024, I need to consider several aspects, including the current situation on the ground, the strategic importance of Chasiv Yar, historical trends in the conflict, and the resolution criteria provided. - -**1. Current Situation and Recent Developments:** - - As of October 22, 2024, Russian forces have continued offensive operations around Chasiv Yar without confirmed advances. - - Reports indicate that Russian forces are making efforts to capture the city, but Ukrainian defenses remain strong. - - The Siverskyi Donets-Donbas Canal is serving as a critical front line, with Ukrainian forces managing to block Russian small infantry groups. - -**2. Strategic Importance:** - - Chasiv Yar holds significant strategic value due to its elevation, offering a potential advantage for advancing on other key cities in Donetsk Oblast. - - Despite its importance, both parties are aware of its significance, so concentrated efforts by Ukraine to protect it can be expected. - -**3. Historical Trends and Base Rates:** - - The conflict in the Donbas region has seen both periods of rapid change and prolonged stalemates. - - Historically, possession of strategically significant urban areas in this conflict can change hands over months rather than weeks. - - Given the time left until the resolution date (a little over two months), substantial shifts in control are less likely under current stalemate conditions. - -**4. Resolution Criteria and Past Predictions:** - - The resolution date is December 31, 2024, with the ISW map being the authoritative source. The map's categorization of control will determine the outcome. - - My past tendency toward overconfidence suggests I should apply a correction to avoid overstating the likelihood of a Russian takeover. - -**5. Uncertainty and Time Frame Analysis:** - - The variability in frontline control in Ukraine-Russia conflicts can be significant, but recent reports suggest Ukrainian defenses are currently robust in the Chasiv Yar area. - - Given two months to the resolution date, the odds of a radical shift given the current status seem limited. - -**6. Adjusting for Overconfidence:** - - Historical data shows only about 35% of similar predictions resolve positively, emphasizing caution. - -**Chain of Thought:** - 1. Assess the recent news and conditions on the ground. - 2. Consider the strategic importance and the resources each side is willing to commit. - 3. Weigh historical data and variability in conflict conditions. - 4. Correct for documented prediction biases (such as overconfidence). - 5. Use all these factors to determine a reasoned probability. - -Taking all these points into account, the likelihood of Russia having control over Chasiv Yar by December 31, 2024, seems relatively low. While not impossible, significant changes would have to occur in a relatively short timeframe, which seems unlikely based on current conditions and historical analysis. - -**Final Probability: 25%**" -29170,"Will William Ruto cease to be President of Kenya before January 1, 2025?","Given the task of forecasting whether William Ruto will cease to be President of Kenya before January 1, 2025, let's proceed with a structured analysis using the provided information, historical precedents, and the political context. - -### Current Context and Analysis: - -1. **Political Unrest**: The intense protests and demands for Ruto's resignation reveal a significant level of political instability. Such unrest can escalate into political crises, including forced removals or resignations. The protests' intensity, significant fatalities, and nationwide reach suggest a highly volatile situation. - -2. **Cabinet Dismissal and Resignations**: The dismissal of most of the cabinet and the controversial resignation of the police chief indicate internal turmoil within the government. These events might signal attempts by Ruto to stabilize his administration, but they also highlight a precarious political climate. - -3. **Expert Opinions and Previous Cases**: Historically, political leaders facing mass protests and demands for resignation do not always step down, particularly in African nations where leaders often have a tighter grip on power. However, abrupt changes can occur, especially if internal political and external pressures mount significantly. - -4. **Timeline Consideration**: There are just over two months until the resolution date, which impacts the likelihood of such a significant political change. Rapid political shifts can occur in unstable environments, but the timeline is narrow, reducing extreme probabilities. - -5. **Base Rates and Precedents**: The historical likelihood of a sitting president in Africa being removed in similar circumstances varies. Coups and forced resignations are not uncommon, but they also depend heavily on the specific country's political landscape, military loyalty, and international pressures. - -6. **Default Resolution Scenario**: If no significant changes occur, Ruto will remain in office, which is the current default scenario given the information at hand. - -7. **Proximity to Resolution Date**: With the resolution date so near, major changes in Ruto's status would require an immediate and precipitating event, which does reduce probabilities relative to a longer timeframe. - -### Probability Assessment: - -With these aspects in mind, the probability of William Ruto ceasing to be President of Kenya before January 1, 2025, requires a balance between acknowledging the current unrest and considering the brevity of the timeframe. Historical data suggest that while such developments are possible, they remain relatively rare without escalated intervention or an immediate triggering event. - -Taking into account my past tendency for overconfidence and recognizing the importance of avoiding extremes, while the political situation is undoubtedly serious, the specific window for Ruto's removal is rather short. - -**Final Probability Assessment:** -Probability: 15%" -29168,"Before Election Day, will Kamala Harris appear on the Joe Rogan podcast?","To forecast the likelihood of Kamala Harris appearing on the Joe Rogan podcast before Election Day, November 5, 2024, we need to consider a variety of factors including strategic importance, past behavior, and current political strategies. - -### Step-by-Step Analysis - -1. **Base Rate and Historical Context**: - - Historically, high-profile political figures may not frequently appear on podcasts that lean significantly towards an audience which may not align with their political base. Joe Rogan's podcast is known for having a more libertarian or right-leaning audience. While politicians do utilize podcasting, such appearances usually align more closely with their existing voter demographics unless a strategic outreach is planned. - - In terms of high-level figures, the appearance on platforms outside of their typical media circuits is notable but not common without strategic incentive. - -2. **Strategic Importance**: - - Kamala Harris appearing on the podcast could be a strategic move to reach younger male voters, a demographic she traditionally struggles with. Given the tight race as indicated by the recent polls, additional outreach could be worthwhile. - - However, the decision also carries the risk of alienating her base or falling into a narrative trap given Joe Rogan's potentially opposing viewership. - -3. **Timeframe for Action**: - - The resolution date is in approximately 12 days. This is a relatively short period, suggesting that if an appearance were planned, there would likely have been stronger signals or announcements by now. Typically, campaign strategies of this nature take time to set up, including negotiations, scheduling, and pre-appearance promotion. - -4. **Recent News and Speculation**: - - As per the assistant’s information, there are reports of negotiations but no confirmation. This suggests that while there is some possibility, nothing is set in stone, and campaigns may be weighing pros and cons. - - The appearance of Trump on the Joe Rogan podcast indicates the relevance of podcasts in this election cycle but also highlights the complexity for Harris’s team given the audience overlap. - -5. **Default Behavior**: - - If no significant changes occur, it is more likely that Harris will not appear, given that she has not set a precedent this cycle of appearing in such oppositional environments. - -6. **Offsetting Overconfidence**: - - Historically, my forecasts have skewed towards overconfidence. Given the high uncertainty and the short timeframe, it's crucial not to assign a very high probability to such speculative forecasts lacking concrete evidence. - -7. **Uncertainty and Distribution**: - - There is inherent uncertainty, but adjusting for base rates and typical patterns, the probability should reflect that high-profile events without strong leads tend not to materialize in politically tense environments. - -### Final Probability Assessment - -Given all the factors, I must assign a probability that incorporates the possibility while recognizing the constraints and norms: - -- **Probability that Kamala Harris will appear on the Joe Rogan podcast before November 5, 2024**: - -- After evaluating all the information I've considered, the final forecast must lean towards unlikely given current evidence and short timeline: - -**Probability: 10%**" -29167,"Before Election Day, will Donald Trump appear on the Call Her Daddy podcast?","Given the task and guidelines, we can systematically break down the thought process to arrive at a rational probability estimate: - -1. **Nature of the Event**: - - This question falls under the ""Other Binary Outcome Event"" category. It pertains to whether a specific and publicized guest appearance will occur on a podcast before a certain date. - -2. **Default Behavior and Campaign Strategies**: - - **Current Status**: Trump has been actively engaging with various media outlets, especially those popular with younger audiences. However, no appearance on ""Call Her Daddy"" is confirmed. - - **Podcast Invitation**: An invitation by itself does not guarantee an appearance. The Vulture report indicates an invitation has been extended, but without confirmation, it remains speculative. - - **Campaign Strategy**: Trump's appearances on notable platforms like Joe Rogan's podcast demonstrate a strategy aimed at maximizing reach to specific demographics. However, not all podcast invitations are necessarily accepted or aligned with campaign priorities. - -3. **Time Frame and Change Potential**: - - With just under two weeks to the resolution date (Election Day on November 5), there is limited time for any last-minute arrangements. The podcast might want a strategic timing closer to the election, influencing the likelihood. - - Media appearances are generally planned in advance due to scheduling, and while last-minute decisions are possible, they are less common. - -4. **Base Rates and Historical Precedent**: - - When reviewing historical precedents of political candidates appearing on podcasts or similar media shows, appearances are generally announced well-in-advance of the actual release. - - This is particularly true for high-profile political figures, where public and media interest are high. - -5. **Novel Factors**: - - The political landscape is dynamic with high media engagement leading up to elections. Podcasts offer a direct and personable approach to voters that has been increasingly utilized. - - However, given Trump's other commitments, the specific choice to appear on this podcast, considering its demographic and reach, might not be prioritized over other appearances. - -6. **Distribution of Likelihood**: - - The cumulative likelihood of an unplanned podcast appearance becoming finalized decreases as we approach the resolution date due to scheduling complexities. - - Analyzing the likelihood across potential shorter timeframes shows a decreasing probability curve as the timeframe shortens. - -7. **Consideration of Overconfidence**: - - As the guidelines suggest, given my own historical overconfidence in forecasts and the general likelihood of events, I must be cautious not to overestimate the probability based on mere speculation. - -Considering these factors, it seems unlikely but not impossible for Trump to appear on the ""Call Her Daddy"" podcast before the resolution date. The base rate of two-week timeframe for unexpected podcast appearances for high-profile political figures favors skepticism. - -After recalibrating for potential biases and inherent uncertainties: - -**Probability: 10%**" -29164,"Will the UK Abolish the Two-Child Benefit Cap before January 1, 2025?","To assess the likelihood of the UK abolishing the two-child benefit cap before January 1, 2025, let's go through a structured analysis: - -### Contextual Factors - -1. **Current Government Stance**: Prime Minister Keir Starmer's Labour government has expressed opposition to abolishing the cap due to financial constraints, despite internal dissent. This is a significant factor that leans towards maintaining the status quo, especially in the absence of a compelling financial plan to support abolition. - -2. **Internal Party Conflict**: The suspension of seven Labour MPs who opposed the policy underscores the division within the party. However, the suspension also indicates that the government is holding a firm line, reducing the likelihood of an abrupt policy reversal due to internal pressure within the given timeframe. - -3. **Public Opinion**: The YouGov poll indicates that 60% of the general public, including 50% of Labour supporters, support retaining the cap. This public sentiment provides a political buffer for the government to maintain the policy, as it aligns with majority public opinion. - -4. **Financial Implications**: Abolishing the cap reportedly costs £2.5 billion annually. Given the government's stated financial constraints, this significant cost makes policy change less likely without a well-defined financial strategy to support it feasibly. - -5. **Timeframe and Legislative Process**: The resolution date is imminent, with less than three months remaining. Introducing and passing new legislation or amendments through Parliament, especially on a contentious issue, typically requires a longer timeframe. There’s no recent indication or progress toward such legislative action, such as a standalone bill or amendment in a finance bill. - -6. **Recent Developments**: In recent budget announcements and welfare reports, there have been no signals or proposed changes directly related to the cap. This absence further suggests that changes to the cap are not on the immediate agenda. - -### Historical and Base Rate Considerations - -- Policy changes similar to abolishing an existing social policy require substantial political will and public support. Historically, such changes occur over months to years, not within weeks, unless there is an urgent catalyst or political crisis driving rapid action, neither of which is evident here. - -- Given the historical rate of positive resolution, and the typical political and legislative inertia, similar policy changes before tight deadlines often do not occur without significant pressures or shifts. - -### Probability Assessment - -Based on the examination above, the probability of the UK abolishing the two-child benefit cap before January 1, 2025, is low. The government's current stance, public opinion, the financial burden of abolishing the cap, and the typical time required for legislative processes imply a low probability of change within the short deadline. - -Considering the tendency for unexpected developments in politics, but also balancing this against my historical overconfidence, I will adjust to avoid an overly confident prediction and consider scenarios that could defy the odds, while still recognizing the prevailing signals and constraints. - -Final Answer: **Probability: 5%**" -29163,"Will JD Vance cease to be Donald Trump's running mate before November 5, 2024?","To assess the probability of JD Vance ceasing to be Donald Trump's running mate before November 5, 2024, let's follow a structured approach: - -### Step 1: Analyze the Context and Recent Information -1. **Recent Activities and Statements**: JD Vance participated in a vice-presidential debate recently, with no indication of stepping down, which supports his continued candidacy. -2. **Controversies and Health**: Past controversies involving JD Vance and his statements don't show an imminent risk of resignation or withdrawal. Unlike Eagleton in 1972, there's no known health issue or major scandal currently putting his candidacy at risk. -3. **Historical Precedent**: The well-known precedent of candidates withdrawing from a ticket due to scandal or health issues (like Eagleton) is relatively rare. Political campaigns tend to be resilient unless faced with substantial and immediate crises. - -### Step 2: Consider Timeframe and Base Rates -1. **Timeframe**: With only about two weeks remaining until the resolution date, the window for any major disruption is narrowing. Political campaigns are highly visible and generally prevent sudden changes this close to an election unless due to extreme circumstances. -2. **Base Rate of VP Withdrawals**: Historically, vice-presidential candidates rarely withdraw from tickets, especially so close to an election. This is often due to the strategic stability parties desire leading up to election day and the complexity of replacing a candidate. - -### Step 3: Weigh Uncertainties and Possible Factors -1. **Unknown Unknowns**: Politics, by its very nature, can be unpredictable. Scandals or significant health issues can emerge without warning. However, the lack of any new negative information about JD Vance reduces this risk. -2. **Structural Factors**: The Republican Party, like any major political party, will try to maintain stability and minimize disruptions unless absolutely necessary. Only a severe misstep or scandal could force such a significant change. - -### Step 4: Adjust for Personal Bias and Scoring Methodology -1. **Compensate for Overconfidence**: Given a noted tendency towards overconfidence and historical data showing lower resolution rates for positive outcomes, be wary of assuming a high probability for unexpected events. -2. **Statistical Analysis**: Assume a conservative forecasting model. The low likelihood of withdrawal within such a narrow timeframe limits the risk percentage. - -### Final Probability Estimation -- Based on the structured analysis, past precedence, the lack of significant catalyst for change, and the short timeframe remaining, the likelihood of JD Vance ceasing to be the running mate before November 5, 2024, is quite low. - -**Probability: 5%** - -This estimation signals a cautious acknowledgment of very low but non-zero risk factors while heavily weighting the current status quo and historical stability patterns." -29162,Will the S&P 500 index go up in November 2024?,"To forecast whether the S&P 500 index will rise during November 2024, let's analyze the situation systematically, considering all pertinent factors. - -### 1. **Historical Base Rates and Seasonality**: -- Historically, stock markets tend to react positively during the fourth quarter, with November being statistically one of the better months for the S&P 500. It would be helpful to quantify this specifically in terms of past November performances, but generally, the market mood is often more optimistic due to end-of-year activities and holiday periods. -- However, it's crucial to note that past performance provides a general context rather than a deterministic forecast. - -### 2. **Economic Indicators and Interest Rates**: -- Current skepticism over aggressive rate cuts by the Federal Reserve suggests economic policies may remain relatively constant in the short term. Rising interest rates often exert downward pressure on stock prices, but if rates stabilize or increase only slightly, the market might absorb this information without significant downside. - -### 3. **Political and Economic Context**: -- The mention of the potential political impact tied to the U.S. presidential election (highlighting Republican candidate Donald Trump) introduces additional uncertainty. Markets sometimes react to perceived policy prospects touted by particular politicians, which can lead to increased volatility. -- Assuming an increase in political uncertainty as November progresses, this aspect could have a dampening effect on stock market optimism. - -### 4. **Current Market Performance**: -- With the most recent performance showing declines, particularly due to fluctuations in the bond market and mixed corporate earnings, this tells us markets are already showing stress signals. -- The bond market's impact, as described, suggests investors are currently wary, looking for stability which is not quite apparent. - -### 5. **Market Analysts Insight**: -- Experts like Goldman Sachs indicating a slowdown suggests the short-term outlook, at least, is tepid. This analyst behavior can sometimes guide institutional strategies, which have a significant impact on market movements. - -### 6. **Volatility and Recent Trends**: -- Recent losses ending a consistent rally indicate that volatility may see an uptick. This instability can lead to sharp moves in either direction, but given the context and the monthly window, a stabilization or a sideways movement could be more reasonable, leading us to expect a modest increase in the probability of the index going up. - -### Combined Analysis: -- We must incorporate the high degree of volatility as expected in November 2024. With less than a week remaining before the start of the forecasted month, most market participants might already be setting positions based on current conditions. -- Balancing these inputs against past observations of fourth-quarter performance, combined with the knowledge that markets are influenced by year-end fiscal strategies, suggests reasons to anticipate a positive movement but not overwhelmingly so given the mixed indicators. - -Given all these considerations and aligning with historical data while accounting for potential bias towards overconfidence observed in past forecasts, I estimate the probability of the S&P 500 rising in November 2024 as follows: - -**Probability: 57%** - -This reflects a cautious optimism, recognizing both positive historical seasonality trends and the mixed signals from current market conditions." -29161,"On October 31, 2024, will VoteHub's Early Voting Tracker show Democrats having 2/3rd or higher of the mail and absentee ballots returned in Pennsylvania?","To evaluate this forecasting question, we need to consider several factors carefully: - -1. **Historical Data and Base Rates**: Historically, Democrats have had a significant share of mail and absentee ballot returns in Pennsylvania. For instance, in past elections such as 2020, a substantial proportion of mail ballots came from registered Democrats, primarily due to broader support for mail voting among Democrats. This historical tendency should serve as a baseline. - -2. **Current Trends and Data**: As of October 24, 2024, we don't have specific data from VoteHub's tracker for the party distribution of early votes in Pennsylvania. However, high voter engagement and technical issues hint at strong participation, though they don’t specify party leanings. - -3. **Timing and Variance**: There is only one week until the resolution date. This limited timeframe implies that drastic changes in trends (which would be necessary to radically shift the party distribution from a plausible current trend) are unlikely. Weekly variance in mail and absentee returns might occur, but extreme changes are improbable unless supported by some significant political event (e.g., heightened Democratic campaign efforts). - -4. **Polls and Political Climate**: Although specific VoteHub data isn’t presently available, political analysts and recent electoral patterns (especially in a politically aligned state like Pennsylvania) can suggest that Democrats might lead in mail voting. However, specifics about the extent to which they lead (i.e., achieving 2/3rds or higher) are not substantiated without hard data. - -5. **Known Unknowns**: Absence of up-to-date data forces reliance on general trends and historical data. VoteHub being nonpartisan is an assurance of data reliability, but the absence of recent updates requires a cautious approach. - -6. **Cognitive Bias Awareness**: Historically, only about 35% of questions have resolved positively, which is lower than intuitive expectations. This warrants a conservative probability estimate concerning the realization of a 66.7% threshold. - -Considering the factors above, we must weigh the historical precedent of high Democratic participation in mail voting against the uncertainty and lack of granular data showing they exceed the two-thirds majority mark distinctly by October 31, 2024. Given historical trends and the current lack of contradictory data but also allowances for unexpected shifts: - -**Probability Estimate**: - -There's a reasonable chance Democrats will have a majority of mail-in ballots, yet achieving the ambitious 2/3rd threshold might be overly optimistic without further specific data. I'll accommodate for potential overconfidence and adjust downward. - -**Final Probability: 40%**" -29160,"Before December 31, 2024, will Tech Crunch report new layoffs at Google or Alphabet?","To predict the likelihood of TechCrunch reporting new layoffs at Google or Alphabet before December 31, 2024, we need to carefully evaluate both current conditions and historical patterns. - -### Analysis: - -1. **Historical Context & Base Rate**: - - Google has announced multiple rounds of layoffs throughout 2024 (January, April, May, and July), which suggests a recent pattern of workforce reductions. - - The tech industry overall has experienced significant layoffs, over 130,000 job cuts in 2024, indicating wider sector trends of restructuring and cost-cutting. - -2. **Recency & Trends**: - - The last reported layoff at Google was in July 2024. For three months, no new layoffs have been reported, suggesting a potential stabilization or pause in such announcements. - - With the only a bit more than two months left till the end of the resolution period, the proximity to the deadline means significant changes might be less probable. However, industry trends could still prevail. - -3. **Current Economic Environment**: - - Economic conditions, such as concerns over economic recession and impacts of artificial intelligence on employment, still persist and might encourage further layoffs. - -4. **Company Strategy**: - - Alphabet (Google's parent company) may continue to streamline its operations to focus on strategic areas. Past behavior suggests that layoffs could be a part of this strategy. - -5. **Potential for New Developments**: - - Although stable in the short term, sudden changes in economic conditions or corporate strategy could result in more layoffs. - -6. **Considerations of Risk**: - - Overconfidence bias from prior forecasts should be compensated by treating the situation conservatively, given TechCrunch's role as the sole determinative source for this resolution. - -### Probability Assessment: - -- The ongoing industry-wide layoffs and Google's recent pattern make additional layoff announcements possible. -- The three-month hiatus in layoffs and the narrowing timeline to December 31, 2024, slightly reduce the likelihood of such an event. -- The overall economic context and Google's history in 2024 slightly skew towards potential further layoffs. - -Considering these factors, I estimate the probability of TechCrunch reporting new layoffs at Google or Alphabet before December 31, 2024, to be **Probability: 45%**. - -This accounts for historical patterns balanced against recent stabilization, company strategy inclinations, and the diminishing time window." -29159,"Will at least 200 Benin Bronzes go from the British Museum to Nigeria before January 1, 2025?","To forecast the probability of at least 200 Benin Bronzes moving from the British Museum to Nigeria by January 1, 2025, we need to carefully evaluate several factors. - -1. **Legal Constraints**: The British Museum has stated legal limitations on the transfer of its collections, making their permanent transfer unlikely without legislative change. Historically, the UK has strong legal frameworks protecting museum collections, which adds a significant barrier. - -2. **Current Holdings and Repatriation Trends**: Although over 900 Bronzes reside at the British Museum, the entire collection is not at risk of immediate repatriation. While some institutions have returned single items, the mass return of 200 items is unprecedented and would likely require extensive negotiation and planning. - -3. **Public and Institutional Pressure**: There is growing pressure for the repatriation of looted artifacts like the Benin Bronzes. However, pressure and rhetoric do not equate to action, especially in the face of legal impediments, and this pressure has yet to manifest in substantive transfers from the British Museum. - -4. **Recent Developments**: Recent cases show some institutions are returning Benin artifacts, yet these have been isolated incidents involving far fewer than 200 artifacts. Furthermore, the timeline involved in such negotiations is usually lengthy, exceeding a few months. - -5. **Timeframe**: With just over two months until the resolution date, the timeframe is severely limited for any significant movement to occur, especially given the bureaucratic and diplomatic hurdles that would need to be overcome. - -6. **Base Rates and Novel Developments**: Historically, mass repatriation from major institutions like the British Museum hasn't occurred rapidly or frequently. No truly novel developments have indicated a departure from these trends besides rising public discourse. - -Considering these factors, the base case (no significant movement) is strongly supported by historical precedent and current legal constraints. Time constraints for a bureaucratically-heavy action like this are also critical. - -Probability of the bronzes moving by the deadline remains low due to these barriers. However, acknowledging my prior overconfidence, I'll make sure to adjust appropriately for uncertainty. - -Given the historical and legal context, my forecast is that the probability of at least 200 Benin Bronzes moving from the British Museum to Nigeria by January 1, 2025, is low but not zero, so a cautious estimate is appropriate. - -Probability: 3%" -29165,"Will Russia have control of Chasiv Yar on December 31, 2024?","Let's approach this step-by-step: - -1. Current situation: As of October 24, 2024, Russian forces have not breached the defense line in Chasiv Yar, despite daily attempts. They've made some advances but haven't gained full control. - -2. Time frame: We have about 2 months until the resolution date (December 31, 2024). This is a relatively short period in terms of significant territorial changes in a war, especially for a well-defended position. - -3. Historical context: The battle for Chasiv Yar has been ongoing since April 2024, with Russia failing to capture it by their May 9 goal. This suggests strong Ukrainian resistance and/or limitations in Russian offensive capabilities. - -4. Strategic importance: Chasiv Yar's elevation makes it strategically significant. This means both sides likely view it as a priority, increasing the intensity of the fight. - -5. Recent trends: Russian forces have been making gradual advances in eastern Ukraine, but at a high cost. Ukrainian forces have been reinforcing their positions in Chasiv Yar. - -6. External factors: The potential influx of Western military aid to Ukraine could shift the balance, but the timing and impact of this are uncertain. - -7. Base rate consideration: In recent years, Russian advances in Ukraine have generally been slow, especially in well-defended areas. Quick captures of strategically important locations have been rare. - -8. Uncertainty factors: Weather conditions, potential strategic shifts, changes in leadership, or unexpected events could influence the outcome. - -Now, let's consider different timeframes: - -- Probability of Russian control in 1 month (by November 24, 2024): 15% -- Probability of Russian control by December 31, 2024 (question timeframe): 25% -- Probability of Russian control within 6 months (by April 24, 2025): 40% - -The increase in probability over time reflects the cumulative chances of Russian success, balanced against potential Ukrainian counteroffensives or stabilization of the front. - -Rationale for the main prediction (25% by December 31, 2024): - -1. The current situation doesn't favor imminent Russian control. -2. Two months is a short time for significant territorial changes, especially for a well-defended, strategically important location. -3. Historical precedent suggests slow Russian advances in similar situations. -4. Ukrainian forces seem committed to defending Chasiv Yar and have been reinforcing their positions. -5. The potential for new Western aid to Ukraine adds uncertainty that slightly reduces the likelihood of Russian success. -6. However, the ongoing Russian offensive and their gradual gains in the region suggest some chance of success, hence not an extremely low probability. - -Given the high stakes nature of this forecast and the tendency towards overconfidence, I'm adjusting my initial assessment slightly downward to account for unforeseen factors and the general difficulty of predicting war outcomes. - -Probability: 25%" -29170,"Will William Ruto cease to be President of Kenya before January 1, 2025?","Let's approach this step-by-step: - -1. Current Situation: - - William Ruto is facing significant challenges, including protests and calls for his resignation. - - He has made concessions, including firing almost his entire cabinet. - - The police chief has resigned amid accusations of excessive force. - -2. Time Frame: - - Today's date is October 24, 2024. - - The resolution date is January 1, 2025. - - This gives approximately 2 months for a change in leadership to occur. - -3. Historical Context: - - Presidential changes outside of elections are relatively rare in Kenya. - - The last non-election change was in 2002 when Daniel arap Moi stepped down after 24 years in power. - -4. Current Political Landscape: - - Ruto has been in office since September 2022, so he's relatively early in his term. - - He still appears to be actively engaged in presidential duties, including international engagements. - -5. Protest Momentum: - - While protests have been intense, they haven't yet reached a level that has forced Ruto from office. - - The cabinet reshuffle may have temporarily reduced some pressure. - -6. Legal and Constitutional Considerations: - - Impeachment would require a majority vote in the National Assembly and a two-thirds majority in the Senate. - - A coup seems unlikely given Kenya's relatively stable democratic history in recent years. - -7. International Factors: - - There's no indication of significant international pressure for Ruto to step down. - -8. Time Probability Distribution: - - Considering the short timeframe (about 2 months), the likelihood of a leadership change is lower than if we had a longer period. - - If we consider a 1-month timeframe, the probability would be even lower, perhaps around 5%. - - If we extend to a 1-year timeframe, the probability might increase to around 25-30% due to more time for political developments. - -9. Base Rate Consideration: - - The base rate for a president leaving office within a 2-month period outside of scheduled elections is quite low, likely less than 5% in stable democracies. - -10. Uncertainty Factors: - - The political situation in Kenya is fluid and could change rapidly. - - There's always a possibility of unforeseen events (health issues, scandals, etc.) - -Given these factors, I believe the probability of Ruto ceasing to be President before January 1, 2025, is relatively low but not negligible. The short timeframe is a significant factor reducing the likelihood. However, the ongoing protests and political pressure create a non-trivial possibility. - -Accounting for my historical tendency to be overconfident and the fact that only about 35% of questions typically resolve positively, I will adjust my estimate slightly downward. - -Probability: 12% - -This reflects a low but not insignificant chance of Ruto leaving office in the given timeframe, considering the current political tensions balanced against the short time period and the relative stability of Kenya's political system." -29168,"Before Election Day, will Kamala Harris appear on the Joe Rogan podcast?","To evaluate the probability of Kamala Harris appearing on the Joe Rogan podcast before Election Day on November 5, 2024, I will consider several factors: - -1. **Discussions and Campaign Strategy**: There have been discussions between Harris's representatives and Rogan's team, but no confirmation yet. These discussions imply at least some interest or potential possibility. - -2. **Strategic Importance**: Harris reaching out to Rogan’s predominantly male audience could be strategically important, especially in a competitive election race. Joe Rogan's podcast has a massive reach, which may be appealing for any political candidate. - -3. **Political Environment and Timing**: Given that we are very close to the November 5 deadline, timing is crucial. There is limited time to secure and manage such an appearance, given campaign schedules and election prep. - -4. **Base Rate Analysis**: Assessing the likelihood based on previous high-profile political appearances on Rogan's podcast, it's known that while political figures do appear, these are relatively rare occurrences, particularly so close to Election Day. - -5. **Uncertainty and Novel Factors**: While Rogan offers a vast audience, there are no guarantees such an appearance would be politically beneficial – it could come with risks of unpredictable discourse. This uncertainty might deter the campaign from following through. - -6. **Default Resolution**: If nothing changes, the default outcome is that the podcast appearance will not happen before November 5. This should heavily weigh into the final probability assessment, especially noting that historically, only about 35% of events resolve positively in such predictions. - -**Probability Distribution**: Given these considerations, I would anticipate a higher likelihood of organization delays, scheduling conflicts, increased scrutiny, and strategic hesitations given the nearness of Election Day. - -Predictive probabilities for time frames: - -- **10/24/2024–10/31/2024**: The probability of the appearance happening in this upcoming week is low, given the current lack of confirmation. -- **10/31/2024–11/4/2024**: Slightly higher probability but still low; last-minute appearances in high-stakes contexts are logistically challenging and uncommon. - -In summary, given all considerations, my calibrated judgment, the understanding of potential overconfidence bias, and the historical precedents, I assess the probability of Kamala Harris appearing on the Joe Rogan podcast before Election Day as follows: - -Probability: 15%" -29167,"Before Election Day, will Donald Trump appear on the Call Her Daddy podcast?","Let's approach this step-by-step: - -1. Base rate: Donald Trump has not appeared on the ""Call Her Daddy"" podcast before. This suggests a low baseline probability. - -2. Time frame: We have about 12 days until the resolution date. This is a very short timeframe, which significantly reduces the likelihood of a new, unannounced event occurring. - -3. Current focus: Trump is scheduled to appear on Joe Rogan's podcast, which targets a similar demographic (young males). This suggests his campaign is already addressing this audience through a different, possibly more aligned, platform. - -4. Invitation: Alex Cooper has invited Trump to the podcast. This slightly increases the probability, but an invitation alone doesn't guarantee an appearance, especially on such short notice. - -5. Campaign strategy: While Trump has been engaging with podcasts, his appearances have been primarily on platforms with hosts or audiences that align more closely with his base. ""Call Her Daddy"" might not fit this pattern as well as other options. - -6. Kamala Harris precedent: Harris's appearance on the podcast might make Trump's team consider it, but it could also make them less likely to follow suit to differentiate their strategy. - -7. Election proximity: With the election so close, Trump's campaign is likely to focus on high-impact, carefully planned appearances rather than potentially risky new ventures. - -8. Scheduling challenges: Organizing and recording a podcast episode, especially with a high-profile guest like a presidential candidate, typically requires more than 12 days of lead time. - -9. Past behavior: Trump's campaign has not shown a pattern of last-minute, surprise media appearances, especially this close to an election. - -10. Risk vs. reward: The potential benefits of reaching the ""Call Her Daddy"" audience might not outweigh the risks of an uncontrolled interview environment for Trump's campaign at this critical juncture. - -Given these factors, I believe the probability of Trump appearing on ""Call Her Daddy"" before Election Day is very low. However, we must account for some uncertainty and the possibility of unexpected events. - -Let's consider different timeframes: -- Probability within 1 week: 0.5% -- Probability within 2 weeks (our actual timeframe): 1% -- Probability within 1 month: 3% -- Probability within 3 months: 8% - -These estimates reflect the increasing likelihood over time, but still maintain a low overall probability due to the factors discussed. - -Adjusting for my historical overconfidence and the general trend of questions resolving negatively, I will slightly increase the probability from my initial estimate. - -Probability: 2%" -29164,"Will the UK Abolish the Two-Child Benefit Cap before January 1, 2025?","Thank you for the detailed instructions. I'll approach this question step-by-step, considering various factors and timelines. - -1. Current political landscape: - - Labour government opposes scrapping the cap due to financial constraints. - - Internal conflict within Labour Party, with 7 MPs suspended for voting against the party line. - - 60% of the public, including 50% of Labour supporters, oppose lifting the cap. - -2. Financial considerations: - - IFS estimates an annual cost of £2.5 billion to abolish the cap. - - OBR has discussed challenges of making significant changes to current welfare policies. - -3. Timeline analysis: - - Today's date: October 24, 2024 - - Resolution date: January 1, 2025 - - Time remaining: Approximately 2 months - -4. Legislative process: - - Abolition could occur through a standalone bill or as part of a finance bill. - - Given the short timeframe, it's unlikely a new bill could be introduced and passed before the resolution date. - -5. Historical precedent: - - The cap has been in place since 2017 without significant changes. - - No recent movements or announcements regarding abolition. - -6. Public sentiment and political will: - - Majority public opposition to lifting the cap. - - Government's stated position against scrapping the cap. - -7. Probability distribution across different timeframes: - - 1 month (by November 24, 2024): 0.1% - - 2 months (by January 1, 2025 - resolution date): 0.5% - - 6 months (by April 24, 2025): 2% - - 1 year (by October 24, 2025): 5% - -Given the short timeframe, current political stance, public opinion, and financial constraints, the probability of the two-child benefit cap being abolished before January 1, 2025, is extremely low. The lack of recent movement or announcements further supports this conclusion. - -However, I need to account for unforeseen circumstances and the possibility of rapid policy shifts, albeit unlikely. The slightly higher probabilities for longer timeframes reflect the potential for change given more time, but even these remain low due to the current political and public sentiment. - -Adjusting for my historical overconfidence and the general tendency for questions to resolve negatively, I'll slightly increase the probability from my initial assessment, but it remains very low. - -Probability: 1% - -This low probability reflects the extremely unlikely scenario of the cap being abolished in such a short timeframe, given the current political, public, and financial factors, while still acknowledging a minute possibility of unforeseen rapid policy change." -29163,"Will JD Vance cease to be Donald Trump's running mate before November 5, 2024?","To approach this question, I'll consider several factors and timelines: - -1. Current situation: - - JD Vance is actively campaigning and supporting Trump - - No signs of discord or intention to step down - - Election is only 12 days away - -2. Historical precedent: - - Thomas Eagleton case in 1972 shows last-minute changes can happen - - However, such cases are extremely rare - -3. Timeframes to consider: - a) Next 6 days (half the remaining time): Probability of Vance stepping down - 0.1% - b) Next 12 days (until resolution date): Probability - 0.2% - c) If the question asked about 1 month: Probability - 0.5% - d) If the question asked about 6 months: Probability - 2% - -4. Factors that could lead to Vance stepping down: - - Health issues (sudden illness or accident) - - Unexpected scandal - - Major disagreement with Trump - - External pressure from the Republican Party - -5. Mitigating factors: - - Very short timeframe until election - - High stakes of the election - - Strong alignment between Trump and Vance - - No recent indicators of any issues - -6. Base rate considerations: - - VP candidate changes this close to an election are extremely rare - - No technological or environmental factors likely to affect this - - Current political climate doesn't suggest higher likelihood than historical precedent - -7. Uncertainty factors: - - Possibility of unknown information coming to light - - Unpredictable nature of Trump's decision-making - -Given the extremely short timeframe and the lack of any indicators suggesting a change, the probability of JD Vance ceasing to be Trump's running mate before November 5, 2024, is very low. However, we must account for the small possibility of unexpected events. - -Adjusting for my tendency to be overconfident and considering the rarity of such events, I would set the probability slightly higher than my initial calculation but still very low. - -Probability: 1% - -This reflects the extremely low likelihood of such a change occurring in the next 12 days, while still acknowledging the small possibility of unforeseen circumstances. The 1% probability accounts for the rarity of the event, the short timeframe, and the need to avoid overconfidence in predicting an almost certain outcome." -29162,Will the S&P 500 index go up in November 2024?,"**Step-by-Step Evaluation Process for Predicting the S&P 500 Index Movement in November 2024** - -### 1. Base Rate Assessment: -- Historical data indicates that, on average, the S&P 500 experiences upward movement approximately 60% of the time for any given month. -- However, considering your note about historical competition outcomes (favoring negative predictions), let's assume a slight adjustment to remain cautious and avoid overconfidence. - -### 2. Current Market Context: -- The S&P 500 index is having recent volatility, with back-to-back losses by October 24, 2024. -- Skepticism exists over potential aggressive Federal Reserve rate cuts, which may keep investor sentiment cautious. -- Political uncertainties such as the impending presidential election and geopolitical tensions could also lead to increased market volatility. - -### 3. Economic Indicators: -- Earnings reports present mixed results, which can lead to a lack of overall confidence in the market. Positive earnings outcomes from some sectors (e.g., Tesla) could drive short-term rallies, but mixed signals can create uncertainty. -- Stable 10-year Treasury note yields at 4.2% indicate a neutral stance for interest rates, indirectly suggesting the potential for stability in stock markets. - -### 4. Timeframe Consideration: -- With only about five weeks until the resolution date, there's limited time for major tectonic shifts. -- Given the usual market fluctuations, even within a month volatility is common. This timeframe means daily or weekly swings could largely impact the index. - -### 5. Potential Catalysts and Risks: -- Positive catalysts: Improved earnings reports, constructive economic data, stable geopolitical climates could foster market optimism. -- Negative influences: Political shocks post-election, Fed's unexpected policy adjustments, or deteriorating international conditions could further impact the market negatively. - -### 6. Final Calculation and Adjustment: -- Accounting for historical data, recent trends, potential catalysts, market volatility, and your tendency toward overconfidence, moderation is crucial. -- Weighting the likelihood of positive resolution at a lower probability than pure historical averages considers recent uncertainty and your historical bias. - -### Conclusion: -Given the weighted consideration for all factors above, a moderate prediction is favorable. You are adjusting down the probability from a simple historical analysis to account more heavily for the identified risks and the competition context of overconfidence. - -**Final Probability Assessment:** - -""Probability: 53%""" -29161,"On October 31, 2024, will VoteHub's Early Voting Tracker show Democrats having 2/3rd or higher of the mail and absentee ballots returned in Pennsylvania?","To determine the probability of VoteHub's Early Voting Tracker showing Democrats with 2/3rds or higher of the mail and absentee ballots returned in Pennsylvania by October 31, 2024, we need to consider several factors: - -1. **Current Status:** As of October 23, 2024, Democrats hold 60.9% of the mail and absentee ballots returned. This percentage is below the required 66.7% for a ""Yes"" resolution. The difference is significant and represents a gap needing to be closed in just a week. - -2. **Trends:** The data suggests a narrowing gap compared to previous election cycles. In 2020, Democrats had a D+41.6 lead, but now it's D+31.7. This indicates that Republican voters are engaging more in early voting, potentially reducing the historically large Democratic lead in mail and absentee voting. - -3. **Time Frame:** We have about one week remaining until the resolution date. While shifts in early voting trends can happen, such a significant swing (from 60.9% to 66.7%) in such a short period is unlikely based on past voting behaviors. - -4. **Behavioral Patterns:** Historically, Democrats are more likely to vote via mail and absentee ballots than Republicans, but the current trend shows Republicans are increasing their share of early votes. - -5. **Base Rates:** Considering past elections, Democrats have had larger shares of mail-in ballots. However, they haven't necessarily increased their percentage significantly within a short time frame close to the resolution date, as seen in current trends. - -6. **Novel Factors:** There are no significant novel political, technological, or environmental factors noted that would suggest a dramatic change in voter behavior within the next week. - -7. **Confidence Adjustment:** Historically, my predictions have been overconfident, and given the current data and time remaining, a significant shift seems improbable. Conservative adjustments are necessary. - -### Calculation of Probability: -Given the above analysis: -- With Democrats currently at 60.9%, assuming an equal rate of Democratic and Republican voting behavior changes in this short time frame does not support a dramatic 6% increase required in Democratic ballots. -- The trend is moving contrary to the shift needed. -- Historical and current behavior suggests the likelihood of Democrats reaching 66.7% is relatively low in such a short window. - -When factoring my historical overconfidence, an estimate around the probability of 66.7% Democratic mail-in reporting seems much more uncertain and unlikely to happen by October 31, 2024. - -Therefore, based on the data, the trend, and the time remaining, a conservative probability estimate is prudent. - -**Probability: 15%**" -29160,"Before December 31, 2024, will Tech Crunch report new layoffs at Google or Alphabet?","Let's approach this step-by-step: - -1. Recent History: - - Google has had layoffs reported in January, April, May, and July 2024. - - The most recent layoff in July 2024 affected Cloud teams. - - This shows a pattern of periodic restructuring throughout the year. - -2. Time Frame: - - Today is October 24, 2024. - - The resolution date is December 31, 2024. - - This gives about 2 months and 1 week for new layoffs to be reported. - -3. Tech Industry Trends: - - The tech industry has seen significant layoffs in 2024, with over 130,000 job cuts across 457 companies as of mid-October. - - This suggests ongoing restructuring in the tech sector. - -4. Google/Alphabet Specific Factors: - - Google has been focusing on AI and cloud services. - - The company may continue to realign its workforce to these strategic priorities. - -5. Economic Factors: - - The broader economic situation and any potential changes in the next two months could influence layoff decisions. - -6. Historical Pattern: - - Google has reported layoffs roughly every 2-3 months in 2024. - - If this pattern continues, another round of layoffs before the end of the year is plausible. - -7. End-of-Year Considerations: - - Companies often make workforce adjustments before the end of the fiscal year. - - However, layoffs during holiday seasons (December) are less common due to PR considerations. - -8. Probability Distribution: - - Given the 2-month timeframe, let's consider shorter and longer periods: - - 1 month (by November 24): 20% chance - - 2 months (by December 24): 40% chance - - 3 months (by January 24, 2025): 55% chance - -9. Base Rate Adjustment: - - Historically, only about 35% of questions resolved positively in past competitions. - - This suggests we should adjust our probability downward slightly. - -10. Uncertainty Factors: - - The exact timing of corporate decisions is inherently uncertain. - - TechCrunch's reporting might lag behind actual layoff events. - -Given these factors, I believe there is a moderate chance of TechCrunch reporting new layoffs at Google or Alphabet before December 31, 2024. The historical pattern suggests it's likely, but the short timeframe and end-of-year considerations slightly reduce the probability. - -Adjusting for the historical 35% positive resolution rate and the uncertainty involved, I'll slightly lower my initial estimate. - -Probability: 38% - -This reflects a cautious estimate given the short timeframe, the historical pattern of layoffs at Google, and the broader tech industry trends, while also accounting for the tendency towards overconfidence in past predictions." -29159,"Will at least 200 Benin Bronzes go from the British Museum to Nigeria before January 1, 2025?","Thank you for the detailed instructions. I'll approach this question step-by-step, considering various factors and timelines. - -1. Current situation: - - The British Museum holds over 900 Benin Bronzes. - - They have refused to transfer ownership, citing legal constraints. - - There's growing pressure for repatriation, but no concrete plans announced. - -2. Timeline consideration: - - Today's date: October 24, 2024 - - Resolution date: January 1, 2025 - - Time remaining: Approximately 2 months - -3. Historical precedent: - - Some institutions (e.g., University of Cambridge) have returned Benin Bronzes. - - The British Museum has not repatriated any significant number of artifacts. - -4. Legal and institutional barriers: - - The British Museum Act of 1963 prevents the museum from giving away its collections. - - Changing this law would require an act of Parliament, which is a time-consuming process. - -5. Political and public pressure: - - There's increasing pressure for repatriation. - - However, this pressure has not led to significant action from the British Museum so far. - -6. Logistical considerations: - - Even if a decision were made to repatriate, organizing the transfer of 200+ artifacts would likely take considerable time. - -7. Alternative timelines: - - Probability of repatriation within 1 month: <1% - - Probability of repatriation within 6 months: 2% - - Probability of repatriation within 1 year: 5% - -8. Base rate consideration: - - Large-scale repatriations of this nature are rare. - - The base rate for such events occurring within a 2-month window is extremely low. - -9. Uncertainty factors: - - Potential for sudden policy changes or diplomatic breakthroughs. - - Possibility of legal workarounds or temporary loan agreements. - -Given the short timeframe, legal barriers, and the museum's historical reluctance to repatriate, the probability of at least 200 Benin Bronzes being returned to Nigeria before January 1, 2025, is extremely low. The logistical challenges alone make it highly improbable within this timeframe, even if a decision were made today. - -However, we must account for some uncertainty and the possibility of unexpected developments. The growing pressure for repatriation and changing attitudes towards colonial artifacts provide a small chance of a breakthrough. - -Considering all factors, and adjusting for potential overconfidence, I would estimate the probability as follows: - -Probability: 1% - -This extremely low probability reflects the significant barriers and short timeframe, while still acknowledging a minute possibility of an unexpected and rapid development in this area." -29209,"Will the Federal Reserve cut interest rates before January 1, 2025?","To forecast the likelihood of the Federal Reserve cutting interest rates before specific dates (November 1, December 1, and January 1, 2025), it's essential to analyze the provided economic data, market expectations, and the Fed's historical behavior. Currently, there are predictions of at least one more 25-basis-point cut for the remaining meetings of the year, suggesting moderate likelihood of further cuts. Economic data shows robust job growth, and inflation is above the 2% target, which complicates the situation. Despite the strong labor market, consumer confidence is waning, which might urge the Fed to make further cuts. However, historically, the Fed tends to be cautious and data-dependent, suggesting a less aggressive approach. - -Moreover, the short timeline and proximity to a major U.S. election add unpredictability, but they might also encourage a conservative monetary policy stance to avoid influencing market volatility. With multiple reports indicating increased skepticism about aggressive policy easing, it seems the likelihood of another rate cut is plausible but not highly certain. The probability decreases as the timeframes get closer to today and increases closer to January 1, 2025, given the pattern of action around official meetings and data releases. - -Given the above factors, the probability of a rate cut is highest by January 1, 2025, and lower for the earlier months. We'll assign probabilities based on this reasoning, while being mindful of not making overly confident predictions due to potential underestimating or overestimating global or domestic developments." -29208,"Will the Federal Reserve cut interest rates before December 1, 2024?","To forecast the likelihood of the Federal Reserve cutting interest rates before specific dates (November 1, December 1, and January 1, 2025), it's essential to analyze the provided economic data, market expectations, and the Fed's historical behavior. Currently, there are predictions of at least one more 25-basis-point cut for the remaining meetings of the year, suggesting moderate likelihood of further cuts. Economic data shows robust job growth, and inflation is above the 2% target, which complicates the situation. Despite the strong labor market, consumer confidence is waning, which might urge the Fed to make further cuts. However, historically, the Fed tends to be cautious and data-dependent, suggesting a less aggressive approach. - -Moreover, the short timeline and proximity to a major U.S. election add unpredictability, but they might also encourage a conservative monetary policy stance to avoid influencing market volatility. With multiple reports indicating increased skepticism about aggressive policy easing, it seems the likelihood of another rate cut is plausible but not highly certain. The probability decreases as the timeframes get closer to today and increases closer to January 1, 2025, given the pattern of action around official meetings and data releases. - -Given the above factors, the probability of a rate cut is highest by January 1, 2025, and lower for the earlier months. We'll assign probabilities based on this reasoning, while being mindful of not making overly confident predictions due to potential underestimating or overestimating global or domestic developments." -29207,"Will the Federal Reserve cut interest rates before November 1, 2024?","To forecast the likelihood of the Federal Reserve cutting interest rates before specific dates (November 1, December 1, and January 1, 2025), it's essential to analyze the provided economic data, market expectations, and the Fed's historical behavior. Currently, there are predictions of at least one more 25-basis-point cut for the remaining meetings of the year, suggesting moderate likelihood of further cuts. Economic data shows robust job growth, and inflation is above the 2% target, which complicates the situation. Despite the strong labor market, consumer confidence is waning, which might urge the Fed to make further cuts. However, historically, the Fed tends to be cautious and data-dependent, suggesting a less aggressive approach. - -Moreover, the short timeline and proximity to a major U.S. election add unpredictability, but they might also encourage a conservative monetary policy stance to avoid influencing market volatility. With multiple reports indicating increased skepticism about aggressive policy easing, it seems the likelihood of another rate cut is plausible but not highly certain. The probability decreases as the timeframes get closer to today and increases closer to January 1, 2025, given the pattern of action around official meetings and data releases. - -Given the above factors, the probability of a rate cut is highest by January 1, 2025, and lower for the earlier months. We'll assign probabilities based on this reasoning, while being mindful of not making overly confident predictions due to potential underestimating or overestimating global or domestic developments." -29212,Will Republican Congressman David Schweikert be re-elected in Arizona's 1st Congressional District in the 2024 election?,"To forecast the likelihood of Congressman David Schweikert being re-elected in Arizona's 1st Congressional District, I will consider several factors, including the current political environment, historical base rates, and potential changes leading up to the election. - -1. **Cook Political Report and FiveThirtyEight Analysis**: The Cook Political Report rates this race as a ""Toss Up,"" indicating a very competitive election. Although FiveThirtyEight gives Republicans a slight 52% chance of maintaining overall control of the House, this prediction is not specific to Schweikert's race. The competitiveness of this district suggests that neither Schweikert nor his opponent has a clear advantage. - -2. **Historical Trends**: Arizona's 1st Congressional District has recently shown some Democratic leanings, with President Biden winning by a small margin in 2020. This indicates that while the district is competitive, it does have a slight Democratic shift that could impact Schweikert's chances. - -3. **Controversial Remarks Impact**: Schweikert's controversial remarks about women and abortions could negatively affect his re-election chances. These remarks may alienate moderate and undecided voters, particularly in a district trending Democratic. - -4. **Base Rates and Default Resolution**: Historically, incumbents have an advantage in congressional elections. However, given the ""Toss Up"" nature of this race and the district's recent Democratic trend, Schweikert's incumbency advantage may be diminished. - -5. **Remaining Time and Uncertainty**: The election is close (just over a week away), which limits the time for new developments to significantly alter the current dynamics. This short timeframe emphasizes the importance of current polling and expert ratings. - -6. **Compensating for Overconfidence**: In past forecasts, overconfidence has been a recognized bias. Given the ""Toss Up"" nature of this race, I should avoid extreme probabilities and recognize high uncertainty. - -Taking all these factors into account, Schweikert's chances of being re-elected seem moderately lower than a coin flip. While incumbency should favor him, the district's recent partisan trends and his controversial statements could lead to an upset. - -After considering the competitive nature of this election, the slight Democratic shift in the district, the timeframe, and my tendency to overestimate certainty, I estimate the probability of David Schweikert being re-elected as follows: - -**Probability: 45%**" -29211,"Will Donald Trump sell any shares of Trump Media before January 1, 2025, as revealed by a Form 4 filing?","Let me think through this step by step: - -1. First, let's establish the timeline: -- Today: October 25, 2024 -- Resolution: January 1, 2025 -- ~2 months and 1 week timeframe - -2. Key factors favoring a NO: -- Trump explicitly stated he doesn't plan to sell -- Stock price jumped on this news, indicating market believes him -- Trump's historical pattern of maintaining control of his assets -- Short timeframe (only ~2 months) reduces likelihood -- Resolution requires both sale AND Form 4 filing being visible on specific website -- Political considerations during campaign season - -3. Key factors favoring a YES: -- Lockup period has expired -- Stock has tripled since lockup expiration -- Significant financial value (~$4.4B) -- Trump's known financial pressures from legal cases -- Other insiders have sold - -4. Timeline analysis: -- If it were to happen within 1 month: 8% -- If it were to happen within 6 months: 25% -- If it were to happen within 1 year: 40% - -5. Specific constraints: -- Must appear on company IR page, not just SEC EDGAR -- Need time for both transaction and filing -- Holiday period at end may affect filing timing - -6. Base rate considerations: -- Most insider sales happen gradually -- Political figures rarely make large stock moves during campaigns -- Similar situations (newly public companies post-lockup) typically see some insider selling - -7. Risk adjustments: -- Given the binary nature and short timeframe, need to adjust down from raw probability -- Must account for both transaction and filing requirements -- Holiday period could affect timing/visibility - -8. Confidence calibration: -- Initial instinct was 15% -- Historical overconfidence suggests moderating -- Short timeframe suggests lower probability -- Multiple required steps (sale + filing + website posting) each reduce probability - -After considering all factors, I believe there's a relatively low but not negligible chance. The short timeframe is a major limiting factor, and Trump's public statement carries weight during campaign season. However, the financial pressures and precedent of other insider sales keep this from being extremely low probability. - -Adjusting for historical overconfidence and the multiple required steps for resolution, and considering the specific resolution criteria: - -Probability: 12%" -29210,"At the end of 2024, will Wikipedia still list Venezuela as ""currently"" blocking access to Twitter/X?","Let's start by breaking down the information and evaluating the different factors relevant to this prediction: - -1. **Current Status**: As of October 25, 2024, Venezuela has an indefinite ban on Twitter/X, announced in August 2024. There have been no updates indicating the lifting of this ban. - -2. **Political and Social Context in Venezuela**: The ban on Twitter/X was instigated by the Venezuelan government to control communication during anti-government protests. Political motivations play a significant role, and given the indefinite nature of the ban, it implies a lack of urgency or pressure from within the government to reverse this decision. Venezuela has a history of media control and censorship, reinforcing the likelihood that the ban could persist. - -3. **Resolution Criteria**: For this question to resolve as ""Yes,"" Wikipedia must still list Venezuela as blocking access to Twitter/X as of January 1, 2025. - -4. **Reliability of Wikipedia**: Given that Wikipedia is a user-edited platform, there could be changes to the entry. However, without new developments lifting the ban or correcting prior information, it's reasonable to assume the current situation will persist. - -5. **Timeframe**: We have just over two months until the resolution date. Political shifts or policy changes are typically slow, especially in a situation like Venezuela's, where government control over media is a strategic tool. This timeframe suggests that significant changes are less likely barring some unexpected political developments or international pressure. - -6. **Base Rates and Similar Historical Events**: In countries with similar political dynamics, such censorships are often prolonged. Typically, these bans remain in place unless there is a strong impetus to reverse them, such as a change in government, significant public pressure, or international intervention. - -7. **Evaluation of Probability Distribution**: Given the context and historical patterns in such scenarios, the default likelihood of ""no change"" (which is a ""Yes"" resolution) has a high base probability. While unforeseen events can always occur, they are statistically less probable given the short timeline. - -8. **Bias Correction**: Historically, my predictions have tilted towards overconfidence with outcomes not aligning as positively. Given this, I'll apply a slight conservative adjustment against a high positive resolution probability that might intuitively seem likely. - -Considering all the factors, the default case is a continuation of the current Wikipedia listing of Venezuela as blocking Twitter. The probability of any change within this timeframe appears low, given precedent and the nature of such decisions. - -**Final Probability Assessment:** - -Considering all the data points and recognizing my historical overconfidence, I will predict a moderate likelihood, slightly adjusting for the potential of unforeseen changes but affirming the initial analysis. - -Probability: 78%" -29206,"Will Mike McCarthy be the Dallas Cowboys head coach on January 1, 2025?","To forecast whether Mike McCarthy will be the Dallas Cowboys head coach on January 1, 2025, we need to break down the available information and evaluate the likelihood of different scenarios. - -1. **Current Performance and Pressures:** - - The Cowboys have started the 2024 season with a 3-3 record and a recent significant loss to the Lions. This performance, coupled with criticism from figures like Troy Aikman and public disappointment from owner Jerry Jones, creates pressure on McCarthy. - - The nature of the criticism and the disappointing record suggest vulnerability for McCarthy's position, particularly since the Cowboys are a franchise accustomed to higher performance expectations. - -2. **Potential Replacements:** - - Speculation about possible replacements like Bill Belichick and Bobby Slowik increases the threat to McCarthy's job security. Belichick's name, in particular, is noteworthy because his association with success could fulfill Jones's desire for a quick turnaround. - - Historical trends show that when high-profile alternatives are considered, coaching changes can become more likely, especially with an owner like Jerry Jones, who is known for making bold decisions. - -3. **Timing and Historical Trends:** - - Although a midseason firing is less common, the offseason presents a typical window for coaching changes. Therefore, significant pressure does not necessarily mean immediate action before the end of the regular season, but the timeframe leading up to January 2025 is crucial. - - Base rates for coaching changes in the NFL generally point to several firings every season, but changes aren't guaranteed, especially late in the season where strategic adjustments could impact the assessment of his performance. - -4. **Recency and Owner's Behavior:** - - Jones's recent comments and the team's performance indicate dissatisfaction, but Jones's past behavior suggests he values stability unless a high-impact alternative is available. It's also relevant to consider that the Cowboys would need a clear path to a better outcome if they were to make a change. - -5. **Evaluation of Prior Outcomes:** - - Historically, about 35% of forecasted questions resolve positively, so initial calibration suggests caring for inflating probabilities based on wishful thinking for changes. - -Given the evidence and base rates, McCarthy's current coaching record, the critical comments by influential figures, and considering alternative options, a change in his position can't be ruled out. However, the specific timing by January 1, 2025, balances the anticipation of potential change with historical precedent that not every season ends with a coaching change. - -Therefore, after considering the above factors, I estimate a reasonable probability of McCarthy being the head coach on January 1, 2025 as: - -**Probability: 55%**" -29205,"Will the Russian government officially ban YouTube before January 1, 2025?","Let me think through this step by step: - -1. Timeline Analysis: -- Today is Oct 25, 2024 -- Resolution date is Jan 1, 2025 -- This gives approximately 2 months and 1 week -- This is a relatively short timeframe for major policy changes - -2. Historical Context: -- Russia has banned other Western platforms (Instagram, Facebook) -- However, YouTube has remained accessible despite previous threats -- The process of banning major platforms typically involves: - * Multiple warnings - * Development of alternatives - * Gradual restrictions before full ban - * Legislative process - -3. Current Situation: -- Recent slowdowns reported (July 2024) -- Russian alternatives being developed (RuTube, VK video) -- Official statements are mixed: - * Klimenko predicts 3-5 months timeline - * Chernyshov walked back statements about full blockade - * No formal government position announced - -4. Weighing Factors: - -Pro-ban: -- Ongoing tension with Western platforms -- Development of domestic alternatives -- Recent technical issues -- Pattern of increasing restrictions - -Against ban: -- YouTube's significant user base in Russia -- Technical complexity of full ban -- Economic impact -- Short timeframe until resolution -- No official government position yet - -5. Timeline Probability Distribution: -- Next 1 month: 15% -- By resolution date (2 months): 25% -- Within 6 months: 45% -- Within 1 year: 65% - -6. Key Considerations: -- The short timeframe is crucial - major policy changes typically take longer -- No formal legislative process has begun -- Technical preparations seem incomplete -- Previous threats haven't materialized - -7. Confidence Adjustment: -- Historical overconfidence noted -- High uncertainty given mixed signals -- Short timeframe increases certainty of ""No"" -- Base rate of similar events suggests longer timeline - -Given the short timeframe, lack of formal process initiation, and historical precedent of longer implementation periods for similar bans, I assess the probability as relatively low but not negligible. The technical preparations and statements suggest movement toward a ban, but the timeline is likely longer than the resolution date. - -Probability: 25%" -29204,"Will the US Steel/Nippon Steel merger collapse before January 1, 2025?","To forecast the likelihood of the US Steel/Nippon Steel merger collapsing before January 1, 2025, I'll consider several factors: - -1. **Historical Base Rates and Past Precedents**: Historically, mergers and acquisitions often face regulatory challenges, especially when national security concerns are involved. The CFIUS and Department of Justice (DOJ) review processes are rigorous, and not all deals survive these challenges. According to a Government Accountability Office (GAO) report, about 10-15% of major mergers face significant issues or collapse due to regulatory scrutiny each year. However, deals involving national strategic assets or foreign ownership typically have higher scrutiny. - -2. **Political and Regulatory Environment**: The US political environment heavily evaluates foreign acquisitions, especially those involving significant economic implications or strategic industries. The strong opposition from President Joe Biden, Vice President Kamala Harris, and bipartisan congressional concerns is a significant factor suggesting potential blockage. The stated opposition and stated intent to formally block the deal are serious considerations. - -3. **Ongoing CFIUS and DOJ Reviews**: There is a possibility of administrative actions, such as CFIUS concluding the merger might threaten national security. While CFIUS cannot directly block the deal, a negative finding would significantly influence the likelihood of a presidential decision to halt the merger. With the presidential election happening, the political landscape could affect the regulatory decisions. The CFIUS allowing to refile and extending the timeline suggests time is being bought to potentially address concerns rather than an outright opposed stance. - -4. **Economic Factors and Industry Trends**: The steel industry plays a crucial role in the U.S. economy. While some experts argue that the merger could revitalize U.S. Steel, the primary concerns around workforce impact, supply chain integrity, and overall control cannot be understated. The benefits have been publicly stated but haven’t been universally accepted in the political discourse. - -5. **Timing and Decision Windows**: The resolution deadline of December by CFIUS and the possibility of actions occurring before year-end complicate the timeline. An imminent decision post-election means more clarity, yet it might not occur by the resolution endpoint. It’s critical to evaluate what actions are feasible within a two-month timeframe. - -6. **Balancing Base Case vs. Current Indicators**: The political pressure and timing suggest significant hurdles remain, increasing the probability of the merger’s collapse. However, historical base rates of mergers generally overcoming regulatory issues mean caution should temper potential overconfidence. - -With these considerations and recognizing potential biases, I will assign a probability to the merger collapsing before January 1, 2025. - -- Given the current opposition and regulatory delays, I estimate a roughly 60% chance of a collapse (through formal blocking or other criteria like mutual/ unilateral termination) occurring within this tight timeframe. -- This reflects the significant political headwinds and complexity of successful acquisition proceedings, but also acknowledges potential mitigating steps Nippon Steel might pursue to address concerns and complete the deal. - -Final prediction: ""Probability: 60%""" -29203,"Will China officially announce export restrictions on any additional metals before January 1, 2025?","Given the question of whether China will officially announce export restrictions on additional metals before January 1, 2025, let's break down the analysis step by step, considering the context, background information, and relevant factors: - -### Context and Background: - -1. **Recent Patterns and Trends**: - - China has announced export restrictions on several key metals: antimony in September 2024, graphite in 2023, and germanium and gallium earlier. This indicates a pattern of leveraging export controls in response to geopolitical pressures, particularly from the U.S. - - Given their significant role in global metal supply chains, China's decision to impose these controls seems strategic, aligning with political and economic objectives. - -2. **Ongoing Trade Tensions**: - - Trade tensions between the U.S. and China have escalated with retaliatory measures on both sides. This could prompt further unilateral trade actions, including imposing more export restrictions by China. - -3. **Speculation and Industry Concerns**: - - Industry experts are concerned about potential restrictions on tungsten and other critical metals. Such speculations are fueled by previous patterns of behavior by the Chinese government. - -### Timeframe Analysis: - -- We have approximately 2 months and a few days left until the resolution date (January 1, 2025). The timeframe is relatively short, which favors the status quo, consistent with the historical baseline where unlikely events remain unlikely over such a brief period. - -- During this period, China's export policy could remain stable unless there is a specific trigger or catalyst, such as a significant geopolitical event or increased U.S. measures, which might prompt a reaction from China. - -### Base Rates and Historical Precedents: - -- Historically, China's series of export restrictions have been concerted and strategic, but not arbitrary or too frequent within short intervals. The time between different export announcements in the past has generally spanned at least several months, suggesting that frequent or rapid successive announcements are less likely unless driven by significant external factors. - -### Evaluation of Current Risk: - -- As of October 2024, there have been no concrete indications of an impending announcement regarding further export restrictions from credible sources or government statements. -- Given the pattern of past actions, further restrictions following the recent antimony control might occur, but within a short time frame and absent clear provocations, such measures seem less likely. - -### Probability Assessment: - -- Taking all these factors into account, especially the short timeframe and past patterns of announcements, we should cautiously balance between favoring the status quo and acknowledging potential geopolitical shifts. -- Given your history of overconfidence, this prediction should lean conservatively. - -### Adjusted Probability Distribution: - -1. **In the next month (till late November 2024)**: Low probability given the immediate aftermath of antimony controls and lack of new provocations. Likelihood ~ 10%. -2. **By the resolution date (January 1, 2025)**: Slightly higher probability within the two months due to potential geopolitical fluctuations but remains low due to base rate of such events and timeframe. Likelihood ~ 20%. - -Therefore, based on the above analysis and compensating for past overconfidence, a cautious prediction is warranted that places a moderately low probability on the event occurring within the specified timeframe: - -**Probability: 20%**" -29202,Will Eric Adams be Mayor of New York City on the 1st of January 2025?,"To forecast whether Eric Adams will be Mayor of New York City on January 1, 2025, I will consider several key factors and establish a probability: - -1. **Indictment and Legal Proceedings**: - - Eric Adams faces serious indictments, including bribery and wire fraud. This creates a significant risk to his tenure. Legal proceedings can be slow, and while there's enough time for developments before the resolution date, these processes often extend over months or years. - -2. **Political Pressure and Calls for Resignation**: - - There is substantial political pressure for Adams to resign, with significant public opposition reflected in the Marist College poll. However, political pressure alone does not necessarily lead to resignation. Adams' apparent determination to stay in office and lack of compelling force might suggest resilience to pressure in the short term. - -3. **Governor's Power and Political Dynamics**: - - Governor Hochul has the authority to remove Adams after a hearing. However, she has indicated hesitance to act on this power. Additionally, removing a mayor is a politically complex and unprecedented move that might not align with Hochul’s interests unless there is overwhelming and urgent necessity. - -4. **Timeframe and Novelty**: - - Historically, indictments alone have not avoided individuals from holding office if they choose to fight while maintaining the position, as direct and immediate removals are rare. However, the timeframe until January 1, 2025, is about two months, posing a considerable uncertainty window. - -5. **Base Rates and Historical Precedent**: - - Resignations due to criminal proceedings and political pressure do occur, but are relatively rare, particularly with strong legal defenses and public figures showing determination to stay despite indictments. - -Statistical Evaluations: - -- **Base Rate Consideration**: Given only about 35% of questions historically resolved positively, caution against assuming Adams will definitively remain in office is necessary. -- **Legal and Political Defenses**: Assuming Adams leverages both, maintaining office in the short term is feasible barring dramatic, unforeseen changes. -- **Bias Adjustment**: Need to adjust for overconfidence, acknowledging there are multiple paths to a positive resolution considering existing pressures and powers. - -Considering all these factors, a reasonable probability estimation balances legal vulnerability with political resilience. The likelihood of Adams being removed or resigning within the specificity of the question is challenging but not heavily skewed. - -To adjust for historical personal overconfidence and given the time left, I cautiously estimate the probability rather than overestimating plaintiff comfort from resisting change. - -**Probability: 65%** - -This reflects significant uncertainty due to potential legal and political developments." -29201,"Will any more United Kingdom MPs be suspended from their party, resign, or change allegiance before 2025?","Let me think through this step by step: - -1) First, let's consider the time frame: -- Only about 2 months until resolution -- This is a relatively short period for political changes -- End of year/holiday period typically has less political activity - -2) Recent history: -- 8 MPs have faced suspension/resignation since July 2024 election -- This suggests about 2 MPs per month on average recently -- However, this was during a particularly contentious period - -3) Base rate analysis: -- Looking at historical data, MP suspensions/changes tend to cluster around controversial votes or scandals -- The rate isn't constant but comes in waves -- End of year typically sees fewer political developments -- Recent cluster might suggest we're past the peak for this cycle - -4) Specific factors: -- Recent controversial votes have already triggered suspensions -- No major controversial legislation expected before 2025 -- Parliament typically less active in December -- Those most likely to change parties often do so earlier in term - -5) Timeline distribution: -- Next 2 weeks: ~5% chance -- Next month: ~15% chance -- By resolution date: ~30% chance -- If the question extended to March 2025: ~45% chance -- If the question extended to June 2025: ~65% chance - -6) Contributing factors: -+ Recent precedent of suspensions -+ Ongoing political tensions -- Short timeframe -- Holiday period -- Most contentious issues already addressed -- Early in new parliament term - -7) Risk adjustments: -- Need to account for overconfidence bias -- Must consider unexpected scandals -- Political situations can change rapidly -- But two months is a short window - -8) Uncertainty analysis: -- High uncertainty due to political nature -- But limited by short timeframe -- Recent events provide good baseline -- Must account for holiday period reducing activity - -Given all these factors, I believe there's a moderate chance of at least one MP being suspended, resigning, or changing allegiance in this period. The recent history suggests activity, but the short timeframe and holiday period significantly reduce the likelihood. I need to adjust down from the recent rate due to timing and regression to mean. - -Probability: 31% - -This reflects: -- The short timeframe -- Holiday period reduction in activity -- Recent precedent of changes -- Adjustment for overconfidence -- Base rate consideration -- Higher uncertainty in political predictions -- End of year typically being quieter politically" -29272,Will a Llama model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 4th Quarter of 2024?,"To assess the likelihood of different models being ranked #1 on the LMSYS Chatbot Arena Leaderboard, we need to consider several aspects. First, the base rate that no rank changes occur in the leaderboard structure is critical, and historically, frequent ranking changes are unlikely in such a short time span given a month until the resolution date. Secondly, we need to evaluate the recent performance of each group of models. - -1. **Llama Models (ID: 29272)**: The Llama model is tied for seventh and another model (Llama-3.1-Nemotron-70B-Instruct) ranks ninth, showing they are strong but not top contenders. Nvidia's enhancement may not be classified under 'llama' due to branding. Heavy user adoption can influence ranks, but not convincingly enough to disrupt from the seventh place. - -2. **Grok Models (ID: 29271)**, **Claude Models (ID: 29270)**: Similar challenges exist for these groups. No specific data is presented in the provided context, and their historical rankings and absence of sudden advances make a top spot unlikely. - -3. **Gemini Models (ID: 29269)** has not been mentioned among potential strongest competitors per the provided information, suggesting similar improbability unless a sudden large leap is enacted. - -4. **OpenAI Models (ID: 29268)**: These models are consistently top-ranked, and there's a strong base rate that they will retain their position given no dramatic competitor gains are visible for the near future. - -The competitive dynamics, even with significant improvements by NVIDIA or in user base growth, lead to a strong likelihood that OpenAI probably maintains its rank, given these factors. - -This evaluation takes into account that technical and market dynamics typically require more time for structural disruptions to manifest in relative rankings. Overall, close attention is paid to the default positions and current changes to consider momentum present in current standings." -29271,Will a Grok model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 4th Quarter of 2024?,"To assess the likelihood of different models being ranked #1 on the LMSYS Chatbot Arena Leaderboard, we need to consider several aspects. First, the base rate that no rank changes occur in the leaderboard structure is critical, and historically, frequent ranking changes are unlikely in such a short time span given a month until the resolution date. Secondly, we need to evaluate the recent performance of each group of models. - -1. **Llama Models (ID: 29272)**: The Llama model is tied for seventh and another model (Llama-3.1-Nemotron-70B-Instruct) ranks ninth, showing they are strong but not top contenders. Nvidia's enhancement may not be classified under 'llama' due to branding. Heavy user adoption can influence ranks, but not convincingly enough to disrupt from the seventh place. - -2. **Grok Models (ID: 29271)**, **Claude Models (ID: 29270)**: Similar challenges exist for these groups. No specific data is presented in the provided context, and their historical rankings and absence of sudden advances make a top spot unlikely. - -3. **Gemini Models (ID: 29269)** has not been mentioned among potential strongest competitors per the provided information, suggesting similar improbability unless a sudden large leap is enacted. - -4. **OpenAI Models (ID: 29268)**: These models are consistently top-ranked, and there's a strong base rate that they will retain their position given no dramatic competitor gains are visible for the near future. - -The competitive dynamics, even with significant improvements by NVIDIA or in user base growth, lead to a strong likelihood that OpenAI probably maintains its rank, given these factors. - -This evaluation takes into account that technical and market dynamics typically require more time for structural disruptions to manifest in relative rankings. Overall, close attention is paid to the default positions and current changes to consider momentum present in current standings." -29270,Will a Claude model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 4th Quarter of 2024?,"To assess the likelihood of different models being ranked #1 on the LMSYS Chatbot Arena Leaderboard, we need to consider several aspects. First, the base rate that no rank changes occur in the leaderboard structure is critical, and historically, frequent ranking changes are unlikely in such a short time span given a month until the resolution date. Secondly, we need to evaluate the recent performance of each group of models. - -1. **Llama Models (ID: 29272)**: The Llama model is tied for seventh and another model (Llama-3.1-Nemotron-70B-Instruct) ranks ninth, showing they are strong but not top contenders. Nvidia's enhancement may not be classified under 'llama' due to branding. Heavy user adoption can influence ranks, but not convincingly enough to disrupt from the seventh place. - -2. **Grok Models (ID: 29271)**, **Claude Models (ID: 29270)**: Similar challenges exist for these groups. No specific data is presented in the provided context, and their historical rankings and absence of sudden advances make a top spot unlikely. - -3. **Gemini Models (ID: 29269)** has not been mentioned among potential strongest competitors per the provided information, suggesting similar improbability unless a sudden large leap is enacted. - -4. **OpenAI Models (ID: 29268)**: These models are consistently top-ranked, and there's a strong base rate that they will retain their position given no dramatic competitor gains are visible for the near future. - -The competitive dynamics, even with significant improvements by NVIDIA or in user base growth, lead to a strong likelihood that OpenAI probably maintains its rank, given these factors. - -This evaluation takes into account that technical and market dynamics typically require more time for structural disruptions to manifest in relative rankings. Overall, close attention is paid to the default positions and current changes to consider momentum present in current standings." -29269,Will a Gemini model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 4th Quarter of 2024?,"To assess the likelihood of different models being ranked #1 on the LMSYS Chatbot Arena Leaderboard, we need to consider several aspects. First, the base rate that no rank changes occur in the leaderboard structure is critical, and historically, frequent ranking changes are unlikely in such a short time span given a month until the resolution date. Secondly, we need to evaluate the recent performance of each group of models. - -1. **Llama Models (ID: 29272)**: The Llama model is tied for seventh and another model (Llama-3.1-Nemotron-70B-Instruct) ranks ninth, showing they are strong but not top contenders. Nvidia's enhancement may not be classified under 'llama' due to branding. Heavy user adoption can influence ranks, but not convincingly enough to disrupt from the seventh place. - -2. **Grok Models (ID: 29271)**, **Claude Models (ID: 29270)**: Similar challenges exist for these groups. No specific data is presented in the provided context, and their historical rankings and absence of sudden advances make a top spot unlikely. - -3. **Gemini Models (ID: 29269)** has not been mentioned among potential strongest competitors per the provided information, suggesting similar improbability unless a sudden large leap is enacted. - -4. **OpenAI Models (ID: 29268)**: These models are consistently top-ranked, and there's a strong base rate that they will retain their position given no dramatic competitor gains are visible for the near future. - -The competitive dynamics, even with significant improvements by NVIDIA or in user base growth, lead to a strong likelihood that OpenAI probably maintains its rank, given these factors. - -This evaluation takes into account that technical and market dynamics typically require more time for structural disruptions to manifest in relative rankings. Overall, close attention is paid to the default positions and current changes to consider momentum present in current standings." -29268,Will an OpenAI model be ranked #1 overall on the LMSYS Chatbot Arena Leaderboard at the end of the 4th Quarter of 2024?,"To assess the likelihood of different models being ranked #1 on the LMSYS Chatbot Arena Leaderboard, we need to consider several aspects. First, the base rate that no rank changes occur in the leaderboard structure is critical, and historically, frequent ranking changes are unlikely in such a short time span given a month until the resolution date. Secondly, we need to evaluate the recent performance of each group of models. - -1. **Llama Models (ID: 29272)**: The Llama model is tied for seventh and another model (Llama-3.1-Nemotron-70B-Instruct) ranks ninth, showing they are strong but not top contenders. Nvidia's enhancement may not be classified under 'llama' due to branding. Heavy user adoption can influence ranks, but not convincingly enough to disrupt from the seventh place. - -2. **Grok Models (ID: 29271)**, **Claude Models (ID: 29270)**: Similar challenges exist for these groups. No specific data is presented in the provided context, and their historical rankings and absence of sudden advances make a top spot unlikely. - -3. **Gemini Models (ID: 29269)** has not been mentioned among potential strongest competitors per the provided information, suggesting similar improbability unless a sudden large leap is enacted. - -4. **OpenAI Models (ID: 29268)**: These models are consistently top-ranked, and there's a strong base rate that they will retain their position given no dramatic competitor gains are visible for the near future. - -The competitive dynamics, even with significant improvements by NVIDIA or in user base growth, lead to a strong likelihood that OpenAI probably maintains its rank, given these factors. - -This evaluation takes into account that technical and market dynamics typically require more time for structural disruptions to manifest in relative rankings. Overall, close attention is paid to the default positions and current changes to consider momentum present in current standings." -29265,"Will CDC’s highest assessment of the risk posed by H5 bird flu to the US general public before January 1, 2025, be ""Low""?","To forecast these two CDC-related questions about H5 bird flu, let's break down the information provided and related factors. - -1. **Current CDC Assessment**: As of the latest updates, the CDC assesses the risk to the general public as 'Low' and seems consistent in this judgment given the current surveillance and containment measures. - -2. **Surveillance and Monitoring**: The CDC is actively monitoring the situation, but there's no evidence of increased human-to-human transmission nor significant changes in the virus's behavior. - -3. **Past and Recent Trends**: Historically, CDC's risk assessments for influenza and similar infections haven't drastically shifted without substantial evidence of increased transmission risk or virulence. - -4. **Time Until Resolution**: With a little over two months until the resolution date, a significant change in risk assessment by the CDC would likely require a sharp increase in human cases or a definitive change in viral transmission dynamics. - -5. **Expert Opinions**: While there are concerns about potential risks, especially among specific populations with occupational exposure, the CDC's focus remains on areas with such exposures, not the general public. - -6. **Probability Distribution**: Given the consistency in CDC's current risk assessment, the evidence pointing towards a low risk remains compelling. However, it's vital to consider the possibilities of new transmission patterns and expert concerns. - -The event resolution is more aligned with 'Other Binary Outcome Event' as it is not strictly quantitative, nor does it align with the other categories. Hence, the probability rates for each scenario warrant careful distribution based on default behavior and recent trends. - -After taking into account these factors, we can estimate: - -- For the CDC's highest assessment to remain 'Low': Given current consistent communication and lack of evidence suggesting imminent risk elevation, a 90% probability seems prudent. -- For an H5 virus to receive a 'high' emergence risk rating: Despite the concerning news, there hasn't been substantial shift to imply a swift increased viral threat, so a 10% probability could account for possible unforeseen developments over the coming months. - -Lastly, reduction in overconfidence necessitates adjusting the predictions towards central probabilities rather than extremes." -29262,"Will an H5 virus receive an ""emergence"" risk rating categorized as ""high"" by the US CDC Influenza Risk Assessment Tool before January 1, 2025?","To forecast these two CDC-related questions about H5 bird flu, let's break down the information provided and related factors. - -1. **Current CDC Assessment**: As of the latest updates, the CDC assesses the risk to the general public as 'Low' and seems consistent in this judgment given the current surveillance and containment measures. - -2. **Surveillance and Monitoring**: The CDC is actively monitoring the situation, but there's no evidence of increased human-to-human transmission nor significant changes in the virus's behavior. - -3. **Past and Recent Trends**: Historically, CDC's risk assessments for influenza and similar infections haven't drastically shifted without substantial evidence of increased transmission risk or virulence. - -4. **Time Until Resolution**: With a little over two months until the resolution date, a significant change in risk assessment by the CDC would likely require a sharp increase in human cases or a definitive change in viral transmission dynamics. - -5. **Expert Opinions**: While there are concerns about potential risks, especially among specific populations with occupational exposure, the CDC's focus remains on areas with such exposures, not the general public. - -6. **Probability Distribution**: Given the consistency in CDC's current risk assessment, the evidence pointing towards a low risk remains compelling. However, it's vital to consider the possibilities of new transmission patterns and expert concerns. - -The event resolution is more aligned with 'Other Binary Outcome Event' as it is not strictly quantitative, nor does it align with the other categories. Hence, the probability rates for each scenario warrant careful distribution based on default behavior and recent trends. - -After taking into account these factors, we can estimate: - -- For the CDC's highest assessment to remain 'Low': Given current consistent communication and lack of evidence suggesting imminent risk elevation, a 90% probability seems prudent. -- For an H5 virus to receive a 'high' emergence risk rating: Despite the concerning news, there hasn't been substantial shift to imply a swift increased viral threat, so a 10% probability could account for possible unforeseen developments over the coming months. - -Lastly, reduction in overconfidence necessitates adjusting the predictions towards central probabilities rather than extremes." -29264,"Will the World Health Organization designate a new COVID-19 Variant of Concern before January 1, 2025?","To forecast the likelihood of the World Health Organization (WHO) designating a new COVID-19 Variant of Concern (VOC) before January 1, 2025, and the lowest COVID-19 hospitalization rate being below 2.0 between October 5, 2024, and January 4, 2025, we need to assess the current context, base rates, and trends. - -### Question 29264: New COVID-19 VOC Designation by WHO -1. **Base Rates and Historical Context**: Historically, the WHO has been conservative with VOC designations, only upgrading a variant's status when there is significant evidence of increased severity, transmissibility, or impact on public health measures and vaccine efficacy. -2. **Current Situation**: As of late October 2024, no new VOC has been identified. The XEC variant is gaining attention but is not currently classified as a VOC. The WHO's decision depends on if XEC or any other variant shows an unexpected increase in severity or public health impact. -3. **Timeframe and Monitoring**: There are a little over two months until the deadline. While the emergence and rapid classification of variants can happen in weeks, it is more common for such processes to take longer unless driven by substantial risk. -4. **Expert Input**: No strong indications from recent reports predict an imminent VOC designation. - -Taking into account the cautious nature of the WHO's decision-making, the current classification of XEC as a VOI, and base rates, the probability of a new VOC being designated by January 1, 2025, appears low but non-negligible due to the unpredictable nature of virus mutations. - -### Question 29263: COVID-19 Hospitalization Rate Below 2.0 -1. **Type of Event**: This is a 'Disease Spread Rate Event' and is subject to fluctuations in COVID-19 spread, seasonality, and public health responses. -2. **Historical Rates and Trends**: Hospitalization rates of COVID-19 have fluctuated but generally trend lower with increased population immunity and therapeutic interventions. However, winter months often correlate with higher rates due to respiratory viruses' seasonality, including COVID-19. -3. **Variance and Stability**: The variance in hospitalization rates is relatively high due to potential waves of infection influenced by new variants or changes in public health interventions. - -Given the season and typical effects of respiratory illnesses, achieving a hospitalization rate consistently below 2.0 by January 4, 2025, may be challenging, but improvements in treatments and immunity might keep it low. Therefore, the probability is moderately low but not insignificant given uncertainty. - -### Conclusion -The forecasts reflect a balanced consideration of current data, historical precedence, the dynamic nature of COVID-19, and biases: -- The probability of a new VOC designation by January 1, 2025, is estimated to be low, but not negligible. -- The probability of the hospitalization rate falling below 2.0 is likewise possible, but not the most likely outcome given current circumstances and seasonal variations." -29263,"Will the lowest COVID-19 hospitalization rate from October 5, 2024, to January 4, 2025, be below 2.0?","To forecast the likelihood of the World Health Organization (WHO) designating a new COVID-19 Variant of Concern (VOC) before January 1, 2025, and the lowest COVID-19 hospitalization rate being below 2.0 between October 5, 2024, and January 4, 2025, we need to assess the current context, base rates, and trends. - -### Question 29264: New COVID-19 VOC Designation by WHO -1. **Base Rates and Historical Context**: Historically, the WHO has been conservative with VOC designations, only upgrading a variant's status when there is significant evidence of increased severity, transmissibility, or impact on public health measures and vaccine efficacy. -2. **Current Situation**: As of late October 2024, no new VOC has been identified. The XEC variant is gaining attention but is not currently classified as a VOC. The WHO's decision depends on if XEC or any other variant shows an unexpected increase in severity or public health impact. -3. **Timeframe and Monitoring**: There are a little over two months until the deadline. While the emergence and rapid classification of variants can happen in weeks, it is more common for such processes to take longer unless driven by substantial risk. -4. **Expert Input**: No strong indications from recent reports predict an imminent VOC designation. - -Taking into account the cautious nature of the WHO's decision-making, the current classification of XEC as a VOI, and base rates, the probability of a new VOC being designated by January 1, 2025, appears low but non-negligible due to the unpredictable nature of virus mutations. - -### Question 29263: COVID-19 Hospitalization Rate Below 2.0 -1. **Type of Event**: This is a 'Disease Spread Rate Event' and is subject to fluctuations in COVID-19 spread, seasonality, and public health responses. -2. **Historical Rates and Trends**: Hospitalization rates of COVID-19 have fluctuated but generally trend lower with increased population immunity and therapeutic interventions. However, winter months often correlate with higher rates due to respiratory viruses' seasonality, including COVID-19. -3. **Variance and Stability**: The variance in hospitalization rates is relatively high due to potential waves of infection influenced by new variants or changes in public health interventions. - -Given the season and typical effects of respiratory illnesses, achieving a hospitalization rate consistently below 2.0 by January 4, 2025, may be challenging, but improvements in treatments and immunity might keep it low. Therefore, the probability is moderately low but not insignificant given uncertainty. - -### Conclusion -The forecasts reflect a balanced consideration of current data, historical precedence, the dynamic nature of COVID-19, and biases: -- The probability of a new VOC designation by January 1, 2025, is estimated to be low, but not negligible. -- The probability of the hospitalization rate falling below 2.0 is likewise possible, but not the most likely outcome given current circumstances and seasonal variations." -29277,"Will country music singer/songwriter Riley Green endorse Donald Trump in the 2024 election before November 5, 2024?","Let me think through this step by step: - -1. Timeline Assessment: -- Only 8 days until resolution -- Very short window for any change from current status -- Late stage of election cycle when endorsements typically slow down - -2. Current Status: -- No current endorsement -- Default resolution is ""No"" if nothing changes -- Would require active change from status quo to resolve ""Yes"" - -3. Riley Green Context: -- Known conservative leanings but not explicitly political -- Has not made any endorsements in 2024 campaign cycle -- No recent statements indicating likelihood of endorsement - -4. Historical Patterns: -- Country music artists have varying approaches to political endorsements -- Some make explicit endorsements, others stay neutral for commercial reasons -- Late endorsements are less common as they have less impact - -5. Timing Factors: -- Very close to election -- Most celebrity endorsements happen earlier in cycle -- Last-minute endorsements usually only happen if there's a specific campaign push - -6. Risk/Reward Analysis for Green: -- Limited upside to last-minute endorsement -- Potential commercial downside -- No apparent pressure or campaign focus on getting his endorsement - -7. Recent Trends: -- No indication of upcoming endorsement -- No recent political statements -- No campaign events featuring Green scheduled - -8. Probability Distribution: -- If looking at 1 month window: 5% -- If looking at 6 month window (past): would have been 15% -- If looking at full year: would have been 25% - -Given the extremely short timeframe, lack of any indicators, and the fact that most endorsements happen earlier in the cycle, I need to assign a very low probability. The 8-day window is particularly constraining, and there's no evidence of any momentum toward an endorsement. - -Adding extra weight to the default case given the short timeline, and accounting for my historical overconfidence, I need to be particularly conservative in this estimate. - -Probability: 3%" -29276,"Before November 5, 2024, will Kalshi ever show Donald Trump with a 70% chance of winning?","Let me think through this step by step: - -1) First, let's consider the timeline: -- Today is October 28, 2024 -- Resolution date is November 5, 2024 -- This gives only 8 days for the condition to be met - -2) Key factors about the specific threshold: -- 70% is a very high probability threshold -- It requires not just Trump leading, but leading by a significant margin -- Markets tend to be more volatile closer to events, but also more informed - -3) Current market conditions: -- 538 shows Trump at 53% -- Other prediction markets favor Trump but not at 70% -- The race is described as ""tight"" by multiple sources - -4) Historical precedents: -- Presidential election markets rarely show 70%+ probability this close to election -- Even when candidates are heavily favored, markets tend to price in uncertainty -- The week before an election typically shows tightening rather than expansion of leads - -5) Time factors: -- With only 8 days, there's limited time for major shifts -- News cycles would need to be extremely favorable to Trump to drive such a large move -- Markets become more liquid and efficient closer to resolution - -6) Mechanism factors: -- Kalshi is a regulated market with sophisticated participants -- Large moves would require significant new information -- The 70% threshold is particularly high for a two-person race this close to election - -7) Breaking down probabilities by timeframe: -- Next 2 days: 1% chance -- Days 3-5: 2% chance -- Final 3 days: 3% chance -- Total cumulative: ~6% - -8) Risk factors: -- Major scandal affecting Biden -- Significant economic news -- International crisis -- However, impact of such events would be muted by early voting already occurring - -9) Mitigating factors: -- Current polling suggests a close race -- Historical precedent suggests tightening in final days -- Market efficiency tends to prevent extreme probabilities - -Given all these factors, I believe the chance of Kalshi showing Trump at 70%+ in the next 8 days is relatively low. The combination of: -- Very short timeframe -- High probability threshold -- Current market conditions showing a close race -- Historical precedent of markets rarely showing such confidence -- Limited potential for game-changing events this close to election - -All suggest this is unlikely. However, it's not impossible given potential black swan events. - -Probability: 6%" -29275,"Will Montana voters approve CI-128, which would add the right to abortion to the state's constitution?","To forecast whether Montana voters will approve CI-128, I'm considering various factors systematically to estimate the probability of resolution in favor. - -**Legal and Political Context:** -- Montana is generally a conservative state, but it has historically protected some abortion rights as demonstrated by legal abortion until fetal viability. -- The initiative would codify abortion rights in the state constitution, a significant legal change. - -**Support and Opposition:** -- **Supporters** of CI-128, like Planned Parenthood Advocates of Montana and ACLU of Montana, have raised substantially more funding, $16.4 million compared to the $200,000 raised by opponents. This financial disparity might enable more effective campaigning and outreach. -- **Opponents** leverage strong religious and conservative networks, which can galvanize voter bases effectively in a conservative state, potentially negating some financial disadvantage. - -**Public Opinion and Campaign Finance:** -- The more substantial funding from supporters indicates strong organizational backing and possibly reflects public interest or confidence in securing votes. -- Opposition claims that the initiative could lead to unrestricted abortion access, which may resonate with conservative voters. - -**Recent Developments and Broader Context:** -- While healthcare professionals support the initiative, citing negative impacts of abortion restrictions on healthcare delivery, vocal religious opposition highlights the contentious nature of the issue. -- With abortion measures appearing on ballots nationwide, voters may be influenced by wider national trends and sentiments regarding reproductive rights. - -**Timing and Historical Base Rates:** -- With only a few days remaining until the vote, large opinion swings are less likely now than in earlier months, giving more weight to present campaign strengths. -- Historically, progressive measures such as this face an uphill battle in conservative states, but changing attitudes and effective campaigning might lead to an upset. - -**Conclusion:** -- The financial advantage and support from healthcare professionals could tilt the scales in favor of CI-128. -- However, the conservative nature of Montana and strong religious opposition are substantial counterweights. -- Given these points and considering my personal tendency towards overconfidence, it's prudent to adjust downwards from initial high-tech support sentiments. - -In conclusion, weighing both sides and the context, I estimate that the probability of CI-128 being approved by Montana voters is ""Probability: 45%"". This reflects a close and contentious situation, slightly tilted against approval due to historical and cultural factors in Montana." -29274,Will Mayor Brandon Scott receive 70% of the vote in the 2024 Baltimore mayoral race?,"Let me think through this step by step: - -1. This is a Competition Event (political election) question. - -2. Key factors: -- Scott won with 70.5% in 2020 -- He's an incumbent Democrat in a heavily Democratic city -- He won the Democratic primary against Sheila Dixon -- His opponent is a Republican in a strongly Democratic city -- His approval ratings have improved after initial vulnerabilities -- The election is in just over a week - -3. Base rate analysis: -- Baltimore consistently elects Democratic mayors with large margins -- Incumbent mayors typically maintain or slightly decrease their vote share -- 70% is a very specific threshold that leaves little room for error - -4. Time considerations: -- With only 8 days until the election, major shifts are unlikely -- Most voters have likely made up their minds -- Early voting may have already started - -5. Statistical considerations: -- 70% is a high threshold -- Even small changes in turnout patterns could affect the percentage -- Third party candidates or write-ins could impact the total -- Need to account for both over and under performance - -6. Risk factors: -- Specific demographic turnout patterns could vary -- Weather on election day could affect turnout -- National political climate could affect turnout -- Local issues could drive unexpected voting patterns - -7. Critical analysis: -- While Scott is very likely to win re-election, hitting exactly 70% or higher is a more challenging target -- His previous 70.5% provides a good benchmark -- The resolution criteria is precise (≥70.00%) -- Given the Democratic lean of Baltimore and his incumbent status, a significant win is likely -- However, exact percentages can vary based on turnout and opposition performance - -8. Uncertainty adjustment: -- Given the short timeframe, we should weight current conditions heavily -- Need to account for natural variance in election results -- Must consider the specific threshold rather than just win/lose - -Given all these factors, I estimate: -- 75% chance of Scott winning with 65-75% of vote -- 45% chance of Scott winning with ≥70% specifically -- 25% chance of Scott winning with <65% or >75% - -The specific threshold of 70% makes this more challenging than just predicting a win. While Scott is heavily favored to win re-election, hitting this exact threshold is less certain. His previous performance suggests it's possible, but electoral dynamics and turnout patterns could easily push the result a few points in either direction. - -Probability: 45%" -29273,Will Democratic Congresswoman Yadira Caraveo be re-elected in Colorado's 8th Congressional District in the 2024 election?,"Let me think through this step by step: - -1. Type of Question: This is a Competition Event (Political Election) - -2. Key Factors: -- Current polling shows a tie -- Significant fundraising advantage for Caraveo -- Super PAC spending slightly favors Caraveo ($12.5M vs $10.6M) -- Incumbent advantage (though only first term) -- District competitiveness - -3. Historical Context: -- Caraveo won in 2022 by a narrow margin (48.4% to 47.7%) -- This is a newly created district (first used in 2022) -- 2024 is a presidential election year, which typically has different turnout patterns -- Colorado has been trending Democratic, but this district remains competitive - -4. Time Considerations: -- We're just 8 days from the election -- Most voters have likely made up their minds -- Early voting is underway -- Limited time for major shifts - -5. Base Rate Analysis: -- House incumbent re-election rates historically around 90-95% -- However, in highly competitive districts, this drops significantly -- In toss-up races, incumbents win about 55-60% of the time - -6. Uncertainty Factors: -- Presidential election impact -- Turnout variables -- Late-breaking developments -- Polling accuracy in close races - -7. Confidence Adjustment: -- Given the extremely close polling -- Short timeframe to resolution -- High stakes nature of the race -- Need to avoid overconfidence - -The fact that we're so close to the election means we should weight current polling and fundraising data more heavily than historical patterns. The tie in polling is particularly significant, but Caraveo's fundraising advantage and slightly higher Super PAC support provide a small edge. - -The incumbent advantage exists but is weaker in a competitive district and with a first-term representative. The presidential election year dynamic adds uncertainty but likely won't dramatically shift district-level dynamics this close to election day. - -Given all factors, I believe Caraveo has a slight advantage but the race remains very close. I need to be careful not to be overconfident given the tight margins and polling tie. - -Probability: 53% - -This reflects the slight incumbent advantage and fundraising edge while acknowledging the extremely competitive nature of the race and the possibility of either outcome." -29267,"For 2024, will there be more than one bank listed on the FDIC's Failed Bank List?","To assess the likelihood of more than one bank being listed on the FDIC's Failed Bank List for 2024 by the resolution date of January 1, 2025, let's go through a comprehensive evaluation of the current situation and available data. - -### Examination of Data: - -1. **Current Status**: - - As of the current date, October 28, 2024, two banks are already on the FDIC's Failed Bank List: Republic First Bank (failed April 26, 2024) and The First National Bank of Lindsay (failed October 18, 2024). - -2. **Remaining Time Frame**: - - There are approximately two months until the resolution date. Historically, bank failures often occur sporadically, and given the complexity of identifying and resolving failing banks, sudden closures within short time frames can occur due to unforeseen financial issues, fraud, or regulatory actions. - -3. **Historical Context**: - - The base rate of bank failures varies significantly from year to year. During periods of economic instability, such as the financial crisis of 2008, failures were more frequent. Conversely, in more stable periods, fewer banks fail. It should be noted that in the aftermath of economic crises, strengthening regulatory frameworks often lead to reduced bank failures in the short term. - -4. **Current Economic and Regulatory Environment**: - - No overt systemic financial crisis is reported as of now, but isolated bank failures still happen. The presence of two failures already in 2024 could indicate sectorial vulnerabilities, increasing the likelihood of additional failures within the remaining timeframe. - -5. **Recent Trends**: - - We should also consider recent news and expert analysis relevant to banking sector health. Given the complexity and interconnectedness of financial institutions, issues within one entity can reveal systemic weaknesses that may affect others. - -### Probability Assessment: - -Given there are already two banks on the Failed Bank List for 2024, the question has already resolved positively (as the minimum criterion of more than one bank is met). The focus now is on the likelihood of additional banks failing before January 1, 2025: - -- **Base Rate Considerations**: Looking at historical bank failure data, multiple banks failing in a year is not unprecedented, even outside of economic crises. Given this year's two closings already, additional further failures within the year are plausible. - -- **Economic Environment**: While no systemic crisis is apparent, localized distress in the banking sector (e.g., increased interest rates, reduced liquidity) means surprises are possible. - -- **Timeframe Considerations**: With about two months remaining in the year, there's a moderate amount of time for changes to occur. Although it's a relatively short span, disruptions causing bank failures can happen swiftly. - -Based on these considerations, and incorporating a buffer against past overconfidence (as I've tended to overestimate), the probability that there will be more than one bank failure resolving positively by the end of the year is 100%, as it has already occurred. - -For the probability of additional banks failing: - -- **Bank Failures by Year's End**: Given the historical context, economic conditions, and remaining timeframe, I estimate a further 25% probability of additional banks being added to the list by the end of the year. - -Final Answer: ""Probability of additional bank failures by year's end: 25%""" -29266,"Will Revolut officially announce it is launching a stablecoin, before January 1, 2025?","Let me think through this step by step: - -1) Type of Question: This is an ""Other Binary Outcome Event"" about a corporate announcement. - -2) Time Analysis: -- Only about 2 months until resolution date -- Very short timeline for a major financial product launch announcement -- Corporate announcements typically need significant lead time for regulatory compliance and internal preparations - -3) Base Rate Analysis: -- Few traditional fintech companies have launched stablecoins -- Those that have considered it (like Robinhood) have ultimately decided against it -- The stablecoin market is dominated by specialized crypto companies -- The regulatory environment is still evolving, especially in Europe - -4) Current State: -- Revolut has only made vague statements about expanding crypto offerings -- No concrete indicators of stablecoin development -- No regulatory filings or partnerships announced that would suggest imminent launch -- Their news page shows no recent crypto-related announcements - -5) Timeline Distribution: -- Next 2 weeks: 1% probability (extremely unlikely given regulatory requirements) -- Next month: 2% probability -- By resolution date (2 months): 5% probability -- Within 6 months: 15% probability -- Within 1 year: 25% probability - -6) Factors Supporting Higher Probability: -- Growing stablecoin market -- Revolut's existing crypto presence -- Bloomberg reporting of consideration - -7) Factors Supporting Lower Probability: -- Very short timeline until resolution -- Regulatory hurdles, especially in EU -- Lack of concrete preparatory steps -- Historical reluctance of traditional fintech to enter stablecoin space -- No evidence of technical infrastructure development - -8) Risk Adjustments: -- Need to account for overconfidence bias -- Short timeline heavily weights toward default (no announcement) -- Regulatory requirements make quick launch unlikely - -9) Special Considerations: -- Resolution criteria specifically requires announcement on Revolut's news page -- Must be explicit about stablecoin launch, not just crypto expansion - -Given the extremely short timeline (2 months), regulatory requirements, lack of preparatory evidence, and specific resolution criteria, I believe this is a low probability event. While Revolut may be exploring stablecoins, the likelihood of an official announcement within this timeframe is small. The 6-month and 1-year probabilities are higher, but that's not relevant to this specific question. - -Adjusting for historical overconfidence and the short timeline, I'm giving this a low but not minimal probability. - -Probability: 5%" -29262,"Will an H5 virus receive an ""emergence"" risk rating categorized as ""high"" by the US CDC Influenza Risk Assessment Tool before January 1, 2025?","Let me think through this step by step: - -1. Current Status: -- Latest H5N1 IRAT emergence risk score is 5.79 (moderate) -- Need to reach ≥8.0 for ""high"" rating -- That's a significant gap to bridge in just ~2 months - -2. Historical Context: -- No H5 virus has received a ""high"" emergence risk rating historically -- Highest known IRAT score was 7.5 for an H1N1 strain -- Changes in ratings typically occur gradually unless there's a significant event - -3. Time Considerations: -- Only ~2 months until resolution -- CDC updates these ratings periodically, not continuously -- Given publication cycles, likely only 1-2 more potential updates before resolution - -4. Risk Factors: -- Ongoing monitoring of H5N1 in dairy cattle -- Recent human cases have been sporadic -- No evidence of sustained human-to-human transmission -- Current control measures appear effective - -5. Timeline Analysis: -- 2 months: ~3% probability (current question) -- 6 months: ~8% probability -- 1 year: ~15% probability -This suggests the probability is not evenly distributed and is actually lower in the near term. - -6. Factors Supporting Higher Probability: -- Ongoing surveillance and testing -- Recent dairy cattle outbreaks -- Continued evolution of virus - -7. Factors Supporting Lower Probability: -- Very short timeframe -- Significant gap between current and ""high"" rating -- Historical precedent -- Conservative nature of CDC ratings -- Need for substantial new evidence to justify such a large increase - -8. Risk Assessment: -- The jump from 5.79 to ≥8.0 would be unprecedented -- Would require significant new evidence of increased transmissibility -- Very short timeline makes this highly unlikely -- CDC tends to be conservative in these assessments - -9. Adjustment for Historical Overconfidence: -- Initial instinct might be 1-2% -- Given past overconfidence, should adjust upward slightly -- Need to account for unknown unknowns - -The extreme shortness of the timeline, the significant gap needed to reach ""high"" risk, and the historical context suggest this is very unlikely. However, we must account for the possibility of rapid developments and our tendency toward overconfidence. - -Probability: 3% - -This reflects: -- The extremely short timeline -- The significant gap between current and required scores -- Historical precedent -- Conservative nature of CDC ratings -- Adjustment for potential overconfidence -- Allowing for small but real possibility of rapid developments" -29265,"Will CDC’s highest assessment of the risk posed by H5 bird flu to the US general public before January 1, 2025, be ""Low""?","To forecast the likelihood of the CDC's highest assessment of the risk posed by H5 bird flu to the US general public being ""Low"" before January 1, 2025, I'll consider the information provided, alongside my general knowledge and the criteria for forming a probability-based forecast. - -### Step-by-Step Analysis: - -1. **Current Status:** - - As of October 18, 2024, the CDC's latest assessment is that the public health risk from H5 bird flu to the general US public is ""Low."" This is a good starting base, as maintaining this status is the default resolution if nothing changes before the deadline. - -2. **Human Cases and Virus Analysis:** - - 31 human cases in 2024, with no sign of mutations for increased human-to-human transmission. This suggests limited impact and aligns with the current ""Low"" risk assessment. - - Genetic tracking shows consistent links to animal exposure cases, further supporting a limited public health risk. - -3. **CDC and State Actions:** - - Enhanced surveillance and monitoring are in place. States are also distributing PPE and vaccines to workers. - - These actions are preventative and appear adequate to maintain control over the spread, which supports a continued ""Low"" risk classification. - -4. **Historical Precedent:** - - Historically, CDC has been conservative in upgrading public health threats. This tends to suggest that a significant increase in risk assessment would require substantial evidence of increased transmission or severity. - -5. **Timeframe and Trend Consideration:** - - There are approximately just over two months until the resolution date. Given the relatively stable situation and robust monitoring, any change in risk assessment would likely need a substantial development. - - A historical lens on similar infectious diseases shows that drastic escalations typically aren't common unless tied to novel mutations or significant epidemic outbreaks. - -6. **Expert Opinions:** - - Both CDC and ECDC assess the risk similarly, explaining confidence in a ""Low"" evaluation. The consensus here suggests no immediate precursors to changing this view. - -### Probability Determination: - -The question is essentially a binary outcome event. The current risk is assessed as ""Low,"" and stable systems (virus monitoring, genetic consistency, state responses) are currently in place to avert escalation. - -#### Scenarios impacting the probability: -- **Low Risk Continues:** Current trajectory, enhancing measures prevent escalation. -- **Risk Escalation:** Would require mutation discovery, increased human-to-human transmission, or significant outbreak. - -Given the current information and assuming no drastic developments (which are not indicated), it's reasonable to assess a conservative estimate. Considering the tendency to overestimate change, and accounting for past overconfidence, a probability that is reassuring yet leaves room for uncertainty and unforeseen events is ideal. - -**Final Probability Calculation:** - -Likelihood of the CDC's risk assessment remaining ""Low"" until resolution: - -- Given the stable situation: ~85% -- Small probability assigned to unforeseen risk escalation due to new data, extreme weather, or political shifts: ~15% - -Balancing the potential unknowns against historical precedent and current actions, I conclude: - -**Probability: 85%**" -29319,"In the 2024 US elections, will the party winning the White House differ from the party winning the House of Representatives?","The questions regard the outcomes of the 2024 US presidential and House of Representatives elections, evaluating if the winning parties for the presidency and House will differ or be the same. Here is a step-by-step analysis: - -1. **Presidential Election:** The current polls show an extremely close race between Kamala Harris and Donald Trump, with slight advantages in battleground states for Trump. Historically, incumbents or previous office holders like Trump can leverage name recognition, but Harris presides in an administration that may either bolster or hinder her campaign resiliency. The divisive political climate and demographics changes make this race particularly unpredictable within just days of the election. - -2. **House of Representatives:** Republicans currently hold a majority but are projected to have only a 52% chance of maintaining control, according to available data. There are numerous battleground House races, but current fundraising and poll advantages for Democrats suggest competitiveness. - -3. **Historical Trends:** Historically, divided governments are not uncommon in US politics. This is particularly relevant given the razor-close political climate. - -4. **Timeframe Considerations:** With approximately a week left before the election and no significant political variance expected, the current trends are most indicative of the outcome. The high uncertainty level suggests maintaining a balance between base rates and current inputs. - -5. **Expert Opinions:** Experts predict various possible outcomes, emphasizing the high uncertainty and likelihood of divided government. - -**Probability Breakdown:** -- **Question 29319:** Given the closely contested nature of both the presidential and House races, and historical examples of divided government, there's a plausible probability that the party winning the presidency differs from the party winning the House, though this is not a certainty. Assigning 60% here due to the high possibility of flip-flops in either race. -- **Question 29318:** With only a slight edge in the House and the presidential race being tight, the probability of Republicans winning both seems moderate. Given the need to hedge against overconfidence, a probability of 40% seems appropriate. -- **Question 29317:** Democrats have indicators showing potential gains, but the House is uncertain, and the presidential race remains competitive. A probability of 25% was assigned to Democrats winning both, understanding that this is less probable currently than the other outcomes given the present data. - -This analysis assumes historical parallels with previous election unpredictability, current dynamic, and polling imperfections, tempering all predictions by the short timespan to election resolution." -29318,"In the 2024 US elections, will Republicans win both the White House and the House of Representatives?","The questions regard the outcomes of the 2024 US presidential and House of Representatives elections, evaluating if the winning parties for the presidency and House will differ or be the same. Here is a step-by-step analysis: - -1. **Presidential Election:** The current polls show an extremely close race between Kamala Harris and Donald Trump, with slight advantages in battleground states for Trump. Historically, incumbents or previous office holders like Trump can leverage name recognition, but Harris presides in an administration that may either bolster or hinder her campaign resiliency. The divisive political climate and demographics changes make this race particularly unpredictable within just days of the election. - -2. **House of Representatives:** Republicans currently hold a majority but are projected to have only a 52% chance of maintaining control, according to available data. There are numerous battleground House races, but current fundraising and poll advantages for Democrats suggest competitiveness. - -3. **Historical Trends:** Historically, divided governments are not uncommon in US politics. This is particularly relevant given the razor-close political climate. - -4. **Timeframe Considerations:** With approximately a week left before the election and no significant political variance expected, the current trends are most indicative of the outcome. The high uncertainty level suggests maintaining a balance between base rates and current inputs. - -5. **Expert Opinions:** Experts predict various possible outcomes, emphasizing the high uncertainty and likelihood of divided government. - -**Probability Breakdown:** -- **Question 29319:** Given the closely contested nature of both the presidential and House races, and historical examples of divided government, there's a plausible probability that the party winning the presidency differs from the party winning the House, though this is not a certainty. Assigning 60% here due to the high possibility of flip-flops in either race. -- **Question 29318:** With only a slight edge in the House and the presidential race being tight, the probability of Republicans winning both seems moderate. Given the need to hedge against overconfidence, a probability of 40% seems appropriate. -- **Question 29317:** Democrats have indicators showing potential gains, but the House is uncertain, and the presidential race remains competitive. A probability of 25% was assigned to Democrats winning both, understanding that this is less probable currently than the other outcomes given the present data. - -This analysis assumes historical parallels with previous election unpredictability, current dynamic, and polling imperfections, tempering all predictions by the short timespan to election resolution." -29317,"In the 2024 US elections, will Democrats win both the White House and the House of Representatives?","The questions regard the outcomes of the 2024 US presidential and House of Representatives elections, evaluating if the winning parties for the presidency and House will differ or be the same. Here is a step-by-step analysis: - -1. **Presidential Election:** The current polls show an extremely close race between Kamala Harris and Donald Trump, with slight advantages in battleground states for Trump. Historically, incumbents or previous office holders like Trump can leverage name recognition, but Harris presides in an administration that may either bolster or hinder her campaign resiliency. The divisive political climate and demographics changes make this race particularly unpredictable within just days of the election. - -2. **House of Representatives:** Republicans currently hold a majority but are projected to have only a 52% chance of maintaining control, according to available data. There are numerous battleground House races, but current fundraising and poll advantages for Democrats suggest competitiveness. - -3. **Historical Trends:** Historically, divided governments are not uncommon in US politics. This is particularly relevant given the razor-close political climate. - -4. **Timeframe Considerations:** With approximately a week left before the election and no significant political variance expected, the current trends are most indicative of the outcome. The high uncertainty level suggests maintaining a balance between base rates and current inputs. - -5. **Expert Opinions:** Experts predict various possible outcomes, emphasizing the high uncertainty and likelihood of divided government. - -**Probability Breakdown:** -- **Question 29319:** Given the closely contested nature of both the presidential and House races, and historical examples of divided government, there's a plausible probability that the party winning the presidency differs from the party winning the House, though this is not a certainty. Assigning 60% here due to the high possibility of flip-flops in either race. -- **Question 29318:** With only a slight edge in the House and the presidential race being tight, the probability of Republicans winning both seems moderate. Given the need to hedge against overconfidence, a probability of 40% seems appropriate. -- **Question 29317:** Democrats have indicators showing potential gains, but the House is uncertain, and the presidential race remains competitive. A probability of 25% was assigned to Democrats winning both, understanding that this is less probable currently than the other outcomes given the present data. - -This analysis assumes historical parallels with previous election unpredictability, current dynamic, and polling imperfections, tempering all predictions by the short timespan to election resolution." -29314,"Will Kamala Harris win New Mexico, New Hampshire, Virginia, and Maine in the 2024 election?","To forecast the probabilities for each question about the 2024 election, I will use the information provided, combined with my knowledge of U.S. electoral history and the current political climate as of late October 2024. The election questions fall into the 'Competition Event' type, so I will consider polling data, historical voting patterns, the current political landscape, and general election dynamics. - -1. **Kamala Harris winning New Mexico, New Hampshire, Virginia, and Maine**: Given the information, Harris has significant leads in New Mexico and Virginia, states with a recent history of Democratic support. In New Mexico, her 9-point lead is substantial, while Virginia's 6-point lead aligns with the state's Democratic-leaning trend since 2008. However, New Hampshire and Maine present more challenges. New Hampshire is currently a battleground, with a narrow margin favoring Trump. Maine, traditionally Democratic, has some potential for divided results due to its district-based electoral vote distribution, but generally leans Democratic in presidential races. Hurried campaigning and potential last-minute shifts in battleground states could influence outcomes, but given the historical context and trends, I anticipate Harris will win at least three of these states, with an approximately 75% likelihood across all four. - -2. **The same presidential candidate winning Michigan and Wisconsin**: Both states are crucial swing territories, often characterized by tight races. They flipped from Obama to Trump in 2016 and then to Biden in 2020. Polling and campaign dynamics in these states usually indicate competitiveness. Typically, if a candidate wins one, they also win the other because they share similar voting demographics and political issues. Given the proximity and similarity, I hypothesize a moderate probability (70%) that the same candidate will carry both. - -3. **Donald Trump winning Arizona and Georgia**: Both states went narrowly to Biden in 2020 and have been close contests historically. Significant demographic changes and political mobilization are factors in both. Trump's potential to recapture these states hinges on his ability to energize his base and mitigate shifts towards more diverse, urban electorates. Given polling trends and increased voter engagement by Democrats, there's a 40% chance he might succeed in winning both. - -Overall, the tightness of the races and the averaging of probabilities from different partial aggregators combined with a short timeline prompt me to avoid extreme confidence in predictions." -29313,Will the same presidential candidate win Michigan and Wisconsin in the 2024 election?,"To forecast the probabilities for each question about the 2024 election, I will use the information provided, combined with my knowledge of U.S. electoral history and the current political climate as of late October 2024. The election questions fall into the 'Competition Event' type, so I will consider polling data, historical voting patterns, the current political landscape, and general election dynamics. - -1. **Kamala Harris winning New Mexico, New Hampshire, Virginia, and Maine**: Given the information, Harris has significant leads in New Mexico and Virginia, states with a recent history of Democratic support. In New Mexico, her 9-point lead is substantial, while Virginia's 6-point lead aligns with the state's Democratic-leaning trend since 2008. However, New Hampshire and Maine present more challenges. New Hampshire is currently a battleground, with a narrow margin favoring Trump. Maine, traditionally Democratic, has some potential for divided results due to its district-based electoral vote distribution, but generally leans Democratic in presidential races. Hurried campaigning and potential last-minute shifts in battleground states could influence outcomes, but given the historical context and trends, I anticipate Harris will win at least three of these states, with an approximately 75% likelihood across all four. - -2. **The same presidential candidate winning Michigan and Wisconsin**: Both states are crucial swing territories, often characterized by tight races. They flipped from Obama to Trump in 2016 and then to Biden in 2020. Polling and campaign dynamics in these states usually indicate competitiveness. Typically, if a candidate wins one, they also win the other because they share similar voting demographics and political issues. Given the proximity and similarity, I hypothesize a moderate probability (70%) that the same candidate will carry both. - -3. **Donald Trump winning Arizona and Georgia**: Both states went narrowly to Biden in 2020 and have been close contests historically. Significant demographic changes and political mobilization are factors in both. Trump's potential to recapture these states hinges on his ability to energize his base and mitigate shifts towards more diverse, urban electorates. Given polling trends and increased voter engagement by Democrats, there's a 40% chance he might succeed in winning both. - -Overall, the tightness of the races and the averaging of probabilities from different partial aggregators combined with a short timeline prompt me to avoid extreme confidence in predictions." -29312,Will Donald Trump win Arizona and Georgia in the 2024 election?,"To forecast the probabilities for each question about the 2024 election, I will use the information provided, combined with my knowledge of U.S. electoral history and the current political climate as of late October 2024. The election questions fall into the 'Competition Event' type, so I will consider polling data, historical voting patterns, the current political landscape, and general election dynamics. - -1. **Kamala Harris winning New Mexico, New Hampshire, Virginia, and Maine**: Given the information, Harris has significant leads in New Mexico and Virginia, states with a recent history of Democratic support. In New Mexico, her 9-point lead is substantial, while Virginia's 6-point lead aligns with the state's Democratic-leaning trend since 2008. However, New Hampshire and Maine present more challenges. New Hampshire is currently a battleground, with a narrow margin favoring Trump. Maine, traditionally Democratic, has some potential for divided results due to its district-based electoral vote distribution, but generally leans Democratic in presidential races. Hurried campaigning and potential last-minute shifts in battleground states could influence outcomes, but given the historical context and trends, I anticipate Harris will win at least three of these states, with an approximately 75% likelihood across all four. - -2. **The same presidential candidate winning Michigan and Wisconsin**: Both states are crucial swing territories, often characterized by tight races. They flipped from Obama to Trump in 2016 and then to Biden in 2020. Polling and campaign dynamics in these states usually indicate competitiveness. Typically, if a candidate wins one, they also win the other because they share similar voting demographics and political issues. Given the proximity and similarity, I hypothesize a moderate probability (70%) that the same candidate will carry both. - -3. **Donald Trump winning Arizona and Georgia**: Both states went narrowly to Biden in 2020 and have been close contests historically. Significant demographic changes and political mobilization are factors in both. Trump's potential to recapture these states hinges on his ability to energize his base and mitigate shifts towards more diverse, urban electorates. Given polling trends and increased voter engagement by Democrats, there's a 40% chance he might succeed in winning both. - -Overall, the tightness of the races and the averaging of probabilities from different partial aggregators combined with a short timeline prompt me to avoid extreme confidence in predictions." -29320,"Will Joe Rogan endorse Donald Trump in the 2024 election before November 5, 2024?","Let me think through this step by step: - -1. Timeline Analysis: -- Only 7 days until resolution -- Very short window for change from current status -- Short timeframes heavily favor the default state - -2. Current Status: -- Rogan has not endorsed Trump -- Recently had Trump on his podcast -- Did not endorse during the podcast despite direct ask -- Previously endorsed RFK Jr. - -3. Historical Patterns: -- Rogan typically takes time to make endorsements -- Has shown willingness to endorse non-mainstream candidates -- Has criticized both major parties -- Generally maintains independence - -4. Recent Context: -- Trump appearance on podcast was significant -- Direct request for endorsement was declined -- RFK Jr. (Rogan's previous choice) has endorsed Trump -- Increased criticism of Harris/Biden administration - -5. Probability Distribution Analysis: -- With only 7 days left, very low chance of major change -- Most endorsements happen with more lead time -- Rogan typically deliberates decisions carefully -- Recent podcast would have been natural time for endorsement - -6. Mitigating Factors: -- RFK Jr.'s endorsement of Trump could influence Rogan -- Increasing criticism of Democrats -- Trump's podcast appearance built rapport - -7. Base Rate Consideration: -- Podcast hosts rarely make last-minute endorsements -- Rogan has historically maintained independence -- Recent opportunity to endorse was not taken - -8. Timeline Probability Distribution: -- Within 1 week (to resolution): 5% -- Within 1 month: 15% -- Within 3 months: 25% -- By election day: 30% - -Given the extremely short timeframe, Rogan's historical behavior patterns, and the fact that he didn't endorse during the recent podcast appearance (which would have been the most natural time), I assess a low probability of endorsement in the next 7 days. The primary factor is the very short remaining time window, combined with Rogan's typical deliberative approach to endorsements. - -Probability: 5%" -29316,"Will the U.S. have passed legislation that requires cybersecurity around AI models before 2025? -","To predict the likelihood of the U.S. passing legislation requiring cybersecurity around AI models before 2025, let's evaluate the available information, apply base rates, consider historical context, and analyze possible developments. - -### Current Environment - -1. **Legislative Activity**: - - **State-Level Action**: Numerous AI-related bills have been introduced at the state level in 2024, but this doesn't guarantee federal attention or a similar pace. - - **Federal Activity**: There's no clear sign of imminent federal legislation specifically targeting cybersecurity for AI models. Given the legislative inertia often seen at the federal level, passing specific cybersecurity legislation quickly is challenging, especially when it's a new domain without an existing precedent in federal law. - -2. **Expert Opinions**: - - Experts acknowledge the challenges of regulating AI, highlighting the tension between innovation and security concerns. The debate is ongoing regarding regulatory focus, suggesting fragmented consensus on immediate action. - -3. **Recent Updates & Progress**: - - While multiple states take individual actions, these don't translate directly to federal policy and, historically, require significant time to galvanize into nationwide legislation. - -### Historical Base Rates - -- Historically, transformative federal legislation, especially related to technology regulation, takes considerable time to develop and pass. Comparing to previous tech regulations (like those around data privacy), years often pass from early legislative introductions to actual law enactment. - -### Timeframe and Momentum - -- Given the tight timeline until January 1, 2025 (just over two months from now), the likelihood drops due to legislative constraints like discussions, amendments, and political negotiations. -- Unlikely events, especially new regulatory measures, are particularly difficult to pass quickly; most legislative bodies require substantial pressure and consensus, which appears fragmented. - -### Probability Distribution - -1. **Within 1 Month**: Given the federal legislative schedule and holiday recesses, the probability is extremely low, likely below 1%. -2. **Before Jan 1, 2025**: As the window of effective days narrows and considering ongoing partisanship, it's improbable that a new, comprehensive legislation requiring cybersecurity measures around AI would pass. - -### Adjustments for Overconfidence - -- Working to correct past overconfidence, I aim to remain conservative in predicting positive outcomes without strong evidence or historical precedent supporting rapid legislative action within this timeline. - -### Conclusion - -- Considering the above factors, the probability of the U.S. passing legislation requiring cybersecurity for AI models by January 1, 2025, remains low. There's insufficient momentum, precedent, or time for action within such a short period, considering both the current legislative environment and historical norms. - -**Probability: 5%**" -29315,Will Donald Trump outperform Rick Scott in Florida in the 2024 election?,"Given the information, the question revolves around predicting whether Donald Trump will receive more votes than Rick Scott in the 2024 election in Florida. Both candidates are Republicans, and according to recent data, both have slight leads over their Democratic opponents. - -Here's my step-by-step reasoning: - -1. **Polling Data**: Trump's lead over Kamala Harris (51%-45%) is slightly larger than Scott's lead over Debbie Mucarsel-Powell (51%-46%). This suggests Trump might outperform Scott by a narrow margin. However, the difference is minimal, and polling errors, while usually small, can be significant enough to make differences in small margins. - -2. **Partisan Leaning**: Florida has become a more reliably Republican state, historically favoring Republican candidates in recent years. This could indicate a general trend favoring both Trump and Scott. However, Trump, as a presidential candidate, might benefit more from this shift due to the higher national focus compared to a senatorial candidate. - -3. **Demographic Support**: Both candidates have similar support from key demographic groups like Hispanics and NPAs, though Trump has a slight edge among NPAs according to the provided margins. - -4. **Gender Gap**: The gender gap is a significant consideration. Trump has a marked lead among men, while Scott's gender gap is less pronounced. This could translate into more overall votes for Trump if the male voter turnout is high. - -5. **Historical Context and Expert Opinion**: Florida has seen tight races historically but has leaned Republican more in recent elections. This historical tendency may favor Trump in terms of overall numbers. - -6. **Timeframe**: With only a week left until the election, significant changes in voter sentiment are unlikely unless a major event occurs. The base case scenario, given current trends, would suggest that both candidates maintain their leads. - -7. **Base Rates and Overconfidence**: Historically, you have been overconfident, and given that a higher percentage of questions resolve negatively, it's essential to factor in inherent uncertainties and potential polling inaccuracies. - -Considering these factors, while both candidates appear to be in a good position in Florida, the narrow difference in polling data and the higher visibility of the presidential race suggest Trump might slightly outperform Scott. However, the margins are narrow, so any unforeseen movements or errors in polling could affect the outcome. - -Final Probability Assessment: -Given the current data trends, demographic supporting nuances, and the minor difference in polling leads, and adjusting for potential overconfidence, I estimate the probability that Trump will outperform Scott in terms of total votes in Florida as: - -**Probability: 57%**" -29311,"Will a European Union ban into non-commercial internal combustion engines take effect before January 1, 2025?","To forecast the probability of a European Union ban on non-commercial internal combustion engines taking effect before January 1, 2025, let's break down the analysis step by step, considering the information provided: - -1. **Current Legislation and Policy Direction**: The EU has planned a ban for 2035, but there is no mention of advancing this ban to 2025. The focus remains on 2035, and there are no indications of legislative action or debate to move this deadline forward dramatically. - -2. **Political Environment**: There has been a recent increase in far-right seats in the EU Parliament, along with a decrease in support for green parties. This shift suggests a less supportive environment for advancing aggressive environmental regulations like an earlier ban on combustion engines. Instead, there's pressure to relax or reconsider such measures, not make them more stringent. - -3. **Industry and Stakeholder Positions**: Automakers and industry voices have expressed concerns about meeting the 2035 deadline, which implies additional resistance to any earlier timelines. Recent calls have been more about adjustment and flexibility rather than immediate bans or accelerated targets. - -4. **Timeframe**: With just over two months left until 2025, the likelihood of a massive policy shift or acceleration regarding a ban seems low. Legislative processes in the EU can be lengthy and complex, typically requiring more time than is available to implement such significant changes by the start of 2025. - -5. **Base Rate and Precedents**: It's historically rare for governments to introduce and enact major industry regulatory shifts on such short notice. Major policy introductions typically involve years of debate, consultation, and adjustment, aligning with the base case that the status quo or moves towards it remain dominant. - -6. **Resolution Criteria Details**: The ban would require complete removal of sales for non-commercial internal combustion engines, including exceptions constraints. Changes needed to meet resolution criteria by the target date further complicate implementation within the available timeframe. - -Based on this analysis, the base case strongly supports that the current legislative and political environment does not favor moving up the ban timeline to before January 1, 2025. Given the time available and the lack of signals indicating imminent legislation, it appears extremely unlikely. - -**Final Probabilities**: -- **Before January 1, 2025**: The probability of this ban taking effect seems exceedingly low, around 1% to 2%, indicating a very minor, albeit not impossible, chance of an unforeseen rapid policy shift. -- **Within 1 year** (e.g., before October 2025): Probably still quite low, perhaps around 5% or slightly higher if considering a new legislative push starting soon but recognizing the difficulty of change without longer preparation. -- **By 2035**: Remaining high, given alignment with the existing target and more ample time for preparation and policy shifts, though not relevant for this specific forecast. - -Taking into account my tendencies for overconfidence and ensuring against this dynamic, my forecast for the event before January 1, 2025, is: - -**Probability: 1%**" -29310,"Will the Secretary of the US Department of Health and Human Services declare a public health emergency for Marburg virus disease before January 1, 2025? -","Let me think about this step by step: - -1. Type of Question: This is a Disease Spread Rate/Binary Outcome hybrid, focusing on government response rather than just spread. - -2. Timeline Analysis: -- Resolution is in ~2 months (Jan 1, 2025) -- Current outbreak is Rwanda's first -- No cases in US currently -- Very short window for situation to dramatically change - -3. Historical Context: -- Marburg is extremely rare globally -- US has never declared a PHE for Marburg -- Recent PHE declarations (COVID, MPox) required actual US cases -- Base rate of PHE declarations for novel diseases is very low - -4. Current Situation: -- Outbreak contained to Rwanda -- CDC assesses US risk as low -- No international spread reported -- Rwanda implementing control measures -- No US cases - -5. Requirements for PHE: -- Typically requires direct threat to US public health -- Usually needs US cases or imminent threat -- Political will to declare emergency - -6. Likelihood Analysis for different timeframes: -- Next 2 weeks: 0.1% (virtually impossible without cases) -- Next 2 months (question timeframe): 1% -- Next 6 months: 3% -- Next year: 5% - -7. Factors that could increase probability: -- International spread -- First US case -- Mutation making virus more transmissible -- Travel-related cases - -8. Factors decreasing probability: -- Very short timeline -- Current containment -- No US cases -- Low assessed risk -- Historical rarity -- Rwanda's control measures - -9. Confidence adjustment: -- Starting with 0.5% base on current situation -- Adjusting up slightly for uncertainty and black swan events -- But maintaining very low probability given timeline constraints - -Given the extremely short timeline, current containment, lack of US cases, and historical precedent, I assess this as very unlikely. However, I want to avoid overconfidence in an extreme prediction while still reflecting the genuine improbability. - -Probability: 1% - -This reflects both the genuine improbability while avoiding overconfidence in an extreme prediction. The 1% accounts for possible black swan events while acknowledging the extremely low likelihood given current circumstances and short timeline." -29309,Will a woman be named the Time Person of the Year for 2024?,"Let me think through this step by step: - -1. Historical Context: -- Time Person of the Year has been awarded since 1927 -- Only a small percentage of solo winners have been women -- However, recent years have shown more openness to female winners (Taylor Swift in 2023) - -2. Timing Analysis: -- We're very close to the decision (about 5 weeks away) -- Most major events that would influence the decision have already happened in 2024 -- Limited time for new developments to significantly change the landscape - -3. Current Landscape: -- 2024 has been dominated by: - * Global conflicts (Israel-Hamas, Ukraine-Russia) - * U.S. Presidential election - * AI developments - * Climate events -- Key female contenders: - * Taylor Swift (unlikely to repeat) - * Kamala Harris (likely only in conjunction with Biden) - * Various political/activist figures - -4. Categorical Analysis: -This is a Competition Event with specific historical patterns. - -5. Base Rate Analysis: -- Women as sole winners represent < 10% of historical picks -- Recent years show increased representation -- But 2023 just had a female winner (Taylor Swift) -- Time rarely picks same category in consecutive years - -6. Specific Factors for 2024: -- Major news stories mostly center on: - * Trump/Biden election - * International conflicts - * Technology leaders -- No single female figure dominates current events -- Time often picks political figures in election years - -7. Mitigating Factors: -- Increased focus on diversity -- Growing influence of women in various fields -- But timing after 2023's female winner works against it - -8. Resolution Criteria Impact: -- Must be sole winner -- Group selections don't count -- This significantly reduces probability - -Given: -- The proximity to resolution date -- The male-dominated major news stories of 2024 -- The recent female winner in 2023 -- The historical base rate -- The requirement for a sole winner -- The tendency toward political figures in election years - -I need to adjust down from historical base rates due to recent female winner and election year dynamics, but up slightly for modern diversity trends. - -Probability: 12%" -29376,"Will Massachusetts voters approve Question 4, which would legalize psychedelic substances for adults 21 years old and older?","Let me think through this step by step: - -1. Type of Question: This is a Competition Event (election/ballot measure) - -2. Recent Polling: -- 42% in favor -- 44% opposed -- 14% undecided -- Very close race with just a week until election - -3. Historical Context: -- Cannabis legalization measures have generally been successful in liberal states -- However, psychedelics are viewed differently than cannabis by many voters -- Recent Oregon measure for psychedelics passed in 2020, but barely (56%-44%) -- Massachusetts is generally progressive but can be conservative on some issues - -4. Timing: -- Only 1 week until resolution -- Very little time for major shifts in public opinion -- Late deciders often break against ballot measures due to status quo bias -- Undecided voters tend to vote ""no"" on drug legalization measures - -5. Key Factors: -- Massachusetts is politically liberal -- But psychedelics are more controversial than cannabis -- Recent polling shows slight opposition -- Detailed regulatory framework may help convince some voters -- 15% tax rate is moderate and shouldn't be a major negative factor -- Public health arguments both for and against - -6. Probability Analysis: -- Starting with recent poll: 42% yes, 44% no, 14% undecided -- Historical pattern suggests undecideds break ~60-40 against drug measures -- This would suggest roughly 46% yes, 54% no final result -- Need to factor in: - * Polling error margin (~3-4%) - * Get out the vote efforts - * Last minute messaging - * Status quo bias - -7. Confidence Check: -- High quality recent polling -- Clear resolution criteria -- Limited time for changes -- Similar historical examples -- But still significant uncertainty - -Given the close polling, historical patterns of undecideds breaking against drug measures, and limited time for change, I assess this as more likely to fail than pass, but not by a huge margin. - -Probability: 43% - -This reflects that while passage is certainly possible, the combination of current slight opposition in polls, likely break of undecideds against, and status quo bias on drug measures makes failure more likely than success. I've adjusted away from the extreme to account for uncertainty and the possibility of polling error or late movement." -29376,"Will Massachusetts voters approve Question 4, which would legalize psychedelic substances for adults 21 years old and older?","Let me think through this step by step: - -1. Type of Question: -This is a Competition Event (political ballot measure), which typically has more predictable outcomes than other types of forecasts. - -2. Recent Polling: -- 42% in favor -- 44% opposed -- 14% undecided -- Poll from September shows a very close race - -3. Historical Context: -- Similar psychedelic/drug legalization measures have had mixed success -- Massachusetts has been relatively progressive on drug policy (legal marijuana) -- However, psychedelics are seen as more extreme than marijuana -- Voter turnout in presidential election years tends to be higher and more diverse - -4. Key Factors: -- This is a presidential election year, which typically brings higher turnout -- Recent polling shows opposition slightly ahead -- Significant undecided voters (14%) -- Strong organized opposition exists -- Less than a week until resolution -- Massachusetts tends to be progressive but this may be too far for many voters - -5. Timeline Analysis: -With only a week until resolution, major changes in public opinion are unlikely. The current polling numbers are likely to be fairly predictive. - -6. Base Rate Considerations: -- Drug legalization measures historically pass about 60% of the time when reaching ballot -- However, psychedelics face more resistance than cannabis -- Massachusetts has passed progressive measures before - -7. Uncertainty Factors: -- Polling could be inaccurate -- Turnout effects unclear -- Undecided voters could break either way -- Late campaign pushes could affect outcome - -Analysis: -Starting with the current polling (42% yes, 44% no, 14% undecided), and considering that: -- Undecided voters often break against change -- The measure is relatively extreme compared to previous drug legislation -- Opposition is slightly ahead in polls -- Very little time remains for significant opinion shifts -- Presidential election year turnout could affect results - -I need to adjust for my historical overconfidence and the fact that we have recent polling data showing a slight lead for ""No."" - -The base case suggests this will fail, but not by a large margin. Given the progressive nature of Massachusetts and historical success of drug reform measures, this still has a reasonable chance of passing. - -Probability: 43% - -This reflects that while passage is certainly possible, the combination of current polling, the nature of the measure, and typical undecided voter behavior suggests failure is more likely than success. The number isn't lower because of Massachusetts' progressive voting history and the general trend toward drug policy liberalization." -29375,"Will Florida voters approve Amendment 3, which would legalize marijuana for adults 21 years old and older?","Let me think through this step by step: - -1. This is a Competition Event question about a ballot initiative. - -2. Key factors to consider: -- Florida requires a 60% supermajority for constitutional amendments -- Recent polling shows 56% support (both FAU and CBS/YouGov polls) -- There's only 1 week until the resolution date -- Historical precedent for marijuana initiatives in Florida and other states -- Current political climate and opposition from state leadership - -3. Critical analysis: -- The 60% threshold is crucial - even with 56% support, there's a significant gap -- Polls often overstate support for marijuana initiatives -- Late opposition from state leadership could sway some voters -- Very short timeframe (1 week) means limited possibility for major shifts -- Younger voters tend to support but have lower turnout in midterms - -4. Historical context: -- Previous Florida marijuana initiatives have failed to reach 60% -- Other states have passed similar measures, but usually with simple majority -- The 60% requirement has killed several Florida initiatives that had majority support - -5. Uncertainty factors: -- Polling accuracy -- Voter turnout, especially among younger voters -- Impact of opposition campaign in final week -- Potential last-minute developments - -6. Mathematical consideration: -- Need to bridge a minimum 4% gap from current polling to threshold -- Must account for polling error margins -- Historical underperformance of polling for marijuana initiatives -- Probability distribution should be weighted toward failure given the supermajority requirement - -7. Timeline analysis: -With only one week remaining, dramatic shifts are unlikely. The current polling and opposition from state leadership suggests this faces an uphill battle to reach 60%. - -8. Base rate consideration: -Similar initiatives requiring supermajority have historically failed more often than succeeded when polling in the mid-50s this close to election day. - -Given all these factors, particularly the challenging 60% threshold, current polling at 56%, historical precedent, and strong opposition from state leadership, I assess that while the initiative will likely get majority support, reaching the required supermajority is improbable. - -The short timeframe until resolution increases confidence in current polling as predictive. - -Adjusting for my historical overconfidence and the fact that getting to 60% requires overcoming multiple hurdles: - -Probability: 35% - -This reflects that while passage is possible, the combination of the supermajority requirement and current polling makes failure more likely than success." -29374,"Will Ohio voters approve Ohio Issue 1, which seeks to end gerrymandering in the state?","To predict the probability of Ohio voters approving Ohio Issue 1, let's break down the information we have and how it can inform our forecast. - -**Issue 1 Overview**: -- This initiative seeks to end gerrymandering by establishing a Citizens Redistricting Commission. -- The language of the ballot is a point of controversy and could impact voter perception and understanding. - -**Ballot Language Controversy and Legal Challenges**: -- The language was deemed fair by the Ohio Supreme Court, despite dissent. This decision may skew the perception of impartiality and potentially decrease support. - -**Support and Opposition Dynamics**: -- The ""yes"" campaign has support from organized labor and some nonpartisan figures, while the Republican Party generally opposes it. Partisan alignment can significantly influence voter turnout and preferences. - -**Voter Impact**: -- The measure's impact on various voter demographics could play a significant role in how it's perceived and supported. - -**Recent Voter Behavior**: -- Voting trends in Ohio have shown that constitutional amendments related to election or districting reforms tend to face an uphill battle, especially when one party perceives a threat to its influence. - -**Base Rates and General Trends**: -- Historically, similar measures have had varying success, often struggling when faced with partisan opposition and potentially unclear language. -- Early voting already started, and any shift in public opinion at this late stage would be difficult. - -**Current Events and Timing**: -- With the election date a week away, there's limited time to change public opinion dramatically. -- Any strong partisan winds or recent developments (e.g., new endorsements, significant campaign ads) in the last week could sway undecided voters, but the window is narrow. - -Overall, the combination of controversial ballot language, strong partisan opposition, and historical difficulties similar measures have faced suggests that while approval is possible, it remains challenging. Given past patterns and the context provided, I have to weigh the possibility conservatively. - -**Adjusting for Confidence**: -- Taking into account my historical overconfidence, I must be cautious about assigning too high a probability where uncertainty remains. -- The past rate of positive resolutions and limited timespan for change add weight to a conservative estimate. - -With these factors considered, my forecast is leaning towards a less than even chance of the measure passing, though not too extreme due to the reasons outlined. - -Final answer: ""Probability: 40%""" -29373,"Will Democrats be ahead in the generic ballot on Election Day, according to 538?","Let me think through this step by step: - -1. Timeline Assessment: -- Only 6 days until resolution -- Very short timeframe for significant changes -- Polls tend to be quite stable in the final week - -2. Current State: -- Polls show an extremely tight race -- Mix of Democratic and Republican leads -- Most recent high-quality polls show slight Democratic advantage -- Early voting trends favor Democrats significantly - -3. Historical Context: -- Generic ballot typically tightens near Election Day -- Final week polls tend to be more predictive than earlier ones -- Democrats often outperform generic ballot polls in early voting -- Republicans typically have stronger Election Day turnout - -4. Key Considerations: -- 538's specific methodology weighs polls differently -- High-quality polls are given more weight -- Recent polls matter more than older ones -- Early voting data suggests Democratic enthusiasm - -5. Risk Factors: -- Only 6 days for changes -- Multiple polls showing different leaders -- Uncertainty about turnout models -- Potential late breaking news - -6. Statistical Analysis: -- Current polling average shows virtual tie -- Most recent high-quality polls lean slightly Democratic -- Early voting data strongly Democratic -- Final week historically fairly stable - -7. Confidence Assessment: -- Very short timeline reduces uncertainty -- Multiple recent polls available -- High-quality pollsters active -- Clear resolution criteria - -Given the extremely tight race, slight Democratic edge in recent high-quality polls, and strong Democratic early voting performance, balanced against historical Election Day Republican turnout advantage, I assess there's a moderate probability Democrats will be ahead in the 538 average. - -The short timeline and multiple recent polls give us good confidence in the current state, with limited time for major shifts. The early voting data is particularly strong for Democrats, though this advantage typically narrows on Election Day. - -I want to avoid overconfidence given the extreme tightness of the race, but the preponderance of evidence suggests a slight Democratic advantage is more likely than not, though not by much. - -Probability: 54%" -29372,Will Democratic Congressman Jared Golden be re-elected in Maine's 2nd Congressional District in the 2024 election?,"In making this prediction about whether Jared Golden will be re-elected in Maine's 2nd Congressional District in the 2024 election, let's take a systematic approach by evaluating the factors at play: - -1. **Recent Context and Dynamics**: - - Jared Golden has shifted his stance on assault weapons, which could influence voters in a district where gun rights are a significant issue, particularly after the Lewiston shooting. His nuanced approach to the campaign suggests he is targeting Trump voters, which could be strategic given the district's conservative leaning in past presidential elections. This pivot might help him buffer against potential losses from his Democratic base who might be more progressive on gun control issues. - -2. **Candidate Dynamics**: - - Golden's opponent, Austin Theriault, has the backing of House Republicans and might appeal to the district's conservative voters. Theriault is a former NASCAR driver, which could attract particular demographics either through personal appeal or national party support efforts. - -3. **Political Environment**: - - Historically, Maine's 2nd Congressional District has been a swing district. While it voted for Trump in 2016 and 2020, Golden was able to secure his position in previous elections, suggesting he has an effective strategy for appealing to his constituents across party lines. - -4. **Election Timing and Uncertainties**: - - The election is just days away, so any major shifts in public opinion would need to be exceptionally rapid. This short timeframe suggests that the current dynamics are unlikely to change drastically unless something significant occurs. - -5. **Base Rate and Similar Historical Occurrences**: - - Historically, incumbents have a high re-election rate, although the margin is smaller in swing districts like this one. Golden's past success in a Trump-favorable district should be considered, but we must also note the variability of voter behavior in such competitive environments. - -6. **Assessment of Recency and Source**: - - There's a lack of recent poll data, which adds uncertainty to any forecast. The focus on gun issues may dominate late-stage voter sentiment, but without polls it's difficult to quantify this impact precisely. - -Considering these points, Jared Golden's strategy and past success in a conservative-leaning district indicate a fairly balanced race with significant uncertainty. I need to consider my past overconfidence bias, which suggests a tempered prediction is appropriate. Given the factors and the historical context: - -- **Base forecast with adjustments for context and strategy**: - - Base rate of incumbency favored, tempered by district's partisan leanings. - - Weight given to recent dynamics such as Golden's stance on guns and strategic campaign adaptions. - -Combining these elements, I estimate that Golden's chances are just slightly above a coin flip but not overwhelmingly secure due to the district's conservative history and the opponent's profile. - -Probability: 55%" -29371,Will Republican Congressman John Duarte be re-elected in California's 13th Congressional District in the 2024 election?,"Let me think through this step by step: - -1. This is a Competition Event (political election), which requires analyzing historical data, current polling, and district dynamics. - -2. Key factors to consider: -- Incumbent advantage (Duarte is incumbent) -- District voting history (swing district) -- Previous margin (extremely close in 2022 - only 564 votes) -- National political environment -- Demographic trends -- Time until election (exactly 1 week) - -3. Historical context: -- 2022 was extremely close (50.2% to 49.8%) -- District is considered highly competitive -- California tends Democratic overall, but this district is truly swing - -4. Current situation: -- Duarte is the incumbent, which typically provides a 2-3% advantage -- Immigration reform bill shows he's positioning as a moderate -- Youth voter turnout could be a wild card -- National environment slightly favors Republicans in House races -- District demographics haven't changed significantly since 2022 - -5. Time consideration: -- With only 7 days until election, major shifts are unlikely -- Most voters have likely made up their minds -- Early voting is already underway - -6. Base rate considerations: -- House incumbent re-election rate historically around 90-95% -- However, in highly competitive districts it's much lower -- Similar California swing districts show closer to 55-60% incumbent retention - -7. Uncertainty factors: -- Lack of recent polling data -- Unknown youth turnout -- National political developments in final week - -Given all these factors, I need to balance: -- The extremely narrow previous victory (suggesting ~50%) -- Incumbent advantage (+2-3%) -- Slight Republican-favoring national environment (+1-2%) -- District competitiveness (high uncertainty) -- Very short timeframe reducing possibility of major changes - -The extremely close previous result and continued competitiveness of the district suggest this is nearly a toss-up, but with slight advantages to Duarte from incumbency and national environment. However, I must avoid overconfidence given the district's history of extremely close races. - -Probability: 54% - -This reflects a slight lean toward Duarte's re-election while acknowledging the high uncertainty and extremely competitive nature of the district. The number is intentionally not too far from 50% given the district's history of razor-thin margins and the lack of definitive polling data." -29370,"Will Oregon voters approve Oregon Measure 117, which would establish ranked-choice voting in the state?","Let me think through this step by step: - -1) First, let's consider the type of question - this is a Competition Event, specifically a ballot measure vote. - -2) Looking at historical precedent: -- Ranked-choice voting measures have had mixed success in different states -- Alaska and Maine have adopted RCV statewide -- Several attempts have failed in other states -- Nevada voters rejected RCV in 2022 -- Local implementations (like Portland) provide mixed signals - -3) Recent polling shows voters are split, which is significant given we're just days from the election. Undecided voters typically break towards ""no"" on ballot measures, as voters tend to default to maintaining status quo when uncertain. - -4) Key factors favoring passage: -- Oregon is generally politically progressive -- Portland's implementation provides local familiarity -- Strong organizing efforts by proponents - -5) Key factors against passage: -- Ballot measures often fail when voters are uncertain -- Complex electoral changes typically face an uphill battle -- Conservative opposition is likely to be unified -- Historical tendency for voters to reject significant electoral changes - -6) Timing considerations: -- We're just 7 days from resolution -- Very little time for significant opinion shifts -- Most voters have likely formed their opinions - -7) Base rate adjustments: -- Historically, similar electoral reform measures have a success rate below 50% -- Oregon's progressive lean provides some positive adjustment -- Recent failures in other states suggest continued skepticism - -8) Uncertainty factors: -- Polling data is limited -- Voter turnout effects are unclear -- Impact of concurrent elections/measures - -9) Probability distribution considerations: -- Given the split polling and historical patterns, this appears to be a genuine toss-up -- Need to account for status quo bias -- Must factor in the tendency for undecideds to break ""no"" - -Given all these factors, I believe this measure faces slightly unfavorable odds. While Oregon's progressive nature helps, the historical tendency for voters to reject complex electoral changes, combined with the status quo bias and the pattern of undecided voters breaking negative on ballot measures, suggests a somewhat lower than 50% chance of passage. - -The closeness to the resolution date means we should have high confidence in current polling, which shows a split electorate. However, accounting for the tendency of undecided voters to break ""no"" and the historical difficulty of passing electoral reform measures, I need to adjust downward from 50%. - -Probability: 43%" -29369,"Will Alaska voters approve Alaska Ballot Measure 2, which would repeal ranked-choice voting and nonpartisan primaries in the state?","Let me think about this step by step: - -1. **Type of Question**: This is a Competition Event, specifically a ballot measure vote. - -2. **Timeline**: The resolution date is November 6, 2024, which is only 7 days away. This means we have very high certainty about the current state of affairs and limited time for major changes. - -3. **Historical Context**: -- Alaska originally passed RCV in 2020 by a very narrow margin (50.55% to 49.45%) -- Most ballot measures to change voting systems tend to fail -- There's typically status quo bias in voting system changes -- The most recent poll (January 2023) showed 53% support for repeal, but that's quite outdated - -4. **Current Factors**: -- Opposition (No on 2) has significantly more funding ($1.29M vs $489K) -- Voters have now experienced RCV in actual elections -- Generally, when people experience RCV, support tends to increase over time -- The opposition having more funding is particularly significant this close to election day - -5. **Base Rate Analysis**: -- Voting system changes typically fail about 65-70% of the time -- When there's significantly more funding on one side, that side wins about 75-80% of the time in ballot measures -- Status quo tends to have advantage in ballot measures - -6. **Key Considerations**: -- The funding disparity is substantial and recent -- The only poll is very outdated (21 months old) -- Voter familiarity with the system has increased -- Opposition (to repeal) has organizational advantages from defending status quo - -7. **Uncertainty Factors**: -- No recent polling -- Unclear voter turnout expectations -- Unknown late campaign developments - -8. **Mathematical Adjustment**: -Starting with base assumption of 50%: -- Adjust -15% for status quo advantage -- Adjust -10% for funding disparity favoring opposition -- Adjust -5% for increasing voter familiarity with RCV -- Add +10% for the January 2023 poll showing majority support -- Add +5% for historical narrow margin of original passage - -This suggests approximately a 35% chance of passage. - -9. **Confidence Check**: -Given the lack of recent polling and the complexity of voting system changes, we should avoid extreme predictions. The 35% estimate feels reasonable given the evidence, while acknowledging significant uncertainty. - -10. **Final Verification**: -- Not too extreme -- Accounts for major known factors -- Reflects funding disparity -- Considers historical patterns -- Acknowledges uncertainty - -Probability: 35%" -29368,"Will SpaceX re-use a Starship booster before January 1, 2025?","Let me think through this step by step: - -1. Timeline Analysis: -- Only 2 months remain until resolution -- Very short window for such a significant technical achievement -- SpaceX typically needs several weeks between launches -- Regulatory approvals add additional time - -2. Technical Progress Assessment: -- First successful booster catch just achieved (Oct 13, 2024) -- Still need to: - * Inspect caught booster - * Certify it for reuse - * Get regulatory approval - * Prepare for relaunch -- No previous reuse of Starship boosters - -3. Historical Context: -- Falcon 9 took about 2 years from first recovery to first reuse -- However, SpaceX now has extensive reuse experience -- Starship system is more complex than Falcon 9 -- First successful catch to reuse timeline likely shorter than Falcon 9, but still significant - -4. Regulatory Considerations: -- FAA approval needed for modified mission profile -- Environmental assessments may be required -- Holiday season could slow approvals - -5. SpaceX's Known Plans: -- Preparing for Flight 6 -- No public indication of reuse plans for 2024 -- Block 2 changes planned for 2025 - -6. Time-Based Probability Distribution: -- By end of November 2024: 2% -- By end of December 2024: 8% -- By end of March 2025: 25% -- By end of June 2025: 45% - -7. Risk Factors: -- Technical challenges from previous flights -- Complex regulatory environment -- Holiday season timing -- Weather considerations - -Given the extremely short timeline, recent first catch, typical SpaceX development cycles, and regulatory requirements, I assess this as highly unlikely before January 1, 2025. While SpaceX has demonstrated rapid progress, two months is an extremely tight window for: -- Post-catch inspection -- Refurbishment -- Certification -- Regulatory approval -- Launch preparation - -Even with SpaceX's accelerated timeline compared to previous programs, this would require an unprecedented speed of development and approval. - -Accounting for my historical overconfidence and the short timeline, but also acknowledging SpaceX's ability to occasionally exceed expectations: - -Probability: 8%" -29367,"Will election night 2024 end without Dave Wasserman, Nate Silver, or Nate Cohn calling the 2024 presidential election?","To evaluate this question, let's break down the factors that influence whether Dave Wasserman, Nate Silver, or Nate Cohn will call the 2024 presidential election by election night (up to 4 AM Eastern on Nov 6). - -### Base Rates and Historical Context -1. **Historical Election Calls**: Review historical data on when U.S. presidential elections are typically called. Historically, many elections have been called before midnight on election night, though circumstances vary. The timelines depend on the nature of the election—close contests often take longer to call. -2. **Past Behavior of Analysts**: All three analysts involved are known for calling elections past before official media calls. This makes a non-call by 4 AM less likely if historical patterns hold. - -### Current Environment -1. **Polling and Key States Analysis**: - - Election experts focus heavily on key swing states like Pennsylvania, Michigan, and Wisconsin. Rapid results in these states can lead to early calls. - - Nate Silver and his analysis on polling saturation in these states suggest a robust data environment that may enable quicker predictive conclusions. - -2. **Prediction Markets and Sentiment**: While they don't directly pertain to the analysts' decisions, they offer insight into expected election outcomes, thus potentially influencing the timing of unofficial calls. - -### Potential Novel Elements -1. **Technological Advances**: Improvements in data collection and analysis might allow for more accurate and timely calls. -2. **Political Environment**: Consider if there is anything particularly novel politically that might lead to extended counting, e.g., increased mail-in ballots. However, given the recent experience with mail-in voting, systems may now be better prepared to handle such loads more quickly. - -### Timing and Probability -1. **Days Until Resolution**: With less than a week until election night, there's minimal time for major changes in the default expectations of early calls. -2. **Distribution Over Time**: Predicting an early call by 4 AM aligns with typical patterns unless there are very close races without preliminary leads in key states. - -### Probability Forecast -Given these considerations: -- Base Rate for Previous Election Calls: Historically leans toward calls being made election night. -- No extraordinary developments suggest deviation from the base rate. -- Close elections tend to delay calls, but with current polling and data saturation, analysts might be able to confidently call results even in closely contested states with partial data. - -Given my earlier tendency to be overconfident, I'll avoid an overly high probability despite strong historical data, due to the random nature and potential unique elements of each election cycle. - -**Probability: 30%** - -This estimate balances the typical behavior of these analysts, improvements in election technology, and potential variability in swing state results with controlled expectations to avoid overconfidence." -29366,"Will Donald Trump says ""win,"" ""won,"" or ""winner"" on election night?","Let me think through this step by step: - -1. Question Type: Competition Event (Election Night Speech) - -2. Historical Precedent: -- 2020: Trump said ""won"" 15 times on election night -- Trump consistently emphasizes winning in campaign speeches -- Base rate is very high for this specific behavior - -3. Key Factors: -a) Timing: -- Only 7 days until resolution -- Very short timeframe means current behavior patterns highly predictive -- Election night is a specific, scheduled event - -b) Trump's Current Behavior: -- Still actively campaigning -- Consistently using victory-oriented language -- Recent rallies show no deviation from past patterns - -4. Positive Factors: -- Trump's consistent messaging style -- Historical precedent from 2020 -- Limited time for behavior change -- High stakes election night scenario -- Trump's tendency to claim victory - -5. Negative Factors: -- Legal scrutiny might affect behavior -- Campaign advisors could recommend different approach -- Different electoral circumstances than 2020 -- Resolution window is relatively narrow (9 hours) - -6. Additional Considerations: -- Words ""win/won/winner"" are common campaign speech elements -- Multiple opportunities during 9-hour window -- Very natural words to use on election night -- Core to Trump's brand/personality - -7. Uncertainty Analysis: -- Short timeline reduces uncertainty -- Specific event reduces variance -- Clear historical precedent -- Limited external factors that could prevent speech - -8. Probability Distribution: -- Base case (following 2020 pattern): ~85% -- Modified for: - * Narrower time window: -5% - * Different electoral context: -5% - * Legal situation impact: -5% - * Possibility of no speech: -5% - -9. Overconfidence Adjustment: -- Initial calculation suggests 65% -- Historical overconfidence tendency suggests reducing by 5% -- But very short timeline and clear precedent supports higher confidence - -Given the extremely short timeline, Trump's consistent behavior patterns, the natural context for these words, and strong historical precedent from 2020, this seems highly likely. However, accounting for uncertainty and potential changes, plus adjusting for overconfidence: - -Probability: 60%" -29366,"Will Donald Trump says ""win,"" ""won,"" or ""winner"" on election night?","Let me think through this step by step: - -1. Question Type: This is a Competition Event, specifically about election night behavior of a political candidate. - -2. Historical Precedent: -- In 2020, Trump said ""won"" 15 times during election night -- Trump consistently uses winning-related language in campaign speeches -- Trump has a documented pattern of claiming victory early - -3. Key Factors: -a) Motivating factors for saying these words: -- Trump's consistent messaging style -- Desire to project strength to supporters -- Pattern of early victory claims -- Current polling showing competitive race - -b) Factors that might prevent saying these words: -- Legal scrutiny from 2020 election cases -- Potential campaign strategy changes -- Possibility of clear early losses -- Possibility of not speaking at all that night - -4. Time Considerations: -- Only 7 days until resolution -- Very little time for fundamental behavior changes -- Campaign strategy likely already set - -5. Base Rate Analysis: -- Looking at Trump's speeches over past year -- Nearly every major speech includes ""win/won/winner"" -- Election nights particularly likely for victory rhetoric - -6. Uncertainty Factors: -- Exact timing of any speech -- Whether he speaks at all -- Potential last-minute strategy changes -- Possible early electoral developments - -7. Statistical Weighting: -- Given Trump's consistent pattern: +70% -- Possibility of no speech: -15% -- Legal context influence: -5% -- Campaign strategy shift: -5% -- Early results impact: -5% - -8. Confidence Adjustment: -- Starting with 85% based on 2020 behavior -- Reducing for overconfidence tendency -- Accounting for uncertainty in speech occurrence -- Considering resolution criteria specificity - -The key factor here is that Trump has extremely consistent messaging patterns, particularly around elections, and there's very little time for fundamental changes. The words ""win,"" ""won,"" or ""winner"" are core to his political vocabulary, especially on election nights. - -However, we must account for the possibility that he might not speak at all, or that circumstances might prevent a typical victory-claiming speech. The specific time window (8:00 PM - 5:00 AM ET) also adds some uncertainty. - -Given all factors, and adjusting for historical overconfidence, I would set the probability at: - -Probability: 78% - -This reflects high likelihood based on past behavior while accounting for various uncertainty factors and the specific resolution criteria." -29433,"Will North Dakota voters approve Initiated Measure 5, which would legalize recreational marijuana in the state?","To forecast the likelihood of Initiated Measures 5 and 29 being approved in North Dakota and South Dakota respectively, we must consider a range of factors including recent polling, historical trends, opposition strength, and national context. - -### North Dakota: Initiated Measure 5 -1. **Polling Data**: Recent polling suggests a tight race with 45% support, 40% opposition, and 15% undecided. This indicates significant uncertainty, but support slightly leads. -2. **Historical Context**: Previously, North Dakota has demonstrated a conservative stance on marijuana legalization. Past attempts at legalization have failed, such as Measure 3 in 2018. -3. **Opposition and Support Campaigns**: The health care and law enforcement communities oppose the measure, alongside the 'Nope on Dope' campaign, which might sway voters concerned about societal impacts. -4. **National Trend**: There is a national trend towards legalization, which might influence some undecided voters positively towards Measure 5. -5. **Current Date**: With only days remaining until resolution, trends are unlikely to shift drastically. -6. **Base Rates and Variability**: Considering historical rejection and the high proportion of undecided voters, a moderate chance of passing should be estimated. - -### South Dakota: Initiated Measure 29 -Given the lack of specific data in the prompt about South Dakota’s Initiated Measure 29, assumptions are required based on regional and national trends: -1. **Previous Trends**: South Dakota narrowly passed an initiative to legalize cannabis in 2020, highlighting a precedent for legalization despite conservative tendencies. -2. **Political Climate**: Similar opposition may exist in South Dakota as in North Dakota, potentially mitigating support. -3. **National Influence**: The same national trends apply here, potentially pushing support slightly higher in comparison to ND. - -In conclusion, given the high degree of undecided voters, historical tendencies, and existing opposition, I forecast a slightly lower probability for the passage of the measure in North Dakota, than is indicated by the polls (compensating for undecided voters tending toward caution or status quo), but a moderately higher likelihood in South Dakota given past successes with legalization measures. - -For both measures, I account for my historic overconfidence by slightly adjusting probabilities away from extremes, centering them more conservatively. - -### Probabilities: -North Dakota Measure 5: Likely outcome suggests about 35% Pass probability considering historical trends and high undecideds. -South Dakota Measure 29: Given somewhat more liberal precedents, set the passing at about 45%." -29428,"Will South Dakota voters approve Initiated Measure 29, which would legalize recreational marijuana in the state?","To forecast the likelihood of Initiated Measures 5 and 29 being approved in North Dakota and South Dakota respectively, we must consider a range of factors including recent polling, historical trends, opposition strength, and national context. - -### North Dakota: Initiated Measure 5 -1. **Polling Data**: Recent polling suggests a tight race with 45% support, 40% opposition, and 15% undecided. This indicates significant uncertainty, but support slightly leads. -2. **Historical Context**: Previously, North Dakota has demonstrated a conservative stance on marijuana legalization. Past attempts at legalization have failed, such as Measure 3 in 2018. -3. **Opposition and Support Campaigns**: The health care and law enforcement communities oppose the measure, alongside the 'Nope on Dope' campaign, which might sway voters concerned about societal impacts. -4. **National Trend**: There is a national trend towards legalization, which might influence some undecided voters positively towards Measure 5. -5. **Current Date**: With only days remaining until resolution, trends are unlikely to shift drastically. -6. **Base Rates and Variability**: Considering historical rejection and the high proportion of undecided voters, a moderate chance of passing should be estimated. - -### South Dakota: Initiated Measure 29 -Given the lack of specific data in the prompt about South Dakota’s Initiated Measure 29, assumptions are required based on regional and national trends: -1. **Previous Trends**: South Dakota narrowly passed an initiative to legalize cannabis in 2020, highlighting a precedent for legalization despite conservative tendencies. -2. **Political Climate**: Similar opposition may exist in South Dakota as in North Dakota, potentially mitigating support. -3. **National Influence**: The same national trends apply here, potentially pushing support slightly higher in comparison to ND. - -In conclusion, given the high degree of undecided voters, historical tendencies, and existing opposition, I forecast a slightly lower probability for the passage of the measure in North Dakota, than is indicated by the polls (compensating for undecided voters tending toward caution or status quo), but a moderately higher likelihood in South Dakota given past successes with legalization measures. - -For both measures, I account for my historic overconfidence by slightly adjusting probabilities away from extremes, centering them more conservatively. - -### Probabilities: -North Dakota Measure 5: Likely outcome suggests about 35% Pass probability considering historical trends and high undecideds. -South Dakota Measure 29: Given somewhat more liberal precedents, set the passing at about 45%." -29430,Will Donald Trump sweep all 7 battleground states in the 2024 election?,"When forecasting the likelihood of Donald Trump sweeping all 7 battleground states in the 2024 election (ID: 29430), we need to consider several factors: current polling data, expert forecasts, historical election data, and the specific resolution criteria. As of October 31, 2024, polling suggests a close race in the battleground states with both candidates having a realistic path to victory in these states, though Harris slightly leads in most. Nate Silver's model indicates a 24.4% chance for Trump to sweep all seven states, highlighting the event's unlikelihood but not impossibility. The competitive nature of the election, coupled with historical trends of polling errors and last-minute voter swings, adds a layer of uncertainty. Given the consistency of polling data and model predictions over the past weeks, it seems more likely than not that Harris will win at least one of these states, which would result in a 'No' resolution. For question 29432 about Trump outperforming Marsha Blackburn in Tennessee, historical voting patterns in Tennessee suggest a strong Republican preference. Although there aren't detailed polls comparing Trump to Blackburn within Tennessee, it's reasonable to predict Trump could leverage high Republican turnout, likely giving him an edge over Blackburn. By considering all available information, specifically expert analyses and current trends, we are accounting for potential surprises while applying a base rate adjusted for current dynamics." -29432,Will Donald Trump outperform Marsha Blackburn in Tennessee in the 2024 election?,"When forecasting the likelihood of Donald Trump sweeping all 7 battleground states in the 2024 election (ID: 29430), we need to consider several factors: current polling data, expert forecasts, historical election data, and the specific resolution criteria. As of October 31, 2024, polling suggests a close race in the battleground states with both candidates having a realistic path to victory in these states, though Harris slightly leads in most. Nate Silver's model indicates a 24.4% chance for Trump to sweep all seven states, highlighting the event's unlikelihood but not impossibility. The competitive nature of the election, coupled with historical trends of polling errors and last-minute voter swings, adds a layer of uncertainty. Given the consistency of polling data and model predictions over the past weeks, it seems more likely than not that Harris will win at least one of these states, which would result in a 'No' resolution. For question 29432 about Trump outperforming Marsha Blackburn in Tennessee, historical voting patterns in Tennessee suggest a strong Republican preference. Although there aren't detailed polls comparing Trump to Blackburn within Tennessee, it's reasonable to predict Trump could leverage high Republican turnout, likely giving him an edge over Blackburn. By considering all available information, specifically expert analyses and current trends, we are accounting for potential surprises while applying a base rate adjusted for current dynamics." -29427,Will Democratic Congressman Don Davis be re-elected in North Carolina's 1st Congressional District in the 2024 election?,"Let me think through this step by step: - -1) **Incumbent Advantage** -- Don Davis is the incumbent Democrat -- Historically, House incumbents win about 90-95% of their races -- However, this advantage may be weaker in competitive districts - -2) **District Demographics & Lean** -- Cook PVI of R+0.7 indicates nearly even partisan split -- This is a competitive district, not safely Democratic -- Being slightly Republican-leaning is concerning for a Democratic incumbent - -3) **Current Polling & Forecasts** -- The Hill/DDHQ shows Davis with 76% chance of winning -- This is very recent (Oct 16) and from a credible source -- However, polls can shift in final weeks - -4) **Campaign Finance** -- Davis has significant cash advantage ($1.8M vs $558K) -- Both candidates have spent heavily -- Financial advantage often correlates with victory - -5) **Political Environment** -- 2024 is a presidential election year -- National environment slightly favors Republicans -- Biden's approval ratings are low, which could hurt down-ballot Democrats - -6) **Timing** -- We're only 6 days from election -- Most minds are made up -- Early voting is already underway -- Major shifts are unlikely this close to election - -7) **Campaign Dynamics** -- Davis has faced some criticism but no major scandals -- His military background helps in a district with military presence -- Republican challenger also has military credentials - -8) **Historical Context** -- Davis won in 2022 with 52.3% of vote -- District has been competitive but Democratic-held recently -- Similar districts have been competitive in 2024 - -Weighing these factors: -- The recent polling showing 76% chance feels slightly optimistic -- The incumbent advantage is strong but moderated by district competitiveness -- The financial advantage is significant -- The timing (6 days out) means current trends likely hold -- The slight Republican lean and national environment create some risk - -I need to adjust for historical overconfidence and the fact that we're dealing with a competitive district in a challenging year for Democrats. However, the incumbent advantage and financial edge are significant factors this close to election day. - -Given all these factors and accounting for uncertainty while avoiding overconfidence, I would set the probability slightly lower than the polls but still favoring Davis. - -Probability: 68%" -29426,Will Republican Congressman Anthony D'Esposito be re-elected in New York's 4th Congressional District in the 2024 election?,"Let me think through this step by step: - -1. **Election Type Analysis** -- This is a House race in a competitive district -- Incumbent advantage exists but is reduced in competitive districts -- 2024 is a presidential election year, which typically has higher turnout - -2. **District Demographics** -- More registered Democrats than Republicans -- Located in Long Island, which has seen recent Republican success -- Suburban district where national issues like crime and cost of living resonate - -3. **Current Situation** -- D'Esposito won by ~10,000 votes in 2022 -- Facing ethics probe, which could impact voters -- Strong fundraising from both candidates -- Rated as ""toss-up"" by nonpartisan analysts - -4. **Time Consideration** -- Election is in 6 days -- Most voters have likely made up their minds -- Late developments unlikely to dramatically shift race - -5. **Historical Context** -- Incumbents generally win ~90% of House races -- But this advantage is lower in competitive districts -- 2022 saw D'Esposito win despite Democratic registration advantage - -6. **Current Environment** -- National environment slightly favors Republicans -- Local Long Island trends favor Republicans -- But presidential year turnout could help Democrats - -7. **Risk Factors** -- Ethics probe could hurt D'Esposito -- Presidential election impact unclear -- Competitive fundraising from challenger - -8. **Confidence Adjustment** -- Given the extremely close timeline (6 days) -- Must heavily weight current polling and situation -- Toss-up rating from respected analysts -- Need to account for historical overconfidence - -This is extremely close to resolution, so we should weight current indicators heavily. While D'Esposito has incumbent advantage and recent Long Island Republican strength, the ethics probe and Democratic registration advantage are significant counterweights. The toss-up rating from nonpartisan analysts is particularly meaningful this close to election day. - -Given all factors and adjusting for historical overconfidence, I would rate D'Esposito's chances as slightly below 50%, primarily due to the ethics probe and Democratic registration advantage in a presidential year. - -Probability: 45%" -29425,Will Netumbo Nandi-Ndaitwah win 55% or more of the vote in the first round of the Namibian presidential election?,"To determine the probability that Netumbo Nandi-Ndaitwah will win 55% or more of the vote in the first round of the 2024 Namibian presidential election, let's break down the situation based on the available information. - -**1. Historical Context and Base Rates:** - - SWAPO and its candidate, Nandi-Ndaitwah, face a significant decline in popularity. SWAPO's support fell from 87% in 2015 to 56% in 2019. - - The base rate for achieving 55% of the vote becomes challenging given these dynamics and historical context. - - Historically, elections where the incumbent party's popularity is declining often result in outcomes below past levels of support. - -**2. Current Political Landscape:** - - SWAPO is contending with youth disenchantment amidst a high youth unemployment rate of 46%. - - There are 15 presidential candidates, which suggests vote-splitting could further hamper Nandi-Ndaitwah's ability to secure 55% in the first round. - - Opposition parties like the IPC, despite not coalescing around a single candidate, are evidently gaining traction in local elections, indicating robust competition. - -**3. Recent Developments and Observations:** - - Nandi-Ndaitwah's candidacy being upheld by the High Court is positive for her campaign but does not directly influence voter sentiment substantially. - - SWAPO's attempts to attract youth votes by promising jobs and including youthful candidates might help, but such promises often take time to shift public perception. - -**4. Uncertainty and Time Factor:** - - The election date is very close (less than a month), so significant shifts in voter sentiment are unlikely within this short timeframe. - - The absence of recent polling data adds uncertainty, but the competitive nature of the election and the mentioned factors likely persist. - -**5. Compensating for Overconfidence:** - - Considering the historical outcomes that only a smaller percentage of questions resolve positively and the current political and economic context, the inclination is to account for bias towards overconfidence in the probability estimation. - -Based on this analysis, the likelihood that Netumbo Nandi-Ndaitwah will achieve 55% or more of the vote seems low. Her party's declining support, the competitive field, and youth disenchantment are significant hurdles, despite her historical candidacy as the first woman for the presidency and other strategic moves by SWAPO. - -**Final Probability Estimate:** -Given these considerations, forecasting a precise probability involves acknowledging low likelihood accompanied by some uncertainty: - -- Probability that Nandi-Ndaitwah secures 55% or more of the vote in the first round: **Probability: 25%**." -29424,"Will Missouri voters approve Missouri Amendment 7, which would prohibit ranked-choice voting in the state?","To assess the likelihood of Missouri voters approving Missouri Amendment 7, it’s important to consider a few key elements and historical precedents that will guide our analysis. - -### Understanding the Context: -1. **Nature of the Amendment**: Missouri Amendment 7 is designed to ban ranked-choice voting and reinforce the prohibition of noncitizen voting, the latter of which is already in place. The dual nature of this amendment—combining two seemingly unrelated issues—could have a significant impact on voter perception and decision-making. - -2. **Current Political Climate**: Missouri is known for its conservative leanings. Often, measures that emphasize nationalist themes like tightening voting rights and rules tend to resonate with conservative-leaning voters. - -3. **Public Awareness and Campaigning**: There's active campaigning both for and against the amendment. This includes organizations like Better Ballot KC arguing against it as misleading. The absence of specific polling data, however, introduces a degree of uncertainty regarding voter sentiment. Historically, ballot measures' outcomes can hinge on the level of public information and campaign effectiveness. - -4. **Past Election Trends**: Historically, people tend to vote based on perceived risks, the clarity of the amendment text, and prevailing societal attitudes. There's a chance that the amendment's language around noncitizen voting could be persuasive to voters, despite the claim being strategically misleading. - -### Analyzing the Factors: -- **Base Rates**: Historically, Missouri has favored conservative policies, which could indicate a predisposition toward approving the amendment, particularly with its anti-immigration undertones. Looking at recent elections, simple and straightforward conservative measures have often been approved. - -- **Unique Aspects**: The combined objection to ranked-choice voting, which may be less understood or less prioritized by voters, and the less relevant clause about noncitizen voting, presents a strategic maneuver to increase the likelihood of approval by leveraging existing voter biases. - -- **Timing and Volatility**: With the vote occurring very soon (November 5), there is limited time for substantial campaign influence or shifts in voter opinion. Historically, ballot measures tend not to undergo significant last-minute opinion changes without significant new information or events. - -### Predictive Assessment: -Due to the combination of these factors, the prediction leans toward a positive resolution for this amendment. However, given potential voter confusion or mixed sentiments about the ranked-choice aspect, there remains a considerable level of uncertainty. - -Since overconfidence has been a past issue, it’s wise to adjust for that by providing some buffer in the probability estimation, leaning slightly cautious but reflecting a conservative advantage. - -**Final Probability Assessment**: -Considering all the above reasoning and adjustments for my previous biases, I estimate: - -**Probability: 65%**" -29423,"Will Kevin Stefanski be the Cleveland Browns head coach on January 1, 2025?","To predict whether Kevin Stefanski will be the Cleveland Browns head coach on January 1, 2025, we must consider several factors, including historical data, recent developments, and the broader context of the NFL. - -### Historical Context and Base Rates: -1. **NFL Coach Tenure:** Historically, NFL head coaches experience significant turnover, with average tenures typically around 3-4 years. Kevin Stefanski became the Browns' head coach in January 2020, meaning as of October 2024, he has been in the role for almost five years — a relatively long tenure by NFL standards. -2. **Cleveland Browns Coaching Stability:** The Browns have had a reputation for frequent coaching changes, which could suggest an elevated probability of a leadership change. -3. **Performance-Related Firings:** NFL coaches are often fired due to poor team performance. The recent five-game losing streak highlights potential performance issues, increasing the risk of Stefanski’s departure. - -### Recent Developments: -1. **Play-Calling Change:** Stefanski's decision to relinquish play-calling duties indicates an awareness of the need for change, which might be an attempt to improve performance and thus his job security. -2. **Speculation & Pressure:** The surrounding speculation about his job security and the high-pressure environment of professional sports can influence decisions rapidly, especially in a performance-driven league like the NFL. - -### Broader Context: -1. **NFL Season Dynamics:** The period between October and January encompasses the latter half of the NFL season, when critical performance evaluation occurs. The Browns' performance in the remainder of the season will heavily influence Stefanski's job status. -2. **Team Dynamics and Management:** Stefanski's relationship with management, players, and staff will play a crucial role. The decision to change play-calling could either improve or further strain these dynamics. - -### Timeframe Consideration: -1. **Short Time Frame:** There are about two months until the resolution date, which doesn’t leave much time for drastic changes unless there is an immediate performance collapse or another significant issue arises. -2. **Historical Precedent for In-Season Coaching Changes:** While mid-season firings do happen, they are less common late in the season unless a team is no longer competitive. - -### Evaluation: -1. Given the past performance, current speculation, pressure, and organizational context, there is a significant risk of Stefaniski's termination. -2. However, the change in play-calling might stabilize the situation if it results in performance improvements. -3. The timeframe is relatively short, and unless there is an undeniable decline or significant incident, stability until the resolution date could be more likely. - -### Probability Assessment: -- Base rate of NFL coach tenure and recent shakiness suggests a considerable chance of a change. -- Given the short time frame and uncertainty about the immediate impact of the play-calling change, a moderate probability of Stefanski remaining seems appropriate. - -**Final Probability: 45%** - -This reflects an elevated risk of change while acknowledging the short period for significant shifts. Given your previous overconfidence bias, this probability conservatively reflects possible outcomes without skewing too far toward certainties." -29431,"Will Kalshi be #1 in the free apps in the Apple App Store on November 5, 2024?","Let me think about this step by step: - -1. CURRENT STATUS -- Kalshi is currently ranked high but not #1 in the App Store -- It reached #7 recently, showing strong momentum -- Just 5 days until resolution - -2. FACTORS SUPPORTING HIGH RANKING -- Presidential election proximity driving interest -- $100M in election bets shows significant engagement -- Recent legal win vs CFTC -- Currently #1 in finance category -- Strong marketing push from CEO - -3. CHALLENGES TO REACHING #1 -- Extremely competitive App Store environment -- #1 spot usually dominated by social media, gaming, or major tech platforms -- Short timeframe (5 days) to climb from #7 to #1 -- Finance apps rarely reach #1 overall -- Current ranking suggests significant gap to #1 - -4. HISTORICAL PRECEDENT -- Very few finance/betting apps have ever reached #1 -- Even during major market events (GameStop, etc.), finance apps peaked lower -- Election-related apps typically see spikes but rarely #1 -- Base rate for finance apps reaching #1 is extremely low - -5. TIMING CONSIDERATIONS -- Resolution date is day of US election -- Peak interest likely on election day -- But competing with news/social media apps that day -- Only needs to be #1 briefly during check period - -6. PROBABILITY DISTRIBUTION ACROSS TIME -- Next 2 days: 2% chance -- Day before election: 5% chance -- Election day: 15% chance -- Week after: 3% chance - -7. ADJUSTMENTS -- Need to adjust down for overconfidence -- Historical 35% positive resolution rate suggests conservatism -- Short timeframe reduces uncertainty but also limits upside potential -- Must account for extreme difficulty of reaching #1 vs just top 10 - -8. FINAL CONSIDERATIONS -- While momentum is strong, gap from #7 to #1 is massive -- Election timing provides unique catalyst -- But competition from news/social apps will be fierce -- Finance category leadership less relevant for overall ranking - -Given all factors, especially the extreme difficulty of reaching #1 overall and the short timeframe, but balanced against the unique election catalyst and current momentum, I estimate: - -Probability: 8% - -This reflects the significant challenge of reaching #1 overall while acknowledging there's a real but small chance given the election timing and current trajectory. The low probability accounts for historical precedent, competitive environment, and the need to adjust for overconfidence." -29429,"Will Hawaii voters approve the constitutional amendment on the ballot in 2024, which would repeal the state's ban on same-sex marriage?","Let me think through this step by step: - -1. Type of Question: Competition Event (electoral ballot measure) - -2. Time Horizon: Very short - just 6 days until resolution - -3. Key Factors to Consider: -- Hawaii is one of the most progressive states on LGBTQ+ rights -- Same-sex marriage already legal in practice since 2013 -- Strong institutional support (Governor, Senator, Teachers Union) -- Part of broader national trend of states securing marriage equality -- National polling shows 69% support for same-sex marriage -- No specific Hawaii polling data available -- Constitutional amendments typically require higher threshold than simple majority - -4. Historical Context: -- Hawaii has historically been progressive on LGBTQ+ rights -- Similar recent marriage equality measures in other states have generally passed -- Constitutional amendments tend to have lower passage rates than regular ballot measures - -5. Risk Factors: -- Constitutional amendments face higher hurdles -- Turnout/voting patterns in 2024 could differ -- Lack of state-specific polling creates uncertainty -- Some voters might see it as unnecessary given existing law - -6. Mitigating Considerations: -- Very short timeframe to resolution reduces uncertainty -- Strong institutional support suggests organized campaign -- No significant organized opposition reported -- Aligns with state's general political leanings - -7. Base Rate Analysis: -- Recent similar measures in other states have passed with 60-70% support -- Hawaii is more progressive than average state -- Constitutional amendments historically pass ~60% of the time when supported by state leadership - -8. Confidence Adjustment: -- Starting with ~75% based on state characteristics -- Adjust down for constitutional amendment hurdle (-5%) -- Adjust down for lack of polling data (-5%) -- Adjust down for general overconfidence correction (-5%) - -This appears to be a likely passage, but not certain. The lack of state-specific polling data and the higher threshold for constitutional amendments necessitate some caution. However, the strong institutional support, progressive nature of Hawaii, and very short timeline to resolution support a relatively high probability. - -Probability: 60%" -29490,Will the US State Department approve more than 20 arms sales globally in the fourth quarter of 2024?,"When forecasting the likelihood of the US State Department approving more than a certain number of arms sales in the fourth quarter of 2024, the base rates and recent historical data provide a crucial foundation. Analyzing the last five years, the average monthly approvals are about 6.7, and monthly fluctuations are prominent. Most notably, August 2024 saw a historically high 22 approvals. For the fourth quarter of this year, October data shows 9 approvals, and while this indicates strong sales activity, November and December's data, historically, do not consistently show high numbers, often closer to average or even below. - -There are several geopolitical factors currently at play, such as recent sales to Taiwan heightening tensions with China and sanctions against multiple countries potentially motivating increased arms purchases. The global geopolitical environment could indeed influence more sales, but consistent historically high months are rare. With only two months left in the quarter, reaching over 25 approvals seems unlikely unless significant and rapid developments occur. - -Evaluating the potential outcomes: -1. For more than 20 approvals (Q1, exceeding the recent peak month of October and aligning with record-breaking highs), the likelihood seems relatively balanced against historical trends (some probability but not overwhelmingly likely). -2. For over 15 approvals (Q2), this is realistic within a typical quarter given the current slight up-trend and geopolitical pressure. -3. Over 25 approvals (Q3) would necessitate sustained high approval rates in November and December, which is historically rare. - -Given these assessments, probabilities were assigned taking into account the average historical baseline, geopolitical tensions, and past patterns of approval numbers. By considering these factors, the joint probability distribution was constructed, ensuring probabilities sum to 100 and accurately reflect uncertainty and confidence in each threshold scenario being met." -29489,Will the US State Department approve more than 15 arms sales globally in the fourth quarter of 2024?,"When forecasting the likelihood of the US State Department approving more than a certain number of arms sales in the fourth quarter of 2024, the base rates and recent historical data provide a crucial foundation. Analyzing the last five years, the average monthly approvals are about 6.7, and monthly fluctuations are prominent. Most notably, August 2024 saw a historically high 22 approvals. For the fourth quarter of this year, October data shows 9 approvals, and while this indicates strong sales activity, November and December's data, historically, do not consistently show high numbers, often closer to average or even below. - -There are several geopolitical factors currently at play, such as recent sales to Taiwan heightening tensions with China and sanctions against multiple countries potentially motivating increased arms purchases. The global geopolitical environment could indeed influence more sales, but consistent historically high months are rare. With only two months left in the quarter, reaching over 25 approvals seems unlikely unless significant and rapid developments occur. - -Evaluating the potential outcomes: -1. For more than 20 approvals (Q1, exceeding the recent peak month of October and aligning with record-breaking highs), the likelihood seems relatively balanced against historical trends (some probability but not overwhelmingly likely). -2. For over 15 approvals (Q2), this is realistic within a typical quarter given the current slight up-trend and geopolitical pressure. -3. Over 25 approvals (Q3) would necessitate sustained high approval rates in November and December, which is historically rare. - -Given these assessments, probabilities were assigned taking into account the average historical baseline, geopolitical tensions, and past patterns of approval numbers. By considering these factors, the joint probability distribution was constructed, ensuring probabilities sum to 100 and accurately reflect uncertainty and confidence in each threshold scenario being met." -29491,Will the US State Department approve more than 25 arms sales globally in the fourth quarter of 2024?,"When forecasting the likelihood of the US State Department approving more than a certain number of arms sales in the fourth quarter of 2024, the base rates and recent historical data provide a crucial foundation. Analyzing the last five years, the average monthly approvals are about 6.7, and monthly fluctuations are prominent. Most notably, August 2024 saw a historically high 22 approvals. For the fourth quarter of this year, October data shows 9 approvals, and while this indicates strong sales activity, November and December's data, historically, do not consistently show high numbers, often closer to average or even below. - -There are several geopolitical factors currently at play, such as recent sales to Taiwan heightening tensions with China and sanctions against multiple countries potentially motivating increased arms purchases. The global geopolitical environment could indeed influence more sales, but consistent historically high months are rare. With only two months left in the quarter, reaching over 25 approvals seems unlikely unless significant and rapid developments occur. - -Evaluating the potential outcomes: -1. For more than 20 approvals (Q1, exceeding the recent peak month of October and aligning with record-breaking highs), the likelihood seems relatively balanced against historical trends (some probability but not overwhelmingly likely). -2. For over 15 approvals (Q2), this is realistic within a typical quarter given the current slight up-trend and geopolitical pressure. -3. Over 25 approvals (Q3) would necessitate sustained high approval rates in November and December, which is historically rare. - -Given these assessments, probabilities were assigned taking into account the average historical baseline, geopolitical tensions, and past patterns of approval numbers. By considering these factors, the joint probability distribution was constructed, ensuring probabilities sum to 100 and accurately reflect uncertainty and confidence in each threshold scenario being met." -29486,"Will it rain more than 100mm in Brasília, Brazil in December 2024?","To forecast the likelihood of rain in Brasília for December 2024, we must consider both historical data and recent trends. Historically, December is Brasília's wettest month, with average rainfall ranging from 215 mm to 253 mm. Despite this, Brasília has just emerged from an unprecedented 167-day drought. However, significant rainfall occurred in October, indicating a potential return to typical wet patterns. - -The influence of climate factors like El Niño and La Niña also plays a significant role. The recent drought was partly attributed to El Niño, but La Niña conditions, which tend to bring more rain, are expected to persist until January. This suggests that December could see substantial rainfall. - -Given the historical average and the recent heavy rain in October after the drought, it's likely that December will exceed 100mm of rainfall. The probability decreases for higher thresholds like 150mm and 200mm, as exceeding these requires more significant rainfall variability, especially given uncertainties about current climate anomalies. - -Taking into account the typical December rainfall, recent drought recovery, and projected La Niña influences, the probability distribution is as follows: -- >100mm is highly likely, given it's below the historical mean. -- >150mm is less probable but still likely given the historical average is above this. -- >200mm is possible but historically less certain, and requires more extreme downpours. - -Therefore, I forecast: -1. P(rain >100mm) = 85% indicative of a strong likelihood of returning to normal rainfall patterns. -2. P(rain >150mm) = 65% reflecting substantial rainfall but less certain than the 100mm threshold due to variability in totals. -3. P(rain >200mm) = 40% as this surpasses the lower bound of the historical average, making it less probable, but still feasible. - -Given our historical and climate context, these forecasts aim to balance historical base rates with present anomalies." -29487,"Will it rain more than 150mm in Brasília, Brazil in December 2024?","To forecast the likelihood of rain in Brasília for December 2024, we must consider both historical data and recent trends. Historically, December is Brasília's wettest month, with average rainfall ranging from 215 mm to 253 mm. Despite this, Brasília has just emerged from an unprecedented 167-day drought. However, significant rainfall occurred in October, indicating a potential return to typical wet patterns. - -The influence of climate factors like El Niño and La Niña also plays a significant role. The recent drought was partly attributed to El Niño, but La Niña conditions, which tend to bring more rain, are expected to persist until January. This suggests that December could see substantial rainfall. - -Given the historical average and the recent heavy rain in October after the drought, it's likely that December will exceed 100mm of rainfall. The probability decreases for higher thresholds like 150mm and 200mm, as exceeding these requires more significant rainfall variability, especially given uncertainties about current climate anomalies. - -Taking into account the typical December rainfall, recent drought recovery, and projected La Niña influences, the probability distribution is as follows: -- >100mm is highly likely, given it's below the historical mean. -- >150mm is less probable but still likely given the historical average is above this. -- >200mm is possible but historically less certain, and requires more extreme downpours. - -Therefore, I forecast: -1. P(rain >100mm) = 85% indicative of a strong likelihood of returning to normal rainfall patterns. -2. P(rain >150mm) = 65% reflecting substantial rainfall but less certain than the 100mm threshold due to variability in totals. -3. P(rain >200mm) = 40% as this surpasses the lower bound of the historical average, making it less probable, but still feasible. - -Given our historical and climate context, these forecasts aim to balance historical base rates with present anomalies." -29488,"Will it rain more than 200mm in Brasília, Brazil in December 2024?","To forecast the likelihood of rain in Brasília for December 2024, we must consider both historical data and recent trends. Historically, December is Brasília's wettest month, with average rainfall ranging from 215 mm to 253 mm. Despite this, Brasília has just emerged from an unprecedented 167-day drought. However, significant rainfall occurred in October, indicating a potential return to typical wet patterns. - -The influence of climate factors like El Niño and La Niña also plays a significant role. The recent drought was partly attributed to El Niño, but La Niña conditions, which tend to bring more rain, are expected to persist until January. This suggests that December could see substantial rainfall. - -Given the historical average and the recent heavy rain in October after the drought, it's likely that December will exceed 100mm of rainfall. The probability decreases for higher thresholds like 150mm and 200mm, as exceeding these requires more significant rainfall variability, especially given uncertainties about current climate anomalies. - -Taking into account the typical December rainfall, recent drought recovery, and projected La Niña influences, the probability distribution is as follows: -- >100mm is highly likely, given it's below the historical mean. -- >150mm is less probable but still likely given the historical average is above this. -- >200mm is possible but historically less certain, and requires more extreme downpours. - -Therefore, I forecast: -1. P(rain >100mm) = 85% indicative of a strong likelihood of returning to normal rainfall patterns. -2. P(rain >150mm) = 65% reflecting substantial rainfall but less certain than the 100mm threshold due to variability in totals. -3. P(rain >200mm) = 40% as this surpasses the lower bound of the historical average, making it less probable, but still feasible. - -Given our historical and climate context, these forecasts aim to balance historical base rates with present anomalies." -29487,"Will it rain more than 150mm in Brasília, Brazil in December 2024?","To predict the outcomes of the presented questions, I've analyzed both historical data and recent climate patterns. Let's address each question individually: - -1. **Rainfall in Brasília more than 150mm in December 2024**: Historically, December is the wettest month in Brasília with averages well above 150mm. Despite recent drought conditions and record heat likely due to El Niño and climate change, the onset of rains in October suggests a return to seasonal norms. With factors pointing towards a recovery of typical patterns, the likelihood of exceeding 150mm remains quite high. I estimate a probability of 85% for this threshold. - -2. **Rainfall in Brasília more than 200mm in December 2024**: While challenging to predict, the historical averages often exceed this mark. Given the return to rain in late October and typical December patterns, there is a robust likelihood of reaching this level, though less certain than for 150mm. I estimate a 70% probability. - -3. **California's Proposition 33 passing in the 2024 general election**: This question falls under the category of a Competition Event. At this point, media coverage, public sentiment, and political landscape around rent control need consideration. The passage of such measures often depends on political mobilization and public opinion. Using similar propositions as a base rate, with a slight regional tilt towards more support for rent control in California, I estimate the probability of passage at around 55%. - -For all questions, I've incorporated historical data, the likelihood of continuation or change in patterns, and nuances from recent developments. The timeframe of over two months before resolution allows for potential fluctuation in conditions and political climate, retaining a level of uncertainty in these forecasts." -29488,"Will it rain more than 200mm in Brasília, Brazil in December 2024?","To predict the outcomes of the presented questions, I've analyzed both historical data and recent climate patterns. Let's address each question individually: - -1. **Rainfall in Brasília more than 150mm in December 2024**: Historically, December is the wettest month in Brasília with averages well above 150mm. Despite recent drought conditions and record heat likely due to El Niño and climate change, the onset of rains in October suggests a return to seasonal norms. With factors pointing towards a recovery of typical patterns, the likelihood of exceeding 150mm remains quite high. I estimate a probability of 85% for this threshold. - -2. **Rainfall in Brasília more than 200mm in December 2024**: While challenging to predict, the historical averages often exceed this mark. Given the return to rain in late October and typical December patterns, there is a robust likelihood of reaching this level, though less certain than for 150mm. I estimate a 70% probability. - -3. **California's Proposition 33 passing in the 2024 general election**: This question falls under the category of a Competition Event. At this point, media coverage, public sentiment, and political landscape around rent control need consideration. The passage of such measures often depends on political mobilization and public opinion. Using similar propositions as a base rate, with a slight regional tilt towards more support for rent control in California, I estimate the probability of passage at around 55%. - -For all questions, I've incorporated historical data, the likelihood of continuation or change in patterns, and nuances from recent developments. The timeframe of over two months before resolution allows for potential fluctuation in conditions and political climate, retaining a level of uncertainty in these forecasts." -29485,[Short fuse] Will California's Proposition 33 (allowing rent control) pass in the 2024 general election?,"To predict the outcomes of the presented questions, I've analyzed both historical data and recent climate patterns. Let's address each question individually: - -1. **Rainfall in Brasília more than 150mm in December 2024**: Historically, December is the wettest month in Brasília with averages well above 150mm. Despite recent drought conditions and record heat likely due to El Niño and climate change, the onset of rains in October suggests a return to seasonal norms. With factors pointing towards a recovery of typical patterns, the likelihood of exceeding 150mm remains quite high. I estimate a probability of 85% for this threshold. - -2. **Rainfall in Brasília more than 200mm in December 2024**: While challenging to predict, the historical averages often exceed this mark. Given the return to rain in late October and typical December patterns, there is a robust likelihood of reaching this level, though less certain than for 150mm. I estimate a 70% probability. - -3. **California's Proposition 33 passing in the 2024 general election**: This question falls under the category of a Competition Event. At this point, media coverage, public sentiment, and political landscape around rent control need consideration. The passage of such measures often depends on political mobilization and public opinion. Using similar propositions as a base rate, with a slight regional tilt towards more support for rent control in California, I estimate the probability of passage at around 55%. - -For all questions, I've incorporated historical data, the likelihood of continuation or change in patterns, and nuances from recent developments. The timeframe of over two months before resolution allows for potential fluctuation in conditions and political climate, retaining a level of uncertainty in these forecasts." -29484,Will the World Health Organization prequalify moxidectin before 2025?,"For each question, a detailed analysis was performed based on available data, trends, and historical patterns. - -1. **WHO Prequalification of Moxidectin (ID: 29484)** - - Current Status: Additional clinical trials required by WHO; no recent updates indicating imminent prequalification. - - Time Frame Considerations: Only 2 months until deadline; significant regulatory processes typically take longer. - - Base Rate: WHO PQ for new drugs can be slow, particularly with new trial requirements. - - Conclusion: Low likelihood of resolution by year's end. Probability: 15% - -2. **California's Proposition 33 (ID: 29485)** - - Type: Competition Event (Election outcome) - - Historical Precedent: Rent control measures have had mixed results in California, with some successes in past elections. - - Current Sentiment: Public opinion on housing issues is crucial; more information on recent polling would refine this forecast. - - Conclusion: Moderate likelihood due to mixed historical results and potential sentiment; however, need for updated polling data found in final prediction. Probability: 60% - -3. **Apple Earnings for Q3 2024 (ID: 29494)** - - Type: Market Price Event - - Historical Earnings: Apple often meets/exceeds analyst expectations; however, macroeconomic factors can influence results. - - Recent Performance: Apple's recent quarterly results need to be checked against expectations for historical trends determination. - - Conclusion: Based on consistent performance, probability of exceeding is moderate but cautious due to broader economic concerns. Probability: 55% - -4. **Plug Power Bankruptcy Before 2025 (ID: 29495)** - - Type: Other Binary Outcome Event - - Financial Health: Analysis of recent financial statements, debt levels, liquidity, and industry trends is required. - - Industry Trends: Recent trends in the clean energy sector could impact financial outcomes. - - Conclusion: Based on available data, a cautious approach is warranted; specific financial indicators haven't shown imminent risk. Probability: 20% - -5. **Closure of USDA Recall on Perdue Foods (ID: 29496)** - - Type: Non-Market Threshold Event - - Typical Length: USDA recalls vary in closure time; additional factors like response resources influence time frames. - - Current Status: No information on progression; standard practices suggest potential closure. - - Conclusion: Past recall timelines suggest moderate probability. Probability: 70% - -6. **Nebraska Initiative 439 vs. 434 Outcome (ID: 29497)** - - Type: Competition Event - - Historical Outcome: Voter sentiment on related issues in Nebraska has been mixed. - - Population Sentiment: Current political environment and social issues weighed in competition. - - Conclusion: Moderate because of similar past experiences, but information not up to date. Probability: 55% - -7. **Sam Altman's Net Worth Exceeding $1 Billion (ID: 29498)** - - Type: Market Price Event - - Current Trends: Examination of Altman's current ventures and their financial outlook is key. - - Economic Environment: A potential indicator of growth in technological sectors. - - Conclusion: Availability of recent reliable valuations provides some direction. Probability: 40% - -8. **Emoji Count Exceeding 3,900 by End of 2024 (ID: 29499)** - - Type: Cumulative Count Event - - Trend: Recent trends show frequent updates/expansions in emoji sets. - - Rate of Addition: Past rates suggest consistency unless something changes. - - Conclusion: Likely based on addition rates. Probability: 85% - -In conclusion, probabilities reflect a combination of historical data, trend analysis, and the current statuses as reflected in the given timeframe. Adjustments have been made to account for potential overconfidence, which has been noted as a past tendency." -29485,[Short fuse] Will California's Proposition 33 (allowing rent control) pass in the 2024 general election?,"For each question, a detailed analysis was performed based on available data, trends, and historical patterns. - -1. **WHO Prequalification of Moxidectin (ID: 29484)** - - Current Status: Additional clinical trials required by WHO; no recent updates indicating imminent prequalification. - - Time Frame Considerations: Only 2 months until deadline; significant regulatory processes typically take longer. - - Base Rate: WHO PQ for new drugs can be slow, particularly with new trial requirements. - - Conclusion: Low likelihood of resolution by year's end. Probability: 15% - -2. **California's Proposition 33 (ID: 29485)** - - Type: Competition Event (Election outcome) - - Historical Precedent: Rent control measures have had mixed results in California, with some successes in past elections. - - Current Sentiment: Public opinion on housing issues is crucial; more information on recent polling would refine this forecast. - - Conclusion: Moderate likelihood due to mixed historical results and potential sentiment; however, need for updated polling data found in final prediction. Probability: 60% - -3. **Apple Earnings for Q3 2024 (ID: 29494)** - - Type: Market Price Event - - Historical Earnings: Apple often meets/exceeds analyst expectations; however, macroeconomic factors can influence results. - - Recent Performance: Apple's recent quarterly results need to be checked against expectations for historical trends determination. - - Conclusion: Based on consistent performance, probability of exceeding is moderate but cautious due to broader economic concerns. Probability: 55% - -4. **Plug Power Bankruptcy Before 2025 (ID: 29495)** - - Type: Other Binary Outcome Event - - Financial Health: Analysis of recent financial statements, debt levels, liquidity, and industry trends is required. - - Industry Trends: Recent trends in the clean energy sector could impact financial outcomes. - - Conclusion: Based on available data, a cautious approach is warranted; specific financial indicators haven't shown imminent risk. Probability: 20% - -5. **Closure of USDA Recall on Perdue Foods (ID: 29496)** - - Type: Non-Market Threshold Event - - Typical Length: USDA recalls vary in closure time; additional factors like response resources influence time frames. - - Current Status: No information on progression; standard practices suggest potential closure. - - Conclusion: Past recall timelines suggest moderate probability. Probability: 70% - -6. **Nebraska Initiative 439 vs. 434 Outcome (ID: 29497)** - - Type: Competition Event - - Historical Outcome: Voter sentiment on related issues in Nebraska has been mixed. - - Population Sentiment: Current political environment and social issues weighed in competition. - - Conclusion: Moderate because of similar past experiences, but information not up to date. Probability: 55% - -7. **Sam Altman's Net Worth Exceeding $1 Billion (ID: 29498)** - - Type: Market Price Event - - Current Trends: Examination of Altman's current ventures and their financial outlook is key. - - Economic Environment: A potential indicator of growth in technological sectors. - - Conclusion: Availability of recent reliable valuations provides some direction. Probability: 40% - -8. **Emoji Count Exceeding 3,900 by End of 2024 (ID: 29499)** - - Type: Cumulative Count Event - - Trend: Recent trends show frequent updates/expansions in emoji sets. - - Rate of Addition: Past rates suggest consistency unless something changes. - - Conclusion: Likely based on addition rates. Probability: 85% - -In conclusion, probabilities reflect a combination of historical data, trend analysis, and the current statuses as reflected in the given timeframe. Adjustments have been made to account for potential overconfidence, which has been noted as a past tendency." -29494,Will Apple earnings for Q3 2024 exceed $1.60 per share?,"For each question, a detailed analysis was performed based on available data, trends, and historical patterns. - -1. **WHO Prequalification of Moxidectin (ID: 29484)** - - Current Status: Additional clinical trials required by WHO; no recent updates indicating imminent prequalification. - - Time Frame Considerations: Only 2 months until deadline; significant regulatory processes typically take longer. - - Base Rate: WHO PQ for new drugs can be slow, particularly with new trial requirements. - - Conclusion: Low likelihood of resolution by year's end. Probability: 15% - -2. **California's Proposition 33 (ID: 29485)** - - Type: Competition Event (Election outcome) - - Historical Precedent: Rent control measures have had mixed results in California, with some successes in past elections. - - Current Sentiment: Public opinion on housing issues is crucial; more information on recent polling would refine this forecast. - - Conclusion: Moderate likelihood due to mixed historical results and potential sentiment; however, need for updated polling data found in final prediction. Probability: 60% - -3. **Apple Earnings for Q3 2024 (ID: 29494)** - - Type: Market Price Event - - Historical Earnings: Apple often meets/exceeds analyst expectations; however, macroeconomic factors can influence results. - - Recent Performance: Apple's recent quarterly results need to be checked against expectations for historical trends determination. - - Conclusion: Based on consistent performance, probability of exceeding is moderate but cautious due to broader economic concerns. Probability: 55% - -4. **Plug Power Bankruptcy Before 2025 (ID: 29495)** - - Type: Other Binary Outcome Event - - Financial Health: Analysis of recent financial statements, debt levels, liquidity, and industry trends is required. - - Industry Trends: Recent trends in the clean energy sector could impact financial outcomes. - - Conclusion: Based on available data, a cautious approach is warranted; specific financial indicators haven't shown imminent risk. Probability: 20% - -5. **Closure of USDA Recall on Perdue Foods (ID: 29496)** - - Type: Non-Market Threshold Event - - Typical Length: USDA recalls vary in closure time; additional factors like response resources influence time frames. - - Current Status: No information on progression; standard practices suggest potential closure. - - Conclusion: Past recall timelines suggest moderate probability. Probability: 70% - -6. **Nebraska Initiative 439 vs. 434 Outcome (ID: 29497)** - - Type: Competition Event - - Historical Outcome: Voter sentiment on related issues in Nebraska has been mixed. - - Population Sentiment: Current political environment and social issues weighed in competition. - - Conclusion: Moderate because of similar past experiences, but information not up to date. Probability: 55% - -7. **Sam Altman's Net Worth Exceeding $1 Billion (ID: 29498)** - - Type: Market Price Event - - Current Trends: Examination of Altman's current ventures and their financial outlook is key. - - Economic Environment: A potential indicator of growth in technological sectors. - - Conclusion: Availability of recent reliable valuations provides some direction. Probability: 40% - -8. **Emoji Count Exceeding 3,900 by End of 2024 (ID: 29499)** - - Type: Cumulative Count Event - - Trend: Recent trends show frequent updates/expansions in emoji sets. - - Rate of Addition: Past rates suggest consistency unless something changes. - - Conclusion: Likely based on addition rates. Probability: 85% - -In conclusion, probabilities reflect a combination of historical data, trend analysis, and the current statuses as reflected in the given timeframe. Adjustments have been made to account for potential overconfidence, which has been noted as a past tendency." -29495,"Will Plug Power file for bankruptcy before January 1, 2025?","For each question, a detailed analysis was performed based on available data, trends, and historical patterns. - -1. **WHO Prequalification of Moxidectin (ID: 29484)** - - Current Status: Additional clinical trials required by WHO; no recent updates indicating imminent prequalification. - - Time Frame Considerations: Only 2 months until deadline; significant regulatory processes typically take longer. - - Base Rate: WHO PQ for new drugs can be slow, particularly with new trial requirements. - - Conclusion: Low likelihood of resolution by year's end. Probability: 15% - -2. **California's Proposition 33 (ID: 29485)** - - Type: Competition Event (Election outcome) - - Historical Precedent: Rent control measures have had mixed results in California, with some successes in past elections. - - Current Sentiment: Public opinion on housing issues is crucial; more information on recent polling would refine this forecast. - - Conclusion: Moderate likelihood due to mixed historical results and potential sentiment; however, need for updated polling data found in final prediction. Probability: 60% - -3. **Apple Earnings for Q3 2024 (ID: 29494)** - - Type: Market Price Event - - Historical Earnings: Apple often meets/exceeds analyst expectations; however, macroeconomic factors can influence results. - - Recent Performance: Apple's recent quarterly results need to be checked against expectations for historical trends determination. - - Conclusion: Based on consistent performance, probability of exceeding is moderate but cautious due to broader economic concerns. Probability: 55% - -4. **Plug Power Bankruptcy Before 2025 (ID: 29495)** - - Type: Other Binary Outcome Event - - Financial Health: Analysis of recent financial statements, debt levels, liquidity, and industry trends is required. - - Industry Trends: Recent trends in the clean energy sector could impact financial outcomes. - - Conclusion: Based on available data, a cautious approach is warranted; specific financial indicators haven't shown imminent risk. Probability: 20% - -5. **Closure of USDA Recall on Perdue Foods (ID: 29496)** - - Type: Non-Market Threshold Event - - Typical Length: USDA recalls vary in closure time; additional factors like response resources influence time frames. - - Current Status: No information on progression; standard practices suggest potential closure. - - Conclusion: Past recall timelines suggest moderate probability. Probability: 70% - -6. **Nebraska Initiative 439 vs. 434 Outcome (ID: 29497)** - - Type: Competition Event - - Historical Outcome: Voter sentiment on related issues in Nebraska has been mixed. - - Population Sentiment: Current political environment and social issues weighed in competition. - - Conclusion: Moderate because of similar past experiences, but information not up to date. Probability: 55% - -7. **Sam Altman's Net Worth Exceeding $1 Billion (ID: 29498)** - - Type: Market Price Event - - Current Trends: Examination of Altman's current ventures and their financial outlook is key. - - Economic Environment: A potential indicator of growth in technological sectors. - - Conclusion: Availability of recent reliable valuations provides some direction. Probability: 40% - -8. **Emoji Count Exceeding 3,900 by End of 2024 (ID: 29499)** - - Type: Cumulative Count Event - - Trend: Recent trends show frequent updates/expansions in emoji sets. - - Rate of Addition: Past rates suggest consistency unless something changes. - - Conclusion: Likely based on addition rates. Probability: 85% - -In conclusion, probabilities reflect a combination of historical data, trend analysis, and the current statuses as reflected in the given timeframe. Adjustments have been made to account for potential overconfidence, which has been noted as a past tendency." -29496,"Will the USDA-posted recall of Perdue Foods LLC's Frozen, Ready-To-Eat Chicken Breast Nugget and Tender Products be closed before November 16, 2024?","For each question, a detailed analysis was performed based on available data, trends, and historical patterns. - -1. **WHO Prequalification of Moxidectin (ID: 29484)** - - Current Status: Additional clinical trials required by WHO; no recent updates indicating imminent prequalification. - - Time Frame Considerations: Only 2 months until deadline; significant regulatory processes typically take longer. - - Base Rate: WHO PQ for new drugs can be slow, particularly with new trial requirements. - - Conclusion: Low likelihood of resolution by year's end. Probability: 15% - -2. **California's Proposition 33 (ID: 29485)** - - Type: Competition Event (Election outcome) - - Historical Precedent: Rent control measures have had mixed results in California, with some successes in past elections. - - Current Sentiment: Public opinion on housing issues is crucial; more information on recent polling would refine this forecast. - - Conclusion: Moderate likelihood due to mixed historical results and potential sentiment; however, need for updated polling data found in final prediction. Probability: 60% - -3. **Apple Earnings for Q3 2024 (ID: 29494)** - - Type: Market Price Event - - Historical Earnings: Apple often meets/exceeds analyst expectations; however, macroeconomic factors can influence results. - - Recent Performance: Apple's recent quarterly results need to be checked against expectations for historical trends determination. - - Conclusion: Based on consistent performance, probability of exceeding is moderate but cautious due to broader economic concerns. Probability: 55% - -4. **Plug Power Bankruptcy Before 2025 (ID: 29495)** - - Type: Other Binary Outcome Event - - Financial Health: Analysis of recent financial statements, debt levels, liquidity, and industry trends is required. - - Industry Trends: Recent trends in the clean energy sector could impact financial outcomes. - - Conclusion: Based on available data, a cautious approach is warranted; specific financial indicators haven't shown imminent risk. Probability: 20% - -5. **Closure of USDA Recall on Perdue Foods (ID: 29496)** - - Type: Non-Market Threshold Event - - Typical Length: USDA recalls vary in closure time; additional factors like response resources influence time frames. - - Current Status: No information on progression; standard practices suggest potential closure. - - Conclusion: Past recall timelines suggest moderate probability. Probability: 70% - -6. **Nebraska Initiative 439 vs. 434 Outcome (ID: 29497)** - - Type: Competition Event - - Historical Outcome: Voter sentiment on related issues in Nebraska has been mixed. - - Population Sentiment: Current political environment and social issues weighed in competition. - - Conclusion: Moderate because of similar past experiences, but information not up to date. Probability: 55% - -7. **Sam Altman's Net Worth Exceeding $1 Billion (ID: 29498)** - - Type: Market Price Event - - Current Trends: Examination of Altman's current ventures and their financial outlook is key. - - Economic Environment: A potential indicator of growth in technological sectors. - - Conclusion: Availability of recent reliable valuations provides some direction. Probability: 40% - -8. **Emoji Count Exceeding 3,900 by End of 2024 (ID: 29499)** - - Type: Cumulative Count Event - - Trend: Recent trends show frequent updates/expansions in emoji sets. - - Rate of Addition: Past rates suggest consistency unless something changes. - - Conclusion: Likely based on addition rates. Probability: 85% - -In conclusion, probabilities reflect a combination of historical data, trend analysis, and the current statuses as reflected in the given timeframe. Adjustments have been made to account for potential overconfidence, which has been noted as a past tendency." -29497,"In Nebraska will Initiative 439, the Right to Abortion Initiative, receive more votes than Initiative 434, the Prohibit Abortions After the 1st Trimester Amendment?","For each question, a detailed analysis was performed based on available data, trends, and historical patterns. - -1. **WHO Prequalification of Moxidectin (ID: 29484)** - - Current Status: Additional clinical trials required by WHO; no recent updates indicating imminent prequalification. - - Time Frame Considerations: Only 2 months until deadline; significant regulatory processes typically take longer. - - Base Rate: WHO PQ for new drugs can be slow, particularly with new trial requirements. - - Conclusion: Low likelihood of resolution by year's end. Probability: 15% - -2. **California's Proposition 33 (ID: 29485)** - - Type: Competition Event (Election outcome) - - Historical Precedent: Rent control measures have had mixed results in California, with some successes in past elections. - - Current Sentiment: Public opinion on housing issues is crucial; more information on recent polling would refine this forecast. - - Conclusion: Moderate likelihood due to mixed historical results and potential sentiment; however, need for updated polling data found in final prediction. Probability: 60% - -3. **Apple Earnings for Q3 2024 (ID: 29494)** - - Type: Market Price Event - - Historical Earnings: Apple often meets/exceeds analyst expectations; however, macroeconomic factors can influence results. - - Recent Performance: Apple's recent quarterly results need to be checked against expectations for historical trends determination. - - Conclusion: Based on consistent performance, probability of exceeding is moderate but cautious due to broader economic concerns. Probability: 55% - -4. **Plug Power Bankruptcy Before 2025 (ID: 29495)** - - Type: Other Binary Outcome Event - - Financial Health: Analysis of recent financial statements, debt levels, liquidity, and industry trends is required. - - Industry Trends: Recent trends in the clean energy sector could impact financial outcomes. - - Conclusion: Based on available data, a cautious approach is warranted; specific financial indicators haven't shown imminent risk. Probability: 20% - -5. **Closure of USDA Recall on Perdue Foods (ID: 29496)** - - Type: Non-Market Threshold Event - - Typical Length: USDA recalls vary in closure time; additional factors like response resources influence time frames. - - Current Status: No information on progression; standard practices suggest potential closure. - - Conclusion: Past recall timelines suggest moderate probability. Probability: 70% - -6. **Nebraska Initiative 439 vs. 434 Outcome (ID: 29497)** - - Type: Competition Event - - Historical Outcome: Voter sentiment on related issues in Nebraska has been mixed. - - Population Sentiment: Current political environment and social issues weighed in competition. - - Conclusion: Moderate because of similar past experiences, but information not up to date. Probability: 55% - -7. **Sam Altman's Net Worth Exceeding $1 Billion (ID: 29498)** - - Type: Market Price Event - - Current Trends: Examination of Altman's current ventures and their financial outlook is key. - - Economic Environment: A potential indicator of growth in technological sectors. - - Conclusion: Availability of recent reliable valuations provides some direction. Probability: 40% - -8. **Emoji Count Exceeding 3,900 by End of 2024 (ID: 29499)** - - Type: Cumulative Count Event - - Trend: Recent trends show frequent updates/expansions in emoji sets. - - Rate of Addition: Past rates suggest consistency unless something changes. - - Conclusion: Likely based on addition rates. Probability: 85% - -In conclusion, probabilities reflect a combination of historical data, trend analysis, and the current statuses as reflected in the given timeframe. Adjustments have been made to account for potential overconfidence, which has been noted as a past tendency." -29498,"Will Sam Altman's net worth exceed $1 billion on the Forbes Real-Time Billionaires List on December 31, 2024?","For each question, a detailed analysis was performed based on available data, trends, and historical patterns. - -1. **WHO Prequalification of Moxidectin (ID: 29484)** - - Current Status: Additional clinical trials required by WHO; no recent updates indicating imminent prequalification. - - Time Frame Considerations: Only 2 months until deadline; significant regulatory processes typically take longer. - - Base Rate: WHO PQ for new drugs can be slow, particularly with new trial requirements. - - Conclusion: Low likelihood of resolution by year's end. Probability: 15% - -2. **California's Proposition 33 (ID: 29485)** - - Type: Competition Event (Election outcome) - - Historical Precedent: Rent control measures have had mixed results in California, with some successes in past elections. - - Current Sentiment: Public opinion on housing issues is crucial; more information on recent polling would refine this forecast. - - Conclusion: Moderate likelihood due to mixed historical results and potential sentiment; however, need for updated polling data found in final prediction. Probability: 60% - -3. **Apple Earnings for Q3 2024 (ID: 29494)** - - Type: Market Price Event - - Historical Earnings: Apple often meets/exceeds analyst expectations; however, macroeconomic factors can influence results. - - Recent Performance: Apple's recent quarterly results need to be checked against expectations for historical trends determination. - - Conclusion: Based on consistent performance, probability of exceeding is moderate but cautious due to broader economic concerns. Probability: 55% - -4. **Plug Power Bankruptcy Before 2025 (ID: 29495)** - - Type: Other Binary Outcome Event - - Financial Health: Analysis of recent financial statements, debt levels, liquidity, and industry trends is required. - - Industry Trends: Recent trends in the clean energy sector could impact financial outcomes. - - Conclusion: Based on available data, a cautious approach is warranted; specific financial indicators haven't shown imminent risk. Probability: 20% - -5. **Closure of USDA Recall on Perdue Foods (ID: 29496)** - - Type: Non-Market Threshold Event - - Typical Length: USDA recalls vary in closure time; additional factors like response resources influence time frames. - - Current Status: No information on progression; standard practices suggest potential closure. - - Conclusion: Past recall timelines suggest moderate probability. Probability: 70% - -6. **Nebraska Initiative 439 vs. 434 Outcome (ID: 29497)** - - Type: Competition Event - - Historical Outcome: Voter sentiment on related issues in Nebraska has been mixed. - - Population Sentiment: Current political environment and social issues weighed in competition. - - Conclusion: Moderate because of similar past experiences, but information not up to date. Probability: 55% - -7. **Sam Altman's Net Worth Exceeding $1 Billion (ID: 29498)** - - Type: Market Price Event - - Current Trends: Examination of Altman's current ventures and their financial outlook is key. - - Economic Environment: A potential indicator of growth in technological sectors. - - Conclusion: Availability of recent reliable valuations provides some direction. Probability: 40% - -8. **Emoji Count Exceeding 3,900 by End of 2024 (ID: 29499)** - - Type: Cumulative Count Event - - Trend: Recent trends show frequent updates/expansions in emoji sets. - - Rate of Addition: Past rates suggest consistency unless something changes. - - Conclusion: Likely based on addition rates. Probability: 85% - -In conclusion, probabilities reflect a combination of historical data, trend analysis, and the current statuses as reflected in the given timeframe. Adjustments have been made to account for potential overconfidence, which has been noted as a past tendency." -29499,"Will there be greater than or equal to 3,900 emojis on December 31, 2024?","For each question, a detailed analysis was performed based on available data, trends, and historical patterns. - -1. **WHO Prequalification of Moxidectin (ID: 29484)** - - Current Status: Additional clinical trials required by WHO; no recent updates indicating imminent prequalification. - - Time Frame Considerations: Only 2 months until deadline; significant regulatory processes typically take longer. - - Base Rate: WHO PQ for new drugs can be slow, particularly with new trial requirements. - - Conclusion: Low likelihood of resolution by year's end. Probability: 15% - -2. **California's Proposition 33 (ID: 29485)** - - Type: Competition Event (Election outcome) - - Historical Precedent: Rent control measures have had mixed results in California, with some successes in past elections. - - Current Sentiment: Public opinion on housing issues is crucial; more information on recent polling would refine this forecast. - - Conclusion: Moderate likelihood due to mixed historical results and potential sentiment; however, need for updated polling data found in final prediction. Probability: 60% - -3. **Apple Earnings for Q3 2024 (ID: 29494)** - - Type: Market Price Event - - Historical Earnings: Apple often meets/exceeds analyst expectations; however, macroeconomic factors can influence results. - - Recent Performance: Apple's recent quarterly results need to be checked against expectations for historical trends determination. - - Conclusion: Based on consistent performance, probability of exceeding is moderate but cautious due to broader economic concerns. Probability: 55% - -4. **Plug Power Bankruptcy Before 2025 (ID: 29495)** - - Type: Other Binary Outcome Event - - Financial Health: Analysis of recent financial statements, debt levels, liquidity, and industry trends is required. - - Industry Trends: Recent trends in the clean energy sector could impact financial outcomes. - - Conclusion: Based on available data, a cautious approach is warranted; specific financial indicators haven't shown imminent risk. Probability: 20% - -5. **Closure of USDA Recall on Perdue Foods (ID: 29496)** - - Type: Non-Market Threshold Event - - Typical Length: USDA recalls vary in closure time; additional factors like response resources influence time frames. - - Current Status: No information on progression; standard practices suggest potential closure. - - Conclusion: Past recall timelines suggest moderate probability. Probability: 70% - -6. **Nebraska Initiative 439 vs. 434 Outcome (ID: 29497)** - - Type: Competition Event - - Historical Outcome: Voter sentiment on related issues in Nebraska has been mixed. - - Population Sentiment: Current political environment and social issues weighed in competition. - - Conclusion: Moderate because of similar past experiences, but information not up to date. Probability: 55% - -7. **Sam Altman's Net Worth Exceeding $1 Billion (ID: 29498)** - - Type: Market Price Event - - Current Trends: Examination of Altman's current ventures and their financial outlook is key. - - Economic Environment: A potential indicator of growth in technological sectors. - - Conclusion: Availability of recent reliable valuations provides some direction. Probability: 40% - -8. **Emoji Count Exceeding 3,900 by End of 2024 (ID: 29499)** - - Type: Cumulative Count Event - - Trend: Recent trends show frequent updates/expansions in emoji sets. - - Rate of Addition: Past rates suggest consistency unless something changes. - - Conclusion: Likely based on addition rates. Probability: 85% - -In conclusion, probabilities reflect a combination of historical data, trend analysis, and the current statuses as reflected in the given timeframe. Adjustments have been made to account for potential overconfidence, which has been noted as a past tendency." -29491,Will the US State Department approve more than 25 arms sales globally in the fourth quarter of 2024?,"To solve the forecasting problem regarding the number of US State Department arms sales approvals in the fourth quarter of 2024, we first need to recognize the nature of the event: it's a cumulative count event. Based on historical data, the monthly average number of approved sales is approximately 6.7. However, we also observe significant variations, with some months like August 2024 reaching as high as 22 approvals. Focusing on the fourth quarter, the month of October has seen high approval activity. However, historical trends suggest that direct extrapolation from October alone is risky due to variability. - -Breaking down the three key questions: -1. Approvals above 15: Based on the historical monthly average of 6.7, the expectation for three months cumulatively is about 20 approvals. Given the high activity in October 2024 and fluctuations, it's reasonable to put a high probability on surpassing 15 approvals in the quarter. We'll assign a probability of 85%. -2. Approvals above 20: Since the average suggests around 20 approvals, and considering October's robust performance, the chance of exceeding 20 is also relatively high, but slightly less certain than surpassing 15. Thus, a 70% probability seems fitting. -3. Approvals above 25: While October's performance was strong, historical data indicates going significantly above 25 might be challenging absent extraordinary circumstances in November and December. Assigning a 35% chance reflects that while possible, it's not the most likely scenario based on variability and historical trends. - -Considering plausible scenarios where the total number of approvals could be less than 15, such scenarios are unlikely given October’s pace. Thus, I assign only a 5% probability of staying below that threshold. - -In summary, the assessment weighs the historical data, the typical number of approvals, and recent trends, along with a consideration for variability in approvals from month to month. My past tendency towards overconfidence is moderated here by assigning possibilities reflective of uncertainty in the direction and scale of approvals." -29490,Will the US State Department approve more than 20 arms sales globally in the fourth quarter of 2024?,"To solve the forecasting problem regarding the number of US State Department arms sales approvals in the fourth quarter of 2024, we first need to recognize the nature of the event: it's a cumulative count event. Based on historical data, the monthly average number of approved sales is approximately 6.7. However, we also observe significant variations, with some months like August 2024 reaching as high as 22 approvals. Focusing on the fourth quarter, the month of October has seen high approval activity. However, historical trends suggest that direct extrapolation from October alone is risky due to variability. - -Breaking down the three key questions: -1. Approvals above 15: Based on the historical monthly average of 6.7, the expectation for three months cumulatively is about 20 approvals. Given the high activity in October 2024 and fluctuations, it's reasonable to put a high probability on surpassing 15 approvals in the quarter. We'll assign a probability of 85%. -2. Approvals above 20: Since the average suggests around 20 approvals, and considering October's robust performance, the chance of exceeding 20 is also relatively high, but slightly less certain than surpassing 15. Thus, a 70% probability seems fitting. -3. Approvals above 25: While October's performance was strong, historical data indicates going significantly above 25 might be challenging absent extraordinary circumstances in November and December. Assigning a 35% chance reflects that while possible, it's not the most likely scenario based on variability and historical trends. - -Considering plausible scenarios where the total number of approvals could be less than 15, such scenarios are unlikely given October’s pace. Thus, I assign only a 5% probability of staying below that threshold. - -In summary, the assessment weighs the historical data, the typical number of approvals, and recent trends, along with a consideration for variability in approvals from month to month. My past tendency towards overconfidence is moderated here by assigning possibilities reflective of uncertainty in the direction and scale of approvals." -29489,Will the US State Department approve more than 15 arms sales globally in the fourth quarter of 2024?,"To solve the forecasting problem regarding the number of US State Department arms sales approvals in the fourth quarter of 2024, we first need to recognize the nature of the event: it's a cumulative count event. Based on historical data, the monthly average number of approved sales is approximately 6.7. However, we also observe significant variations, with some months like August 2024 reaching as high as 22 approvals. Focusing on the fourth quarter, the month of October has seen high approval activity. However, historical trends suggest that direct extrapolation from October alone is risky due to variability. - -Breaking down the three key questions: -1. Approvals above 15: Based on the historical monthly average of 6.7, the expectation for three months cumulatively is about 20 approvals. Given the high activity in October 2024 and fluctuations, it's reasonable to put a high probability on surpassing 15 approvals in the quarter. We'll assign a probability of 85%. -2. Approvals above 20: Since the average suggests around 20 approvals, and considering October's robust performance, the chance of exceeding 20 is also relatively high, but slightly less certain than surpassing 15. Thus, a 70% probability seems fitting. -3. Approvals above 25: While October's performance was strong, historical data indicates going significantly above 25 might be challenging absent extraordinary circumstances in November and December. Assigning a 35% chance reflects that while possible, it's not the most likely scenario based on variability and historical trends. - -Considering plausible scenarios where the total number of approvals could be less than 15, such scenarios are unlikely given October’s pace. Thus, I assign only a 5% probability of staying below that threshold. - -In summary, the assessment weighs the historical data, the typical number of approvals, and recent trends, along with a consideration for variability in approvals from month to month. My past tendency towards overconfidence is moderated here by assigning possibilities reflective of uncertainty in the direction and scale of approvals." -29488,"Will it rain more than 200mm in Brasília, Brazil in December 2024?","To evaluate the likelihood of different rainfall scenarios in Brasília, Brazil for December 2024, several factors must be considered: - -1. **Historical Context**: December is typically the wettest month for Brasília, with historical averages ranging from 215mm to 253mm of rainfall. This sets a baseline expectation for significant rainfall. - -2. **Recent Weather Trends**: Recent reports indicate a record-breaking drought for 167 days, followed by heavy rains in October. While drought conditions prevailed due to La Niña, a weakening of it could lead to normalized or even excessive rainfall as the region enters its wet season, historically beginning in November. - -3. **Climate Change Effects**: With 2024 marked as one of the hottest years, ongoing climate changes may lead to extreme variability, including intense localized downpours or prolonged dry spells. However, Brasília's historical trends suggest December should see substantial rain. - -4. **Recent Forecasts and Patterns**: While direct forecasts for December are not available yet, the transition to the rainy season typically involves considerable rainfall events, especially after the drought conditions. - -Given the interplay of these factors, the base rate suggests a strong possibility of exceeding 100mm, with a reasonable likelihood of surpassing 150mm. The >200mm threshold is plausible, albeit with more uncertainty due to the recent drought and climate irregularities. - -**Probability Assignments**: -- **Less than 100mm**: Highly unlikely given the historical averages and the transition to the wet season, despite recent drought conditions. -- **100mm to 150mm**: A significant portion of the historical norm falls within this range, but given the likelihood of a wet December post-drought, the probability is moderate. -- **150mm to 200mm**: More likely than not, as it's within typical historical averages and expected December patterns post-drought. -- **More than 200mm**: Given historical averages and the transition from extreme drought, this is plausible but with higher uncertainty due to recent climatic volatility. - -Adjustments for overconfidence are applied, bearing in mind recent trends and historical data, leading to conservative estimations for high rainfall events." -29487,"Will it rain more than 150mm in Brasília, Brazil in December 2024?","To evaluate the likelihood of different rainfall scenarios in Brasília, Brazil for December 2024, several factors must be considered: - -1. **Historical Context**: December is typically the wettest month for Brasília, with historical averages ranging from 215mm to 253mm of rainfall. This sets a baseline expectation for significant rainfall. - -2. **Recent Weather Trends**: Recent reports indicate a record-breaking drought for 167 days, followed by heavy rains in October. While drought conditions prevailed due to La Niña, a weakening of it could lead to normalized or even excessive rainfall as the region enters its wet season, historically beginning in November. - -3. **Climate Change Effects**: With 2024 marked as one of the hottest years, ongoing climate changes may lead to extreme variability, including intense localized downpours or prolonged dry spells. However, Brasília's historical trends suggest December should see substantial rain. - -4. **Recent Forecasts and Patterns**: While direct forecasts for December are not available yet, the transition to the rainy season typically involves considerable rainfall events, especially after the drought conditions. - -Given the interplay of these factors, the base rate suggests a strong possibility of exceeding 100mm, with a reasonable likelihood of surpassing 150mm. The >200mm threshold is plausible, albeit with more uncertainty due to the recent drought and climate irregularities. - -**Probability Assignments**: -- **Less than 100mm**: Highly unlikely given the historical averages and the transition to the wet season, despite recent drought conditions. -- **100mm to 150mm**: A significant portion of the historical norm falls within this range, but given the likelihood of a wet December post-drought, the probability is moderate. -- **150mm to 200mm**: More likely than not, as it's within typical historical averages and expected December patterns post-drought. -- **More than 200mm**: Given historical averages and the transition from extreme drought, this is plausible but with higher uncertainty due to recent climatic volatility. - -Adjustments for overconfidence are applied, bearing in mind recent trends and historical data, leading to conservative estimations for high rainfall events." -29486,"Will it rain more than 100mm in Brasília, Brazil in December 2024?","To evaluate the likelihood of different rainfall scenarios in Brasília, Brazil for December 2024, several factors must be considered: - -1. **Historical Context**: December is typically the wettest month for Brasília, with historical averages ranging from 215mm to 253mm of rainfall. This sets a baseline expectation for significant rainfall. - -2. **Recent Weather Trends**: Recent reports indicate a record-breaking drought for 167 days, followed by heavy rains in October. While drought conditions prevailed due to La Niña, a weakening of it could lead to normalized or even excessive rainfall as the region enters its wet season, historically beginning in November. - -3. **Climate Change Effects**: With 2024 marked as one of the hottest years, ongoing climate changes may lead to extreme variability, including intense localized downpours or prolonged dry spells. However, Brasília's historical trends suggest December should see substantial rain. - -4. **Recent Forecasts and Patterns**: While direct forecasts for December are not available yet, the transition to the rainy season typically involves considerable rainfall events, especially after the drought conditions. - -Given the interplay of these factors, the base rate suggests a strong possibility of exceeding 100mm, with a reasonable likelihood of surpassing 150mm. The >200mm threshold is plausible, albeit with more uncertainty due to the recent drought and climate irregularities. - -**Probability Assignments**: -- **Less than 100mm**: Highly unlikely given the historical averages and the transition to the wet season, despite recent drought conditions. -- **100mm to 150mm**: A significant portion of the historical norm falls within this range, but given the likelihood of a wet December post-drought, the probability is moderate. -- **150mm to 200mm**: More likely than not, as it's within typical historical averages and expected December patterns post-drought. -- **More than 200mm**: Given historical averages and the transition from extreme drought, this is plausible but with higher uncertainty due to recent climatic volatility. - -Adjustments for overconfidence are applied, bearing in mind recent trends and historical data, leading to conservative estimations for high rainfall events." -29499,"Will there be greater than or equal to 3,900 emojis on December 31, 2024?","Let me think through this step by step: - -1. First, let's establish where we are now: -- It's November 1, 2024 -- We're looking at December 31, 2024 (about 2 months away) -- The question is about reaching ≥3,900 emojis -- We know there were 3,633 emojis in Unicode 14.0 (2021) - -2. Key timing considerations: -- Unicode versions are typically announced around World Emoji Day (July 17) -- By resolution date, we'll likely be on Unicode 16.0 or 17.0 -- Major changes are unlikely in the next 2 months - -3. Historical growth analysis: -- 2018 (v11.0): 2,789 -- 2021 (v14.0): 3,633 -- This shows ~844 emoji increase over 3 years -- Average growth of ~281 emojis per year - -4. Reaching the target: -- Need 267 more emojis from v14.0 to reach 3,900 -- Based on historical growth rate, this seems achievable -- However, growth may be slowing as core concepts are covered - -5. Risk factors: -- Emoji additions tend to be smaller in recent years -- Major platform vendors may be less aggressive about additions -- Cultural/political factors could slow growth - -6. Positive factors: -- Continuous demand for representation -- Technical capability to add more -- Historical precedent of steady growth - -7. Timeline analysis: -- Being so close to resolution date, major changes are unlikely -- The current version's emoji count is likely already set -- We need to focus on what's already implemented - -8. Confidence adjustment: -- Given the specific threshold and short timeframe -- Historical overconfidence needs to be factored in -- This is a Non-Market Threshold Value Event - -9. Final considerations: -- The relatively short time to resolution increases certainty -- The target seems achievable based on historical growth -- But we need to be conservative given past overconfidence - -Based on this analysis, I believe there's a good chance we've already reached or will reach 3,900 emojis by the end of 2024, but I need to account for uncertainty and historical overconfidence. The short timeline to resolution actually helps reduce uncertainty. - -Probability: 75% - -This reflects: -- Strong historical growth trend -- Short time to resolution -- Adjustment for overconfidence -- Conservative estimate given the specific threshold -- Consideration of slowing growth rates -- High likelihood current version is already set" -29498,"Will Sam Altman's net worth exceed $1 billion on the Forbes Real-Time Billionaires List on December 31, 2024?","To forecast whether Sam Altman's net worth will exceed $1.1 billion on the Forbes Real-Time Billionaires List on December 31, 2024, we need to carefully analyze the given information and consider various factors that could influence the outcome. - -1. **Current Status and Gap to Target**: As of October 8, 2024, Sam Altman's net worth was exactly $1 billion. The resolution criteria specify that his net worth needs to be at least $1.1 billion to resolve positively. Therefore, a gain of at least $100 million is required within the next two months for the question to resolve as ""Yes."" - -2. **Recent Developments**: - - **OpenAI's Growth**: OpenAI's success with ChatGPT and GPT-4 has been a substantial contributor to Altman's net worth. However, the impact of such growth on his personal net worth in a two-month span needs to be significant and fast-moving, which is not typical for a static increase unless there's a big valuation change or liquidity event. - - - **Corporate Restructuring**: If OpenAI undergoes restructuring that increases Altman's equity stake, this could potentially increase his net worth. But such restructurings typically take time, and the closeness to the resolution date complicates significant changes before the end of the year. - -3. **Other Investments**: - - Altman's diverse investment portfolio, including in Reddit and Helion, adds potential upside. However, it's uncertain whether any of those investments will experience major value accelerations within the next two months. - -4. **Forbes and Real-Time Updates**: Forbes' real-time list can fluctuate based on market conditions and values changing daily. This variability means that short-term increases in net worth are possible but unpredictable. - -5. **Broader Economic Environment**: Considering the economic environment is crucial. If there are no major economic downturns or market crashes, the stabilization might maintain or slightly increase valuations, but a large $100 million increase still seems challenging in such a short time. - -6. **Timeframe Consideration**: Given the short time left until the resolution date (two months), significant movements in net worth are unlikely without a catalytic event, like a company going public or a major investment gaining recognition for substantial value increases. These are not indicated in the data provided. - -7. **Historical Outcomes**: Historically, it's rare for someone to gain $100 million in net worth based solely on existing investments without notable new investments or liquidity events. - -8. **Base Rate and Adjustment for Overconfidence**: Historically, about 35% of such binary outcome questions resolve positively. Given a tendency towards overconfidence in past competitions, adjustments should guide towards conservatism. - -Taking all these considerations into account, the probability of Sam Altman's net worth exceeding $1.1 billion by December 31, 2024 seems relatively low without unexpected developments. The base case maintains his net worth around its current level, with no significant foreseen changes. - -Probability: 20%" -29497,"In Nebraska will Initiative 439, the Right to Abortion Initiative, receive more votes than Initiative 434, the Prohibit Abortions After the 1st Trimester Amendment?","To forecast the outcome of whether Nebraska Initiative 439 will receive more votes than Initiative 434, we should take a structured approach based on the information given, the historical context, statistical reasoning, and possible biases in interpreting the data. - -### Context Analysis - -1. **Competing Ballot Measures**: Both measures directly address abortion, a polarizing issue. Initiative 439 seeks to protect abortion rights until about 24 weeks, while Initiative 434 aims for more restrictive measures. This sets the stage for a contentious vote with strong advocacy on both sides. - -2. **Current Polling**: The polls indicate a narrow lead for Initiative 439 or a close competition between both (44% support for 439 with 46% for 434). Given these narrow margins, there's a lot of uncertainty. Historically, polling can underrepresent either side due to social desirability bias, especially on sensitive issues like abortion. - -3. **Campaign Efforts**: Financial backing is slightly higher for Initiative 434, suggesting strong organized support for restrictionist policies. However, the financial difference isn't overwhelming, and both sides being well-funded might lead to sophisticated campaigning that could sway opinions. - -4. **Legal Context**: Nebraska's existing law on abortion might suggest voter leanings towards restrictions, potentially benefiting Initiative 434. However, existing laws may also galvanize pro-choice voters aiming to roll back current restrictions. - -5. **Tactics and Confusion**: Reports of misleading tactics could cause confusion among voters. This generally favors the status quo or the option more clearly defined in public discourse. - -6. **Implications**: This vote could serve as a proxy for broader national sentiments on abortion, which might boost turnout for the more publicly visible or emotionally resonant option. - -### Historical and Statistical Considerations - -- **Base Rate of Similar Measures**: Historically, measures expanding rights (in this context, abortion rights) in conservative-leaning areas like Nebraska have a challenging path to success. Abortion restriction measures often perform better in such states. - -- **State Political Climate**: Nebraska tends to be more conservative, which can tilt the outcome towards more restrictive laws. However, national sentiment can heavily influence state-level voting on such high-stakes issues. - -- **Short Timeframe**: Given the proximity to the vote (less than a week), opinions are unlikely to shift dramatically unless a significant last-minute event occurs. This suggests a modest chance for major change in the polling figures. - -### Psychological Considerations - -- **Crowded Information Environment**: Voter confusion due to similar names and misinformation could lead to voting errors, usually benefiting the status quo. - -- **Overconfidence Adjustment**: Historically being overconfident means I should slightly adjust my probabilities to account for the natural inclination to overestimate positive outcomes. - -### Final Assessment - -**Probability Calculation:** -- Initiative 439 has a narrow lead or is perceived as viable but with significant undecideds. -- Conservative leanings and close polling suggest a tight outcome, potentially favoring 434 if undecided voters prioritize historical or default policy settings. - -**Potential Outcome Distribution:** -- Initiative 439 receiving more votes: Likely around 40-50% given the close polling and the state's conservative tendencies. - -**Calculation Balance:** -- Balancing my adjustments historically, considering the nuanced close nature of the polls, base rates, voter leanings, funding differences, and potential for confusion tilting towards restriction measures: - -**Final Probability:** Probability that Initiative 439 receives more votes than Initiative 434: **45%**. - -Thus, my final answer is: - -""Probability: 45%""" -29496,"Will the USDA-posted recall of Perdue Foods LLC's Frozen, Ready-To-Eat Chicken Breast Nugget and Tender Products be closed before November 16, 2024?","Let me think through this step by step: - -1. This is a food safety recall question, which falls under Other Binary Outcome Event. - -2. Key timeline considerations: -- Recall initiated in August 2024 (about 2.5 months ago) -- Resolution date is November 16, 2024 (about 2 weeks away) -- Products have ""best by"" date of March 2025 - -3. Important factors: -- Metal contamination is a serious food safety issue -- Large recall (167,000 pounds) -- No reported injuries/illnesses -- Consumer complaints led to discovery -- Voluntary recall by Perdue -- Products likely still in consumers' freezers due to long shelf life - -4. Historical context: -- Food recalls typically remain active for 3-6 months -- Metal contamination recalls often take longer than biological contamination recalls -- Consumer freezer storage extends the timeframe -- FSIS is typically cautious about closing recalls - -5. Timeline analysis: -- Given typical 3-6 month timeframe, November would be around 3 months -- However, frozen products with long shelf life typically extend this -- Two weeks is a very short window for status change - -6. Risk factors: -- No injuries reported (positive) -- Voluntary recall (positive) -- But frozen storage means products still in circulation -- Metal contamination requires thorough investigation - -7. Probability distribution: -- 1 month from now: 15% -- 2 months from now: 35% -- 3 months from now: 60% -- 6 months from now: 90% - -Given: -- Very short timeline (2 weeks) -- Products still likely in freezers -- No urgency to close with March 2025 best-by date -- FSIS's typically cautious approach -- Metal contamination requiring thorough verification - -The probability of closure in just two weeks seems quite low. The base case strongly suggests the recall will remain active, and there's insufficient time for typical closure procedures. - -Adjusting for historical overconfidence and the short timeline, but acknowledging some chance of quick resolution due to no reported injuries: - -Probability: 12%" -29495,"Will Plug Power file for bankruptcy before January 1, 2025?","To determine the likelihood of Plug Power filing for bankruptcy before January 1, 2025, we need to methodically evaluate the available information and apply forecasting principles. Here's a step-by-step breakdown: - -1. **Financial Challenges**: Plug Power's financial situation is concerning, as highlighted by their SEC filings, which raise substantial doubt about their ability to continue as a going concern. This indicates a high risk of financial distress; however, doubt alone does not imply imminent bankruptcy within the next two months. - -2. **Industry Context**: The hydrogen fuel cell industry faces both challenges and opportunities. While there are competitive and regulatory pressures, government initiatives for sustainable energy could potentially benefit companies in this space. The industry context adds uncertainty, but recent regulatory support might provide a cushion against immediate bankruptcy. - -3. **Timeframe**: The question concerns a relatively short timeframe (2 months). Since unlikely events are less probable in shorter timeframes, the probability of filing for bankruptcy in this period is inherently lower than if the timeframe were longer. - -4. **Economic and Market Conditions**: General economic conditions, such as potential increases in operational costs due to regulatory changes, might strain the company further, but no drastic economic shifts are provided that significantly tip the scales. - -5. **Base Rates**: Historically, how often do companies facing ""going concern"" issues file for bankruptcy within a few months? While it's common for companies in severe financial distress to consider bankruptcy, not all do so immediately. This base rate suggests a moderate probability. - -6. **Recent Developments and Expert Opinion**: Lack of concrete expert opinions on their immediate bankruptcy risk requires us to rely more heavily on the financial indicators already present. - -Given the requirement to avoid overconfidence and my historical bias towards overestimation, I should temper the probability further. Additionally, the proximity to the resolution date allows for only a moderate deterioration of conditions before the filing would likely occur. - -In conclusion, while Plug Power is at serious financial risk, the immediate occurrence of bankruptcy filing in the next two months is not highly probable without more severe indications of financial failure or lack of liquidity to continue operations. Keeping my potential overconfidence in check and balancing the data presented: - -- **Probability of Plug Power filing for bankruptcy before January 1, 2025**: I would estimate a probability of around 25%. This percentage accounts for significant financial distress but respects the improbability of sudden bankruptcy given the typical business operations and industry support mechanisms. - -Final Answer: -- **Probability: 25%**" -29494,Will Apple earnings for Q3 2024 exceed $1.60 per share?,"Let me think through this step by step: - -1. First, looking at the research assistant responses, I notice a major discrepancy. One claims Apple has already reported Q3 2024 earnings at $1.64, but this is impossible since we're in November 2023 and the resolution date is November 2024. This information should be disregarded. - -2. The question is about Q3 2024 (July-September 2024), with earnings to be reported in early November 2024. - -3. This is a Non-Market Threshold Value Event, specifically about exceeding $1.60 EPS. - -4. Let's look at key factors: -- Current consensus is exactly $1.60 -- We're forecasting almost a full year ahead -- Long-term forecasts tend to be less accurate -- Apple has historically been good at managing earnings expectations - -5. Historical context: -- Apple typically beats consensus estimates about 75% of the time -- However, the margin of beating is often small -- The $1.60 threshold is exactly at consensus, not below it - -6. Risk factors for 2024: -- Macroeconomic uncertainty -- Potential recession risks -- Competition in smartphone market -- AI integration challenges -- China market concerns - -7. Positive factors: -- Strong services growth -- iPhone 16 launch will be recent -- Historical execution strength -- Potential AI features rollout - -8. Temporal considerations: -- A year is a long time for conditions to change -- Q3 includes new iPhone launch period -- September quarter typically strong for Apple - -9. Statistical analysis: -- Given exact consensus threshold -- Historical beat rate ~75% -- But need to discount for time uncertainty -- And account for macroeconomic headwinds - -10. Adjustments: -- Starting from 75% historical beat rate -- Reduce by 15% for long forecast window -- Reduce by 10% for macroeconomic uncertainty -- Add 5% for iPhone launch timing -- Add 5% for services growth momentum - -Final calculation: 75% - 15% - 10% + 5% + 5% = 60% - -This accounts for Apple's strong historical performance while acknowledging the significant uncertainty of a long-term forecast and current headwinds. The probability is not extremely high because we're forecasting far in advance and the threshold is exactly at consensus, not below it. - -Probability: 60%" -29493,Will Gukesh Dommaraju win the 2024 World Chess Championship?,"Let me think about this step by step: - -1) First, let's consider this as a Competition Event with two clear participants and a scheduled match. - -2) Key factors: -- Current ratings: Gukesh 2794 (World #5) vs Ding 2728 (World #21) -- Head-to-head classical record: Ding leads 2-0-1 -- Match format: 14 classical games + potential tiebreaks -- Venue: Singapore -- Timeline: Starting November 25, less than a month away - -3) Historical context: -- World Championship matches often favor the champion -- However, Ding's form has declined significantly since becoming champion -- The rating gap (66 points) is significant but not insurmountable -- Recent champions (Carlsen, Anand) were typically higher rated than challengers - -4) Specific considerations: -- Gukesh's strong performance in Candidates shows excellent form -- At 18, he's less experienced in match play -- Ding's recent tournament results have been concerning -- The match length (14 games) gives more opportunity for the stronger player - -5) Statistical analysis: -- With a 66-point rating gap, statistical models typically give the higher-rated player about 58-60% chances in classical chess -- However, match play is different from tournament play -- Champion's advantage historically worth about 30-40 rating points -- Adjusting for match conditions might reduce Gukesh's advantage to equivalent of 30-35 points - -6) Uncertainty factors: -- Gukesh's lack of experience in championship matches -- Ding's recent poor form could continue or reverse -- Psychological pressure on both players -- Singapore conditions (neutral venue) - -7) Base rate consideration: -- Challengers have won approximately 40% of world championship matches historically -- Recent matches have been very close (last two decided in tiebreaks) - -8) Time factor: -- With match starting in less than a month, major form changes are unlikely -- Current ratings and form are highly relevant - -Given all factors, I believe Gukesh has a slight advantage based on current form and rating, but this is moderated by: -- Championship match experience deficit -- Historical advantage for champions -- Head-to-head record favoring Ding -- Match play vs tournament play considerations - -I need to avoid overconfidence, as this is a complex event with many variables. While Gukesh's current form is excellent, world championship matches have unique pressures. - -Probability: 54% - -This reflects Gukesh's rating advantage and strong recent form, while accounting for the moderating factors of match play, championship experience, and historical precedent. It's close to even odds but slightly favoring Gukesh, which feels appropriate given the various factors at play." -29492,Will there be a white Christmas in at least 4 of these 9 large European cities in 2024?,"Let me think about this step by step: - -1. First, let's understand what we need - snow on Dec 24/25 in at least 4 of 9 specific European cities: Istanbul, Moscow, London, St. Petersburg, Berlin, Madrid, Rome, Bucharest, and Paris. - -2. Let's group these cities by historical likelihood of Christmas snow: -- High likelihood: Moscow, St. Petersburg -- Medium likelihood: Berlin, Bucharest -- Low likelihood: London, Paris -- Very low likelihood: Istanbul, Madrid, Rome - -3. Historical data points: -- Moscow and St. Petersburg typically have around 70% chance of snow in late December -- Berlin and Bucharest around 20-30% -- London and Paris around 5-10% -- Madrid, Rome, Istanbul less than 5% - -4. Key considerations: -- We need 4 out of 9 cities (44%) -- Even getting the two Russian cities doesn't get us halfway there -- Need at least 2 more cities with snow from the remaining 7 -- Climate change has been reducing snow probability in recent years -- El Niño conditions expected for winter 2024-25, which typically means: - * Warmer temperatures in Northern Europe - * More precipitation but less likely as snow - -5. Probability analysis: -- Almost certain to get Moscow & St. Petersburg (0.7 × 0.7 = ~0.49) -- Need 2 more from remaining cities -- Berlin & Bucharest combined probability (~0.25 × 0.25 = ~0.06) -- Other cities very unlikely to contribute - -6. Mathematical approach: -- Base probability of getting exactly 4 cities with snow, given likely correlations and weather patterns, is around 15-20% -- Probability of getting 5+ cities is much lower, perhaps 5% - -7. Adjustments: -- El Niño effect: -5% -- Climate change trend: -2% -- Weather correlation between cities: +2% - -This is a challenging forecast because we need multiple relatively unlikely events to occur simultaneously. While Moscow and St. Petersburg are likely to have snow, getting two more cities from the remaining group is quite difficult. The El Niño conditions expected for winter 2024-25 further reduce the likelihood. - -Since we only need two days (Dec 24-25) to show snow, this helps slightly compared to needing sustained snow cover, but it's still a high bar to clear. - -Probability: 20% - -This accounts for the high likelihood of Russian cities having snow, but the significant challenge of getting two more cities to experience snow on those specific days, especially given El Niño conditions and the general warming trend in European winters." -29485,[Short fuse] Will California's Proposition 33 (allowing rent control) pass in the 2024 general election?,"Let me think through this step by step: - -1. Recent Context: -- Most recent poll shows 51% support, 46% oppose, 3% undecided -- Very close to election date (4 days away) -- Similar rent control measure (Prop 21) failed in 2020 with only 40.1% support - -2. Campaign Dynamics: -- Opposition has significantly more funding ($32M vs $12M) -- Major newspaper editorial boards recommending ""No"" -- Opposition historically tends to be more effective in California ballot measures - -3. Historical Precedent: -- Similar measures have consistently failed in California -- 2020's Prop 21 failed by a large margin -- Housing crisis has worsened since 2020, but sentiment against rent control remains strong - -4. Current Environment: -- Housing affordability remains a major issue -- High interest rates affecting housing market -- Growing concern about housing supply -- Economists generally oppose rent control - -5. Timing Factors: -- Being so close to election date, major shifts in sentiment are unlikely -- Late deciders tend to break toward ""No"" on California propositions -- Undecided voters (3%) unlikely to significantly change outcome - -6. Base Rate Considerations: -- California voters have consistently rejected rent control measures -- Progressive ballot measures often poll better than they perform -- Similar measures nationwide have generally failed - -Key Adjustments: -- The 51% support in polling likely overestimates actual support -- Need to account for historically consistent rejection of rent control -- Must consider the strong funding advantage for opposition -- Recent polling is more relevant than 2020 results, but pattern is important - -Given: -- The consistent historical pattern of rejection -- Strong opposition funding -- Editorial board positions -- Tendency for undecideds to break ""No"" -- Very short time until resolution -- Pattern of polls overestimating support for progressive measures - -I assess that while support appears slightly higher than in 2020, the measure is still likely to fail. The 51% support in polling will likely translate to less than 50% in actual voting, consistent with historical patterns. - -Probability: 32% - -This reflects that while conditions are somewhat more favorable than 2020, the fundamentals still strongly favor rejection, with only a moderate chance of passage given the consistent historical pattern and campaign dynamics." -29484,Will the World Health Organization prequalify moxidectin before 2025?,"Let me think through this step by step: - -1. Timeline Analysis: -- Today is Nov 1, 2024 -- Resolution date is Jan 1, 2025 -- This gives only 2 months for prequalification to occur -- This is a very short window for regulatory processes - -2. Current Status: -- WHO has ""unexpectedly required"" extensive extra clinical trials -- This suggests significant unexpected hurdles -- No indication these trials have been completed -- GiveWell's 80% confidence is for 2027, not 2025 -- Recent grant in June 2024 suggests process is still ongoing - -3. Historical Context: -- WHO prequalification typically takes years -- Drug already has FDA approval since 2018 -- 6 years have passed since FDA approval without WHO prequalification -- Extra clinical trials requirement suggests at least several more months needed - -4. Process Requirements: -- WHO prequalification involves multiple steps: - * Clinical trials completion - * Data analysis - * Application review - * Site inspections - * Final determination -- Each step typically takes months - -5. Urgency Factors: -- Clear medical need exists -- Alternative (ivermectin) already available -- No indication of emergency fast-tracking - -6. Timeline Probabilities: -- Next 2 months (by Jan 2025): Very low probability -- By end of 2025: ~30% -- By 2027: Align with GiveWell's 80% - -7. Risk Adjustments: -- Regulatory processes rarely complete faster than expected -- Short timeframe makes positive resolution extremely unlikely -- Need to adjust for historical overconfidence - -Given: -- The extremely short timeframe (2 months) -- Known additional clinical trial requirements -- No indication of near-term completion -- Typical regulatory timeline length -- Recent grant suggesting ongoing work needed -- Need to complete multiple process steps - -The probability of WHO prequalification completing in just the next two months is extremely low. Even considering potential unknown accelerating factors, the timeline is too compressed for reasonable likelihood of completion. - -Probability: 3%" -29542,"Before January 5, 2025, will the KP lineage constitute less than 50% of the COVID-19 variants monitored in the US?","Predictions: [20, 20, 15, 15, 10] -Median reasoning: To forecast these questions, we need to consider recent trends in COVID-19 variant proportions in the US and the likelihood that these trends will continue or change by early January 2025. - -1. **Current Situation (as of October 26, 2024):** - - The KP lineage has a combined prevalence of 68%, and KP.3.1.1 specifically accounts for 57% of the variants. - - The XEC variant has increased sharply to 17% from 2% in just over a month. - -2. **Factors to Consider:** - - **Competition Between Variants:** The rapid rise of the XEC variant indicates strong competition for dominance with KP. If XEC continues to grow, it could reduce the KP lineage to below 50%. - - **Time Frame:** There are about two months remaining until the resolution date. This is a relatively short period for such a substantial shift, especially if the KP lineage has structural or evolutionary advantages that have sustained its high prevalence. - - **Historical Trends and Base Rates:** Variants can quickly change prevalent landscape, but a change from 68% to below 50% in two months is quite rapid and uncommon without a clear driver. - - **Public Health Factors:** Vaccine effectiveness against new variants and public health responses (e.g., vaccination rates) can influence variant dynamics. However, the high level of KP could suggest more resistance to quick changes. - -3. **Probabilities for Each Question:** - - For KP to constitute less than 50%, the growth of XEC and potentially other variants must be sufficient to outcompete KP lineage substantially – a possible but challenging prospect in just 2 months without clear accelerators beyond current drives. - - For XEC to account for at least 50% dominance, it would need to continue its recent surge and double its proportion, largely displacing KP lineage. - -Given these considerations, a conservative likelihood is warranted due to the need for dramatic shifts unsupported by historical scope or current public health adjustments. - -4. **Overconfidence Adjustment:** In previous forecasts, overconfidence has been an issue. We ensure predictions allow for inherent unpredictabilities. - -Based on the above analysis, we assess the final probabilities as follows:" -29541,"Will the XEC COVID-19 variant account for at least 50% of the variants monitored in the US before January 5, 2025?","Predictions: [20, 20, 15, 15, 10] -Median reasoning: To forecast these questions, we need to consider recent trends in COVID-19 variant proportions in the US and the likelihood that these trends will continue or change by early January 2025. - -1. **Current Situation (as of October 26, 2024):** - - The KP lineage has a combined prevalence of 68%, and KP.3.1.1 specifically accounts for 57% of the variants. - - The XEC variant has increased sharply to 17% from 2% in just over a month. - -2. **Factors to Consider:** - - **Competition Between Variants:** The rapid rise of the XEC variant indicates strong competition for dominance with KP. If XEC continues to grow, it could reduce the KP lineage to below 50%. - - **Time Frame:** There are about two months remaining until the resolution date. This is a relatively short period for such a substantial shift, especially if the KP lineage has structural or evolutionary advantages that have sustained its high prevalence. - - **Historical Trends and Base Rates:** Variants can quickly change prevalent landscape, but a change from 68% to below 50% in two months is quite rapid and uncommon without a clear driver. - - **Public Health Factors:** Vaccine effectiveness against new variants and public health responses (e.g., vaccination rates) can influence variant dynamics. However, the high level of KP could suggest more resistance to quick changes. - -3. **Probabilities for Each Question:** - - For KP to constitute less than 50%, the growth of XEC and potentially other variants must be sufficient to outcompete KP lineage substantially – a possible but challenging prospect in just 2 months without clear accelerators beyond current drives. - - For XEC to account for at least 50% dominance, it would need to continue its recent surge and double its proportion, largely displacing KP lineage. - -Given these considerations, a conservative likelihood is warranted due to the need for dramatic shifts unsupported by historical scope or current public health adjustments. - -4. **Overconfidence Adjustment:** In previous forecasts, overconfidence has been an issue. We ensure predictions allow for inherent unpredictabilities. - -Based on the above analysis, we assess the final probabilities as follows:" -29550,"Will ""slaughterbots"" kill at least 50 people outside a military conflict by 2025? - -","To assess the probability of ""slaughterbots"" killing at least 50 people outside a military conflict by January 1, 2025, let's break down the available information and approach this step-by-step: - -1. **Base Rate and Historical Context**: - - There is no known historical precedent for autonomous drones (or ""slaughterbots"") causing 50 civilian fatalities outside military conflicts. - - Previous instances of autonomous weaponry use have been within military contexts, and no reports have emerged about their use in civilian settings as described in the question. - -2. **Technological Capabilities**: - - While AI and drone technology have advanced significantly, including developments in swarm drones, there is no direct evidence of these technologies being used lethally outside military contexts. - - Significant advancements in AI navigation and facial recognition increase the potential for misuse, but specific instances of misuse involving 50 fatalities have not been documented. - -3. **Regulatory Environment**: - - The failure of the 2021 UN resolution indicates a lack of global consensus on regulating autonomous lethal weapons. However, this lack of regulation could facilitate misuse, though no surge in such incidents has been observed recently. - - While the regulatory environment is permissive, initiating a large-scale use of ""slaughterbots"" in civilian contexts would likely attract significant attention and backlash. - -4. **Timeframe**: - - With less than two months until the resolution date, the timeline is very short for any significant change in the current dynamics. - - Historically, changes in the use of military technologies in civilian contexts are gradual due to ethical, political, and logistical constraints. - -5. **Recent Developments**: - - There have been notable advancements and testing, such as Ukraine's military exploring swarm drones, but these are confined to military use. - - There are no credible media reports of slaughterbot use in civilian casualties as described, nor any indication of ongoing plans by non-state actors to deploy such technology imminently. - -6. **Expert Opinions and Advocacy**: - - Experts like Max Tegmark express concerns about potential misuse, yet the spectrum of realistic threat versus speculative fear remains debatable as indicated by Paul Scharre's criticism. - -7. **Consideration of Uncertainty**: - - While acknowledging the potential for rare, high-impact incidents, the probability of such an event occurring in the specified timeframe with credible media reporting seems low. - - Overconfidence adjustment: Historically 35% positivity rate in analogous predictions indicates a need for a more conservative estimate considering potential unforeseen factors. - -Taking into account all these factors, the probability of ""slaughterbots"" causing 50 civilian fatalities outside a military conflict by January 1, 2025, appears very low, given the short timespan, lack of precedent, and absence of current factual indicators heading toward such an incident. - -Final Answer: ""Probability: 1%""" -29549,"Will lab-grown complex vital organs be used successfully in humans before 2025? -","Let me think through this step by step: - -1. Time Horizon Analysis: -- Only about 2 months remain until resolution -- For complex medical breakthroughs, this is an extremely short window -- Most medical advances require lengthy testing and approval processes - -2. Current State Assessment: -- No lab-grown complex vital organs have yet been successfully transplanted in humans -- Simpler structures (vaginas, urethras, bladders) have been achieved -- Animal testing shows promise but significant gaps remain -- The 100-day survival requirement adds additional complexity - -3. Technical Requirements: -- Need successful organ growth -- Need successful transplantation -- Need 100 days of survival without life support -- Need medical professional verification of substantial benefit -- All steps must complete before January 1, 2025 - -4. Historical Context: -- Progress in this field has been steady but slow -- Moving from animal trials to human trials typically takes years -- No indication of imminent human trials for complex organs - -5. Timeline Analysis: -- If we consider a 5-year window (2025-2030), probability might be 30-40% -- If we consider a 2-year window (2024-2026), probability might be 15-20% -- For our ~2-month window, probability must be significantly lower - -6. Risk Factors: -- Regulatory approval requirements -- Safety protocols -- Technical challenges -- Time needed for organ growth -- Time needed for recovery monitoring - -7. Success Requirements: -- Would need trials to already be in progress -- Would need regulatory fast-tracking -- Would need immediate success -- No major news of such trials has emerged - -Given the extremely short timeframe remaining, the complexity of the achievement required, and the lack of any indication that human trials are imminent, I must assign a very low probability. While significant progress is being made in the field, the specific requirements (successful transplant + 100 days survival + before 2025) make this highly unlikely in the next two months. - -Even if a breakthrough were announced tomorrow, the 100-day survival requirement alone would make it nearly impossible to verify success before the resolution date. - -Probability: 1%" -29548,"Will there be a culturally significant development in aging research before January 1, 2025? -","Let me think about this step by step: - -1. Time Horizon Analysis: -- Only about 2 months remain until resolution -- Very short timeframe for major developments -- End of year typically has fewer major announcements due to holidays - -2. Resolution Criteria Analysis: -- Multiple paths to resolution, but all require significant public visibility -- Wikipedia ""In the news"" is a high bar -- Presidential debates unlikely in this period -- Time Person of the Year is possible but announced in December - -3. Historical Base Rate: -- Aging research rarely makes mainstream news -- When it does, it's usually tied to specific breakthroughs -- No major aging breakthroughs have made Wikipedia's ""In the news"" in recent years -- Time Person of the Year has never been awarded for aging research - -4. Current Context: -- No indication of imminent major breakthroughs -- Funding opportunities mentioned are routine, not extraordinary -- Research initiatives described are incremental -- No signs of paradigm-shifting results about to be published - -5. Timeline Distribution: -- Next 2 months: 2% probability -- Next 6 months: 5% probability -- Next year: 12% probability -- This suggests the short timeframe severely limits likelihood - -6. Specific Resolution Path Analysis: -- Wikipedia ""In the news"": 1% chance -- History.com yearly summary: 0.5% chance -- Presidential debate mention: 0% chance (no debates scheduled) -- Time Person of the Year: 1% chance - -7. Mitigating Factors: -- Multiple resolution paths provide some cushion -- Field is active with ongoing research -- Increased public interest in longevity - -8. Limiting Factors: -- Extremely short timeframe -- High bar for ""cultural significance"" -- End of year timing -- No major pending announcements - -9. Confidence Adjustment: -- Initial instinct was 1% -- Historical overconfidence adjustment suggests moving toward 5% -- Very short timeframe suggests staying conservative - -Given the extremely short timeframe, multiple but high-bar resolution criteria, and lack of pending major announcements, this seems very unlikely. However, accounting for multiple resolution paths and avoiding overconfidence in extreme predictions, I'll adjust slightly upward from my initial assessment. - -Probability: 3%" -29547,"Will Elon Musk be the richest person in the world on December 31, 2024, according to Forbes?","To forecast whether Elon Musk will remain the richest person in the world according to the Forbes list on December 31, 2024, let's break down the relevant information and analyze it. - -1. **Current Status**: As of November 1, 2024, Elon Musk is currently the richest person with a net worth of $263.8 billion. The next richest individuals are Larry Ellison and Jeff Bezos. Musk holds a sizable lead in his net worth, and based solely on this, he is currently positioned to remain in first place. - -2. **Wealth Volatility**: Musk's wealth is notably volatile, primarily because it is closely tied to Tesla's stock price, which has experienced significant fluctuations. However, in just under two months, significant shifts in net worth could occur, especially in the volatile tech sector. - -3. **Prediction Markets**: The prediction market on Kalshi gives Musk an 82% chance of retaining his status as the richest person at the year's end. This is a strong indicator of current sentiment and how others view his likelihood of maintaining his position. - -4. **Recent Changes**: Despite a $7 billion dip in Musk's net worth due to Tesla, he remains in the lead. This suggests that even with considerable losses in Tesla's stock, Musk’s lead is substantial enough to absorb some level of volatility. - -5. **Economic Environment**: Without specific indicators pointing toward an imminent economic downturn or a specific reason for Tesla stock to dramatically plummet, the default assumption based on current conditions supports a continued lead for Musk. - -6. **Base Rates and Uncertainty**: Historically, shifts in the top position of the richest individuals are not common within such a short time frame barring extraordinary circumstances. However, due to the inherent uncertainty and potential for market shifts, overconfidence should be tempered. - -7. **Volatility and Base Case**: Considering the limited time frame until the resolution date, the likelihood of maintaining the status quo is higher compared to a longer-term forecast. The volatility of Musk's primary asset, Tesla, adds variability, but the proximity to year-end favors less radical change. - -8. **Potential Novel Events**: Consider potential risks such as political or legal challenges, breakthroughs in other companies, or unexpected market developments. These factors must be considered but currently seem unlikely to immediately impact Musk’s standing by year-end. - -In evaluating these factors, it's crucial to incorporate my historical tendency toward overconfidence and adjust accordingly. Given Musk's current financial lead, market predictors, and the relatively short timeline to the end of the year, the probability remains high that he will still be the richest person, but I will account for significant wealth volatility as a risk. - -**Probability Calculation**: -- Baseline probability reflects the current prediction market's stance (82%). -- Temper with a slight adjustment for volatility and overconfidence (factor this down to avoid being overly confident): Decrease slightly. - -**Final Assessment**: -**Probability: 75%** - -This adjustment reflects a cautious confidence in Musk’s odds, taking into account potential risks and the recent tendency towards overconfidence in prior estimates." -29546,"Will Oregon voters approve Measure 118, which would initiate a universal basic income in the state, funded by corporate tax?","Let me think through this step by step: - -1. Type of Question: This is a Competition Event (political ballot measure) - -2. Base Rate Analysis: -- State-level UBI initiatives are extremely rare -- Progressive ballot measures in Oregon have mixed success -- Corporate tax increases generally face strong opposition -- Recent trend shows voters being more fiscally conservative post-pandemic - -3. Key Factors: -- Only 2 days until resolution -- Bipartisan opposition is a major red flag -- Progressive groups rescinding support is particularly concerning -- Corporate opposition likely to be well-funded -- Complex economic policy changes tend to face uphill battles -- $1,600 per person is a significant amount that may raise skepticism -- 3% tax on large corporations could mobilize strong business opposition - -4. Timing Considerations: -- With election tomorrow, voter opinions are largely set -- Late opposition from progressive groups likely to have significant impact -- Little time for new developments to change trajectory - -5. Comparative Analysis: -- Similar broad economic policy changes rarely pass at state level -- Corporate tax measures historically face strong opposition -- UBI is still considered experimental by many voters - -6. Risk Factors: -- Economic uncertainty makes voters more cautious -- Complex funding mechanism may create voter hesitation -- Bipartisan opposition suggests broad-based concerns - -7. Positive Factors: -- Oregon has progressive voting history -- Direct cash benefits can be appealing to voters -- Tech industry backing provides resources - -Key considerations for probability: -- The bipartisan opposition is particularly damaging -- Progressive groups withdrawing support likely fatal -- Complex economic policies rarely pass on first attempt -- Timing (right before election) means current opposition likely to hold -- Recent voter trends favor fiscal conservatism - -Given these factors, especially the broad opposition coalition and withdrawal of progressive support, this measure appears very unlikely to pass. However, Oregon's progressive voting history and the appeal of direct payments provide some possibility of success. - -The combination of opposition from both traditional conservative and progressive groups, along with the measure's complexity and significant economic implications, makes passage highly improbable but not impossible. - -Probability: 15%" -29545,Will Donald Trump Lose in New York State by under 6.0 points in the 2024 election?,"To forecast whether Donald Trump will lose by under 6.0 points in the 2024 election in New York State, I'll analyze the contextual clues, historical data, recent trends, and polling data, while being careful not to overweigh any single source of evidence. - -### Historical Context: -1. **2020 Presidential Election**: Joe Biden defeated Donald Trump by 23.1 points. This indicates a strong Democratic base in New York State. -2. **2022 Gubernatorial Election**: The Democratic governor, Kathy Hochul, won by 6.39 points, which suggests a somewhat narrower margin compared to prior elections but still a decisive lead. -3. The recent historical context shows a consistent Democratic dominance, though the margins have varied. - -### Recent Polling Data: -1. **October 2024 Polling**: Kamala Harris has been leading Trump by an average of 14 points, an indicator that Trump trails significantly behind. -2. **NYT/Siena Poll**: Shows Harris at 66% vs. Trump's 27% in New York City, suggesting Trump has made some inroads but remains far behind typical levels needed for a dramatic upset. - -### Event Weighting and Historical Base Rates: -1. **Rally at MSG**: Trump's rally on October 27, 2024, is a tactical move to gain visibility but is unlikely to substantially shift statewide preferences given the historical precedent. -2. **New York's Overall Political Lean**: Historically, New York is a Democratic-leaning state, and there's little evidence suggesting a political shift large enough (e.g., >17-point swing needed) to bring Trump's loss to under 6.0 points. - -### Analysis Time Frame: -1. **Short-term Change Feasibility**: With the election imminent, significant shifts in voter preference are unlikely, especially given the consistent polling data stretching through October. -2. **Historical Variance**: Elections in New York have not historically shown dramatic last-minute swings of this magnitude. - -### Key Probability Considerations: -- Historical trends strongly favor a Democratic win by a larger margin than in 2022. -- Recent polling supports a comfortable Democratic lead. -- Limited time for substantial reversal in opinion makes sudden shifts less probable. -- Margin analysis suggests that even a considerable Trump gain unlikely results in a <6-point defeat. - -### Rationale: -- While there are some indicators of Trump gaining in certain areas like NYC, the overall evidence strongly suggests that the margin remains well above 6.0% in favor of the Democratic candidate. -- Given the consistent Democratic advantage historically and as reflected in recent polls, a forecast below 6.0 points seems unlikely. -- Historical and present data converge on a similar conclusion, emphasizing limited variance in New York's political outcome. - -Thus, when considering the balance of probabilities, historical context, consistent polling results, and time constraints, the likelihood of Trump losing by under 6.0 points in New York State is low. - -### Conclusion: -Final forecast: Probability: 5%" -29544,"Will Kamala Harris be president before January 1, 2025?","To determine the probability of Kamala Harris becoming president before January 1, 2025, we'll analyze several factors: the current situation, historical precedents, and potential scenarios. - -### Current Situation: - -1. **Election Status**: Kamala Harris is the Democratic nominee following Joe Biden's withdrawal. The presidential election is tomorrow, November 5, 2024. No official results will be available for several hours to weeks, which could affect the timing of any potential transition. - -2. **Biden’s Health**: Though there were earlier discussions about Biden's mental fitness, there is no recent news suggesting an immediate threat to his ability to complete his term just a couple months before its end. - -### Historical Precedents: - -1. **Presidential Succession**: Historically, vice presidents have assumed the presidency due to death or resignation, but such events are rare. There is limited historical precedent for a vice president succeeding due to incapacitation without death or resignation. - -2. **Impeachment and Resignation**: The links to Metaculus questions on Biden's impeachment provide no strong evidence of an active process that would lead to his removal before the end of his term. - -### Potential Scenarios: - -1. **Health Issues**: Any sudden developments regarding Biden's health could result in Harris assuming the presidency. Given no alarming reports, such a scenario remains improbable in the near term. - -2. **Impeachment or Resignation**: While theoretically possible, there are no compelling indications of ongoing processes that could force Biden from office before January 1, 2025. - -3. **Election Outcome**: Even if Harris wins the 2024 presidential election, she wouldn't become president based on election results until after the New Year, due to the certification timeline. - -### Timeframe and Resolution: - -Given that there are less than two months until January 1, 2025, and considering the base rate of vice presidential succession due to unforeseen circumstances is low, the probability hinges largely on Biden either resigning or being incapacitated. As the timeframe is short, the window for anomalies is limited, resulting in a minor overall probability. - -### Probability Distribution: - -1. **Early November (Current Date)**: There are no immediate indications of change; high stability assumption. -2. **Mid-December to New Year's Eve**: Increased political activity, but still unlikely without preceding signals. - -### Final Calculation: - -Given the historical rarity, current political stability, and tight timeframe, we must err on the side of a low probability, reflecting the limited likelihood of any significant changes. - -**Probability: 2%**" -29598,Will the number of FBI firearms background checks initiated through the NICS exceed 2.6M for October 2024?,"Predictions: [100, 99, 1, 99, 10] -Median reasoning: To forecast the likelihood of the FBI NICS background checks exceeding the given thresholds for October 2024, we begin by looking at the current data and trends. - -1. **Base Rates**: As of the latest data, the unadjusted NICS figure for October 2024 is 2,298,383. This is significantly below both the 2.4M and 2.6M thresholds. Importantly, the adjusted figure is even lower at 1,302,857. Historically, there is seldom a drastic increase in such figures between the end of the reporting period and the resolution of the question. - -2. **Election Influence**: While the 2024 U.S. presidential election context might suggest increased firearm purchases, clear indicators of increased checks in October are not present in the existing data. The slight recent upward trend and the election context are already factored into the October figures. - -3. **Trend Analysis**: Historical data shows a long-term increase in firearms sales with fluctuations due to socio-political events. However, recent figures (both unadjusted and adjusted) are lower than the three explicit thresholds mentioned in the questions. - -4. **Timeliness and Validity of Sources**: The data from the FBI, seen as reliably current and authoritative, shows figures well-documented up to this point. - -5. **Possible Outcomes**: Given the current data and short time until the resolution date, significant changes in October’s figures are highly unlikely. - -Based on the current figures and the historical trend analysis, the probability distribution for the thresholds in question is outlined below. We also consider a possible increase above the highest threshold, but this is quite unlikely. - -- **Exceeding 2.6M**: Given the current figure of 2,298,383, achieving over 2.6M would require an unrealistic jump. Thus, the probability is very low. -- **Exceeding 2.4M**: While more plausible than 2.6M, it still requires a significant and unlikely increase in recorded sales/checks. -- **Exceeding 2.2M**: The October figure based on recent data is already known to exceed this value." -29597,Will the number of FBI firearms background checks initiated through the NICS exceed 2.4M for October 2024?,"Predictions: [100, 99, 1, 99, 10] -Median reasoning: To forecast the likelihood of the FBI NICS background checks exceeding the given thresholds for October 2024, we begin by looking at the current data and trends. - -1. **Base Rates**: As of the latest data, the unadjusted NICS figure for October 2024 is 2,298,383. This is significantly below both the 2.4M and 2.6M thresholds. Importantly, the adjusted figure is even lower at 1,302,857. Historically, there is seldom a drastic increase in such figures between the end of the reporting period and the resolution of the question. - -2. **Election Influence**: While the 2024 U.S. presidential election context might suggest increased firearm purchases, clear indicators of increased checks in October are not present in the existing data. The slight recent upward trend and the election context are already factored into the October figures. - -3. **Trend Analysis**: Historical data shows a long-term increase in firearms sales with fluctuations due to socio-political events. However, recent figures (both unadjusted and adjusted) are lower than the three explicit thresholds mentioned in the questions. - -4. **Timeliness and Validity of Sources**: The data from the FBI, seen as reliably current and authoritative, shows figures well-documented up to this point. - -5. **Possible Outcomes**: Given the current data and short time until the resolution date, significant changes in October’s figures are highly unlikely. - -Based on the current figures and the historical trend analysis, the probability distribution for the thresholds in question is outlined below. We also consider a possible increase above the highest threshold, but this is quite unlikely. - -- **Exceeding 2.6M**: Given the current figure of 2,298,383, achieving over 2.6M would require an unrealistic jump. Thus, the probability is very low. -- **Exceeding 2.4M**: While more plausible than 2.6M, it still requires a significant and unlikely increase in recorded sales/checks. -- **Exceeding 2.2M**: The October figure based on recent data is already known to exceed this value." -29596,Will the number of FBI firearms background checks initiated through the NICS exceed 2.2M for October 2024?,"Predictions: [100, 99, 1, 99, 10] -Median reasoning: To forecast the likelihood of the FBI NICS background checks exceeding the given thresholds for October 2024, we begin by looking at the current data and trends. - -1. **Base Rates**: As of the latest data, the unadjusted NICS figure for October 2024 is 2,298,383. This is significantly below both the 2.4M and 2.6M thresholds. Importantly, the adjusted figure is even lower at 1,302,857. Historically, there is seldom a drastic increase in such figures between the end of the reporting period and the resolution of the question. - -2. **Election Influence**: While the 2024 U.S. presidential election context might suggest increased firearm purchases, clear indicators of increased checks in October are not present in the existing data. The slight recent upward trend and the election context are already factored into the October figures. - -3. **Trend Analysis**: Historical data shows a long-term increase in firearms sales with fluctuations due to socio-political events. However, recent figures (both unadjusted and adjusted) are lower than the three explicit thresholds mentioned in the questions. - -4. **Timeliness and Validity of Sources**: The data from the FBI, seen as reliably current and authoritative, shows figures well-documented up to this point. - -5. **Possible Outcomes**: Given the current data and short time until the resolution date, significant changes in October’s figures are highly unlikely. - -Based on the current figures and the historical trend analysis, the probability distribution for the thresholds in question is outlined below. We also consider a possible increase above the highest threshold, but this is quite unlikely. - -- **Exceeding 2.6M**: Given the current figure of 2,298,383, achieving over 2.6M would require an unrealistic jump. Thus, the probability is very low. -- **Exceeding 2.4M**: While more plausible than 2.6M, it still requires a significant and unlikely increase in recorded sales/checks. -- **Exceeding 2.2M**: The October figure based on recent data is already known to exceed this value." -29591,"Will the CDC report more than 80% of the tested influenza sequences as influenza A during the 2024-25 season through the week ending Dec 21, 2024?","Predictions: [66, 55, 50, 55, 65] -Median reasoning: To arrive at the probabilities for each question, I analyzed historical data, recent trends, and relevant background information. - -### Question 1 (29591): Will the CDC report more than 80% of the tested influenza sequences as influenza A during the 2024-25 season through the week ending Dec 21, 2024? - -The current trends show a strong prevalence of Influenza A (94.8% as of week 43), mainly the A(H3N2) and A(H1N1) strains. Historically, there can be fluctuations in Influenza A vs. B dominance, but the current data points towards a higher percentage of Influenza A cases. With the information at hand, including the vaccine targeting mostly Influenza A strains and low early season activity of Influenza B, it is reasonable to expect more than 80% Influenza A cases. However, historical trends of bifurcated seasons with later Influenza B waves warrant consideration. - -### Question 2 & 3 (Will weekly total influenza hospitalizations in the US exceed 2,000 for Nov 23 and Dec 7, 2024?) - -Influenza hospitalizations tend to increase as the flu season progresses. The recent high prevalence of Influenza A and the dominance of the more virulent A(H3N2) subtype could contribute to higher hospitalization rates. However, the extent and speed of this increase depend on the progression of the current flu season. Given the potential for increasing flu activity and hospitalizations, I would estimate a moderate probability of exceeding 2,000 hospitalizations during these weeks due to still being early in the season post-Oct 26, yet respecting local variations and the variability of the flu season's impact. - -The timelines for all questions are short, meaning significant deviations from current trends are less likely. Thus, my predictions aim to balance recent data and historical variability. - -Compensating for my past tendency towards overconfidence, I've arrived at the probabilities below: - -- Question 1: [Will Influenza A > 80%?] This is a cumulative count event focusing on the percentage of influenza A. Based on current high percentages and the nature of the season's initial strains, I'm estimating a 70% probability. -- Question 2: [Nov 23 hospitalizations > 2,000?] Estimates suggest moderate activity. I'm placing a 40% probability on exceeding the threshold. -- Question 3: [Dec 7 hospitalizations > 2,000?] Given potential rising activity, this has a higher chance than for Nov 23. I'm predicting a 55% probability." -29590,"Will the weekly total number of influenza hospitalizations for the United States for the week of Dec 7, 2024 be more than 2,000?","Predictions: [66, 55, 50, 55, 65] -Median reasoning: To arrive at the probabilities for each question, I analyzed historical data, recent trends, and relevant background information. - -### Question 1 (29591): Will the CDC report more than 80% of the tested influenza sequences as influenza A during the 2024-25 season through the week ending Dec 21, 2024? - -The current trends show a strong prevalence of Influenza A (94.8% as of week 43), mainly the A(H3N2) and A(H1N1) strains. Historically, there can be fluctuations in Influenza A vs. B dominance, but the current data points towards a higher percentage of Influenza A cases. With the information at hand, including the vaccine targeting mostly Influenza A strains and low early season activity of Influenza B, it is reasonable to expect more than 80% Influenza A cases. However, historical trends of bifurcated seasons with later Influenza B waves warrant consideration. - -### Question 2 & 3 (Will weekly total influenza hospitalizations in the US exceed 2,000 for Nov 23 and Dec 7, 2024?) - -Influenza hospitalizations tend to increase as the flu season progresses. The recent high prevalence of Influenza A and the dominance of the more virulent A(H3N2) subtype could contribute to higher hospitalization rates. However, the extent and speed of this increase depend on the progression of the current flu season. Given the potential for increasing flu activity and hospitalizations, I would estimate a moderate probability of exceeding 2,000 hospitalizations during these weeks due to still being early in the season post-Oct 26, yet respecting local variations and the variability of the flu season's impact. - -The timelines for all questions are short, meaning significant deviations from current trends are less likely. Thus, my predictions aim to balance recent data and historical variability. - -Compensating for my past tendency towards overconfidence, I've arrived at the probabilities below: - -- Question 1: [Will Influenza A > 80%?] This is a cumulative count event focusing on the percentage of influenza A. Based on current high percentages and the nature of the season's initial strains, I'm estimating a 70% probability. -- Question 2: [Nov 23 hospitalizations > 2,000?] Estimates suggest moderate activity. I'm placing a 40% probability on exceeding the threshold. -- Question 3: [Dec 7 hospitalizations > 2,000?] Given potential rising activity, this has a higher chance than for Nov 23. I'm predicting a 55% probability." -29589,"Will the weekly total number of influenza hospitalizations for the United States for the week of Nov 23, 2024 be more than 2,000?","Predictions: [66, 55, 50, 55, 65] -Median reasoning: To arrive at the probabilities for each question, I analyzed historical data, recent trends, and relevant background information. - -### Question 1 (29591): Will the CDC report more than 80% of the tested influenza sequences as influenza A during the 2024-25 season through the week ending Dec 21, 2024? - -The current trends show a strong prevalence of Influenza A (94.8% as of week 43), mainly the A(H3N2) and A(H1N1) strains. Historically, there can be fluctuations in Influenza A vs. B dominance, but the current data points towards a higher percentage of Influenza A cases. With the information at hand, including the vaccine targeting mostly Influenza A strains and low early season activity of Influenza B, it is reasonable to expect more than 80% Influenza A cases. However, historical trends of bifurcated seasons with later Influenza B waves warrant consideration. - -### Question 2 & 3 (Will weekly total influenza hospitalizations in the US exceed 2,000 for Nov 23 and Dec 7, 2024?) - -Influenza hospitalizations tend to increase as the flu season progresses. The recent high prevalence of Influenza A and the dominance of the more virulent A(H3N2) subtype could contribute to higher hospitalization rates. However, the extent and speed of this increase depend on the progression of the current flu season. Given the potential for increasing flu activity and hospitalizations, I would estimate a moderate probability of exceeding 2,000 hospitalizations during these weeks due to still being early in the season post-Oct 26, yet respecting local variations and the variability of the flu season's impact. - -The timelines for all questions are short, meaning significant deviations from current trends are less likely. Thus, my predictions aim to balance recent data and historical variability. - -Compensating for my past tendency towards overconfidence, I've arrived at the probabilities below: - -- Question 1: [Will Influenza A > 80%?] This is a cumulative count event focusing on the percentage of influenza A. Based on current high percentages and the nature of the season's initial strains, I'm estimating a 70% probability. -- Question 2: [Nov 23 hospitalizations > 2,000?] Estimates suggest moderate activity. I'm placing a 40% probability on exceeding the threshold. -- Question 3: [Dec 7 hospitalizations > 2,000?] Given potential rising activity, this has a higher chance than for Nov 23. I'm predicting a 55% probability." -29600,"Will the USDA-posted recall of Hickory Hollow's ready-to-eat jerky products be closed before December 1, 2024?","To determine the probability that the USDA-posted recall of Hickory Hollow's ready-to-eat jerky products will be closed before December 1, 2024, I will assess the available information, base rates of similar events, and the timeframe we are dealing with. - -**Assessment of Current Information:** - -1. **Current Recall Status**: As of November 2, 2024, the recall is still active. The lack of any updates indicates no signs of the recall closure. - -2. **Reason for Recall**: The recall was due to the lack of federal inspection which is a serious issue. A resolution requires intervention or corrective measures to ensure compliance with federal regulations. - -3. **Historical Base Rates**: Typically, recalls of food products by the USDA are resolved when the company involved has rectified the issues that led to the recall or when all questionable products have been accounted for. The closure of recalls before issues are fully resolved is rare. - -4. **Timeframe**: The resolution date is less than a month away. In general, bureaucratic processes to close a recall can be time-consuming, especially if involving compliance with federal inspections. - -5. **Health Concerns**: There are no confirmed adverse reactions reported as of yet, which might reduce the urgency but not necessarily affect the resolution directly. - -**Historical Analysis and Base Rates**: - -- Recalls involving federal inspection issues typically have a regulatory pathway that must be followed before closure. Browning through FSIS records, many recalls remain active for extended periods, especially if production issues or inspection failures are the root cause. -- Food recalls generally resolve within months but considering the intervening factor of non-compliance with inspection rules, this could extend the expected resolution time. - -**Possible Outcomes and Consideration of Timeframe**: - -- Less than a month remains, and given past overconfidence, leaning towards a lower probability accounts for unknown variables and typical resolution timelines. -- If hypothetically this question had been for a 2-month window, the probability might slightly increase but only marginally. For a year-long view, the probability of resolution could be significantly higher, perhaps exceeding 80-90%, assuming no further complications arise. - -**Probability Calculations**: - -Given the current state of the recall, historical tendencies, and the short period until the resolution date, I lean towards a cautious estimate: - -1. **Probability the recall is closed by December 1, 2024**: Low, given limited time and typical procedural pace. -2. **Estimations for Different Timeframes**: - - Resolving in the next few weeks (by December 1): Low likelihood. - - Resolving within 1-2 more months (post-December 1): Somewhat higher likelihood, potentially reaching 20-30%. - - Resolving within 1 year: High likelihood, potentially reaching 90%. - -**Final Proability**: - -Considering all factors and aiming to curb overconfidence, while also accounting for typical recall closure timelines, I estimate the probability at: - -Probability: 20%" -29599,"Will Noodles & Co. file for bankruptcy before January 1, 2025?","Let me think through this step by step: - -1. Type of Question: This is an Other Binary Outcome Event, specifically about corporate bankruptcy. - -2. Time Frame Analysis: -- Only about 2 months until resolution -- Very short window for dramatic changes -- Most corporate bankruptcies take longer to develop -- Company is actively taking corrective actions - -3. Base Rate Analysis: -- Restaurant bankruptcy rates historically low (2-3% annually) -- Fast casual segment has been challenging but not catastrophic -- Post-pandemic stress continuing but most vulnerable companies already failed -- Recent notable restaurant bankruptcies (Red Lobster) show pattern of much longer decline - -4. Company Specific Factors: -+ Negative: -- Ongoing losses -- Rising debt/interest expenses -- Closing underperforming locations -+ Positive: -- Still taking proactive measures -- Has access to capital markets -- No immediate debt maturity crisis reported -- Management actively restructuring - -5. Timeline Distribution: -- Next 2 months (till Jan 2025): ~3% -- Next 6 months: ~8% -- Next 12 months: ~15% -- The probability is not evenly distributed and increases over time - -6. Key Considerations: -- Very short timeline makes bankruptcy less likely -- Usually see more warning signs/missed payments before filing -- No reports of missed payments or critical debt issues -- Store closures are often last attempt before bankruptcy, but also can prevent it - -7. Confidence Adjustment: -- Initial estimate might be overconfident given historical patterns -- Need to account for limited information and short timeline -- Restaurant industry has shown resilience despite challenges - -The company shows clear stress signals but is taking corrective action. The extremely short timeline (2 months) is the most significant factor here - even troubled companies rarely go from current operations to bankruptcy that quickly without more severe warning signs or a catastrophic event. The store closures, while concerning, are actually a positive sign that management is taking action to avoid bankruptcy. - -While longer-term risks exist, the probability of bankruptcy specifically before January 1, 2025 is quite low given the compressed timeline and lack of immediate crisis indicators. - -Probability: 3%" -29595,Will Nvidia earnings for Q3 2024 exceed $0.74 per share?,"To assess the probability of Nvidia's Q3 2024 earnings exceeding $0.74 per share, let's walk through the available information step by step: - -1. **Analyst Consensus**: The consensus estimate for earnings is $0.74 per share. This is our initial baseline, as it's what analysts collectively project based on current market trends and Nvidia's performance. - -2. **Historical Performance**: Nvidia has beaten earnings estimates in almost every quarter over the past five years. This historical performance suggests a pattern of outperformance, likely driven by their strong market position and high demand for their products. This factor may increase the probability of exceeding the consensus. - -3. **Demand and Product Outlook**: CEO Jensen Huang and CFO Colette Kress have expressed positive outlooks, emphasizing high demand for Nvidia's GPUs, including their latest release, the Blackwell chips. This suggests potential upside against the consensus estimate due to ongoing and robust demand. - -4. **Revenue Projections**: Analysts project revenue of $32.9 billion for Q3 2024, supporting the view that Nvidia's growth trajectory remains strong. Revenue growth often translates into better-than-expected earnings per share, provided cost structures are managed well. - -5. **Valuation Metrics**: Nvidia's forward 12-month earnings multiple, while lower than its five-year median, indicates that the market sees the company as relatively favorably valued. This might anticipate continued positive performance and investor confidence. - -6. **Market Context and External Factors**: With the earnings report due shortly, significant changes are less likely to impact projected earnings. Historical data and current trends are therefore more reliable indicators close to the resolution date. - -7. **Base Rate Consideration**: The historical likelihood of earnings exceeding estimates in this case should be factored in. Given Nvidia's strong track record, there is a higher probability compared to other companies that it may exceed the consensus. - -8. **Compensation for Overconfidence**: Historically, only around 35% of questions resolved positively, indicating a tendency towards overconfidence. It’s crucial to counterbalance this by moderate adjustments of our probability. - -Taking into account all of the above factors, the probability that Nvidia's earnings for Q3 2024 will exceed $0.74 per share leans towards a positive outcome, but the overconfidence bias should prevent assigning a very high probability. - -Final Prediction: -After weighing the strong historical performance, current market conditions, demand projections, and potential for only minor near-term variability, but also adjusting for the overall competition overconfidence, I conclude: - -Probability: 68%" -29594,"Will Robinhood officially announce it is launching a stablecoin, before January 1, 2025?","Let me think through this step by step: - -1. Timeline Analysis: -- We're looking at a ~2 month window (Nov 5, 2024 to Jan 1, 2025) -- This is a very short timeframe for a major financial product launch -- Short timeframes favor the status quo significantly - -2. Recent Context: -- Robinhood is already involved in USDG, a consortium-based stablecoin -- This likely reduces their immediate incentive to launch their own competing product -- The spokesperson's ""no imminent plans"" statement, while not recent, aligns with their consortium approach - -3. Market Analysis: -- Stablecoin market is growing but concentrated -- Regulatory environment remains uncertain -- Major players (Tether, USDC) dominate the space -- New entrants tend to be consortiums rather than individual companies - -4. Historical Precedents: -- Financial companies rarely launch competing products right after joining consortiums -- Product launches typically take 6-12 months of preparation and regulatory review -- Most crypto/fintech companies announce intentions well before actual launches - -5. Resolution Criteria: -- Must be officially announced on their specific blog -- Must be their own stablecoin, not just participation in USDG -- High bar for resolution as it requires specific communication channel - -6. Risk Factors: -- Very short timeframe -- Already committed to USDG -- Regulatory hurdles -- Technical implementation requirements -- Marketing and rollout planning needed - -7. Alternative Timeframe Analysis: -- 1 month probability: 1% -- 6 months probability: 8% -- 1 year probability: 15% -- The probability is not equally distributed across months due to planning/implementation requirements - -8. Positive Factors: -- They're clearly interested in the space -- Have technical capabilities -- Have regulatory relationships -- Have customer base - -However, given: -- The extremely short timeframe -- Their recent commitment to USDG -- The specific resolution criteria -- The regulatory environment -- Historical patterns of product launches -- The need for extensive preparation - -I assess there's a very low probability of Robinhood announcing their own stablecoin in this timeframe. The USDG involvement likely satisfies their near-term stablecoin strategy. The timeframe is too short for a major product launch, especially given no current indicators of preparation. - -Adjusting for historical overconfidence and the short timeframe, but maintaining some probability for unexpected developments: - -Probability: 3%" -29593,"Will US airline passenger volume for the Tuesday and Wednesday before Thanksgiving 2024 be up year over year, according to the TSA?","To determine the likelihood that US airline passenger volumes for the Tuesday and Wednesday before Thanksgiving will exceed the previous year's total, we'll conduct a detailed analysis based on available data and trends. - -**Base Rates and Historical Context:** -1. **Default Expectations:** The base rate for this type of event is essential to consider. Historically, travel tends to increase year over year, especially after pandemic-related travel restrictions have been lifted and normalcy is restored. Given that 2023 had high passenger volumes, the question revolves around whether this upward trajectory will continue. - -2. **Historical Patterns:** Thanksgiving week, particularly the Tuesday and Wednesday prior to Thanksgiving, consistently sees high volumes due to the holiday's cultural significance. This pattern informs a likely increase in 2024 assuming similar societal activities and customs. - -**Current Trends and Projections:** -1. **Travel Recovery Post-Pandemic:** As indicated, travel volumes in the U.S. are near or at pre-pandemic levels in 2024, reflecting a strong recovery trend. Given this return to normalcy and possibly pent-up demand that may not have been fully realized until 2024, volumes might be expected to rise further. - -2. **Current Year Trends:** Reports and surveys project a busier Thanksgiving travel season for 2024. The TSA and airlines are preparing for record-breaking volumes, which supports the notion of increased passenger numbers. - -3. **Economic Indicators & Consumer Behavior:** While economic downturns can affect travel behavior, 2024 projections suggest ongoing strong demand. Seasonal travelers' enthusiasm and increasing consumer confidence play a role in driving up demand. - -**Potential Counteracting Factors:** -1. **Any Disruptive Events:** External disruptors like geopolitical tensions, economic crises, or a rise in COVID-19 cases could negatively affect travel volumes. However, there are no significant threats visible as of now that might impede travel significantly. - -2. **Capacity Constraints:** Any limitations on airline capacity, staffing issues, or TSA bottlenecks at checkpoints might hinder the ability to accommodate increased traveler numbers, though these seem well-managed based on TSA preparations. - -**Analyzing Uncertainty and Probabilities:** -Given the short time frame until resolution and limited variability expected, it's reasonable to emphasize base rates with a cautious optimism bias. The expectation for a potential significant increase should be tempered with historical restraint, especially given my tendency towards overconfidence. - -- **Comparable Year-on-Year Volume Increases:** In a typical year, volumes increase modestly. Anticipating another 1-5% increase over last year is plausible based on current indicators. - -- **Variability Considerations:** Travel numbers can fluctuate due to factors like weather, but advance purchase trends help solidify predictions. - -Based on the balance of these factors and compensating for my historical overconfidence bias, I estimate the likelihood of an increase in US airline passenger volume for the specified dates as follows: - -Probability: 65%" -29592,"Will Sadiq Khan cease to be mayor of London before January 1, 2025?","To evaluate the probability of Sadiq Khan ceasing to be mayor of London before January 1, 2025, I will go through the information and break down the considerations methodically: - -1. **Election Context**: Sadiq Khan was recently re-elected for a third term in the 2024 mayoral election. This is a substantial indicator that he has a strong political mandate and the support necessary to continue in office. The base rate for politicians voluntarily resigning shortly after securing re-election is generally low. - -2. **Calls for Resignation**: The call for resignation, such as the one from Nile Gardiner, seems to be a routine political criticism rather than a credible threat to Khan's tenure. Such calls are fairly common in political offices and rarely lead to an actual resignation barring significant scandals or health issues. - -3. **Base Rates and Historical Precedent**: I would consider the base rate of mayors who resign shortly after re-election to be very low unless there's a significant triggering event. Historically, there are few examples of mayors of major cities stepping down under such circumstances absent extraordinary pressures. - -4. **Current Situation and Stability**: There does not appear to be any immediate scandal or health concern that would force Sadiq Khan to resign. His recent activities such as engagement with budget issues indicate he is discussing future plans and governance instead of preparing to leave office. - -5. **Political Environment**: Without any unusual political, economic, or legal pressures, factors that might prompt an early exit appear negligible. His recent electoral victory suggests stability in the near term. - -6. **Timeline**: The time frame is approximately 8 weeks until the resolution date. Given that there have been no significant issues so far, the chance of an unexpected resignation is low in such a short period. - -7. **Overconfidence and Past Trends**: Acknowledging a tendency towards overconfidence, a compensatory adjustment is necessary. It's crucial to not assume destabilizing events can't occur, even if they are statistically unlikely. - -8. **Outcome Distribution**: Given these considerations, the outcome remains very much in favor of Khan continuing his role unabated until at least the resolution date. - -Given all these factors, including historical precedents and the generally low base rate of such resignations shortly after an election, the likelihood of Sadiq Khan ceasing to be mayor before the specified date is low. - -**Probability: 3%**" -29588,"Will three or more Frontier AI Labs issue a joint statement committing to constrain their AI's capabilities before 2025? -","To evaluate the likelihood of three or more Frontier AI Labs issuing a joint statement to constrain their AI capabilities before January 1, 2025, let's break down the key components of the question and assess the current environment, trends, and the probability of such an event occurring within the specified timeframe. - -### Assessment of Variables and Context: - -1. **Current Date and Timeline:** - - Today is November 5, 2024. The resolution date is January 1, 2025, leaving less than two months for a joint statement to be issued. - - The short timeframe means this event must occur quickly, which tends to favor the continuation of the status quo and lowers the likelihood of significant change. - -2. **Definition and Criteria:** - - Three or more Frontier Labs must issue a joint statement clearly committing to constraining their AI's capabilities. - - ""Frontier AI Labs"" currently include OpenAI, Anthropic, Microsoft, and Google DeepMind. - - Statements of this nature have not been standard industry practice or observed in recent years in a sufficiently public or formal joint statement. - -3. **Recent Industry and Regulatory Trends:** - - There is a notable trend towards AI safety and governance, with several governmental and international initiatives focusing on responsible AI usage. - - While this creates a favorable environment for discussions around AI constraints, the translation of discussions into commitments, especially joint ones, has not traditionally happened quickly. - -4. **Historical Base Rates:** - - Historically, similar industries have been slow to self-regulate without significant external pressure or incidents prompting collective action. - - No occurrence of such a high-profile joint commitment among these labs has been observed in the past under similar circumstances. - -5. **Likelihood of Catalysts:** - - Major new regulations, significant AI-related incidents, or influential leadership changes could be catalysts, but there’s no clear evidence of such immediate triggers currently. - -6. **Strategic Interests and Alignment:** - - While these labs might align on safety in some aspects, competitive interests might dissuade joint public commitments, particularly those constraining technological capabilities. - -### Probability Calculation: - -Given these analyses, the base rate for such a specific, cooperative, and restrictive announcement occurring within the next two months seems quite low. Sociopolitical momentum for AI safety and governance is increasing, but not typically through collective statements of constraint absent a strong immediate driver. - -Given that superforecasters are typically cautious about overconfidence, and past experience suggests being wary of assuming normalized, even distributions of likelihood due to historic overconfidence, it is best to approach this forecast conservatively. - -**Timeframe Probability Analysis:** - - **Before January 1, 2025:** Low likelihood due to time constraints and lack of strong catalysts. - - **Within 6 months or a year:** Possibly higher probability as more time allows for developing regulatory and industry trends to influence outcomes, but still not highly probable without stronger impetus or progression of current developments. - -In conclusion, there is a non-negligible chance of such a statement occurring due to increasing pressures and dialogues around AI governance, but the immediate probability given the remaining time is still low. - -**Final Forecast:** -**Probability: 8%**" -29640,"Before December 31, 2024, will Tech Crunch report new layoffs at Microsoft?","Let me think through this step by step: - -1. Timeline Analysis: -- We have about 8 weeks until resolution (Nov 6 to Dec 31) -- This is a relatively short timeframe -- The most recent Microsoft layoffs were in September 2024 (2 months ago) - -2. Historical Pattern Analysis: -- Microsoft has had multiple layoff rounds in 2024 -- Tech companies typically space out layoffs by at least several months -- End of year/Q4 is often when companies announce restructuring -- However, having just had layoffs in September reduces likelihood of immediate additional cuts - -3. Resolution Criteria Specifics: -- Must be reported specifically by TechCrunch tracker -- Must specifically mention Microsoft (not subsidiaries) -- Must occur and be reported before Dec 31 -- Very specific source requirement reduces probability - -4. Current Context: -- No immediate signals of pending layoffs -- Company financially healthy -- But tech industry still seeing ongoing layoffs -- Economic uncertainty continues - -5. Timeline Probability Distribution: -- Next 2 weeks: 5% chance -- Next 4 weeks: 15% chance -- Next 8 weeks (full period): 25% chance -- Next 6 months (if question allowed): 45% chance - -6. Risk Factors: -- Recent layoffs reduce immediate likelihood -- Short timeframe reduces probability -- Specific source requirement adds uncertainty -- But ongoing tech industry trends increase possibility - -7. Base Rate Consideration: -- Microsoft averages 2-3 layoff rounds per year historically -- Already had major rounds in 2024 -- End of year timing somewhat increases probability -- But recent September layoffs decrease near-term likelihood - -8. Adjusting for Historical Overconfidence: -- Initial instinct might be 30-35% -- Need to account for overconfidence bias -- Short timeframe should reduce confidence -- Recent layoffs should reduce confidence further - -The key factors pulling the probability down are: -- Very recent September layoffs -- Short timeframe -- Specific source requirement -- Company's general financial health - -Factors pulling probability up: -- General tech industry trends -- Historical pattern of multiple yearly layoffs -- End of year timing - -Given the recent September layoffs and short timeframe, I believe the probability should be lower than might initially seem intuitive. The specific source requirement also adds uncertainty. - -Probability: 22%" -29639,"Before January 1, 2025, will Stripe announce on the news section of its website that it is planning an IPO?","Let me think through this step by step: - -1. **Timeline Analysis**: -- Resolution date is January 1, 2025 -- Today is November 6, 2024 -- This means we have roughly 8 weeks to resolution -- This is a relatively short timeframe for a major corporate announcement - -2. **Base Rate Consideration**: -- Large private tech companies typically telegraph IPO intentions months in advance -- Companies often avoid major announcements during holiday periods (December) -- Most IPO announcements come during Q1 or Q3 -- The short remaining window makes this particularly unlikely - -3. **Company-Specific Factors**: -- Stripe has consistently indicated no rush to IPO -- Recent tender offer in February 2024 reduces pressure -- $65B-90B valuation means they have significant leverage -- Bridge acquisition shows focus on growth, not exit -- CEO's June 2024 statement against rushing to IPO is particularly relevant - -4. **Market Conditions**: -- Current market conditions are mixed for tech IPOs -- Interest rates remain high -- 2025-2026 is seen as more likely timing by analysts - -5. **Resolution Criteria Specifics**: -- Must be announced specifically on Stripe's news section -- Many companies announce through other channels first -- This makes resolution even less likely within timeframe - -6. **Timeline Distribution Analysis**: -- Next 4 weeks (by Dec 6): 2% chance -- Next 8 weeks (by Jan 1): 5% chance -- Next 6 months: 15% chance -- Next 12 months: 35% chance -- This shows most likelihood is beyond our resolution window - -7. **Uncertainty Factors**: -- Market conditions could improve rapidly -- Private investor pressure could increase -- However, these are unlikely to manifest in such short timeframe - -8. **Historical Precedent**: -- Similar-sized tech companies typically plan IPOs over 6-12 months -- Holiday season (December) historically has very few major corporate announcements -- The specific website announcement requirement adds another limiting factor - -Given all these factors, and particularly considering: -- The very short timeframe remaining -- The specific resolution criteria requiring website announcement -- Recent company statements against rushing to IPO -- The holiday season timing -- Historical precedents -- The need to avoid overconfidence - -I would assign a relatively low probability to this occurring within the specified timeframe. - -Probability: 5% - -This accounts for the possibility while acknowledging the significant constraints and short timeline. It's low but not negligible, as there's always some chance of unexpected developments." -29638,Will armed conflicts between the Republic of China (Taiwan) and the People's Republic of China (PRC) lead to at least 100 deaths before 2025?,"Let me think through this step by step: - -1. Timeline Assessment: -- We only have about 7-8 weeks until resolution -- This is an extremely short window for a major armed conflict to develop and result in 100+ deaths -- Most military buildups and conflicts take months or years to develop - -2. Historical Base Rates: -- There have been no direct armed conflicts between PRC and ROC resulting in deaths in decades -- The last significant armed conflict was in the 1950s -- Recent incidents have involved military drills and posturing but no direct conflict - -3. Current Situation Analysis: -- Tensions are elevated with military drills from both sides -- Recent US arms sales to Taiwan increase tensions -- However, both sides have strong economic incentives to avoid conflict -- China's current focus appears to be on economic challenges and internal stability - -4. Resolution Criteria Analysis: -- Requires 100+ deaths from direct military engagement -- Needs verification from multiple credible sources -- Could be cumulative across multiple incidents -- Bar is relatively high for such a short timeframe - -5. Time Distribution: -- 1 month probability: 0.5% -- 2 months probability: 1% -- 6 months probability: 3% -- 1 year probability: 5% -- The distribution is not linear, with higher risk during military exercises - -6. Risk Factors: -+ Military exercises increase accident risk -+ Growing tensions and provocative actions -- Winter weather typically reduces military activity -- Holiday season traditionally sees reduced operations -- Economic interdependence remains a deterrent -- International pressure against escalation - -7. Uncertainty Considerations: -- Very short timeframe reduces uncertainty range -- Clear resolution criteria helps bound possibilities -- Strong status quo bias for such a short period - -Given the extremely short timeframe until resolution (less than 2 months), the high bar for resolution (100+ deaths), and the strong status quo bias, I need to assign a low probability. While tensions are high, the likelihood of escalation to deadly conflict in this specific timeframe is very low, though not zero due to accident risk during exercises and ongoing tensions. - -Comparing to other timeline probabilities and accounting for historical overconfidence, I arrive at: - -Probability: 2% - -This reflects the very short timeframe, while acknowledging some non-zero risk from ongoing military activities and tensions. The probability is low but not minimal, as accident risk during exercises provides a real, if small, possibility of escalation." -29637,Will the International Criminal Court bring charges against Benjamin Netanyahu before 2025?,"Let me think about this step by step: - -1. Time Analysis: -- We have less than 2 months until resolution (January 1, 2025) -- ICC investigations and legal proceedings typically take years, not months -- The conflict and potential crimes are still ongoing, making investigation more complex - -2. Historical Precedents: -- The ICC has never brought charges against a sitting Israeli PM -- ICC cases against heads of state are rare and face significant political hurdles -- When the ICC has brought charges against heads of state (e.g., Sudan's al-Bashir), the process took many years - -3. Jurisdictional Factors: -- Israel is not a member of the ICC -- ICC claims jurisdiction over Palestine territories -- This jurisdictional complexity could further slow any potential charges - -4. Current Status: -- Investigation is in preliminary stages -- South Africa's referral is recent (Nov 2023) -- Ongoing conflict makes evidence gathering difficult -- Recent judge replacement suggests potential delays - -5. Timeline Analysis: -Probability distribution across different timeframes: -- Next 2 months (by Jan 2025): Very low due to procedural requirements -- By end of 2025: Still low but higher (~15%) -- By end of 2026: More possible (~25-30%) -- Ever: Moderate possibility (~40%) - -6. Accelerating Factors: -+ International pressure is high -+ Clear documentation of events -+ Multiple country referrals - -7. Decelerating Factors: -- Political complications -- Need for thorough investigation -- Procedural requirements -- US opposition to ICC action against Israel -- Complex nature of ongoing conflict - -8. Risk Adjustment: -- Base rate of ICC charges against heads of state is very low -- Two months is an extremely short timeline for ICC action -- Must adjust for historical overconfidence - -Given the extremely short timeline (less than 2 months), the typical length of ICC investigations (years), and the complex political and jurisdictional issues involved, the probability of charges being brought specifically before January 1, 2025 is very low. While international pressure is high and evidence may be substantial, the institutional constraints and typical timeline of ICC proceedings make this highly unlikely in such a short period. - -Probability: 3% - -This accounts for a small but non-zero chance of an expedited process due to international pressure, while recognizing the significant procedural and practical barriers to such rapid action by the ICC." -29636,"Before December 1, 2024, will laws be in place requiring that AI systems that emulate humans must reveal to people that they are AI? -","Let me think about this step by step: - -1. Timeline Analysis: -- Only 25 days remain until resolution -- For new laws to be in place by Dec 1, they would likely need to be already far along in the legislative process -- The extremely short timeframe is a major limiting factor - -2. Base Rate Analysis: -- Laws requiring AI disclosure are relatively novel -- Most tech regulation takes years to implement -- Few countries have comprehensive AI regulations in place -- When legislation does pass, implementation usually has longer lead times - -3. Current Status: -- No major jurisdictions currently have such laws in place -- While there's discussion of AI regulation, most focus is on broader issues like safety and accountability -- The EU AI Act, while comprehensive, doesn't specifically mandate this -- US regulations are still in early stages - -4. Historical Precedent: -- Similar technology disclosure requirements (like bot disclosure laws) typically took 1-2 years minimum from proposal to implementation -- Google's Duplex self-disclosure was voluntary, not legally mandated -- Corporate self-regulation has been more common than legal requirements - -5. Time Distribution Analysis: -- 1 month probability (by Dec 1): Very low due to legislative timelines -- 6 month probability: ~15% -- 1 year probability: ~30% -- 2 year probability: ~50% - -6. Limiting Factors: -- Legislative bodies move slowly -- Implementation time needed after passage -- Complexity of defining ""emulate humans"" in law -- Technical challenges in enforcement - -7. Accelerating Factors: -- Increasing public concern about AI deception -- Rapid AI advancement creating urgency -- Existing regulatory frameworks to build upon - -Given the extremely short timeframe (25 days), the typical speed of legislation, and the lack of any laws currently near implementation, the probability must be very low. While there's growing momentum for AI regulation, the specific requirement for human-emulating AI to self-disclose is not currently a legislative priority in any major jurisdiction. - -Adjusting for historical overconfidence and the short timeline, but acknowledging some small possibility of a surprise fast-track regulation: - -Probability: 3% - -This reflects that while the event is very unlikely in this timeframe, it's not completely impossible (as a 1% would suggest). The 3% accounts for some small chance of an emergency regulation or fast-tracked law in response to a specific incident, while acknowledging the extreme difficulty of passing and implementing such legislation in just 25 days." -29635,"Will Kalshi outrank Coinbase in the top free Finance apps on December 31, 2024?","Let me think through this step by step: - -1. Current State: -- Kalshi is currently ranked #1 -- Coinbase is #35 -- This represents a significant gap, but app store rankings can be highly volatile - -2. Time Horizon: -- We're looking at Dec 31, 2024 -- That's about 8 weeks away -- This is a relatively short timeframe, which means current rankings should carry more weight - -3. Key Drivers: -- Kalshi's surge is primarily driven by election betting -- Post-election (Nov 5) interest will likely decline -- Coinbase's ranking is tied to crypto market activity -- Historical precedent shows Coinbase consistently ranks highly in finance apps - -4. Historical Context: -- Coinbase has maintained strong app store presence for years -- Kalshi only recently surged due to election -- Previous election-related apps typically see sharp dropoffs after elections - -5. Risk Factors: -- Crypto market volatility could affect Coinbase -- Kalshi needs to maintain user engagement post-election -- App store rankings can change dramatically day-to-day - -6. Timeline Analysis: -- Next 2 weeks: High probability Kalshi maintains lead (80%) -- Next 4 weeks: Moderate probability (60%) -- By Dec 31: Lower probability as election effects fade - -7. Key Considerations: -- Election momentum will likely fade significantly by Dec 31 -- Coinbase has stronger long-term user base -- Kalshi needs new catalysts to maintain ranking -- Holiday period typically favors established financial apps - -8. Comparative Analysis: -- Similar election-focused apps historically drop significantly within 4-6 weeks post-election -- Crypto apps maintain more consistent rankings with occasional volatility - -Given these factors, I believe Kalshi is unlikely to maintain its lead through December 31. While it's currently ahead, the election catalyst will fade, and Coinbase's established user base and broader appeal will likely reassert itself. However, given Kalshi's current strong position and potential for new features/catalysts, it's not impossible. - -The short timeframe (8 weeks) makes the current rankings more relevant, but post-election behavior patterns suggest a high likelihood of ranking decline. - -Adjusting for historical overconfidence and the fact that Kalshi does currently lead, I'll moderate my bearish view somewhat. - -Probability: 25% - -This reflects that while I expect Kalshi to drop below Coinbase by Dec 31, there's still a meaningful chance they could maintain their position through new initiatives or sustained election-related interest." -29634,"Will the US restrict access outside the US to some APIs to generative AI before 2025? -","Let me think through this step by step: - -1. Timeline Analysis: -- Only about 2 months remain until resolution -- This is an extremely short window for new legislation or major policy changes -- Most significant policy changes take 6-12 months minimum to implement - -2. Current State: -- No existing restrictions on API access based on location -- No pending legislation specifically addressing this -- Executive orders could theoretically implement this faster than legislation - -3. Recent Developments: -- Growing concern about AI safety -- Industry push for legislation -- But focus is on safety standards, not geographic restrictions -- Meta/defense partnerships suggest some movement toward controlled access - -4. Historical Precedents: -- Digital content (Netflix, Spotify) restrictions took years to implement -- Export controls on technology typically face significant resistance -- Similar technology restrictions (like cryptocurrency) have been slow to materialize - -5. Technical Feasibility: -- API restrictions by location are technically feasible -- But implementation would be complex and require significant lead time -- Companies would need time to comply - -6. Political Climate: -- Election year approaching -- Congress typically slower during election seasons -- Bipartisan concern about AI, but geographic restrictions not a primary focus - -7. Probability Distribution Analysis: -- 1 month: 2% chance -- 2 months (question timeline): 5% chance -- 6 months: 15% chance -- 12 months: 30% chance - -Key Factors Reducing Probability: -- Extremely short timeline -- Lack of current legislative momentum -- Technical complexity of implementation -- Industry likely to resist - -Key Factors Increasing Probability: -- Growing security concerns -- Precedent of digital content restrictions -- Executive action possibility -- Recent focus on AI regulation - -Given the extremely short timeline (less than 2 months), the lack of current legislation in process, and the typical speed of implementing such significant changes, I need to be quite conservative in my estimate. While there's clear movement toward more AI regulation, geographic API restrictions specifically by year-end 2024 would be surprisingly quick. - -The 12-month probability being much higher (30%) suggests this could happen eventually, but the 2-month window is very tight. - -Accounting for historical overconfidence and the fact that positive resolutions are less common than expected, I'll adjust slightly downward from my initial analysis. - -Probability: 5%" -29633,"Will OpenAI's o1 remain the top LLM in all categories of Chatbot Arena on November 30, 2024? -","Let me think about this step by step: - -1. Time Frame Analysis: -- Only 24 days until resolution -- Very short timeframe for major changes -- New models typically take months to develop and deploy - -2. Current Status: -- o1 is currently #1 in all categories -- This has been consistent since its September launch -- Must maintain lead in ALL 9 categories - -3. Historical Context: -- Previous top models (GPT-4, Claude) typically maintained leadership for several months -- However, no previous model dominated ALL categories simultaneously -- The requirement to lead in all 9 categories makes this very stringent - -4. Competitive Landscape: -- Major competitors (Google, Anthropic, Meta) are actively developing -- End of year is often a time for major releases -- But 24 days is very short for development and deployment cycle - -5. Risk Analysis: -- Only needs to lose in ONE category to resolve No -- 9 different opportunities for failure -- Even small improvements by competitors in specialized areas (math, coding) could break dominance -- Style control and hard prompts categories are particularly vulnerable - -6. Mitigating Factors: -- o1's architecture seems particularly robust -- Chain of thought capabilities give it advantages across categories -- OpenAI likely to respond quickly to any competitive threats - -7. Additional Considerations: -- Short timeframe heavily favors status quo -- But multiple categories requirement creates multiplication of risk -- Even 95% chance of maintaining each category = ~63% overall - -8. Timeline Distribution: -- 1 week: 85% chance of maintaining lead -- 2 weeks: 75% chance -- 1 month: 65% chance (our timeframe) -- 3 months: 40% chance -- 6 months: 20% chance - -This suggests the time window is favorable but not guaranteed. - -The multiplication of risk across 9 categories is the biggest factor here. Even with a strong model and short timeframe, maintaining dominance across ALL categories is challenging. A competitor only needs one breakthrough in one area to invalidate. - -However, the extremely short timeframe (24 days) is the strongest factor in favor of maintaining status quo. Major model releases typically take longer than this to develop, test, and deploy. - -Balancing these factors, and accounting for historical overconfidence: - -Probability: 72% - -(This reflects the very short timeframe favoring status quo, but acknowledging the multiplicative risk of needing to maintain leadership across all 9 categories)" -29632,"Will Mike Tyson have a public boxing match before January 1, 2025?","Let me think through this step by step: - -1. Type of Question: This is a Competition Event question, specifically about a scheduled boxing match. - -2. Time Analysis: -- The match is scheduled for November 15, 2024 -- Resolution date is January 1, 2025 -- This gives about 1.5 months for the scheduled match to occur, plus additional time if rescheduled - -3. Key Factors: -a) Positive indicators: -- Match is officially scheduled -- Netflix involvement adds legitimacy -- Significant financial interests -- Recent sources confirm plans - -b) Risk factors: -- Tyson's age (58 years old) -- History of postponements -- Health/regulatory concerns possible -- Exhibition matches often face more scrutiny - -4. Historical Context: -- Tyson's last exhibition match was in 2020 vs Roy Jones Jr. -- Jake Paul has been reliable in following through with scheduled fights -- Boxing exhibitions have had ~80% completion rate when scheduled - -5. Timeline Distribution: -- November 15, 2024: 70% chance of happening on schedule -- By December 1, 2024: +10% additional chance -- By January 1, 2025: +5% additional chance -- After January 1, 2025 or never: 15% chance - -6. Confidence Adjustment: -- Base prediction would be 85% (70+10+5) -- Need to adjust for: - * Historical overconfidence (-5%) - * Age/health risks (-5%) - * Regulatory uncertainty (-5%) - * Time proximity advantage (+5%) - -7. Final Analysis: -The match has significant momentum and commercial backing, with Netflix's involvement being particularly notable. However, given Tyson's age and the exhibition nature, there are meaningful risks. The relatively short timeframe to resolution actually works in favor of the prediction, as there's less time for things to go wrong. - -After adjusting for historical overconfidence and weighing all factors: - -Probability: 75%" -29631,Will Atlético Mineiro win the 2024 Copa Libertadores?,"Let me think about this step by step: - -1) This is a Competition Event - specifically a major football final match. - -2) Key factors to consider: -- Single match final (not two-legged) -- Neutral venue in Buenos Aires -- Both teams are Brazilian, reducing home continent advantage -- Atlético Mineiro has previous Libertadores experience and success -- Botafogo is in their first final -- Recent form of both teams -- Historical precedent for finals - -3) Breaking down advantages: -Pro Atlético Mineiro: -- Previous tournament victory (2013) -- Unbeaten in current campaign -- Strong defensive record -- Experience in big matches - -Pro Botafogo: -- Strong attacking record -- Momentum from preliminary rounds -- Element of surprise as first-time finalists - -4) Historical context: -- In Libertadores finals, experience tends to matter -- However, first-time finalists have won before -- Brazilian teams have dominated recent editions -- Single-leg finals tend to be more unpredictable than two-leg ties - -5) Statistical considerations: -- In a single match between relatively evenly matched teams, the base probability shouldn't be too far from 50% -- Need to adjust for experience factor and current form -- Final being played at neutral venue reduces home advantage factor - -6) Risk factors: -- Single match means higher variance -- Month away allows for form changes/injuries -- Previous performance doesn't guarantee future success - -7) Comparable situations: -- Recent Libertadores finals between Brazilian teams have been closely contested -- Experience advantage has historically provided about a 5-10% edge - -Given all these factors, I would slightly favor Atlético Mineiro but not by a large margin. Their experience and defensive solidity are advantages, but the single-match format increases variance and Botafogo has shown they can compete at this level. - -I need to account for overconfidence bias and the fact that this is a single match where anything can happen. - -Starting from a 50% base for an even match, adding: -+5% for experience -+3% for current form -+2% for defensive record --3% for single match variance --2% for time until match - -Probability: 55%" -29630,"On December 31, 2024, will exactly 4 US states have Waymo's rider-only ride-hailing service available to the general public? -","To determine the probability of exactly 4 US states having Waymo's rider-only ride-hailing service available to the general public by December 31, 2024, let's work through the available information and considerations step-by-step. - -### Current Status: -- Waymo operates in California and Arizona, making 2 states as of early 2024. -- Expansion plans to Texas (Austin) suggest they will add a third state. - -### Expansion Plans and Announcements: -- There are no publicly announced plans for a fourth state by 2024. The major public announcements have focused on technological advancements and scaling operations within the known markets. -- Waymo's recent funding indicates a focus on deepening geographical coverage in existing states and cities, rather than adding new states. - -### Potential for Expansion: -- Given the timeline and Waymo's existing rate of scaling (based on reaching Texas by late 2024), it's highly unlikely they could announce and accomplish a fourth state rollout by the end of the year. Deployments typically involve significant regulatory approvals and infrastructure setup. -- Historically, such undertakings do not happen within the span of under two months. - -### Timing and Remaining Period: -- Current date is November 6, 2024, with a resolution date of December 31, 2024. This leaves around 50 days until resolution. -- Given the timeline and logistical constraints as mentioned above, it's improbable for an additional state rollout beyond Texas within this period. - -### Base Rates and Historical Precedents: -- Consider similar rollouts by Waymo and other companies: achieving such expansions typically takes months to years of agreements, safety verification, and market deployments. -- No historical precedent exists for Waymo introducing services in new states unannounced in such short timeframes. - -### Default Resolution: -- The current status without any further expansion will result in 3 states. -- No evidence suggests a change—technically or logistically—can feasibly happen within this time period. - -### Uncertainty and Confidence: -- Considering my overconfidence bias, I'm cautious against giving overly confident predictions, especially with no supporting evidence for an expansion to a fourth state. - -### Conclusion: -Given the lack of announcements or indications of any fourth state, and understanding the practical and legal hurdles involved in such an expansion, the probability of precisely 4 states having Waymo's service by December 31, 2024, is extremely low. - -Final Answer: **Probability: 5%**" -29635,"Will Kalshi outrank Coinbase in the top free Finance apps on December 31, 2024?","Let me think through this step by step: - -1. Current Status: -- Kalshi is currently ranked #1 -- Coinbase is #35 -- This represents a significant gap - -2. Time Horizon: -- We have about 7 weeks until resolution -- This is a relatively short timeframe for major ranking changes -- However, app rankings can be quite volatile day-to-day - -3. Key Factors: -- US Election results will be known by resolution -- Post-election interest in political betting may decline -- Crypto market conditions affect Coinbase usage -- Holiday season trading patterns -- Historical precedent of app rankings - -4. Historical Analysis: -- Finance app rankings typically show high volatility -- Established apps like Coinbase tend to maintain relatively stable positions -- New feature launches (like election betting) often show initial spikes followed by decline -- Seasonal patterns show increased finance app usage during year-end - -5. Unique Circumstances: -- Kalshi's election betting feature is novel -- Post-election engagement likely to decrease -- Coinbase has stronger brand recognition and established user base -- Year-end financial planning could boost both apps - -6. Timeline Analysis: -- If looking at 2 weeks: 80% chance Kalshi maintains lead -- If looking at 3 months: 40% chance -- If looking at 6 months: 20% chance -This suggests declining probability over time - -7. Risk Factors: -- Post-election engagement drop -- Crypto market volatility -- Competition from other finance apps -- Marketing spend changes -- App store algorithm changes - -8. Confidence Adjustment: -- Need to account for historical overconfidence -- Short timeframe increases confidence in current status -- But post-election period adds uncertainty - -Given: -- The short timeframe (7 weeks) -- Current large ranking gap -- Post-election engagement likely to decrease -- Holiday season financial activity -- Historical tendency for regression to mean -- Need to avoid overconfidence - -I estimate that while Kalshi currently leads strongly, the post-election period will likely see decreased engagement, but the timeframe is short enough that some momentum should remain. The holiday season financial activity could help maintain some engagement. - -Probability: 35% - -I've deliberately adjusted down from what might seem intuitive given current rankings to account for historical overconfidence and the likely post-election engagement drop. The short timeframe helps Kalshi's chances, but the post-election period and natural regression to mean are significant factors working against maintaining the lead." -29596,Will the number of FBI firearms background checks initiated through the NICS exceed 2.2M for October 2024?,"@johnnycaffeine these questions should be annulled, the Oct 2024 value was already published at question time" -29707,Will China's youth unemployment rate be greater than 19.0 for October 2024?,"Predictions: [20, 20, 15, 20, 10] -Median reasoning: To forecast China's youth unemployment rate for October 2024, we need to consider several factors and recent data trends. The unemployment rate reached 17.1% in July 2024 and, under the new calculation method of excluding students, 18.8% in August 2024, then decreased to 17.6% in September 2024. The new calculation method makes current rates more reflective of employment challenges and changes seen in recent months. Factors such as the economic challenges posed by the slumping real estate market, high debt levels, and looming tariffs from the United States suggest potential upward pressure on unemployment. However, China's broader economic stabilization efforts, slight improvements in the overall employed rate from August to September 2024, and strong policy intents indicate potential downward pressure on further increases in youth unemployment. Given these conflicting pressures, the chance of a return to above 19.0% seems less likely in the immediate term given the September rate decrease. We must also consider the high baseline from past months, but this has already shown signs of reversing. Thus, we predict the highest likelihood for a youth unemployment rate range that includes the lowest reported recent values (less than or equal to 17.0%) and the least likely chance of surpassing 19.0%. Given potential fluctuations around this trend, we also recognize some possibility for slight increases or decreases around the more central rates." -29706,Will China's youth unemployment rate be greater than 18.0 and less than or equal to 19.0 for October 2024?,"Predictions: [20, 20, 15, 20, 10] -Median reasoning: To forecast China's youth unemployment rate for October 2024, we need to consider several factors and recent data trends. The unemployment rate reached 17.1% in July 2024 and, under the new calculation method of excluding students, 18.8% in August 2024, then decreased to 17.6% in September 2024. The new calculation method makes current rates more reflective of employment challenges and changes seen in recent months. Factors such as the economic challenges posed by the slumping real estate market, high debt levels, and looming tariffs from the United States suggest potential upward pressure on unemployment. However, China's broader economic stabilization efforts, slight improvements in the overall employed rate from August to September 2024, and strong policy intents indicate potential downward pressure on further increases in youth unemployment. Given these conflicting pressures, the chance of a return to above 19.0% seems less likely in the immediate term given the September rate decrease. We must also consider the high baseline from past months, but this has already shown signs of reversing. Thus, we predict the highest likelihood for a youth unemployment rate range that includes the lowest reported recent values (less than or equal to 17.0%) and the least likely chance of surpassing 19.0%. Given potential fluctuations around this trend, we also recognize some possibility for slight increases or decreases around the more central rates." -29705,Will China's youth unemployment rate be greater than 17.0 and less than or equal to 18.0 for October 2024?,"Predictions: [20, 20, 15, 20, 10] -Median reasoning: To forecast China's youth unemployment rate for October 2024, we need to consider several factors and recent data trends. The unemployment rate reached 17.1% in July 2024 and, under the new calculation method of excluding students, 18.8% in August 2024, then decreased to 17.6% in September 2024. The new calculation method makes current rates more reflective of employment challenges and changes seen in recent months. Factors such as the economic challenges posed by the slumping real estate market, high debt levels, and looming tariffs from the United States suggest potential upward pressure on unemployment. However, China's broader economic stabilization efforts, slight improvements in the overall employed rate from August to September 2024, and strong policy intents indicate potential downward pressure on further increases in youth unemployment. Given these conflicting pressures, the chance of a return to above 19.0% seems less likely in the immediate term given the September rate decrease. We must also consider the high baseline from past months, but this has already shown signs of reversing. Thus, we predict the highest likelihood for a youth unemployment rate range that includes the lowest reported recent values (less than or equal to 17.0%) and the least likely chance of surpassing 19.0%. Given potential fluctuations around this trend, we also recognize some possibility for slight increases or decreases around the more central rates." -29704,Will China's youth unemployment rate be less than or equal to 17.0 for October 2024?,"Predictions: [20, 20, 15, 20, 10] -Median reasoning: To forecast China's youth unemployment rate for October 2024, we need to consider several factors and recent data trends. The unemployment rate reached 17.1% in July 2024 and, under the new calculation method of excluding students, 18.8% in August 2024, then decreased to 17.6% in September 2024. The new calculation method makes current rates more reflective of employment challenges and changes seen in recent months. Factors such as the economic challenges posed by the slumping real estate market, high debt levels, and looming tariffs from the United States suggest potential upward pressure on unemployment. However, China's broader economic stabilization efforts, slight improvements in the overall employed rate from August to September 2024, and strong policy intents indicate potential downward pressure on further increases in youth unemployment. Given these conflicting pressures, the chance of a return to above 19.0% seems less likely in the immediate term given the September rate decrease. We must also consider the high baseline from past months, but this has already shown signs of reversing. Thus, we predict the highest likelihood for a youth unemployment rate range that includes the lowest reported recent values (less than or equal to 17.0%) and the least likely chance of surpassing 19.0%. Given potential fluctuations around this trend, we also recognize some possibility for slight increases or decreases around the more central rates." -29703,"Will West Virginia have 1.4 million or more residents living in drought on December 31, 2024?","Let me think through this step by step: - -1. Type of Question: This is a Non-Market Threshold Value Event, specifically about population in drought conditions exceeding 1.4M by a specific date. - -2. Current Situation: -- West Virginia is experiencing its worst drought in 20+ years -- Recent data indicates severe conditions -- Burning bans and water shortages are in effect -- Total WV population is approximately 1.8M - -3. Timeline Analysis: -- We're looking at Dec 31, 2024 (about 7 weeks away) -- Winter months typically see reduced drought conditions -- However, drought recovery can be slow even with precipitation - -4. Historical Context: -- Major droughts in WV are relatively rare -- Current drought is historically significant -- Winter typically brings more precipitation in this region - -5. Population Analysis: -- Need 1.4M+ residents in drought areas -- This represents about 78% of WV's population -- High threshold requiring widespread drought conditions - -6. Key Factors: -- Short timeframe (7 weeks) means current conditions weighted heavily -- Winter season typically brings more precipitation -- El Niño conditions could affect precipitation patterns -- Drought recovery takes time even with precipitation - -7. Probability Distribution: -- Next 2 weeks: 65% chance of maintaining current conditions -- 1 month: 55% chance -- By Dec 31: 45% chance -- 3 months: 30% chance - -8. Uncertainty Factors: -- Exact population counts in drought areas not provided -- Winter precipitation patterns can be variable -- Resolution source accessibility - -Given: -- The high threshold (1.4M represents a large portion of WV's population) -- The short timeframe -- Winter typically bringing more precipitation -- But drought recovery taking time -- Historical rarity of such widespread drought conditions -- Current severe conditions -- Need for sustained drought affecting most of the state - -I need to adjust for historical overconfidence and account for the fact that this is an unusually high population threshold that requires sustained widespread drought conditions. - -The 1.4M threshold is particularly important - it requires maintaining drought conditions across a very large portion of the state's population centers. While conditions are severe now, winter precipitation could reduce affected areas enough to drop below this threshold. - -Accounting for all factors and adjusting for historical overconfidence, I assess: - -Probability: 42% - -This reflects: -- High current drought severity -- But likelihood of some winter precipitation -- High population threshold requirement -- Historical rarity of such widespread conditions -- Short timeframe limiting major changes -- But also allowing for some recovery" -29702,Will Luke Combs win an award in any of these 2024 CMA Awards categories?,"Let me think through this step by step: - -1. This is a Competition Event type question, specifically about entertainment awards. - -2. Key factors to consider: -- Luke Combs is nominated in multiple categories -- He's a major star in country music -- Historical performance at CMA Awards -- Current industry momentum -- Competition in each category - -3. Recent relevant context: -- Combs has been very successful commercially -- He won Entertainer of the Year in 2021 and 2022 -- Strong competition this year from Morgan Wallen and Lainey Wilson -- The categories mentioned are the most prestigious ones - -4. Historical analysis: -- Repeat wins are common but three consecutive Entertainer of the Year wins are rare -- Being nominated in multiple categories increases chances -- Past winners often continue to win in different categories - -5. Competition assessment: -- Morgan Wallen has had a massive year commercially -- Lainey Wilson has significant momentum -- Chris Stapleton remains a perennial favorite - -6. Time factors: -- This is a scheduled event in less than 2 weeks -- Voting is likely already complete -- No external factors likely to influence outcome - -7. Uncertainty considerations: -- Multiple categories increase chances -- Strong competition in each category -- Industry politics and voting patterns - -8. Base rate adjustment: -- Multiple nominations historically translate to roughly 40-50% win rate -- Need to adjust down due to extremely strong competition this year -- Recent consecutive wins might work against him - -Given all factors, I believe Combs has a decent but not overwhelming chance. The multiple categories help, but strong competition and potential voter fatigue after recent wins work against him. I'll adjust slightly down from base rate due to these factors. - -Probability: 35%" -29701,"Will Astro Bot win in any of these categories at the 2024 Golden Joystick Awards? -","Let me think through this step by step: - -1. First, let's consider what we're evaluating - Astro Bot needs to win in at least one of three specific categories: -- Best Audio Design -- Best Soundtrack -- Console Game of the Year - -2. Key factors to consider: -- Voting has already closed for most categories (Nov 1) -- Only about 2 weeks until results -- Astro Bot received very positive reviews and acclaim -- Competition is strong, especially from Final Fantasy VII Rebirth -- The audio categories are new splits from previous years - -3. Analyzing advantages: -+ Astro Bot is known for excellent audio design -+ Strong critical reception -+ Multiple nominations increase chances -+ PlayStation exclusive games often do well at GJ Awards - -4. Disadvantages: -- Split audio categories may dilute votes -- Strong competition in all categories -- Historical tendency for major franchises (like FF7) to win -- Being nominated in multiple categories doesn't guarantee wins - -5. Historical context: -- Previous PlayStation exclusives have mixed success -- New category splits make historical comparisons difficult -- Golden Joystick tends to favor established franchises - -6. Time consideration: -- With voting closed or closing very soon, and resolution in ~2 weeks, there's very little uncertainty left in the process -- Results are essentially ""locked in"" at this point - -7. Category-specific analysis: -- Best Audio Design: ~25% chance (strong but competitive) -- Best Soundtrack: ~20% chance (tough against FF7) -- Console Game: ~15% chance (very competitive category) - -8. Mathematical combination: -- Need at least one win -- Categories are not entirely independent (correlation in voting patterns) -- Combined probability needs to account for overlap - -Given the multiple nomination advantage but strong competition, closed voting, and historical patterns, I estimate: - -Probability: 42% - -This accounts for: -- Multiple chances to win -- Strong reception -- Already completed voting -- Tough competition -- Historical patterns -- Some correlation between categories -- Slight adjustment downward for typical overconfidence - -The probability is meaningful but not dominant because while Astro Bot is well-positioned, it faces serious competition in each category and needs to win at least one of three specific categories, not just any category it's nominated in." -29700,Will John Thune succeed Mitch McConnell as Republican Leader of the US Senate?,"Let me think through this step by step: - -1. Type of Question: This is a Competition Event, specifically a political leadership contest. - -2. Timeline Analysis: -- Resolution date is December 1, 2024 -- We're at November 7, 2024 -- The vote is scheduled for November 13, 2024 -- This is a very short timeframe with a specific scheduled event - -3. Key Factors: -- Thune is currently Minority Whip, the #2 position -- Historical precedent favors succession from Whip to Leader -- Main competition is from John Cornyn and potentially Rick Scott -- Trump's influence could be significant but may be limited in a secret ballot -- The presidential election results could affect dynamics - -4. Historical Base Rates: -- Senate party leadership transitions typically favor the deputy leader -- McConnell himself was Whip before becoming leader -- However, leadership contests can be unpredictable due to secret balloting - -5. Specific Considerations: -- Very short timeline (less than a week to vote) -- Secret ballot means public statements may not match private votes -- Thune has been actively campaigning and building support -- As Whip, he has existing relationships with caucus members -- Multiple sources identify him as frontrunner - -6. Risk Factors: -- Secret ballot could produce surprise results -- Trump intervention could sway some votes -- Three-way race could split traditional voting blocs - -7. Probability Distribution Analysis: -- Base case strongly favors Thune given position and timing -- Limited time for major shifts in support -- Secret ballot adds uncertainty -- Multiple candidates reduce likelihood of any single candidate - -Given these factors, I believe Thune has a strong advantage but not overwhelmingly so. The secret ballot and multiple candidates create significant uncertainty. Historical precedent and current position favor him, but not decisively. - -I need to adjust for my tendency toward overconfidence and account for the general tendency of questions to resolve negatively. - -Considering the very short timeline and Thune's structural advantages, balanced against the uncertainty factors, I would rate his chances as moderately favorable but not overwhelming. - -Probability: 65%" -29699,"Will either major party candidate in California’s 13th Congressional District concede the election before December 1, 2024?","To forecast whether either major party candidate in California’s 13th Congressional District will concede before December 1, 2024, let's consider the key elements of the situation: - -1. **Historical Context and Base Rates**: Historically, California, especially in districts like CA-13, has had prolonged vote counts due to the state's extensive use of mail-in ballots and verification processes. This context resulted in a delayed concession in 2022. Given the parallels between 2022 and 2024 (including the same candidates and conditions for a tight race), it is reasonable to expect a similar timeline. - -2. **Current Status of the Race**: The race remains very close as of November 7, 2024. Adam Gray currently holds a slight lead, which could influence whether a concession happens. However, the small margin suggests that neither candidate is likely to make an early concession before more votes are counted. - -3. **Potential for Outcome Change**: Given the current voter lead and historical tendency for slow vote counts, it is plausible that results won't be fully clear by December 1, potentially delaying any concession past this date. Observing the election dynamics, as we approach December 1, there is a window of approximately three weeks. In past elections with similar circumstances, results took several weeks to finalize. - -4. **Political Incentives and Candidate Behavior**: Both candidates are seen as moderates, typically signaling pragmatic political behavior. If the vote count begins to show a clear trend before December 1, a concession could occur, but given the district's history, the likelihood of clarity before this date seems lower. - -5. **Expert Analysis and Trends**: Expert expectations and historical data suggest a high likelihood that the race will remain unresolved by early December, continuing past behavior of slow result declaration. - -6. **Base Rates for Similar Events**: Taking historical precedence and noting that roughly 35% of such questions resolve positively, and in this instance, the factors lean toward a prolonged process aligning with historical trends and the November election nature in CA. - -Considering these factors, we should assume a base likelihood that nothing will substantially change from previous similar elections. The history of district electoral results and expectations for this election cycle weigh heavily here, particularly recalling the prolonged result declaration in 2022. - -Ultimately, acknowledging my tendency for overconfidence in past forecasts, applying a conservative adjustment, given the recency of the relevant evidence: - -Overall, the probability that either candidate will concede by the resolution date is modest, but definitely not negligible, especially if a trend emerges in the count sooner than expected. - -**Probability: 35%**" -29698,"Will Merck stock continuously remain below its all-time high before January 1, 2025?","To assess the likelihood that Merck stock remains below its all-time high continuously before January 1, 2025, let's break down the relevant factors in detail: - -1. **Current Stock Price**: Merck's stock is trading at $101.58, significantly below the all-time high of $134.63. The stock would need to rise over 30% in a short period to reach or exceed this high. - -2. **Market Trends and Forecasts**: - - Analysts have an average price target of $133.56 for Merck, close to the all-time high but not surpassing it. This indicates potential growth but not definitively reaching the record high within the given timeframe. - - The current consensus is ""Strong Buy,"" suggesting the stock is undervalued, but the potential upside might be limited in the short-term due to time constraints. - -3. **Political and Economic Context**: - - The presidential race between Harris and Trump adds political uncertainty which could inject volatility into the markets. - - Economic factors such as recent interest rate cuts may boost stock performance, but the timeframe is short for a significant rally. - -4. **Recent Financial Performance**: Merck has shown solid financial performance with growing sales, particularly for KEYTRUDA. However, this is already partially reflected in the stock's current valuation. - -5. **Historical Context**: Historically, drastic stock movements over a short period are relatively rare without extraordinary catalysts such as mergers, breakthroughs, or crises. - -6. **Resolution Date**: The timeframe is limited (less than two months), decreasing the probability of reaching a new high given current trading levels and typical price volatility. - -Considering the above factors: -- **Base Rate Analysis**: The historical base rate of stocks making substantial gains within a few months without a major catalyst is relatively low. -- **Variance and Expectations**: Given the current level of the stock and analyst expectations, achieving over 30% growth in less than two months is unlikely. -- **Time Constraints**: The short period remaining before the resolution date significantly dampens the probability of a substantial stock movement. - -Given my tendency towards past overconfidence, I will moderate my estimate to account for potential unforeseen market movements but remain cautious given the current stock level and timeframe. - -Therefore, based on the evaluation of all factors, I believe the probability that Merck will remain below its all-time high before January 1, 2025, is relatively high: - -""Probability: 85%""" -29697,"Will Thomas Massie be announced as the nominee for Secretary of Agriculture before January 1, 2025?","Let me think through this step by step: - -1. First, let's consider the type of question - this is a Binary Outcome Event about a political appointment. - -2. Timeline analysis: -- Current date: Nov 7, 2024 -- Resolution date: Jan 1, 2025 -- This is less than 2 months (~55 days) -- This would be during a presidential transition period, assuming Trump wins - -3. Key factors: -- Massie has explicitly expressed interest in the role -- Trump team would likely be making cabinet decisions during this period -- Historical precedent shows most cabinet picks are announced in December/January of transition -- Massie is a sitting congressman with relevant experience -- Trump values loyalty, and Massie has been supportive - -4. Base rate considerations: -- How often do early rumored cabinet picks become the actual nominee? Historically quite low -- Many names are typically floated for each position -- Trump's first term showed he often went with unexpected picks -- Being ""open to"" a role is very different from being selected - -5. Time distribution: -- If this were to happen, most likely timeframe would be Dec 2024 -- Very unlikely before December (transition team typically not fully formed) -- Some chance in early January, but question cuts off before inauguration - -6. Limiting factors: -- Trump needs to win the election first -- Other candidates may be under consideration -- Massie might be more valuable to Trump in Congress -- No strong indication Trump is seriously considering him yet - -7. Probability distribution across time: -- Next 2 weeks: 1% -- By Dec 15: 10% -- By Jan 1: 15% -- By Inauguration: 25% - -8. Key uncertainties: -- Election outcome -- Other potential candidates -- Trump's prioritization of this position -- Transition team preferences - -Given the short timeframe, requirement for multiple contingent events (Trump winning, choosing Massie over others, announcing before Jan 1), and historical tendency for early cabinet speculation to be wrong, I need to assign a relatively low probability. - -The explicit interest from Massie and the typical timing of cabinet announcements during transition provide some positive signal, but not enough to overcome the base rate skepticism. - -Adjusting for my historical overconfidence and the general principle that specific predicted outcomes are less likely than they initially appear: - -Probability: 12%" -29696,"Will the Jackson Hole ski resort be listed as Open by On the Snow's Ski Report on November 29, 2024?","To forecast whether the Jackson Hole ski resort will be open on November 29, 2024, I need to consider several key factors using the information provided and my general knowledge: - -### Analysis - -1. **Projected Opening Date**: - - Jackson Hole is planning to open on November 29, 2024. This indicates a company expectation of being open and preparing accordingly. Historically, most ski resorts strive to open close to their planned dates due to the economic importance of the early season. - -2. **Snow Conditions**: - - Currently, there is no significant snowfall reported that could suggest opening earlier than planned. Lack of substantial early snowfall might mean relying on future snow or artificial snowmaking, which is common in the ski industry. - -3. **Weather Patterns**: - - La Niña has been mentioned with a 79% likelihood, which typically suggests drier conditions for parts of the Rockies. This could potentially hinder early season snow accumulation, affecting the resort’s ability to open if insufficient snowmaking capabilities are present. - -4. **Current Timeframe**: - - With 22 days until the resolution date, there is still enough time for snow to fall, particularly as late November tends to see increasing snowfall volumes in the region as winter approaches. - -5. **General Ski Season Context**: - - Ski areas in regions like Colorado have already opened, which suggests that winter conditions are starting to stabilize. Although these are not direct predictors of Wyoming conditions, broader patterns across Western US ski resorts signal movement into the ski season. - -### Past Data and Base Rates - -- Historically, many ski resorts tend to meet their projected opening dates barring extreme weather conditions or unforeseen mechanical issues with snowmaking equipment. -- The opening of ski resorts tends to fluctuate with a probability leaning towards opening on time, considering the industry's business model heavily relies on favorable early openings. - -### Considerations of Uncertainty - -- While La Niña could play a role in limiting natural snowfall, snowmaking advancements and the commitment of resorts to stick to planned openings allow for some mitigation of this risk. -- Unknown factors like equipment failures, unexpected warm weather events, or significant snowfall shortages should be considered, but generally pose a smaller risk relative to the norm. - -### Adjusting for Bias and Confidence - -- I have historically been overconfident, so I will aim to moderate my certainty. Given that approximately 35% of such questions resolve positively according to historical data, I must ensure sufficient caution. - -Considering all these factors, my final probability accounts for moderate uncertainty due to weather unpredictability and slight risk from La Niña, balanced with the planned opening and the resort's ability to compensate with snowmaking. - -### Final Probability Estimate - -Taking into account all considerations, the probability that Jackson Hole ski resort will be listed as open on November 29, 2024, is likely above the historical 35% positive resolution for such events due to proactive ski resort management and weather conditions typical of the season. - -**Probability: 65%**" -29695,Will there be major civil unrest or rioting in the US before 1 January 2025?,"To predict the likelihood of major civil unrest or rioting in the US before January 1, 2025, we need to consider various factors, the historical context, current events, and socio-political dynamics. - -### Historical Context and Base Rate: -Past large-scale civil unrest and rioting in the US, such as the 1992 Los Angeles riots and the 2020 George Floyd protests, occurred under specific social tensions and trigger events. Based on the resolution criteria defined (at least 10 deaths or 1,000 arrests in a given period related to unrest), such events are not common. Historically, years without such incidents are more frequent than years with them. The base rate for events meeting these criteria is likely low – potentially in the single-digit percentages annually. - -### Current Context: -1. **Election-Related Tensions**: - - The 2024 US Presidential election could serve as a catalyst for unrest, given the high-profile and contentious nature of the election. - - The presence of polarizing candidates and rhetoric (as detailed concerning Trump's campaign) adds to the potential for unrest. - - However, the election has already passed, lowering the immediate trigger mechanism. - -2. **Security Preparedness**: - - The activation of the National Guard and heightened law enforcement alertness may act as a deterrent or a mitigation factor against unrest escalating to the levels specified. - - Historical examples show that heightened security often defuses potential situations before they escalate to the required severity. - -3. **Public Sentiment and Expert Opinions**: - - Key sources and experts suggest a heightened possibility of unrest or violence near political events (e.g., Electoral Vote Certification), but this is conjectural. - -### Temporal Consideration and Timeframe: -- With under two months left before the resolution date, the time frame for an incident of the required magnitude to develop and resolve is limited. -- Events of this kind often have a fair amount of buildup or warning signals, neither of which have substantially manifested post-election. - -### Probabilistic Assessment: -Given the context, it seems prudent to hedge against overconfidence. The specific resolution criteria make reaching the required thresholds somewhat challenging under current conditions. However, we should not entirely dismiss the possibility given the socio-political climate. - -1. **Base Probability**: The low historical frequency of such events gives a low base rate – potentially in the low single-digits annually. -2. **Adjusted for Current Context**: Adjust upwards slightly to account for heightened tensions and current warnings without overrating them – perhaps adding a few percentage points above the base rate. -3. **Temporal Limitation**: The under-two-month window caps the likelihood, reinforcing the prediction remains on the lower end. - -While there is heightened tension, the constraints of time and preventative measures balance against a significant increase in probability. Balancing all components, a final probability of major civil unrest occurring before January 1, 2025, and meeting the specified criteria is: - -**Probability: 12%**" -29694,"On December 31, 2024, will exactly 4 US states have Waymo's rider-only ride-hailing service available to the general public?","Let me think through this step by step: - -1) Current State (Nov 2024): -- Confirmed operating in 2 states: California and Arizona -- Announced/planned expansion to Texas (Austin) and Georgia (Atlanta) - -2) Timeline Analysis: -- Only ~7 weeks until resolution date -- Very short timeframe for major operational changes -- Launching in a new state requires significant regulatory work and infrastructure - -3) Progress Assessment: -- Texas (Austin) expansion seems well-advanced in planning -- Georgia (Atlanta) less certain but actively in progress -- No other states with concrete near-term plans announced - -4) Historical Expansion Rate: -- Waymo has been deliberate and cautious in expansion -- Typically takes 6-12 months from announcement to public launch -- Track record shows they usually meet announced expansion targets - -5) Risk Factors: -- Regulatory delays could prevent planned launches -- Technical challenges in new environments -- Winter weather conditions might impact launch timing -- Public or political pushback could delay expansion - -6) Scenario Analysis: -A) Most likely (60%): Exactly 4 states by year-end -- Current 2 plus successful launch in both planned expansions - -B) Under 4 states (30%): -- Delays in one or both planned expansions -- Regulatory or technical setbacks - -C) Over 4 states (10%): -- Extremely unlikely given timeframe -- Would require surprise announcement and extremely rapid deployment - -7) Confidence Adjustments: -- Short timeline increases confidence in current trajectory -- Limited time for unexpected changes -- Clear resolution criteria -- Strong historical data on expansion pace - -8) Additional Considerations: -- Recent funding suggests resources available -- Company has demonstrated ability to execute -- Regulatory environment generally stable -- No major negative incidents recently - -The question asks specifically about EXACTLY 4 states, which is actually the most likely outcome given: -- Current operations in 2 states -- 2 announced expansions -- Very short timeline for additional surprises -- Track record of meeting announced targets -- Limited time for regulatory surprises in either direction - -Probability: 60%" -29698,"Will Merck stock continuously remain below its all-time high before January 1, 2025?","Let me think through this step by step: - -1. **Current Situation** -- Current price: $101.58 -- All-time high: $134.63 -- Gap to reach ATH: 32.5% increase needed -- Time remaining: ~54 days - -2. **Historical Context** -- Stock has been showing bearish sentiment -- 3.64% price volatility over 30 days -- Stocks rarely make new ATHs in short timeframes unless there's major news - -3. **Timeline Analysis** -- Very short time window (less than 2 months) -- December typically has lower volatility due to holiday season -- Year-end tax-loss harvesting could create additional selling pressure - -4. **Market Forecasts** -- Short-term forecast only shows minimal gains -- Long-term forecast for 2025 ($90.19-$119.20) is well below ATH -- Even analyst consensus target ($133.56) is below ATH - -5. **Risk Factors** -- Only needs to touch ATH once to resolve NO -- Market could react strongly to unexpected positive news -- Pharma stocks can be volatile on drug trial results -- General market could have end-of-year rally - -6. **Mathematical Analysis** -- 32.5% gain needed in 54 days -- Daily volatility suggests <1% standard deviation moves -- Would need sustained upward momentum or major catalyst - -7. **Probability Distribution** -For stock to hit ATH: -- Next 2 weeks: ~2% chance -- Next month: ~5% chance -- By resolution date: ~8% chance -- By mid-2025: ~20% chance - -8. **Confidence Adjustment** -- Historical tendency for overconfidence suggests tempering the YES probability -- Market questions often have fat-tail distributions -- Need to account for unknown unknowns - -Given the short timeframe, significant price gap to ATH, and current market conditions, this seems likely to resolve YES (stock stays below ATH). However, we must account for the possibility of unexpected events and the fact that we only need one touch of the ATH for a NO resolution. - -Starting with a base 95% probability for YES (stock stays below ATH), I'll adjust down to account for: -- Market volatility (-3%) -- Potential positive catalysts (-2%) -- Historical overconfidence tendency (-2%) -- Fat-tail distribution possibility (-1%) - -Probability: 87%" -29695,Will there be major civil unrest or rioting in the US before 1 January 2025?,"To evaluate the probability of major civil unrest or rioting occurring in the US before January 1, 2025, we can break down the analysis into a few key components: - -1. **Historical Base Rates**: Examining recent history, major civil unrest events in the US, like the George Floyd protests in 2020 and the Capitol riots in January 2021, occur under high-tension conditions. These events were relatively rare but not unprecedented, highlighting a non-zero base rate for such occurrences. Notably, these events were driven by significant catalysts, such as high-profile incidents or disputes over election outcomes. - -2. **Current Context**: The upcoming resolution date follows closely after a US presidential election, which can serve as a major catalyst for unrest, especially if the results are contested or controversial. The period after elections, particularly when results are disputed, typically sees heightened tensions. - -3. **Current Tensions and Indicators**: The ongoing intense political rhetoric, warnings from federal law enforcement, and preparations by the National Guard and other agencies indicate a real potential for unrest. Trump's rhetoric and the already charged atmosphere around the election increase the risk. Historical precedents show that contested elections can lead to notable unrest. - -4. **Timeframe Consideration**: The timeline from now until January 1, 2025, is approximately eight weeks, a period that coincides with significant political events (e.g., certification of electoral votes). Historically, this timeframe aligns with when tensions from electoral disputes can peak, increasing the probability of unrest during this period. - -5. **Statistical Modeling**: While we don't have precise statistical models for forecasting this exact event due to its complexity and rare occurrence, we can combine historical base rates with current indicators. The context of the election and the current political climate suggest a higher-than-average risk of civil unrest. - -Given the election context, the charged political atmosphere, and the potential for an electoral dispute, I estimate a higher probability than under typical conditions. However, acknowledging my tendency for overconfidence and the need to be cautious given the ambiguous nature of predicting such events, I'll moderate the final probability to account for uncertainty. - -Final Consideration: Given the research, historical precedent, and current indicators, there seems to be a substantial chance of civil unrest reaching the criteria for resolution within the timeframe. Balancing this with a necessary caution against overconfidence: - -**Probability: 25%**" -29801,"Will the number of active US oil drilling rigs be less than 585 on November 29, 2024?","Predictions: [20, 5, 45, 5, 5] -Median reasoning: To assess the probability of the different outcomes related to the U.S. oil drilling rig counts, I considered the following: - -1. **Current Data and Recent Trends:** - - The U.S. oil rig count, as reported on November 1, 2024, is 479, with a total rig count (including gas rigs) at 585. This suggests stability in the rig count over the previous weeks. Given the Brier scoring, the initial focus is on making precise predictions on whether the count will fall below, within, or above the stated thresholds. - -2. **Historical Context and Patterns:** - - The historical data shows the total U.S. rig count has decreased by 6.40% over the past year, with specific oil rigs decreasing by roughly 4.96%. These data points are important in understanding the trend direction but emphasize stability more recently. - -3. **Recent Stability and Proximity to Resolution Date:** - - Given the stability of the rig count over the past weeks and the current level near the threshold compared to the time gap to resolution (~3 weeks), significant fluctuations are unlikely barring sudden market or policy developments. This temporal proximity means that the base case—stability—is weighted more heavily. - -4. **Market and Political Influences:** - - Market updates suggest attempts to manage production, with OPEC+ maintaining production cuts which might influence oil prices but not immediately impact the U.S. rig count in this short timeframe. Political conditions, like the upcoming potential shift in U.S. policy, may not have concrete impacts until after the inauguration. - -5. **The Rig Count Ranges:** - - Focused primarily on three likely outcomes: below 585, between 585 and 590, and above 590. Historical and recent data prioritize the likelihood of the count being near its current position. Improbable that it would rise significantly above 590 given current trends due to a lack of immediate policy implementation and stable oil production outlook. - -6. **Likelihood Estimation:** - - Assessing probabilities across outcomes, I anticipate lower probability for significant jumps given economic and political constraints, with more weight on stable or slightly fluctuating counts around the current figures. - -Overall, predictions reflect both base rates, market/policy context, and upcoming changes—tempered by the understanding that sudden major changes in rig activity are less feasible in just a few weeks without a surfacing direct trigger." -29800,"Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on November 29, 2024?","Predictions: [20, 5, 45, 5, 5] -Median reasoning: To assess the probability of the different outcomes related to the U.S. oil drilling rig counts, I considered the following: - -1. **Current Data and Recent Trends:** - - The U.S. oil rig count, as reported on November 1, 2024, is 479, with a total rig count (including gas rigs) at 585. This suggests stability in the rig count over the previous weeks. Given the Brier scoring, the initial focus is on making precise predictions on whether the count will fall below, within, or above the stated thresholds. - -2. **Historical Context and Patterns:** - - The historical data shows the total U.S. rig count has decreased by 6.40% over the past year, with specific oil rigs decreasing by roughly 4.96%. These data points are important in understanding the trend direction but emphasize stability more recently. - -3. **Recent Stability and Proximity to Resolution Date:** - - Given the stability of the rig count over the past weeks and the current level near the threshold compared to the time gap to resolution (~3 weeks), significant fluctuations are unlikely barring sudden market or policy developments. This temporal proximity means that the base case—stability—is weighted more heavily. - -4. **Market and Political Influences:** - - Market updates suggest attempts to manage production, with OPEC+ maintaining production cuts which might influence oil prices but not immediately impact the U.S. rig count in this short timeframe. Political conditions, like the upcoming potential shift in U.S. policy, may not have concrete impacts until after the inauguration. - -5. **The Rig Count Ranges:** - - Focused primarily on three likely outcomes: below 585, between 585 and 590, and above 590. Historical and recent data prioritize the likelihood of the count being near its current position. Improbable that it would rise significantly above 590 given current trends due to a lack of immediate policy implementation and stable oil production outlook. - -6. **Likelihood Estimation:** - - Assessing probabilities across outcomes, I anticipate lower probability for significant jumps given economic and political constraints, with more weight on stable or slightly fluctuating counts around the current figures. - -Overall, predictions reflect both base rates, market/policy context, and upcoming changes—tempered by the understanding that sudden major changes in rig activity are less feasible in just a few weeks without a surfacing direct trigger." -29799,"Will the number of active US oil drilling rigs exceed 590 on November 29, 2024?","Predictions: [20, 5, 45, 5, 5] -Median reasoning: To assess the probability of the different outcomes related to the U.S. oil drilling rig counts, I considered the following: - -1. **Current Data and Recent Trends:** - - The U.S. oil rig count, as reported on November 1, 2024, is 479, with a total rig count (including gas rigs) at 585. This suggests stability in the rig count over the previous weeks. Given the Brier scoring, the initial focus is on making precise predictions on whether the count will fall below, within, or above the stated thresholds. - -2. **Historical Context and Patterns:** - - The historical data shows the total U.S. rig count has decreased by 6.40% over the past year, with specific oil rigs decreasing by roughly 4.96%. These data points are important in understanding the trend direction but emphasize stability more recently. - -3. **Recent Stability and Proximity to Resolution Date:** - - Given the stability of the rig count over the past weeks and the current level near the threshold compared to the time gap to resolution (~3 weeks), significant fluctuations are unlikely barring sudden market or policy developments. This temporal proximity means that the base case—stability—is weighted more heavily. - -4. **Market and Political Influences:** - - Market updates suggest attempts to manage production, with OPEC+ maintaining production cuts which might influence oil prices but not immediately impact the U.S. rig count in this short timeframe. Political conditions, like the upcoming potential shift in U.S. policy, may not have concrete impacts until after the inauguration. - -5. **The Rig Count Ranges:** - - Focused primarily on three likely outcomes: below 585, between 585 and 590, and above 590. Historical and recent data prioritize the likelihood of the count being near its current position. Improbable that it would rise significantly above 590 given current trends due to a lack of immediate policy implementation and stable oil production outlook. - -6. **Likelihood Estimation:** - - Assessing probabilities across outcomes, I anticipate lower probability for significant jumps given economic and political constraints, with more weight on stable or slightly fluctuating counts around the current figures. - -Overall, predictions reflect both base rates, market/policy context, and upcoming changes—tempered by the understanding that sudden major changes in rig activity are less feasible in just a few weeks without a surfacing direct trigger." -29794,"Will Elon Musk’s net worth differ from the highest other net worth on the Forbes Real-Time Billionaires list by greater than or equal to $100 billion as of January 1, 2025?","Predictions: [15, 5, 10, 10, 20] -Median reasoning: To evaluate the likelihood of each outcome regarding Elon Musk's net worth difference with the highest other net worth, we start by considering the historical and recent data trends. As of November 7, 2024, Musk's net worth is $290 billion, leading Jeff Bezos' $228 billion by $62 billion. The current lead is significant, but $38 billion short of the $100 billion threshold for the first question. - -First, considering the volatility of billionaire net worth due to stock market fluctuations, significant changes are possible. However, with less than two months until January 1, 2025, a $38 billion increase in the gap is unlikely. Historically, such drastic fluctuations in net worth are uncommon over short periods, barring extreme market events. So the base case or default resolution if no significant changes occur favors a smaller gap. - -Current economic conditions and the market's positive response to Trump's re-election could further benefit Musk, but these effects might be more gradual than explosive in the time frame given. - -- For a difference greater than or equal to $100 billion: Historically rare changes in short periods make this unlikely, assigning a 15% probability. -- For between $70 billion and less than $100 billion: This scenario is more plausible if Tesla's stock continues performing well, assigning a 25% probability. -- For between $40 billion and less than $70 billion: Given the current $62 billion gap, this is the most stable range barring any significant market fluctuations, assigning a 40% probability. -- For greater than zero and less than $40 billion: With a significant current lead of $62 billion, this outcome would require substantial decreases in Tesla's valuation, assigned a 10% probability. -- For Musk's net worth less than or equal to the highest other net worth: Unlikely within two months without drastic market collapse or significant gains by competitors, assigning a 10% probability. - -To account for the uncertainty and avoid overconfidence, non-extreme predictions are provided across scenarios, capturing potential outcomes while reflecting the current situation." -29793,"Will Elon Musk’s net worth differ from the highest other net worth on the Forbes Real-Time Billionaires list by greater than $70 billion and less than $100 billion as of January 1, 2025?","Predictions: [15, 5, 10, 10, 20] -Median reasoning: To evaluate the likelihood of each outcome regarding Elon Musk's net worth difference with the highest other net worth, we start by considering the historical and recent data trends. As of November 7, 2024, Musk's net worth is $290 billion, leading Jeff Bezos' $228 billion by $62 billion. The current lead is significant, but $38 billion short of the $100 billion threshold for the first question. - -First, considering the volatility of billionaire net worth due to stock market fluctuations, significant changes are possible. However, with less than two months until January 1, 2025, a $38 billion increase in the gap is unlikely. Historically, such drastic fluctuations in net worth are uncommon over short periods, barring extreme market events. So the base case or default resolution if no significant changes occur favors a smaller gap. - -Current economic conditions and the market's positive response to Trump's re-election could further benefit Musk, but these effects might be more gradual than explosive in the time frame given. - -- For a difference greater than or equal to $100 billion: Historically rare changes in short periods make this unlikely, assigning a 15% probability. -- For between $70 billion and less than $100 billion: This scenario is more plausible if Tesla's stock continues performing well, assigning a 25% probability. -- For between $40 billion and less than $70 billion: Given the current $62 billion gap, this is the most stable range barring any significant market fluctuations, assigning a 40% probability. -- For greater than zero and less than $40 billion: With a significant current lead of $62 billion, this outcome would require substantial decreases in Tesla's valuation, assigned a 10% probability. -- For Musk's net worth less than or equal to the highest other net worth: Unlikely within two months without drastic market collapse or significant gains by competitors, assigning a 10% probability. - -To account for the uncertainty and avoid overconfidence, non-extreme predictions are provided across scenarios, capturing potential outcomes while reflecting the current situation." -29792,"Will Elon Musk’s net worth differ from the highest other net worth on the Forbes Real-Time Billionaires list by greater than or equal to $40 billion and less than or equal to $70 billion as of January 1, 2025?","Predictions: [15, 5, 10, 10, 20] -Median reasoning: To evaluate the likelihood of each outcome regarding Elon Musk's net worth difference with the highest other net worth, we start by considering the historical and recent data trends. As of November 7, 2024, Musk's net worth is $290 billion, leading Jeff Bezos' $228 billion by $62 billion. The current lead is significant, but $38 billion short of the $100 billion threshold for the first question. - -First, considering the volatility of billionaire net worth due to stock market fluctuations, significant changes are possible. However, with less than two months until January 1, 2025, a $38 billion increase in the gap is unlikely. Historically, such drastic fluctuations in net worth are uncommon over short periods, barring extreme market events. So the base case or default resolution if no significant changes occur favors a smaller gap. - -Current economic conditions and the market's positive response to Trump's re-election could further benefit Musk, but these effects might be more gradual than explosive in the time frame given. - -- For a difference greater than or equal to $100 billion: Historically rare changes in short periods make this unlikely, assigning a 15% probability. -- For between $70 billion and less than $100 billion: This scenario is more plausible if Tesla's stock continues performing well, assigning a 25% probability. -- For between $40 billion and less than $70 billion: Given the current $62 billion gap, this is the most stable range barring any significant market fluctuations, assigning a 40% probability. -- For greater than zero and less than $40 billion: With a significant current lead of $62 billion, this outcome would require substantial decreases in Tesla's valuation, assigned a 10% probability. -- For Musk's net worth less than or equal to the highest other net worth: Unlikely within two months without drastic market collapse or significant gains by competitors, assigning a 10% probability. - -To account for the uncertainty and avoid overconfidence, non-extreme predictions are provided across scenarios, capturing potential outcomes while reflecting the current situation." -29791,"Will Elon Musk’s net worth differ from the highest other net worth on the Forbes Real-Time Billionaires list by greater than zero and less than $40 billion as of January 1, 2025?","Predictions: [15, 5, 10, 10, 20] -Median reasoning: To evaluate the likelihood of each outcome regarding Elon Musk's net worth difference with the highest other net worth, we start by considering the historical and recent data trends. As of November 7, 2024, Musk's net worth is $290 billion, leading Jeff Bezos' $228 billion by $62 billion. The current lead is significant, but $38 billion short of the $100 billion threshold for the first question. - -First, considering the volatility of billionaire net worth due to stock market fluctuations, significant changes are possible. However, with less than two months until January 1, 2025, a $38 billion increase in the gap is unlikely. Historically, such drastic fluctuations in net worth are uncommon over short periods, barring extreme market events. So the base case or default resolution if no significant changes occur favors a smaller gap. - -Current economic conditions and the market's positive response to Trump's re-election could further benefit Musk, but these effects might be more gradual than explosive in the time frame given. - -- For a difference greater than or equal to $100 billion: Historically rare changes in short periods make this unlikely, assigning a 15% probability. -- For between $70 billion and less than $100 billion: This scenario is more plausible if Tesla's stock continues performing well, assigning a 25% probability. -- For between $40 billion and less than $70 billion: Given the current $62 billion gap, this is the most stable range barring any significant market fluctuations, assigning a 40% probability. -- For greater than zero and less than $40 billion: With a significant current lead of $62 billion, this outcome would require substantial decreases in Tesla's valuation, assigned a 10% probability. -- For Musk's net worth less than or equal to the highest other net worth: Unlikely within two months without drastic market collapse or significant gains by competitors, assigning a 10% probability. - -To account for the uncertainty and avoid overconfidence, non-extreme predictions are provided across scenarios, capturing potential outcomes while reflecting the current situation." -29790,"Will Elon Musk’s net worth be less than or equal to the highest other net worth on the Forbes Real-Time Billionaires list as of January 1, 2025?","Predictions: [15, 5, 10, 10, 20] -Median reasoning: To evaluate the likelihood of each outcome regarding Elon Musk's net worth difference with the highest other net worth, we start by considering the historical and recent data trends. As of November 7, 2024, Musk's net worth is $290 billion, leading Jeff Bezos' $228 billion by $62 billion. The current lead is significant, but $38 billion short of the $100 billion threshold for the first question. - -First, considering the volatility of billionaire net worth due to stock market fluctuations, significant changes are possible. However, with less than two months until January 1, 2025, a $38 billion increase in the gap is unlikely. Historically, such drastic fluctuations in net worth are uncommon over short periods, barring extreme market events. So the base case or default resolution if no significant changes occur favors a smaller gap. - -Current economic conditions and the market's positive response to Trump's re-election could further benefit Musk, but these effects might be more gradual than explosive in the time frame given. - -- For a difference greater than or equal to $100 billion: Historically rare changes in short periods make this unlikely, assigning a 15% probability. -- For between $70 billion and less than $100 billion: This scenario is more plausible if Tesla's stock continues performing well, assigning a 25% probability. -- For between $40 billion and less than $70 billion: Given the current $62 billion gap, this is the most stable range barring any significant market fluctuations, assigning a 40% probability. -- For greater than zero and less than $40 billion: With a significant current lead of $62 billion, this outcome would require substantial decreases in Tesla's valuation, assigned a 10% probability. -- For Musk's net worth less than or equal to the highest other net worth: Unlikely within two months without drastic market collapse or significant gains by competitors, assigning a 10% probability. - -To account for the uncertainty and avoid overconfidence, non-extreme predictions are provided across scenarios, capturing potential outcomes while reflecting the current situation." -29789,"Will 12-15 U.S. federal executive department heads be announced by the incoming administration between the election and December 16, 2024?","Predictions: [7, 1, 1, 0, 3] -Median reasoning: To predict the likelihood of different ranges of U.S. federal executive department heads being announced by the incoming administration (Donald Trump) before December 16, 2024, I consider several factors: - -1. **Base Rate and Historical Context**: Historically, new administrations announce many (if not all) cabinet members within weeks of winning the election. Given the 15 executive department heads, it is common for administrations to announce a significant portion during this period. Trump's previous transition in 2016 serves as a reference point, where major department head appointments occurred quickly. - -2. **Political Context and Current Transition Progress**: With Trump having secured both the presidency and a Republican majority in Congress, the transition and confirmation processes are expected to be relatively smooth. This facilitates the likelihood of more rapid announcements compared to a potential scenario of party division. - -3. **Timeframe and Default Scenario**: Today's date is November 8, 2024, which gives the Trump administration over a month to announce cabinet nominees before December 16, 2024. This ample time within the historical norm supports a base case involving many cabinet announcements. - -4. **Current Indicators Within the Administration**: Potential nominees are already being considered and discussed publicly, suggesting that Trump is actively working towards filling these key positions. - -5. **Uncertainty and Variance**: While historical trends and the existing political environment increase the probability of numerous announcements, uncertainties remain, such as unexpected delays or strategic changes by Trump. However, these uncertainties don't outweigh the overall likelihood of typical transition behavior. - -Considering all of these factors, I assign probabilities based on likelihoods deduced from historical trends, current context, and timeframe: - -- 'Will 12-15 department heads be announced?': High likelihood given historical trends and the specific factors mentioned. -- 'Will 10 or 11 department heads be announced?': Moderately likely if the administration prioritizes key roles but paces remaining appointments. -- 'Will 8 or 9 department heads be announced?': Lower probability, as this would be atypical given usual practices and the current situation. -- 'Will 6 or 7 department heads be announced?': Even lower likelihood, not reflective of standard process. -- 'Will 4 or 5 department heads be announced?': Very unlikely, as this would suggest significant deviation from norm or serious delays. -- 'Will 0-3 department heads be announced?': Highly improbable barring major unforeseen events." -29788,"Will 10 or 11 U.S. federal executive department heads be announced by the incoming administration between the election and December 16, 2024?","Predictions: [7, 1, 1, 0, 3] -Median reasoning: To predict the likelihood of different ranges of U.S. federal executive department heads being announced by the incoming administration (Donald Trump) before December 16, 2024, I consider several factors: - -1. **Base Rate and Historical Context**: Historically, new administrations announce many (if not all) cabinet members within weeks of winning the election. Given the 15 executive department heads, it is common for administrations to announce a significant portion during this period. Trump's previous transition in 2016 serves as a reference point, where major department head appointments occurred quickly. - -2. **Political Context and Current Transition Progress**: With Trump having secured both the presidency and a Republican majority in Congress, the transition and confirmation processes are expected to be relatively smooth. This facilitates the likelihood of more rapid announcements compared to a potential scenario of party division. - -3. **Timeframe and Default Scenario**: Today's date is November 8, 2024, which gives the Trump administration over a month to announce cabinet nominees before December 16, 2024. This ample time within the historical norm supports a base case involving many cabinet announcements. - -4. **Current Indicators Within the Administration**: Potential nominees are already being considered and discussed publicly, suggesting that Trump is actively working towards filling these key positions. - -5. **Uncertainty and Variance**: While historical trends and the existing political environment increase the probability of numerous announcements, uncertainties remain, such as unexpected delays or strategic changes by Trump. However, these uncertainties don't outweigh the overall likelihood of typical transition behavior. - -Considering all of these factors, I assign probabilities based on likelihoods deduced from historical trends, current context, and timeframe: - -- 'Will 12-15 department heads be announced?': High likelihood given historical trends and the specific factors mentioned. -- 'Will 10 or 11 department heads be announced?': Moderately likely if the administration prioritizes key roles but paces remaining appointments. -- 'Will 8 or 9 department heads be announced?': Lower probability, as this would be atypical given usual practices and the current situation. -- 'Will 6 or 7 department heads be announced?': Even lower likelihood, not reflective of standard process. -- 'Will 4 or 5 department heads be announced?': Very unlikely, as this would suggest significant deviation from norm or serious delays. -- 'Will 0-3 department heads be announced?': Highly improbable barring major unforeseen events." -29787,"Will 8 or 9 U.S. federal executive department heads be announced by the incoming administration between the election and December 16, 2024?","Predictions: [7, 1, 1, 0, 3] -Median reasoning: To predict the likelihood of different ranges of U.S. federal executive department heads being announced by the incoming administration (Donald Trump) before December 16, 2024, I consider several factors: - -1. **Base Rate and Historical Context**: Historically, new administrations announce many (if not all) cabinet members within weeks of winning the election. Given the 15 executive department heads, it is common for administrations to announce a significant portion during this period. Trump's previous transition in 2016 serves as a reference point, where major department head appointments occurred quickly. - -2. **Political Context and Current Transition Progress**: With Trump having secured both the presidency and a Republican majority in Congress, the transition and confirmation processes are expected to be relatively smooth. This facilitates the likelihood of more rapid announcements compared to a potential scenario of party division. - -3. **Timeframe and Default Scenario**: Today's date is November 8, 2024, which gives the Trump administration over a month to announce cabinet nominees before December 16, 2024. This ample time within the historical norm supports a base case involving many cabinet announcements. - -4. **Current Indicators Within the Administration**: Potential nominees are already being considered and discussed publicly, suggesting that Trump is actively working towards filling these key positions. - -5. **Uncertainty and Variance**: While historical trends and the existing political environment increase the probability of numerous announcements, uncertainties remain, such as unexpected delays or strategic changes by Trump. However, these uncertainties don't outweigh the overall likelihood of typical transition behavior. - -Considering all of these factors, I assign probabilities based on likelihoods deduced from historical trends, current context, and timeframe: - -- 'Will 12-15 department heads be announced?': High likelihood given historical trends and the specific factors mentioned. -- 'Will 10 or 11 department heads be announced?': Moderately likely if the administration prioritizes key roles but paces remaining appointments. -- 'Will 8 or 9 department heads be announced?': Lower probability, as this would be atypical given usual practices and the current situation. -- 'Will 6 or 7 department heads be announced?': Even lower likelihood, not reflective of standard process. -- 'Will 4 or 5 department heads be announced?': Very unlikely, as this would suggest significant deviation from norm or serious delays. -- 'Will 0-3 department heads be announced?': Highly improbable barring major unforeseen events." -29786,"Will 6 or 7 U.S. federal executive department heads be announced by the incoming administration between the election and December 16, 2024?","Predictions: [7, 1, 1, 0, 3] -Median reasoning: To predict the likelihood of different ranges of U.S. federal executive department heads being announced by the incoming administration (Donald Trump) before December 16, 2024, I consider several factors: - -1. **Base Rate and Historical Context**: Historically, new administrations announce many (if not all) cabinet members within weeks of winning the election. Given the 15 executive department heads, it is common for administrations to announce a significant portion during this period. Trump's previous transition in 2016 serves as a reference point, where major department head appointments occurred quickly. - -2. **Political Context and Current Transition Progress**: With Trump having secured both the presidency and a Republican majority in Congress, the transition and confirmation processes are expected to be relatively smooth. This facilitates the likelihood of more rapid announcements compared to a potential scenario of party division. - -3. **Timeframe and Default Scenario**: Today's date is November 8, 2024, which gives the Trump administration over a month to announce cabinet nominees before December 16, 2024. This ample time within the historical norm supports a base case involving many cabinet announcements. - -4. **Current Indicators Within the Administration**: Potential nominees are already being considered and discussed publicly, suggesting that Trump is actively working towards filling these key positions. - -5. **Uncertainty and Variance**: While historical trends and the existing political environment increase the probability of numerous announcements, uncertainties remain, such as unexpected delays or strategic changes by Trump. However, these uncertainties don't outweigh the overall likelihood of typical transition behavior. - -Considering all of these factors, I assign probabilities based on likelihoods deduced from historical trends, current context, and timeframe: - -- 'Will 12-15 department heads be announced?': High likelihood given historical trends and the specific factors mentioned. -- 'Will 10 or 11 department heads be announced?': Moderately likely if the administration prioritizes key roles but paces remaining appointments. -- 'Will 8 or 9 department heads be announced?': Lower probability, as this would be atypical given usual practices and the current situation. -- 'Will 6 or 7 department heads be announced?': Even lower likelihood, not reflective of standard process. -- 'Will 4 or 5 department heads be announced?': Very unlikely, as this would suggest significant deviation from norm or serious delays. -- 'Will 0-3 department heads be announced?': Highly improbable barring major unforeseen events." -29785,"Will 4 or 5 U.S. federal executive department heads be announced by the incoming administration between the election and December 16, 2024?","Predictions: [7, 1, 1, 0, 3] -Median reasoning: To predict the likelihood of different ranges of U.S. federal executive department heads being announced by the incoming administration (Donald Trump) before December 16, 2024, I consider several factors: - -1. **Base Rate and Historical Context**: Historically, new administrations announce many (if not all) cabinet members within weeks of winning the election. Given the 15 executive department heads, it is common for administrations to announce a significant portion during this period. Trump's previous transition in 2016 serves as a reference point, where major department head appointments occurred quickly. - -2. **Political Context and Current Transition Progress**: With Trump having secured both the presidency and a Republican majority in Congress, the transition and confirmation processes are expected to be relatively smooth. This facilitates the likelihood of more rapid announcements compared to a potential scenario of party division. - -3. **Timeframe and Default Scenario**: Today's date is November 8, 2024, which gives the Trump administration over a month to announce cabinet nominees before December 16, 2024. This ample time within the historical norm supports a base case involving many cabinet announcements. - -4. **Current Indicators Within the Administration**: Potential nominees are already being considered and discussed publicly, suggesting that Trump is actively working towards filling these key positions. - -5. **Uncertainty and Variance**: While historical trends and the existing political environment increase the probability of numerous announcements, uncertainties remain, such as unexpected delays or strategic changes by Trump. However, these uncertainties don't outweigh the overall likelihood of typical transition behavior. - -Considering all of these factors, I assign probabilities based on likelihoods deduced from historical trends, current context, and timeframe: - -- 'Will 12-15 department heads be announced?': High likelihood given historical trends and the specific factors mentioned. -- 'Will 10 or 11 department heads be announced?': Moderately likely if the administration prioritizes key roles but paces remaining appointments. -- 'Will 8 or 9 department heads be announced?': Lower probability, as this would be atypical given usual practices and the current situation. -- 'Will 6 or 7 department heads be announced?': Even lower likelihood, not reflective of standard process. -- 'Will 4 or 5 department heads be announced?': Very unlikely, as this would suggest significant deviation from norm or serious delays. -- 'Will 0-3 department heads be announced?': Highly improbable barring major unforeseen events." -29784,"Will 0-3 U.S. federal executive department heads be announced by the incoming administration between the election and December 16, 2024?","Predictions: [7, 1, 1, 0, 3] -Median reasoning: To predict the likelihood of different ranges of U.S. federal executive department heads being announced by the incoming administration (Donald Trump) before December 16, 2024, I consider several factors: - -1. **Base Rate and Historical Context**: Historically, new administrations announce many (if not all) cabinet members within weeks of winning the election. Given the 15 executive department heads, it is common for administrations to announce a significant portion during this period. Trump's previous transition in 2016 serves as a reference point, where major department head appointments occurred quickly. - -2. **Political Context and Current Transition Progress**: With Trump having secured both the presidency and a Republican majority in Congress, the transition and confirmation processes are expected to be relatively smooth. This facilitates the likelihood of more rapid announcements compared to a potential scenario of party division. - -3. **Timeframe and Default Scenario**: Today's date is November 8, 2024, which gives the Trump administration over a month to announce cabinet nominees before December 16, 2024. This ample time within the historical norm supports a base case involving many cabinet announcements. - -4. **Current Indicators Within the Administration**: Potential nominees are already being considered and discussed publicly, suggesting that Trump is actively working towards filling these key positions. - -5. **Uncertainty and Variance**: While historical trends and the existing political environment increase the probability of numerous announcements, uncertainties remain, such as unexpected delays or strategic changes by Trump. However, these uncertainties don't outweigh the overall likelihood of typical transition behavior. - -Considering all of these factors, I assign probabilities based on likelihoods deduced from historical trends, current context, and timeframe: - -- 'Will 12-15 department heads be announced?': High likelihood given historical trends and the specific factors mentioned. -- 'Will 10 or 11 department heads be announced?': Moderately likely if the administration prioritizes key roles but paces remaining appointments. -- 'Will 8 or 9 department heads be announced?': Lower probability, as this would be atypical given usual practices and the current situation. -- 'Will 6 or 7 department heads be announced?': Even lower likelihood, not reflective of standard process. -- 'Will 4 or 5 department heads be announced?': Very unlikely, as this would suggest significant deviation from norm or serious delays. -- 'Will 0-3 department heads be announced?': Highly improbable barring major unforeseen events." -29798,"Will ModivCare file for bankruptcy before January 1, 2025?","To forecast whether ModivCare will file for bankruptcy before January 1, 2025, let's consider multiple factors, including financial health indicators, market sentiment, and historical base rates for bankruptcy filings in similar situations. - -### Current Situation and Analysis: - -1. **Financial Health:** - - ModivCare faces challenges with debt covenants and liquidity. - - Lenders granted a waiver extension until December 15, 2024, suggesting they believe there's a chance the company can resolve current issues without resorting to bankruptcy. - - Stock prices plummeting from a 52-week high of $12.50 to a current price of $2.50 indicate a severe market lack of confidence. - -2. **Market Sentiment:** - - A significant stock price drop (80% from the peak) reflects investors' concerns about the company's viability. - - This pressure could persist, influencing strategic decisions if recovery measures aren't successful quickly. - -3. **Timeline:** - - With a resolution date slightly over six weeks away, the remaining time is relatively short for any major turnaround, especially in dealing with complex financial restructuring and strategic alternatives. - -4. **Base Rates:** - - Historical data from similar companies struggling with debt indicates that bankruptcy filings are not uncommon but vary based on several mitigating actions and external support (loans, sell-offs, mergers). - - It's crucial here to balance the urgency of the situation with typical base rates for bankruptcy, which often range around 5-10% within a quarter for companies under severe financial stress. - -5. **Qualitative Factors:** - - The company's effort to explore strategic alternatives indicates they are actively addressing the issues, potentially finding solutions such as refinancing or asset sales to avoid filing for bankruptcy. - - However, the proximity to the loan covenant waiver expiration significantly heightens the risk if no clear path is realized by then. - -### Estimation: - -Considering these factors, the probability of ModivCare filing for bankruptcy is anchored by the severity and immediacy of their financial predicament, the temporary liquidity relief provided, and the short runway to resolution. Given the current corporate landscape, liquidity crunch, and deadline for the waiver extension: - -- The likelihood of filing within one month increases as the waiver extension deadline (December 15, 2024) approaches without a resolution, making this period critical. -- There is also a slightly lower chance of filing after that immediate period but before January 1, 2025, subject to unforeseen events or last-minute resolutions. - -Adjusting for past overconfidence trends and under the consideration that ModivCare needs rapid, effective action (amidst existing market skepticism), a reasonable estimate based on both strategic and temporal factors would be around 30%. - -Therefore, my final probability estimate is: - -**Probability: 30%**" -29797,"Will IBM raise its Pay-as-you-go quantum computing prices before January 1, 2025?","To predict whether IBM will raise its Pay-as-you-go quantum computing prices before January 1, 2025, let's consider several factors: - -1. **Base Rate and Historical Context**: Historically, IBM has not frequently changed their quantum computing pricing structure. Large companies often announce price changes well in advance, and they are typically driven by cost changes or strategic market positioning. - -2. **Current Status and Announcements**: The information available up to now shows no announcement from IBM about plans to change pricing before the specified date. The focus of recent press releases has been on data center expansions and technological advancements rather than pricing. - -3. **Timing**: With less than two months until the resolution date, the likelihood of a last-minute price change is reduced. Companies often avoid abrupt changes that can disrupt customer relations. The trend seems focused on growth and expansion rather than immediate monetization changes. - -4. **Market Trends and Novel Events**: While the quantum computing market is rapidly evolving, the lack of specific and recent pricing discussions in public sources suggests a stable pricing strategy for now. Additionally, no new economic or technological events have been indicated to spur a price rise on such a short timeframe. - -5. **Competition and Economic Conditions**: IBM is positioning itself as a leader in the quantum computing space. Pricing changes are typically announced in response to competitive pressures or economic factors. There is no evidence currently suggesting such external pressures. - -6. **Base Case and Default Resolution**: Given the lack of announcements and historical precedence, the base case is that prices will not increase by the resolution date. It is also crucial to weigh the possibility of unforeseen announcements, though they remain slim at this time. - -7. **Overconfidence Adjustment**: Recognizing my historical tendency towards overconfidence, I will adjust my estimation to account for the unpredictability of market actions and announcements, albeit still focusing on the base case due to the lack of current evidence for change. - -Given these factors, the probability of IBM increasing its Pay-as-you-go quantum computing prices before January 1, 2025, appears low. Taking into account the little time left before the resolution date, historical base rates, and current public information, I adjust for overconfidence and propose that: - -Probability: 10%" -29796,"Will Matt Eberflus cease to be the Chicago Bears head coach before January 1, 2025?","Let me think through this step by step: - -1. Timeline Assessment: -- Today is Nov 8, 2024 -- Resolution date is Jan 1, 2025 -- This is approximately 7-8 weeks -- NFL regular season typically ends around early January -- Most coaching changes happen either mid-season or right after season end - -2. Historical Base Rates: -- Mid-season NFL head coach firings are relatively rare -- About 15-20% of NFL teams change coaches each year -- Mid-season changes typically happen by Week 8-10 if they're going to happen -- Bears organization historically conservative with changes - -3. Current Situation: -- Bears record is poor (14-28 overall under Eberflus) -- Team has had meetings about his future -- Recent losses and fan dissatisfaction -- Reports suggest he'll finish season but likely won't return in 2025 - -4. Key Factors: -- Bears ownership's historical preference for stability -- Time remaining in season (approaching late season) -- Team's current trajectory -- GM Ryan Poles' position and influence - -5. Timeline Probability Distribution: -- Next 2 weeks: 10% chance -- By end of November: 15% chance -- By end of December: 25% chance -- By January 1st: 25% chance -- After January 1st: 75% chance (if it happens) - -6. Mitigating Factors: -- Bears might improve performance -- Cost considerations of mid-season change -- Lack of obvious interim replacement -- Historical reluctance for mid-season changes - -7. Confidence Adjustment: -- Initial estimate might be overconfident -- Need to account for organizational inertia -- Must consider the specific short timeframe -- Historical 35% positive resolution rate - -Given all these factors, while it seems likely Eberflus won't be the Bears coach for 2025, the specific timeframe (before January 1, 2025) makes this less likely. The Bears' historical preference for stability and the short remaining time frame are significant factors. While fan dissatisfaction is high, the organization seems more likely to wait until after the season ends to make any change. - -The most likely scenario is that he finishes the season and is let go after January 1st, which would resolve as ""No"" for this question. - -Probability: 25%" -29795,"On November 29, 2024, Will Apple have the largest market cap in the world?","To assess the probability that Apple will have the largest market cap on November 29, 2024, we need to consider several key factors and trends. - -### Current Situation and Background -- **Nvidia's lead**: As of November 6, 2024, Nvidia is ahead of Apple in market capitalization, valued at $3.43 trillion compared to Apple's $3.391 trillion. -- **AI Boom**: Nvidia's rise is closely tied to the ongoing AI revolution, a secular trend likely to sustain Nvidia's valuation. -- **Recent Movement**: The gap between the companies is approximately $39 billion. Given the market dynamics, this is a non-trivial difference, and any reversal would require significant market shifts. - -### Market Dynamics -- **Volatility**: Market caps can change rapidly due to stock price movements, influenced by earnings reports, product announcements, macroeconomic factors, or external shocks. -- **Short Timeframe**: With only three weeks until the resolution date, significant volatility or company-specific catalysts would be required for Apple to surpass Nvidia. -- **Historical Context**: While Apple has been a consistent leader in market cap previously, the tech sector's volatility and Nvidia's current strength in a lucrative market segment (AI) can sustain its lead. - -### Base Rates and Historical Precedence -- Historically, once a company secures a lead in market cap, especially when it aligns with a major industry-wide trend like AI, it tends to maintain that position unless there are notable shifts or announcements from competitors. -- We should note an internal bias towards overconfidence and ensure that we steer towards a conservative estimation. The historical base rate of only 35% of prediction events resolving positively should be a correction factor here. - -### Possible Market Shifts -- **Positive for Apple**: An unexpected positive catalyst, such as a new product launch, a significant increase in estimated growth, or favorable macroeconomic news. -- **Negative for Nvidia**: A critical setback or sector-wide technology disruption. - -### Adjustments for Bias and Confidence -- Considering our tendency towards overconfidence, earlier predictions might skew towards Apple bouncing back strongly. However, trends are currently in favor of Nvidia due to technological advancements and investor sentiment backing AI stocks. -- Apple's resilience makes it a contender if market sentiment shifts, but this phenomena is not the base-case expectation in the short span provided. - -### Final Probability Assessment -Given the current data, market trends, and our need to err on the side of caution (countering overconfidence bias), we determine the likelihood: - -Apple regaining its position by November 29, 2024, would typically be less likely than Nvidia maintaining its lead given no expected disruptive events. - -Therefore, the final probability of Apple having the largest market cap on November 29, 2024, is: - -**Probability: 25%**" -29783,Will the 2024 World Chess Champion be decided in the first 10 games?,"To assess whether the 2024 World Chess Champion will be decided in the first 10 games, we'll need to consider several factors including the players' strengths, historical precedent, and the dynamics of high-level chess matches. - -1. **Match Structure and Base Rates**: The format is a best of 14 games, with a player needing at least 7.5 points to win. Historical World Chess Championships often involve many drawn games due to the deep preparation and high skill level, typically extending close to or full match length. For instance, recent matches have seen a high percentage of draws making early resolution less likely. - -2. **Player Analysis**: - - **Ding Liren**: Despite a slightly lower current rating, Ding has extensive experience and has been highly ranked for several years. His previous achievements and long unbeaten streak indicate resilience, likely resulting in fewer decisive losses. - - **Gukesh Dommaraju**: As a rapidly rising talent with a higher rating, Gukesh may have a momentum advantage, but less experience in such crucial matches. - -3. **Statistical Analysis**: Match dynamics in championships often heavily favor draws. For a match to be decided in 10 games, a player needs a decisive score of 7.5, meaning a combination of wins, losses, or draws totaling a clear advantage. Historically, matches are less likely to be decided so early, beyond the norm of needing to play 12+ games. - -4. **Current Chess Trends**: While Gukesh's rating suggests potential dominance, high-level matches often demonstrate tight contests due to equal skill and preparation levels. Hence, the match extending beyond 10 games is more consistent with past outcomes. - -5. **Other Considerations**: Unlikely events such as forfeiture or disqualification are rare and typically don't impact the resolution criteria defined here. - -**Empirical Estimate**: Considering the draw tendency, historical data, and both players' current form, it's sensible to lean towards the match extending beyond 10 games, given the competitive nature and high skill level of both participants. Decision within the first 10 games is possible but not highly probable. - -6. **Compensating for Overconfidence**: Historically, chess matches at this level specific to determining a champion often stretch the full length. Given my personal track record of overconfidence, I'll weigh this caution in decision-making further. - -In conclusion, synthesizing these elements, my estimated probability that the 2024 World Chess Champion will be decided in the first 10 games is moderately low. - -**Final Probability: 20%**" -29879,Will the new Prime Minister of Iceland belong to the Social Democratic Alliance after the 2024 parliamentary elections?,"Predictions: [30, 15, 22, 25, 20] -Median reasoning: To approach this problem, we have to consider several factors, including recent political developments in Iceland, the state of polling, historical precedents, and the dynamics of coalition governments. - -1. **Political Context and Current Events**: The recent collapse of the coalition government and the announcement of early elections create a volatile political environment. The Social Democratic Alliance is leading in polls, which suggests a favorable outlook for them in the upcoming elections. However, Iceland's political system often results in coalition governments due to the proportional representation model. - -2. **Polling and Public Sentiment**: Current polls indicate that the Social Democratic Alliance is leading with 26.1% of support. This gives them a strong position going into the election. However, leading in polls doesn't guarantee that they will secure the position of Prime Minister, as coalition agreements are critical. - -3. **Historical Precedents and Coalition Dynamics**: Historically, Icelandic governments often require coalitions. The Social Democratic Alliance might need to negotiate with other parties to form a government. The Independence Party, having been part of the ruling coalition, might face difficulties due to recent controversies. - -4. **Resolution Criteria and Timing**: The resolution is contingent on the Social Democratic Alliance having the Prime Minister before January 1, 2025. This makes the immediate aftermath of the election and subsequent coalition talks very important. - -5. **Risks and Uncertainties**: Coalition negotiations could extend beyond the resolution date, leaving uncertainty about whether a Prime Minister from the Social Democratic Alliance will be appointed in time. Alternatively, other parties could form a coalition that excludes the Social Democrats from the Prime Minister's position. - -Considering these factors, the probability distribution needs to reflect both the current polling advantage of the Social Democrats and the inherent uncertainty in coalition negotiations. I estimate the Social Democratic Alliance's chance to secure the Prime Minister position at about 41%, given their polling advantage but tempered by the requisite coalition and negotiation complexities. For the Left-Green Movement, Progressive Party, or Independence Party to secure this position, let's distribute smaller probabilities commensurate with their standings and coalition potential." -29878,Will the new Prime Minister of Iceland belong to the Left-Green Movement after the 2024 parliamentary elections?,"Predictions: [30, 15, 22, 25, 20] -Median reasoning: To approach this problem, we have to consider several factors, including recent political developments in Iceland, the state of polling, historical precedents, and the dynamics of coalition governments. - -1. **Political Context and Current Events**: The recent collapse of the coalition government and the announcement of early elections create a volatile political environment. The Social Democratic Alliance is leading in polls, which suggests a favorable outlook for them in the upcoming elections. However, Iceland's political system often results in coalition governments due to the proportional representation model. - -2. **Polling and Public Sentiment**: Current polls indicate that the Social Democratic Alliance is leading with 26.1% of support. This gives them a strong position going into the election. However, leading in polls doesn't guarantee that they will secure the position of Prime Minister, as coalition agreements are critical. - -3. **Historical Precedents and Coalition Dynamics**: Historically, Icelandic governments often require coalitions. The Social Democratic Alliance might need to negotiate with other parties to form a government. The Independence Party, having been part of the ruling coalition, might face difficulties due to recent controversies. - -4. **Resolution Criteria and Timing**: The resolution is contingent on the Social Democratic Alliance having the Prime Minister before January 1, 2025. This makes the immediate aftermath of the election and subsequent coalition talks very important. - -5. **Risks and Uncertainties**: Coalition negotiations could extend beyond the resolution date, leaving uncertainty about whether a Prime Minister from the Social Democratic Alliance will be appointed in time. Alternatively, other parties could form a coalition that excludes the Social Democrats from the Prime Minister's position. - -Considering these factors, the probability distribution needs to reflect both the current polling advantage of the Social Democrats and the inherent uncertainty in coalition negotiations. I estimate the Social Democratic Alliance's chance to secure the Prime Minister position at about 41%, given their polling advantage but tempered by the requisite coalition and negotiation complexities. For the Left-Green Movement, Progressive Party, or Independence Party to secure this position, let's distribute smaller probabilities commensurate with their standings and coalition potential." -29877,Will the new Prime Minister of Iceland belong to the Progressive Party after the 2024 parliamentary elections?,"Predictions: [30, 15, 22, 25, 20] -Median reasoning: To approach this problem, we have to consider several factors, including recent political developments in Iceland, the state of polling, historical precedents, and the dynamics of coalition governments. - -1. **Political Context and Current Events**: The recent collapse of the coalition government and the announcement of early elections create a volatile political environment. The Social Democratic Alliance is leading in polls, which suggests a favorable outlook for them in the upcoming elections. However, Iceland's political system often results in coalition governments due to the proportional representation model. - -2. **Polling and Public Sentiment**: Current polls indicate that the Social Democratic Alliance is leading with 26.1% of support. This gives them a strong position going into the election. However, leading in polls doesn't guarantee that they will secure the position of Prime Minister, as coalition agreements are critical. - -3. **Historical Precedents and Coalition Dynamics**: Historically, Icelandic governments often require coalitions. The Social Democratic Alliance might need to negotiate with other parties to form a government. The Independence Party, having been part of the ruling coalition, might face difficulties due to recent controversies. - -4. **Resolution Criteria and Timing**: The resolution is contingent on the Social Democratic Alliance having the Prime Minister before January 1, 2025. This makes the immediate aftermath of the election and subsequent coalition talks very important. - -5. **Risks and Uncertainties**: Coalition negotiations could extend beyond the resolution date, leaving uncertainty about whether a Prime Minister from the Social Democratic Alliance will be appointed in time. Alternatively, other parties could form a coalition that excludes the Social Democrats from the Prime Minister's position. - -Considering these factors, the probability distribution needs to reflect both the current polling advantage of the Social Democrats and the inherent uncertainty in coalition negotiations. I estimate the Social Democratic Alliance's chance to secure the Prime Minister position at about 41%, given their polling advantage but tempered by the requisite coalition and negotiation complexities. For the Left-Green Movement, Progressive Party, or Independence Party to secure this position, let's distribute smaller probabilities commensurate with their standings and coalition potential." -29876,Will the new Prime Minister of Iceland belong to the Independence Party after the 2024 parliamentary elections?,"Predictions: [30, 15, 22, 25, 20] -Median reasoning: To approach this problem, we have to consider several factors, including recent political developments in Iceland, the state of polling, historical precedents, and the dynamics of coalition governments. - -1. **Political Context and Current Events**: The recent collapse of the coalition government and the announcement of early elections create a volatile political environment. The Social Democratic Alliance is leading in polls, which suggests a favorable outlook for them in the upcoming elections. However, Iceland's political system often results in coalition governments due to the proportional representation model. - -2. **Polling and Public Sentiment**: Current polls indicate that the Social Democratic Alliance is leading with 26.1% of support. This gives them a strong position going into the election. However, leading in polls doesn't guarantee that they will secure the position of Prime Minister, as coalition agreements are critical. - -3. **Historical Precedents and Coalition Dynamics**: Historically, Icelandic governments often require coalitions. The Social Democratic Alliance might need to negotiate with other parties to form a government. The Independence Party, having been part of the ruling coalition, might face difficulties due to recent controversies. - -4. **Resolution Criteria and Timing**: The resolution is contingent on the Social Democratic Alliance having the Prime Minister before January 1, 2025. This makes the immediate aftermath of the election and subsequent coalition talks very important. - -5. **Risks and Uncertainties**: Coalition negotiations could extend beyond the resolution date, leaving uncertainty about whether a Prime Minister from the Social Democratic Alliance will be appointed in time. Alternatively, other parties could form a coalition that excludes the Social Democrats from the Prime Minister's position. - -Considering these factors, the probability distribution needs to reflect both the current polling advantage of the Social Democrats and the inherent uncertainty in coalition negotiations. I estimate the Social Democratic Alliance's chance to secure the Prime Minister position at about 41%, given their polling advantage but tempered by the requisite coalition and negotiation complexities. For the Left-Green Movement, Progressive Party, or Independence Party to secure this position, let's distribute smaller probabilities commensurate with their standings and coalition potential." -29879,Will the new Prime Minister of Iceland belong to the Social Democratic Alliance after the 2024 parliamentary elections?,"Predictions: [30, 45, 45, 60, 35] -Median reasoning: The task involves forecasting the likelihood of different parties in Iceland getting the prime minister position following the parliamentary elections. Given the political landscape, it's crucial to consider the breakdown of past coalitions, the latest polls, and the timeline until resolution. - -1. **Current Political Scenario:** The Icelandic political climate is marked by coalition governments, as seen in the 2017 and 2021 elections. The current coalition has dissolved due to policy disagreements, and snap elections are scheduled for November 30, 2024. - -2. **Party Strengths and Public Support:** Recent polls show varied support across parties, with the Independence Party and the Social Democratic Alliance gaining some attention. However, polling figures for the Social Democratic Alliance (ranging from 13.5% to 15.9%) do not indicate a dominance over other parties like the Independence Party or the Progressive Party, which have traditionally been influential. - -3. **Base Rates and Historical Context:** Historically, coalition politics in Iceland make it less likely for a single party to lead without forming alliances. Past elections have not seen a clear win for the Social Democratic Alliance in terms of the prime minister position, typically being a part of coalitions instead of leading them outright. - -4. **Timeline and Variability:** The resolution date is about a month after the elections, allowing for coalition talks. Iceland's government formation processes can be lengthy, but the current political urgency might expedite results, though a clear mandate might still be challenging for the Social Democratic Alliance. - -5. **Analysis of Likelihoods:** Given the recent polls and the historical coalition patterns, while different parties have varying chances based on the coalition-building effectiveness, the Independence Party holds a stronger position with Bjarni Benediktsson currently in power and possibly retaining influence, pointing to a broader occurrence of prime ministers from other parties. The Social Democratic Alliance, given the low poll numbers and historical patterns, has a less likely chance. - -6. **Overconfidence Adjustment:** Considering potential overconfidence, I account for higher competition from heavily present parties and coalitions. Therefore, probabilities are adjusted to ensure a conservative forecasting approach. - -Based on these parameters, my assessments of probabilities for each party leading to the prime ministership are as follows." -29878,Will the new Prime Minister of Iceland belong to the Left-Green Movement after the 2024 parliamentary elections?,"Predictions: [30, 45, 45, 60, 35] -Median reasoning: The task involves forecasting the likelihood of different parties in Iceland getting the prime minister position following the parliamentary elections. Given the political landscape, it's crucial to consider the breakdown of past coalitions, the latest polls, and the timeline until resolution. - -1. **Current Political Scenario:** The Icelandic political climate is marked by coalition governments, as seen in the 2017 and 2021 elections. The current coalition has dissolved due to policy disagreements, and snap elections are scheduled for November 30, 2024. - -2. **Party Strengths and Public Support:** Recent polls show varied support across parties, with the Independence Party and the Social Democratic Alliance gaining some attention. However, polling figures for the Social Democratic Alliance (ranging from 13.5% to 15.9%) do not indicate a dominance over other parties like the Independence Party or the Progressive Party, which have traditionally been influential. - -3. **Base Rates and Historical Context:** Historically, coalition politics in Iceland make it less likely for a single party to lead without forming alliances. Past elections have not seen a clear win for the Social Democratic Alliance in terms of the prime minister position, typically being a part of coalitions instead of leading them outright. - -4. **Timeline and Variability:** The resolution date is about a month after the elections, allowing for coalition talks. Iceland's government formation processes can be lengthy, but the current political urgency might expedite results, though a clear mandate might still be challenging for the Social Democratic Alliance. - -5. **Analysis of Likelihoods:** Given the recent polls and the historical coalition patterns, while different parties have varying chances based on the coalition-building effectiveness, the Independence Party holds a stronger position with Bjarni Benediktsson currently in power and possibly retaining influence, pointing to a broader occurrence of prime ministers from other parties. The Social Democratic Alliance, given the low poll numbers and historical patterns, has a less likely chance. - -6. **Overconfidence Adjustment:** Considering potential overconfidence, I account for higher competition from heavily present parties and coalitions. Therefore, probabilities are adjusted to ensure a conservative forecasting approach. - -Based on these parameters, my assessments of probabilities for each party leading to the prime ministership are as follows." -29877,Will the new Prime Minister of Iceland belong to the Progressive Party after the 2024 parliamentary elections?,"Predictions: [30, 45, 45, 60, 35] -Median reasoning: The task involves forecasting the likelihood of different parties in Iceland getting the prime minister position following the parliamentary elections. Given the political landscape, it's crucial to consider the breakdown of past coalitions, the latest polls, and the timeline until resolution. - -1. **Current Political Scenario:** The Icelandic political climate is marked by coalition governments, as seen in the 2017 and 2021 elections. The current coalition has dissolved due to policy disagreements, and snap elections are scheduled for November 30, 2024. - -2. **Party Strengths and Public Support:** Recent polls show varied support across parties, with the Independence Party and the Social Democratic Alliance gaining some attention. However, polling figures for the Social Democratic Alliance (ranging from 13.5% to 15.9%) do not indicate a dominance over other parties like the Independence Party or the Progressive Party, which have traditionally been influential. - -3. **Base Rates and Historical Context:** Historically, coalition politics in Iceland make it less likely for a single party to lead without forming alliances. Past elections have not seen a clear win for the Social Democratic Alliance in terms of the prime minister position, typically being a part of coalitions instead of leading them outright. - -4. **Timeline and Variability:** The resolution date is about a month after the elections, allowing for coalition talks. Iceland's government formation processes can be lengthy, but the current political urgency might expedite results, though a clear mandate might still be challenging for the Social Democratic Alliance. - -5. **Analysis of Likelihoods:** Given the recent polls and the historical coalition patterns, while different parties have varying chances based on the coalition-building effectiveness, the Independence Party holds a stronger position with Bjarni Benediktsson currently in power and possibly retaining influence, pointing to a broader occurrence of prime ministers from other parties. The Social Democratic Alliance, given the low poll numbers and historical patterns, has a less likely chance. - -6. **Overconfidence Adjustment:** Considering potential overconfidence, I account for higher competition from heavily present parties and coalitions. Therefore, probabilities are adjusted to ensure a conservative forecasting approach. - -Based on these parameters, my assessments of probabilities for each party leading to the prime ministership are as follows." -29876,Will the new Prime Minister of Iceland belong to the Independence Party after the 2024 parliamentary elections?,"Predictions: [30, 45, 45, 60, 35] -Median reasoning: The task involves forecasting the likelihood of different parties in Iceland getting the prime minister position following the parliamentary elections. Given the political landscape, it's crucial to consider the breakdown of past coalitions, the latest polls, and the timeline until resolution. - -1. **Current Political Scenario:** The Icelandic political climate is marked by coalition governments, as seen in the 2017 and 2021 elections. The current coalition has dissolved due to policy disagreements, and snap elections are scheduled for November 30, 2024. - -2. **Party Strengths and Public Support:** Recent polls show varied support across parties, with the Independence Party and the Social Democratic Alliance gaining some attention. However, polling figures for the Social Democratic Alliance (ranging from 13.5% to 15.9%) do not indicate a dominance over other parties like the Independence Party or the Progressive Party, which have traditionally been influential. - -3. **Base Rates and Historical Context:** Historically, coalition politics in Iceland make it less likely for a single party to lead without forming alliances. Past elections have not seen a clear win for the Social Democratic Alliance in terms of the prime minister position, typically being a part of coalitions instead of leading them outright. - -4. **Timeline and Variability:** The resolution date is about a month after the elections, allowing for coalition talks. Iceland's government formation processes can be lengthy, but the current political urgency might expedite results, though a clear mandate might still be challenging for the Social Democratic Alliance. - -5. **Analysis of Likelihoods:** Given the recent polls and the historical coalition patterns, while different parties have varying chances based on the coalition-building effectiveness, the Independence Party holds a stronger position with Bjarni Benediktsson currently in power and possibly retaining influence, pointing to a broader occurrence of prime ministers from other parties. The Social Democratic Alliance, given the low poll numbers and historical patterns, has a less likely chance. - -6. **Overconfidence Adjustment:** Considering potential overconfidence, I account for higher competition from heavily present parties and coalitions. Therefore, probabilities are adjusted to ensure a conservative forecasting approach. - -Based on these parameters, my assessments of probabilities for each party leading to the prime ministership are as follows." -29874,"Will the number of active US oil drilling rigs be less than 585 on November 29, 2024?","Predictions: [10, 5, 5, 10, 5] -Median reasoning: Let's first clarify the questions: - -1. Will the number of active US oil drilling rigs be less than 585 on November 29, 2024? -2. Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on November 29, 2024? -3. Will the number of active US drilling rigs exceed 590 on November 29, 2024? - -Given that the current total US rig count is 585, and it has remained unchanged recently, the proximity to the resolution date (November 29, 2024, is only a few weeks away), suggests that significant changes are unlikely unless influenced by drastic economic or geopolitical factors. - -Considering recent trends: -- There has been a consistent decline over the past year. -- The global context indicates a stable-to-slightly-decreasing trend in offshore as well as total US oil rig count. - -Additional factors: -- Economic pressures including declining oil and gas prices and high operation costs could discourage increasing the rig count. -- Though Donald Trump's election could have theoretically increased optimism in the oil sector, there is skepticism about a rapid increase in production due to structural market factors. -- Concerns about plateauing U.S. shale production and a reported industry shift towards natural gas over crude oil further support the stable or declining trend. - -Analysis: -- With the current rig count at the critical threshold and historical data indicating a decline, it's more likely that the number will stay below 585 or at this level rather than increase beyond 590. -- The time for substantial variance is limited, and the base rate scenario supports stability or slight decrease. Thus, based on the criteria, significant changes appear unlikely. - -Probabilities: -1. Probability of less than 585 rigs: Given the base rate and trend, this possibility is significant, around 65%. -2. Probability of between 585 and 590 rigs: Given 585 is the current rate, it's weighted around 30%, but any rise is constrained to this range by prior conditions. -3. Probability of exceeding 590 rigs: Given historical declines and context, this is significantly lower, around 5%, due to a lack of supporting factors for a sharp increase. - -Potential for variance and unexpected factors is acknowledged, but given the current data, these outcomes support economies and reduce resource allocation for the possibility of changes happening so quickly. - -Given past tendency of overconfidence, conservative adjustments are incorporated into these probabilities." -29873,"Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on November 29, 2024?","Predictions: [10, 5, 5, 10, 5] -Median reasoning: Let's first clarify the questions: - -1. Will the number of active US oil drilling rigs be less than 585 on November 29, 2024? -2. Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on November 29, 2024? -3. Will the number of active US drilling rigs exceed 590 on November 29, 2024? - -Given that the current total US rig count is 585, and it has remained unchanged recently, the proximity to the resolution date (November 29, 2024, is only a few weeks away), suggests that significant changes are unlikely unless influenced by drastic economic or geopolitical factors. - -Considering recent trends: -- There has been a consistent decline over the past year. -- The global context indicates a stable-to-slightly-decreasing trend in offshore as well as total US oil rig count. - -Additional factors: -- Economic pressures including declining oil and gas prices and high operation costs could discourage increasing the rig count. -- Though Donald Trump's election could have theoretically increased optimism in the oil sector, there is skepticism about a rapid increase in production due to structural market factors. -- Concerns about plateauing U.S. shale production and a reported industry shift towards natural gas over crude oil further support the stable or declining trend. - -Analysis: -- With the current rig count at the critical threshold and historical data indicating a decline, it's more likely that the number will stay below 585 or at this level rather than increase beyond 590. -- The time for substantial variance is limited, and the base rate scenario supports stability or slight decrease. Thus, based on the criteria, significant changes appear unlikely. - -Probabilities: -1. Probability of less than 585 rigs: Given the base rate and trend, this possibility is significant, around 65%. -2. Probability of between 585 and 590 rigs: Given 585 is the current rate, it's weighted around 30%, but any rise is constrained to this range by prior conditions. -3. Probability of exceeding 590 rigs: Given historical declines and context, this is significantly lower, around 5%, due to a lack of supporting factors for a sharp increase. - -Potential for variance and unexpected factors is acknowledged, but given the current data, these outcomes support economies and reduce resource allocation for the possibility of changes happening so quickly. - -Given past tendency of overconfidence, conservative adjustments are incorporated into these probabilities." -29872,"Will the number of active US drilling rigs exceed 590 on November 29, 2024?","Predictions: [10, 5, 5, 10, 5] -Median reasoning: Let's first clarify the questions: - -1. Will the number of active US oil drilling rigs be less than 585 on November 29, 2024? -2. Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on November 29, 2024? -3. Will the number of active US drilling rigs exceed 590 on November 29, 2024? - -Given that the current total US rig count is 585, and it has remained unchanged recently, the proximity to the resolution date (November 29, 2024, is only a few weeks away), suggests that significant changes are unlikely unless influenced by drastic economic or geopolitical factors. - -Considering recent trends: -- There has been a consistent decline over the past year. -- The global context indicates a stable-to-slightly-decreasing trend in offshore as well as total US oil rig count. - -Additional factors: -- Economic pressures including declining oil and gas prices and high operation costs could discourage increasing the rig count. -- Though Donald Trump's election could have theoretically increased optimism in the oil sector, there is skepticism about a rapid increase in production due to structural market factors. -- Concerns about plateauing U.S. shale production and a reported industry shift towards natural gas over crude oil further support the stable or declining trend. - -Analysis: -- With the current rig count at the critical threshold and historical data indicating a decline, it's more likely that the number will stay below 585 or at this level rather than increase beyond 590. -- The time for substantial variance is limited, and the base rate scenario supports stability or slight decrease. Thus, based on the criteria, significant changes appear unlikely. - -Probabilities: -1. Probability of less than 585 rigs: Given the base rate and trend, this possibility is significant, around 65%. -2. Probability of between 585 and 590 rigs: Given 585 is the current rate, it's weighted around 30%, but any rise is constrained to this range by prior conditions. -3. Probability of exceeding 590 rigs: Given historical declines and context, this is significantly lower, around 5%, due to a lack of supporting factors for a sharp increase. - -Potential for variance and unexpected factors is acknowledged, but given the current data, these outcomes support economies and reduce resource allocation for the possibility of changes happening so quickly. - -Given past tendency of overconfidence, conservative adjustments are incorporated into these probabilities." -29887,"Will either major party candidate in California’s 22nd Congressional District concede the election before December 1, 2024?","Let me think through this step by step: - -1) First, let's consider the timeline: -- Today is Nov 11 -- Resolution date is Dec 1 -- This gives us about 20 days - -2) Key factors supporting a concession: -- Close race indicated by polling -- Historical precedent from 2022 where concession occurred on Nov 22 -- We're already 6 days post-election - -3) Factors against a concession: -- California's known slow vote counting process -- Mail-in ballots still being counted -- No indication yet of either candidate planning to concede -- Tight margins make candidates less likely to concede early - -4) Timeline analysis: -- In 2022, concession came 17 days after election -- We're currently at day 6 post-election -- Have 14 more days until resolution date -- This timeline allows for a similar pattern to 2022 - -5) Base rate considerations: -- Most House races are called much earlier -- But CA-22 specifically has recent history of slow counting -- 2022 precedent shows concession possible within this window - -6) Uncertainty factors: -- Mail-in ballot volume unknown -- Current vote margin unknown -- Candidate temperaments and strategies unclear -- Political environment more charged than 2022 - -7) Timeline probability distribution: -- Days 1-7: Very low probability (already passed) -- Days 8-14: 15% cumulative -- Days 15-21: 25% cumulative -- Days 22-30: 35% cumulative -- Beyond Dec 1: 65% probability - -8) Risk adjustments: -- Need to adjust for historical overconfidence -- Consider asymmetric risks of early vs. late concession -- Account for increased political polarization - -Given the above analysis, I believe there's a moderate chance of concession before December 1. The 2022 precedent is important but not determinative. The tight timeline and California's slow counting process are significant limiting factors. However, the existence of recent precedent for concession within this window prevents me from going too low with the probability. - -Adjusting for historical overconfidence and the general tendency for questions to resolve negatively, I'll reduce my initial estimate. - -Probability: 37%" -29886,Will Jelly Roll win an award in any of these 2024 CMA Awards categories?,"To forecast whether Jelly Roll will win an award in any of the specified categories at the 2024 CMA Awards, I'll consider several aspects: - -1. **Background Analysis**: - - Jelly Roll is nominated for Entertainer of the Year, Album of the Year for *Whitsitt Chapel*, and possibly Male Vocalist of the Year, as these are the categories specified in the resolution criteria. - - The prediction article from November 7, 2024, suggests Jelly Roll is a contender but not favored to win Entertainer of the Year, with Chris Stapleton being the favorite in that category. Predictions from industry experts often provide a strong indication of likely outcomes, though they are not always correct. - - Jelly Roll has received two Grammy nominations for 2025, indicating recognition and momentum in his career. However, this success does not directly translate into guaranteed wins at the CMA Awards, as voting bodies and criteria may differ. - -2. **Recent Success and Trends**: - - The success of Jelly Roll's albums, such as *Whitsitt Chapel* and *Beautifully Broken*, and his current high profile in country music (e.g., Austin City Limits spotlight) contribute to his candidacy strength. - - No specific mention of him being the frontrunner for Male Vocalist of the Year or Album of the Year in the provided data, aside from his nomination. - -3. **Historical and Base Rate Considerations**: - - In previous CMA Awards, newcomers or less-established names occasionally win major awards, but it is generally less common for an artist to win against bigger, more entrenched names. - - The importance of recent attention in media can sometimes signify potential surprise victories. - -4. **Resolution Time Frame and Variance**: - - The event will occur on November 20, 2024, which is just nine days away, leaving minimal time for significant changes in metrics or perceptions among voters, suggesting the base case should be heavily considered. - - Unlikely events are particularly less likely as we approach the resolution date without new strong evidence. - -5. **Importance of Expert Predictions**: - - The expert prediction cites Chris Stapleton as likely to win Entertainer of the Year, a category Jelly Roll is nominated for, which suggests a lower likelihood for Jelly Roll in that category. - - Given no substantial push or predictors for Album or Male Vocalist of the Year, these too remain less likely. - -6. **Assessing Overconfidence**: - - Historically, I have been overly confident. To avoid this, I balance selling points with skepticism over easy jumps to conclusions, keeping the nuances in mind. - -After considering these points, I balance my assessment toward a cautious probability given historical precedents, industry trends, and current expert sentiment, while correcting for potential overconfidence. - -**Final Probability Assessment**: -Given the evidence and trends, I'll assign a moderate probability that Jelly Roll will win one of the awards in the specified categories but lower than if expert predictions strongly favored him. - -Probability: 32%" -29885,"Will Texas have 15.0 million or more residents living in drought on December 31, 2024?","To tackle this forecasting challenge effectively, let's break down the information, assess base rates, and consider contextual factors. Here’s a step-by-step analysis: - -### Current Situation and Base Conditions -1. **Current Drought Conditions**: Recent data on drought conditions indicate that over 50% of the lower 48 states, including major portions of Texas, are experiencing significant drought issues. Important insight comes from [Drought.gov](https://www.drought.gov/states/texas), highlighting that drought conditions are indeed severe and widespread. - -2. **Current Population Impact**: As of the provided information, it's not mentioned precisely how many Texas residents are currently experiencing drought. For forecasting purposes, we need to check historical trends on how drought typically impacts populations and evaluate if it's probable for the count to meet or exceed 15 million in the near future. - -### Key Factors and Influences - -1. **Infrastructure and Resources**: News suggests Texas lacks sufficient infrastructure to tackle severe drought, exemplified by cities like Buda going to Stage 3 drought restrictions. This implies potentially increasing numbers of impacted residents. - -2. **Weather Patterns and Forecasts**: Historical weather patterns indicate that late autumn and early winter may not significantly alleviate current drought conditions, given Texas' typical precipitation cycles and any anomalies that might be present this year. Continued monitoring of weather forecasts is essential. - -3. **Recent Changes and Developments**: - - **Restrictions and Local Responses**: Some areas are lifting restrictions (e.g., Denton), while others impose more stringent measures, suggesting variability across the state. However, the trend points towards worsening conditions, hinting at a possible increase in affected residents. - -4. **Historical Base Rate Analysis**: An effective base rate evaluation would involve analyzing past instances where Texas has experienced drought conditions affecting large populations, particularly around similar times of the year. - -### Assessing Likelihood - -- **Timeframe Considered**: There are only about 7 weeks until the resolution date. This relatively short duration must account for variances and potential changes in weather patterns. Short timeframes typically limit the potential for substantial deviations from current conditions, which is why the immediate drought landscape is critical. - -- **Current Impact**: As current information doesn’t specify exactly how many residents are affected right now, a conservative base estimation assumes near current levels or slightly escalated percentages compared to earlier this year due to recent local actions (Buda’s Stage 3). - -- **Probability Judgement**: Consider proximity to the threshold of 15.0 million. If current impacts are not significantly below this, trends suggest it could plausibly edge closer to or surpass this threshold given increasing drought severity and infrastructure challenges. - -### Final Probability Assessment - -Given these considerations, we must balance between overconfidence in trenchant analysis of Texas's drought trends and the lesson learned from past overconfident estimates with more favorable judgment for quantitatively assessed uncertainties. - -Taking all these factors into account, a reasonable probability estimate for 15 million or more Texas residents being in drought by the end of 2024 is: - -**Probability: 70%**. - -This considers ongoing trends, consistent conditional analysis, likely exacerbations of drought effects, and relevant base rates, concluding toward a slightly above-moderate likelihood of resolution in the affirmative due to current susceptible dynamics and environmental uncertainties." -29884,"Before January 1, 2025, will Anthropic announce on the news section of its website that it is planning an IPO?","Let me think through this step by step: - -1. Timeline Analysis: -- Only about 7 weeks until resolution -- Very short window for such a major corporate decision -- IPO announcements typically take months of preparation -- End of year/holiday season further reduces effective business days - -2. Current Status: -- No public hints or preparations for IPO -- Recent focus on private funding (Amazon talks) -- $18B private valuation is already substantial -- Company appears well-funded through private markets - -3. Market Context: -- Tech IPO market has been relatively quiet -- Similar AI companies (except ARM) staying private -- Private funding readily available for AI companies -- No immediate pressure to go public - -4. Historical Precedents: -- AI companies of this size/age rarely IPO this quickly -- Most wait several more years post major funding -- Recent Amazon investment talks suggest private path -- Similar companies (OpenAI, DeepMind) remained private - -5. Specific Resolution Requirements: -- Must be announced on specific website section -- Very precise requirements reduce probability -- Website section might be unavailable (technical requirement) -- Holiday period reduces likelihood of major announcements - -6. Timeline Probability Distribution: -- Next 2 months (resolution period): 3% -- Next 6 months: 8% -- Next 12 months: 15% -- Next 24 months: 25% - -7. Factors Reducing Probability: -- Very short timeframe -- Recent private funding focus -- Holiday season timing -- Specific website requirement -- No current preparation signals - -8. Factors Increasing Probability: -- High valuation makes IPO viable -- General market interest in AI -- Potential strategic benefits -- Competitor activities - -9. Special Considerations: -- Resolution criteria requiring specific website announcement adds extra hurdle -- End of year timing makes major corporate announcements less likely -- Recent Amazon investment talks suggest private path preferred - -Given the extremely short timeframe, specific resolution requirements, recent private funding focus, and typical IPO announcement patterns, I need to assign a fairly low probability. The 7-week window during holiday season is particularly constraining. - -Adjusting for my historical overconfidence and the general tendency for negative resolutions, while considering the full probability distribution: - -Probability: 3% - -This reflects both the inherent uncertainty in corporate decisions while acknowledging the significant constraints of the timeline and specific resolution requirements. The low probability primarily stems from the very short timeframe rather than a judgment about Anthropic's long-term IPO prospects." -29883,"Will the world remain ""normal"" through 2024, according to the specified criteria?","To determine the probability that the world will remain ""normal"" through 2024 according to the specified criteria, we need to evaluate each of the listed potential events and their likelihoods, considering the short time frame left until the resolution date. - -1. **Development of Artificial General Intelligence (AGI):** - - The probability of AGI development before 2030 is given as 40%. However, given no major announcements or breakthroughs have occurred as of November 2024, and the typical pace of such developments, the likelihood before January 2025 is very low. Assuming an even distribution across years, it would be around 5% for any given year. With only seven months of 2024 and one month of 2025, we might estimate well under 1% for the remainder of 2024. - -2. **Global Population Decline:** - - A 36% chance by 2100 implies this is a long-term risk. A sudden 10% decrease is an unlikely catastrophic event within a few weeks. The annual likelihood adjusted would likely be under 1% for any short term. Thus, it's very improbable for 2024-2025. - -3. **Global GDP Increase (30% or more in one year):** - - This scenario also has a cumulative chance of 13% by 2030, suggesting low short-term likelihood. For any individual year it's roughly 1-2%, making it similarly improbable for the remainder of 2024. - -4. **Alien Technosignature Detection:** - - With a 3% chance by 2050, discoveries of this nature are exceptionally unlikely in the immediate term, justifying a probability near 0% for 2024. - -5. **Worldview-Shattering Explanation for UAPs:** - - At a 1.4% chance before July 2028, this suggests such a revelation is very rare. Again, the likelihood is near 0% in the immediate future. - -6. **World War III:** - - With a 25% chance before 2050, assuming an even distribution, the chance for any given year is around 1%. Given no global conflicts exhibit the signs of escalating to WWIII imminently, this is probably also under 1% by year's end 2024. - -7. **Nuclear Detonation in War:** - - Similar to World War III, a 23% chance before 2050 suggests annual likelihood of roughly 0.9%. Current geopolitical climates don't indicate an imminent threat, allowing us to reduce this likelihood for the short term. - -For all calculated probabilities being near zero, it's critical to remember that the question resolves positively (world remains ""normal"") if none of these happen by January 2025. The cumulative probability of any single catastrophic event happening becomes even smaller. - -Base rates for each event align strategy towards expecting non-occurrence in the immediate future due to the inherent stability in global systemic processes. Even considering the low probabilities, together they don't significantly drive the likelihood of resolution towards the ""No"" outcome. - -Moreover, the time frame left for these events to manifest (7 weeks) is critically short, particularly given the need for global, public acknowledgment of each criterion. Thus, historical inertia towards non-occurrence still heavily dominates the prediction landscape here. - -Account for historical competitive biases, and resist overconfidence, even though the likelihood of resolution ""No"" is cumulatively less than the distribution across vast future years. - -**Probability: 98%**" -29882,"Will a mouse be confirmed to have lived for 2,500 days before January 1, 2025? -","To forecast the likelihood that a mouse will be confirmed to have lived for 2,500 days before January 1, 2025, we need to carefully weigh the current state of research, historical precedents, potential for breakthroughs, and the timeframe provided. - -Let's break down the factors: - -1. **Historical Context and Records**: The longest-lived laboratory mouse, as noted in the provided background, is approximately 1,800 days, which is significantly less than 2,500 days. This longevity record from a growth hormone receptor knockout mouse suggests that extending beyond this lifespan would require substantial scientific breakthroughs. - -2. **Recent Research and Developments**: Recent studies cited do not indicate significant advancements that could push mouse lifespans toward 2,500 days. For instance, the studies on APP/PS1 and APPNL-F/NL-F mice showed lifespans far below the required 2,500 days. No current research suggests imminent breakthroughs that would extend life expectancies to this degree. - -3. **Timeframe Considerations**: Between today's date (November 11, 2024) and the resolution date (January 1, 2025), there is a very short period of just over a month. It is unlikely for groundbreaking research, publication, and confirmation of a 2,500-day mouse lifespan to occur in this time, especially considering the usual pace and peer-review cycles within academic research. - -4. **Uncertainty and Default Outcomes**: Given the history of such records and no immediate signs of extraordinary breakthroughs, the baseline expectation would lean toward no mouse reaching 2,500 days by the deadline. Base rates suggest these types of breakthroughs are rare and more incremental rather than revolutionary. - -5. **Overconfidence Bias Adjustment**: Acknowledging historical overconfidence, the forecast should lean conservative and avoid overly optimistic predictions without hard evidence or trends indicating otherwise. - -6. **Probability Assignment**: - - **Likelihood before January 1, 2025**: With the profound gap between current scientific standing and the needed breakthrough in such a short timespan, the odds are quite low. - - **Considering shorter timeframes**: If contemplating the possibility of advancements in shorter spans (e.g., a month), it diminishes further given the required steps and verification for scientific claims. - -In conclusion, with careful consideration of the factors mentioned, the likelihood of a mouse being confirmed to have lived for 2,500 days before January 1, 2025, is extremely low. I will assign a probability that accounts for past patterns, current research, and timeframe constraints, but also compensates for any potential bias in underestimating unlikely events. - -**Probability: 1%**" -29881,"Will there be a large-scale armed conflict in Russia before January 1, 2025? -","Let me think through this step by step: - -1) First, considering the base rate: -- Large-scale armed conflicts in Russia are historically rare -- The last comparable events were the Chechen Wars in the 1990s/2000s -- The 1991 Soviet coup attempt didn't reach the death threshold -- Most internal conflicts in Russia's history occurred during major transitions or after military defeats - -2) Timeline analysis: -- Only ~7 weeks remain until resolution -- This is an extremely short window for a conflict to both emerge and reach the specified thresholds -- The death threshold (1,000) and participant threshold (100) are significant numbers to reach in such a short time - -3) Current conditions: -- Putin maintains strong control over security forces -- No visible organized opposition with military capabilities -- Economic stress exists but not at revolutionary levels -- Military is engaged in Ukraine, limiting internal deployment options -- No clear signs of imminent uprising or coup - -4) Risk factors: -+ Ongoing war strain -+ Some economic instability -+ Historical precedent of military failures leading to internal conflict -- But these typically developed over longer periods - -5) Probability distribution across time: -- 6 months: ~4% -- 1 year: ~8% -- 2 years: ~12% -- Therefore, 7 weeks should be significantly lower - -6) Specific resolution criteria considerations: -- Need for 1,000 deaths is a high bar for short timeframe -- 100 participants requirement suggests organized resistance -- Wikipedia page requirement adds verification threshold -- These combine to make spontaneous events less likely to qualify - -7) Mitigating factors: -- Strong state security apparatus -- Winter conditions typically reduce conflict likelihood -- Holiday period approaching -- No visible organizing opposition - -Given the extremely short timeframe, high thresholds for qualification, and current stability of control mechanisms, this seems very unlikely in the specified window, though not impossible. - -The base rate adjusted for current conditions would suggest maybe 8-10% annually, but given only 7 weeks remain and the holiday period, this should be significantly reduced. - -I need to adjust down for overconfidence, but the extremely short timeline and high thresholds are genuine limiting factors. - -Probability: 2%" -29880,"Will George Soros be ranked in the top 400 richest people in the world on December 31, 2024?","Let me think through this step by step: - -1) First, let's consider the timeframe: -- We're looking at December 31, 2024 -- That's only about 7 weeks away -- This short timeframe significantly reduces the likelihood of major changes - -2) Current situation: -- As of October 8, 2024, Soros was ranked 424th -- He needs to move up at least 24 spots to reach 400th place -- His net worth was reported as $7.2B in September 2024 - -3) This is a Non-Market Threshold Value Event, but with characteristics of Market Price Events since net worth fluctuates with market conditions. - -4) Key factors to consider: -- Very short timeframe (7 weeks) -- Needs significant relative movement (24 spots) -- Market volatility affects all billionaires somewhat similarly -- Year-end tax-loss harvesting and portfolio rebalancing could affect rankings -- Rankings tend to be relatively stable in short periods - -5) Historical precedent: -- Billionaire rankings typically don't show dramatic shifts over such short periods -- Exception: Major company stock price movements or corporate events -- No indication of pending major events for Soros's holdings - -6) Analysis of movement needed: -- To move up 24 spots would likely require either: - a) Significant increase in Soros's wealth - b) Significant decrease in others' wealth - c) Combination of both -- In 7 weeks, this is a substantial hurdle - -7) Considering probabilities across different timeframes: -- 1 month: ~8% chance -- 2 months (our timeframe): ~12% chance -- 6 months: ~25% chance - -8) Risk factors: -- Market volatility could work either way -- Year-end tax considerations -- No major known catalysts - -Given: -- The very short timeframe -- Current ranking significantly below threshold -- No obvious catalysts -- Need for substantial relative movement -- Historical stability of rankings over short periods -- Year-end timing considerations - -I need to adjust for historical overconfidence and the fact that this would require a significant positive movement in a short time. - -While not impossible, the combination of factors makes this unlikely in the given timeframe. The short time window is particularly constraining. - -Probability: 12%" -29875,"Will Russia have significantly expanded its controlled territory in Ukraine on January 1, 2025?","Let me think about this step by step: - -1) First, let's consider the timeline: -- We're looking at just under 2 months until resolution -- This is a very short timeframe for significant territorial changes in modern warfare -- The winter season typically slows military operations - -2) Current situation analysis: -- Russia has been largely defensive for months -- Ukraine's counteroffensive has made limited gains -- Neither side has shown ability to make major breakthroughs recently -- Russian forces are entrenched in current positions - -3) Resolution criteria examination: -- Russia must maintain ALL current key positions (5 locations) -- AND capture at least 2 new major cities from a list of 7 -- These are major population centers, not small territories -- Taking major cities requires significant forces and time - -4) Historical precedent: -- In the last 12 months, neither side has captured any major cities -- The last major city captures were in 2022 -- Winter offensives are historically rare and difficult -- Modern urban warfare is extremely slow - -5) Specific requirements analysis: -- To succeed, Russia needs to: - a) Hold all current territory (likely) - b) Launch major successful offensive (very unlikely in 2 months) - c) Capture two major cities (extremely unlikely in 2 months) - -6) Risk factors: -- Ukraine has Western support -- Russia has shown no recent capability for major offensive operations -- Winter conditions will hamper any offensive operations -- Two months is an extremely short timeline for major territorial changes - -7) Mitigating factors: -- Ukraine's forces are stretched thin -- Western aid discussions are complicated by other global events -- Russia has been mobilizing additional forces - -8) Timeline probability distribution: -- 2 months (by Jan 1, 2025): 3% -- 6 months: 8% -- 1 year: 15% -- 2 years: 25% - -The extremely short timeline combined with the specific requirement of capturing TWO major cities (not just territory) makes this highly unlikely. The winter season further reduces the probability. While Russia maintains significant military power, the logistics and time required for such advances make this nearly impossible in the given timeframe. - -I need to avoid overconfidence, but given the specific criteria and very short timeline, this is genuinely a low probability event. - -Probability: 3%" -29887,"Will either major party candidate in California’s 22nd Congressional District concede the election before December 1, 2024?","The question is about whether either major party candidate in California’s 22nd Congressional District will concede the election before December 1, 2024. We are seeing a common political question which falls under the ""Competition Event"" type. - -### Context and Current Situation: - -1. **Historical Context**: In 2022, the election in CA-22 was resolved with a concession by November 22, which is 19 days after the election date (November 8 in 2022). This is a relevant base rate as a starting point for our estimate given the similar circumstances. - -2. **Current Election Status**: - - **Close Race**: The sources indicate close results and a tightly contested race, which means a concession may be less likely without complete vote counting. - - **Uncertainty of Outcome**: The election is still uncertain, as indicated by the incomplete vote counting and a narrow margin [2][3][4]. - -3. **Timeline Considerations**: Today is November 11, and the deadline for concessive action is December 1. There are 20 days remaining. - -4. **Role of Concession**: - - Concessions usually happen if one candidate sees an insurmountable deficit or based on strategic calculations. - - With the race close and affecting control of the chamber, candidates might be more cautious in conceding prematurely. - -5. **Recent Articles**: Articles like the one from The Hill mention a win, but this is not officially called [1][3]. California is known for delayed vote counts impacting final decisions due to logistical and procedural factors [2]. - -### Probability Considerations: - -- **Base Rate Influence**: Historically, similar situations resolved with a concession just before the end of November. -- **Current Election Dynamics**: While parties typically concede when the path to victory becomes implausible, the close nature and significant stakes of this race might delay a decision. -- **Uncertainty Factors**: Historically, only about 35% of forecasting questions resolve positively, suggesting most outcomes do not meet forecasted conditions. This aligns with the typical cautious timing of concessions. - -### Comprehensive Scenario Evaluation: - -- Given more days for events to unfold, consider that results become clearer, leading one candidate to concede by the end of the month. -- Weighing the importance of timeline; historically, close results take a while, but 20 days could suffice for clarity and potential concession. -- The recent past event (2022 concession on November 22) mentioned provides optimism on probability but lessens due to tight competitions currently highlighted. - -### Conclusion: - -Considering the base rate of past times when concessions were made in similarly contested situations and the tension added by current control significance: - -- **Base Probability** for resolution before December 1: Marks at a moderate percentage due to the close race and high stakes involved. -- **Adjust for Conservative Probabilities**: Historically, overconfidence mitigation aims for a conservative forecasting approach. - -Hence, I would estimate: - -""Probability: 55%"" - -This probability considers the typical timeline, historical contexts, and leverage given to tighter race conditions and high stakes for potential control of Congress. While the intrinsic uncertainty remains, the coordination of factors suggests moderate likelihood aligning concession actions within the resolving period." -29872,"Will the number of active US drilling rigs exceed 590 on November 29, 2024?","Predictions: [63, 35, 45, 10, 45] -Median reasoning: To forecast the probability of the number of active US drilling rigs exceeding, being between, and being less than the specified numbers by November 29, 2024, we need to consider several factors. - -First, we must acknowledge that the current US rig count is 585, with no movement from the previous week and a declining trend over the past year (a reduction of about 6.4%). This indicates a steady or slightly decreasing trend, suggesting a cautious approach by the industry amidst volatile oil prices. - -Second, since the timeframe to the resolution date is only a few weeks away, significant deviations from the current trend are less probable unless market dynamics change dramatically, which there is currently no indication of happening. - -Third, historical trends, recent data, and expert opinions suggest limited capacity and incentive to increase the rig count substantially in the near term. There have been no notable changes in policy or market factors that hint at an immediate increase. - -Taking into account these considerations, the likelihood of the rig count exceeding 590 appears low, with most indications pointing towards it either staying around the current number or potentially dropping further, thus - -1. The probability of the number of rigs exceeding 590 is low as it would require an unusually swift and unexpected increase in drilling activity, which is usually constrained by market and economic factors. -2. The probability of rigs being between 585 and 590 is moderate as it aligns with the current stability in rig numbers, given minimal change from current reports and market forecasts. -3. The probability of rigs being below 585 is slightly higher than remaining stable due to the decreasing trend seen over the past year. - -Thus, it’s essential to weigh heavily on the status quo while acknowledging that minor deviations are possible but unlikely to exceed high thresholds." -29873,"Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on November 29, 2024?","Predictions: [63, 35, 45, 10, 45] -Median reasoning: To forecast the probability of the number of active US drilling rigs exceeding, being between, and being less than the specified numbers by November 29, 2024, we need to consider several factors. - -First, we must acknowledge that the current US rig count is 585, with no movement from the previous week and a declining trend over the past year (a reduction of about 6.4%). This indicates a steady or slightly decreasing trend, suggesting a cautious approach by the industry amidst volatile oil prices. - -Second, since the timeframe to the resolution date is only a few weeks away, significant deviations from the current trend are less probable unless market dynamics change dramatically, which there is currently no indication of happening. - -Third, historical trends, recent data, and expert opinions suggest limited capacity and incentive to increase the rig count substantially in the near term. There have been no notable changes in policy or market factors that hint at an immediate increase. - -Taking into account these considerations, the likelihood of the rig count exceeding 590 appears low, with most indications pointing towards it either staying around the current number or potentially dropping further, thus - -1. The probability of the number of rigs exceeding 590 is low as it would require an unusually swift and unexpected increase in drilling activity, which is usually constrained by market and economic factors. -2. The probability of rigs being between 585 and 590 is moderate as it aligns with the current stability in rig numbers, given minimal change from current reports and market forecasts. -3. The probability of rigs being below 585 is slightly higher than remaining stable due to the decreasing trend seen over the past year. - -Thus, it’s essential to weigh heavily on the status quo while acknowledging that minor deviations are possible but unlikely to exceed high thresholds." -29874,"Will the number of active US oil drilling rigs be less than 585 on November 29, 2024?","Predictions: [63, 35, 45, 10, 45] -Median reasoning: To forecast the probability of the number of active US drilling rigs exceeding, being between, and being less than the specified numbers by November 29, 2024, we need to consider several factors. - -First, we must acknowledge that the current US rig count is 585, with no movement from the previous week and a declining trend over the past year (a reduction of about 6.4%). This indicates a steady or slightly decreasing trend, suggesting a cautious approach by the industry amidst volatile oil prices. - -Second, since the timeframe to the resolution date is only a few weeks away, significant deviations from the current trend are less probable unless market dynamics change dramatically, which there is currently no indication of happening. - -Third, historical trends, recent data, and expert opinions suggest limited capacity and incentive to increase the rig count substantially in the near term. There have been no notable changes in policy or market factors that hint at an immediate increase. - -Taking into account these considerations, the likelihood of the rig count exceeding 590 appears low, with most indications pointing towards it either staying around the current number or potentially dropping further, thus - -1. The probability of the number of rigs exceeding 590 is low as it would require an unusually swift and unexpected increase in drilling activity, which is usually constrained by market and economic factors. -2. The probability of rigs being between 585 and 590 is moderate as it aligns with the current stability in rig numbers, given minimal change from current reports and market forecasts. -3. The probability of rigs being below 585 is slightly higher than remaining stable due to the decreasing trend seen over the past year. - -Thus, it’s essential to weigh heavily on the status quo while acknowledging that minor deviations are possible but unlikely to exceed high thresholds." -29872,"Will the number of active US drilling rigs exceed 590 on November 29, 2024?","Predictions: [15, 75, 30, 20, 20] -Median reasoning: In order to predict whether the number of active US drilling rigs will exceed 590 on November 29, 2024, we need to consider current trends, past data, and any potential influencing factors. - -Key data points include: -- The current rig count is 585 as per the most recent data from November 8, 2024. -- The rig count has remained stable at 585 for several weeks, with no significant upward or downward changes. -- The rig count has decreased from 625 a year ago, and the number of oil rigs stands at 480, down from 504 this time last year. -- Historical data suggests that the rig count has not shown volatility in the short term in the past months, indicating a base case expectation of stability rather than growth. - -Analyzing recent trends: -- There has been no strong indication of an uptick in the number of rigs. The stability of the rig count around 585 suggests industrial inertia. -- Economic factors, such as decreased global oil demand and higher operating costs, further suppress expectations for a rapid increase in rig numbers. -- Political influence, such as President-Elect Donald Trump's pro-drilling stance, may eventually lead to increased activity, but these policy effects will take time to materialize and are unlikely to significantly affect the rig count in the short period remaining until the prediction deadline. -- Market conditions, including reduced demand from China, less urgency for increased rigs. - -Given the data and the short window remaining for change, we should expect the most probable outcome to be continuing stability around the current 585 rigs, with probabilities adjusted for some variance but unlikely to cross the threshold of 590. - -Forecasting the probability distribution: -1. For the rig count exceeding 590: Given the stability in numbers and the lack of upward driving forces in the near term, the likelihood of exceeding 590 by November 29, 2024, is very low. -2. For the rig count being between 585 and 590 inclusive: The count sits exactly at 585, indicating the highest probability range as a probable status quo. -3. For the rig count being less than 585: Given the stability and absence of downward trends, a minor chance for a decrease exists but is smaller than maintaining current numbers. - -Considering these factors, I assign the following probabilities: -- Exceeding 590: 5% -- Between 585 and 590 inclusive: 75% -- Less than 585: 20%" -29873,"Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on November 29, 2024?","Predictions: [15, 75, 30, 20, 20] -Median reasoning: In order to predict whether the number of active US drilling rigs will exceed 590 on November 29, 2024, we need to consider current trends, past data, and any potential influencing factors. - -Key data points include: -- The current rig count is 585 as per the most recent data from November 8, 2024. -- The rig count has remained stable at 585 for several weeks, with no significant upward or downward changes. -- The rig count has decreased from 625 a year ago, and the number of oil rigs stands at 480, down from 504 this time last year. -- Historical data suggests that the rig count has not shown volatility in the short term in the past months, indicating a base case expectation of stability rather than growth. - -Analyzing recent trends: -- There has been no strong indication of an uptick in the number of rigs. The stability of the rig count around 585 suggests industrial inertia. -- Economic factors, such as decreased global oil demand and higher operating costs, further suppress expectations for a rapid increase in rig numbers. -- Political influence, such as President-Elect Donald Trump's pro-drilling stance, may eventually lead to increased activity, but these policy effects will take time to materialize and are unlikely to significantly affect the rig count in the short period remaining until the prediction deadline. -- Market conditions, including reduced demand from China, less urgency for increased rigs. - -Given the data and the short window remaining for change, we should expect the most probable outcome to be continuing stability around the current 585 rigs, with probabilities adjusted for some variance but unlikely to cross the threshold of 590. - -Forecasting the probability distribution: -1. For the rig count exceeding 590: Given the stability in numbers and the lack of upward driving forces in the near term, the likelihood of exceeding 590 by November 29, 2024, is very low. -2. For the rig count being between 585 and 590 inclusive: The count sits exactly at 585, indicating the highest probability range as a probable status quo. -3. For the rig count being less than 585: Given the stability and absence of downward trends, a minor chance for a decrease exists but is smaller than maintaining current numbers. - -Considering these factors, I assign the following probabilities: -- Exceeding 590: 5% -- Between 585 and 590 inclusive: 75% -- Less than 585: 20%" -29874,"Will the number of active US oil drilling rigs be less than 585 on November 29, 2024?","Predictions: [15, 75, 30, 20, 20] -Median reasoning: In order to predict whether the number of active US drilling rigs will exceed 590 on November 29, 2024, we need to consider current trends, past data, and any potential influencing factors. - -Key data points include: -- The current rig count is 585 as per the most recent data from November 8, 2024. -- The rig count has remained stable at 585 for several weeks, with no significant upward or downward changes. -- The rig count has decreased from 625 a year ago, and the number of oil rigs stands at 480, down from 504 this time last year. -- Historical data suggests that the rig count has not shown volatility in the short term in the past months, indicating a base case expectation of stability rather than growth. - -Analyzing recent trends: -- There has been no strong indication of an uptick in the number of rigs. The stability of the rig count around 585 suggests industrial inertia. -- Economic factors, such as decreased global oil demand and higher operating costs, further suppress expectations for a rapid increase in rig numbers. -- Political influence, such as President-Elect Donald Trump's pro-drilling stance, may eventually lead to increased activity, but these policy effects will take time to materialize and are unlikely to significantly affect the rig count in the short period remaining until the prediction deadline. -- Market conditions, including reduced demand from China, less urgency for increased rigs. - -Given the data and the short window remaining for change, we should expect the most probable outcome to be continuing stability around the current 585 rigs, with probabilities adjusted for some variance but unlikely to cross the threshold of 590. - -Forecasting the probability distribution: -1. For the rig count exceeding 590: Given the stability in numbers and the lack of upward driving forces in the near term, the likelihood of exceeding 590 by November 29, 2024, is very low. -2. For the rig count being between 585 and 590 inclusive: The count sits exactly at 585, indicating the highest probability range as a probable status quo. -3. For the rig count being less than 585: Given the stability and absence of downward trends, a minor chance for a decrease exists but is smaller than maintaining current numbers. - -Considering these factors, I assign the following probabilities: -- Exceeding 590: 5% -- Between 585 and 590 inclusive: 75% -- Less than 585: 20%" -29939,Will the Federal Register list 3 or more executive orders by President Joe Biden for November 2024? ,"Predictions: [10, 5, 20, 10, 20] -Median reasoning: To predict the number of executive orders listed in the Federal Register for November 2024, we must consider several factors. Historically, President Joe Biden has issued executive orders relatively frequently, but the number can vary significantly month to month. The dataset provided highlights two executive orders in November 2024 related to national emergencies. It's plausible that another could be announced, but not guaranteed or evident from the current information. - -1. Base Rate Analysis: Biden's pace for executive orders has been variable. While sometimes multiple orders occur in a short period, more than two in a month is not unprecedented but not highly frequent either. - -2. Timeframe Consideration: The date is November 12th, leaving a bit less than three weeks until the month's end. In terms of routine executive action, this is enough time for additional orders but not a certainty, especially given the lull that sometimes accompanies mid-month periods. - -3. Expert Opinions & Past Trends: Neither research assistant identified a third confirmed executive order for November. The nature of government and the need for executive orders for emergencies or updates could influence Biden to issue more, but past data suggests it's not always an everyday occurrence. - -4. External & Novel Influences: The international and domestic environments may press for additional orders, yet none are obviously impending. - -In conclusion, given the historical frequency of 2-3 executive orders a month, coupled with the current observations (only two known), the most likely outcome is the listing of 1 or 2 executive orders. Therefore, I assign the highest probability to the questions based on historical administrative cadence and the existing state of documentation." -29938,Will the Federal Register list 1 or 2 executive orders by President Joe Biden for November 2024? ,"Predictions: [10, 5, 20, 10, 20] -Median reasoning: To predict the number of executive orders listed in the Federal Register for November 2024, we must consider several factors. Historically, President Joe Biden has issued executive orders relatively frequently, but the number can vary significantly month to month. The dataset provided highlights two executive orders in November 2024 related to national emergencies. It's plausible that another could be announced, but not guaranteed or evident from the current information. - -1. Base Rate Analysis: Biden's pace for executive orders has been variable. While sometimes multiple orders occur in a short period, more than two in a month is not unprecedented but not highly frequent either. - -2. Timeframe Consideration: The date is November 12th, leaving a bit less than three weeks until the month's end. In terms of routine executive action, this is enough time for additional orders but not a certainty, especially given the lull that sometimes accompanies mid-month periods. - -3. Expert Opinions & Past Trends: Neither research assistant identified a third confirmed executive order for November. The nature of government and the need for executive orders for emergencies or updates could influence Biden to issue more, but past data suggests it's not always an everyday occurrence. - -4. External & Novel Influences: The international and domestic environments may press for additional orders, yet none are obviously impending. - -In conclusion, given the historical frequency of 2-3 executive orders a month, coupled with the current observations (only two known), the most likely outcome is the listing of 1 or 2 executive orders. Therefore, I assign the highest probability to the questions based on historical administrative cadence and the existing state of documentation." -29937,Will the Federal Register list exactly zero executive orders by President Joe Biden for November 2024? ,"Predictions: [10, 5, 20, 10, 20] -Median reasoning: To predict the number of executive orders listed in the Federal Register for November 2024, we must consider several factors. Historically, President Joe Biden has issued executive orders relatively frequently, but the number can vary significantly month to month. The dataset provided highlights two executive orders in November 2024 related to national emergencies. It's plausible that another could be announced, but not guaranteed or evident from the current information. - -1. Base Rate Analysis: Biden's pace for executive orders has been variable. While sometimes multiple orders occur in a short period, more than two in a month is not unprecedented but not highly frequent either. - -2. Timeframe Consideration: The date is November 12th, leaving a bit less than three weeks until the month's end. In terms of routine executive action, this is enough time for additional orders but not a certainty, especially given the lull that sometimes accompanies mid-month periods. - -3. Expert Opinions & Past Trends: Neither research assistant identified a third confirmed executive order for November. The nature of government and the need for executive orders for emergencies or updates could influence Biden to issue more, but past data suggests it's not always an everyday occurrence. - -4. External & Novel Influences: The international and domestic environments may press for additional orders, yet none are obviously impending. - -In conclusion, given the historical frequency of 2-3 executive orders a month, coupled with the current observations (only two known), the most likely outcome is the listing of 1 or 2 executive orders. Therefore, I assign the highest probability to the questions based on historical administrative cadence and the existing state of documentation." -29948,"Will the US sanction Venezuelan oil again by 2025? -","To forecast the likelihood of the US sanctioning Venezuelan oil again by 2025, we'll carefully consider the provided sources, the current geopolitical context, and the likelihood of policy changes in a short period. - -### Analysis of Information: - -1. **Current Status and Immediate History:** - - The article from November 8 confirms that the US has reimposed sanctions on Venezuelan oil as of now, due to political issues concerning democratic reforms. The immediate history suggests that sanctions, once lifted, were reimposed, indicating a volatility in US-Venezuela relations due to political instability in Venezuela. - -2. **Resolution Criteria:** - - The reimposition since November 2024 already satisfies the condition of the query about sanctioning Venezuelan oil before January 1, 2025. Therefore, the outcome has already occurred, and further changes aren't necessary for resolution. - -3. **Timing and Variability:** - - Given the resolution window until December 31, 2024, there is limited time for reversal given historical precedent that such sanctions tend to remain in place unless substantial geopolitical or policy changes occur. The Biden administration reimposed sanctions due to unmet political reform commitments, and there is no clear indication these conditions will be met shortly. - -4. **Base Rates and Historical Context:** - - Historically, sanctions like these, once reimposed, tend to remain due to the complex nature of international relations and the time needed for political reforms. Given the skepticism of Venezuela meeting the terms of the Barbados Agreement quickly, it's unlikely that there will be a retraction of sanctions within the next several weeks. - -### Conclusion: - -As of November 12, 2024, the US has already reimposed sanctions, satisfying the conditions set for the January 1, 2025 deadline. There is no indication, given the short timeframe, that the sanctions will be lifted again before 2025. Thus, the probability that the US will sanction Venezuelan oil again before 2025 is essentially certain given the current situation. - -**Final Forecast:** Probability: 99%" -29947,Will the S&P 500 index go up in December 2024?,"Let me think through this step by step: - -1. First, let's consider the type of question - this is a Market Price Event for a major index. - -2. Historical Context: -- December is traditionally a positive month for stocks (the ""Santa Claus rally"") -- However, markets have been very strong in 2024 (up 36.04% YoY as of October) -- There was a slight decline (-0.99%) from September to October - -3. Timing Considerations: -- This forecast is for December 2024, just about 2-3 weeks from now -- The short time frame means current trends and momentum are highly relevant -- We're looking at month-over-month change, not yearly - -4. Base Rate Analysis: -- Historically, the S&P 500 rises about 60% of months -- December specifically has been positive about 75% of historical instances -- However, after very strong years (>30% gains), subsequent months tend to show more modest returns - -5. Current Market Conditions: -- Recent Dow Jones breakthrough above 44,000 indicates positive momentum -- Markets have already had a very strong year, which could limit further upside -- Interest rates are high but may have peaked -- Inflation has moderated but remains above target - -6. Risk Factors: -- Potential for profit-taking after strong year -- Geopolitical tensions remain -- Fed policy uncertainty -- Holiday trading volumes can increase volatility - -7. Statistical Weighting: -- Historical December positive rate: 75% -- Current market momentum: Positive -- Technical indicators: Mixed after strong run -- Economic indicators: Mixed but stable - -8. Uncertainty Adjustment: -- Very short time horizon increases confidence in current trends -- But year-end dynamics can be unpredictable -- Need to account for historical overconfidence - -Given all these factors, I'm going to start with the historical December success rate of 75% but adjust downward for: -- Already strong year performance (-10%) -- Current market conditions (+5%) -- Economic uncertainty (-5%) -- Short time horizon certainty (+5%) -- Historical overconfidence adjustment (-5%) - -Probability: 65% - -This reflects both the historically positive seasonality while accounting for the unusually strong prior performance and various risk factors. The short time horizon gives more weight to current momentum, but I've tempered the forecast due to the tendency for forecasters to be overconfident." -29939,Will the Federal Register list 3 or more executive orders by President Joe Biden for November 2024? ,"Predictions: [40, 10, 25, 10, 5] -Median reasoning: To determine the probabilities for the number of executive orders by President Joe Biden listed in the Federal Register for November 2024, I need to consider historical trends, the specificity of available information, and the time remaining until the resolution date. Firstly, it is common for presidents to issue executive orders throughout their term, but not at a constant or predictable rate every month. Based on historical data, President Biden has issued executive orders in past months, averaging about 2-4 per month, though this number can fluctuate. - -Given today's date, November 12, 2024, we are already a significant way through the month and have limited evidence of executive orders directly related to November 2024 in the current background data. The only relevant action taken in November so far is the continuation of a national emergency, which is not a new executive order. This suggests at least one executive action, but not necessarily an order, indicating that at least some executive activity is occurring. - -With the available data, there is no strong evidence suggesting the issuance of three or more new executive orders. The lack of cited new executive orders for this month in the provided resources leans against the proposition of three or more orders. Moreover, this administration's recent pace in issuing executive orders does not notably suggest an abnormal increase in output for this timeframe. - -Considering that executive orders are often tied to immediate or strategic policy needs, there may still be one or two orders processed in the remaining days of November. The likelihood of zero executive orders is low given the administration's historical behavior and ongoing executive actions. The probability needs to reflect these nuances: a lower chance of three or more orders, still allowing for a few, but unlikely none at all. - -After analyzing the likelihood of each scenario and adjusting for historical trends and the remaining days for potential executive actions before the resolution date, I frame the probabilities as follows:" -29938,Will the Federal Register list 1 or 2 executive orders by President Joe Biden for November 2024? ,"Predictions: [40, 10, 25, 10, 5] -Median reasoning: To determine the probabilities for the number of executive orders by President Joe Biden listed in the Federal Register for November 2024, I need to consider historical trends, the specificity of available information, and the time remaining until the resolution date. Firstly, it is common for presidents to issue executive orders throughout their term, but not at a constant or predictable rate every month. Based on historical data, President Biden has issued executive orders in past months, averaging about 2-4 per month, though this number can fluctuate. - -Given today's date, November 12, 2024, we are already a significant way through the month and have limited evidence of executive orders directly related to November 2024 in the current background data. The only relevant action taken in November so far is the continuation of a national emergency, which is not a new executive order. This suggests at least one executive action, but not necessarily an order, indicating that at least some executive activity is occurring. - -With the available data, there is no strong evidence suggesting the issuance of three or more new executive orders. The lack of cited new executive orders for this month in the provided resources leans against the proposition of three or more orders. Moreover, this administration's recent pace in issuing executive orders does not notably suggest an abnormal increase in output for this timeframe. - -Considering that executive orders are often tied to immediate or strategic policy needs, there may still be one or two orders processed in the remaining days of November. The likelihood of zero executive orders is low given the administration's historical behavior and ongoing executive actions. The probability needs to reflect these nuances: a lower chance of three or more orders, still allowing for a few, but unlikely none at all. - -After analyzing the likelihood of each scenario and adjusting for historical trends and the remaining days for potential executive actions before the resolution date, I frame the probabilities as follows:" -29937,Will the Federal Register list exactly zero executive orders by President Joe Biden for November 2024? ,"Predictions: [40, 10, 25, 10, 5] -Median reasoning: To determine the probabilities for the number of executive orders by President Joe Biden listed in the Federal Register for November 2024, I need to consider historical trends, the specificity of available information, and the time remaining until the resolution date. Firstly, it is common for presidents to issue executive orders throughout their term, but not at a constant or predictable rate every month. Based on historical data, President Biden has issued executive orders in past months, averaging about 2-4 per month, though this number can fluctuate. - -Given today's date, November 12, 2024, we are already a significant way through the month and have limited evidence of executive orders directly related to November 2024 in the current background data. The only relevant action taken in November so far is the continuation of a national emergency, which is not a new executive order. This suggests at least one executive action, but not necessarily an order, indicating that at least some executive activity is occurring. - -With the available data, there is no strong evidence suggesting the issuance of three or more new executive orders. The lack of cited new executive orders for this month in the provided resources leans against the proposition of three or more orders. Moreover, this administration's recent pace in issuing executive orders does not notably suggest an abnormal increase in output for this timeframe. - -Considering that executive orders are often tied to immediate or strategic policy needs, there may still be one or two orders processed in the remaining days of November. The likelihood of zero executive orders is low given the administration's historical behavior and ongoing executive actions. The probability needs to reflect these nuances: a lower chance of three or more orders, still allowing for a few, but unlikely none at all. - -After analyzing the likelihood of each scenario and adjusting for historical trends and the remaining days for potential executive actions before the resolution date, I frame the probabilities as follows:" -29948,"Will the US sanction Venezuelan oil again by 2025? -","Let me think through this step by step: - -1) Timeline Assessment: -- Today is Nov 12, 2024 -- Resolution date is Dec 31, 2024 -- Only ~7 weeks remain -- This is a very short window for policy changes - -2) Current Status: -- Sanctions were lifted in October 2023 -- According to sources, they were reimposed in April 2024 -- Some waivers extend to May 2025 -- CITGO protection extends to March 2025 - -3) Key Factors: -- Very short timeframe until resolution -- Holiday season typically slows policy changes -- Current administration likely wants stability through year-end -- Oil markets prefer predictability -- Venezuelan compliance with agreements is key factor - -4) Historical Base Rates: -- Sanctions changes typically follow major political developments -- Changes usually telegraphed weeks/months in advance -- End-of-year period typically maintains status quo -- Policy shifts more common around quarterly reviews - -5) Timeline Distribution Analysis: -- Next 2 weeks: 5% chance (holiday slowdown) -- Next 4 weeks: 15% chance -- Next 7 weeks (resolution window): 25% chance -- Next 6 months: 45% chance -- Next year: 65% chance - -6) Risk Factors: -- Venezuelan non-compliance with agreements -- U.S. domestic political pressure -- Oil market disruptions -- Regional stability concerns - -7) Mitigating Factors: -- Very short timeline remaining in 2024 -- Administrative processes take time -- Holiday period reduces likelihood -- Current waivers extend well into 2025 - -8) Confidence Adjustment: -- Starting estimate: 30% -- Adjust down for short timeline: -5% -- Adjust down for holiday period: -3% -- Adjust up for Venezuelan compliance risks: +3% - -Given the extremely short timeline remaining in 2024, the holiday period slowdown, and the existence of waivers extending into 2025, I believe the likelihood of new sanctions being imposed in the next 7 weeks is relatively low. While there are risks of Venezuelan non-compliance, the administrative and political processes required for sanctions changes typically take longer than the remaining window. - -Adjusting for historical overconfidence and the general tendency for negative resolutions, I'll further reduce my final estimate slightly. - -Probability: 23%" -29947,Will the S&P 500 index go up in December 2024?,"Let me analyze this Market Price Event question step by step: - -1. Historical December Performance: -- The S&P 500 has historically performed well in December (known as the ""Santa Claus Rally"") -- Studies show December is positive about 75% of the time historically -- However, this is a general trend and specific circumstances matter more - -2. Current Market Conditions: -- Market is up significantly YoY (36.04%) -- Recent slight decline (-0.99% month over month) -- Dow Jones recently hit new highs above 44,000 -- We're seeing high valuations historically - -3. Key Factors for December 2024: -- Fed policy and interest rates will be crucial -- Election aftermath could create volatility -- Year-end tax-loss harvesting and window dressing -- Potential recession concerns -- Corporate earnings expectations - -4. Time Horizon Analysis: -- Question resolves in about 6 weeks -- Comparing final trading day December vs November -- Short timeframe reduces uncertainty somewhat -- Still enough time for significant moves - -5. Risk Factors: -- Market already up substantially in 2024 -- High valuations could limit upside -- Geopolitical risks remain -- Interest rates still historically high - -6. Base Rate Adjustment: -- Historical 75% December positive rate -- Adjust down for: - * Already strong 2024 performance - * Higher than average valuations - * Economic uncertainty -- Adjust up for: - * Recent market momentum - * Traditional seasonal strength - -7. Resolution Criteria Analysis: -- Only needs to be higher than November close -- Doesn't need to be significantly higher -- Single day comparison reduces noise - -Given all factors, I believe the base rate of 75% needs to be adjusted down due to the strong prior performance and high valuations, but not dramatically due to positive momentum and seasonal factors. - -The recent market strength and seasonal tendencies suggest better than 50% odds, but the high valuations and year-to-date gains suggest being more conservative than the historical 75% success rate. - -Balancing these factors and accounting for general forecaster overconfidence, I estimate: - -Probability: 65%" -29946,"Before December 31, 2024, will Tech Crunch report new layoffs at Netflix?","Let me think about this step by step: - -1. Timeline Analysis: -- Only about 7 weeks remain until resolution -- This is a relatively short timeframe for a layoff event to both occur and be reported by TechCrunch -- The reported potential layoffs from Variety suggest imminent action - -2. Historical Pattern Analysis: -- Netflix has had sporadic layoffs in recent years -- Previous layoffs were generally in hundreds of employees -- They've had layoffs in both 2022 and 2023 -- Already had layoffs of 150 employees in May 2024 - -3. Source Credibility Assessment: -- Variety is reporting potential imminent layoffs -- This is a credible industry publication -- However, resolution depends specifically on TechCrunch reporting -- There could be lag time between announcement and TechCrunch updating their tracker - -4. Economic Context: -- Tech sector continues to face challenges -- Streaming market remains highly competitive -- Q4 often sees companies making final yearly adjustments - -5. Resolution Criteria Specifics: -- Must appear on TechCrunch's tracker specifically -- Must be listed under Oct/Nov/Dec 2024 -- Only Netflix proper counts, not subsidiaries -- TechCrunch must continue maintaining the tracker - -6. Risk Factors: -- TechCrunch could miss reporting it -- Layoffs might be delayed beyond December -- Layoffs might be too small to make the tracker -- TechCrunch could stop updating their tracker - -7. Timeline Probability Distribution: -- Next 2 weeks (given Variety report): ~20% -- Next 4 weeks: ~15% -- Next 7 weeks (full period): ~10% -- If this extended to 3 months: ~35% -- If this extended to 6 months: ~50% - -8. Confidence Adjustment: -- Base case is currently ""No"" as it's not yet reported -- Recent Variety report increases probability significantly -- Need to account for historical overconfidence -- Short timeframe reduces probability - -Given the very specific resolution criteria (must appear on TechCrunch tracker), the short timeframe, but balanced against the credible report of imminent layoffs, I estimate: - -Probability: 42% - -This accounts for both the strong signal from Variety about imminent layoffs (pushing probability up) and the short timeframe and specific resolution requirements (pushing probability down). The 42% reflects that while layoffs seem likely soon, there are several failure points in the resolution criteria that could prevent a ""Yes"" resolution even if layoffs do occur." -29945,Will Morgan Wallen win an award in any of these 2024 CMA Awards categories?,"To assess the probability of Morgan Wallen winning an award in any of the specified 2024 CMA Awards categories, we will carefully consider various factors including historical data, current expert opinions, and other contextual elements. - -1. **Historical Considerations**: Morgan Wallen is leading the nominations at the CMA Awards, which indicates he is a significant player in the country music scene. Historically, artists with multiple nominations have a decent chance of winning at least one award, although this isn’t guaranteed. - -2. **Current Expert Opinions**: Country Central suggests that Chris Stapleton and Luke Combs are stronger contenders for major categories like Entertainer of the Year. Although it's noted that Wallen is a nominee, he may face challenges due to his independent label status, potentially affecting his chances. - -3. **Timing and Recency**: Today’s date is November 12, 2024, and the event is scheduled for November 20, 2024, giving us a narrow window before the event occurs. Thus, the probability should not heavily lean towards short-term trends or unexpected swings. However, Wallen's numerous nominations are a positive indicator. - -4. **Base Rates and Novel Context**: Given his nominations, it would be reasonable to assume a certain baseline probability of winning at least one award, even against strong competitors. His popularity and support from fans could counterbalance the industry dynamics slightly. - -5. **Outcome Categories**: Wallen is nominated in two major categories specifically mentioned (Entertainer of the Year and Male Vocalist of the Year). While his chances in either are debated, the combined probability across various categories slightly increases his odds. - -6. **Probability Distribution and Event Type**: This is an ""Other Binary Outcome Event"", where careful analysis of probability distribution is critical. Since we are predicting the result of a scheduled event, and given base rates, we should maintain caution in estimating too high or too low a probability due to the Brier score consideration. - -7. **Adjustment for Bias**: Considering the historical tendency toward overconfidence, an adjustment is made to slightly temper optimistic estimates. - -After evaluating these considerations, including the recency of information, competition strength, and historical trends, the probability of Wallen winning in at least one of the nominated categories is adjusted to be neither too high nor too low, reflecting the competitive nature of the awards and uncertain elements like industry support. - -**Probability: 45%**" -29944,"Will Helldivers 2 win in any of these categories at the 2024 Golden Joystick Awards? -","Let me think about this step by step: - -1. First, let's consider the categories Helldivers 2 is nominated in: -- Best Multiplayer Game -- Console Game of the Year -- Best Game Trailer - -2. Key factors to consider: -- Helldivers 2 has been very successful commercially and critically -- The game has strong competition in each category -- These are public voted awards, which can favor popular games -- The game has generated significant buzz and community engagement - -3. Let's break down each category's chances: - -Best Multiplayer Game: -- Strong competition from established franchises -- Helldivers 2 has received praise specifically for multiplayer -- Estimate: 25% chance - -Console Game of the Year: -- Extremely competitive category -- Several major AAA titles as competition -- Estimate: 15% chance - -Best Game Trailer: -- Generally less competitive category -- Helldivers 2 had good but not revolutionary trailers -- Estimate: 10% chance - -4. Mathematical consideration: -- Need to calculate probability of winning at least one -- P(winning at least one) = 1 - P(losing all) -- P(losing all) = 0.75 * 0.85 * 0.9 = 0.574 -- P(winning at least one) = 1 - 0.574 = 0.426 - -5. Adjusting factors: -- Public voting tends to favor popular mainstream titles -- Record number of votes suggests high engagement -- Short time until resolution (10 days) means little uncertainty -- Historical precedent shows underdogs rarely win multiple nominations - -6. Base rate consideration: -- In previous years, games with multiple nominations typically win at least one -- However, they tend to be major AAA titles -- Helldivers 2, while successful, is not from a major publisher - -7. Final adjustment: -- Need to account for historical overconfidence -- Multiple categories increases chances but competition is steep -- Public voting adds volatility - -Given all these factors, I believe there's a moderate chance of Helldivers 2 winning at least one category. The mathematical probability of 42.6% needs to be adjusted down slightly due to historical overconfidence and the strength of competition. - -Probability: 38%" -29943,"Will Trump Media & Technology Group (the owner of Truth Social) stock continuously remain below its all-time high before January 1, 2025?","To forecast the probability of Trump Media & Technology Group (TMTG) stock remaining below its all-time high of $79.38 before January 1, 2025, let's break down the factors influencing this forecast. - -### Background Analysis: -1. **Stock Performance Post Trump's Reelection**: TMTG's stock surged to over $45 but cooled to below $36, indicating volatility but stabilizing below the all-time high[5]. -2. **Current Economic and Market Sentiment**: The sources highlight a cooling off of the ""Trump trade,"" with minimal changes in the Dow Jones and Nasdaq, implying a more stabilized market environment not conducive to dramatic stock soaring[1][3]. -3. **Volatility and Political Influence**: TMTG's stock has shown high volatility and a significant dependence on Trump's political moves. While volatility suggests potential swings, the specific mention that TMTG had a muted reaction to Trump's reelection implies that significant upward movement may not be imminent[4][5]. -4. **Company Fundamentals and Investor Sentiment**: The sources do not reflect TMTG as part of the standout performers post-election, indicating that investors might not have fundamentally bullish sentiments on this stock in the short term[2]. - -### Timeframe Considerations: -- **Current Date**: It is November 12, 2024, with a resolution timeframe of approximately 1.5 months. -- **Historical Trends and Base Rates**: Based on the historical interaction of political events and stock responses, sudden spikes to all-time highs are rare within a short timeframe without a significant trigger. -- **Time for Change**: The short period left until the resolution date further decreases the likelihood of a dramatic upward stock movement, given previous reactions. - -### Uncertainty and Volatility: -- **Weekly Variance**: Given the volatility mentioned, while there is potential for sizable swings, the fact that the ""muted reaction"" occurred following a significant event like Trump's reelection is critical[4]. - -### Probability Distribution: -- **Short-Term (1.5 Months)**: Given the immediate timeframe, and accounting for past overconfidence, we will skew the probability towards the default of the stock remaining below the all-time high. -- **Medium-Term (6 Months - Another Benchmark)**: While not directly needed, forecasting a 6-month scenario might not significantly change unless new catalysts arise. - -Using this data and analysis: -- **Base Case Likelihood**: The base case assumption favors a resolution as ""Yes"" that the stock remains below $79.38. -- **No Significant Economic Indicators Suggest Major Stock Surge Before Jan 1, 2025**: The economic sentiment and political environment do not indicate an artificial push to historical highs imminently. - -### Conclusion: -Given the conservative base rate forecast preference, the muted reaction to major news, and short timeframe until the date, a cautious estimate would be: - -Probability of TMTG remaining below its all-time high by January 1, 2025: **85%**" -29942,"Will Elon Musk or Dana White be chosen for a paid position within the Trump White House before January 1, 2025?","To address the problem of forecasting whether Elon Musk or Dana White will be chosen for a paid position within the Trump White House before January 1, 2025, I'll follow a structured analysis. - -### Step-by-Step Analysis: - -1. **Base Rate and Historical Precedent:** - - Historically, positions in the White House, especially in such a polarized political environment, are predominantly filled by politically established figures or those with significant governmental experience. While Trump has been known to appoint outsiders or non-traditional figures, those usually align closely with recognized political agendas or offer strategic advantages. - - The specificity of this situation—choosing either Musk or White—narrows the field considerably compared to a broader spectrum of possible appointees. Although both individuals have notable public profiles and varying degrees of support from Trump, their formal inclusion in an official, salaried capacity is unprecedented. - -2. **Analysis of Given Information:** - - **Elon Musk:** There are indications of a strong, albeit informal, relationship with Trump. His significant financial support and regular presence at Mar-a-Lago hint at influence but likely in an advisory role, avoiding official titles due to the complexities of Senate confirmation and Musk's high-profile business commitments. - - **Dana White:** While a vocal supporter, his association with controversial figures may hinder a formal role. His current trajectory suggests influence within Trump's circles but not necessarily a salaried position. - - The recent coverage doesn't report any concrete developments or intentions of formal roles for either individual, which suggests limited recent progress in these directions. - -3. **Current Political Factors and Timing:** - - As of late 2024, Trump is beginning to fill his administration with figures like Susie Wiles and Richard Grenell, displaying preferences for those with essential experience or prior collaborations. - - The immediate timeline until the resolution date (about 1.5 months) is notably short, generally a difficult period for substantial, unexpected changes, especially significant appointments. - -4. **Prediction for Different Timeframes:** - - **Within 1 month (by mid-December 2024):** Very minimal likelihood exists for Moses's government role changes, especially given the upcoming holiday season. - - **Within the remaining time until January 1, 2025:** Again, the compressed timeframe and intricate nature of appointments limit the likelihood of such appointments. - -5. **Additional Considerations:** - - Administrative complexity and Trump's decision-making style suggest a focus on political veterans or staunch political allies by January 2025. Neither Musk nor White fits neatly into these prerequisites based on currently available information and trends. - -6. **Adjusted Probability:** - - Accounting for prior overconfidence, the actual likelihood of this happening is lower than initially estimated without analysis. No substantial indications exist that deviate from normative historical trends for such short-term high-profile appointments. - -Based on this analysis, the most rational forecast given this situation is: - -**Probability: 5%**" -29941,"Will the Alta ski resort be listed as Open by On the Snow's Ski Report on November 22, 2024?","Let me think through this step by step: - -1. Question Type: This is a Scheduled Event with binary outcome, similar to a Competition Event but with weather dependency. - -2. Timeline Analysis: -- Current date: Nov 12, 2024 -- Resolution date: Nov 22, 2024 -- Only 10 days difference, which means high certainty about current conditions and plans - -3. Key Factors: -- Official planned opening date matches resolution date exactly -- Multiple recent sources confirm Nov 22 as planned date -- All sources note ""weather permitting"" caveat -- This is a major ski resort with significant snowmaking capabilities - -4. Historical Context: -- Alta typically aims for mid-to-late November openings -- They have infrastructure and experience in meeting planned opening dates -- Snowmaking capabilities provide buffer against marginal natural conditions - -5. Risk Factors: -- Weather dependency is the main risk -- Only 10 days to resolution means current conditions highly relevant -- ""Conditions permitting"" appears in multiple sources, indicating real uncertainty - -6. Confidence Adjustments: -- Multiple independent sources increase confidence -- Very short timeline reduces uncertainty about external factors -- But weather dependency is significant -- Need to adjust for historical overconfidence - -7. Probability Distribution: -- Base case (planned opening proceeds): ~85% -- Weather disruption risk: ~15% -- Need to adjust down slightly for overconfidence bias - -The fact that this is only 10 days out and multiple recent sources confirm the date increases confidence substantially. However, the explicit ""weather permitting"" caveats in all sources, combined with the need to adjust for overconfidence, suggests we should moderate our prediction. - -The resolution criteria are very specific about checking the OnTheSnow report, which adds a small additional uncertainty factor. - -Given the very short timeline, recent confirmations, but acknowledging weather dependency and historical overconfidence, I will adjust down from my initial ~85% to: - -Probability: 78%" -29940,"Will the cheapest new Tesla Model 3 be listed as under $43,000 on December 30, 2024?","Let me think through this step by step as a Market Price Event: - -1. Current State Analysis: -- Price is currently $42,490, which is $510 below the threshold -- This price has been stable recently -- We're only looking ~6 weeks into the future - -2. Time Horizon Consideration: -- Very short time frame (less than 2 months) -- Limited opportunity for major pricing changes -- Tesla typically makes pricing changes with some advance notice - -3. Historical Pattern Analysis: -- Tesla does make frequent price adjustments -- Changes can be significant but are usually telegraphed -- End of quarter/year timing could affect pricing strategy -- No recent announcements of planned changes - -4. Risk Factors: -Upward pressure: -- End of year pricing adjustments -- Potential supply chain issues -- Inflation concerns -- Desire to boost Q4 revenue - -Downward pressure: -- Competition from other EV makers -- Need to maintain market share -- Recent price stability -- Consumer price sensitivity - -5. Timeline Analysis: -- If we extended this to 6 months: 65% probability -- If we shortened to 2 weeks: 90% probability -- Given ~6 weeks: higher confidence than 6 months but lower than 2 weeks - -6. Key Considerations: -- Very close to threshold ($510 margin) -- Short timeframe reduces uncertainty -- End of year timing increases risk -- No announced changes yet - -7. Confidence Adjustment: -- Base case is strongly ""Yes"" given current price -- But need to account for: - * Historical overconfidence - * Small margin to threshold - * End of year timing - * Tesla's history of price changes - -Given the very short timeframe, current stable price, and lack of announced changes, I would normally be very confident in a ""Yes"". However, I need to account for: -- The small $510 buffer to threshold -- Tesla's history of price adjustments -- End of year timing considerations -- My historical overconfidence - -The short timeframe is the strongest factor here, significantly limiting the likelihood of major changes. - -Probability: 84% - -This reflects high confidence due to the short timeframe and current price, but accounts for the small buffer to threshold and Tesla's pricing volatility history." -30014,Will China's youth unemployment rate be greater than 19.0 for November 2024?,"Predictions: [15, 20, 15, 15, 35] -Median reasoning: To provide a rational forecast for the youth unemployment rate in China for November 2024, let's first consolidate the data and trends provided. The last known rate was 17.6% in September 2024. The decreasing trend from 21.3% in June to 17.6% in September suggests a significant drop. Seasonal factors and economic pressures, such as the large number of graduates entering the job market, explain this fluctuation. However, the recent decrease in the unemployment rate is likely influenced by seasonal hiring patterns as well as possible economic recoveries and stabilization tactics. - -Analyzing the stimulus package reveals doubts about its immediate impact on unemployment rates due to its focus on local government debts rather than direct stimulus involving job creation. The looming possibility of increased tariffs or trade tensions due to Donald Trump's policies could also negatively impact economic conditions and employment. - -Given that the unemployment rate for September is already below 19%, it's plausible that this trend can continue, especially considering typical hiring in the last quarter and recent efforts to stabilize the labor market despite structural issues. The substantial jump back to rates above 19% seems improbable without significant negative catalysts in the short span left before the resolution. - -Considering the probabilities: -- Greater than 19.0% would require a significant adverse change in hiring patterns or economic shock. -- Greater than 18.0% and less than or equal to 19.0% remains a possibility, considering higher statistical variability or slight negative trends not visible yet. -- I lean more toward the rate staying stable or even improving slightly due to continued seasonal effects, leading to my forecast: - -- Greater than 19.0%: Lower probability due to the downward trend and lack of significant negative forecasts short-term. -- Greater than 18.0% and less than or equal to 19.0%: Moderate chance considering historical shifts and gradual trends. -- Greater than 17.0% and less than or equal to 18.0%: Higher likelihood due to existing trend and seasonality. -- Less than or equal to 17.0%: Less likely, but not impossible as economic strategies may succeed better than expected. - -In assessing overall probabilities, ensuring a full 100% distribution, I predict the following outcomes:" -30013,Will China's youth unemployment rate be greater than 18.0 and less than or equal to 19.0 for November 2024?,"Predictions: [15, 20, 15, 15, 35] -Median reasoning: To provide a rational forecast for the youth unemployment rate in China for November 2024, let's first consolidate the data and trends provided. The last known rate was 17.6% in September 2024. The decreasing trend from 21.3% in June to 17.6% in September suggests a significant drop. Seasonal factors and economic pressures, such as the large number of graduates entering the job market, explain this fluctuation. However, the recent decrease in the unemployment rate is likely influenced by seasonal hiring patterns as well as possible economic recoveries and stabilization tactics. - -Analyzing the stimulus package reveals doubts about its immediate impact on unemployment rates due to its focus on local government debts rather than direct stimulus involving job creation. The looming possibility of increased tariffs or trade tensions due to Donald Trump's policies could also negatively impact economic conditions and employment. - -Given that the unemployment rate for September is already below 19%, it's plausible that this trend can continue, especially considering typical hiring in the last quarter and recent efforts to stabilize the labor market despite structural issues. The substantial jump back to rates above 19% seems improbable without significant negative catalysts in the short span left before the resolution. - -Considering the probabilities: -- Greater than 19.0% would require a significant adverse change in hiring patterns or economic shock. -- Greater than 18.0% and less than or equal to 19.0% remains a possibility, considering higher statistical variability or slight negative trends not visible yet. -- I lean more toward the rate staying stable or even improving slightly due to continued seasonal effects, leading to my forecast: - -- Greater than 19.0%: Lower probability due to the downward trend and lack of significant negative forecasts short-term. -- Greater than 18.0% and less than or equal to 19.0%: Moderate chance considering historical shifts and gradual trends. -- Greater than 17.0% and less than or equal to 18.0%: Higher likelihood due to existing trend and seasonality. -- Less than or equal to 17.0%: Less likely, but not impossible as economic strategies may succeed better than expected. - -In assessing overall probabilities, ensuring a full 100% distribution, I predict the following outcomes:" -30012,Will China's youth unemployment rate be greater than 17.0 and less than or equal to 18.0 for November 2024?,"Predictions: [15, 20, 15, 15, 35] -Median reasoning: To provide a rational forecast for the youth unemployment rate in China for November 2024, let's first consolidate the data and trends provided. The last known rate was 17.6% in September 2024. The decreasing trend from 21.3% in June to 17.6% in September suggests a significant drop. Seasonal factors and economic pressures, such as the large number of graduates entering the job market, explain this fluctuation. However, the recent decrease in the unemployment rate is likely influenced by seasonal hiring patterns as well as possible economic recoveries and stabilization tactics. - -Analyzing the stimulus package reveals doubts about its immediate impact on unemployment rates due to its focus on local government debts rather than direct stimulus involving job creation. The looming possibility of increased tariffs or trade tensions due to Donald Trump's policies could also negatively impact economic conditions and employment. - -Given that the unemployment rate for September is already below 19%, it's plausible that this trend can continue, especially considering typical hiring in the last quarter and recent efforts to stabilize the labor market despite structural issues. The substantial jump back to rates above 19% seems improbable without significant negative catalysts in the short span left before the resolution. - -Considering the probabilities: -- Greater than 19.0% would require a significant adverse change in hiring patterns or economic shock. -- Greater than 18.0% and less than or equal to 19.0% remains a possibility, considering higher statistical variability or slight negative trends not visible yet. -- I lean more toward the rate staying stable or even improving slightly due to continued seasonal effects, leading to my forecast: - -- Greater than 19.0%: Lower probability due to the downward trend and lack of significant negative forecasts short-term. -- Greater than 18.0% and less than or equal to 19.0%: Moderate chance considering historical shifts and gradual trends. -- Greater than 17.0% and less than or equal to 18.0%: Higher likelihood due to existing trend and seasonality. -- Less than or equal to 17.0%: Less likely, but not impossible as economic strategies may succeed better than expected. - -In assessing overall probabilities, ensuring a full 100% distribution, I predict the following outcomes:" -30011,Will China's youth unemployment rate be less than or equal to 17.0 for November 2024?,"Predictions: [15, 20, 15, 15, 35] -Median reasoning: To provide a rational forecast for the youth unemployment rate in China for November 2024, let's first consolidate the data and trends provided. The last known rate was 17.6% in September 2024. The decreasing trend from 21.3% in June to 17.6% in September suggests a significant drop. Seasonal factors and economic pressures, such as the large number of graduates entering the job market, explain this fluctuation. However, the recent decrease in the unemployment rate is likely influenced by seasonal hiring patterns as well as possible economic recoveries and stabilization tactics. - -Analyzing the stimulus package reveals doubts about its immediate impact on unemployment rates due to its focus on local government debts rather than direct stimulus involving job creation. The looming possibility of increased tariffs or trade tensions due to Donald Trump's policies could also negatively impact economic conditions and employment. - -Given that the unemployment rate for September is already below 19%, it's plausible that this trend can continue, especially considering typical hiring in the last quarter and recent efforts to stabilize the labor market despite structural issues. The substantial jump back to rates above 19% seems improbable without significant negative catalysts in the short span left before the resolution. - -Considering the probabilities: -- Greater than 19.0% would require a significant adverse change in hiring patterns or economic shock. -- Greater than 18.0% and less than or equal to 19.0% remains a possibility, considering higher statistical variability or slight negative trends not visible yet. -- I lean more toward the rate staying stable or even improving slightly due to continued seasonal effects, leading to my forecast: - -- Greater than 19.0%: Lower probability due to the downward trend and lack of significant negative forecasts short-term. -- Greater than 18.0% and less than or equal to 19.0%: Moderate chance considering historical shifts and gradual trends. -- Greater than 17.0% and less than or equal to 18.0%: Higher likelihood due to existing trend and seasonality. -- Less than or equal to 17.0%: Less likely, but not impossible as economic strategies may succeed better than expected. - -In assessing overall probabilities, ensuring a full 100% distribution, I predict the following outcomes:" -30009,"Will Nebraska have 1.5 million or more residents living in drought on December 31, 2024?","Predictions: [40, 20, 65, 30, 30] -Median reasoning: To forecast the likelihood of Nebraska and Alabama having a specified number of residents living in drought conditions by December 31, 2024, I carefully considered the available information, including recent drought data, forecasts, and regional conditions. - -### Nebraska Analysis: -1. **Current Conditions**: As of early November 2024, Nebraska is experiencing severe drought, with large portions of the state under at least Severe Drought and some areas in Extreme Drought. -2. **Regional Trends**: The broader regional context indicates persistent and expanding drought conditions in surrounding states like South Dakota. -3. **Weather Patterns**: The shift towards a La Niña pattern during winter typically exacerbates drought conditions, suggesting minimal relief. -4. **Base Rates and Historical Context**: Given historical precedent, Nebraska often experiences prolonged droughts during such climatic patterns. -5. **Resolution Criteria**: Resolves as 'Yes' if 1.5 million or more residents are in drought. -6. **Time Frame and Variance**: There is little time for significant improvements, and recent trends point towards worsening conditions rather than relief. -Based on these factors, it is highly probable that over 1.5 million Nebraska residents will continue to live in drought conditions by the resolution date. - -### Alabama Analysis: -1. **Regional Drought Conditions**: No recent specific data outlines severe, widespread drought in Alabama to the extent seen in Nebraska. -2. **Forecast Patterns**: The forecasts indicate improving conditions in the Southern Plains and South, suggesting possible alleviation in areas like Alabama. -3. **Base Rates and Historical Context**: Historically, Alabama does experience periodic drought, but current trends do not confirm a severe or extreme event affecting over 2 million residents. -4. **Resolution Criteria**: Resolves as 'Yes' if 2.0 million or more residents are in drought. -5. **Time Frame and Variance**: Similar to Nebraska, but the context is less severe, suggesting lower probabilities of meeting the threshold in Alabama. -Considering these aspects, it seems less likely that Alabama will have over 2.0 million residents in drought conditions by the resolution date. - -My final estimates are as follows: - -**Predictions:** -- Nebraska (ID: 30009): A high probability given persistent severe drought and recent environmental patterns that aren't indicating improvement. -- Alabama (ID: 30003): A lower probability, given the current trends suggesting potential improvements in drought conditions. - -These predictions were carefully vetted for overconfidence, particularly considering how often I have previously overestimated outcomes." -30003,"Will Alabama have 2.0 million or more residents living in drought on December 31, 2024?","Predictions: [40, 20, 65, 30, 30] -Median reasoning: To forecast the likelihood of Nebraska and Alabama having a specified number of residents living in drought conditions by December 31, 2024, I carefully considered the available information, including recent drought data, forecasts, and regional conditions. - -### Nebraska Analysis: -1. **Current Conditions**: As of early November 2024, Nebraska is experiencing severe drought, with large portions of the state under at least Severe Drought and some areas in Extreme Drought. -2. **Regional Trends**: The broader regional context indicates persistent and expanding drought conditions in surrounding states like South Dakota. -3. **Weather Patterns**: The shift towards a La Niña pattern during winter typically exacerbates drought conditions, suggesting minimal relief. -4. **Base Rates and Historical Context**: Given historical precedent, Nebraska often experiences prolonged droughts during such climatic patterns. -5. **Resolution Criteria**: Resolves as 'Yes' if 1.5 million or more residents are in drought. -6. **Time Frame and Variance**: There is little time for significant improvements, and recent trends point towards worsening conditions rather than relief. -Based on these factors, it is highly probable that over 1.5 million Nebraska residents will continue to live in drought conditions by the resolution date. - -### Alabama Analysis: -1. **Regional Drought Conditions**: No recent specific data outlines severe, widespread drought in Alabama to the extent seen in Nebraska. -2. **Forecast Patterns**: The forecasts indicate improving conditions in the Southern Plains and South, suggesting possible alleviation in areas like Alabama. -3. **Base Rates and Historical Context**: Historically, Alabama does experience periodic drought, but current trends do not confirm a severe or extreme event affecting over 2 million residents. -4. **Resolution Criteria**: Resolves as 'Yes' if 2.0 million or more residents are in drought. -5. **Time Frame and Variance**: Similar to Nebraska, but the context is less severe, suggesting lower probabilities of meeting the threshold in Alabama. -Considering these aspects, it seems less likely that Alabama will have over 2.0 million residents in drought conditions by the resolution date. - -My final estimates are as follows: - -**Predictions:** -- Nebraska (ID: 30009): A high probability given persistent severe drought and recent environmental patterns that aren't indicating improvement. -- Alabama (ID: 30003): A lower probability, given the current trends suggesting potential improvements in drought conditions. - -These predictions were carefully vetted for overconfidence, particularly considering how often I have previously overestimated outcomes." -30008,Will Chris Stapleton win an award in any of these 2024 CMA Awards categories?,"Predictions: [55, 70, 65, 60, 60] -Median reasoning: To make an informed prediction on Chris Stapleton's potential success at the 2024 CMA Awards, several factors must be considered. First, despite the lack of information about Chris Stapleton’s nominations in the provided articles, clarification from a research assistant confirms that Stapleton is nominated for Album of the Year, Male Vocalist of the Year, and Single of the Year. Since these are prominent categories and Stapleton is a respected and accomplished artist within the country music scene, his chances appear notable. Historically, Stapleton has been very successful at award shows, which significantly bolsters his probability of winning in at least one of these categories. The forecast period is very short, just one week, which means little new information is likely to emerge that could dramatically alter base expectations. Therefore, it's fair to say Stapleton has a high probability of winning at least one award. - -For the second question regarding Lainey Wilson or Luke Combs winning the 2024 CMA Award for Entertainer of the Year, we don’t have direct evidence of the nominations. However, both are included in performance lineups and previous years' records suggest they are significant contenders. If both are nominated for Entertainer of the Year, which is not confirmed, their high profiles in country music right now likely make them strong candidates. Given the lack of precise nomination details, a moderate probability estimate considers both historical potential and the uncertainty around the nomination. - -In conclusion, while considering the typical base rates and Chris Stapleton’s historical performance at awards, I project a high probability of him winning an award. However, given the nature of competition and historical context, I will moderate my prediction slightly from being excessively certain. Similarly, while the chances for Lainey Wilson or Luke Combs look promising, the exact level of certainty is tempered by less clear nomination specifics." -30004,Will Lainey Wilson or Luke Combs win the 2024 CMA Award forEntertainer of the Year?,"Predictions: [55, 70, 65, 60, 60] -Median reasoning: To make an informed prediction on Chris Stapleton's potential success at the 2024 CMA Awards, several factors must be considered. First, despite the lack of information about Chris Stapleton’s nominations in the provided articles, clarification from a research assistant confirms that Stapleton is nominated for Album of the Year, Male Vocalist of the Year, and Single of the Year. Since these are prominent categories and Stapleton is a respected and accomplished artist within the country music scene, his chances appear notable. Historically, Stapleton has been very successful at award shows, which significantly bolsters his probability of winning in at least one of these categories. The forecast period is very short, just one week, which means little new information is likely to emerge that could dramatically alter base expectations. Therefore, it's fair to say Stapleton has a high probability of winning at least one award. - -For the second question regarding Lainey Wilson or Luke Combs winning the 2024 CMA Award for Entertainer of the Year, we don’t have direct evidence of the nominations. However, both are included in performance lineups and previous years' records suggest they are significant contenders. If both are nominated for Entertainer of the Year, which is not confirmed, their high profiles in country music right now likely make them strong candidates. Given the lack of precise nomination details, a moderate probability estimate considers both historical potential and the uncertainty around the nomination. - -In conclusion, while considering the typical base rates and Chris Stapleton’s historical performance at awards, I project a high probability of him winning an award. However, given the nature of competition and historical context, I will moderate my prediction slightly from being excessively certain. Similarly, while the chances for Lainey Wilson or Luke Combs look promising, the exact level of certainty is tempered by less clear nomination specifics." -30015,"Will Warren Buffett be ranked in the top 5 of the Forbes Real-Time Billionaires List on December 31, 2024?","To evaluate the likelihood of Warren Buffett being in the top 5 of Forbes' Real-Time Billionaires List by December 31, 2024, let's go through a structured analysis: - -### Current Rankings and Financial Gap -- As of October 8, 2024, Warren Buffett was ranked 6th, with a net worth of $142.2 billion. -- The 5th place was held by Bernard Arnault & family, with a net worth of $183.1 billion. -- Buffett needs to bridge a gap of $40.9 billion to surpass Arnault, assuming other rankings remain constant. - -### Market Volatility and Net Worth Changes -- Recent months have shown significant net worth increases for top billionaires driven by stock market fluctuations. -- Buffett’s wealth is heavily tied to the performance of Berkshire Hathaway, a diversified conglomerate. - -### Historical Trends and Base Rates -- Buffett has consistently been among the top billionaires over the years, but significant ascension in rankings typically requires major capital gains, often from stock market surges. -- Historically, Buffett’s net worth exhibits less volatility compared to tech billionaires whose wealth can surge rapidly due to high-growth tech stock movements. - -### Novel Elements and Economic Context -- Economic conditions, particularly stock market trends, will heavily influence the net worth of these individuals. Given the proximity to the resolution date, sudden large-scale economic changes typically required for such jumps are less probable. -- There are no specific indicators suggesting why Buffett’s wealth might dramatically increase over the next month and a half. - -### Timeline -- Time frame: November 13, 2024, to December 31, 2024. -- Given it's mid-November, there's limited time for significant movements in billionaire rankings. - -### Conclusion -- Given the necessary net worth increase required and the stability of Buffett's assets, the probability of large-scale shifts pushing him into the top five within the next month and a half is quite low. -- With past insights showing overconfidence tendencies, I'll adjust slightly towards a conservative estimation, reflecting realistic assessment in highly competitive rankings. - -Therefore, evaluating all factors, the probability is: - -Probability: 5%" -30010,"Will the Crimean Bridge be hit with an attack before January 1, 2025?","Let me think about this step by step: - -1. Time Analysis: -- Only about 7 weeks remain until resolution (Nov 13 to Jan 1) -- This is a relatively short window for a major military operation -- Winter conditions may affect operational capabilities - -2. Historical Pattern Analysis: -- 3 known attacks since 2022 (2 successful, 1 failed) -- Roughly one attack attempt every 6-8 months -- Last reported attack attempt was in August 2023, over a year ago - -3. Strategic Importance: -- Bridge remains crucial for Russian logistics -- Ukraine has demonstrated both capability and intent to strike it -- Defense systems likely enhanced after previous attacks - -4. Recent Context: -- November 13 attacks show continued interest in the region -- But these were smaller-scale drone attacks, not bridge hits -- Ukraine's resources are more constrained now than in early 2024 - -5. Timeline Distribution: -- 1 month probability: ~8% -- 2 months (question timeframe): ~15% -- 6 months: ~35% -- 12 months: ~60% - -6. Constraining Factors: -- Winter weather reduces operational windows -- Enhanced Russian air defenses -- Ukraine's limited advanced weapons availability -- Holiday period may affect military operations - -7. Enabling Factors: -- Continued Ukrainian motivation -- Previous success demonstrates capability -- Strategic value remains high - -8. Risk Adjustments: -- Historical overconfidence adjustment needed -- Base rate of successful attacks is relatively low -- Need to account for increased difficulty of winter operations - -Given the short timeframe, winter conditions, and historical attack frequency, I need to be conservative. While Ukraine has both capability and intent, the specific window is quite short and comes during a challenging operational period. - -The earlier 2024 predictions about attacks were made under different circumstances, and many predicted events (like the May 7 attack) did not materialize. - -Accounting for all factors and adjusting for historical overconfidence, I assess that while an attack remains possible, the probability within this specific timeframe is relatively low. - -Probability: 15%" -30007,"Will Apple stock continuously remain below its all-time high before January 1, 2025?","To determine the probability of Apple stock continuously remaining below its all-time high of $237.49 before January 1, 2025, let's apply a structured analysis: - -### Recent Trends and Current Data -1. **Recent Prices and Trends**: - - Current price is $226.99. - - The 50-day and 14-day RSI trends indicate a slightly bearish momentum. - - Recent closing prices have been close to the all-time high but have not broken it. - -2. **Forecasts and Expert Opinions**: - - CoinCodex indicates an increase to $236.33 by December 7, which is close to the all-time high but not beyond it. - - Metadoro suggests strong potential due to iPhone sales, which could push the price higher. - - LongForecast suggests potential price increases, going beyond the all-time high in January 2025. - -3. **Market Sentiment**: - - General bullish sentiment with a forecasted steady increase. - - Strong sales data and positive market sentiment could catalyze a breach of the all-time high before the deadline. - -### Timeframe Considerations -- There is approximately a month and a half until the resolution date. Given the proximity to the all-time high, this allows for multiple trading days where the price could potentially exceed $237.49. - -- Historically, stock prices can exhibit significant day-to-day and intraday volatility, which makes it feasible for Apple to break its all-time high within a few trading sessions if positive momentum continues. - -### Base Rates and Historical Context -- Historically, stocks nearing their all-time highs have a reasonable probability of breaching those levels, especially in bullish scenarios with strong fundamentals as in Apple's case. - -- Considering the tech sector's historical performance in similar scenarios, there is a noteworthy likelihood of breaking all-time highs following strong quarterly numbers and a bullish outlook. - -### Weighing the Default Scenario -- If everything remains status quo, it seems the likelihood is against remaining below the all-time high given the current close proximity and strong indicators of upward movement. - -### Overconfidence Check -- Historical competition data shows overconfidence can skew predictions, so it's important to avoid giving an overly aggressive probability of the stock breaking its high. - -### Distribution of Probabilities -- To consider shorter or longer timeframes, if I assume: - - 15% chance the stock might break the high within two weeks. - - 25% within four weeks. - - Approximately 40% by the resolution date, considering increasing momentum and positive market signals. - -### Conclusion -Based on the trends, market sentiments, and time until resolution, the likelihood of Apple stock remaining below its all-time high until January 1, 2025, is relatively balanced by factors pointing towards a potential rise past it, although there are counter significant bullish indicators. - -Final Answer: **Probability: 45%**" -30006,"Will the Hudson Bay Mountain ski resort be listed as Open by On the Snow's Ski Report on December 8, 2024?","Let me think through this step by step: - -1. Base Case Analysis: -- Hudson Bay Mountain Resort has operated for many years -- They have a planned opening date of December 7, 2024 -- The question resolves on December 8, giving them a buffer day -- Historical opening dates are typically in early December - -2. Current Conditions: -- Nearby resorts are reporting significant snow (79cm at Revelstoke) -- There's forecasted snowfall in the region -- No reported issues or concerns about opening - -3. Risk Factors: -- 2023's historic drought caused delays -- El Niño conditions could affect snowfall -- Climate change increasing weather variability -- Technical or operational issues could delay opening - -4. Timeline Analysis: -- We're about 3.5 weeks from the resolution date -- This is enough time for conditions to change significantly -- But also close enough that current conditions are meaningful - -5. Historical Pattern Analysis: -- Ski resorts typically open as planned unless there are severe issues -- Delays are relatively rare but do happen (as seen in 2023) -- When delays occur, they're usually weather-related - -6. Probability Distribution: -- Base success rate: ~80% (historical opening success) -- Weather risk adjustment: -10% (El Niño year) -- Buffer day advantage: +5% (extra day to resolve issues) -- Technical risk: -5% (standard operational risk) - -7. Confidence Adjustments: -- Reducing confidence due to historical overconfidence -- Accounting for unknown variables -- Considering the binary nature of the outcome - -8. Additional Considerations: -- Resort has financial incentive to open on time -- Early season openings often operate with limited runs -- Resolution criteria only requires ""Open"" status, not full operation - -The planned opening date of December 7 gives them one day of buffer before the resolution date. Current conditions appear favorable with nearby resorts having good snow coverage. However, we must account for the El Niño conditions and the previous year's drought-related delays. - -Given these factors, and adjusting for historical overconfidence, I estimate: - -Probability: 68% - -This reflects a moderately optimistic outlook based on current conditions and plans, while acknowledging significant uncertainty and risk factors. The number is deliberately moved away from extremes due to the competition scoring system and historical overconfidence." -30005,"Will Dogecoin close at $0.420 a share before January 1, 2025?","Let me think through this step by step: - -1. Question Type: This is a Market Price Event, specifically for cryptocurrency. - -2. Current Situation: -- DOGE is at $0.36 as of Nov 12, 2024 -- Recently hit intraday high of $0.43457 -- Needs to hit closing price of $0.420 -- About 7 weeks until resolution - -3. Technical Analysis: -- RSI indicates oversold, suggesting possible correction -- MVRV ratio at 45.65% suggests room for growth -- Recent momentum is strongly positive -- High volatility (24.89% over last month) - -4. Historical Context: -- Crypto markets are highly volatile -- DOGE has shown ability to make large moves quickly -- Already very close to target price ($0.36 vs $0.420) -- Has exceeded this price multiple times historically - -5. Timeline Analysis: -- For 1 week: 25% chance (very short term) -- For 1 month: 45% chance -- For 7 weeks (actual timeframe): 65% chance -- For 6 months: 80% chance - -6. Positive Factors: -- Strong recent momentum -- Already close to target price (needs ~16.7% increase) -- High trading volume -- Positive market sentiment -- Recent Trump-related catalyst -- Potential ETF speculation - -7. Negative Factors: -- Crypto markets are unpredictable -- Could face regulatory headwinds -- Technical indicators suggest possible short-term correction -- Closing price requirement (not intraday) makes it harder - -8. Uncertainty Adjustments: -- Starting with 65% based on timeline analysis -- Adjust down 5% for overconfidence bias -- Add 3% for current strong momentum -- Subtract 2% for closing price vs intraday requirement - -The target price is only about 16.7% above current levels, which is very achievable in crypto markets, especially given the current momentum and catalyst-rich environment. The recent intraday high shows it's already capable of reaching these levels. However, we need a closing price, not just intraday, which is harder. - -Seven weeks is a reasonable amount of time for this move, especially given current momentum. The primary risk is a broader market reversal or specific negative catalyst for DOGE. - -Given all factors and adjusting for historical overconfidence, I believe this has a good chance of occurring before the resolution date. - -Probability: 61%" -30068,Will Kelsea Ballerini or Lainey Wilson win the 2024 CMA Award for Female Vocalist of the Year?,"Let me think through this step by step: - -1. Type of Question: Competition Event (Awards Show) - -2. Key Factors to Consider: -- There are 5 total nominees -- Both Kelsea Ballerini and Lainey Wilson are major names in country music -- Current momentum and recent success of each artist -- Historical voting patterns -- Just one week until resolution - -3. Analysis: -- Lainey Wilson has had a particularly strong year in country music -- She's nominated in multiple categories and is a performer -- Won this award last year (2023) -- Has maintained strong presence and momentum -- Kelsea Ballerini has also had a solid year -- Both are performing at the show -- Other nominees (Ashley McBryde, Megan Moroney, Kacey Musgraves) are also strong contenders - -4. Historical Context: -- CMA tends to reward consistent commercial success -- Repeat winners are common in this category -- Lainey Wilson's recent dominance suggests strong voter support - -5. Probability Distribution: -- Lainey Wilson: ~40% chance -- Kelsea Ballerini: ~20% chance -- Other nominees combined: ~40% chance - -6. Resolution Criteria Check: -- Question resolves YES if either Wilson OR Ballerini wins -- Combined probability should be used - -7. Uncertainty Factors: -- Voting already complete -- No major events likely to influence outcome in next week -- Strong historical data available -- Multiple viable candidates - -8. Base Rate Adjustment: -- Question asks for combined probability of two out of five nominees -- Both are leading contenders -- Need to adjust for overconfidence - -9. Final Calculation: -40% (Wilson) + 20% (Ballerini) = 60% -Adjusting for overconfidence and strong competition: -5% - -The combined probability of either Lainey Wilson or Kelsea Ballerini winning is reasonably high, but not overwhelming given the strong field. Wilson's recent success and momentum gives her better odds, while Ballerini remains a solid contender. The presence of other strong nominees requires some downward adjustment to avoid overconfidence. - -Probability: 55%" -30074,"Will 1 Euro be worth less than 1 US Dollar before 2025? -","Let me think about this step by step as a Market Price Event forecast: - -1. Current Situation: -- EUR/USD is currently trading well above parity -- We have only about 6 weeks until resolution date -- The pair would need to drop significantly in a short time - -2. Historical Context: -- EUR/USD has only briefly touched parity once in recent years (Sept-Oct 2022) -- This happened during extreme circumstances (energy crisis, Ukraine war escalation) -- The pair has generally maintained levels above 1.05 in 2023-2024 - -3. Time Analysis: -- With only 6 weeks until resolution, we need a very rapid movement -- Daily volatility in EUR/USD typically doesn't exceed 0.5-1% -- Would need sustained downward pressure or shock event - -4. Supporting Factors for Drop: -- Political uncertainty in Germany -- Potential Trump victory effects -- Some analysts predicting parity - -5. Factors Against Drop: -- Most forecasts show strengthening or stability -- ECB likely to maintain rates -- Current level (~1.07) requires significant movement -- Market has priced in many negative scenarios - -6. Timeline Distribution: -- Next 2 weeks: 3% chance -- Next 6 weeks (resolution): 8% chance -- Next 6 months: 15% chance -- Next 12 months: 25% chance - -7. Adjustment Factors: -- Short timeframe heavily reduces likelihood -- Current level requires ~7% drop -- Historical precedent suggests extreme moves are rare -- Must account for overconfidence bias - -8. Risk Analysis: -- Major shock events could trigger rapid movement -- But timeframe severely limits probability -- Current technical and fundamental analysis suggests stability - -Given the very short timeframe, current exchange rate level, and required magnitude of movement, the probability needs to be relatively low. However, can't completely discount possibility of shock events or rapid deterioration. - -Considering all factors and adjusting for historical overconfidence, while acknowledging some legitimate bearish factors: - -Probability: 8%" -30073,"Will the United Nations have exactly 193 member states on December 31, 2024?","Let me think through this step by step: - -1. Current State: -- UN has 193 members as of November 2024 -- This has been stable since South Sudan joined in 2011 (13 years) -- Resolution date is only about 6 weeks away - -2. Historical Analysis: -- UN membership changes are rare and slow -- Last change was in 2011 -- Before that, Montenegro in 2006 -- Average rate of new members has dramatically slowed since the 1960s-1990s period - -3. Time Window Analysis: -- Only 6 weeks until resolution -- UN procedures are bureaucratic and slow -- Security Council meetings and General Assembly votes take significant time -- Holiday period typically has reduced diplomatic activity - -4. Current Candidates: -- Palestine: Most likely candidate but faces US veto -- Kosovo: Faces Russian veto -- Others (Taiwan, South Ossetia, etc.): Extremely unlikely due to geopolitical situation - -5. Risk Factors for Change: -- No active applications in final stages -- No scheduled votes -- No immediate independence referendums -- No active territorial disputes likely to resolve in 6 weeks - -6. Timeline Distribution: -- 1 month probability: 99.9% -- 3 months probability: 99% -- 6 months probability: 98% -- 1 year probability: 95% - -7. Special Considerations: -- Resolution criteria is very specific (exactly 193) -- Both increase and decrease would cause negative resolution -- Decrease extremely unlikely (would require member expulsion or dissolution) -- Multiple simultaneous additions virtually impossible - -8. Confidence Adjustment: -- Base case strongly favors status quo -- Very short timeframe -- Clear resolution criteria -- Limited uncertainty factors -- But accounting for historical overconfidence - -Given the extremely short timeframe, the bureaucratic nature of UN membership changes, the lack of active candidates in final stages, and the holiday period, I assess there is a very high probability the member count will remain at 193. - -However, I need to account for: -- Historical overconfidence -- Unknown factors -- The fact that both increase and decrease would cause negative resolution -- The specific requirement of ""exactly 193"" - -Probability: 98% - -This high probability is justified by the extremely short timeframe and strong status quo, but still leaves room for unforeseen developments. A more conservative estimate might be warranted for longer timeframes, but for just 6 weeks, the institutional inertia is nearly insurmountable." -30072,"Before 2025, will OpenAI's commercial operations cease to be governed by its nonprofit board of directors? -","Let me think through this step by step: - -1) Timeline Analysis: -- Today is Nov 14, 2024 -- Resolution date is Dec 31, 2024 -- Only ~6 weeks remaining -- Very short timeframe for major corporate governance changes - -2) Historical Context: -- OpenAI had major governance crisis in Nov 2023 -- Board was reconstituted after that crisis -- System has been relatively stable since then -- Previous crisis involved attempted removal of Sam Altman -- No major governance changes in past year - -3) Resolution Criteria Analysis: -Two main paths to ""Yes"": -a) Formal restructuring/transfer of control -b) Mass employee departure (>50% within 180 days) - -4) Current Situation: -- No public signs of imminent governance changes -- Company appears stable after 2023 crisis -- Major products and developments continuing -- No recent board conflicts reported - -5) Timeline Distribution Analysis: -For context, likelihood of governance change: -- Next 6 weeks (question period): Very low -- Next 3 months: Low -- Next 6 months: Moderate -- Next 12 months: Higher - -6) Risk Factors: -- Previous crisis shows potential for sudden changes -- Commercial pressures vs nonprofit mission tension exists -- Microsoft relationship could push for changes -- But very short remaining timeline mitigates these - -7) Base Rate Consideration: -- Major tech company governance changes are rare -- Usually take months to execute when planned -- Even more rare in 6-week window -- Previous crisis took days to resolve but was unique - -8) Mitigating Factors: -- Recent stability suggests low immediate risk -- Short timeline heavily favors status quo -- Major changes typically require longer planning -- Holiday period further reduces likelihood - -Given the extremely short timeline remaining (6 weeks), recent stability, and absence of current warning signs, the probability of such a major change occurring before year-end is quite low. However, given the previous crisis and ongoing tensions between commercial and nonprofit missions, it's not negligible. - -The very short timeline is the strongest factor here - major corporate governance changes typically take months to execute even when planned, and there's no public indication of such plans. - -I need to adjust for historical overconfidence but also recognize that the short timeline heavily favors status quo. - -Probability: 8%" -30071,"Will Kalshi outrank the Wells Fargo Mobile App in the top free Finance apps on December 31, 2024?","Let me think about this step by step: - -1. Current situation analysis: -- Wells Fargo is currently ranked #13 in Finance apps -- Kalshi would need to gain significant users to surpass an established major bank's app -- The resolution date is only about 6 weeks away, which is a very short timeframe - -2. Recent developments for Kalshi: -- Successfully surpassed Coinbase in rankings after enabling election betting -- Added interest payments on deposits -- Showing momentum in user acquisition -- Election betting could drive significant near-term growth - -3. Constraining factors: -- Wells Fargo has a massive existing user base who need their app for basic banking -- Very short timeframe until resolution (6 weeks) -- Bank apps tend to be sticky due to necessity -- Holiday season may reduce new user acquisition - -4. Historical precedents: -- Very rare for newer fintech apps to surpass major bank apps in rankings -- When it happens, usually takes years, not weeks -- App store rankings can be volatile but tend to revert to mean - -5. Timeline analysis: -- 6 weeks is extremely short for such a significant ranking change -- Even with strong momentum, displacing a major bank app would be unprecedented in this timeframe -- If we extended this to 6 months or 1 year, probability would be higher - -6. Risk factors: -- App store rankings can be volatile -- Marketing pushes can temporarily boost rankings -- Holiday season could affect usage patterns -- Wells Fargo could have technical issues that drop their ranking - -7. Statistical considerations: -- Need to account for both the difficulty of the achievement and the very short timeline -- Must avoid overconfidence given the unprecedented nature -- Base rate of similar achievements suggests very low probability - -Given the extremely short timeline (6 weeks) and the magnitude of achievement required (surpassing a major bank app), this would be a truly exceptional event. While Kalshi has shown impressive momentum, the timeframe is too short for this to be likely. - -If the question were about a 6-month timeline, I might give it 15-20% probability. For a 1-year timeline, perhaps 25-30%. But for just 6 weeks, the probability needs to be significantly lower. - -The recent positive developments for Kalshi prevent this from being a near-zero probability, but the timeframe makes it very unlikely. - -Probability: 8%" -30070,"Will a new nuclear-armed state emerge before January 1, 2025?","Let me think through this step by step: - -1. Base Rate Analysis: -- Since 1945, only 9 countries have successfully developed nuclear weapons -- That's roughly one new nuclear state every 8-9 years on average -- The last new nuclear state was North Korea (first test in 2006) -- This suggests the base rate for any given ~6 week period is extremely low - -2. Current Situation: -- Iran is the most likely candidate, having enriched uranium to near weapons-grade -- Technical capability: Iran could produce material for one bomb in under a week -- However, weaponization requires more than just material (testing, delivery systems) -- No other countries appear close to nuclear capability - -3. Timeline Analysis: -- Only 6-7 weeks until resolution date -- Historical precedent shows nuclear weapons development typically announced via tests -- Tests require significant preparation and are usually detected in advance -- No current indicators of imminent testing from any country - -4. Probability Distribution across different timeframes: -- Next 6 weeks (until resolution): ~2% -- Next 6 months: ~5% -- Next year: ~10% -- Next 2 years: ~15% - -5. Risk Factors: -- Iran's advanced program and geopolitical tensions -- Reduced international monitoring -- Regional security dynamics - -6. Mitigating Factors: -- International pressure and sanctions -- Technical challenges of weaponization -- Short timeframe until resolution -- Diplomatic costs of testing -- Winter weather conditions less favorable for testing - -7. Resolution Criteria Analysis: -- Requires not just development but also testing -- Needs credible evidence/confirmation -- Multiple verification sources required -- High bar for resolution as ""Yes"" - -8. Uncertainty Adjustments: -- Very short timeframe increases certainty of no change -- Historical overconfidence adjustment needed -- Base rate dominance for short-term predictions - -Given the extremely short timeframe (6-7 weeks), the high bar for resolution criteria, and the historical rarity of new nuclear states, I need to heavily weight the base case of no change. While Iran has the technical capability, the likelihood of them conducting and announcing a test in this specific window is very low. Even with Iran's capabilities, the combination of technical, political, and timing factors make this highly unlikely in such a short window. - -Adjusting for historical overconfidence and the strong tendency toward status quo in short timeframes: - -Probability: 3% - -This reflects the extremely low base rate, the short timeframe, and the high bar for resolution, while still acknowledging the non-zero possibility given Iran's capabilities and current geopolitical tensions." -30069,"Will Arkansas have 1 million or more residents living in drought on December 31, 2024?","Let me think about this step by step: - -1) First, let's consider the type of question - this is a Non-Market Threshold Value Event about population affected by drought conditions. - -2) Time horizon assessment: -- Only about 6 weeks until resolution -- Winter season in Northern Hemisphere -- Short enough timeline that seasonal patterns matter more than long-term climate trends - -3) Current situation: -- Recent rainfall has improved conditions in Arkansas -- Still have moderate and severe drought in eastern/southern parts -- Overall US drought conditions affecting 43.4% of country -- Arkansas population is approximately 3.1 million people - -4) Seasonal considerations: -- December/January typically see increased precipitation in Arkansas -- Winter months generally have lower drought risk than summer/fall -- El Niño conditions predicted for winter 2024-25, typically bringing wetter conditions to Arkansas - -5) Historical patterns: -- Arkansas drought conditions tend to improve in winter months -- Recent rainfall showing improvement trajectory -- Population centers (Little Rock etc.) receiving precipitation - -6) Resolution criteria specifics: -- Need ≥1 million residents in drought areas -- Only needs one measurement on Dec 31 -- About 1/3 of state population would need to be in drought - -7) Risk factors: -- Short timeline reduces uncertainty -- Recent improvements suggest downward trend -- Winter seasonal patterns favorable -- El Niño typically reduces drought risk - -8) Confidence adjustments: -- Given recent improvements and seasonal patterns, likelihood seems low -- However, need to account for possibility of dry spell -- Must consider that current conditions still show significant drought areas -- Should avoid extreme probability given inherent weather uncertainty - -9) Timeline distribution: -- Chance of drought affecting 1M+ by Dec 15: 15% -- Chance by Dec 31: 25% -- Chance by Jan 15 (if question continued): 20% -- Shows decreasing probability given seasonal patterns - -Given the recent rainfall, improving conditions, typical winter patterns, El Niño influence, and short timeline, I assess the probability as relatively low but not negligible. The current drought areas could persist despite seasonal improvements, but reaching 1M+ residents seems unlikely given current trajectory. - -Probability: 25%" -30067,"Will Google have over a 90% global search engine market share, according to StatCounter, on December 31, 2024?","Let me think through this step by step: - -1. Type of Question: This is a Non-Market Threshold Value Event, specifically about market share percentage at a specific future date. - -2. Timeline Analysis: -- Resolution date is December 31, 2024 -- Only about 6 weeks away from current date -- Very short timeframe for significant market share changes - -3. Current State: -- Google currently at ~91.62% (latest data) -- Slight decline from 93.37% in 2023 -- Needs to stay above 90.00% exactly - -4. Recent Trends: -- Small but steady decline over past year (~1.75% drop) -- Bing gained about 0.5% in past year -- Other competitors showing minimal gains - -5. Statistical Analysis: -- At current rate of decline (~1.75%/year) -- Over 6 weeks, proportional decline would be ~0.20% -- This would put Google at ~91.42% at resolution date -- Comfortable buffer above 90% threshold - -6. Risk Factors: -- AI competition from ChatGPT/Bing -- Privacy concerns -- Regulatory pressures -- However, these move slowly and 6 weeks is very short - -7. Positive Factors: -- Google's entrenched position -- Network effects -- Brand recognition -- Own AI capabilities (Gemini) - -8. Historical Context: -- Google has maintained >90% share for many years -- Market share changes typically happen gradually -- No precedent for rapid shifts in search market - -9. Resolution Criteria: -- Exact threshold of 90.00% -- StatCounter specific measurement -- Clear resolution source - -Given the very short timeframe (6 weeks), current buffer zone (~1.6% above threshold), and historical stability, this seems highly likely to resolve positively. The main risk is the exact threshold of 90.00%, which creates some uncertainty even with favorable conditions. - -The current trend would need to accelerate significantly to drop below 90% in just 6 weeks, which seems unlikely given market dynamics and historical patterns. - -However, need to account for: -- Some uncertainty in measurements -- Possibility of unforeseen events -- Historical overconfidence adjustment - -While raw analysis suggests 95%+ probability, I'll adjust down significantly to account for overconfidence bias and exact threshold requirements. - -Probability: 88%" -30066,Will either Matt Dolan or Jane Timken be announced as the successor to occupy JD Vance's Senate seat?,"Let me think about this step by step: - -1) First, let's consider the timeline: -- Today is Nov 14, 2024 -- Resolution date is Jan 1, 2025 -- That's about 6-7 weeks -- Vance doesn't need to vacate until Jan 20, 2025, but DeWine likely wants to announce before then - -2) Key factors favoring Dolan or Timken: -- Both are established Ohio Republicans -- Both have previous Senate campaign experience -- Both have relationships with DeWine -- They represent different wings of the party (Dolan more moderate, Timken more Trump-aligned) - -3) Factors against Dolan or Timken: -- Multiple other strong candidates in consideration -- DeWine consulting with Trump could favor other choices -- Frank LaRose and Dave Yost have strong credentials -- DeWine might choose someone unexpected - -4) Historical precedents: -- Senate appointments typically go to established party figures -- Governors often choose someone who can win the next election -- Base rate suggests ""known"" candidates are more likely - -5) Timeline considerations: -- DeWine likely to announce by mid-December to allow for transition -- If not announced by Jan 1, question resolves No -- Most likely announcement window is early-mid December - -6) Probability distribution across time: -- Next 2 weeks: 15% chance of any announcement -- Weeks 3-4: 40% chance of any announcement -- Weeks 5-6: 30% chance of any announcement -- After Jan 1: 15% chance of announcement - -7) Given an announcement occurs before Jan 1: -- Dolan chances: ~25% -- Timken chances: ~20% -- All others combined: ~55% - -8) Mathematical calculation: -- 85% chance of announcement before Jan 1 -- Combined 45% chance it's Dolan or Timken if announced -- 85% × 45% = 38.25% - -9) Adjustment factors: -- Recent reports suggest multiple viable candidates -- Trump's influence could reduce chances of moderate picks -- Need to adjust for historical overconfidence -- Base rate of 35% positive resolutions - -After consideration and adjusting for overconfidence, I believe there's a significant but not majority chance that either Dolan or Timken will be selected. While they're both strong candidates, the field is crowded with other viable options, and Trump's influence could favor different choices. - -Probability: 35%" -30065,"Will Lara Trump be chosen for a paid position within the Trump White House before January 1, 2025?","Let me think through this step by step: - -1) First, let's consider the timeline: -- We're forecasting from Nov 14, 2024 to Jan 1, 2025 -- This is approximately 6-7 weeks -- This would be during a presidential transition period, assuming Trump wins - -2) Key factors favoring a ""Yes"" resolution: -- Lara Trump is already deeply involved in Trump's orbit -- She's gained political experience as RNC co-chair -- Family connections obviously strong -- Trump has shown preference for family members in key positions previously - -3) Key factors favoring a ""No"" resolution: -- Short timeline (only 6-7 weeks) -- No current signals she's being considered for White House role -- She seems more focused on party leadership/Senate possibilities -- Ivanka/Jared experience might make Trump more hesitant about family appointments - -4) Historical base rates: -- In 2016 transition, family appointments were announced relatively early -- But those were primarily Ivanka/Jared -- Generally, most key White House staff positions are announced in first few weeks after election - -5) Timeline distribution analysis: -- If it were to happen, most likely in first 3 weeks after election -- Probability decreases as we get closer to January -- Would be unusual for major staff announcements very close to January 1 - -6) Confidence adjustment: -- Given the very specific timeline and criteria, should lean conservative -- Need to account for general overconfidence bias -- High uncertainty due to political nature and multiple dependencies - -7) Dependencies: -- Requires Trump victory first -- Requires him wanting to appoint her -- Requires her wanting the position -- Requires announcement within specific timeframe - -8) Key timing consideration: -- Most key staff announcements happen within first month post-election -- Later announcements more likely for lower-level positions - -Given all these factors, I believe the probability is relatively low but not negligible. The short timeline and multiple dependencies are key limiting factors, but her close involvement and family connection provide some possibility. - -Breaking down probabilities: -- By Dec 1: 8% -- By Dec 15: +4% -- By Jan 1: +3% - -Probability: 15% - -This accounts for the short timeline, multiple dependencies, historical precedent of family appointments, while avoiding overconfidence given the uncertainty and specific criteria needed for resolution." -30121,Will Violet be the closest color to the 2025 Pantone Color of the Year?,"Predictions: [30, 18, 20, 13, 20] -Median reasoning: To forecast the likelihood of each color being closest to the 2025 Pantone Color of the Year, we need to evaluate both the historical trends and the insights from current articles. Analyzing the historical data, we note the following: Red has appeared four times, Blue three times, and other colors like Violet and Green once each in the last ten years. No color has appeared more than twice consecutively, suggesting there might not be a preference for frequent repetitions. The articles and sources suggest a trend towards warm, natural hues and rich tones with some highlighting purple shades, which may indicate a leaning towards Violet and similar tones like Blue. The information provided by sources like ELLE Decor and Businesswire supports the emergence of purple hues, yet there is no definitive indication that Violet is the likeliest candidate, especially since past Pantone selections tend to balance previous iterations by introducing different yet complementary tones. With upcoming color trends emphasizing earthy, warm tones and nuanced colors like Purple Basil, which blends violet and mauve, Violet's probability may be moderate. However, it's essential to note the potential overconfidence in these predictions given that Pantone's choices are often unexpected. The forecast must be distributed evenly with conservative estimates given the impending announcement. Base rates suggest a higher chance of Red or Blue due to previous selections, but external predictions of purple hues raise some likelihood for Violet this year. The other colors, while present in trend discussions, seem less represented or likely based on the current thematic leanings. Given that the resolution is near and the information remains speculative, my estimates account for both historical and predictive indicators without leaning too heavily towards speculative predictions not deeply supported by consistent evidence." -30120,Will Blue be the closest color to the 2025 Pantone Color of the Year?,"Predictions: [30, 18, 20, 13, 20] -Median reasoning: To forecast the likelihood of each color being closest to the 2025 Pantone Color of the Year, we need to evaluate both the historical trends and the insights from current articles. Analyzing the historical data, we note the following: Red has appeared four times, Blue three times, and other colors like Violet and Green once each in the last ten years. No color has appeared more than twice consecutively, suggesting there might not be a preference for frequent repetitions. The articles and sources suggest a trend towards warm, natural hues and rich tones with some highlighting purple shades, which may indicate a leaning towards Violet and similar tones like Blue. The information provided by sources like ELLE Decor and Businesswire supports the emergence of purple hues, yet there is no definitive indication that Violet is the likeliest candidate, especially since past Pantone selections tend to balance previous iterations by introducing different yet complementary tones. With upcoming color trends emphasizing earthy, warm tones and nuanced colors like Purple Basil, which blends violet and mauve, Violet's probability may be moderate. However, it's essential to note the potential overconfidence in these predictions given that Pantone's choices are often unexpected. The forecast must be distributed evenly with conservative estimates given the impending announcement. Base rates suggest a higher chance of Red or Blue due to previous selections, but external predictions of purple hues raise some likelihood for Violet this year. The other colors, while present in trend discussions, seem less represented or likely based on the current thematic leanings. Given that the resolution is near and the information remains speculative, my estimates account for both historical and predictive indicators without leaning too heavily towards speculative predictions not deeply supported by consistent evidence." -30119,Will Green be the closest color to the 2025 Pantone Color of the Year?,"Predictions: [30, 18, 20, 13, 20] -Median reasoning: To forecast the likelihood of each color being closest to the 2025 Pantone Color of the Year, we need to evaluate both the historical trends and the insights from current articles. Analyzing the historical data, we note the following: Red has appeared four times, Blue three times, and other colors like Violet and Green once each in the last ten years. No color has appeared more than twice consecutively, suggesting there might not be a preference for frequent repetitions. The articles and sources suggest a trend towards warm, natural hues and rich tones with some highlighting purple shades, which may indicate a leaning towards Violet and similar tones like Blue. The information provided by sources like ELLE Decor and Businesswire supports the emergence of purple hues, yet there is no definitive indication that Violet is the likeliest candidate, especially since past Pantone selections tend to balance previous iterations by introducing different yet complementary tones. With upcoming color trends emphasizing earthy, warm tones and nuanced colors like Purple Basil, which blends violet and mauve, Violet's probability may be moderate. However, it's essential to note the potential overconfidence in these predictions given that Pantone's choices are often unexpected. The forecast must be distributed evenly with conservative estimates given the impending announcement. Base rates suggest a higher chance of Red or Blue due to previous selections, but external predictions of purple hues raise some likelihood for Violet this year. The other colors, while present in trend discussions, seem less represented or likely based on the current thematic leanings. Given that the resolution is near and the information remains speculative, my estimates account for both historical and predictive indicators without leaning too heavily towards speculative predictions not deeply supported by consistent evidence." -30118,Will Yellow be the closest color to the 2025 Pantone Color of the Year?,"Predictions: [30, 18, 20, 13, 20] -Median reasoning: To forecast the likelihood of each color being closest to the 2025 Pantone Color of the Year, we need to evaluate both the historical trends and the insights from current articles. Analyzing the historical data, we note the following: Red has appeared four times, Blue three times, and other colors like Violet and Green once each in the last ten years. No color has appeared more than twice consecutively, suggesting there might not be a preference for frequent repetitions. The articles and sources suggest a trend towards warm, natural hues and rich tones with some highlighting purple shades, which may indicate a leaning towards Violet and similar tones like Blue. The information provided by sources like ELLE Decor and Businesswire supports the emergence of purple hues, yet there is no definitive indication that Violet is the likeliest candidate, especially since past Pantone selections tend to balance previous iterations by introducing different yet complementary tones. With upcoming color trends emphasizing earthy, warm tones and nuanced colors like Purple Basil, which blends violet and mauve, Violet's probability may be moderate. However, it's essential to note the potential overconfidence in these predictions given that Pantone's choices are often unexpected. The forecast must be distributed evenly with conservative estimates given the impending announcement. Base rates suggest a higher chance of Red or Blue due to previous selections, but external predictions of purple hues raise some likelihood for Violet this year. The other colors, while present in trend discussions, seem less represented or likely based on the current thematic leanings. Given that the resolution is near and the information remains speculative, my estimates account for both historical and predictive indicators without leaning too heavily towards speculative predictions not deeply supported by consistent evidence." -30117,Will Orange be the closest color to the 2025 Pantone Color of the Year?,"Predictions: [30, 18, 20, 13, 20] -Median reasoning: To forecast the likelihood of each color being closest to the 2025 Pantone Color of the Year, we need to evaluate both the historical trends and the insights from current articles. Analyzing the historical data, we note the following: Red has appeared four times, Blue three times, and other colors like Violet and Green once each in the last ten years. No color has appeared more than twice consecutively, suggesting there might not be a preference for frequent repetitions. The articles and sources suggest a trend towards warm, natural hues and rich tones with some highlighting purple shades, which may indicate a leaning towards Violet and similar tones like Blue. The information provided by sources like ELLE Decor and Businesswire supports the emergence of purple hues, yet there is no definitive indication that Violet is the likeliest candidate, especially since past Pantone selections tend to balance previous iterations by introducing different yet complementary tones. With upcoming color trends emphasizing earthy, warm tones and nuanced colors like Purple Basil, which blends violet and mauve, Violet's probability may be moderate. However, it's essential to note the potential overconfidence in these predictions given that Pantone's choices are often unexpected. The forecast must be distributed evenly with conservative estimates given the impending announcement. Base rates suggest a higher chance of Red or Blue due to previous selections, but external predictions of purple hues raise some likelihood for Violet this year. The other colors, while present in trend discussions, seem less represented or likely based on the current thematic leanings. Given that the resolution is near and the information remains speculative, my estimates account for both historical and predictive indicators without leaning too heavily towards speculative predictions not deeply supported by consistent evidence." -30116,Will Red be the closest color to the 2025 Pantone Color of the Year?,"Predictions: [30, 18, 20, 13, 20] -Median reasoning: To forecast the likelihood of each color being closest to the 2025 Pantone Color of the Year, we need to evaluate both the historical trends and the insights from current articles. Analyzing the historical data, we note the following: Red has appeared four times, Blue three times, and other colors like Violet and Green once each in the last ten years. No color has appeared more than twice consecutively, suggesting there might not be a preference for frequent repetitions. The articles and sources suggest a trend towards warm, natural hues and rich tones with some highlighting purple shades, which may indicate a leaning towards Violet and similar tones like Blue. The information provided by sources like ELLE Decor and Businesswire supports the emergence of purple hues, yet there is no definitive indication that Violet is the likeliest candidate, especially since past Pantone selections tend to balance previous iterations by introducing different yet complementary tones. With upcoming color trends emphasizing earthy, warm tones and nuanced colors like Purple Basil, which blends violet and mauve, Violet's probability may be moderate. However, it's essential to note the potential overconfidence in these predictions given that Pantone's choices are often unexpected. The forecast must be distributed evenly with conservative estimates given the impending announcement. Base rates suggest a higher chance of Red or Blue due to previous selections, but external predictions of purple hues raise some likelihood for Violet this year. The other colors, while present in trend discussions, seem less represented or likely based on the current thematic leanings. Given that the resolution is near and the information remains speculative, my estimates account for both historical and predictive indicators without leaning too heavily towards speculative predictions not deeply supported by consistent evidence." -30122,"Before January 1, 2025, will the International Criminal Court issue an arrest warrant for Benjamin Netanyahu?","Predictions: [25, 30, 15, 10, 12] -Median reasoning: To predict the likelihood of the ICC issuing an arrest warrant for Benjamin Netanyahu before January 1, 2025, and any warrants in November or December 2024, we must consider several factors including historical base rates, current evidence, and geopolitical contexts. - -1. **Base Rate Analysis**: The ICC has historically taken lengthy periods for investigations and issuing arrest warrants due to its complex legal process and the need for substantial evidence. It has issued 42 arrest warrants in over two decades, indicating a low base rate. - -2. **Current Context**: Given the geopolitical tensions and lobbying by powerful nations like the US and G7 to prevent such warrants, there is significant international pressure against issuing a warrant soon. Furthermore, existing procedural delays and allegations of bias against the ICC complicate matters further. - -3. **Timing Considerations**: With only a few weeks until the resolution date, the potential for the ICC to pivot quickly and issue an arrest warrant is limited. Historically, actions like these by the ICC do not respond swiftly without significant cause. - -4. **Specific Factors Against Issuance**: The potential influence of Donald Trump’s presidency, existing diplomatic pressures, and Israel’s strategic resistance suggest that the likelihood of warrant issuance remains low. Additionally, the ICC is under scrutiny due to its internal issues, which may delay decisive actions like issuing arrest warrants. - -Thus, based on historical precedents, geopolitical influences, and current information, the probabilities of an ICC arrest warrant being issued for Netanyahu and the issuance of any arrest warrants before January 1, 2025, remain low. Accounting for historical overconfidence, a more moderate estimation is justified." -30113,Will the International Criminal Court issue any warrants in November or December 2024?,"Predictions: [25, 30, 15, 10, 12] -Median reasoning: To predict the likelihood of the ICC issuing an arrest warrant for Benjamin Netanyahu before January 1, 2025, and any warrants in November or December 2024, we must consider several factors including historical base rates, current evidence, and geopolitical contexts. - -1. **Base Rate Analysis**: The ICC has historically taken lengthy periods for investigations and issuing arrest warrants due to its complex legal process and the need for substantial evidence. It has issued 42 arrest warrants in over two decades, indicating a low base rate. - -2. **Current Context**: Given the geopolitical tensions and lobbying by powerful nations like the US and G7 to prevent such warrants, there is significant international pressure against issuing a warrant soon. Furthermore, existing procedural delays and allegations of bias against the ICC complicate matters further. - -3. **Timing Considerations**: With only a few weeks until the resolution date, the potential for the ICC to pivot quickly and issue an arrest warrant is limited. Historically, actions like these by the ICC do not respond swiftly without significant cause. - -4. **Specific Factors Against Issuance**: The potential influence of Donald Trump’s presidency, existing diplomatic pressures, and Israel’s strategic resistance suggest that the likelihood of warrant issuance remains low. Additionally, the ICC is under scrutiny due to its internal issues, which may delay decisive actions like issuing arrest warrants. - -Thus, based on historical precedents, geopolitical influences, and current information, the probabilities of an ICC arrest warrant being issued for Netanyahu and the issuance of any arrest warrants before January 1, 2025, remain low. Accounting for historical overconfidence, a more moderate estimation is justified." -30114,Will Astro Bot win the Game of the Year 2024 award?,"Let me think through this step by step: - -1. Type of Question: This is a Competition Event question, specifically about an awards ceremony. - -2. Key Considerations: -- The resolution date is very close (less than a month away) -- The Game Awards have a consistent history and format -- We have strong current data (reviews, sales, expert predictions) -- Multiple strong competitors exist - -3. Historical Analysis: -- Past GOTY winners typically come from major franchises or groundbreaking new IPs -- Platform games rarely win GOTY at The Game Awards -- Critical acclaim (94 Metacritic) is very strong, but not unprecedented -- 1.5M sales is good but not exceptional - -4. Competition Assessment: -- Final Fantasy VII Rebirth appears to be the strongest competitor -- Multiple sources consistently mention both games as frontrunners -- Other strong contenders like Dragon Age: The Veilguard add uncertainty -- Platform games historically have lower chances at major awards - -5. Timing Factors: -- Nominations haven't been announced yet -- Very short time until resolution (< 1 month) -- Limited time for significant changes in perception - -6. Positive Factors: -- Universal critical acclaim -- PlayStation's 30th anniversary significance -- Strong sales performance -- Consistent mention as a frontrunner - -7. Negative Factors: -- Genre historically underrepresented in GOTY -- Strong competition from established franchises -- Platform exclusivity might limit voter base -- Not a ""revolutionary"" title compared to some past winners - -8. Uncertainty Considerations: -- Award voting can be unpredictable -- Jury composition affects outcomes -- Marketing push in final weeks could affect perception - -Given these factors, I need to be careful not to overestimate based on recent positive coverage. While Astro Bot is clearly a strong contender, historical patterns suggest platform games face an uphill battle for GOTY. The presence of strong competitors, particularly Final Fantasy VII Rebirth, significantly impacts the probability. - -The short timeframe until resolution means current indicators are highly relevant, but the genre bias and competition are significant limiting factors. - -Considering all factors and adjusting for historical overconfidence, I estimate: - -Probability: 28% - -This reflects strong contender status while acknowledging significant headwinds from genre bias, competition, and historical patterns at The Game Awards." -30124,"On December 31, 2024, will Cruise operate a rider-only ride-hailing service anywhere in the United States? - -","Let me think about this step by step: - -1. Timeline Analysis: -- Only 46 days until resolution -- This is an extremely short window for a company to restart operations after major setbacks -- Regulatory approval processes typically take months - -2. Current Status: -- Not currently operating rider-only service anywhere -- Only conducting limited testing with safety drivers -- Recent admission of falsifying reports (November 2024) suggests continued regulatory challenges -- Still under NHTSA investigation - -3. Requirements for Success: -- Need regulatory approvals -- Need to rebuild public trust -- Need to demonstrate safety improvements -- Need to roll out service to general public (not just waitlist) - -4. Historical Precedents: -- After similar shutdowns, AV companies typically take 6-12 months to resume operations -- Waymo's restart after COVID took several months even without safety incidents -- Cruise's recent admission of falsifying reports likely extends typical timeline - -5. Positive Factors: -- Already resumed limited testing in multiple cities -- Has infrastructure and vehicles in place -- Strong financial backing from GM - -6. Negative Factors: -- Recent regulatory fine and admission of wrongdoing -- Ongoing NHTSA investigation -- Public trust issues -- Financial losses -- Winter weather conditions in December add complexity - -7. Timeline Distribution Analysis: -- 1 month probability: ~1% -- 2 months (by resolution): ~3% -- 6 months: ~15% -- 12 months: ~40% - -8. Resolution Criteria Analysis: -- Must be fully public (no waitlist) -- Must be truly rider-only -- Must be operating on December 31 specifically -- All these criteria make success less likely - -Given the extremely short timeline, recent regulatory issues, and strict resolution criteria, I assess there is a very low probability of success. The admission of falsifying reports just days ago is particularly damaging. While Cruise might resume limited operations in 2025, the likelihood of full public rider-only service by December 31, 2024, is quite low. - -I need to adjust for historical overconfidence, but in this case, the timeline is so short and the recent news so negative that a very low probability is warranted. - -Probability: 3%" -30123,"Will the US enact export controls for some generative AI software before 2025? -","To forecast whether the US will enact export controls for some generative AI software before 2025, I need to examine several factors and follow structured reasoning while accounting for possible biases and uncertainties. - -### Analysis of the Current Situation and Background Information: - -1. **Current Status**: As of November 2024, there are no specific export controls targeting generative AI software, only on hardware like AI chips. The lack of existing regulations makes the status quo lean towards a ""No"" resolution unless significant changes occur soon. - -2. **Trends in Policy**: - - The US has been actively enforcing export controls on technology related to AI hardware, most recently with actions against TSMC to limit China's access to AI chips. This demonstrates a trend of increased caution and regulation related to advanced technology with national security implications. - - Discussions and reports indicate consideration towards stricter controls on AI beyond hardware (e.g., Fact Sheet from October 2024), which suggests policymakers are thinking about software, although concrete proposals or actions are not yet publicized. - -3. **Time Frame and Variance**: - - The time remaining until the resolution date (December 31, 2024) is about six weeks. Generally, policy changes of this magnitude, especially involving international trade regulations, require considerable time for discussion, debate, drafting, and implementation. - - Historical variance in export control policies suggests changes tend to unfold over months and usually require broader legislative or executive consensus, reducing the probability of such a drastic change within the short timeframe. - -4. **Historical Base Rates**: - - Looking historically at technology export controls, these are usually hardware-driven before they trickle down to software, partly because software export is inherently less controllable and measure-intensive. - - No generative AI software-specific export controls have been enacted historically, impacting the base case probability. - -5. **Expert Opinions and Recent Developments**: - - Recent discussions on expanding export bans beyond China reflect broader aspirations, but these have not translated into explicit controls on software. - - Given that hardware-related restrictions have been a focus, the extension to software requires distinct groundwork, particularly concerning definition, technology readiness for such controls, and international cooperation. - -### Synthesis and Probability Assessment: - -Given the factors above, it seems unlikely that new regulations targeting generative AI software will appear before the end of 2024. The timeline is too short for the typical regulatory process, broader political circumstances need to support such an effort, and there is little evidence of immediate official intention to impose software-specific controls. - -### Alternative Time Frames: - -- **1-Month Outlook**: The probability of enacting generative AI software export controls in the next month is very low, likely in the single digits due to the short timeframe. -- **1-Year Outlook (Through 2025)**: Over a longer period, it becomes more plausible as ongoing regulatory attention on AI and technological security issues may converge on software, possibly reaching 20-30%. - -These assessments influence, but do not solely dictate, the probability for the immediate question focus. Given the context and a history of past overconfidence, I'll position my final forecast with balanced caution. - -**Final Probability**: - -Considering all factors, the probability that the US will enact export controls for some generative AI software before 2025 is limited by the time remaining and current legislative inertia. There isn't sufficient momentum or urgency apparent in public or political dialogues to suggest an imminent resolution. - -**Probability**: 8%" -30115,"Will North Korea deploy military personnel to Ukraine before January 1, 2025?","Let me think through this step by step: - -1. Timeline Assessment: -- Only about 6 weeks remain until resolution (Nov 15 to Jan 1) -- This is a very short window for significant military deployments -- Winter conditions may affect military movements - -2. Current Status: -- NK troops are confirmed in Kursk Oblast (Russia) -- Some combat engagement reported with Ukrainian forces -- No confirmed presence of 100+ NK troops on Ukrainian soil yet - -3. Key Factors: -- NK troops are already positioned near Ukraine -- Russia and NK have strengthened military cooperation -- Winter conditions might limit major offensive operations -- The threshold (100 troops) is relatively low -- Pre-2014 borders give more territory to consider - -4. Historical Context: -- NK has never deployed troops to Europe in modern history -- This level of NK-Russia cooperation is unprecedented -- Russia has previously used foreign fighters (Wagner, etc.) - -5. Timeline Probability Distribution: -- Next 2 weeks: 15% (immediate deployment) -- 2-4 weeks: 25% (planned operation) -- 4-6 weeks: 35% (including holiday period) -- Within 3 months: 45% (longer timeframe) -- Within 6 months: 60% (much longer timeframe) - -6. Risk Factors: -- International reaction could deter deployment -- NK might prefer advisory/support roles from Russia -- Ukraine's defensive capabilities -- Weather conditions - -7. Scenario Analysis: -Base Case (40%): NK troops remain in Kursk, limited engagement -Positive Case (35%): Small force enters Ukraine for combat -Negative Case (25%): Political/military constraints prevent deployment - -8. Confidence Adjustments: -- Short timeframe reduces likelihood -- High uncertainty due to unprecedented situation -- Need to account for historical overconfidence -- Base rate of similar military deployments is low - -Given: -- The troops are already positioned nearby -- Recent combat engagement reported -- Low threshold (100 troops) -- Very short timeframe -- Winter conditions -- Historical precedent of overconfidence -- Need for conservative estimate given uncertainty - -I believe there's a significant but not overwhelming chance of this occurring within the timeframe. The presence of troops in Kursk and recent engagements suggest movement is possible, but the short timeline and winter conditions are limiting factors. - -Probability: 35% - -This accounts for the short timeframe, current positioning, historical overconfidence, and the relatively low threshold while acknowledging the significant uncertainty and unprecedented nature of the situation." -30112,"Will a country change its official name before Jan 1, 2025?","To forecast the probability of a country changing its official name before January 1, 2025, we start by analyzing past data, trends, and the context surrounding such changes. The probability of occurrence lies within the ""Other Binary Outcome Event"" category, as it is a binary and non-quantitative question not fitting other specified categories. - -### Analysis and Research: -1. **Historical Base Rate**: - - Country name changes are relatively rare. For example, a country may change its name once a decade. - - The decision is usually driven by significant political, historical, or national reconciliation motives, which are extraordinary circumstances. - -2. **Current Information**: - - Multiple sources, such as Wikipedia 2024, African Arguments 2024, and the CIA World Factbook, do not report any expected name changes within the remaining months of 2024. - - Traditionally, even if discussions or sentiments lead towards a name change, these processes are often prolonged and might involve referendums, legislative approval, and diplomatic negotiations, which could further delay a change. - -3. **Remaining Timeframe**: - - There are only about six weeks left until the resolution date. This short timeframe reduces the likelihood of a country changing its name, given the typical duration and complexity involved in such processes. - -4. **Bias and Recency in Data**: - - Current data up to November 2024 does not indicate any impending changes, suggesting a strong base case against the event occurring. - - There are no significant global events or trends currently that align with historical catalysts for name changes. - -5. **Base Case & Overconfidence Check**: - - Historically, I've been overconfident and thus should lean conservatively against overestimating rare events. - - Given the rarity, short timeline, and lack of substantial recent evidence supporting a pending name change, the base case strongly suggests no change. - -By putting all this together, we observe that while possible, the probability is low that a country will change its official name in the defined period due to various constraints such as typical timelines for legal and international recognition processes. - -### Additional Timeframe Considerations: -- **Within 1 Month**: Extremely unlikely given typical lag in diplomatic processes alone. -- **Within 6 Months (post 2025)**: Slightly higher, as more time allows for the start and completion of such complex negotiations. - -Given the above considerations, the probability of a country changing its official name before January 1, 2025, given this data and analysis, can be projected as: - -**Probability: 2%**" -30111,"Will the USDA's Center for Veterinary Biologics grant a license for a highly pathogenic avian influenza H5 vaccine in dairy cattle before January 1, 2025?","To forecast whether the USDA's Center for Veterinary Biologics (USDA-CVB) will grant a license for an H5 vaccine in dairy cattle before January 1, 2025, I’ll consider several factors systematically. - -### Base Rates and Historical Context - -1. **Timeframe and Process**: New vaccine licensing typically requires substantial data from trials for safety, efficacy, and regulatory compliance. Given that we have less than two months until January 1, 2025, this is a limited timeframe for completing these steps. - -2. **Conditional vs. Full Licensure**: Conditional licensure has fewer requirements than full licensure, potentially increasing the probability of resolution as ""Yes."" However, given that data would be submitted in December, the timeframe for review is very tight. - -3. **Historical Decision Timelines**: Historically, regulatory agencies can take from weeks to several months to review licensing applications, even under expedited processes. - -### Current Situation and Developments - -1. **Medgene Labs Field Trials**: The pivotal point is the completion of Medgene Labs' trials expected in December 2024. They intend to share results with USDA then. This gives USDA very limited time to review and decide before the January 1, 2025 deadline. - -2. **Regulatory Focus and Alerts**: The USDA is notably proactive in dealing with HPAI due to public health concerns, as exemplified by the ongoing efforts and wider context involving H5N1 spillover concerns. - -3. **Expert Opinions and Updates**: Recent updates indicate the USDA-CVB is preparing for potential licensures, shown by supporting developments and accepting applications. However, updates suggest uncertainty about whether licensing will occur before the deadline. - -### Influencing Variables and Uncertainty Consideration - -1. **Public Health Urgency**: The potential spillover threat from HPAI to humans raises the urgency. This might push regulatory bodies to expedite processes, but physical and procedural constraints still exist. - -2. **Research Assistant Insights**: They underscore the regulatory support while also highlighting uncertainties inherent in a last-minute process. - -3. **Overconfidence Calibration**: Historical outcomes resolve positively about 35% of the time, instructing caution against assuming equal likelihood given the scenario's inherent hurdles and tight deadlines. - -### Probability Estimation - -1. **Overall Context**: The required pathogen's impact, USDA's proactive regulatory stance, and the potential justification for expedited licensure contribute positively. However, the procedural timeline (especially document submission in December) restrains likelihood. - -2. **Event Probability in the Context**: Weighing these aspects, potential expedited action by the USDA, juxtaposed with procedural norms and the tight timeframe, lead to a restrained probability, markedly below a 50% threshold. - -Given the constraints and analyses, the probability of USDA-CVB issuing a license for a highly pathogenic avian influenza H5 vaccine in dairy cattle by January 1, 2025 is reduced, although not negligible due to urgency considerations: - -**Probability: 20%**" -30110,Will Bitcoin dominance exceed 63.0% on any single day in November or December 2024?,"Let me think through this step by step: - -1. Question Type: This is a Non-Market Threshold Value Event, specifically about Bitcoin dominance exceeding 63.0%. - -2. Current Status: -- BTC dominance just reached 60.0% on November 13, 2024 -- There's been a steady rise recently -- It needs to gain another 3% to hit the threshold - -3. Time Consideration: -- Only about 1.5 months until resolution -- This is a relatively short timeframe for such a movement -- The shorter timeframe reduces uncertainty but also limits potential for large changes - -4. Historical Context: -- BTC dominance has been volatile historically -- Hit a low of 38.3% in 2022 -- Current level of 60% is already a significant recovery -- 63% would be a major milestone not seen in several years - -5. Current Factors: -- Trump's re-election and crypto-friendly stance -- Spot Bitcoin ETF approval already priced in -- Current ""Extreme Greed"" sentiment (84 on Fear & Greed index) -- Market is already quite extended - -6. Probability Distribution Analysis: -Next 2 weeks: 15% chance -December: 25% chance -If it were a 6-month window: 45% chance -If it were a 12-month window: 60% chance - -7. Risk Factors: -- Market already showing signs of extreme bullishness -- Historical resistance around 60-62% level -- Short timeframe limits upside potential -- Current technical indicators suggest possible short-term pullback - -8. Positive Factors: -- Strong momentum -- Institutional adoption -- Favorable political environment -- Only needs 3% increase from current levels - -9. Scenario Analysis: -Most likely: BTC dominance stays between 58-62% range -Bullish case: Breaks above 63% on strong momentum -Bearish case: Pulls back to 55-58% range - -10. Confidence Adjustment: -- Initial estimate: 35% -- Adjusting for historical overconfidence -- Considering the short timeframe -- Accounting for current extreme sentiment - -Given the relatively short timeframe, the already extended nature of the market, and historical resistance levels, while also considering the strong momentum and favorable environment, I believe there's a moderate chance of reaching 63% dominance. - -However, I need to adjust for typical overconfidence and the fact that we're already at extreme sentiment levels. - -Probability: 28%" -30203,Will Lainey Wilson or Chris Stapleton win the 2024 CMA Award for Single of the Year?,"Let me think through this step by step: - -1. First, this is a Competition Event type question, specifically for an awards ceremony. - -2. Key considerations: -- This is for a major country music award (CMA) -- We have only 3 days until resolution -- We have two specific artists who could fulfill a ""Yes"" resolution -- We know all nominees and their songs - -3. Historical context: -- Chris Stapleton has significant CMA success historically -- Lainey Wilson has been rising rapidly in country music -- Both are respected within the industry -- Both songs were commercially successful - -4. Current factors: -- Morgan Wallen leading nominations could split attention -- Both Stapleton and Wilson are performing at the ceremony -- ""White Horse"" and ""Watermelon Moonshine"" both had strong commercial performance - -5. Probability analysis: -- With 5 nominees total, baseline probability would be 40% for these two combined -- Stapleton's industry respect adds ~5% -- Wilson's current momentum adds ~5% -- The fact that they represent 2 of 5 nominees increases chances significantly -- Recent award show trends suggest spreading recognition across artists - -6. Mitigating factors: -- Other nominees are also strong contenders -- Morgan Wallen's commercial success this year -- Potential vote splitting between Stapleton and Wilson - -7. Timeline considerations: -- With only 3 days until resolution, we have high certainty in our information -- Voting is already completed -- No external factors can influence the outcome now - -8. Confidence adjustment: -- Starting from 40% baseline (2/5 nominees) -- Adding 10% for combined strength of both artists -- Subtracting 5% for strong competition -- Adding 5% for industry standing - -9. Final analysis: -- The combination of two strong contenders -- Both artists' current industry position -- Historical CMA voting patterns -- The strength of their respective singles -- The fact that they represent 40% of nominees - -After careful consideration and adjusting for historical overconfidence, I believe these two artists combined have a slightly better than even chance of winning, but not dramatically so. - -Probability: 52%" -30202,"Will the CDC confirm a case of Marburg virus disease in a US resident before January 1, 2025?","Let me think about this step by step: - -1. First, let's consider the base rate: -- Marburg virus has never had a confirmed case in a US resident historically -- Similar viruses (like Ebola) have had very few US cases, even during major outbreaks -- When cases did occur in the US, they were typically medical workers returning from affected areas - -2. Timeline analysis: -- We have about 6 weeks until the resolution date -- This is a very short window for a rare event -- The Rwanda outbreak is ongoing but not rapidly expanding -- Travel restrictions and monitoring are already in place - -3. Key factors increasing probability: -- Active outbreak in Rwanda -- US healthcare workers potentially involved in response -- Established travel routes between regions -- Modern air travel making spread possible - -4. Key factors decreasing probability: -- Very short timeframe (6 weeks) -- Strong US infection control protocols -- Limited direct contact between US residents and outbreak area -- CDC's enhanced screening measures -- Historical rarity of such events - -5. Timeline probability distribution: -- Next 6 weeks (until resolution): ~0.8% -- Next 3 months: ~1.5% -- Next 6 months: ~3% -- Next year: ~5% -This shows the event becomes more likely over longer timeframes, but remains rare - -6. Risk factors specific to this period: -- Holiday travel season could increase movement -- But enhanced awareness and screening likely offsets this -- Healthcare protocols are well-established for this type of threat - -7. Comparison to similar events: -- Ebola cases in US (2014): 4 cases total during major outbreak -- But that outbreak was much larger and longer -- This situation has fewer risk factors - -8. Confidence adjustment: -- Initial estimate might be too high given historical overconfidence -- Need to account for the extremely short timeline -- Must consider the specific CDC confirmation requirement - -Given the extremely short timeline (6 weeks), the historical absence of US Marburg cases, strong preventive measures in place, and the specific requirement for CDC confirmation (not just identification), I believe the probability is very low but not zero. - -Considering all factors and adjusting for historical overconfidence, particularly given the short timeline: - -Probability: 1% - -This reflects the extremely low but non-zero chance of occurrence in such a short timeframe, while acknowledging that such events are possible but historically very rare, even during active outbreaks." -30201,Will the Federal Register list 3 or more executive orders by President Joe Biden for December 2024? ,"Predictions: [15, 10, 10, 10, 10] -Median reasoning: To assess the likelihood of different counts of executive orders by President Joe Biden for December 2024, we need to consider both past behaviors and current political context. Historically, executive orders are tools presidents often use, especially during the lame duck period if there is a change in administration or political urgency. Given the November 2024 election results and the impending change of administration, there is heightened pressure for Biden to solidify his policy legacy via executive orders. Past data show Biden has issued executive orders at varying paces, but considering the current urgency, a spike is plausible. - -1. **Base Rate and Urgency**: Presidents have historically issued last-minute executive orders. Biden's situation in November 2024 is unique with the Democratic base urging swift action before President-elect Trump takes office. Such political pressure often leads to increased use of executive actions. -2. **Current Pace and Projections**: Biden has issued multiple executive orders during his presidency. While recent months may not indicate a clear upward trend, the recent electoral loss context should heavily motivate more actions. -3. **Specific Plans and Announcements**: There's mention of a nearly complete cybersecurity executive order and other potential actions in response to international (Ukraine-related) events. These specific plans suggest imminent action. -4. **Timestamp and Variability**: With the resolution date only a few weeks away, the likelihood of maintaining or increasing the baseline frequency of executive actions is high due to urgency and already partially completed plans. -5. **Evaluation by December End**: Paying attention to the practicality and time available, it's feasible that Biden can issue 3 or more executive orders given the existing pressure points and trends. -6. **Risk Evaluation**: Considering risks of overconfidence, we should temper our probabilities while recognizing patterns in historical behavior during such political transitions. - -Given the above considerations, the probabilities are apportioned as follows: -- Zero EOs is unlikely given the urgency, so a low probability. -- 1 or 2 EOs also seems less probable due to political pressure. -- ≥3 EOs is the more likely event under current circumstances, balancing history, recent announcements, and political dynamics." -30200,Will the Federal Register list 1 or 2 executive orders by President Joe Biden for December 2024? ,"Predictions: [15, 10, 10, 10, 10] -Median reasoning: To assess the likelihood of different counts of executive orders by President Joe Biden for December 2024, we need to consider both past behaviors and current political context. Historically, executive orders are tools presidents often use, especially during the lame duck period if there is a change in administration or political urgency. Given the November 2024 election results and the impending change of administration, there is heightened pressure for Biden to solidify his policy legacy via executive orders. Past data show Biden has issued executive orders at varying paces, but considering the current urgency, a spike is plausible. - -1. **Base Rate and Urgency**: Presidents have historically issued last-minute executive orders. Biden's situation in November 2024 is unique with the Democratic base urging swift action before President-elect Trump takes office. Such political pressure often leads to increased use of executive actions. -2. **Current Pace and Projections**: Biden has issued multiple executive orders during his presidency. While recent months may not indicate a clear upward trend, the recent electoral loss context should heavily motivate more actions. -3. **Specific Plans and Announcements**: There's mention of a nearly complete cybersecurity executive order and other potential actions in response to international (Ukraine-related) events. These specific plans suggest imminent action. -4. **Timestamp and Variability**: With the resolution date only a few weeks away, the likelihood of maintaining or increasing the baseline frequency of executive actions is high due to urgency and already partially completed plans. -5. **Evaluation by December End**: Paying attention to the practicality and time available, it's feasible that Biden can issue 3 or more executive orders given the existing pressure points and trends. -6. **Risk Evaluation**: Considering risks of overconfidence, we should temper our probabilities while recognizing patterns in historical behavior during such political transitions. - -Given the above considerations, the probabilities are apportioned as follows: -- Zero EOs is unlikely given the urgency, so a low probability. -- 1 or 2 EOs also seems less probable due to political pressure. -- ≥3 EOs is the more likely event under current circumstances, balancing history, recent announcements, and political dynamics." -30199,Will the Federal Register list exactly zero executive orders by President Joe Biden for December 2024? ,"Predictions: [15, 10, 10, 10, 10] -Median reasoning: To assess the likelihood of different counts of executive orders by President Joe Biden for December 2024, we need to consider both past behaviors and current political context. Historically, executive orders are tools presidents often use, especially during the lame duck period if there is a change in administration or political urgency. Given the November 2024 election results and the impending change of administration, there is heightened pressure for Biden to solidify his policy legacy via executive orders. Past data show Biden has issued executive orders at varying paces, but considering the current urgency, a spike is plausible. - -1. **Base Rate and Urgency**: Presidents have historically issued last-minute executive orders. Biden's situation in November 2024 is unique with the Democratic base urging swift action before President-elect Trump takes office. Such political pressure often leads to increased use of executive actions. -2. **Current Pace and Projections**: Biden has issued multiple executive orders during his presidency. While recent months may not indicate a clear upward trend, the recent electoral loss context should heavily motivate more actions. -3. **Specific Plans and Announcements**: There's mention of a nearly complete cybersecurity executive order and other potential actions in response to international (Ukraine-related) events. These specific plans suggest imminent action. -4. **Timestamp and Variability**: With the resolution date only a few weeks away, the likelihood of maintaining or increasing the baseline frequency of executive actions is high due to urgency and already partially completed plans. -5. **Evaluation by December End**: Paying attention to the practicality and time available, it's feasible that Biden can issue 3 or more executive orders given the existing pressure points and trends. -6. **Risk Evaluation**: Considering risks of overconfidence, we should temper our probabilities while recognizing patterns in historical behavior during such political transitions. - -Given the above considerations, the probabilities are apportioned as follows: -- Zero EOs is unlikely given the urgency, so a low probability. -- 1 or 2 EOs also seems less probable due to political pressure. -- ≥3 EOs is the more likely event under current circumstances, balancing history, recent announcements, and political dynamics." -30198,"Will the USDA-posted recall affecting Dearborn Sausage Company, Inc be closed before December 1, 2024?","### Analysis - -**Context and Background**: -- **Recall Nature**: The recall was initiated due to a mislabeling issue leading to undeclared allergens in approximately 1,944 pounds of ready-to-eat wieners by Dearborn Sausage Company. Historically, recalls related to misbranding and allergens tend to remain active until a satisfactory resolution is confirmed by the relevant authorities. -- **Current Status and Updates**: As of November 11, 2024, there have been no updates indicating closure of this recall. With today's date being November 18, 2024, there is only about 12 days until the resolution date, leaving limited time for the status to change. - -**Base Rates and Historical Data**: -- **Base Rate of Recall Closures**: While the specific timeline for closure can vary, most recalls do not resolve rapidly, especially if they involve undeclared allergens which require thorough investigation and confirmation of corrective actions. -- **Recent Developments**: The absence of new information or progress towards closure as of mid-November suggests a low likelihood of resolution. Recalls with no adverse reactions reported may be deprioritized relative to those with confirmed health impacts, potentially extending the duration. - -**Considerations**: -- **Timing**: Given we are close to the end date without major updates, the probability of closure in the next two weeks is likely low. Historical averages suggest that significant movement towards closure would typically be reported if imminent. -- **Default Outcome**: The default assumption, barring substantial updates, should be that the status remains unchanged, i.e., active. -- **Overconfidence Adjustment**: Accounting for past tendencies towards overconfidence, the probability should be moderated towards a more conservative estimate. - -**Statistical Analysis**: -- **Probability of Closure Before Dec 1, 2024**: Based on typical recall timelines and the specifics of this case with no recent changes, and adjusting for a possible bias of overconfidence, there's a low likelihood of closure by December 1. However, it's not impossible, just unlikely. - -### Conclusion -The historical pattern, limited time remaining, and lack of updates all suggest a low probability of this recall being closed by the deadline. Therefore, a reasonable estimate for the probability of closure by December 1, 2024, is as follows: - -**Probability: 15%**" -30197,"Will iRobot file for bankruptcy before January 1, 2025?","Let me think about this step by step: - -1. First, let's consider the timeframe: -- Only about 6 weeks until resolution -- This is a very short window for a bankruptcy filing -- Most companies, even in distress, take longer to enter bankruptcy -- End of year timing makes immediate bankruptcy less likely as companies often wait for new fiscal years - -2. Financial Analysis: -- Negative cash flow and operating losses are concerning -- Low Altman Z-Score of 1.03 indicates distress -- But still has $195M market cap -- Companies usually exhaust other options before bankruptcy -- Recent 23% US revenue growth is positive sign -- International market declines concerning but not catastrophic - -3. Leadership and Operations: -- Planned executive transitions suggest orderly process -- Already executed significant cost-cutting (41% headcount reduction) -- ""Elevate"" turnaround strategy in place -- These actions typically buy time - -4. Base Rate Analysis: -- Public companies of iRobot's size rarely file for bankruptcy with such short notice -- Most distressed companies attempt debt restructuring, asset sales, or strategic alternatives first -- Holiday season is traditionally stronger for consumer products - -5. Timeline Distribution: -- Next 6 weeks (by Jan 1, 2025): ~8% probability -- Next 3 months (by March 2025): ~15% probability -- Next 6 months (by June 2025): ~25% probability -- Next 12 months (by Dec 2025): ~35% probability - -This shows the risk increases over time, but is lower in the immediate term. - -6. Risk Factors: -- Failed Amazon deal removed strategic option -- Competitive pressures increasing -- But holiday season could provide cash flow boost -- Still has market value and potential strategic value - -7. Mitigating Factors: -- Recent cost-cutting measures -- Still generating significant revenue -- Brand value remains strong -- Other strategic alternatives likely available - -Given the very short timeframe until resolution, while the company is clearly distressed, it would be unusual for a bankruptcy filing to occur so quickly, especially during the holiday season. The company has already taken significant steps to reduce costs and appears to be executing an orderly transition plan. While bankruptcy risk is significant over a longer timeframe, the probability of filing before January 1, 2025 is relatively low. - -Adjusting for historical overconfidence and the short timeline, but acknowledging the serious financial challenges: - -Probability: 12%" -30196,"Will Bausch Health, Inc., file for bankruptcy before January 1, 2025?","Let me think through this step by step: - -1. **Question Type**: This is an Other Binary Outcome Event, specifically about corporate bankruptcy filing. - -2. **Timeline Analysis**: -- Only 44 days remain until resolution -- Very short timeframe for major corporate action -- End of year timing is relevant (companies often make major moves before year end) - -3. **Current Status**: -- $22B in debt (very significant) -- Strong Q3 2024 performance reported -- Official denial of bankruptcy plans -- Ongoing debt restructuring negotiations -- Creditor-shareholder standoff over Bausch + Lomb spinoff - -4. **Base Rate Considerations**: -- Large pharmaceutical companies rarely file for bankruptcy -- When companies officially deny bankruptcy rumors, they usually don't file within 2-3 months -- However, creditor support for bankruptcy is a serious red flag - -5. **Timeline Distribution**: -- Next 2 weeks (through December): 2% chance -- December 1-31: 4% chance -- If looking at full year 2025: Would estimate 15-20% chance -- Probability not evenly distributed; year-end slightly higher chance - -6. **Key Factors Weighing Against Filing by Jan 1**: -- Very short timeframe remaining -- Recent strong financial performance -- Official company denial -- Complex stakeholder situation would take time to resolve - -7. **Key Factors Supporting Possible Filing**: -- Massive debt load -- Creditor support for bankruptcy -- Stalled negotiations -- Year-end timing - -8. **Risk Adjustment**: -- Need to adjust for historical overconfidence -- Major corporate actions rarely happen this quickly -- But can't completely discount possibility given debt situation - -9. **Uncertainty Factors**: -- Hidden negotiations we don't know about -- Potential sudden deterioration in financial position -- Creditor actions could force hand - -Given the extremely short timeframe (44 days), recent strong performance, and official denial, I believe the probability is quite low. However, the massive debt load and creditor support for bankruptcy means it can't be completely discounted. The year-end timing slightly increases the probability compared to mid-year. - -Adjusting for historical overconfidence and considering the short timeline, while accounting for the serious red flags: - -Probability: 6% - -This reflects the very short timeframe combined with current indicators, while still acknowledging the non-zero possibility given the serious underlying issues. The probability would be significantly higher if asking about all of 2025." -30195,"Will Nick Sirianni cease to be the Philadelphia Eagles head coach before January 1, 2025?","To forecast the likelihood of Nick Sirianni ceasing to be the head coach of the Philadelphia Eagles before January 1, 2025, we need to carefully evaluate the given information and contextual factors, considering the short-term nature of the timeline and historical precedents. - -**Current Context:** - -1. **Team Record and Performance:** The Eagles are currently 7-2, which is a strong performance. Typically, head coaches of NFL teams with winning records late in the season face a lower risk of being fired unless there are severe off-field issues or conflicts with management. - -2. **Support and Criticism:** While there has been criticism of Sirianni's decisions, notably in play-calling, key voices such as former player Jason Kelce publicly support him. This support can mitigate some of the pressure from fans and the media. - -3. **Management Dynamics:** An insider report suggests that Sirianni feels isolated and there is mounting pressure. This potential strain on his relationship with the team owner and front office does increase the risk somewhat. - -4. **Timeframe:** The resolution date is quite near—less than two months away. Significant changes in coaching staff, especially mid-season, are not common unless the team is performing poorly or there is a major off-field issue. - -**Base Rates and Historical Precedents:** - -- Mid-season coaching changes are uncommon for teams with a winning record, as front offices typically prefer stability to avoid disrupting the team's performance. -- There is no indication of new, unexpected factors (political, technological, etc.) that might skew the historical base rate. - -**Indicators of Potential Change:** - -- Current evidence does not strongly indicate any forthcoming moves to replace Sirianni. The criticisms, while present, do not appear to outweigh the current performance level and support from certain influential figures. - -**Probability Distribution and Uncertainty:** - -While there are elements of criticism and pressure, these are largely offset by the team's performance and supportive voices. Given the short timeframe, the likelihood of a change is reduced. - -Balancing these factors with historical data, contemporary context, and the nature of organizational dynamics in the NFL, it's reasonable to estimate that the probability Sirianni will be dismissed before January 1, 2025, is low. - -Taking into account the possibility of unforeseen developments and previous tendencies toward overconfidence: -- The probability is somewhere between 5% and 10%. - -Final Estimate: -""Probability: 8%""" -30194,"On December 30, 2024, Will Nvidia have the largest market cap in the world?","Predictions: [60, 50, 50, 40, 55] -Median reasoning: We are tasked with forecasting which company—Nvidia, Microsoft, or Apple—will have the largest market cap on December 30, 2024. A Brier score will be used to evaluate our predictions, so we must provide precise probabilities. The market cap data as of November 14, 2024, indicates that Nvidia is a strong performer but has recently experienced fluctuations in its stock price. Nvidia remains the #3 company, suggesting consistent competition from Microsoft and Apple. Historically, movements in market cap rankings can occur due to sudden stock price changes, investor sentiment, and market dynamics, especially in the volatile technology sector. However, with only 42 days until the resolution date, it is crucial to weigh the default scenario heavily and recognize the limited time for substantial changes. Nvidia's continued focus on AI, HPC, gaming, and VR sectors has fueled its growth, yet a 3.26% recent drop in stock price could indicate volatility. Apple's and Microsoft's more established market positions and track records make them formidable competitors close to the resolution deadline. Given Nvidia's rank and the rapid timeframe, the probability of Nvidia surpassing both companies is unlikely. Thus, for Nvidia, I estimate a probability of about 5%. Microsoft and Apple both have strong potential to hold or strengthen their positions, so I will allocate 50% for Apple and 45% for Microsoft, considering current trends, base cases, and competitive standing. Balancing the recent performance trends of these tech giants with historical base rates of such shifts, our probabilities reflect their potential to become the largest market cap within the timeframe." -30193,"On December 30, 2024, Will Microsoft have the largest market cap in the world?","Predictions: [60, 50, 50, 40, 55] -Median reasoning: We are tasked with forecasting which company—Nvidia, Microsoft, or Apple—will have the largest market cap on December 30, 2024. A Brier score will be used to evaluate our predictions, so we must provide precise probabilities. The market cap data as of November 14, 2024, indicates that Nvidia is a strong performer but has recently experienced fluctuations in its stock price. Nvidia remains the #3 company, suggesting consistent competition from Microsoft and Apple. Historically, movements in market cap rankings can occur due to sudden stock price changes, investor sentiment, and market dynamics, especially in the volatile technology sector. However, with only 42 days until the resolution date, it is crucial to weigh the default scenario heavily and recognize the limited time for substantial changes. Nvidia's continued focus on AI, HPC, gaming, and VR sectors has fueled its growth, yet a 3.26% recent drop in stock price could indicate volatility. Apple's and Microsoft's more established market positions and track records make them formidable competitors close to the resolution deadline. Given Nvidia's rank and the rapid timeframe, the probability of Nvidia surpassing both companies is unlikely. Thus, for Nvidia, I estimate a probability of about 5%. Microsoft and Apple both have strong potential to hold or strengthen their positions, so I will allocate 50% for Apple and 45% for Microsoft, considering current trends, base cases, and competitive standing. Balancing the recent performance trends of these tech giants with historical base rates of such shifts, our probabilities reflect their potential to become the largest market cap within the timeframe." -30192,"On December 30, 2024, Will Apple have the largest market cap in the world?","Predictions: [60, 50, 50, 40, 55] -Median reasoning: We are tasked with forecasting which company—Nvidia, Microsoft, or Apple—will have the largest market cap on December 30, 2024. A Brier score will be used to evaluate our predictions, so we must provide precise probabilities. The market cap data as of November 14, 2024, indicates that Nvidia is a strong performer but has recently experienced fluctuations in its stock price. Nvidia remains the #3 company, suggesting consistent competition from Microsoft and Apple. Historically, movements in market cap rankings can occur due to sudden stock price changes, investor sentiment, and market dynamics, especially in the volatile technology sector. However, with only 42 days until the resolution date, it is crucial to weigh the default scenario heavily and recognize the limited time for substantial changes. Nvidia's continued focus on AI, HPC, gaming, and VR sectors has fueled its growth, yet a 3.26% recent drop in stock price could indicate volatility. Apple's and Microsoft's more established market positions and track records make them formidable competitors close to the resolution deadline. Given Nvidia's rank and the rapid timeframe, the probability of Nvidia surpassing both companies is unlikely. Thus, for Nvidia, I estimate a probability of about 5%. Microsoft and Apple both have strong potential to hold or strengthen their positions, so I will allocate 50% for Apple and 45% for Microsoft, considering current trends, base cases, and competitive standing. Balancing the recent performance trends of these tech giants with historical base rates of such shifts, our probabilities reflect their potential to become the largest market cap within the timeframe." -30191,"Will South Carolina have 400,000 or more residents living in drought on December 31, 2024?","To determine the probability that South Carolina will have 400,000 or more residents living in drought on December 31, 2024, we need to analyze the given data and apply forecasting principles carefully. - -### Assessment of Background Information: - -1. **Current Drought Situation (as of mid-November 2024):** - - Drought conditions are worsening in South Carolina, especially in coastal areas ([1], [4]). - - The Southeast has experienced a very dry October, leading to an expansion of drought ([2], [5]). - -2. **Forecast for the Coming Weeks:** - - There is a weak La Niña pattern, potentially resulting in below-normal precipitation from December through February across the Southeast, including South Carolina ([Recent Updates]). - -3. **Drought Area Extent and Severity:** - - As of November 14, moderate drought has expanded in coastal areas of South Carolina ([4]). - - The percentage of the Southeast experiencing abnormally dry conditions is 57%, with 19% in moderate drought or worse ([5]). - -4. **Variability in Rainfall:** - - Recent rainfall has provided relief in some areas but left others worsening ([1]). - -### Evaluation and Analysis: - -- **Historical and Seasonal Trends:** - Given the patterns of drought in South Carolina and the forecasted climate conditions, we have a reasonable base rate to suggest continued drought conditions. - -- **Timeframe:** - With only about 1.5 months until the resolution date, significant deviation from the current trend (drought worsening) is less likely. Seasonal trends and climatic conditions suggest that drought conditions may persist or even worsen, aligning with the forecasted weak La Niña effect. - -- **Drought Resolution and Mitigation:** - While significant rainfall or effective drought mitigation efforts could shift trends, these are uncertain and not currently indicated in the forecasts. - -- **Expert Analysis:** - Frequent reports of deteriorating conditions from agricultural agents and existing burn bans suggest heightened awareness and ongoing drought impacts. - -- **Population Impact:** - The criteria focus on whether 400,000 residents will be affected. Recent expansions of the drought suggest this number could be reached or exceeded, especially parallel to climatic forecasts pointing to worsening conditions. - -### Probabilistic Assessment: - -- With the base rates and forecasts indicating worsening drought, combined with historical climate patterns during La Niña-like conditions, there is a higher likelihood that the threshold of residents affected will be met. - -- Considering factors of uncertainty and variability, while ensuring not to overpredict due to overconfidence: A fair balanced probability should lean towards a positive resolution. - -### Final Thought Process: - -- The question inherently lines up as a Cumulative Count Event concerning drought extent. -- Default resolution, absent changes, suggests a 'Yes.' -- Overconfidence consideration: Compensate by slightly pulling probability back from extreme values. -- Expert trends and forecasts indicate moving towards conditions conducive for drought continuation. - -**Final Forecast:** - -Considering all these factors together: - -**Probability: 65%**" -30190,"Will Microsoft stock continuously remain below its all-time high before January 1, 2025?","Let me think about this step by step: - -1. **Question Type**: This is a Market Price Event combined with a Non-Market Threshold Value Event, as it involves both stock price and an all-time high threshold. - -2. **Time Analysis**: -- Only about 6 weeks until resolution -- Short timeframe reduces uncertainty -- Market has more trading days due to holiday season, but also typically lower volume - -3. **Current Status**: -- Current price: $415.00 -- All-time high: $468.35 -- Gap to breach: ~12.8% -- Currently trading below both 50-day and 200-day moving averages - -4. **Historical Context**: -- Technology stocks have been volatile in 2024 -- Microsoft has strong fundamentals but faces valuation concerns -- 12.8% move in 6 weeks is significant but not unprecedented - -5. **Statistical Analysis**: -- To reach $468.35, needs ~2% weekly growth consistently -- Historical volatility suggests this is possible but not highly probable -- Short time window makes sustained growth more challenging - -6. **Different Timeframes**: -- 1 month probability: ~15% -- 6 weeks (actual timeline): ~20% -- 3 months: ~35% -- 6 months: ~50% - -7. **Key Considerations**: -- Question resolves NO if price touches $468.35 even once -- Only needs one breakthrough day to resolve NO -- Holiday season typically has lower volume but can be more volatile -- Analyst consensus price targets suggest long-term upside -- Recent institutional selling pressure - -8. **Risk Factors**: -- Market sentiment around AI and tech sector -- Macro economic conditions -- Year-end portfolio rebalancing -- Political uncertainty - -9. **Base Rate Analysis**: -- Stocks typically don't make new all-time highs in short windows -- But when near previous highs (~12.8% away), breakthrough is more likely -- Technology sector has been strong performer - -10. **Final Considerations**: -- Short timeframe favors current status quo -- But only needs one breakthrough for NO -- Multiple potential catalysts could drive price movement -- Question resolves YES only if price stays below continuously - -Given the short timeframe but significant upside potential, and considering that we only need one breakthrough day for a NO resolution, while requiring continuous containment for a YES resolution, I believe there's a higher probability of seeing at least one breakthrough day. - -Probability: 20% - -(This means 80% chance of hitting the all-time high at least once before January 1, 2025. The short timeframe and current price gap make it challenging, but the asymmetric resolution criteria - needing just one touch versus continuous containment - skews the probability toward NO.)" -30286,"Will Astro Bot or Final Fantasy 7 Rebirth win Console Game of the Year in the 2024 Golden Joystick Awards? -","Let me think through this step by step: - -1) First, looking at the type of question - this is a Competition Event, specifically an awards ceremony where voting has already concluded. - -2) Key facts: -- Both Astro Bot and FF7 Rebirth have 5 nominations each in Golden Joystick Awards -- Both are also strong contenders in other awards (Game Awards) -- Voting has already concluded -- There are 6 total nominees -- Resolution is in just 3 days - -3) Analysis of advantages for these games: -- FF7 Rebirth has massive franchise recognition and fan base -- Astro Bot has received critical acclaim and represents innovative VR gaming -- Both games represent different gaming approaches (traditional vs VR) - -4) Looking at historical patterns: -- Golden Joystick Awards often align with popular consensus -- AAA titles like Final Fantasy tend to perform well -- However, innovative games can sometimes upset traditional favorites - -5) Considering the competition: -- Zelda: Echoes of Wisdom is likely strong competition -- Dragon's Dogma 2 and Helldivers 2 have their own dedicated followings -- Prince of Persia: The Lost Crown received positive reviews - -6) Key factors for probability: -- Having 2 out of 6 nominees gives a base probability of ~33% -- Both games being leading nominees increases this -- However, strong competition from other titles -- Very short timeframe to resolution (3 days) means high certainty in current situation - -7) Risk adjustments: -- Need to account for historical overconfidence -- Must consider that either game needs to win, not both -- Competition from other strong titles -- Limited data on voter preferences - -Given that: -- Having two strong contenders out of six increases chances -- Both games are leading nominees -- The question resolves in just 3 days -- Voting is already complete -- Need to account for strong competition - -I would estimate that having two of the strongest contenders gives us approximately a 45% chance of success. This factors in: -- Base probability from nominee count -- Strength of these particular nominees -- Competition from other games -- Historical voting patterns -- Adjustment for typical overconfidence - -Probability: 45%" -30285,Will Planet Nine be discovered before 2025?,"The question at hand asks whether Planet Nine will be discovered before January 1, 2025. Let's break down the various factors influencing the probability of this event occurring. - -### Background and Context - -1. **Proposed Existence and Evidence**: The hypothesis of Planet Nine's existence primarily stems from observed gravitational influences in the outer solar system, as described by Batygin and Brown. Their work, along with others, provides indirect evidence but not any direct optical observation needed for a positive resolution to the question. - -2. **Recent Updates**: - - Articles from recent months suggest that while there is continued debate over Planet Nine's existence, with some astronomers skeptical, new detection methods, technology, and facilities are being developed. - - The Vera C. Rubin Observatory, however, will not be operational until 2025, which is beyond the resolution date for this question. Therefore, any potential increased ability to detect Planet Nine through this observatory does not affect our forecast. - - The proposed novel detection methods using arrays of telescopes seem promising but their feasibility and implementation are uncertain and not within the stated timeline to resolution. - -3. **General Scientific Progress and Limitations**: Scientific discoveries of this nature often span years to decades. Large direct observational discoveries in astronomy historically take considerable time and continuous efforts due to the vastness and complexity of space. The indirect evidence for Planet Nine remains convincing for some but lacks the direct observation needed to confirm its existence by the resolution date. - -### Default Behavior and Time Constraints -- As of now, Planet Nine has not been directly observed, which means that the default outcome is negative (i.e., it will not be discovered). -- There are less than two months before the resolution date, which is typically too short for major breakthroughs in such contexts. -- Existing methods haven't found the planet yet, which underlines the base rate of discovery being extremely low without significant technological advances or novel methods yielding quick results. - -### Base Rates and Historical Context -- Consider the base rate for significant astronomical discoveries being proven over short timescales. Typically, discoveries such as new planets or significant astro phenomena have verification and observational work taking multiple years. - -### Final Estimation -Given the above factors, the likelihood of Planet Nine being directly observed within the given timeframe is low. The recency of technological advancements (most notably the Rubin Observatory) won't come into play until post the resolution date. Likewise, the proposed methods using small telescopes for occultation studies are targeted over a longer timeframe. - -Synthesizing all these insights, I would estimate the probability that Planet Nine will be discovered before January 1, 2025, as low: - -**Probability: 1%**" -30284,"Will Larry Ellison be ranked in the top 2 of the Forbes Real-Time Billionaires List on December 31, 2024?","To forecast whether Larry Ellison will be ranked in the top 2 of the Forbes Real-Time Billionaires List on December 31, 2024, we'll break down the problem using a structured approach: - -### Current Situation and Data Analysis -1. **Current Rankings & Net Worth**: - - As of the most reliable recent data (Bloomberg, Nov 18, 2024): - - Elon Musk is at $326 billion. - - Jeff Bezos is at $223 billion. - - Larry Ellison is at $200 billion. - -2. **Past Rankings**: - - Historically, Larry Ellison has maintained a high position on the list but has not frequently been in the top 2. - -3. **Time Remaining**: - - There are approximately 1.5 months left until the resolution date. The changes necessary for Ellison to move into a top 2 position must occur within this period. - -### Variability and Volatility -1. **Market Volatility**: - - Billionaire net worths can fluctuate dramatically due to market changes, particularly those heavily invested in tech stocks like Musk, Bezos, and Ellison. - -2. **Net Worth Dynamics**: - - Large increases in net worth typically result from major gains in stock prices or significant company events (e.g., IPOs, big acquisitions). - -3. **Ellison's Position**: - - Ellison's net worth would need to increase significantly compared to Bezos, assuming Musk remains uncontested as the richest. - -### Base Rates and Historical Trends -1. **Base Rate of Rank Changes**: - - Historically, shifts in the billionaire rankings in such a short time period happen but are uncommon without significant market events. - -2. **No Major Anticipated Events**: - - Currently, there are no known large upcoming events or trends that might favor or disadvantage Ellison against Bezos, such as anticipated product releases or major investments. - -### Conclusion -- **Probability Assessment**: - - **Default Resolution**: Currently, Ellison is not in the top 2; he is ranked 3rd with some margin behind Bezos. The probability should heavily account for this default. - - **Volatility and Lack of Catalyst**: While the billionaire rankings can certainly change rapidly, the lack of a clear catalyst or trend favoring Ellison makes it less likely. - - **Probability Adjustment for Overconfidence**: Historically, only about 35% of questions resolve positively rather than assumed 50%, which should inform a more cautious stance. - -Given these factors, I will assign a relatively low probability, keeping in mind the possibility of unexpected changes but the high likelihood of stability given the short timeframe and current trends. - -**Probability: 15%**" -30283,"Will the number of active US oil drilling rigs be less than 585 on December 27, 2024?","Predictions: [5, 10, 10, 15, 10] -Median reasoning: As of November 19, 2024, the current US rig count is 584, having experienced a slight downward trend but relatively stable overall. Recent weeks have shown modest fluctuations around this level. The background information highlights both a continued downward trend and some stability, which suggests that changes in the rig count are likely to remain incremental in the short term. - -The immediate factors influencing rig count include geopolitical tensions, economic pressures, and market conditions. These factors imply a limited appetite for significant increases or decreases in rig count in the near term. Despite forecasts of increased oil production through the end of 2024, the economic and geopolitical environment does not indicate a surge in drilling rig numbers over the next month. - -With only about a month until the resolution date, significant changes in rig count are unlikely based on historical stability and recent trends. Historically, notable shifts in rig counts occur over longer periods, especially when driven by economic factors rather than sudden policy shifts or technological breakthroughs. - -Given these considerations, I forecast the probabilities as follows: -- Probability for the count being less than 585 is relatively high given the current rig count is 584 and has been trending slightly downwards or stable. -- Probability for the count being between 585 and 590 is relatively low because it requires an increase in the current count, which appears unlikely based on the current trajectory. -- Probability for the count exceeding 590 is even lower for the same reasons. - -To ensure the Brier score accuracy, I assign probabilities based on these considerations, balancing the stable recent data with potential external influences and remembering past competition bias towards overconfidence." -30282,"Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on December 27, 2024?","Predictions: [5, 10, 10, 15, 10] -Median reasoning: As of November 19, 2024, the current US rig count is 584, having experienced a slight downward trend but relatively stable overall. Recent weeks have shown modest fluctuations around this level. The background information highlights both a continued downward trend and some stability, which suggests that changes in the rig count are likely to remain incremental in the short term. - -The immediate factors influencing rig count include geopolitical tensions, economic pressures, and market conditions. These factors imply a limited appetite for significant increases or decreases in rig count in the near term. Despite forecasts of increased oil production through the end of 2024, the economic and geopolitical environment does not indicate a surge in drilling rig numbers over the next month. - -With only about a month until the resolution date, significant changes in rig count are unlikely based on historical stability and recent trends. Historically, notable shifts in rig counts occur over longer periods, especially when driven by economic factors rather than sudden policy shifts or technological breakthroughs. - -Given these considerations, I forecast the probabilities as follows: -- Probability for the count being less than 585 is relatively high given the current rig count is 584 and has been trending slightly downwards or stable. -- Probability for the count being between 585 and 590 is relatively low because it requires an increase in the current count, which appears unlikely based on the current trajectory. -- Probability for the count exceeding 590 is even lower for the same reasons. - -To ensure the Brier score accuracy, I assign probabilities based on these considerations, balancing the stable recent data with potential external influences and remembering past competition bias towards overconfidence." -30281,"Will the number of active US oil drilling rigs exceed 590 on December 27, 2024?","Predictions: [5, 10, 10, 15, 10] -Median reasoning: As of November 19, 2024, the current US rig count is 584, having experienced a slight downward trend but relatively stable overall. Recent weeks have shown modest fluctuations around this level. The background information highlights both a continued downward trend and some stability, which suggests that changes in the rig count are likely to remain incremental in the short term. - -The immediate factors influencing rig count include geopolitical tensions, economic pressures, and market conditions. These factors imply a limited appetite for significant increases or decreases in rig count in the near term. Despite forecasts of increased oil production through the end of 2024, the economic and geopolitical environment does not indicate a surge in drilling rig numbers over the next month. - -With only about a month until the resolution date, significant changes in rig count are unlikely based on historical stability and recent trends. Historically, notable shifts in rig counts occur over longer periods, especially when driven by economic factors rather than sudden policy shifts or technological breakthroughs. - -Given these considerations, I forecast the probabilities as follows: -- Probability for the count being less than 585 is relatively high given the current rig count is 584 and has been trending slightly downwards or stable. -- Probability for the count being between 585 and 590 is relatively low because it requires an increase in the current count, which appears unlikely based on the current trajectory. -- Probability for the count exceeding 590 is even lower for the same reasons. - -To ensure the Brier score accuracy, I assign probabilities based on these considerations, balancing the stable recent data with potential external influences and remembering past competition bias towards overconfidence." -30280,"Will AMC Theatres file for bankruptcy before January 1, 2025?","Let me think through this step by step: - -1. This is an Other Binary Outcome Event, specifically about corporate bankruptcy within a very short timeframe (approximately 6 weeks from today to resolution). - -2. Key factors making bankruptcy more likely: -- Significant debt load ($4.1B) -- Negative shareholder equity (-$1.7B) -- Poor cash flow metrics -- Underperforming stock price -- Industry headwinds - -3. Key factors making bankruptcy less likely in this timeframe: -- Recent successful debt refinancing to 2029 -- Very short timeline to resolution (only ~6 weeks) -- Holiday season typically brings stronger box office -- No immediate debt payments due given refinancing -- Management actively working to avoid bankruptcy - -4. Timeline analysis: -- 6 week probability (question timeframe): Lower due to extremely short horizon -- 6 month probability: Moderately higher -- 1 year probability: Significantly higher -- 2 year probability: Highest risk period - -5. Base rate considerations: -- Major theater chains rarely file for bankruptcy during holiday seasons -- Chapter 11 filings typically follow failed refinancing attempts, not successful ones -- Companies usually exhaust all other options before filing -- Bankruptcy processes typically take months of preparation - -6. Special considerations: -- The recent refinancing suggests immediate bankruptcy is unlikely -- Management would likely wait until after holiday season if considering filing -- The extremely short timeline to resolution is crucial here -- Most corporate bankruptcies are telegraphed months in advance - -7. Uncertainty factors: -- Box office performance can be highly variable -- Market conditions could change rapidly -- Unknown private negotiations or plans - -Given the very short timeline to resolution (6 weeks), recent successful refinancing, and typical corporate behavior patterns, I assess the probability as quite low for this specific timeframe. While AMC faces serious long-term challenges, an immediate bankruptcy filing before January 1, 2025 seems unlikely. - -If asked about longer timeframes, I would estimate: -- 6 months: 15% -- 1 year: 25% -- 2 years: 40% - -For the specific question timeframe (by January 1, 2025), given the extremely short horizon and recent developments, I need to adjust significantly downward from those longer-term probabilities. - -Probability: 8%" -30279,Will the number of FBI firearms background checks initiated through the NICS exceed 3.0M for November 2024?,"Predictions: [95, 80, 85, 80, 85] -Median reasoning: To predict the likelihood of NICS firearm background checks exceeding various threshold values for November 2024, we must evaluate the current trends, background data, and historical patterns. - -**Recent Trends and Data:** -- **Historical Base Rates:** The long-term trend has seen increasing firearm sales, though sales have dampened somewhat since the 2020 surge. However, since July 2024, there's been an increase in year-over-year numbers. October 2024 saw a 5.2% increase in background checks compared to the previous month, indicating a potentially upward trajectory. - -- **Seasonal Effects:** November typically sees increased firearm sales due to hunting seasons and holiday shopping, supporting the possibility of elevated background checks exceeding thresholds. - -- **Political and Economic Context:** The political climate as the U.S. election approaches can drive urgency in firearm purchases. Although some data suggest decreased checks compared to the previous year or increased regulatory friction, there is also potential for increased panic buying due to legislative uncertainties, offsetting these effects. - -- **Current Numbers:** The checks in October 2024 were about 2.37M. The steady increase from September suggests potential for checks nearing or exceeding 3.0M if the trend continues. However, 3.0M would be a significant jump within a month, thus requiring careful consideration. - -**Probability Analysis:** -- **Exceeding 2.4M:** Given trends and historical November increases, the probability is quite high. -- **Exceeding 2.6M:** Fairly high probability based on year-over-year trends and October data. -- **Exceeding 2.8M:** Possible, but less likely given current numbers. Generating over 400,000 additional checks from October is a steep climb, even with the political climate contributing. -- **Exceeding 3.0M:** While possible due to any surges in pre-election pressures, this would be unprecedented based on recent monthly figures. - -**Weighting:** The October increase, political climate, and seasonal patterns are weighted more heavily due to recency and potential impact. Ensure not to overindex on political and economic surge potentials without solid historical backing for such increases. - -Base rates and established seasonal influences should thus heavily influence predictions, particularly given the statistical penalties for overconfident incorrect predictions." -30278,Will the number of FBI firearms background checks initiated through the NICS exceed 2.8M for November 2024?,"Predictions: [95, 80, 85, 80, 85] -Median reasoning: To predict the likelihood of NICS firearm background checks exceeding various threshold values for November 2024, we must evaluate the current trends, background data, and historical patterns. - -**Recent Trends and Data:** -- **Historical Base Rates:** The long-term trend has seen increasing firearm sales, though sales have dampened somewhat since the 2020 surge. However, since July 2024, there's been an increase in year-over-year numbers. October 2024 saw a 5.2% increase in background checks compared to the previous month, indicating a potentially upward trajectory. - -- **Seasonal Effects:** November typically sees increased firearm sales due to hunting seasons and holiday shopping, supporting the possibility of elevated background checks exceeding thresholds. - -- **Political and Economic Context:** The political climate as the U.S. election approaches can drive urgency in firearm purchases. Although some data suggest decreased checks compared to the previous year or increased regulatory friction, there is also potential for increased panic buying due to legislative uncertainties, offsetting these effects. - -- **Current Numbers:** The checks in October 2024 were about 2.37M. The steady increase from September suggests potential for checks nearing or exceeding 3.0M if the trend continues. However, 3.0M would be a significant jump within a month, thus requiring careful consideration. - -**Probability Analysis:** -- **Exceeding 2.4M:** Given trends and historical November increases, the probability is quite high. -- **Exceeding 2.6M:** Fairly high probability based on year-over-year trends and October data. -- **Exceeding 2.8M:** Possible, but less likely given current numbers. Generating over 400,000 additional checks from October is a steep climb, even with the political climate contributing. -- **Exceeding 3.0M:** While possible due to any surges in pre-election pressures, this would be unprecedented based on recent monthly figures. - -**Weighting:** The October increase, political climate, and seasonal patterns are weighted more heavily due to recency and potential impact. Ensure not to overindex on political and economic surge potentials without solid historical backing for such increases. - -Base rates and established seasonal influences should thus heavily influence predictions, particularly given the statistical penalties for overconfident incorrect predictions." -30277,Will the number of FBI firearms background checks initiated through the NICS exceed 2.6M for November 2024?,"Predictions: [95, 80, 85, 80, 85] -Median reasoning: To predict the likelihood of NICS firearm background checks exceeding various threshold values for November 2024, we must evaluate the current trends, background data, and historical patterns. - -**Recent Trends and Data:** -- **Historical Base Rates:** The long-term trend has seen increasing firearm sales, though sales have dampened somewhat since the 2020 surge. However, since July 2024, there's been an increase in year-over-year numbers. October 2024 saw a 5.2% increase in background checks compared to the previous month, indicating a potentially upward trajectory. - -- **Seasonal Effects:** November typically sees increased firearm sales due to hunting seasons and holiday shopping, supporting the possibility of elevated background checks exceeding thresholds. - -- **Political and Economic Context:** The political climate as the U.S. election approaches can drive urgency in firearm purchases. Although some data suggest decreased checks compared to the previous year or increased regulatory friction, there is also potential for increased panic buying due to legislative uncertainties, offsetting these effects. - -- **Current Numbers:** The checks in October 2024 were about 2.37M. The steady increase from September suggests potential for checks nearing or exceeding 3.0M if the trend continues. However, 3.0M would be a significant jump within a month, thus requiring careful consideration. - -**Probability Analysis:** -- **Exceeding 2.4M:** Given trends and historical November increases, the probability is quite high. -- **Exceeding 2.6M:** Fairly high probability based on year-over-year trends and October data. -- **Exceeding 2.8M:** Possible, but less likely given current numbers. Generating over 400,000 additional checks from October is a steep climb, even with the political climate contributing. -- **Exceeding 3.0M:** While possible due to any surges in pre-election pressures, this would be unprecedented based on recent monthly figures. - -**Weighting:** The October increase, political climate, and seasonal patterns are weighted more heavily due to recency and potential impact. Ensure not to overindex on political and economic surge potentials without solid historical backing for such increases. - -Base rates and established seasonal influences should thus heavily influence predictions, particularly given the statistical penalties for overconfident incorrect predictions." -30276,Will the number of FBI firearms background checks initiated through the NICS exceed 2.4M for November 2024?,"Predictions: [95, 80, 85, 80, 85] -Median reasoning: To predict the likelihood of NICS firearm background checks exceeding various threshold values for November 2024, we must evaluate the current trends, background data, and historical patterns. - -**Recent Trends and Data:** -- **Historical Base Rates:** The long-term trend has seen increasing firearm sales, though sales have dampened somewhat since the 2020 surge. However, since July 2024, there's been an increase in year-over-year numbers. October 2024 saw a 5.2% increase in background checks compared to the previous month, indicating a potentially upward trajectory. - -- **Seasonal Effects:** November typically sees increased firearm sales due to hunting seasons and holiday shopping, supporting the possibility of elevated background checks exceeding thresholds. - -- **Political and Economic Context:** The political climate as the U.S. election approaches can drive urgency in firearm purchases. Although some data suggest decreased checks compared to the previous year or increased regulatory friction, there is also potential for increased panic buying due to legislative uncertainties, offsetting these effects. - -- **Current Numbers:** The checks in October 2024 were about 2.37M. The steady increase from September suggests potential for checks nearing or exceeding 3.0M if the trend continues. However, 3.0M would be a significant jump within a month, thus requiring careful consideration. - -**Probability Analysis:** -- **Exceeding 2.4M:** Given trends and historical November increases, the probability is quite high. -- **Exceeding 2.6M:** Fairly high probability based on year-over-year trends and October data. -- **Exceeding 2.8M:** Possible, but less likely given current numbers. Generating over 400,000 additional checks from October is a steep climb, even with the political climate contributing. -- **Exceeding 3.0M:** While possible due to any surges in pre-election pressures, this would be unprecedented based on recent monthly figures. - -**Weighting:** The October increase, political climate, and seasonal patterns are weighted more heavily due to recency and potential impact. Ensure not to overindex on political and economic surge potentials without solid historical backing for such increases. - -Base rates and established seasonal influences should thus heavily influence predictions, particularly given the statistical penalties for overconfident incorrect predictions." -30275,"Will Kalshi outrank Robinhood in the top free Finance apps on December 31, 2024?","Let me think through this step by step: - -1. First, let's consider the timeframe: -- Only about 6 weeks until resolution -- This is a very short window for major ranking changes -- App store rankings tend to be relatively stable for established apps - -2. Current situation: -- Robinhood is #22 in Finance apps -- Kalshi managed to surpass Coinbase at one point -- Both apps offer election betting, but with different approaches - -3. Key factors: -- Robinhood has massive brand recognition and a broader feature set -- Kalshi is more specialized in prediction markets -- The election betting feature is just one component of Robinhood's offering -- App store rankings are heavily influenced by download velocity - -4. Historical precedent: -- It's extremely rare for newer, specialized apps to outrank established fintech leaders -- Temporary spikes in rankings can occur but are usually not sustained -- Finance app rankings tend to be particularly sticky - -5. Statistical considerations: -- The resolution date is very close -- The default case (Kalshi not outranking Robinhood) is strong -- Would need exceptional circumstances to overcome the current gap - -6. Growth potential analysis: -- Kalshi would need significant sustained growth -- Or Robinhood would need to drop substantially -- Neither seems likely in such a short timeframe - -7. Risk factors: -- News events could cause temporary ranking shifts -- Holiday season could affect download patterns -- But these are unlikely to cause sustained ranking changes - -8. Key constraints: -- The resolution specifically requires Kalshi to have a higher rank -- Equal ranking would resolve as No -- Only looks at one specific day (Dec 31) - -9. Comparative analysis: -- While Kalshi has shown impressive growth -- The gap to overcome is still substantial -- Robinhood's diverse feature set provides ranking stability - -Given: -- The extremely short timeframe -- The significant current ranking gap -- The stability of finance app rankings -- The need for sustained rather than temporary ranking changes -- The specific resolution criteria -- The historical rarity of such events - -I need to be very conservative in estimating the probability of such a significant shift in just 6 weeks. - -While Kalshi has shown impressive momentum, the combination of the short timeframe and the need to overcome an established player makes this highly unlikely. - -Probability: 8% - -This accounts for some possibility of a major shift while acknowledging the significant barriers and short timeframe. I've adjusted down from my initial estimate of 15% to account for historical overconfidence and the extremely short timeline remaining." -30274,"Will a bilateral ceasefire be announced in the Ukraine war, before January 1, 2025?","Let me think through this step by step: - -1. Timeline Analysis: -- Only 43 days until resolution -- Trump won't take office until Jan 20, 2025, after the resolution date -- Very short window for major diplomatic changes - -2. Current Situation: -- Active fighting continues -- No serious ceasefire negotiations underway -- Russia showing no signs of compromise -- Ukraine still receiving Western support - -3. Historical Precedents: -- Similar conflicts (Syria, Afghanistan) took years to reach ceasefires -- Previous attempted ceasefires in Ukraine have failed -- Winter traditionally impacts military operations but hasn't led to ceasefires - -4. Key Factors: -- Trump's election victory creates uncertainty -- But he can't directly influence before taking office -- Russia's position remains uncompromising -- Ukraine lacks leverage for favorable terms -- Winter conditions might affect military operations - -5. Timeline Probability Distribution: -- Next 43 days (until Jan 1, 2025): Very low probability -- By March 2025 (post-Trump inauguration): Higher (~15%) -- By end of 2025: Much higher (~30%) -- This suggests the Jan 1 deadline comes too soon - -6. Positive Factors: -- Trump's election creating diplomatic movement -- Winter conditions could encourage talks -- Growing war fatigue on both sides -- Economic pressures on Russia - -7. Negative Factors: -- Very short timeframe -- Russia's continued offensive stance -- No active high-level negotiations -- Resolution criteria require comprehensive ceasefire -- Neither side showing willingness to compromise - -8. Uncertainty Considerations: -- The timeframe is the biggest limiting factor -- Even if parties wanted to negotiate, implementing a ceasefire takes time -- Resolution criteria require both sides to announce agreement - -Given the extremely short timeframe, Russia's current stance, and the lack of active negotiations, I assess the probability as very low. While Trump's election has created some diplomatic movement, the January 1st deadline comes before he takes office, severely limiting his influence. The comprehensive nature of the required ceasefire (not allowing partial measures) further reduces the likelihood. - -I need to avoid overconfidence, but the short timeframe and current situation make this highly unlikely. However, given the potential for surprise diplomatic breakthroughs and the uncertainty introduced by Trump's election, I can't completely rule it out. - -Probability: 4%" -30328,"Will WeWork continuously operate through January 1, 2025, without announcing Adam Neumann's return as CEO?","Analyzing this question step by step involves considering both aspects of the resolution criteria: whether Adam Neumann will return as CEO and whether WeWork will continue operating. - -### Initial Evaluation: - -1. **Adam Neumann's Return as CEO**: - - **Recent Activities**: News and information point to Neumann being engaged in ventures outside of WeWork, such as investments with Andreessen Horowitz and in Canada Global. These suggest he is focused on new and different business projects. - - **Base Rates**: Historically, founders or former CEOs returning to their previous companies are rare unless there's significant pressure or incentive. Considering Neumann's controversial tenure at WeWork, the company likely seeks to maintain a distance from past controversies. - - **Announcement Timing**: Given that there are only a few weeks until the resolution date, the probability of a sudden announcement change is generally low. - -2. **WeWork's Continued Operations**: - - **Industry Trends**: WeWork has been dealing with financial difficulties, a common scenario in the coworking space due to economic challenges. However, a sudden cessation of operations just weeks away is rather low probability given the lack of any significant baseline indicators. - - **Financial Health**: While WeWork's financial strain could lead to a closure, transitions like these take time and show signs prior to happening. There's no current indication of an imminent shutdown before January 1, 2025. - -### Probability Analysis: - -- **Unbiased Default Outcome**: If no significant announcements or changes occur, the question will resolve as ""Yes"" by default. - -- **Assessment of Likely Outcomes and Uncertainty**: - - The likelihood of Neumann returning is low based on his current direction and previous company strategy. - - The chance of WeWork ceasing operations is slightly higher but still low considering the proximity to the resolution date. - -- **Timeframe Consideration**: With the short time until resolution, any potential for change (like a CEO return or operations ceasing) drops significantly compared to scenarios with extended timeframes. - -- **Historical Resolution Trend**: Historically, only 35% of binary outcome competition questions resolve positively (as a ""Yes""), which implies being cautious with confidence in any one outcome. - -- **Adjusting for Overconfidence**: Given past tendencies to be overconfident, slightly lowering the probability from initial estimates helps mitigate this bias. - -### Conclusion: - -Combining all aspects, including recent Neumann activities, WeWork's operational status, default resolution assumptions, and correcting for past overconfidence, I estimate the probability of the question resolving as ""Yes"" by January 1, 2025. - -**Probability: 85%**" -30327,"Will any of the Waltons be worth $100 billion or more on December 31, 2024, according to Forbes?","Let me think through this step by step: - -1) Question Type: This is a Non-Market Threshold Value Event, as we're looking at whether net worth will exceed a specific threshold. - -2) Current Status: -- Rob Walton: ~$100B -- Jim Walton: ~$57-99B (sources differ) -- Alice Walton: ~$92B - -3) Key Considerations: -- Resolution is only 41 days away -- Wealth primarily tied to Walmart stock (WMT) -- Recent trend shows family wealth declining ($14B drop in 13 months) -- Holiday season could impact Walmart performance -- Sources differ on current values, but Forbes is resolution source - -4) Analyzing Time Windows: -- Very short timeframe until resolution -- Market volatility in December typically lower due to holidays -- Year-end tax-loss harvesting could affect stock prices - -5) Base Rate Analysis: -- Very few individuals have ever reached $100B -- Those who have typically did so through tech companies -- Retail margins historically thinner than tech -- Walmart faces increased competition from Amazon - -6) Key Factors Affecting Probability: -- Already having one member at threshold (Rob) -- Two others within striking distance -- Short timeframe reduces volatility risk -- But also reduces upside potential -- Recent declining trend -- Economic headwinds including inflation - -7) Statistical Considerations: -- WMT stock would need roughly 8-10% growth for Alice to reach threshold -- Given 41-day window, this is possible but not highly probable -- Rob needs to maintain current level -- Historical volatility suggests maintaining $100B exactly is unlikely - -8) Probability Distribution: -- Base case: Rob maintains near $100B: ~40% chance -- Alice reaches $100B: ~15% chance -- Jim reaches $100B: ~15% chance -- Combined probability of at least one achieving: Higher than individual probabilities but with overlap - -9) Confidence Adjustment: -- Sources conflict on current values -- Forbes (resolution source) typically more conservative -- Need to adjust for historical overconfidence -- Short timeframe increases confidence in current status quo - -Given Rob is reportedly at the threshold but may fluctuate, Alice and Jim are close but would need significant gains, and considering the very short timeframe, I estimate: - -Probability: 45% - -This accounts for: -- High likelihood Rob stays near current level -- Small but real chance others reach threshold -- Economic headwinds -- Historical overconfidence adjustment -- Short timeframe reducing both upside and downside risks -- Conservative Forbes valuations -- Recent declining wealth trend" -30326,"Will iHeartMedia continuously operate through January 1, 2025, without filing for bankruptcy?","To forecast whether iHeartMedia will file for bankruptcy before January 1, 2025, I'll carefully examine the factors influencing their financial health, assess the probability of such an event in the given timeframe, and consider relevant base rates and historical occurrences. - -1. **Background and Current Financial Position:** - - iHeartMedia's history includes a Chapter 11 bankruptcy filing in 2019 due to unsustainable debt levels, which still seem to be an issue as they engage in debt restructuring efforts. - - The company must contend with interest payments exceeding operating income, necessitating these debt restructuring measures in a currently high-interest rate environment. - - Recent significant layoffs may indicate financial distress but could also be strategic restructuring to improve financial health. - -2. **Recent Developments:** - - Engaging in debt restructuring efforts through exchange offers and consent solicitations shows proactive management, indicating potential avoidance of bankruptcy if these efforts are successful. - - There's skepticism about the effectiveness of layoffs alone to resolve financial issues, which could suggest potential risks of restructuring efforts falling short. - -3. **Timeframe:** - - The forecasted period is relatively short (approximately 6 weeks), reducing the likelihood of a bankruptcy filing. There isn't sufficient time for significant operational changes that might worsen their condition to an extent requiring bankruptcy. - -4. **Base Rates and Historical Context:** - - Historical data shows that only 35% of questions resolve positively in past forecasting competitions, meaning unexpected outcomes are common. - - iHeartMedia's previous bankruptcy (2019) involved a lengthy process of financial decline before filing. A similar rapid and unforeseen financial deterioration within six weeks seems unlikely unless current restructuring efforts completely fail. - - The radio industry and iHeartMedia are affected by macroeconomic trends, including rising interest rates, but these are known constraints and factored into current strategies. - -5. **Probability Distribution:** - - Given the time available, the proactive steps in debt restructuring, and the historical context, a conservative approach is prudent. There is a risk of overconfidence, which must be tempered with the understanding that unexpected financial developments could arise. - -Considering these factors, the probability of iHeartMedia filing for bankruptcy before January 1, 2025, is relatively low, mainly due to the limited timeframe for such a development and ongoing restructuring efforts aimed at avoiding bankruptcy. There is uncertainty, but the default ""Yes"" resolution to continue operations should be weighted heavily. - -**Final Probability:** Probability: 85%" -30325,"Will the domestic opening weekend box office revenue of Kraven the Hunter, scheduled to open December 13, 2024, exceed $20,000,000?","Let me think through this step by step: - -1. Type of Question: This is a Non-Market Threshold Value Event for a scheduled entertainment release. - -2. Key Factors to Consider: -- Only 23 days until release -- Specific threshold of $20M opening weekend -- R-rating limits audience -- December release during holiday season -- Competition from other films -- Recent track record of similar films - -3. Recent Forecasts: -- Box Office Theory's range of $16M-$24M with $20M target is highly relevant -- Multiple sources confirming this range -- These are professional forecasts from just days ago - -4. Market Context: -- R-rated superhero films have mixed track record -- December is typically strong for movies but split audience -- Heavy competition from family films during holiday season -- Sony's Marvel films have underperformed recently - -5. Statistical Analysis: -- $20M is exactly at the median forecast -- Range suggests roughly normal distribution around $20M -- With $16M-$24M range, standard deviation ~$2M -- This puts $20M at a crucial inflection point - -6. Time Consideration: -- Very close to release date -- Marketing campaign well underway -- Tracking numbers unlikely to shift dramatically -- Weather could still impact opening weekend - -7. Historical Context: -- Similar Sony Marvel films often underperform expectations -- R-rated superhero films typically open lower than PG-13 -- December releases tend to have legs but smaller openings - -8. Risk Factors: -- Exact $20M threshold makes this particularly tight -- Any underperformance means ""No"" -- Must exceed, not just meet, $20M - -Given the exact threshold matches the median forecast, but considering: -- Historical underperformance of similar films -- R-rating limitation -- Need to exceed not just meet $20M -- Heavy holiday competition -- Recent Sony Marvel track record - -I believe this has slightly less than 50% chance of exceeding $20M. The proximity to release date gives high confidence in current tracking, and the need to exceed rather than meet $20M tips this slightly negative. - -Probability: 45%" -30324,"Will we detect an exoplanet atmosphere with >5% oxygen atmosphere before 2025? -","To forecast the probability of detecting an exoplanet atmosphere with more than 5% oxygen before January 1, 2025, I'll consider several factors, including current scientific capabilities, recent developments in exoplanet research, the time left until the resolution date, and base rates for similar discoveries. - -### 1. Scientific Capabilities and Developments - -- **Current Detection Techniques**: Currently, detecting exoplanet atmospheres mostly relies on transmission spectroscopy and direct imaging. While these methods have been successful in identifying elements and molecules such as water, sodium, and trace amounts of oxygen, determining the precise composition percentages still poses a challenge. - -- **Recent Discoveries and Technologies**: As of the latest updates, JWST has demonstrated its ability to identify water, clouds, and sulfur in exoplanet atmospheres. However, nothing indicates a breakthrough in identifying and measuring high oxygen concentrations with the precision required (>5%). - -- **Potential Future Missions**: The Habitable Worlds Observatory has been mentioned as a promising future endeavor for improved analysis of exoplanet composition, but significant advancements from this mission wouldn't be expected in the immediate near future given the timeline constraints. - -### 2. Timing and Base Rates - -- **Timeline Until Resolution**: As today's date is November 20, 2024, there are about six weeks remaining until the resolution date of January 1, 2025. Significant scientific discoveries, especially those requiring validation and peer review, typically take more time than this to emerge and be confirmed. - -- **Historical Base Rates**: Discoveries in exoplanetary atmospheres that have fundamentally changed our understanding (e.g., detecting various molecules) are rare and often take years of consideration, technological development, and serendipitous innovation. - -### 3. Probability Assessment - -- **Default Resolution**: Given the absence of confirmed techniques for measuring exact atmospheric composition percentages, especially high oxygen levels, the default outcome without any new developments would be a resolution of ""No."" - -- **Recent News Weighting**: None of the recent developments suggest imminent breakthroughs in measuring >5% oxygen in exoplanet atmospheres within the timeframe. The detection of sulfur and water is promising but does not directly relate to oxygen measurements at the necessary precision. - -### Conclusion and Probability Formulation - -Considering these factors: - -- The possibility of an unexpected breakthrough in measuring oxygen levels accurately before the deadline is low. -- Significant discoveries, especially those that can be announced and confirmed within six weeks, are rare without prior substantial evidence or leading indicators already visible. -- Historically only approximately 35% of forecasts resolve positively, which supports conservative estimates in uncertain conditions. - -Based on the above analysis, the probability that we will detect an exoplanet atmosphere with more than 5% oxygen before January 1, 2025 is: - -**Probability: 5%**" -30323,"Before December 31, 2024, will Tech Crunch report new layoffs at Google or Alphabet?","Predictions: [7, 20, 30, 20, 10] -Median reasoning: ### Question Analysis - -1. **Google/Alphabet Layoffs**: - - Historical data shows that Google has had several layoff events in 2024, with the latest being in July. - - The broader tech sector continues to experience layoffs due to economic pressures; over 130,000 job cuts have occurred among tech companies in 2024. - - Significant legal pressures due to antitrust issues could drive further restructuring, potentially prompting more layoffs. - - However, there is no specific mention or rumor of new Google or Alphabet layoffs post-July that would fulfill the resolution criteria. - -2. **Tesla Layoffs**: - - Tesla, like other tech companies, has been part of the broader layoff trend in 2024. - - Unlike Google, there are no specific indicators or legal pressures mentioned that would imply an imminent layoff at Tesla before the end of 2024. - -### Base Rates and Trends - -For both companies, while base rates suggest that layoffs in the tech industry are common in 2024, there are no concrete, recent indications that either company has layoffs pending in the examination period. - -### Timeframe Consideration - -Today's date is November 20, 2024, which leaves approximately six weeks until the resolution date. This is a relatively short period for significant organizational change that would be reported by Tech Crunch without prior rumor or leak. - -### Probability Assessment - -For Google/Alphabet, given their recent larger-scale layoffs and continued legal pressures, there is a moderate chance of additional layoffs. Given the context and legal pressures, a realistic estimate would place the probability of further layoffs at **30%**. - -For Tesla, the possibility is present but there are fewer direct indicators than for Google, and thus the probability is lower, at **20%**." -30322,"Before December 31, 2024, will Tech Crunch report new layoffs at Tesla?","Predictions: [7, 20, 30, 20, 10] -Median reasoning: ### Question Analysis - -1. **Google/Alphabet Layoffs**: - - Historical data shows that Google has had several layoff events in 2024, with the latest being in July. - - The broader tech sector continues to experience layoffs due to economic pressures; over 130,000 job cuts have occurred among tech companies in 2024. - - Significant legal pressures due to antitrust issues could drive further restructuring, potentially prompting more layoffs. - - However, there is no specific mention or rumor of new Google or Alphabet layoffs post-July that would fulfill the resolution criteria. - -2. **Tesla Layoffs**: - - Tesla, like other tech companies, has been part of the broader layoff trend in 2024. - - Unlike Google, there are no specific indicators or legal pressures mentioned that would imply an imminent layoff at Tesla before the end of 2024. - -### Base Rates and Trends - -For both companies, while base rates suggest that layoffs in the tech industry are common in 2024, there are no concrete, recent indications that either company has layoffs pending in the examination period. - -### Timeframe Consideration - -Today's date is November 20, 2024, which leaves approximately six weeks until the resolution date. This is a relatively short period for significant organizational change that would be reported by Tech Crunch without prior rumor or leak. - -### Probability Assessment - -For Google/Alphabet, given their recent larger-scale layoffs and continued legal pressures, there is a moderate chance of additional layoffs. Given the context and legal pressures, a realistic estimate would place the probability of further layoffs at **30%**. - -For Tesla, the possibility is present but there are fewer direct indicators than for Google, and thus the probability is lower, at **20%**." -30321,"Will South Carolina have 300,000 or more residents living in drought on December 31, 2024?","Let me think through this step by step: - -1. First, let's consider the timeline: -- Today is November 20, 2024 -- Resolution date is December 31, 2024 -- This is only about 41 days away, which is a relatively short timeframe - -2. This is a Non-Market Threshold Value Event, focusing on a specific population threshold (300,000) in drought conditions. - -3. Key factors to consider: -- Current drought conditions in South Carolina and broader region -- Seasonal patterns for December -- Population distribution in South Carolina -- Historical drought patterns -- Recent trends showing worsening conditions in Eastern U.S. - -4. Important considerations: -- Winter months typically see less drought in South Carolina -- However, the background indicates drought conditions are ""creeping back"" -- Eastern U.S. has seen worsening conditions recently -- With only 41 days until resolution, current conditions are highly relevant - -5. Risk factors: -- Short timeframe reduces uncertainty -- Winter typically brings more precipitation -- But climate patterns have been unusual recently -- Population centers matter more than geographic area - -6. Analysis of threshold: -- 300,000 residents is about 5.7% of South Carolina's population -- This is a relatively low threshold -- Even moderate drought conditions in one populous area could trigger this - -7. Historical context: -- South Carolina has experienced significant droughts in recent years -- Winter droughts are less common but not unprecedented -- The threshold seems achievable given recent patterns - -8. Confidence adjustment: -- Given the short timeframe, current conditions should weigh heavily -- Need to avoid overconfidence in either direction -- Recent worsening trends are significant but could reverse - -9. Timeline distribution: -- Next 2 weeks: 35% chance -- By resolution date: 45% chance -- If extended to January 31: 50% chance -- If extended to March 31: 60% chance - -Given the recent worsening trends in the Eastern U.S., the relatively low population threshold, and the short timeframe, but considering the winter season typically brings more precipitation and the need to avoid overconfidence, I assess this as moderately likely but not certain. - -Probability: 45%" -30326,"Will iHeartMedia continuously operate through January 1, 2025, without filing for bankruptcy?","To forecast whether iHeartMedia will file for bankruptcy before January 1, 2025, let's break down the factors influencing this prediction: - -### Contextual Analysis: - -1. **Debt Restructuring Efforts**: iHeartMedia is actively engaging in debt restructuring through exchange offers and consent solicitations. Companies often make such efforts to improve their financial positions and avoid bankruptcy, suggesting a deliberate approach to avert filing. However, the success of these efforts is uncertain and dependent on market conditions and creditor agreements. - -2. **Financial Challenges**: Despite positive operating income, iHeartMedia's interest obligations surpass these earnings. High debt levels combined with declining revenues pose significant risks, especially in a high-interest rate environment, which makes refinancing more expensive and challenging. - -3. **Operational Adjustments**: Recent layoffs underscore cost-cutting measures, yet they have been criticized for not addressing deeper, structural financial issues. This suggests potential instability in the face of continuing financial pressures. - -4. **Industry Context**: The radio industry, in general, is confronting structural changes and competition from digital platforms, which can affect iHeartMedia's revenue streams and financial stability. - -5. **Historical Context**: Companies in similar situations often either restructure effectively to avoid bankruptcy or eventually file if restructuring fails. - -### Timeframe Considerations: - -- There are only about six weeks until the deadline, which limits significant changes. The short timeframe naturally reduces the likelihood of dramatic financial deteriorations resulting in bankruptcy filings. -- Given past patterns, bankruptcies, especially announced ones, likely develop over months, often signaled by failed restructuring, multiple layoffs, or drastic drops in stock prices. - -### Base Rates and Probabilities: - -- Considering the historical base rate, only about 35% of predictions for such binary outcomes resolved positively. However, instead of over-relying on this, I'll assess specifics here. - -- Novelty factors like economic volatility and media industry challenges should be considered too, which can both increase or decrease default likelihoods. - -### Probability Distribution and Calculation: - -1. Given iHeartMedia's proactive debt restructuring, the likelihood of a near-term bankruptcy does appear low. -2. Historical precedent and the short timeframe reduce the probability of a bankruptcy filing within 41 days. -3. Considering this timeframe and status quo, along with personal tendency towards overconfidence, I'll adjust expectations conservatively. - -### Summary and Final Probability: - -While significant financial challenges exist, iHeartMedia's active restructuring and current operational status suggest it will most likely avoid filing in the immediate future, particularly within the next six weeks. Nonetheless, uncertainty and external economic factors call for cautious optimism. - -**Probability of iHeartMedia filing for bankruptcy by January 1, 2025: 18%** - -Factors like ongoing restructuring efforts and the short timeframe until the deadline outweigh the procedural complexities and existing financial risks." -30328,"Will WeWork continuously operate through January 1, 2025, without announcing Adam Neumann's return as CEO?","To forecast whether WeWork will continuously operate through January 1, 2025, without announcing Adam Neumann's return as CEO, we'll take into consideration multiple dynamics and contextual information. - -1. **Current Situation of WeWork and Adam Neumann:** - - Adam Neumann has shown active engagement with a new venture, Flow, and other real estate projects, notably with no signs or credible reports suggesting he is planning to return to WeWork as CEO. - - Media reports from sources like MediaPost, Globes, and The Real Deal emphasize his current focus and commitment to his new ventures, which reduces the likelihood of him returning to his previous role at WeWork within the short timeframe. - -2. **WeWork's Operational Status:** - - WeWork has been facing significant challenges, including financial instability and varying occupancy rates, which knowledge from the scenario up to November 2023 would indicate. - - However, given the information until November 2024, there is no explicit indication that WeWork is on the brink of ceasing operations in the next month. - -3. **Historical Context and Base Rates:** - - Historically, CEOs return not solely based on individual desires but complex corporate governance dynamics. Such returns—especially after a significant ousting like Neumann's—are relatively rare. - - The base rate of a high-profile ousted CEO like Neumann returning to the same company where he had a contentious departure is low, barring extraordinary circumstances or strategic shifts. - -4. **Near-Term Resolution Timeline:** - - With about a month remaining until the resolution date, the timeframe is quite limited for any major corporate shift, particularly the reinstatement of a CEO or a company ceasing operations suddenly without current strong indicators. - -5. **Consideration of Bias and Overconfidence:** - - Given the guidance to avoid overconfidence and considering historic resolution percentages of around 35% positive outcomes, my assessment should cautiously weigh the high likelihood of WeWork continuing operations without Neumann's return. - - By weighing the default status heavily and considering no substantial trend or announcement pointing in another direction, maintaining an overestimated positive outcome is unlikely. - -6. **Forecast Analysis:** - - Continued focus by Neumann on new ventures coupled with no strong indicator of WeWork failing operations rapidly—or of Neumann's imminent return—points toward a higher probability of resolution as **Yes** (i.e., WeWork continues without Neumann's return). - -Based on my analysis and to compensate for potential overconfidence: -- **Probability: 85%** that WeWork will continuously operate through January 1, 2025, without announcing Adam Neumann's return as CEO. This percentage reflects the high base case likelihood with a small possibility remaining for unexpected outcomes." -30326,"Will iHeartMedia continuously operate through January 1, 2025, without filing for bankruptcy?","Let me think through this step by step: - -1. Timeline Analysis: -- Only about 40 days until resolution -- Very short timeframe reduces uncertainty -- Bankruptcy filings typically take months of preparation -- Recent exchange offers (Nov 18) suggest active debt management - -2. Financial Health Indicators: -- Positive operating income but interest payments exceed it -- Recent layoffs indicate cost-cutting measures -- Proactive debt restructuring attempts via exchange offers -- Higher interest rate environment creates pressure - -3. Historical Context: -- Previous Chapter 11 filing in 2019 -- Radio industry continues facing structural challenges -- Base rate of second bankruptcies within 5 years is meaningful -- But most occur after longer periods, not within months - -4. Short-term vs Long-term Risk: -- Long-term risk appears significant -- But immediate term (40 days) risk is much lower -- Exchange offers suggest some lender cooperation -- Holiday season typically provides stronger advertising revenue - -5. Resolution Criteria Specifics: -- Only counts if iHeartMedia Inc itself files -- Subsidiary bankruptcies don't count -- Must be filed before January 1, 2025 -- Just needs filing, not court approval - -6. Risk Factors: -- Very short timeframe greatly reduces risk -- Recent debt exchange offers suggest no imminent filing -- Holiday season provides cash flow buffer -- But underlying financial stress remains significant - -7. Probability Distribution: -- 6-month probability might be around 25% -- 1-year probability might be around 35% -- 40-day probability must be significantly lower -- Must account for preparation time needed for filing - -Given the extremely short timeframe, recent debt management actions, and typical bankruptcy preparation periods, while acknowledging the serious underlying financial challenges, I assess that a bankruptcy filing in the next 40 days is unlikely, though not impossible. - -The broader financial challenges are concerning, but the timescale heavily weights toward continued operation through January 1. - -Probability: 92%" -30328,"Will WeWork continuously operate through January 1, 2025, without announcing Adam Neumann's return as CEO?","To predict whether WeWork will continuously operate through January 1, 2025, without announcing Adam Neumann as its CEO, we need to carefully consider various factors and follow a structured analytical process. - -1. **Understanding the Event Type:** - This is an ""Other Binary Outcome Event"" because we are assessing whether two specific conditions will not occur within a given timeframe: the reappointment of Adam Neumann as CEO and the cessation of WeWork operations. - -2. **Current Context and Background Check:** - - Adam Neumann's recent ventures, like Flow and Canada Global, indicate his focus is currently elsewhere. There is substantial investment and activity documented in these ventures.[1][2][3] - - Given Neumann's well-publicized and somewhat tumultuous departure from WeWork in 2019, his sudden return seems unlikely, especially given his apparent satisfaction with, and focus on, other projects. - - There have been no credible reports or rumors suggesting Neumann's return to WeWork imminently. - -3. **WeWork's Current Operational Status:** - - As of the latest information available, WeWork continues to operate. Despite previous financial struggles and bankruptcy threats, there are no recent announcements or credible reports suggesting operational cessation before the resolution date under consideration. - -4. **Time Frame and Dynamics:** - - The time until the resolution date is approximately 1.5 months. This is a relatively short period for any major corporate change like reappointing a controversial CEO or operational shutdown, which generally requires longer gestational periods. - -5. **Base Rates and Historical Precedent:** - - Historically, corporate leadership changes of this nature, especially involving founders with controversial pasts, do not occur frequently and are often preceded by extensive board deliberations and media speculation. - - The base rate of companies abruptly ceasing operations without months of preceding financial distress signals is low; WeWork, by this point, should have shown more significant trouble signs if bankruptcy were imminent before January 1, 2025. - -6. **Applying Overconfidence Bias Check:** - - Acknowledging past overconfidence and ensuring more distributed outcomes based on uncertainty, the likelihood of negative resolution (either Neumann's return or cessation of operations) should not be overestimated. - -7. **Summary and Final Probability Assessment:** - - Given the focus of Neumann on new ventures with no indications of interest or intention towards WeWork, coupled with WeWork's current operational status, the probability of a non-resolution based on the criteria is high. - - Factoring in the short time frame and generally low base rates, the probability of WeWork operating continuously without announcing Neumann's return as CEO up to January 1, 2025, felt prudent. - -Thus, the adjusted probability, accounting for the range of uncertainties and potential outcomes, is as follows: - -Probability: 92%" -30364,"Will the Mt Seymour ski resort be listed as Open by On the Snow's Ski Report on December 13, 2024?","Let me think about this step by step: - -1. Timeline Analysis: -- Current date: Nov 21, 2024 -- Target date: Dec 13, 2024 -- This is only about 3 weeks away, which reduces uncertainty significantly - -2. Historical Context: -- Mt Seymour is a well-established ski resort in Vancouver area -- Typically aims to open in early-mid December -- Weather patterns in the region are fairly predictable for this time of year - -3. Current Conditions: -- First snow of the season has already fallen -- Weather experts predicting snowier winter than last year -- 16 inches of new snow predicted in next 3 days -- Current snowpack is only 24% of normal, which is concerning - -4. Key Factors: -- Resort has officially announced Dec 13 as estimated opening date -- This shows confidence from resort management -- The resolution date exactly matches their planned opening -- Weather forecasts support likelihood of sufficient snow - -5. Risk Factors: -- Low current snowpack could delay opening -- Unexpected warm weather could melt snow -- Technical issues could delay opening -- Labor or operational issues could cause delays - -6. Similar Events Analysis: -- Ski resorts generally hit their announced opening dates about 70-80% of the time when announced this close -- Weather forecasts 3 weeks out are reasonably reliable -- El Niño conditions this year might affect snowfall - -7. Mitigating Factors: -- Resort has snowmaking capabilities -- Strong incentive to open on announced date -- Already receiving natural snowfall -- Expert predictions of snowier winter - -Given the short timeline, official announcement matching resolution date, current conditions, and weather predictions, I would normally be quite confident. However, the low current snowpack is concerning, and I need to adjust for historical overconfidence. - -The most concerning factor is the current snowpack being only 24% of normal, which significantly increases risk of delay. However, predicted snowfall and snowmaking capabilities partially mitigate this. - -Probability: 72%" -30363,"Will Joe Biden no longer be US President before January 1, 2025? - -","To forecast the likelihood that Joe Biden will no longer be US President before January 1, 2025, we need to assess various factors with an understanding of both historical data and the current political context. - -1. **Base Rates and Historical Context:** - - Historically, US presidents rarely leave office before their term ends unless due to death, resignation, or impeachment. In modern history, Nixon is the only president who resigned, and Clinton faced impeachment but was acquitted. Deaths in office are rare. - -2. **Current Political Landscape:** - - Recent articles outline that impeachment discussions have been ongoing, particularly from Republican figures. However, the impeachment inquiry concluded without significant steps toward impeachment. - - The event in Ukraine, where Biden authorized missiles that increased Republican calls for impeachment, adds tension but does not automatically lead to impeachment proceedings or removal. - -3. **Remaining Timeframe:** - - The timeline is very short – approximately six weeks remain. This short timeframe limits the likelihood of Biden leaving office, as initiating and completing an impeachment process with removal would be highly unusual and unprecedented in such a time span. - -4. **Public and Political Sentiment:** - - Prediction markets like Metaculus and Polymarket provide aggregate forecasts with low probabilities for impeachment and removal before the end of Biden's current term. These platforms aggregate broad insights and often provide credible baselines. - -5. **Current Default:** - - By default, Biden remains president if no extraordinary steps occur. Given no articles of impeachment are currently enacted, and no signs from Biden suggesting voluntary resignation or other health concerns, the default case is a significant factor. - -6. **Potential Novel Factors:** - - There are geopolitical tensions (e.g., with Ukraine) and domestic political dynamics (e.g., Republican control of the House), but these alone do not alter the historical precedent significantly enough in such a short period. - -Considering these factors and recognizing the inherent uncertainty and rarity of such events, an overestimation of likelihood would skew the Brier score negatively due to the low base probability. - -I will adjust my overconfidence from prior experiences and remember that generally, a much lower percentage of such events resolve positively compared to a naive 50% assumption. Given the data, historical precedent, and current context, this event seems highly unlikely to resolve positively. - -**Probability: 2%**" -30362,"Will Alphabet/Google continuously operate through January 1, 2025, without announcing Eric Schmidt's return as CEO or as a member of its Board of Directors?","Given the task and parameters provided, I'll systematically assess all key aspects to predict the probability of Alphabet/Google operating continuously through January 1, 2025, without announcing Eric Schmidt's return as CEO or to its Board of Directors. - -**Step 1: Evaluate Current Context and Trends** -1. **Historical Context**: Eric Schmidt has not held a position at Alphabet since 2017 and no recent credible sources hint at his potential return. -2. **Current Leadership Stability**: Sundar Pichai, serving as the current CEO, has made significant strategic announcements mainly around Alphabet's focus on AI and operational stability. Additionally, there is no current strategic shift or crisis instigating a leadership change that typically requires someone of Eric Schmidt's caliber. -3. **General Media and News**: There are no new reports or rumors in mainstream media about Eric Schmidt's return. - -**Step 2: Assess Market and Political Environment** -1. **Financial and Market Conditions**: Alphabet is in good financial health, with a stable market position. Stable organizations generally do not see rapid leadership changes unless under new pressure, which is presently absent. -2. **Technological and Strategic Focus**: The current focus on AI and tech innovation is consistent with existing leadership, further reducing the likelihood of past leaders being re-engaged. - -**Step 3: Analyze Timeframe Specifics** -1. **Timeframe Considerations**: The prediction has to hold only until January 1, 2025, which is approximately six weeks from the current date. Within such a short span, it is unlikely for Alphabet to announce and negotiate such a significant leadership change if it isn’t already appearing in reliable news sources. -2. **Default Continuation**: The probability of continuation without major leadership revelation is significantly high given the absence of indications or signs towards a shift. - -**Step 4: Base Rate Analysis** -1. **Past Precedent**: Historically, tech giants often maintain their leadership structure unless there’s a considerable operational or financial disruption. Such disruptions usually come with information leakage that would have, at this point, been detected in media or industry analysis. -2. **Eric Schmidt's Potential Return**: As a seasoned executive and former leader, such a return would likely have been flagged in the industry if it were plausible. - -**Step 5: Adjust for Past Bias** -1. **Overconfidence Correction**: Historically, forecasts tend to lean overly optimistic—compensating by slightly adjusting for this tendency is wise. - -**Conclusion and Probability Distribution** -- Given the information and analyses above, the logical conclusion is that the likelihood of Alphabet/Google operating as specified (without changes regarding Eric Schmidt) through January 1, 2025, is exceptionally high. -- Considering all factors and my predisposition to overconfidence historically, I estimate the event’s probability on the cautious side while still reflecting the overwhelming likelihood of continuity without changes. - -Probability: 97%" -30361,"Will the yield curve be non-inverted on Friday December 27, 2024?","To forecast the probability of the yield curve being non-inverted on December 27, 2024, we need to consider the relevant information given and apply a structured process to arrive at a reasoned prediction. - -### Key Considerations: - -1. **Current Status and Context:** - - As of November 20, 2024, the yield curve is slightly positive at 0.10. This indicates that currently, the curve is non-inverted. - -2. **Historical Context and Trends:** - - Yield curve inversions have historically been followed by a return to normalcy as market conditions and policies adjust over time. However, the timeline for this change is not always consistent. - -3. **Monetary Policy and Fed Actions:** - - Recent trends suggest an effort by the Federal Reserve to promote a non-inverted yield curve through monetary policies, including potential interest rate cuts. - -4. **Variability and Volatility:** - - The yield curve is subject to economic surprises, investor sentiment, and unexpected geopolitical events that could influence short-term rates and long-term yields. - -5. **Time Frame:** - - With the resolution date being just over a month away, there is limited time for significant market shifts. Typically, major economic changes influencing the yield curve occur over longer periods. - -6. **Base Rates and Historical Analogues:** - - Historically, yield curves do spend more time in a non-inverted state than inverted, especially after a period of inversion. - -### Assessment and Probability Calculation: - -**Assessing Recent Sources:** -- **Normalization Trend:** Given that longer-term yields have been rising more sharply than short-term yields, which indicates movement towards a sustained non-inverted state. -- **Recent Stability:** With current data showing a positive spread, we can infer a baseline that favors no inversion unless a significant disruption occurs. - -**Risk Considerations:** -- Market events are always in flux, and with recent volatility (mentioned in source [1]), we should continue to recognize some probability of inversion. - -**Compensation for Overconfidence:** -- While the data currently supports a non-inverted forecast, my historical overconfidence suggests the need to moderate the estimate slightly to allow for unexpected developments. - -### Conclusion: - -Given the current information, ongoing trends, Fed actions, and the close proximity of the resolution date, the probability that the yield curve will remain non-inverted on December 27, 2024, is assessed at a fair balance of optimism and caution. However, due to historical overconfidence on my part, I'll slightly adjust this towards a more conservative estimate than initial impressions might suggest. - -**Probability: 75%**" -30360,"Will Comex Gold Spot prices remain below $2,800 per ounce through December 27, 2024, according to Yahoo Finance?","To evaluate the probability that Comex Gold Spot prices will remain below $2,800 per ounce through December 27, 2024, we'll need to consider several key factors: - -1. **Current Price Levels and Trends:** - - As of November 20-21, 2024, the Comex Gold Spot price is reported at approximately $2,675-$2,627 per ounce from sources such as Bloomberg and FX Empire. - - Analysts' expectations from Trading Economics project a year-end price of approximately $2,749.48. - -2. **Market Context and Drivers:** - - Gold has seen an increase throughout 2024, driven by factors including central bank buying, inflation concerns, and anticipated Federal Reserve rate cuts. - - Potential drivers for increased prices include geopolitical tensions, particularly due to escalations in the Middle East and the conflict between Russia and Ukraine. - -3. **Historical Volatility and Trading Dynamics:** - - The historical context shows considerable volatility in gold prices, suggesting potential for significant price shifts even in a short time frame. - - Recent technical analysis cited by TradingView suggests mixed signals, with potential for both upward and downward movements in price. - -4. **Time Consideration and Market Behavior:** - - With just over a month remaining before the resolution date, gold prices have a limited window for significant movement beyond current levels. - - The base case of remaining below $2,800 is currently favored given recent prices and forward expectations. - -5. **Statistical Base Rates and Additional Factors:** - - Past gold price behavior in similar situations often shows resilience, with sharp upward spikes being relatively less common without strong catalysts. - - Existing geopolitical tensions could act as such a catalyst, but substantial additional news or shifts would likely be needed to breach $2,800. - -Considering these factors, the forecast suggests the probability of gold spot prices crossing the $2,800 threshold is not overwhelmingly high but needs caution given current global uncertainties. - -Historically, over the last month, prices have remained significantly below $2,800, and the forecast and market sentiment appear to support continued levels below this threshold. However, given some risks present and the historical tendency for larger-than-expected shifts, a moderate degree of uncertainty remains. - -Given past tendencies towards overconfidence, and the fact that only 35% of questions resolve positively relative to the expectation of 50%, let's cautiously and conservatively adjust our confidence downwards, reflecting the dynamic and sometimes unpredictable nature of commodity markets like gold. - -Final thoughtful probability estimation, taking into account all noted factors: ""Probability: 80%""" -30359,"Will Rivian make any posts at its Stories site before Jan 1, 2025?","To forecast whether Rivian will make any posts on its Stories site before January 1, 2025, let's approach this systematically: - -1. **Recent Activity and Posting Frequency**: As of the latest data, Rivian's most recent post was on November 14, 2024. The proximity of this post to the forecast date suggests a pattern of regular updates. Additionally, posts in October and November indicate that Rivian maintains an active presence on its Stories site. - -2. **Content Strategy and Future Events**: Rivian's Stories generally include content that aligns with its broader marketing and engagement strategy. The announcement of the R2 Tour with dates extending into early 2025 suggests a strong likelihood of continued posts, particularly regarding tour updates or related promotional content, which aligns with their past behavior in updating Stories corresponding to major company initiatives. - -3. **Base Rates and Historical Trends**: Historically, companies with a marketing strategy similar to Rivian's, especially in competitive and evolving industries like electric vehicles, maintain a robust digital presence to engage audiences. The frequency of content during recent months serves as a reasonable base rate for future content before the target date. - -4. **Likelihood of Change in Behavior**: The likelihood of Rivian deviating from its current posting pattern within a short time frame (around 6 weeks) is low, particularly since companies usually ramp up content around ongoing initiatives. Considering the upcoming R2 Tour scheduled events, the trend suggests continued activity. - -5. **Page Accessibility Concerns**: The resolution condition that the webpage must be accessible adds a potential variability factor if unforeseen technical obstacles arise. However, unless there's an anticipated issue, we should assume the site will remain operational. - -6. **Balancing Confidence and Historical Overconfidence**: Recognizing that past judgments can stem from overconfidence, it is essential to apply a slightly more conservative probability than instinct might initially suggest, without veering too much given the strong recent activity and external drivers for new posts. - -7. **Allocation and Distribution over Time**: Given this short timeframe and Rivian's regular posting pattern into late November, it's reasonable to estimate a fairly linear probability of ongoing activity. The concentration of events indicates a higher likelihood of posts in December as well. - -**Conclusion**: -Given Rivian's regular use of its Stories platform, recent activity, and the planned R2 Tour, the probability of Rivian making another post before January 1, 2025, is high. Accounting for potential variables such as overconfidence and external issues, a slightly prudent prediction would still result in a high likelihood of a new post. - -Probability: 85%" -30358,"Will lithium carbonate (CNY/T) prices continuously remain above 70,000 before December 21, 2024?","Let me think through this step by step: - -1. Timeline Assessment: -- Only 30 days until resolution -- Very short timeframe reduces uncertainty -- Need continuous above-70,000 price for the entire period - -2. Current Price Analysis: -- Currently at CNY 79,000 -- Recently rose from three-year low of CNY 71,000 -- Provides ~9,000 CNY buffer above threshold -- Multiple recent sources confirm current price range - -3. Recent Price Movement Factors: -+ Chinese government EV subsidies -+ Supply curtailments (190 tons) -+ Increased purchasing activity -+ Battery manufacturer restocking -- Overall market oversupply -- Historically high stocks -- Recent price volatility - -4. Short-term Market Dynamics: -- Only needs to hold for 30 days -- Supply changes take months to impact -- Demand changes more immediate -- Current price showing stability above 75,000 -- Buffer zone provides safety margin - -5. Risk Factors: -- Single day below 70,000 fails resolution -- Market still showing volatility -- Historical precedent of rapid price changes -- Year-end often sees market adjustments - -6. Statistical Considerations: -- Need 30 consecutive daily closes above 70,000 -- Each day has small chance of dipping -- Compound probability across period -- Recent price stability suggests >90% daily probability -- Compounded over 30 days reduces certainty - -7. Final Analysis: -- Very short timeframe favors current conditions -- Significant buffer above threshold -- Multiple positive price supports -- But continuous requirement is stringent -- Need to account for compound probability -- Recent stability suggests high likelihood - -Given the short timeframe, current buffer, and market supports, but accounting for the stringent continuous requirement and compounding daily probabilities, I assess: - -Probability: 83%" -30357,"Will the domestic opening weekend box office revenue of The Lord of The Rings: The War of the Rohirrim, scheduled to open December 13, 2024, exceed $20,000,000?","Let me think through this step by step: - -1) This is a Non-Market Threshold Value Event, specifically about box office performance. - -2) Key considerations: -- Single forecast of exactly $20M from BoxOfficeTheory -- This is an animated LOTR film, which is quite different from previous live-action entries -- December release, traditionally strong for family films -- Competition from other films including Wicked Part 1 -- Only ~3 weeks until resolution -- Exact threshold at the current prediction - -3) Historical context: -- Previous LOTR films were massive hits but were live action and had much larger budgets -- Animated films in December typically perform well -- $20M would be modest for a franchise film -- Theater attendance has been somewhat volatile post-pandemic - -4) Timing factors: -- December is typically strong for family films -- Pre-Christmas weekend can be solid but not peak -- Very close to resolution date, so less uncertainty - -5) Market conditions: -- Theater attendance recovering but still unpredictable -- Animation has been hit-or-miss recently -- LOTR brand recognition remains strong but this is a different format - -6) Resolution specifics: -- Needs to EXCEED $20M, not meet it -- Very specific threshold matching current prediction -- Short time until resolution increases confidence in current estimates - -7) Statistical considerations: -- Box office predictions tend to be somewhat accurate within a few weeks -- Equal probability of slight over/under performance -- Current forecast exactly at threshold creates natural 50/50 split before adjusting - -8) Risk factors: -- Marketing campaign effectiveness unclear -- Audience interest in animated LOTR uncertain -- Competition from other releases -- Historical tendency for questions to resolve negatively - -Given: -- The exact match of forecast to threshold -- Short time until resolution -- Historical tendency for negative resolution -- Animated format being different from main franchise -- Need to exceed rather than meet threshold -- December timing being generally favorable -- Recent animation performance being mixed - -I believe there's a slight bias toward not exceeding the threshold, given the need to exceed rather than meet it and the historical tendency toward negative resolutions. - -Probability: 45%" -30356,Will the Dairy Recall Tracker maintained by the Center for Dairy Research show more than 1 recall for December 2024?,"Let me think through this step by step: - -1. First, let's consider the base rate of dairy recalls: -- Dairy recalls are relatively uncommon -- Most months see 0-1 recalls -- Multiple recalls in a single month are rare events - -2. Timeframe analysis: -- We're predicting for December 2024 -- Only about 1 month away -- Short timeframe means less uncertainty but also less time for multiple events - -3. HPAI (Bird Flu) context: -- While mentioned in background, dairy recalls are not typically directly related to bird flu -- This may be overemphasized in the background information -- Most dairy recalls are due to bacterial contamination, foreign objects, or labeling issues - -4. Resolution criteria specifics: -- Needs MORE THAN 1 recall -- Must be specifically labeled as recalls (not alerts or notices) -- Must be visible on specific tracker website -- Very specific criteria makes this more challenging - -5. Near-term forecast considerations: -- Being only one month away, we should heavily weight current conditions -- No indication of unusual circumstances that would drive multiple recalls -- Holiday season can sometimes see slightly higher food safety scrutiny - -6. Statistical analysis: -- If average month has 0-1 recalls -- Multiple recalls would be ~2 standard deviations from mean -- Normal distribution would suggest roughly 15-20% chance - -7. Risk factors: -- Holiday production increases -- Winter weather can affect transportation -- But these are regular seasonal factors - -8. Mitigating factors: -- Modern food safety practices -- Short timeframe -- Need for MULTIPLE recalls, not just one - -9. Adjusting for historical overconfidence: -- Initial instinct might be 10-15% -- Given past overconfidence, should adjust upward -- But not too far given specific criteria - -Given the short timeframe, specific criteria requiring multiple recalls, and baseline low frequency of dairy recalls, while accounting for seasonal factors and potential overconfidence, I would set this probability relatively low but not extremely low. - -Probability: 22%" -30122,"Before January 1, 2025, will the International Criminal Court issue an arrest warrant for Benjamin Netanyahu?",[@Tom_Metaculus](/accounts/profile/120279) should this have a \<1 weight due to [https://www.metaculus.com/questions/30113/](https://www.metaculus.com/questions/30113/) ? -30362,"Will Alphabet/Google continuously operate through January 1, 2025, without announcing Eric Schmidt's return as CEO or as a member of its Board of Directors?","Let me think through this step by step: - -1. Time Frame Analysis: -- Only about 40 days until resolution -- Very short timeframe reduces likelihood of major changes -- End of year/holiday period typically has reduced corporate activity - -2. Current Situation: -- Sundar Pichai is current CEO -- No indication of leadership instability -- Company facing regulatory challenges but performing well financially -- Started paying dividends, showing stability - -3. Historical Context: -- Eric Schmidt hasn't been involved in leadership since 2017 -- At 68 years old, return to CEO role would be unusual -- No precedent of former Google CEOs returning -- Board membership more plausible than CEO role - -4. Market/Company Conditions: -- Google/Alphabet stable despite regulatory pressures -- No crisis that might prompt leadership change -- Strong AI position in competitive market -- Stock performance relatively solid - -5. Specific Resolution Criteria: -- Requires announcement before Jan 1, 2025 -- Includes both CEO and board positions -- Company must continue operating -- Temporary roles don't count - -6. Risk Factors: -- Regulatory pressure could prompt leadership changes -- AI competition could drive strategic shifts -- Board composition could change in response to challenges - -7. Mitigating Factors: -- Very short timeframe -- No current indicators of change -- Holiday period reduces likelihood of major announcements -- Schmidt's current focus on other ventures - -8. Probability Distribution Analysis: -- Base case (no change): Very high probability -- CEO return: Extremely unlikely in this timeframe -- Board return: Low but slightly higher probability than CEO -- Company cessation: Negligible probability - -Given the extremely short timeframe, stable company performance, lack of indicators, and holiday period, I assess a very high probability of the current situation continuing through January 1, 2025. However, accounting for unknown factors and the possibility of surprise announcements, especially regarding board membership, I need to leave some probability for alternative outcomes. - -Probability: 97%" -30362,"Will Alphabet/Google continuously operate through January 1, 2025, without announcing Eric Schmidt's return as CEO or as a member of its Board of Directors?","Let me think through this step by step: - -1. First, assessing the time frame: -- Only about 41 days until resolution -- This is a very short window for major leadership changes -- Corporate governance changes typically take months to implement - -2. Current situation: -- Sundar Pichai is firmly established as CEO -- No recent indicators of leadership instability -- Company facing major challenges (DOJ case) that favor leadership stability - -3. Historical context: -- Schmidt hasn't been CEO since 2011 (13 years) -- He hasn't been on the board since 2017 (7 years) -- No precedent of former CEOs returning to Google/Alphabet - -4. Risk factors to consider: -- DOJ antitrust pressure -- AI industry competition -- Stock performance -- Board satisfaction with current leadership - -5. Mitigating factors: -- Schmidt's age (69) -- His current focus on AI startups and advisory roles -- No recent involvement in Alphabet operations -- Short timeframe until resolution - -6. Base rate analysis: -- CEO returns are rare in tech (Jobs at Apple being a notable exception) -- Board member returns are slightly more common but still rare -- Emergency leadership changes typically happen due to scandals or crises - -7. Additional considerations: -- The resolution criteria includes company cessation of operations -- This is extremely unlikely for a company of Alphabet's size in 41 days -- The interim/acting CEO exclusion reduces some risk scenarios - -8. Timeline distribution: -- 1 month probability would be slightly lower -- 6 month probability would be moderately higher -- 1 year probability would be notably higher -- Distribution is not equal across time periods due to corporate governance cycles - -9. Confidence adjustment: -- Need to account for historical overconfidence -- Short timeframe justifies higher confidence in status quo -- Multiple resolution criteria slightly increase risk - -Given the extremely short timeframe, the stability of current leadership, lack of any indicators pointing to Schmidt's return, and the company's strong operational position, the probability of this resolving ""No"" is very low. However, we must account for unknown unknowns and the multiple resolution criteria. - -Probability: 97%" -30362,"Will Alphabet/Google continuously operate through January 1, 2025, without announcing Eric Schmidt's return as CEO or as a member of its Board of Directors?","To assess the likelihood that Eric Schmidt will return to Alphabet/Google as CEO or board member by January 1, 2025, we will break down the available information and apply a systematic forecasting approach. - -1. **Event Type and Base Rate Consideration**: - - This is an ""Other Binary Outcome Event,"" where we need to determine a binary result based on whether a former executive will return to an influential position within Alphabet/Google. - - Historically, the return of former CEOs to major tech companies is quite rare. While not impossible, such events are typically low-frequency occurrences. - -2. **Timeframe Consideration**: - - The resolution date is approximately six weeks away. This short timeframe means that significant corporate changes, such as a CEO or board member appointment, are less likely compared to this happening over a longer duration. - -3. **Current Company Climate**: - - Alphabet is facing legal challenges from the Department of Justice regarding its Chrome browser, which could potentially alter its strategic direction. - - Despite these challenges, there is no current indication from expert analyses or recent company filings of significant executive changes involving Eric Schmidt. - - Alphabet continues to demonstrate financial resilience, evidenced by the introduction of a dividend, suggesting continued stability rather than a reactionary change at the executive level. - -4. **Recent News and Statements**: - - None of the recent or relevant articles suggest any movement towards Eric Schmidt's involvement or appointment. - - The company's focus appears to be on operational performance and addressing legal challenges, not on leadership changes involving past executives. - -5. **Base Rates and Contextual Changes**: - - The historical precedent and base rate of CEOs returning to similar roles in the tech industry is low, particularly within stable organizations with current leadership in place. - - No new strategic, technological, or environmental shifts suggest a higher probability of Schmidt's return that hasn't been seen in the past. - -6. **Evaluation Adjustments for Overconfidence**: - - Considering the tendency towards overconfidence noted in past forecasting efforts, it's crucial to slightly adjust downward any probability estimates that might initially seem higher than supported by evidence. - -Given the current information, the analysis suggests a strong likelihood that the answer will resolve as ""Yes"" based on current company operations without Eric Schmidt's return as CEO or board member. Given the short resolution window and lack of signals indicating a change, the chance of a surprise appointment is low. - -Final Probability Output: -- Probability that Alphabet/Google will continue to operate without announcing Eric Schmidt's return to CEO or the board by January 1, 2025, is: - -**Probability: 97%**" -30362,"Will Alphabet/Google continuously operate through January 1, 2025, without announcing Eric Schmidt's return as CEO or as a member of its Board of Directors?","Let me think about this step by step: - -1. Timeline Assessment: -- Only about 40 days until resolution -- Very short timeframe for major leadership changes -- Board appointments typically have longer lead times and planning - -2. Current Situation: -- Google/Alphabet is performing well financially -- No signs of leadership crisis or need for change -- Sundar Pichai remains CEO with no indication of departure -- No recent involvement of Schmidt in company operations - -3. Historical Context: -- Schmidt hasn't been involved in leadership since 2017 -- At 68 years old, returning to CEO would be unusual -- Board changes typically happen at scheduled times (annual meetings) -- No precedent of former CEOs returning to Google/Alphabet - -4. Structural Factors: -- Multiple resolution criteria (CEO, board member, or company closure) -- Company closure extremely unlikely given financial strength -- Board appointments possible but rare mid-fiscal year -- CEO changes typically telegraphed well in advance - -5. Risk Factors: -- Short time window reduces likelihood of major changes -- No visible catalyst for leadership change -- Company performing well, reducing need for shake-up -- Schmidt involved in other ventures and AI initiatives externally - -6. Probability Distribution: -- Base case (no change): ~98% -- Board appointment: ~1.5% -- CEO return: ~0.4% -- Company closure: ~0.1% - -7. Additional Considerations: -- Major tech companies rarely make leadership changes during holiday season -- Board changes typically announced at shareholder meetings -- No rumors or speculation in industry about Schmidt's return - -8. Comparative Timelines: -- 1 month probability: 99% -- 3 months probability: 97% -- 6 months probability: 95% -- 1 year probability: 90% - -Given the extremely short timeframe, strong company performance, and absence of any indicators suggesting Schmidt's return or appointment, combined with the holiday season timing, I assess a very high probability of the current situation continuing. - -Adjusting for historical overconfidence and the multiple resolution criteria, but maintaining strong conviction due to the short timeline: - -Probability: 97%" -30362,"Will Alphabet/Google continuously operate through January 1, 2025, without announcing Eric Schmidt's return as CEO or as a member of its Board of Directors?","Let me think through this step by step: - -1. First, let's consider the timeframe: -- Only about 40 days until resolution -- Very short window for major leadership changes -- End of year/holiday period typically has fewer major corporate changes - -2. Base rate analysis: -- CEO returns are extremely rare in major tech companies -- Even rarer for former CEOs to return to board positions after leaving -- Schmidt has been gone from leadership for 7+ years -- No precedent of Google/Alphabet bringing back former executives - -3. Current situation: -- Company facing DOJ pressure over Chrome -- Stock price recently dropped -- But company still performing well overall -- No indication of leadership instability -- Current CEO Sundar Pichai appears secure in position - -4. Factors making Schmidt's return unlikely: -- He's been focused on AI initiatives outside Google -- No recent involvement with company operations -- Age (69) makes executive return unlikely -- Current board seems stable -- No public statements suggesting interest - -5. Factors that could prompt return: -- Crisis management needed (but current issues aren't severe enough) -- Board shake-up (no signs) -- Health/personal issues with current leadership (no indication) - -6. Resolution criteria analysis: -- Multiple ways to resolve ""No"": CEO appointment, board appointment, or company closure -- Company closure extremely unlikely in 40 days -- Board appointments typically planned well in advance -- Emergency CEO changes very rare without major scandal - -7. Probability distribution across timeframes: -- Next 40 days: Very low probability -- 6 months: Still very low -- 1 year: Low but slightly higher -- Multiple years: Moderate but still low - -Given the extremely short timeframe, stable company situation, lack of any indicators suggesting leadership changes, and the multiple independent unlikely events needed for a ""No"" resolution, I assess this as highly likely to resolve as ""Yes."" - -However, accounting for unknown unknowns and the multiple paths to a ""No"" resolution, plus my historical overconfidence, I'll adjust slightly away from extreme confidence. - -Probability: 97%" -30410,"Will Virgin Galactic fail to make make any new posts on its News page before January 1, 2025? ","Let me think about this step by step: - -1. First, let's consider the timeframe: -- Today is Nov 22, 2024 -- Resolution is Jan 1, 2025 -- That's about 40 days -- Last post was July 10, 2024, over 4 months ago - -2. Key considerations for Virgin Galactic: -- They are an active commercial spaceflight company -- Regular business operations typically require communications -- Investor relations typically demand updates -- End of year/new year period often sees corporate communications - -3. Analysis of baseline posting frequency: -- Having no posts for 4+ months is unusual -- But they've already demonstrated they can go this long -- The longer the gap, the more likely some update will be needed -- 40 days is a significant window for something newsworthy to occur - -4. Resolution criteria details: -- Only needs one post on specific webpage -- Page being down resolves as No -- Technical issues would resolve as No - -5. Risk factors: -- Company could be in quiet period -- But most quiet periods don't last this long -- Holiday season could reduce likelihood of posts -- But year-end updates are common - -6. Probability distribution analysis: -- Base rate suggests high likelihood of at least one post in 40 days -- Given 4 month gap, suggests possible strategic communications shift -- But complete silence for 6 months would be highly unusual -- Need to factor in ""page down"" scenario resolving as No - -7. Timeline comparison: -- In 20 days: 25% chance -- In 40 days (actual timeframe): 65% chance -- In 80 days: 85% chance - -8. Adjusting for historical overconfidence: -- Initial instinct was 75% chance of a post -- Need to factor in demonstrated ability to not post -- Consider holiday season impact -- Account for overconfidence bias - -Given the relatively long window of 40 days, the business necessity of communications, and the unusual length of current silence, but accounting for demonstrated ability to not post and adjusting for overconfidence... - -Probability: 35% - -(This represents the chance they will fail to post, per the question wording)" -30409,Will the Boeing Starliner Commercial Crew program make it to 2025 without being cancelled?,"Let me think through this step by step: - -1. Time horizon assessment: -- Only about 5-6 weeks until resolution date -- Very short timeframe for major program changes -- Most aerospace decisions take months of review - -2. Current status: -- Program is actively experiencing serious issues -- Boeing has taken another large financial hit -- Boeing considering selling space division -- NASA has already hedged with SpaceX backup - -3. Historical precedents: -- Major aerospace programs rarely get cancelled abruptly -- Even troubled programs usually wind down gradually -- Boeing's corporate culture resists admitting defeat -- NASA typically prefers to maintain redundancy - -4. Financial considerations: -- Boeing has already invested $1.85B -- Additional charges being taken -- But sunk costs shouldn't affect forward-looking decisions - -5. Technical analysis: -- Multiple serious technical issues remain unresolved -- No clear timeline for fixes -- Recent crewed test flight had significant problems -- Required another test flight seems increasingly likely - -6. Institutional factors: -- NASA wants multiple providers -- Boeing's reputation at stake -- Political considerations favor continuation -- But patience may be wearing thin - -7. Resolution criteria specifics: -- Only needs to ""not be cancelled"" by Jan 1, 2025 -- Doesn't require successful completion of anything -- Very low bar for ""Yes"" resolution - -8. Short-term likelihood assessment: -- Next 2 weeks: ~95% chance of continuation -- Next month: ~90% chance -- By resolution date: ~85% chance - -Key factors favoring continuation in this timeframe: -- Institutional inertia -- Short timeline -- High bar for cancellation -- Political considerations - -Key factors favoring cancellation: -- Mounting technical issues -- Financial losses -- Loss of confidence -- Potential corporate restructuring - -Given the very short timeline to resolution, the institutional factors involved, and the relatively low bar for a ""Yes"" resolution (simply not being cancelled), I believe there's a high probability of the program continuing through January 1, 2025, even if it's eventually cancelled later in 2025. - -The timeline is too short for major organizational changes, and both NASA and Boeing would need significant preparation time to formally end the program. - -Probability: 85%" -30408,"Will the US weekly 30-year fixed mortgage rate be below 7.00% on December 31, 2024, according to Freddie Mac?","Let me think through this step by step: - -1. First, let's consider the timeline: -- We're looking at just over 5 weeks from now (Nov 22 to Dec 31) -- This is a relatively short timeframe for significant rate changes - -2. Current situation: -- Rate is at 6.84%, which is 0.16 percentage points below the 7.00% threshold -- This provides a buffer, but not a large one -- Recent trend has been upward - -3. Historical context: -- Rates have been volatile in 2024 -- They've crossed above and below 7% multiple times -- Weekly changes typically range from 0.05% to 0.25% - -4. Expert predictions: -- Fannie Mae predicts 6% by year-end -- MBA predicts 6.3% by year-end -- However, these predictions were made earlier and recent data has shown upward pressure - -5. Key factors to consider: -- Fed policy is unlikely to change before year-end -- Economic data remains mixed -- Year-end typically sees less volatility due to reduced market activity -- Current rate provides a small buffer below 7% - -6. Risk factors: -- Treasury yields remain elevated -- Government borrowing concerns persist -- Economic data could surprise to the upside - -7. Timing considerations: -- Year-end typically sees less market activity -- Only 5 weekly reports remain until resolution -- Each report represents a significant chance for movement - -8. Statistical analysis: -- With 0.16% buffer and typical weekly movements of 0.05-0.25% -- Probability distribution suggests more downside protection than upside risk -- Current position below threshold provides advantage - -Given the short timeframe, current buffer below 7%, and typically reduced year-end volatility, I believe the rate is more likely to stay below 7% than cross above it. However, the buffer is small enough that we can't be extremely confident. - -I need to adjust for historical overconfidence and the general tendency for negative resolutions. - -Looking at a 1-month timeline would give about 75% confidence, while a 3-month timeline would be closer to 60% confidence. Given we're at about 5 weeks, and considering the year-end timing, I'll adjust accordingly. - -Probability: 68%" -30407,Will Bluesky reach 30 million users before 1 January 2025?,"To forecast whether Bluesky will reach 30 million users by January 1, 2025, we need to assess current growth trends, potential influences, and the time remaining. - -1. **Current User Count and Growth Rate:** - - The latest data indicate that Bluesky has over 21 million users as of November 22, 2024, and it has been growing at a rate of 1 million users per day since the US presidential election. - - If this rate continues, Bluesky would reach 30 million users in approximately 9 days. - -2. **Time Until Resolution:** - - As of today, November 22, 2024, there are 39 days until the resolution date, December 31, 2024. - - This time frame offers ample opportunity for Bluesky to reach the goal if the growth trend remains stable. - -3. **Factors Supporting Growth:** - - Increased growth can be attributed to user dissatisfaction with X (formerly Twitter), changes in X’s policies, and content moderation issues, fueling migration to Bluesky. - - Bluesky's attraction is also bolstered by celebrity endorsements and media coverage. - - It is the No. 1 free app in the US iPhone App Store, indicating high public interest and adoption. - -4. **Potential Risks or Slowing Factors:** - - Rapid growth might not be sustainable at the same high rate, and user acquisition might slow down due to market saturation or unforeseen challenges. - - It's vital to consider whether technical issues or negative media could detract from or slow down adoption. - -5. **Historical Base Rates:** - - Typically, fast-growing social media platforms that reach an adoption tipping point can sustain high growth if momentum is maintained. - - However, such rapid and continuous user growth is relatively rare, which suggests careful consideration of sustainability. - -6. **Conclusion and Probability Estimation:** - - With the user count already surpassing 21 million and rapid daily growth, the chances of reaching 30 million within 39 days appear significant. - - While historical caution against overconfidence indicates a need to slightly temper the probability, the data strongly support a high likelihood of reaching the target. - -Overall, considering the current user base, growth pattern, period remaining, and drivers of growth, there is a high probability that Bluesky will reach 30 million users before the resolution date. - -**Final Probability Estimation:** Given past overconfidence and the need to adjust for potential slowdowns or unexpected changes, I estimate the probability of Bluesky reaching 30 million users before January 1, 2025, as: - -Probability: 85%" -30406,"Will at least one of Andrea Bocelli's concerts at Madison Square Garden on December 18 or 19, 2024 sell out?","Let me think through this step by step: - -1) First, let's look at what we know: -- Two concerts scheduled for Dec 18-19, 2024 -- Currently tickets are available for both shows -- We have about 4 weeks until the concerts -- Only need one show to sell out for a ""Yes"" -- Must be officially sold out through primary channels (Ticketmaster) - -2) Key factors to consider: -- MSG capacity is around 20,000 -- Holiday season timing could increase demand -- Tickets have been on sale for months already and haven't sold out -- Current economy and high ticket prices may dampen demand -- Multiple ticket platforms still showing availability - -3) Historical context: -- Bocelli has performed at MSG many times -- Not all his shows sell out, though some do -- Holiday shows typically have stronger demand -- Current ticket availability suggests moderate but not overwhelming demand - -4) Timeline analysis: -- Most concert sellouts happen either: - a) Immediately during presale/initial sale - b) In final days before show -- Being 4 weeks out with good availability suggests lower probability - -5) Market factors: -- Higher inflation and economic uncertainty in 2024 -- Entertainment spending more discretionary -- Competition from other holiday entertainment options -- High ticket prices ($93+ starting) may limit demand - -6) Risk adjustments: -- Two chances for sellout increases probability -- But current availability for both shows suggests neither is close -- Need to adjust for historical overconfidence -- Holiday timing provides modest upside - -7) Resolution criteria specifics: -- Must be officially declared sold out -- Secondary market irrelevant -- Need documented evidence from venue/Ticketmaster - -Given that: -- Tickets are readily available now -- We're only 4 weeks out -- No indication of rapidly increasing demand -- Economic headwinds -- But considering holiday timing and two chances - -I would estimate: -- 15% chance of selling out in next 2 weeks -- Additional 20% chance in final 2 weeks -- Two shows gives roughly 1.5x multiplier effect - -Accounting for overconfidence bias and the current availability status, I believe the total probability of at least one show selling out is: - -Probability: 28%" -30405,Will Ukraine use ATACMS to strike deep within Russia before 1 January 2025?,"To assess the probability of Ukraine using ATACMS to strike deep within Russia before January 1, 2025, we need to carefully dissect the current geopolitical context, potential catalysts, and historical precedent, while keeping in mind the resolution criteria. - -### Current Geopolitical Context and Recent Developments -1. **US Authorization**: As of November 17, 2024, the US has authorized Ukraine to use ATACMS for strikes deep within Russian territory. This is a significant policy shift, indicating an increase in military support for Ukraine. This move may embolden Ukrainian forces to pursue deeper strikes. - -2. **Ukrainian Military Actions**: Ukraine recently hit a weapons arsenal in Bryansk using missiles, showing their capability and willingness to strike within Russia, though Bryansk is excluded from the resolution criteria. - -3. **Russian Red Line**: Russia's statement labeling any use of ATACMS as a ""red line"" signals that such an action could provoke a significant retaliatory or escalatory response. This dynamic introduces a significant deterrent effect. - -4. **Presence of North Korean Troops**: The presence of North Korean troops in support of Russian forces further complicates the geopolitical landscape, potentially increasing pressure on Ukraine to demonstrate military capabilities elsewhere. - -### Base Rates and Historical Precedent -- Historically, the base rate of similar escalatory actions by Ukraine (militarily targeting deep Russian territory) has been low, mainly due to fears of escalation and international diplomatic fallout. -- Novel factors include the recent authorization and ongoing conflict, which might slightly increase the likelihood compared to historical precedent. - -### Timeframe and Uncertainty -- The remaining time frame until January 1, 2025, is relatively short, about six weeks, which limits the opportunities for a significant strategic shift or new operational campaigns. -- Given that such deep strikes would likely carry significant risks of escalation, Ukraine might restrain its use of ATACMS in this manner unless provoked by a major event. - -### Distribution Over Time -Considering whether the potential for a strike could be more likely in a shorter-term versus the longer term, the short time until the resolution date suggests that the likelihood of such an action occurring diminishes with each passing day without a significant provocation or strategic justification. - -### Balancing Factors and Final Probability -- **Argument for a Positive Resolution**: US authorization might encourage Ukraine to use the missiles as a demonstration of capability, especially in response to provocations. -- **Argument Against**: Major geopolitical risks and the potential for escalation, as warned by Russia, could deter such actions from occurring within the stipulated timeframe. - -Given these factors and the likelihood of bias towards action due to strategic deterrence, while still recognizing the deterrent factor and potential overconfidence, a balanced forecast is needed. - -Thus, after weighing all the considerations, I conclude: - -**Probability: 20%**" -30404,Will Nvidia have the largest market cap in the world at the end of 2024?,"Let me think about this step by step: - -1. Current Market Cap Situation: -- Microsoft: ~$3.0T -- Apple: ~$2.9T -- Nvidia: ~$1.2T -This shows Nvidia needs to more than double while others stay flat, or grow significantly faster than them. - -2. Time Horizon: -- Only about 5 weeks until resolution -- Very short timeframe for such a dramatic change in market cap rankings -- Stock movements tend to be more muted during holiday season - -3. Recent Performance & Trends: -- Nvidia has had extraordinary growth in 2023-2024 -- However, recent AI advancement slowdown reports could impact sentiment -- Competition in AI chips increasing from AMD, Intel, and others -- Market generally seems to be pricing in very optimistic scenarios already - -4. Historical Precedent: -- Companies rarely leap to #1 market cap position in short timeframes -- Last major switch was Apple/Microsoft/Saudi Aramco trading places -- Those moves happened over months/years, not weeks -- No company has ever doubled market cap in 5 weeks - -5. Technical Analysis: -- To reach #1, Nvidia would need roughly: - * 150% increase while others stay flat - * Or 200%+ increase if others grow moderately -- This would require unprecedented daily gains - -6. Risk Factors: -- General market uncertainty -- AI hype could cool further -- Competition increasing -- Valuation already very high - -7. Positive Factors: -- Strong AI market position -- Excellent execution historically -- Could benefit from year-end window dressing - -Given: -- The extremely short timeframe -- The massive gap to close -- Historical precedents -- Current market conditions -- Season (typically lower volatility) - -While Nvidia has shown remarkable growth, achieving #1 market cap in just 5 weeks would require truly unprecedented movement. Even if bullish on Nvidia long-term, the probability of this specific outcome in this timeframe must be very low. - -Probability: 2%" -30403,Will the domestic opening weekend box office revenue of Mufasa: The Lion King exceed that of Sonic the Hedgehog 3?,"Let me think about this step by step: - -1. Historical Performance: -- The Lion King (2019) opened at $191M -- Sonic 1 opened at $58M, Sonic 2 at $72M -- This shows strong growth for Sonic franchise -- But Mufasa is a prequel, not a direct sequel, which typically perform worse - -2. Current Projections: -- Mufasa: $57M-69M (target $61M) -- Sonic 3: $53M-62M (target $56M) -- These are early projections before marketing campaigns - -3. Key Factors: -- Same release date (Dec 20, 2024) -- Heavy competition period -- Both are established franchises -- Marketing hasn't started for either - -4. Comparative Analysis: -- Sonic has shown consistent growth between installments -- Lion King prequels often perform worse than originals -- December release is different from previous films in both franchises -- Current projections give Mufasa slight edge - -5. Uncertainty Factors: -- Marketing campaigns could significantly shift expectations -- Holiday season timing affects both equally -- Strong competition from other films -- Early tracking numbers often change - -6. Base Rate Considerations: -- Prequels typically earn 60-70% of original film -- Third films in franchises often see 10-20% decline -- December releases often have longer legs but smaller openings - -7. Risk Analysis: -- Being overconfident in either direction is risky -- Projections are close enough that either could win -- Marketing campaigns could significantly shift expectations -- Holiday season adds additional uncertainty - -Given the current projections showing a slight edge to Mufasa, but accounting for: -- Historical franchise performance -- Prequel vs sequel dynamics -- Uncertainty this far out -- Need to avoid overconfidence -- Holiday season timing -- Marketing campaign potential - -The probability should reflect a slight advantage to Mufasa but not be overconfident given the numerous uncertainties and close projections. - -Probability: 57%" -30464,"Will the year-over-year increase in rent in Providence, RI in November 2024 be greater than or equal to 5.0%, according to Zillow?","Predictions: [85, 85, 80, 85, 80] -Median reasoning: In evaluating the probability of each event, it's critical to consider both the specific background context provided and broader market trends. - -For the Providence question (ID: 30464), the latest data indicates a year-over-year rent increase of 4.76% for November 2024, which is below the 5.0% threshold. Despite prior reports of higher increases, recent data suggests a slowdown. With the resolution date imminent, and considering the high importance of the recent data, it's unlikely that additional information would cause this estimate to exceed 5.0%. Therefore, I assign a lower probability to this achieving a Yes resolution. - -For the Los Angeles question (ID: 30463), the threshold of 2.0% is modest. Given the data and typical market conditions in large urban areas like Los Angeles where demand remains strong, it's reasonable to assume that rent increases will meet or exceed this threshold. Grouped with the noted economic pressures and slight national slowdown, I still view this as more likely than not. - -For Orlando (ID: 30462), with a 1.0% threshold, the likelihood of exceeding this is fairly high given both national trends and the specific economic factors related to urban areas in Florida, where demand generally tends to support rent increases above such modest levels. - -Considering the near-immediate resolution date and potential variance in small gaps, I weighed heavily on latest available data and historical market patterns. My confidence levels account for standard deviations typical in such economic data and my bias towards overconfidence in past forecasting has been consciously down-weighted." -30463,"Will the year-over-year increase in rent in Los Angeles, CA in November 2024 be greater than or equal to 2.0%, according to Zillow?","Predictions: [85, 85, 80, 85, 80] -Median reasoning: In evaluating the probability of each event, it's critical to consider both the specific background context provided and broader market trends. - -For the Providence question (ID: 30464), the latest data indicates a year-over-year rent increase of 4.76% for November 2024, which is below the 5.0% threshold. Despite prior reports of higher increases, recent data suggests a slowdown. With the resolution date imminent, and considering the high importance of the recent data, it's unlikely that additional information would cause this estimate to exceed 5.0%. Therefore, I assign a lower probability to this achieving a Yes resolution. - -For the Los Angeles question (ID: 30463), the threshold of 2.0% is modest. Given the data and typical market conditions in large urban areas like Los Angeles where demand remains strong, it's reasonable to assume that rent increases will meet or exceed this threshold. Grouped with the noted economic pressures and slight national slowdown, I still view this as more likely than not. - -For Orlando (ID: 30462), with a 1.0% threshold, the likelihood of exceeding this is fairly high given both national trends and the specific economic factors related to urban areas in Florida, where demand generally tends to support rent increases above such modest levels. - -Considering the near-immediate resolution date and potential variance in small gaps, I weighed heavily on latest available data and historical market patterns. My confidence levels account for standard deviations typical in such economic data and my bias towards overconfidence in past forecasting has been consciously down-weighted." -30462,"Will the year-over-year increase in rent in Orlando, FL in November 2024 be greater than or equal to 1.0%, according to Zillow?","Predictions: [85, 85, 80, 85, 80] -Median reasoning: In evaluating the probability of each event, it's critical to consider both the specific background context provided and broader market trends. - -For the Providence question (ID: 30464), the latest data indicates a year-over-year rent increase of 4.76% for November 2024, which is below the 5.0% threshold. Despite prior reports of higher increases, recent data suggests a slowdown. With the resolution date imminent, and considering the high importance of the recent data, it's unlikely that additional information would cause this estimate to exceed 5.0%. Therefore, I assign a lower probability to this achieving a Yes resolution. - -For the Los Angeles question (ID: 30463), the threshold of 2.0% is modest. Given the data and typical market conditions in large urban areas like Los Angeles where demand remains strong, it's reasonable to assume that rent increases will meet or exceed this threshold. Grouped with the noted economic pressures and slight national slowdown, I still view this as more likely than not. - -For Orlando (ID: 30462), with a 1.0% threshold, the likelihood of exceeding this is fairly high given both national trends and the specific economic factors related to urban areas in Florida, where demand generally tends to support rent increases above such modest levels. - -Considering the near-immediate resolution date and potential variance in small gaps, I weighed heavily on latest available data and historical market patterns. My confidence levels account for standard deviations typical in such economic data and my bias towards overconfidence in past forecasting has been consciously down-weighted." -30461,"Will the year-over-year increase in prices of homes in Baltimore, MD in November 2024 be greater than or equal to 2.0%, according to Zillow?","Predictions: [25, 15, 55, 30, 30] -Median reasoning: To forecast these housing market questions accurately, I considered various factors: recent trends from Baltimore, Dallas, and Hartford, local market dynamics, mortgage rates, and national housing forecasts. - -1. **Baltimore, MD (Question ID: 30461)**: - - We have data suggesting a 5.7% year-over-year increase for October 2024. Given the timeframe, there is a positive momentum in pricing with a recent boost in sales (12.4% increase). Despite high mortgage rates, the local supply-demand imbalance and the recent trend point to a likelihood that November will continue this behavior. Base rate from recent years aligns, as Baltimore has seen steady increases when supply is tight. - - Estimated Probability: 85%. - -2. **Dallas, TX (Question ID: 30460)**: - - The broader U.S. market shows stabilization with Zillow predicting a gradual national growth. In Dallas, the market's behavior tends to follow national trends closely. However, the increase of exactly 0.0% sets a low threshold, making it very likely that housing prices have at least not decreased, especially considering modest gains in sales elsewhere in the dataset. - - Estimated Probability: 90%. - -3. **Hartford, CT (Question ID: 30459)**: - - The 7.0% threshold is quite significant compared to many U.S. cities' expectations. Hartford does not have the same strong growth figures as Baltimore or other major metros looking at historical data, recent stability in mortgage rates, and supply trends. Thus, this is less likely but not impossible with consideration to overall market pressures. - - Estimated Probability: 30%. - -Overall, while the initial conditions look promising for general price increases due to restricted supply and steady demand, each region has specific factors that must be carefully weighed." -30460,"Will the year-over-year increase in prices of homes in Dallas, TX in November 2024 be greater than or equal to 0.0%, according to Zillow?","Predictions: [25, 15, 55, 30, 30] -Median reasoning: To forecast these housing market questions accurately, I considered various factors: recent trends from Baltimore, Dallas, and Hartford, local market dynamics, mortgage rates, and national housing forecasts. - -1. **Baltimore, MD (Question ID: 30461)**: - - We have data suggesting a 5.7% year-over-year increase for October 2024. Given the timeframe, there is a positive momentum in pricing with a recent boost in sales (12.4% increase). Despite high mortgage rates, the local supply-demand imbalance and the recent trend point to a likelihood that November will continue this behavior. Base rate from recent years aligns, as Baltimore has seen steady increases when supply is tight. - - Estimated Probability: 85%. - -2. **Dallas, TX (Question ID: 30460)**: - - The broader U.S. market shows stabilization with Zillow predicting a gradual national growth. In Dallas, the market's behavior tends to follow national trends closely. However, the increase of exactly 0.0% sets a low threshold, making it very likely that housing prices have at least not decreased, especially considering modest gains in sales elsewhere in the dataset. - - Estimated Probability: 90%. - -3. **Hartford, CT (Question ID: 30459)**: - - The 7.0% threshold is quite significant compared to many U.S. cities' expectations. Hartford does not have the same strong growth figures as Baltimore or other major metros looking at historical data, recent stability in mortgage rates, and supply trends. Thus, this is less likely but not impossible with consideration to overall market pressures. - - Estimated Probability: 30%. - -Overall, while the initial conditions look promising for general price increases due to restricted supply and steady demand, each region has specific factors that must be carefully weighed." -30459,"Will the year-over-year increase in prices of homes in Hartford, CT in November 2024 be greater than or equal to 7.0%, according to Zillow?","Predictions: [25, 15, 55, 30, 30] -Median reasoning: To forecast these housing market questions accurately, I considered various factors: recent trends from Baltimore, Dallas, and Hartford, local market dynamics, mortgage rates, and national housing forecasts. - -1. **Baltimore, MD (Question ID: 30461)**: - - We have data suggesting a 5.7% year-over-year increase for October 2024. Given the timeframe, there is a positive momentum in pricing with a recent boost in sales (12.4% increase). Despite high mortgage rates, the local supply-demand imbalance and the recent trend point to a likelihood that November will continue this behavior. Base rate from recent years aligns, as Baltimore has seen steady increases when supply is tight. - - Estimated Probability: 85%. - -2. **Dallas, TX (Question ID: 30460)**: - - The broader U.S. market shows stabilization with Zillow predicting a gradual national growth. In Dallas, the market's behavior tends to follow national trends closely. However, the increase of exactly 0.0% sets a low threshold, making it very likely that housing prices have at least not decreased, especially considering modest gains in sales elsewhere in the dataset. - - Estimated Probability: 90%. - -3. **Hartford, CT (Question ID: 30459)**: - - The 7.0% threshold is quite significant compared to many U.S. cities' expectations. Hartford does not have the same strong growth figures as Baltimore or other major metros looking at historical data, recent stability in mortgage rates, and supply trends. Thus, this is less likely but not impossible with consideration to overall market pressures. - - Estimated Probability: 30%. - -Overall, while the initial conditions look promising for general price increases due to restricted supply and steady demand, each region has specific factors that must be carefully weighed." -30458,"Will Florida Governor Ron Desantis refrain from naming the replacement for Marco Rubio's Senate seat before January 1, 2025?","This question regarding whether Florida Governor Ron DeSantis will refrain from naming a replacement for Marco Rubio's Senate seat before January 1, 2025 falls under the category of ""Other Binary Outcome Event."" The task involves assessing the likelihood of a specific decision being made within a defined time frame. - -1. **Identify the Current Baseline and Announced Intentions**: - - Governor DeSantis has publicly announced that he is aiming to appoint Rubio's Senate replacement by early January 2025. He has not made the decision yet, and it appears no appointment will happen before the end of the year. [3], [5] This is a strong signal that refraining from naming a replacement before January 1, 2025, is the current baseline scenario. - -2. **Analyze the News and Timelines**: - - It's late November 2024, and DeSantis has indicated the governor is conducting preliminary to more intensive vetting, intending to conclude the process possibly in early January. This aligns with his announced timeline, further substantiating that it is unlikely for him to name a successor before the end of 2024. The possibility of an unexpected decision seems low due to his strategic approach [3]. - - Several candidates are being considered, yet there is no indication that this process would be expedited to complete the selection in December [1], [2]. - -3. **Understand Political and Procedural Context**: - - Appointments to such a high-profile position are usually deliberated with care, considering both political and logistical factors. An unplanned or rushed decision goes against the generally cautious and strategic handling of such replacements. - - The political environment and typical decision-making timelines for similar scenarios indicate that last-minute appointments are relatively infrequent unless prompted by urgent necessity, which isn’t currently apparent. - -4. **Historical Precedent and Base Rates**: - - Historically, political appointments, especially at the Senate level, tend to follow thorough procedural paths with clear goals for public accountability and strategic alignment. The governor's statements indicate he is following such a path. - -5. **Assess Probability**: - - Given the firm public statements and the lack of immediate urgency, the probability that DeSantis refrains from naming a replacement before January 1 is notably higher. - -Combining these points considering base rates, expressed intentions, and historical context results in a strong likelihood that we will not see an appointment before January 1, 2025. - -**Probability Calculation**: -- Given the current status and the governor's statements, I estimate the probability that DeSantis does not name a replacement before the resolution date at 80%. - -Final answer: -""Probability: 80%""" -30457,"Will the domestic opening weekend box office revenue of Y2K, scheduled to open December 6, 2024, exceed $5,000,000?","Let me think about this step by step: - -1. Movie Type & Scale: -- Y2K appears to be a smaller-scale comedy film -- No major stars mentioned -- Period piece set in 1999 -- Limited marketing visibility so far -- December release during competitive holiday season - -2. Box Office Context: -- $5M opening weekend is a relatively low threshold -- December releases often have longer legs but smaller openings -- Coming out against strong holiday competition -- No major forecasts available yet suggests limited confidence/awareness - -3. Comparable Recent Films: -- Small/mid-budget comedies in 2023-2024 have struggled -- Theater attendance for non-blockbusters remains challenged -- Period comedies have particularly struggled recently -- December counter-programming often performs poorly against tentpoles - -4. Specific Factors: -- Release date is just 2 weeks away with minimal buzz -- No major marketing campaign visible yet -- Y2K nostalgia angle could help but might be niche -- Holiday season means higher ticket prices but more competition - -5. Distribution: -- No information about theater count -- Limited time for marketing ramp-up -- Screen availability will be tight in December - -6. Historical Precedent: -- Most wide releases clear $5M opening -- But many limited/platform releases don't -- Without confirmed wide release status, this is concerning - -7. Risk Factors: -- Very close to release with minimal information -- No box office tracking available -- No clear marketing strategy visible -- Challenging release window - -Given: -- The extremely short timeline to release -- Lack of marketing presence -- Absence of tracking data -- Competitive December window -- Historical struggles of similar films -- No confirmation of release scale - -I assess this has a relatively low probability of clearing $5M opening weekend. The lack of information this close to release is particularly concerning. While the threshold is low, there's significant downside risk of a limited release or poor performance. - -Probability: 25%" -30456,"Will SpaceX fail to make any new posts on its Updates page before January 1, 2025? ","Let me think through this step by step: - -1) First, let's understand the timeline: -- Today is November 25, 2024 -- Resolution is January 1, 2025 -- That's approximately 37 days -- Last post was September 10, 2024 (about 76 days ago) - -2) Base rate analysis: -- SpaceX historically posts updates irregularly -- The current ~76 day gap is long but not unprecedented -- Updates typically coincide with major milestones or developments - -3) Upcoming events that could trigger posts: -- Starship test flights planned -- Regular Starlink launches continuing -- End of year traditionally sees some company updates/summaries - -4) Resolution criteria specifics: -- Only posts on spacex.com/updates count -- Page being down resolves as No -- Must be timestamped before January 1, 2025 - -5) Key factors favoring posts: -- Active launch schedule -- Starship development progress -- Company tendency to document major milestones -- End of year timing - -6) Key factors against posts: -- Current long gap shows reduced priority on website updates -- SpaceX increasingly uses X/Twitter for communications -- Short timeframe (37 days) limits opportunities -- Holiday season may reduce corporate communications - -7) Timeline probability distribution: -- Next 2 weeks: 25% chance of post -- Week 3-4: 20% chance of post -- Final week: 15% chance of post -- Beyond Jan 1: Remaining probability - -8) Adjusting for overconfidence: -- Historical 35% positive resolution rate -- Current gap suggests communication strategy shift -- Need to account for uncertainty in such a short timeframe - -9) Final considerations: -- Question asks for probability of NO posts -- Must account for compound probability -- Short timeframe increases confidence in status quo -- Multiple planned events increase posting likelihood - -The question asks for the probability of failing to post (resolving Yes), but given the active launch schedule, planned Starship tests, and end-of-year timing, I believe there's a higher chance they will post than not. However, the current long gap suggests some structural changes in their communication strategy. - -Probability: 35% - -(This represents a 65% chance they WILL post, adjusted down from my initial estimate to account for historical overconfidence and the current posting gap)" -30455,Will Brazil ban the 6x1 work schedule before 1 January 2025?,"Let me think through this step by step: - -1) Timeline Assessment: -- From today (Nov 25, 2024) to resolution (Jan 1, 2025) is only about 5 weeks -- This is an extremely short time for major labor law changes -- Legislative processes typically take months or years, especially for significant changes - -2) Current Status Analysis: -- The proposal is under discussion but hasn't been approved -- There are protests and support from some officials -- Minister França's statement about inevitability doesn't specify timing -- No concrete legislative timeline is mentioned - -3) Base Rate Considerations: -- Labor law changes typically take significant time in Brazil -- Major work schedule changes usually require extensive debate and transition periods -- Similar labor reforms in other countries often take 1-2 years minimum - -4) Supporting Factors: -+ Strong worker support and protests -+ Some government official backing -+ Positive pilot project results -+ International precedent (Germany example) - -5) Opposing Factors: -- Very short timeframe until resolution -- Need for economic impact studies -- Potential resistance from businesses -- Complex implementation requirements -- Holiday season could slow legislative process - -6) Timeline Distribution: -Next 5 weeks (by Jan 1, 2025): 15% -By mid-2025: 35% -By end of 2025: 55% -By end of 2026: 70% - -7) Uncertainty Factors: -- Limited recent precedent for such rapid labor law changes -- Unclear legislative priority level -- Unknown level of business opposition -- Holiday season impact on legislative schedule - -8) Overconfidence Adjustment: -- Initial instinct might be 25% given the momentum -- Need to adjust down due to extremely short timeline -- Historical tendency for overconfidence in similar predictions - -Given the extremely short timeline, the complexity of labor law changes, and the holiday season timing, while there is momentum for the change, it's highly unlikely to be implemented in just 5 weeks. Even with strong support, the legislative and implementation processes typically take much longer. - -Probability: 15%" -30500,"Will the temperature in Denver, CO remain below 70 degrees F for the entire month of December 2024?","Let me think through this step by step: - -1. Question Type: This is a Non-Market Threshold Value Event about temperature not exceeding a specific value over a defined period. - -2. Historical Data Analysis: -- December 2022: Max high was 64°F -- December 2023: Max high was 71°F -This shows it's possible to exceed 70°F in December in Denver. - -3. Time Consideration: -- We're forecasting for December 2024, starting in just a few days -- Need ALL days to stay below 70°F for 31 consecutive days -- Only one day needs to exceed 70°F for a ""No"" resolution - -4. Base Rate Analysis: -- Denver's climate typically has cold Decembers -- But can experience occasional warm spells due to chinook winds -- Recent year (2023) showed it's quite possible to exceed 70°F - -5. Risk Factors: -- Climate change trending toward warmer winters -- Only need one day above 70°F for failure -- 31 days is a long period to maintain threshold -- Denver's variable winter weather patterns - -6. Supporting Factors: -- December is typically one of Denver's coldest months -- Most December days are well below 70°F -- Short daylight hours limit solar heating -- Recent data point from 2022 stayed under threshold - -7. Statistical Considerations: -- Need success on all 31 days -- If each day has a 98% chance of staying under 70°F -- (0.98)^31 = 0.54 or 54% chance of all days succeeding - -8. Key Uncertainties: -- Potential weather pattern shifts -- Climate change effects -- Chinook wind events -- Accuracy of historical data - -Given that: -- We need perfect success over 31 days -- Recent history shows threshold can be exceeded -- Climate trending warmer -- But December is typically cold -- And most days are well below threshold - -The most likely scenario is that temperatures stay below 70°F, but the length of time and recent counter-example make this far from certain. The multiplication of daily probabilities suggests significant risk of at least one warm day. - -Adjusting for historical overconfidence and considering the strict success criteria requiring all 31 days to stay under threshold, I need to be conservative in my estimate. - -Probability: 65%" -30499,"Will the temperature in Phoenix, AZ remain below 80 degrees F in the entire month of December 2024?","Let me think through this step by step: - -1. Historical Context: -- December 2022 max: 74°F -- December 2023 max: 84°F -- This shows recent precedent for both staying under and exceeding 80°F - -2. Time Period Analysis: -- We need ALL days in December 2024 to stay below 80°F -- This makes it a harder threshold to meet - only one day needs to exceed 80°F for a ""No"" -- December is typically one of the coolest months in Phoenix - -3. Climate Factors: -- La Niña conditions are predicted to be weak -- This reduces confidence in typical La Niña patterns (warmer winters) -- Climate change trends suggest gradually increasing temperatures year over year - -4. Statistical Consideration: -- Need 31 consecutive days to all stay below 80°F -- Even with a 97% daily chance of staying below 80°F, the probability of all 31 days doing so would be (0.97)^31 = 39% -- One warm front or unusual weather pattern could cause resolution to fail - -5. Recent Data Weight: -- 2023 having exceeded 80°F is particularly relevant as it's the most recent data point -- Shows the threshold is definitely possible to breach in modern conditions - -6. Resolution Criteria: -- Uses official weather station data -- Clear resolution source -- No ambiguity in measurement - -7. Current Trends: -- General warming trend in Phoenix -- More extreme temperature events in recent years -- December 2023 breaking 80°F is significant - -8. Risk Assessment: -- Only needs one day over 80°F to fail -- Must consider both typical winter patterns and potential anomalies -- Recent precedent shows the threshold can be breached - -Given that: -- We need ALL days to stay below 80°F -- We have recent precedent of breaking 80°F -- There's a warming trend -- Only one day needs to exceed for failure -- But December is typically cool - -I believe this has a lower probability of success than might first appear, especially given the all-or-nothing nature of needing every single day to comply. - -Probability: 35%" -30498,"Will the temperature in Chicago, IL remain below 60 degrees F for the entire month of December 2024?","Let me think through this step by step: - -1) First, let's consider the historical data: -- December 2022: Highest temp was 57°F -- December 2023: Highest temp was 60°F -This shows it's possible but challenging to stay under 60°F all month - -2) Climate factors: -- We're in a El Niño pattern, which typically brings warmer winters to the northern US -- Long-term climate change trends suggest warmer winters overall -- Current forecasts suggest warmer than average temperatures - -3) Statistical considerations: -- We need ALL 31 days to stay under 60°F -- Only need one day to exceed 60°F for a ""No"" -- This makes it quite challenging - each day is another opportunity to fail - -4) Time horizon: -- Question resolves in about 5 weeks -- Weather predictions beyond 2 weeks are notoriously unreliable -- December is typically cold in Chicago but can have warm spells - -5) Base rate analysis: -- Looking at past Decembers, it's relatively rare to stay under 60°F the entire month -- Recent years show we're getting closer to 60°F even in winter -- Climate change makes new temperature records more likely - -6) Resolution criteria specifics: -- Uses Midway Airport weather station -- Missing data won't count -- Only needs one >60°F day to resolve ""No"" - -7) Risk factors: -- Only need one warm day to fail -- El Niño typically brings warmer winters -- Climate change increasing likelihood of warm spells - -8) Protective factors: -- December is traditionally very cold in Chicago -- Recent years have stayed under 60°F -- Most December days are well below this threshold - -Given that we only need one day over 60°F to fail, and considering: -- El Niño conditions -- Warming trends -- The challenge of maintaining under 60°F for all 31 days -- Recent years barely staying under the threshold -- But balanced against December being traditionally very cold - -I need to be careful not to be overconfident, but the combination of factors suggests this is unlikely to succeed. - -Probability: 28%" -30497,"Will the temperature in Anchorage, Alaska ever go below 0 degrees F in the entire month of December 2024?","Let me think about this step by step: - -1. Base Rate Analysis: -- December in Anchorage typically has multiple days below 0°F -- However, there are exceptions (like 2019) -- This suggests a strong but not guaranteed base rate - -2. Time Consideration: -- December 2024 starts in just a few days -- We have a full month for the event to occur -- Only need one instance below 0°F in 31 days - -3. Current Conditions & Short-term Forecasts: -- Currently 26.1°F -- Short-term forecast shows temps between 10-21°F -- QWeather's 30-day forecast predicts 11 days below -10°F - -4. Long-term Trends: -- NOAA predicts below-normal temperatures for the region -- This winter is expected to be colder than average - -5. Climate Change Impact: -- While warming trends exist, one month is too short to significantly impact typical winter patterns -- December 2019's exception shows possibility of warmer winters - -6. Resolution Requirements: -- Only need one reading below 0°F -- Using official Iowa State University data -- Multiple opportunities (31 days) for occurrence - -7. Risk Assessment: -- Primary risk is unusually warm winter like 2019 -- Multiple independent chances for success -- Strong historical precedent - -8. Uncertainty Factors: -- Weather forecasts become less reliable beyond 7-10 days -- Climate variability increasing -- Possible equipment/reporting issues - -9. Probability Distribution: -- Historical data suggests ~90% probability -- Recent warming trends reduce this -- 2019 precedent shows possibility of no sub-zero days -- Multiple opportunities increase likelihood -- Short time frame increases forecast confidence - -Given the multiple opportunities, strong historical precedent, and current forecasts indicating below-normal temperatures, but accounting for increasing climate variability and the 2019 precedent, and adjusting for typical overconfidence: - -Probability: 82%" -30496,"Will the temperature in Jacksonville, FL ever exceed 80 degrees F in the entire month of December 2024?","In evaluating whether the temperature in Jacksonville, FL will exceed 80 degrees F in the month of December 2024, we must take into account multiple factors including historical data, current weather forecasts, and typical December climate conditions for the region. - -1. **Historical Data and Base Rates**: - - Historically, December in Jacksonville tends to be cooler, with average high temperatures typically ranging from the mid-60s to low-70s Fahrenheit. Exceeding 80 degrees is relatively rare but not impossible. - - Specific historical temperature data would show that hitting over 80°F in December is an outlier event, albeit one that can happen due to variability in weather patterns. - -2. **Current Forecasts**: - - The research provided shows no indication from long-range forecasts or expert opinions that December 2024 will see temperatures over 80°F. The specific predictions listed show highs below 80°F, with forecasts indicating temperatures will generally be cooler than average. - - However, weather forecasts, especially long-term ones, can often be uncertain and changeable. Yet, they offer a consistent picture of cooler weather for this period. - -3. **Climatological Considerations**: - - December represents the start of winter in the Northern Hemisphere, so statistically, the likelihood of experiencing an 80°F day is lower relative to other months. - - It is important to note any potential anomalies, such as heatwaves, that could theoretically raise the temperature unexpectedly. However, there is no mention of such conditions in the provided forecasts. - -4. **Remaining Time Until Resolution**: - - With the resolution date being just a few days into December, there is limited time for any significant temperature shifts that would result in exceeding 80°F. - - The overall window for an 80-degree day is the entire month, but with less than a full month left until resolution, the likelihood window is effectively smaller. - -5. **Adjusting for Overconfidence**: - - Given past tendencies towards overconfidence, I’ll ensure that my prediction remains grounded in the assessed probability distribution without overestimating the likelihood of atypical events. - -6. **Default Resolution**: - - If we presume that the default climate and weather conditions continue without substantial deviation, the probability of exceeding 80°F remains low. - -Considering the above factors and weighing the base rate against the present forecasts and conditions, it seems unlikely that Jacksonville will surpass the 80°F mark in December 2024. - -**Final Probability Calculation**: -- Base rate of temperature exceeding 80°F during December is low from historical data, perhaps single-digit percentages. -- Given 0 indications from current forecasts and the narrowing time window, certainty increases toward a ""No"" resolution. - -After careful evaluation: -- I will assign a probability of 10% that Jacksonville, FL will experience a temperature exceeding 80 degrees F at any point during December 2024. - -""Probability: 10%""" -30495,"Will the temperature in New York, NY ever go below 25 degrees F in the entire month of December 2024?","To assess the probability of the temperature in New York, NY dropping below 25 degrees F in December 2024, we'll systematically navigate through the given data and forecasts, considering historical trends, current climate forecasts, and base rates of similar occurrences. - -### Step 1: Base Rate and Historical Trends -Historically, temperatures in New York City do occasionally dip below 25°F in December. However, this doesn't happen every year. In the past two years, it occurred in December 2022 but not in December 2023. This suggests that while cold periods are possible, they aren't guaranteed each December. - -### Step 2: Current Climate Forecasts -The various forecasts have provided a mixed outlook: - -1. **Almanac.com**: Predicts above-average temperatures for December 2024 with no specific indication of temperatures below 25°F. -2. **FOX 5 NY**: Indicates potential cold air moving into the region after Thanksgiving, which could lead to colder temperatures. However, there's no definitive assertion of temperatures dropping below 25°F. -3. **CPC Outlook**: Suggests above-normal temperatures for the Northeast during December 2024, which includes New York City. - -These forecasts collectively lean towards milder conditions with cold spells possible but uncertain. - -### Step 3: Evaluate the Potential for Cold Spells -The FOX 5 NY report introduces the potential for a cold snap, which may be consistent with historical patterns where abrupt temperature drops occur in December. Additionally, with several unspecified potential cold periods, the probability of at least one occurrence below 25°F isn't negligible. - -### Step 4: Calendar Time Analysis -Given the current date is November 27, 2024, we have all of December 2024 to consider daily temperature variations. For a large city like New York, there can be significant daily fluctuation during winter months due to weather systems. - -### Step 5: Base Rate Adjustment for Novel Factors -Considering environmental factors, climate change can potentially impact traditional weather patterns. This may lean toward warmer winters with fewer extreme cold spells. However, it does not eliminate the possibility of a single day with sub-25°F temperatures. - -### Step 6: Final Probability Assessment -Given the mixed but slightly leaning above-norm forecasts, historical precedent for sporadic cold periods in December, and the full month ahead for variations, we need to synthesize these factors. Striking a balance between potential overconfidence and the realistic chance of occurrence suggests a cautious but informed probability. - -Thus, based on the analysis: -- Probable cold periods need consideration, but the overall warmer forecast tempers extreme low probabilities. -- Mitigating slightly for past overconfident forecasts towards a more neutral, historically validated outcome. - -### Conclusion -Considering all factors, I will estimate the probability of New York's temperature dropping below 25°F in December 2024 as: - -Probability: 35%" -30494,"Will the temperature in San Francisco, CA ever go below 40 degrees F in the entire month of December 2024?","Let me think through this step by step: - -1. First, let's look at historical data: -- December 2022: lowest was 40°F (right at threshold) -- December 2023: lowest was 46.13°F (well above threshold) - -2. Consider the current forecasts: -- Multiple sources suggesting warmer than normal December -- Drier conditions predicted, which typically correlates with greater temperature variation -- Recent trends showing warmer winters in general due to climate change - -3. Time factors: -- We're forecasting for the entire month of December 2024 -- This gives 31 opportunities for temperature to drop below 40°F -- Only need one instance to resolve Yes - -4. Statistical considerations: -- The threshold of 40°F is very specific -- Recent years show temperatures approaching but rarely breaking below 40°F -- Warmer climate trends suggest less likelihood of extreme cold - -5. Location factors: -- San Francisco's coastal location moderates temperature extremes -- Urban heat island effect helps maintain higher minimums -- Downtown location specified (typically warmer than outlying areas) - -6. Resolution criteria specifics: -- Must be below 40°F (not equal to) -- Must be recorded at specific downtown station -- Missing data won't count - -7. Uncertainty analysis: -- Long-term forecasts are inherently uncertain -- One year of data showing 46.13°F minimum isn't enough to establish strong trend -- Climate change increases uncertainty but generally biases toward warmer - -8. Timeline comparison: -- 1 week probability: ~1% -- 2 weeks probability: ~3% -- Full month probability: ~8% -- Three month winter period: ~15% - -9. Risk adjustment: -- Base rate suggests rare occurrence -- Recent years show close but not below threshold -- Must account for overconfidence tendency - -Given all factors, I believe there's a small but notable chance of temperatures dropping below 40°F. The downtown location and coastal influence make it unlikely, but with 31 opportunities and winter conditions, it's not impossible. The threshold being specifically below 40°F (not equal to) is important, as even 2022's lowest exactly matched but didn't go below. - -Adjusting for historical overconfidence and accounting for uncertainty over a full month period: - -Probability: 8%" -30493,"Will Donald Trump's 538 favorability be greater than or equal to 44.0% on December 30, 2024?","Let me think about this step by step: - -1. First, let's consider the timeline: -- We're looking at December 30, 2024, which is about 13 months away -- This is a long time horizon, which increases uncertainty - -2. Current Status: -- Trump's favorability has historically fluctuated between 35-45% -- Recent political events, including multiple indictments, have affected his ratings -- He's currently the frontrunner for the Republican nomination - -3. Key Factors: -- The resolution date is after the 2024 election -- If Trump wins the election, his favorability might increase due to ""rally around the winner"" effect -- If he loses, his favorability might decrease -- Legal proceedings and their outcomes could significantly impact favorability - -4. Historical Context: -- Trump's favorability has been relatively stable over long periods -- Major events (like Jan 6th, indictments) have caused temporary shifts -- Presidential candidates typically see favorability changes during campaign season - -5. Timeframe Analysis: -- Next month (Dec 2023): 40% chance of ≥44.0% -- By June 2024: 42% chance -- By November 2024: 45% chance -- By December 30, 2024: 43% chance - -6. Risk Factors: -- Multiple ongoing legal cases -- Potential economic changes -- Campaign dynamics -- Post-election sentiment - -7. Statistical Considerations: -- Mean reversion suggests staying near current levels -- Volatility tends to increase during campaign season -- Post-election period typically shows some stabilization - -8. Mitigating Factors: -- Long time horizon increases uncertainty -- Need to adjust for historical overconfidence -- Base rate of similar political figures' favorability - -9. Additional Considerations: -- 44.0% is a relatively high threshold historically for Trump -- Post-election period might see less volatility -- Media coverage impacts during campaign vs. post-election - -After considering these factors and accounting for historical overconfidence, I believe Trump reaching 44.0% favorability by December 30, 2024 is possible but not highly likely. The long timeline adds uncertainty, but the threshold is historically challenging for Trump to maintain. - -Probability: 43%" -30492,"Will astronauts Suni Williams and Butch Wilmore still be in space on December 30, 2024?","To forecast the likelihood of astronauts Suni Williams and Butch Wilmore still being in space on December 30, 2024, let's break down the information: - -1. **Current Situation and Plans:** - - Both astronauts have been on the ISS for five months, initially planning for an eight-day mission due to unforeseen circumstances. - - A rescue mission is slated for February 2025, indicating a planned extended stay beyond the resolution date of December 30, 2024. - -2. **Food and Health:** - - Challenges with food supplies are apparent, but Suni Williams has publicly refuted health concerns and confirmed maintaining her well-being through regular exercise. - - NASA continues efforts to keep the astronauts healthy, which reduces the likelihood of urgent health-related evacuation. - -3. **Incidents on ISS:** - - The emergency triggered by a toxic smell from the Progress MS-29 cargo ship was handled effectively by the crew, suggesting competent crisis management and no immediate need for early return. - -4. **Updates and Implications:** - - The latest updates emphasize that no sudden change or earlier-than-planned return is indicated, supporting the February 2025 rescue timetable. - - External contingencies (such as a rapidly deteriorating situation or an emergency launch capacity by other agencies) are possibilities but are not hinted at in any credible reports. - -5. **Historical Precedent:** - - Historically, astronauts have remained on the ISS for the duration planned unless emergencies necessitate early return. Here, no such emergencies are reported. - -6. **Resolution Criteria:** - - The resolution is straightforward: if the astronauts are still in space, with no new expeditions scheduled before December 30, 2024, the probability should reflect that they will be in space at the resolution time. - -By methodically assessing the factors, the dominant indicators are a well-publicized February timeline for the rescue and stabilization of the situation aboard the ISS, with no emergence of earlier intervention needs. Considering possible unpredictabilities (like sudden emergencies), albeit low probability, we incorporate some uncertainty. - -Adjusting for overconfidence bias and other unforeseen changes based on historical precedent, I estimate the probability of both astronauts being in space on December 30, 2024, to be very high, but not absolute due to the consideration of rare emergency events. - -**Probability: 95%**" -30491,"On December 31, 2024, will exactly 4 US states have Waymo's rider-only ride-hailing service available to the general public? -","Let me think through this step by step: - -1. Current Status: -- Waymo operates in 2 states (CA and AZ) -- Plans announced for TX expansion -- Very near-term timeline (about 1 month until resolution) - -2. Time Analysis: -- Only about 4-5 weeks until resolution -- Historical pace of expansion is slow and methodical -- New state launches typically take months of preparation -- Very short window for any major changes - -3. Expansion Patterns: -- Waymo has been cautious with expansion -- Takes months to years between new state launches -- Regulatory and safety considerations slow progress -- Weather and infrastructure requirements limit quick expansion - -4. Known Plans: -- Texas (Austin) is clearly next -- Partnership with Uber mentions Atlanta -- But Atlanta launch timing is not specified -- No other concrete state expansion plans public - -5. Probability Distribution: -- Most likely: 3 states (current 2 + Texas) -- Next likely: 2 states (status quo) -- Less likely: 4 states (would require two launches) -- Very unlikely: >4 states - -6. Key Considerations: -- Resolution requires EXACTLY 4 states -- Must be fully operational by Dec 31 -- Must be open to general public -- No waitlist allowed - -7. Risk Analysis: -- Texas launch could be delayed -- Atlanta unlikely to launch this quickly -- No other states in advanced planning stages -- Very tight timeline for multiple launches - -8. Historical Context: -- Waymo typically announces expansions months in advance -- Testing phases last several months -- End of year timing makes launches less likely -- Weather considerations for winter launches - -Given the extremely short timeline until resolution (just over a month), the requirement for EXACTLY 4 states, and Waymo's historically cautious approach, I think the probability is quite low. While they will likely reach 3 states with Texas, getting to exactly 4 would require another state launch in just weeks with no current public plans. - -The most likely scenario is 3 states by year-end, with 2 states being the second most likely outcome if the Texas launch gets delayed. - -Probability: 8%" -30540,"Will Joe Biden's 538 approval rating be greater than or equal to 38.5% on December 30, 2024?","To predict whether Joe Biden's 538 approval rating will be greater than or equal to 38.5% on December 30, 2024, we'll consider several factors: - -1. **Current Approval Ratings**: The latest polls show Biden's approval rating at or below 38.5%. Specifically, Gallup (37%) and Emerson College (36%) both have him below the threshold, which suggests recent challenges in his approval metrics. - -2. **Trend Analysis**: Biden's approval ratings have seen a decline in recent months, with some fluctuations. Despite economic improvements post-election, discontent with his handling of particular issues has persisted, which hasn't significantly boosted his approval. - -3. **Time Frame**: With approximately four weeks until the resolution date, there is limited time for a substantial change in Biden's approval rating. Typically, approval ratings do not see drastic shifts in such a short period unless significant events alter public perception. - -4. **Base Rates and Historical Context**: Presidential approval ratings don't generally jump significantly in the short term based on historical precedent, unless there are dramatic domestic or international developments that shift public sentiment. - -5. **Political and Economic Climate**: Although there is increased economic optimism, dissatisfaction regarding key issues such as government and immigration remain dominant concerns according to polls. This suggests only a partial impact on overall approval ratings. - -6. **Expert Opinions and Poll Insights**: Both expert assessments and polls emphasize the challenges Biden faces, with his closest competitor, Trump, experiencing improved favorability. This competitive dynamic is likely to maintain pressure on Biden's approval prospects. - -7. **Potential for Change**: While political dynamics can rapidly change, such changes are generally more likely to occur over longer time frames or due to significant events. The short time until the resolution date limits the scope for such shifts. - -Balancing these considerations, the probability that Biden's 538 approval rating will exceed or meet 38.5% by the specified date appears low. Historically, similar situations have been less likely to resolve positively within short time frames without substantial game-changers. - -Considering my past tendency towards overconfidence, I will adjust slightly towards caution in my prediction. - -**Final Probability**: 25%" -30541,"Will Kamala Harris's 538 approval rating be greater than or equal to 44.0% on December 30, 2024?","Let me think about this step by step: - -1. Current Situation: -- According to the data provided, Harris had a 47% favorability rating as of November 12, 2024 -- However, favorability ratings and approval ratings can differ significantly -- The recent presidential election loss likely has impacted public perception - -2. Historical Context: -- Vice presidential approval ratings typically correlate with presidential approval ratings -- After electoral defeats, politicians often see temporary dips in approval -- December tends to show relatively stable polling due to holiday season reducing news cycles - -3. Timeline Analysis: -- Only about a month remains until the resolution date -- This is a very short window for significant changes -- The period includes holidays which typically reduce political news coverage - -4. Key Factors: -- Recent election loss creates downward pressure -- Post-election transition period could affect public perception -- Holiday season typically stabilizes ratings -- Current administration will be in lame duck period - -5. Statistical Considerations: -- Short timeframe reduces volatility potential -- Need to account for polling lag in 538's methodology -- Historical post-election approval patterns suggest stabilization - -6. Risk Factors: -- Post-election transition activities could affect ratings -- Potential policy announcements or public appearances -- Year-end political developments - -7. Confidence Assessment: -- Given the short timeframe and recent election results -- Current ratings suggest below 44% is more likely -- Limited potential for major shifts in next month -- Need to account for historical overconfidence - -8. Additional Timeline Analysis: -- By January 31, 2025: 25% probability -- By March 31, 2025: 30% probability -- Current window has reduced likelihood due to post-election environment - -The most critical factors are the recent election loss and the short timeframe. While Harris had higher favorability recently, the election outcome and transition period are likely to suppress approval ratings. The holiday season and lame duck period further reduce likelihood of positive movement. - -Given these factors, and accounting for historical overconfidence, I assess there is a lower probability of reaching 44% approval by December 30, 2024. - -Probability: 28%" -30542,"Will JD Vance's 538 favorability be greater than or equal to 39.0% on December 30, 2024?","To make an informed prediction about JD Vance's 538 favorability rating, we need to analyze several factors, utilizing the information given and general knowledge. - -1. **Current Context:** - Given that today’s date is November 27, 2024, we are approximately one month away from the resolution date. Our task is to assess whether JD Vance's favorability rating will be at least 39.0% on December 30, 2024. - -2. **Absence of Specific Data:** - There's an absence of recent favorability data specific to JD Vance. This is a significant limitation. Therefore, we need to rely on broader political context and historical trends to formulate our prediction. - -3. **Political Environment:** - From the provided research assistant notes, the 2024 presidential election seems to have been a crucial event recently, presumably impacting political figures' favorability ratings. However, the cited sources do not provide clear insights or trends regarding JD Vance. Typically, moments like the aftermath of an election can lead either to increased visibility and potentially higher favorability if a politician plays a significant role or helps a successful campaign, or to a decline if they are associated with less popular positions or outcomes. - -4. **Historical Favorability Trends:** - While we lack specific numbers, it is generally useful to consider JD Vance's historical favorability. Previously, as a political figure, his favorability is likely influenced by his political alignment and national events. Typically, favorability ratings for politicians do not experience drastic changes in short periods unless there are significant national events or scandals. - -5. **Base Rate Consideration:** - Historically, favorability ratings show moderate fluctuations rather than dramatic spikes or drops without triggering events. Therefore, if his favorability were significantly below 39.0% previously, it would be unlikely to jump by a large margin in such a short period without new data to suggest such a trend. - -6. **Time Frame and Variance:** - With about a month to go until the resolution date, the potential for significant changes in favorability is limited. Short-term movement in such ratings generally remains relatively subdued unless driven by major, influential news stories or events. - -7. **Potential Novel Factors:** - Considering current political, economic, and social climates, nothing in the shared briefings directly suggests an extraordinary positive turn for JD Vance. - -8. **Forecast Approach:** - Given the uncertainty and the absence of specific trends indicating an upward swing beyond the 39.0% mark in favorability, the base case is likely still the best guide for this short time frame. Given my known tendency to overestimate probabilities, a more conservative estimate should be applied. - -9. **Assessment and Final Probability:** - Without evidence suggesting a significant change in favorability, I will employ a conservative base rate, acknowledging that drastic changes are unlikely in the absence of specific catalysts. - -Given these contemplations and calculations, the probability that JD Vance's 538 favorability will be greater than or equal to 39.0% by December 30, 2024, is assessed as: ""Probability: 40%""" -30543,"Will the US Congress's 538 approval rating be greater than or equal to 22.0% on December 30, 2024?","To evaluate the likelihood of the US Congress's 538 approval rating being greater than or equal to 22.0% on December 30, 2024, we need to consider several factors: - -1. **Current Data Point**: The most recent Gallup poll from November 25, 2024, shows Congress with a 19% approval rating. This is the most significant data point provided, as it is the closest to the resolution date and demonstrates a slight increase from 16% prior to the election. Still, this is below the threshold of 22%. - -2. **Timeframe and Trend**: With approximately a month left until the resolution date, there is limited time for significant changes in public opinion to occur. A 3 percentage point increase is needed to reach the 22% threshold. Historical trends in approval ratings for Congress typically show slow movements unless major legislative actions or events occur that drastically shift public perception. - -3. **Base Rates and Historical Context**: Congress's approval ratings have historically been low, often below 25%. A significant jump in a short period is uncommon, barring significant political or social events that could change perceptions rapidly. - -4. **Recent Political and Economic Context**: The political situation may not necessarily lead to positive shifts in Congressional approval. The recent election and concerns related to economic conditions may contribute to voter dissatisfaction, as seen in the negative trends shown before the slight uptick. - -5. **Potential for Variability**: Daily and weekly fluctuations in approval ratings are generally minor unless major news occurs. Given the immediate data and historical patterns, the potential for dramatic increases in approval is low. - -6. **Considering Past Forecasting Bias**: Historically, only about 35% of questions in similar forecasting competitions resolve positively. Applying this understanding helps moderate any overconfidence in predicting a positive outcome. - -Taking into account the above factors, there does appear to be a small trend towards a higher approval rating, but not enough data suggests that Congress’s approval rating will surpass 22% by the end of December 2024 without any upcoming drastic events that could influence public opinion significantly. - -Given these considerations, my final forecast is that there is a low probability of Congress's approval ratings reaching or exceeding 22.0% by December 30, 2024. - -Probability: 15%" -30544,"Will the US Supreme Court's 538 approval rating be greater than or equal to 41.0% on December 30, 2024?","To address the question of whether the US Supreme Court's 538 approval rating will be 41.0% or higher on December 30, 2024, we first need to understand the current context and any factors that could influence changes in the approval rating over the next month. - -**1. Current Approval Rating:** -Since the exact current approval rating from 538 isn't directly provided, I'll assume a hypothetical scenario based on past trends and general sentiment. Let's assume that, as of November 27, 2024, the approval rating is approximately 39%. Historically, Supreme Court approval ratings have remained relatively stable, especially within short timeframes, unless significant events affect public perception. - -**2. Historical Stability and Volatility:** -Supreme Court approval ratings tend to show less volatility than other branches due to the court's relatively apolitical nature and the longer-term impact of decisions. While there can be fluctuations, drastic changes over a one-month period are unusual without significant external events. - -**3. Political Environment and Recent Events:** -Consideration must be given to any recent Supreme Court decisions or political controversies that might impact public opinion. If there have been contentious rulings or significant political pressure, this could sway the approval rating. Otherwise, if recent activity has been relatively stable, major changes are less likely. - -**4. Methodological Considerations:** -Understanding 538's methodology is important; they use adjustments for poll quality, recency, and sample size. This means any new data incorporated into their model closer to the resolution date could adjust the rating slightly, but likely not drastically within such a short period. - -**5. Timeframe Analysis:** -With only a month to the resolution date, the ability for public opinion to shift the approval rating by 2% (39% to 41%) is limited, especially given the typical base stability of Supreme Court ratings. The likelihood of such a shift without a catalyzing event is relatively low. - -**6. Base Rates and Historical Context:** -Historically, public opinion on the Supreme Court has not shown rapid changes without specific controversial rulings or political events, which do not appear to be present in this scenario. This historical stability suggests a lower probability of variance in public opinion over a short period. - -**7. Compensation for Overconfidence:** -Given past experiences of overconfidence, and the tendency for only about 35% of questions to resolve positively, I'll adjust expectations accordingly. Current analysis suggests an unlikely but not impossible scenario for reaching the 41% threshold. - -Considering these points, the probability of the US Supreme Court's 538 approval rating being at or above 41% by December 30, 2024, seems relatively low, given the stability of such ratings over a short period and the lack of significant recent events. Therefore, the final probability estimate for the approval rating reaching the specified threshold by the resolution date is: - -Probability: 30%" -30539,"Will the CDC report less than 50 cases of H5N1 in persons tested in the United States before January 1, 2025?","Let me think about this step by step: - -1. Current Situation Assessment: -- As of November 22, 2024, there are already 55 reported H5N1 cases in the US -- This exceeds the threshold of 50 cases specified in the question -- The resolution date is January 1, 2025, about 5 weeks away - -2. Historical Context: -- H5N1 cases in the US were historically very rare before 2024 -- The recent outbreak associated with dairy cows is unprecedented -- The spread has been relatively rapid since April 2024 - -3. Timeline Analysis: -- The question asks about ""less than 50 cases"" -- We're already at 55 cases -- For this to resolve as ""Yes,"" the CDC would need to revise their numbers downward - -4. Factors that could lead to downward revision: -- Further testing revealing some cases were not H5N1 specifically -- Reclassification of cases -- Data corrections or updates -- Methodology changes in counting - -5. Probability Assessment: -- For a ""Yes"" resolution, we need the CDC to revise numbers down by at least 6 cases -- Historical precedent shows CDC rarely makes such large downward revisions -- The short timeframe (5 weeks) makes major methodology changes unlikely -- Recent confirmations (like the child case) strengthen the current count - -6. Risk Factors: -- CDC's note about difficulty isolating virus subtypes -- Some cases are listed as H5 without confirmed N1 subtype -- However, CDC is specifically tracking H5N1 separately - -7. Considering different timeframes: -- In 2 weeks: 1% chance of revision below 50 -- In 5 weeks (resolution): 2% chance -- In 3 months: 5% chance -- In 6 months: 8% chance - -The key factor here is that we're already past the threshold, and the CDC would need to make a significant downward revision in a short timeframe. While such revisions are possible, they are quite rare, especially of this magnitude. The recent confirmation of new cases and the continued spread pattern make it even less likely that previous cases would be declassified. - -Probability: 2%" -30538,"Will the lowest temperature recorded in Anchorage, Alaska during the entire month of December 2024 be lower than the lowest temperature recorded in Chicago, IL for the month?","To approach this forecasting question, we need to assess the probability that the lowest temperature recorded in Anchorage, Alaska, during December 2024 will be lower than that recorded in Chicago, IL. We'll follow a structured approach to do that. - -### Historical Context and Base Rate - -1. **Historical Comparisons**: Historically, it is relatively unusual for Chicago to have a lower minimum temperature than Anchorage in December. Although it has happened before in December 2017, it seems a unique case rather than a common occurrence. This provides a baseline probability favoring Anchorage, consistent with its climate being typically colder than that of Chicago during the winter months. - -2. **Climatological Differences**: Anchorage is generally colder in winter than Chicago due to its northern latitude and climate, favoring colder monthly low temperatures. - -### Current Conditions and Trends - -1. **Anchorage's Recent Weather**: As mentioned by the research assistant, Anchorage has experienced a cold start to the winter, including temperatures as low as 3 °F in mid-November. This suggests a continuation of colder temperatures could be expected into December. - -2. **La Niña Influence**: NOAA's forecast indicating a weak La Niña historically suggests colder temperatures in Alaska. This aligns with below-average temperature predictions for Anchorage, increasing the likelihood of colder conditions continuing through December. - -3. **Chicago's Recent Weather**: Chicago has been experiencing above-average temperatures recently, which hints that it may encounter warmer winter temperatures compared to historical averages. - -4. **NOAA's Winter Forecast**: This forecast highlights cooler-than-normal conditions for the northern U.S., including Alaska, which supports the expected continuation of colder than average temperatures in Anchorage, as well as potentially warmer conditions in the lower 48, including Chicago. - -### Timeframe Considerations - -1. **Short-Term Dynamics**: Given it's late November, there’s limited time for a drastic departure from current weather patterns. This reduces variability and increases confidence in current trends playing out into December. - -2. **Uncertainty**: Despite aligning factors, weather remains inherently unpredictable, especially with the variance that can occur day to day. However, the trends and forecasts lean towards Anchorage maintaining colder lows than Chicago. - -### Statistical Adjustment - -Taking into account my tendency towards overconfidence, I wish to correct this by factoring in an appropriate margin of error. Historically, my forecasts have more often resolved as negative than positive, reinforcing the need for cautious optimism in prediction. - -### Conclusion - -In consideration of all the above factors, we can conclude that while weather remains unpredictable, the current conditions, combined with historical trends and recent forecasts, suggest it is more likely than not for Anchorage to record a lower temperature than Chicago in December 2024. - -Probability: 65%" -30537,"Will the lowest temperature recorded in Miami, FL over the entire month of December 2024 never be lower than the highest temperature recorded in Anchorage Alaska for the month?","Let me think through this step by step: - -1. First, let's understand what we're predicting: -- Miami's lowest December 2024 temperature must be HIGHER than Anchorage's highest December 2024 temperature -- This has not happened in the past 5 years -- The closest case was in 2020: Miami low of 47°F vs Anchorage high of 45°F - -2. Historical context: -- Miami typically sees December lows in the 40s-50s°F -- Anchorage typically sees December highs in the 30s-40s°F -- The gap needed is very small - just a few degrees difference could make it happen - -3. Current conditions and trends: -- 2024 has been an exceptionally warm year globally -- El Niño conditions are present, which typically: - * Brings cooler winters to Florida - * Brings warmer winters to Alaska -- This actually makes the conditions more favorable than typical years - -4. Time consideration: -- We're predicting just for December 2024, starting in 4 days -- Short timeline reduces uncertainty -- Winter weather patterns are already established - -5. Statistical analysis: -- Need just one day where Miami's low > Anchorage's high -- Given 31 days, multiple opportunities -- But historical data shows this hasn't happened in 5 years -- Came within 2 degrees once (47 vs 45) -- Probability of any given day succeeding is very low -- But having 31 chances increases odds - -6. Risk factors: -- Cold fronts in Florida -- Warm fronts in Alaska -- Data availability issues -- Missing data doesn't count - -7. Resolution criteria specifics: -- Only using specific weather stations -- Missing data excluded -- Clear resolution source - -Given: -- The extremely rare historical occurrence (0 in 5 years) -- But very close calls (within 2 degrees) -- Current El Niño conditions favorable -- Record warm year -- Multiple opportunities within the month -- Short timeline reducing uncertainty - -I believe this has a real but low chance of occurring. The base rate of 0 in 5 years suggests very low odds, but the close calls and current conditions suggest it's possible. I'll adjust slightly upward from the pure historical rate due to current conditions, but remain conservative given the rarity. - -Probability: 12%" -30535,Will Elena Lasconi win the second round of the 2024 presidential election in Romania?,"Predictions: [45, 54, 46, 45, 48] -Median reasoning: To forecast the probability of Elena Lasconi and Calin Georgescu winning the second round of the 2024 presidential election in Romania, we need to consider a variety of factors detailed in the background information. - -1. **Results from the First Round**: Georgescu secured 22.94% of the votes, while Lasconi received 19.18%. Georgescu's lead is not substantial, and the diaspora votes played a crucial role in Lasconi's advancement, suggesting that her candidacy has significant support outside the immediate voting populace. - -2. **Candidate Profiles and Campaigns**: Lasconi, aligning with pro-European ideals, may attract centrists and former supporters of traditional parties (PSD and PNL), given the general population's potential resistance to far-right policies. Georgescu's campaign as a far-right populist might have capped appeal beyond his current support base, as suggested by his reliance on anti-establishment sentiment. - -3. **Political Climate & Uncertainty**: The political landscape is fraught with unpredictability following the first round's shocking developments. Given the short time frame until the elections, there's limited opportunity for dramatic shifts in public sentiment. - -4. **Base Rates and Historical Context**: Historically, runoff elections favor candidates who can coalesce remaining voters from eliminated candidates. Lasconi may have an edge in appealing to pro-European, centrist voters, though it's contingent on effective campaigning and voter mobilization. - -5. **Expert Opinions and Analyses**: While lacking explicit polling data for the second round, expert analysis suggests a competitive match-up. With the unpredictability of the first round, we should avoid overconfidence in forecasting. - -Given these considerations, we should assign probabilities that reflect significant uncertainty yet recognize the competitive nature of the race. - -- **Elena Lasconi winning**: Given her ability to mobilize diaspora support and potential to gain centrist votes, I'd estimate a moderate likelihood, despite Georgescu's initial lead. - -- **Calin Georgescu winning**: As he leads from the first round and carries momentum, though his campaign might not expand beyond current support limits. - -Considering these factors, I'll assign: -- Elena Lasconi: 54% -- Calin Georgescu: 46% - -These reflect a fairly balanced probability given the information and voter dynamics." -30536,Will Calin Georgescu win the second round of the 2024 presidential election in Romania?,"Predictions: [45, 54, 46, 45, 48] -Median reasoning: To forecast the probability of Elena Lasconi and Calin Georgescu winning the second round of the 2024 presidential election in Romania, we need to consider a variety of factors detailed in the background information. - -1. **Results from the First Round**: Georgescu secured 22.94% of the votes, while Lasconi received 19.18%. Georgescu's lead is not substantial, and the diaspora votes played a crucial role in Lasconi's advancement, suggesting that her candidacy has significant support outside the immediate voting populace. - -2. **Candidate Profiles and Campaigns**: Lasconi, aligning with pro-European ideals, may attract centrists and former supporters of traditional parties (PSD and PNL), given the general population's potential resistance to far-right policies. Georgescu's campaign as a far-right populist might have capped appeal beyond his current support base, as suggested by his reliance on anti-establishment sentiment. - -3. **Political Climate & Uncertainty**: The political landscape is fraught with unpredictability following the first round's shocking developments. Given the short time frame until the elections, there's limited opportunity for dramatic shifts in public sentiment. - -4. **Base Rates and Historical Context**: Historically, runoff elections favor candidates who can coalesce remaining voters from eliminated candidates. Lasconi may have an edge in appealing to pro-European, centrist voters, though it's contingent on effective campaigning and voter mobilization. - -5. **Expert Opinions and Analyses**: While lacking explicit polling data for the second round, expert analysis suggests a competitive match-up. With the unpredictability of the first round, we should avoid overconfidence in forecasting. - -Given these considerations, we should assign probabilities that reflect significant uncertainty yet recognize the competitive nature of the race. - -- **Elena Lasconi winning**: Given her ability to mobilize diaspora support and potential to gain centrist votes, I'd estimate a moderate likelihood, despite Georgescu's initial lead. - -- **Calin Georgescu winning**: As he leads from the first round and carries momentum, though his campaign might not expand beyond current support limits. - -Considering these factors, I'll assign: -- Elena Lasconi: 54% -- Calin Georgescu: 46% - -These reflect a fairly balanced probability given the information and voter dynamics." -30580,Will Tesla increase its quarterly vehicle deliveries in Q4 2024 on a year-over-year basis?,"Let me think through this step by step: - -1. First, let's identify this as a Non-Market Threshold Value Event with clear historical data. - -2. Key baseline: Tesla needs to deliver more than 484,507 vehicles in Q4 2024 for a ""Yes"" resolution. - -3. Recent delivery trajectory: -- Q4 2023: 484,507 -- Q1 2024: 386,810 -- Q2 2024: 443,956 -- Q3 2024: 462,890 - -4. Key factors supporting growth: -- Strong China performance with potential 180,000 deliveries -- Historical pattern of Q4 being strongest quarter -- Continuing production ramp-up -- Price cuts and incentives driving demand - -5. Factors against growth: -- Declining margins suggesting limited room for further price cuts -- Increased competition in EV space -- Economic headwinds (high interest rates) -- Mixed performance in Europe and US - -6. Statistical analysis: -- Average quarterly growth YoY: ~6-7% -- Q4 is typically 10-15% higher than Q3 -- 484,507 * 1.06 = ~513,577 (basic YoY projection) -- 462,890 * 1.12 = ~518,437 (Q3 to Q4 seasonal adjustment) - -7. Risk adjustments: -- Market saturation increasing -- Competition intensifying -- Economic uncertainty -- But Tesla has strong execution history in Q4 - -8. Time consideration: -- Only about a month until resolution -- Limited time for major changes to current trajectory -- Most Q4 production/delivery plans already in motion - -9. Base rate consideration: -- Tesla has historically grown YoY in most quarters -- But growth rate is slowing -- Recent quarters show more volatility - -Given all factors, I see this as slightly above 50-50, but with significant uncertainty. The strong China performance and typical Q4 seasonal boost are positive factors, but increasing competition and economic headwinds create meaningful risk. The short time to resolution reduces uncertainty somewhat. - -The threshold of 484,507 is achievable but challenging, requiring about a 4.7% increase from Q4 2023. - -Probability: 58%" -30579,"Will US airline passenger volume for the week of Christmas through New Years Eve 2024 be up year over year, according to the TSA?","Let me think through this step by step: - -1) First, let's look at the baseline: We need to exceed 17,307,363 passengers for the specific week. - -2) Recent trends and data points: -- TSA estimates 6% increase for Thanksgiving travel -- Growth rate slowed to 3% in October -- Airlines for America projects record holiday season -- International traffic expected to be flat but pickup around Christmas - -3) Key factors to consider: -a) Economic conditions: -- Interest rates remain high -- Some inflation concerns persist -- Employment remains strong -- Consumer spending has been resilient - -b) Industry conditions: -- Airlines have largely recovered from post-COVID disruptions -- Better staffing levels than previous years -- More efficient operations -- 2023 was already a record year, making growth harder - -4) Risk factors: -- Weather events could impact travel -- High base from 2023 makes growth more challenging -- Economic uncertainty might affect discretionary travel -- Airline ticket prices remain elevated - -5) Statistical analysis: -- 6% Thanksgiving growth suggests strong holiday travel -- 3% October growth provides more conservative baseline -- Historical year-over-year growth typically 2-4% in normal years -- Need less than 1% growth to exceed the threshold - -6) Confidence adjustment: -- Question resolves in about 1 month -- Short timeframe reduces uncertainty -- Strong recent data points -- Clear resolution criteria -- Need very modest growth to resolve positively - -Given that we only need minimal growth over 2023's numbers, combined with strong recent travel data and positive industry projections, this seems likely to resolve positively. However, we should account for: -- The high base from 2023 -- Potential weather disruptions -- Economic headwinds -- Historical overconfidence - -The growth needed is very modest, and multiple indicators suggest continued strong travel demand. While being cautious about overconfidence, the short timeline and clear indicators support a relatively high probability. - -Probability: 71%" -30578,"Will the median person on Bloomberg's Billionaires Index be from the United States on December 20, 2024?","Let me think through this step by step: - -1. First, let's identify the question type: This is a Non-Market Threshold Value Event with binary outcome elements. - -2. Timeline analysis: -- Resolution date is December 20, 2024 -- Current date is November 29, 2024 -- Only 21 days of potential change -- This is a very short timeframe, heavily favoring current status quo - -3. Current status: -- 6 out of 10 people ranked 246-255 are from US (60% around median) -- 190 out of 500 total are from US (38% overall) -- The median position (250th) is right in the middle of the 246-255 range - -4. Key considerations: -- Very short timeframe means minimal opportunity for significant changes -- Wealth rankings tend to be relatively stable over short periods -- Year-end effects might cause some rebalancing but unlikely to shift dramatically -- Market movements would likely affect US and non-US billionaires similarly in this timeframe - -5. Historical context: -- US has consistently maintained strong representation in global billionaire lists -- Short-term ranking changes tend to be minimal without major market events -- Year-end portfolio adjustments might cause minor shifts - -6. Confidence factors: -- Very short timeframe increases confidence in current distribution -- Direct data about the relevant ranking range (246-255) is highly valuable -- Recent data (within last week) increases reliability - -7. Risk factors: -- Year-end tax loss harvesting could affect rankings -- Currency fluctuations could affect relative wealth -- Market volatility in December could affect rankings - -Given the very short timeframe and the current 60% US representation in the relevant range, with some adjustment for uncertainty and historical overconfidence, but heavily weighing the current status due to the extremely short timeline: - -Probability: 57%" -30577,"Will marijuana still be listed as a US Schedule I controlled substance on December 31, 2024?","Let me think through this step by step: - -1. Current Status: -- Marijuana is currently Schedule I -- We're only ~32 days from resolution date -- The default state (Schedule I) continues unless actively changed - -2. Process Requirements: -- DEA must complete rulemaking process -- Public hearings are required -- Implementation period would follow any decision - -3. Recent Developments: -- Most critically, the DEA hearing has been officially delayed until 2025 -- This is a major procedural requirement that cannot be bypassed -- The delay announcement is very recent and highly credible - -4. Timeline Analysis: -- Only 32 days remain until resolution -- The hearing itself hasn't even started -- After hearings, implementation would take additional time -- No reasonable administrative path exists to complete before Dec 31 - -5. Alternative Pathways: -- Congressional action could theoretically bypass DEA process -- However, Congress is highly unlikely to act in December -- No emergency powers exist for expedited rescheduling - -6. Historical Context: -- Drug scheduling changes typically take many months to years -- No precedent exists for rushing this type of change -- Base rate of major drug policy changes is very low in short timeframes - -7. Resolution Criteria: -- Based solely on Wikipedia list status on Dec 31 -- Clear resolution source reduces uncertainty -- No indication of potential Wikipedia edit wars on this topic - -8. Risk Factors: -- Very short timeframe remaining -- Official delay announcement -- No viable alternative pathways -- Strong institutional inertia - -Given the official delay of hearings until 2025, the extremely short remaining timeline, and the complete lack of alternative pathways, I assess there is virtually no chance of marijuana being removed from Schedule I by December 31, 2024. Even accounting for my historical overconfidence and leaving room for truly unexpected developments: - -Probability: 98%" -30576,"Will SpaceX fail to make any new posts on its Updates page before January 1, 2025? ","To predict whether SpaceX will fail to make any new posts on its Updates page before January 1, 2025, I'll consider several factors, including past behavior, trends, and the current activity level of SpaceX. - -### Historical Context and Base Rates -SpaceX is a highly dynamic company with frequent activities, including satellite launches and test flights, as highlighted by the recently mentioned launches. Historically, SpaceX has been proactive in updating the public about its activities, suggesting that updates are likely when significant events occur. - -### Current Activity and External Signals -The information available indicates that SpaceX is engaged in several high-profile projects and launches, evidenced by 21 additional launches planned before the end of the year (as noted in [1]). This suggests a high level of operational activity, which may correlate with public-facing updates. - -### Consideration of Update Page Behavior -The recent post on the Updates page (September 10, 2024) marks a gap of over two months without a new update. While this could suggest a potential lull in communications, it is equally plausible that the timing of significant events, such as upcoming launches or test results, could drive new posts. - -### Assessment of Novel Influences -The presence of significant projects, like preparing for a return to the moon and progress on Mars missions, is noteworthy. These endeavors would generally warrant communication milestones, potentially leading to updates on the page. - -### Time Frame Considerations -Given the need for a post within 33 days (from November 29, 2024, to January 1, 2025), there is a limited window for a new update to occur. With ongoing activities and the potential for end-of-year summaries or milestone achievements, there remain multiple opportunities for SpaceX to make a post. - -### Likelihood Analysis -1. **Short Term (Within December):** Given the likelihood of significant development, I would surmise that there's a reasonable chance of an update in this timeframe. -2. **Total 33-Day Period to Resolution Date:** Considering the historical tendencies of SpaceX, recent activities, and planned launches within a short timeframe, there is a moderate probability for updates. - -### Confidence Calibration -Given my historical overconfidence and the general tendency for lower probabilities of positive resolution, I should err on the side of caution and account for the uncertainty inherent in behavior predicting specific corporate actions. - -### Final Probability Assessment -Considering all factors, while the activity level supports the likelihood of an update, historical overconfidence corrections, and the limited timeframe suggest a moderate probability of a post being made. Balancing these elements, the probability that SpaceX will fail to make any new post before the deadline is estimated at: - -Probability: 35%" -30575,"Will the lowest temperature recorded in Anchorage, Alaska during the entire month of December 2024 be lower than the lowest temperature recorded in Chicago, IL for the month?","Let me think about this step by step: - -1. First, let's consider what we're comparing: -- Anchorage International Airport vs Chicago Midway Airport -- Only December 2024 -- Only looking at the lowest temperature recorded - -2. Key timing factors: -- This is for next month -- We have very near-term forecasting available -- Winter weather patterns are already established - -3. Historical analysis: -- It happened in 2017 (Anchorage warmer by 8°F) -- Almost happened in 2022 (Anchorage colder by 3°F) -- This suggests it's relatively rare but possible - -4. Geographic/Climate considerations: -- Anchorage typically has more stable winter temperatures due to maritime influence -- Chicago gets more extreme cold snaps due to Arctic air masses -- Chicago's record lows tend to be lower than Anchorage's in winter - -5. Current conditions and forecasts: -- NOAA predicting below-normal temperatures for Alaska region -- But Chicago can get sudden severe cold snaps -- Lake effect can influence Chicago temperatures significantly - -6. Statistical weighting: -- Looking at December data for past 10 years -- Chicago tends to have more volatile temperature swings -- Anchorage more consistently cold but not usually extreme - -7. Risk factors: -- Only need one very cold day in Anchorage relative to Chicago -- But Chicago more likely to get Arctic blast events -- December is prime time for polar vortex events affecting Chicago - -8. Resolution criteria specifics: -- Using official weather stations -- Full month comparison -- Clear data sources - -Given that: -- This is a rare occurrence historically -- Chicago more prone to extreme cold snaps -- We're looking at near-term forecast -- Need sustained pattern for full month -- NOAA forecast suggests colder Alaska temps -- But Chicago's variability means higher chance of extreme lows - -The historical data suggests this happens roughly 15-20% of the time, but current conditions and forecasts slightly increase the probability. However, we shouldn't overweight the recent NOAA forecast as Chicago's temperature volatility remains the dominant factor. - -Probability: 23%" -30626,Will the US Federal Reserve's balance sheet have under $4.3 trillion in U.S. treasury securities at the end of December 2024?,"Predictions: [70, 35, 70, 40] -Median reasoning: In analyzing the set of questions regarding the Federal Reserve's balance sheet, it is crucial to consider both the available data and the trends while accounting for all uncertainties and biases. Let's break down each question step by step: - -1. **U.S. Treasury Securities under $4.3 trillion**: Based on the given data, as of August 7, 2024, the Federal Reserve held $4,413,650 million in U.S. Treasury securities, and the reduction rate has been consistent at $25 billion per month. By December 2024, this reduction amounts to $125 billion, leading to a projected holding of approximately $4,288,650 million. Given the limited time until resolution and the consistent monthly reduction, achieving less than $4.3 trillion appears highly probable. - -2. **Reverse Repurchase Agreements under $550 billion**: Unfortunately, no specific data on the current level of reverse repurchase agreements was provided in your background information. However, considering the Federal Reserve's broader strategy of tightening policies, the amount of reverse repurchase agreements might decline in the coming weeks, but the uncertainty is high due to lack of specific numbers and current economic conditions. - -3. **Total Assets under $6.8 trillion**: The trend has been towards reduction, with the balance sheet reported to be $6.99 trillion in early November 2024, the lowest since May 2020. Given the QT process is underway and assuming no significant policy reversals, the reduction in assets should continue. If the reductions in treasury securities and other assets continue at the observed consistent rate, the likelihood of the total falling below $6.8 trillion is relatively high, due to this ongoing QT process. However, due to uncertainties and the variability in other balance sheet components, this probability will be less than that of the treasury securities. - -Overall, confidence in the U.S. Treasury securities falling below $4.3 trillion is high, while the others face uncertainties due to lacking specific current data or projections. - -Therefore, I assign a high but cautious probability to the first question, and slightly lower probabilities to the other two questions to account for uncertainties and potential economic and policy shifts." -30625,Will the US Federal Reserve's balance sheet have under $550 billion in reverse repurchase agreements at the end of December 2024?,"Predictions: [70, 35, 70, 40] -Median reasoning: In analyzing the set of questions regarding the Federal Reserve's balance sheet, it is crucial to consider both the available data and the trends while accounting for all uncertainties and biases. Let's break down each question step by step: - -1. **U.S. Treasury Securities under $4.3 trillion**: Based on the given data, as of August 7, 2024, the Federal Reserve held $4,413,650 million in U.S. Treasury securities, and the reduction rate has been consistent at $25 billion per month. By December 2024, this reduction amounts to $125 billion, leading to a projected holding of approximately $4,288,650 million. Given the limited time until resolution and the consistent monthly reduction, achieving less than $4.3 trillion appears highly probable. - -2. **Reverse Repurchase Agreements under $550 billion**: Unfortunately, no specific data on the current level of reverse repurchase agreements was provided in your background information. However, considering the Federal Reserve's broader strategy of tightening policies, the amount of reverse repurchase agreements might decline in the coming weeks, but the uncertainty is high due to lack of specific numbers and current economic conditions. - -3. **Total Assets under $6.8 trillion**: The trend has been towards reduction, with the balance sheet reported to be $6.99 trillion in early November 2024, the lowest since May 2020. Given the QT process is underway and assuming no significant policy reversals, the reduction in assets should continue. If the reductions in treasury securities and other assets continue at the observed consistent rate, the likelihood of the total falling below $6.8 trillion is relatively high, due to this ongoing QT process. However, due to uncertainties and the variability in other balance sheet components, this probability will be less than that of the treasury securities. - -Overall, confidence in the U.S. Treasury securities falling below $4.3 trillion is high, while the others face uncertainties due to lacking specific current data or projections. - -Therefore, I assign a high but cautious probability to the first question, and slightly lower probabilities to the other two questions to account for uncertainties and potential economic and policy shifts." -30624,Will the US Federal Reserve's balance sheet have less than $6.8 trillion in assets at the end of December 2024?,"Predictions: [70, 35, 70, 40] -Median reasoning: In analyzing the set of questions regarding the Federal Reserve's balance sheet, it is crucial to consider both the available data and the trends while accounting for all uncertainties and biases. Let's break down each question step by step: - -1. **U.S. Treasury Securities under $4.3 trillion**: Based on the given data, as of August 7, 2024, the Federal Reserve held $4,413,650 million in U.S. Treasury securities, and the reduction rate has been consistent at $25 billion per month. By December 2024, this reduction amounts to $125 billion, leading to a projected holding of approximately $4,288,650 million. Given the limited time until resolution and the consistent monthly reduction, achieving less than $4.3 trillion appears highly probable. - -2. **Reverse Repurchase Agreements under $550 billion**: Unfortunately, no specific data on the current level of reverse repurchase agreements was provided in your background information. However, considering the Federal Reserve's broader strategy of tightening policies, the amount of reverse repurchase agreements might decline in the coming weeks, but the uncertainty is high due to lack of specific numbers and current economic conditions. - -3. **Total Assets under $6.8 trillion**: The trend has been towards reduction, with the balance sheet reported to be $6.99 trillion in early November 2024, the lowest since May 2020. Given the QT process is underway and assuming no significant policy reversals, the reduction in assets should continue. If the reductions in treasury securities and other assets continue at the observed consistent rate, the likelihood of the total falling below $6.8 trillion is relatively high, due to this ongoing QT process. However, due to uncertainties and the variability in other balance sheet components, this probability will be less than that of the treasury securities. - -Overall, confidence in the U.S. Treasury securities falling below $4.3 trillion is high, while the others face uncertainties due to lacking specific current data or projections. - -Therefore, I assign a high but cautious probability to the first question, and slightly lower probabilities to the other two questions to account for uncertainties and potential economic and policy shifts." -30623,"Will Bitcoin be worth at least 200,000 Dogecoin on December 30, 2024?","To forecast whether Bitcoin will be worth at least 200,000 Dogecoin on December 30, 2024, we need to evaluate the available data on current prices, market trends, and the inherent volatility of cryptocurrency markets. - -### Current Status - -- **Bitcoin Price**: Approximately $97,000, yielding a ratio of about 250,731 Dogecoin per Bitcoin at the current Dogecoin price of $0.387128. This suggests that Bitcoin comfortably exceeds the 200,000 Dogecoin threshold at present. -- **Dogecoin Price**: $0.387128, with predictions indicating mixed future trends. Some sources suggest a possible decline in Dogecoin's value. - -### Market Dynamics - -1. **Bitcoin Analysis**: - - Significant growth of 129.63% YTD. - - Potential for continued increase due to supply constraints and rising institutional demand. - - Historically, Bitcoin has shown high volatility both downwards and upwards. - -2. **Dogecoin Analysis**: - - Substantial year-to-date returns of 375.27%, with recent volatility showing significant price changes. - - Mixed predictions suggest both potential for decline and the possibility for further increase. - -### Considerations - -- **Time Frame**: With less than a month until the resolution date, it's unlikely for extreme market shifts to affect Bitcoin to the extent that it drops below the 200,000 Dogecoin threshold. Both cryptocurrencies are highly volatile, but extremely significant changes are less probable in such a short time. - -- **Market Volatility**: The cryptocurrency market is notably volatile; however, rapid shifts are required to alter the current status significantly. - -- **Base Rate and Historical Context**: Historically, such significant relative price shifts between major cryptocurrencies over one month are rare, suggesting it is sensible to consider a base case biased towards the current situation maintaining or marginally altering. - -### Calculation and Adjustments - -- Current calculations show a substantial margin beyond the target ratio. -- Given the strong current positioning of Bitcoin relative to Dogecoin, the probability of Bitcoin maintaining a position above 200,000 Dogecoin is high. -- However, compensating for historical overconfidence and potential market volatility, we must not assume certainty. - -### Probability Forecast - -Considering the above analysis, we account for possible shifts but heavily weigh the current scenario as the stable base case. This leads to a high probability but not absolute certainty. - -Final Probability: 85% - -This prediction considers the current market ratios, the time frame, historical volatility, and biases towards overconfidence." -30622,Will the 2024 return of professional investor Howard Marks beat the return of any of the politicans tracked by Quiver Quantitative?,"To predict whether Howard Marks' 2024 return will outperform the return of any of the politicians tracked by Quiver Quantitative, I'll assess the available information and weigh it carefully. - -1. **Howard Marks’ Investment Strategy and Portfolio**: - - Howard Marks is a well-regarded investor known for risk management and strategic portfolio adjustments. His focus on diversified holdings and consistent, quality investments can offer stability, although such an approach may sometimes forego extraordinary short-term gains. - - Recent portfolio adjustments reflect an adaptive strategy to market conditions, which potentially positions him well for solid returns. - -2. **Comparison with Politician Portfolios**: - - The politicians' stocks are informed by different motivations compared to Marks' disciplined investment strategy. Their returns could vary significantly based on short-term market intelligence, policy changes, or other less predictable factors. - - Historical data suggest that some politicians have indeed seen substantial returns in specific cases. However, professional investors typically have a more methodological approach, which overall tends to align with market averages or better. - -3. **Market Conditions**: - - Market volatility and interest rate adjustments are critical factors currently influencing investment returns. While these conditions create opportunities, they also introduce risk, making specific outcomes hard to predict. - - Marks' experience and focus on risk control might act as a buffer against adverse market conditions that could randomly impact politicians' stock returns. - -4. **Resolution Timeframe**: - - There are only a few weeks until the resolution date, indicating reduced scope for significant changes in rankings based on portfolio returns. This limits the variance and suggests that current trends may hold, making it difficult for either side to drastically outperform the other unless there has already been a clear disparity in performance. - -5. **Base Rates and Historical Performance**: - - Historically, politicians' portfolios have shown intermittent success compared to seasoned investors, but there's no consistent long-term outperformance against experienced investors like Marks. Markets generally reward consistency and risk management, traits heavily emphasized by Marks. - -6. **Potential Bias**: - - The background information may suggest confidence in Marks' strategy. I must adjust for overconfidence based on historical performance and potential for random variance in short periods. - -Considering these factors, the base case, current trends, market volatility, and the slightly unpredictable nature of politician trading performance, I estimate that while Marks is a strong performer, the outcome of this competition involves significant uncertainty given the short time frame and variance potential. - -Therefore, the probability that Howard Marks' return will exceed the return of any politician tracked by Quiver Quantitative by the end of 2024 is: - -Probability: 45%" -30621,"Will Ghana declare a winner in its presidential election before December 17, 2024?","Let me think through this step by step: - -1. First, let's consider the timeline: -- Election date: December 7, 2024 -- Question deadline: December 17, 2024 -- This gives 10 days for results to be declared - -2. Historical precedent: -- Ghana has held successful elections since 1992 -- Results are typically declared within 48-72 hours -- The 2020 election results were declared on December 9 (within 72 hours) -- The 2016 election results were declared within 48 hours - -3. Positive factors: -- Strong democratic tradition -- Presence of international observers -- Clear electoral commission procedures -- Only 12 qualified candidates -- Two main parties dominate - -4. Risk factors: -- Increased polarization -- Economic challenges -- ""Do-or-die"" rhetoric from both parties -- Possible runoff if no candidate gets >50% - -5. Timeline analysis: -- Even with delays, 10 days is a significant buffer -- A potential runoff would be scheduled later, not affect initial declaration -- International observers increase pressure for timely results - -6. Institutional strength: -- Electoral Commission has experience -- Digital systems in place -- Clear constitutional framework -- International support - -7. Adjustment factors: -- Past overconfidence warning suggests tempering optimistic assessment -- High stakes nature of election could increase litigation risk -- But 10-day window provides substantial buffer - -Given Ghana's strong track record of declaring results within 72 hours, and the 10-day window provided, even accounting for increased tensions and possible delays, the probability of declaration within this timeframe remains high. However, we should adjust downward from historical 95%+ success rate due to increased polarization and economic pressures. - -The main risk would be legal challenges preventing declaration, but these typically come after initial declarations in Ghana's system. - -Accounting for overconfidence correction and increased political tension, but recognizing the strong institutional framework and ample time buffer: - -Probability: 88%" -30620,Will Nancy Pelosi's stock portfolio beat the return of Congressman Donald Beyer's portfolio in 2024?,"Predictions: [40, 30, 35, 20, 35] -Median reasoning: To address these forecasts, I will analyze each event as a Market Price Event, given that they revolve around comparative market return performances of investment portfolios relative to different benchmarks and other portfolios. We have until December 31, 2024, to observe outcomes, giving a 29-day assessment window. For such resolution-based predictions, recency, historical performance, and market conditions heavily influence the outcome. - -1. **Pelosi vs Beyer Portfolio (30620)**: Without precise information on Beyer's holdings or significant outperforming actions, we rely on the default assumption and Pelosi's past performance. Pelosi's past demonstrated her strategic moves and impressive long-term return but also raises potential ethical questions. Pelosi's major sell indicated strategic foresight, potentially e.g., avoiding underperformance. Generally, unless Beyer's is exceptionally curated or Pelosi makes significantly poor decisions moving forward, she has strong comparatives. However, given the uncertainty of Beyer's exact position, and potential unforeseen market swings, estimate around 60% chance of Pelosi outperforming Beyer. - -2. **Pelosi vs S&P 500 (30619)**: The S&P 500's strong gain of 26% in 2024 and recent record highs suggest a formidable benchmark. Pelosi's strategic moves, like her timely Microsoft sale, show market sensitivity. Exposure in tech and finance could see varying impacts given speculative volatility around these sectors for December. Much of Pelosi's portfolio overlapped significantly with S&P components, affecting how differently they might perform. Her portfolio's all-time high may suggest some insulation against downside risks, but outperforming the exceptionally strong S&P gains is challenging. Estimate around 40% chance to beat this index. - -3. **Pelosi vs Berkshire Hathaway (30618)**: Buffett's caution around market overvaluations and tactical cash holdings could work to his advantage if downturns arise. While Pelosi's active trading could leverage rapid gains, Berkshire's diversified, wealth-heavy strategies provide stability and may excel in volatility spells. Given this contrast, her portfolio might not exceed Berkshire's conservative gamble unless specific outperformances occur in sectors she holds. Estimate around 35% chance to beat Berkshire. - -Overall, given the correlation imperatives, assume significant potential variance exists, with Pelosi possibly diverging less from broad market trends when risky bets don't play out as expected." -30619,Will Nancy Pelosi's stock portfolio beat the S&P 500 in 2024?,"Predictions: [40, 30, 35, 20, 35] -Median reasoning: To address these forecasts, I will analyze each event as a Market Price Event, given that they revolve around comparative market return performances of investment portfolios relative to different benchmarks and other portfolios. We have until December 31, 2024, to observe outcomes, giving a 29-day assessment window. For such resolution-based predictions, recency, historical performance, and market conditions heavily influence the outcome. - -1. **Pelosi vs Beyer Portfolio (30620)**: Without precise information on Beyer's holdings or significant outperforming actions, we rely on the default assumption and Pelosi's past performance. Pelosi's past demonstrated her strategic moves and impressive long-term return but also raises potential ethical questions. Pelosi's major sell indicated strategic foresight, potentially e.g., avoiding underperformance. Generally, unless Beyer's is exceptionally curated or Pelosi makes significantly poor decisions moving forward, she has strong comparatives. However, given the uncertainty of Beyer's exact position, and potential unforeseen market swings, estimate around 60% chance of Pelosi outperforming Beyer. - -2. **Pelosi vs S&P 500 (30619)**: The S&P 500's strong gain of 26% in 2024 and recent record highs suggest a formidable benchmark. Pelosi's strategic moves, like her timely Microsoft sale, show market sensitivity. Exposure in tech and finance could see varying impacts given speculative volatility around these sectors for December. Much of Pelosi's portfolio overlapped significantly with S&P components, affecting how differently they might perform. Her portfolio's all-time high may suggest some insulation against downside risks, but outperforming the exceptionally strong S&P gains is challenging. Estimate around 40% chance to beat this index. - -3. **Pelosi vs Berkshire Hathaway (30618)**: Buffett's caution around market overvaluations and tactical cash holdings could work to his advantage if downturns arise. While Pelosi's active trading could leverage rapid gains, Berkshire's diversified, wealth-heavy strategies provide stability and may excel in volatility spells. Given this contrast, her portfolio might not exceed Berkshire's conservative gamble unless specific outperformances occur in sectors she holds. Estimate around 35% chance to beat Berkshire. - -Overall, given the correlation imperatives, assume significant potential variance exists, with Pelosi possibly diverging less from broad market trends when risky bets don't play out as expected." -30618,Will Nancy Pelosi's stock portfolio beat Berkshire Hathaway in 2024?,"Predictions: [40, 30, 35, 20, 35] -Median reasoning: To address these forecasts, I will analyze each event as a Market Price Event, given that they revolve around comparative market return performances of investment portfolios relative to different benchmarks and other portfolios. We have until December 31, 2024, to observe outcomes, giving a 29-day assessment window. For such resolution-based predictions, recency, historical performance, and market conditions heavily influence the outcome. - -1. **Pelosi vs Beyer Portfolio (30620)**: Without precise information on Beyer's holdings or significant outperforming actions, we rely on the default assumption and Pelosi's past performance. Pelosi's past demonstrated her strategic moves and impressive long-term return but also raises potential ethical questions. Pelosi's major sell indicated strategic foresight, potentially e.g., avoiding underperformance. Generally, unless Beyer's is exceptionally curated or Pelosi makes significantly poor decisions moving forward, she has strong comparatives. However, given the uncertainty of Beyer's exact position, and potential unforeseen market swings, estimate around 60% chance of Pelosi outperforming Beyer. - -2. **Pelosi vs S&P 500 (30619)**: The S&P 500's strong gain of 26% in 2024 and recent record highs suggest a formidable benchmark. Pelosi's strategic moves, like her timely Microsoft sale, show market sensitivity. Exposure in tech and finance could see varying impacts given speculative volatility around these sectors for December. Much of Pelosi's portfolio overlapped significantly with S&P components, affecting how differently they might perform. Her portfolio's all-time high may suggest some insulation against downside risks, but outperforming the exceptionally strong S&P gains is challenging. Estimate around 40% chance to beat this index. - -3. **Pelosi vs Berkshire Hathaway (30618)**: Buffett's caution around market overvaluations and tactical cash holdings could work to his advantage if downturns arise. While Pelosi's active trading could leverage rapid gains, Berkshire's diversified, wealth-heavy strategies provide stability and may excel in volatility spells. Given this contrast, her portfolio might not exceed Berkshire's conservative gamble unless specific outperformances occur in sectors she holds. Estimate around 35% chance to beat Berkshire. - -Overall, given the correlation imperatives, assume significant potential variance exists, with Pelosi possibly diverging less from broad market trends when risky bets don't play out as expected." -30617,"Will the BLS report an increase in nonfarm payroll numbers of ≥175,000 for November 2024?","Predictions: [80, 70, 85, 85, 85] -Median reasoning: To forecast the probabilities, we must account for several factors, including recent trends, market expectations, and historical context. The provided background information shows that the recent nonfarm payroll report for October was quite low, only increasing by 12,000 jobs, and was significantly below expectations due to external disruptions like weather and strikes. This sets a low base case for subsequent months. Historically, nonfarm payroll numbers can be volatile, especially after significant disruptions, but rebounds are possible if disruptions are temporary. Market expectations, as mentioned in the research assistant's summary, suggest optimism for November, expecting a rebound with an increase around 200,000. However, these projections might be overly optimistic, given the weak recent data, and should be tempered by caution. - -For the questions: -1. The likelihood of an increase greater than or equal to 175,000: Considering the low growth in October, the base rate suggests caution. While there is some optimism for recovery, potential external disruptions and a cautionary note from historical situations suggest a moderate probability around 40%. - -2. For an increase greater than or equal to 75,000: Given the expectation of a rebound and historical precedent for recovery, the probability is higher here. An increase of 75,000 is more attainable, so this is estimated around 70%. - -3. For any increase at all: Historical precedents and base rates strongly suggest there will be at least some increase, given the revisions and adjustments over time, and some level of economic recovery, placing this probability higher, at around 85%. - -Overall, while there is potential for a rebound, conservative estimates account for uncertainty, especially given the recent historical low and external disruptions impacting confidence in a drastic immediate recovery." -30616,"Will the BLS report an increase in nonfarm payroll numbers of ≥75,000 for November 2024?","Predictions: [80, 70, 85, 85, 85] -Median reasoning: To forecast the probabilities, we must account for several factors, including recent trends, market expectations, and historical context. The provided background information shows that the recent nonfarm payroll report for October was quite low, only increasing by 12,000 jobs, and was significantly below expectations due to external disruptions like weather and strikes. This sets a low base case for subsequent months. Historically, nonfarm payroll numbers can be volatile, especially after significant disruptions, but rebounds are possible if disruptions are temporary. Market expectations, as mentioned in the research assistant's summary, suggest optimism for November, expecting a rebound with an increase around 200,000. However, these projections might be overly optimistic, given the weak recent data, and should be tempered by caution. - -For the questions: -1. The likelihood of an increase greater than or equal to 175,000: Considering the low growth in October, the base rate suggests caution. While there is some optimism for recovery, potential external disruptions and a cautionary note from historical situations suggest a moderate probability around 40%. - -2. For an increase greater than or equal to 75,000: Given the expectation of a rebound and historical precedent for recovery, the probability is higher here. An increase of 75,000 is more attainable, so this is estimated around 70%. - -3. For any increase at all: Historical precedents and base rates strongly suggest there will be at least some increase, given the revisions and adjustments over time, and some level of economic recovery, placing this probability higher, at around 85%. - -Overall, while there is potential for a rebound, conservative estimates account for uncertainty, especially given the recent historical low and external disruptions impacting confidence in a drastic immediate recovery." -30615,Will the BLS report an increase in nonfarm payroll numbers for November 2024?,"Predictions: [80, 70, 85, 85, 85] -Median reasoning: To forecast the probabilities, we must account for several factors, including recent trends, market expectations, and historical context. The provided background information shows that the recent nonfarm payroll report for October was quite low, only increasing by 12,000 jobs, and was significantly below expectations due to external disruptions like weather and strikes. This sets a low base case for subsequent months. Historically, nonfarm payroll numbers can be volatile, especially after significant disruptions, but rebounds are possible if disruptions are temporary. Market expectations, as mentioned in the research assistant's summary, suggest optimism for November, expecting a rebound with an increase around 200,000. However, these projections might be overly optimistic, given the weak recent data, and should be tempered by caution. - -For the questions: -1. The likelihood of an increase greater than or equal to 175,000: Considering the low growth in October, the base rate suggests caution. While there is some optimism for recovery, potential external disruptions and a cautionary note from historical situations suggest a moderate probability around 40%. - -2. For an increase greater than or equal to 75,000: Given the expectation of a rebound and historical precedent for recovery, the probability is higher here. An increase of 75,000 is more attainable, so this is estimated around 70%. - -3. For any increase at all: Historical precedents and base rates strongly suggest there will be at least some increase, given the revisions and adjustments over time, and some level of economic recovery, placing this probability higher, at around 85%. - -Overall, while there is potential for a rebound, conservative estimates account for uncertainty, especially given the recent historical low and external disruptions impacting confidence in a drastic immediate recovery." -30624,Will the US Federal Reserve's balance sheet have less than $6.8 trillion in assets at the end of December 2024?,"Let me think through this step by step: - -1. Current Situation: -- Latest data shows $6.905 trillion (Nov 26, 2024) -- Already below the threshold of $6.8 trillion -- Only ~3.5 weeks until resolution - -2. Trend Analysis: -- Clear downward trend over past year -- Decreased from $7.811T to $6.905T (≈$906B reduction) -- Average weekly reduction recently has been small but consistent - -3. Key Considerations: -- Very short timeframe until resolution (weeks not months) -- Fed has consistently communicated continued balance sheet reduction -- Powell explicitly stated drawdown continues even with rate discussions -- Year-end can sometimes see temporary balance sheet fluctuations - -4. Risk Factors: -- Potential market stress requiring Fed intervention -- Year-end banking system liquidity needs -- Unexpected economic shocks - -5. Statistical Analysis: -- Current level: $6.905T -- Target threshold: $6.8T -- Buffer: $105B above threshold -- Recent weekly changes have been small ($10-20B) -- 3.5 weeks remaining - -6. Historical Context: -- Fed has maintained steady reduction pace -- No major policy changes announced -- Year-end historically sees some volatility but usually manageable - -7. Confidence Assessment: -- Very short time horizon reduces uncertainty -- Current trajectory supports continuation -- Already close to threshold -- Limited time for major policy shifts - -Given the extremely short timeframe until resolution, the current level being already close to but above the threshold, and typical year-end dynamics, I assess that while the trend is downward, there may not be enough time for sufficient reduction to consistently stay below $6.8T by end of December. - -The base case strongly suggests the balance sheet will remain slightly above $6.8T, though it's close. With only weeks remaining, dramatic changes are unlikely without major external shocks. - -Adjusting for historical overconfidence and the short timeline, I'll weight heavily toward the current state while accounting for the clear downward trend and Fed guidance. - -Probability: 35%" -30625,Will the US Federal Reserve's balance sheet have under $550 billion in reverse repurchase agreements at the end of December 2024?,"Let me think through this step by step: - -1. Current Situation Analysis: -- Latest reported value is $569,509 million (Nov 27, 2024) -- Target threshold is $550,000 million -- Only about $19.5 billion difference from current to target -- Resolution date is December 28, less than 4 weeks away - -2. Recent Trend Assessment: -- Clear downward trend in reverse repo amounts -- Significant decrease from earlier in 2024 -- Rate of decline has been steady but not precipitous - -3. Timing Considerations: -- Very short timeframe until resolution (less than 4 weeks) -- December often sees increased market activity due to year-end positioning -- Holiday season can affect market behavior - -4. Technical Factors: -- Fed policy remains consistent -- No major policy changes announced -- Current rate environment stable - -5. Historical Pattern Analysis: -- RRP levels tend to show some volatility -- Year-end periods can see temporary spikes -- Weekly variations typically under $50 billion - -6. Risk Assessment: -- Short timeframe reduces likelihood of major changes -- Current level very close to threshold -- Year-end dynamics could increase volatility - -7. Probability Distribution: -- Current value: $569,509M -- Target: <$550,000M -- Need: ~$19.5B decrease -- Weekly variance historically: ~$10-50B - -Given: -- The extremely short timeframe (less than 4 weeks) -- The relatively small gap to target ($19.5B) -- The consistent downward trend -- But accounting for year-end effects -- And the possibility of temporary spikes - -The most likely scenario is that the downward trend continues but may be affected by year-end dynamics. The gap is small enough that it could be bridged in normal market operations, but the timing (year-end) adds uncertainty. - -Being careful not to be overconfident given the short timeframe and year-end considerations, but acknowledging the clear downward trend and small gap to target: - -Probability: 42%" -30626,Will the US Federal Reserve's balance sheet have under $4.3 trillion in U.S. treasury securities at the end of December 2024?,"To address the question of whether the US Federal Reserve's balance sheet will have under $4.3 trillion in U.S. Treasury securities by the end of December 2024, I will break down the issue into several components and evaluate them step by step. - -### Analysis: - -1. **Current Holdings and Reductions:** - - As of August 7, 2024, the Fed held approximately $4.414 trillion in U.S. Treasury securities. - - The Fed is reducing its holdings by $25 billion per month according to the tapering schedule mentioned in the background information. - - This indicates a reduction of about $100 billion over the four months from August to December. - -2. **Projection of Treasury Holdings:** - - Starting from $4.414 trillion in August and applying the $25 billion reduction per month, by December 2024, the Fed's U.S. Treasury securities holdings could be projected to reduce to approximately $4.314 trillion. - - This projection still results in the Fed holding more than $4.3 trillion, specifically above the $4.3 trillion threshold but nearing it. - -3. **Time Available for Change:** - - With less than a month remaining for the resolution date and consistent reduction patterns, a large deviation from the anticipated $4.314 trillion amount seems unlikely barring an unexpected policy shift or massive sale. - -4. **Market and Policy Environment:** - - There's no significant information suggesting imminent drastic changes in Fed policy that might increase the rate of reduction in Treasury holdings. - - The Fed’s quantitative tightening strategy appears steady without signals for acceleration in reductions beyond the known amounts. - -5. **Consideration of Variance and Historical Context:** - - There is potential daily and weekly variance in the Fed’s balance sheet due to operational market activities, but these are generally controlled and unlikely to cause a deviation large enough to result in U.S. Treasury holdings falling below $4.3 trillion by end of December. - - Historically, reductions have been methodical and planned, aiming at market stability which aligns with the weight of gradual reduction rather than unexpected market shocks. - -6. **Base Rates and Historical Precedent:** - - The base rate for reaching such a specific threshold in such a short time is low given the precision required. Historical adjustments have not resulted in such substantial and rapid decreases without preemptive announcements or market conditions necessitating urgent action. - -7. **Evaluation Addressing Overconfidence:** - - While the forecast suggests an outcome slightly above the $4.3 trillion threshold, I must adjust for overconfidence. Thus, while initial projection is slightly over the threshold, any unforeseen minor upticks in reduction, though unlikely, must be considered. - -### Conclusion: -Based on current tapering practices and historical precedent, the U.S. Federal Reserve is likely to hold just above $4.3 trillion in U.S. Treasury securities by the end of December 2024. The projected continued reduction remains consistent with $25 billion per month. - -**Final Prediction:** -- Accounting for some uncertainty (possible variations, unanticipated minor adjustments in Fed operations, and typical forecasts): - -**Probability of resolution (having under $4.3 trillion): 20%** - -This probability reflects the slim chance of reduction acceleration or variance leading to holdings under $4.3 trillion, heavily weighed by current trends and operational consistencies." -30620,Will Nancy Pelosi's stock portfolio beat the return of Congressman Donald Beyer's portfolio in 2024?,"Predictions: [40, 52, 40, 45, 48] -Median reasoning: Analyzing each question step by step: - -1. Pelosi vs Beyer (30620): -- Historical data shows Pelosi has consistently outperformed many other congressional traders -- Her 2023 return of 65% demonstrates strong performance -- She has advantages in information access and sophisticated trading strategies -- However, there's significant uncertainty in comparing to Beyer specifically -- Base rate suggests ~60% chance of outperformance - -2. Pelosi vs S&P 500 (30619): -- The S&P 500 has had strong performance in 2024 -- Historical data shows Pelosi beating the market more often than not -- Her technology-heavy portfolio aligns well with current market leaders -- Recent market conditions favor her investment style -- Base rate and current positioning suggests ~55% chance - -3. Pelosi vs Berkshire (30618): -- Berkshire has underperformed in recent bull markets due to conservative positioning -- Buffett's large cash position may continue to be a drag on returns -- However, Berkshire tends to outperform in market uncertainty -- Berkshire's diversification provides stability -- Given market conditions, estimate ~45% chance Pelosi outperforms - -Key considerations across all predictions: -- Very short time remaining in 2024 (less than 1 month) -- Current market momentum likely to persist short-term -- High market valuations create some downside risk -- Pelosi's tech-heavy portfolio could be volatile -- Need to avoid overconfidence given the short timeframe - -Given the limited time remaining in 2024, current positions and momentum will heavily influence outcomes. The predictions reflect both historical base rates and current market conditions, while accounting for uncertainty in such short-term comparisons." -30619,Will Nancy Pelosi's stock portfolio beat the S&P 500 in 2024?,"Predictions: [40, 52, 40, 45, 48] -Median reasoning: Analyzing each question step by step: - -1. Pelosi vs Beyer (30620): -- Historical data shows Pelosi has consistently outperformed many other congressional traders -- Her 2023 return of 65% demonstrates strong performance -- She has advantages in information access and sophisticated trading strategies -- However, there's significant uncertainty in comparing to Beyer specifically -- Base rate suggests ~60% chance of outperformance - -2. Pelosi vs S&P 500 (30619): -- The S&P 500 has had strong performance in 2024 -- Historical data shows Pelosi beating the market more often than not -- Her technology-heavy portfolio aligns well with current market leaders -- Recent market conditions favor her investment style -- Base rate and current positioning suggests ~55% chance - -3. Pelosi vs Berkshire (30618): -- Berkshire has underperformed in recent bull markets due to conservative positioning -- Buffett's large cash position may continue to be a drag on returns -- However, Berkshire tends to outperform in market uncertainty -- Berkshire's diversification provides stability -- Given market conditions, estimate ~45% chance Pelosi outperforms - -Key considerations across all predictions: -- Very short time remaining in 2024 (less than 1 month) -- Current market momentum likely to persist short-term -- High market valuations create some downside risk -- Pelosi's tech-heavy portfolio could be volatile -- Need to avoid overconfidence given the short timeframe - -Given the limited time remaining in 2024, current positions and momentum will heavily influence outcomes. The predictions reflect both historical base rates and current market conditions, while accounting for uncertainty in such short-term comparisons." -30618,Will Nancy Pelosi's stock portfolio beat Berkshire Hathaway in 2024?,"Predictions: [40, 52, 40, 45, 48] -Median reasoning: Analyzing each question step by step: - -1. Pelosi vs Beyer (30620): -- Historical data shows Pelosi has consistently outperformed many other congressional traders -- Her 2023 return of 65% demonstrates strong performance -- She has advantages in information access and sophisticated trading strategies -- However, there's significant uncertainty in comparing to Beyer specifically -- Base rate suggests ~60% chance of outperformance - -2. Pelosi vs S&P 500 (30619): -- The S&P 500 has had strong performance in 2024 -- Historical data shows Pelosi beating the market more often than not -- Her technology-heavy portfolio aligns well with current market leaders -- Recent market conditions favor her investment style -- Base rate and current positioning suggests ~55% chance - -3. Pelosi vs Berkshire (30618): -- Berkshire has underperformed in recent bull markets due to conservative positioning -- Buffett's large cash position may continue to be a drag on returns -- However, Berkshire tends to outperform in market uncertainty -- Berkshire's diversification provides stability -- Given market conditions, estimate ~45% chance Pelosi outperforms - -Key considerations across all predictions: -- Very short time remaining in 2024 (less than 1 month) -- Current market momentum likely to persist short-term -- High market valuations create some downside risk -- Pelosi's tech-heavy portfolio could be volatile -- Need to avoid overconfidence given the short timeframe - -Given the limited time remaining in 2024, current positions and momentum will heavily influence outcomes. The predictions reflect both historical base rates and current market conditions, while accounting for uncertainty in such short-term comparisons." -30615,Will the BLS report an increase in nonfarm payroll numbers for November 2024?,"Predictions: [40, 40, 50, 40, 35] -Median reasoning: Let's analyze this step by step: - -1. Recent Context: -- October 2024 showed only 12,000 jobs added, significantly below expectations -- This was heavily influenced by temporary factors (strikes, weather) -- The unemployment rate remained stable at 4.1% - -2. Base Rate Analysis: -- Looking at historical nonfarm payroll data, increases of: - * >0: ~85% of months - * >75,000: ~75% of months - * >175,000: ~45% of months historically, but lower in recent years - -3. Current Factors: -- The resolution date is only 4 days away, giving high confidence in current trends -- Market expectations are showing ~195,000-200,000 for November -- Recovery expected from temporary October disruptions -- However, there are signs of broader labor market cooling - -4. Distribution Analysis: -- Probability of negative growth: ~15% -- 0 to 74,999: ~10% -- 75,000 to 174,999: ~35% -- 175,000+: ~40% - -5. Confidence Adjustments: -- Given the very short timeframe, we can be more confident in current expectations -- Recent volatility suggests some caution in extreme predictions -- Market consensus seems well-aligned around 200,000 - -6. Final Probability Distribution: -- Will increase (>0): 85% -- Will increase ≥75,000: 75% -- Will increase ≥175,000: 40% - -These probabilities reflect both the current market expectations for a rebound and historical patterns, while accounting for recent labor market cooling and avoiding overconfidence given past volatility." -30616,"Will the BLS report an increase in nonfarm payroll numbers of ≥75,000 for November 2024?","Predictions: [40, 40, 50, 40, 35] -Median reasoning: Let's analyze this step by step: - -1. Recent Context: -- October 2024 showed only 12,000 jobs added, significantly below expectations -- This was heavily influenced by temporary factors (strikes, weather) -- The unemployment rate remained stable at 4.1% - -2. Base Rate Analysis: -- Looking at historical nonfarm payroll data, increases of: - * >0: ~85% of months - * >75,000: ~75% of months - * >175,000: ~45% of months historically, but lower in recent years - -3. Current Factors: -- The resolution date is only 4 days away, giving high confidence in current trends -- Market expectations are showing ~195,000-200,000 for November -- Recovery expected from temporary October disruptions -- However, there are signs of broader labor market cooling - -4. Distribution Analysis: -- Probability of negative growth: ~15% -- 0 to 74,999: ~10% -- 75,000 to 174,999: ~35% -- 175,000+: ~40% - -5. Confidence Adjustments: -- Given the very short timeframe, we can be more confident in current expectations -- Recent volatility suggests some caution in extreme predictions -- Market consensus seems well-aligned around 200,000 - -6. Final Probability Distribution: -- Will increase (>0): 85% -- Will increase ≥75,000: 75% -- Will increase ≥175,000: 40% - -These probabilities reflect both the current market expectations for a rebound and historical patterns, while accounting for recent labor market cooling and avoiding overconfidence given past volatility." -30617,"Will the BLS report an increase in nonfarm payroll numbers of ≥175,000 for November 2024?","Predictions: [40, 40, 50, 40, 35] -Median reasoning: Let's analyze this step by step: - -1. Recent Context: -- October 2024 showed only 12,000 jobs added, significantly below expectations -- This was heavily influenced by temporary factors (strikes, weather) -- The unemployment rate remained stable at 4.1% - -2. Base Rate Analysis: -- Looking at historical nonfarm payroll data, increases of: - * >0: ~85% of months - * >75,000: ~75% of months - * >175,000: ~45% of months historically, but lower in recent years - -3. Current Factors: -- The resolution date is only 4 days away, giving high confidence in current trends -- Market expectations are showing ~195,000-200,000 for November -- Recovery expected from temporary October disruptions -- However, there are signs of broader labor market cooling - -4. Distribution Analysis: -- Probability of negative growth: ~15% -- 0 to 74,999: ~10% -- 75,000 to 174,999: ~35% -- 175,000+: ~40% - -5. Confidence Adjustments: -- Given the very short timeframe, we can be more confident in current expectations -- Recent volatility suggests some caution in extreme predictions -- Market consensus seems well-aligned around 200,000 - -6. Final Probability Distribution: -- Will increase (>0): 85% -- Will increase ≥75,000: 75% -- Will increase ≥175,000: 40% - -These probabilities reflect both the current market expectations for a rebound and historical patterns, while accounting for recent labor market cooling and avoiding overconfidence given past volatility." -30677,Will the average daily temperature for December 2024 recorded by the Barrow/Port Rogers NWS weather station (on the Arctic coast of Alaska) exceed the 1945-2023 average for December?,"Predictions: [15, 10, 10, 10, 20] -Median reasoning: Let's analyze this step by step: - -1. Historical Context: -- Arctic temperatures are rising 3x faster than global average -- The region has shown consistent warming trends -- December 2024 is very close (resolution is in less than a month) - -2. Temperature Thresholds Analysis: -- We need to determine likelihood of exceeding historical average by: - * Any amount (Q30677) - * ≥4°F (Q30678) - * ≥6°F (Q30679) - -3. Current Conditions & Near-term Forecasts: -- 2024 is tracking to be the warmest year on record globally -- Arctic sea ice extent is at record lows -- These conditions typically correlate with higher temperatures - -4. Base Rate Consideration: -- Given the extremely short timeframe (December has already started) -- Weather patterns are somewhat set for the immediate future -- Major deviations are less likely in such a short window - -5. Probability Distribution: -- Most likely: Exceeding historical average by 0-4°F (45%) -- Next most likely: Exceeding by 4-6°F (25%) -- Then: Below historical average (20%) -- Least likely: Exceeding by 6°F+ (10%) - -6. Time Horizon Impact: -- With resolution just weeks away, current conditions and near-term forecasts should be weighted heavily -- Long-term trends matter less given the short timeframe - -7. Final Calculation: -- Probability of exceeding historical average (Q30677): 80% (sum of all excess scenarios) -- Probability of exceeding by ≥4°F (Q30678): 35% (sum of 4-6°F and >6°F scenarios) -- Probability of exceeding by ≥6°F (Q30679): 10% - -Given the extremely short timeline and the need to avoid overconfidence, these probabilities reflect both the strong warming trend in the Arctic and the inherent uncertainty in short-term weather predictions." -30678,Will the average daily temperature for December 2024 recorded by the Barrow/Port Rogers NWS weather station (on the Arctic coast of Alaska) exceed the 1945-2023 average for December by ≥4 degrees F?,"Predictions: [15, 10, 10, 10, 20] -Median reasoning: Let's analyze this step by step: - -1. Historical Context: -- Arctic temperatures are rising 3x faster than global average -- The region has shown consistent warming trends -- December 2024 is very close (resolution is in less than a month) - -2. Temperature Thresholds Analysis: -- We need to determine likelihood of exceeding historical average by: - * Any amount (Q30677) - * ≥4°F (Q30678) - * ≥6°F (Q30679) - -3. Current Conditions & Near-term Forecasts: -- 2024 is tracking to be the warmest year on record globally -- Arctic sea ice extent is at record lows -- These conditions typically correlate with higher temperatures - -4. Base Rate Consideration: -- Given the extremely short timeframe (December has already started) -- Weather patterns are somewhat set for the immediate future -- Major deviations are less likely in such a short window - -5. Probability Distribution: -- Most likely: Exceeding historical average by 0-4°F (45%) -- Next most likely: Exceeding by 4-6°F (25%) -- Then: Below historical average (20%) -- Least likely: Exceeding by 6°F+ (10%) - -6. Time Horizon Impact: -- With resolution just weeks away, current conditions and near-term forecasts should be weighted heavily -- Long-term trends matter less given the short timeframe - -7. Final Calculation: -- Probability of exceeding historical average (Q30677): 80% (sum of all excess scenarios) -- Probability of exceeding by ≥4°F (Q30678): 35% (sum of 4-6°F and >6°F scenarios) -- Probability of exceeding by ≥6°F (Q30679): 10% - -Given the extremely short timeline and the need to avoid overconfidence, these probabilities reflect both the strong warming trend in the Arctic and the inherent uncertainty in short-term weather predictions." -30679,Will the average daily temperature for December 2024 recorded by the Barrow/Port Rogers NWS weather station (on the Arctic coast of Alaska) exceed the 1945-2023 average for December by ≥6 degrees F?,"Predictions: [15, 10, 10, 10, 20] -Median reasoning: Let's analyze this step by step: - -1. Historical Context: -- Arctic temperatures are rising 3x faster than global average -- The region has shown consistent warming trends -- December 2024 is very close (resolution is in less than a month) - -2. Temperature Thresholds Analysis: -- We need to determine likelihood of exceeding historical average by: - * Any amount (Q30677) - * ≥4°F (Q30678) - * ≥6°F (Q30679) - -3. Current Conditions & Near-term Forecasts: -- 2024 is tracking to be the warmest year on record globally -- Arctic sea ice extent is at record lows -- These conditions typically correlate with higher temperatures - -4. Base Rate Consideration: -- Given the extremely short timeframe (December has already started) -- Weather patterns are somewhat set for the immediate future -- Major deviations are less likely in such a short window - -5. Probability Distribution: -- Most likely: Exceeding historical average by 0-4°F (45%) -- Next most likely: Exceeding by 4-6°F (25%) -- Then: Below historical average (20%) -- Least likely: Exceeding by 6°F+ (10%) - -6. Time Horizon Impact: -- With resolution just weeks away, current conditions and near-term forecasts should be weighted heavily -- Long-term trends matter less given the short timeframe - -7. Final Calculation: -- Probability of exceeding historical average (Q30677): 80% (sum of all excess scenarios) -- Probability of exceeding by ≥4°F (Q30678): 35% (sum of 4-6°F and >6°F scenarios) -- Probability of exceeding by ≥6°F (Q30679): 10% - -Given the extremely short timeline and the need to avoid overconfidence, these probabilities reflect both the strong warming trend in the Arctic and the inherent uncertainty in short-term weather predictions." -30676,"Will be Donald Trump's net favorability rating on December 27, 2024 be less than -6?","Predictions: [40, 15, 15, 15, 20] -Median reasoning: Let's analyze this step by step: - -1. Current Status: -- As of November 21, Trump's net favorability was -6 -- This was his highest rating in 4 years -- Recent improvement from -8.6 on November 4 - -2. Time Considerations: -- Only ~3 weeks until resolution date -- Short timeframe reduces likelihood of major changes -- Holiday period typically has less political news coverage - -3. Historical Analysis: -- Trump's net favorability has been consistently negative -- Recent improvement coincides with election victory -- Previous post-election periods showed temporary bumps - -4. Recent Trends: -- Positive momentum since election -- Improvement from -8.6 to -6 in ~2 weeks -- Rate of improvement likely to slow - -5. Key Factors: -- Post-election bounce effect typically temporary -- Holiday period reduces news cycle impact -- Short timeframe limits potential for major shifts - -6. Distribution Analysis: -- Current -6 represents recent peak -- More room for decline than improvement given historical patterns -- Extreme changes unlikely in short period - -Given the short timeframe and holiday period, dramatic changes are unlikely. The current -6 rating represents a recent peak, and historical patterns suggest some regression is more likely than continued improvement. However, the post-election period could sustain current levels. - -Probability Distribution: -- Less than -6: 45% (some regression likely) -- Between -6 and -4: 40% (current range most likely) -- Greater than -4: 15% (significant improvement unlikely) - -Confidence is moderate due to short timeframe and holiday period reducing volatility." -30675,"Will be Donald Trump's net favorability rating on December 27, 2024 be greater than or equal to -6 and less than or equal to -4?","Predictions: [40, 15, 15, 15, 20] -Median reasoning: Let's analyze this step by step: - -1. Current Status: -- As of November 21, Trump's net favorability was -6 -- This was his highest rating in 4 years -- Recent improvement from -8.6 on November 4 - -2. Time Considerations: -- Only ~3 weeks until resolution date -- Short timeframe reduces likelihood of major changes -- Holiday period typically has less political news coverage - -3. Historical Analysis: -- Trump's net favorability has been consistently negative -- Recent improvement coincides with election victory -- Previous post-election periods showed temporary bumps - -4. Recent Trends: -- Positive momentum since election -- Improvement from -8.6 to -6 in ~2 weeks -- Rate of improvement likely to slow - -5. Key Factors: -- Post-election bounce effect typically temporary -- Holiday period reduces news cycle impact -- Short timeframe limits potential for major shifts - -6. Distribution Analysis: -- Current -6 represents recent peak -- More room for decline than improvement given historical patterns -- Extreme changes unlikely in short period - -Given the short timeframe and holiday period, dramatic changes are unlikely. The current -6 rating represents a recent peak, and historical patterns suggest some regression is more likely than continued improvement. However, the post-election period could sustain current levels. - -Probability Distribution: -- Less than -6: 45% (some regression likely) -- Between -6 and -4: 40% (current range most likely) -- Greater than -4: 15% (significant improvement unlikely) - -Confidence is moderate due to short timeframe and holiday period reducing volatility." -30674,"Will be Donald Trump's net favorability rating on December 27, 2024 be greater than -4?","Predictions: [40, 15, 15, 15, 20] -Median reasoning: Let's analyze this step by step: - -1. Current Status: -- As of November 21, Trump's net favorability was -6 -- This was his highest rating in 4 years -- Recent improvement from -8.6 on November 4 - -2. Time Considerations: -- Only ~3 weeks until resolution date -- Short timeframe reduces likelihood of major changes -- Holiday period typically has less political news coverage - -3. Historical Analysis: -- Trump's net favorability has been consistently negative -- Recent improvement coincides with election victory -- Previous post-election periods showed temporary bumps - -4. Recent Trends: -- Positive momentum since election -- Improvement from -8.6 to -6 in ~2 weeks -- Rate of improvement likely to slow - -5. Key Factors: -- Post-election bounce effect typically temporary -- Holiday period reduces news cycle impact -- Short timeframe limits potential for major shifts - -6. Distribution Analysis: -- Current -6 represents recent peak -- More room for decline than improvement given historical patterns -- Extreme changes unlikely in short period - -Given the short timeframe and holiday period, dramatic changes are unlikely. The current -6 rating represents a recent peak, and historical patterns suggest some regression is more likely than continued improvement. However, the post-election period could sustain current levels. - -Probability Distribution: -- Less than -6: 45% (some regression likely) -- Between -6 and -4: 40% (current range most likely) -- Greater than -4: 15% (significant improvement unlikely) - -Confidence is moderate due to short timeframe and holiday period reducing volatility." -30673,Will New Zealand report <275 whooping cough cases for weeks 51 and 52?,"Predictions: [35, 30, 30, 35, 30] -Median reasoning: Let's analyze this step by step: - -1. CURRENT SITUATION: -- 263 cases were reported over four weeks (mid-Oct to mid-Nov) -- This averages to about 66 cases per week -- An epidemic has been declared, suggesting cases are likely to increase - -2. TEMPORAL CONSIDERATIONS: -- We're forecasting weeks 51-52 (Dec 20-27) -- This is about 4-5 weeks after the epidemic declaration -- Pertussis typically shows exponential growth in early epidemic stages - -3. FACTORS SUGGESTING HIGHER CASES: -- Early stage of declared epidemic -- Historical patterns show rapid spread once declared -- Holiday season could increase transmission -- Winter season in Northern Hemisphere (though summer in NZ) - -4. FACTORS SUGGESTING LOWER CASES: -- Public health response and awareness following declaration -- Vaccination push, especially for vulnerable groups -- Summer in New Zealand may reduce transmission - -5. MATHEMATICAL ANALYSIS: -- Base rate: 66 cases/week -- Conservative growth estimate: 50% increase every 2 weeks -- This suggests ~150 cases/week by weeks 51-52 -- Total projected: ~300 cases - -6. PROBABILITY DISTRIBUTION: -- <275 cases: 25% (lower than expected given epidemic growth) -- 275-375 cases: 45% (most likely range given current trajectory) -- >375 cases: 30% (significant but not majority chance of rapid spread) - -7. CONFIDENCE ADJUSTMENT: -- Given the early stage of the epidemic and uncertainty in growth rate -- Adjusted probabilities to be less extreme -- Accounted for overconfidence bias - -The most likely scenario is 275-375 cases, as this represents reasonable epidemic growth while accounting for public health response. The <275 scenario is less likely but possible if control measures are very effective. The >375 scenario represents rapid spread but is moderated by summer season and control measures." -30672,Will New Zealand report greater than or equal to 275 and less than or equal to 375 whooping cough cases for weeks 51 and 52?,"Predictions: [35, 30, 30, 35, 30] -Median reasoning: Let's analyze this step by step: - -1. CURRENT SITUATION: -- 263 cases were reported over four weeks (mid-Oct to mid-Nov) -- This averages to about 66 cases per week -- An epidemic has been declared, suggesting cases are likely to increase - -2. TEMPORAL CONSIDERATIONS: -- We're forecasting weeks 51-52 (Dec 20-27) -- This is about 4-5 weeks after the epidemic declaration -- Pertussis typically shows exponential growth in early epidemic stages - -3. FACTORS SUGGESTING HIGHER CASES: -- Early stage of declared epidemic -- Historical patterns show rapid spread once declared -- Holiday season could increase transmission -- Winter season in Northern Hemisphere (though summer in NZ) - -4. FACTORS SUGGESTING LOWER CASES: -- Public health response and awareness following declaration -- Vaccination push, especially for vulnerable groups -- Summer in New Zealand may reduce transmission - -5. MATHEMATICAL ANALYSIS: -- Base rate: 66 cases/week -- Conservative growth estimate: 50% increase every 2 weeks -- This suggests ~150 cases/week by weeks 51-52 -- Total projected: ~300 cases - -6. PROBABILITY DISTRIBUTION: -- <275 cases: 25% (lower than expected given epidemic growth) -- 275-375 cases: 45% (most likely range given current trajectory) -- >375 cases: 30% (significant but not majority chance of rapid spread) - -7. CONFIDENCE ADJUSTMENT: -- Given the early stage of the epidemic and uncertainty in growth rate -- Adjusted probabilities to be less extreme -- Accounted for overconfidence bias - -The most likely scenario is 275-375 cases, as this represents reasonable epidemic growth while accounting for public health response. The <275 scenario is less likely but possible if control measures are very effective. The >375 scenario represents rapid spread but is moderated by summer season and control measures." -30671,Will New Zealand report >375 whooping cough cases for weeks 51 and 52?,"Predictions: [35, 30, 30, 35, 30] -Median reasoning: Let's analyze this step by step: - -1. CURRENT SITUATION: -- 263 cases were reported over four weeks (mid-Oct to mid-Nov) -- This averages to about 66 cases per week -- An epidemic has been declared, suggesting cases are likely to increase - -2. TEMPORAL CONSIDERATIONS: -- We're forecasting weeks 51-52 (Dec 20-27) -- This is about 4-5 weeks after the epidemic declaration -- Pertussis typically shows exponential growth in early epidemic stages - -3. FACTORS SUGGESTING HIGHER CASES: -- Early stage of declared epidemic -- Historical patterns show rapid spread once declared -- Holiday season could increase transmission -- Winter season in Northern Hemisphere (though summer in NZ) - -4. FACTORS SUGGESTING LOWER CASES: -- Public health response and awareness following declaration -- Vaccination push, especially for vulnerable groups -- Summer in New Zealand may reduce transmission - -5. MATHEMATICAL ANALYSIS: -- Base rate: 66 cases/week -- Conservative growth estimate: 50% increase every 2 weeks -- This suggests ~150 cases/week by weeks 51-52 -- Total projected: ~300 cases - -6. PROBABILITY DISTRIBUTION: -- <275 cases: 25% (lower than expected given epidemic growth) -- 275-375 cases: 45% (most likely range given current trajectory) -- >375 cases: 30% (significant but not majority chance of rapid spread) - -7. CONFIDENCE ADJUSTMENT: -- Given the early stage of the epidemic and uncertainty in growth rate -- Adjusted probabilities to be less extreme -- Accounted for overconfidence bias - -The most likely scenario is 275-375 cases, as this represents reasonable epidemic growth while accounting for public health response. The <275 scenario is less likely but possible if control measures are very effective. The >375 scenario represents rapid spread but is moderated by summer season and control measures." -30681,"Will Candy Meehan survive the recall vote in Norwood, Colorado, scheduled for December 10, 2024?","To predict whether Candy Meehan will survive the recall vote in Norwood, Colorado, scheduled for December 10, 2024, I must carefully evaluate the information available and consider historical patterns for similar events. This is a political event, which falls under the category of a ""Competition Event."" - -**1. Context and Allegations:** -- The recall petition is centered on allegations of inadequate leadership, violation of the Code of Conduct for Elected Officials, inappropriate use of government funds, and causing strife between local entities. These issues might resonate with voters concerned about governance and ethics. -- Meehan has denied these allegations, framing the recall as a politically motivated attempt by a small group to create division. - -**2. Importance of Allegations:** -- The allegations being civil rather than criminal reduces the intensity of the charges in the eyes of some voters, which might favor Meehan. -- Nevertheless, the ethical concerns can still hold significant sway, depending on public perception and local political dynamics. - -**3. Historical Recalls and Base Rates:** -- Historically, recalls are challenging to succeed but not impossible. The outcome largely depends on local political dynamics, public sentiment, incumbents' popularity, and how well-organized the recall effort is. -- In small communities, such as Norwood, local issues can sometimes lead to a successful recall if there is strong community sentiment against the incumbent. - -**4. Timing and Current Dynamics:** -- There is a short amount of time (one week) until the recall vote, meaning significant shifts in public opinion are less likely to occur suddenly. -- As of now, no clear public opinion polls or expert analyses suggest a decisive outcome. - -**5. Default Scenario and Likelihood Adjustments:** -- The default scenario, if nothing further evolves, is typically that the incumbent survives, as incumbents generally have structural advantages, such as name recognition and established support networks. -- Given that the information available does not strongly indicate a turning of the tide against Meehan and considering past performance biases in such events, the probability of her surviving the recall is weighed towards the status quo. - -**6. Addressing My Overconfidence:** -- It is crucial to account for historical biases where I might have been overconfident. Ensuring I align with a probability distribution that reflects greater uncertainty, rather than leaning heavily towards a specific outcome. - -Overall, weighing these factors together, the incumbency advantage, the non-criminal nature of the allegations, and the limited time for any major shifts, I conclude: - -Probability: 65%" -30680,"Will the state of Delaware's Division of Corporations list OpenAI as an ""Exempt"" entity on December 31, 2024?","Let me think through this step by step: - -1) Current Status: -- OpenAI is currently listed as ""Exempt"" in Delaware -- There are public plans to restructure from non-profit to for-profit -- This is receiving scrutiny from Delaware's AG - -2) Timeline Analysis: -- Today is December 3, 2024 -- Resolution date is December 31, 2024 -- Only 28 days remain -- Corporate restructuring typically takes months, especially with regulatory scrutiny - -3) Factors Supporting Status Quo (Remaining Exempt): -- Corporate changes, especially with AG scrutiny, move slowly -- Holiday season may further slow administrative processes -- Current status is ""Exempt"" -- Short timeframe until resolution - -4) Factors Supporting Change (Not Exempt): -- Clear intention to restructure -- Significant pressure from stakeholders -- Microsoft's large ownership stake -- Delaware AG's involvement suggests active review - -5) Key Considerations: -- The resolution date is very close -- Corporate status changes require formal filings -- Holiday period typically slows administrative processes -- AG scrutiny adds additional complexity - -6) Base Rate Analysis: -- Corporate restructurings typically take 3-6 months minimum -- Regulatory reviews often extend timelines -- End-of-year timing typically slows processes -- Status quo tends to persist in short timeframes - -7) Risk Adjustments: -- High-profile nature might expedite process -- But AG involvement likely prevents rushing -- Holiday season reduces likelihood of major changes - -Given the extremely short timeframe (28 days), current exempt status, typical corporate change timelines, AG scrutiny, and holiday season timing, the most likely outcome is that the status remains unchanged through December 31, 2024. While change is coming, it's unlikely to be completed in this timeframe. - -The presence of AG scrutiny particularly suggests this won't be rushed through before year-end. The holiday season timing further reduces the likelihood of major administrative changes. - -Adjusting for historical overconfidence and the short timeline, but acknowledging uncertainty: - -Probability: 85%" -30670,"Will a ceasefire between Israel and Lebanon/Hezbollah be active on January 1, 2025?","Let me think through this step by step: - -1. Timeline Analysis: -- Current date is December 3, 2024 -- Resolution date is January 1, 2025 -- This is approximately 29 days -- The ceasefire began November 27, 2024 -- The ceasefire is supposed to last 60 days (until late January 2025) - -2. Key Factors: -- The ceasefire is currently active -- It's designed to last well past the resolution date -- There have been minor violations but no major breakdown -- International monitoring is in place -- Both sides have shown restraint despite provocations - -3. Historical Context: -- Previous Israel-Hezbollah ceasefires have been relatively stable -- The 2006 ceasefire has largely held for years -- Winter traditionally sees reduced military activity -- End-of-year period often sees diplomatic pushes for peace - -4. Risk Factors: -- Recent violations could escalate -- Complex withdrawal requirements could cause friction -- Regional tensions remain high -- Historical volatility between parties - -5. Positive Factors: -- International monitoring -- Both sides showing commitment -- Short timeframe to resolution -- Winter conditions favor maintaining ceasefire -- International diplomatic pressure - -6. Timeline Distribution: -- Next week: 85% chance ceasefire holds -- Next two weeks: 80% chance -- By January 1: 75% chance -- By late January: 65% chance -- By March: 50% chance - -7. Uncertainty Considerations: -- Very short time window reduces uncertainty -- Clear resolution criteria -- Reliable monitoring sources -- Current status quo favors maintenance - -8. Risk Adjustments: -- Adjusting down slightly for historical overconfidence -- Accounting for regional volatility -- Considering the specific resolution date falls within agreed ceasefire period -- Factoring in winter seasonal effects - -Given that: -- The resolution date is well within the agreed 60-day ceasefire period -- There's strong international monitoring -- The winter season reduces military activity -- The timeframe is very short -- Both sides have shown restraint -- But accounting for historical volatility and potential for breakdown - -Probability: 75%" -30669,"Will any more of Trump's announced Cabinet picks drop out before January 1, 2025?","Let me think through this step by step: - -1. Timeline Analysis: -- Only 29 days until resolution -- Very short window for changes -- Most cabinet vetting happens early in transition -- Historical precedent shows most withdrawals happen early in process - -2. Current Situation: -- One withdrawal already (Gaetz) -- Multiple controversial picks identified -- Initial vetting phase mostly complete -- Senate confirmation hearings haven't started yet - -3. Historical Base Rates: -- Trump's first transition had relatively few withdrawals -- Most recent presidents had 0-2 withdrawals during transition -- Most withdrawals happened early in process, not final month - -4. Risk Factors: -- Several controversial nominees (Hegseth, Gabbard) -- Intense media scrutiny -- Opposition research actively happening -- Senate confirmation process looming - -5. Mitigating Factors: -- Short timeframe (29 days) -- Most major vetting already done -- Trump team likely learned from Gaetz situation -- Holiday period typically slower for political news -- Replacement nominations get harder closer to inauguration - -6. Probability Distribution Analysis: -- Next 7 days: 8% chance -- Days 8-15: 6% chance -- Days 16-29: 4% chance -- Cumulative probability around 18% - -7. Confidence Adjustment: -- Historical overconfidence suggests widening bands -- Base rate of positive resolutions (35%) suggests moderate probability -- Short timeframe suggests lower probability -- Already had one withdrawal suggests slightly higher probability - -8. Additional Considerations: -- Holiday period reduces media/political activity -- Trump team likely more careful after Gaetz -- Senate not in session reduces immediate pressure -- Time pressure makes finding replacements harder - -After adjusting for overconfidence and considering the very short timeframe, I believe there's a moderate but significant chance of another withdrawal. The holiday period and previous withdrawal slightly reduce the likelihood, but several controversial picks remain under scrutiny. - -Probability: 22%" -30680,"Will the state of Delaware's Division of Corporations list OpenAI as an ""Exempt"" entity on December 31, 2024?","Let me think through this step by step: - -1) Current Status: -- OpenAI is currently listed as ""Exempt"" in Delaware's corporate registry -- This reflects its 501(c)(3) non-profit status -- The resolution date is December 31, 2024, less than 4 weeks away - -2) Announced Changes: -- OpenAI has publicly announced plans to transition to a for-profit structure -- This would typically mean losing ""Exempt"" status -- However, the actual implementation timeline is unclear - -3) Timeline Analysis: -- The Reuters article about removing non-profit control is from September -- Three months have passed without the change being implemented -- Only 4 weeks remain until resolution -- Corporate structure changes, especially involving non-profits, typically take months -- End of year/holiday period typically slows corporate filings - -4) Process Considerations: -- Changing corporate structure requires: - * Board approval (already likely obtained) - * Legal documentation - * IRS notifications - * State filing updates - * Potential regulatory review - -5) Base Rate Analysis: -- Non-profit to for-profit conversions are relatively rare -- When they do occur, the process typically takes 6-12 months -- End-of-year corporate status changes are less common due to tax implications - -6) Risk Factors: -- OpenAI has shown ability to move quickly on major changes -- Recent leadership crisis might have delayed implementation -- Delaware is generally efficient with corporate filings -- But holiday period could slow processing - -7) Key Timing Considerations: -- With less than 4 weeks remaining: - * Documentation would need to be complete - * Filings would need to be processed - * Status would need to be updated in system - -Given: -- The very short timeframe remaining -- Current ""Exempt"" status -- Typical processing times -- Holiday period slowdown -- Complexity of conversion process - -The base case strongly favors maintaining current status through December 31, though plans are in motion for eventual change. The extremely short timeline until resolution makes any change unlikely to be completed in time. - -Probability: 85%" -30741,"Will the US national average retail price of regular gasoline be less than $3.020 for the week of December 9, 2024?","Predictions: [5, 15, 15, 15, 5] -Median reasoning: Let's analyze this step by step: - -1. Current Situation: -- Most recent data shows gas prices around $3.01/gallon -- Strong downward trend over past weeks -- Historically low seasonal demand -- High inventory levels (+4.6M barrels) - -2. Time Horizon: -- Resolution is for week of Dec 9, just 5 days away -- Very short timeframe means current prices are highly predictive -- Weekly price changes typically small (<$0.05) unless major event - -3. Key Factors: -- Winter seasonality typically keeps prices lower -- High inventories suggest continued price stability -- OPEC+ cuts somewhat balanced by high US production -- No major supply disruptions on horizon - -4. Statistical Analysis: -- Current price $3.01 is just below target range -- Weekly volatility typically +/- $0.03 -- Given 5-day timeline, dramatic moves unlikely -- 68% of price moves within one standard deviation - -5. Range Analysis: -- Target range $3.020-$3.080 is narrow ($0.06) -- Current price very close to lower bound -- Small upward move would put price in range -- Large moves unlikely in short term - -6. Probability Distribution: -- Below $3.020: 45% - Current price below range, momentum down -- $3.020-$3.080: 40% - Narrow but achievable range -- Above $3.080: 15% - Would require significant catalyst - -Given very short timeframe, current price level, and seasonal factors, prices are most likely to either stay near current levels (below range) or drift slightly higher into range. Significant upward moves are least likely scenario." -30742,"Will the US national average retail price of regular gasoline between $3.020 and $3.080 (inclusive) for the week of December 9, 2024?","Predictions: [5, 15, 15, 15, 5] -Median reasoning: Let's analyze this step by step: - -1. Current Situation: -- Most recent data shows gas prices around $3.01/gallon -- Strong downward trend over past weeks -- Historically low seasonal demand -- High inventory levels (+4.6M barrels) - -2. Time Horizon: -- Resolution is for week of Dec 9, just 5 days away -- Very short timeframe means current prices are highly predictive -- Weekly price changes typically small (<$0.05) unless major event - -3. Key Factors: -- Winter seasonality typically keeps prices lower -- High inventories suggest continued price stability -- OPEC+ cuts somewhat balanced by high US production -- No major supply disruptions on horizon - -4. Statistical Analysis: -- Current price $3.01 is just below target range -- Weekly volatility typically +/- $0.03 -- Given 5-day timeline, dramatic moves unlikely -- 68% of price moves within one standard deviation - -5. Range Analysis: -- Target range $3.020-$3.080 is narrow ($0.06) -- Current price very close to lower bound -- Small upward move would put price in range -- Large moves unlikely in short term - -6. Probability Distribution: -- Below $3.020: 45% - Current price below range, momentum down -- $3.020-$3.080: 40% - Narrow but achievable range -- Above $3.080: 15% - Would require significant catalyst - -Given very short timeframe, current price level, and seasonal factors, prices are most likely to either stay near current levels (below range) or drift slightly higher into range. Significant upward moves are least likely scenario." -30740,"Will the US national average retail price of regular gasoline be greater than $3.080 for the week of December 9, 2024?","Predictions: [5, 15, 15, 15, 5] -Median reasoning: Let's analyze this step by step: - -1. Current Situation: -- Most recent data shows gas prices around $3.01/gallon -- Strong downward trend over past weeks -- Historically low seasonal demand -- High inventory levels (+4.6M barrels) - -2. Time Horizon: -- Resolution is for week of Dec 9, just 5 days away -- Very short timeframe means current prices are highly predictive -- Weekly price changes typically small (<$0.05) unless major event - -3. Key Factors: -- Winter seasonality typically keeps prices lower -- High inventories suggest continued price stability -- OPEC+ cuts somewhat balanced by high US production -- No major supply disruptions on horizon - -4. Statistical Analysis: -- Current price $3.01 is just below target range -- Weekly volatility typically +/- $0.03 -- Given 5-day timeline, dramatic moves unlikely -- 68% of price moves within one standard deviation - -5. Range Analysis: -- Target range $3.020-$3.080 is narrow ($0.06) -- Current price very close to lower bound -- Small upward move would put price in range -- Large moves unlikely in short term - -6. Probability Distribution: -- Below $3.020: 45% - Current price below range, momentum down -- $3.020-$3.080: 40% - Narrow but achievable range -- Above $3.080: 15% - Would require significant catalyst - -Given very short timeframe, current price level, and seasonal factors, prices are most likely to either stay near current levels (below range) or drift slightly higher into range. Significant upward moves are least likely scenario." -30737,Will November 2024 have a global temperature increase of >1.31 degrees?,"Predictions: [15, 5, 15, 20, 20] -Median reasoning: Let's analyze this step by step: - -1. This is a Non-Market Threshold Value Event about global temperature anomalies. - -2. Current Context: -- We're trying to predict November 2024 temperature anomaly -- Currently in early December 2024 -- Resolution will be based on NOAA's official data -- The three ranges are: <1.25°, 1.25-1.31°, and >1.31° - -3. Key Factors: -- 2024 has been exceptionally warm, likely to be warmest year on record -- El Niño conditions have been contributing to high temperatures -- Recent months have shown anomalies well above 1.31° -- La Niña might be developing, which could moderate temperatures - -4. Analysis: -- Recent temperature anomalies have been very high, often exceeding 1.5°C -- The specified range (1.25-1.31°) is quite narrow (only 0.06°C) -- Given recent trends and the extremely narrow range, it's more likely the temperature will be above 1.31° -- Historical variability suggests some chance of lower temperatures - -5. Adjusting for Time Frame: -- With November 2024 already past, we're just waiting for official data -- This eliminates most uncertainty about future changes -- The delay in data reporting shouldn't affect the outcome - -6. Statistical Distribution: -- Recent months suggest a right-skewed distribution -- The narrow middle range (1.25-1.31°) represents about 15% of likely outcomes -- Values above 1.31° are most likely given recent trends (~70%) -- Values below 1.25° are less likely but possible (~15%) - -7. Confidence Level: -- High confidence in the overall distribution given recent data -- The resolution criteria are very specific and based on a reliable source - -Therefore: -- P(>1.31°) = 70% -- P(1.25-1.31°) = 15% -- P(<1.25°) = 15%" -30738,Will November 2024 have a global temperature increase of <1.25 degrees?,"Predictions: [15, 5, 15, 20, 20] -Median reasoning: Let's analyze this step by step: - -1. This is a Non-Market Threshold Value Event about global temperature anomalies. - -2. Current Context: -- We're trying to predict November 2024 temperature anomaly -- Currently in early December 2024 -- Resolution will be based on NOAA's official data -- The three ranges are: <1.25°, 1.25-1.31°, and >1.31° - -3. Key Factors: -- 2024 has been exceptionally warm, likely to be warmest year on record -- El Niño conditions have been contributing to high temperatures -- Recent months have shown anomalies well above 1.31° -- La Niña might be developing, which could moderate temperatures - -4. Analysis: -- Recent temperature anomalies have been very high, often exceeding 1.5°C -- The specified range (1.25-1.31°) is quite narrow (only 0.06°C) -- Given recent trends and the extremely narrow range, it's more likely the temperature will be above 1.31° -- Historical variability suggests some chance of lower temperatures - -5. Adjusting for Time Frame: -- With November 2024 already past, we're just waiting for official data -- This eliminates most uncertainty about future changes -- The delay in data reporting shouldn't affect the outcome - -6. Statistical Distribution: -- Recent months suggest a right-skewed distribution -- The narrow middle range (1.25-1.31°) represents about 15% of likely outcomes -- Values above 1.31° are most likely given recent trends (~70%) -- Values below 1.25° are less likely but possible (~15%) - -7. Confidence Level: -- High confidence in the overall distribution given recent data -- The resolution criteria are very specific and based on a reliable source - -Therefore: -- P(>1.31°) = 70% -- P(1.25-1.31°) = 15% -- P(<1.25°) = 15%" -30739,Will November 2024 have a global temperature increase of greater than or equal to 1.25 degrees and less than or equal to 1.31 degrees?,"Predictions: [15, 5, 15, 20, 20] -Median reasoning: Let's analyze this step by step: - -1. This is a Non-Market Threshold Value Event about global temperature anomalies. - -2. Current Context: -- We're trying to predict November 2024 temperature anomaly -- Currently in early December 2024 -- Resolution will be based on NOAA's official data -- The three ranges are: <1.25°, 1.25-1.31°, and >1.31° - -3. Key Factors: -- 2024 has been exceptionally warm, likely to be warmest year on record -- El Niño conditions have been contributing to high temperatures -- Recent months have shown anomalies well above 1.31° -- La Niña might be developing, which could moderate temperatures - -4. Analysis: -- Recent temperature anomalies have been very high, often exceeding 1.5°C -- The specified range (1.25-1.31°) is quite narrow (only 0.06°C) -- Given recent trends and the extremely narrow range, it's more likely the temperature will be above 1.31° -- Historical variability suggests some chance of lower temperatures - -5. Adjusting for Time Frame: -- With November 2024 already past, we're just waiting for official data -- This eliminates most uncertainty about future changes -- The delay in data reporting shouldn't affect the outcome - -6. Statistical Distribution: -- Recent months suggest a right-skewed distribution -- The narrow middle range (1.25-1.31°) represents about 15% of likely outcomes -- Values above 1.31° are most likely given recent trends (~70%) -- Values below 1.25° are less likely but possible (~15%) - -7. Confidence Level: -- High confidence in the overall distribution given recent data -- The resolution criteria are very specific and based on a reliable source - -Therefore: -- P(>1.31°) = 70% -- P(1.25-1.31°) = 15% -- P(<1.25°) = 15%" -30744,"Will Dogecoin close at below $0.50 a share every day before January 1, 2025?","Let me think through this step by step: - -1. First, let's consider the timeframe: -- Only about 4 weeks until resolution -- Very short window for major price movements -- Recent high of $0.43457 still well below threshold -- Current price around $0.40 - -2. Historical context: -- Dogecoin has only briefly exceeded $0.50 a few times in its history -- Most notably during the 2021 crypto bull run -- Current bull market hasn't pushed it near those levels yet -- Even with Trump-related surge, stayed below $0.45 - -3. Technical analysis: -- Recent failure to break $0.50 resistance -- Significant drop in open interest ($380M) -- Current market showing signs of correction -- Extreme greed index suggests potential for pullback - -4. Time-based probability distribution: -- Next week: 95% chance stays below $0.50 -- Next two weeks: 92% chance -- Until Jan 1: 88% chance -- If timeline was longer (6 months): 70% chance - -5. Market factors: -- Crypto typically less volatile during holiday season -- No major catalysts expected before year-end -- Recent Trump-related surge already priced in -- Technical resistance at $0.50 proven strong - -6. Risk factors: -- Crypto markets can be highly volatile -- Unexpected news could cause spikes -- Meme coin susceptibility to social media -- Whale movements can cause quick jumps - -7. Confidence adjustments: -- Short timeframe increases confidence -- Recent failed breakout attempt is significant -- Multiple resistance tests make further attempts less likely -- Need to account for some uncertainty - -Given the very short timeframe, recent failed attempt to break $0.50, and typical year-end market dynamics, it's highly likely Dogecoin will stay below $0.50 through January 1. However, need to account for crypto's inherent volatility and possibility of unexpected events. - -Probability: 88%" -30743,"Will the Alpental ski resort be listed as Open by On the Snow's Ski Report on December 6, 2024?","Let me think through this step by step: - -1. Current Situation: -- Today is December 4, 2024 -- Resolution date is December 6, 2024 (just 2 days away) -- Alpental is currently closed -- This is a Non-Market Threshold Value Event - -2. Historical Data: -- 10-year average opening date is December 12 -- Needs significant snow base to open, especially compared to other Summit areas -- Summit West typically opens first -- Current snow depth is insufficient for opening - -3. Time Analysis: -- Only 2 days until resolution -- Weather patterns and snow accumulation typically don't change dramatically in 48 hours -- Major snowstorms would need to occur immediately to change current trajectory - -4. Critical Factors: -- Current snow base is well below operating requirements -- No immediate major snowstorms forecasted -- Opening requires not just snow accumulation but also preparation time -- Resort typically announces openings in advance - -5. Base Rate Consideration: -- Historical December 6 openings would be earlier than average -- La Niña conditions might help but impact is more long-term -- 2 days is extremely short for conditions to change sufficiently - -6. Uncertainty Factors: -- Weather can be unpredictable -- Snow-making capabilities could supplement natural snow -- Resort might push for early opening due to business pressures - -7. Resolution Criteria Analysis: -- Binary outcome (Open/Closed on specific website) -- Very specific date and time -- No ambiguity in resolution criteria - -8. Timeline Distribution: -- Probability of opening by Dec 6: Very low due to current conditions -- Probability of opening by Dec 12: Moderate (historical average) -- Probability of opening by Dec 19: Higher -- Probability of opening by Dec 26: Much higher - -Given the extremely short timeframe (2 days), current insufficient snow conditions, typical preparation time needed, and the historical average opening being December 12, the probability of Alpental being open on December 6 is very low. The 2-day window is particularly constraining, as even if perfect conditions manifested immediately, there's minimal time for preparation. - -Adjusting for historical overconfidence but recognizing the extremely short timeline and current conditions, I'll set a low but not minimum probability. - -Probability: 8%" -30736,"Will Daniel Penny be convicted in connection with the death of Jordan Neely before January 1, 2025?","To forecast whether Daniel Penny will be convicted in connection with the death of Jordan Neely by January 1, 2025, I must carefully evaluate the current state of the trial and consider base rates for similar cases. Here are my considerations: - -1. **Current Status of the Trial:** - - The jury in Daniel Penny's trial is currently deliberating. - - As of December 4, 2024, the jury requested additional evidence such as video footage, indicating they are evaluating the case in depth. This suggests they are taking their decision seriously and considering all available evidence. - - The charges against Penny are serious: manslaughter and criminally negligent homicide. Both require proving that Penny's actions were reckless or negligent and resulted in Neely's death. - -2. **Timeline Considerations:** - - With less than a month until the resolution date, the window for a conviction is narrow. Jury deliberations have commenced, meaning the case could resolve relatively quickly, but the holiday season may impact deliberation schedules. - - If the jury does not reach a conclusion by year-end, or if they deadlock, the resolution will be ""No."" A hung jury is a possible outcome in high-stakes cases with strong public opinions. - -3. **Precedents and Base Rates:** - - Historically, convictions in high-profile, contentious cases can be challenging, with outcomes often leaning toward acquittals or no-decisions if there's substantial doubt about the actions and intentions. This is worth considering given the divided nature of the arguments (self-defense vs. recklessness). - - The nature of the charges (manslaughter and criminally negligent homicide) don't always lead to swift convictions unless there is clear evidence and legal alignment on the interpretation of Penny's actions. - -4. **Public and Juror Pressure:** - - There is significant public interest in this case, which may influence the jury’s urgency to arrive at a verdict. - - Media coverage and societal debates surrounding the case can add pressure to deliver a verdict, but do not necessarily predict the jury's actual conclusion. - -5. **My Past Biases and Correction:** - - Acknowledge my tendency to be overconfident in positive resolutions. Given the historical data that only about 35% of questions resolve positively, I must adjust my expectations to lean more toward the possibility of no conviction by the deadline. - -**Probability Assessments:** -- **Conviction by January 1, 2025:** Given the jury is already deliberating, there is some non-negligible chance of a quick verdict, but based on the complexity and historical patterns in similar cases, I estimate the likelihood at 35%. -- **No Conviction by January 1, 2025:** Considering the possibilities of a hung jury, lengthy deliberations, or acquittal, this remains the more likely scenario. - -My final answer balances these considerations and accounts for potential overconfidence by skewing the probability toward a less positive outcome than initially instinctive, in light of past patterns and the remaining short timeframe. - -**Probability: 35%**" -30735,"Will former First Lady of Pakistan Bushra Bibi be arrested before January 1, 2025?","To forecast the probability of Bushra Bibi being arrested before January 1, 2025, let's break down the situation based on the information provided, considering relevant factors and base rates: - -### Current Situation - -1. **Arrest Warrants and Legal Proceedings:** - - Multiple arrest warrants, including non-bailable ones, have been issued against Bushra Bibi for various charges, such as inciting violence and a £190 million case. - - A specific indictment date was set for a case regarding alleged illegal sale of gifts. These legal actions suggest significant momentum towards the possibility of arrest. - -2. **Political Context:** - - Political instability in Pakistan is notable, as indicated by the protest activity and unrest related to former Prime Minister Imran Khan’s detention. - - The issuance of these warrants by both anti-terrorism and accountability courts indicates serious legal challenges. - -3. **Timeframe:** - - The timeframe is relatively short, less than a month until the resolution date. Given the current legal scenario, there's pressure that might accelerate actions such as an arrest. - -### Analysis of Probability - -- **Legal Environment:** Given the multiple warrants and ongoing cases, the legal environment is intense and favors a high likelihood of arrest. The non-bailable nature of the warrants suggests a non-negligible immediate threat. - -- **Base Rates and Precedents:** Historically, high-profile political figures often evade arrest for extended periods due to political maneuvering and interventions. However, the issuance of non-bailable warrants and indictments implies stronger commitment from authorities. - -- **Time Limitation:** The short timeframe means any developments must occur within about four weeks. The closer the timeframe, the lesser the probability of unlikely events occurring, but with active warrants, this is less relevant. - -### Calculation - -1. **Short-Term Estimate:** Given the multiple active warrants and the urgent legal proceedings, I estimate a relatively high probability that an arrest could occur this month. - -2. **Consideration for Base Rates and Uncertainties:** Adjusting for historical trends in the region and my own tendency for overconfidence, I add caution to the likelihood of an arrest being immediate. While legal strategies might delay immediate arrest, the situation's ongoing nature cannot be ignored. - -3. **Final Probability Estimate:** - -Considering the active warrants, political dynamics, short timeframe, and historical arrest patterns, the probability of Bushra Bibi being arrested before January 1, 2025 is: - -Probability: 65%" -30743,"Will the Alpental ski resort be listed as Open by On the Snow's Ski Report on December 6, 2024?","Let me think through this step by step: - -1. Current Status: -- Resort is currently closed -- No announced opening date -- We're looking at just 1 day from now for resolution - -2. Historical Context: -- 10-year average opening date is December 12 -- December 6 is earlier than average opening -- Current snow depths are insufficient for typical operations - -3. Weather and Conditions: -- La Niña conditions suggest potentially favorable snow conditions -- However, immediate forecasts don't indicate massive snowfall -- Takes time to prepare slopes even after sufficient snow - -4. Operational Considerations: -- Needs significant base depth for safety -- Requires staff preparation and training -- Usually announces openings in advance -- Current depths (11 inches at base) well below typical operating requirements - -5. Timeline Analysis: -- Resolution is tomorrow -- Extremely short window for conditions to change -- No current announcement of pending opening -- Typically requires several days notice to prepare for opening - -6. Risk Adjustment: -- Base case is currently ""closed"" -- Extremely short timeline (1 day) makes change from status quo unlikely -- Historical precedent suggests early December openings are possible but not common -- Need to adjust for typical overconfidence - -7. Probability Distribution: -- If looking at next week: 15% -- If looking at next two weeks: 40% -- If looking at by end of December: 75% - -For tomorrow specifically, given: -- No current announcement -- Insufficient snow depth -- Typical advance notice needed -- Earlier than average date -- Just one day window - -The probability needs to be very low. While early December openings do happen, the combination of current conditions and extremely short timeline make this highly unlikely. However, need to leave some probability for unexpected developments. - -Probability: 3%" -30783,"Will Xi Jinping continue leading China through January 1, 2025?","Given the context of this question and historical precedence, let's carefully analyze the current situation regarding Xi Jinping's leadership in China. - -**Default Situation:** -Xi Jinping has recently secured an unprecedented third term, as reported by multiple sources such as Telegrafi, Foreign Analysis, and Foreign Policy. This consolidates his position as China's leader. The CCP’s structure and current political climate show a strong concentration of power around Xi, further supported by his previous successful terms. Historically, regime changes or leadership disruptions in China at this level of consolidation under single-party rule are highly rare without major internal or external disruptions. - -**Timeframe Analysis:** -With less than a month until January 1, 2025, the timeframe for any major shift is exceedingly tight. Political systems, especially those with strong centralized control like China's, resist rapid leadership changes unless triggered by extreme events such as a coup, health issues, or large-scale societal unrest. With this short timeframe, the probability of such destabilizing events occurring is notably low. - -**Current Political Climate:** -Reports from credible sources, including Foreign Analysis and Oxford Analytica, indicate that Xi Jinping continues to focus on consolidating his power and has plans for a potential fourth term, suggesting no immediate threat to his leadership. The new Politburo Standing Committee is filled with his loyalists, providing further insulation against destabilization. - -**Recent Events:** -Recent articles like those from VOA and Foreign Policy emphasize the focus on economic challenges and international relations, such as U.S. tariffs, but there is no information pointing to an imminent threat to Xi’s position. These issues, while significant, are aspects of governance that further drive the need for a stable and consistent leadership approach. - -**Base Case and Unlikely Scenarios:** -Historically, the probability of a sudden leadership change within a concentrated power structure of a one-party state, absent significant turmoil, is very low. Any event that could challenge Xi's leadership, such as health crises or political coups, are outliers rather than predictable trends, with even less probability of transition occurring within the next 26 days. - -Considering all these aspects: - -- The likelihood of Xi Jinping continuing to lead China through January 1, 2025, is extremely high given the current political consolidation, lack of immediate threats, and short timeframe. - -Therefore, the probability percentage reflecting Xi Jinping's continuance in leadership through the specified date should be set with high confidence, after accounting for historical overconfidence in forecasts as well. - -Final answer: ""Probability: 98%""" -30782,"Will Emmanuel Macron be President of France on December 31, 2024?","Let me think through this step by step: - -1. First, consider the timeline: -- Resolution is December 31, 2024 -- Only 26 days from current date -- This is an extremely short timeframe for a presidential change - -2. Current situation: -- Macron has a constitutional mandate until 2027 -- No formal mechanism exists to force his resignation -- Current crisis is about PM/government, not presidency directly -- Macron has explicitly stated he won't resign - -3. Historical precedents: -- No French president has ever resigned due to political pressure -- De Gaulle resigned in 1969, but that was voluntary and planned -- French system designed for presidential stability -- Government/PM changes are common, presidential changes are not - -4. Mechanisms for removal: -- Impeachment requires proof of serious misconduct -- No current allegations rise to this level -- Voluntary resignation only realistic path -- Health issues always possible but no indication - -5. Risk factors: -- Political crisis is serious but primarily affects government -- Protests/unrest possible but historically don't force presidents out -- No indication of personal scandal or health issues -- Economic challenges present but not extreme - -6. Time consideration: -- 26 days is extremely short for any major change -- Holiday period typically sees reduced political activity -- Most government offices partially closed late December - -7. Mathematical analysis: -- Base rate of presidential resignations: extremely low -- Probability needs to be very high given short timeframe -- Even if crisis worsens, institutional inertia favors stability -- Must account for ""black swan"" events but with low probability - -8. Confidence adjustment: -- Short timeframe increases confidence -- Clear resolution criteria -- Strong institutional factors -- Well-documented situation - -Given the extremely short timeframe, clear constitutional position, historical precedents, and Macron's explicit statements, combined with the holiday period and institutional stability, I assess a very high probability he remains president. - -However, I must account for some small probability of unexpected events (health, personal decision, extreme crisis escalation). - -Probability: 98%" -30781,"Will Bashar al-Assad be President of Syria on December 31, 2024?","Let me think through this step by step: - -1) Time Horizon Assessment: -- Only 26 days until resolution date -- Very short timeframe for major regime change -- Winter conditions typically slow military operations - -2) Current Situation Analysis: -- Unprecedented rebel gains in past week -- Capture of Aleppo is significant -- Assad still controls Damascus and coastal regions -- Has backing of Russia and Iran - -3) Historical Context: -- Assad has survived multiple serious challenges since 2011 -- Previous rebel advances have often been reversed -- International backing has been crucial to regime survival -- Winter offensives historically less successful - -4) Specific Recent Developments: -- Loss of Aleppo is severe blow -- Iran's FM visit shows continued support -- Russian involvement remains significant -- Opposition gains are rapid but recent - -5) Base Rate Consideration: -- Dictators rarely fall within 30-day windows -- When they do fall, it's usually after longer periods of decline -- Winter is traditionally regime-favorable season -- Regional powers typically prevent sudden collapse - -6) Institutional Factors: -- Assad retains control of state apparatus -- Still has significant military forces -- Controls capital and key regions -- International recognition remains - -7) Short-Term Variables: -- Weather limiting major operations -- International intervention possible -- Opposition coordination challenges -- Regime consolidation potential - -Given the extremely short timeframe (26 days), despite the serious setbacks, Assad's long history of survival, continued international backing, and control of key institutions suggest he is more likely than not to remain in power through December 31. The recent losses are severe but the timeframe is too short for complete regime collapse, barring assassination or voluntary departure, which seem unlikely. - -However, the situation is more precarious than any time in recent years, and the speed of recent losses is concerning for regime stability. The base rate of dictator survival in 30-day windows, even during crises, is high, but this situation warrants significant uncertainty. - -Adjusting for historical overconfidence and the recent dramatic developments, while still weighing the extremely short timeframe heavily: - -Probability: 78%" -30780,Will Miami have at least 5 days of rain in December 2024?,"Let me think through this step by step: - -1) First, let's consider the time frame: -- It's December 5th, and we need 5 days of rain in the remaining 26 days of December -- This is a relatively short-term forecast, so we should weigh current conditions heavily - -2) Historical context for Miami in December: -- Miami typically experiences its dry season from December through April -- December averages about 2.5 inches of rain -- Usually has 6-8 rainy days in December historically -- But there's significant variance year to year - -3) Current conditions: -- Miami is currently ""Abnormally Dry"" according to Drought.gov -- The Almanac predicts below-average precipitation -- Current forecasts show mostly sunny conditions in the near term - -4) Weighing factors: -POSITIVE: -- Historical base rate suggests 6-8 rainy days is normal -- Even in dry season, Miami can get afternoon thunderstorms -- Still 26 days remaining in the month - -NEGATIVE: -- Current drought conditions -- Below average precipitation predicted -- We're in El Niño, which typically means drier winters in Florida -- Already 5 days into December with no significant rain - -5) Statistical consideration: -- Need 5 days out of remaining 26 (about 19% of days) -- Historical December average suggests this should be achievable -- But current conditions suggest below normal precipitation - -6) Timeline analysis: -- If we extended this to January, probability would increase to ~65% -- If this was just for next week, probability would be ~15% -- For full December, need to account for current conditions while recognizing plenty of time remains - -7) Confidence adjustment: -- Given the short timeframe and clear resolution criteria -- But also accounting for weather variability -- Need to avoid overconfidence given past bias - -Given the dry conditions, El Niño influence, and forecasts for below-average precipitation, but balanced against the historical frequency of December rain in Miami and the relatively achievable threshold of just 5 days, I will predict lower than historical average but not extremely low. - -Probability: 42%" -30779,"Will Yoon Suk Yeol be President of South Korea on December 31, 2024?","Let me think through this step by step: - -1) First, let's consider the time frame: -- Only 26 days until resolution -- Very short window for major changes -- Institutional stability typically high for presidential positions - -2) Current situation analysis: -- Yoon Suk Yeol is currently president -- Has declared martial law (significant escalation) -- Facing calls for resignation -- Thousands of professors protesting - -3) Historical precedents: -- South Korean presidents have resigned before (Park Geun-hye) -- But such transitions typically take months/years -- Martial law declarations often strengthen short-term position -- Most calls for resignation don't result in actual resignation - -4) Institutional factors: -- South Korea has strong democratic institutions -- Constitutional process for removal is complex -- Martial law gives president additional powers -- Opposition needs super-majority for impeachment - -5) Timeline considerations: -- 26 days is very short for constitutional removal -- Resignation would require voluntary action -- Protests typically take months to force change -- Year-end period often sees reduced political activity - -6) Risk factors: -- Martial law could backfire -- Military could theoretically intervene -- Public protests could escalate -- International pressure could increase - -7) Stabilizing factors: -- Institutional inertia -- Complex removal process -- Holiday period approaching -- Short timeframe -- Martial law powers - -8) Probability distribution analysis: -- Base case (stays in power): ~90% -- Voluntary resignation: ~5% -- Forced removal: ~3% -- Other scenarios: ~2% - -Given the extremely short timeframe (26 days), current institutional control (including martial law powers), and historical precedents of how long presidential removals typically take, even in crisis situations, I assess that while the situation is serious, the likelihood of Yoon not being president by December 31 is relatively low. - -The martial law declaration, while concerning, actually strengthens his short-term position, even if it might weaken it long-term. - -Adjusting for historical overconfidence and the tendency for questions to resolve negatively, I'll reduce my initial assessment somewhat, but still maintain a high probability given the short timeframe. - -Probability: 87%" -30778,"Will Ghana declare a winner in its presidential election before January 1, 2025?","Let me think through this step by step: - -1. First, let's establish the key timeline: -- Today is December 5, 2024 -- Election is scheduled for December 7, 2024 -- Resolution deadline is January 1, 2025 -- This gives approximately 3.5 weeks for declaration after election - -2. Historical precedent: -- Ghana has generally declared presidential election winners within 72 hours of voting -- 2020 election results were declared on December 9 (2 days after voting) -- 2016 results were declared within 3 days -- Ghana has a strong democratic tradition in West Africa - -3. Risk factors for delay: -- Current political environment is more polarized than usual -- Economic conditions are poor, increasing tension -- Opposition leader has specifically mentioned only accepting ""transparent and fair"" results -- Both parties are treating this as ""do-or-die"" -- Polls show a relatively clear leader (reducing dispute likelihood) - -4. Timeline analysis: -- Even with significant disputes, having over 3 weeks provides substantial buffer -- A delay beyond January 1 would be highly unusual -- Most likely declaration timeline would be December 9-10 -- Worst case scenarios still likely resolve within 2 weeks - -5. Mitigating factors: -- Electoral Commission has experience managing disputes -- International pressure would mount for timely declaration -- Current polls showing clear leader reduces risk of prolonged disputes -- Ghana's democratic institutions remain relatively strong - -6. Probability distribution across timeframes: -- Within 3 days (by Dec 10): 65% -- Within 1 week (by Dec 14): 80% -- Within 2 weeks (by Dec 21): 90% -- By January 1: 93% - -Adjusting for historical overconfidence and the current political climate, I need to reduce my confidence slightly. The base rate strongly favors declaration within the timeline, but there are legitimate risk factors this cycle. - -Probability: 91%" -30834,"Will the closing price of CAC 40 be less than 7,200 on December 31, 2024?","Predictions: [35, 45, 45, 25, 30] -Median reasoning: Let's analyze this step by step: - -1. Current Context: -- CAC 40 has shown resilience despite political turbulence -- Recent market forecasts predict a closing value around 7481 for December 2024 -- Only about 3 weeks until resolution -- Political instability with PM collapse, but markets have largely priced this in - -2. Technical Analysis: -- Recent trading patterns show support above 7200 -- Market has been relatively stable despite political uncertainty -- Short timeframe (3 weeks) reduces likelihood of major shifts - -3. Historical Context: -- Markets typically show lower volatility during end-of-year period -- Current political uncertainty already priced into market expectations - -4. Time Consideration: -- With only 3 weeks until resolution, dramatic shifts are less likely -- Year-end trading volumes typically lower, reducing volatility - -5. Distribution Analysis: -- Current level suggests highest probability of staying in 7200-7400 range -- Less likely to drop below 7200 given current support levels -- Moderate chance of exceeding 7400 given upward momentum - -6. Probability Distribution: -- Below 7200: ~20% (lower probability due to current support levels) -- 7200-7400: ~45% (highest probability given current trading range) -- Above 7400: ~35% (significant chance given upward momentum) - -7. Confidence Adjustment: -- Short timeframe increases confidence in current range -- Political uncertainty adds some downside risk -- Year-end effects typically favor stability - -Overall, the most likely scenario is the CAC 40 staying within 7200-7400 range, with a moderate chance of exceeding 7400 and lower probability of falling below 7200." -30833,"Will the closing price of CAC 40 be less than or equal to 7,400 and greater than or equal to 7,200 on December 31, 2024?","Predictions: [35, 45, 45, 25, 30] -Median reasoning: Let's analyze this step by step: - -1. Current Context: -- CAC 40 has shown resilience despite political turbulence -- Recent market forecasts predict a closing value around 7481 for December 2024 -- Only about 3 weeks until resolution -- Political instability with PM collapse, but markets have largely priced this in - -2. Technical Analysis: -- Recent trading patterns show support above 7200 -- Market has been relatively stable despite political uncertainty -- Short timeframe (3 weeks) reduces likelihood of major shifts - -3. Historical Context: -- Markets typically show lower volatility during end-of-year period -- Current political uncertainty already priced into market expectations - -4. Time Consideration: -- With only 3 weeks until resolution, dramatic shifts are less likely -- Year-end trading volumes typically lower, reducing volatility - -5. Distribution Analysis: -- Current level suggests highest probability of staying in 7200-7400 range -- Less likely to drop below 7200 given current support levels -- Moderate chance of exceeding 7400 given upward momentum - -6. Probability Distribution: -- Below 7200: ~20% (lower probability due to current support levels) -- 7200-7400: ~45% (highest probability given current trading range) -- Above 7400: ~35% (significant chance given upward momentum) - -7. Confidence Adjustment: -- Short timeframe increases confidence in current range -- Political uncertainty adds some downside risk -- Year-end effects typically favor stability - -Overall, the most likely scenario is the CAC 40 staying within 7200-7400 range, with a moderate chance of exceeding 7400 and lower probability of falling below 7200." -30832,"Will the closing price of CAC 40 be greater than 7,400 on December 31, 2024?","Predictions: [35, 45, 45, 25, 30] -Median reasoning: Let's analyze this step by step: - -1. Current Context: -- CAC 40 has shown resilience despite political turbulence -- Recent market forecasts predict a closing value around 7481 for December 2024 -- Only about 3 weeks until resolution -- Political instability with PM collapse, but markets have largely priced this in - -2. Technical Analysis: -- Recent trading patterns show support above 7200 -- Market has been relatively stable despite political uncertainty -- Short timeframe (3 weeks) reduces likelihood of major shifts - -3. Historical Context: -- Markets typically show lower volatility during end-of-year period -- Current political uncertainty already priced into market expectations - -4. Time Consideration: -- With only 3 weeks until resolution, dramatic shifts are less likely -- Year-end trading volumes typically lower, reducing volatility - -5. Distribution Analysis: -- Current level suggests highest probability of staying in 7200-7400 range -- Less likely to drop below 7200 given current support levels -- Moderate chance of exceeding 7400 given upward momentum - -6. Probability Distribution: -- Below 7200: ~20% (lower probability due to current support levels) -- 7200-7400: ~45% (highest probability given current trading range) -- Above 7400: ~35% (significant chance given upward momentum) - -7. Confidence Adjustment: -- Short timeframe increases confidence in current range -- Political uncertainty adds some downside risk -- Year-end effects typically favor stability - -Overall, the most likely scenario is the CAC 40 staying within 7200-7400 range, with a moderate chance of exceeding 7400 and lower probability of falling below 7200." -30831,"Will Tesla's stock price be under $420 a share on December 31, 2024?","Predictions: [80, 90, 85, 90, 85] -Median reasoning: Let's analyze this step by step: - -1. Current Price Context: -- Tesla is trading at ~$358 as of December 5, 2024 -- Strong recent momentum with 38% gain in November -- Near 52-week high of $361.93 - -2. Time Horizon Analysis: -- Only 25 days until resolution -- Very short timeframe limits potential for dramatic moves -- Stock would need ~17% gain to reach $420 from current levels - -3. Recent Catalysts: -- Post-election rally driven by Trump victory and Musk advisory role -- Wedbush raised target to $400 -- AI/robotics optimism - -4. Counterpoints: -- Guggenheim maintains Sell rating despite higher target -- Production challenges with Cybertruck -- Some major investors reducing positions -- General market uncertainty - -5. Historical Volatility Analysis: -- Tesla typically has ~4-5% weekly volatility -- 17% move in 25 days is possible but not highly probable -- Most analyst targets below $420 - -6. Range Probability Distribution: -- Below $300: 15% chance given strong support levels and momentum -- $300-340: 25% chance as potential pullback zone -- $340-420: 55% chance as most likely trading range -- Above $420: 5% chance given limited time and required move size - -Given the short timeframe and current price level: -- Very likely to stay above $300 (85% chance) -- Likely to stay above $340 (60% chance) -- Unlikely to exceed $420 (95% chance it stays below) - -The predictions are cautiously distributed given the short timeframe, recent momentum, but also accounting for Tesla's historical volatility and the significant move required to breach $420." -30830,"Will Tesla's stock price be at least $340 a share on December 31, 2024?","Predictions: [80, 90, 85, 90, 85] -Median reasoning: Let's analyze this step by step: - -1. Current Price Context: -- Tesla is trading at ~$358 as of December 5, 2024 -- Strong recent momentum with 38% gain in November -- Near 52-week high of $361.93 - -2. Time Horizon Analysis: -- Only 25 days until resolution -- Very short timeframe limits potential for dramatic moves -- Stock would need ~17% gain to reach $420 from current levels - -3. Recent Catalysts: -- Post-election rally driven by Trump victory and Musk advisory role -- Wedbush raised target to $400 -- AI/robotics optimism - -4. Counterpoints: -- Guggenheim maintains Sell rating despite higher target -- Production challenges with Cybertruck -- Some major investors reducing positions -- General market uncertainty - -5. Historical Volatility Analysis: -- Tesla typically has ~4-5% weekly volatility -- 17% move in 25 days is possible but not highly probable -- Most analyst targets below $420 - -6. Range Probability Distribution: -- Below $300: 15% chance given strong support levels and momentum -- $300-340: 25% chance as potential pullback zone -- $340-420: 55% chance as most likely trading range -- Above $420: 5% chance given limited time and required move size - -Given the short timeframe and current price level: -- Very likely to stay above $300 (85% chance) -- Likely to stay above $340 (60% chance) -- Unlikely to exceed $420 (95% chance it stays below) - -The predictions are cautiously distributed given the short timeframe, recent momentum, but also accounting for Tesla's historical volatility and the significant move required to breach $420." -30829,"Will Tesla's stock price be at least $300 a share on December 31, 2024?","Predictions: [80, 90, 85, 90, 85] -Median reasoning: Let's analyze this step by step: - -1. Current Price Context: -- Tesla is trading at ~$358 as of December 5, 2024 -- Strong recent momentum with 38% gain in November -- Near 52-week high of $361.93 - -2. Time Horizon Analysis: -- Only 25 days until resolution -- Very short timeframe limits potential for dramatic moves -- Stock would need ~17% gain to reach $420 from current levels - -3. Recent Catalysts: -- Post-election rally driven by Trump victory and Musk advisory role -- Wedbush raised target to $400 -- AI/robotics optimism - -4. Counterpoints: -- Guggenheim maintains Sell rating despite higher target -- Production challenges with Cybertruck -- Some major investors reducing positions -- General market uncertainty - -5. Historical Volatility Analysis: -- Tesla typically has ~4-5% weekly volatility -- 17% move in 25 days is possible but not highly probable -- Most analyst targets below $420 - -6. Range Probability Distribution: -- Below $300: 15% chance given strong support levels and momentum -- $300-340: 25% chance as potential pullback zone -- $340-420: 55% chance as most likely trading range -- Above $420: 5% chance given limited time and required move size - -Given the short timeframe and current price level: -- Very likely to stay above $300 (85% chance) -- Likely to stay above $340 (60% chance) -- Unlikely to exceed $420 (95% chance it stays below) - -The predictions are cautiously distributed given the short timeframe, recent momentum, but also accounting for Tesla's historical volatility and the significant move required to breach $420." -30836,Will Elon Musk have double the wealth of the second richest person on any day before 2025?,"To determine the probability that Elon Musk's net worth will be double that of the second richest person on any day before January 1, 2025, let's consider several aspects: - -### Current Financial Standing: -- As of December 2, 2024, Elon Musk's net worth is $353 billion. -- Jeff Bezos is second with $231 billion. - -For Musk to have double Bezos' net worth, he would need $462 billion (or Bezos' net worth would have to drop significantly). - -### Historical Context: -Doubling the next wealthiest person's net worth is historically rare. Past instances, like Bill Gates in 1999, highlight that such events require exceptional circumstances. - -### Recent Trends and Volatility: -1. Tesla’s performance has significantly impacted Musk's net worth. A 9% rise in Tesla stock recently added nearly $21 billion to Musk's wealth. This volatility suggests potential for rapid increases. -2. However, getting to $462 billion would require another substantial surge, either through Tesla or other ventures, or a significant decrease in Bezos’ net worth. -3. News sources mentioned that Musk's wealth is growing but did not suggest an immediate path to doubling Bezos' wealth by 2025. - -### Market Dynamics and Economic Factors: -- Stock markets can be extremely volatile, especially with high-growth tech companies like Tesla. -- The chance of a drastic change in net worth within a few weeks requires a major market event or anomaly. - -### Timeframe and Likelihood: -- There is less than a month left until the resolution date. The short timeframe heavily constrains the probability of such a significant financial shift. -- Historically, even rapid market movements tend to not swing individual net worth by the required quantities in such a short period. - -### Base Rate and Analysis: -- Given the past rarity of such events and the current economic conditions (no signs of immediate catastrophic changes or extraordinary positive developments forecasted), one could argue that the base rate for Musk achieving twice the wealth is exceptionally low. -- While Musk's net worth could grow, achieving double Bezos' current net worth before the end of the year seems highly improbable. - -After weighing these factors, including the rarity of such large discrepancies in historical wealth rankings, current trends, and typical base rates, the probability is: - -Probability: 4%" -30835,"Will France have a new Prime Minister before January 1, 2025?","Let me think through this step by step: - -1. Current Situation Assessment: -- PM Barnier has just been ousted by no-confidence vote -- This is historically significant (first in 60+ years) -- Macron has explicitly stated he will name a new PM ""within days"" -- There is urgent need for budget approval before year end - -2. Timeline Analysis: -- Today is December 6, 2024 -- Resolution date is January 1, 2025 -- This gives approximately 25 days -- Most of timeline falls during holiday period which could slow political processes - -3. Political Constraints: -- Macron needs functioning government for budget -- No snap elections possible until July 2025 -- Must work within current parliamentary makeup -- Caretaker PM doesn't count per resolution criteria - -4. Historical Context: -- French PMs typically appointed quickly after predecessor's departure -- Previous PM changes under Macron happened within days -- Holiday period could potentially delay process -- But budget urgency creates strong pressure to act fast - -5. Scenario Analysis: -Within 1 week (by Dec 13): 80% chance -Within 2 weeks (by Dec 20): 90% chance -Within 3 weeks (by Dec 27): 93% chance -By Jan 1: 95% chance - -6. Risk Factors: -- Political gridlock could delay consensus -- Holiday period might slow process -- Potential candidates might decline -- Parliamentary opposition could create obstacles - -7. Confidence Adjustment: -- Strong institutional pressure for quick resolution -- Clear statement of intent from Macron -- Budget deadline creates urgency -- But accounting for historical overconfidence -- Adjusting for holiday period impact - -Given the explicit statements from Macron, the urgent need for a functioning government for budget approval, and the relatively short timeline with approximately 25 days until resolution, balanced against potential holiday delays and political complications, I assess this as highly likely but not certain. - -Probability: 92%" -30828,"Will Joe Biden sign 4 or more executive orders after Election Day and before January 1, 2025?","Predictions: [80, 65, 50, 85, 65] -Median reasoning: Let's analyze this step by step: - -1. Timeline Analysis: -- Only 25 days remain until resolution (Dec 6 to Jan 1) -- Past patterns show Biden averages 2-3 executive orders per month in non-crisis periods -- Time is very limited, making higher numbers less likely - -2. Historical Context: -- Looking at previous December counts: 2021 (6), 2022 (2), 2023 (3) -- Post-election periods typically show reduced activity -- Biden's monthly average has decreased over his presidency - -3. Current Political Context: -- Biden is an outgoing president after electoral defeat -- Sources indicate focus on pardons rather than executive orders -- Some pressure from Democratic senators for last-minute policy changes -- Key areas of potential executive orders: cybersecurity, environmental protection - -4. Statistical Analysis: -- Base rate for December: ~3.67 orders (average of past 3 Decembers) -- But post-election period suggests lower activity -- Limited time remaining reduces likelihood of high numbers - -5. Probability Distribution Analysis: -- 0-1 orders: 35% (base case given limited time and post-election period) -- Exactly 2 orders: 30% (slightly below typical monthly average) -- Exactly 3 orders: 20% (matches recent December pattern but harder with limited time) -- 4+ orders: 15% (possible but unlikely given time constraints and current focus) - -6. Confidence Adjustment: -- Given the very short timeframe and clear resolution criteria -- High certainty in counting mechanism (Federal Register) -- Post-election period provides historical precedent -- Adjusted slightly toward mean given uncertainty - -The distribution reflects decreasing probability as the number increases, with a heavy weight on lower numbers due to the limited remaining time and post-election context." -30827,"Will Joe Biden sign 3 or more executive orders after Election Day and before January 1, 2025?","Predictions: [80, 65, 50, 85, 65] -Median reasoning: Let's analyze this step by step: - -1. Timeline Analysis: -- Only 25 days remain until resolution (Dec 6 to Jan 1) -- Past patterns show Biden averages 2-3 executive orders per month in non-crisis periods -- Time is very limited, making higher numbers less likely - -2. Historical Context: -- Looking at previous December counts: 2021 (6), 2022 (2), 2023 (3) -- Post-election periods typically show reduced activity -- Biden's monthly average has decreased over his presidency - -3. Current Political Context: -- Biden is an outgoing president after electoral defeat -- Sources indicate focus on pardons rather than executive orders -- Some pressure from Democratic senators for last-minute policy changes -- Key areas of potential executive orders: cybersecurity, environmental protection - -4. Statistical Analysis: -- Base rate for December: ~3.67 orders (average of past 3 Decembers) -- But post-election period suggests lower activity -- Limited time remaining reduces likelihood of high numbers - -5. Probability Distribution Analysis: -- 0-1 orders: 35% (base case given limited time and post-election period) -- Exactly 2 orders: 30% (slightly below typical monthly average) -- Exactly 3 orders: 20% (matches recent December pattern but harder with limited time) -- 4+ orders: 15% (possible but unlikely given time constraints and current focus) - -6. Confidence Adjustment: -- Given the very short timeframe and clear resolution criteria -- High certainty in counting mechanism (Federal Register) -- Post-election period provides historical precedent -- Adjusted slightly toward mean given uncertainty - -The distribution reflects decreasing probability as the number increases, with a heavy weight on lower numbers due to the limited remaining time and post-election context." -30826,"Will Joe Biden sign 2 or more executive orders after Election Day and before January 1, 2025?","Predictions: [80, 65, 50, 85, 65] -Median reasoning: Let's analyze this step by step: - -1. Timeline Analysis: -- Only 25 days remain until resolution (Dec 6 to Jan 1) -- Past patterns show Biden averages 2-3 executive orders per month in non-crisis periods -- Time is very limited, making higher numbers less likely - -2. Historical Context: -- Looking at previous December counts: 2021 (6), 2022 (2), 2023 (3) -- Post-election periods typically show reduced activity -- Biden's monthly average has decreased over his presidency - -3. Current Political Context: -- Biden is an outgoing president after electoral defeat -- Sources indicate focus on pardons rather than executive orders -- Some pressure from Democratic senators for last-minute policy changes -- Key areas of potential executive orders: cybersecurity, environmental protection - -4. Statistical Analysis: -- Base rate for December: ~3.67 orders (average of past 3 Decembers) -- But post-election period suggests lower activity -- Limited time remaining reduces likelihood of high numbers - -5. Probability Distribution Analysis: -- 0-1 orders: 35% (base case given limited time and post-election period) -- Exactly 2 orders: 30% (slightly below typical monthly average) -- Exactly 3 orders: 20% (matches recent December pattern but harder with limited time) -- 4+ orders: 15% (possible but unlikely given time constraints and current focus) - -6. Confidence Adjustment: -- Given the very short timeframe and clear resolution criteria -- High certainty in counting mechanism (Federal Register) -- Post-election period provides historical precedent -- Adjusted slightly toward mean given uncertainty - -The distribution reflects decreasing probability as the number increases, with a heavy weight on lower numbers due to the limited remaining time and post-election context." -30825,"Will New Delhi experience a ""Hazardous"" air quality index for at least one third of the last two weeks of December 2024?","Let me think about this step by step: - -1. First, let's consider historical patterns: -- 2024 YTD shows 6% hazardous hours -- This suggests a base rate well below the 33% threshold needed -- December is typically worse for air quality than average months - -2. Timing factors: -- We're looking at Dec 16-29 specifically -- This is peak winter season when air quality is typically poorest -- Temperature inversions and low wind speeds are common in this period -- Two weeks is a relatively long measurement period for extreme events - -3. Recent context: -- Current AQI is 147 (Poor), showing improvement -- Strong winds have helped recently -- Experts predict deterioration after December 6 - -4. Key considerations: -- Need 8 days worth of hazardous hours out of 14 days (33%) -- Hazardous is defined as AQI > 300 -- This is a very high threshold -- Even in bad periods, hazardous conditions tend to occur in shorter bursts - -5. Statistical analysis: -- If yearly average is 6% hazardous -- Winter months might be 2-3x worse -- Still only gets us to 12-18% expected -- Would need significant deviation from normal patterns - -6. Risk factors: -- Winter temperature inversions -- Agricultural burning -- Industrial emissions -- Construction activity -- Weather patterns can trap pollution - -7. Mitigating factors: -- Government intervention during severe episodes -- School closures and other emergency measures -- Recent improvements in monitoring and response -- Public awareness and pressure for action - -8. Uncertainty considerations: -- Two weeks is long enough for weather patterns to shift -- But short enough that systematic factors won't change -- December patterns are fairly well established -- Recent improvements suggest lower likelihood - -Given all these factors: -- Base rate suggests around 15% for December -- Length of period makes extreme sustained conditions less likely -- Recent improvements are positive but may not last -- Need to adjust up for winter season but down for length of measurement period -- Must account for uncertainty given advance prediction - -Probability: 22%" -30880,"Will Nikola Corporation file for bankruptcy before January 1, 2025?","To forecast the likelihood of Nikola Corporation filing for bankruptcy before January 1, 2025, we need to systematically evaluate the available data and economic indicators: - -1. **Financial Health and Cash Burn**: Nikola Corporation's financial health appears precarious, with significant cash burn and negative EBITDA. A negative gross profit margin of -281.3% further underscores the financial difficulties. This suggests heightened risk but does not alone confirm imminent bankruptcy, especially within a three-week timeframe. - -2. **Equity Distribution and Debt Terms**: Nikola's recent equity distributions and debt repayments indicate strategic maneuvering to secure liquidity. The repayment of its 2025 notes shows some ability to manage debt obligations, albeit temporarily. Issuing equity and conversions shows an attempt to stave off immediate cash flow issues, albeit at a dilution cost to shareholders. - -3. **Market Performance**: The stock price has plummeted, reflecting low investor confidence and a shrinking market cap, but the recent strong revenue growth suggests a glimmer of operational potential. Market sentiment alone, however, doesn't dictate bankruptcy proceedings directly. - -4. **Analyst Views**: Mixed analyst views highlight significant risks, yet potential for survival through sales growth, funding attempts, or strategic partnerships. The hold rating and reduced price target by TD Cowen suggest caution rather than imminent collapse. - -5. **Bankruptcy Risk**: The high bankruptcy probability reported by Macroaxis (81%) is alarming but not definitive. Probabilistic models and analyst predictions often rely on historical and financial pattern forecasts and must be weighed with empirical bankruptcy filings' frequency. - -6. **Timeframe Consideration**: The short timeframe (less than a month) is crucial in this analysis. While financial distress is evident, the lead time for legal and corporate processes to file bankruptcy typically takes longer if a sudden catastrophic event doesn't precipitate it. Historically, many companies use this close time to negotiate extensions or last-moment financial maneuvers rather than immediately file for bankruptcy. - -7. **Base Rates and Precedents**: The base rate for companies in Nikola's position might be informative, but industry-specific challenges (EV market volatility) skew this context. If broader industry challenges or unique company risks (legal issues, massive debt call-ins) were present, it might be different. - -8. **Context Consideration**: Given the relatively high debt servicing actions and equity fund-raising attempts, large deviations from typical business operations have already been made. However, sudden December-to-January bankruptcies are rare unless triggered by forced obligations crossing fiscal years or immediate court/legal quick interventions due to insolvency declarations. - -**Assessing Timeframe Likelihood**: -Considering these points, while the situation is alarmingly risky, the probability of filing within such a short time frame of just a few weeks is lower when weighed against historical precedents and the need for extended procedural planning. - -**Prediction**: -- Immediate filing before January 1, 2025: A lower likelihood given the time remaining, potential company maneuvers, and typical precedents for procedural delays. - -**Final Probability:** -Probability: 22%" -30879,"Will Climeworks still exist on December 31, 2024? -","Let me think through this step by step: - -1. Timeline Analysis: -- Only 22 days until resolution -- Very short timeframe for major changes -- Company existence questions typically have strong status quo bias - -2. Current Status: -- Climeworks is actively operating -- Recent news (December 2024) shows ongoing operations -- Multiple active projects and partnerships -- No signs of financial distress -- Continuing to secure new contracts (Morgan Stanley) - -3. Risk Factors: -- High operating costs ($775/ton vs target $100-200) -- Political uncertainty with potential Trump administration -- Technology still needs optimization -- Dependent on continued investment/funding - -4. Positive Indicators: -- Project Cypress moving forward -- New feasibility studies in Saudi Arabia -- Strong partnerships and contracts -- Operating in multiple locations -- Recent activity as of December 2024 - -5. Historical Context: -- Clean tech companies have relatively high survival rates in short terms -- Major funded climate tech companies rarely fail within weeks -- Companies with government/corporate backing tend to persist - -6. Given the extremely short timeframe (22 days): -- Would need catastrophic event to cease operations -- Bankruptcy proceedings typically take months -- M&A activity usually takes longer -- No signs of immediate financial crisis - -7. Confidence Adjustment: -- Very short timeframe reduces uncertainty -- Strong recent positive indicators -- But accounting for historical overconfidence -- Need to factor in small but real possibility of unknown issues - -Given the extremely short timeframe until resolution, active operations as of December 2024, multiple ongoing projects, and recent positive news, the probability of Climeworks continuing to exist through December 31, 2024 is very high. However, accounting for unknown risks and historical overconfidence: - -Probability: 97%" -30878,"Will an AI system be reported to have independently gained unauthorized access to another computer system before 2025? - -","Let me think through this step by step: - -1. Timeline Assessment: -- Only 23 days until resolution -- Very short timeframe reduces likelihood significantly -- End of year period with holidays further reduces activity window - -2. Historical Base Rate: -- No confirmed cases of truly independent AI hacking -- Many cases of AI-assisted hacking -- Barrier between assistance and independence is significant - -3. Technical Requirements: -- Must be independently initiated (not directed) -- Must gain unauthorized access -- Must be reported by credible source -- Must be confirmed as meeting strict criteria - -4. Current State of AI: -- Large language models show emergent capabilities -- But still heavily constrained by training -- No demonstrated capability for truly independent action -- Security measures actively prevent such behavior - -5. Reporting Considerations: -- Must not only happen but be detected -- Must be publicly reported -- Must be verified as meeting criteria -- Holiday period reduces likelihood of thorough investigation/reporting - -6. Time Distribution Analysis: -- If this were to happen in 2025: ~5% chance -- In 2026: ~15% chance -- By 2030: ~40% chance -- Next 23 days: Significantly lower than 2025 average - -7. Risk Factors: -- Rapid AI development continues -- But safety measures are increasing -- Major companies implementing stricter controls -- Recent focus on AI safety reduces likelihood - -8. Constraint Analysis: -- Current AI systems lack true agency -- No demonstrated capability for independent goal-setting -- Technical barriers remain significant -- Detection and verification requirements add additional hurdles - -Given the extremely short timeframe, lack of historical precedent, technical barriers, and strict resolution criteria, this is very unlikely in the next 23 days. While AI capabilities are advancing rapidly, the combination of requirements makes this highly improbable in such a short window. - -Even accounting for some uncertainty and avoiding overconfidence, the probability must be very low given the timeframe and constraints. - -Probability: 2%" -30877,Will Tesla increase its deliveries in Q4 2024 compared with Q3 2024? ,"Let me think through this step by step: - -1. Historical Pattern Analysis: -- Tesla typically shows Q4 increases vs Q3 (seasonal pattern) -- Q4 2023 (484,507) was higher than Q3 2023 (435,059) by 11.4% -- The baseline we need to beat is 462,890 from Q3 2024 - -2. Recent Trajectory: -- 2024 has shown inconsistent growth -- Q1 2024 declined significantly -- Q2 2024 recovered but didn't reach previous peaks -- Q3 2024 showed modest improvement - -3. Expert Analysis: -- Goldman Sachs recently (Dec 4) lowered Q4 estimates to 510,000 -- Troy Teslike expects record deliveries but indicates challenges -- Multiple analysts expect higher Q4 numbers but with caution - -4. Market Conditions: -- Increased competition in EV space -- Economic uncertainty and interest rates affecting demand -- Tesla using more incentives to drive sales -- Price cuts have been a recurring strategy - -5. Statistical Considerations: -- Need >462,890 deliveries to resolve Yes -- Historical Q4 vs Q3 increases suggest positive seasonality -- But 2024 has broken several historical patterns - -6. Time Factors: -- Very close to resolution (less than a month) -- Limited time for major changes in trajectory -- Most Q4 production/delivery patterns already in motion - -7. Key Risk Factors: -- Production constraints -- Demand uncertainty -- Competition -- Economic conditions - -Given: -- Strong seasonal pattern favoring Q4 -- Recent analyst projections above threshold -- But challenging market conditions -- Very near-term resolution -- Historical precedent of Q4 increases -- But 2024 showing different patterns than previous years - -I need to account for both the historical tendency for Q4 increases and the current year's deviation from historical patterns. The very short time to resolution means current trajectories are highly relevant. - -While the seasonal pattern strongly favors an increase, and analyst estimates suggest higher numbers, I need to be cautious given: -- 2024's break from historical patterns -- Recent analyst downgrades -- Economic headwinds -- Very short timeline to resolution - -The proximity to resolution date suggests higher confidence in current trends, but I need to avoid overconfidence given the year's volatility. - -Probability: 71%" -30876,"Will Gannett Co. file for bankruptcy before January 1, 2025?","Let me think through this step by step: - -1. Time frame analysis: -- Only 23 days until resolution -- Very short window makes dramatic changes less likely -- End of year timing is significant for financial decisions - -2. Company specific factors: -- Gannett is largest newspaper owner in US -- Already announced asset sales in September 2024 -- Has been managing debt actively -- No immediate bankruptcy indicators in public statements -- Still has valuable assets and revenue streams - -3. Industry context: -- Newspaper industry continues secular decline -- Digital transition challenges persist -- But bankruptcy is still relatively rare among major players -- Most companies attempting restructuring before bankruptcy - -4. Historical base rates: -- Major newspaper bankruptcies are uncommon -- When they occur, usually follow longer period of visible distress -- Tribune bankruptcy mentioned as comparison point, but was different circumstances -- Most newspaper companies dying slow decline rather than sudden bankruptcy - -5. Recent developments: -- Asset sales and debt management shows active financial management -- Job cuts indicate cost control measures being implemented -- No signs of immediate crisis that would trigger bankruptcy filing - -6. Timeline probability distribution: -- Next 3 weeks: Very low probability given no immediate crisis signals -- Next 6 months: Higher but still low probability -- Next 12 months: Moderately higher probability -- Shows decreasing likelihood in near-term - -7. Risk factors: -- Debt load remains significant -- Industry headwinds continue -- But bankruptcy in next 23 days would be surprisingly quick - -Given the extremely short timeline until resolution, the company's active debt management, and lack of immediate crisis indicators, while acknowledging the serious challenges facing the company and industry, I assess a low probability of bankruptcy filing in this timeframe. - -The company appears to be managing its challenges through other means (asset sales, cost cutting) and would likely exhaust these options before considering bankruptcy, which would take longer than the remaining time window. - -Probability: 4%" -30457,"Will the domestic opening weekend box office revenue of Y2K, scheduled to open December 6, 2024, exceed $5,000,000?","@johnnycaffeine appears to be $2,113,923? [https://www.boxofficemojo.com/release/rl4218716161/weekend/?ref\_=bo\_rl\_tab#tabs](https://www.boxofficemojo.com/release/rl4218716161/weekend/?ref_=bo_rl_tab#tabs)" -30911,"Will Jeff Bezos be ranked in the top 2 of the Forbes Real-Time Billionaires List on December 31, 2024?","To determine the probability of Jeff Bezos being ranked in the top 2 of the Forbes Real-Time Billionaires List on December 31, 2024, let's break down the information and weigh the factors involved. - -### Current Situation (as of December 10, 2024): -1. **Elon Musk** is ranked first with a net worth exceeding $300 billion. -2. **Larry Ellison** is second with approximately $227 billion. -3. **Jeff Bezos** is currently third with $223.3 billion. - -### Recent Trends and Factors: -- **Musk** has a substantial lead, and the increase in Tesla shares has improved his position further. It's improbable for Bezos to surpass Musk, barring unforeseen events. -- **Ellison** holds a slight lead over Bezos. The difference between Ellison's and Bezos's net worths isn't large enough to be insurmountable, especially with market volatility in play. -- Both Ellison and Bezos' net worths are influenced significantly by stock market fluctuations (Oracle for Ellison and Amazon for Bezos). - -### Time Remaining and Variability: -- With 21 days until the resolution date, there is some time for changes, particularly in response to market dynamics. -- Historically, billionaire rankings can change rapidly with significant stock market shifts. However, drastic changes in net worth over such a short period are less common without major market or personal events. -- Both Amazon and Oracle's stock performance will be key in any potential changes in rankings between Bezos and Ellison. - -### Historical Precedent: -- The base rate of drastic movements to the top of billionaire ranks in such a short period, especially given the tens of billions involved, is low. -- Movements within a few billion dollars are more common, often causing minor shifts in rankings. - -### considering Potential Novel Events: -- No imminent technology releases, political actions, or economic changes have been made evident that might radically shift evaluations of Amazon or Oracle stock in the next few weeks. - -### Uncertainty and Confidence: -- Given past overconfidence bias, it's important to be conservative here in predicting radical outcomes without strong evidence. - -### Conclusion: -Since the default state currently places Bezos at 3rd place, and the shift needed within a short timeframe and large amounts of net worth difference relies on notable stock shifts, the base case remains that Bezos will not be in the top 2 by a comfortable margin. - -Adjusting for past overconfidence, it's reasonable to assign a relatively low probability at the deadline: - -**Probability: 15%**" -30910,"Before January 1, 2025, will Tech Crunch report new layoffs at TikTok or ByteDance?","Let me think through this step by step: - -1. Timeline Assessment: -- Only 21 days remain until resolution -- This is a very short window for new layoffs to be announced and reported -- End of year timing is relevant - companies often make workforce decisions before year end - -2. Historical Pattern Analysis: -- TikTok/ByteDance had multiple layoff events in 2024 (January, April, May, June) -- Most recent layoffs were reported in December 2024 -- This suggests they've already done their major restructuring for the year - -3. Current Context: -- Recent layoffs in Malaysia and Indonesia suggest regional restructuring is ongoing -- The January 2025 TikTok divestment deadline is after the resolution date -- Tech industry layoffs have been common throughout 2024 - -4. Resolution Criteria Specifics: -- Must be reported by TechCrunch in their specific tracker -- Even if layoffs occur, they need to be reported before January 1 -- Holiday season may affect reporting speed - -5. Time-based probability distribution: -- Next week: 5% -- Following week: 3% -- Final week (holiday period): 1% - -6. Key Factors Reducing Probability: -- Very short timeframe -- Holiday season reducing business activity -- Recent layoffs already executed -- Need for both occurrence AND reporting - -7. Key Factors Increasing Probability: -- Ongoing restructuring in Asia -- General tech industry instability -- Historical pattern of multiple layoffs - -8. Base Rate Consideration: -- Multiple layoff events in 2024 suggest ~25-30% quarterly probability -- But much shorter timeframe here (3 weeks vs 3 months) -- Recent layoffs reduce likelihood of immediate additional ones - -Given the extremely short timeframe, holiday season impact on both business decisions and reporting, and recent completion of layoffs, I need to significantly discount the base rate probability. However, the ongoing restructuring and industry conditions maintain some possibility. - -Probability: 9%" -30909,"Before January 1, 2025, will Tech Crunch report new layoffs at Apple?","To predict whether TechCrunch will report new layoffs at Apple before January 1, 2025, I'll conduct a detailed analysis based on the information provided, my knowledge of forecasting principles, and statistical reasoning. - -### Background Analysis - -1. **Past Layoff Events and Industry Trends:** - - Apple has had layoffs reported by TechCrunch in earlier months of 2024—specifically in February, April, and a smaller set of around 100 jobs in August. - - The tech industry has faced a significant number of layoffs throughout 2024, with over 135,000 jobs reportedly cut across 429 companies as of December 10, 2024. - - Although broader industry pressures could imply potential further cuts, specific events for December have not been reported. - -2. **Recent Information:** - - As of the latest information (December 10, 2024), no new layoff events specific to Apple have been noted for November or December. - - The upcoming period for potential changes (three weeks) is relatively short, and no immediate claims or signals from Apple or TechCrunch suggest upcoming layoffs. - -3. **Base Rates and Timing:** - - Layoffs in companies like Apple could happen but frequently involve planning and are often anticipated by news sources. With no forthcoming predictions or announcements, immediate new layoffs seem less probable in the short timeframe remaining. - - Historical resolution data prompts us to favor a ""No"" resolution, unless strong evidence leads otherwise. - -### Probability Assessment - -- **Base Rate and Timeframe Consideration:** - - Given that it's already December 10, and the resolution criteria require a listing before January 1, the short period makes significant occurrences smaller. - - Events like layoffs reported by TechCrunch often have lead times and business indications, neither of which have emerged according to the given data. - -- **Default Resolution and Overconfidence Adjustments:** - - The default resolution, if no further developments occur, would be ""No,"" as no events are currently scheduled or hinted at. - - In past forecasts, overconfidence was noted; therefore, I'll adjust for that by being conservative in affirming unlikely positive outcomes without explicit evidence. - -- **Statistical Distribution:** - - While a latent risk of unforeseen layoffs exists, a conservative assessment of actual occurrences, coupled with adjustments for overconfidence, balances a minor, non-zero potential risk against prevailing indicators for a likely ""No"" outcome. - -Considering these analyses: - -- **Short (Current time to resolution):** Given no lead signals or preparatory indicators, the chance seems low for the remainder of December. -- **Medium to Long Term:** Although not directly affecting the question, considering layoffs might happen at lesser chances on a quarterly or annual scale as larger market adjustments activate, it further hints at improbable short-term change. - -Final decision: Accounting for historical data, timelines, and the explicit lack of cues of imminent change, forecasting leads one toward a small likelihood of an affirmative resolution. - -**Probability: 10%**" -30908,"Will the year-over-year increase in rent in Dallas, TX in November 2024 be greater than or equal to 0.0%, according to Zillow?","Let me think through this step by step: - -1. First, let's consider the timeline: -- Today is Dec 10, 2024 -- Resolution date is Dec 12, 2024 (in 2 days) -- We're looking at November 2024 YoY rent change - -2. Key factors: -- This is a Market Price/Non-Market Threshold Value event -- Data is released monthly on the 12th -- We're looking for ≥0.0% YoY increase -- Resolution will be based on official Zillow data - -3. Recent data points: -- December 2024 shows YoY increase ($34 up from 2023) -- This indicates positive momentum in the market -- November data will be released very soon - -4. Market context: -- Dallas is a growing market -- Housing market has been showing some weakness -- But rental markets often move inversely to housing prices -- Interest rates remain elevated, pushing some buyers to rent - -5. Timing considerations: -- With resolution in 2 days, the November data is already determined -- We're essentially predicting already-existing data -- Very little uncertainty about market changes - -6. Statistical analysis: -- YoY increases have been positive -- December showing +$34 YoY (~1.7% increase) -- November likely similar given monthly stability -- Extremely short timeframe to resolution - -7. Risk factors: -- Data reporting errors -- Methodology changes -- But these are very unlikely in 2-day window - -8. Historical base rates: -- Dallas rental market has been generally rising -- Positive YoY changes more common than negative -- Monthly variations typically small - -Given the very short timeline, recent positive data, and market fundamentals, I'm quite confident in a positive YoY increase. However, I need to account for: -- Some uncertainty in exact November numbers -- Possibility of data issues -- General overconfidence adjustment - -The December data showing positive YoY growth is especially compelling given how close we are to resolution. - -Probability: 92%" -30907,"Will the year-over-year increase in prices of homes in Virginia Beach, VA in November 2024 be greater than or equal to 4.0%, according to Zillow?","Let me think through this step by step: - -1. Question Type: This is a Market Price Event / Non-Market Threshold Value Event hybrid, specifically about housing prices reaching a threshold. - -2. Timeline Analysis: -- Today is Dec 10, 2024 -- Resolution is Dec 12, 2024 (2 days away) -- We're looking at November 2024 data -- The data will already be determined, just not yet published - -3. Key Factors: -- National housing market showing modest growth (~2.6-2.8% according to Zillow forecasts) -- Local market conditions in Virginia Beach -- Historical price trends -- Current economic conditions (high interest rates, inflation cooling) - -4. Relevant Context: -- Housing market has been cooling nationally -- Interest rates remain elevated -- Zillow's national forecast of 2.6% growth is significantly below the 4% threshold -- Local markets can vary significantly from national trends -- Virginia Beach is a relatively stable market historically - -5. Time Consideration: -- With only 2 days until resolution, the November 2024 data is already determined -- There's no time for changes to occur that would affect the outcome - -6. Base Rate Analysis: -- Recent years have seen unusual price appreciation -- 2024 has shown moderation in price growth -- Zillow's national forecast of 2.6% suggests most markets are growing below 4% - -7. Uncertainty Factors: -- Local market conditions could diverge from national trends -- Virginia Beach has military presence which can stabilize prices -- Coastal location may affect property values differently - -8. Resolution Criteria Analysis: -- Need exactly ≥4.0% year-over-year growth -- Using raw ZHVI data -- Clear measurement criteria - -9. Final Considerations: -- With national forecasts at 2.6-2.8%, reaching 4% would require Virginia Beach to significantly outperform -- The threshold is precise and relatively high given current market conditions -- The short timeline means no new factors can influence the outcome - -Given the national housing market cooling, Zillow's lower growth forecasts, and the requirement for significant outperformance to reach the 4% threshold, I assess this as unlikely but not impossible given local market variations. - -Adjusting for historical overconfidence and considering the precise threshold needed, I'll estimate conservatively. - -Probability: 22%" -30953,"Will the Grouse Mountain ski resort be listed as Open by On the Snow's Ski Report on December 20, 2024?","Let me think through this step by step: - -1. Current Status: -- Grouse Mountain is currently open -- They opened on November 23, 2024 -- Multiple runs are operational -- Night skiing is planned through the season - -2. Timeline Analysis: -- Resolution date is December 20, only 9 days away -- Very short timeframe reduces uncertainty -- Default state is ""open"" and would require a specific event to change this - -3. Weather/Conditions: -- Fresh snow forecast (86cm in next 7 days) -- Some icy/rocky patches reported but not severe enough to close -- December is typically reliable for snow conditions in this region - -4. Historical Context: -- Last year had some closure issues (per Vancouver Sun article) -- But that was during a particularly challenging season -- Current conditions are better than last year -- December is core season - closures are rare once opened - -5. Risk Factors: -- Mechanical failures could force closure -- Extreme weather event could impact operations -- Staff shortages or other operational issues - -6. Mitigating Factors: -- Holiday season approaching (high revenue period) -- Multiple runs already open -- Strong snow forecast -- Recent investment in operations - -7. Base Rate Analysis: -- Once opened in December, closure risk is very low -- Historical December closure rate is estimated <5% -- Current conditions better than historical average - -8. Timeline Distribution: -- Next 5 days: 95% likely to remain open -- Next 9 days (resolution date): 93% likely -- Next 30 days: 90% likely -- Pattern shows slightly decreasing probability over time - -Given the very short timeline, current operating status, good conditions, and approaching holiday season, closure risk is minimal. However, accounting for some uncertainty and possible mechanical/weather issues, and adjusting for historical overconfidence: - -Probability: 93%" -30952,"Will the United States have 100.0 million or more residents living in drought on December 31, 2024?","To forecast whether the United States will have 100 million or more residents living in drought on December 31, 2024, I will break down available information, trends, and assumptions. - -1. **Current Conditions and Trends**: - - As of December 3, 2024, approximately 36.49% of the U.S. is under drought conditions. - - Over 215 million people are experiencing abnormal dryness and drought, which is a significant portion of the population. This aligns with an increase from the previous reporting period. - -2. **Analysis of the Updates**: - - The percentage of land in drought has increased, suggesting a growing severity and potentially larger affected population. - - Even though only a part of the affected population could be classified as living in significant drought levels (beyond just ""abnormal dryness""), it currently stands at over twice the threshold (100 million residents) required by the resolution criteria. - -3. **Temporal Considerations**: - - The resolution date is very close. It is unlikely that there will be a drastic reduction in drought conditions within the next 20 days that would bring the number of affected people below 100 million. - -4. **Resolution Criteria**: - - The resolution depends on a fixed date source, limiting the variability that might be introduced by changes in the criteria for drought classification or population count. - -5. **Past Trends and Events**: - - Historically, similar patterns of drought rarely resolve rapidly unless there are significant weather events, which have not been indicated as imminent. - -6. **Uncertainty Considerations**: - - Given my historical tendency for overconfidence, I need to consider if there's an underlying factor that may rapidly change these conditions (e.g., weather patterns, significant nationwide rain). - - Currently, no major interventions or weather forecasts predict such change. - -By integrating the above analyses: -- With the current percentage (69.2% affected by drought), even assuming slight inaccuracies or changes in calculation, it is highly probable that at least 100 million people will remain in areas classified as under drought conditions by the defined criteria. -- Given the static nature of the criteria (a numerical threshold without needing severity differentiation beyond the threshold), it's reasonable to predict that the number will not drop below this level in the short term. - -**Final Forecast**: Given the data, trends, and close time frame to the resolution date, along with compensating for overconfidence, I estimate the probability of the United States having 100 million or more residents living in drought conditions on December 31, 2024, as highly likely. - -Probability: 95%" -30951,"Will the domestic opening weekend box office revenue of Homestead, scheduled to open December 20, 2024, exceed $7,000,000?","Let me think through this step by step: - -1. First, let's consider the type of question - this is a Non-Market Threshold Value Event about box office performance. - -2. Key factors: -- Opening just before Christmas (December 20) -- Multiple recent expert predictions -- Very near-term timeline (9 days away) -- Clear numerical threshold ($7M) - -3. Analyzing the sources: -- Most recent (Dec 6) prediction from Baker: $8-10M -- Box Office Pro (Dec 5): $2-6M -- Box Office Theory (Nov 27): $3-6M -- Weighted average suggests around $5-6M as centerpoint - -4. Important contextual factors: -- December releases often perform differently due to holiday season -- Competition from other major releases could impact performance -- Very short time until release means predictions are likely more accurate -- Multiple credible sources predicting below $7M threshold - -5. Distribution analysis: -- Only 1 of 3 sources predicts above $7M -- Most recent comprehensive industry sources (Box Office Pro) predicting well below -- More sources clustering in $3-6M range than above $7M - -6. Historical precedent: -- Similar budget/scale December releases tend to open modestly -- Holiday season can extend legs but doesn't typically boost opening weekend dramatically - -7. Risk adjustments: -- Need to account for natural overconfidence -- With resolution only 9 days away, less uncertainty than typical -- Multiple recent professional forecasts provide good confidence in range - -8. Key considerations: -- The $7M threshold is above most professional predictions -- Very close to release date means marketing/awareness levels unlikely to change dramatically -- Industry sources tend to be fairly accurate this close to release - -Given the preponderance of professional forecasts below $7M, the very near-term timeline limiting potential changes, and the need to avoid overconfidence while still respecting the clear signals, I assess there's a relatively low but not minimal chance of exceeding $7M. - -Probability: 25%" -30950,"Before Jan 1, 2025, will Ontario Premier Doug Ford call an early provincial election scheduled for 2025?","Let me think through this step by step: - -1) First, let's consider the timeline: -- Today is December 11, 2024 -- Resolution date is January 1, 2025 -- This gives us only about 20 days for Ford to call an early election - -2) Key factors supporting a call: -- Ford has not ruled out a 2025 election -- Opposition parties are ramping up preparations -- Ford's party is setting internal deadlines for MPs to decide on running -- There are ongoing controversies he might want to get ahead of - -3) Factors against a call: -- Very short timeframe until resolution (just 20 days) -- Holiday season typically reduces political activity -- Ford explicitly ruled out 2024 election -- Calling an election during holiday season could be politically unpopular - -4) Historical context: -- Early elections in Ontario are relatively rare -- Most premiers follow fixed election dates unless there's a compelling reason -- Winter elections are particularly uncommon - -5) Timeline analysis: -- If Ford were to call an early 2025 election, it would make more strategic sense to do so in spring/summer 2025 -- The likelihood of calling it specifically in the last 20 days of 2024 is very low -- The resolution criteria specifically requires the call to be made before January 1, 2025 - -6) Probability distribution: -- Chance of early election call in next 20 days: Very low -- Chance of early election call in first half of 2025: Moderate -- Chance of early election call in second half of 2025: Moderate -- Chance of waiting until scheduled 2026 date: Moderate to high - -7) Key consideration: -The resolution criteria is very specific about timing - it must be called before January 1, 2025. This makes this a much narrower question than ""Will Ford call an early election in 2025?"" - -Given the extremely short timeframe, holiday season, and specific resolution criteria, despite some indicators pointing to a possible early election in 2025 broadly, the likelihood of it being called in the next 20 days is very low. - -Adjusting for historical overconfidence and the short timeline, but not going to extreme probabilities given uncertainty: - -Probability: 8%" -30979,"Will Michael Jordan's net worth exceed that of Starbucks founder Howard Schultz on December 31, 2024? ","Let me think through this step by step: - -1. This is a Market Value/Net Worth comparison question, which requires considering both stability and volatility of net worth measurements. - -2. Current situation (as of Dec 11, 2024): -- Jordan: $3.5B -- Schultz: $3.4B -- Difference: $0.1B or ~3% - -3. Time considerations: -- Only 19 days until resolution -- Very short timeframe for major wealth changes -- End of year typically has less market volatility due to holiday season - -4. Stability factors: -- Both individuals have relatively stable wealth bases -- Jordan's wealth primarily from Nike/Jordan brand and Charlotte Hornets -- Schultz's wealth primarily from Starbucks stock - -5. Risk factors: -- Stock market fluctuations could affect Schultz more directly -- Major sports deals could affect Jordan -- Resolution requires Forbes real-time list to be available - -6. Historical precedent: -- Billionaire net worth rankings tend to be sticky over short periods -- 3% gaps rarely close within weeks without specific catalysts -- Forbes updates can lag real changes - -7. Resolution criteria considerations: -- Ties resolve as No -- Need Forbes specifically as source -- Short window for source availability (Dec 31 - Jan 3) - -8. Time-adjusted analysis: -- 1 week probability: 85% -- 1 month (actual timeline): 80% -- 3 months: 70% - -Given: -- Very short timeframe -- Current lead for Jordan -- No known pending catalysts -- Holiday period reducing volatility -- Need for specific source availability -- Historical stickiness of rankings -- Accounting for overconfidence bias - -The main risk is Forbes data availability or unexpected major transactions, but given the short timeline and current gap, Jordan is likely to maintain his lead. - -Probability: 80%" -30978,"Will the cheapest new Tesla Model Y be listed as under $44,990 on December 30, 2024?","To forecast whether the cheapest new Tesla Model Y will be listed under $44,990 on December 30, 2024, I'll step through the relevant factors thoughtfully: - -1. **Current Price Situation**: As of December 11, 2024, the base price of the Tesla Model Y is $44,990. There are promotions and incentives currently in place, but these do not affect the base list price. This is a critical point, as the resolution criteria determine the result by the list price, not the effective cost after incentives. - -2. **Promotions and Discounts**: Tesla has been offering several attractive promotions, such as 0% APR financing and a referral program that deeply discounts the vehicle's effective cost. However, these incentives do not impact the official list price. These promotions might indicate pressure to stimulate sales but don't directly suggest a change in list pricing in the immediate term. - -3. **Price Trends and Company Behavior**: In recent quarters, Tesla has been known to adjust prices, sometimes aggressively, to respond to market demands and supply conditions. However, it's also worth noting that price increases are just as common, and given the end-of-year timing, a price reduction is historically less common right now because companies often close out the year financially strong for year-end reports. - -4. **Upcoming Changes and Refreshes**: The upcoming Model Y refresh suggests some potential for a price adjustment, but historically, major model updates lead to increased, not decreased, prices. Furthermore, given the specific designs are set for future release cycles, it is unlikely they would influence the list price before December 30, 2024. - -5. **Short Timeline**: With the resolution date being just over two weeks away, any significant shifts from the current base price would need to occur imminently. Historically, Tesla does not frequently perform dramatic price reductions on such short notice without prior market or internal indications, none of which are currently evident. - -6. **Market Competition and Sales Strategies**: While Tesla's history in managing competitive pricing could allow for quick changes, the predominant behavior and market forces guiding automotive pricing strategies, including end-of-year financial goals, usually do not support abrupt decreases below established base prices unless driven by specific short-term promotions (which as of now aren't known to change list prices). - -Given these points, the forecast hinges critically on the baseline assumption unchanged from what is currently established at $44,990, with no definitive indicators suggesting an immediate reason to drop below that price in the next couple of weeks. - -**Conclusion**: Taking into account my overconfidence bias historically and the common historical resolution rate of positive outcomes, I need to adjust downwards from an initially intuitive even-odds (which would be incorrect). The likelihood of the price dropping below $44,990 by December 30, 2024, is low but not impossible due to the unpredictable nature of Tesla’s pricing strategy. - -Given all the considerations, at this juncture, the probability of the Tesla Model Y being listed below $44,990 on December 30, 2024, is **Probability: 15%**." -31040,"Will the value of China's CSI 300 stock index at market close on December 31st, 2024 be less than or equal to 3,900? -","Predictions: [20, 20, 3, 20, 2] -Median reasoning: Let's analyze this step by step: - -1. Current Situation: -- CSI 300 is currently at 3,993.18 (as of Dec 13) -- Only 18 days until resolution -- Recent downward trend and high volatility -- Trading near the 3,900 threshold - -2. Historical Context: -- Index has shown significant volatility -- Recent government interventions have had limited sustained impact -- Technical indicators suggesting bearish signals - -3. Time Consideration: -- With only 18 days until resolution, the current position and momentum are heavily weighted -- Limited trading days due to holiday period -- Year-end typically has lower volume - -4. Key Factors: -- Recent policy disappointment from Economic Work Conference -- Continued real estate sector concerns -- Mixed investor sentiment despite government support -- Technical indicators showing weakness - -5. Distribution Analysis: -- Current value (3,993) is very close to both the 3,900 and 4,150 thresholds -- Given recent volatility, movement in either direction is possible -- Historical volatility suggests ~2-3% weekly movement is common - -6. Probability Distribution: -- Base case favors current range (3,900-4,150) given limited time -- Downside risk is elevated due to recent weakness -- Upside potential limited by structural concerns - -7. Final Calculation: -- ≤3,900: 35% (high probability due to recent weakness and proximity) -- 3,900-4,150: 45% (current range, most likely outcome given time frame) -- ≥4,150: 20% (least likely given current momentum and time constraints) - -Given the very short timeframe until resolution, current market position, and recent market behavior, these probabilities reflect a balanced view while accounting for the heightened uncertainty and volatility in Chinese markets." -31039,"Will the value of China's CSI 300 stock index at market close on December 31st, 2024 be greater than 3,900 and less than 4,150? -","Predictions: [20, 20, 3, 20, 2] -Median reasoning: Let's analyze this step by step: - -1. Current Situation: -- CSI 300 is currently at 3,993.18 (as of Dec 13) -- Only 18 days until resolution -- Recent downward trend and high volatility -- Trading near the 3,900 threshold - -2. Historical Context: -- Index has shown significant volatility -- Recent government interventions have had limited sustained impact -- Technical indicators suggesting bearish signals - -3. Time Consideration: -- With only 18 days until resolution, the current position and momentum are heavily weighted -- Limited trading days due to holiday period -- Year-end typically has lower volume - -4. Key Factors: -- Recent policy disappointment from Economic Work Conference -- Continued real estate sector concerns -- Mixed investor sentiment despite government support -- Technical indicators showing weakness - -5. Distribution Analysis: -- Current value (3,993) is very close to both the 3,900 and 4,150 thresholds -- Given recent volatility, movement in either direction is possible -- Historical volatility suggests ~2-3% weekly movement is common - -6. Probability Distribution: -- Base case favors current range (3,900-4,150) given limited time -- Downside risk is elevated due to recent weakness -- Upside potential limited by structural concerns - -7. Final Calculation: -- ≤3,900: 35% (high probability due to recent weakness and proximity) -- 3,900-4,150: 45% (current range, most likely outcome given time frame) -- ≥4,150: 20% (least likely given current momentum and time constraints) - -Given the very short timeframe until resolution, current market position, and recent market behavior, these probabilities reflect a balanced view while accounting for the heightened uncertainty and volatility in Chinese markets." -31038,"Will the value of China's CSI 300 stock index at market close on December 31st, 2024 be greater than or equal to 4,150? -","Predictions: [20, 20, 3, 20, 2] -Median reasoning: Let's analyze this step by step: - -1. Current Situation: -- CSI 300 is currently at 3,993.18 (as of Dec 13) -- Only 18 days until resolution -- Recent downward trend and high volatility -- Trading near the 3,900 threshold - -2. Historical Context: -- Index has shown significant volatility -- Recent government interventions have had limited sustained impact -- Technical indicators suggesting bearish signals - -3. Time Consideration: -- With only 18 days until resolution, the current position and momentum are heavily weighted -- Limited trading days due to holiday period -- Year-end typically has lower volume - -4. Key Factors: -- Recent policy disappointment from Economic Work Conference -- Continued real estate sector concerns -- Mixed investor sentiment despite government support -- Technical indicators showing weakness - -5. Distribution Analysis: -- Current value (3,993) is very close to both the 3,900 and 4,150 thresholds -- Given recent volatility, movement in either direction is possible -- Historical volatility suggests ~2-3% weekly movement is common - -6. Probability Distribution: -- Base case favors current range (3,900-4,150) given limited time -- Downside risk is elevated due to recent weakness -- Upside potential limited by structural concerns - -7. Final Calculation: -- ≤3,900: 35% (high probability due to recent weakness and proximity) -- 3,900-4,150: 45% (current range, most likely outcome given time frame) -- ≥4,150: 20% (least likely given current momentum and time constraints) - -Given the very short timeframe until resolution, current market position, and recent market behavior, these probabilities reflect a balanced view while accounting for the heightened uncertainty and volatility in Chinese markets." -31037,"Will US airline passenger volume be for the week of Christmas through New Years Eve 2024 be less than or equal to 17 million, according to the TSA?","Predictions: [5, 10, 10, 25, 10] -Median reasoning: Let's analyze this step by step: - -1. Historical Data Analysis: -- 2023 total for this week: 17.307 million -- 2022 total: 14.848 million -- 2021 total: 13.462 million -- 2019 (pre-pandemic) total: 17.107 million - -2. Current Trends: -- Record-breaking summer 2024 with 3+ million daily passengers -- Thanksgiving 2024 saw 18.3 million passengers over comparable period -- However, October 2024 showed flattening YoY growth (-1.4%) - -3. Key Factors: -- AAA projecting record holiday travel for 2024 -- Strong consumer preference for travel experiences continues -- Operational challenges including ATC staffing issues -- Some capacity constraints from aircraft delivery delays - -4. Statistical Analysis: -- Base case (2023) is 17.307 million -- YoY growth has been slowing but still positive -- Daily variation typically 2.0-2.7 million in this period -- Standard deviation suggests 95% confidence interval of roughly +/- 1.5 million - -5. Probability Distribution Analysis: -- Most likely outcome centered around 17.5-18.5 million -- Bell curve distribution with tails for operational disruptions -- Higher likelihood of being above 2023 numbers but not dramatically - -6. Final Distribution: -≤17M: 15% (significant operational disruptions or weather events needed) -17M-18M: 45% (slight growth from 2023, most likely range) -18M-19M: 30% (strong but not record-breaking growth) ->19M: 10% (would require exceptional circumstances) - -Confidence Adjustment: -- Resolution date is very near (about 2 weeks) -- Limited time for major changes to current trajectory -- Historical data provides strong baseline -- Weather uncertainty remains key variable - -The probabilities reflect a conservative estimate given the proximity to resolution, with the most weight given to recent historical data and current trends, while accounting for operational uncertainties." -31036,"Will US airline passenger volume be for the week of Christmas through New Years Eve 2024 be greater than 17 million and less than 18 million, according to the TSA?","Predictions: [5, 10, 10, 25, 10] -Median reasoning: Let's analyze this step by step: - -1. Historical Data Analysis: -- 2023 total for this week: 17.307 million -- 2022 total: 14.848 million -- 2021 total: 13.462 million -- 2019 (pre-pandemic) total: 17.107 million - -2. Current Trends: -- Record-breaking summer 2024 with 3+ million daily passengers -- Thanksgiving 2024 saw 18.3 million passengers over comparable period -- However, October 2024 showed flattening YoY growth (-1.4%) - -3. Key Factors: -- AAA projecting record holiday travel for 2024 -- Strong consumer preference for travel experiences continues -- Operational challenges including ATC staffing issues -- Some capacity constraints from aircraft delivery delays - -4. Statistical Analysis: -- Base case (2023) is 17.307 million -- YoY growth has been slowing but still positive -- Daily variation typically 2.0-2.7 million in this period -- Standard deviation suggests 95% confidence interval of roughly +/- 1.5 million - -5. Probability Distribution Analysis: -- Most likely outcome centered around 17.5-18.5 million -- Bell curve distribution with tails for operational disruptions -- Higher likelihood of being above 2023 numbers but not dramatically - -6. Final Distribution: -≤17M: 15% (significant operational disruptions or weather events needed) -17M-18M: 45% (slight growth from 2023, most likely range) -18M-19M: 30% (strong but not record-breaking growth) ->19M: 10% (would require exceptional circumstances) - -Confidence Adjustment: -- Resolution date is very near (about 2 weeks) -- Limited time for major changes to current trajectory -- Historical data provides strong baseline -- Weather uncertainty remains key variable - -The probabilities reflect a conservative estimate given the proximity to resolution, with the most weight given to recent historical data and current trends, while accounting for operational uncertainties." -31035,"Will US airline passenger volume be for the week of Christmas through New Years Eve 2024 be greater than or equal to 18 million and less than or equal to 19 million, according to the TSA?","Predictions: [5, 10, 10, 25, 10] -Median reasoning: Let's analyze this step by step: - -1. Historical Data Analysis: -- 2023 total for this week: 17.307 million -- 2022 total: 14.848 million -- 2021 total: 13.462 million -- 2019 (pre-pandemic) total: 17.107 million - -2. Current Trends: -- Record-breaking summer 2024 with 3+ million daily passengers -- Thanksgiving 2024 saw 18.3 million passengers over comparable period -- However, October 2024 showed flattening YoY growth (-1.4%) - -3. Key Factors: -- AAA projecting record holiday travel for 2024 -- Strong consumer preference for travel experiences continues -- Operational challenges including ATC staffing issues -- Some capacity constraints from aircraft delivery delays - -4. Statistical Analysis: -- Base case (2023) is 17.307 million -- YoY growth has been slowing but still positive -- Daily variation typically 2.0-2.7 million in this period -- Standard deviation suggests 95% confidence interval of roughly +/- 1.5 million - -5. Probability Distribution Analysis: -- Most likely outcome centered around 17.5-18.5 million -- Bell curve distribution with tails for operational disruptions -- Higher likelihood of being above 2023 numbers but not dramatically - -6. Final Distribution: -≤17M: 15% (significant operational disruptions or weather events needed) -17M-18M: 45% (slight growth from 2023, most likely range) -18M-19M: 30% (strong but not record-breaking growth) ->19M: 10% (would require exceptional circumstances) - -Confidence Adjustment: -- Resolution date is very near (about 2 weeks) -- Limited time for major changes to current trajectory -- Historical data provides strong baseline -- Weather uncertainty remains key variable - -The probabilities reflect a conservative estimate given the proximity to resolution, with the most weight given to recent historical data and current trends, while accounting for operational uncertainties." -31034,"Will US airline passenger volume be for the week of Christmas through New Years Eve 2024 be greater than 19 million, according to the TSA?","Predictions: [5, 10, 10, 25, 10] -Median reasoning: Let's analyze this step by step: - -1. Historical Data Analysis: -- 2023 total for this week: 17.307 million -- 2022 total: 14.848 million -- 2021 total: 13.462 million -- 2019 (pre-pandemic) total: 17.107 million - -2. Current Trends: -- Record-breaking summer 2024 with 3+ million daily passengers -- Thanksgiving 2024 saw 18.3 million passengers over comparable period -- However, October 2024 showed flattening YoY growth (-1.4%) - -3. Key Factors: -- AAA projecting record holiday travel for 2024 -- Strong consumer preference for travel experiences continues -- Operational challenges including ATC staffing issues -- Some capacity constraints from aircraft delivery delays - -4. Statistical Analysis: -- Base case (2023) is 17.307 million -- YoY growth has been slowing but still positive -- Daily variation typically 2.0-2.7 million in this period -- Standard deviation suggests 95% confidence interval of roughly +/- 1.5 million - -5. Probability Distribution Analysis: -- Most likely outcome centered around 17.5-18.5 million -- Bell curve distribution with tails for operational disruptions -- Higher likelihood of being above 2023 numbers but not dramatically - -6. Final Distribution: -≤17M: 15% (significant operational disruptions or weather events needed) -17M-18M: 45% (slight growth from 2023, most likely range) -18M-19M: 30% (strong but not record-breaking growth) ->19M: 10% (would require exceptional circumstances) - -Confidence Adjustment: -- Resolution date is very near (about 2 weeks) -- Limited time for major changes to current trajectory -- Historical data provides strong baseline -- Weather uncertainty remains key variable - -The probabilities reflect a conservative estimate given the proximity to resolution, with the most weight given to recent historical data and current trends, while accounting for operational uncertainties." -31033,"Will the undiagnosed disease currently spreading in the Democratic Republic of the Congo be identified as COVID-19 before January 7, 2025?","Predictions: [4, 8, 15, 12, 20] -Median reasoning: Let's analyze this step by step: - -1. Timeline Analysis: -- We have about 3.5 weeks until resolution (Dec 13 to Jan 7) -- This is a relatively short timeframe for disease identification in a remote area -- The rainy season and access difficulties compound the timeline challenges - -2. Current Evidence: -- Initial samples show malaria presence -- Symptoms align with multiple respiratory illnesses -- WHO teams are actively investigating but facing logistical challenges -- All severe cases are in malnourished children - -3. Base Rates: -- Malaria is endemic in the region -- Respiratory diseases are common, especially during rainy season -- Past undiagnosed outbreaks in DRC often turn out to be known endemic diseases - -4. COVID-19 Specific Analysis: -- COVID-19 testing is readily available and would likely be one of the first tests done -- Current variant tracking shows COVID-19 cases are primarily mild -- The age distribution (mostly young children) doesn't match typical COVID-19 patterns - -5. RSV Analysis: -- RSV commonly affects young children -- Symptoms align with RSV -- Testing capabilities for RSV may be limited in the region - -6. Influenza Analysis: -- Symptoms align well with influenza -- Current season is conducive to influenza spread -- Testing capabilities should be available - -7. Resolution Requirements: -- Must be officially confirmed by WHO -- Must be certain identification, not suspected -- Must be majority of cases - -Given: -- The short timeline -- Logistical challenges -- Initial malaria findings -- Strict resolution criteria - -I assess: -- COVID-19: 4% chance (symptoms match but age distribution doesn't, and testing is readily available) -- RSV: 8% chance (symptoms and age distribution match, but testing may be limited) -- Influenza: 12% chance (good symptom match and seasonal timing) - -The remaining 76% probability is split between other causes (mainly malaria, which has already tested positive, and possibly multiple concurrent infections). - -Given the short timeline and strict WHO confirmation requirement, these probabilities are intentionally conservative." -31032,"Will the undiagnosed disease currently spreading in the Democratic Republic of the Congo be identified as RSV before January 7, 2025?","Predictions: [4, 8, 15, 12, 20] -Median reasoning: Let's analyze this step by step: - -1. Timeline Analysis: -- We have about 3.5 weeks until resolution (Dec 13 to Jan 7) -- This is a relatively short timeframe for disease identification in a remote area -- The rainy season and access difficulties compound the timeline challenges - -2. Current Evidence: -- Initial samples show malaria presence -- Symptoms align with multiple respiratory illnesses -- WHO teams are actively investigating but facing logistical challenges -- All severe cases are in malnourished children - -3. Base Rates: -- Malaria is endemic in the region -- Respiratory diseases are common, especially during rainy season -- Past undiagnosed outbreaks in DRC often turn out to be known endemic diseases - -4. COVID-19 Specific Analysis: -- COVID-19 testing is readily available and would likely be one of the first tests done -- Current variant tracking shows COVID-19 cases are primarily mild -- The age distribution (mostly young children) doesn't match typical COVID-19 patterns - -5. RSV Analysis: -- RSV commonly affects young children -- Symptoms align with RSV -- Testing capabilities for RSV may be limited in the region - -6. Influenza Analysis: -- Symptoms align well with influenza -- Current season is conducive to influenza spread -- Testing capabilities should be available - -7. Resolution Requirements: -- Must be officially confirmed by WHO -- Must be certain identification, not suspected -- Must be majority of cases - -Given: -- The short timeline -- Logistical challenges -- Initial malaria findings -- Strict resolution criteria - -I assess: -- COVID-19: 4% chance (symptoms match but age distribution doesn't, and testing is readily available) -- RSV: 8% chance (symptoms and age distribution match, but testing may be limited) -- Influenza: 12% chance (good symptom match and seasonal timing) - -The remaining 76% probability is split between other causes (mainly malaria, which has already tested positive, and possibly multiple concurrent infections). - -Given the short timeline and strict WHO confirmation requirement, these probabilities are intentionally conservative." -31031,"Will the undiagnosed disease currently spreading in the Democratic Republic of the Congo be identified as Influenza before January 7, 2025?","Predictions: [4, 8, 15, 12, 20] -Median reasoning: Let's analyze this step by step: - -1. Timeline Analysis: -- We have about 3.5 weeks until resolution (Dec 13 to Jan 7) -- This is a relatively short timeframe for disease identification in a remote area -- The rainy season and access difficulties compound the timeline challenges - -2. Current Evidence: -- Initial samples show malaria presence -- Symptoms align with multiple respiratory illnesses -- WHO teams are actively investigating but facing logistical challenges -- All severe cases are in malnourished children - -3. Base Rates: -- Malaria is endemic in the region -- Respiratory diseases are common, especially during rainy season -- Past undiagnosed outbreaks in DRC often turn out to be known endemic diseases - -4. COVID-19 Specific Analysis: -- COVID-19 testing is readily available and would likely be one of the first tests done -- Current variant tracking shows COVID-19 cases are primarily mild -- The age distribution (mostly young children) doesn't match typical COVID-19 patterns - -5. RSV Analysis: -- RSV commonly affects young children -- Symptoms align with RSV -- Testing capabilities for RSV may be limited in the region - -6. Influenza Analysis: -- Symptoms align well with influenza -- Current season is conducive to influenza spread -- Testing capabilities should be available - -7. Resolution Requirements: -- Must be officially confirmed by WHO -- Must be certain identification, not suspected -- Must be majority of cases - -Given: -- The short timeline -- Logistical challenges -- Initial malaria findings -- Strict resolution criteria - -I assess: -- COVID-19: 4% chance (symptoms match but age distribution doesn't, and testing is readily available) -- RSV: 8% chance (symptoms and age distribution match, but testing may be limited) -- Influenza: 12% chance (good symptom match and seasonal timing) - -The remaining 76% probability is split between other causes (mainly malaria, which has already tested positive, and possibly multiple concurrent infections). - -Given the short timeline and strict WHO confirmation requirement, these probabilities are intentionally conservative." -31030,"Will Blue Origin launch its New Glenn rocket before January 1, 2025?","Let me think through this step by step: - -1. Timeline Assessment: -- Only 18 days remain until resolution -- No hot-fire test has been completed yet -- Regulatory approvals still pending -- Holiday period approaching which could slow progress - -2. Critical Path Analysis: -- Need to complete hot-fire test -- Analyze test data -- Get FAA approval -- Prepare for actual launch -- Execute launch - -3. Historical Context: -- Blue Origin has consistently missed deadlines -- New rockets typically face delays in first launches -- Holiday period traditionally sees reduced activity -- First launches often require multiple attempt windows - -4. Current Status: -- Rocket is on pad -- Company maintains public confidence -- But critical milestones remain incomplete -- No specific launch date announced - -5. Risk Factors: -- Weather in Florida in December -- Technical issues during hot-fire -- FAA approval timeline -- Holiday staffing -- First-time launch complexity - -6. Comparable Timelines: -- Similar new rockets typically need 1-2 months from hot-fire to launch -- FAA approval process usually takes weeks -- Weather delays common in winter - -7. Probability Distribution: -By January 1: 15% -By February 1: 35% -By March 1: 60% -By June 1: 85% - -Given the extremely tight timeline, pending critical tests, regulatory requirements, and historical context of rocket development, combined with the holiday period, I assess there's a low probability of launch before January 1. - -The company's public confidence is noted but weighted against the practical constraints and typical timelines for similar milestones in the industry. - -Probability: 15%" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {6: 10, 7: 90, 10: 18, 20: 25, 40: 35, 60: 42, 80: 52, 90: 62} - -Model's Answer: Let me think through this step by step as a superforecaster: - -1) First, this is a Non-Market Threshold Value Event about box office performance during a specific 5-day period. - -2) Key positive factors: -- Christmas release window is traditionally strong for movies -- Timothée Chalamet is a current box office draw (Wonka, Dune) -- Musical biopics have done well historically (Bohemian Rhapsody, Rocketman) -- James Mangold has success with musical biopics (Walk the Line) - -3) Key negative factors: -- Bob Dylan, while legendary, may have less broad modern appeal than recent musical biopic subjects -- Competition from other holiday releases -- Current movie market is somewhat unpredictable post-pandemic -- Period piece musical dramas can be harder sells than action/family fare during holidays - -4) Most recent forecasts suggest $35-55M for 5-day, with $37M as point estimate. This is from trusted industry sources and relatively recent. - -5) Comparable recent musical biopics: -- Elvis (2022): $51.4M in first 5 days -- Rocketman (2019): $40.4M in first 5 days -- Bohemian Rhapsody (2018): $61.2M in first 5 days - -6) For a low-end scenario (~10th percentile): -Poor reviews combine with niche subject matter and holiday competition to severely underperform, pulling in only $15-20M over 5 days (25% probability) - -7) For a high-end scenario (~90th percentile): -Exceptional reviews, Chalamet's star power, and strong holiday timing drive significant overperformance to $60-65M (25% probability) - -8) The middle range of $35-45M seems most likely (50% probability), aligning with recent forecasts and comparable films. - -9) Given the holiday timing and recent comparables, I'll weight slightly toward the higher end of ranges while maintaining appropriate uncertainty bands. - -Final probability distribution: - -Percentile 10: 18 -Percentile 20: 25 -Percentile 40: 35 -Percentile 60: 42 -Percentile 80: 52 -Percentile 90: 62 - -This distribution reflects: -- Central tendency around expert forecasts -- Wide bands to account for uncertainty -- Slight positive skew due to holiday timing -- Grounding in recent comparable performances -- Recognition that both significant under and overperformance are possible but unlikely" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {10: 15, 80: 45, 20: 20, 40: 30, 60: 38, 90: 50} - -Model's Answer: Let me think through this carefully as a superforecaster: - -This is a Non-Market Threshold Value Event with some elements of a Competition Event, as movies compete for audience attention. - -Key considerations: - -1. Timing: Christmas opening is prime movie-going time, which supports higher numbers -2. Competition: Will be competing with other major releases like Wonka and Aquaman -3. Genre/Appeal: Musical biopics have shown varying success - ""Elvis"" opened to $31.2M in 2022 -4. Star Power: Timothée Chalamet has growing appeal but isn't yet a guaranteed box office draw -5. Reviews: Initial 74% on Rotten Tomatoes is decent but not exceptional - -Low outcome scenario (10-20th percentile): -- Poor weather across key markets during holiday week -- Strong competition from other films -- Mixed word of mouth -- Limited appeal beyond Dylan fans -- Could result in $15-20M for 5-day period - -High outcome scenario (80-90th percentile): -- Excellent word of mouth -- Strong critical acclaim for Chalamet's performance -- Weather cooperates nationwide -- Holiday timing maximizes attendance -- Could reach $45-50M for 5-day period - -The Box Office Theory forecast of $35M for 5 days seems reasonable as a median expectation, given: -- Christmas falling on a Wednesday maximizes potential viewing days -- Historical performance of similar musical biopics -- Current tracking numbers -- Star power and director credentials - -I'll set relatively wide confidence intervals due to: -- Uncertainty about weather -- Varying appeal of Bob Dylan to different demographics -- Competition from other films -- Economic factors affecting holiday spending - -Percentile 10: 15 -Percentile 20: 20 -Percentile 40: 30 -Percentile 60: 38 -Percentile 80: 45 -Percentile 90: 50" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {10: 20, 20: 30, 30: 38, 40: 44, 50: 48, 60: 52, 70: 57, 80: 63, 90: 70, 95: 75, 99: 85} - -Model's Answer: Let me think through this step by step: - -1) First, let's evaluate the reliability and recency of our sources: -- Box Office Pro (Dec 13) is most recent and typically reliable for projections -- Box Office Theory (Nov 22) is older but provides specific 5-day forecasts -- Critical reception data is recent and moderately positive - -2) Key factors for a low outcome: -- Competition from other Christmas releases -- Mixed critical reception (74% RT isn't spectacular) -- Biographical films can be niche -- Bob Dylan, while iconic, may not draw younger audiences -Probability of significantly underperforming ($15M or less for 5 days): ~20% - -3) Key factors for a high outcome: -- Christmas period typically strong for movies -- Timothée Chalamet's star power -- Director's track record with biopics -- Holiday timing allowing for multiple viewing days -Probability of significantly overperforming ($45M+ for 5 days): ~15% - -4) Historical context: -- Recent musical biopics have varied widely in performance -- Christmas releases typically see 2.5-3x their Friday-Sunday numbers across 5 days -- Taking Box Office Pro's $15-20M three-day forecast, this suggests $37.5-60M potential - -5) Considering uncertainty: -- We're 9 days from release -- Marketing push could affect numbers -- Weather could impact theater attendance -- Competition from other releases could shift - -6) Base case analysis: -- Using Box Office Pro's midpoint ($17.5M for 3 days) -- Multiplying by typical 5-day Christmas multiplier (2.75x) -- This suggests around $48M as a median outcome - -7) Final distribution considerations: -- Wide confidence intervals due to multiple uncertainties -- Slightly right-skewed distribution -- Floor around $10M barring disaster -- Ceiling around $75M for exceptional performance - -"" -Percentile 1: 10 -Percentile 5: 15 -Percentile 10: 20 -Percentile 20: 30 -Percentile 30: 38 -Percentile 40: 44 -Percentile 50: 48 -Percentile 60: 52 -Percentile 70: 57 -Percentile 80: 63 -Percentile 90: 70 -Percentile 95: 75 -Percentile 99: 85 -""" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {1: 6, 5: 10, 10: 15, 20: 22, 30: 27, 40: 30, 50: 32, 60: 35, 70: 38, 80: 42, 90: 48, 95: 55, 99: 65} - -Model's Answer: Let me think through this step by step. - -Step one: Let's assess the movie's characteristics and market conditions. This is a musical biopic starring Timothée Chalamet as Bob Dylan, directed by James Mangold who has experience with musical biopics (Walk the Line). The Christmas release timing is traditionally strong for box office performance. - -Step two: Looking at comparable musical biopics in recent years: -- Bohemian Rhapsody (2018): $51.1M opening weekend -- Elvis (2022): $31.2M opening weekend -- Whitney Houston: I Wanna Dance with Somebody (2022): $4.8M opening weekend -- Rocketman (2019): $25.7M opening weekend - -Step three: Consider specific advantages and disadvantages: -Advantages: -- Timothée Chalamet is a current leading star with strong draw -- Christmas release timing is optimal -- James Mangold has proven track record -Disadvantages: -- Bob Dylan, while legendary, may have less broad contemporary appeal than Elvis or Queen -- Competition from other holiday releases -- Possible audience fatigue with musical biopics - -Step four: Analyzing the forecasts provided, Box Office Theory's $35M 5-day prediction seems well-reasoned given the comparable films and timing. The more conservative $20M week 1 forecast from Beatrice Baker suggests some downside risk. - -Step five: For a low case scenario (10th percentile or lower), factors could include: -- Poor critical reception -- Strong competition from other holiday releases -- Weather events affecting theater attendance -- Marketing misfire -This could result in earnings as low as $8-12M for the 5-day period. - -Step six: For a high case scenario (90th percentile or higher), factors could include: -- Exceptional reviews -- Strong word-of-mouth -- Viral social media momentum -- Weather conducive to theater attendance -This could push earnings up to $50-60M for the 5-day period. - -Step seven: Given the historical performance of similar films, the timing, and current market conditions, I'll center my distribution around $32M with significant spread to account for uncertainty. - -"" -Percentile 1: 6 -Percentile 5: 10 -Percentile 10: 15 -Percentile 20: 22 -Percentile 30: 27 -Percentile 40: 30 -Percentile 50: 32 -Percentile 60: 35 -Percentile 70: 38 -Percentile 80: 42 -Percentile 90: 48 -Percentile 95: 55 -Percentile 99: 65 -""" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {0.1: 8, 1: 12, 5: 18, 10: 22, 15: 25, 20: 28, 25: 30, 30: 32, 35: 34, 40: 36, 45: 38, 50: 40, 55: 42, 60: 44, 65: 46, 70: 48, 75: 51, 80: 54, 85: 58, 90: 63, 95: 70, 99: 85, 99.9: 100} - -Model's Answer: Let me think through this step by step. - -Step one: First, I need to consider the nature of this question - it's a cumulative count event for box office earnings over a specific 5-day period. The timing (Christmas opening through Sunday) is particularly significant as this is traditionally one of the strongest periods for movie attendance. - -Step two: Looking at the sources, the most recent forecast from Box Office Pro (Dec 14) suggests $15M-$20M for just the three-day weekend, while the earlier Box Office Theory forecast suggested $35M for the five-day period. The more recent forecast seems more credible, and suggests the five-day period could be higher than the earlier $35M forecast. - -Step three: Consider comparable films. This is a musical biopic starring Timothée Chalamet, directed by James Mangold. Recent comparable films include ""Elvis"" which opened to $31.2M in a regular three-day weekend, and ""Bohemian Rhapsody"" which opened to $51.1M. However, these were summer/fall releases, not Christmas releases. - -Step four: The Christmas timing is crucial. Movies opening on Christmas Day typically see strong numbers through the holiday week. Recent Christmas Day openings like ""Little Women"" (2019) earned $29.2M in its first five days, while ""Les Misérables"" (2012) earned $66.7M in its first five days. - -Step five: Potential downside risks include: -- Competition from other holiday releases -- Mixed critical reception -- Bob Dylan's appeal to younger audiences -- Potential winter weather impacts -Likelihood of significant underperformance (<$20M): 15% - -Step six: Potential upside factors include: -- Timothée Chalamet's strong following -- Holiday timing boost -- Strong critical reception -- Mangold's track record with biopics -Likelihood of significant overperformance (>$50M): 20% - -Step seven: Given all factors, I expect the most likely outcome to be between $30M-$45M, but with wide tails due to the uncertainty of holiday releases and audience reception. - -Percentile 0.1: 8 -Percentile 1: 12 -Percentile 5: 18 -Percentile 10: 22 -Percentile 15: 25 -Percentile 20: 28 -Percentile 25: 30 -Percentile 30: 32 -Percentile 35: 34 -Percentile 40: 36 -Percentile 45: 38 -Percentile 50: 40 -Percentile 55: 42 -Percentile 60: 44 -Percentile 65: 46 -Percentile 70: 48 -Percentile 75: 51 -Percentile 80: 54 -Percentile 85: 58 -Percentile 90: 63 -Percentile 95: 70 -Percentile 99: 85 -Percentile 99.9: 100" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {0.1: 8, 1: 12, 5: 15, 10: 18, 15: 21, 20: 24, 25: 27, 30: 29, 35: 31, 40: 33, 45: 34, 50: 35, 55: 37, 60: 39, 65: 41, 70: 43, 75: 45, 80: 48, 85: 51, 90: 55, 95: 60, 99: 67, 99.9: 72} - -Model's Answer: Let me think through this carefully as a superforecaster: - -Step one: This is a Non-Market Threshold Value Event, specifically about box office earnings during a holiday period. The timing (Christmas opening) is particularly significant for box office performance. - -Step two: Key factors supporting a higher outcome: -- Christmas week is historically strong for movies -- Timothée Chalamet has strong recent success (Wonka, Dune) -- James Mangold has proven success with music biopics (Walk the Line) -- Early tracking shows decent interest -- Strong early reviews reported - -Step three: Key factors supporting a lower outcome: -- Music biopics have been inconsistent performers -- Bob Dylan may appeal more to older audiences -- Competition during holiday season could be significant -- Economic conditions might affect discretionary spending -- Weather during Christmas week could affect turnout - -Step four: Looking at recent comparable music biopics and adjusting for the Christmas timing and star power: -- Elvis (2022) opened to $31.2M in 3 days (summer) -- Bohemian Rhapsody (2018) opened to $51.1M in 3 days (fall) -- Rocketman (2019) opened to $25.7M in 3 days (summer) - -Step five: The 5-day holiday period typically sees about 40-50% more earnings than a regular 3-day weekend. The Box Office Theory forecast of $35M for 5 days seems reasonable as a median estimate, considering the holiday boost but also accounting for potentially mixed appeal. - -Step six: Given the uncertainty and various factors, I need to set wide confidence intervals. The 90% confidence interval should probably span from about $15M to $55M to account for various scenarios including weather issues, unexpected competition, or breakout success. - -Percentile 0.1: 8 -Percentile 1: 12 -Percentile 5: 15 -Percentile 10: 18 -Percentile 15: 21 -Percentile 20: 24 -Percentile 25: 27 -Percentile 30: 29 -Percentile 35: 31 -Percentile 40: 33 -Percentile 45: 34 -Percentile 50: 35 -Percentile 55: 37 -Percentile 60: 39 -Percentile 65: 41 -Percentile 70: 43 -Percentile 75: 45 -Percentile 80: 48 -Percentile 85: 51 -Percentile 90: 55 -Percentile 95: 60 -Percentile 99: 67 -Percentile 99.9: 72" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {1: 12, 5: 15, 10: 18, 20: 22, 30: 26, 40: 29, 50: 32, 60: 35, 70: 38, 80: 42, 90: 47, 95: 52, 99: 60, 99.9: 71} - -Model's Answer: Let me think through this step by step. - -Step one: This is a Non-Market Threshold Value Event question about box office earnings. The key uncertainty is the 5-day opening period for a major biographical film during a prime movie-going season. - -Step two: The most relevant and recent forecast comes from Box Office Theory (Nov 22), predicting a 5-day opening of $35M. B.A. Baker's prediction (Dec 6) is notably lower at $20M for the first week. This significant disagreement suggests high uncertainty. - -Step three: Looking at comparable films, biographical music films have shown wide variance in performance. ""Elvis"" opened to $31.2M in its first weekend in summer 2022, while ""Bob Marley: One Love"" is tracking for a more modest opening. Timothée Chalamet's star power and the Christmas release date are positive factors, but Dylan's story might appeal to a more selective audience. - -Step four: The Christmas timing is crucial. December 25-29, 2024 includes the holiday and subsequent weekend, traditionally strong box office days. However, competition from other releases could impact performance. - -Step five: For a low outcome scenario (around $15M): Poor reviews, limited appeal beyond Dylan fans, and strong competition from other holiday releases could significantly dampen attendance. Likelihood: 20% - -Step six: For a high outcome scenario (around $45M): Strong reviews, Chalamet's draw, holiday timing, and broad nostalgic appeal could drive strong attendance. Likelihood: 15% - -Step seven: The base case scenario ($30-35M) aligns with Box Office Theory's prediction and seems reasonable given comparable films and timing. Likelihood: 65% - -Step eight: Given the proximity to release (just over a week), major shifts in expectations are unlikely, but marketing push and early reviews could still impact opening numbers. - -Percentile .1: 9 -Percentile 1: 12 -Percentile 5: 15 -Percentile 10: 18 -Percentile 20: 22 -Percentile 30: 26 -Percentile 40: 29 -Percentile 50: 32 -Percentile 60: 35 -Percentile 70: 38 -Percentile 80: 42 -Percentile 90: 47 -Percentile 95: 52 -Percentile 99: 60 -Percentile 99.9: 71" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {1: 12, 5: 15, 7: 17, 9: 19, 11: 20, 13: 21, 15: 22, 17: 23, 19: 24, 21: 25, 23: 26, 25: 27, 27: 28, 29: 29, 31: 30, 33: 31, 35: 32, 37: 33, 39: 34, 41: 35, 43: 36, 45: 37, 47: 38, 49: 39, 51: 40, 53: 41, 55: 42, 57: 43, 59: 44, 61: 45, 63: 46, 65: 47, 67: 48, 69: 49, 71: 50, 73: 51, 75: 52, 77: 53, 79: 54, 81: 55, 83: 56, 85: 57, 87: 58, 89: 59, 91: 61, 93: 63, 95: 65, 99: 72, 99.9: 80} - -Model's Answer: Let me think through this step by step as a superforecaster. - -Step one: Consider the base case. We have two primary forecasts - Box Office Theory predicting $35M for the 5-day period, and B.A. Baker predicting around $20M. The more recent Box Office Theory prediction should be weighted more heavily, but both indicate uncertainty in the market. - -Step two: Analyze comparable films. Recent musical biopics have shown wide variance: -- ""Elvis"" (2022): $31.2M opening weekend -- ""Whitney Houston: I Wanna Dance with Somebody"" (2022): $4.8M opening weekend -- ""Bohemian Rhapsody"" (2018): $51.1M opening weekend - -Step three: Consider specific factors for this release: -- Positive: Christmas release window traditionally strong for movies -- Positive: Timothée Chalamet's star power and recent success with Wonka -- Negative: Bob Dylan, while iconic, may have less mainstream appeal than Elvis or Queen -- Negative: Potential competition from other holiday releases - -Step four: Account for uncertainties: -- Weather could impact theater attendance -- Last-minute marketing push effectiveness -- Critical reviews yet to be released -- Word-of-mouth impact - -Step five: Consider extreme scenarios: -Low case ($10M or below): Extremely poor reviews, weather issues, stronger competition than expected, limited appeal outside core Dylan fans. -Probability: ~5% - -High case ($50M+): Exceptional reviews, strong word-of-mouth, broader appeal than expected, perfect weather conditions. -Probability: ~5% - -Step six: Given the recent forecast updates and Christmas timing, I lean toward the higher end of estimates but maintain wide confidence intervals due to significant uncertainty. - -Percentile .1: 8 -Percentile 1: 12 -Percentile 5: 15 -Percentile 7: 17 -Percentile 9: 19 -Percentile 11: 20 -Percentile 13: 21 -Percentile 15: 22 -Percentile 17: 23 -Percentile 19: 24 -Percentile 21: 25 -Percentile 23: 26 -Percentile 25: 27 -Percentile 27: 28 -Percentile 29: 29 -Percentile 31: 30 -Percentile 33: 31 -Percentile 35: 32 -Percentile 37: 33 -Percentile 39: 34 -Percentile 41: 35 -Percentile 43: 36 -Percentile 45: 37 -Percentile 47: 38 -Percentile 49: 39 -Percentile 51: 40 -Percentile 53: 41 -Percentile 55: 42 -Percentile 57: 43 -Percentile 59: 44 -Percentile 61: 45 -Percentile 63: 46 -Percentile 65: 47 -Percentile 67: 48 -Percentile 69: 49 -Percentile 71: 50 -Percentile 73: 51 -Percentile 75: 52 -Percentile 77: 53 -Percentile 79: 54 -Percentile 81: 55 -Percentile 83: 56 -Percentile 85: 57 -Percentile 87: 58 -Percentile 89: 59 -Percentile 91: 61 -Percentile 93: 63 -Percentile 95: 65 -Percentile 99: 72 -Percentile 99.9: 80" -30927,"How much will A Complete Unknown earn in domestic gross earnings during the 5-day period from its Christmas 2024 opening through Sunday December 29, 2024?","Extracted Percentile Values: {1: 12, 5: 15, 10: 18, 15: 20, 20: 22, 25: 24, 30: 26, 35: 28, 40: 30, 45: 31, 46: 31.2, 47: 31.4, 48: 31.6, 49: 31.8, 50: 32, 51: 32.3, 52: 32.6, 53: 32.9, 54: 33.2, 55: 33.5, 60: 35, 65: 37, 70: 40, 75: 43, 80: 46, 85: 50, 90: 55, 95: 62, 99: 72, 99.9: 80} - -Model's Answer: Let me think through this step by step as a superforecaster: - -Step one: This is a Non-Market Threshold Value Event for a movie's box office performance during a specific 5-day window. - -Step two: The timing is crucial - Christmas opening through December 29, which includes the lucrative Christmas to New Year period. This period typically sees higher theater attendance than normal. - -Step three: Key factors suggesting a lower outcome: -- Musical biopics have had mixed performance recently -- Competition from other holiday releases -- Potential weather impacts during winter release -- Box Office Pro's more conservative forecast of $15-20M for just the weekend portion -Likelihood of significantly underperforming ($15M or less for 5 days): ~10% - -Step four: Key factors suggesting a higher outcome: -- Star power of Timothée Chalamet, who has recent success -- Director James Mangold's track record with ""Walk the Line"" -- Holiday timing advantage -- Box Office Theory's optimistic $35M forecast -Likelihood of significantly overperforming ($50M+ for 5 days): ~15% - -Step five: Recent comparable musical biopics during non-holiday periods have averaged $25-35M opening weekends. The holiday timing should provide a 20-30% boost to these numbers. - -Step six: The resolution criteria specifically looks at domestic box office only, and includes the full 5-day period rather than just the weekend. - -Step seven: The range minimum of 10.0 and maximum of 70.0 seem reasonable given historical precedent, though I'll assign small probabilities outside this range to account for extreme scenarios. - -Step eight: Weighing all factors, the median outcome appears to be around $32M, with a wide distribution to account for uncertainty: - -Percentile .1: 8 -Percentile 1: 12 -Percentile 5: 15 -Percentile 10: 18 -Percentile 15: 20 -Percentile 20: 22 -Percentile 25: 24 -Percentile 30: 26 -Percentile 35: 28 -Percentile 40: 30 -Percentile 45: 31 -Percentile 46: 31.2 -Percentile 47: 31.4 -Percentile 48: 31.6 -Percentile 49: 31.8 -Percentile 50: 32 -Percentile 51: 32.3 -Percentile 52: 32.6 -Percentile 53: 32.9 -Percentile 54: 33.2 -Percentile 55: 33.5 -Percentile 60: 35 -Percentile 65: 37 -Percentile 70: 40 -Percentile 75: 43 -Percentile 80: 46 -Percentile 85: 50 -Percentile 90: 55 -Percentile 95: 62 -Percentile 99: 72 -Percentile 99.9: 80" -31010,"Who will send out more tweets in the 10-day period from December 21-December 30, 2024--Elon Musk, The New York Times, or The Economist?","EXTRACTED_PROBABILITIES: [8, 85, 7] - -Model's Answer: Let me analyze this step by step as a superforecaster: - -1. First, let's categorize this as a Competition Event with Cumulative Count characteristics. - -2. Let's look at recent tweet frequency data from SocialBlade: -- NYTimes typically posts 150-200+ tweets per day -- The Economist averages 50-80 tweets per day -- Elon Musk varies greatly, averaging 10-30 tweets per day but with high variance - -3. Key timing considerations: -- This is during Christmas/New Year holiday period -- News organizations typically maintain high posting volumes even during holidays -- Personal accounts like Musk's often show reduced activity during holidays -- Only 4 days until measurement period begins - -4. Historical precedent: -- News organizations maintain consistent posting schedules -- The NYTimes has historically posted more than The Economist -- Musk's posting frequency has declined somewhat since peak Twitter acquisition period - -5. Special factors: -- Holiday period likely reduces all posting somewhat -- News organizations use scheduling tools and maintain 24/7 coverage -- No major anticipated events during this period that would drive unusual volume - -6. Base rate analysis: -Over 10 days: -- NYTimes: ~1500-2000 tweets expected -- The Economist: ~500-800 tweets expected -- Musk: ~100-300 tweets expected with high variance - -7. Uncertainty factors: -- News org posting is highly predictable -- Musk's posting is highly variable -- Holiday period adds some uncertainty -- Short timeframe reduces likelihood of major changes - -8. Final probabilities after multiple reviews and uncertainty adjustments: - -Option_A: 8 -Option_B: 85 -Option_C: 7" -31010,"Who will send out more tweets in the 10-day period from December 21-December 30, 2024--Elon Musk, The New York Times, or The Economist?","EXTRACTED_PROBABILITIES: [15, 75, 10] - -Model's Answer: Let me think about this step by step: - -1) First, let's consider the question type - this is a Competition Event with a clear resolution mechanism using Social Blade data. - -2) Given the very near-term resolution (within 2 weeks), current behavior patterns should be weighted heavily. - -3) Looking at recent Social Blade data for December 2023: -- NYTimes averages around 100-150 tweets per day -- The Economist averages 30-50 tweets per day -- Elon Musk varies widely but typically 15-40 tweets per day - -4) Key considerations: -- Holiday period could reduce institutional posting -- NYTimes maintains 24/7 news coverage even during holidays -- Elon Musk's posting is highly variable and personal -- The Economist has more consistent but lower volume - -5) Factors affecting uncertainty: -- Very short timeframe reduces uncertainty -- Holiday period adds some variance -- News events could spike institutional posting -- Musk's posting is inherently unpredictable - -6) Historical precedent: -- NYTimes consistently leads in volume -- The Economist maintains steady but lower volume -- Musk's posting varies significantly - -7) Risk factors: -- Technical issues could affect any account -- Major news events could spike institutional posting -- Personal events could affect Musk's posting - -8) Given the short timeframe and clear historical patterns, I'll weight current behavior heavily while accounting for holiday effects and uncertainty. - -Option_A: 15 -Option_B: 75 -Option_C: 10" -31010,"Who will send out more tweets in the 10-day period from December 21-December 30, 2024--Elon Musk, The New York Times, or The Economist?","EXTRACTED_PROBABILITIES: [15, 75, 10] - -Model's Answer: Let me think through this step by step: - -1. First, let's consider this is a Competition Event type question, comparing tweet volumes over a specific 10-day period. - -2. The time period (Dec 21-30, 2024) includes Christmas and year-end holidays, which could affect posting patterns: -- News organizations might reduce staff/posting -- Elon Musk may have more personal time to tweet -- The Economist typically has lighter coverage during holidays - -3. Let's analyze typical posting volumes (though current data would be ideal): -- NYTimes typically posts 100+ times daily -- The Economist posts roughly 25-50 times daily -- Musk's posting is highly variable, from 5-50+ daily - -4. Holiday period considerations: -- NYTimes maintains 24/7 news coverage even during holidays -- The Economist typically reduces content during holidays -- Musk's posting may increase during downtime - -5. Specific factors for December 2024: -- Presidential election aftermath could drive high news volume -- Economic year-end reporting increases business news -- Tech industry typically slower during holidays - -6. Risk factors/uncertainties: -- Platform changes could affect posting abilities -- Account suspensions/technical issues -- Major breaking news events -- Personal schedule changes - -7. Base rate considerations: -- News organizations are more consistent -- Personal accounts more variable -- Holiday periods typically show reduced volume - -8. Recent trends suggest NYTimes maintains highest volume despite platform changes - -9. The short timeframe (4 days from now) means current patterns likely persist - -10. Conservative estimate accounting for holidays and uncertainties: - -Elon Musk: @elonmusk: 15 -The NY Times: @nytimes: 75 -The Economist: @TheEconomist: 10" -31010,"Who will send out more tweets in the 10-day period from December 21-December 30, 2024--Elon Musk, The New York Times, or The Economist?","EXTRACTED_PROBABILITIES: [45, 40, 15] - -Model's Answer: Let me analyze this step by step: - -1. Type of Question: This is a Competition Event, comparing tweet volumes over a specific period. - -2. Timeline Analysis: -- Only 4 days until measurement period begins -- Very short-term forecast (2 weeks) -- Holiday period which may affect behavior - -3. Historical Patterns: -- NYTimes typically posts 50-100 tweets daily -- The Economist posts 20-40 tweets daily -- Musk's posting varies greatly, from 5-50+ daily - -4. Key Considerations: -- Holiday period (Christmas/New Year's) may reduce institutional posting -- News organizations have scheduled content even during holidays -- Personal accounts like Musk's more variable but less structured - -5. Recent Context: -- No major anticipated events that would dramatically change behavior -- Short timeframe means current patterns likely to persist -- Holiday period might reduce Musk's posting somewhat - -6. Uncertainty Factors: -- Holiday impact on posting behavior -- Potential breaking news events -- Platform changes or policy shifts - -7. Base Rate Analysis: -- News organizations maintain more consistent posting schedules -- NYTimes historically posts more than The Economist -- Musk's posting highly variable but generally high volume - -8. Special Considerations: -- Christmas/New Year period may reduce institutional posting -- News cycle typically slower during holidays -- Personal posting patterns less predictable - -9. Risk Assessment: -- Very short timeline reduces likelihood of major changes -- Holiday period adds uncertainty -- Need to account for historical overconfidence - -Final Probabilities: - -Option_A: 45 -Option_B: 40 -Option_C: 15" -31012,"How many wins will the Cleveland Cavaliers have in the 2024-25 NBA basketball season on January 1, 2025?","EXTRACTED_PROBABILITIES: [35, 45, 20] - -Model's Answer: Let me think through this step by step: - -1) First, let's establish the current situation: -- Cavaliers are 22-4 through about 26 games -- There are 6 remaining games until January 1st -- Current win percentage is .846 - -2) Analyzing upcoming schedule: -- Milwaukee Bucks (tough) -- Philadelphia 76ers (tough) -- Utah Jazz (winnable) -- Denver Nuggets (very tough, away) -- Golden State Warriors (moderate, away) -- LA Lakers (tough, away, back-to-back) - -3) Key considerations: -- Back-to-back games at end of December -- 4 of 6 games against strong playoff teams -- 4 of 6 games are away games -- Team is currently overperforming expectations -- Short time horizon reduces uncertainty - -4) Statistical analysis: -- Even maintaining .846 win% (unlikely), expected wins in 6 games ≈ 5.1 -- More realistic .600 win% for tough stretch = 3.6 wins -- Current 22 wins + probable 3-4 wins = 25-26 wins - -5) Base rate considerations: -- Teams rarely maintain .800+ win rates -- Road games typically have lower win rates -- Back-to-backs typically reduce win probability - -Given the tough schedule, likelihood of some regression, and road-heavy schedule, I expect 3-4 wins in this stretch, putting them most likely at 25-26 wins. - -Option_A: 35 -Option_B: 45 -Option_C: 20" -30964,"How many US cases of H5N1 will the CDC report on December 31, 2024?","Extracted Percentile Values: {1: 30, 5: 35, 10: 38, 15: 40, 20: 42, 25: 44, 30: 46, 35: 48, 40: 50, 45: 52, 46: 53, 47: 54, 48: 55, 49: 56, 50: 57, 51: 58, 52: 59, 53: 60, 54: 61, 55: 62, 60: 64, 65: 66, 70: 68, 75: 70, 80: 72, 85: 75, 90: 78, 95: 82, 99: 87, 99.9: 90} - -Model's Answer: Let me think through this step by step. - -Step one: Review current known data points -- CDC officially reports 27 H5N1 cases as of October 25, 2024 -- PAHO report indicates 57 cases as of December 3, 2024 -- Recent reports suggest 60 confirmed cases as of December 16 -- Louisiana just reported its first case and California added 2 more cases - -Step two: Assess data reliability and discrepancies -- Official CDC website shows lower numbers due to reporting changes -- PAHO data comes from direct communication with US health authorities -- Recent news reports align with PAHO's higher numbers -- The discrepancy appears to be due to reporting methodology rather than actual case differences - -Step three: Consider time remaining until resolution -- Only 14 days remain until December 31 -- Holiday period may slow down case reporting -- Recent uptick in cases suggests continued spread -- Winter season typically associated with higher transmission - -Step four: Analyze rate of change -- Approximately 3-4 new cases per week recently -- Geographic spread continuing (new state affected) -- Ongoing exposure risk from affected dairy herds -- No human-to-human transmission observed - -Step five: Consider potential scenarios -Low scenario (30-40 cases): -- CDC maintains strict classification criteria -- Holiday period delays reporting -- Some cases get classified as general H5 rather than H5N1 -Likelihood: 25% - -High scenario (65-75 cases): -- CDC aligns reporting with other sources -- Continued spread from dairy exposure -- Rapid case classification and reporting -Likelihood: 20% - -Step six: Account for uncertainties -- Reporting methodology changes -- Holiday impact on surveillance -- Weather conditions affecting transmission -- Potential new exposure sources - -Final rationale: Given the current reporting of 60 cases, limited time remaining, and holiday period, I expect minimal but continued case growth. The main uncertainty is whether CDC will align its reporting with other sources by year-end. - -Percentile .1: 28 -Percentile 1: 30 -Percentile 5: 35 -Percentile 10: 38 -Percentile 15: 40 -Percentile 20: 42 -Percentile 25: 44 -Percentile 30: 46 -Percentile 35: 48 -Percentile 40: 50 -Percentile 45: 52 -Percentile 46: 53 -Percentile 47: 54 -Percentile 48: 55 -Percentile 49: 56 -Percentile 50: 57 -Percentile 51: 58 -Percentile 52: 59 -Percentile 53: 60 -Percentile 54: 61 -Percentile 55: 62 -Percentile 60: 64 -Percentile 65: 66 -Percentile 70: 68 -Percentile 75: 70 -Percentile 80: 72 -Percentile 85: 75 -Percentile 90: 78 -Percentile 95: 82 -Percentile 99: 87 -Percentile 99.9: 90" -30964,"How many US cases of H5N1 will the CDC report on December 31, 2024?","Extracted Percentile Values: {1: 30, 5: 35, 10: 40, 15: 45, 20: 50, 25: 55, 30: 58, 35: 61, 40: 64, 45: 66, 46: 67, 47: 67, 48: 68, 49: 68, 50: 69, 51: 69, 52: 70, 53: 70, 54: 71, 55: 71, 60: 73, 65: 75, 70: 77, 75: 79, 80: 81, 85: 83, 90: 85, 95: 87, 99: 89, 99.9: 90} - -Model's Answer: Let me think through this step by step. - -Step one: Analyzing the current situation -- The CDC officially reports 27 H5N1 cases as of October 25, 2024 -- PAHO reports 57 cases as of December 3, 2024 -- Recent news indicates 60 cases as of December 16, 2024 -- However, the resolution criteria specifically requires CDC-reported H5N1 subtype cases - -Step two: Time consideration -- Only 14 days remain until resolution -- CDC updates appear to lag behind actual cases -- The reporting delay means some current cases may not be officially counted by December 31 - -Step three: Rate of change analysis -- From October 25 to December 16 (52 days), officially reported cases increased by 33 -- This suggests approximately 0.63 new cases per day -- At this rate, we might expect 8-9 more cases by December 31 - -Step four: Considering low-end scenario -- CDC maintains strict classification criteria -- Some current probable cases might not be confirmed as H5N1 specifically -- Holiday period might slow down testing and reporting -- Low-end scenario would be current official number plus minimal additional confirmations - -Step five: Considering high-end scenario -- Current outbreak is still active -- California continues to report new cases -- Louisiana just reported its first case, suggesting possible geographic spread -- Increased surveillance might lead to better detection -- Holiday gatherings could increase transmission opportunities - -Step six: Uncertainty factors -- CDC's specific H5N1 reporting criteria vs general H5 cases -- Lag time in official reporting -- Holiday period impact on testing and reporting -- Potential for sudden cluster outbreaks - -Based on this analysis, I expect the most likely outcome to be between 65-75 cases, but with significant uncertainty given the reporting criteria specifics. - -Percentile .1: 28 -Percentile 1: 30 -Percentile 5: 35 -Percentile 10: 40 -Percentile 15: 45 -Percentile 20: 50 -Percentile 25: 55 -Percentile 30: 58 -Percentile 35: 61 -Percentile 40: 64 -Percentile 45: 66 -Percentile 46: 67 -Percentile 47: 67 -Percentile 48: 68 -Percentile 49: 68 -Percentile 50: 69 -Percentile 51: 69 -Percentile 52: 70 -Percentile 53: 70 -Percentile 54: 71 -Percentile 55: 71 -Percentile 60: 73 -Percentile 65: 75 -Percentile 70: 77 -Percentile 75: 79 -Percentile 80: 81 -Percentile 85: 83 -Percentile 90: 85 -Percentile 95: 87 -Percentile 99: 89 -Percentile 99.9: 90" -31066,"Which state will have the highest real GDP growth rate in Q3 2024: California, Texas or Illinois?","EXTRACTED_PROBABILITIES: [55, 15, 30] - -Model's Answer: Let me think about this step by step: - -1) First, let's consider the timing - this resolves in just 2 days for Q3 2024 data. The question is about official BEA data that will be released, so we need to consider what economic activity has already happened and will be reported. - -2) Looking at historical patterns from BEA releases: -- Texas has consistently outperformed in recent quarters due to energy sector strength -- California has shown resilience but faces headwinds from tech sector adjustments -- Illinois has underperformed both states consistently - -3) Recent economic indicators favor Texas: -- Strong oil and gas production -- Continued business migration -- Manufacturing growth -- Population growth driving consumption - -4) Key factors for California: -- Tech sector challenges -- High interest rates affecting housing -- Some population outflow -- But still benefits from diverse economy - -5) Illinois situation: -- Manufacturing contraction -- Population loss -- Limited growth drivers -- Higher tax burden - -6) The extremely short time to resolution means we should heavily weight recent performance and known Q3 2024 factors rather than speculative changes. - -7) Statistical analysis of past quarterly GDP growth rates suggests: -- Texas typically leads by 0.3-0.8 percentage points -- California usually second -- Illinois consistently trails - -8) Accounting for uncertainty and avoiding overconfidence, while recognizing Texas's clear advantages: - -Texas: 55 -Illinois: 15 -California: 30" -31005,What will be the U.S. International Investment Position in the 3rd Quarter of 2024?,"Extracted Percentile Values: {1: -23.75, 5: -23.5, 10: -23.25, 15: -23.1, 20: -23.0, 25: -22.9, 30: -22.8, 35: -22.7, 40: -22.65, 45: -22.6, 46: -22.59, 47: -22.58, 48: -22.57, 49: -22.56, 50: -22.55, 51: -22.54, 52: -22.53, 53: -22.52, 54: -22.51, 55: -22.5, 60: -22.45, 65: -22.4, 70: -22.3, 75: -22.2, 80: -22.1, 85: -22.0, 90: -21.8, 95: -21.5, 99: -21.0, 99.9: -20.5} - -Model's Answer: Let me think through this step by step. - -Step one: This is a Non-Market Threshold Value Event, measuring the U.S. International Investment Position. The most recent value from Q2 2024 was -$22.52 trillion. - -Step two: The resolution date is December 27, 2024, just 9 days away, when the Q3 2024 data will be released. This means we're looking at data that has already occurred but hasn't been released yet, significantly reducing uncertainty. - -Step three: Historical patterns show that the U.S. IIP has been consistently negative and generally declining over time, but with quarterly variations. The rate of decline has somewhat moderated in recent periods. - -Step four: Looking at potential low (more negative) outcomes: -- Further deterioration of the U.S. dollar during Q3 could have increased the negative position -- Global market volatility could have reduced the value of U.S. foreign assets -- Increased foreign investment in U.S. assets would increase liabilities -Likelihood: 30% chance of significant movement in this direction - -Step five: Looking at potential high (less negative) outcomes: -- Strong U.S. market performance in Q3 could have increased the value of U.S. foreign assets -- Dollar strengthening could have improved the position -- Foreign divestment from U.S. assets would reduce liabilities -Likelihood: 20% chance of significant movement in this direction - -Step six: Given the short timeframe to resolution and that we're looking at historical data, the most likely outcome is a modest change from the Q2 value of -$22.52 trillion. Quarterly changes typically don't exceed ±$0.5 trillion unless there are major market events. - -Final distribution, considering the high recent value and recent trends: - -Percentile .1: -24.00 -Percentile 1: -23.75 -Percentile 5: -23.50 -Percentile 10: -23.25 -Percentile 15: -23.10 -Percentile 20: -23.00 -Percentile 25: -22.90 -Percentile 30: -22.80 -Percentile 35: -22.70 -Percentile 40: -22.65 -Percentile 45: -22.60 -Percentile 46: -22.59 -Percentile 47: -22.58 -Percentile 48: -22.57 -Percentile 49: -22.56 -Percentile 50: -22.55 -Percentile 51: -22.54 -Percentile 52: -22.53 -Percentile 53: -22.52 -Percentile 54: -22.51 -Percentile 55: -22.50 -Percentile 60: -22.45 -Percentile 65: -22.40 -Percentile 70: -22.30 -Percentile 75: -22.20 -Percentile 80: -22.10 -Percentile 85: -22.00 -Percentile 90: -21.80 -Percentile 95: -21.50 -Percentile 99: -21.00 -Percentile 99.9: -20.50" -31005,What will be the U.S. International Investment Position in the 3rd Quarter of 2024?,"Extracted Percentile Values: {1: -23.8, 5: -23.5, 10: -23.3, 15: -23.1, 20: -23.0, 25: -22.9, 30: -22.8, 35: -22.7, 40: -22.65, 45: -22.6, 46: -22.59, 47: -22.58, 48: -22.57, 49: -22.56, 50: -22.55, 51: -22.54, 52: -22.53, 53: -22.52, 54: -22.51, 55: -22.5, 60: -22.4, 65: -22.3, 70: -22.2, 75: -22.1, 80: -22.0, 85: -21.9, 90: -21.8, 95: -21.5, 99: -21.2, 99.9: -21.0} - -Model's Answer: Let me think through this step by step. - -Step one: Consider the current position and recent trends. The Q2 2024 position was -$22.52 trillion, and there has been a general trend of increasing negative position over time. - -Step two: Evaluate the time horizon. We're looking at Q3 2024 data, which has already occurred but hasn't been released yet. This significantly reduces uncertainty compared to a future forecast. - -Step three: Consider historical volatility. Quarter-to-quarter changes in the IIP typically range from a few hundred billion to about a trillion dollars, with rare larger moves during significant market events. - -Step four: Analyze current economic conditions. The dollar has been relatively stable, and market movements in Q3 2024 were not particularly extreme. Interest rates remained elevated but stable. - -Step five: Consider potential low-side scenario. A significant strengthening of U.S. assets relative to liabilities could improve the position to around -21.5 trillion. Given the short timeframe and that Q3 is already complete, this is unlikely but possible. Likelihood: 10% - -Step six: Consider potential high-side scenario. A deterioration of U.S. asset values or increase in liabilities could push the position more negative to around -23.5 trillion. Given current conditions, this is also unlikely but possible. Likelihood: 10% - -Step seven: Given that Q3 is already complete and we're just waiting for the official release, the most likely outcome is a moderate change from the Q2 position, with a slight bias toward a more negative position based on historical trends. - -Percentile .1: -24.0 -Percentile 1: -23.8 -Percentile 5: -23.5 -Percentile 10: -23.3 -Percentile 15: -23.1 -Percentile 20: -23.0 -Percentile 25: -22.9 -Percentile 30: -22.8 -Percentile 35: -22.7 -Percentile 40: -22.65 -Percentile 45: -22.6 -Percentile 46: -22.59 -Percentile 47: -22.58 -Percentile 48: -22.57 -Percentile 49: -22.56 -Percentile 50: -22.55 -Percentile 51: -22.54 -Percentile 52: -22.53 -Percentile 53: -22.52 -Percentile 54: -22.51 -Percentile 55: -22.5 -Percentile 60: -22.4 -Percentile 65: -22.3 -Percentile 70: -22.2 -Percentile 75: -22.1 -Percentile 80: -22.0 -Percentile 85: -21.9 -Percentile 90: -21.8 -Percentile 95: -21.5 -Percentile 99: -21.2 -Percentile 99.9: -21.0" -31005,What will be the U.S. International Investment Position in the 3rd Quarter of 2024?,"Extracted Percentile Values: {1: -23.7, 5: -23.3, 10: -23.1, 15: -22.9, 20: -22.8, 25: -22.7, 30: -22.65, 35: -22.6, 40: -22.56, 45: -22.54, 46: -22.53, 47: -22.53, 48: -22.52, 49: -22.52, 50: -22.51, 51: -22.51, 52: -22.5, 53: -22.5, 54: -22.49, 55: -22.48, 60: -22.45, 65: -22.4, 70: -22.35, 75: -22.3, 80: -22.2, 85: -22.1, 90: -21.9, 95: -21.7, 99: -21.3, 99.9: -21.0} - -Model's Answer: Let me think through this step by step. - -Step one: First, let's understand what we're predicting. The US International Investment Position (IIP) for Q3 2024 will be released in just 9 days, on December 27, 2024. The most recent data point we have is from Q2 2024, showing -$22.52 trillion. - -Step two: Given the extremely short timeframe until resolution (9 days), and that this measures end-of-quarter positions which have already occurred (Q3 ended September 30), this is essentially a question about what number will be reported, not what will actually happen in the future. - -Step three: Looking at historical trends, the IIP has been steadily becoming more negative over time, but with quarterly variations. From the chart provided, we can see typical quarter-to-quarter changes are often in the range of $0.2-0.5 trillion, though larger moves do occur. - -Step four: For a low (more negative) outcome scenario: If there was significant USD appreciation in Q3, combined with falling US asset values abroad and/or rising foreign-owned US assets, we could see the position worsen to around -$23.0 trillion or potentially even -$23.5 trillion. However, given normal quarterly variation, moves beyond this would be quite unlikely. - -Step five: For a high (less negative) outcome scenario: If there was USD depreciation and/or favorable asset price movements, we might see improvement to around -$22.0 trillion. Given the long-term trend toward more negative positions, and typical quarterly variations, improvements beyond this would be surprising. - -Step six: Given the very short time until resolution and that we're measuring a past quarter, I want to keep the distribution relatively tight around the most recent value of -$22.52 trillion, with some allowance for typical quarterly variation but not extreme moves. - -Here are my percentile predictions: - -Percentile .1: -24.0 -Percentile 1: -23.7 -Percentile 5: -23.3 -Percentile 10: -23.1 -Percentile 15: -22.9 -Percentile 20: -22.8 -Percentile 25: -22.7 -Percentile 30: -22.65 -Percentile 35: -22.6 -Percentile 40: -22.56 -Percentile 45: -22.54 -Percentile 46: -22.53 -Percentile 47: -22.53 -Percentile 48: -22.52 -Percentile 49: -22.52 -Percentile 50: -22.51 -Percentile 51: -22.51 -Percentile 52: -22.50 -Percentile 53: -22.50 -Percentile 54: -22.49 -Percentile 55: -22.48 -Percentile 60: -22.45 -Percentile 65: -22.4 -Percentile 70: -22.35 -Percentile 75: -22.3 -Percentile 80: -22.2 -Percentile 85: -22.1 -Percentile 90: -21.9 -Percentile 95: -21.7 -Percentile 99: -21.3 -Percentile 99.9: -21.0" -31005,What will be the U.S. International Investment Position in the 3rd Quarter of 2024?,"Extracted Percentile Values: {1: -23.5, 5: -23.2, 10: -23.0, 15: -22.9, 20: -22.8, 25: -22.7, 30: -22.65, 35: -22.6, 40: -22.55, 45: -22.52, 46: -22.51, 47: -22.5, 48: -22.49, 49: -22.48, 50: -22.47, 51: -22.46, 52: -22.45, 53: -22.44, 54: -22.43, 55: -22.42, 60: -22.35, 65: -22.3, 70: -22.25, 75: -22.2, 80: -22.1, 85: -22.0, 90: -21.8, 95: -21.5, 99: -21.2, 99.9: -21.0} - -Model's Answer: Let me think about this step by step. - -Step one: First, I note that this is a Non-Market Threshold Value Event, and we have very recent data (Q2 2024 at -$22.52 trillion) and the resolution date is extremely close (December 27, 2024). - -Step two: The timing is crucial here - we're currently on December 18, 2024, and the Q3 data will be released in just 9 days. This means the data is already determined and is just waiting to be published. This significantly reduces uncertainty compared to a longer-term forecast. - -Step three: Looking at historical trends, the U.S. net international investment position has been consistently negative and generally becoming more negative over time. The rate of change between quarters has typically been in the hundreds of billions, rather than trillions. - -Step four: Given the extremely short time until resolution and that the Q3 data is already determined (just not published), I should heavily weight the scenario close to the most recent known value (-$22.52 trillion from Q2). - -Step five: For a low-end scenario (more negative position), factors could include: -- Continued foreign investment in U.S. assets -- Potential market value declines in U.S. holdings abroad -- Dollar strength reducing the value of foreign assets -Likelihood: About 40% chance of being more negative than Q2 - -Step six: For a high-end scenario (less negative position), factors could include: -- Market gains in U.S. holdings abroad -- Dollar weakness increasing the value of foreign assets -- Reduced foreign investment in U.S. -Likelihood: About 60% chance of being less negative than Q2 - -Step seven: Given the extremely short timeframe until resolution and that this is historical data that's already determined, I should keep my distribution relatively tight around the Q2 value, with most probability mass between -23.0 and -22.0 trillion. - -Percentile .1: -23.8 -Percentile 1: -23.5 -Percentile 5: -23.2 -Percentile 10: -23.0 -Percentile 15: -22.9 -Percentile 20: -22.8 -Percentile 25: -22.7 -Percentile 30: -22.65 -Percentile 35: -22.6 -Percentile 40: -22.55 -Percentile 45: -22.52 -Percentile 46: -22.51 -Percentile 47: -22.50 -Percentile 48: -22.49 -Percentile 49: -22.48 -Percentile 50: -22.47 -Percentile 51: -22.46 -Percentile 52: -22.45 -Percentile 53: -22.44 -Percentile 54: -22.43 -Percentile 55: -22.42 -Percentile 60: -22.35 -Percentile 65: -22.3 -Percentile 70: -22.25 -Percentile 75: -22.2 -Percentile 80: -22.1 -Percentile 85: -22.0 -Percentile 90: -21.8 -Percentile 95: -21.5 -Percentile 99: -21.2 -Percentile 99.9: -21.0" -30406,"Will at least one of Andrea Bocelli's concerts at Madison Square Garden on December 18 or 19, 2024 sell out?","@johnnycaffeine not sure how this gets resolved after-the-fact - -on the quarterly cup thread someone claims they saw tickets on Ticketmaster shortly before the shows, but not sure how to have an authoritative answer now" -30953,"Will the Grouse Mountain ski resort be listed as Open by On the Snow's Ski Report on December 20, 2024?",@johnnycaffeine reported open -30406,"Will at least one of Andrea Bocelli's concerts at Madison Square Garden on December 18 or 19, 2024 sell out?","@histerio yea it is very possible that there were handfuls of tickets available the day of, but that for purposes of the venue it was effectively sold out" -30252,"Will at least one of Andrea Bocelli's concerts at Madison Square Garden on December 18 or 19, 2024 sell out?","@citizen That isn't relevant to ""before the respective performance date"" though, that's on the performance date" -30951,"Will the domestic opening weekend box office revenue of Homestead, scheduled to open December 20, 2024, exceed $7,000,000?","@johnnycaffeine reported as $6,066,710, should resolve as No" -30281,"Will the number of active US oil drilling rigs exceed 590 on December 27, 2024?",@johnnycaffeine 589 -30361,"Will the yield curve be non-inverted on Friday December 27, 2024?",@johnnycaffeine .31 -30674,"Will be Donald Trump's net favorability rating on December 27, 2024 be greater than -4?",edit: ignore -30282,"Will the number of active US oil drilling rigs be greater than or equal to 585 and less than or equal to 590 on December 27, 2024?",@johnnycaffeine 589 -30283,"Will the number of active US oil drilling rigs be less than 585 on December 27, 2024?",@johnnycaffeine 589 -31057,"How many unique posters will Bluesky have on December 29, 2024?","@johnnycaffeine 828,408" -30978,"Will the cheapest new Tesla Model Y be listed as under $44,990 on December 30, 2024?","@johnnycaffeine still exactly $44,990" -29120,"Will the 500th richest person on Bloomberg's Billionaires Index have $6.5 billion or more on Monday December 30, 2024?",@johnnycaffeine **$6.18B** -30192,"On December 30, 2024, Will Apple have the largest market cap in the world?",@johnnycaffeine yes -30193,"On December 30, 2024, Will Microsoft have the largest market cap in the world?",@johnnycaffeine no -30194,"On December 30, 2024, Will Nvidia have the largest market cap in the world?",@johnnycaffeine no -30406,"Will at least one of Andrea Bocelli's concerts at Madison Square Garden on December 18 or 19, 2024 sell out?",@johnnycaffeine doesn't look like you resolved it -31031,"Will the undiagnosed disease currently spreading in the Democratic Republic of the Congo be identified as Influenza before January 7, 2025?",@johnnycaffeine I assume these should all be resolved as no? a bunch of different respiratory illnesses + malaria: [https://www.who.int/emergencies/disease-outbreak-news/item/2024-DON547](https://www.who.int/emergencies/disease-outbreak-news/item/2024-DON547) -29590,"Will the weekly total number of influenza hospitalizations for the United States for the week of Dec 7, 2024 be more than 2,000?","@johnnycaffeine 2024-12-07,""US"",""US"",6342,1.89361539145639" -29541,"Will the XEC COVID-19 variant account for at least 50% of the variants monitored in the US before January 5, 2025?","@johnnycaffeine if I understand the resolution criteria correctly, the highest was 45%" -30870,"Will the undiagnosed disease currently spreading in the Democratic Republic of the Congo be identified as Influenza, RSV, or COVID-19 before January 7, 2025?","@BrunoParga that explanation sounds fairly unambiguous that it was ""predominantly caused by"" none of these diseases in particular - -each may have contributed, but none were predominantly caused by given the malaria" -31370,The AI Forecasting Benchmark for Q1 Is Live!,"@(Zaldath) They have been talking about that since the beginning of the Q3 tournament, just took a while to figure out the best way to do it" -31370,The AI Forecasting Benchmark for Q1 Is Live!,"@(histerio) Thanks, congrats to everyone this quarter! - -Like jms said, I definitely adjusted for the Yes bias. I think the earliest questions that resolved resulted in a handful of particularly negative scores that took a while to dig out of. I also shifted the relative weights of different model providers over the course of the quarter, as I found that some models followed instructions a lot better than others! " -31435,[PRACTICE] What will be the U.S. International Investment Position in the 4th Quarter of 2024?,"Extracted Percentile Values: {1: -24.0, 5: -23.85, 10: -23.75, 15: -23.7, 20: -23.67, 25: -23.65, 30: -23.63, 35: -23.62, 40: -23.61, 45: -23.605, 46: -23.604, 47: -23.603, 48: -23.602, 49: -23.601, 50: -23.6, 51: -23.599, 52: -23.598, 53: -23.597, 54: -23.596, 55: -23.595, 60: -23.59, 65: -23.58, 70: -23.57, 75: -23.55, 80: -23.53, 85: -23.5, 90: -23.45, 95: -23.35, 99: -23.2, 99.9: -23.0} - -Model's Answer: Let me think through this step by step. - -Step one: Evaluate the most recent data point and its reliability -- We have confirmed Q4 2024 data showing -$23.60 trillion -- This is very recent (December 27, 2024) and comes directly from the BEA -- Since today is January 19, 2025, this data is less than a month old -- As this is Q4 2024 data, this is actually the exact period we're trying to predict - -Step two: Consider the resolution criteria -- The question resolves based on the initial release -- The initial release has already occurred on December 27, 2024 -- Any revisions after the resolution date (March 31, 2025) won't affect the outcome -- This dramatically reduces uncertainty since we already have the initial release - -Step three: Analyze potential for revision before resolution -- The BEA does occasionally revise their initial releases -- However, revisions to IIP tend to be relatively small, usually less than 2% -- The time window for revision before resolution is about 2 months -- Historical patterns show most significant revisions occur during annual updates, not quarterly ones - -Step four: Evaluate recent trends -- Q2 2024 was -$22.52 trillion -- Movement to Q4 2024 at -$23.60 trillion shows continued deterioration -- This represents about a -4.8% change over two quarters -- The trend matches broader economic indicators showing increased foreign investment in US assets - -Step five: Consider extreme scenarios -Low case (less negative IIP): -- Significant upward revision due to found calculation error -- Major revaluation of US assets abroad -- Probability: Very low (~1%) given the short timeframe and nature of the metric - -High case (more negative IIP): -- Downward revision due to newly discovered liabilities -- Currency fluctuations affecting valuations -- Probability: Very low (~1%) given the short timeframe - -Given all this, I should concentrate most probability mass very close to -23.60, with small tails for potential revisions. - -Percentile .1: -24.20 -Percentile 1: -24.00 -Percentile 5: -23.85 -Percentile 10: -23.75 -Percentile 15: -23.70 -Percentile 20: -23.67 -Percentile 25: -23.65 -Percentile 30: -23.63 -Percentile 35: -23.62 -Percentile 40: -23.61 -Percentile 45: -23.605 -Percentile 46: -23.604 -Percentile 47: -23.603 -Percentile 48: -23.602 -Percentile 49: -23.601 -Percentile 50: -23.60 -Percentile 51: -23.599 -Percentile 52: -23.598 -Percentile 53: -23.597 -Percentile 54: -23.596 -Percentile 55: -23.595 -Percentile 60: -23.59 -Percentile 65: -23.58 -Percentile 70: -23.57 -Percentile 75: -23.55 -Percentile 80: -23.53 -Percentile 85: -23.50 -Percentile 90: -23.45 -Percentile 95: -23.35 -Percentile 99: -23.20 -Percentile 99.9: -23.00" -31600,[PRACTICE] What will be the U.S. International Investment Position in the 4th Quarter of 2024?,"Extracted Percentile Values: {1: -25.3, 5: -24.8, 10: -24.5, 15: -24.2, 20: -24.0, 25: -23.8, 30: -23.6, 35: -23.4, 40: -23.2, 45: -23.1, 46: -23.0, 47: -23.0, 48: -22.9, 49: -22.9, 50: -22.8, 51: -22.8, 52: -22.7, 53: -22.7, 54: -22.6, 55: -22.6, 60: -22.4, 65: -22.2, 70: -22.0, 75: -21.8, 80: -21.6, 85: -21.4, 90: -21.2, 95: -20.8, 99: -20.3, 99.9: -19.8} - -Model's Answer: Let me think through this step by step. - -Step one: Assessment of the most recent data point -The last known value from Q2 2024 was -$22.52 trillion. This serves as our anchor point, but it's important to note this data is now about 6 months old. - -Step two: Analysis of recent trends and factors -- Sharp drop in net long-term transactions (from $159.1B to $79.0B) suggests weakening foreign investment -- However, there was still a net TIC inflow of $159.9B -- Global AI boom and U.S. interest rate cuts have increased cross-border investments -- Reduction in 10-year Treasury yield to 4.6% - -Step three: Consideration of time horizon -We're forecasting Q4 2024, and today is January 19, 2025. The actual value has already been determined, we're just waiting for the official release. This means we should be less speculative and more focused on known trends through the end of 2024. - -Step four: Analysis of low-case scenario -A significantly more negative IIP could result from: -- Continued decline in foreign investment appetite -- Market volatility causing devaluation of U.S. assets abroad -- Increased U.S. borrowing -Likelihood: 25% chance of position declining beyond -24 trillion - -Step five: Analysis of high-case scenario -A less negative IIP could result from: -- Strong foreign investment maintaining asset values -- Positive market performance increasing U.S. asset values abroad -- Reduction in U.S. borrowing needs -Likelihood: 20% chance of position improving above -21 trillion - -Step six: Base case consideration -Given the recent data and trends, the most likely outcome is a moderate deterioration from the Q2 position, centering around -23 trillion. - -Step seven: Distribution assessment -Given the resolution is relatively soon and the value is already determined (just not published), we should have a relatively tight distribution around our expected value, but still accounting for uncertainty in the underlying factors. - -"" -Percentile .1: -25.8 -Percentile 1: -25.3 -Percentile 5: -24.8 -Percentile 10: -24.5 -Percentile 15: -24.2 -Percentile 20: -24.0 -Percentile 25: -23.8 -Percentile 30: -23.6 -Percentile 35: -23.4 -Percentile 40: -23.2 -Percentile 45: -23.1 -Percentile 46: -23.0 -Percentile 47: -23.0 -Percentile 48: -22.9 -Percentile 49: -22.9 -Percentile 50: -22.8 -Percentile 51: -22.8 -Percentile 52: -22.7 -Percentile 53: -22.7 -Percentile 54: -22.6 -Percentile 55: -22.6 -Percentile 60: -22.4 -Percentile 65: -22.2 -Percentile 70: -22.0 -Percentile 75: -21.8 -Percentile 80: -21.6 -Percentile 85: -21.4 -Percentile 90: -21.2 -Percentile 95: -20.8 -Percentile 99: -20.3 -Percentile 99.9: -19.8 -""" -31601,[PRACTICE] Will humans go extinct before 2100?,"Let me think through this step by step: - -1. Timeline Analysis: -- Resolution date is April 11, 2025 (less than 3 months from now) -- Question asks about extinction by 2100 -- For a ""Yes"" resolution by April 2025, we'd need to have definitive proof that extinction is inevitable by 2100 - -2. Base Rate Considerations: -- Humans have existed for ~300,000 years -- No previous extinction events for our species -- Extremely strong survival instinct and adaptability -- Multiple isolated populations globally provide redundancy - -3. Expert Opinions: -- Oxford conference's 19% by 2100 likely represents selection bias -- More mainstream estimates typically range 1-5% for century-scale extinction risk -- Recent concerns about AI from Geoffrey Hinton (10-20% in 10-20 years) deserve weight -- Climate projections show severe challenges but not guaranteed extinction - -4. Resolution Criteria Analysis: -- Requires ALL humans to die (extremely high bar) -- Includes humans anywhere in observable universe -- Must be known by April 2025 that this will happen - -5. Short-term vs Long-term Probability: -- 3-month probability much lower than 75-year probability -- Need for certainty by April 2025 dramatically reduces likelihood -- Most catastrophic scenarios take years/decades to unfold - -6. Risk Categories: -Near-term (by April 2025): -- Nuclear war: 0.1% -- AI catastrophe: 0.2% -- Pandemic: 0.1% -- Asteroid impact: 0.001% - -By 2100: -- Climate disaster: 2% -- AI risks: 5% -- Nuclear war: 3% -- Pandemic: 2% -- Other: 1% - -7. Key Factors Reducing Probability: -- Short timeframe to resolution -- Need for certainty of future extinction -- Human resilience and geographic distribution -- Multiple independent populations -- No historical precedent - -8. Factors Increasing Probability: -- Multiple concurrent risks -- Accelerating technological threats -- Climate change trajectory -- Growing nuclear tensions - -Given the extremely short resolution timeframe (less than 3 months), we would need to discover something by April 2025 that makes human extinction by 2100 certain. This is orders of magnitude less likely than actual extinction by 2100. - -While long-term extinction risks are non-trivial (perhaps 5-15% by 2100), the probability of having certainty about this by April 2025 is much lower. - -Adjusting for historical overconfidence and the requirement for certainty in such a short timeframe: - -Probability: 2%" -31730,"For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?","EXTRACTED_PROBABILITIES: [0, 55, 35, 8, 2] - -Model's Answer: Option_A: 0 -Option_B: 55 -Option_C: 35 -Option_D: 8 -Option_E: 2" -31731,What percentage of the vote will Alexander Lukashenko receive in the 2025 Belarusian presidential election?,"Extracted Percentile Values: {1: 65, 5: 70, 10: 73, 15: 75, 20: 76, 25: 77, 30: 78, 35: 79, 40: 80, 45: 80, 46: 81, 47: 81, 48: 81, 49: 81, 50: 82, 51: 82, 52: 82, 53: 82, 54: 83, 55: 83, 60: 84, 65: 85, 70: 86, 75: 87, 80: 88, 85: 89, 90: 91, 95: 93, 99: 96, 99.9: 98} - -Model's Answer: Let me think through this step by step. - -Step one: First, I need to consider the base case of Lukashenko's previous election results. In 2020, he officially received 80.1% of the vote, and previous elections showed similar high percentages, typically between 75-85%. - -Step two: The election is extremely soon - just 6 days away. This drastically reduces uncertainty and the likelihood of major changes or unexpected events affecting the outcome. The electoral machinery is already in place. - -Step three: Given the research showing continued strong institutional control, suppression of opposition, and Russian support, there's little reason to expect significant deviation from previous patterns. The presence of five registered candidates suggests a veneer of competition but unlikely to materially affect the outcome. - -Step four: Considering the low likelihood scenarios: -- Low scenario (60-70%): Could occur if there's last-minute internal dissent or if Lukashenko wants to project more legitimacy by showing a more ""reasonable"" victory margin. Likelihood: ~10% -- High scenario (90-100%): Could occur if Lukashenko wants to project absolute control and dominance, especially given the geopolitical context with Russia. Some sources suggest this is possible. Likelihood: ~15% - -Step five: The most likely outcome (75-85%) aligns with historical patterns and the current political environment. Given the extremely short timeframe until resolution, radical departures from this range are unlikely. - -Step six: The resolution criteria specifically notes that this will be based on official results regardless of electoral fraud evidence, which reduces uncertainty about how to interpret the results. - -Given all this, I'll center the distribution around 80-85% with wider tails to account for uncertainty, but not too wide given the short timeframe to resolution. - -Percentile .1: 60 -Percentile 1: 65 -Percentile 5: 70 -Percentile 10: 73 -Percentile 15: 75 -Percentile 20: 76 -Percentile 25: 77 -Percentile 30: 78 -Percentile 35: 79 -Percentile 40: 80 -Percentile 45: 80 -Percentile 46: 81 -Percentile 47: 81 -Percentile 48: 81 -Percentile 49: 81 -Percentile 50: 82 -Percentile 51: 82 -Percentile 52: 82 -Percentile 53: 82 -Percentile 54: 83 -Percentile 55: 83 -Percentile 60: 84 -Percentile 65: 85 -Percentile 70: 86 -Percentile 75: 87 -Percentile 80: 88 -Percentile 85: 89 -Percentile 90: 91 -Percentile 95: 93 -Percentile 99: 96 -Percentile 99.9: 98" -31732,"Will the bubble in the Magnificent Seven pop before March 28, 2025?","Let me think through this step by step: - -1. Understanding the Resolution Criteria: -- Need 4+ of the Magnificent Seven to close 50%+ below all-time highs -- Only need this to happen once before March 28, 2025 -- We have about 2.5 months until resolution - -2. Historical Context: -- Tech stock crashes of 2000-2002 (many stocks fell >80%) -- 2008 Financial Crisis (broad market decline ~50%) -- 2022 Tech selloff (many growth stocks fell 50-80%) -- However, the Magnificent Seven specifically have been more resilient - -3. Current Situation: -- None are currently near 50% down from ATH -- Strong recent performance (60.5% return in 2024) -- High valuations and market concentration -- AI narrative providing fundamental support - -4. Time Analysis: -- 2.5 months is a very short period for such a dramatic decline -- Would likely require a major catalyst or crisis -- Historical crashes typically took 6-18 months to reach -50% - -5. Base Rates: -- Major market crashes (>50%) occur roughly every 10-15 years -- Individual large tech stocks falling >50% more common -- But coordinated 50% drops in 4+ mega-caps extremely rare - -6. Risk Factors: -- Extreme market concentration -- High valuations -- Interest rate environment -- Potential policy changes -- Earnings disappointments could trigger selloff - -7. Supporting Factors: -- Strong earnings growth -- AI revolution continues -- Cash-rich balance sheets -- Market dominance -- Federal Reserve likely to cut rates - -8. Timeline Distribution: -- 1 month: 3% probability -- 2.5 months (question timeline): 8% probability -- 6 months: 15% probability -- 12 months: 25% probability - -9. Adjustments: -- Short timeframe reduces probability significantly -- Recent strong performance reduces near-term risk -- High uncertainty suggests avoiding extreme predictions -- Historical overconfidence adjustment needed - -The extremely short timeframe (2.5 months) is the biggest factor here. While valuations are stretched and risks exist, coordinated 50% drops in 4+ mega-caps typically take much longer to develop. The recent strong performance and continued AI narrative provide near-term support. However, the extreme market concentration and high valuations create vulnerability. - -Given the base rates of major crashes, the short timeframe, and adjusting for historical overconfidence, I arrive at: - -Probability: 8%" -31611,[PRACTICE] Will humans go extinct before 2100?,"Let me think through this step by step: - -1) First, let's consider the timeline: -- Question asks about extinction by 2100 -- Resolution date is April 11, 2025 -- This means we're really asking ""Will we know by April 2025 that humanity will definitely go extinct by 2100?"" - -2) Base rate analysis: -- Humans have existed for roughly 300,000 years -- No previous extinction events -- Base rate suggests extremely low probability - -3) Resolution criteria analysis: -- Requires ALL humans to die (not just civilization collapse) -- Includes humans anywhere in observable universe -- Must be known by April 2025 -- Very high bar for positive resolution - -4) Key risk factors by 2100: -- Climate change: Severe but unlikely to cause complete extinction -- Nuclear war: Could cause massive casualties but unlikely to kill everyone -- AI: Emerging risk, but complete extinction within 75 years seems unlikely -- Pandemics: Natural or engineered, but unlikely to achieve 100% mortality -- Asteroid impact: Detectable in advance, low probability -- Other cosmic events: Very low probability - -5) Short-term considerations (by April 2025): -- We would need clear, irrefutable evidence of inevitable extinction -- Major catastrophes take time to unfold -- Even if a catastrophic event occurred tomorrow, surviving until 2100 would still be possible -- Nearly impossible to prove zero survivors by 2100 - -6) Expert opinion analysis: -- Oxford conference's 19% by 2100 likely heavily biased -- More mainstream estimates typically range 1-5% -- Need to discount further for short resolution timeframe - -7) Temporal distribution: -- 3 month resolution window vs 75 year question period -- Even if extinction risk by 2100 was 5%, the chance of knowing this definitively by April 2025 would be far lower -- Need to divide any long-term probability by significant factor - -8) Uncertainty considerations: -- Extremely difficult to predict with confidence -- Many unknown variables -- Need to avoid overconfidence - -9) Mathematical adjustment: -- Taking 5% risk by 2100 -- Dividing by ~300 for the 3-month vs 75-year window -- Adjusting for certainty requirement -- Considering base rates -- Accounting for historical overconfidence - -Given all these factors, especially the extremely short resolution window and the requirement for certainty about complete extinction, I believe the probability must be very low, but not zero given the various existential risks humanity faces. - -Probability: 1%" -31612,[PRACTICE] Will someone born before 2001 live to be 150?,"Let me think about this step by step: - -1. First, let's consider the timeframe: -- Resolution date is April 12, 2025 (less than 3 months away) -- For a ""yes"" resolution, someone born before 2001 would need to reach 150 by 2150 -- This means they'd need to survive 125+ more years from now - -2. Current state of longevity: -- Record holder Jeanne Calment lived to 122 -- Current oldest people are around 119 -- No significant breakthrough in extending maximum lifespan yet -- Studies suggest biological limit around 125 years - -3. Technical requirements per resolution criteria: -- Must be of sound mind -- No cryopreservation time counts -- Must maintain primarily same body (no mind uploading) -- Must be born before 2001 - -4. Short-term vs long-term probability: -- For next 3 months: Extremely unlikely anyone reaches 150 -- By 2150: More possible but still very challenging -- Technology would need massive breakthroughs - -5. Base rate analysis: -- In human history, no one has lived past 122 -- Rate of increase in maximum lifespan has plateaued -- Current centenarians show no sign of approaching 150 - -6. Technological considerations: -- Anti-aging research is ongoing but far from breakthrough -- Focus is on healthspan rather than maximum lifespan -- No current technology close to enabling 150-year lifespan - -7. Near-term resolution factors: -- Question resolves in less than 3 months -- Would require someone to already be approaching 150 -- No candidates currently close - -8. Statistical analysis: -- Currently impossible in 3-month window -- Even long-term (by 2150) probability is very low -- Given biological limits and technological requirements - -9. Adjusting for overconfidence: -- Even though it seems impossible in 3 months -- Need to account for unknown factors -- Historical tendency to be overconfident - -10. Final considerations: -- Resolution criteria are very strict -- Time window is extremely short -- Would need unprecedented breakthrough and immediate implementation - -For this question, given the extremely short resolution timeframe (less than 3 months), and the fact that no one is currently even close to 150 years old, combined with the strict resolution criteria, the probability must be extremely low. Even accounting for unknown factors and adjusting for overconfidence, the probability of resolution within this timeframe is minimal. - -Probability: 1%" -31615,[PRACTICE] Will there be a US-China war before 2035?,"Let me think through this step by step: - -1. Timeline Analysis: -- Resolution date is April 15, 2025, less than 3 months from now -- For a war with 1,000+ battle deaths to occur, tensions would need to escalate extremely rapidly -- Historical precedent suggests major conflicts typically have longer build-up periods - -2. Base Rate Analysis: -- US-China have never had direct military conflict reaching 1,000 battle deaths -- Last major power direct conflict was decades ago -- Nuclear deterrence has prevented direct conflicts between nuclear powers - -3. Current Situation Assessment: -- Tensions exist but primarily diplomatic/economic -- Military posturing present but contained -- Recent Trump-Xi communication suggests diplomatic channels open -- Both sides actively working to prevent accidental escalation - -4. Short-term vs Long-term Risk: -For 1,000+ battle deaths: -- Next 3 months (to resolution): Extremely low probability -- By 2027 (potential Taiwan timeline): ~3-5% -- By 2035 (question deadline): ~8-12% -- The probability is not evenly distributed across time periods - -5. Risk Factors: -- Taiwan situation is tense but stable -- Military buildups are gradual -- Economic interdependence remains strong -- Multiple diplomatic channels active - -6. Mitigating Factors: -- Nuclear deterrence -- Economic consequences -- International pressure -- Military preparation gaps -- Mutual desire to avoid direct conflict - -7. Resolution Criteria Analysis: -- 1,000 battle deaths is a high threshold -- Must be direct US-China conflict -- Proxy conflicts don't count -- Must be verified by credible sources - -8. Near-term Scenario Analysis: -- Accidental escalation: Very low probability -- Intentional conflict: Extremely low probability -- Required escalation time: Longer than resolution window -- Current diplomatic status: Active engagement - -For this specific resolution window (by April 15, 2025), given: -- The extremely short timeframe -- High threshold for battle deaths -- Current diplomatic engagement -- Lack of immediate triggers -- Historical precedents -- Nuclear deterrence -- Economic interdependence - -I need to significantly discount longer-term risk factors and focus on near-term probability. - -Even accounting for uncertainty and potential black swan events, the probability of meeting the resolution criteria within this extremely short timeframe is very low. - -Probability: 1%" -31610,[PRACTICE] What will be the U.S. International Investment Position in the 4th Quarter of 2024?,"Extracted Percentile Values: {1: -24.9, 5: -24.5, 10: -24.3, 15: -24.2, 20: -24.1, 25: -24.0, 30: -23.95, 35: -23.9, 40: -23.85, 45: -23.82, 46: -23.81, 47: -23.8, 48: -23.79, 49: -23.78, 50: -23.77, 51: -23.76, 52: -23.75, 53: -23.74, 54: -23.73, 55: -23.72, 60: -23.65, 65: -23.6, 70: -23.5, 75: -23.4, 80: -23.3, 85: -23.2, 90: -23.0, 95: -22.8, 99: -22.5, 99.9: -22.2} - -Model's Answer: Let me think through this step by step carefully. - -Step one: First, let's establish what we know with certainty. We have Q2 2024 data (-$22.52 trillion) and Q3 2024 data (-$23.60 trillion), showing a significant deterioration of -$1.08 trillion in one quarter. The trend is clearly negative. - -Step two: Analyzing the rate of change, we see that Q3's deterioration was driven by both financial transactions (-$479.5B) and other position changes (-$577.2B). This suggests a combination of structural and market-driven factors affecting the position. - -Step three: The period we're forecasting (Q4 2024) has already ended, and we're just waiting for the official release. This significantly reduces uncertainty compared to a forward-looking forecast. Most of the major economic and market events that would affect this number have already occurred. - -Step four: Historical precedent shows that quarter-to-quarter changes in IIP typically don't exceed $1-1.5 trillion unless there are extreme market movements. The Q3 change was already quite substantial at -$1.08 trillion. - -Step five: The base case suggests continued deterioration but likely at a slower pace than Q3, given that some of Q3's decline was due to market valuation effects that might not have repeated as strongly in Q4. - -Step six: For a low (more negative) outcome scenario (-24.5 trillion or worse), we would need to see continued strong foreign investment into U.S. markets combined with declining values of U.S. foreign assets. This is possible but less likely given the already substantial move in Q3. Likelihood: ~15% - -Step seven: For a high (less negative) outcome scenario (-22.5 trillion or better), we would need to see either a significant reversal in market valuations or substantial U.S. acquisition of foreign assets. Given the known market conditions in Q4 2024, this seems unlikely. Likelihood: ~10% - -Step eight: The most probable range appears to be between -23.2 and -24.0 trillion, centered around -23.8 trillion, which represents a more modest deterioration from Q3's -23.60 trillion. - -Here are my percentile predictions: - -Percentile .1: -25.2 -Percentile 1: -24.9 -Percentile 5: -24.5 -Percentile 10: -24.3 -Percentile 15: -24.2 -Percentile 20: -24.1 -Percentile 25: -24.0 -Percentile 30: -23.95 -Percentile 35: -23.9 -Percentile 40: -23.85 -Percentile 45: -23.82 -Percentile 46: -23.81 -Percentile 47: -23.80 -Percentile 48: -23.79 -Percentile 49: -23.78 -Percentile 50: -23.77 -Percentile 51: -23.76 -Percentile 52: -23.75 -Percentile 53: -23.74 -Percentile 54: -23.73 -Percentile 55: -23.72 -Percentile 60: -23.65 -Percentile 65: -23.6 -Percentile 70: -23.5 -Percentile 75: -23.4 -Percentile 80: -23.3 -Percentile 85: -23.2 -Percentile 90: -23.0 -Percentile 95: -22.8 -Percentile 99: -22.5 -Percentile 99.9: -22.2" -31614,[PRACTICE] How much global warming by 2100?,"Extracted Percentile Values: {1: 1.4, 5: 1.8, 10: 2.0, 15: 2.2, 20: 2.3, 25: 2.4, 30: 2.5, 35: 2.6, 40: 2.7, 45: 2.8, 46: 2.82, 47: 2.84, 48: 2.86, 49: 2.88, 50: 2.9, 51: 2.92, 52: 2.94, 53: 2.96, 54: 2.98, 55: 3.0, 60: 3.1, 65: 3.2, 70: 3.3, 75: 3.5, 80: 3.7, 85: 4.0, 90: 4.3, 95: 4.8, 99: 5.5, 99.9: 6.5} - -Model's Answer: Let me think through this carefully as a superforecaster. - -Step one: First, I need to recognize that this is actually a trick question. While the background provides extensive information about climate change projections for 2100, the resolution date is April 14, 2025 - less than 3 months from now. This completely changes how I should approach this forecast. - -Step two: The question asks for the temperature difference between 2100 and 1880, but will resolve in April 2025 based on projections available at that time. Therefore, I need to forecast what the scientific consensus and official projections will be as of April 2025, not what will actually happen in 2100. - -Step three: Looking at the current scientific consensus from the research provided: -- Most experts (76.3% of IPCC authors) project at least 2.5°C warming by 2100 -- The median expert projection is around 2.7°C -- Current warming is already at ~1.55°C above pre-industrial levels -- Projections range from 0.8°C to 6.0°C, with most falling between 2-4°C - -Step four: Given the short timeframe until resolution (less than 3 months), major changes to scientific consensus are unlikely. The base case should be weighted heavily toward current projections. - -Step five: Considering potential low-end scenarios: -- A major new study showing negative feedback loops or cooling effects could emerge (very unlikely in 3 months) -- Current temperature spike could be determined to be temporary -Likelihood: Very low (<5%) of consensus shifting below 2°C by April - -Step six: Considering potential high-end scenarios: -- New research showing accelerated warming or tipping points -- Updated models incorporating recent record temperatures -Likelihood: Moderate (~15%) of consensus shifting above 3.5°C by April - -Step seven: The question bounds are 0-10°C, but scientific projections almost entirely fall within 1-5°C. Given explicit bounds aren't specified, I can focus probability mass in this range. - -Given this analysis, I'll center most probability mass around current consensus of 2.5-3°C, with wider tails to account for uncertainty: - -Percentile .1: 1.0 -Percentile 1: 1.4 -Percentile 5: 1.8 -Percentile 10: 2.0 -Percentile 15: 2.2 -Percentile 20: 2.3 -Percentile 25: 2.4 -Percentile 30: 2.5 -Percentile 35: 2.6 -Percentile 40: 2.7 -Percentile 45: 2.8 -Percentile 46: 2.82 -Percentile 47: 2.84 -Percentile 48: 2.86 -Percentile 49: 2.88 -Percentile 50: 2.9 -Percentile 51: 2.92 -Percentile 52: 2.94 -Percentile 53: 2.96 -Percentile 54: 2.98 -Percentile 55: 3.0 -Percentile 60: 3.1 -Percentile 65: 3.2 -Percentile 70: 3.3 -Percentile 75: 3.5 -Percentile 80: 3.7 -Percentile 85: 4.0 -Percentile 90: 4.3 -Percentile 95: 4.8 -Percentile 99: 5.5 -Percentile 99.9: 6.5" -31613,[PRACTICE] What type of pathogen will cause the next pandemic?,"EXTRACTED_PROBABILITIES: [3, 1, 2, 94] - -Model's Answer: Let me think through this step by step: - -1. First, let's consider the time frame: -- Resolution date is April 13, 2025 (less than 3 months away) -- The pandemic must cause 25M+ deaths between 2022-2032 -- Given the short resolution timeframe, we need strong evidence of an emerging threat - -2. Historical precedents for pandemics: -- 1918 Spanish Flu (H1N1): Orthomyxoviridae -- 2009 Swine Flu (H1N1): Orthomyxoviridae -- 2020 COVID-19: Coronaviridae -- Most major pandemics have been influenza-based - -3. Current situation analysis: -- H5N1 (Orthomyxoviridae) showing concerning evolution -- Recent coronavirus variants haven't shown pandemic potential -- No other major viral threats currently emerging -- Non-viral pathogens historically rare for pandemics - -4. Requirements for pandemic potential: -- Need high transmissibility -- Need significant lethality -- Need lack of population immunity -- Need time to spread globally - -5. Time constraints consideration: -- Less than 3 months to resolution -- Very short time for a new pandemic to emerge -- Most likely resolution is ""no pandemic"" given criteria -- Need extremely high threshold (25M deaths) - -6. Current surveillance and preparedness: -- Enhanced global monitoring post-COVID -- Better response capabilities -- Improved vaccine development -- Better understanding of containment - -7. Weighing factors: -- Historical precedent favors Orthomyxoviridae -- Current H5N1 evolution concerning -- Very high death threshold makes all outcomes unlikely -- Short timeframe reduces likelihood further - -8. Risk factors: -- Antigenic shift possible with influenza -- Current global health systems stressed -- Climate change affecting disease patterns -- Increased global travel and connectivity - -Given all these factors, and accounting for the extremely high threshold of 25M deaths and short timeframe, while maintaining appropriate uncertainty: - -Option_A: 3 -Option_B: 1 -Option_C: 2 -Option_D: 94" -31616,[PRACTICE] Which team will win the 2024/2025 UEFA Champions League?,"EXTRACTED_PROBABILITIES: [24, 12, 11, 13, 9, 8, 5, 6, 3, 4, 5] - -Model's Answer: Let me think about this step by step: - -1) First, let's assess the current situation: -- Liverpool leads the group with perfect record -- Barcelona second with strong performance -- Resolution is in ~3 months -- New format impacts traditional dynamics - -2) Historical context: -- Past winners show elite clubs dominate -- English teams have been strong recently -- Real Madrid has historical advantage in knockouts -- Surprise winners are rare (~5-10% historically) - -3) Current form analysis: -- Liverpool showing exceptional form both UCL/domestic -- Barcelona performing above expectations -- Man City/Real Madrid struggling but have quality -- Leverkusen dark horse given domestic form - -4) Time factor considerations: -- ~3 months until resolution -- Injuries/form can change significantly -- Knockout stages increase variance -- Key matches upcoming could shift standings - -5) Key uncertainties: -- New format impact -- Current leaders maintaining form -- Lower-ranked teams' qualification chances -- Injury risks to key players - -6) Weighting factors: -- Current form (35%) -- Historical performance (25%) -- Squad depth/quality (20%) -- Current standings (20%) - -7) Probability distribution adjustment: -- Need wide distribution given uncertainty -- Account for ""Another Team"" possibility -- Avoid overconfidence in current leaders -- Consider knockout stage variance - -Final probabilities: - -Option_A: 24 -Option_B: 12 -Option_C: 11 -Option_D: 13 -Option_E: 9 -Option_F: 8 -Option_G: 5 -Option_H: 6 -Option_I: 3 -Option_J: 4 -Option_K: 5" -31618,"[PRACTICE] How many seconds until midnight will the Doomsday Clock show on January 29, 2025?","EXTRACTED_PROBABILITIES: [3, 7, 15, 45, 18, 9, 3] - -Model's Answer: Let me think about this step by step: - -1) First, let's consider the base case - currently at 90 seconds. Since resolution is in just 8-9 days, strong bias toward status quo. - -2) Historical movement patterns: -- Clock typically moves in increments of 30 seconds or 1 minute -- Has never moved more than 5 minutes in a single update -- Strong tendency to move closer to midnight in recent decades -- Only one move away from midnight since 1991 - -3) Current global situation analysis: -Key negative factors: -- Ongoing Ukraine war -- Middle East conflicts -- Climate concerns -- AI risks -- Political instability with Trump likely returning -- Nuclear tensions - -Key positive factors: -- No new major escalations -- Some climate progress -- International cooperation on AI safety - -4) Timing considerations: -- Very short window until announcement -- Major changes unlikely in 8 days -- Strong status quo bias - -5) Movement likelihood analysis: -- Most likely: No change (90 seconds) -- Second most likely: Small move closer (80-89 or 91-100) -- Less likely: Larger moves in either direction -- Very unlikely: Extreme moves (59 or fewer, 121+) - -6) Recent betting markets suggest 60% chance of moving closer - -7) Accounting for historical overconfidence and uncertainty: - -Option_A: 3 -Option_B: 7 -Option_C: 15 -Option_D: 45 -Option_E: 18 -Option_F: 9 -Option_G: 3" -31619,[PRACTICE] How many pounds of chicken per capita will the US consume in 2025?,"Extracted Percentile Values: {1: 96, 5: 98, 10: 99, 15: 100, 20: 101, 25: 101.5, 30: 102, 35: 102.4, 40: 102.8, 45: 103, 46: 103.1, 47: 103.15, 48: 103.2, 49: 103.25, 50: 103.3, 51: 103.35, 52: 103.4, 53: 103.45, 54: 103.5, 55: 103.6, 60: 104, 65: 104.5, 70: 105, 75: 105.5, 80: 106, 85: 107, 90: 108, 95: 109, 99: 111, 99.9: 113} - -Model's Answer: Let me think about this step by step: - -Step one: Evaluate the most recent and reliable data points. The USDA forecast cited in Chick-News projecting 103.3 lbs per capita for 2025 seems the most directly relevant and authoritative source, especially given its recent date (December 2024) and institutional credibility. - -Step two: Consider historical trends and volatility. Chicken consumption has shown steady growth from 28lb in 1960 to 101.2lb in 2023, but with notable drops during economic downturns (7-8% during recessions). This suggests both an underlying upward trend and potential for short-term variations. - -Step three: Assess the time horizon. With the resolution date being April 19, 2025, and today being January 20, 2025, we're looking at a relatively short three-month window. This short timeframe suggests limited potential for major deviations from current trajectories. - -Step four: Evaluate potential downside risks. A significant drop would likely require: -- A sudden economic shock -- A major food safety scandal -- Supply chain disruptions -- Bird flu outbreak -Given the short timeframe, these risks are relatively low but not impossible, suggesting maybe a 10% chance of falling below 100 lbs. - -Step five: Consider upside potential. Factors that could drive higher consumption: -- Continued inflation making chicken relatively more attractive vs. other proteins -- Strong economic growth increasing overall meat consumption -- Marketing campaigns or health trends favoring chicken -The short timeframe limits upside potential, but the trend is positive. - -Step six: Account for measurement uncertainty. The National Chicken Council's final numbers might differ slightly from USDA projections, adding another layer of uncertainty. - -Given these factors, I'll center my distribution around 103.3 lbs (the USDA projection) with wider tails to account for uncertainty: - -Percentile .1: 94 -Percentile 1: 96 -Percentile 5: 98 -Percentile 10: 99 -Percentile 15: 100 -Percentile 20: 101 -Percentile 25: 101.5 -Percentile 30: 102 -Percentile 35: 102.4 -Percentile 40: 102.8 -Percentile 45: 103 -Percentile 46: 103.1 -Percentile 47: 103.15 -Percentile 48: 103.2 -Percentile 49: 103.25 -Percentile 50: 103.3 -Percentile 51: 103.35 -Percentile 52: 103.4 -Percentile 53: 103.45 -Percentile 54: 103.5 -Percentile 55: 103.6 -Percentile 60: 104 -Percentile 65: 104.5 -Percentile 70: 105 -Percentile 75: 105.5 -Percentile 80: 106 -Percentile 85: 107 -Percentile 90: 108 -Percentile 95: 109 -Percentile 99: 111 -Percentile 99.9: 113" -31742,How many arms sales globally will the US State Department approve in February 2025?,"EXTRACTED_PROBABILITIES: [20, 50, 30] - -Model's Answer: To forecast the number of US State Department arms sales approvals in February 2025, we should consider past trends, current geopolitical context, and available data. Here's a step-by-step thought process: - -1. **Historical Trends**: Review past months' data on arms approvals. The frequency of sales in recent months, such as January 2025, indicates active military sales activity. Typically, months with heightened geopolitical tension or legislative seasons might show increased arms sales approvals. - -2. **Base Rate Estimation**: The frequency of approvals in similar historical periods can help estimate probabilities. The provided examples underscore continuous high-level activity, suggesting >4 approvals might be more common under current conditions. - -3. **Geopolitical Context**: Ongoing tensions in regions like the Asia-Pacific and Middle East, increased military collaborations, and conflicts such as the situation in Ukraine imply elevated demand from allies for US arms. This supports the trend toward higher sales numbers. - -4. **Current Month Context**: Given that January witnessed multiple approvals, and with geopolitical tensions likely prompting more ally support, February could continue this trend. However, variance in approvals can occur month-to-month based on operational and diplomatic factors. - -5. **Legislative and Regulatory Impact**: Although specific legislative actions might not directly affect approval counts within the month, general policies favoring arms exports to allies could sustain high approval numbers. - -6. **Uncertainty and Recency**: Considering the date is January 21, 2025, within a short time frame until March 1 for resolution, anticipate some continued default behavior but don't rule out unexpected events or approvals surges. - -Considering all factors, here's the final joint probability distribution for the options: - -Option_0-4: 20 -Option_5-9: 50 -Option_>9: 30" -31744,"Will the USDA-posted recall by Pork Dynasty Inc. of Ready-to-Eat Fried Pork Skin Products issued September 27, 2024 be closed before March 1, 2025?","To evaluate the probability that the USDA-posted recall by Pork Dynasty Inc. will be closed before March 1, 2025, I'll consider several factors: - -### Overview - -1. **Understanding the Recall and its Type**: - - The recall involves undeclared soy allergens, typically treated with urgency due to potential health risks. This increases the likelihood of swift action. - - Historical data suggests that food recalls due to allergens are handled relatively quickly, given the need to protect consumer health. - -2. **Current Timeline**: - - The recall was issued on September 27, 2024. As of today, January 21, 2025, approximately four months have passed. - - The process involves notifying the public, withdrawing the products, and verification by the FSIS. - -3. **Common Recall Duration**: - - Food recalls can often be resolved within a few months if all processes go smoothly. The presence of an undeclared allergen strengthens the motivation for a timely resolution. - -4. **Time left until the resolution date**: - - There are approximately five weeks remaining until the resolution date of March 1, 2025. - -### Baseline Probability - -Considering similar past recalls for undeclared allergens, the resolution time often ranges around three to six months. Since approximately four months have passed since the recall announcement, we're in the expected timeframe for potential resolution. - -### Variables to Consider - -- **Effectiveness of the Recall**: - - If Pork Dynasty Inc. has effectively implemented corrective actions, the probability increases. FSIS has rigorous verification standards which could delay closing the recall if not properly implemented. - -- **FSIS's Timeliness**: - - Delays can occur if FSIS's workload is high or prioritization shifts. However, as allergens are typically prioritized, this could favor the recall being resolved within the remaining time. - -- **Potential Delays**: - - Unexpected complications, such as logistical issues in product removal or prolonged verification processes, could slow resolution. - -### Analysis - -1. **Past Recall Trends**: Previous recalls with similar contexts typically resolve within three to six months. The four-month mark suggests we're mid-process, aligning well with trends. - -2. **Uncertainty and Distribution**: Since recalls can close any time within this window without a strict timeline, the distribution of probabilities is not uniform. Past competition insights and your tendency towards overconfidence necessitate caution. - -3. **Compensating for Overconfidence**: Historical tendency towards a 35% resolution rate across all prediction competitions suggests a cautious middle-ground in predictions. Lean towards a conservative estimate to mitigate past overconfidence bias. - -### Conclusion - -Taking into account the urgency due to undeclared allergens, typical timelines for similar cases, the current state of the recall process, and the allowance for unforeseen delays, the probability that the recall will be resolved before March 1, 2025, seems moderate. Balancing these factors with past overconfidence issues, I will place my probability estimate slightly lower than pure trend analysis might suggest. - -**Probability: 65%**" -31743,"How much will it rain in Brasília, Brazil in February 2025?","Extracted Percentile Values: {0.1: 15, 1: 30, 5: 45, 10: 60, 15: 75, 20: 90, 25: 105, 30: 120, 35: 135, 40: 150, 45: 160, 46: 162, 47: 164, 48: 166, 49: 168, 50: 170, 51: 172, 52: 174, 53: 176, 54: 178, 55: 180, 60: 195, 65: 210, 70: 225, 75: 240, 80: 255, 85: 270, 90: 285, 95: 310, 99: 350, 99.9: 390} - -Model's Answer: Let me think through this step by step: - -Step one: First, I'll establish the baseline. Historical data shows February is typically a rainy month in Brasília, with average rainfall around 178-197mm and about 18-19 rainy days. This gives us a strong central tendency to work from. - -Step two: Consider recent conditions and trends. The record drought in 2024 and extreme heat are significant, but more recent data shows a return to rain patterns. January 2025 has already seen rainfall alerts, suggesting the drought pattern has broken. - -Step three: Evaluate the La Niña effect. Current forecasts indicate a weak La Niña pattern, which typically brings below-average rainfall to central Brazil. However, this effect isn't uniform and can still allow for significant precipitation events. - -Step four: Assess time horizon and variance. We're forecasting just one month ahead, which reduces uncertainty compared to longer-term forecasts. However, monthly rainfall can still vary significantly from historical averages. - -Step five: Consider extreme scenarios: -Low case (around 50mm): Continuation of drier than normal conditions due to lingering effects of 2024's extreme weather patterns and weak La Niña influence. -Likelihood: About 10% - -High case (above 300mm): Intense storm systems combined with atmospheric instability leading to multiple heavy rainfall events. -Likelihood: About 10% - -Step six: Account for known unknowns. Weather systems can be unpredictable beyond a few weeks, and climate change is increasing variability. This suggests we should have wider confidence intervals than historical data alone would indicate. - -Step seven: Final reasoning: Given the recent return to rain patterns, but considering the La Niña influence, I expect slightly below-average rainfall for February 2025. However, I'm maintaining wide confidence intervals due to increased climate variability and recent extreme weather patterns. - -Percentile 0.1: 15 -Percentile 1: 30 -Percentile 5: 45 -Percentile 10: 60 -Percentile 15: 75 -Percentile 20: 90 -Percentile 25: 105 -Percentile 30: 120 -Percentile 35: 135 -Percentile 40: 150 -Percentile 45: 160 -Percentile 46: 162 -Percentile 47: 164 -Percentile 48: 166 -Percentile 49: 168 -Percentile 50: 170 -Percentile 51: 172 -Percentile 52: 174 -Percentile 53: 176 -Percentile 54: 178 -Percentile 55: 180 -Percentile 60: 195 -Percentile 65: 210 -Percentile 70: 225 -Percentile 75: 240 -Percentile 80: 255 -Percentile 85: 270 -Percentile 90: 285 -Percentile 95: 310 -Percentile 99: 350 -Percentile 99.9: 390" -31754,"Who will be ranked 2nd on the Forbes Real-Time Billionaires List on March 29, 2025?","EXTRACTED_PROBABILITIES: [8, 5, 25, 7, 50, 5] - -Model's Answer: Let me think about this step by step: - -1. Current Rankings and Recent Movement: -- Elon Musk is firmly #1 ($433.9B) -- Jeff Bezos is #2 ($239.4B) -- Mark Zuckerberg is #3 ($211.8B) -- Larry Ellison around $209.7B -- Bernard Arnault has been declining recently - -2. Time Horizon Analysis: -- Only about 2 months until resolution -- Short timeframe reduces likelihood of major ranking changes -- Stock market volatility can still cause significant wealth changes - -3. Key Company Performance Factors: -- Amazon showing strong performance with AI initiatives -- Meta/Facebook beating expectations -- Tesla having some production concerns -- LVMH seeing luxury market rebound -- Oracle showing cloud growth - -4. Historical Precedent: -- Billionaire rankings tend to be relatively stable over 2-month periods -- Tech stock volatility can cause rapid wealth changes -- Current #2 position has ~15-20% gap to #3 - -5. Risk Factors: -- Stock market volatility -- Tech sector performance -- Regulatory changes -- Company-specific news -- Global economic conditions - -6. Confidence Considerations: -- Short timeframe increases confidence in current rankings -- Need to account for historical overconfidence -- Must consider ""Someone else"" possibility -- Market volatility requires broader probability distribution - -7. Final Analysis: -- Bezos has strong position with recent gains -- Zuckerberg showing momentum but significant gap to close -- Musk unlikely to fall to #2 given large lead -- Others would need substantial gains to reach #2 - -Option_A: 8 -Option_B: 5 -Option_C: 25 -Option_D: 7 -Option_E: 50 -Option_F: 5" -31756,Will Eric Adams be Mayor of New York City on the 1st of April 2025?,"Let me think through this step by step: - -1. Timeline Analysis: -- Resolution date is April 1, 2025 -- Current date is January 22, 2025 -- This means we need to forecast ~70 days ahead - -2. Base Case: -- Adams is currently mayor -- No immediate resignation announced -- Default state continues unless changed - -3. Key Risk Factors: -a) Federal corruption trial -- Scheduled for April 2025 -- Trial likely won't conclude before April 1 -- Being on trial itself doesn't remove him from office - -b) Political Pressure -- Very low approval ratings (39%) -- Significant opposition within party -- But no formal removal proceedings initiated - -c) Legal Framework: -- Removal would require conviction or formal process -- Resignation would be voluntary -- No precedent of NYC mayor being removed while in office - -4. Historical Base Rates: -- No NYC mayor has ever been removed from office -- Few have resigned mid-term -- When facing legal troubles, mayors typically finish their terms - -5. Time-based probability distribution: -- Next 30 days: 5% chance of departure -- 31-60 days: 8% chance -- 61-70 days: 4% chance -- Cumulative probability increases but not linearly - -6. Mitigating Factors: -- Adams consistently states he won't resign -- No formal removal process started -- Trial unlikely to conclude before April 1 -- Political removal process would take months - -7. Risk Amplifiers: -- Unprecedented federal indictment -- Extremely low approval ratings -- Potential new evidence or charges -- Political pressure for resignation - -8. Cross-check against similar situations: -- Other indicted politicians often remain in office during trials -- Local officials typically stay until convicted -- Political pressure rarely forces resignation without conviction - -9. Adjusting for Historical Overconfidence: -- Initial estimate might be too optimistic -- Need to account for unprecedented situation -- Must consider unknown factors - -10. Final Analysis: -- Base case strongly favors remaining in office -- Limited time for removal process -- Trial timeline works in his favor -- But situation is unprecedented -- Must account for unknown factors - -Given the short timeframe, legal processes required, and historical precedents, while accounting for unprecedented circumstances and unknown factors, I estimate: - -Probability: 82% - -This reflects high likelihood of continuation but acknowledges significant risks and uncertainty in an unprecedented situation." -31755,"What will the Warren Buffett Indicator (Wilshire 5000 to GDP Ratio) be on January 31, 2025?","Extracted Percentile Values: {1: 180, 5: 190, 10: 195, 15: 198, 20: 200, 25: 202, 30: 203, 35: 204, 40: 205, 45: 206, 46: 206, 47: 207, 48: 207, 49: 207, 50: 208, 51: 208, 52: 208, 53: 209, 54: 209, 55: 209, 60: 210, 65: 211, 70: 212, 75: 213, 80: 214, 85: 216, 90: 218, 95: 222, 99: 228, 99.9: 235} - -Model's Answer: Let me think through this step by step. - -Step one: Evaluate the most recent data. The latest confirmed value from the resolution source (Longtermtrends) was 200.31 on December 18, 2024. Other sources indicate current values between 205-210%, suggesting an upward trend. - -Step two: Consider the time horizon. We're forecasting just 9 days into the future (January 22 to January 31, 2025). This very short timeframe significantly reduces the likelihood of major changes from current levels. - -Step three: Analyze recent volatility. The Buffett Indicator typically doesn't show extreme day-to-day volatility. Historical data suggests weekly movements are usually within 1-3% range unless there's a major market event. - -Step four: Evaluate market conditions. The stock market has been strong recently, with the S&P 500 showing significant gains. GDP growth is moderate but stable at 2.3% annualized. These factors support current elevated levels. - -Step five: Consider potential downside scenarios. A quick market correction could drop the ratio by 5-10% even in this short timeframe, though this is unlikely given the short period and current market momentum. The probability of a larger drop is very small given the timeframe. - -Step six: Consider potential upside scenarios. Further market gains could push the ratio higher, but there's likely resistance given already elevated levels. A 2-3% increase is possible but larger gains are less likely in such a short period. - -Step seven: Account for measurement lag. The GDP component of the ratio changes quarterly and is unlikely to significantly impact the next 9 days. The main driver will be market capitalization changes. - -Step eight: Low case scenario (175-185): Would require a sharp market correction of 10-15% in just over a week. While possible, this is highly unlikely given the timeframe (about 1% chance). - -Step nine: High case scenario (215-225): Would require continued strong market performance and possibly some GDP adjustment. More likely than the low case but still relatively unlikely given the short timeframe (about 5% chance). - -Given the very short timeframe and current trends, I expect the most likely outcome to be between 205-210, with a wider distribution to account for market volatility and uncertainty. - -Percentile .1: 170 -Percentile 1: 180 -Percentile 5: 190 -Percentile 10: 195 -Percentile 15: 198 -Percentile 20: 200 -Percentile 25: 202 -Percentile 30: 203 -Percentile 35: 204 -Percentile 40: 205 -Percentile 45: 206 -Percentile 46: 206 -Percentile 47: 207 -Percentile 48: 207 -Percentile 49: 207 -Percentile 50: 208 -Percentile 51: 208 -Percentile 52: 208 -Percentile 53: 209 -Percentile 54: 209 -Percentile 55: 209 -Percentile 60: 210 -Percentile 65: 211 -Percentile 70: 212 -Percentile 75: 213 -Percentile 80: 214 -Percentile 85: 216 -Percentile 90: 218 -Percentile 95: 222 -Percentile 99: 228 -Percentile 99.9: 235" -31794,Will the S&P 500 index go up in January 2025?,"Let me think about this step by step: - -1. Question Type Analysis: -This is a Market Price Event, specifically about the S&P 500 index movement over a specific timeframe (January 2025). - -2. Timeline Analysis: -- Current date: January 23, 2025 -- Resolution date: February 1, 2025 -- This means we're looking at just over a week of market movement - -3. Recent Performance: -- S&P 500 is currently at 5910.00 -- Recently hit all-time highs around 6,101 -- Showing some signs of technical resistance -- Currently experiencing a slight pullback - -4. Historical January Performance: -- January historically has a positive bias (known as the ""January effect"") -- However, after strong year-end rallies, January often sees profit-taking -- The S&P 500 had an exceptionally strong 2024 - -5. Current Market Conditions: -- High valuations -- Concentrated gains in tech sector -- Mixed economic indicators -- Strong earnings reports, particularly in tech -- Expectations of Fed rate cuts - -6. Short-Term Considerations: -- With only about a week left in January, the window for significant movement is limited -- Daily volatility becomes more important given the short timeframe -- Technical analysis suggests possible near-term resistance - -7. Probability Analysis: -- Base rate for monthly gains is roughly 60% -- Given the short remaining time frame, current momentum becomes more important -- Recent pullback from highs suggests some consolidation -- High valuations increase downside risk -- Strong earnings season provides support - -8. Risk Factors: -- Technical resistance levels -- Profit-taking after recent highs -- Concentration risk in tech sector -- Economic uncertainty - -9. Supporting Factors: -- Strong earnings -- Fed policy support -- Historical January bias -- Positive momentum from 2024 - -Given the very short time remaining in January, current market dynamics become more important than longer-term factors. The recent achievement of all-time highs followed by a pullback suggests some consolidation is likely. While the longer-term outlook remains positive, the compressed timeframe increases uncertainty. - -The market would need to overcome current resistance levels in a very short period to close higher than December. Given the recent strong performance and technical setup, combined with historical January performance, but tempered by the very short remaining time and current pullback, I estimate a moderately positive probability. - -Probability: 55%" -31793,"What will US airline passenger volume be for Presidents' Day Weekend (February 14-17, 2025), according to the TSA?","Extracted Percentile Values: {1: 7500000, 5: 8000000, 10: 8300000, 15: 8500000, 20: 8700000, 25: 8900000, 30: 9000000, 35: 9100000, 40: 9200000, 45: 9300000, 46: 9320000, 47: 9340000, 48: 9360000, 49: 9380000, 50: 9400000, 51: 9420000, 52: 9440000, 53: 9460000, 54: 9480000, 55: 9500000, 60: 9600000, 65: 9700000, 70: 9800000, 75: 9900000, 80: 10000000, 85: 10200000, 90: 10400000, 95: 10700000, 99: 11000000, 99.9: 11500000} - -Model's Answer: Let me think through this step by step: - -Step one: First, I'll analyze the timing and seasonality. Presidents' Day Weekend 2025 is about three weeks away, which means travel patterns are already becoming somewhat clear, though weather and last-minute bookings could still impact final numbers. - -Step two: Looking at recent data trends, we see very strong holiday travel numbers from the 2024-2025 winter season, with record-breaking numbers during Thanksgiving and Christmas/New Year periods. This suggests continued robust travel demand. - -Step three: For historical comparison, let's consider that TSA typically sees around 2-2.5 million passengers on normal days, with holiday periods showing significant spikes. A four-day weekend like Presidents' Day typically sees elevated but not peak numbers compared to major holidays like Thanksgiving or Christmas. - -Step four: The Airlines for America (A4A) projection of 9.5 million passengers over the four-day period seems reasonable given recent trends and represents about 2.375 million passengers per day on average, which aligns with current daily volumes plus a holiday premium. - -Step five: Considering potential downside risks: -- Severe winter weather could significantly impact travel (15% chance of major weather disruption) -- Economic concerns could reduce discretionary travel -- Airline operational issues -These factors could push numbers down to around 8 million for the period. - -Step six: Considering potential upside factors: -- Continued strong travel demand as seen in recent holidays -- Relatively mild winter weather forecast -- School breaks aligning with the weekend -These could push numbers up to around 10.5 million. - -Step seven: Given that this is a relatively near-term forecast with good historical data and clear seasonal patterns, we can be reasonably confident in a central range, but should still maintain wide tails to account for uncertainties. - -Here's my probability distribution: - -Percentile .1: 7000000 -Percentile 1: 7500000 -Percentile 5: 8000000 -Percentile 10: 8300000 -Percentile 15: 8500000 -Percentile 20: 8700000 -Percentile 25: 8900000 -Percentile 30: 9000000 -Percentile 35: 9100000 -Percentile 40: 9200000 -Percentile 45: 9300000 -Percentile 46: 9320000 -Percentile 47: 9340000 -Percentile 48: 9360000 -Percentile 49: 9380000 -Percentile 50: 9400000 -Percentile 51: 9420000 -Percentile 52: 9440000 -Percentile 53: 9460000 -Percentile 54: 9480000 -Percentile 55: 9500000 -Percentile 60: 9600000 -Percentile 65: 9700000 -Percentile 70: 9800000 -Percentile 75: 9900000 -Percentile 80: 10000000 -Percentile 85: 10200000 -Percentile 90: 10400000 -Percentile 95: 10700000 -Percentile 99: 11000000 -Percentile 99.9: 11500000" -31865,How many Grammy awards will Taylor Swift win in 2025?,"EXTRACTED_PROBABILITIES: [20, 45, 25, 10] - -Model's Answer: Let me think about this step by step: - -1. Time Frame Analysis: -- Resolution date is February 3, 2025, just 11 days away -- Grammy ceremony is imminent, reducing uncertainty -- Voting has likely already concluded - -2. Current Nominations: -- Taylor Swift has 6 nominations -- This provides the maximum possible wins -- Having multiple nominations increases chances of at least one win - -3. Historical Analysis: -- Swift has consistently won Grammys when nominated -- However, rarely wins more than 1-2 per ceremony -- Past performance shows moderate success rate - -4. Competition Assessment: -- Beyoncé leads with 11 nominations -- Strong competition from Billie Eilish and others -- Divided vote potential in major categories - -5. Category Strength: -- Album of the Year facing strong competition -- Concert film category showing potential weakness -- Mixed predictions across other categories - -6. Recent Momentum: -- Strong commercial success -- Positive industry presence -- But possible ""award fatigue"" factor - -7. Base Rate Analysis: -- Most multiple nominees win 0-2 awards -- Complete shutouts are uncommon for major artists -- 3+ wins are rare except for sweep years - -8. Uncertainty Factors: -- Grammy voting can be unpredictable -- Industry politics play a role -- Voter fatigue vs. continued momentum - -9. Statistical Distribution: -- Should follow roughly normal distribution -- Peak likelihood around 1-2 wins -- Tails at 0 and 3+ less likely but possible - -10. Final Analysis: -- High likelihood of at least one win given nominations -- Multiple wins possible but not guaranteed -- Complete shutout or sweep less probable - -Option_A: 20 -Option_B: 45 -Option_C: 25 -Option_D: 10" -31810,"At the end of March 2025, will Wikipedia still list all these countries as ""currently"" blocking access to Twitter/X?","To accurately predict the likelihood that Twitter/X will still be listed as blocked in all specified countries on Wikipedia, let's carefully assess the available information and factors at play. - -### Default State and Contextual Analysis -1. **Default Status:** As of now, Twitter/X is known to be blocked in all eight countries mentioned: China, Iran, Myanmar, North Korea, Pakistan, Russia, Turkmenistan, and Venezuela. This consistency implies a strong baseline of censorship in these countries, well-supported by historical precedent. - -2. **Global Trends and Recent Developments:** Recent information, as per the research assistants, suggests that these countries have maintained or reinforced stringent internet controls: - - **China** and **North Korea:** Highly unlikely to alter their well-known internet censorship regimes given their history and strict governance. - - **Iran** and **Russia:** These countries have ongoing political tensions and public discontent, leading them to historically maintain tight control over social media platforms. - - **Pakistan** and **Myanmar:** With political unrest and governance challenges, censorship serves as a tool for control in these regions. - - **Turkmenistan** and **Venezuela:** Known for heavy-handed control over media and information access, unlikely to change rapidly without substantial political shifts. - -3. **Temporal Proximity:** The resolution date is just over two months away, which minimizes the probability of significant policy shifts. Given the countries' trends and current sociopolitical climates, a short timeframe further solidifies the likelihood of maintaining the status quo. - -4. **Recent News and Updates:** - - No substantial news has indicated any imminent changes towards lifting the blocks. Indeed, most reports suggest either reinforcement of current censorship laws or continued inaccessibility. - - We've identified recent developments from reliable news sources, pointing to entrenched government control over internet platforms growing stronger rather than weaker. - -### Assessing Resolution Criteria -The resolution is contingent upon Wikipedia listing all the specified countries as blocking Twitter/X. There are several layers to consider here: -- **Wikipedia Reliability:** Wikipedia's internet censorship entries are typically well-maintained and updated due to their high relevance and frequent citations, supported by the ongoing management observed. -- **Potential Edit Wars or Disputes:** Given that consensus issues or edit wars could affect visibility temporarily, it's crucial this possibility is considered. However, these incidents are rare and Wikipedia policies generally prioritize consensus. - -### Prediction & Statistical Consideration -1. **Base Rate of No Significant Change:** Given the consistency in existing conditions, no significant reports of upcoming policy reversals, and historical precedence, the likelihood of continued block status is high if no changes occur. -2. **Time-Based Likelihood Distribution:** - - **Short Term Changes (<2 months):** Improbable due to entrenched systems and political constancy. - - **Extended Period Adjustments (>2 months):** More time might allow for political shifts or negotiations, but within our timeframe, the status is less likely to change. - -### Addressing Overconfidence -Historically, my forecasts have shown overconfidence, so careful adjustment is necessary. Factoring this in, while maintaining evidence-based reasoning, will help resist overestimation. - -### Conclusion -Based on the strong default state of ongoing censorship in these countries, lack of any significant developments suggesting changes, and the stringent controls characterizing each government, a high probability that Wikipedia will still list these countries as blocking Twitter/X by the resolution date is justified. - -**Probability: 85%**" -31808,What will be Egypt's inflation rate for January 2025?,"EXTRACTED_PROBABILITIES: [70, 23, 7] - -Model's Answer: To forecast Egypt's inflation rate for January 2025, it's essential to synthesize the information and apply a structured analysis: - -1. **Recent Trends and References**: - - The latest data indicates a headline inflation rate of 24.060% as of December 2024. - - Both local and international sources forecast a decline in inflation, citing various economic improvements and tight monetary policies. - - The Central Bank of Egypt’s monetary policy continues to maintain high interest rates to stabilize inflation. - -2. **Near-Term Outlook**: - - The expectation, as derived from multiple sources, is that inflation will continue to fall due to favorable base effects, economic stabilization, and persisted monetary policies. - -3. **Past Pattern Recognition**: - - Historically, inflation rates show variability but clear trends in the presence of policy interventions, especially in controlled currency and external stability measures. - - The trajectory over the last months shows a consistent decrease, suggesting a lower likelihood of inflation reversing or exceeding previous levels in the short span before February 2025. - -4. **Other Considerations**: - - Geopolitical and currency factors currently appear stable, promoting economic confidence. - - Most forecasts estimate January’s inflation potentially falling below the 25% mark, which aligns with the recent trend of year-long declines. - -5. **Uncertainty**: - - While the global economy and potential unexpected local shocks present uncertainty, the base case, largely supported by multiple independent sources, suggests ongoing moderation in inflation. - -6. **Probability Assignments**: - - Considering all data, the likelihood of inflation being ‘Less than 25%’ is most significant, but recognizing the proximity of recent figures just above 24%, I factor in conservatism to avoid overconfidence. - -Option_A: 70 -Option_B: 23 -Option_C: 7" -31809,What will be the monthly cargo volumes at the Port of NY–NJ for January 2025?,"Extracted Percentile Values: {1: 550000, 5: 600000, 10: 625000, 15: 640000, 20: 655000, 25: 670000, 30: 685000, 35: 700000, 40: 715000, 45: 730000, 46: 732000, 47: 734000, 48: 736000, 49: 738000, 50: 740000, 51: 742000, 52: 744000, 53: 746000, 54: 748000, 55: 750000, 60: 765000, 65: 780000, 70: 795000, 75: 810000, 80: 825000, 85: 840000, 90: 855000, 95: 875000, 99: 900000, 99.9: 925000} - -Model's Answer: Let me think through this step by step. - -Step one: First, I'll analyze the current situation and recent data. The most recent authoritative data shows 721,284 TEUs for November 2024 at the Port of NY-NJ. This provides a solid baseline for our projection. - -Step two: Looking at seasonality, January volumes are typically influenced by pre-Lunar New Year shipping rushes from Asia. In 2025, Lunar New Year falls on January 29, which means much of the pre-holiday surge should be captured in January numbers. - -Step three: The National Retail Federation and other sources are projecting a 10-12% year-over-year increase in overall U.S. port volumes for January 2025. This is a strong indicator, though we should be careful not to assume NY-NJ will exactly match this growth rate. - -Step four: Several factors support higher volumes: -- Pre-tariff shipping surge due to anticipated policy changes -- Resolution of labor disputes reducing uncertainty -- Strong global trade momentum -- Historical pattern of January increases due to Lunar New Year - -Step five: Factors that could lead to lower volumes: -- Potential economic slowdown -- Weather disruptions (common in January) -- Potential diversion to other ports -- Possibility of operational issues - -Step six: Looking at historical volatility, monthly TEU volumes at NY-NJ typically fluctuate within a 10-15% range month-to-month. Given we're forecasting just one month ahead and have recent data, this helps narrow our confidence intervals. - -Step seven: The base case suggests volumes around 750,000 TEUs, considering recent trends and seasonal patterns. A very low case (below 600,000) would require significant disruptions or economic shocks, while a very high case (above 850,000) would require exceptional circumstances like unprecedented pre-tariff rushing. - -Step eight: Given the short timeframe and relatively stable indicators, I'll assign more weight around the central tendency while still accounting for uncertainty. - -Percentile .1: 520000 -Percentile 1: 550000 -Percentile 5: 600000 -Percentile 10: 625000 -Percentile 15: 640000 -Percentile 20: 655000 -Percentile 25: 670000 -Percentile 30: 685000 -Percentile 35: 700000 -Percentile 40: 715000 -Percentile 45: 730000 -Percentile 46: 732000 -Percentile 47: 734000 -Percentile 48: 736000 -Percentile 49: 738000 -Percentile 50: 740000 -Percentile 51: 742000 -Percentile 52: 744000 -Percentile 53: 746000 -Percentile 54: 748000 -Percentile 55: 750000 -Percentile 60: 765000 -Percentile 65: 780000 -Percentile 70: 795000 -Percentile 75: 810000 -Percentile 80: 825000 -Percentile 85: 840000 -Percentile 90: 855000 -Percentile 95: 875000 -Percentile 99: 900000 -Percentile 99.9: 925000" -31731,What percentage of the vote will Alexander Lukashenko receive in the 2025 Belarusian presidential election?,"preliminary results are 86.82% - -[https://www.sb.by/en/central-election-commission-announced-preliminary-results-of-presidential-election-in-belarus-aleksa.html](https://www.sb.by/en/central-election-commission-announced-preliminary-results-of-presidential-election-in-belarus-aleksa.html) - -\> As reported by Mr. Karpenko to BelTA, the Central Election Commission plans to sum up the final results of the Presidential election in Belarus on February 3rd. “In line with the legislation, we are given 10 days after the election, the main voting day, to summarise the final results. We plan to hold a CEC meeting on February 3rd,” he said." diff --git a/pgodzinai_comments.ipynb b/pgodzinai_comments.ipynb deleted file mode 100644 index 29e2202..0000000 --- a/pgodzinai_comments.ipynb +++ /dev/null @@ -1,187 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First result complete structure:\n", - "{\n", - " \"id\": 161249,\n", - " \"author\": {\n", - " \"id\": 191935,\n", - " \"username\": \"pgodzinai\",\n", - " \"is_bot\": true,\n", - " \"is_staff\": false\n", - " },\n", - " \"parent_id\": null,\n", - " \"root_id\": null,\n", - " \"created_at\": \"2024-07-08T23:47:46.145432Z\",\n", - " \"edited_at\": \"2024-07-08T23:47:46.145432Z\",\n", - " \"is_soft_deleted\": false,\n", - " \"text\": \"PERPLEXITY\\n\\nHere is a concise rundown of the news relevant to the question:\\n\\n- Nvidia's market capitalization has surged significantly, reaching $3.34 trillion, surpassing Microsoft and Apple to become the most valuable public company.\\n- Nvidia's rapid growth is attributed to the increasing demand for its graphics processing units (GPUs) in the development of artificial intelligence (AI) systems.\\n- Apple's market capitalization is slightly below $3.4 trillion, while Microsoft leads with a market capitalization just exceeding $3.4 trillion.\\n- Experts believe Nvidia will likely be the first to reach a $4 trillion market capitalization due to the high demand for its AI-focused hardware.\\n- Despite concerns about Nvidia's valuation being overstretched and increasing competition in the semiconductor market, analysts remain bullish on its growth trajectory.\\n- Apple is also seen as a strong contender for reaching the $4 trillion market cap, leveraging AI to drive customer upgrades and attract new iPhone users.\\n\\nRecent updates indicate that Nvidia's market capitalization continues to grow, with some experts predicting it will reach $4 trillion first. However, Apple's growth prospects are also promising, particularly with its integration of AI capabilities into its latest devices. The current market capitalization values are:\\n\\n- Nvidia: $3.34 trillion\\n- Apple: slightly below $3.4 trillion\\n\\nExpert opinions suggest that Nvidia's momentum may continue, but Apple's growth potential and relatively lower valuation make it a strong contender as well.\\n\\n#########\\n\\nGPT\\n\\nGiven the high stakes and complex factors involved in predicting market capitalization, I will approach this methodically by evaluating relevant data, trends, and factors influencing Nvidia and Apple\\u2019s market performance. I will also consider statistical analysis and historical precedents.\\n\\n### Current Context and Data:\\n1. **Current Market Capitalization:**\\n - Nvidia: $3.34 trillion\\n - Apple: slightly below $3.4 trillion\\n\\n2. **Trends:**\\n - Nvidia has experienced significant growth, primarily due to its dominance in the AI chip market.\\n - Apple continues to perform strongly, especially with its integration of AI capabilities into its latest devices and strong consumer demand.\\n\\n3. **Market Forecasts and Expert Opinions:**\\n - Many analysts are bullish on Nvidia due to the increasing demand for AI hardware.\\n - Apple is also seen as a potential candidate for a $4 trillion market cap.\\n\\n### Factors to Consider:\\n1. **Temporal Cushion (9 days until resolution):**\\n - The market can experience significant movements within short periods, given volatility and investor sentiment.\\n - Nine days provide time for any unexpected news or events to impact market caps.\\n\\n2. **Historical Volatility:**\\n - Tech stocks are known for high volatility, especially companies like Nvidia and Apple.\\n\\n3. **Comparative Dynamics:**\\n - Nvidia\\u2019s rapid rise is due to a high demand but could be sensitive to shifts in market sentiment or economic indicators.\\n - Apple benefits from a more diversified product line and consistent revenue streams.\\n\\n### Base Rates and Historical Precedents:\\n- Historically, rapid surges in market valuation, such as Nvidia\\u2019s recent growth, tend to stabilize or correct over short timeframes, especially if perceived as overstretched.\\n- Apple\\u2019s valuation tends to be more stable due to its diversified, entrenched position in consumer electronics and services.\\n\\n### Statistical Considerations:\\n- Using historical market data, compute daily standard deviation:\\n - Assume a 2% daily volatility (standard deviation) for Nvidia and 1.5% for Apple based on their historical performance.\\n- Over 9 days:\\n - Nvidia could experience roughly \\\\(2\\\\% \\\\cdot \\\\sqrt{9} \\u2248 6%\\\\) movement.\\n - Apple could experience roughly \\\\(1.5\\\\% \\\\cdot \\\\sqrt{9} \\u2248 4.5%\\\\) movement.\\n\\n### Default Resolution:\\n- As of now, Apple has a slightly higher market cap.\\n- Without significant new developments, the default outcome favors Apple maintaining its lead due to the factors discussed.\\n\\n### Potential Novel Developments:\\n- **Technology and Product Announcements:** Any significant product release or technological breakthrough can impact valuations.\\n- **Economic Indicators:** Broader market economic factors like interest rate changes or economic reports can influence investor behavior.\\n- **Geopolitical Events:** Unpredictable geopolitical events can impact tech stock performance.\\n\\n### Adjusted Probabilities after Considering Factors:\\n1. **Nvidia\\u2019s continued upward trajectory**: Given Nvidia\\u2019s current momentum and the high demand for AI hardware, assume a 60% probability they will maintain or increase their valuation preference compared to Apple.\\n2. **Apple\\u2019s consistent growth**: Apple\\u2019s stability and potential AI integration pose a 40% probability they will close the marginal gap given current valuations and trends.\\n\\n### Final Assessment:\\nGiven the above calculations, data analysis, and expert opinions, the comprehensive reasoning leans towards a slight favorability for Nvidia.\\n\\nGiven the nine-day period and diverse influencing factors, the uncertainty remains, yet steady trends prefer Nvidia in this high volatility period.\\n\\n### Probability:\\nConsidering all the outlined angles and thorough reassessment:\\n\\n**Probability: 62%**\\n\\nThis suggests there\\u2019s a 62% chance that Nvidia\\u2019s market capitalization will be larger than Apple\\u2019s on July 17, 2024.\",\n", - " \"on_post\": 25871,\n", - " \"on_post_data\": {\n", - " \"id\": 25871,\n", - " \"title\": \"On July 17, 2024, will Nvidia's market capitalization be larger than Apple's?\"\n", - " },\n", - " \"included_forecast\": {\n", - " \"start_time\": \"2024-07-08T23:47:45.615050Z\",\n", - " \"probability_yes\": 0.62,\n", - " \"probability_yes_per_category\": null,\n", - " \"continuous_cdf\": null,\n", - " \"quartiles\": null,\n", - " \"range_min\": null,\n", - " \"range_max\": null,\n", - " \"zero_point\": null,\n", - " \"options\": null,\n", - " \"question_type\": \"binary\"\n", - " },\n", - " \"is_private\": false,\n", - " \"vote_score\": 0,\n", - " \"changed_my_mind\": {\n", - " \"count\": 0,\n", - " \"for_this_user\": false\n", - " },\n", - " \"mentioned_users\": [],\n", - " \"user_vote\": 0,\n", - " \"author_staff_permission\": null,\n", - " \"is_current_content_translated\": false,\n", - " \"key_factors\": []\n", - "}\n" - ] - } - ], - "source": [ - "import requests\n", - "import json\n", - "\n", - "url = 'https://www.metaculus.com/api/comments/'\n", - "params = {\n", - " 'author': '191935',\n", - " 'limit': 1000,\n", - " 'is_private': 'false'\n", - "}\n", - "headers = {\n", - " 'accept': 'application/json'\n", - "}\n", - "\n", - "try:\n", - " response = requests.get(url, params=params, headers=headers)\n", - " response.raise_for_status()\n", - " data = response.json()\n", - " \n", - " # Print the first result with full structure\n", - " if 'results' in data and data['results']:\n", - " print(\"First result complete structure:\")\n", - " print(json.dumps(data['results'][0], indent=2))\n", - " else:\n", - " print(\"No results found\")\n", - "\n", - "except requests.exceptions.RequestException as e:\n", - " print(f\"An error occurred: {e}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total comments in API response: 936\n", - "Number of comments in this request: 936\n", - "\n", - "Saved 936 comments to metaculus_comments.csv\n", - "\n", - "Sample (first row):\n", - "Post ID: 25871\n", - "Post Title: On July 17, 2024, will Nvidia's market capitalization be larger than Apple's?\n", - "Comment Text: PERPLEXITY\n", - "\n", - "Here is a concise rundown of the news relevant to the question:\n", - "\n", - "- Nvidia's market capit...\n" - ] - } - ], - "source": [ - "import requests\n", - "import csv\n", - "\n", - "url = 'https://www.metaculus.com/api/comments/'\n", - "params = {\n", - " 'author': '191935',\n", - " 'limit': 1000,\n", - " 'is_private': 'false'\n", - "}\n", - "headers = {\n", - " 'accept': 'application/json'\n", - "}\n", - "\n", - "try:\n", - " response = requests.get(url, params=params, headers=headers)\n", - " response.raise_for_status()\n", - " data = response.json()\n", - " \n", - " # Print total count\n", - " total_count = data.get('count', 0)\n", - " results_count = len(data['results'])\n", - " print(f\"Total comments in API response: {total_count}\")\n", - " print(f\"Number of comments in this request: {results_count}\")\n", - " \n", - " # Save to CSV\n", - " with open('metaculus_comments.csv', 'w', newline='', encoding='utf-8') as f:\n", - " writer = csv.writer(f)\n", - " writer.writerow(['post_id', 'post_title', 'comment_text'])\n", - " \n", - " for result in data['results']:\n", - " writer.writerow([\n", - " result['on_post_data']['id'],\n", - " result['on_post_data']['title'],\n", - " result['text']\n", - " ])\n", - " \n", - " print(f\"\\nSaved {results_count} comments to metaculus_comments.csv\")\n", - " \n", - " # Print first row as sample\n", - " if data['results']:\n", - " first = data['results'][0]\n", - " print(\"\\nSample (first row):\")\n", - " print(f\"Post ID: {first['on_post_data']['id']}\")\n", - " print(f\"Post Title: {first['on_post_data']['title']}\")\n", - " print(f\"Comment Text: {first['text'][:100]}...\")\n", - "\n", - "except requests.exceptions.RequestException as e:\n", - " print(f\"An error occurred: {e}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "usr", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 492be08fb1eb848903454f9162e51cde4824b515 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 07:49:31 -0600 Subject: [PATCH 03/26] Added pseudocode that for a refactor, and data models that can be used in creating test data --- .gitignore | 1 + AI_BENCHMARKING_ANALYSIS.ipynb | 7 +- main.py | 0 pytest.ini | 13 ++ refactored_notebook/data_models.py | 52 ++++++ refactored_notebook/pseudocode_for_main.py | 162 +++++++++++++++++++ refactored_notebook/simulated_tournament.py | 64 ++++++++ tests/generate_test_data.py | 3 + test_functions.py => tests/test_functions.py | 0 9 files changed, 296 insertions(+), 6 deletions(-) delete mode 100644 main.py create mode 100644 pytest.ini create mode 100644 refactored_notebook/data_models.py create mode 100644 refactored_notebook/pseudocode_for_main.py create mode 100644 refactored_notebook/simulated_tournament.py create mode 100644 tests/generate_test_data.py rename test_functions.py => tests/test_functions.py (100%) diff --git a/.gitignore b/.gitignore index 39111a6..14cb0d1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ .venv/ .env __pycache__ +.personal/ \ No newline at end of file diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 0f510e4..482161d 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -2074,12 +2074,7 @@ "# Print WEIGHTED average for pro_median\n", "print(\"PRO MEDIAN\")\n", "pro_median_baseline = df_pro_baseline_long[df_pro_baseline_long['forecaster'] == 'pro_median']\n", - "print(f'Average baseline: {(pro_median_baseline[\"score\"] * pro_median_baseline[\"question_weight\"]).sum() / pro_median_baseline[\"question_weight\"].sum()}')\n", - "\n", - "# Same for pgodzinai in df_bot_scores (this differs from the bot team results later on because it's on ALL his questions)\n", - "print(\"pgodzinai MEDIAN\")\n", - "pgodzinai_baseline = df_bot_scores[df_bot_scores['forecaster'] == 'pgodzinai']\n", - "print(f'Average baseline: {(pgodzinai_baseline[\"score\"] * pgodzinai_baseline[\"question_weight\"]).sum() / pgodzinai_baseline[\"question_weight\"].sum()}')" + "print(f'Average baseline: {(pro_median_baseline[\"score\"] * pro_median_baseline[\"question_weight\"]).sum() / pro_median_baseline[\"question_weight\"].sum()}')" ] }, { diff --git a/main.py b/main.py deleted file mode 100644 index e69de29..0000000 diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..531adbb --- /dev/null +++ b/pytest.ini @@ -0,0 +1,13 @@ +[pytest] +python_files = test_*.py +pythonpath="./" +# log_level=DEBUG +# log_cli=true +# asyncio_mode = auto +# addopts=-nauto --durations=10 + +# log_file = logs/latest-pytest-outputs.log +# log_cli_format = %(threadName)s - %(asctime)s - %(levelname)s - %(name)s - %(funcName)s - %(message)s +# log_cli_date_format = %Y-%m-%d %H:%M:%S +# log_file_format = %(threadName)s - %(asctime)s - %(levelname)s - %(name)s - %(funcName)s - %(message)s +# log_file_date_format = %Y-%m-%d %H:%M:%S diff --git a/refactored_notebook/data_models.py b/refactored_notebook/data_models.py new file mode 100644 index 0000000..6452dae --- /dev/null +++ b/refactored_notebook/data_models.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +from datetime import datetime +from typing import Literal + +from pydantic import BaseModel +from enum import Enum + +class ResolutionType(Enum): + YES = "yes" + NO = "no" + ANNULLED = "annulled" + AMBIGUOUS = "ambiguous" + +class Forecast(BaseModel): + question: Question + user: User + prediction: list[float] # binary, MC, or numeric + predcition_for_correct_answer: float + prediction_time: datetime + comment: str | None = None + + def get_spot_baseline_score(self, resolution: ResolutionType) -> Score: + raise NotImplementedError("Not implemented") + + def get_spot_peer_score(self, resolution: ResolutionType, other_users_forecasts: list[Forecast]) -> Score: + # assert only one forecast per user + # assert that forecasts are in time range of question + raise NotImplementedError("Not implemented") + +class Score(BaseModel): + score: float + type: Literal["spot_peer", "spot_baseline"] + forecast: Forecast + users_used_in_scoring: list[User] | None# Empty if baseline + +class Question(BaseModel): + question_text: str + resolution: ResolutionType + weight: float + spot_scoring_time: datetime + +class User(BaseModel): + name: str + type: Literal["pro", "bot", "cp"] + is_aggregate: bool + aggregated_users: list[User] + + @property + def is_metac_bot(self) -> bool: + return "metac-" in self.name + diff --git a/refactored_notebook/pseudocode_for_main.py b/refactored_notebook/pseudocode_for_main.py new file mode 100644 index 0000000..3df7559 --- /dev/null +++ b/refactored_notebook/pseudocode_for_main.py @@ -0,0 +1,162 @@ +from __future__ import annotations + +from typing import Literal, Callable +from datetime import datetime +from pydantic import BaseModel +from refactored_notebook.data_models import User, Forecast, Question, Score +from refactored_notebook.simulated_tournament import SimulatedTournament + +# TODO: Since I'm already creating spot score calculations, +# I might as well just input forecasts rather than scores into the tournament +# Though I will also need to check for spot scoring timing/ +# I should check that the scoring matches the original scoring though +# TODO: Rather than the seperate tournament creation for pros and bots, create a +# "Create tournament from tournament" function that takes in a tournament and +# a function that returns users. The function uses the tournament to make the new users +# a new tournament with full scores is created. + + +def set_up_data(path_to_data: str) -> dict[str, SimulatedTournament]: + + def load_initial_tournament(path_to_data: str) -> dict[str, SimulatedTournament]: + # Load the data + # Match questions between the tournaments + # Raise errors (or require manual matching) if there are differences in the questions + bot_tournament = None + pro_tournament = None + return { + "bot_tournament": bot_tournament, + "pro_tournament": pro_tournament, + } + + def caculate_spot_peer_score_for_user(all_forecasts_for_question: list[Forecast], user: User) -> Score: + # Assert forecasts are all for the same question + # Assert that there is only one forecast per user + # Filter for last forecast of each user that is before the spot scoring time (possibly do in previous step) + # Calculate the score for the user (weighted by question weight) + raise NotImplementedError("Not implemented") + + def caculate_spot_baseline_score(forecasts_for_user: list[Forecast]) -> Score: + # Find last forecast for user that is before the spot scoring time + # Calculate the score for the user (weighted by question weight) + raise NotImplementedError("Not implemented") + + def caculate_all_scores_for_forecasts(forecasts: list[Forecast]) -> list[Score]: + # Find questions + # For each question + # For each user + # Calculate spot peer score + # Calculate spot baseline score + raise NotImplementedError("Not implemented") + + def get_bot_team_user_with_size(original_tournament: SimulatedTournament, team_size: int) -> tuple[User, list[Forecast]]: + # Create a new user for the team + # Create forecasts for the team + # Calculate the scores for the user + raise NotImplementedError("Not implemented") + + def get_all_bot_teams_as_users(original_tournament: SimulatedTournament) -> list[tuple[User, list[Forecast]]]: + users_and_forecasts = [] + for team_size in range(1, len(original_tournament.users)): + users_and_forecasts.extend(get_bot_team_user_with_size(original_tournament, team_size)) + return users_and_forecasts + + def get_best_bot_team_user(bot_tournament: SimulatedTournament) -> list[tuple[User, list[Forecast]]]: + # Simulate bot team tournament + # Grab the user and forecasts for the best bot team + raise NotImplementedError("Not implemented") + + def get_pro_median_user(pro_tournament: SimulatedTournament) -> list[tuple[User, list[Forecast]]]: + # Create new user + # Create forecasts for the median + raise NotImplementedError("Not implemented") + + def get_pro_median_and_bot_median_users(bot_tournament: SimulatedTournament, pro_tournament: SimulatedTournament) -> list[tuple[User, list[Forecast]]]: + # Get the pro median user + # Get the bot median user + # Return the two users and their forecasts + raise NotImplementedError("Not implemented") + + def create_tournament( + original_tournament: SimulatedTournament, + new_users: list[tuple[User, list[Forecast]]], + remove_all_old_users: bool = False + ) -> SimulatedTournament: + # TODO: Also add parameter for filtering questions (or choosing new ones like only binaries) + # assert that the forecasts given each have a corresonding question and vise versa for each user + # Create scores for the new users and recaculate for old users + # Make a new tournament with all the new scores + raise NotImplementedError("Not implemented") + + original_tournament = load_initial_tournament(path_to_data) + original_bot_tournament = original_tournament["bot_tournament"] + original_pro_tournament = original_tournament["pro_tournament"] + bot_team_only_tournament = create_tournament( + original_bot_tournament, + get_all_bot_teams_as_users(original_bot_tournament), + remove_all_old_users=True + ) + pro_v_bot_head_to_head_tournament = create_tournament( + original_bot_tournament, + get_pro_median_and_bot_median_users(original_bot_tournament, original_pro_tournament), + remove_all_old_users=True + ) + + return { + "original_bot_tournament": original_bot_tournament, + "original_pro_tournament": original_pro_tournament, + "bot_team_only_tournament": bot_team_only_tournament, + "pro_v_bot_head_to_head_tournament": pro_v_bot_head_to_head_tournament, + } + + +def display_everything(score_sets: dict[str, SimulatedTournament]) -> None: + + forecasts_to_display = score_sets["original_bot_tournament"].forecasts + + def display_calibration_curve(forecasts: list[Forecast]) -> None: + # Each user has its own line and a 90% confidence interval + raise NotImplementedError("Not implemented") + + def display_discrimination_curve(forecasts: list[Forecast]) -> None: + # Each user has its own bar + raise NotImplementedError("Not implemented") + + def display_spot_peer_score_table(tournament: SimulatedTournament, users_to_display: list[User] | None = None) -> None: + # Filter for peer scores + # make sure all scores are peer scores + # make sure that all scores use the same users for calculation + + # Add these stats as a property of the simulated tournament scores + # Caculate average spot peer score + # Caculate sum of spot peer scores + # Find confidence interval + # Weighted question count (sum of weights) + # Show in table with a row for each user + # Filter by users_to_display if provided + raise NotImplementedError("Not implemented") + + def display_best_and_worse_scoring_questions(tournament: SimulatedTournament) -> None: + # Assert there are only 2 users + # Find the score differences between each question + # Show the top 5 and bottom 5 questions, forecasts for those questions, the resolution, and the score difference + raise NotImplementedError("Not implemented") + + def display_general_tournament_stats(bot_tournament: SimulatedTournament, pro_tournament: SimulatedTournament) -> None: + # Display num pro questions + # Display num bot questions + # Display num pro users + # Display num bot users + raise NotImplementedError("Not implemented") + + + metac_bots = [user for user in score_sets["original_bot_tournament"].users if user.is_metac_bot] + + display_calibration_curve(forecasts_to_display) + display_discrimination_curve(forecasts_to_display) + display_spot_peer_score_table(score_sets["original_bot_tournament"]) + display_spot_peer_score_table(score_sets["original_bot_tournament"], users_to_display=metac_bots) + display_spot_peer_score_table(score_sets["bot_team_only_tournament"]) + display_spot_peer_score_table(score_sets["pro_v_bot_head_to_head_tournament"]) + display_best_and_worse_scoring_questions(score_sets["pro_v_bot_head_to_head_tournament"]) + display_general_tournament_stats(score_sets["original_bot_tournament"], score_sets["original_pro_tournament"]) \ No newline at end of file diff --git a/refactored_notebook/simulated_tournament.py b/refactored_notebook/simulated_tournament.py new file mode 100644 index 0000000..eddbfc5 --- /dev/null +++ b/refactored_notebook/simulated_tournament.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +from pydantic import BaseModel +from refactored_notebook.data_models import User, Question, Forecast, Score + + +class SimulatedTournament(BaseModel): + forecasts: list[Forecast] + + @property + def users(self) -> set[User]: + users = set() + for forecast in self.forecasts: + users.add(forecast.user) + return users + + @property + def questions(self) -> set[Question]: + questions = set() + for forecast in self.forecasts: + questions.add(forecast.question) + return questions + + @property + def scores(self) -> list[Score]: + spot_peer_scores = [] + spot_baseline_scores = [] + for forecast in self.forecasts: + forecasts_from_other_users = [ + f + for f in self.forecasts + if f.question == forecast.question and f.user != forecast.user + ] + spot_peer_scores.append( + forecast.get_spot_peer_score( + forecast.question.resolution, forecasts_from_other_users + ) + ) + spot_baseline_scores.append( + forecast.get_spot_baseline_score(forecast.question.resolution) + ) + return spot_peer_scores + spot_baseline_scores + + def get_ranking_by_spot_peer_score_lower_t_bound( + self, confidence_level: float + ) -> list[tuple[User, float]]: + # Get all spot peer scores + # create a confidence interval for the spot peer score + # Sort by lower bound + raise NotImplementedError("Not implemented") + + def get_ranking_by_spot_peer_score_sum(self) -> list[tuple[User, float]]: + # Get all spot peer scores + # Sort by spot peer score + raise NotImplementedError("Not implemented") + + def get_ranking_by_spot_peer_score_bootstrap_lower_bound( + self, confidence_level: float + ) -> list[tuple[User, float]]: + # Get all spot peer scores + # bootstrap the spot peer scores + # create a confidence interval for the spot peer score + # Sort by lower bound + raise NotImplementedError("Not implemented") diff --git a/tests/generate_test_data.py b/tests/generate_test_data.py new file mode 100644 index 0000000..8ef6257 --- /dev/null +++ b/tests/generate_test_data.py @@ -0,0 +1,3 @@ +from refactored_notebook.data_models import User, Question, Forecast, Score + + diff --git a/test_functions.py b/tests/test_functions.py similarity index 100% rename from test_functions.py rename to tests/test_functions.py From f4a9e4ee2984303d26c3a458dc7ebd48b3462be1 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 08:29:43 -0600 Subject: [PATCH 04/26] Converted baseline scoring function to be independent of dataframes --- refactored_notebook/scoring.py | 87 ++++++++++++++++++++++++++++++++++ tests/generate_test_data.py | 2 + tests/test_scoring.py | 14 ++++++ 3 files changed, 103 insertions(+) create mode 100644 refactored_notebook/scoring.py create mode 100644 tests/test_scoring.py diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py new file mode 100644 index 0000000..4054f3c --- /dev/null +++ b/refactored_notebook/scoring.py @@ -0,0 +1,87 @@ +from typing import Literal + +import numpy as np + + +def calculate_spot_peer_score( + forecast_for_correct_answer: float, + other_users_forecasts_for_correct_answer: list[float], +) -> float: + raise NotImplementedError("Not implemented") + + +def calculate_spot_baseline_score( + forecast: list[float] | None, # binary: [p_yes, p_no], multiple choice: [p_a, p_b, p_c], numeric: [p_0, p_1, p_2, ...] + resolution: bool | str | float | None, # binary: bool, multiple choice: str, numeric: float + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, +) -> float: + """ + Question type can be infered from resolution type + """ + + if forecast is None or resolution is None: + raise NotImplementedError("Havent decided how to handle null forecasts and resolutions") + + if len(forecast) == 0: + raise ValueError("Forecast is empty") + + baseline_score = None + + if isinstance(resolution, bool): + if len(forecast) != 1 or len(forecast) != 2: + raise ValueError("Binary questions must have exactly one forecast and two options (for yes or 'yes and no')") + + forecast_val = float(forecast[0]) + baseline_prob = 0.5 + if resolution: + prob_for_resolution = forecast_val + else: + prob_for_resolution = 1 - forecast_val + elif isinstance(resolution, str): + if options is None: + raise ValueError("Options are required for multiple choice questions") + + if len(forecast) != len(options): + raise ValueError("Forecast and options have different lengths") + + pmf = [float(p) for p in forecast] + options = [str(opt) for opt in options] + resolution_idx = options.index(str(resolution)) + prob_for_resolution = pmf[resolution_idx] + baseline_prob = 1 / len(pmf) + elif isinstance(resolution, float): + if range_min is None or range_max is None: + raise ValueError("Range min and range max are required for numeric questions") + + cdf = [float(p) for p in forecast] + pmf = [cdf[0]] + [cdf[i] - cdf[i-1] for i in range(1, len(cdf))] # @Ben check: is this a correct conversion? + pmf.append(1 - cdf[-1]) + + resolution = float(resolution) + + bin_edges = np.linspace(range_min, range_max, 200) + resolution_idx = np.searchsorted(bin_edges, resolution, side='right') + + if resolution_idx >= len(pmf): + raise ValueError("Resolution is out of bounds") + + prob_for_resolution = pmf[resolution_idx] + baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin + + else: + raise ValueError("Unknown question type") + + if prob_for_resolution <= 0 or baseline_prob <= 0: + raise ValueError("Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue") + + baseline_score = np.log2(prob_for_resolution / baseline_prob) + + if isinstance(resolution, float): + baseline_score /= 2 # Numeric scores are halved + + weighted_score = baseline_score * question_weight + + return weighted_score diff --git a/tests/generate_test_data.py b/tests/generate_test_data.py index 8ef6257..5b68133 100644 --- a/tests/generate_test_data.py +++ b/tests/generate_test_data.py @@ -1,3 +1,5 @@ from refactored_notebook.data_models import User, Question, Forecast, Score +# TODO: Things to test: +# - peer rankings \ No newline at end of file diff --git a/tests/test_scoring.py b/tests/test_scoring.py new file mode 100644 index 0000000..ce27003 --- /dev/null +++ b/tests/test_scoring.py @@ -0,0 +1,14 @@ + +# TODO: +# For each of Multiple Choice, Binary, and Numeric questions +# - Test spot peer score +# - forecast this is further away than others gets worse scores (with 1-5 forecasts) +# - forecast this is closer to the resolution gets better scores (with 1-5 forecasts) +# - If everyone has the same forecast, the score is 0 +# - The sum (average?) of everyone's scores is 0 +# - The score for a weighted question is weighted by the question weight +# - Test spot baseline score +# - 0 with 50% forecast, ? for a uniform distribution, and 0 for uniform multiple choice questions +# - better score when closer to resolution, and worse when further away (for forecasts on both sides of 50% forecast) +# - The score for a weighted question is weighted by the question weight +# - Run a test of some forecasts from the site, and make sure the score generated matches the score the site gives From 3ed53c911076b12b54af56c3e5279fc1c3845f7e Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 12:56:08 -0600 Subject: [PATCH 05/26] Added baseline scoring tests --- AI_BENCHMARKING_ANALYSIS.ipynb | 2 +- functions.py | 519 +++++++++++---------- refactored_notebook/data_models.py | 12 +- refactored_notebook/pseudocode_for_main.py | 3 +- refactored_notebook/scoring.py | 71 ++- tests/generate_test_data.py | 5 - tests/test_end_to_end.py | 18 + tests/test_scoring.py | 186 ++++++++ 8 files changed, 546 insertions(+), 270 deletions(-) delete mode 100644 tests/generate_test_data.py create mode 100644 tests/test_end_to_end.py diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 482161d..619d445 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -3378,7 +3378,7 @@ "outputs": [], "source": [ "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", - "# @Ben: Check -> This was originally 'calculate_all_peer_scores'. NOt sure the correct function alternative\n" + "# @Check: -> This was originally 'calculate_all_peer_scores'. NOt sure the correct function alternative\n" ] }, { diff --git a/functions.py b/functions.py index d0bf79f..42c14de 100644 --- a/functions.py +++ b/functions.py @@ -1,25 +1,27 @@ -import pandas as pd -import numpy as np +import ast +import math +import random +import re +from datetime import datetime + import matplotlib.pyplot as plt -from scipy.stats import norm +import numpy as np +import pandas as pd from scipy import stats from scipy.optimize import minimize_scalar -from scipy.stats import binom -import re -from datetime import datetime -import random -import math -import ast +from scipy.stats import binom, norm + +from refactored_notebook.scoring import calculate_spot_baseline_score + def extract_forecast(df): # Extract the forecast from whichever column it's in - df['forecast'] = df['probability_yes'].combine_first( - df['probability_yes_per_category'].combine_first( - df['continuous_cdf'] - ) + df["forecast"] = df["probability_yes"].combine_first( + df["probability_yes_per_category"].combine_first(df["continuous_cdf"]) ) return df + def process_forecasts(df): """ Process a dataframe of forecasts by: @@ -39,23 +41,24 @@ def process_forecasts(df): Processed DataFrame with last forecasts """ # Extract the forecast value - df['forecast'] = df['probability_yes'].combine_first( - df['probability_yes_per_category'].combine_first( - df['continuous_cdf'] - ) + df["forecast"] = df["probability_yes"].combine_first( + df["probability_yes_per_category"].combine_first(df["continuous_cdf"]) ) # Sort by created_at to ensure chronological order - df = df.sort_values(by='created_at') + df = df.sort_values(by="created_at") # Take the last forecast for each (forecaster, question_id) pair - df = df.groupby(['question_id', 'forecaster']).last().reset_index() + df = df.groupby(["question_id", "forecaster"]).last().reset_index() # Drop the original forecast columns as they're now redundant - df = df.drop(['probability_yes', 'probability_yes_per_category', 'continuous_cdf'], axis=1) + df = df.drop( + ["probability_yes", "probability_yes_per_category", "continuous_cdf"], axis=1 + ) return df + def add_is_median(df): """ Marks exactly one row per question_id as the median. @@ -69,22 +72,23 @@ def add_is_median(df): pandas.DataFrame: DataFrame with an additional 'is_median' column. """ # Initialize median column - df['is_median'] = False + df["is_median"] = False # For each question_id - for qid in df['question_id'].unique(): + for qid in df["question_id"].unique(): # Get just the rows for this question - question_mask = df['question_id'] == qid + question_mask = df["question_id"] == qid question_df = df[question_mask] # Get the median value index (middle position after sorting) - median_idx = question_df['forecast'].sort_values().index[len(question_df)//2] + median_idx = question_df["forecast"].sort_values().index[len(question_df) // 2] # Mark that row - df.loc[median_idx, 'is_median'] = True + df.loc[median_idx, "is_median"] = True return df + def add_median_rows(df, prefix): """ For each row where is_median=True, creates a duplicate row with forecaster='median'. @@ -97,16 +101,21 @@ def add_median_rows(df, prefix): pandas.DataFrame: Original DataFrame plus duplicate rows for medians. """ # Get the median rows - median_rows = df[df['is_median']].copy() + median_rows = df[df["is_median"]].copy() # Change forecaster to 'median' - median_rows['forecaster'] = f'{prefix}_median' + median_rows["forecaster"] = f"{prefix}_median" # Combine original and new median rows - whole = pd.concat([df, median_rows], ignore_index=True).sort_values('question_id').drop_duplicates(['question_id', 'forecaster']) + whole = ( + pd.concat([df, median_rows], ignore_index=True) + .sort_values("question_id") + .drop_duplicates(["question_id", "forecaster"]) + ) return whole + def calculate_weighted_stats(df): """ Calculates weighted statistics (mean, sum, standard error, confidence intervals) for each forecaster. @@ -120,12 +129,12 @@ def calculate_weighted_stats(df): results = [] # For each forecaster - for forecaster in df['forecaster'].unique(): - forecaster_data = df[df['forecaster'] == forecaster] + for forecaster in df["forecaster"].unique(): + forecaster_data = df[df["forecaster"] == forecaster] # Get scores and weights - scores = forecaster_data['score'] - weights = forecaster_data['question_weight'] + scores = forecaster_data["score"] + weights = forecaster_data["question_weight"] # Calculate weighted mean weighted_mean = np.average(scores, weights=weights) @@ -133,26 +142,28 @@ def calculate_weighted_stats(df): # Calculate weighted standard error # Using weighted variance formula - weighted_var = np.average((scores - weighted_mean)**2, weights=weights) + weighted_var = np.average((scores - weighted_mean) ** 2, weights=weights) n = len(scores) weighted_se = np.sqrt(weighted_var / n) # Calculate t-statistic for 95% confidence interval - t_value = stats.t.ppf(0.975, n-1) + t_value = stats.t.ppf(0.975, n - 1) ci_lower = weighted_mean - (t_value * weighted_se) - results.append({ - 'forecaster': forecaster, - 'weighted_mean': weighted_mean, - 'weighted_sum': weighted_sum, - 'n_questions': n, - 'ci_lower': ci_lower, - 'weighted_se': weighted_se - }) + results.append( + { + "forecaster": forecaster, + "weighted_mean": weighted_mean, + "weighted_sum": weighted_sum, + "n_questions": n, + "ci_lower": ci_lower, + "weighted_se": weighted_se, + } + ) # Convert to dataframe and sort by lower bound results_df = pd.DataFrame(results) - return results_df.sort_values('weighted_sum', ascending=False) + return results_df.sort_values("weighted_sum", ascending=False) def make_wide(df_bot_peer, df_pro_bot_resolved_questions): @@ -166,33 +177,38 @@ def make_wide(df_bot_peer, df_pro_bot_resolved_questions): Returns: pandas.DataFrame: Wide-format DataFrame with question weights merged. """ - df_pivoted = df_bot_peer.pivot(index='bot_question_id', columns='forecaster', values='score') + df_pivoted = df_bot_peer.pivot( + index="bot_question_id", columns="forecaster", values="score" + ) df_pivoted = df_pivoted.reset_index() df_pivoted = df_pivoted.reindex(sorted(df_pivoted.columns), axis=1) # Step 4: Move 'question_id' to be the first column cols = df_pivoted.columns.tolist() - cols = ['bot_question_id'] + [col for col in cols if col != 'bot_question_id'] + cols = ["bot_question_id"] + [col for col in cols if col != "bot_question_id"] df_pivoted = df_pivoted[cols] all_columns = df_pivoted.columns.tolist() ## Remove 'question_id' and 'bot_median' from the list if they exist - all_columns = [col for col in all_columns if col not in ['bot_question_id']] - new_column_order = ['bot_question_id'] + all_columns + all_columns = [col for col in all_columns if col not in ["bot_question_id"]] + new_column_order = ["bot_question_id"] + all_columns df_pivoted = df_pivoted[new_column_order] df_bot_peer_wide = df_pivoted - df_bot_peer_wide['bot_question_id'] = pd.to_numeric(df_bot_peer_wide['bot_question_id'], errors='coerce') + df_bot_peer_wide["bot_question_id"] = pd.to_numeric( + df_bot_peer_wide["bot_question_id"], errors="coerce" + ) # Join with df_pro_bot_resolved_questions to get question weights df_bot_peer_wide = pd.merge( df_bot_peer_wide, - df_pro_bot_resolved_questions[['bot_question_id', 'question_weight']], - on='bot_question_id', - how='left' + df_pro_bot_resolved_questions[["bot_question_id", "question_weight"]], + on="bot_question_id", + how="left", ) return df_bot_peer_wide + """ Options from https://stats.stackexchange.com/questions/47325/bias-correction-in-weighted-variance I didn't think (B) beared trying, but could be wrong. - MGH @@ -200,6 +216,7 @@ def make_wide(df_bot_peer, df_pro_bot_resolved_questions): the bias in the sample variance. """ + def calc_weighted_std_dev(df3, bot, weighted_score, weighted_count, weight_col): """ Calculates the weighted standard deviation using Molly's method - (A) from stack exchange post. @@ -215,7 +232,11 @@ def calc_weighted_std_dev(df3, bot, weighted_score, weighted_count, weight_col): float: Weighted standard deviation. """ weighted_average = weighted_score / weighted_count - return np.sqrt(((df3[bot] - weighted_average) ** 2 * df3[weight_col]).sum() / (weighted_count - 1)) + return np.sqrt( + ((df3[bot] - weighted_average) ** 2 * df3[weight_col]).sum() + / (weighted_count - 1) + ) + def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col): """ @@ -233,10 +254,14 @@ def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) """ weighted_average = weighted_score / weighted_count return np.sqrt( - (df3[weight_col] * (df3[bot] - weighted_average) ** 2).sum() / - (df3[weight_col].sum() * (1 - (df3[weight_col] ** 2).sum() / (df3[weight_col].sum() ** 2))) + (df3[weight_col] * (df3[bot] - weighted_average) ** 2).sum() + / ( + df3[weight_col].sum() + * (1 - (df3[weight_col] ** 2).sum() / (df3[weight_col].sum() ** 2)) + ) ) + def weighted_bootstrap_analysis(df_bot_peer_wide, bots, NUM, ITER): """ Performs weighted bootstrap analysis to calculate confidence intervals and medians. @@ -250,10 +275,11 @@ def weighted_bootstrap_analysis(df_bot_peer_wide, bots, NUM, ITER): Returns: pandas.DataFrame: DataFrame with confidence intervals and medians for each bot. """ + # Function to perform a single bootstrap iteration def single_bootstrap(df): # Weighted sampling of questions - sampled_df = df.sample(n=NUM, weights='question_weight', replace=True) + sampled_df = df.sample(n=NUM, weights="question_weight", replace=True) # Calculate total weighted score for each bot return sampled_df[bots].sum() @@ -271,32 +297,35 @@ def single_bootstrap(df): median = results_df.median() # Create output DataFrame - output_df = pd.DataFrame({ - '2.5% CI': ci_low, - '10% CI': ci_10, - 'Median': median, - '90% CI': ci_90, - '97.5% CI': ci_high - }) + output_df = pd.DataFrame( + { + "2.5% CI": ci_low, + "10% CI": ci_10, + "Median": median, + "90% CI": ci_90, + "97.5% CI": ci_high, + } + ) # Sort by median descending - output_df = output_df.sort_values('Median', ascending=False) + output_df = output_df.sort_values("Median", ascending=False) return output_df + def get_median_forecast_multiple_choice(row, forecasts): """ Given a row (with 'options' and 'resolution') and a list of forecasts (each a list of floats), returns the median probability assigned to the resolution option. """ - options = row['options'] - resolution = row['resolution'] + options = row["options"] + resolution = row["resolution"] try: resolution_idx = options.index(resolution) - #print(f"Resolution '{resolution}' found at index {resolution_idx} in options {options}") + # print(f"Resolution '{resolution}' found at index {resolution_idx} in options {options}") except ValueError: - #print(f"Resolution '{resolution}' not found in options {options} — returning np.nan") + # print(f"Resolution '{resolution}' not found in options {options} — returning np.nan") return np.nan # Resolution not found in options probs = [] @@ -309,14 +338,15 @@ def get_median_forecast_multiple_choice(row, forecasts): continue if not probs: - #print(f"NO PROBS collected for multiple-choice question {row.get('bot_question_id')} — returning np.nan") + # print(f"NO PROBS collected for multiple-choice question {row.get('bot_question_id')} — returning np.nan") return np.nan return np.nanmedian(probs) + def get_median_forecast(row, bots): """ - @BEN: Check + @Check: Calculates the median forecast for a given set of bots, handling different question types properly. @@ -327,7 +357,7 @@ def get_median_forecast(row, bots): Returns: pandas.Series: Median forecast for each row. """ - q_type = row['type'] + q_type = row["type"] forecasts = [] for bot in bots: @@ -345,7 +375,7 @@ def get_median_forecast(row, bots): if not forecasts: return np.nan - if q_type == 'numeric': + if q_type == "numeric": forecasts = [f for f in forecasts if isinstance(f, list)] if not forecasts: @@ -356,36 +386,42 @@ def get_median_forecast(row, bots): return mean_cdf - elif q_type == 'binary': + elif q_type == "binary": probs = [] for f in forecasts: try: val = float(f) probs.append(val) except (ValueError, TypeError): - print(f' Invalid forecast: {f} — error {e}') + print(f" Invalid forecast: {f} — error {e}") continue if not probs: - print(f" >>> NO PROBS collected for binary question {row.get('bot_question_id')} — returning np.nan") + print( + f" >>> NO PROBS collected for binary question {row.get('bot_question_id')} — returning np.nan" + ) return np.nan print(f" >>> Collected {len(probs)} forecasts: {probs}") return np.nanmedian(probs) - elif q_type == 'multiple_choice': + elif q_type == "multiple_choice": return get_median_forecast_multiple_choice(row, forecasts) else: raise ValueError(f"Unknown question type: {q_type}") -def calculate_all_peer_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[str]) -> pd.DataFrame: +def calculate_all_peer_scores( + df_bot_team_forecasts: pd.DataFrame, teams: list[str] +) -> pd.DataFrame: """ Takes in a df that has a row for each question, a column for each team, and a forecast as that columns value Changes the df so that the forecast is now the score for that question """ - raise NotImplementedError("I accidentally implemented baseline scoring here unfortunately") + raise NotImplementedError( + "I accidentally implemented baseline scoring here unfortunately" + ) score_df = df_bot_team_forecasts.copy() team_scores = calculate_weighted_scores(df_bot_team_forecasts, teams) for team in teams: @@ -396,7 +432,7 @@ def calculate_all_peer_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[s def calculate_weighted_scores(df_bot_team_forecasts, teams): """ - @BEN: check + @Check: Calculates weighted scores for each team based on their forecasts and question weights. @@ -410,76 +446,30 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): team_scores = {team: 0.0 for team in teams} for _, row in df_bot_team_forecasts.iterrows(): - q_type = row['type'] - resolution = row['resolution'] - options = row.get('options') - range_min = row.get('range_min') - range_max = row.get('range_max') - question_weight = row['question_weight'] + q_type = row["type"] + resolution = row["resolution"] + options = row.get("options") + range_min = row.get("range_min") + range_max = row.get("range_max") + question_weight = row["question_weight"] for team in teams: forecast = row[team] - if forecast is None or (isinstance(forecast, float) and np.isnan(forecast)): - continue - - baseline_score = None - try: - if q_type == 'binary': - forecast_val = float(forecast) - baseline_prob = 0.5 - if resolution == 'yes': - p_team = forecast_val - elif resolution == 'no': - p_team = 1 - forecast_val - else: - continue # Skip if invalid resolution - - elif q_type == 'multiple_choice': - pmf = [float(p) for p in forecast] - options = [str(opt) for opt in options] - resolution_idx = options.index(str(resolution)) - p_team = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) - - elif q_type == 'numeric': - cdf = [float(p) for p in forecast] - pmf = [cdf[0]] + [cdf[i] - cdf[i-1] for i in range(1, len(cdf))] - pmf.append(1 - cdf[-1]) - - resolution = float(resolution) - if range_min is None or range_max is None: - continue - bin_edges = np.linspace(range_min, range_max, 200) - resolution_idx = np.searchsorted(bin_edges, resolution, side='right') - - if resolution_idx >= len(pmf): - continue # Skip if out of bounds - - p_team = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin - - else: - continue # Unknown question type - - if p_team <= 0 or baseline_prob <= 0: - continue # Avoid log(0) issues - - baseline_score = np.log2(p_team / baseline_prob) - - if q_type == 'numeric': - baseline_score /= 2 # Numeric scores are halved - - weighted_score = baseline_score * question_weight + weighted_score = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) team_scores[team] += weighted_score except (ValueError, TypeError, IndexError): + # @Ben: Does skipping introduce any problems? continue # Be robust to bad/missing data return pd.Series(team_scores) -def calculate_t_test(df_input, bot_list, weight_col='question_weight'): + +def calculate_t_test(df_input, bot_list, weight_col="question_weight"): """ Calculates weighted statistics, including t-test and p-values, for multiple bots. @@ -521,7 +511,9 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): if weighted_count > 2: # Only calculate if we have enough data weighted_average = weighted_score / weighted_count - weighted_std_dev = calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) + weighted_std_dev = calc_weighted_std_dev2( + df3, bot, weighted_score, weighted_count, weight_col + ) std_error = weighted_std_dev / np.sqrt(weighted_count) t_statistic = (weighted_average - 0) / std_error @@ -532,11 +524,13 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): lower_bound = weighted_average - t_crit * std_error # Calculate CDF and p-value - cdf = stats.t.cdf(t_statistic, df=weighted_count-1) + cdf = stats.t.cdf(t_statistic, df=weighted_count - 1) p_value = 2 * min(cdf, 1 - cdf) # Two-tailed p-value else: # Not enough data - weighted_average = weighted_score / weighted_count if weighted_count > 0 else np.nan + weighted_average = ( + weighted_score / weighted_count if weighted_count > 0 else np.nan + ) weighted_std_dev = np.nan std_error = np.nan t_statistic = np.nan @@ -547,50 +541,48 @@ def calculate_t_test(df_input, bot_list, weight_col='question_weight'): p_value = np.nan # Store results - df_W_leaderboard.loc[bot, 'W_score'] = weighted_score - df_W_leaderboard.loc[bot, 'W_count'] = weighted_count - df_W_leaderboard.loc[bot, 'W_ave'] = weighted_average - df_W_leaderboard.loc[bot, 'W_stdev'] = weighted_std_dev - df_W_leaderboard.loc[bot, 'std_err'] = std_error - df_W_leaderboard.loc[bot, 't_stat'] = t_statistic - df_W_leaderboard.loc[bot, 't_crit'] = t_crit - df_W_leaderboard.loc[bot, 'upper_bound'] = upper_bound - df_W_leaderboard.loc[bot, 'lower_bound'] = lower_bound - df_W_leaderboard.loc[bot, 'cdf'] = cdf - df_W_leaderboard.loc[bot, 'p_value'] = p_value + df_W_leaderboard.loc[bot, "W_score"] = weighted_score + df_W_leaderboard.loc[bot, "W_count"] = weighted_count + df_W_leaderboard.loc[bot, "W_ave"] = weighted_average + df_W_leaderboard.loc[bot, "W_stdev"] = weighted_std_dev + df_W_leaderboard.loc[bot, "std_err"] = std_error + df_W_leaderboard.loc[bot, "t_stat"] = t_statistic + df_W_leaderboard.loc[bot, "t_crit"] = t_crit + df_W_leaderboard.loc[bot, "upper_bound"] = upper_bound + df_W_leaderboard.loc[bot, "lower_bound"] = lower_bound + df_W_leaderboard.loc[bot, "cdf"] = cdf + df_W_leaderboard.loc[bot, "p_value"] = p_value # Format and round the results - df_W_leaderboard['W_score'] = df_W_leaderboard['W_score'].round(1) + df_W_leaderboard["W_score"] = df_W_leaderboard["W_score"].round(1) # Store numerical p-values temporarily for sorting - df_W_leaderboard['_p_value_sort'] = df_W_leaderboard['p_value'] + df_W_leaderboard["_p_value_sort"] = df_W_leaderboard["p_value"] # Format p-values as percentages - df_W_leaderboard['p_value'] = df_W_leaderboard['p_value'].apply( + df_W_leaderboard["p_value"] = df_W_leaderboard["p_value"].apply( lambda x: f"{x:.6f}" if pd.notnull(x) else "NA" ) # Round other columns - df_W_leaderboard[['W_ave', 'W_count', 'lower_bound', 'upper_bound']] = \ - df_W_leaderboard[['W_ave', 'W_count', 'lower_bound', 'upper_bound']].round(1) + df_W_leaderboard[["W_ave", "W_count", "lower_bound", "upper_bound"]] = ( + df_W_leaderboard[["W_ave", "W_count", "lower_bound", "upper_bound"]].round(1) + ) # Sort by the numerical p-values df_W_leaderboard = df_W_leaderboard.sort_values( - by='W_score', - ascending=False, - na_position='last' + by="W_score", ascending=False, na_position="last" ) # Drop the temporary sorting column - df_W_leaderboard = df_W_leaderboard.drop('_p_value_sort', axis=1) + df_W_leaderboard = df_W_leaderboard.drop("_p_value_sort", axis=1) return df_W_leaderboard - def calculate_head_to_head(row, a, b): """ - @BEN: Check... + @Check:... Calculates the head-to-head score for two forecasters. Positive if 'a' did better than 'b', negative if 'b' did better than 'a'. @@ -603,42 +595,50 @@ def calculate_head_to_head(row, a, b): Returns: float: Head-to-head score. """ - q_type = row['type'] - resolution = row['resolution'] - options = row['options'] - range_min = row.get('range_min') - range_max = row.get('range_max') + q_type = row["type"] + resolution = row["resolution"] + options = row["options"] + range_min = row.get("range_min") + range_max = row.get("range_max") forecast_a = row[a] forecast_b = row[b] - if q_type == 'binary': - if (resolution == 'yes') or (resolution == 1): + if q_type == "binary": + if (resolution == "yes") or (resolution == 1): return 100 * np.log(forecast_a / forecast_b) - elif (resolution == 'no') or (resolution == 0): + elif (resolution == "no") or (resolution == 0): return 100 * np.log((1 - forecast_a) / (1 - forecast_b)) else: return np.nan - elif q_type == 'multiple_choice': + elif q_type == "multiple_choice": # Parse forecast_a if it's a string if isinstance(forecast_a, str): forecast_a = ast.literal_eval(forecast_a) - options = ast.literal_eval(row['options']) if isinstance(row['options'], str) else row['options'] - resolution_idx = options.index(str(row['resolution'])) + options = ( + ast.literal_eval(row["options"]) + if isinstance(row["options"], str) + else row["options"] + ) + resolution_idx = options.index(str(row["resolution"])) forecast_a = forecast_a[resolution_idx] # Parse forecast_b if it's a string if isinstance(forecast_b, str): forecast_b = ast.literal_eval(forecast_b) - options = ast.literal_eval(row['options']) if isinstance(row['options'], str) else row['options'] - resolution_idx = options.index(str(row['resolution'])) + options = ( + ast.literal_eval(row["options"]) + if isinstance(row["options"], str) + else row["options"] + ) + resolution_idx = options.index(str(row["resolution"])) forecast_b = forecast_b[resolution_idx] # Now both are floats with the prob assigned to the correct bin return 100 * np.log(forecast_a / forecast_b) - elif q_type == 'numeric': + elif q_type == "numeric": # Ensure both forecasts are Python lists if isinstance(forecast_a, str): forecast_a = ast.literal_eval(forecast_a) @@ -656,10 +656,10 @@ def calculate_head_to_head(row, a, b): cdf_a = forecast_a cdf_b = forecast_b - pmf_a = [cdf_a[0]] + [cdf_a[i] - cdf_a[i-1] for i in range(1, len(cdf_a))] + pmf_a = [cdf_a[0]] + [cdf_a[i] - cdf_a[i - 1] for i in range(1, len(cdf_a))] pmf_a.append(1 - cdf_a[-1]) - pmf_b = [cdf_b[0]] + [cdf_b[i] - cdf_b[i-1] for i in range(1, len(cdf_b))] + pmf_b = [cdf_b[0]] + [cdf_b[i] - cdf_b[i - 1] for i in range(1, len(cdf_b))] pmf_b.append(1 - cdf_b[-1]) bin_edges = np.linspace(range_min, range_max, 200) @@ -671,7 +671,9 @@ def calculate_head_to_head(row, a, b): else: try: resolution_val = float(resolution) - resolution_idx = np.searchsorted(bin_edges, resolution_val, side='right') + resolution_idx = np.searchsorted( + bin_edges, resolution_val, side="right" + ) except ValueError: print(f"Bad resolution value: {resolution}") return np.nan @@ -685,7 +687,10 @@ def calculate_head_to_head(row, a, b): return 100 * np.log(p_a / p_b) -def plot_head_to_head_distribution(df_forecasts, col='head_to_head', vs=('Bot Team', 'Pros')): + +def plot_head_to_head_distribution( + df_forecasts, col="head_to_head", vs=("Bot Team", "Pros") +): """ Plots the distribution of head-to-head scores and fits a Gaussian curve. @@ -706,23 +711,23 @@ def plot_head_to_head_distribution(df_forecasts, col='head_to_head', vs=('Bot Te # Create the histogram plt.figure(figsize=(10, 6)) - n, bins, patches = plt.hist(data, bins=30, density=True, alpha=0.7, color='skyblue') + n, bins, patches = plt.hist(data, bins=30, density=True, alpha=0.7, color="skyblue") # Generate points for the fitted Gaussian curve x = np.linspace(min(data), max(data), 100) y = norm.pdf(x, mean, std) # Plot the fitted Gaussian curve - plt.plot(x, y, 'r-', linewidth=2, label='Fitted Gaussian') + plt.plot(x, y, "r-", linewidth=2, label="Fitted Gaussian") # Customize the plot - plt.title(f'{vs[0]} Head-to-Head Scores vs {vs[1]}') - plt.xlabel('Head-to-Head Score') - plt.ylabel('Density') + plt.title(f"{vs[0]} Head-to-Head Scores vs {vs[1]}") + plt.xlabel("Head-to-Head Score") + plt.ylabel("Density") plt.legend() # Add text annotation for the mean - #plt.text(0.95, 0.95, f'Mean: {mean:.2f}', transform=plt.gca().transAxes, verticalalignment='top', horizontalalignment='right') + # plt.text(0.95, 0.95, f'Mean: {mean:.2f}', transform=plt.gca().transAxes, verticalalignment='top', horizontalalignment='right') # Display the plot plt.show() @@ -730,6 +735,7 @@ def plot_head_to_head_distribution(df_forecasts, col='head_to_head', vs=('Bot Te # Print the average print(f"The average of 'head_to_head' is: {mean:.2f}") + def calculate_calibration_curve(forecasts, resolutions, weights): """ Calculates a calibration curve for forecasts. @@ -787,6 +793,7 @@ def calculate_calibration_curve(forecasts, resolutions, weights): "calibration_curve": calibration_curve, } + def plot_calibration_curve(df, column_name, label, color): """ Plots a calibration curve with confidence intervals. @@ -801,29 +808,36 @@ def plot_calibration_curve(df, column_name, label, color): None """ # Filter to binary questions in case the DataFrame has other types (0 or 1 INT or 'yes'/'no' STR) - df = df[df['resolution'].isin(['yes', 'no', 1, 0])] + df = df[df["resolution"].isin(["yes", "no", 1, 0])] - y_true = df['resolution'] + y_true = df["resolution"] y_pred = df[column_name] weights = [1.0 for _ in y_true] - calibration_curve = calculate_calibration_curve(y_pred, y_true, weights)['calibration_curve'] - prob_true = [item['average_resolution'] for item in calibration_curve] - bin_center = [(item['bin_lower'] + item['bin_upper']) / 2 for item in calibration_curve] - ci_lower = [item['lower_confidence_interval'] for item in calibration_curve] - ci_upper = [item['upper_confidence_interval'] for item in calibration_curve] - - plt.plot(bin_center, prob_true, marker='o', linewidth=2, label=label, color=color) + calibration_curve = calculate_calibration_curve(y_pred, y_true, weights)[ + "calibration_curve" + ] + prob_true = [item["average_resolution"] for item in calibration_curve] + bin_center = [ + (item["bin_lower"] + item["bin_upper"]) / 2 for item in calibration_curve + ] + ci_lower = [item["lower_confidence_interval"] for item in calibration_curve] + ci_upper = [item["upper_confidence_interval"] for item in calibration_curve] + + plt.plot(bin_center, prob_true, marker="o", linewidth=2, label=label, color=color) plt.fill_between(bin_center, ci_lower, ci_upper, alpha=0.2, color=color) for x, y in zip(bin_center, prob_true): if x is None or y is None: continue - plt.annotate(f'({x:.2f}, {y:.2f})', - (x, y), - textcoords="offset points", - xytext=(0,10), - ha='center', - color=color, - fontsize=8) + plt.annotate( + f"({x:.2f}, {y:.2f})", + (x, y), + textcoords="offset points", + xytext=(0, 10), + ha="center", + color=color, + fontsize=8, + ) + def calculate_confidence(predictions, outcomes): """ @@ -840,9 +854,11 @@ def calculate_confidence(predictions, outcomes): bins = pd.cut(predictions, bins=10) # Calculate mean prediction and actual outcome for each bin - grouped = pd.DataFrame({'prediction': predictions, 'outcome': outcomes}).groupby(bins) - mean_prediction = grouped['prediction'].mean() - mean_outcome = grouped['outcome'].mean() + grouped = pd.DataFrame({"prediction": predictions, "outcome": outcomes}).groupby( + bins + ) + mean_prediction = grouped["prediction"].mean() + mean_outcome = grouped["outcome"].mean() # Calculate the difference between mean prediction and mean outcome confidence_diff = mean_prediction - mean_outcome @@ -850,6 +866,7 @@ def calculate_confidence(predictions, outcomes): # Return the average difference (excluding NaN values) return np.nanmean(confidence_diff) + def interpret_confidence(score): """ Interprets the confidence score. @@ -867,6 +884,7 @@ def interpret_confidence(score): else: return "Perfectly calibrated" + def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): """ Creates histograms to compare discrimination between bot and pro teams. @@ -887,12 +905,15 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): bins = np.linspace(0, 1, 6) # Top plot: Questions that resolved 1 - ax1.hist([df[df[resolution_col] == 1][bot_col], - df[df[resolution_col] == 1][pro_col]], - bins=bins, label=['Bot Team', 'Pro Team'], alpha=0.7) - ax1.set_title('Questions that Resolved \'Yes\'') - ax1.set_xlabel('Assigned Probability') - ax1.set_ylabel('Frequency') + ax1.hist( + [df[df[resolution_col] == 1][bot_col], df[df[resolution_col] == 1][pro_col]], + bins=bins, + label=["Bot Team", "Pro Team"], + alpha=0.7, + ) + ax1.set_title("Questions that Resolved 'Yes'") + ax1.set_xlabel("Assigned Probability") + ax1.set_ylabel("Frequency") ax1.legend() # Set integer y-ticks for top plot @@ -900,12 +921,15 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): ax1.set_yticks(range(0, ymax1 + 1, 2)) # Bottom plot: Questions that resolved 0 - ax2.hist([df[df[resolution_col] == 0][bot_col], - df[df[resolution_col] == 0][pro_col]], - bins=bins, label=['Bot Team', 'Pro Team'], alpha=0.7) - ax2.set_title('Questions that Resolved \'No\'') - ax2.set_xlabel('Assigned Probability') - ax2.set_ylabel('Frequency') + ax2.hist( + [df[df[resolution_col] == 0][bot_col], df[df[resolution_col] == 0][pro_col]], + bins=bins, + label=["Bot Team", "Pro Team"], + alpha=0.7, + ) + ax2.set_title("Questions that Resolved 'No'") + ax2.set_xlabel("Assigned Probability") + ax2.set_ylabel("Frequency") ax2.legend() # Set integer y-ticks for bottom plot @@ -916,6 +940,7 @@ def create_discrimination_histogram(df, bot_col, pro_col, resolution_col): plt.tight_layout() plt.show() + def get_weighted_score(df_forecasts): """ Calculates the weighted total score for forecasts. @@ -927,13 +952,15 @@ def get_weighted_score(df_forecasts): float: Weighted total score. """ # Calculate the weighted score for each row - df_forecasts['weighted_score'] = df_forecasts['head_to_head'] * df_forecasts['question_weight'] + df_forecasts["weighted_score"] = ( + df_forecasts["head_to_head"] * df_forecasts["question_weight"] + ) # Calculate the total weighted score - total_weighted_score = df_forecasts['weighted_score'].sum() + total_weighted_score = df_forecasts["weighted_score"].sum() # Calculate the sum of weights - total_weight = df_forecasts['question_weight'].sum() + total_weight = df_forecasts["question_weight"].sum() # Calculate the weighted total score weighted_total_score = total_weighted_score / total_weight @@ -942,8 +969,10 @@ def get_weighted_score(df_forecasts): return weighted_total_score + # ====== CODE FROM LUKE, REFACTORED BY CHATGPT ======= + def string_location_to_scaled_location(string_location, question_row): """ Converts a string location to a scaled location based on question type. @@ -978,6 +1007,7 @@ def string_location_to_scaled_location(string_location, question_row): # question.type == "numeric" return float(string_location) + def scaled_location_to_unscaled_location(scaled_location, question_row): """ Converts a scaled location to an unscaled location based on question type. @@ -1001,12 +1031,16 @@ def scaled_location_to_unscaled_location(scaled_location, question_row): if zero_point is not None: deriv_ratio = (range_max - zero_point) / max((range_min - zero_point), 1e-7) return ( - np.log((scaled_location - range_min) * (deriv_ratio - 1) + (range_max - range_min)) + np.log( + (scaled_location - range_min) * (deriv_ratio - 1) + + (range_max - range_min) + ) - np.log(range_max - range_min) ) / np.log(deriv_ratio) return (scaled_location - range_min) / (range_max - range_min) + def nominal_location_to_cdf_location(nominal_location, question_data): """ Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to @@ -1042,6 +1076,7 @@ def nominal_location_to_cdf_location(nominal_location, question_data): unscaled_location = (scaled_location - range_min) / (range_max - range_min) return unscaled_location + def get_cdf_at(cdf, unscaled_location): """ Retrieves the CDF value at a given unscaled location. @@ -1061,7 +1096,7 @@ def get_cdf_at(cdf, unscaled_location): if index_scaled_location.is_integer(): return cdf[int(index_scaled_location)] # linear interpolation step - left_index = int(index_scaled_location) # This is the floor, which is what we want + left_index = int(index_scaled_location) # This is the floor, which is what we want right_index = left_index + 1 left_value = cdf[left_index] right_value = cdf[right_index] @@ -1069,8 +1104,10 @@ def get_cdf_at(cdf, unscaled_location): index_scaled_location - left_index ) + # ======== END OF LUKE'S CODE ========== + def cdf_between(row, cdf, lower_bound, upper_bound): """ Calculates the probability between two bounds using the CDF. @@ -1086,7 +1123,8 @@ def cdf_between(row, cdf, lower_bound, upper_bound): """ a = get_cdf_at(cdf, nominal_location_to_cdf_location(lower_bound, row)) b = get_cdf_at(cdf, nominal_location_to_cdf_location(upper_bound, row)) - return (b - a) + return b - a + def extract_year(title): """ @@ -1098,9 +1136,10 @@ def extract_year(title): Returns: int or None: Extracted year or None if not found. """ - match = re.search(r'\b(19|20)\d{2}\b', title) + match = re.search(r"\b(19|20)\d{2}\b", title) return int(match.group(0)) if match else None + def extract_month(title): """ Extracts the month from a title string. @@ -1111,9 +1150,13 @@ def extract_month(title): Returns: str or None: Extracted month or None if not found. """ - match = re.search(r'\b(January|February|March|April|May|June|July|August|September|October|November|December)\b', title) + match = re.search( + r"\b(January|February|March|April|May|June|July|August|September|October|November|December)\b", + title, + ) return match.group(0) if match else None + def compute_cp_baseline_score(value): """ Gracefully computes the cp_baseline_score. @@ -1134,6 +1177,7 @@ def compute_cp_baseline_score(value): # Handle any unexpected errors return np.nan + def process_forecast_values(df): """ Adds a 'bucket_forecast_value' column to the DataFrame (for interpreting CP distribution as a @@ -1146,34 +1190,40 @@ def process_forecast_values(df): Returns: pandas.DataFrame: Updated DataFrame with 'bucket_forecast_value' column added. """ + def compute_bucket_forecast_value(row): # Handle binary_version_tuple gracefully - if pd.isna(row['binary_version_tuple']) or not isinstance(row['binary_version_tuple'], (list, tuple)): + if pd.isna(row["binary_version_tuple"]) or not isinstance( + row["binary_version_tuple"], (list, tuple) + ): return None # Extract the first and second elements of the tuple - comparison_type = row['binary_version_tuple'][0] - string_location = row['binary_version_tuple'][1] + comparison_type = row["binary_version_tuple"][0] + string_location = row["binary_version_tuple"][1] # Skip if comparison_type is 'complicated' - if comparison_type == 'complicated': + if comparison_type == "complicated": return None # Compute forecast_value using the extracted string_location - forecast_value = get_cdf_at(row['cdf'], nominal_location_to_cdf_location(string_location, row)) + forecast_value = get_cdf_at( + row["cdf"], nominal_location_to_cdf_location(string_location, row) + ) # Apply logic based on comparison_type - if comparison_type == 'less': + if comparison_type == "less": return forecast_value - elif comparison_type == 'greater': + elif comparison_type == "greater": return 1 - forecast_value return None # Apply the function to each row and overwrite forecast_value (currently contains cdf, which we no longer need) - df['forecast_values'] = df.apply(compute_bucket_forecast_value, axis=1) + df["forecast_values"] = df.apply(compute_bucket_forecast_value, axis=1) return df + def parse_options_array(options_str): """ Parse options string that looks like an array into an actual array. @@ -1194,13 +1244,14 @@ def parse_options_array(options_str): except: # If that fails, try custom parsing # Strip brackets and split by comma - cleaned = options_str.strip('[]') + cleaned = options_str.strip("[]") # Split by comma, but respect quotes import re + # Match items in quotes with commas inside parts = re.findall(r'"([^"]*)"', cleaned) if parts: return parts # Simple fallback: just split by comma and strip quotes - return [p.strip().strip('"\'') for p in cleaned.split(',')] + return [p.strip().strip("\"'") for p in cleaned.split(",")] diff --git a/refactored_notebook/data_models.py b/refactored_notebook/data_models.py index 6452dae..1aaa3f4 100644 --- a/refactored_notebook/data_models.py +++ b/refactored_notebook/data_models.py @@ -4,18 +4,14 @@ from typing import Literal from pydantic import BaseModel -from enum import Enum -class ResolutionType(Enum): - YES = "yes" - NO = "no" - ANNULLED = "annulled" - AMBIGUOUS = "ambiguous" +ResolutionType = bool | str | float | None # binary, MC, numeric, or 'annulled/ambiguous' +ForecastType = list[float] | None # binary: [p_yes, p_no], multiple choice: [p_a, p_b, p_c], numeric: [p_0, p_1, p_2, ...] class Forecast(BaseModel): question: Question user: User - prediction: list[float] # binary, MC, or numeric + prediction: ForecastType predcition_for_correct_answer: float prediction_time: datetime comment: str | None = None @@ -32,7 +28,7 @@ class Score(BaseModel): score: float type: Literal["spot_peer", "spot_baseline"] forecast: Forecast - users_used_in_scoring: list[User] | None# Empty if baseline + users_used_in_scoring: list[User] | None # Empty if baseline class Question(BaseModel): question_text: str diff --git a/refactored_notebook/pseudocode_for_main.py b/refactored_notebook/pseudocode_for_main.py index 3df7559..6660cc4 100644 --- a/refactored_notebook/pseudocode_for_main.py +++ b/refactored_notebook/pseudocode_for_main.py @@ -130,7 +130,8 @@ def display_spot_peer_score_table(tournament: SimulatedTournament, users_to_disp # Add these stats as a property of the simulated tournament scores # Caculate average spot peer score # Caculate sum of spot peer scores - # Find confidence interval + # Find confidence interval w/ t test + # find confidence interval with bootstrapping # Weighted question count (sum of weights) # Show in table with a row for each user # Filter by users_to_display if provided diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 4054f3c..62a2c3f 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -1,7 +1,7 @@ -from typing import Literal - import numpy as np +from refactored_notebook.data_models import ForecastType, ResolutionType + def calculate_spot_peer_score( forecast_for_correct_answer: float, @@ -11,28 +11,42 @@ def calculate_spot_peer_score( def calculate_spot_baseline_score( - forecast: list[float] | None, # binary: [p_yes, p_no], multiple choice: [p_a, p_b, p_c], numeric: [p_0, p_1, p_2, ...] - resolution: bool | str | float | None, # binary: bool, multiple choice: str, numeric: float - options: list[str] | None, - range_min: float | None, - range_max: float | None, - question_weight: float, + forecast: ForecastType, + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, + question_weight: float = 1.0, ) -> float: """ Question type can be infered from resolution type + Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ + + is_binary = isinstance(resolution, bool) + is_multiple_choice = isinstance(resolution, str) + is_numeric = isinstance(resolution, float) or isinstance(resolution, int) + + if forecast is None or resolution is None: - raise NotImplementedError("Havent decided how to handle null forecasts and resolutions") + raise NotImplementedError( + "Havent decided how to handle null forecasts or anulled resolutions" + ) if len(forecast) == 0: raise ValueError("Forecast is empty") - baseline_score = None + if not is_numeric and any(p <= 0 or p >= 1 for p in forecast): + # @Check: Is it valid to have a numeric forecast with 0 probability for a number? + raise ValueError("Forecast contains probabilities outside of 0 to 1 range") + - if isinstance(resolution, bool): - if len(forecast) != 1 or len(forecast) != 2: - raise ValueError("Binary questions must have exactly one forecast and two options (for yes or 'yes and no')") + if is_binary: + if len(forecast) != 1 and len(forecast) != 2: + raise ValueError( + "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" + ) forecast_val = float(forecast[0]) baseline_prob = 0.5 @@ -40,7 +54,7 @@ def calculate_spot_baseline_score( prob_for_resolution = forecast_val else: prob_for_resolution = 1 - forecast_val - elif isinstance(resolution, str): + elif is_multiple_choice: if options is None: raise ValueError("Options are required for multiple choice questions") @@ -52,32 +66,47 @@ def calculate_spot_baseline_score( resolution_idx = options.index(str(resolution)) prob_for_resolution = pmf[resolution_idx] baseline_prob = 1 / len(pmf) - elif isinstance(resolution, float): + elif is_numeric: if range_min is None or range_max is None: - raise ValueError("Range min and range max are required for numeric questions") + raise ValueError( + "Range min and range max are required for numeric questions" + ) + if len(forecast) != 201: + raise ValueError("CDF should have 201 bins") + previous_prob = 0 + for current_prob in forecast: + if current_prob < previous_prob: + raise ValueError("CDF should be in increasing order") + previous_prob = current_prob cdf = [float(p) for p in forecast] - pmf = [cdf[0]] + [cdf[i] - cdf[i-1] for i in range(1, len(cdf))] # @Ben check: is this a correct conversion? + pmf = [cdf[0]] + [ + cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) + ] # @Check: is this a correct conversion? pmf.append(1 - cdf[-1]) resolution = float(resolution) bin_edges = np.linspace(range_min, range_max, 200) - resolution_idx = np.searchsorted(bin_edges, resolution, side='right') + resolution_idx = np.searchsorted(bin_edges, resolution, side="right") if resolution_idx >= len(pmf): raise ValueError("Resolution is out of bounds") prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin + baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins else: raise ValueError("Unknown question type") if prob_for_resolution <= 0 or baseline_prob <= 0: - raise ValueError("Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue") + raise ValueError( + "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" + ) - baseline_score = np.log2(prob_for_resolution / baseline_prob) + baseline_score = np.log2( + prob_for_resolution / baseline_prob + ) * 100 # @Check: check correctness (also shouldn't this be natural log?) if isinstance(resolution, float): baseline_score /= 2 # Numeric scores are halved diff --git a/tests/generate_test_data.py b/tests/generate_test_data.py deleted file mode 100644 index 5b68133..0000000 --- a/tests/generate_test_data.py +++ /dev/null @@ -1,5 +0,0 @@ -from refactored_notebook.data_models import User, Question, Forecast, Score - - -# TODO: Things to test: -# - peer rankings \ No newline at end of file diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py new file mode 100644 index 0000000..233e769 --- /dev/null +++ b/tests/test_end_to_end.py @@ -0,0 +1,18 @@ +from refactored_notebook.data_models import User, Question, Forecast, Score + + +# Generate test csvs to input into the notebook, and assert the below tests pass + +# Things that could go wrong: +# - bad math in scoring +# - didn't load in data correctly +# - bad filtering/manipulation of scoring data (did we take out the right people) +# - make sure to determine the bot team only by the bot-only questions +# - make sure best bot team is decided by baseline score comparison to each other +# - make sure best bots for bot team are decided by lower bound of t test +# - Confidence interval code is wrong +# - make sure that there are large intervals if only a few forecasts, and small intervals if many forecasts +# - make sure bootstrap and t tests indicate the same things generally +# ... continue through and consider other final outputs (e.g. calibration curve) + + diff --git a/tests/test_scoring.py b/tests/test_scoring.py index ce27003..6195e67 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -1,3 +1,6 @@ +import pytest + +from refactored_notebook.scoring import calculate_spot_baseline_score # TODO: # For each of Multiple Choice, Binary, and Numeric questions @@ -12,3 +15,186 @@ # - better score when closer to resolution, and worse when further away (for forecasts on both sides of 50% forecast) # - The score for a weighted question is weighted by the question weight # - Run a test of some forecasts from the site, and make sure the score generated matches the score the site gives + + +def generate_uniform_cdf(num_points: int) -> list[float]: + return [(i + 1) / num_points for i in range(num_points)] + + +def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[float]: + assert correct_index >= 0 and correct_index <= 201 + length_of_cdf = 201 + cdf = [] + for i in range(length_of_cdf): + if i < correct_index: + cdf.append(float(i / length_of_cdf)) + else: + cdf.append(0.99) + + if inverse_cdf: + cdf = [1 - c for c in cdf] + + return cdf + + + + +@pytest.mark.parametrize( + "forecast,resolution,options,range_min,range_max,question_weight,expected", + [ + # Binary: uniform forecast, should be 0 + ([0.5], True, None, None, None, 1.0, 0.0), + ([0.5], False, None, None, None, 1.0, 0.0), + ([0.5, 0.5], False, None, None, None, 1.0, 0.0), + # Multiple Choice: uniform forecast, should be 0 + ([1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None, 1.0, 0.0), + ([0.25, 0.25, 0.25, 0.25], "B", ["A", "B", "C", "D"], None, None, 1.0, 0.0), + # Numeric: uniform CDF, should be 0 + (generate_uniform_cdf(201), 0.5, None, 0.0, 1.0, 1.0, 0.0), + ], +) +def test_baseline_score_is_0_with_uniform_prediction( + forecast: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, + expected: float, +): + score = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score - expected) == pytest.approx(0) + + +def test_binary_baseline_score_when_perfect_forecast(): + score = calculate_spot_baseline_score( + forecast=[0.99999999], + resolution=True, + ) + assert score == pytest.approx(100) + + +def test_binary_baseline_if_completly_incorrect_forecast(): + score = calculate_spot_baseline_score( + forecast=[0.0000001], + resolution=True, + ) + assert score == pytest.approx(-897) + + +def test_numeric_baseline_when_perfect_forecast(): + correct_index = 30 + length_of_cdf = 201 + index_to_answer_ratio = 3 + correct_answer = correct_index * index_to_answer_ratio + range_max = length_of_cdf * index_to_answer_ratio + + score = calculate_spot_baseline_score( + forecast=generate_perfect_cdf(correct_index), + resolution=correct_answer, + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(183) + + +def test_numeric_baseline_if_completly_incorrect_forecast(): + correct_index = 30 + length_of_cdf = 201 + index_to_answer_ratio = 3 + correct_answer = correct_index * index_to_answer_ratio + range_max = length_of_cdf * index_to_answer_ratio + + score = calculate_spot_baseline_score( + forecast=generate_perfect_cdf(correct_index), + resolution=correct_answer, + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(-230) + + +def test_multiple_choice_perfect_forecast(): + forecast_for_answer_a = 0.999999999 + num_other_forecasts = 7 + other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts + score = calculate_spot_baseline_score( + forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, + resolution="A", + options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], + ) + assert score == pytest.approx(100) + + +def test_multiple_choice_if_completly_incorrect_forecast(): + forecast_for_answer_c = 0.999999999 + other_forecasts = (1 - forecast_for_answer_c) / 2 + score = calculate_spot_baseline_score( + forecast=[other_forecasts, other_forecasts, forecast_for_answer_c], + resolution="C", + options=["A", "B", "C"], + ) + assert score == pytest.approx(-232) + + +@pytest.mark.parametrize( + "forecast_closer,forecast_further,resolution,options,range_min,range_max", + [ + # Binary: closer to True + ([0.8], [0.2], True, None, None, None), + # Binary: closer to False + ([0.2], [0.8], False, None, None, None), + # Multiple Choice: closer to "A" + ([0.7, 0.2, 0.1], [0.1, 0.2, 0.7], "A", ["A", "B", "C"], None, None), + # Numeric: CDF with more mass near 0.5 vs near 0.0 + ([0.1] * 52 + [0.9] * 149, [0.9] * 52 + [0.1] * 149, 0.5, None, 0.0, 1.0), + ], +) +def test_baseline_score_better_when_closer( + forecast_closer: list[float], + forecast_further: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + score_closer = calculate_spot_baseline_score( + forecast_closer, resolution, options, range_min, range_max, 1.0 + ) + score_further = calculate_spot_baseline_score( + forecast_further, resolution, options, range_min, range_max, 1.0 + ) + assert score_closer > score_further + + +@pytest.mark.parametrize( + "forecast,resolution,options,range_min,range_max,question_weight", + [ + # Binary + ([0.8], True, None, None, None, 2.0), + # Multiple Choice + ([0.7, 0.2, 0.1], "A", ["A", "B", "C"], None, None, 0.5), + # Numeric + ([0.1] * 50 + [0.9] * 149, 0.5, None, 0.0, 1.0, 3.0), + ], +) +def test_baseline_score_weighted( + forecast: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, +): + score_unweighted = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, 1.0 + ) + score_weighted = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 + + + From 7a3c6a0d2b0564c4251427385fa35d7f2f4ea6fe Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 13:27:27 -0600 Subject: [PATCH 06/26] Added peer scoring tests --- refactored_notebook/scoring.py | 61 ++++++---- tests/test_scoring.py | 208 +++++++++++++++++++++++++++++++-- 2 files changed, 241 insertions(+), 28 deletions(-) diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 62a2c3f..080aea4 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -4,8 +4,13 @@ def calculate_spot_peer_score( - forecast_for_correct_answer: float, - other_users_forecasts_for_correct_answer: list[float], + forecast: ForecastType, + forecast_for_other_users: list[ForecastType], + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, + question_weight: float = 1.0, ) -> float: raise NotImplementedError("Not implemented") @@ -23,12 +28,41 @@ def calculate_spot_baseline_score( Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ + prob_for_resolution, baseline_prob = ( + _determine_probability_for_resolution_and_baseline( + forecast, resolution, options, range_min, range_max + ) + ) + + if prob_for_resolution <= 0 or baseline_prob <= 0: + raise ValueError( + "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" + ) + + baseline_score = ( + np.log2(prob_for_resolution / baseline_prob) * 100 + ) # @Check: check correctness (also shouldn't this be natural log?) + + if isinstance(resolution, float): + baseline_score /= 2 # Numeric scores are halved + + weighted_score = baseline_score * question_weight + + return weighted_score + + +def _determine_probability_for_resolution_and_baseline( + forecast: ForecastType, + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, +) -> tuple[float, float]: is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) is_numeric = isinstance(resolution, float) or isinstance(resolution, int) - if forecast is None or resolution is None: raise NotImplementedError( "Havent decided how to handle null forecasts or anulled resolutions" @@ -41,7 +75,6 @@ def calculate_spot_baseline_score( # @Check: Is it valid to have a numeric forecast with 0 probability for a number? raise ValueError("Forecast contains probabilities outside of 0 to 1 range") - if is_binary: if len(forecast) != 1 and len(forecast) != 2: raise ValueError( @@ -94,23 +127,11 @@ def calculate_spot_baseline_score( raise ValueError("Resolution is out of bounds") prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins + baseline_prob = 1 / len( + pmf + ) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins else: raise ValueError("Unknown question type") - if prob_for_resolution <= 0 or baseline_prob <= 0: - raise ValueError( - "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" - ) - - baseline_score = np.log2( - prob_for_resolution / baseline_prob - ) * 100 # @Check: check correctness (also shouldn't this be natural log?) - - if isinstance(resolution, float): - baseline_score /= 2 # Numeric scores are halved - - weighted_score = baseline_score * question_weight - - return weighted_score + return prob_for_resolution, baseline_prob diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 6195e67..3a4dd7c 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -1,6 +1,11 @@ +import numpy as np import pytest -from refactored_notebook.scoring import calculate_spot_baseline_score +from refactored_notebook.data_models import ForecastType +from refactored_notebook.scoring import ( + calculate_spot_baseline_score, + calculate_spot_peer_score, +) # TODO: # For each of Multiple Choice, Binary, and Numeric questions @@ -10,12 +15,10 @@ # - If everyone has the same forecast, the score is 0 # - The sum (average?) of everyone's scores is 0 # - The score for a weighted question is weighted by the question weight -# - Test spot baseline score -# - 0 with 50% forecast, ? for a uniform distribution, and 0 for uniform multiple choice questions -# - better score when closer to resolution, and worse when further away (for forecasts on both sides of 50% forecast) -# - The score for a weighted question is weighted by the question weight # - Run a test of some forecasts from the site, and make sure the score generated matches the score the site gives +################################### HELPER FUNCTIONS ################################### + def generate_uniform_cdf(num_points: int) -> list[float]: return [(i + 1) / num_points for i in range(num_points)] @@ -37,6 +40,198 @@ def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[ return cdf +################################### PEER SCORES ################################### + + +@pytest.mark.parametrize( + "forecasts,resolution,options,range_min,range_max", + [ + # Binary: forecast closer to resolution gets better score + ( + [[0.9], [0.7], [0.5], [0.3], [0.1]], + True, + None, + None, + None, + ), + # Multiple Choice: forecast closer to resolution gets better score + ( + [ + [0.9, 0.1, 0.0], + [0.7, 0.2, 0.1], + [0.5, 0.3, 0.2], + [0.3, 0.4, 0.3], + [0.1, 0.2, 0.7], + ], + "A", + ["A", "B", "C"], + None, + None, + ), + # Numeric: forecast CDFs with more mass near resolution get better score + ( + [ + [0.1] * 100 + [0.9] * 101, # most mass above 0.5 + [0.2] * 100 + [0.8] * 101, + [0.5] * 201, + [0.8] * 100 + [0.2] * 101, + [0.9] * 100 + [0.1] * 101, # most mass below 0.5 + ], + 0.5, + None, + 0.0, + 1.0, + ), + ], +) +def test_better_forecast_means_better_peer_score( + forecasts: list[list[float]], + resolution: bool | str | float, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + expected_order: list[int], +): + scores = [ + calculate_spot_peer_score( + forecast, + [f for i, f in enumerate(forecasts) if i != idx], + resolution, + options, + range_min, + range_max, + 1.0, + ) + for idx, forecast in enumerate(forecasts) + ] + # Scores should be ordered as expected (descending) + sorted_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True) + assert sorted_indices == expected_order + + +@pytest.mark.parametrize( + "question_type,forecast,resolution,options,range_min,range_max", + [ + ("binary", [0.5], True, None, None, None), + ("mc", [1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None), + ("numeric", [0.5] * 201, 0.5, None, 0.0, 1.0), + ], +) +def test_peer_score_zero_when_all_same( + question_type: str, + forecast: list[float], + resolution: bool | str | float, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + forecasts = [forecast for _ in range(5)] + scores = [ + calculate_spot_peer_score( + f, + [f2 for i2, f2 in enumerate(forecasts) if i2 != i], + resolution, + options, + range_min, + range_max, + 1.0, + ) + for i, f in enumerate(forecasts) + ] + for score in scores: + assert score == pytest.approx(0) + + +@pytest.mark.parametrize( + "forecasts,resolution,options,range_min,range_max", + [ + # Binary + ([[0.7], [0.3], [0.5]], True, None, None, None), + # Multiple Choice + ( + [[0.7, 0.2, 0.1], [0.1, 0.7, 0.2], [0.2, 0.1, 0.7]], + "A", + ["A", "B", "C"], + None, + None, + ), + # Numeric + ( + [[0.1] * 100 + [0.9] * 101, [0.9] * 100 + [0.1] * 101, [0.5] * 201], + 0.5, + None, + 0.0, + 1.0, + ), + ], +) +def test_peer_score_average_zero( + forecasts: list[list[float]], + resolution: bool | str | float, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + scores = [ + calculate_spot_peer_score( + forecast, + [f for i, f in enumerate(forecasts) if i != idx], + resolution, + options, + range_min, + range_max, + 1.0, + ) + for idx, forecast in enumerate(forecasts) + ] + assert np.mean(scores) == pytest.approx(0) + + +@pytest.mark.parametrize( + "forecasts,resolution,options,range_min,range_max,weight", + [ + # Binary + ([[0.7], [0.3], [0.5]], True, None, None, None, 2.0), + # Multiple Choice + ( + [[0.7, 0.2, 0.1], [0.1, 0.7, 0.2], [0.2, 0.1, 0.7]], + "A", + ["A", "B", "C"], + None, + None, + 0.5, + ), + # Numeric + ( + [[0.1] * 100 + [0.9] * 101, [0.9] * 100 + [0.1] * 101, [0.5] * 201], + 0.5, + None, + 0.0, + 1.0, + 3.0, + ), + ], +) +def test_peer_score_weighted( + forecasts: list[ForecastType], + resolution: bool | str | float, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + weight: float, +): + for idx, forecast in enumerate(forecasts): + other_forecasts = [f for i, f in enumerate(forecasts) if i != idx] + score_unweighted = calculate_spot_peer_score( + forecast, other_forecasts, resolution, options, range_min, range_max, 1.0 + ) + score_weighted = calculate_spot_peer_score( + forecast, other_forecasts, resolution, options, range_min, range_max, weight + ) + assert score_weighted == pytest.approx(score_unweighted * weight) + + +################################### BASELINE SCORES ################################### @pytest.mark.parametrize( @@ -195,6 +390,3 @@ def test_baseline_score_weighted( forecast, resolution, options, range_min, range_max, question_weight ) assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 - - - From 44ccf43255a343c91fd666f7ca62c5ad176a1c88 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Fri, 2 May 2025 13:46:40 -0600 Subject: [PATCH 07/26] Minor updates --- tests/test_scoring.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 3a4dd7c..7080b1f 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -27,12 +27,13 @@ def generate_uniform_cdf(num_points: int) -> list[float]: def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[float]: assert correct_index >= 0 and correct_index <= 201 length_of_cdf = 201 + perfect_forecast = 0.99999 cdf = [] for i in range(length_of_cdf): if i < correct_index: - cdf.append(float(i / length_of_cdf)) + cdf.append(1 - perfect_forecast) else: - cdf.append(0.99) + cdf.append(perfect_forecast) if inverse_cdf: cdf = [1 - c for c in cdf] @@ -90,7 +91,6 @@ def test_better_forecast_means_better_peer_score( options: list[str] | None, range_min: float | None, range_max: float | None, - expected_order: list[int], ): scores = [ calculate_spot_peer_score( @@ -104,9 +104,8 @@ def test_better_forecast_means_better_peer_score( ) for idx, forecast in enumerate(forecasts) ] - # Scores should be ordered as expected (descending) sorted_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True) - assert sorted_indices == expected_order + assert sorted_indices == list(range(len(scores))), "Scores should be ordered as expected (descending)" @pytest.mark.parametrize( @@ -230,6 +229,12 @@ def test_peer_score_weighted( ) assert score_weighted == pytest.approx(score_unweighted * weight) +# TODO: Test the below +# Best score for MC and binary is 996 +# Worst score for MC and binary is -996 +# Best score for numeric is 408 +# Worst score for numeric is -408 +# @Check: Can we even validate this (won't we need infinite other forecasters to get max score?) ################################### BASELINE SCORES ################################### From c0f76b63a0ed7a205723f1f8e35d60abba9a69dd Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 04:35:49 -0600 Subject: [PATCH 08/26] Added some comments --- AI_BENCHMARKING_ANALYSIS.ipynb | 2 +- tests/test_end_to_end.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 619d445..864dc28 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -3378,7 +3378,7 @@ "outputs": [], "source": [ "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", - "# @Check: -> This was originally 'calculate_all_peer_scores'. NOt sure the correct function alternative\n" + "# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention." ] }, { diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index 233e769..76bbe91 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -10,6 +10,7 @@ # - make sure to determine the bot team only by the bot-only questions # - make sure best bot team is decided by baseline score comparison to each other # - make sure best bots for bot team are decided by lower bound of t test +# - make sure that worse bots come out on bottom # - Confidence interval code is wrong # - make sure that there are large intervals if only a few forecasts, and small intervals if many forecasts # - make sure bootstrap and t tests indicate the same things generally From a374c2d4871edc867dd6ff4d777ed043bd194be2 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 04:45:41 -0600 Subject: [PATCH 09/26] Added peer score function from previous versions --- AI_BENCHMARKING_ANALYSIS.ipynb | 3773 +++++++---------- functions.py | 174 +- .../bootstrapped_h2h_bot_vs_pros.csv | 30 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 30 +- 4 files changed, 1816 insertions(+), 2191 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 864dc28..510d463 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 82, "metadata": { "id": "ISzIoto4hnoG" }, @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -358,7 +358,7 @@ " dtype='object')" ] }, - "execution_count": 9, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -424,7 +424,7 @@ "dtype: object" ] }, - "execution_count": 11, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -435,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -446,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -514,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -693,7 +693,7 @@ "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, - "execution_count": 16, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -704,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -747,7 +747,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 18, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -759,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -802,15 +802,6 @@ " 1.738353\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 8.829587\n", - " 3337.760404\n", - " 409\n", - " 5.839419\n", - " 1.521098\n", - " \n", - " \n", " 4\n", " metac-o1-preview\n", " 8.465638\n", @@ -820,6 +811,15 @@ " 2.298000\n", " \n", " \n", + " 15\n", + " bot_median\n", + " 8.215149\n", + " 3105.490478\n", + " 409\n", + " 5.145245\n", + " 1.561660\n", + " \n", + " \n", " 24\n", " manticAI\n", " 6.510835\n", @@ -844,15 +844,15 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.829587 3337.760404 409 5.839419 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", + "15 bot_median 8.215149 3105.490478 409 5.145245 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.521098 \n", "4 2.298000 \n", + "15 1.561660 \n", "24 3.029040 \n", "1 2.309106 " ] @@ -968,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 101, "metadata": { "id": "BmAFBHIhK77X" }, @@ -1017,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -1441,7 +1441,7 @@ " np.int64(35705)}" ] }, - "execution_count": 21, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -1462,7 +1462,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 103, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1512,7 +1512,7 @@ " \n", " 3\n", " bot_median\n", - " 8806.147044\n", + " 8671.898307\n", " \n", " \n", " 4\n", @@ -1533,7 +1533,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8806.147044\n", + "3 bot_median 8671.898307\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1639,7 +1639,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -1658,7 +1658,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 105, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1703,7 +1703,7 @@ " \n", " 2\n", " bot_median\n", - " 3711.510468\n", + " 3347.538115\n", " \n", " \n", " 3\n", @@ -1938,7 +1938,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3711.510468\n", + "2 bot_median 3347.538115\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -1986,7 +1986,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 24, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -2028,7 +2028,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -2047,7 +2047,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ @@ -2056,7 +2056,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -2064,9 +2064,7 @@ "output_type": "stream", "text": [ "PRO MEDIAN\n", - "Average baseline: 44.964801909223056\n", - "pgodzinai MEDIAN\n", - "Average baseline: 16.482817250003514\n" + "Average baseline: 44.964801909223056\n" ] } ], @@ -2079,7 +2077,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -2258,7 +2256,7 @@ "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, - "execution_count": 28, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -2269,7 +2267,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 110, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2334,7 +2332,7 @@ " NaN\n", " ...\n", " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", " [0.35000000000000003,0.30000000000000004,0.250...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", @@ -2357,7 +2355,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", @@ -2382,8 +2380,8 @@ " NaN\n", " ...\n", " 0.1\n", - " 0.15\n", " 0.1\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2405,7 +2403,7 @@ " NaN\n", " [0.16,0.47,0.37]\n", " ...\n", - " [0.25,0.6,0.15]\n", + " [0.3,0.55,0.15]\n", " [0.2,0.6,0.2]\n", " [0.15,0.55,0.3]\n", " NaN\n", @@ -2429,8 +2427,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", - " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2469,22 +2467,22 @@ "\n", " CatrachoCaster ... metac-o1 \\\n", "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", - "1 NaN ... [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "1 NaN ... [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", "2 NaN ... 0.1 \n", - "3 [0.16,0.47,0.37] ... [0.25,0.6,0.15] \n", - "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + "3 [0.16,0.47,0.37] ... [0.3,0.55,0.15] \n", + "4 NaN ... [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.15 \n", + "2 0.1 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.1 NaN \n", + "2 0.15 NaN \n", "3 [0.15,0.55,0.3] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", @@ -2574,7 +2572,7 @@ " NaN\n", " ...\n", " 0.95\n", - " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.95\n", @@ -2597,8 +2595,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.35\n", - " 0.4\n", + " 0.3\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2621,7 +2619,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.9\n", + " 0.8\n", " 0.95\n", " NaN\n", " NaN\n", @@ -2645,9 +2643,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", + " 0.7\n", " 0.85\n", - " 0.3\n", + " 0.25\n", " NaN\n", " 0.85\n", " 0.85\n", @@ -2695,16 +2693,16 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.35 \n", - "96 None 0.97 0.85 NaN NaN ... 0.9 \n", - "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "95 None 0.05 0.95 NaN NaN ... 0.3 \n", + "96 None 0.97 0.85 NaN NaN ... 0.8 \n", + "97 None 0.666 0.8 NaN NaN ... 0.7 \n", "98 None 0.03 0.3 NaN NaN ... 0.05 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", - "94 0.9 NaN NaN 0.95 0.95 NaN \n", - "95 0.4 NaN NaN 0.15 NaN NaN \n", + "94 0.95 NaN NaN 0.95 0.95 NaN \n", + "95 0.85 NaN NaN 0.15 NaN NaN \n", "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "97 0.85 0.25 NaN 0.85 0.85 NaN \n", "98 0.05 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", @@ -2773,7 +2771,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -2795,7 +2793,7 @@ " dtype='object')" ] }, - "execution_count": 30, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -2806,7 +2804,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -2816,7 +2814,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 31, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -2831,7 +2829,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -2843,7 +2841,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -2852,7 +2850,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -2914,7 +2912,7 @@ " NaN\n", " ...\n", " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", @@ -2937,7 +2935,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...]\n", + " [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...]\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", " NaN\n", @@ -2962,8 +2960,8 @@ " NaN\n", " ...\n", " 0.1\n", - " 0.15\n", " 0.1\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2985,7 +2983,7 @@ " NaN\n", " [0.16,0.47,0.37]\n", " ...\n", - " [0.25,0.6,0.15]\n", + " [0.3,0.55,0.15]\n", " [0.2,0.6,0.2]\n", " [0.15,0.55,0.3]\n", " NaN\n", @@ -3009,9 +3007,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...]\n", - " [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...]\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.2066666667, 0.2133333333, 0.22, 0.2266666667, 0.2333333333, 0.24, 0.2466666667, 0.2533333333, 0.26, 0.2666666667, 0.2733333333, 0.28, 0.2866666667, 0.2933333333, 0.3, 0.3066666667, 0.3133333333, 0.32, 0.3266666667, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3933333333, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...]\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.6066666667, 0.6133333333, 0.62, 0.6266666667, ...]\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...]\n", + " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...]\n", " NaN\n", " [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", " [0.0, 0.0001141583, 0.0002446967, 0.0003862688, 0.0005272579, 0.0006650709, 0.0008243437, 0.0011074433, 0.0016696544, 0.0025699094, 0.0037138357, 0.0049708626, 0.0062610152, 0.0075426566, 0.0089765864, 0.0111726822, 0.0147311078, 0.0195212559, 0.0249547717, 0.0306181288, 0.0363105138, 0.0419407763, 0.0476011969, 0.053516341, 0.0598014349, 0.0663689162, 0.0730761187, 0.0798334547, 0.0865904866, 0.0933196582, 0.1000172031, 0.1066924089, 0.1133554776, 0.1200140176, 0.1266729489, 0.1333343989, 0.1399984689, 0.1466644317, 0.1533314439, 0.1599988203, 0.1666661444, 0.1733332523, 0.1800001372, 0.1866668598, 0.1933334943, 0.2000000995, 0.2066667101, 0.2133333393, 0.2199999878, 0.22666665, 0.2333333196, 0.2399999916, 0.2466666631, 0.2533333329, 0.2600000011, 0.2666666681, 0.2733333345, 0.2800000007, 0.286666667, 0.2933333334, 0.2999999999, 0.3066666665, 0.3133333332, 0.3199999999, 0.3266666666, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3934628939, 0.400837331, 0.40925763, 0.4186848364, 0.428718413, 0.4390353607, 0.4494419812, 0.4597974687, 0.4700329298, 0.4801500685, 0.4901790777, 0.500153105, 0.5101028922, 0.5200515519, 0.5300114112, 0.5398722838, 0.5492279015, 0.5576212737, 0.5650210292, 0.571743695, 0.5780856137, 0.5842571713, 0.5904328096, 0.5967209586, 0.603152213, 0.6097133168, ...]\n", @@ -3054,26 +3052,26 @@ "3 NaN NaN [0.16,0.47,0.37] ... \n", "4 NaN NaN NaN ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...] \n", - "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", + " metac-o1 \\\n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...] \n", + "2 0.1 \n", + "3 [0.3,0.55,0.15] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.6066666667, 0.6133333333, 0.62, 0.6266666667, ...] \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", + "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", - "2 0.15 \n", + "2 0.1 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", "\n", " metac-perplexity \\\n", "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.1 \n", + "2 0.15 \n", "3 [0.15,0.55,0.3] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.2066666667, 0.2133333333, 0.22, 0.2266666667, 0.2333333333, 0.24, 0.2466666667, 0.2533333333, 0.26, 0.2666666667, 0.2733333333, 0.28, 0.2866666667, 0.2933333333, 0.3, 0.3066666667, 0.3133333333, 0.32, 0.3266666667, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3933333333, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3182,7 +3180,7 @@ " NaN\n", " ...\n", " 0.95\n", - " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.95\n", @@ -3205,8 +3203,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.35\n", - " 0.4\n", + " 0.3\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3229,7 +3227,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.9\n", + " 0.8\n", " 0.95\n", " NaN\n", " NaN\n", @@ -3253,9 +3251,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", + " 0.7\n", " 0.85\n", - " 0.3\n", + " 0.25\n", " NaN\n", " 0.85\n", " 0.85\n", @@ -3303,16 +3301,16 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.35 \n", - "96 None 0.97 0.85 NaN NaN ... 0.9 \n", - "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "95 None 0.05 0.95 NaN NaN ... 0.3 \n", + "96 None 0.97 0.85 NaN NaN ... 0.8 \n", + "97 None 0.666 0.8 NaN NaN ... 0.7 \n", "98 None 0.03 0.3 NaN NaN ... 0.05 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", - "94 0.9 NaN NaN 0.95 0.95 NaN \n", - "95 0.4 NaN NaN 0.15 NaN NaN \n", + "94 0.95 NaN NaN 0.95 0.95 NaN \n", + "95 0.85 NaN NaN 0.15 NaN NaN \n", "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "97 0.85 0.25 NaN 0.85 0.85 NaN \n", "98 0.05 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", @@ -3373,7 +3371,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -3383,7 +3381,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -3444,9 +3442,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3468,9 +3466,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3492,9 +3490,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3516,9 +3514,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3540,9 +3538,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3586,11 +3584,11 @@ "13 [0.05,0.45,0.45,0.05] 0.643473 2.597381 1.762901 \n", "\n", " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "0 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "3 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "6 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "9 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "13 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "0 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "3 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "6 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "9 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "13 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", "\n", " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", "0 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", @@ -3663,9 +3661,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3687,9 +3685,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3711,9 +3709,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3735,9 +3733,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3759,9 +3757,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3805,11 +3803,11 @@ "92 [0.001,0.359,0.55,0.08,0.01] 0.643473 2.597381 1.762901 \n", "\n", " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "81 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "82 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "83 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "91 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", - "92 ... 21.041046 10.134917 20.283821 -2.987997 9.735149 \n", + "81 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "82 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "83 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "91 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "92 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", "\n", " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", "81 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", @@ -3882,9 +3880,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3906,9 +3904,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3930,9 +3928,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3954,9 +3952,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3978,9 +3976,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4003,18 +4001,18 @@ "16 33876 33751 no 1.0 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "2 None 0.013 0.643473 2.597381 1.762901 ... 21.041046 \n", - "5 None 0.45 0.643473 2.597381 1.762901 ... 21.041046 \n", - "8 None 0.95 0.643473 2.597381 1.762901 ... 21.041046 \n", - "12 None 0.9 0.643473 2.597381 1.762901 ... 21.041046 \n", - "16 None 0.058 0.643473 2.597381 1.762901 ... 21.041046 \n", + "2 None 0.013 0.643473 2.597381 1.762901 ... 20.222117 \n", + "5 None 0.45 0.643473 2.597381 1.762901 ... 20.222117 \n", + "8 None 0.95 0.643473 2.597381 1.762901 ... 20.222117 \n", + "12 None 0.9 0.643473 2.597381 1.762901 ... 20.222117 \n", + "16 None 0.058 0.643473 2.597381 1.762901 ... 20.222117 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "5 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "8 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "12 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "16 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "2 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "5 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "8 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "12 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "16 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "2 -2.173212 2.411469 14.267308 2.372721 \n", @@ -4087,9 +4085,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4111,9 +4109,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4135,9 +4133,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4159,9 +4157,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4183,9 +4181,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 21.041046\n", - " 10.134917\n", - " 20.283821\n", + " 20.222117\n", + " 6.738936\n", + " 20.60531\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4208,18 +4206,18 @@ "98 35387 35367 no 0.85 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.643473 2.597381 1.762901 ... 21.041046 \n", - "95 None 0.05 0.643473 2.597381 1.762901 ... 21.041046 \n", - "96 None 0.97 0.643473 2.597381 1.762901 ... 21.041046 \n", - "97 None 0.666 0.643473 2.597381 1.762901 ... 21.041046 \n", - "98 None 0.03 0.643473 2.597381 1.762901 ... 21.041046 \n", + "94 None 0.95 0.643473 2.597381 1.762901 ... 20.222117 \n", + "95 None 0.05 0.643473 2.597381 1.762901 ... 20.222117 \n", + "96 None 0.97 0.643473 2.597381 1.762901 ... 20.222117 \n", + "97 None 0.666 0.643473 2.597381 1.762901 ... 20.222117 \n", + "98 None 0.03 0.643473 2.597381 1.762901 ... 20.222117 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "95 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "96 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "97 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", - "98 10.134917 20.283821 -2.987997 9.735149 3.537037 \n", + "94 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "95 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "96 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "97 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "98 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 -2.173212 2.411469 14.267308 2.372721 \n", @@ -4243,7 +4241,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -4285,7 +4283,7 @@ " \n", " 2\n", " bot_median\n", - " 3711.510468\n", + " 3347.538115\n", " \n", " \n", " 3\n", @@ -4520,7 +4518,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3711.510468\n", + "2 bot_median 3347.538115\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4568,7 +4566,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 37, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -4579,7 +4577,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -4588,13 +4586,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", + "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", "mean metac-o1 forecast on questions that resolved no: 26.0%\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -4663,7 +4661,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -4720,7 +4718,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ @@ -4733,7 +4731,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 122, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4795,18 +4793,18 @@ " \n", " 3\n", " 4\n", - " acm_bot\n", - " 2239.058675\n", - " 85\n", - " 81.25\n", + " bot_median\n", + " 2500.508853\n", + " 97\n", + " 93.10\n", " \n", " \n", " 4\n", " 5\n", - " bot_median\n", - " 2196.323052\n", - " 97\n", - " 93.10\n", + " acm_bot\n", + " 2239.058675\n", + " 85\n", + " 81.25\n", " \n", " \n", " 5\n", @@ -5153,8 +5151,8 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 acm_bot 2239.058675 85 \n", - "4 5 bot_median 2196.323052 97 \n", + "3 4 bot_median 2500.508853 97 \n", + "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", @@ -5202,8 +5200,8 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 81.25 \n", - "4 93.10 \n", + "3 93.10 \n", + "4 81.25 \n", "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", @@ -5248,7 +5246,7 @@ "46 52.10 " ] }, - "execution_count": 41, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -5317,7 +5315,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -5398,6 +5396,20 @@ " 0.000036\n", " \n", " \n", + " bot_median\n", + " 2500.5\n", + " 93.1\n", + " 26.9\n", + " 62.117260\n", + " 6.437800\n", + " 4.171971\n", + " 1.985277\n", + " 39.6\n", + " 14.1\n", + " 0.999966\n", + " 0.000068\n", + " \n", + " \n", " acm_bot\n", " 2239.1\n", " 81.2\n", @@ -5412,20 +5424,6 @@ " 0.000025\n", " \n", " \n", - " bot_median\n", - " 2196.3\n", - " 93.1\n", - " 23.6\n", - " 59.192687\n", - " 6.134698\n", - " 3.845505\n", - " 1.985277\n", - " 35.8\n", - " 11.4\n", - " 0.999889\n", - " 0.000221\n", - " \n", - " \n", " metac-claude-3-5-sonnet-20240620\n", " 2018.1\n", " 91.5\n", @@ -6022,8 +6020,8 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", + "bot_median 2500.5 93.1 26.9 62.117260 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", - "bot_median 2196.3 93.1 23.6 59.192687 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", @@ -6071,8 +6069,8 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", + "bot_median 6.437800 4.171971 1.985277 39.6 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", - "bot_median 6.134698 3.845505 1.985277 35.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", @@ -6120,8 +6118,8 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", + "bot_median 14.1 0.999966 0.000068 \n", "acm_bot 15.3 0.999987 0.000025 \n", - "bot_median 11.4 0.999889 0.000221 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", @@ -6166,7 +6164,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 42, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } @@ -6182,7 +6180,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 124, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -6215,8 +6213,6 @@ " t_statistic = (weighted_average - 0) / std_error\n", "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", " t_statistic = (weighted_average - 0) / std_error\n", "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", @@ -6259,44 +6255,30 @@ " \n", " \n", " \n", - " metac-o1\n", - " 1998.9\n", - " 95.0\n", - " 21.0\n", - " 3.570999e-15\n", - " 3.663768e-16\n", - " 5.743007e+16\n", - " 1.98475\n", - " 21.0\n", - " 21.0\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " metac-perplexity\n", - " 1927.0\n", + " 1957.5\n", " 95.0\n", - " 20.3\n", + " 20.6\n", " 0.000000e+00\n", " 0.000000e+00\n", " inf\n", " 1.98475\n", - " 20.3\n", - " 20.3\n", + " 20.6\n", + " 20.6\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " bot_median\n", - " 1698.8\n", + " metac-o1\n", + " 1921.1\n", " 95.0\n", - " 17.9\n", + " 20.2\n", " 0.000000e+00\n", " 0.000000e+00\n", " inf\n", " 1.98475\n", - " 17.9\n", - " 17.9\n", + " 20.2\n", + " 20.2\n", " 1.0\n", " 0.000000\n", " \n", @@ -6315,6 +6297,20 @@ " 0.000000\n", " \n", " \n", + " bot_median\n", + " 1655.0\n", + " 95.0\n", + " 17.4\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 4.755070e+16\n", + " 1.98475\n", + " 17.4\n", + " 17.4\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " manticAI\n", " 1378.2\n", " 95.0\n", @@ -6358,99 +6354,85 @@ " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " 1136.7\n", - " 95.0\n", - " 12.0\n", - " 3.570999e-15\n", - " 3.663768e-16\n", - " 3.265969e+16\n", - " 1.98475\n", - " 12.0\n", - " 12.0\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", - " GreeneiBot2\n", - " 1115.4\n", + " 1235.2\n", " 95.0\n", - " 11.7\n", - " 5.356499e-15\n", - " 5.495652e-16\n", - " 2.136428e+16\n", + " 13.0\n", + " 1.785500e-15\n", + " 1.831884e-16\n", + " 7.097519e+16\n", " 1.98475\n", - " 11.7\n", - " 11.7\n", + " 13.0\n", + " 13.0\n", " 1.0\n", " 0.000000\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " 1091.6\n", + " 1180.5\n", " 95.0\n", - " 11.5\n", - " 5.356499e-15\n", - " 5.495652e-16\n", - " 2.090764e+16\n", + " 12.4\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " inf\n", " 1.98475\n", - " 11.5\n", - " 11.5\n", + " 12.4\n", + " 12.4\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " NextWorldLab\n", - " 1050.3\n", + " metac-deepseek-r1\n", + " 1166.0\n", " 95.0\n", - " 11.1\n", + " 12.3\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 6.035038e+16\n", + " 6.700213e+16\n", " 1.98475\n", - " 11.1\n", - " 11.1\n", + " 12.3\n", + " 12.3\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " metac-grok-2-1212\n", - " 1047.4\n", + " metac-Llama-3.1\n", + " 1154.9\n", " 95.0\n", - " 11.0\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " inf\n", + " 12.2\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 3.318128e+16\n", " 1.98475\n", - " 11.0\n", - " 11.0\n", + " 12.2\n", + " 12.2\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " metac-gpt-4o\n", - " 1002.0\n", + " GreeneiBot2\n", + " 1119.2\n", " 95.0\n", - " 10.5\n", - " 3.570999e-15\n", - " 3.663768e-16\n", - " 2.878879e+16\n", + " 11.8\n", + " 1.785500e-15\n", + " 1.831884e-16\n", + " 6.431060e+16\n", " 1.98475\n", - " 10.5\n", - " 10.5\n", + " 11.8\n", + " 11.8\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " metac-Llama-3.1\n", - " 973.0\n", + " NextWorldLab\n", + " 1050.3\n", " 95.0\n", - " 10.2\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " inf\n", + " 11.1\n", + " 1.785500e-15\n", + " 1.831884e-16\n", + " 6.035038e+16\n", " 1.98475\n", - " 10.2\n", - " 10.2\n", + " 11.1\n", + " 11.1\n", " 1.0\n", " 0.000000\n", " \n", @@ -6483,16 +6465,16 @@ " 0.000000\n", " \n", " \n", - " metac-o1-preview\n", - " 962.8\n", + " metac-grok-2-1212\n", + " 932.3\n", " 95.0\n", - " 10.1\n", + " 9.8\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 5.532510e+16\n", + " 5.357005e+16\n", " 1.98475\n", - " 10.1\n", - " 10.1\n", + " 9.8\n", + " 9.8\n", " 1.0\n", " 0.000000\n", " \n", @@ -6511,16 +6493,16 @@ " 0.000000\n", " \n", " \n", - " metac-exa\n", - " 919.9\n", + " metac-Gemini-Exp-1206\n", + " 910.2\n", " 95.0\n", - " 9.7\n", + " 9.6\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 5.285939e+16\n", + " 5.230332e+16\n", " 1.98475\n", - " 9.7\n", - " 9.7\n", + " 9.6\n", + " 9.6\n", " 1.0\n", " 0.000000\n", " \n", @@ -6539,16 +6521,16 @@ " 0.000000\n", " \n", " \n", - " metac-deepseek-r1\n", - " 802.0\n", + " metac-exa\n", + " 836.7\n", " 95.0\n", - " 8.4\n", + " 8.8\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 4.608683e+16\n", + " 4.808056e+16\n", " 1.98475\n", - " 8.4\n", - " 8.4\n", + " 8.8\n", + " 8.8\n", " 1.0\n", " 0.000000\n", " \n", @@ -6581,30 +6563,30 @@ " 0.000000\n", " \n", " \n", - " cookics_bot_TEST\n", - " 612.4\n", + " metac-o1-preview\n", + " 640.2\n", " 95.0\n", - " 6.4\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 3.518949e+16\n", + " 6.7\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 7.357383e+16\n", " 1.98475\n", - " 6.4\n", - " 6.4\n", + " 6.7\n", + " 6.7\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " 548.0\n", + " cookics_bot_TEST\n", + " 596.4\n", " 95.0\n", - " 5.8\n", + " 6.3\n", " 0.000000e+00\n", " 0.000000e+00\n", " inf\n", " 1.98475\n", - " 5.8\n", - " 5.8\n", + " 6.3\n", + " 6.3\n", " 1.0\n", " 0.000000\n", " \n", @@ -6665,6 +6647,20 @@ " 0.000000\n", " \n", " \n", + " metac-gpt-4o\n", + " 280.3\n", + " 95.0\n", + " 3.0\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 3.221541e+16\n", + " 1.98475\n", + " 3.0\n", + " 3.0\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " InstitutPelFutur\n", " 256.0\n", " 95.0\n", @@ -6791,6 +6787,20 @@ " 0.000000\n", " \n", " \n", + " RPM_bot\n", + " 71.4\n", + " 95.0\n", + " 0.8\n", + " 1.115937e-16\n", + " 1.144927e-17\n", + " 6.560693e+16\n", + " 1.98475\n", + " 0.8\n", + " 0.8\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " 4Shadower\n", " 61.1\n", " 95.0\n", @@ -6819,20 +6829,6 @@ " 0.000000\n", " \n", " \n", - " RPM_bot\n", - " 52.6\n", - " 95.0\n", - " 0.6\n", - " 1.115937e-16\n", - " 1.144927e-17\n", - " 4.834420e+16\n", - " 1.98475\n", - " 0.6\n", - " 0.6\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " andrewsiah\n", " 0.0\n", " 95.0\n", @@ -6908,35 +6904,35 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev \\\n", - "metac-o1 1998.9 95.0 21.0 3.570999e-15 \n", - "metac-perplexity 1927.0 95.0 20.3 0.000000e+00 \n", - "bot_median 1698.8 95.0 17.9 0.000000e+00 \n", + "metac-perplexity 1957.5 95.0 20.6 0.000000e+00 \n", + "metac-o1 1921.1 95.0 20.2 0.000000e+00 \n", "acm_bot 1680.6 95.0 17.7 3.570999e-15 \n", + "bot_median 1655.0 95.0 17.4 3.570999e-15 \n", "manticAI 1378.2 95.0 14.5 0.000000e+00 \n", "twsummerbot 1355.4 95.0 14.3 1.785500e-15 \n", "jkraybill_bot 1354.5 95.0 14.3 1.785500e-15 \n", - "metac-claude-3-5-sonnet-20240620 1136.7 95.0 12.0 3.570999e-15 \n", - "GreeneiBot2 1115.4 95.0 11.7 5.356499e-15 \n", - "metac-claude-3-5-sonnet-latest 1091.6 95.0 11.5 5.356499e-15 \n", + "metac-claude-3-5-sonnet-20240620 1235.2 95.0 13.0 1.785500e-15 \n", + "metac-claude-3-5-sonnet-latest 1180.5 95.0 12.4 0.000000e+00 \n", + "metac-deepseek-r1 1166.0 95.0 12.3 1.785500e-15 \n", + "metac-Llama-3.1 1154.9 95.0 12.2 3.570999e-15 \n", + "GreeneiBot2 1119.2 95.0 11.8 1.785500e-15 \n", "NextWorldLab 1050.3 95.0 11.1 1.785500e-15 \n", - "metac-grok-2-1212 1047.4 95.0 11.0 0.000000e+00 \n", - "metac-gpt-4o 1002.0 95.0 10.5 3.570999e-15 \n", - "metac-Llama-3.1 973.0 95.0 10.2 0.000000e+00 \n", "Grizeu_Bot 966.4 95.0 10.2 0.000000e+00 \n", "SynapseSeer 964.7 95.0 10.2 1.785500e-15 \n", - "metac-o1-preview 962.8 95.0 10.1 1.785500e-15 \n", + "metac-grok-2-1212 932.3 95.0 9.8 1.785500e-15 \n", "mmBot 924.8 95.0 9.7 0.000000e+00 \n", - "metac-exa 919.9 95.0 9.7 1.785500e-15 \n", + "metac-Gemini-Exp-1206 910.2 95.0 9.6 1.785500e-15 \n", "annabot 854.4 95.0 9.0 1.785500e-15 \n", - "metac-deepseek-r1 802.0 95.0 8.4 1.785500e-15 \n", + "metac-exa 836.7 95.0 8.8 1.785500e-15 \n", "VeritasAI 802.0 95.0 8.4 1.785500e-15 \n", "laylaps 723.4 95.0 7.6 8.927498e-16 \n", - "cookics_bot_TEST 612.4 95.0 6.4 1.785500e-15 \n", - "metac-Gemini-Exp-1206 548.0 95.0 5.8 0.000000e+00 \n", + "metac-o1-preview 640.2 95.0 6.7 8.927498e-16 \n", + "cookics_bot_TEST 596.4 95.0 6.3 0.000000e+00 \n", "MWG 520.8 95.0 5.5 8.927498e-16 \n", "ajf-bot 481.2 95.0 5.1 1.785500e-15 \n", "pgodzinai 336.0 95.0 3.5 8.927498e-16 \n", "KevinTestBot 314.5 95.0 3.3 8.927498e-16 \n", + "metac-gpt-4o 280.3 95.0 3.0 8.927498e-16 \n", "InstitutPelFutur 256.0 95.0 2.7 8.927498e-16 \n", "Bot_Pepa 246.8 95.0 2.6 0.000000e+00 \n", "CumulativeBot 241.1 95.0 2.5 4.463749e-16 \n", @@ -6946,9 +6942,9 @@ "bean_bot 200.0 95.0 2.1 0.000000e+00 \n", "X_bot 181.4 95.0 1.9 0.000000e+00 \n", "CatrachoCaster 167.5 95.0 1.8 4.463749e-16 \n", + "RPM_bot 71.4 95.0 0.8 1.115937e-16 \n", "4Shadower 61.1 95.0 0.6 2.231875e-16 \n", "krm-bot 60.8 95.0 0.6 1.115937e-16 \n", - "RPM_bot 52.6 95.0 0.6 1.115937e-16 \n", "andrewsiah 0.0 95.0 0.0 0.000000e+00 \n", "cobyj-bot 0.0 95.0 0.0 0.000000e+00 \n", "pianobot -206.5 95.0 -2.2 4.463749e-16 \n", @@ -6956,35 +6952,35 @@ "minefrac1 -283.9 95.0 -3.0 4.463749e-16 \n", "\n", " std_err t_stat t_crit \\\n", - "metac-o1 3.663768e-16 5.743007e+16 1.98475 \n", "metac-perplexity 0.000000e+00 inf 1.98475 \n", - "bot_median 0.000000e+00 inf 1.98475 \n", + "metac-o1 0.000000e+00 inf 1.98475 \n", "acm_bot 3.663768e-16 4.828449e+16 1.98475 \n", + "bot_median 3.663768e-16 4.755070e+16 1.98475 \n", "manticAI 0.000000e+00 inf 1.98475 \n", "twsummerbot 1.831884e-16 7.788325e+16 1.98475 \n", "jkraybill_bot 1.831884e-16 7.783286e+16 1.98475 \n", - "metac-claude-3-5-sonnet-20240620 3.663768e-16 3.265969e+16 1.98475 \n", - "GreeneiBot2 5.495652e-16 2.136428e+16 1.98475 \n", - "metac-claude-3-5-sonnet-latest 5.495652e-16 2.090764e+16 1.98475 \n", + "metac-claude-3-5-sonnet-20240620 1.831884e-16 7.097519e+16 1.98475 \n", + "metac-claude-3-5-sonnet-latest 0.000000e+00 inf 1.98475 \n", + "metac-deepseek-r1 1.831884e-16 6.700213e+16 1.98475 \n", + "metac-Llama-3.1 3.663768e-16 3.318128e+16 1.98475 \n", + "GreeneiBot2 1.831884e-16 6.431060e+16 1.98475 \n", "NextWorldLab 1.831884e-16 6.035038e+16 1.98475 \n", - "metac-grok-2-1212 0.000000e+00 inf 1.98475 \n", - "metac-gpt-4o 3.663768e-16 2.878879e+16 1.98475 \n", - "metac-Llama-3.1 0.000000e+00 inf 1.98475 \n", "Grizeu_Bot 0.000000e+00 inf 1.98475 \n", "SynapseSeer 1.831884e-16 5.543440e+16 1.98475 \n", - "metac-o1-preview 1.831884e-16 5.532510e+16 1.98475 \n", + "metac-grok-2-1212 1.831884e-16 5.357005e+16 1.98475 \n", "mmBot 0.000000e+00 inf 1.98475 \n", - "metac-exa 1.831884e-16 5.285939e+16 1.98475 \n", + "metac-Gemini-Exp-1206 1.831884e-16 5.230332e+16 1.98475 \n", "annabot 1.831884e-16 4.909363e+16 1.98475 \n", - "metac-deepseek-r1 1.831884e-16 4.608683e+16 1.98475 \n", + "metac-exa 1.831884e-16 4.808056e+16 1.98475 \n", "VeritasAI 1.831884e-16 4.608352e+16 1.98475 \n", "laylaps 9.159420e-17 8.313180e+16 1.98475 \n", - "cookics_bot_TEST 1.831884e-16 3.518949e+16 1.98475 \n", - "metac-Gemini-Exp-1206 0.000000e+00 inf 1.98475 \n", + "metac-o1-preview 9.159420e-17 7.357383e+16 1.98475 \n", + "cookics_bot_TEST 0.000000e+00 inf 1.98475 \n", "MWG 9.159420e-17 5.985647e+16 1.98475 \n", "ajf-bot 1.831884e-16 2.764898e+16 1.98475 \n", "pgodzinai 9.159420e-17 3.861639e+16 1.98475 \n", "KevinTestBot 9.159420e-17 3.614852e+16 1.98475 \n", + "metac-gpt-4o 9.159420e-17 3.221541e+16 1.98475 \n", "InstitutPelFutur 9.159420e-17 2.941623e+16 1.98475 \n", "Bot_Pepa 0.000000e+00 inf 1.98475 \n", "CumulativeBot 4.579710e-17 5.542703e+16 1.98475 \n", @@ -6994,9 +6990,9 @@ "bean_bot 0.000000e+00 inf 1.98475 \n", "X_bot 0.000000e+00 inf 1.98475 \n", "CatrachoCaster 4.579710e-17 3.849373e+16 1.98475 \n", + "RPM_bot 1.144927e-17 6.560693e+16 1.98475 \n", "4Shadower 2.289855e-17 2.810106e+16 1.98475 \n", "krm-bot 1.144927e-17 5.586129e+16 1.98475 \n", - "RPM_bot 1.144927e-17 4.834420e+16 1.98475 \n", "andrewsiah 0.000000e+00 NaN 1.98475 \n", "cobyj-bot 0.000000e+00 NaN 1.98475 \n", "pianobot 4.579710e-17 -4.745305e+16 1.98475 \n", @@ -7004,35 +7000,35 @@ "minefrac1 4.579710e-17 -6.524424e+16 1.98475 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "metac-o1 21.0 21.0 1.0 0.000000 \n", - "metac-perplexity 20.3 20.3 1.0 0.000000 \n", - "bot_median 17.9 17.9 1.0 0.000000 \n", + "metac-perplexity 20.6 20.6 1.0 0.000000 \n", + "metac-o1 20.2 20.2 1.0 0.000000 \n", "acm_bot 17.7 17.7 1.0 0.000000 \n", + "bot_median 17.4 17.4 1.0 0.000000 \n", "manticAI 14.5 14.5 1.0 0.000000 \n", "twsummerbot 14.3 14.3 1.0 0.000000 \n", "jkraybill_bot 14.3 14.3 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-20240620 12.0 12.0 1.0 0.000000 \n", - "GreeneiBot2 11.7 11.7 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-latest 11.5 11.5 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-20240620 13.0 13.0 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-latest 12.4 12.4 1.0 0.000000 \n", + "metac-deepseek-r1 12.3 12.3 1.0 0.000000 \n", + "metac-Llama-3.1 12.2 12.2 1.0 0.000000 \n", + "GreeneiBot2 11.8 11.8 1.0 0.000000 \n", "NextWorldLab 11.1 11.1 1.0 0.000000 \n", - "metac-grok-2-1212 11.0 11.0 1.0 0.000000 \n", - "metac-gpt-4o 10.5 10.5 1.0 0.000000 \n", - "metac-Llama-3.1 10.2 10.2 1.0 0.000000 \n", "Grizeu_Bot 10.2 10.2 1.0 0.000000 \n", "SynapseSeer 10.2 10.2 1.0 0.000000 \n", - "metac-o1-preview 10.1 10.1 1.0 0.000000 \n", + "metac-grok-2-1212 9.8 9.8 1.0 0.000000 \n", "mmBot 9.7 9.7 1.0 0.000000 \n", - "metac-exa 9.7 9.7 1.0 0.000000 \n", + "metac-Gemini-Exp-1206 9.6 9.6 1.0 0.000000 \n", "annabot 9.0 9.0 1.0 0.000000 \n", - "metac-deepseek-r1 8.4 8.4 1.0 0.000000 \n", + "metac-exa 8.8 8.8 1.0 0.000000 \n", "VeritasAI 8.4 8.4 1.0 0.000000 \n", "laylaps 7.6 7.6 1.0 0.000000 \n", - "cookics_bot_TEST 6.4 6.4 1.0 0.000000 \n", - "metac-Gemini-Exp-1206 5.8 5.8 1.0 0.000000 \n", + "metac-o1-preview 6.7 6.7 1.0 0.000000 \n", + "cookics_bot_TEST 6.3 6.3 1.0 0.000000 \n", "MWG 5.5 5.5 1.0 0.000000 \n", "ajf-bot 5.1 5.1 1.0 0.000000 \n", "pgodzinai 3.5 3.5 1.0 0.000000 \n", "KevinTestBot 3.3 3.3 1.0 0.000000 \n", + "metac-gpt-4o 3.0 3.0 1.0 0.000000 \n", "InstitutPelFutur 2.7 2.7 1.0 0.000000 \n", "Bot_Pepa 2.6 2.6 1.0 0.000000 \n", "CumulativeBot 2.5 2.5 1.0 0.000000 \n", @@ -7042,9 +7038,9 @@ "bean_bot 2.1 2.1 1.0 0.000000 \n", "X_bot 1.9 1.9 1.0 0.000000 \n", "CatrachoCaster 1.8 1.8 1.0 0.000000 \n", + "RPM_bot 0.8 0.8 1.0 0.000000 \n", "4Shadower 0.6 0.6 1.0 0.000000 \n", "krm-bot 0.6 0.6 1.0 0.000000 \n", - "RPM_bot 0.6 0.6 1.0 0.000000 \n", "andrewsiah 0.0 0.0 NaN NA \n", "cobyj-bot 0.0 0.0 NaN NA \n", "pianobot -2.2 -2.2 0.0 0.000000 \n", @@ -7052,7 +7048,7 @@ "minefrac1 -3.0 -3.0 0.0 0.000000 " ] }, - "execution_count": 43, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -7078,7 +7074,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -7088,7 +7084,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 126, "metadata": { "cellView": "form", "colab": { @@ -8002,7 +7998,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 45, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -8041,7 +8037,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -8051,7 +8047,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -8256,7 +8252,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 47, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -8267,7 +8263,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 129, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -8329,7 +8325,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -8751,7 +8747,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 131, "metadata": { "cellView": "form", "colab": { @@ -8801,131 +8797,131 @@ " \n", " \n", " metac-o1\n", - " 6.0\n", - " 7.2\n", - " 9.6\n", - " 11.9\n", - " 13.1\n", + " 6.2\n", + " 7.4\n", + " 9.7\n", + " 11.8\n", + " 13.2\n", " \n", " \n", " metac-o1-preview\n", - " 3.7\n", - " 5.2\n", - " 8.3\n", - " 11.2\n", + " 3.9\n", + " 5.4\n", + " 8.4\n", + " 11.4\n", " 12.8\n", " \n", " \n", " manticAI\n", - " 0.2\n", - " 2.1\n", - " 5.5\n", - " 8.8\n", - " 10.5\n", + " 0.1\n", + " 2.0\n", + " 5.4\n", + " 8.6\n", + " 10.2\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.4\n", - " 1.9\n", - " 4.9\n", - " 7.5\n", - " 8.9\n", + " 0.5\n", + " 2.0\n", + " 5.0\n", + " 7.9\n", + " 9.5\n", " \n", " \n", " acm_bot\n", - " 0.2\n", + " 0.1\n", " 1.8\n", - " 4.7\n", - " 7.7\n", - " 9.1\n", + " 4.5\n", + " 7.5\n", + " 8.8\n", " \n", " \n", " metac-perplexity\n", " -2.2\n", - " 0.0\n", - " 4.3\n", + " 0.2\n", + " 4.1\n", " 7.8\n", - " 9.9\n", + " 9.5\n", " \n", " \n", " GreeneiBot2\n", - " -1.2\n", - " 0.4\n", - " 3.9\n", - " 7.0\n", + " -0.8\n", + " 0.7\n", + " 4.0\n", + " 7.2\n", " 8.7\n", " \n", " \n", " twsummerbot\n", - " 0.3\n", + " -0.1\n", " 1.5\n", " 3.9\n", - " 6.1\n", - " 7.4\n", - " \n", - " \n", - " pgodzinai\n", - " -3.4\n", - " -1.2\n", - " 3.2\n", - " 7.3\n", - " 9.6\n", + " 6.3\n", + " 7.7\n", " \n", " \n", " cookics_bot_TEST\n", - " -0.2\n", - " 0.8\n", - " 2.9\n", - " 5.0\n", + " 0.0\n", + " 1.0\n", + " 3.0\n", + " 4.9\n", " 5.8\n", " \n", " \n", - " CumulativeBot\n", - " -0.1\n", - " 0.9\n", + " pgodzinai\n", + " -3.5\n", + " -1.1\n", + " 2.8\n", + " 6.8\n", + " 8.9\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-latest\n", + " -1.4\n", + " 0.0\n", " 2.7\n", - " 4.6\n", - " 5.4\n", + " 5.1\n", + " 6.2\n", " \n", " \n", " SynapseSeer\n", - " 0.4\n", - " 1.1\n", + " 0.3\n", + " 1.0\n", " 2.6\n", - " 4.1\n", - " 4.8\n", + " 4.0\n", + " 5.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -1.3\n", - " -0.0\n", + " CumulativeBot\n", + " -0.3\n", + " 0.7\n", " 2.5\n", - " 5.0\n", - " 6.2\n", + " 4.4\n", + " 5.4\n", " \n", " \n", " jkraybill_bot\n", - " -3.5\n", - " -1.7\n", - " 1.7\n", - " 5.0\n", + " -3.7\n", + " -1.8\n", + " 1.8\n", + " 4.9\n", " 6.4\n", " \n", " \n", " metac-exa\n", - " -4.8\n", - " -2.2\n", - " 1.7\n", - " 5.6\n", - " 7.8\n", + " -5.0\n", + " -2.4\n", + " 1.5\n", + " 5.4\n", + " 7.4\n", " \n", " \n", " metac-deepseek-r1\n", - " -2.0\n", - " -0.8\n", - " 1.3\n", + " -1.7\n", + " -0.6\n", + " 1.4\n", " 3.4\n", - " 4.6\n", + " 4.5\n", " \n", " \n", " MWG\n", @@ -8936,228 +8932,228 @@ " 2.8\n", " \n", " \n", - " andrewsiah\n", - " -0.8\n", - " -0.6\n", - " -0.0\n", - " 0.6\n", - " 0.9\n", - " \n", - " \n", " pianobot\n", - " -1.2\n", + " -1.3\n", " -0.8\n", - " -0.0\n", + " 0.0\n", " 0.7\n", - " 1.0\n", + " 1.1\n", " \n", " \n", " cobyj-bot\n", - " -1.5\n", + " -1.4\n", " -0.9\n", " -0.0\n", - " 0.8\n", - " 1.3\n", + " 0.9\n", + " 1.4\n", + " \n", + " \n", + " andrewsiah\n", + " -0.9\n", + " -0.6\n", + " -0.0\n", + " 0.5\n", + " 0.9\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.2\n", + " -0.3\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", " annabot\n", - " -3.5\n", + " -3.2\n", " -2.3\n", " -0.4\n", - " 1.2\n", - " 2.1\n", + " 1.3\n", + " 2.0\n", " \n", " \n", " bean_bot\n", " -3.2\n", " -2.3\n", - " -0.5\n", - " 1.1\n", - " 1.8\n", + " -0.4\n", + " 1.2\n", + " 1.9\n", " \n", " \n", " KevinTestBot\n", - " -4.1\n", - " -2.8\n", + " -3.9\n", + " -2.7\n", " -0.6\n", - " 1.6\n", - " 2.7\n", + " 1.3\n", + " 2.4\n", " \n", " \n", " CatrachoCaster\n", - " -2.2\n", - " -1.7\n", + " -2.3\n", + " -1.8\n", " -0.8\n", " 0.2\n", - " 0.7\n", + " 0.8\n", " \n", " \n", " jonahsingerbot\n", " -3.0\n", - " -2.2\n", + " -2.1\n", " -0.8\n", - " 0.5\n", - " 1.0\n", + " 0.4\n", + " 1.1\n", " \n", " \n", " krm-bot\n", - " -3.5\n", - " -2.7\n", - " -0.9\n", - " 0.7\n", + " -3.7\n", + " -2.8\n", + " -1.0\n", + " 0.6\n", " 1.7\n", " \n", " \n", " ProfessorSP\n", - " -4.6\n", - " -3.3\n", - " -1.0\n", + " -4.1\n", + " -3.2\n", + " -1.1\n", " 1.1\n", - " 2.1\n", - " \n", - " \n", - " mmBot\n", - " -7.5\n", - " -5.4\n", - " -1.5\n", - " 2.4\n", - " 4.7\n", + " 2.3\n", " \n", " \n", " metac-grok-2-1212\n", " -6.6\n", - " -4.8\n", + " -4.7\n", + " -1.4\n", + " 1.8\n", + " 3.5\n", + " \n", + " \n", + " mmBot\n", + " -7.2\n", + " -5.5\n", " -1.5\n", - " 1.9\n", - " 3.6\n", + " 2.2\n", + " 4.0\n", " \n", " \n", " 4Shadower\n", - " -4.6\n", - " -3.6\n", - " -1.6\n", + " -4.7\n", + " -3.8\n", + " -1.7\n", " 0.2\n", - " 1.2\n", + " 1.3\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.5\n", + " -4.5\n", + " -1.8\n", + " 0.9\n", + " 2.4\n", " \n", " \n", " swingswish\n", - " -5.2\n", + " -5.4\n", " -4.0\n", " -1.9\n", " -0.2\n", - " 0.5\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.2\n", - " -4.9\n", - " -2.0\n", - " 0.9\n", - " 2.4\n", + " 0.6\n", " \n", " \n", " RPM_bot\n", - " -4.9\n", + " -4.8\n", " -3.8\n", " -2.1\n", " -0.7\n", - " -0.2\n", + " -0.1\n", " \n", " \n", " InstitutPelFutur\n", - " -9.1\n", + " -9.0\n", " -6.4\n", - " -2.4\n", - " 1.7\n", - " 4.0\n", + " -2.5\n", + " 1.6\n", + " 3.6\n", " \n", " \n", " wunderplumb\n", - " -6.2\n", + " -6.4\n", " -4.9\n", - " -2.4\n", + " -2.7\n", " -0.2\n", - " 1.1\n", + " 0.8\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.8\n", + " -6.7\n", " -5.3\n", " -2.7\n", " 0.0\n", - " 1.5\n", + " 1.7\n", " \n", " \n", " NextWorldLab\n", - " -8.8\n", - " -6.8\n", - " -3.4\n", - " -0.3\n", - " 1.5\n", + " -8.3\n", + " -6.6\n", + " -3.6\n", + " -0.7\n", + " 1.2\n", " \n", " \n", " Bot_Pepa\n", - " -7.0\n", + " -7.2\n", " -5.9\n", - " -3.9\n", + " -4.0\n", " -2.0\n", - " -1.1\n", + " -1.3\n", " \n", " \n", " laylaps\n", - " -10.1\n", - " -7.9\n", + " -10.3\n", + " -8.0\n", " -4.0\n", - " -0.1\n", + " -0.2\n", " 2.1\n", " \n", " \n", " VeritasAI\n", - " -8.0\n", - " -6.8\n", - " -4.4\n", - " -2.0\n", - " -0.7\n", + " -7.7\n", + " -6.6\n", + " -4.2\n", + " -1.9\n", + " -0.6\n", " \n", " \n", " minefrac1\n", - " -7.9\n", - " -6.8\n", - " -4.6\n", - " -2.7\n", - " -1.5\n", + " -7.8\n", + " -6.7\n", + " -4.8\n", + " -2.8\n", + " -1.6\n", " \n", " \n", " Grizeu_Bot\n", - " -9.3\n", + " -9.2\n", " -7.7\n", - " -5.1\n", - " -2.5\n", - " -1.0\n", + " -4.9\n", + " -2.4\n", + " -1.1\n", " \n", " \n", " metac-gpt-4o\n", - " -10.4\n", - " -9.0\n", - " -6.1\n", - " -3.0\n", - " -1.4\n", + " -10.5\n", + " -8.9\n", + " -5.8\n", + " -2.8\n", + " -1.3\n", " \n", " \n", " ajf-bot\n", - " -15.0\n", - " -12.6\n", + " -15.6\n", + " -12.8\n", " -8.4\n", - " -4.2\n", - " -2.2\n", + " -4.0\n", + " -1.9\n", " \n", " \n", "\n", @@ -9165,54 +9161,54 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.0 7.2 9.6 11.9 13.1\n", - "metac-o1-preview 3.7 5.2 8.3 11.2 12.8\n", - "manticAI 0.2 2.1 5.5 8.8 10.5\n", - "metac-Gemini-Exp-1206 0.4 1.9 4.9 7.5 8.9\n", - "acm_bot 0.2 1.8 4.7 7.7 9.1\n", - "metac-perplexity -2.2 0.0 4.3 7.8 9.9\n", - "GreeneiBot2 -1.2 0.4 3.9 7.0 8.7\n", - "twsummerbot 0.3 1.5 3.9 6.1 7.4\n", - "pgodzinai -3.4 -1.2 3.2 7.3 9.6\n", - "cookics_bot_TEST -0.2 0.8 2.9 5.0 5.8\n", - "CumulativeBot -0.1 0.9 2.7 4.6 5.4\n", - "SynapseSeer 0.4 1.1 2.6 4.1 4.8\n", - "metac-claude-3-5-sonnet-latest -1.3 -0.0 2.5 5.0 6.2\n", - "jkraybill_bot -3.5 -1.7 1.7 5.0 6.4\n", - "metac-exa -4.8 -2.2 1.7 5.6 7.8\n", - "metac-deepseek-r1 -2.0 -0.8 1.3 3.4 4.6\n", + "metac-o1 6.2 7.4 9.7 11.8 13.2\n", + "metac-o1-preview 3.9 5.4 8.4 11.4 12.8\n", + "manticAI 0.1 2.0 5.4 8.6 10.2\n", + "metac-Gemini-Exp-1206 0.5 2.0 5.0 7.9 9.5\n", + "acm_bot 0.1 1.8 4.5 7.5 8.8\n", + "metac-perplexity -2.2 0.2 4.1 7.8 9.5\n", + "GreeneiBot2 -0.8 0.7 4.0 7.2 8.7\n", + "twsummerbot -0.1 1.5 3.9 6.3 7.7\n", + "cookics_bot_TEST 0.0 1.0 3.0 4.9 5.8\n", + "pgodzinai -3.5 -1.1 2.8 6.8 8.9\n", + "metac-claude-3-5-sonnet-latest -1.4 0.0 2.7 5.1 6.2\n", + "SynapseSeer 0.3 1.0 2.6 4.0 5.0\n", + "CumulativeBot -0.3 0.7 2.5 4.4 5.4\n", + "jkraybill_bot -3.7 -1.8 1.8 4.9 6.4\n", + "metac-exa -5.0 -2.4 1.5 5.4 7.4\n", + "metac-deepseek-r1 -1.7 -0.6 1.4 3.4 4.5\n", "MWG -1.6 -0.8 0.7 2.1 2.8\n", - "andrewsiah -0.8 -0.6 -0.0 0.6 0.9\n", - "pianobot -1.2 -0.8 -0.0 0.7 1.0\n", - "cobyj-bot -1.5 -0.9 -0.0 0.8 1.3\n", - "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "annabot -3.5 -2.3 -0.4 1.2 2.1\n", - "bean_bot -3.2 -2.3 -0.5 1.1 1.8\n", - "KevinTestBot -4.1 -2.8 -0.6 1.6 2.7\n", - "CatrachoCaster -2.2 -1.7 -0.8 0.2 0.7\n", - "jonahsingerbot -3.0 -2.2 -0.8 0.5 1.0\n", - "krm-bot -3.5 -2.7 -0.9 0.7 1.7\n", - "ProfessorSP -4.6 -3.3 -1.0 1.1 2.1\n", - "mmBot -7.5 -5.4 -1.5 2.4 4.7\n", - "metac-grok-2-1212 -6.6 -4.8 -1.5 1.9 3.6\n", - "4Shadower -4.6 -3.6 -1.6 0.2 1.2\n", - "swingswish -5.2 -4.0 -1.9 -0.2 0.5\n", - "metac-claude-3-5-sonnet-20240620 -6.2 -4.9 -2.0 0.9 2.4\n", - "RPM_bot -4.9 -3.8 -2.1 -0.7 -0.2\n", - "InstitutPelFutur -9.1 -6.4 -2.4 1.7 4.0\n", - "wunderplumb -6.2 -4.9 -2.4 -0.2 1.1\n", - "metac-Llama-3.1 -6.8 -5.3 -2.7 0.0 1.5\n", - "NextWorldLab -8.8 -6.8 -3.4 -0.3 1.5\n", - "Bot_Pepa -7.0 -5.9 -3.9 -2.0 -1.1\n", - "laylaps -10.1 -7.9 -4.0 -0.1 2.1\n", - "VeritasAI -8.0 -6.8 -4.4 -2.0 -0.7\n", - "minefrac1 -7.9 -6.8 -4.6 -2.7 -1.5\n", - "Grizeu_Bot -9.3 -7.7 -5.1 -2.5 -1.0\n", - "metac-gpt-4o -10.4 -9.0 -6.1 -3.0 -1.4\n", - "ajf-bot -15.0 -12.6 -8.4 -4.2 -2.2" + "pianobot -1.3 -0.8 0.0 0.7 1.1\n", + "cobyj-bot -1.4 -0.9 -0.0 0.9 1.4\n", + "andrewsiah -0.9 -0.6 -0.0 0.5 0.9\n", + "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "annabot -3.2 -2.3 -0.4 1.3 2.0\n", + "bean_bot -3.2 -2.3 -0.4 1.2 1.9\n", + "KevinTestBot -3.9 -2.7 -0.6 1.3 2.4\n", + "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", + "jonahsingerbot -3.0 -2.1 -0.8 0.4 1.1\n", + "krm-bot -3.7 -2.8 -1.0 0.6 1.7\n", + "ProfessorSP -4.1 -3.2 -1.1 1.1 2.3\n", + "metac-grok-2-1212 -6.6 -4.7 -1.4 1.8 3.5\n", + "mmBot -7.2 -5.5 -1.5 2.2 4.0\n", + "4Shadower -4.7 -3.8 -1.7 0.2 1.3\n", + "metac-claude-3-5-sonnet-20240620 -6.5 -4.5 -1.8 0.9 2.4\n", + "swingswish -5.4 -4.0 -1.9 -0.2 0.6\n", + "RPM_bot -4.8 -3.8 -2.1 -0.7 -0.1\n", + "InstitutPelFutur -9.0 -6.4 -2.5 1.6 3.6\n", + "wunderplumb -6.4 -4.9 -2.7 -0.2 0.8\n", + "metac-Llama-3.1 -6.7 -5.3 -2.7 0.0 1.7\n", + "NextWorldLab -8.3 -6.6 -3.6 -0.7 1.2\n", + "Bot_Pepa -7.2 -5.9 -4.0 -2.0 -1.3\n", + "laylaps -10.3 -8.0 -4.0 -0.2 2.1\n", + "VeritasAI -7.7 -6.6 -4.2 -1.9 -0.6\n", + "minefrac1 -7.8 -6.7 -4.8 -2.8 -1.6\n", + "Grizeu_Bot -9.2 -7.7 -4.9 -2.4 -1.1\n", + "metac-gpt-4o -10.5 -8.9 -5.8 -2.8 -1.3\n", + "ajf-bot -15.6 -12.8 -8.4 -4.0 -1.9" ] }, - "execution_count": 50, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -9235,7 +9231,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 132, "metadata": { "cellView": "form", "colab": { @@ -9288,28 +9284,20 @@ " \n", " \n", " \n", - " metac-o1\n", - " 21.0\n", - " 21.0\n", - " 21.0\n", - " 21.0\n", - " 21.0\n", - " \n", - " \n", " metac-perplexity\n", - " 20.3\n", - " 20.3\n", - " 20.3\n", - " 20.3\n", - " 20.3\n", + " 20.6\n", + " 20.6\n", + " 20.6\n", + " 20.6\n", + " 20.6\n", " \n", " \n", - " bot_median\n", - " 17.9\n", - " 17.9\n", - " 17.9\n", - " 17.9\n", - " 17.9\n", + " metac-o1\n", + " 20.2\n", + " 20.2\n", + " 20.2\n", + " 20.2\n", + " 20.2\n", " \n", " \n", " acm_bot\n", @@ -9320,6 +9308,14 @@ " 17.7\n", " \n", " \n", + " bot_median\n", + " 17.4\n", + " 17.4\n", + " 17.4\n", + " 17.4\n", + " 17.4\n", + " \n", + " \n", " manticAI\n", " 14.5\n", " 14.5\n", @@ -9345,27 +9341,43 @@ " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " 12.0\n", - " 12.0\n", - " 12.0\n", - " 12.0\n", - " 12.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", " \n", " \n", - " GreeneiBot2\n", - " 11.7\n", - " 11.7\n", - " 11.7\n", - " 11.7\n", - " 11.7\n", + " metac-claude-3-5-sonnet-latest\n", + " 12.4\n", + " 12.4\n", + " 12.4\n", + " 12.4\n", + " 12.4\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " 11.5\n", - " 11.5\n", - " 11.5\n", - " 11.5\n", - " 11.5\n", + " metac-deepseek-r1\n", + " 12.3\n", + " 12.3\n", + " 12.3\n", + " 12.3\n", + " 12.3\n", + " \n", + " \n", + " metac-Llama-3.1\n", + " 12.2\n", + " 12.2\n", + " 12.2\n", + " 12.2\n", + " 12.2\n", + " \n", + " \n", + " GreeneiBot2\n", + " 11.8\n", + " 11.8\n", + " 11.8\n", + " 11.8\n", + " 11.8\n", " \n", " \n", " NextWorldLab\n", @@ -9376,30 +9388,6 @@ " 11.1\n", " \n", " \n", - " metac-grok-2-1212\n", - " 11.0\n", - " 11.0\n", - " 11.0\n", - " 11.0\n", - " 11.0\n", - " \n", - " \n", - " metac-gpt-4o\n", - " 10.5\n", - " 10.5\n", - " 10.5\n", - " 10.5\n", - " 10.5\n", - " \n", - " \n", - " metac-Llama-3.1\n", - " 10.2\n", - " 10.2\n", - " 10.2\n", - " 10.2\n", - " 10.2\n", - " \n", - " \n", " Grizeu_Bot\n", " 10.2\n", " 10.2\n", @@ -9416,12 +9404,12 @@ " 10.2\n", " \n", " \n", - " metac-o1-preview\n", - " 10.1\n", - " 10.1\n", - " 10.1\n", - " 10.1\n", - " 10.1\n", + " metac-grok-2-1212\n", + " 9.8\n", + " 9.8\n", + " 9.8\n", + " 9.8\n", + " 9.8\n", " \n", " \n", " mmBot\n", @@ -9432,12 +9420,12 @@ " 9.7\n", " \n", " \n", - " metac-exa\n", - " 9.7\n", - " 9.7\n", - " 9.7\n", - " 9.7\n", - " 9.7\n", + " metac-Gemini-Exp-1206\n", + " 9.6\n", + " 9.6\n", + " 9.6\n", + " 9.6\n", + " 9.6\n", " \n", " \n", " annabot\n", @@ -9448,12 +9436,12 @@ " 9.0\n", " \n", " \n", - " metac-deepseek-r1\n", - " 8.4\n", - " 8.4\n", - " 8.4\n", - " 8.4\n", - " 8.4\n", + " metac-exa\n", + " 8.8\n", + " 8.8\n", + " 8.8\n", + " 8.8\n", + " 8.8\n", " \n", " \n", " VeritasAI\n", @@ -9472,20 +9460,20 @@ " 7.6\n", " \n", " \n", - " cookics_bot_TEST\n", - " 6.4\n", - " 6.4\n", - " 6.4\n", - " 6.4\n", - " 6.4\n", + " metac-o1-preview\n", + " 6.7\n", + " 6.7\n", + " 6.7\n", + " 6.7\n", + " 6.7\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " 5.8\n", - " 5.8\n", - " 5.8\n", - " 5.8\n", - " 5.8\n", + " cookics_bot_TEST\n", + " 6.3\n", + " 6.3\n", + " 6.3\n", + " 6.3\n", + " 6.3\n", " \n", " \n", " MWG\n", @@ -9520,6 +9508,14 @@ " 3.3\n", " \n", " \n", + " metac-gpt-4o\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " 3.0\n", + " \n", + " \n", " InstitutPelFutur\n", " 2.7\n", " 2.7\n", @@ -9592,15 +9588,15 @@ " 1.8\n", " \n", " \n", - " 4Shadower\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", + " RPM_bot\n", + " 0.8\n", + " 0.8\n", + " 0.8\n", + " 0.8\n", + " 0.8\n", " \n", " \n", - " krm-bot\n", + " 4Shadower\n", " 0.6\n", " 0.6\n", " 0.6\n", @@ -9608,7 +9604,7 @@ " 0.6\n", " \n", " \n", - " RPM_bot\n", + " krm-bot\n", " 0.6\n", " 0.6\n", " 0.6\n", @@ -9661,35 +9657,35 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 21.0 21.0 21.0 21.0 21.0\n", - "metac-perplexity 20.3 20.3 20.3 20.3 20.3\n", - "bot_median 17.9 17.9 17.9 17.9 17.9\n", + "metac-perplexity 20.6 20.6 20.6 20.6 20.6\n", + "metac-o1 20.2 20.2 20.2 20.2 20.2\n", "acm_bot 17.7 17.7 17.7 17.7 17.7\n", + "bot_median 17.4 17.4 17.4 17.4 17.4\n", "manticAI 14.5 14.5 14.5 14.5 14.5\n", "twsummerbot 14.3 14.3 14.3 14.3 14.3\n", "jkraybill_bot 14.3 14.3 14.3 14.3 14.3\n", - "metac-claude-3-5-sonnet-20240620 12.0 12.0 12.0 12.0 12.0\n", - "GreeneiBot2 11.7 11.7 11.7 11.7 11.7\n", - "metac-claude-3-5-sonnet-latest 11.5 11.5 11.5 11.5 11.5\n", + "metac-claude-3-5-sonnet-20240620 13.0 13.0 13.0 13.0 13.0\n", + "metac-claude-3-5-sonnet-latest 12.4 12.4 12.4 12.4 12.4\n", + "metac-deepseek-r1 12.3 12.3 12.3 12.3 12.3\n", + "metac-Llama-3.1 12.2 12.2 12.2 12.2 12.2\n", + "GreeneiBot2 11.8 11.8 11.8 11.8 11.8\n", "NextWorldLab 11.1 11.1 11.1 11.1 11.1\n", - "metac-grok-2-1212 11.0 11.0 11.0 11.0 11.0\n", - "metac-gpt-4o 10.5 10.5 10.5 10.5 10.5\n", - "metac-Llama-3.1 10.2 10.2 10.2 10.2 10.2\n", "Grizeu_Bot 10.2 10.2 10.2 10.2 10.2\n", "SynapseSeer 10.2 10.2 10.2 10.2 10.2\n", - "metac-o1-preview 10.1 10.1 10.1 10.1 10.1\n", + "metac-grok-2-1212 9.8 9.8 9.8 9.8 9.8\n", "mmBot 9.7 9.7 9.7 9.7 9.7\n", - "metac-exa 9.7 9.7 9.7 9.7 9.7\n", + "metac-Gemini-Exp-1206 9.6 9.6 9.6 9.6 9.6\n", "annabot 9.0 9.0 9.0 9.0 9.0\n", - "metac-deepseek-r1 8.4 8.4 8.4 8.4 8.4\n", + "metac-exa 8.8 8.8 8.8 8.8 8.8\n", "VeritasAI 8.4 8.4 8.4 8.4 8.4\n", "laylaps 7.6 7.6 7.6 7.6 7.6\n", - "cookics_bot_TEST 6.4 6.4 6.4 6.4 6.4\n", - "metac-Gemini-Exp-1206 5.8 5.8 5.8 5.8 5.8\n", + "metac-o1-preview 6.7 6.7 6.7 6.7 6.7\n", + "cookics_bot_TEST 6.3 6.3 6.3 6.3 6.3\n", "MWG 5.5 5.5 5.5 5.5 5.5\n", "ajf-bot 5.1 5.1 5.1 5.1 5.1\n", "pgodzinai 3.5 3.5 3.5 3.5 3.5\n", "KevinTestBot 3.3 3.3 3.3 3.3 3.3\n", + "metac-gpt-4o 3.0 3.0 3.0 3.0 3.0\n", "InstitutPelFutur 2.7 2.7 2.7 2.7 2.7\n", "Bot_Pepa 2.6 2.6 2.6 2.6 2.6\n", "CumulativeBot 2.5 2.5 2.5 2.5 2.5\n", @@ -9699,9 +9695,9 @@ "bean_bot 2.1 2.1 2.1 2.1 2.1\n", "X_bot 1.9 1.9 1.9 1.9 1.9\n", "CatrachoCaster 1.8 1.8 1.8 1.8 1.8\n", + "RPM_bot 0.8 0.8 0.8 0.8 0.8\n", "4Shadower 0.6 0.6 0.6 0.6 0.6\n", "krm-bot 0.6 0.6 0.6 0.6 0.6\n", - "RPM_bot 0.6 0.6 0.6 0.6 0.6\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "pianobot -2.2 -2.2 -2.2 -2.2 -2.2\n", @@ -9709,7 +9705,7 @@ "minefrac1 -3.0 -3.0 -3.0 -3.0 -3.0" ] }, - "execution_count": 51, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -9730,7 +9726,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 133, "metadata": {}, "outputs": [], "source": [ @@ -9740,7 +9736,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -9800,7 +9796,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 135, "metadata": { "cellView": "form", "colab": { @@ -10289,7 +10285,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 54, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -10310,7 +10306,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 136, "metadata": {}, "outputs": [], "source": [ @@ -10319,7 +10315,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -10358,7 +10354,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 138, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" @@ -10368,511 +10364,505 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.02]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.98]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.4]\n", " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.01]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.75]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.05]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " >>> Collected 2 forecasts: [0.15, 0.1]\n", + " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.2, 0.7]\n", - " >>> Collected 2 forecasts: [0.95, 0.9]\n", - " >>> Collected 2 forecasts: [0.85, 0.75]\n", + " >>> Collected 2 forecasts: [0.85, 0.9]\n", + " >>> Collected 2 forecasts: [0.85, 0.85]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.65, 0.6]\n", + " >>> Collected 2 forecasts: [0.6, 0.6]\n", " >>> Collected 2 forecasts: [0.7, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.15, 0.05]\n", - " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.35, 0.85]\n", - " >>> Collected 2 forecasts: [0.25, 0.6]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.25]\n", + " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.7, 0.8]\n", + " >>> Collected 2 forecasts: [0.05, 0.3]\n", + " >>> Collected 2 forecasts: [0.05, 0.25]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", " >>> Collected 2 forecasts: [0.15, 0.25]\n", " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.25]\n", - " >>> Collected 2 forecasts: [0.02, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.02]\n", - " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.3, 0.3]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", - " >>> Collected 2 forecasts: [0.98, 0.98]\n", - " >>> Collected 2 forecasts: [0.7, 0.4]\n", - " >>> Collected 2 forecasts: [0.35, 0.3]\n", - " >>> Collected 2 forecasts: [0.3, 0.55]\n", - " >>> Collected 2 forecasts: [0.1, 0.02]\n", - " >>> Collected 2 forecasts: [0.8, 0.8]\n", - " >>> Collected 2 forecasts: [0.99, 0.99]\n", + " >>> Collected 2 forecasts: [0.1, 0.35]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.1, 0.4]\n", + " >>> Collected 2 forecasts: [0.4, 0.35]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.98, 0.96]\n", + " >>> Collected 2 forecasts: [0.4, 0.3]\n", + " >>> Collected 2 forecasts: [0.3, 0.25]\n", + " >>> Collected 2 forecasts: [0.3, 0.6]\n", + " >>> Collected 2 forecasts: [0.01, 0.02]\n", + " >>> Collected 2 forecasts: [0.7, 0.7]\n", " >>> Collected 2 forecasts: [0.99, 0.99]\n", - " >>> Collected 2 forecasts: [0.35, 0.1]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.9, 0.65]\n", - " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.95, 0.98]\n", + " >>> Collected 2 forecasts: [0.95, 0.15]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.75]\n", + " >>> Collected 2 forecasts: [0.6, 0.4]\n", " >>> Collected 2 forecasts: [0.85, 0.85]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.3, 0.2]\n", - " >>> Collected 2 forecasts: [0.75, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.35]\n", + " >>> Collected 2 forecasts: [0.75, 0.75]\n", " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.15, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.1, 0.15]\n", " >>> Collected 2 forecasts: [0.1, 0.03]\n", - " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.95]\n", - " >>> Collected 2 forecasts: [0.4, 0.35]\n", - " >>> Collected 2 forecasts: [0.95, 0.9]\n", - " >>> Collected 2 forecasts: [0.85, 0.8]\n", + " >>> Collected 2 forecasts: [0.8, 0.9]\n", + " >>> Collected 2 forecasts: [0.95, 0.95]\n", + " >>> Collected 2 forecasts: [0.85, 0.3]\n", + " >>> Collected 2 forecasts: [0.95, 0.8]\n", + " >>> Collected 2 forecasts: [0.85, 0.7]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 3 forecasts: [0.15, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", " >>> Collected 3 forecasts: [0.2, 0.7, 0.62]\n", - " >>> Collected 3 forecasts: [0.95, 0.9, 0.82]\n", - " >>> Collected 3 forecasts: [0.85, 0.75, 0.85]\n", + " >>> Collected 3 forecasts: [0.85, 0.9, 0.82]\n", + " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.65, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.6, 0.6, nan]\n", " >>> Collected 3 forecasts: [0.7, 0.3, nan]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, 0.25]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.35, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.6, 0.108]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.16]\n", + " >>> Collected 3 forecasts: [0.1, 0.25, 0.25]\n", + " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.3, 0.108]\n", + " >>> Collected 3 forecasts: [0.05, 0.25, 0.16]\n", " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", " >>> Collected 3 forecasts: [0.15, 0.25, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.25, 0.125]\n", - " >>> Collected 3 forecasts: [0.02, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.05, 0.02, 0.03]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.3, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, 0.115]\n", - " >>> Collected 3 forecasts: [0.98, 0.98, 0.97]\n", - " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.35, 0.3, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.3, 0.55, 0.17]\n", - " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", - " >>> Collected 3 forecasts: [0.8, 0.8, 0.875]\n", + " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.03]\n", + " >>> Collected 3 forecasts: [0.1, 0.4, 0.35]\n", + " >>> Collected 3 forecasts: [0.4, 0.35, 0.35]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, 0.115]\n", + " >>> Collected 3 forecasts: [0.98, 0.96, 0.97]\n", + " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", + " >>> Collected 3 forecasts: [0.3, 0.25, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.3, 0.6, 0.17]\n", + " >>> Collected 3 forecasts: [0.01, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", - " >>> Collected 3 forecasts: [0.99, 0.99, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.35, 0.1, 0.4166666666666666]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.65, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.35, 0.6, 0.875]\n", + " >>> Collected 3 forecasts: [0.95, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.95, 0.15, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.75, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", " >>> Collected 3 forecasts: [0.85, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.3, 0.2, 0.16]\n", - " >>> Collected 3 forecasts: [0.75, 0.85, 0.67]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.2, 0.35, 0.16]\n", + " >>> Collected 3 forecasts: [0.75, 0.75, 0.67]\n", " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.3925]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.086]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, 0.285]\n", + " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", " >>> Collected 3 forecasts: [0.1, 0.03, 0.02]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", - " >>> Collected 3 forecasts: [0.4, 0.35, nan]\n", - " >>> Collected 3 forecasts: [0.95, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.95, 0.95]\n", + " >>> Collected 3 forecasts: [0.85, 0.3, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.8, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", - " >>> Collected 4 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999]\n", - " >>> Collected 4 forecasts: [0.85, 0.75, 0.85, 0.884]\n", + " >>> Collected 4 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.65, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.6, nan, nan]\n", " >>> Collected 4 forecasts: [0.7, 0.3, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.35, 0.85, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.6, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.1, 0.25, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.05, 0.3, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.05, 0.25, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.25, 0.15, 0.144]\n", + " >>> Collected 4 forecasts: [0.15, 0.25, 0.15, 0.12]\n", " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.25, 0.125, 0.212]\n", - " >>> Collected 4 forecasts: [0.02, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.98, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.3, 0.55, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.8, 0.8, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.4, 0.35, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.96, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.3, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.75, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.3, 0.2, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.85, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.35, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.4, 0.35, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.85, 0.3, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.8, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.71]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.65, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.6, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.7, 0.3, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.85, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.6, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.25, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.3, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.25, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.3, 0.55, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.3, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.3, 0.2, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.85, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.2, 0.35, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.4, 0.35, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.85, 0.3, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.8, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.65, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.6, 0.6, nan, nan, nan, 0.7]\n", " >>> Collected 6 forecasts: [0.7, 0.3, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15]\n", " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", - " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65]\n", + " >>> Collected 7 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", + " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15]\n", " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15]\n", - " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38]\n", - " >>> Collected 7 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2]\n", + " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35]\n", + " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", - " >>> Collected 7 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9]\n", - " >>> Collected 7 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", - " >>> Collected 7 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07]\n", + " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", + " >>> Collected 7 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", + " >>> Collected 7 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513]\n", - " >>> Collected 8 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95]\n", - " >>> Collected 8 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", - " >>> Collected 8 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", + " >>> Collected 8 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", - " >>> Collected 8 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", + " >>> Collected 8 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", + " >>> Collected 8 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.3]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", + " >>> Collected 9 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.65]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", - " >>> Collected 9 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.65]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85]\n", " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.1]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.8]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", + " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.15, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.3, nan]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.75, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", - " >>> Collected 10 forecasts: [0.65, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.2, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.35, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.6, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.16, 0.652, nan, 0.275, 0.25, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", + " >>> Collected 10 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.1, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.65, 0.293]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.38, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.8, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.65, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85, 0.6659999999999999]\n", " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.99, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.35, 0.1, 0.4166666666666666, 0.2, 0.336, 0.325, 0.25, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.1, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.8, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } @@ -10906,7 +10896,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ @@ -10916,238 +10906,8 @@ }, { "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
typeoptionsresolutionmetac-o1-previewmedian_forecast_5_botsmedian_forecast_8_bots
0multiple_choice[0, 1, 2-3, 4-6, >6]0[0.02,0.7,0.2,0.07,0.01]0.0174630.1
1numericNaN86.82[0.05,0.0506666667,0.0513333333,0.052,0.052666...[0.037750000000000006, 0.03822284245, 0.038700...[0.0402, 0.040728273960000005, 0.04126011788, ...
2binaryNaNno0.150.0850.125
3multiple_choice[0-4, 5-9, >9]5-9[0.2,0.6,0.2]0.60.5125
4numericNaN119.2[0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...[0.0, 0.00318255036, 0.00637055762, 0.00956313...[0.0, 0.0028936984428571426, 0.005791294657142...
.....................
342binaryNaNyes0.90.9050.9025
351binaryNaNno0.40.350.2085
355binaryNaNyes0.950.90.772
361binaryNaNno0.850.80.755
364binaryNaNno0.050.050.046
\n", - "

99 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " type options resolution \\\n", - "0 multiple_choice [0, 1, 2-3, 4-6, >6] 0 \n", - "1 numeric NaN 86.82 \n", - "2 binary NaN no \n", - "3 multiple_choice [0-4, 5-9, >9] 5-9 \n", - "4 numeric NaN 119.2 \n", - ".. ... ... ... \n", - "342 binary NaN yes \n", - "351 binary NaN no \n", - "355 binary NaN yes \n", - "361 binary NaN no \n", - "364 binary NaN no \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.15 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", - ".. ... \n", - "342 0.9 \n", - "351 0.4 \n", - "355 0.95 \n", - "361 0.85 \n", - "364 0.05 \n", - "\n", - " median_forecast_5_bots \\\n", - "0 0.017463 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.085 \n", - "3 0.6 \n", - "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", - ".. ... \n", - "342 0.905 \n", - "351 0.35 \n", - "355 0.9 \n", - "361 0.8 \n", - "364 0.05 \n", - "\n", - " median_forecast_8_bots \n", - "0 0.1 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.125 \n", - "3 0.5125 \n", - "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", - ".. ... \n", - "342 0.9025 \n", - "351 0.2085 \n", - "355 0.772 \n", - "361 0.755 \n", - "364 0.046 \n", - "\n", - "[99 rows x 6 columns]" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_bot_team_forecasts[['type', 'options', 'resolution', 'metac-o1-preview', 'median_forecast_5_bots', 'median_forecast_8_bots']]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, + "execution_count": 140, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sum of weights: 95.0, Number of questions: 99\n" - ] - } - ], - "source": [ - "# Sanity check\n", - "a = df_bot_team_forecasts['question_weight'].sum()\n", - "b = df_bot_team_forecasts.shape[0] # number of rows in df_bot_team_forecasts\n", - "print(f'Sum of weights: {a}, Number of questions: {b}')" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3-FedHpWV_1v", - "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" - }, "outputs": [ { "data": { @@ -11170,486 +10930,215 @@ " \n", " \n", " \n", - " bot_question_id\n", - " question_weight\n", - " resolution\n", " type\n", " options\n", - " range_min\n", - " range_max\n", - " metac-o1-preview\n", - " metac-o1\n", - " pgodzinai\n", - " ...\n", - " median_forecast_1_bots\n", - " median_forecast_2_bots\n", - " median_forecast_3_bots\n", - " median_forecast_4_bots\n", - " median_forecast_5_bots\n", - " median_forecast_6_bots\n", - " median_forecast_7_bots\n", - " median_forecast_8_bots\n", - " median_forecast_9_bots\n", - " median_forecast_10_bots\n", - " \n", - " \n", - " \n", - " \n", - " 0\n", - " 31262\n", - " 1.0\n", - " 0\n", - " multiple_choice\n", - " [0, 1, 2-3, 4-6, >6]\n", - " NaN\n", - " NaN\n", - " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.014925742574257425,0.5137871287128712,0.334...\n", - " ...\n", - " 0.02\n", - " 0.21\n", - " 0.02\n", - " 0.017463\n", - " 0.017463\n", - " 0.02\n", - " 0.1\n", - " 0.1\n", - " 0.02\n", - " 0.02\n", - " \n", - " \n", - " 1\n", - " 31263\n", - " 1.0\n", - " 86.82\n", - " numeric\n", - " NaN\n", - " 60.0\n", - " 100.0\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", - " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", - " ...\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", - " [0.03366666666666667, 0.03409436576666667, 0.0...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.041833333333333333, 0.04238467275, 0.042938...\n", - " [0.041833333333333333, 0.04238467275, 0.042938...\n", - " \n", - " \n", - " 2\n", - " 31264\n", - " 1.0\n", - " no\n", - " binary\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 0.15\n", - " 0.1\n", - " 0.07\n", - " ...\n", - " 0.15\n", - " 0.125\n", - " 0.1\n", - " 0.085\n", - " 0.085\n", - " 0.1\n", - " 0.125\n", - " 0.125\n", - " 0.15\n", - " 0.15\n", - " \n", - " \n", - " 3\n", - " 31274\n", - " 1.0\n", - " 5-9\n", - " multiple_choice\n", - " [0-4, 5-9, >9]\n", - " NaN\n", - " NaN\n", - " [0.2,0.6,0.2]\n", - " [0.25,0.6,0.15]\n", - " [0.27499999999999997,0.5125,0.21249999999999997]\n", - " ...\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.55625\n", - " 0.5125\n", - " 0.5125\n", - " 0.55625\n", - " 0.5125\n", - " \n", - " \n", - " 4\n", - " 31275\n", - " 1.0\n", - " 119.2\n", - " numeric\n", - " NaN\n", - " 0.0\n", - " 400.0\n", - " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", - " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", - " ...\n", - " [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07...\n", - " [0.0, 0.00642857145, 0.01285714285, 0.01928571...\n", - " [0.0, 0.004323767066666667, 0.0086529941333333...\n", - " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", - " [0.0, 0.00318255036, 0.00637055762, 0.00956313...\n", - " [0.0, 0.00295931485, 0.0059231771, 0.008890847...\n", - " [0.0, 0.0028936984428571426, 0.005791294657142...\n", - " [0.0, 0.0028936984428571426, 0.005791294657142...\n", - " [0.0, 0.0028097639124999995, 0.005622938375, 0...\n", - " [0.0, 0.0026433398111111108, 0.005289711211111...\n", - " \n", - " \n", - "\n", - "

5 rows × 27 columns

\n", - "" - ], - "text/plain": [ - " bot_question_id question_weight resolution type \\\n", - "0 31262 1.0 0 multiple_choice \n", - "1 31263 1.0 86.82 numeric \n", - "2 31264 1.0 no binary \n", - "3 31274 1.0 5-9 multiple_choice \n", - "4 31275 1.0 119.2 numeric \n", - "\n", - " options range_min range_max \\\n", - "0 [0, 1, 2-3, 4-6, >6] NaN NaN \n", - "1 NaN 60.0 100.0 \n", - "2 NaN NaN NaN \n", - "3 [0-4, 5-9, >9] NaN NaN \n", - "4 NaN 0.0 400.0 \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.15 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", - "\n", - " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", - "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", - "\n", - " pgodzinai ... \\\n", - "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", - "1 [0.001,0.001060875,0.0011396,0.0012863125,0.00... ... \n", - "2 0.07 ... \n", - "3 [0.27499999999999997,0.5125,0.21249999999999997] ... \n", - "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... ... \n", - "\n", - " median_forecast_1_bots \\\n", - "0 0.02 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.15 \n", - "3 0.6 \n", - "4 [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07... \n", - "\n", - " median_forecast_2_bots \\\n", - "0 0.21 \n", - "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", - "2 0.125 \n", - "3 0.6 \n", - "4 [0.0, 0.00642857145, 0.01285714285, 0.01928571... \n", - "\n", - " median_forecast_3_bots \\\n", - "0 0.02 \n", - "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", - "2 0.1 \n", - "3 0.6 \n", - "4 [0.0, 0.004323767066666667, 0.0086529941333333... \n", - "\n", - " median_forecast_4_bots \\\n", - "0 0.017463 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.085 \n", - "3 0.6 \n", - "4 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", - "\n", - " median_forecast_5_bots \\\n", - "0 0.017463 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.085 \n", - "3 0.6 \n", - "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", - "\n", - " median_forecast_6_bots \\\n", - "0 0.02 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.1 \n", - "3 0.55625 \n", - "4 [0.0, 0.00295931485, 0.0059231771, 0.008890847... \n", - "\n", - " median_forecast_7_bots \\\n", - "0 0.1 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.125 \n", - "3 0.5125 \n", - "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", - "\n", - " median_forecast_8_bots \\\n", - "0 0.1 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.125 \n", - "3 0.5125 \n", - "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", - "\n", - " median_forecast_9_bots \\\n", - "0 0.02 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", - "3 0.55625 \n", - "4 [0.0, 0.0028097639124999995, 0.005622938375, 0... \n", - "\n", - " median_forecast_10_bots \n", - "0 0.02 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", - "3 0.5125 \n", - "4 [0.0, 0.0026433398111111108, 0.005289711211111... \n", - "\n", - "[5 rows x 27 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", "
bot_question_idquestion_weightresolutiontypeoptionsrange_minrange_maxmetac-o1-previewmetac-o1pgodzinai...median_forecast_1_botsmedian_forecast_2_botsmedian_forecast_3_botsmedian_forecast_4_botsmedian_forecast_5_botsmedian_forecast_6_botsmedian_forecast_7_botsmedian_forecast_5_botsmedian_forecast_8_botsmedian_forecast_9_botsmedian_forecast_10_bots
342353451.00yes0multiple_choice[0, 1, 2-3, 4-6, >6]0[0.010416666666666666,0.20833333333333334,0.04...0.0126710.032463
1numericNaN86.82[0.05,0.0506666667,0.0513333333,0.052,0.052666...[0.037750000000000006, 0.038231012375000005, 0...[0.0402, 0.0407348099, 0.04127318978, 0.041825...
2binaryNaNno0.10.0850.1
3multiple_choice[0-4, 5-9, >9]5-9[0.2,0.6,0.2]0.550.5125
4numericNaN119.2[0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...[0.0, 0.0022111217800000003, 0.00442770048, 0....[0.0, 0.002199820885714286, 0.0044035395571428...
.....................
342binaryNaN0.90.95yes0.95...0.90.9250.950.92750.9050.920.9050.90250.90.9
351353541.00nobinaryNaNNaNNaN0.40.35NaN...0.40.3750.3750.3750.350.20250.350.20850.350.238no0.850.30.1835
355353581.00yesbinaryNaNNaNNaN0.950.9NaN...yes0.950.9250.9250.9250.90.850.80.7720.80.8140.775
361353640.85nobinaryNaNNaNNaN0.850.80.85...0.850.825no0.850.8250.80.7550.80.7550.80.7550.710.704
364353670.85nobinaryNaNNaNNaN0.050.050.05...0.050.050.050.050.05no0.050.050.0460.050.05
\n", - "

5 rows × 27 columns

\n", + "

99 rows × 6 columns

\n", "
" ], "text/plain": [ - " bot_question_id question_weight resolution type options range_min \\\n", - "342 35345 1.00 yes binary NaN NaN \n", - "351 35354 1.00 no binary NaN NaN \n", - "355 35358 1.00 yes binary NaN NaN \n", - "361 35364 0.85 no binary NaN NaN \n", - "364 35367 0.85 no binary NaN NaN \n", - "\n", - " range_max metac-o1-preview metac-o1 pgodzinai ... \\\n", - "342 NaN 0.9 0.95 0.95 ... \n", - "351 NaN 0.4 0.35 NaN ... \n", - "355 NaN 0.95 0.9 NaN ... \n", - "361 NaN 0.85 0.8 0.85 ... \n", - "364 NaN 0.05 0.05 0.05 ... \n", - "\n", - " median_forecast_1_bots median_forecast_2_bots median_forecast_3_bots \\\n", - "342 0.9 0.925 0.95 \n", - "351 0.4 0.375 0.375 \n", - "355 0.95 0.925 0.925 \n", - "361 0.85 0.825 0.85 \n", - "364 0.05 0.05 0.05 \n", - "\n", - " median_forecast_4_bots median_forecast_5_bots median_forecast_6_bots \\\n", - "342 0.9275 0.905 0.92 \n", - "351 0.375 0.35 0.2025 \n", - "355 0.925 0.9 0.85 \n", - "361 0.825 0.8 0.755 \n", - "364 0.05 0.05 0.05 \n", - "\n", - " median_forecast_7_bots median_forecast_8_bots median_forecast_9_bots \\\n", - "342 0.905 0.9025 0.9 \n", - "351 0.35 0.2085 0.35 \n", - "355 0.8 0.772 0.8 \n", - "361 0.8 0.755 0.8 \n", - "364 0.05 0.046 0.05 \n", - "\n", - " median_forecast_10_bots \n", - "342 0.9 \n", - "351 0.238 \n", - "355 0.814 \n", - "361 0.755 \n", - "364 0.05 \n", + " type options resolution \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] 0 \n", + "1 numeric NaN 86.82 \n", + "2 binary NaN no \n", + "3 multiple_choice [0-4, 5-9, >9] 5-9 \n", + "4 numeric NaN 119.2 \n", + ".. ... ... ... \n", + "342 binary NaN yes \n", + "351 binary NaN no \n", + "355 binary NaN yes \n", + "361 binary NaN no \n", + "364 binary NaN no \n", "\n", - "[5 rows x 27 columns]" + " metac-o1-preview \\\n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.1 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + ".. ... \n", + "342 0.95 \n", + "351 0.85 \n", + "355 0.95 \n", + "361 0.85 \n", + "364 0.05 \n", + "\n", + " median_forecast_5_bots \\\n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.038231012375000005, 0... \n", + "2 0.085 \n", + "3 0.55 \n", + "4 [0.0, 0.0022111217800000003, 0.00442770048, 0.... \n", + ".. ... \n", + "342 0.95 \n", + "351 0.3 \n", + "355 0.8 \n", + "361 0.71 \n", + "364 0.05 \n", + "\n", + " median_forecast_8_bots \n", + "0 0.032463 \n", + "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", + "2 0.1 \n", + "3 0.5125 \n", + "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", + ".. ... \n", + "342 0.92 \n", + "351 0.1835 \n", + "355 0.775 \n", + "361 0.704 \n", + "364 0.046 \n", + "\n", + "[99 rows x 6 columns]" ] }, + "execution_count": 140, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" + } + ], + "source": [ + "df_bot_team_forecasts[['type', 'options', 'resolution', 'metac-o1-preview', 'median_forecast_5_bots', 'median_forecast_8_bots']]" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sum of weights: 95.0, Number of questions: 99\n" + ] + } + ], + "source": [ + "# Sanity check\n", + "a = df_bot_team_forecasts['question_weight'].sum()\n", + "b = df_bot_team_forecasts.shape[0] # number of rows in df_bot_team_forecasts\n", + "print(f'Sum of weights: {a}, Number of questions: {b}')" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "3-FedHpWV_1v", + "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" + }, + "outputs": [ { "data": { "text/html": [ @@ -11679,52 +11168,52 @@ " \n", " 0\n", " 1\n", - " 15.75\n", + " 16.52\n", " \n", " \n", " 1\n", " 2\n", - " 26.31\n", + " 26.94\n", " \n", " \n", " 2\n", " 3\n", - " 27.15\n", + " 28.15\n", " \n", " \n", " 3\n", " 4\n", - " 27.65\n", + " 27.95\n", " \n", " \n", " 4\n", " 5\n", - " 27.58\n", + " 28.09\n", " \n", " \n", " 5\n", " 6\n", - " 27.57\n", + " 28.10\n", " \n", " \n", " 6\n", " 7\n", - " 27.05\n", + " 26.82\n", " \n", " \n", " 7\n", " 8\n", - " 27.45\n", + " 27.00\n", " \n", " \n", " 8\n", " 9\n", - " 26.23\n", + " 26.79\n", " \n", " \n", " 9\n", " 10\n", - " 26.47\n", + " 26.71\n", " \n", " \n", "\n", @@ -11732,19 +11221,19 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 15.75\n", - "1 2 26.31\n", - "2 3 27.15\n", - "3 4 27.65\n", - "4 5 27.58\n", - "5 6 27.57\n", - "6 7 27.05\n", - "7 8 27.45\n", - "8 9 26.23\n", - "9 10 26.47" + "0 1 16.52\n", + "1 2 26.94\n", + "2 3 28.15\n", + "3 4 27.95\n", + "4 5 28.09\n", + "5 6 28.10\n", + "6 7 26.82\n", + "7 8 27.00\n", + "8 9 26.79\n", + "9 10 26.71" ] }, - "execution_count": 81, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -11775,16 +11264,16 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai']" ] }, - "execution_count": 62, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -11798,7 +11287,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -11807,7 +11296,7 @@ "(424, 47)" ] }, - "execution_count": 63, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -11818,7 +11307,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ @@ -11836,7 +11325,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -11893,20 +11382,20 @@ " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", - " [0.02,0.7,0.2,0.07,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", " [0.4,0.35,0.2,0.04,0.01]\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", " ...\n", - " 0.02\n", - " 0.21\n", - " 0.02\n", - " 0.017463\n", - " 0.017463\n", - " 0.02\n", - " 0.1\n", - " 0.1\n", - " 0.02\n", - " 0.02\n", + " 0.010417\n", + " 0.205208\n", + " 0.014926\n", + " 0.012671\n", + " 0.012671\n", + " 0.014926\n", + " 0.032463\n", + " 0.032463\n", + " 0.014926\n", + " 0.014926\n", " \n", " \n", " 1\n", @@ -11918,19 +11407,19 @@ " 60.0\n", " 100.0\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", - " [0.03366666666666667, 0.03409436576666667, 0.0...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.041833333333333333, 0.04238467275, 0.042938...\n", - " [0.041833333333333333, 0.04238467275, 0.042938...\n", + " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", + " [0.03366666666666667, 0.034105259000000006, 0....\n", + " [0.037750000000000006, 0.038231012375000005, 0...\n", + " [0.037750000000000006, 0.038231012375000005, 0...\n", + " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", + " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", + " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", + " [0.041833333333333333, 0.042417897133333334, 0...\n", + " [0.041833333333333333, 0.042417897133333334, 0...\n", " \n", " \n", " 2\n", @@ -11941,20 +11430,20 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.15\n", + " 0.1\n", " 0.1\n", " 0.07\n", " ...\n", - " 0.15\n", - " 0.125\n", + " 0.1\n", + " 0.1\n", " 0.1\n", " 0.085\n", " 0.085\n", " 0.1\n", - " 0.125\n", - " 0.125\n", - " 0.15\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " \n", " \n", " 3\n", @@ -11966,18 +11455,18 @@ " NaN\n", " NaN\n", " [0.2,0.6,0.2]\n", - " [0.25,0.6,0.15]\n", + " [0.3,0.55,0.15]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", " ...\n", " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.6\n", - " 0.55625\n", + " 0.575\n", + " 0.55\n", + " 0.575\n", + " 0.55\n", + " 0.53125\n", " 0.5125\n", " 0.5125\n", - " 0.55625\n", + " 0.53125\n", " 0.5125\n", " \n", " \n", @@ -11989,20 +11478,20 @@ " NaN\n", " 0.0\n", " 400.0\n", - " [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", " ...\n", - " [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07...\n", - " [0.0, 0.00642857145, 0.01285714285, 0.01928571...\n", - " [0.0, 0.004323767066666667, 0.0086529941333333...\n", - " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", - " [0.0, 0.00318255036, 0.00637055762, 0.00956313...\n", - " [0.0, 0.00295931485, 0.0059231771, 0.008890847...\n", - " [0.0, 0.0028936984428571426, 0.005791294657142...\n", - " [0.0, 0.0028936984428571426, 0.005791294657142...\n", - " [0.0, 0.0028097639124999995, 0.005622938375, 0...\n", - " [0.0, 0.0026433398111111108, 0.005289711211111...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", + " [0.0, 0.0024830850250000002, 0.004970265075000...\n", + " [0.0, 0.0022111217800000003, 0.00442770048, 0....\n", + " [0.0, 0.0021497910333333338, 0.004304129483333...\n", + " [0.0, 0.002199820885714286, 0.0044035395571428...\n", + " [0.0, 0.002199820885714286, 0.0044035395571428...\n", + " [0.0, 0.0023415099375000002, 0.00468643045, 0....\n", + " [0.0, 0.002227114055555556, 0.0044572597222222...\n", " \n", " \n", "\n", @@ -12025,18 +11514,18 @@ "4 NaN 0.0 400.0 \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.15 \n", + "2 0.1 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0... \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-o1 \\\n", "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + "3 [0.3,0.55,0.15] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " pgodzinai ... \\\n", "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", @@ -12046,79 +11535,79 @@ "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... ... \n", "\n", " median_forecast_1_bots \\\n", - "0 0.02 \n", + "0 0.010417 \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.15 \n", + "2 0.1 \n", "3 0.6 \n", - "4 [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07... \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 0.21 \n", - "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", - "2 0.125 \n", - "3 0.6 \n", - "4 [0.0, 0.00642857145, 0.01285714285, 0.01928571... \n", + "0 0.205208 \n", + "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", + "2 0.1 \n", + "3 0.575 \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_3_bots \\\n", - "0 0.02 \n", - "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", + "0 0.014926 \n", + "1 [0.03366666666666667, 0.034105259000000006, 0.... \n", "2 0.1 \n", - "3 0.6 \n", - "4 [0.0, 0.004323767066666667, 0.0086529941333333... \n", + "3 0.55 \n", + "4 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", "\n", " median_forecast_4_bots \\\n", - "0 0.017463 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.038231012375000005, 0... \n", "2 0.085 \n", - "3 0.6 \n", - "4 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "3 0.575 \n", + "4 [0.0, 0.0024830850250000002, 0.004970265075000... \n", "\n", " median_forecast_5_bots \\\n", - "0 0.017463 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.038231012375000005, 0... \n", "2 0.085 \n", - "3 0.6 \n", - "4 [0.0, 0.00318255036, 0.00637055762, 0.00956313... \n", + "3 0.55 \n", + "4 [0.0, 0.0022111217800000003, 0.00442770048, 0.... \n", "\n", " median_forecast_6_bots \\\n", - "0 0.02 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "0 0.014926 \n", + "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", "2 0.1 \n", - "3 0.55625 \n", - "4 [0.0, 0.00295931485, 0.0059231771, 0.008890847... \n", + "3 0.53125 \n", + "4 [0.0, 0.0021497910333333338, 0.004304129483333... \n", "\n", " median_forecast_7_bots \\\n", - "0 0.1 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.125 \n", + "0 0.032463 \n", + "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", + "2 0.1 \n", "3 0.5125 \n", - "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", + "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", "\n", " median_forecast_8_bots \\\n", - "0 0.1 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.125 \n", + "0 0.032463 \n", + "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", + "2 0.1 \n", "3 0.5125 \n", - "4 [0.0, 0.0028936984428571426, 0.005791294657142... \n", + "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", "\n", " median_forecast_9_bots \\\n", - "0 0.02 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", - "3 0.55625 \n", - "4 [0.0, 0.0028097639124999995, 0.005622938375, 0... \n", + "0 0.014926 \n", + "1 [0.041833333333333333, 0.042417897133333334, 0... \n", + "2 0.1 \n", + "3 0.53125 \n", + "4 [0.0, 0.0023415099375000002, 0.00468643045, 0.... \n", "\n", " median_forecast_10_bots \n", - "0 0.02 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", + "0 0.014926 \n", + "1 [0.041833333333333333, 0.042417897133333334, 0... \n", + "2 0.1 \n", "3 0.5125 \n", - "4 [0.0, 0.0026433398111111108, 0.005289711211111... \n", + "4 [0.0, 0.002227114055555556, 0.0044572597222222... \n", "\n", "[5 rows x 27 columns]" ] }, - "execution_count": 65, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -12129,7 +11618,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 147, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12177,14 +11666,14 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -15.1905\n" + "Weighted Total Score: -13.5599\n" ] } ], @@ -12194,7 +11683,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 149, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12206,7 +11695,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -12218,7 +11707,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -15.16\n" + "The average of 'head_to_head' is: -13.46\n" ] } ], @@ -12228,7 +11717,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 150, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12274,17 +11763,17 @@ " \n", " \n", " head_to_head\n", - " -1443.1\n", + " -1288.2\n", " 93.1\n", - " -15.5\n", - " 86.181587\n", - " 8.931813\n", - " -1.735425\n", + " -13.8\n", + " 86.437183\n", + " 8.958303\n", + " -1.544559\n", " 1.985277\n", - " 2.2\n", - " -33.2\n", - " 0.043005\n", - " 0.086010\n", + " 3.9\n", + " -31.6\n", + " 0.062941\n", + " 0.125882\n", " \n", " \n", "\n", @@ -12292,13 +11781,13 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -1443.1 93.1 -15.5 86.181587 8.931813 -1.735425 \n", + "head_to_head -1288.2 93.1 -13.8 86.437183 8.958303 -1.544559 \n", "\n", " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.985277 2.2 -33.2 0.043005 0.086010 " + "head_to_head 1.985277 3.9 -31.6 0.062941 0.125882 " ] }, - "execution_count": 69, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -12311,7 +11800,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 151, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12357,44 +11846,44 @@ " \n", " \n", " \n", - " 335\n", - " How many cubic meters of water produced and su...\n", - " [0.146083333325, 0.1540953797, 0.1622041748, 0...\n", - " [0.0346238299,0.0364286012,0.0383259676,0.0403...\n", - " 130027.0\n", - " -265.7\n", - " \n", - " \n", " 279\n", " What will Kalshi's rank in the iPhone Top Free...\n", - " 0.063\n", + " 0.05\n", " [0.02,0.01,0.015,0.015,0.05,0.89]\n", " Not in top 50\n", - " -264.8\n", + " -287.9\n", + " \n", + " \n", + " 335\n", + " How many cubic meters of water produced and su...\n", + " [0.167, 0.17296050626666667, 0.179050010833333...\n", + " [0.0346238299,0.0364286012,0.0383259676,0.0403...\n", + " 130027.0\n", + " -187.3\n", " \n", " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " 0.14\n", + " 0.15\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", - " -176.9\n", + " -170.0\n", " \n", " \n", - " 151\n", - " How many earthquakes of magnitude ≥ 4 will hap...\n", - " [0.0, 0.0032810261, 0.0065908451250000005, 0.0...\n", - " [0.0,0.0158237002,0.0235315723,0.0279864362,0....\n", - " 0.0\n", - " -157.3\n", + " 71\n", + " Will OpenAI, Anthropic, or Perplexity run an a...\n", + " 0.16\n", + " 0.55\n", + " yes\n", + " -123.5\n", " \n", " \n", - " 47\n", - " What will be Donald Trump's net worth, accordi...\n", - " 0.17\n", - " [0.6,0.2,0.1,0.075,0.025]\n", - " 0-$6 billion, inclusive\n", - " -126.1\n", + " 87\n", + " How many movies will be new on Netflix's globa...\n", + " 0.28\n", + " [0.01,0.064,0.926]\n", + " 2 or more\n", + " -119.6\n", " \n", " \n", "\n", @@ -12402,35 +11891,35 @@ ], "text/plain": [ " title \\\n", - "335 How many cubic meters of water produced and su... \n", "279 What will Kalshi's rank in the iPhone Top Free... \n", + "335 How many cubic meters of water produced and su... \n", "121 How many movies will be new on Netflix's top 1... \n", - "151 How many earthquakes of magnitude ≥ 4 will hap... \n", - "47 What will be Donald Trump's net worth, accordi... \n", + "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", + "87 How many movies will be new on Netflix's globa... \n", "\n", " bot_team_median \\\n", - "335 [0.146083333325, 0.1540953797, 0.1622041748, 0... \n", - "279 0.063 \n", - "121 0.14 \n", - "151 [0.0, 0.0032810261, 0.0065908451250000005, 0.0... \n", - "47 0.17 \n", - "\n", - " pro_median \\\n", - "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... \n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] \n", - "121 [0.005,0.017,0.157,0.821] \n", - "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... \n", - "47 [0.6,0.2,0.1,0.075,0.025] \n", - "\n", - " resolution head_to_head \n", - "335 130027.0 -265.7 \n", - "279 Not in top 50 -264.8 \n", - "121 3 or more -176.9 \n", - "151 0.0 -157.3 \n", - "47 0-$6 billion, inclusive -126.1 " + "279 0.05 \n", + "335 [0.167, 0.17296050626666667, 0.179050010833333... \n", + "121 0.15 \n", + "71 0.16 \n", + "87 0.28 \n", + "\n", + " pro_median resolution \\\n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", + "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... 130027.0 \n", + "121 [0.005,0.017,0.157,0.821] 3 or more \n", + "71 0.55 yes \n", + "87 [0.01,0.064,0.926] 2 or more \n", + "\n", + " head_to_head \n", + "279 -287.9 \n", + "335 -187.3 \n", + "121 -170.0 \n", + "71 -123.5 \n", + "87 -119.6 " ] }, - "execution_count": 70, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -12452,7 +11941,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -12495,26 +11984,26 @@ " \n", " 85\n", " Will Elon Musk attend the Super Bowl in 2025?\n", - " 0.1685\n", + " 0.125\n", " 0.755\n", " no\n", - " 122.2\n", + " 127.3\n", " \n", " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " 0.017463\n", + " 0.014926\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", - " 286.0\n", + " 270.3\n", " \n", " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.051569126225, 0.10695714615, 0.1599563...\n", + " [0.0, 0.025806875566666665, 0.0571614027666666...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 491.5\n", + " 531.1\n", " \n", " \n", " 211\n", @@ -12527,7 +12016,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.972\n", + " 0.95\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12545,11 +12034,11 @@ "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "85 0.1685 \n", - "0 0.017463 \n", - "189 [0.0, 0.051569126225, 0.10695714615, 0.1599563... \n", + "85 0.125 \n", + "0 0.014926 \n", + "189 [0.0, 0.025806875566666665, 0.0571614027666666... \n", "211 0.99 \n", - "214 0.972 \n", + "214 0.95 \n", "\n", " pro_median resolution \\\n", "85 0.755 no \n", @@ -12559,14 +12048,14 @@ "214 0.95 annulled \n", "\n", " head_to_head \n", - "85 122.2 \n", - "0 286.0 \n", - "189 491.5 \n", + "85 127.3 \n", + "0 270.3 \n", + "189 531.1 \n", "211 NaN \n", "214 NaN " ] }, - "execution_count": 71, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -12579,7 +12068,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -12603,7 +12092,7 @@ "dtype: object" ] }, - "execution_count": 72, + "execution_count": 153, "metadata": {}, "output_type": "execute_result" } @@ -12617,7 +12106,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -12672,10 +12161,10 @@ " NaN\n", " 31268\n", " 1.0\n", - " 0.017463\n", + " 0.014926\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 286.007699\n", - " 286.007699\n", + " 270.308741\n", + " 270.308741\n", " \n", " \n", " 1\n", @@ -12690,10 +12179,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.03366666666666667, 0.034105259000000006, 0....\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -76.357515\n", - " -76.357515\n", + " -79.442225\n", + " -79.442225\n", " \n", " \n", " 2\n", @@ -12708,10 +12197,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", - " -7.574597\n", - " -7.574597\n", + " -9.227528\n", + " -9.227528\n", " \n", " \n", " 3\n", @@ -12726,10 +12215,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " 0.6\n", + " 0.55\n", " [0.16,0.44,0.4]\n", - " 31.015493\n", - " 31.015493\n", + " 22.314355\n", + " 22.314355\n", " \n", " \n", " 4\n", @@ -12744,10 +12233,10 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", + " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 28.578581\n", - " 28.578581\n", + " 25.971582\n", + " 25.971582\n", " \n", " \n", "\n", @@ -12776,28 +12265,28 @@ "4 NaN 0.0 400.0 31281 \n", "\n", " question_weight bot_team_median \\\n", - "0 1.0 0.017463 \n", - "1 1.0 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 1.0 0.085 \n", - "3 1.0 0.6 \n", - "4 1.0 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "0 1.0 0.014926 \n", + "1 1.0 [0.03366666666666667, 0.034105259000000006, 0.... \n", + "2 1.0 0.1 \n", + "3 1.0 0.55 \n", + "4 1.0 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 286.007699 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -76.357515 \n", - "2 0.013 -7.574597 \n", - "3 [0.16,0.44,0.4] 31.015493 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 28.578581 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -79.442225 \n", + "2 0.013 -9.227528 \n", + "3 [0.16,0.44,0.4] 22.314355 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 25.971582 \n", "\n", " weighted_score \n", - "0 286.007699 \n", - "1 -76.357515 \n", - "2 -7.574597 \n", - "3 31.015493 \n", - "4 28.578581 " + "0 270.308741 \n", + "1 -79.442225 \n", + "2 -9.227528 \n", + "3 22.314355 \n", + "4 25.971582 " ] }, - "execution_count": 73, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } @@ -12808,7 +12297,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 155, "metadata": {}, "outputs": [], "source": [ @@ -12820,7 +12309,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 156, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12832,7 +12321,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -12876,7 +12365,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 157, "metadata": {}, "outputs": [], "source": [ @@ -12886,7 +12375,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 158, "metadata": {}, "outputs": [ { @@ -12939,7 +12428,7 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", " \n", " \n", @@ -12955,7 +12444,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.66\n", + " 0.62\n", " 0.45\n", " \n", " \n", @@ -12971,7 +12460,7 @@ " NaN\n", " 31294\n", " 1.0\n", - " 0.86\n", + " 0.85\n", " 0.95\n", " \n", " \n", @@ -13026,9 +12515,9 @@ "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", "\n", " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "2 NaN NaN 31270 1.0 0.085 \n", - "5 NaN NaN 31282 1.0 0.66 \n", - "8 NaN NaN 31294 1.0 0.86 \n", + "2 NaN NaN 31270 1.0 0.1 \n", + "5 NaN NaN 31282 1.0 0.62 \n", + "8 NaN NaN 31294 1.0 0.85 \n", "10 NaN NaN 1.0 NaN \n", "13 NaN NaN 31338 1.0 0.85 \n", "\n", @@ -13040,7 +12529,7 @@ "13 0.9 " ] }, - "execution_count": 77, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -13051,7 +12540,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 159, "metadata": {}, "outputs": [ { @@ -13102,7 +12591,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 160, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13163,10 +12652,10 @@ " NaN\n", " 31268\n", " 1.0\n", - " 0.017463\n", + " 0.014926\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 286.007699\n", - " 286.007699\n", + " 270.308741\n", + " 270.308741\n", " \n", " \n", " 1\n", @@ -13181,10 +12670,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.03366666666666667, 0.034105259000000006, 0....\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -76.357515\n", - " -76.357515\n", + " -79.442225\n", + " -79.442225\n", " \n", " \n", " 2\n", @@ -13199,10 +12688,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", - " -7.574597\n", - " -7.574597\n", + " -9.227528\n", + " -9.227528\n", " \n", " \n", " 3\n", @@ -13217,10 +12706,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " 0.6\n", + " 0.55\n", " [0.16,0.44,0.4]\n", - " 31.015493\n", - " 31.015493\n", + " 22.314355\n", + " 22.314355\n", " \n", " \n", " 4\n", @@ -13235,10 +12724,10 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.00369737075, 0.0073988365, 0.011103060...\n", + " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 28.578581\n", - " 28.578581\n", + " 25.971582\n", + " 25.971582\n", " \n", " \n", "\n", @@ -13267,25 +12756,25 @@ "4 NaN 0.0 400.0 31281 \n", "\n", " question_weight bot_team_median \\\n", - "0 1.0 0.017463 \n", - "1 1.0 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 1.0 0.085 \n", - "3 1.0 0.6 \n", - "4 1.0 [0.0, 0.00369737075, 0.0073988365, 0.011103060... \n", + "0 1.0 0.014926 \n", + "1 1.0 [0.03366666666666667, 0.034105259000000006, 0.... \n", + "2 1.0 0.1 \n", + "3 1.0 0.55 \n", + "4 1.0 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 286.007699 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -76.357515 \n", - "2 0.013 -7.574597 \n", - "3 [0.16,0.44,0.4] 31.015493 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 28.578581 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -79.442225 \n", + "2 0.013 -9.227528 \n", + "3 [0.16,0.44,0.4] 22.314355 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 25.971582 \n", "\n", " weighted_score \n", - "0 286.007699 \n", - "1 -76.357515 \n", - "2 -7.574597 \n", - "3 31.015493 \n", - "4 28.578581 " + "0 270.308741 \n", + "1 -79.442225 \n", + "2 -9.227528 \n", + "3 22.314355 \n", + "4 25.971582 " ] }, "metadata": {}, @@ -13343,10 +12832,10 @@ " NaN\n", " 35380\n", " 1.00\n", - " 0.9275\n", " 0.95\n", - " -2.396919\n", - " -2.396919\n", + " 0.95\n", + " 0.000000\n", + " 0.000000\n", " \n", " \n", " 351\n", @@ -13361,10 +12850,10 @@ " NaN\n", " 35381\n", " 1.00\n", - " 0.375\n", + " 0.575\n", " 0.05\n", - " -41.871033\n", - " -41.871033\n", + " -80.437282\n", + " -80.437282\n", " \n", " \n", " 355\n", @@ -13379,10 +12868,10 @@ " NaN\n", " 35385\n", " 1.00\n", - " 0.925\n", + " 0.875\n", " 0.97\n", - " -4.750233\n", - " -4.750233\n", + " -10.307219\n", + " -10.307219\n", " \n", " \n", " 361\n", @@ -13397,10 +12886,10 @@ " NaN\n", " 35386\n", " 0.85\n", - " 0.825\n", + " 0.85\n", " 0.666\n", - " -64.635502\n", - " -54.940177\n", + " -80.050570\n", + " -68.042984\n", " \n", " \n", " 364\n", @@ -13440,17 +12929,17 @@ "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", "\n", " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "342 NaN NaN 35380 1.00 0.9275 \n", - "351 NaN NaN 35381 1.00 0.375 \n", - "355 NaN NaN 35385 1.00 0.925 \n", - "361 NaN NaN 35386 0.85 0.825 \n", + "342 NaN NaN 35380 1.00 0.95 \n", + "351 NaN NaN 35381 1.00 0.575 \n", + "355 NaN NaN 35385 1.00 0.875 \n", + "361 NaN NaN 35386 0.85 0.85 \n", "364 NaN NaN 35387 0.85 0.05 \n", "\n", " pro_median head_to_head weighted_score \n", - "342 0.95 -2.396919 -2.396919 \n", - "351 0.05 -41.871033 -41.871033 \n", - "355 0.97 -4.750233 -4.750233 \n", - "361 0.666 -64.635502 -54.940177 \n", + "342 0.95 0.000000 0.000000 \n", + "351 0.05 -80.437282 -80.437282 \n", + "355 0.97 -10.307219 -10.307219 \n", + "361 0.666 -80.050570 -68.042984 \n", "364 0.03 -2.083409 -1.770897 " ] }, @@ -13464,7 +12953,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[80], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[160], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:839\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 837\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 839\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 842\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", diff --git a/functions.py b/functions.py index 42c14de..2035207 100644 --- a/functions.py +++ b/functions.py @@ -392,7 +392,7 @@ def get_median_forecast(row, bots): try: val = float(f) probs.append(val) - except (ValueError, TypeError): + except (ValueError, TypeError) as e: print(f" Invalid forecast: {f} — error {e}") continue @@ -412,24 +412,6 @@ def get_median_forecast(row, bots): raise ValueError(f"Unknown question type: {q_type}") -def calculate_all_peer_scores( - df_bot_team_forecasts: pd.DataFrame, teams: list[str] -) -> pd.DataFrame: - """ - Takes in a df that has a row for each question, a column for each team, and a forecast as that columns value - Changes the df so that the forecast is now the score for that question - """ - raise NotImplementedError( - "I accidentally implemented baseline scoring here unfortunately" - ) - score_df = df_bot_team_forecasts.copy() - team_scores = calculate_weighted_scores(df_bot_team_forecasts, teams) - for team in teams: - score_for_team = team_scores[team] - score_df[team] = score_for_team - return score_df - - def calculate_weighted_scores(df_bot_team_forecasts, teams): """ @Check: @@ -1255,3 +1237,157 @@ def parse_options_array(options_str): # Simple fallback: just split by comma and strip quotes return [p.strip().strip("\"'") for p in cleaned.split(",")] + + +def calculate_peer_score_numeric(row, bot_col, pro_col='pro_median'): + """Calculate peer score for numeric questions""" + try: + # Check if bot didn't provide a forecast + if pd.isna(row[bot_col]): + return np.nan + + resolution_value = row['resolution'] + + # Get the CDF values + bot_cdf = row[bot_col] + pro_median_cdf = row[pro_col] + + # Handle special cases + if resolution_value == 'below_lower_bound': + # Use first point in CDF + if isinstance(bot_cdf, (list, np.ndarray)) and len(bot_cdf) > 0: + bot_prob = bot_cdf[0] + else: + return np.nan + + if isinstance(pro_median_cdf, (list, np.ndarray)) and len(pro_median_cdf) > 0: + pro_median_prob = pro_median_cdf[0] + else: + return np.nan + + elif resolution_value == 'above_upper_bound': + # Use (1 - last point in CDF) + if isinstance(bot_cdf, (list, np.ndarray)) and len(bot_cdf) > 0: + bot_prob = 1 - bot_cdf[-1] + else: + return np.nan + + if isinstance(pro_median_cdf, (list, np.ndarray)) and len(pro_median_cdf) > 0: + pro_median_prob = 1 - pro_median_cdf[-1] + else: + return np.nan + + else: + # Convert to float if it's a numeric resolution + try: + resolution_float = float(resolution_value) + + # Convert CDF to PMF + if isinstance(bot_cdf, (list, np.ndarray)) and isinstance(pro_median_cdf, (list, np.ndarray)): + # Convert CDFs to PMFs + bot_pmf = np.diff(np.concatenate([[0], bot_cdf])) + pro_pmf = np.diff(np.concatenate([[0], pro_median_cdf])) + + # Use nominal_location_to_cdf_location to find the appropriate bucket + cdf_location = nominal_location_to_cdf_location(resolution_float, row) + + # Find the appropriate bucket index + bucket_index = min(int(cdf_location * (len(bot_pmf) - 1)), len(bot_pmf) - 1) + + # Get probabilities + bot_prob = bot_pmf[bucket_index] + pro_median_prob = pro_pmf[bucket_index] + else: + return np.nan + except: + return np.nan + + # Ensure non-zero probabilities + bot_prob = max(bot_prob, 1e-10) + pro_median_prob = max(pro_median_prob, 1e-10) + + # Calculate peer score and divide by 2 for continuous questions + return np.log(bot_prob / pro_median_prob) / 2 + + except Exception as e: + # Print the specific error for debugging + return np.nan + +def calculate_peer_score_binary(row, bot_col, pro_col='pro_median'): + """Calculate peer score for binary questions""" + if row['resolution'] == 'yes': + return np.log(row[bot_col] / row[pro_col]) + else: # resolution is 'no' + return np.log((1 - row[bot_col]) / (1 - row[pro_col])) + +def parse_cdf_string(cdf_string): + """Parse CDF string into numpy array""" + return np.array([float(x) for x in cdf_string.strip('[]').split(',')]) + +def calculate_peer_score_multiple_choice(row, bot_col, pro_col='pro_median'): + """Calculate peer score for multiple choice questions""" + # Check if bot didn't provide a forecast (NaN) + if pd.isna(row[bot_col]): + return np.nan + + # Get the resolution value and options + resolution_value = row['resolution'] + options = row['options_parsed'] if 'options_parsed' in row else row['options'] + + # Find the index of the resolution in options array + resolution_str = str(resolution_value) + + try: + resolution_index = options.index(resolution_str) + + # Get the forecasts + bot_pmf_raw = row[bot_col] + pro_pmf_raw = row[pro_col] + + # Parse string representations of arrays if needed + if isinstance(bot_pmf_raw, str): + bot_pmf = [float(x) for x in bot_pmf_raw.strip('[]').split(',')] + else: + bot_pmf = bot_pmf_raw + + if isinstance(pro_pmf_raw, str): + pro_pmf = [float(x) for x in pro_pmf_raw.strip('[]').split(',')] + else: + pro_pmf = pro_pmf_raw + + # Get the probabilities at the correct index + bot_prob = bot_pmf[resolution_index] + pro_prob = pro_pmf[resolution_index] + + # Calculate peer score + return np.log(bot_prob / pro_prob) + except Exception as e: + # If any error occurs, return NaN + return np.nan + +def calculate_peer_score(row, bot_col, pro_col='pro_median'): + """Calculate peer score based on question type""" + if row['type'] == 'binary': + return calculate_peer_score_binary(row, bot_col, pro_col) + elif row['type'] == 'multiple_choice': + return calculate_peer_score_multiple_choice(row, bot_col, pro_col) + elif row['type'] == 'numeric': + return calculate_peer_score_numeric(row, bot_col, pro_col) + else: + # Unknown question type; return NaN + return np.nan + +def calculate_all_peer_scores(df, all_bots, pro_col='pro_median'): + """Calculate peer scores for all bots""" + # Create a new DataFrame to store peer scores + df_peer = df.copy() + + # Calculate peer score for each bot + for bot in all_bots: + df_peer[bot] = 100 * df.apply(lambda row: calculate_peer_score(row, bot, pro_col), axis=1) + + # Calculate peer score for bot_team_median + df_peer["bot_team_median"] = 100 * df.apply( + lambda row: calculate_peer_score(row, 'bot_median', pro_col), axis=1) + + return df_peer diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 5811dc4..265e32d 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,33 +1,33 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI -metac-o1,21.0,21.0,21.0,21.0,21.0 -metac-perplexity,20.3,20.3,20.3,20.3,20.3 -bot_median,17.9,17.9,17.9,17.9,17.9 +metac-perplexity,20.6,20.6,20.6,20.6,20.6 +metac-o1,20.2,20.2,20.2,20.2,20.2 acm_bot,17.7,17.7,17.7,17.7,17.7 +bot_median,17.4,17.4,17.4,17.4,17.4 manticAI,14.5,14.5,14.5,14.5,14.5 twsummerbot,14.3,14.3,14.3,14.3,14.3 jkraybill_bot,14.3,14.3,14.3,14.3,14.3 -metac-claude-3-5-sonnet-20240620,12.0,12.0,12.0,12.0,12.0 -GreeneiBot2,11.7,11.7,11.7,11.7,11.7 -metac-claude-3-5-sonnet-latest,11.5,11.5,11.5,11.5,11.5 +metac-claude-3-5-sonnet-20240620,13.0,13.0,13.0,13.0,13.0 +metac-claude-3-5-sonnet-latest,12.4,12.4,12.4,12.4,12.4 +metac-deepseek-r1,12.3,12.3,12.3,12.3,12.3 +metac-Llama-3.1,12.2,12.2,12.2,12.2,12.2 +GreeneiBot2,11.8,11.8,11.8,11.8,11.8 NextWorldLab,11.1,11.1,11.1,11.1,11.1 -metac-grok-2-1212,11.0,11.0,11.0,11.0,11.0 -metac-gpt-4o,10.5,10.5,10.5,10.5,10.5 -metac-Llama-3.1,10.2,10.2,10.2,10.2,10.2 Grizeu_Bot,10.2,10.2,10.2,10.2,10.2 SynapseSeer,10.2,10.2,10.2,10.2,10.2 -metac-o1-preview,10.1,10.1,10.1,10.1,10.1 +metac-grok-2-1212,9.8,9.8,9.8,9.8,9.8 mmBot,9.7,9.7,9.7,9.7,9.7 -metac-exa,9.7,9.7,9.7,9.7,9.7 +metac-Gemini-Exp-1206,9.6,9.6,9.6,9.6,9.6 annabot,9.0,9.0,9.0,9.0,9.0 -metac-deepseek-r1,8.4,8.4,8.4,8.4,8.4 +metac-exa,8.8,8.8,8.8,8.8,8.8 VeritasAI,8.4,8.4,8.4,8.4,8.4 laylaps,7.6,7.6,7.6,7.6,7.6 -cookics_bot_TEST,6.4,6.4,6.4,6.4,6.4 -metac-Gemini-Exp-1206,5.8,5.8,5.8,5.8,5.8 +metac-o1-preview,6.7,6.7,6.7,6.7,6.7 +cookics_bot_TEST,6.3,6.3,6.3,6.3,6.3 MWG,5.5,5.5,5.5,5.5,5.5 ajf-bot,5.1,5.1,5.1,5.1,5.1 pgodzinai,3.5,3.5,3.5,3.5,3.5 KevinTestBot,3.3,3.3,3.3,3.3,3.3 +metac-gpt-4o,3.0,3.0,3.0,3.0,3.0 InstitutPelFutur,2.7,2.7,2.7,2.7,2.7 Bot_Pepa,2.6,2.6,2.6,2.6,2.6 CumulativeBot,2.5,2.5,2.5,2.5,2.5 @@ -37,9 +37,9 @@ jonahsingerbot,2.2,2.2,2.2,2.2,2.2 bean_bot,2.1,2.1,2.1,2.1,2.1 X_bot,1.9,1.9,1.9,1.9,1.9 CatrachoCaster,1.8,1.8,1.8,1.8,1.8 +RPM_bot,0.8,0.8,0.8,0.8,0.8 4Shadower,0.6,0.6,0.6,0.6,0.6 krm-bot,0.6,0.6,0.6,0.6,0.6 -RPM_bot,0.6,0.6,0.6,0.6,0.6 andrewsiah,0.0,0.0,0.0,0.0,0.0 cobyj-bot,0.0,0.0,0.0,0.0,0.0 pianobot,-2.2,-2.2,-2.2,-2.2,-2.2 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index b364ee5..889922c 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,33 +1,33 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -metac-o1,1998.9,95.0,21.0,3.570999300115835e-15,3.663767977230083e-16,5.743007173754146e+16,1.9847501794262088,21.0,21.0,1.0,0.000000 -metac-perplexity,1927.0,95.0,20.3,0.0,0.0,inf,1.9847501794262088,20.3,20.3,1.0,0.000000 -bot_median,1698.8,95.0,17.9,0.0,0.0,inf,1.9847501794262088,17.9,17.9,1.0,0.000000 +metac-perplexity,1957.5,95.0,20.6,0.0,0.0,inf,1.9847501794262088,20.6,20.6,1.0,0.000000 +metac-o1,1921.1,95.0,20.2,0.0,0.0,inf,1.9847501794262088,20.2,20.2,1.0,0.000000 acm_bot,1680.6,95.0,17.7,3.570999300115835e-15,3.663767977230083e-16,4.828448927545706e+16,1.9847501794262088,17.7,17.7,1.0,0.000000 +bot_median,1655.0,95.0,17.4,3.570999300115835e-15,3.663767977230083e-16,4.755070072324921e+16,1.9847501794262088,17.4,17.4,1.0,0.000000 manticAI,1378.2,95.0,14.5,0.0,0.0,inf,1.9847501794262088,14.5,14.5,1.0,0.000000 twsummerbot,1355.4,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.788325122257914e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 jkraybill_bot,1354.5,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.783286397381174e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 -metac-claude-3-5-sonnet-20240620,1136.7,95.0,12.0,3.570999300115835e-15,3.663767977230083e-16,3.26596902511772e+16,1.9847501794262088,12.0,12.0,1.0,0.000000 -GreeneiBot2,1115.4,95.0,11.7,5.3564989501737525e-15,5.495651965845125e-16,2.1364275625153532e+16,1.9847501794262088,11.7,11.7,1.0,0.000000 -metac-claude-3-5-sonnet-latest,1091.6,95.0,11.5,5.3564989501737525e-15,5.495651965845125e-16,2.0907644050343052e+16,1.9847501794262088,11.5,11.5,1.0,0.000000 +metac-claude-3-5-sonnet-20240620,1235.2,95.0,13.0,1.7854996500579174e-15,1.8318839886150415e-16,7.097519447336572e+16,1.9847501794262088,13.0,13.0,1.0,0.000000 +metac-claude-3-5-sonnet-latest,1180.5,95.0,12.4,0.0,0.0,inf,1.9847501794262088,12.4,12.4,1.0,0.000000 +metac-deepseek-r1,1166.0,95.0,12.3,1.7854996500579174e-15,1.8318839886150415e-16,6.700213221693384e+16,1.9847501794262088,12.3,12.3,1.0,0.000000 +metac-Llama-3.1,1154.9,95.0,12.2,3.570999300115835e-15,3.663767977230083e-16,3.3181275591894544e+16,1.9847501794262088,12.2,12.2,1.0,0.000000 +GreeneiBot2,1119.2,95.0,11.8,1.7854996500579174e-15,1.8318839886150415e-16,6.4310595726389144e+16,1.9847501794262088,11.8,11.8,1.0,0.000000 NextWorldLab,1050.3,95.0,11.1,1.7854996500579174e-15,1.8318839886150415e-16,6.035037516349447e+16,1.9847501794262088,11.1,11.1,1.0,0.000000 -metac-grok-2-1212,1047.4,95.0,11.0,0.0,0.0,inf,1.9847501794262088,11.0,11.0,1.0,0.000000 -metac-gpt-4o,1002.0,95.0,10.5,3.570999300115835e-15,3.663767977230083e-16,2.87887889373382e+16,1.9847501794262088,10.5,10.5,1.0,0.000000 -metac-Llama-3.1,973.0,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 Grizeu_Bot,966.4,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 SynapseSeer,964.7,95.0,10.2,1.7854996500579174e-15,1.8318839886150415e-16,5.5434396730578184e+16,1.9847501794262088,10.2,10.2,1.0,0.000000 -metac-o1-preview,962.8,95.0,10.1,1.7854996500579174e-15,1.8318839886150415e-16,5.5325101025506376e+16,1.9847501794262088,10.1,10.1,1.0,0.000000 +metac-grok-2-1212,932.3,95.0,9.8,1.7854996500579174e-15,1.8318839886150415e-16,5.357004504213439e+16,1.9847501794262088,9.8,9.8,1.0,0.000000 mmBot,924.8,95.0,9.7,0.0,0.0,inf,1.9847501794262088,9.7,9.7,1.0,0.000000 -metac-exa,919.9,95.0,9.7,1.7854996500579174e-15,1.8318839886150415e-16,5.285938770788284e+16,1.9847501794262088,9.7,9.7,1.0,0.000000 +metac-Gemini-Exp-1206,910.2,95.0,9.6,1.7854996500579174e-15,1.8318839886150415e-16,5.230331909359555e+16,1.9847501794262088,9.6,9.6,1.0,0.000000 annabot,854.4,95.0,9.0,1.7854996500579174e-15,1.8318839886150415e-16,4.909363317298574e+16,1.9847501794262088,9.0,9.0,1.0,0.000000 -metac-deepseek-r1,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608683275523464e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 +metac-exa,836.7,95.0,8.8,1.7854996500579174e-15,1.8318839886150415e-16,4.808056144499867e+16,1.9847501794262088,8.8,8.8,1.0,0.000000 VeritasAI,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608352429717695e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 laylaps,723.4,95.0,7.6,8.927498250289587e-16,9.159419943075207e-17,8.313179820692651e+16,1.9847501794262088,7.6,7.6,1.0,0.000000 -cookics_bot_TEST,612.4,95.0,6.4,1.7854996500579174e-15,1.8318839886150415e-16,3.5189490119492424e+16,1.9847501794262088,6.4,6.4,1.0,0.000000 -metac-Gemini-Exp-1206,548.0,95.0,5.8,0.0,0.0,inf,1.9847501794262088,5.8,5.8,1.0,0.000000 +metac-o1-preview,640.2,95.0,6.7,8.927498250289587e-16,9.159419943075207e-17,7.357383207755715e+16,1.9847501794262088,6.7,6.7,1.0,0.000000 +cookics_bot_TEST,596.4,95.0,6.3,0.0,0.0,inf,1.9847501794262088,6.3,6.3,1.0,0.000000 MWG,520.8,95.0,5.5,8.927498250289587e-16,9.159419943075207e-17,5.985647068886487e+16,1.9847501794262088,5.5,5.5,1.0,0.000000 ajf-bot,481.2,95.0,5.1,1.7854996500579174e-15,1.8318839886150415e-16,2.7648981076196796e+16,1.9847501794262088,5.1,5.1,1.0,0.000000 pgodzinai,336.0,95.0,3.5,8.927498250289587e-16,9.159419943075207e-17,3.8616390554277256e+16,1.9847501794262088,3.5,3.5,1.0,0.000000 KevinTestBot,314.5,95.0,3.3,8.927498250289587e-16,9.159419943075207e-17,3.614851659932975e+16,1.9847501794262088,3.3,3.3,1.0,0.000000 +metac-gpt-4o,280.3,95.0,3.0,8.927498250289587e-16,9.159419943075207e-17,3.221540864953186e+16,1.9847501794262088,3.0,3.0,1.0,0.000000 InstitutPelFutur,256.0,95.0,2.7,8.927498250289587e-16,9.159419943075207e-17,2.9416230195900824e+16,1.9847501794262088,2.7,2.7,1.0,0.000000 Bot_Pepa,246.8,95.0,2.6,0.0,0.0,inf,1.9847501794262088,2.6,2.6,1.0,0.000000 CumulativeBot,241.1,95.0,2.5,4.463749125144793e-16,4.579709971537604e-17,5.542702538240192e+16,1.9847501794262088,2.5,2.5,1.0,0.000000 @@ -37,9 +37,9 @@ jonahsingerbot,212.9,95.0,2.2,4.463749125144793e-16,4.579709971537604e-17,4.8945 bean_bot,200.0,95.0,2.1,0.0,0.0,inf,1.9847501794262088,2.1,2.1,1.0,0.000000 X_bot,181.4,95.0,1.9,0.0,0.0,inf,1.9847501794262088,1.9,1.9,1.0,0.000000 CatrachoCaster,167.5,95.0,1.8,4.463749125144793e-16,4.579709971537604e-17,3.8493725321790856e+16,1.9847501794262088,1.8,1.8,1.0,0.000000 +RPM_bot,71.4,95.0,0.8,1.1159372812861984e-16,1.144927492884401e-17,6.560692777870449e+16,1.9847501794262088,0.8,0.8,1.0,0.000000 4Shadower,61.1,95.0,0.6,2.2318745625723967e-16,2.289854985768802e-17,2.810105705323094e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 krm-bot,60.8,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,5.586128771835555e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 -RPM_bot,52.6,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,4.834419627569585e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 andrewsiah,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA cobyj-bot,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA pianobot,-206.5,95.0,-2.2,4.463749125144793e-16,4.579709971537604e-17,-4.745304957283875e+16,1.9847501794262088,-2.2,-2.2,0.0,0.000000 From 881193b074221a3980030cb8d7a4d51b853e3cc5 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 07:27:27 -0600 Subject: [PATCH 10/26] Refactored spot peer scoring functions --- AI_BENCHMARKING_ANALYSIS.ipynb | 3015 +++++++++-------- functions.py | 187 +- .../bootstrapped_h2h_bot_vs_pros.csv | 26 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 28 +- refactored_notebook/scoring.py | 193 +- tests/test_scoring.py | 2 +- 6 files changed, 1715 insertions(+), 1736 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 510d463..fe42ead 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 1, "metadata": { "id": "ISzIoto4hnoG" }, @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -358,7 +358,7 @@ " dtype='object')" ] }, - "execution_count": 90, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -424,7 +424,7 @@ "dtype: object" ] }, - "execution_count": 92, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -435,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -446,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -514,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -693,7 +693,7 @@ "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, - "execution_count": 97, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -704,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -727,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -747,7 +747,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 99, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -759,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -793,6 +793,15 @@ " \n", " \n", " \n", + " 15\n", + " bot_median\n", + " 9.993738\n", + " 3777.832847\n", + " 409\n", + " 7.260052\n", + " 1.390626\n", + " \n", + " \n", " 12\n", " metac-o1\n", " 9.674740\n", @@ -811,15 +820,6 @@ " 2.298000\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 8.215149\n", - " 3105.490478\n", - " 409\n", - " 5.145245\n", - " 1.561660\n", - " \n", - " \n", " 24\n", " manticAI\n", " 6.510835\n", @@ -843,16 +843,16 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", + "15 bot_median 9.993738 3777.832847 409 7.260052 \n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", - "15 bot_median 8.215149 3105.490478 409 5.145245 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", + "15 1.390626 \n", "12 1.738353 \n", "4 2.298000 \n", - "15 1.561660 \n", "24 3.029040 \n", "1 2.309106 " ] @@ -968,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 20, "metadata": { "id": "BmAFBHIhK77X" }, @@ -1017,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1441,7 +1441,7 @@ " np.int64(35705)}" ] }, - "execution_count": 102, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1462,7 +1462,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 22, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1501,18 +1501,18 @@ " \n", " \n", " 1\n", - " metac-o1\n", - " 8861.959039\n", + " bot_median\n", + " 9389.288325\n", " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 8849.559824\n", + " metac-o1\n", + " 8861.959039\n", " \n", " \n", " 3\n", - " bot_median\n", - " 8671.898307\n", + " metac-o1-preview\n", + " 8849.559824\n", " \n", " \n", " 4\n", @@ -1531,9 +1531,9 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 metac-o1 8861.959039\n", - "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8671.898307\n", + "1 bot_median 9389.288325\n", + "2 metac-o1 8861.959039\n", + "3 metac-o1-preview 8849.559824\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1639,7 +1639,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1658,7 +1658,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 24, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1697,13 +1697,13 @@ " \n", " \n", " 1\n", - " metac-o1\n", - " 3864.168122\n", + " bot_median\n", + " 4077.448023\n", " \n", " \n", " 2\n", - " bot_median\n", - " 3347.538115\n", + " metac-o1\n", + " 3864.168122\n", " \n", " \n", " 3\n", @@ -1937,8 +1937,8 @@ "text/plain": [ " bot Peer Score\n", "Rank \n", - "1 metac-o1 3864.168122\n", - "2 bot_median 3347.538115\n", + "1 bot_median 4077.448023\n", + "2 metac-o1 3864.168122\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -1986,7 +1986,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 105, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2028,7 +2028,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -2047,7 +2047,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -2056,7 +2056,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2077,7 +2077,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2256,7 +2256,7 @@ "6 [0.001,0.56,0.36,0.059,0.02] False " ] }, - "execution_count": 109, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2267,7 +2267,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 29, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2331,8 +2331,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", + " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.02,0.7,0.2,0.07,0.01]\n", " [0.35000000000000003,0.30000000000000004,0.250...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", @@ -2355,7 +2355,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", @@ -2380,8 +2380,8 @@ " NaN\n", " ...\n", " 0.1\n", + " 0.05\n", " 0.1\n", - " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2405,7 +2405,7 @@ " ...\n", " [0.3,0.55,0.15]\n", " [0.2,0.6,0.2]\n", - " [0.15,0.55,0.3]\n", + " [0.1,0.6,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2427,7 +2427,7 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", @@ -2466,24 +2466,24 @@ "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... NaN NaN \n", "\n", " CatrachoCaster ... metac-o1 \\\n", - "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", - "1 NaN ... [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", + "0 NaN ... [0.45,0.3,0.15,0.05,0.05] \n", + "1 NaN ... [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", "2 NaN ... 0.1 \n", "3 [0.16,0.47,0.37] ... [0.3,0.55,0.15] \n", - "4 NaN ... [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.02,0.7,0.2,0.07,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", + "2 0.05 \n", "3 [0.2,0.6,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.15 NaN \n", - "3 [0.15,0.55,0.3] NaN \n", + "2 0.1 NaN \n", + "3 [0.1,0.6,0.3] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2595,8 +2595,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.3\n", - " 0.85\n", + " 0.65\n", + " 0.15\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2619,8 +2619,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", - " 0.95\n", + " 0.85\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2643,9 +2643,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.7\n", + " 0.8\n", " 0.85\n", - " 0.25\n", + " 0.3\n", " NaN\n", " 0.85\n", " 0.85\n", @@ -2667,9 +2667,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.1\n", " 0.05\n", - " 0.05\n", - " 0.03\n", + " 0.1\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2693,17 +2693,17 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.3 \n", - "96 None 0.97 0.85 NaN NaN ... 0.8 \n", - "97 None 0.666 0.8 NaN NaN ... 0.7 \n", - "98 None 0.03 0.3 NaN NaN ... 0.05 \n", + "95 None 0.05 0.95 NaN NaN ... 0.65 \n", + "96 None 0.97 0.85 NaN NaN ... 0.85 \n", + "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "98 None 0.03 0.3 NaN NaN ... 0.1 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.85 NaN NaN 0.15 NaN NaN \n", - "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.25 NaN 0.85 0.85 NaN \n", - "98 0.05 0.03 NaN 0.15 0.05 NaN \n", + "95 0.15 NaN NaN 0.15 NaN NaN \n", + "96 0.9 NaN NaN 0.9 NaN NaN \n", + "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "98 0.05 0.1 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -2771,7 +2771,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2793,7 +2793,7 @@ " dtype='object')" ] }, - "execution_count": 111, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2804,7 +2804,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -2814,7 +2814,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 112, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -2829,7 +2829,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -2841,7 +2841,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2850,7 +2850,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -2911,8 +2911,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", + " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.02,0.7,0.2,0.07,0.01]\n", " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", @@ -2935,9 +2935,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...]\n", + " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...]\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", - " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", + " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", " NaN\n", " [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...]\n", " [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...]\n", @@ -2960,8 +2960,8 @@ " NaN\n", " ...\n", " 0.1\n", + " 0.05\n", " 0.1\n", - " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2985,7 +2985,7 @@ " ...\n", " [0.3,0.55,0.15]\n", " [0.2,0.6,0.2]\n", - " [0.15,0.55,0.3]\n", + " [0.1,0.6,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -3007,8 +3007,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.6066666667, 0.6133333333, 0.62, 0.6266666667, ...]\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...]\n", + " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...]\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...]\n", " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...]\n", " NaN\n", " [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", @@ -3052,26 +3052,26 @@ "3 NaN NaN [0.16,0.47,0.37] ... \n", "4 NaN NaN NaN ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...] \n", - "2 0.1 \n", - "3 [0.3,0.55,0.15] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.208, 0.216, 0.224, 0.232, 0.24, 0.248, 0.256, 0.264, 0.272, 0.28, 0.288, 0.296, 0.304, 0.312, 0.32, 0.328, 0.336, 0.344, 0.352, 0.36, 0.368, 0.376, 0.384, 0.392, 0.4, 0.408, 0.416, 0.424, 0.432, 0.44, 0.448, 0.456, 0.464, 0.472, 0.48, 0.488, 0.496, 0.504, 0.512, 0.52, 0.528, 0.536, 0.544, 0.552, 0.56, 0.568, 0.576, 0.584, 0.592, 0.6, 0.6066666667, 0.6133333333, 0.62, 0.6266666667, ...] \n", + " metac-o1 \\\n", + "0 [0.45,0.3,0.15,0.05,0.05] \n", + "1 [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...] \n", + "2 0.1 \n", + "3 [0.3,0.55,0.15] \n", + "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", + "0 [0.02,0.7,0.2,0.07,0.01] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", - "2 0.1 \n", + "2 0.05 \n", "3 [0.2,0.6,0.2] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...] \n", "\n", - " metac-perplexity \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", - "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.15 \n", - "3 [0.15,0.55,0.3] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", + " metac-perplexity \\\n", + "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", + "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", + "2 0.1 \n", + "3 [0.1,0.6,0.3] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3203,8 +3203,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.3\n", - " 0.85\n", + " 0.65\n", + " 0.15\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3227,8 +3227,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", - " 0.95\n", + " 0.85\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3251,9 +3251,9 @@ " NaN\n", " NaN\n", " ...\n", - " 0.7\n", + " 0.8\n", " 0.85\n", - " 0.25\n", + " 0.3\n", " NaN\n", " 0.85\n", " 0.85\n", @@ -3275,9 +3275,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.1\n", " 0.05\n", - " 0.05\n", - " 0.03\n", + " 0.1\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3301,17 +3301,17 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.3 \n", - "96 None 0.97 0.85 NaN NaN ... 0.8 \n", - "97 None 0.666 0.8 NaN NaN ... 0.7 \n", - "98 None 0.03 0.3 NaN NaN ... 0.05 \n", + "95 None 0.05 0.95 NaN NaN ... 0.65 \n", + "96 None 0.97 0.85 NaN NaN ... 0.85 \n", + "97 None 0.666 0.8 NaN NaN ... 0.8 \n", + "98 None 0.03 0.3 NaN NaN ... 0.1 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.85 NaN NaN 0.15 NaN NaN \n", - "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.25 NaN 0.85 0.85 NaN \n", - "98 0.05 0.03 NaN 0.15 0.05 NaN \n", + "95 0.15 NaN NaN 0.15 NaN NaN \n", + "96 0.9 NaN NaN 0.9 NaN NaN \n", + "97 0.85 0.3 NaN 0.85 0.85 NaN \n", + "98 0.05 0.1 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -3371,9 +3371,27 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'calculate_peer_score' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1245\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;66;03m# options = row['options_parsed'] if 'options_parsed' in row else row['options']\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;66;03m# # Get the forecasts\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;66;03m# bot_pmf_raw = row[bot_col]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \n\u001b[1;32m 1243\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1245\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_peer_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1248\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_peer_score(row, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m'\u001b[39m, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1245\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;66;03m# options = row['options_parsed'] if 'options_parsed' in row else row['options']\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;66;03m# # Get the forecasts\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;66;03m# bot_pmf_raw = row[bot_col]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \n\u001b[1;32m 1243\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1245\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_peer_score\u001b[49m(row, bot, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1248\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_peer_score(row, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m'\u001b[39m, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'calculate_peer_score' is not defined" + ] + } + ], "source": [ "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", "# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention." @@ -3381,7 +3399,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -3442,9 +3460,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3466,9 +3484,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3490,9 +3508,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3514,9 +3532,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3538,9 +3556,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3584,11 +3602,11 @@ "13 [0.05,0.45,0.45,0.05] 0.643473 2.597381 1.762901 \n", "\n", " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "0 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "3 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "6 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "9 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "13 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "0 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "3 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "6 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "9 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "13 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", "\n", " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", "0 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", @@ -3661,9 +3679,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3685,9 +3703,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3709,9 +3727,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3733,9 +3751,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3757,9 +3775,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3803,11 +3821,11 @@ "92 [0.001,0.359,0.55,0.08,0.01] 0.643473 2.597381 1.762901 \n", "\n", " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "81 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "82 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "83 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "91 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", - "92 ... 20.222117 6.738936 20.60531 -2.987997 9.735149 \n", + "81 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "82 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "83 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "91 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", + "92 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", "\n", " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", "81 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", @@ -3880,9 +3898,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3904,9 +3922,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3928,9 +3946,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3952,9 +3970,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -3976,9 +3994,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4001,18 +4019,18 @@ "16 33876 33751 no 1.0 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "2 None 0.013 0.643473 2.597381 1.762901 ... 20.222117 \n", - "5 None 0.45 0.643473 2.597381 1.762901 ... 20.222117 \n", - "8 None 0.95 0.643473 2.597381 1.762901 ... 20.222117 \n", - "12 None 0.9 0.643473 2.597381 1.762901 ... 20.222117 \n", - "16 None 0.058 0.643473 2.597381 1.762901 ... 20.222117 \n", + "2 None 0.013 0.643473 2.597381 1.762901 ... 16.605891 \n", + "5 None 0.45 0.643473 2.597381 1.762901 ... 16.605891 \n", + "8 None 0.95 0.643473 2.597381 1.762901 ... 16.605891 \n", + "12 None 0.9 0.643473 2.597381 1.762901 ... 16.605891 \n", + "16 None 0.058 0.643473 2.597381 1.762901 ... 16.605891 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "5 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "8 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "12 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "16 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "2 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "5 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "8 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "12 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "16 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "2 -2.173212 2.411469 14.267308 2.372721 \n", @@ -4085,9 +4103,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4109,9 +4127,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4133,9 +4151,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4157,9 +4175,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4181,9 +4199,9 @@ " 2.597381\n", " 1.762901\n", " ...\n", - " 20.222117\n", - " 6.738936\n", - " 20.60531\n", + " 16.605891\n", + " 6.665593\n", + " 18.102498\n", " -2.987997\n", " 9.735149\n", " 3.537037\n", @@ -4206,18 +4224,18 @@ "98 35387 35367 no 0.85 binary \n", "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.643473 2.597381 1.762901 ... 20.222117 \n", - "95 None 0.05 0.643473 2.597381 1.762901 ... 20.222117 \n", - "96 None 0.97 0.643473 2.597381 1.762901 ... 20.222117 \n", - "97 None 0.666 0.643473 2.597381 1.762901 ... 20.222117 \n", - "98 None 0.03 0.643473 2.597381 1.762901 ... 20.222117 \n", + "94 None 0.95 0.643473 2.597381 1.762901 ... 16.605891 \n", + "95 None 0.05 0.643473 2.597381 1.762901 ... 16.605891 \n", + "96 None 0.97 0.643473 2.597381 1.762901 ... 16.605891 \n", + "97 None 0.666 0.643473 2.597381 1.762901 ... 16.605891 \n", + "98 None 0.03 0.643473 2.597381 1.762901 ... 16.605891 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "95 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "96 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "97 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", - "98 6.738936 20.60531 -2.987997 9.735149 3.537037 \n", + "94 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "95 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "96 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "97 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", + "98 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 -2.173212 2.411469 14.267308 2.372721 \n", @@ -4241,7 +4259,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 197, "metadata": {}, "outputs": [ { @@ -4282,13 +4300,13 @@ " \n", " \n", " 2\n", - " bot_median\n", - " 3347.538115\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 3\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3060.137114\n", " \n", " \n", " 4\n", @@ -4518,8 +4536,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3347.538115\n", - "3 metac-o1-preview 3162.155445\n", + "2 metac-o1-preview 3162.155445\n", + "3 bot_median 3060.137114\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -4566,7 +4584,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 118, + "execution_count": 197, "metadata": {}, "output_type": "execute_result" } @@ -4577,7 +4595,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 198, "metadata": {}, "outputs": [ { @@ -4586,13 +4604,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", - "mean metac-o1 forecast on questions that resolved no: 26.0%\n" + "mean metac-o1 forecast on questions that resolved yes: 71.0%\n", + "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -4661,7 +4679,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 199, "metadata": {}, "outputs": [ { @@ -4718,7 +4736,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 200, "metadata": {}, "outputs": [], "source": [ @@ -4731,7 +4749,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 201, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4794,7 +4812,7 @@ " 3\n", " 4\n", " bot_median\n", - " 2500.508853\n", + " 2273.115089\n", " 97\n", " 93.10\n", " \n", @@ -5151,7 +5169,7 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2500.508853 97 \n", + "3 4 bot_median 2273.115089 97 \n", "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", @@ -5246,7 +5264,7 @@ "46 52.10 " ] }, - "execution_count": 122, + "execution_count": 201, "metadata": {}, "output_type": "execute_result" } @@ -5315,7 +5333,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -5397,17 +5415,17 @@ " \n", " \n", " bot_median\n", - " 2500.5\n", + " 2273.1\n", " 93.1\n", - " 26.9\n", - " 62.117260\n", - " 6.437800\n", - " 4.171971\n", + " 24.4\n", + " 58.936587\n", + " 6.108156\n", + " 3.997253\n", " 1.985277\n", - " 39.6\n", - " 14.1\n", - " 0.999966\n", - " 0.000068\n", + " 36.5\n", + " 12.3\n", + " 0.999935\n", + " 0.000129\n", " \n", " \n", " acm_bot\n", @@ -6020,7 +6038,7 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2500.5 93.1 26.9 62.117260 \n", + "bot_median 2273.1 93.1 24.4 58.936587 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", @@ -6069,7 +6087,7 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.437800 4.171971 1.985277 39.6 \n", + "bot_median 6.108156 3.997253 1.985277 36.5 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", @@ -6118,7 +6136,7 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 14.1 0.999966 0.000068 \n", + "bot_median 12.3 0.999935 0.000129 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", @@ -6164,7 +6182,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 123, + "execution_count": 202, "metadata": {}, "output_type": "execute_result" } @@ -6180,7 +6198,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 203, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -6256,29 +6274,15 @@ " \n", " \n", " metac-perplexity\n", - " 1957.5\n", - " 95.0\n", - " 20.6\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " inf\n", - " 1.98475\n", - " 20.6\n", - " 20.6\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", - " metac-o1\n", - " 1921.1\n", + " 1719.7\n", " 95.0\n", - " 20.2\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " inf\n", + " 18.1\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 4.940951e+16\n", " 1.98475\n", - " 20.2\n", - " 20.2\n", + " 18.1\n", + " 18.1\n", " 1.0\n", " 0.000000\n", " \n", @@ -6298,15 +6302,43 @@ " \n", " \n", " bot_median\n", - " 1655.0\n", + " 1610.4\n", " 95.0\n", - " 17.4\n", + " 17.0\n", " 3.570999e-15\n", " 3.663768e-16\n", - " 4.755070e+16\n", + " 4.626691e+16\n", " 1.98475\n", - " 17.4\n", - " 17.4\n", + " 17.0\n", + " 17.0\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", + " metac-o1\n", + " 1577.6\n", + " 95.0\n", + " 16.6\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 4.532462e+16\n", + " 1.98475\n", + " 16.6\n", + " 16.6\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-20240620\n", + " 1405.9\n", + " 95.0\n", + " 14.8\n", + " 3.570999e-15\n", + " 3.663768e-16\n", + " 4.039354e+16\n", + " 1.98475\n", + " 14.8\n", + " 14.8\n", " 1.0\n", " 0.000000\n", " \n", @@ -6353,13 +6385,13 @@ " 0.000000\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " 1235.2\n", + " metac-exa\n", + " 1233.6\n", " 95.0\n", " 13.0\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 7.097519e+16\n", + " 7.088710e+16\n", " 1.98475\n", " 13.0\n", " 13.0\n", @@ -6367,72 +6399,44 @@ " 0.000000\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " 1180.5\n", + " GreeneiBot2\n", + " 1163.2\n", " 95.0\n", - " 12.4\n", + " 12.2\n", " 0.000000e+00\n", " 0.000000e+00\n", " inf\n", " 1.98475\n", - " 12.4\n", - " 12.4\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", - " metac-deepseek-r1\n", - " 1166.0\n", - " 95.0\n", - " 12.3\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 6.700213e+16\n", - " 1.98475\n", - " 12.3\n", - " 12.3\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", - " metac-Llama-3.1\n", - " 1154.9\n", - " 95.0\n", - " 12.2\n", - " 3.570999e-15\n", - " 3.663768e-16\n", - " 3.318128e+16\n", - " 1.98475\n", " 12.2\n", " 12.2\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " GreeneiBot2\n", - " 1119.2\n", + " NextWorldLab\n", + " 1050.3\n", " 95.0\n", - " 11.8\n", + " 11.1\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 6.431060e+16\n", + " 6.035038e+16\n", " 1.98475\n", - " 11.8\n", - " 11.8\n", + " 11.1\n", + " 11.1\n", " 1.0\n", " 0.000000\n", " \n", " \n", - " NextWorldLab\n", - " 1050.3\n", + " metac-Llama-3.1\n", + " 997.0\n", " 95.0\n", - " 11.1\n", + " 10.5\n", " 1.785500e-15\n", " 1.831884e-16\n", - " 6.035038e+16\n", + " 5.728816e+16\n", " 1.98475\n", - " 11.1\n", - " 11.1\n", + " 10.5\n", + " 10.5\n", " 1.0\n", " 0.000000\n", " \n", @@ -6465,16 +6469,16 @@ " 0.000000\n", " \n", " \n", - " metac-grok-2-1212\n", - " 932.3\n", + " metac-claude-3-5-sonnet-latest\n", + " 949.9\n", " 95.0\n", - " 9.8\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 5.357005e+16\n", + " 10.0\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " inf\n", " 1.98475\n", - " 9.8\n", - " 9.8\n", + " 10.0\n", + " 10.0\n", " 1.0\n", " 0.000000\n", " \n", @@ -6493,20 +6497,6 @@ " 0.000000\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " 910.2\n", - " 95.0\n", - " 9.6\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 5.230332e+16\n", - " 1.98475\n", - " 9.6\n", - " 9.6\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " annabot\n", " 854.4\n", " 95.0\n", @@ -6521,20 +6511,6 @@ " 0.000000\n", " \n", " \n", - " metac-exa\n", - " 836.7\n", - " 95.0\n", - " 8.8\n", - " 1.785500e-15\n", - " 1.831884e-16\n", - " 4.808056e+16\n", - " 1.98475\n", - " 8.8\n", - " 8.8\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " VeritasAI\n", " 802.0\n", " 95.0\n", @@ -6549,6 +6525,20 @@ " 0.000000\n", " \n", " \n", + " metac-grok-2-1212\n", + " 775.1\n", + " 95.0\n", + " 8.2\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " inf\n", + " 1.98475\n", + " 8.2\n", + " 8.2\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " laylaps\n", " 723.4\n", " 95.0\n", @@ -6563,13 +6553,27 @@ " 0.000000\n", " \n", " \n", + " metac-Gemini-Exp-1206\n", + " 701.9\n", + " 95.0\n", + " 7.4\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 8.065986e+16\n", + " 1.98475\n", + " 7.4\n", + " 7.4\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " metac-o1-preview\n", - " 640.2\n", + " 633.2\n", " 95.0\n", " 6.7\n", " 8.927498e-16\n", " 9.159420e-17\n", - " 7.357383e+16\n", + " 7.277309e+16\n", " 1.98475\n", " 6.7\n", " 6.7\n", @@ -6591,6 +6595,20 @@ " 0.000000\n", " \n", " \n", + " metac-deepseek-r1\n", + " 545.5\n", + " 95.0\n", + " 5.7\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 6.268723e+16\n", + " 1.98475\n", + " 5.7\n", + " 5.7\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " MWG\n", " 520.8\n", " 95.0\n", @@ -6619,6 +6637,20 @@ " 0.000000\n", " \n", " \n", + " metac-gpt-4o\n", + " 451.6\n", + " 95.0\n", + " 4.8\n", + " 8.927498e-16\n", + " 9.159420e-17\n", + " 5.190358e+16\n", + " 1.98475\n", + " 4.8\n", + " 4.8\n", + " 1.0\n", + " 0.000000\n", + " \n", + " \n", " pgodzinai\n", " 336.0\n", " 95.0\n", @@ -6647,20 +6679,6 @@ " 0.000000\n", " \n", " \n", - " metac-gpt-4o\n", - " 280.3\n", - " 95.0\n", - " 3.0\n", - " 8.927498e-16\n", - " 9.159420e-17\n", - " 3.221541e+16\n", - " 1.98475\n", - " 3.0\n", - " 3.0\n", - " 1.0\n", - " 0.000000\n", - " \n", - " \n", " InstitutPelFutur\n", " 256.0\n", " 95.0\n", @@ -6788,15 +6806,15 @@ " \n", " \n", " RPM_bot\n", - " 71.4\n", + " 118.6\n", " 95.0\n", - " 0.8\n", - " 1.115937e-16\n", - " 1.144927e-17\n", - " 6.560693e+16\n", + " 1.2\n", + " 4.463749e-16\n", + " 4.579710e-17\n", + " 2.726486e+16\n", " 1.98475\n", - " 0.8\n", - " 0.8\n", + " 1.2\n", + " 1.2\n", " 1.0\n", " 0.000000\n", " \n", @@ -6904,35 +6922,35 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev \\\n", - "metac-perplexity 1957.5 95.0 20.6 0.000000e+00 \n", - "metac-o1 1921.1 95.0 20.2 0.000000e+00 \n", + "metac-perplexity 1719.7 95.0 18.1 3.570999e-15 \n", "acm_bot 1680.6 95.0 17.7 3.570999e-15 \n", - "bot_median 1655.0 95.0 17.4 3.570999e-15 \n", + "bot_median 1610.4 95.0 17.0 3.570999e-15 \n", + "metac-o1 1577.6 95.0 16.6 3.570999e-15 \n", + "metac-claude-3-5-sonnet-20240620 1405.9 95.0 14.8 3.570999e-15 \n", "manticAI 1378.2 95.0 14.5 0.000000e+00 \n", "twsummerbot 1355.4 95.0 14.3 1.785500e-15 \n", "jkraybill_bot 1354.5 95.0 14.3 1.785500e-15 \n", - "metac-claude-3-5-sonnet-20240620 1235.2 95.0 13.0 1.785500e-15 \n", - "metac-claude-3-5-sonnet-latest 1180.5 95.0 12.4 0.000000e+00 \n", - "metac-deepseek-r1 1166.0 95.0 12.3 1.785500e-15 \n", - "metac-Llama-3.1 1154.9 95.0 12.2 3.570999e-15 \n", - "GreeneiBot2 1119.2 95.0 11.8 1.785500e-15 \n", + "metac-exa 1233.6 95.0 13.0 1.785500e-15 \n", + "GreeneiBot2 1163.2 95.0 12.2 0.000000e+00 \n", "NextWorldLab 1050.3 95.0 11.1 1.785500e-15 \n", + "metac-Llama-3.1 997.0 95.0 10.5 1.785500e-15 \n", "Grizeu_Bot 966.4 95.0 10.2 0.000000e+00 \n", "SynapseSeer 964.7 95.0 10.2 1.785500e-15 \n", - "metac-grok-2-1212 932.3 95.0 9.8 1.785500e-15 \n", + "metac-claude-3-5-sonnet-latest 949.9 95.0 10.0 0.000000e+00 \n", "mmBot 924.8 95.0 9.7 0.000000e+00 \n", - "metac-Gemini-Exp-1206 910.2 95.0 9.6 1.785500e-15 \n", "annabot 854.4 95.0 9.0 1.785500e-15 \n", - "metac-exa 836.7 95.0 8.8 1.785500e-15 \n", "VeritasAI 802.0 95.0 8.4 1.785500e-15 \n", + "metac-grok-2-1212 775.1 95.0 8.2 0.000000e+00 \n", "laylaps 723.4 95.0 7.6 8.927498e-16 \n", - "metac-o1-preview 640.2 95.0 6.7 8.927498e-16 \n", + "metac-Gemini-Exp-1206 701.9 95.0 7.4 8.927498e-16 \n", + "metac-o1-preview 633.2 95.0 6.7 8.927498e-16 \n", "cookics_bot_TEST 596.4 95.0 6.3 0.000000e+00 \n", + "metac-deepseek-r1 545.5 95.0 5.7 8.927498e-16 \n", "MWG 520.8 95.0 5.5 8.927498e-16 \n", "ajf-bot 481.2 95.0 5.1 1.785500e-15 \n", + "metac-gpt-4o 451.6 95.0 4.8 8.927498e-16 \n", "pgodzinai 336.0 95.0 3.5 8.927498e-16 \n", "KevinTestBot 314.5 95.0 3.3 8.927498e-16 \n", - "metac-gpt-4o 280.3 95.0 3.0 8.927498e-16 \n", "InstitutPelFutur 256.0 95.0 2.7 8.927498e-16 \n", "Bot_Pepa 246.8 95.0 2.6 0.000000e+00 \n", "CumulativeBot 241.1 95.0 2.5 4.463749e-16 \n", @@ -6942,7 +6960,7 @@ "bean_bot 200.0 95.0 2.1 0.000000e+00 \n", "X_bot 181.4 95.0 1.9 0.000000e+00 \n", "CatrachoCaster 167.5 95.0 1.8 4.463749e-16 \n", - "RPM_bot 71.4 95.0 0.8 1.115937e-16 \n", + "RPM_bot 118.6 95.0 1.2 4.463749e-16 \n", "4Shadower 61.1 95.0 0.6 2.231875e-16 \n", "krm-bot 60.8 95.0 0.6 1.115937e-16 \n", "andrewsiah 0.0 95.0 0.0 0.000000e+00 \n", @@ -6952,35 +6970,35 @@ "minefrac1 -283.9 95.0 -3.0 4.463749e-16 \n", "\n", " std_err t_stat t_crit \\\n", - "metac-perplexity 0.000000e+00 inf 1.98475 \n", - "metac-o1 0.000000e+00 inf 1.98475 \n", + "metac-perplexity 3.663768e-16 4.940951e+16 1.98475 \n", "acm_bot 3.663768e-16 4.828449e+16 1.98475 \n", - "bot_median 3.663768e-16 4.755070e+16 1.98475 \n", + "bot_median 3.663768e-16 4.626691e+16 1.98475 \n", + "metac-o1 3.663768e-16 4.532462e+16 1.98475 \n", + "metac-claude-3-5-sonnet-20240620 3.663768e-16 4.039354e+16 1.98475 \n", "manticAI 0.000000e+00 inf 1.98475 \n", "twsummerbot 1.831884e-16 7.788325e+16 1.98475 \n", "jkraybill_bot 1.831884e-16 7.783286e+16 1.98475 \n", - "metac-claude-3-5-sonnet-20240620 1.831884e-16 7.097519e+16 1.98475 \n", - "metac-claude-3-5-sonnet-latest 0.000000e+00 inf 1.98475 \n", - "metac-deepseek-r1 1.831884e-16 6.700213e+16 1.98475 \n", - "metac-Llama-3.1 3.663768e-16 3.318128e+16 1.98475 \n", - "GreeneiBot2 1.831884e-16 6.431060e+16 1.98475 \n", + "metac-exa 1.831884e-16 7.088710e+16 1.98475 \n", + "GreeneiBot2 0.000000e+00 inf 1.98475 \n", "NextWorldLab 1.831884e-16 6.035038e+16 1.98475 \n", + "metac-Llama-3.1 1.831884e-16 5.728816e+16 1.98475 \n", "Grizeu_Bot 0.000000e+00 inf 1.98475 \n", "SynapseSeer 1.831884e-16 5.543440e+16 1.98475 \n", - "metac-grok-2-1212 1.831884e-16 5.357005e+16 1.98475 \n", + "metac-claude-3-5-sonnet-latest 0.000000e+00 inf 1.98475 \n", "mmBot 0.000000e+00 inf 1.98475 \n", - "metac-Gemini-Exp-1206 1.831884e-16 5.230332e+16 1.98475 \n", "annabot 1.831884e-16 4.909363e+16 1.98475 \n", - "metac-exa 1.831884e-16 4.808056e+16 1.98475 \n", "VeritasAI 1.831884e-16 4.608352e+16 1.98475 \n", + "metac-grok-2-1212 0.000000e+00 inf 1.98475 \n", "laylaps 9.159420e-17 8.313180e+16 1.98475 \n", - "metac-o1-preview 9.159420e-17 7.357383e+16 1.98475 \n", + "metac-Gemini-Exp-1206 9.159420e-17 8.065986e+16 1.98475 \n", + "metac-o1-preview 9.159420e-17 7.277309e+16 1.98475 \n", "cookics_bot_TEST 0.000000e+00 inf 1.98475 \n", + "metac-deepseek-r1 9.159420e-17 6.268723e+16 1.98475 \n", "MWG 9.159420e-17 5.985647e+16 1.98475 \n", "ajf-bot 1.831884e-16 2.764898e+16 1.98475 \n", + "metac-gpt-4o 9.159420e-17 5.190358e+16 1.98475 \n", "pgodzinai 9.159420e-17 3.861639e+16 1.98475 \n", "KevinTestBot 9.159420e-17 3.614852e+16 1.98475 \n", - "metac-gpt-4o 9.159420e-17 3.221541e+16 1.98475 \n", "InstitutPelFutur 9.159420e-17 2.941623e+16 1.98475 \n", "Bot_Pepa 0.000000e+00 inf 1.98475 \n", "CumulativeBot 4.579710e-17 5.542703e+16 1.98475 \n", @@ -6990,7 +7008,7 @@ "bean_bot 0.000000e+00 inf 1.98475 \n", "X_bot 0.000000e+00 inf 1.98475 \n", "CatrachoCaster 4.579710e-17 3.849373e+16 1.98475 \n", - "RPM_bot 1.144927e-17 6.560693e+16 1.98475 \n", + "RPM_bot 4.579710e-17 2.726486e+16 1.98475 \n", "4Shadower 2.289855e-17 2.810106e+16 1.98475 \n", "krm-bot 1.144927e-17 5.586129e+16 1.98475 \n", "andrewsiah 0.000000e+00 NaN 1.98475 \n", @@ -7000,35 +7018,35 @@ "minefrac1 4.579710e-17 -6.524424e+16 1.98475 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "metac-perplexity 20.6 20.6 1.0 0.000000 \n", - "metac-o1 20.2 20.2 1.0 0.000000 \n", + "metac-perplexity 18.1 18.1 1.0 0.000000 \n", "acm_bot 17.7 17.7 1.0 0.000000 \n", - "bot_median 17.4 17.4 1.0 0.000000 \n", + "bot_median 17.0 17.0 1.0 0.000000 \n", + "metac-o1 16.6 16.6 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-20240620 14.8 14.8 1.0 0.000000 \n", "manticAI 14.5 14.5 1.0 0.000000 \n", "twsummerbot 14.3 14.3 1.0 0.000000 \n", "jkraybill_bot 14.3 14.3 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-20240620 13.0 13.0 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-latest 12.4 12.4 1.0 0.000000 \n", - "metac-deepseek-r1 12.3 12.3 1.0 0.000000 \n", - "metac-Llama-3.1 12.2 12.2 1.0 0.000000 \n", - "GreeneiBot2 11.8 11.8 1.0 0.000000 \n", + "metac-exa 13.0 13.0 1.0 0.000000 \n", + "GreeneiBot2 12.2 12.2 1.0 0.000000 \n", "NextWorldLab 11.1 11.1 1.0 0.000000 \n", + "metac-Llama-3.1 10.5 10.5 1.0 0.000000 \n", "Grizeu_Bot 10.2 10.2 1.0 0.000000 \n", "SynapseSeer 10.2 10.2 1.0 0.000000 \n", - "metac-grok-2-1212 9.8 9.8 1.0 0.000000 \n", + "metac-claude-3-5-sonnet-latest 10.0 10.0 1.0 0.000000 \n", "mmBot 9.7 9.7 1.0 0.000000 \n", - "metac-Gemini-Exp-1206 9.6 9.6 1.0 0.000000 \n", "annabot 9.0 9.0 1.0 0.000000 \n", - "metac-exa 8.8 8.8 1.0 0.000000 \n", "VeritasAI 8.4 8.4 1.0 0.000000 \n", + "metac-grok-2-1212 8.2 8.2 1.0 0.000000 \n", "laylaps 7.6 7.6 1.0 0.000000 \n", + "metac-Gemini-Exp-1206 7.4 7.4 1.0 0.000000 \n", "metac-o1-preview 6.7 6.7 1.0 0.000000 \n", "cookics_bot_TEST 6.3 6.3 1.0 0.000000 \n", + "metac-deepseek-r1 5.7 5.7 1.0 0.000000 \n", "MWG 5.5 5.5 1.0 0.000000 \n", "ajf-bot 5.1 5.1 1.0 0.000000 \n", + "metac-gpt-4o 4.8 4.8 1.0 0.000000 \n", "pgodzinai 3.5 3.5 1.0 0.000000 \n", "KevinTestBot 3.3 3.3 1.0 0.000000 \n", - "metac-gpt-4o 3.0 3.0 1.0 0.000000 \n", "InstitutPelFutur 2.7 2.7 1.0 0.000000 \n", "Bot_Pepa 2.6 2.6 1.0 0.000000 \n", "CumulativeBot 2.5 2.5 1.0 0.000000 \n", @@ -7038,7 +7056,7 @@ "bean_bot 2.1 2.1 1.0 0.000000 \n", "X_bot 1.9 1.9 1.0 0.000000 \n", "CatrachoCaster 1.8 1.8 1.0 0.000000 \n", - "RPM_bot 0.8 0.8 1.0 0.000000 \n", + "RPM_bot 1.2 1.2 1.0 0.000000 \n", "4Shadower 0.6 0.6 1.0 0.000000 \n", "krm-bot 0.6 0.6 1.0 0.000000 \n", "andrewsiah 0.0 0.0 NaN NA \n", @@ -7048,7 +7066,7 @@ "minefrac1 -3.0 -3.0 0.0 0.000000 " ] }, - "execution_count": 124, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } @@ -7074,7 +7092,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ @@ -7084,7 +7102,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 205, "metadata": { "cellView": "form", "colab": { @@ -7998,7 +8016,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 126, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } @@ -8037,7 +8055,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 206, "metadata": {}, "outputs": [], "source": [ @@ -8047,7 +8065,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 207, "metadata": {}, "outputs": [ { @@ -8252,7 +8270,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 128, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } @@ -8263,7 +8281,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 208, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -8325,7 +8343,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 209, "metadata": {}, "outputs": [ { @@ -8747,7 +8765,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 210, "metadata": { "cellView": "form", "colab": { @@ -8797,363 +8815,363 @@ " \n", " \n", " metac-o1\n", - " 6.2\n", + " 6.1\n", " 7.4\n", " 9.7\n", - " 11.8\n", - " 13.2\n", + " 12.0\n", + " 13.1\n", " \n", " \n", " metac-o1-preview\n", - " 3.9\n", - " 5.4\n", - " 8.4\n", - " 11.4\n", + " 3.5\n", + " 5.0\n", + " 8.2\n", + " 11.1\n", " 12.8\n", " \n", " \n", " manticAI\n", - " 0.1\n", - " 2.0\n", + " 0.3\n", + " 2.1\n", " 5.4\n", " 8.6\n", - " 10.2\n", + " 10.4\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.5\n", - " 2.0\n", + " 0.7\n", + " 2.2\n", " 5.0\n", - " 7.9\n", - " 9.5\n", + " 7.8\n", + " 9.2\n", " \n", " \n", " acm_bot\n", - " 0.1\n", - " 1.8\n", - " 4.5\n", - " 7.5\n", - " 8.8\n", + " -0.1\n", + " 1.4\n", + " 4.6\n", + " 7.6\n", + " 9.2\n", " \n", " \n", " metac-perplexity\n", - " -2.2\n", - " 0.2\n", + " -1.4\n", + " 0.5\n", " 4.1\n", - " 7.8\n", + " 7.9\n", " 9.5\n", " \n", " \n", " GreeneiBot2\n", - " -0.8\n", + " -1.1\n", " 0.7\n", - " 4.0\n", + " 3.9\n", " 7.2\n", - " 8.7\n", + " 8.8\n", " \n", " \n", " twsummerbot\n", - " -0.1\n", + " 0.1\n", " 1.5\n", " 3.9\n", - " 6.3\n", - " 7.7\n", + " 6.1\n", + " 7.4\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.0\n", - " 1.0\n", + " 0.1\n", + " 1.1\n", " 3.0\n", - " 4.9\n", - " 5.8\n", + " 5.1\n", + " 6.1\n", " \n", " \n", " pgodzinai\n", - " -3.5\n", - " -1.1\n", - " 2.8\n", - " 6.8\n", - " 8.9\n", + " -4.2\n", + " -1.3\n", + " 2.9\n", + " 7.0\n", + " 9.0\n", + " \n", + " \n", + " CumulativeBot\n", + " -0.2\n", + " 0.9\n", + " 2.6\n", + " 4.4\n", + " 5.2\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.4\n", - " 0.0\n", - " 2.7\n", + " -1.2\n", + " 0.1\n", + " 2.6\n", " 5.1\n", - " 6.2\n", + " 6.1\n", " \n", " \n", " SynapseSeer\n", - " 0.3\n", - " 1.0\n", - " 2.6\n", + " 0.1\n", + " 0.9\n", + " 2.4\n", " 4.0\n", - " 5.0\n", + " 4.7\n", " \n", " \n", - " CumulativeBot\n", - " -0.3\n", - " 0.7\n", - " 2.5\n", - " 4.4\n", - " 5.4\n", + " metac-exa\n", + " -5.1\n", + " -2.5\n", + " 1.8\n", + " 5.7\n", + " 7.9\n", " \n", " \n", " jkraybill_bot\n", - " -3.7\n", - " -1.8\n", - " 1.8\n", + " -3.6\n", + " -1.5\n", + " 1.7\n", " 4.9\n", " 6.4\n", " \n", " \n", - " metac-exa\n", - " -5.0\n", - " -2.4\n", - " 1.5\n", - " 5.4\n", - " 7.4\n", - " \n", - " \n", " metac-deepseek-r1\n", - " -1.7\n", - " -0.6\n", - " 1.4\n", + " -2.1\n", + " -0.8\n", + " 1.2\n", " 3.4\n", - " 4.5\n", + " 4.4\n", " \n", " \n", " MWG\n", " -1.6\n", " -0.8\n", - " 0.7\n", - " 2.1\n", + " 0.6\n", + " 2.2\n", " 2.8\n", " \n", " \n", " pianobot\n", - " -1.3\n", - " -0.8\n", + " -1.1\n", + " -0.7\n", " 0.0\n", " 0.7\n", - " 1.1\n", - " \n", - " \n", - " cobyj-bot\n", - " -1.4\n", - " -0.9\n", - " -0.0\n", - " 0.9\n", - " 1.4\n", + " 1.0\n", " \n", " \n", " andrewsiah\n", " -0.9\n", - " -0.6\n", + " -0.5\n", " -0.0\n", - " 0.5\n", + " 0.6\n", " 0.9\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", - " annabot\n", - " -3.2\n", - " -2.3\n", - " -0.4\n", - " 1.3\n", - " 2.0\n", + " cobyj-bot\n", + " -1.5\n", + " -0.9\n", + " -0.1\n", + " 0.8\n", + " 1.4\n", " \n", " \n", - " bean_bot\n", - " -3.2\n", - " -2.3\n", + " KevinTestBot\n", + " -3.9\n", + " -2.8\n", " -0.4\n", + " 1.4\n", + " 2.4\n", + " \n", + " \n", + " annabot\n", + " -3.4\n", + " -2.5\n", + " -0.5\n", " 1.2\n", - " 1.9\n", + " 2.1\n", " \n", " \n", - " KevinTestBot\n", - " -3.9\n", - " -2.7\n", - " -0.6\n", - " 1.3\n", - " 2.4\n", + " bean_bot\n", + " -3.4\n", + " -2.4\n", + " -0.5\n", + " 1.1\n", + " 2.0\n", " \n", " \n", " CatrachoCaster\n", - " -2.3\n", - " -1.8\n", - " -0.8\n", + " -2.2\n", + " -1.7\n", + " -0.7\n", " 0.2\n", - " 0.8\n", + " 0.7\n", " \n", " \n", " jonahsingerbot\n", " -3.0\n", - " -2.1\n", + " -2.2\n", " -0.8\n", " 0.4\n", - " 1.1\n", + " 1.0\n", " \n", " \n", " krm-bot\n", " -3.7\n", - " -2.8\n", + " -2.7\n", " -1.0\n", - " 0.6\n", - " 1.7\n", + " 0.7\n", + " 1.5\n", " \n", " \n", " ProfessorSP\n", - " -4.1\n", + " -4.5\n", " -3.2\n", " -1.1\n", - " 1.1\n", - " 2.3\n", + " 1.0\n", + " 1.9\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.6\n", - " -4.7\n", - " -1.4\n", - " 1.8\n", - " 3.5\n", + " -6.2\n", + " -4.9\n", + " -1.3\n", + " 2.0\n", + " 3.6\n", " \n", " \n", " mmBot\n", - " -7.2\n", - " -5.5\n", + " -7.4\n", + " -5.3\n", " -1.5\n", " 2.2\n", " 4.0\n", " \n", " \n", " 4Shadower\n", - " -4.7\n", - " -3.8\n", - " -1.7\n", - " 0.2\n", - " 1.3\n", + " -4.6\n", + " -3.7\n", + " -1.6\n", + " 0.4\n", + " 1.2\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.5\n", - " -4.5\n", - " -1.8\n", - " 0.9\n", - " 2.4\n", + " RPM_bot\n", + " -4.9\n", + " -3.7\n", + " -1.9\n", + " -0.6\n", + " -0.0\n", " \n", " \n", " swingswish\n", - " -5.4\n", + " -5.3\n", " -4.0\n", " -1.9\n", - " -0.2\n", - " 0.6\n", + " -0.1\n", + " 0.8\n", " \n", " \n", - " RPM_bot\n", - " -4.8\n", - " -3.8\n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.2\n", + " -4.9\n", " -2.1\n", - " -0.7\n", - " -0.1\n", + " 0.8\n", + " 2.2\n", " \n", " \n", " InstitutPelFutur\n", - " -9.0\n", - " -6.4\n", - " -2.5\n", - " 1.6\n", + " -9.1\n", + " -6.5\n", + " -2.4\n", + " 1.9\n", " 3.6\n", " \n", " \n", " wunderplumb\n", - " -6.4\n", - " -4.9\n", - " -2.7\n", + " -5.9\n", + " -4.8\n", + " -2.5\n", " -0.2\n", - " 0.8\n", + " 0.9\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.7\n", - " -5.3\n", - " -2.7\n", + " -6.9\n", + " -5.2\n", + " -2.8\n", " 0.0\n", - " 1.7\n", + " 1.5\n", " \n", " \n", " NextWorldLab\n", - " -8.3\n", - " -6.6\n", - " -3.6\n", - " -0.7\n", - " 1.2\n", + " -8.6\n", + " -6.9\n", + " -3.7\n", + " -0.5\n", + " 1.1\n", " \n", " \n", " Bot_Pepa\n", - " -7.2\n", - " -5.9\n", - " -4.0\n", - " -2.0\n", - " -1.3\n", + " -7.0\n", + " -6.0\n", + " -3.9\n", + " -1.9\n", + " -0.9\n", " \n", " \n", " laylaps\n", - " -10.3\n", - " -8.0\n", - " -4.0\n", + " -9.6\n", + " -7.6\n", + " -3.9\n", " -0.2\n", - " 2.1\n", + " 1.7\n", " \n", " \n", " VeritasAI\n", - " -7.7\n", - " -6.6\n", - " -4.2\n", - " -1.9\n", - " -0.6\n", + " -7.9\n", + " -6.7\n", + " -4.3\n", + " -2.0\n", + " -0.7\n", " \n", " \n", " minefrac1\n", - " -7.8\n", - " -6.7\n", - " -4.8\n", - " -2.8\n", - " -1.6\n", + " -7.9\n", + " -6.9\n", + " -4.7\n", + " -2.6\n", + " -1.4\n", " \n", " \n", " Grizeu_Bot\n", " -9.2\n", - " -7.7\n", + " -7.6\n", " -4.9\n", - " -2.4\n", + " -2.3\n", " -1.1\n", " \n", " \n", " metac-gpt-4o\n", - " -10.5\n", - " -8.9\n", + " -10.2\n", + " -8.8\n", " -5.8\n", - " -2.8\n", - " -1.3\n", + " -3.1\n", + " -1.5\n", " \n", " \n", " ajf-bot\n", - " -15.6\n", - " -12.8\n", + " -15.2\n", + " -12.9\n", " -8.4\n", - " -4.0\n", - " -1.9\n", + " -4.5\n", + " -2.3\n", " \n", " \n", "\n", @@ -9161,54 +9179,54 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.2 7.4 9.7 11.8 13.2\n", - "metac-o1-preview 3.9 5.4 8.4 11.4 12.8\n", - "manticAI 0.1 2.0 5.4 8.6 10.2\n", - "metac-Gemini-Exp-1206 0.5 2.0 5.0 7.9 9.5\n", - "acm_bot 0.1 1.8 4.5 7.5 8.8\n", - "metac-perplexity -2.2 0.2 4.1 7.8 9.5\n", - "GreeneiBot2 -0.8 0.7 4.0 7.2 8.7\n", - "twsummerbot -0.1 1.5 3.9 6.3 7.7\n", - "cookics_bot_TEST 0.0 1.0 3.0 4.9 5.8\n", - "pgodzinai -3.5 -1.1 2.8 6.8 8.9\n", - "metac-claude-3-5-sonnet-latest -1.4 0.0 2.7 5.1 6.2\n", - "SynapseSeer 0.3 1.0 2.6 4.0 5.0\n", - "CumulativeBot -0.3 0.7 2.5 4.4 5.4\n", - "jkraybill_bot -3.7 -1.8 1.8 4.9 6.4\n", - "metac-exa -5.0 -2.4 1.5 5.4 7.4\n", - "metac-deepseek-r1 -1.7 -0.6 1.4 3.4 4.5\n", - "MWG -1.6 -0.8 0.7 2.1 2.8\n", - "pianobot -1.3 -0.8 0.0 0.7 1.1\n", - "cobyj-bot -1.4 -0.9 -0.0 0.9 1.4\n", - "andrewsiah -0.9 -0.6 -0.0 0.5 0.9\n", - "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", - "annabot -3.2 -2.3 -0.4 1.3 2.0\n", - "bean_bot -3.2 -2.3 -0.4 1.2 1.9\n", - "KevinTestBot -3.9 -2.7 -0.6 1.3 2.4\n", - "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.1 -0.8 0.4 1.1\n", - "krm-bot -3.7 -2.8 -1.0 0.6 1.7\n", - "ProfessorSP -4.1 -3.2 -1.1 1.1 2.3\n", - "metac-grok-2-1212 -6.6 -4.7 -1.4 1.8 3.5\n", - "mmBot -7.2 -5.5 -1.5 2.2 4.0\n", - "4Shadower -4.7 -3.8 -1.7 0.2 1.3\n", - "metac-claude-3-5-sonnet-20240620 -6.5 -4.5 -1.8 0.9 2.4\n", - "swingswish -5.4 -4.0 -1.9 -0.2 0.6\n", - "RPM_bot -4.8 -3.8 -2.1 -0.7 -0.1\n", - "InstitutPelFutur -9.0 -6.4 -2.5 1.6 3.6\n", - "wunderplumb -6.4 -4.9 -2.7 -0.2 0.8\n", - "metac-Llama-3.1 -6.7 -5.3 -2.7 0.0 1.7\n", - "NextWorldLab -8.3 -6.6 -3.6 -0.7 1.2\n", - "Bot_Pepa -7.2 -5.9 -4.0 -2.0 -1.3\n", - "laylaps -10.3 -8.0 -4.0 -0.2 2.1\n", - "VeritasAI -7.7 -6.6 -4.2 -1.9 -0.6\n", - "minefrac1 -7.8 -6.7 -4.8 -2.8 -1.6\n", - "Grizeu_Bot -9.2 -7.7 -4.9 -2.4 -1.1\n", - "metac-gpt-4o -10.5 -8.9 -5.8 -2.8 -1.3\n", - "ajf-bot -15.6 -12.8 -8.4 -4.0 -1.9" + "metac-o1 6.1 7.4 9.7 12.0 13.1\n", + "metac-o1-preview 3.5 5.0 8.2 11.1 12.8\n", + "manticAI 0.3 2.1 5.4 8.6 10.4\n", + "metac-Gemini-Exp-1206 0.7 2.2 5.0 7.8 9.2\n", + "acm_bot -0.1 1.4 4.6 7.6 9.2\n", + "metac-perplexity -1.4 0.5 4.1 7.9 9.5\n", + "GreeneiBot2 -1.1 0.7 3.9 7.2 8.8\n", + "twsummerbot 0.1 1.5 3.9 6.1 7.4\n", + "cookics_bot_TEST 0.1 1.1 3.0 5.1 6.1\n", + "pgodzinai -4.2 -1.3 2.9 7.0 9.0\n", + "CumulativeBot -0.2 0.9 2.6 4.4 5.2\n", + "metac-claude-3-5-sonnet-latest -1.2 0.1 2.6 5.1 6.1\n", + "SynapseSeer 0.1 0.9 2.4 4.0 4.7\n", + "metac-exa -5.1 -2.5 1.8 5.7 7.9\n", + "jkraybill_bot -3.6 -1.5 1.7 4.9 6.4\n", + "metac-deepseek-r1 -2.1 -0.8 1.2 3.4 4.4\n", + "MWG -1.6 -0.8 0.6 2.2 2.8\n", + "pianobot -1.1 -0.7 0.0 0.7 1.0\n", + "andrewsiah -0.9 -0.5 -0.0 0.6 0.9\n", + "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", + "cobyj-bot -1.5 -0.9 -0.1 0.8 1.4\n", + "KevinTestBot -3.9 -2.8 -0.4 1.4 2.4\n", + "annabot -3.4 -2.5 -0.5 1.2 2.1\n", + "bean_bot -3.4 -2.4 -0.5 1.1 2.0\n", + "CatrachoCaster -2.2 -1.7 -0.7 0.2 0.7\n", + "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", + "krm-bot -3.7 -2.7 -1.0 0.7 1.5\n", + "ProfessorSP -4.5 -3.2 -1.1 1.0 1.9\n", + "metac-grok-2-1212 -6.2 -4.9 -1.3 2.0 3.6\n", + "mmBot -7.4 -5.3 -1.5 2.2 4.0\n", + "4Shadower -4.6 -3.7 -1.6 0.4 1.2\n", + "RPM_bot -4.9 -3.7 -1.9 -0.6 -0.0\n", + "swingswish -5.3 -4.0 -1.9 -0.1 0.8\n", + "metac-claude-3-5-sonnet-20240620 -6.2 -4.9 -2.1 0.8 2.2\n", + "InstitutPelFutur -9.1 -6.5 -2.4 1.9 3.6\n", + "wunderplumb -5.9 -4.8 -2.5 -0.2 0.9\n", + "metac-Llama-3.1 -6.9 -5.2 -2.8 0.0 1.5\n", + "NextWorldLab -8.6 -6.9 -3.7 -0.5 1.1\n", + "Bot_Pepa -7.0 -6.0 -3.9 -1.9 -0.9\n", + "laylaps -9.6 -7.6 -3.9 -0.2 1.7\n", + "VeritasAI -7.9 -6.7 -4.3 -2.0 -0.7\n", + "minefrac1 -7.9 -6.9 -4.7 -2.6 -1.4\n", + "Grizeu_Bot -9.2 -7.6 -4.9 -2.3 -1.1\n", + "metac-gpt-4o -10.2 -8.8 -5.8 -3.1 -1.5\n", + "ajf-bot -15.2 -12.9 -8.4 -4.5 -2.3" ] }, - "execution_count": 131, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" } @@ -9231,7 +9249,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 211, "metadata": { "cellView": "form", "colab": { @@ -9285,19 +9303,11 @@ " \n", " \n", " metac-perplexity\n", - " 20.6\n", - " 20.6\n", - " 20.6\n", - " 20.6\n", - " 20.6\n", - " \n", - " \n", - " metac-o1\n", - " 20.2\n", - " 20.2\n", - " 20.2\n", - " 20.2\n", - " 20.2\n", + " 18.1\n", + " 18.1\n", + " 18.1\n", + " 18.1\n", + " 18.1\n", " \n", " \n", " acm_bot\n", @@ -9309,11 +9319,27 @@ " \n", " \n", " bot_median\n", - " 17.4\n", - " 17.4\n", - " 17.4\n", - " 17.4\n", - " 17.4\n", + " 17.0\n", + " 17.0\n", + " 17.0\n", + " 17.0\n", + " 17.0\n", + " \n", + " \n", + " metac-o1\n", + " 16.6\n", + " 16.6\n", + " 16.6\n", + " 16.6\n", + " 16.6\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-20240620\n", + " 14.8\n", + " 14.8\n", + " 14.8\n", + " 14.8\n", + " 14.8\n", " \n", " \n", " manticAI\n", @@ -9335,36 +9361,20 @@ " jkraybill_bot\n", " 14.3\n", " 14.3\n", - " 14.3\n", - " 14.3\n", - " 14.3\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-20240620\n", - " 13.0\n", - " 13.0\n", - " 13.0\n", - " 13.0\n", - " 13.0\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-latest\n", - " 12.4\n", - " 12.4\n", - " 12.4\n", - " 12.4\n", - " 12.4\n", + " 14.3\n", + " 14.3\n", + " 14.3\n", " \n", " \n", - " metac-deepseek-r1\n", - " 12.3\n", - " 12.3\n", - " 12.3\n", - " 12.3\n", - " 12.3\n", + " metac-exa\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", + " 13.0\n", " \n", " \n", - " metac-Llama-3.1\n", + " GreeneiBot2\n", " 12.2\n", " 12.2\n", " 12.2\n", @@ -9372,14 +9382,6 @@ " 12.2\n", " \n", " \n", - " GreeneiBot2\n", - " 11.8\n", - " 11.8\n", - " 11.8\n", - " 11.8\n", - " 11.8\n", - " \n", - " \n", " NextWorldLab\n", " 11.1\n", " 11.1\n", @@ -9388,6 +9390,14 @@ " 11.1\n", " \n", " \n", + " metac-Llama-3.1\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " 10.5\n", + " \n", + " \n", " Grizeu_Bot\n", " 10.2\n", " 10.2\n", @@ -9404,12 +9414,12 @@ " 10.2\n", " \n", " \n", - " metac-grok-2-1212\n", - " 9.8\n", - " 9.8\n", - " 9.8\n", - " 9.8\n", - " 9.8\n", + " metac-claude-3-5-sonnet-latest\n", + " 10.0\n", + " 10.0\n", + " 10.0\n", + " 10.0\n", + " 10.0\n", " \n", " \n", " mmBot\n", @@ -9420,14 +9430,6 @@ " 9.7\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " 9.6\n", - " 9.6\n", - " 9.6\n", - " 9.6\n", - " 9.6\n", - " \n", - " \n", " annabot\n", " 9.0\n", " 9.0\n", @@ -9436,14 +9438,6 @@ " 9.0\n", " \n", " \n", - " metac-exa\n", - " 8.8\n", - " 8.8\n", - " 8.8\n", - " 8.8\n", - " 8.8\n", - " \n", - " \n", " VeritasAI\n", " 8.4\n", " 8.4\n", @@ -9452,6 +9446,14 @@ " 8.4\n", " \n", " \n", + " metac-grok-2-1212\n", + " 8.2\n", + " 8.2\n", + " 8.2\n", + " 8.2\n", + " 8.2\n", + " \n", + " \n", " laylaps\n", " 7.6\n", " 7.6\n", @@ -9460,6 +9462,14 @@ " 7.6\n", " \n", " \n", + " metac-Gemini-Exp-1206\n", + " 7.4\n", + " 7.4\n", + " 7.4\n", + " 7.4\n", + " 7.4\n", + " \n", + " \n", " metac-o1-preview\n", " 6.7\n", " 6.7\n", @@ -9476,6 +9486,14 @@ " 6.3\n", " \n", " \n", + " metac-deepseek-r1\n", + " 5.7\n", + " 5.7\n", + " 5.7\n", + " 5.7\n", + " 5.7\n", + " \n", + " \n", " MWG\n", " 5.5\n", " 5.5\n", @@ -9492,6 +9510,14 @@ " 5.1\n", " \n", " \n", + " metac-gpt-4o\n", + " 4.8\n", + " 4.8\n", + " 4.8\n", + " 4.8\n", + " 4.8\n", + " \n", + " \n", " pgodzinai\n", " 3.5\n", " 3.5\n", @@ -9508,14 +9534,6 @@ " 3.3\n", " \n", " \n", - " metac-gpt-4o\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " \n", - " \n", " InstitutPelFutur\n", " 2.7\n", " 2.7\n", @@ -9589,11 +9607,11 @@ " \n", " \n", " RPM_bot\n", - " 0.8\n", - " 0.8\n", - " 0.8\n", - " 0.8\n", - " 0.8\n", + " 1.2\n", + " 1.2\n", + " 1.2\n", + " 1.2\n", + " 1.2\n", " \n", " \n", " 4Shadower\n", @@ -9657,35 +9675,35 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-perplexity 20.6 20.6 20.6 20.6 20.6\n", - "metac-o1 20.2 20.2 20.2 20.2 20.2\n", + "metac-perplexity 18.1 18.1 18.1 18.1 18.1\n", "acm_bot 17.7 17.7 17.7 17.7 17.7\n", - "bot_median 17.4 17.4 17.4 17.4 17.4\n", + "bot_median 17.0 17.0 17.0 17.0 17.0\n", + "metac-o1 16.6 16.6 16.6 16.6 16.6\n", + "metac-claude-3-5-sonnet-20240620 14.8 14.8 14.8 14.8 14.8\n", "manticAI 14.5 14.5 14.5 14.5 14.5\n", "twsummerbot 14.3 14.3 14.3 14.3 14.3\n", "jkraybill_bot 14.3 14.3 14.3 14.3 14.3\n", - "metac-claude-3-5-sonnet-20240620 13.0 13.0 13.0 13.0 13.0\n", - "metac-claude-3-5-sonnet-latest 12.4 12.4 12.4 12.4 12.4\n", - "metac-deepseek-r1 12.3 12.3 12.3 12.3 12.3\n", - "metac-Llama-3.1 12.2 12.2 12.2 12.2 12.2\n", - "GreeneiBot2 11.8 11.8 11.8 11.8 11.8\n", + "metac-exa 13.0 13.0 13.0 13.0 13.0\n", + "GreeneiBot2 12.2 12.2 12.2 12.2 12.2\n", "NextWorldLab 11.1 11.1 11.1 11.1 11.1\n", + "metac-Llama-3.1 10.5 10.5 10.5 10.5 10.5\n", "Grizeu_Bot 10.2 10.2 10.2 10.2 10.2\n", "SynapseSeer 10.2 10.2 10.2 10.2 10.2\n", - "metac-grok-2-1212 9.8 9.8 9.8 9.8 9.8\n", + "metac-claude-3-5-sonnet-latest 10.0 10.0 10.0 10.0 10.0\n", "mmBot 9.7 9.7 9.7 9.7 9.7\n", - "metac-Gemini-Exp-1206 9.6 9.6 9.6 9.6 9.6\n", "annabot 9.0 9.0 9.0 9.0 9.0\n", - "metac-exa 8.8 8.8 8.8 8.8 8.8\n", "VeritasAI 8.4 8.4 8.4 8.4 8.4\n", + "metac-grok-2-1212 8.2 8.2 8.2 8.2 8.2\n", "laylaps 7.6 7.6 7.6 7.6 7.6\n", + "metac-Gemini-Exp-1206 7.4 7.4 7.4 7.4 7.4\n", "metac-o1-preview 6.7 6.7 6.7 6.7 6.7\n", "cookics_bot_TEST 6.3 6.3 6.3 6.3 6.3\n", + "metac-deepseek-r1 5.7 5.7 5.7 5.7 5.7\n", "MWG 5.5 5.5 5.5 5.5 5.5\n", "ajf-bot 5.1 5.1 5.1 5.1 5.1\n", + "metac-gpt-4o 4.8 4.8 4.8 4.8 4.8\n", "pgodzinai 3.5 3.5 3.5 3.5 3.5\n", "KevinTestBot 3.3 3.3 3.3 3.3 3.3\n", - "metac-gpt-4o 3.0 3.0 3.0 3.0 3.0\n", "InstitutPelFutur 2.7 2.7 2.7 2.7 2.7\n", "Bot_Pepa 2.6 2.6 2.6 2.6 2.6\n", "CumulativeBot 2.5 2.5 2.5 2.5 2.5\n", @@ -9695,7 +9713,7 @@ "bean_bot 2.1 2.1 2.1 2.1 2.1\n", "X_bot 1.9 1.9 1.9 1.9 1.9\n", "CatrachoCaster 1.8 1.8 1.8 1.8 1.8\n", - "RPM_bot 0.8 0.8 0.8 0.8 0.8\n", + "RPM_bot 1.2 1.2 1.2 1.2 1.2\n", "4Shadower 0.6 0.6 0.6 0.6 0.6\n", "krm-bot 0.6 0.6 0.6 0.6 0.6\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", @@ -9705,7 +9723,7 @@ "minefrac1 -3.0 -3.0 -3.0 -3.0 -3.0" ] }, - "execution_count": 132, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -9726,7 +9744,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 212, "metadata": {}, "outputs": [], "source": [ @@ -9736,7 +9754,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 213, "metadata": {}, "outputs": [ { @@ -9796,7 +9814,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 214, "metadata": { "cellView": "form", "colab": { @@ -10285,7 +10303,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 135, + "execution_count": 214, "metadata": {}, "output_type": "execute_result" } @@ -10306,7 +10324,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 215, "metadata": {}, "outputs": [], "source": [ @@ -10315,7 +10333,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 216, "metadata": {}, "outputs": [ { @@ -10354,7 +10372,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 217, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" @@ -10364,506 +10382,506 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.02]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.3]\n", " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.98]\n", " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.35]\n", " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.01]\n", - " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.97]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.75]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.2, 0.7]\n", - " >>> Collected 2 forecasts: [0.85, 0.9]\n", - " >>> Collected 2 forecasts: [0.85, 0.85]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.6, 0.6]\n", - " >>> Collected 2 forecasts: [0.7, 0.3]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.8, 0.7]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.8, 0.6]\n", + " >>> Collected 2 forecasts: [0.7, 0.35]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.25]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", - " >>> Collected 2 forecasts: [0.7, 0.8]\n", - " >>> Collected 2 forecasts: [0.05, 0.3]\n", - " >>> Collected 2 forecasts: [0.05, 0.25]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.15, 0.25]\n", - " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.35]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.4]\n", - " >>> Collected 2 forecasts: [0.4, 0.35]\n", + " >>> Collected 2 forecasts: [0.2, 0.35]\n", " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.98, 0.96]\n", - " >>> Collected 2 forecasts: [0.4, 0.3]\n", - " >>> Collected 2 forecasts: [0.3, 0.25]\n", - " >>> Collected 2 forecasts: [0.3, 0.6]\n", - " >>> Collected 2 forecasts: [0.01, 0.02]\n", - " >>> Collected 2 forecasts: [0.7, 0.7]\n", + " >>> Collected 2 forecasts: [0.7, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.5]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.1, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.3]\n", + " >>> Collected 2 forecasts: [0.95, 0.95]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.02, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.25, 0.35]\n", + " >>> Collected 2 forecasts: [0.3, 0.3]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.98, 0.98]\n", + " >>> Collected 2 forecasts: [0.4, 0.4]\n", + " >>> Collected 2 forecasts: [0.35, 0.3]\n", + " >>> Collected 2 forecasts: [0.3, 0.55]\n", + " >>> Collected 2 forecasts: [0.1, 0.02]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", " >>> Collected 2 forecasts: [0.99, 0.99]\n", - " >>> Collected 2 forecasts: [0.95, 0.98]\n", + " >>> Collected 2 forecasts: [0.97, 0.99]\n", " >>> Collected 2 forecasts: [0.95, 0.15]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.75]\n", - " >>> Collected 2 forecasts: [0.6, 0.4]\n", - " >>> Collected 2 forecasts: [0.85, 0.85]\n", + " >>> Collected 2 forecasts: [0.9, 0.8]\n", + " >>> Collected 2 forecasts: [0.35, 0.4]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.2, 0.35]\n", + " >>> Collected 2 forecasts: [0.25, 0.3]\n", " >>> Collected 2 forecasts: [0.75, 0.75]\n", - " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.3, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.3]\n", " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.1, 0.03]\n", - " >>> Collected 2 forecasts: [0.8, 0.9]\n", - " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.85, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.03]\n", + " >>> Collected 2 forecasts: [0.85, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.95]\n", + " >>> Collected 2 forecasts: [0.9, 0.3]\n", " >>> Collected 2 forecasts: [0.95, 0.8]\n", - " >>> Collected 2 forecasts: [0.85, 0.7]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", - " >>> Collected 3 forecasts: [0.2, 0.7, 0.62]\n", - " >>> Collected 3 forecasts: [0.85, 0.9, 0.82]\n", - " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.6, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.3, nan]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.82]\n", + " >>> Collected 3 forecasts: [0.8, 0.7, 0.85]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.8, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.35, nan]\n", " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.25, 0.25]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.05, 0.3, 0.108]\n", - " >>> Collected 3 forecasts: [0.05, 0.25, 0.16]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.25, 0.15]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.35, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.5, 0.108]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.16]\n", + " >>> Collected 3 forecasts: [0.1, 0.15, 0.95]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.03]\n", - " >>> Collected 3 forecasts: [0.1, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.4, 0.35, 0.35]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, 0.115]\n", - " >>> Collected 3 forecasts: [0.98, 0.96, 0.97]\n", - " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", - " >>> Collected 3 forecasts: [0.3, 0.25, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.3, 0.6, 0.17]\n", - " >>> Collected 3 forecasts: [0.01, 0.02, 0.12]\n", - " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", + " >>> Collected 3 forecasts: [0.02, 0.05, 0.034]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.03]\n", + " >>> Collected 3 forecasts: [0.25, 0.35, 0.35]\n", + " >>> Collected 3 forecasts: [0.3, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", + " >>> Collected 3 forecasts: [0.98, 0.98, 0.97]\n", + " >>> Collected 3 forecasts: [0.4, 0.4, 0.285]\n", + " >>> Collected 3 forecasts: [0.35, 0.3, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.3, 0.55, 0.17]\n", + " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.875]\n", " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", - " >>> Collected 3 forecasts: [0.95, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.97, 0.99, 0.9233333333333332]\n", " >>> Collected 3 forecasts: [0.95, 0.15, 0.4166666666666666]\n", " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.75, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.85, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.35, 0.4, 0.875]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.2, 0.35, 0.16]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", " >>> Collected 3 forecasts: [0.75, 0.75, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.086]\n", + " >>> Collected 3 forecasts: [0.3, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.1, 0.15, 0.086]\n", " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.1, 0.03, 0.02]\n", - " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.95, 0.95, 0.95]\n", - " >>> Collected 3 forecasts: [0.85, 0.3, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.03, 0.02]\n", + " >>> Collected 3 forecasts: [0.85, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", + " >>> Collected 3 forecasts: [0.9, 0.3, nan]\n", " >>> Collected 3 forecasts: [0.95, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999]\n", - " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.3, nan, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.82, 0.794]\n", + " >>> Collected 4 forecasts: [0.8, 0.7, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.35, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.25, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.05, 0.3, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.05, 0.25, 0.16, 0.652]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.25, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.35, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.85, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.05, 0.5, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.95, 0.052]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.4, 0.35, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.96, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.3, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.02, 0.05, 0.034, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999]\n", + " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.98, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.3, 0.55, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.875, 0.92]\n", " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", " >>> Collected 4 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2]\n", " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.75, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.35, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, nan]\n", " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.3, 0.15, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.086, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.85, 0.3, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.03, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.9, 0.3, nan, nan]\n", " >>> Collected 4 forecasts: [0.95, 0.8, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.3, nan, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.35, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.25, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.3, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.25, 0.16, 0.652, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.35, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.85, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.5, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.3, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.3, 0.55, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999]\n", " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", " >>> Collected 5 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336]\n", " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.2, 0.35, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, nan, 0.05]\n", " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.3, 0.15, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.086, nan, 0.12]\n", " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.85, 0.3, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.15, 0.03, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.9, 0.3, nan, nan, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.8, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.6, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.3, nan, nan, nan, 0.65]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.35, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", " >>> Collected 6 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225]\n", " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1]\n", " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055]\n", " >>> Collected 6 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", - " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2]\n", - " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35]\n", - " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", " >>> Collected 7 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2]\n", + " >>> Collected 7 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05]\n", " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07]\n", - " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", - " >>> Collected 7 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", - " >>> Collected 7 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan]\n", + " >>> Collected 7 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02]\n", + " >>> Collected 7 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", + " >>> Collected 7 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", " >>> Collected 8 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", - " >>> Collected 8 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", - " >>> Collected 8 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", - " >>> Collected 9 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85]\n", + " >>> Collected 8 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", + " >>> Collected 8 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.65]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.35]\n", + " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", " >>> Collected 9 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.1]\n", + " >>> Collected 9 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.8]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", - " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.9, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.8, nan, 0.85, 0.546]\n", + " >>> Collected 9 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.95]\n", + " >>> Collected 9 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85]\n", + " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75, 0.638]\n", + " >>> Collected 10 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.6, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.3, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.25, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.05, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.2, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.4, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.65, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.3, 0.6, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.35, 0.289]\n", + " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", " >>> Collected 10 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.35, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.1, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.8, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.85, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.95, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -10896,7 +10914,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 218, "metadata": {}, "outputs": [], "source": [ @@ -10906,7 +10924,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 219, "metadata": {}, "outputs": [ { @@ -10944,9 +10962,9 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " 0.012671\n", - " 0.032463\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " 0.014505\n", + " 0.097463\n", " \n", " \n", " 1\n", @@ -10954,26 +10972,26 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.037750000000000006, 0.038231012375000005, 0...\n", - " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", + " [0.037750000000000006, 0.038250620225000004, 0...\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", " \n", " \n", " 2\n", " binary\n", " NaN\n", " no\n", - " 0.1\n", + " 0.05\n", + " 0.063\n", " 0.085\n", - " 0.1\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.2,0.6,0.2]\n", - " 0.55\n", - " 0.5125\n", + " [0.15,0.65,0.2]\n", + " 0.56\n", + " 0.56\n", " \n", " \n", " 4\n", @@ -10981,8 +10999,8 @@ " NaN\n", " 119.2\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0, 0.0022111217800000003, 0.00442770048, 0....\n", - " [0.0, 0.002199820885714286, 0.0044035395571428...\n", + " [0.0, 0.00207778844, 0.00416103382, 0.00624884...\n", + " [0.0, 0.002104582785714286, 0.0042130633714285...\n", " \n", " \n", " ...\n", @@ -10998,16 +11016,16 @@ " binary\n", " NaN\n", " yes\n", - " 0.95\n", - " 0.95\n", - " 0.92\n", + " 0.9\n", + " 0.905\n", + " 0.9025\n", " \n", " \n", " 351\n", " binary\n", " NaN\n", " no\n", - " 0.85\n", + " 0.9\n", " 0.3\n", " 0.1835\n", " \n", @@ -11018,7 +11036,7 @@ " yes\n", " 0.95\n", " 0.8\n", - " 0.775\n", + " 0.8\n", " \n", " \n", " 361\n", @@ -11026,8 +11044,8 @@ " NaN\n", " no\n", " 0.85\n", - " 0.71\n", - " 0.704\n", + " 0.8\n", + " 0.755\n", " \n", " \n", " 364\n", @@ -11058,48 +11076,48 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", - "342 0.95 \n", - "351 0.85 \n", + "342 0.9 \n", + "351 0.9 \n", "355 0.95 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.038231012375000005, 0... \n", - "2 0.085 \n", - "3 0.55 \n", - "4 [0.0, 0.0022111217800000003, 0.00442770048, 0.... \n", + "0 0.014505 \n", + "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "2 0.063 \n", + "3 0.56 \n", + "4 [0.0, 0.00207778844, 0.00416103382, 0.00624884... \n", ".. ... \n", - "342 0.95 \n", + "342 0.905 \n", "351 0.3 \n", "355 0.8 \n", - "361 0.71 \n", + "361 0.8 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 0.032463 \n", - "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", - "2 0.1 \n", - "3 0.5125 \n", - "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", + "0 0.097463 \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.085 \n", + "3 0.56 \n", + "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", ".. ... \n", - "342 0.92 \n", + "342 0.9025 \n", "351 0.1835 \n", - "355 0.775 \n", - "361 0.704 \n", + "355 0.8 \n", + "361 0.755 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" ] }, - "execution_count": 140, + "execution_count": 219, "metadata": {}, "output_type": "execute_result" } @@ -11110,7 +11128,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 220, "metadata": {}, "outputs": [ { @@ -11130,7 +11148,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 221, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11168,52 +11186,52 @@ " \n", " 0\n", " 1\n", - " 16.52\n", + " 18.17\n", " \n", " \n", " 1\n", " 2\n", - " 26.94\n", + " 24.94\n", " \n", " \n", " 2\n", " 3\n", - " 28.15\n", + " 26.48\n", " \n", " \n", " 3\n", " 4\n", - " 27.95\n", + " 26.48\n", " \n", " \n", " 4\n", " 5\n", - " 28.09\n", + " 26.77\n", " \n", " \n", " 5\n", " 6\n", - " 28.10\n", + " 26.92\n", " \n", " \n", " 6\n", " 7\n", - " 26.82\n", + " 25.83\n", " \n", " \n", " 7\n", " 8\n", - " 27.00\n", + " 26.50\n", " \n", " \n", " 8\n", " 9\n", - " 26.79\n", + " 25.22\n", " \n", " \n", " 9\n", " 10\n", - " 26.71\n", + " 25.45\n", " \n", " \n", "\n", @@ -11221,19 +11239,19 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 16.52\n", - "1 2 26.94\n", - "2 3 28.15\n", - "3 4 27.95\n", - "4 5 28.09\n", - "5 6 28.10\n", - "6 7 26.82\n", - "7 8 27.00\n", - "8 9 26.79\n", - "9 10 26.71" + "0 1 18.17\n", + "1 2 24.94\n", + "2 3 26.48\n", + "3 4 26.48\n", + "4 5 26.77\n", + "5 6 26.92\n", + "6 7 25.83\n", + "7 8 26.50\n", + "8 9 25.22\n", + "9 10 25.45" ] }, - "execution_count": 142, + "execution_count": 221, "metadata": {}, "output_type": "execute_result" } @@ -11264,16 +11282,21 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 222, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai']" + "['metac-o1-preview',\n", + " 'metac-o1',\n", + " 'pgodzinai',\n", + " 'GreeneiBot2',\n", + " 'manticAI',\n", + " 'acm_bot']" ] }, - "execution_count": 143, + "execution_count": 222, "metadata": {}, "output_type": "execute_result" } @@ -11287,7 +11310,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 223, "metadata": {}, "outputs": [ { @@ -11296,7 +11319,7 @@ "(424, 47)" ] }, - "execution_count": 144, + "execution_count": 223, "metadata": {}, "output_type": "execute_result" } @@ -11307,7 +11330,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 224, "metadata": {}, "outputs": [], "source": [ @@ -11325,7 +11348,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 225, "metadata": {}, "outputs": [ { @@ -11382,18 +11405,18 @@ " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.4,0.3,0.2,0.05,0.05]\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", " ...\n", - " 0.010417\n", - " 0.205208\n", + " 0.014083\n", + " 0.207042\n", " 0.014926\n", - " 0.012671\n", - " 0.012671\n", + " 0.014505\n", + " 0.014505\n", " 0.014926\n", - " 0.032463\n", - " 0.032463\n", + " 0.097463\n", + " 0.097463\n", " 0.014926\n", " 0.014926\n", " \n", @@ -11407,19 +11430,19 @@ " 60.0\n", " 100.0\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", - " [0.03366666666666667, 0.034105259000000006, 0....\n", - " [0.037750000000000006, 0.038231012375000005, 0...\n", - " [0.037750000000000006, 0.038231012375000005, 0...\n", - " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", - " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", - " [0.0402, 0.0407348099, 0.04127318978, 0.041825...\n", - " [0.041833333333333333, 0.042417897133333334, 0...\n", - " [0.041833333333333333, 0.042417897133333334, 0...\n", + " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", + " [0.03366666666666667, 0.0341314028, 0.03460208...\n", + " [0.037750000000000006, 0.038250620225000004, 0...\n", + " [0.037750000000000006, 0.038250620225000004, 0...\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.041833333333333333, 0.042403191266666675, 0...\n", + " [0.041833333333333333, 0.042403191266666675, 0...\n", " \n", " \n", " 2\n", @@ -11430,20 +11453,20 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.1\n", + " 0.05\n", " 0.1\n", " 0.07\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.05\n", + " 0.075\n", + " 0.07\n", + " 0.063\n", + " 0.063\n", + " 0.07\n", " 0.085\n", " 0.085\n", " 0.1\n", " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", " \n", " \n", " 3\n", @@ -11454,19 +11477,19 @@ " [0-4, 5-9, >9]\n", " NaN\n", " NaN\n", - " [0.2,0.6,0.2]\n", - " [0.3,0.55,0.15]\n", + " [0.15,0.65,0.2]\n", + " [0.29,0.56,0.14999999999999997]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", " ...\n", - " 0.6\n", - " 0.575\n", - " 0.55\n", - " 0.575\n", - " 0.55\n", - " 0.53125\n", - " 0.5125\n", - " 0.5125\n", - " 0.53125\n", + " 0.65\n", + " 0.605\n", + " 0.56\n", + " 0.59\n", + " 0.56\n", + " 0.53625\n", + " 0.56\n", + " 0.56\n", + " 0.53625\n", " 0.5125\n", " \n", " \n", @@ -11479,19 +11502,19 @@ " 0.0\n", " 400.0\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0033333333,0.0066666667,0.01,0.01333333...\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", " ...\n", " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", - " [0.0, 0.0024830850250000002, 0.004970265075000...\n", - " [0.0, 0.0022111217800000003, 0.00442770048, 0....\n", - " [0.0, 0.0021497910333333338, 0.004304129483333...\n", - " [0.0, 0.002199820885714286, 0.0044035395571428...\n", - " [0.0, 0.002199820885714286, 0.0044035395571428...\n", - " [0.0, 0.0023415099375000002, 0.00468643045, 0....\n", - " [0.0, 0.002227114055555556, 0.0044572597222222...\n", + " [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0...\n", + " [0.0, 0.0024824972, 0.004970454466666667, 0.00...\n", + " [0.0, 0.00231641835, 0.00463693175, 0.00696020...\n", + " [0.0, 0.00207778844, 0.00416103382, 0.00624884...\n", + " [0.0, 0.002038679916666667, 0.0040819072666666...\n", + " [0.0, 0.002104582785714286, 0.0042130633714285...\n", + " [0.0, 0.002104582785714286, 0.0042130633714285...\n", + " [0.0, 0.0023970654875000003, 0.0047975415625, ...\n", + " [0.0, 0.002276496766666667, 0.0045560251555555...\n", " \n", " \n", "\n", @@ -11514,18 +11537,18 @@ "4 NaN 0.0 400.0 \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", + "0 [0.4,0.3,0.2,0.05,0.05] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.3,0.55,0.15] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "3 [0.29,0.56,0.14999999999999997] \n", + "4 [0.0,0.0033333333,0.0066666667,0.01,0.01333333... \n", "\n", " pgodzinai ... \\\n", "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", @@ -11535,79 +11558,79 @@ "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... ... \n", "\n", " median_forecast_1_bots \\\n", - "0 0.010417 \n", + "0 0.014083 \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.1 \n", - "3 0.6 \n", + "2 0.05 \n", + "3 0.65 \n", "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 0.205208 \n", - "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", - "2 0.1 \n", - "3 0.575 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "0 0.207042 \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.075 \n", + "3 0.605 \n", + "4 [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0... \n", "\n", " median_forecast_3_bots \\\n", "0 0.014926 \n", - "1 [0.03366666666666667, 0.034105259000000006, 0.... \n", - "2 0.1 \n", - "3 0.55 \n", - "4 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", + "1 [0.03366666666666667, 0.0341314028, 0.03460208... \n", + "2 0.07 \n", + "3 0.56 \n", + "4 [0.0, 0.0024824972, 0.004970454466666667, 0.00... \n", "\n", " median_forecast_4_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.038231012375000005, 0... \n", - "2 0.085 \n", - "3 0.575 \n", - "4 [0.0, 0.0024830850250000002, 0.004970265075000... \n", + "0 0.014505 \n", + "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "2 0.063 \n", + "3 0.59 \n", + "4 [0.0, 0.00231641835, 0.00463693175, 0.00696020... \n", "\n", " median_forecast_5_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.038231012375000005, 0... \n", - "2 0.085 \n", - "3 0.55 \n", - "4 [0.0, 0.0022111217800000003, 0.00442770048, 0.... \n", + "0 0.014505 \n", + "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "2 0.063 \n", + "3 0.56 \n", + "4 [0.0, 0.00207778844, 0.00416103382, 0.00624884... \n", "\n", " median_forecast_6_bots \\\n", "0 0.014926 \n", - "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", - "2 0.1 \n", - "3 0.53125 \n", - "4 [0.0, 0.0021497910333333338, 0.004304129483333... \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.07 \n", + "3 0.53625 \n", + "4 [0.0, 0.002038679916666667, 0.0040819072666666... \n", "\n", " median_forecast_7_bots \\\n", - "0 0.032463 \n", - "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", - "2 0.1 \n", - "3 0.5125 \n", - "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", + "0 0.097463 \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.085 \n", + "3 0.56 \n", + "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", "\n", " median_forecast_8_bots \\\n", - "0 0.032463 \n", - "1 [0.0402, 0.0407348099, 0.04127318978, 0.041825... \n", - "2 0.1 \n", - "3 0.5125 \n", - "4 [0.0, 0.002199820885714286, 0.0044035395571428... \n", + "0 0.097463 \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.085 \n", + "3 0.56 \n", + "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", "\n", " median_forecast_9_bots \\\n", "0 0.014926 \n", - "1 [0.041833333333333333, 0.042417897133333334, 0... \n", + "1 [0.041833333333333333, 0.042403191266666675, 0... \n", "2 0.1 \n", - "3 0.53125 \n", - "4 [0.0, 0.0023415099375000002, 0.00468643045, 0.... \n", + "3 0.53625 \n", + "4 [0.0, 0.0023970654875000003, 0.0047975415625, ... \n", "\n", " median_forecast_10_bots \n", "0 0.014926 \n", - "1 [0.041833333333333333, 0.042417897133333334, 0... \n", + "1 [0.041833333333333333, 0.042403191266666675, 0... \n", "2 0.1 \n", "3 0.5125 \n", - "4 [0.0, 0.002227114055555556, 0.0044572597222222... \n", + "4 [0.0, 0.002276496766666667, 0.0045560251555555... \n", "\n", "[5 rows x 27 columns]" ] }, - "execution_count": 146, + "execution_count": 225, "metadata": {}, "output_type": "execute_result" } @@ -11618,7 +11641,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 226, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11666,14 +11689,14 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 227, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -13.5599\n" + "Weighted Total Score: -15.6339\n" ] } ], @@ -11683,7 +11706,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 228, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11695,7 +11718,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA18AAAIjCAYAAAD80aFnAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAe7BJREFUeJzt3Xt8j/X/x/HnZ+exE5nNGFvIOXOcOcsyx68JoXIu+RYRKlToKCJKhfp+f+ggWqSSZC1RiBxLoZJTmDltY2KzXb8/ru8+65PRaPtcOzzut9t187ne1/tzfV7Xx8fs+bmu6/22GYZhCAAAAABQoFysLgAAAAAASgLCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwCg0Dt48KBsNpsWLlxodSmFEu8PABQNhC8ARd7ChQtls9kclvLly6tdu3b67LPPbni/zz//vFasWPG3/dq2bXvF6+e2TJky5YZrcZawsDB17do1121fffWVbDabPvjgAydXdX0WL16s2bNnF8i+i8P7c/DgQQ0ePFhVq1aVl5eXgoOD1bp1a02ePNnq0oqksLCwK372tGrVSh9++KHVpQEohNysLgAA8svTTz+t8PBwGYahEydOaOHChercubM++eSTq/7CfC3PP/+8evXqpdjY2Gv2e/zxx3Xvvffa17/77ju98sormjhxomrVqmVvv/XWW6+7Bly/xYsXa/fu3Ro9erTVpRQ6v/76q5o0aSJvb28NGTJEYWFhOn78uLZv365p06bpqaeesrrEIikiIkJjx46VJB07dkzz58/XHXfcoblz52r48OEWVwegMCF8ASg2OnXqpMaNG9vXhw4dqqCgIL333ns3FL7y6vbbb3dY9/Ly0iuvvKLbb79dbdu2LbDXBa7XrFmzdP78ee3cuVNVqlRx2JaUlOTUWtLS0lS6dGmnvmZBqVixou655x77+oABA1StWjXNmjXrquHr8uXLysrKkoeHh7PKBFAIcNkhgGIrICBA3t7ecnNz/J4pLS1NY8eOVWhoqDw9PVWjRg3NmDFDhmHY+9hsNqWlpWnRokX2y4kGDRr0j+r57LPP1KpVK5UuXVq+vr7q0qWLfvzxR4c+33//vQYNGqSbb77ZfknYkCFDdPr0aYd+U6ZMkc1m088//6x77rlH/v7+CgwM1JNPPinDMHTkyBF1795dfn5+Cg4O1syZM/9R7ddy9OhRDRkyREFBQfL09FSdOnX0f//3fw590tPTNWnSJDVq1Ej+/v4qXbq0WrVqpbVr116xv+TkZA0aNEj+/v4KCAjQwIEDlZycnKda2rZtq08//VSHDh2y/72FhYXZtyclJdlDuZeXl+rXr69Fixb9k8P/W4Xp/dm/f78qVap0RfCSpPLly1/R9tlnn6lNmzby9fWVn5+fmjRposWLFzv0iYuLU6NGjeTt7a1y5crpnnvu0dGjRx36DBo0SD4+Ptq/f786d+4sX19f3X333ZKkrKwszZ49W3Xq1JGXl5eCgoJ0//336+zZsw772Lp1q2JiYlSuXDl5e3srPDxcQ4YMuebxdu3aVTfffHOu26Kiohy+rImPj1fLli0VEBAgHx8f1ahRQxMnTrzm/q8mODhYtWrV0oEDByTl3JM3Y8YMzZ49W1WrVpWnp6d++uknSdKXX35p/9kQEBCg7t27a8+ePQ77PHfunEaPHq2wsDB5enqqfPnyuv3227V9+/YbqhGANTjzBaDYSElJ0alTp2QYhpKSkjRnzhydP3/e4RtpwzD0r3/9S2vXrtXQoUMVERGhzz//XI888oiOHj2qWbNmSZLefvtt3XvvvWratKmGDRsmSapateoN1/b2229r4MCBiomJ0bRp03ThwgXNnTtXLVu21I4dO+wBIT4+Xr/99psGDx6s4OBg/fjjj3rjjTf0448/6ttvv5XNZnPYb58+fVSrVi298MIL+vTTT/Xss8+qbNmymj9/vm677TZNmzZN7777rsaNG6cmTZqodevWf1trRkaGTp06dUV7SkrKFW0nTpxQs2bNZLPZNGLECAUGBuqzzz7T0KFDlZqaar/0LzU1Vf/5z3/Ur18/3XfffTp37pz++9//KiYmRlu2bFFERIQk8++ne/fu+uabbzR8+HDVqlVLH374oQYOHJin9/nxxx9XSkqKfv/9d/vfpY+PjyTpjz/+UNu2bfXrr79qxIgRCg8PV1xcnAYNGqTk5GSNGjUqT69RlN+fKlWq6IsvvtCXX36p22677Zp9Fy5cqCFDhqhOnTqaMGGCAgICtGPHDq1evVp33XWXvc/gwYPVpEkTTZ06VSdOnNDLL7+sDRs2aMeOHQoICLDv7/Lly4qJiVHLli01Y8YMlSpVSpJ0//332/fz0EMP6cCBA3r11Ve1Y8cObdiwQe7u7kpKSlKHDh0UGBio8ePHKyAgQAcPHtTy5cuveQx9+vTRgAED9N1336lJkyb29kOHDunbb7/Viy++KEn68ccf1bVrV9166616+umn5enpqV9//VUbNmzI0/v6VxkZGTpy5Ihuuukmh/YFCxbo4sWLGjZsmDw9PVW2bFl98cUX6tSpk26++WZNmTJFf/zxh+bMmaMWLVpo+/bt9p8Nw4cP1wcffKARI0aodu3aOn36tL755hvt2bNHDRs2vKE6AVjAAIAibsGCBYakKxZPT09j4cKFDn1XrFhhSDKeffZZh/ZevXoZNpvN+PXXX+1tpUuXNgYOHHjd9cTFxRmSjLVr1xqGYRjnzp0zAgICjPvuu8+hX2JiouHv7+/QfuHChSv299577xmSjPXr19vbJk+ebEgyhg0bZm+7fPmyUalSJcNmsxkvvPCCvf3s2bOGt7d3no6lSpUqub6Xf17i4uLs/YcOHWpUqFDBOHXqlMN++vbta/j7+9uP5/Lly8alS5cc+pw9e9YICgoyhgwZYm/L/vuZPn26w3G1atXKkGQsWLDgb4+hS5cuRpUqVa5onz17tiHJeOedd+xt6enpRlRUlOHj42Okpqb+7b6L+vuze/duw9vb25BkREREGKNGjTJWrFhhpKWlOfRLTk42fH19jcjISOOPP/5w2JaVlWUYhvnelS9f3qhbt65Dn5UrVxqSjEmTJtnbBg4caEgyxo8f77Cvr7/+2pBkvPvuuw7tq1evdmj/8MMPDUnGd999d83j+6uUlBTD09PTGDt2rEP79OnTDZvNZhw6dMgwDMOYNWuWIck4efLkde3fMMzPRIcOHYyTJ08aJ0+eNHbt2mX07dvXkGSMHDnSMAzDOHDggCHJ8PPzM5KSkhyeHxERYZQvX944ffq0vW3Xrl2Gi4uLMWDAAHubv7+/8eCDD153fQAKFy47BFBsvPbaa4qPj1d8fLzeeecdtWvXTvfee6/Dt+OrVq2Sq6urHnroIYfnjh07VoZh/KPREa8mPj5eycnJ6tevn06dOmVfXF1dFRkZ6XBpmbe3t/3xxYsXderUKTVr1kyScr286M8Dfbi6uqpx48YyDENDhw61twcEBKhGjRr67bff8lRvZGSk/X388zJjxgyHfoZhaNmyZerWrZsMw3A4tpiYGKWkpNhrdnV1td/bkpWVpTNnzujy5ctq3Lixw3GtWrVKbm5u+ve//+1wXCNHjsxT7deyatUqBQcHq1+/fvY2d3d3PfTQQzp//rzWrVuXp/0U5fenTp062rlzp+655x4dPHhQL7/8smJjYxUUFKQ333zT3i8+Pl7nzp3T+PHj5eXl5bCP7LOvW7duVVJSkh544AGHPl26dFHNmjX16aefXvH6f65bMi9Z9Pf31+233+7w/jRq1Eg+Pj72fxvZZ9BWrlypjIyMPB2rJPn5+alTp056//33HS4rXrp0qZo1a6bKlSs77P+jjz5SVlZWnvefbc2aNQoMDFRgYKDq16+vuLg49e/fX9OmTXPo17NnTwUGBtrXjx8/rp07d2rQoEEqW7asvf3WW2/V7bffrlWrVtnbAgICtHnzZh07duy66wNQeHDZIYBio2nTpg73cPTr108NGjTQiBEj1LVrV3l4eOjQoUMKCQmRr6+vw3OzRyU8dOhQvtf1yy+/SNJVL/Py8/OzPz5z5oyeeuopLVmy5IoBEHK7rC37l8ds/v7+8vLyUrly5a5o/+t9Y1dTrlw5RUdHX9H+13vnTp48qeTkZL3xxht64403ct3Xn49h0aJFmjlzpvbu3evwC3R4eLj98aFDh1ShQgX7pYLZatSo4bD+xx9/XPF+BAcHX/O4Dh06pOrVq8vFxfF7x7/+3aekpOiPP/6wb/fw8HD4xbgovD/Xcsstt+jtt99WZmamfvrpJ61cuVLTp0/XsGHDFB4erujoaO3fv1+SVLdu3avuJ/v9yu21a9asqW+++cahzc3NTZUqVXJo++WXX5SSkpLr/WZSzvvTpk0b9ezZU0899ZRmzZqltm3bKjY2VnfddZc8PT2vebx9+vTRihUrtGnTJjVv3lz79+/Xtm3bHKYj6NOnj/7zn//o3nvv1fjx49W+fXvdcccd6tWr1xWfl9xERkbq2Weflc1mU6lSpVSrVi2HSy6z/fnvUrr2e1irVi19/vnn9oFJpk+froEDByo0NFSNGjVS586dNWDAgKve0wagcCJ8ASi2XFxc1K5dO7388sv65ZdfVKdOHUvqyP4m/e233841IPz5l/Y777xTGzdu1COPPKKIiAj5+PgoKytLHTt2zPUbeVdX1zy1SXL45j8/ZNdzzz33XPWeo+zh9d955x0NGjRIsbGxeuSRR1S+fHm5urpq6tSp9l/0r8fSpUs1ePBgh7b8Or5Ro0Y5DMLRpk0bffXVV9e9Hyvfn7xwdXVVvXr1VK9ePUVFRaldu3Z69913cw2W+cHT0/OKIJOVlaXy5cvr3XffzfU52WeJsudP+/bbb/XJJ5/o888/15AhQzRz5kx9++23V4TRP+vWrZtKlSql999/X82bN9f7778vFxcX9e7d297H29tb69ev19q1a/Xpp59q9erVWrp0qW677TatWbPmqv+msl0tkP/Vn89sX68777zTPn/YmjVr9OKLL2ratGlavny5OnXqdMP7BeBchC8Axdrly5clSefPn5eUM+DAuXPnHM5+7d271749218Ht7hR2QN1lC9f/pq/oJ09e1YJCQl66qmnNGnSJHt79pmzwiYwMFC+vr7KzMz82188P/jgA918881avny5w/v614l9q1SpooSEBJ0/f97hF+p9+/Y59IuJiVF8fHyur3W1v7cqVaro+++/V1ZWlkMI+Ovf/aOPPuowSEuZMmWueWxXY+X7c72yzxgfP35cUs5ndvfu3apWrVquz8l+v/bt23fFWd19+/blOqLiX1WtWlVffPGFWrRokadg0qxZMzVr1kzPPfecFi9erLvvvltLlixxuPz2r0qXLq2uXbsqLi5OL730kpYuXapWrVopJCTEoZ+Li4vat2+v9u3b66WXXtLzzz+vxx9/XGvXri2wQPrn9/Cv9u7dq3LlyjkMx1+hQgU98MADeuCBB5SUlKSGDRvqueeeI3wBRQj3fAEotjIyMrRmzRp5eHjYLy3r3LmzMjMz9eqrrzr0nTVrlmw2m8MvMaVLl87zEN7XEhMTIz8/Pz3//PO53q9y8uRJSTlnrP56BufPl0cVJq6ururZs6eWLVum3bt3X7E9+7iy+0qOx7Z582Zt2rTJ4TmdO3fW5cuXNXfuXHtbZmam5syZ49CvQoUKio6OdliylS5dOtdLNDt37qzExEQtXbrU3nb58mXNmTNHPj4+atOmjSSpdu3aDvtt1KhRnt6Pv7Ly/bmar7/+OtfPYPa9RdmXv3Xo0EG+vr6aOnWqLl686NA3u8bGjRurfPnymjdvni5dumTf/tlnn2nPnj3q0qXL39Zz5513KjMzU88888wV2y5fvmz/93f27Nkr/l1kjwD559e+mj59+ujYsWP6z3/+o127dqlPnz4O28+cOXPFc65n/zeqQoUKioiI0KJFixx+1uzevVtr1qxR586dJZl/x3/9TJcvX14hISEFWh+A/MeZLwDFxmeffWY/i5GUlKTFixfrl19+0fjx4+33VXXr1k3t2rXT448/roMHD6p+/fpas2aNPvroI40ePdphOPlGjRrpiy++0EsvvaSQkBCFh4crMjLyuuvy8/PT3Llz1b9/fzVs2FB9+/ZVYGCgDh8+rE8//VQtWrTQq6++Kj8/P7Vu3VrTp09XRkaGKlasqDVr1tjnCiqMXnjhBa1du1aRkZG67777VLt2bZ05c0bbt2/XF198Yf+ltmvXrlq+fLl69OihLl266MCBA5o3b55q165tPyspmX8/LVq00Pjx43Xw4EHVrl1by5cvzzVMXU2jRo20dOlSjRkzRk2aNJGPj4+6deumYcOGaf78+Ro0aJC2bdumsLAwffDBB9qwYYNmz559xX2AxfH9mTZtmrZt26Y77rjDfsnj9u3b9dZbb6ls2bL2oe/9/Pw0a9Ys3XvvvWrSpInuuusulSlTRrt27dKFCxe0aNEiubu7a9q0aRo8eLDatGmjfv362YeaDwsL08MPP/y39bRp00b333+/pk6dqp07d6pDhw5yd3fXL7/8ori4OL388svq1auXFi1apNdff109evRQ1apVde7cOb355pvy8/OzB5RryZ5bbNy4cfZQ/GdPP/201q9fry5duqhKlSpKSkrS66+/rkqVKqlly5Z5em9v1IsvvqhOnTopKipKQ4cOtQ817+/vrylTpkgy5/iqVKmSevXqpfr168vHx0dffPGFvvvuuwKdww9AAbBiiEUAyE+5DTXv5eVlREREGHPnzrUPjZ3t3LlzxsMPP2yEhIQY7u7uRvXq1Y0XX3zxin579+41WrdubR+aO6/Dzv91qPlsa9euNWJiYgx/f3/Dy8vLqFq1qjFo0CBj69at9j6///670aNHDyMgIMDw9/c3evfubRw7dsyQZEyePNneL3uo+b8OjT1w4ECjdOnSV9TUpk0bo06dOn9be5UqVYwuXbrkum3t2rVXDKVuGIZx4sQJ48EHHzRCQ0MNd3d3Izg42Gjfvr3xxhtv2PtkZWUZzz//vFGlShXD09PTaNCggbFy5Upj4MCBVwwLf/r0aaN///6Gn5+f4e/vb/Tv39/YsWNHnoeaP3/+vHHXXXcZAQEBhiSH/Z84ccIYPHiwUa5cOcPDw8OoV69envaZrai/Pxs2bDAefPBBo27duoa/v7/h7u5uVK5c2Rg0aJCxf//+K/p//PHHRvPmzQ1vb2/Dz8/PaNq0qfHee+859Fm6dKnRoEEDw9PT0yhbtqxx9913G7///rtDn6t9LrO98cYbRqNGjQxvb2/D19fXqFevnvHoo48ax44dMwzDMLZv327069fPqFy5suHp6WmUL1/e6Nq1q8O/nb9z9913G5KM6OjoK7YlJCQY3bt3N0JCQgwPDw8jJCTE6Nevn/Hzzz//7X6v9ZnIlj3U/Isvvpjr9i+++MJo0aKF/X3u1q2b8dNPP9m3X7p0yXjkkUeM+vXrG76+vkbp0qWN+vXrG6+//vrf1gegcLEZRj7fgQ0AAAAAuAL3fAEAAACAExC+AAAAAMAJCF8AAAAA4ASELwAAAABwAsIXAAAAADgB4QsAAAAAnIBJlm9QVlaWjh07Jl9fX9lsNqvLAQAAAGARwzB07tw5hYSEyMXl6ue3CF836NixYwoNDbW6DAAAAACFxJEjR1SpUqWrbid83SBfX19J5hvs5+dncTUAAAAArJKamqrQ0FB7RrgawtcNyr7U0M/Pj/AFAAAA4G9vR2LADQAAAABwAsIXAAAAADgB4QsAAAAAnIB7vgAAAFBkZWZmKiMjw+oyUMy5urrKzc3tH08xRfgCAABAkXT+/Hn9/vvvMgzD6lJQApQqVUoVKlSQh4fHDe+D8AUAAIAiJzMzU7///rtKlSqlwMDAf3xGArgawzCUnp6ukydP6sCBA6pevfo1J1K+FsIXAAAAipyMjAwZhqHAwEB5e3tbXQ6KOW9vb7m7u+vQoUNKT0+Xl5fXDe2HATcAAABQZHHGC85yo2e7HPaRD3UAAAAAAP4G4QsAAAAAnIDwBQAAABQCbdu21ejRo532egsXLlRAQIDTXq8gOfu9u1GELwAAAMBJBg0aJJvNdsXy66+/avny5XrmmWfsfcPCwjR79myH51sRmNauXauuXbsqMDBQXl5eqlq1qvr06aP169c7tY5r+et7V1gRvgAAAAAn6tixo44fP+6whIeHq2zZsvL19bW6PAevv/662rdvr5tuuklLly7Vvn379OGHH6p58+Z6+OGHrS7PrjC+d7khfAEAAABO5OnpqeDgYIfF1dXV4dK5tm3b6tChQ3r44YftZ8e++uorDR48WCkpKfa2KVOmSJIuXbqkcePGqWLFiipdurQiIyP11VdfObzuwoULVblyZZUqVUo9evTQ6dOnr1nn4cOHNXr0aI0ePVqLFi3SbbfdpipVqujWW2/VqFGjtHXrVnvf06dPq1+/fqpYsaJKlSqlevXq6b333nPYX25n8iIiIuzHYBiGpkyZosqVK8vT01MhISF66KGH7H1ff/11Va9eXV5eXgoKClKvXr3s2/562eHbb7+txo0by9fXV8HBwbrrrruUlJRk3/7VV1/JZrMpISFBjRs3VqlSpdS8eXPt27fvmu/JP8U8XwAAACgeGjeWEhOd/7rBwdKfgkh+WL58uerXr69hw4bpvvvuk2Se3Zk9e7YmTZpkDwk+Pj6SpBEjRuinn37SkiVLFBISog8//FAdO3bUDz/8oOrVq2vz5s0aOnSopk6dqtjYWK1evVqTJ0++Zg3Lli1TRkaGHn300Vy3/3mY/4sXL6pRo0Z67LHH5Ofnp08//VT9+/dX1apV1bRp0zwd87JlyzRr1iwtWbJEderUUWJionbt2iVJ2rp1qx566CG9/fbbat68uc6cOaOvv/76qvvKyMjQM888oxo1aigpKUljxozRoEGDtGrVKod+jz/+uGbOnKnAwEANHz5cQ4YM0YYNG/JU740gfAEAAKB4SEyUjh61uoq/tXLlSntokqROnTopLi7OoU/ZsmXl6upqP3OTzd/fXzabzaHt8OHDWrBggQ4fPqyQkBBJ0rhx47R69WotWLBAzz//vF5++WV17NjRHqRuueUWbdy4UatXr75qnT///LP8/PwcXmvZsmUaOHCgfX3Tpk2qV6+eKlasqHHjxtnbR44cqc8//1zvv/9+nsPX4cOHFRwcrOjoaLm7u6ty5cr25x4+fFilS5dW165d5evrqypVqqhBgwZX3deQIUPsj2+++Wa98soratKkic6fP+/w3j/33HNq06aNJGn8+PHq0qWLLl68eMOTKP8dwhcAAACKhz+FhML8uu3atdPcuXPt66VLl/5HL//DDz8oMzNTt9xyi0P7pUuXdNNNN0mS9uzZox49ejhsj4qKumb4kq6cxDomJkY7d+7U0aNH1bZtW2VmZkqSMjMz9fzzz+v999/X0aNHlZ6erkuXLqlUqVJ5Po7evXtr9uzZuvnmm9WxY0d17txZ3bp1k5ubm26//XZVqVLFvq1jx47q0aPHVfe/bds2TZkyRbt27dLZs2eVlZUlyQxxtWvXtve79dZb7Y8rVKggSUpKSlLlypXzXPf1IHwBAACgeMjnS/8KSunSpVWtWrV829/58+fl6uqqbdu2ydXV1WHbn8/yXK/q1asrJSVFiYmJ9rNfPj4+qlatmtzcHGPEiy++qJdfflmzZ89WvXr1VLp0aY0ePVrp6en2Pi4uLjIMw+F5GRkZ9sehoaHat2+fvvjiC8XHx+uBBx7Qiy++qHXr1snX11fbt2/XV199pTVr1mjSpEmaMmWKvvvuuytGf0xLS1NMTIxiYmL07rvvKjAwUIcPH1ZMTIxDPZLk7u5uf5wdNLODWkFgwA0AAACgEPLw8LCfWbpWW4MGDZSZmamkpCRVq1bNYckOTbVq1dLmzZsdnvftt99e8/V79eold3d3TZs27W9r3bBhg7p376577rlH9evX180336yff/7ZoU9gYKCOHz9uX09NTdWBAwcc+nh7e6tbt2565ZVX9NVXX2nTpk364YcfJElubm6Kjo7W9OnT9f333+vgwYP68ssvr6hl7969On36tF544QW1atVKNWvWdBhsw0qc+QIAJ4rbn1Jg++5d1b/A9g0AcL6wsDCtX79effv2laenp8qVK6ewsDCdP39eCQkJql+/vkqVKqVbbrlFd999twYMGKCZM2eqQYMGOnnypBISEnTrrbeqS5cueuihh9SiRQvNmDFD3bt31+eff/63lxxWrlxZM2fO1KhRo3TmzBkNGjRI4eHhOnPmjN555x1Jsp9pq169uj744ANt3LhRZcqU0UsvvaQTJ044XOJ32223aeHCherWrZsCAgI0adIkhzN1CxcuVGZmpiIjI1WqVCm988478vb2VpUqVbRy5Ur99ttvat26tcqUKaNVq1YpKytLNWrUyLVuDw8PzZkzR8OHD9fu3bsLzRxgnPkCAAAACqGnn35aBw8eVNWqVRUYGChJat68uYYPH64+ffooMDBQ06dPlyQtWLBAAwYM0NixY1WjRg3Fxsbqu+++s9+71KxZM7355pt6+eWXVb9+fa1Zs0ZPPPHE39YwcuRIrVmzRidPnlSvXr1UvXp1de7cWQcOHNDq1atVr149SdITTzyhhg0bKiYmRm3btlVwcLBiY2Md9jVhwgS1adNGXbt2VZcuXRQbG6uqVavatwcEBOjNN99UixYtdOutt+qLL77QJ598optuukkBAQFavny5brvtNtWqVUvz5s3Te++9pzp16lxRc2BgoBYuXKi4uDjVrl1bL7zwgmbMmHFDfwf5zWb89cJL5Elqaqr8/f2VkpIiPz8/q8sBUERw5gsA8sfFixd14MABhYeHF9jIdMCfXeszl9dswJkvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAECRxdhxcJb8+KwRvgAAAFDkZM8PlZ6ebnElKCkuXLggSXJ3d7/hfTDJMgAAAIocNzc3lSpVSidPnpS7u7tcXDingIJhGIYuXLigpKQkBQQEOEwMfb0IXwAAAChybDabKlSooAMHDujQoUNWl4MSICAgQMHBwf9oH4QvAAAAFEkeHh6qXr06lx6iwLm7u/+jM17ZCF8AAAAoslxcXOTl5WV1GUCecHEsAAAAADgB4QsAAAAAnIDwBQAAAABOQPgCAAAAACcgfAEAAACAExC+AAAAAMAJCF8AAAAA4ASELwAAAABwAsIXAAAAADgB4QsAAAAAnIDwBQAAAABOQPgCAAAAACewPHy99tprCgsLk5eXlyIjI7Vly5Zr9o+Li1PNmjXl5eWlevXqadWqVQ7bly9frg4dOuimm26SzWbTzp07HbafOXNGI0eOVI0aNeTt7a3KlSvroYceUkpKSn4fGgAAAADYWRq+li5dqjFjxmjy5Mnavn276tevr5iYGCUlJeXaf+PGjerXr5+GDh2qHTt2KDY2VrGxsdq9e7e9T1pamlq2bKlp06bluo9jx47p2LFjmjFjhnbv3q2FCxdq9erVGjp0aIEcIwAAAABIks0wDMOqF4+MjFSTJk306quvSpKysrIUGhqqkSNHavz48Vf079Onj9LS0rRy5Up7W7NmzRQREaF58+Y59D148KDCw8O1Y8cORUREXLOOuLg43XPPPUpLS5Obm1uufS5duqRLly7Z11NTUxUaGqqUlBT5+fnl9ZABlHBx+wvuLHvvqv4Ftm8AAHB1qamp8vf3/9tsYNmZr/T0dG3btk3R0dE5xbi4KDo6Wps2bcr1OZs2bXLoL0kxMTFX7Z9X2W/S1YKXJE2dOlX+/v72JTQ09B+9JgAAAICSxbLwderUKWVmZiooKMihPSgoSImJibk+JzEx8br657WOZ555RsOGDbtmvwkTJiglJcW+HDly5IZfEwAAAEDJc/VTPSVAamqqunTpotq1a2vKlCnX7Ovp6SlPT0/nFAYAAACg2LHszFe5cuXk6uqqEydOOLSfOHFCwcHBuT4nODj4uvpfy7lz59SxY0f5+vrqww8/lLu7+3XvAwAAAADyyrLw5eHhoUaNGikhIcHelpWVpYSEBEVFReX6nKioKIf+khQfH3/V/leTmpqqDh06yMPDQx9//LG8vLyu/wAAAAAA4DpYetnhmDFjNHDgQDVu3FhNmzbV7NmzlZaWpsGDB0uSBgwYoIoVK2rq1KmSpFGjRqlNmzaaOXOmunTpoiVLlmjr1q1644037Ps8c+aMDh8+rGPHjkmS9u3bJ8k8axYcHGwPXhcuXNA777yj1NRUpaamSpICAwPl6urqzLcAAAAAQAlhafjq06ePTp48qUmTJikxMVERERFavXq1fVCNw4cPy8Ul5+Rc8+bNtXjxYj3xxBOaOHGiqlevrhUrVqhu3br2Ph9//LE9vElS3759JUmTJ0/WlClTtH37dm3evFmSVK1aNYd6Dhw4oLCwsII6XAAAAAAlmKXzfBVleR3LHwD+jHm+AAAofgr9PF8AAAAAUJIQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBNYHr5ee+01hYWFycvLS5GRkdqyZcs1+8fFxalmzZry8vJSvXr1tGrVKofty5cvV4cOHXTTTTfJZrNp586dV+zj4sWLevDBB3XTTTfJx8dHPXv21IkTJ/LzsAAAAADAgaXha+nSpRozZowmT56s7du3q379+oqJiVFSUlKu/Tdu3Kh+/fpp6NCh2rFjh2JjYxUbG6vdu3fb+6Slpally5aaNm3aVV/34Ycf1ieffKK4uDitW7dOx44d0x133JHvxwcAAAAA2WyGYRhWvXhkZKSaNGmiV199VZKUlZWl0NBQjRw5UuPHj7+if58+fZSWlqaVK1fa25o1a6aIiAjNmzfPoe/BgwcVHh6uHTt2KCIiwt6ekpKiwMBALV68WL169ZIk7d27V7Vq1dKmTZvUrFmzPNWempoqf39/paSkyM/P73oPHUAJFbc/pcD23buqf4HtGwAAXF1es4FlZ77S09O1bds2RUdH5xTj4qLo6Ght2rQp1+ds2rTJob8kxcTEXLV/brZt26aMjAyH/dSsWVOVK1e+5n4uXbqk1NRUhwUAAAAA8sqy8HXq1CllZmYqKCjIoT0oKEiJiYm5PicxMfG6+l9tHx4eHgoICLiu/UydOlX+/v72JTQ0NM+vCQAAAACWD7hRVEyYMEEpKSn25ciRI1aXBAAAAKAIcbPqhcuVKydXV9crRhk8ceKEgoODc31OcHDwdfW/2j7S09OVnJzscPbr7/bj6ekpT0/PPL8OAAAAAPyZZWe+PDw81KhRIyUkJNjbsrKylJCQoKioqFyfExUV5dBfkuLj46/aPzeNGjWSu7u7w3727dunw4cPX9d+AAAAAOB6WHbmS5LGjBmjgQMHqnHjxmratKlmz56ttLQ0DR48WJI0YMAAVaxYUVOnTpUkjRo1Sm3atNHMmTPVpUsXLVmyRFu3btUbb7xh3+eZM2d0+PBhHTt2TJIZrCTzjFdwcLD8/f01dOhQjRkzRmXLlpWfn59GjhypqKioPI90CAAAAADXy9Lw1adPH508eVKTJk1SYmKiIiIitHr1avugGocPH5aLS87JuebNm2vx4sV64oknNHHiRFWvXl0rVqxQ3bp17X0+/vhje3iTpL59+0qSJk+erClTpkiSZs2aJRcXF/Xs2VOXLl1STEyMXn/9dSccMQAAAICSytJ5vooy5vkCcCOY5wsAgOKn0M/zBQAAAAAlCeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJzA8vD12muvKSwsTF5eXoqMjNSWLVuu2T8uLk41a9aUl5eX6tWrp1WrVjlsNwxDkyZNUoUKFeTt7a3o6Gj98ssvDn1+/vlnde/eXeXKlZOfn59atmyptWvX5vuxAQAAAEA2S8PX0qVLNWbMGE2ePFnbt29X/fr1FRMTo6SkpFz7b9y4Uf369dPQoUO1Y8cOxcbGKjY2Vrt377b3mT59ul555RXNmzdPmzdvVunSpRUTE6OLFy/a+3Tt2lWXL1/Wl19+qW3btql+/frq2rWrEhMTC/yYAQAAAJRMNsMwDKtePDIyUk2aNNGrr74qScrKylJoaKhGjhyp8ePHX9G/T58+SktL08qVK+1tzZo1U0REhObNmyfDMBQSEqKxY8dq3LhxkqSUlBQFBQVp4cKF6tu3r06dOqXAwECtX79erVq1kiSdO3dOfn5+io+PV3R0dJ5qT01Nlb+/v1JSUuTn5/dP3woAJUTc/pQC23fvqv4Ftm8AAHB1ec0Glp35Sk9P17Zt2xzCjouLi6Kjo7Vp06Zcn7Np06YrwlFMTIy9/4EDB5SYmOjQx9/fX5GRkfY+N910k2rUqKG33npLaWlpunz5subPn6/y5curUaNGV6330qVLSk1NdVgAAAAAIK8sC1+nTp1SZmamgoKCHNqDgoKuevlfYmLiNftn/3mtPjabTV988YV27NghX19feXl56aWXXtLq1atVpkyZq9Y7depU+fv725fQ0NDrO2AAAAAAJZrlA244m2EYevDBB1W+fHl9/fXX2rJli2JjY9WtWzcdP378qs+bMGGCUlJS7MuRI0ecWDUAAACAos6y8FWuXDm5urrqxIkTDu0nTpxQcHBwrs8JDg6+Zv/sP6/V58svv9TKlSu1ZMkStWjRQg0bNtTrr78ub29vLVq06Kr1enp6ys/Pz2EBAAAAgLyyLHx5eHioUaNGSkhIsLdlZWUpISFBUVFRuT4nKirKob8kxcfH2/uHh4crODjYoU9qaqo2b95s73PhwgVJ5v1lf+bi4qKsrKx/fmAAAAAAkAs3K198zJgxGjhwoBo3bqymTZtq9uzZSktL0+DBgyVJAwYMUMWKFTV16lRJ0qhRo9SmTRvNnDlTXbp00ZIlS7R161a98cYbksz7uUaPHq1nn31W1atXV3h4uJ588kmFhIQoNjZWkhngypQpo4EDB2rSpEny9vbWm2++qQMHDqhLly6WvA8AAAAAij9Lw1efPn108uRJTZo0SYmJiYqIiNDq1avtA2YcPnzY4QxV8+bNtXjxYj3xxBOaOHGiqlevrhUrVqhu3br2Po8++qjS0tI0bNgwJScnq2XLllq9erW8vLwkmZc7rl69Wo8//rhuu+02ZWRkqE6dOvroo49Uv359574BAAAAAEoMS+f5KsqY5wvAjWCeLwAAip9CP88XAAAAAJQkhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATkD4AgAAAAAnIHwBAAAAgBMQvgAAAADACQhfAAAAAOAEhC8AAAAAcALCFwAAAAA4AeELAAAAAJyA8AUAAAAATnBD4eu3337L7zoAAAAAoFi7ofBVrVo1tWvXTu+8844uXryY3zUBAAAAQLFzQ+Fr+/btuvXWWzVmzBgFBwfr/vvv15YtW/K7NgAAAAAoNm4ofEVEROjll1/WsWPH9H//9386fvy4WrZsqbp16+qll17SyZMn87tOAAAAACjS/tGAG25ubrrjjjsUFxenadOm6ddff9W4ceMUGhqqAQMG6Pjx4/lVJwAAAAAUaf8ofG3dulUPPPCAKlSooJdeeknjxo3T/v37FR8fr2PHjql79+75VScAAAAAFGluN/Kkl156SQsWLNC+ffvUuXNnvfXWW+rcubNcXMwsFx4eroULFyosLCw/awUAAACAIuuGwtfcuXM1ZMgQDRo0SBUqVMi1T/ny5fXf//73HxUHAAAAAMXFDYWv+Ph4Va5c2X6mK5thGDpy5IgqV64sDw8PDRw4MF+KBAAAAICi7obu+apatapOnTp1RfuZM2cUHh7+j4sCAAAAgOLmhsKXYRi5tp8/f15eXl7/qCAAAAAAKI6u67LDMWPGSJJsNpsmTZqkUqVK2bdlZmZq8+bNioiIyNcCAQAAAKA4uK7wtWPHDknmma8ffvhBHh4e9m0eHh6qX7++xo0bl78VAgAAAEAxcF3ha+3atZKkwYMH6+WXX5afn1+BFAUAAAAAxc0NjXa4YMGC/K4DAAAAAIq1PIevO+64QwsXLpSfn5/uuOOOa/Zdvnz5Py4MAAAAAIqTPIcvf39/2Ww2+2MAAAAAQN7lOXz9+VJDLjsEAAAAgOtzQ/N8/fHHH7pw4YJ9/dChQ5o9e7bWrFmTb4UBAAAAQHFyQ+Gre/fueuuttyRJycnJatq0qWbOnKnu3btr7ty5+VogAAAAABQHNxS+tm/frlatWkmSPvjgAwUHB+vQoUN666239Morr+RrgQAAAABQHNxQ+Lpw4YJ8fX0lSWvWrNEdd9whFxcXNWvWTIcOHcrXAgEAAACgOLih8FWtWjWtWLFCR44c0eeff64OHTpIkpKSkph4GQAAAABycUPha9KkSRo3bpzCwsIUGRmpqKgoSeZZsAYNGuRrgQAAAABQHOR5qPk/69Wrl1q2bKnjx4+rfv369vb27durR48e+VYcAAAAABQXNxS+JCk4OFjBwcEObU2bNv3HBQEAAABAcXRD4SstLU0vvPCCEhISlJSUpKysLIftv/32W74UBwAAAADFxQ2Fr3vvvVfr1q1T//79VaFCBdlstvyuCwAAAACKlRsKX5999pk+/fRTtWjRIr/rAQAAAIBi6YZGOyxTpozKli2b37UAAAAAQLF1Q+HrmWee0aRJk3ThwoX8rgcAAAAAiqUbuuxw5syZ2r9/v4KCghQWFiZ3d3eH7du3b8+X4gAAAACguLih8BUbG5vPZQAAAABA8XZD4Wvy5Mn5XQcAAAAAFGs3dM+XJCUnJ+s///mPJkyYoDNnzkgyLzc8evRovhUHAAAAAMXFDZ35+v777xUdHS1/f38dPHhQ9913n8qWLavly5fr8OHDeuutt/K7TgAAAAAo0m7ozNeYMWM0aNAg/fLLL/Ly8rK3d+7cWevXr8+34gAAAACguLih8PXdd9/p/vvvv6K9YsWKSkxM/MdFAQAAAEBxc0Phy9PTU6mpqVe0//zzzwoMDPzHRQEAAABAcXND4etf//qXnn76aWVkZEiSbDabDh8+rMcee0w9e/bM1wIBAAAAoDi4ofA1c+ZMnT9/XoGBgfrjjz/Upk0bVatWTb6+vnruuefyu0YAAAAAKPJuaLRDf39/xcfHa8OGDdq1a5fOnz+vhg0bKjo6Or/rAwAAAIBi4brDV1ZWlhYuXKjly5fr4MGDstlsCg8PV3BwsAzDkM1mK4g6AQAAAKBIu67LDg3D0L/+9S/de++9Onr0qOrVq6c6dero0KFDGjRokHr06FFQdQIAAABAkXZd4WvhwoVav369EhIStGPHDr333ntasmSJdu3apS+++EJffvnldU+w/NprryksLExeXl6KjIzUli1brtk/Li5ONWvWlJeXl+rVq6dVq1Y5bDcMQ5MmTVKFChXk7e2t6Oho/fLLL1fs59NPP1VkZKS8vb1VpkwZxcbGXlfdAAAAAHA9rit8vffee5o4caLatWt3xbbbbrtN48eP17vvvpvn/S1dulRjxozR5MmTtX37dtWvX18xMTFKSkrKtf/GjRvVr18/DR06VDt27FBsbKxiY2O1e/due5/p06frlVde0bx587R582aVLl1aMTExunjxor3PsmXL1L9/fw0ePFi7du3Shg0bdNddd13HOwEAAAAA18dmGIaR187BwcFavXq1IiIict2+Y8cOderUKc8TLUdGRqpJkyZ69dVXJZn3k4WGhmrkyJEaP378Ff379OmjtLQ0rVy50t7WrFkzRUREaN68eTIMQyEhIRo7dqzGjRsnSUpJSVFQUJAWLlyovn376vLlywoLC9NTTz2loUOH5vXQr5Camip/f3+lpKTIz8/vhvcDoGSJ259SYPvuXdW/wPYNAACuLq/Z4LrOfJ05c0ZBQUFX3R4UFKSzZ8/maV/p6enatm2bwwiJLi4uio6O1qZNm3J9zqZNm64YUTEmJsbe/8CBA0pMTHTo4+/vr8jISHuf7du36+jRo3JxcVGDBg1UoUIFderUyeHsWW4uXbqk1NRUhwUAAAAA8uq6wldmZqbc3K4+QKKrq6suX76cp32dOnVKmZmZV4S5oKCgq545S0xMvGb/7D+v1ee3336TJE2ZMkVPPPGEVq5cqTJlyqht27Y6c+bMVeudOnWq/P397UtoaGiejhMAAAAApOscat4wDA0aNEienp65br906VK+FFWQsrKyJEmPP/64evbsKUlasGCBKlWqpLi4ON1///25Pm/ChAkaM2aMfT01NZUABgAAACDPrit8DRw48G/7DBgwIE/7KleunFxdXXXixAmH9hMnTig4ODjX5wQHB1+zf/afJ06cUIUKFRz6ZN+nlt1eu3Zt+3ZPT0/dfPPNOnz48FXr9fT0vGroBAAAAIC/c13ha8GCBfn2wh4eHmrUqJESEhLsw7xnZWUpISFBI0aMyPU5UVFRSkhI0OjRo+1t8fHxioqKkiT7ZM8JCQn2sJWamqrNmzfr3//+tySpUaNG8vT01L59+9SyZUtJUkZGhg4ePKgqVark2/EBAAAAwJ9dV/jKb2PGjNHAgQPVuHFjNW3aVLNnz1ZaWpoGDx4syTyLVrFiRU2dOlWSNGrUKLVp00YzZ85Uly5dtGTJEm3dulVvvPGGJMlms2n06NF69tlnVb16dYWHh+vJJ59USEiIPeD5+flp+PDhmjx5skJDQ1WlShW9+OKLkqTevXs7/00AAAAAUCJYGr769OmjkydPatKkSUpMTFRERIRWr15tHzDj8OHDcnHJGROkefPmWrx4sZ544glNnDhR1atX14oVK1S3bl17n0cffVRpaWkaNmyYkpOT1bJlS61evVpeXl72Pi+++KLc3NzUv39//fHHH4qMjNSXX36pMmXKOO/gAQAAAJQo1zXPF3IwzxeAG8E8XwAAFD8FMs8XAAAAAODGEL4AAAAAwAkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAAAAJyB8AQAAAIATEL4AAAAAwAkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAAAAJyB8AQAAAIATEL4AAAAAwAkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAAAAJyB8AQAAAIATEL4AAAAAwAkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhCwAAAACcwM3qAgCgMInbn2J1CTesoGvvXdW/QPcPAEBxx5kvAAAAAHACwhcAAAAAOAHhCwAAAACcgPAFAAAAAE5A+AIAAAAAJ2C0QwAoYLaMDJU6ekQ+hw9IhqH0gDJK9y+j9IAyyvD1k1xdrS4RAAA4AeELAPKJLT1dgVs3ye/XvfI5uF8+hw7I5+B+lT56WC6XL+f6HMNmU4avn9L9y+h85XCdaHWbElu3V2r1WpLN5uQjAAAABYnwBQD/RFaWyn23UZU/+UChn62QR0rydT3dZhjySE2RR2qKfI4cVPCGtar/wpO6EBSixNbtldg6Wkkt2ijDL6BAygcAAM5D+AKA62UY8t/zgyp/8oEqf7JMpRKPXrXr5VKlda7KzTofVlXnq9ysLDc3eaSclUdKsjySz5qPk8/K8+xpeaTmTJJc6sQx3Rz3tm6Oe1tZrq461aiZfh46Qsdv68gZMQAAiijCFwDkVWamwj58TzXenCO//fuu2HzZu5SO3t5FSVGt7WHrYmBQ3sKSYcj3t18UvP4LBX39pcpv/kauly5KklwyM1V+ywaV37JByTXraM+/x+n3jv/iXjEAAIoYm2EYhtVFFEWpqany9/dXSkqK/Pz8rC4HQD6J25+Sa3vQ+gTdOn2SAvb+6NCe5eamxFbtdfhfvXWsfSdlliqdL3W4XPxDgVs2KvjrBFVY+7l8D+532J56c3Xt/fcYHe7aS4a7e7685t/pXdXfKa8DAEBRk9dsQPi6QYQvoHj6a/jy3/ODbn1hkoI3rHVoP9k4Sof/1Vu/d4pVepmyBVuUYajCl6tV6/UZumnXNodN50OraO/9D+tgr3tkuBXsxQyELwAAckf4KmCEL8A6Vzs7lZ+8jx9V3VnPqsqHS2T704/JM/Ua6PvHntbJZq0KvIYrGIbKb1ynWq+9qPJbNjhsOnNrQ22Z9rrOVa9ZYC9P+AIAIHd5zQZMsgwAf5aVpRrzZ6tTdCOFLX/PHrzSKlXWt7P+o4RlCdYEL0my2ZTUoq3WLf5Ua99bpcRW7e2byn6/Xbd3b6Nb3nxFysy0pj4AAHBNnPm6QZz5AqxTUGe+PE+eUOS4+xW04St7W7qfv/Y8ME6/9h+mLE/PAnndf+Km7ZvVePwI+f32i73tVIOm+m766zofXi1fX4szXwAA5I4zXwBwHYLWJ6hD15b24GXYbPp50L+16sud+vnekYUyeEnS6YaRiv94vfYNHSHjf6MqltuxRR26tlT1Ba9LWVkWVwgAALIRvgCUaLaMDNWbPlmth/SU1+mTkqQ/gipo3VsfadcTU5URUMbiCv9elpe3vp/wrNYu+UznqtwsSXK9dFERz01U27u7qNTRwxZXCAAAJMIXgBKs1JGDate3k2q+8bK97XjbDlrz8dc6GdXawspuzOlGzRS/8hv9MvB+e1vgd5vU/o72Krt9i4WVAQAAifAFoIQKWfOJbv9Xa920a6skKcvdXTsnPKtv3lii9JvKWVzdjcv0LqWdT07T2ndXKq1SZUmS1+mTantPN4V+HGdxdQAAlGyELwAlTrWFc9Xigf7yOJcqSTofGqYvl36uX4aOkFyKx4/FU5EtFb9inZL+NzKja/olNRtzn2q/PFVinCUAACxRPH7LAIC8MAzVmz5ZDZ6dYG863OUOxX+8XmdvbWhhYQUjI6CM1v/fMv3Wu7+9rc6caYp8+F65XPzDwsoAACiZCF8ASgRbRoaaPPpvh/u7fhrxiDbP/q8u+xbf6SIMDw9te/4V7Rr/jH00xMorl6ntPf+S56kki6sDAKBkIXwBKPZc086r5bC+CvtwiSRzGPltT83Uj6Mfl/4XSIo1m00/3ztSG+a+q8ulSkuSbtr5ndrfcZv8fv7J4uIAACg5CF8AijXP/w02Efx1giQp08NTm15dpN/uHmpxZc53PLqzvlzymS4EV5QklT72u9re3VX+P31vcWUAAJQMhC8AxVbpwwfV7s4Ylf1hhyQp3c9f6xd9qKMx/7K4Muuk1L5VCcsTdKZeA0mS59kzajOguwJ+3GVxZQAAFH+ELwDFks/B/WrXJ0a+h36TJF0ICtHaJZ/pVJPmFldmvYvlg7XurY90qkFTSZJn8lkzgO3eaW1hAAAUc4QvAMVOqd8PqU3/7vI+eUKSlFq1hr6MW6PUW2pbXFnhcdnXT18vWKZTjZpJkjxSkglgAAAUsEIRvl577TWFhYXJy8tLkZGR2rJlyzX7x8XFqWbNmvLy8lK9evW0atUqh+2GYWjSpEmqUKGCvL29FR0drV9++SXXfV26dEkRERGy2WzauXNnfh0SAIt4JR5Tm/7/Uqnjv0uSkmvU1toln+mPkEoWV1b4XPbx1df/jdPJxlGSJI/UFLUZ0F1lvt9ucWUAABRPloevpUuXasyYMZo8ebK2b9+u+vXrKyYmRklJuQ+BvHHjRvXr109Dhw7Vjh07FBsbq9jYWO3evdveZ/r06XrllVc0b948bd68WaVLl1ZMTIwuXrx4xf4effRRhYSEFNjxAXAez1NJajOgu3yOHJIkpVa9Revf+kjpZcpaXFnhZQ9gTXICWOuBsSqza5vFlQEAUPzYDMMwrCwgMjJSTZo00auvvipJysrKUmhoqEaOHKnx48df0b9Pnz5KS0vTypUr7W3NmjVTRESE5s2bJ8MwFBISorFjx2rcuHGSpJSUFAUFBWnhwoXq27ev/XmfffaZxowZo2XLlqlOnTrasWOHIiIi8lR3amqq/P39lZKSIj+/4jtHEFAYxe1PuaLN4+wZtbmnqwL2mUOnnw8N09r3VuliMF+u5IVr2nm1vK+Pym/ZIEnK8PHT+oXLdSaisb1P76r+VpUHAEChltdsYOmZr/T0dG3btk3R0dH2NhcXF0VHR2vTpk25PmfTpk0O/SUpJibG3v/AgQNKTEx06OPv76/IyEiHfZ44cUL33Xef3n77bZUqVepva7106ZJSU1MdFgCFg3tqsloP6mEPXhcqVNK6dz4meF2HzNI++uY/7yspsqUkyf18qloN6SnfX/ZaXBkAAMWHpeHr1KlTyszMVFBQkEN7UFCQEhMTc31OYmLiNftn/3mtPoZhaNCgQRo+fLgaN26svJg6dar8/f3tS2hoaJ6eB6BguaadV8uhd6rM/4ZK/6N8sL56+2NdqFjZ4sqKnsxSpc0A1qyVpP9dgjikl7yPH7W4MgAAigfL7/mywpw5c3Tu3DlNmDAhz8+ZMGGCUlJS7MuRI0cKsEIAeeFy6aJa3t9P5XaYg/RcKnOT1i1aobSwmy2urOjK9C6lDfMW60zdCElSqeO/q9XQ3nJPTba0LgAAigNLw1e5cuXk6uqqEydOOLSfOHFCwcHBuT4nODj4mv2z/7xWny+//FKbNm2Sp6en3NzcVK1aNUlS48aNNXDgwFxf19PTU35+fg4LAAtlZanJYw+q/LdfSzInUF731gqdq17T4sKKvss+vvrmP+/rfGiYJMn/55/U4v67pFwGLQIAAHlnafjy8PBQo0aNlJCQYG/LyspSQkKCoqKicn1OVFSUQ39Jio+Pt/cPDw9XcHCwQ5/U1FRt3rzZ3ueVV17Rrl27tHPnTu3cudM+VP3SpUv13HPP5esxAigYdV96VpVXLpMkXfYupfULliulVj2Lqyo+LpUrr68XLNPFsuUkSYHfbZT695cyMy2uDACAosvN6gLGjBmjgQMHqnHjxmratKlmz56ttLQ0DR48WJI0YMAAVaxYUVOnTpUkjRo1Sm3atNHMmTPVpUsXLVmyRFu3btUbb7whSbLZbBo9erSeffZZVa9eXeHh4XryyScVEhKi2NhYSVLlyo73gvj4+EiSqlatqkqVmAsIKOzClyxSrXkvSZIMFxd9O/u/Olu/kcVVFT/nw6rqm/+8r7Z3d5XbHxekDz6QHn5YevllyWazujwAAIocy8NXnz59dPLkSU2aNEmJiYmKiIjQ6tWr7QNmHD58WC4uOSfomjdvrsWLF+uJJ57QxIkTVb16da1YsUJ169a193n00UeVlpamYcOGKTk5WS1bttTq1avl5eXl9OMDkM8+/1wNJ4+xr+54cpqOt+9kYUHF29lbG2rTq4vUYlhfuWRmSnPmSBUrSo89ZnVpAAAUOZbP81VUMc8XYIFdu6RWraRz5yRJ+4Y8qO8ncqmwM1RZ9q6aPvZgTsOiRdKAAdYVBABAIVIk5vkCgDw7elTq0sUevH6P6abvxz9jcVElx6Ged0vPP5/TcO+90tdfW1cQAABFEOELQOF37pwZvI6a802drt9YW2bMl1z4EeZU48dLD/7v7FdGhnTHHdLBg5aWBABAUcJvLgAKt8uXpTvvNC85lKSbb9aGN95Tpncpa+sqiWw2afZs6fbbzfVTp6R//ct+NhIAAFwb4QtA4fbII9Lq1ebjMmWkVat06aZAa2sqydzcpKVLpVtuMdd/+MEcgj4ry9q6AAAoAghfAAqvt94yz7RIkru7tGKFVKOGlRVBMkPwxx9L/v7m+kcfSZMmWVsTAABFAOELQOH03XfSsGE566++KrVubV09cFSjhnkGLPu+u+eek5YssbYmAAAKOcIXgMInMVHq0UO6dMlcHz7cMYihcIiJkWbOzFkfPFjautW6egAAKOQIXwAKl/R0qVcv+8iGatlSevlla2vC1Y0aJQ0ZYj6+eFHq3l06dszamgAAKKQIXwAKl4cekjZsMB9XqiR98IHk4WFtTbg6m016/XWpRQtz/dgx86zlxYvW1gUAQCFE+AJQeMyfby6S5OkpffihFBRkbU34e56e0vLlUuXK5vqWLeYZMQAA4IDwBaBw+OYbacSInPU335QaN7auHlyf8uXNUQ+9vMz1N96Q/u//rK0JAIBChvAFwHq//y717GlOqCxJDz9szh2FoiUiIufMpSQ98IC0bZtl5QAAUNgQvgBYKz1duvNOKSnJXG/fXpo+3dqacOMGDDBDl2SOVtmzp3T6tLU1AQBQSBC+AFjrkUekTZvMx1WqmHNHublZWxP+mVmzpGbNzMeHDkl33y1lZlpbEwAAhQDhC4B1liyRXnnFfOzhYY5seNNN1taEf87DQ4qLkwIDzfXPP5eeesramgAAKAQIXwCssWePdO+9Oetz5jDARnFSqZJ5FtPlf//NPPOMtHKltTUBAGAxwhcA5zt/3rwXKC3NXB8wQLrvPmtrQv5r10564YWc9XvukX791bp6AACwGOELgHMZhhm09uwx1+vVk+bONSfrRfEzbpx0xx3m45QUM3RfuGBtTQAAWITwBcC5Xn3VvNdLkvz8pGXLpFKlrK0JBcdmkxYskGrUMNe//95xPjcAAEoQwhcA59m0SRo7Nmd94UKpenXLyoGT+PlJy5fnhOwFC8wFAIAShvAFwDlOnpR695YyMsz1Rx6RevSwtiY4T+3ajhMwP/ig9MMP1tUDAIAFCF8ACl5WljnYwtGj5nrr1tLzz1tbE5zvnnukYcPMx3/8YYbxc+esrQkAACcifAEoeM8/L61ZYz4OCmIi5ZLs5ZeliAjz8b59ZhgzDEtLAgDAWQhfAArW2rXS5MnmYxcX6b33pOBga2uCdby8zAmYfX3N9SVLHC9HBACgGCN8ASg4iYlSv37mZYeSNGWKOfcTSrZq1RwH3Bg1Stq2zbp6AABwEsIXgIKRmSnddZd04oS5fvvt0sSJ1taEwqNnTzN0SVJ6unn/V3KypSUBAFDQCF8ACsbTT5uXHEpSSIj0zjuSq6u1NaFwmT5datrUfHzggDR4MPd/AQCKNcIXgPwXHy8984z5OPs+r/Llra0JhY+Hh/T++1KZMub6ihXS7NlWVgQAQIEifAHIX8eOSXffnXMG49lnzaHlgdxUqSK9/XbO+qOPSps3W1cPAAAFiPAFIP9cvmwOsHHypLneqZP02GPW1oTCr0sXM3RJ5meoTx/pzBlrawIAoAAQvgDkn8mTpfXrzceVKklvvWVedgj8nWeflVq0MB8fOsT9XwCAYonfigDkj9WrzcmUJXNgjSVLpHLlrK0JRYe7u3lv4E03mesffyzNmmVtTQAA5DPCF4B/7vffpf79c9anTs05iwHkVWio4/1fjz0mffutdfUAAJDPCF8A/pns+7xOnTLXu3aVxo61tiYUXZ06SePHm48vX5buvJP7vwAAxQbhC8A/88QT0jffmI8rV5YWLeI+L/wzzzwjtWxpPj5yRBo4UMrKsrYmAADyAb8hAbhxn34qTZtmPnZzk5YulcqWtbYmFH1ubub9X9n3DK5cKb30krU1AQCQDwhfAG7MkSPSgAE569OnS82aWVcPipdKlRzv/xo/Xtqwwbp6AADIB4QvANcvI8NxLqbu3aXRoy0tCcVQx47ShAnm48xM8zOXPYccAABFEOELwPWbOFHatMl8HBYmLVgg2WyWloRi6umnpdatzcdHj5qjanL/FwCgiCJ8Abg+n3wizZhhPnZ3l95/XypTxtqaUHxl3/9Vvry5/vnnOfPJAQBQxBC+AOTdgQOO93nNmCE1aWJdPSgZQkKkxYtzzq5Onix9+aW1NQEAcAMIXwDy5tIlc86l5GRz/Y47pJEjLS0JJUj79tJTT5mPs7Kku+6Sjh+3tiYAAK4T4QtA3owZI23daj6uVk36v//jPi841+OPSx06mI9PnDAn97582dqaAAC4DoQvAH9v8WLp9dfNx15eUlyc5O9vbU0oeVxcpHfekSpWNNfXrTMvQQQAoIggfAG4tp9+koYNy1l/7TUpIsKyclDCBQaak3m7uprrzz8vffaZtTUBAJBHhC8AV3f+vNSrl5SWZq4PHiwNGWJtTUCLFtILL+Ss33OPdPiwdfUAAJBHhC8AuTMMafhwac8ec/3WW6VXX7W2JiDb2LHm5N6SOdl3797moDAAABRihC8AuZs/X3r3XfOxr6/0wQdSqVLW1gRks9nMyb3Dw831LVuk0aMtLQkAgL9D+AJwpa1bpVGjctb/7/+k6tWtqwfITZky0rJl5iAwkjRvnrRokbU1AQBwDYQvAI5Onzbv80pPN9dHjzbXgcKoQQNp7tyc9eHDpZ07LSsHAIBrIXwByJGZac6ddOiQud6smTRtmrU1AX9n0CDp/vvNxxcvmhOAnz1raUkAAOSG8AUgxxNPSPHx5uPy5c37vDw8rK0JyIuXX5aaNDEfHzgg9e8vZWVZWxMAAH/hZnUBAKwRtz/FYb3i6o/U/H/Dd2e5umrd7AU6ddFH+ks/oFDy9DS/LGjY0Lx09tNPpeeek5580urKAACw48wXAPn+sldNHnvQvr5rwrM61bSFhRUBN6ByZWnJEsnlf/+1TZ4srV5tbU0AAPwJ4Qso4dzOpajFA/fIPe28JOnQv+7UrwOHW1wVcIOio6VnnjEfG4Z0993SwYOWlgQAQDbCF1CSZWWp6SP/lu+BXyVJybXqattzs805lICiavx46V//Mh+fOSP16CFduGBtTQAAiPAFlGg1572kil+skiSl+/lr42vvKNObiZRRxLm4mPN9Vatmru/cKQ0ZYp4JAwDAQoUifL322msKCwuTl5eXIiMjtWXLlmv2j4uLU82aNeXl5aV69epp1apVDtsNw9CkSZNUoUIFeXt7Kzo6Wr/88ot9+8GDBzV06FCFh4fL29tbVatW1eTJk5WePa8RUAIEr4tX3VnPSZIMm03fzvqv0iqHWVsUkF8CAqSPPpJ8fMz1pUul/w0oAwCAVSwPX0uXLtWYMWM0efJkbd++XfXr11dMTIySkpJy7b9x40b169dPQ4cO1Y4dOxQbG6vY2Fjt3r3b3mf69Ol65ZVXNG/ePG3evFmlS5dWTEyMLl68KEnau3evsrKyNH/+fP3444+aNWuW5s2bp4kTJzrlmAHL/fyzIh++V7b/nQnY/fDjOtEm2uKigHxWu7b07rs5l9E+/rj0ySfW1gQAKNFshmHtdRiRkZFq0qSJXn31VUlSVlaWQkNDNXLkSI0fP/6K/n369FFaWppWrlxpb2vWrJkiIiI0b948GYahkJAQjR07VuPGjZMkpaSkKCgoSAsXLlTfvn1zrePFF1/U3Llz9dtvv+Wp7tTUVPn7+yslJUV+fn7Xe9iAdc6eNSdP/vlnSdLR6M7a+Po7OSPEAVfRu6q/1SXcmOeeM+ewkyRfX2nzZqlWLWtrAgAUK3nNBpb+tpWenq5t27YpOjrnG3cXFxdFR0dr06ZNuT5n06ZNDv0lKSYmxt7/wIEDSkxMdOjj7++vyMjIq+5TMgNa2bJlr7r90qVLSk1NdViAIufyZalPH3vwSrmltrbMmE/wQvE2caLUu7f5+Nw5qXt380sIAACczNLfuE6dOqXMzEwFBQU5tAcFBSkxMTHX5yQmJl6zf/af17PPX3/9VXPmzNH9999/1VqnTp0qf39/+xIaGnrtgwMKo7Fjpfh4SdKlMjfpm/mLddnH1+KigAJms0kLFkj165vrv/wi9esnZWZaWxcAoMQp8V93Hz16VB07dlTv3r113333XbXfhAkTlJKSYl+OHDnixCqBfPDGG9Irr5iP3d218bW3dCE0zNKSAKcpXVpasUIqV85c//xzc0h6AACcyNLwVa5cObm6uurEiRMO7SdOnFBwcHCuzwkODr5m/+w/87LPY8eOqV27dmrevLneeOONa9bq6ekpPz8/hwUoMr76SnrwwZz1uXN1qmkLy8oBLBEWJn3wgeTmZq7PmCG9/balJQEAShZLw5eHh4caNWqkhIQEe1tWVpYSEhIUFRWV63OioqIc+ktSfHy8vX94eLiCg4Md+qSmpmrz5s0O+zx69Kjatm2rRo0aacGCBXLhnhcUV7/9JvXsad7vJUkPPywNHWptTYBV2rTJOQMsSffeK339tXX1AABKFDerCxgzZowGDhyoxo0bq2nTppo9e7bS0tI0ePBgSdKAAQNUsWJFTZ06VZI0atQotWnTRjNnzlSXLl20ZMkSbd261X7mymazafTo0Xr22WdVvXp1hYeH68knn1RISIhiY2Ml5QSvKlWqaMaMGTp58qS9nqudcQOKpNRUqVs36cwZc71jR2n6dGtrQpEVtz+lwPbt1JEUhw+Xvv9emjdPSk+XYmOlTZukW25xXg0AgBLJ8vDVp08fnTx5UpMmTVJiYqIiIiK0evVq+4AZhw8fdjgr1bx5cy1evFhPPPGEJk6cqOrVq2vFihWqW7euvc+jjz6qtLQ0DRs2TMnJyWrZsqVWr14tLy8vSeaZsl9//VW//vqrKlWq5FCPxSPvA/nn8mVzUIGffjLXa9aUlizJueQKKKlsNmnOHOnAAfPerzNnpM6dzQAWGGh1dQCAYszyeb6KKub5QqFmGNK//y3Nn2+ulykjbdkiVatm71KQZzGA62XJHGKpqVLLltIPP5jrzZtLCQnS/76oAwAgr4rEPF8ACsgLL+QEL3d3c5CBPwUvAJL8/KRPP5VCQsz1jRulgQOlrCxr6wIAFFuEL6C4efttc1LZbAsWSLfdZl09QGEWGiqtXGkORS9J778vPf64tTUBAIotwhdQnMTHS0OG5KxPnSrdfbd19QBFQYMG5v2Q2fcXv/CC9Oab1tYEACiWCF9AcbFrl+OQ8g88ID32mLU1AUVF167Syy/nrP/739KaNdbVAwAolghfQHFw+LA5Wtu5c+Z69+7mXEY2m7V1AUXJiBHS6NHm48xM88uMrVstLQkAULwQvoCi7uxZqVMn6dgxcz0yUlq8WHJ1tbYuoCiaMcOc90uSzp8358bbs8fSkgAAxQfhCyjKLl6UevTImcurWjXpk0+kUqWsrQsoqlxdzS8vWrc210+flm6/XTp0yNq6AADFAuELKKrS06VevaR168z1wEBp9WomiQX+KW9v6eOPpYYNzfWjR80AlpRkbV0AgCKP8AUURZcvS/fcY85RJJnDZK9cKVWtam1dQHHh729+mVGjhrn+yy9STIyUwuTkAIAbR/gCipqsLOnee6W4OHPdy8sMXk2bWlsXUNwEBpojHlaqZK7v3Cl16yZduGBpWQCAoovwBRQlhmGOyLZokbnu7i4tXy61bWtpWUCxVbmyOX9euXLm+tdfS3feKWVkWFsXAKBIInwBRYVhSI8+Ks2da667upoTw3bqZG1dQHFXs6Z5CaKvr7n+6afSwIHmcPQAAFwHwhdQVDz9tDkMtmTO37VwoXTHHZaWBJQYjRqZg3B4eprr770nDRiQM6k5AAB5QPgCioIZM6QpU3LW580zB9wA4Dxt25r3Wrq7m+uLF0v9+xPAAAB5RvgCCruZM6VHHslZnzVLGjbMunqAkqxbN2nZspwAtmSJdNdd3AMGAMgTwhdQWBmGeanhuHE5bc88I40ebVlJAGQGsA8/lDw8zPW4OKlfPwIYAOBvEb6AwsgwpPHjpcmTc9qeflp6/HHragKQo0sXacWKnHvAli0zR0FMT7e0LABA4Ub4AgqbrCxp5Ehp+vSctpdekp580hxoA0Dh0KmT9NFH5lx7khnGeveWLl2ytCwAQOFF+AIKk8xMcwLl114z1202c3CNhx+2ti4AuYuJkT75JCeAffyx1LOn9Mcf1tYFACiUCF9AYZGRId19t7Rggbnu4mJOpnz//dbWBeDaoqPNub+8vc31Tz81286csbYuAEChQ/gCCoOLF6VevaSlS811Nzfzcf/+1tYFIG9uu01atUry8THXN26UWrSQDh2yti4AQKFC+AKsduqU+S35xx+b656e5r0jvXpZWhaA69S2rbRunRQUZK7v3Ss1by59/72lZQEACg/CF2Cln3+WmjWTNmww10uVMi9Z6tLF2roA3JiGDc2zXtWrm+vHjkmtWklr11pbFwCgUCB8AVb5+mspKkrav99cDw6W1q+X2re3ti4A/8zNN5sBLDLSXE9NlTp2zLmsGABQYhG+ACu8+67jDfn16kmbN0uNGllbF4D8Ua6clJCQcxY7PV3q21eaPdvSsgAA1iJ8Ac5kGNIzz0j33JMzGWuHDtI330iVK1tbG4D8Vbq0ef/mkCE5bQ8/LA0bxlxgAFBCEb4AZ0lPN38JmzQpp+2++6SVKyU/P+vqAlBw3Nyk//zH8d/9m2+ag3McO2ZZWQAAaxC+AGc4ckRq00ZauDCnbfp0af58yd3dsrIAOIHNJj31lPT22zmTMX/7rXmZcfZgOwCAEoHwBRS0NWukBg3MX7Yk85evuDjpkUfMX8oAlAz33GOGrexLjBMTpXbtpHnzzEuSAQDFHuELKCiZmea33R07SqdPm21hYeYoh8zhBZRMDRtKW7eaoUuSMjKkf/+b+8AAoIQgfAEF4dQpqXNnacqUnG+0u3aVtm2TGje2tDQAFgsMNM+IP/xwTtt//mNemnzwoGVlAQAKHuELyG/ffmteZrhmjbnu4iJNnSp99JFUtqy1tQEoHNzcpJdecrwPbPNmqX59cyoKAECxRPgC8ktmpjmIRuvW0u+/m21BQeZcP+PHmyEMAP4s+z6wsDBzPTXVbLvrLik52crKAAAFgN8Ggfzw889Sq1bSY4+Z93BI5vqOHeaQ0gBwNQ0bSjt3mqEr23vvSbfeKq1bZ1lZAID8R/gC/omsLGn2bPNSoU2bzDabzQxhX34pVahgaXkAigh/f/MSxPfeMx9L5hQV7dqZZ86zJ2UHABRphC/gRu3fb57Vevhh6eJFs61aNembb6QXXjDv6QCA69G3r/T99zlnzA1DmjZNatZM2rXL0tIAAP+czTCYXORGpKamyt/fXykpKfLz87O6HDhTVpY5L88jj0gXLuS0P/SQObBGqVLW1XYd4vanWF0C4DS9q/pbXcL1ycyUZs6Unngi51JmV1fz58xTT0m+vtbWBwBwkNdswJkv4Hrs2GEOB/3ggznBKzxc+uor6eWXi0zwAlDIubpKjz5qjp5aq5bZlpkpzZplri9fzsTMAFAEEb6AvDh1Sho+XGrUyLysMNvw4eYlQm3aWFcbgOKrYUPzS59nn80Zkv7oUalnT6lbN+nAAWvrAwBcF8IXcC2XL0uvvipVry7Nn5/zTfMtt0jx8dLcuZKPj7U1AijePD2lxx+XfvxR6tQpp/3TT6U6dczLnS9dsq4+AECeEb6Aq1m71pwseeTInPl2fHykF1+UfvhBio62tDwAJczNN5uB64MPpJAQs+2PP6SJE6UaNaRFi8xLEwEAhRbhC/irnTul2Fjpttuk3btz2gcONOfzGjdO8vCwqjoAJZnNZl5yuHevNHp0zuTthw5JgwaZ01589BH3gwFAIUX4ArLt2iX16GGe7froo5z2Jk3Mm94XLmTeLgCFg6+vOfjG9u2OlyL++KP55VHz5kzQDACFEOEL+P5785vkiAhpxYqc9pAQ6b//NYNXZKRV1QHA1dWvL61aZY64GhWV0/7tt+ZcYR075kwADwCwHOELJdeOHVKvXuYvL8uX57SHhEhz5piTKA8ZknNZDwAUVm3aSBs2mGft69TJaf/8c/MsWIsW5s857gkDAEvxWyVKlowMKS5OatXKHMJ52bKcbRUqmHN17d8vjRiRM6wzABQFNpv0r3+Zl1AvWiRVqZKzbeNG8wx/jRrS6687ThAPAHAawhdKhpMnpeeeMydEvvNOx7m6goOl2bPN0PXQQ4QuAEWbq6s0YIC0b5+0YIFUt27Otv37zUniK1eWnnxSOnbMujoBoAQifKH4Mgxp82ZzBLBKlaQnnjAnJ81Wq5b02mvSb79Jo0ZJ3t6WlQoA+c7T0/z59/330urVjtNjnD5tTtwcGip16WIOX89cYQBQ4AhfKH5+/VV66inz8ppmzczLb9LTzW3Zl+XEx5ujgj3wAKELQPFms0kxMebPvZ07pf79JTc3c1tWljlgR+/eUsWK5hdRO3daWS0AFGs2w2AykBuRmpoqf39/paSkyM/Pz+pyFLc/pcD23buqf4HtO98kJUlLl0rvvmue7fqLdD9/HbhzgH69e6guhIY5v74bUNDve0F+ZoDCpiD/PRX0v6UCqf3336U33zSn0Dh8+MrtDRpId99tTr9x8835//pFQIn/fxXIJ0XyZ+QNyGs2cHNiTUD+OnhQ+vRT6eOPpYSEK0fxstmUFNlSh7rfqSNdeyrTu5QlZQJAoVOpknmFwOTJ0tq10v/9nzka4sWL5vYdO8xl3Djp1lvNENajh/nYZrO2dgAowghfKDoyM825a1auNJfdu3PvV7+++Y1tv35ad8nXuTUCQFHi4iK1b28uycnSkiXmIB1btuT0+f57c3nqKXPQoh49pK5dzXnFGKAIAK4L4QuFl2GYo3WtXy+tW2fOV3P6dO59Q0Olu+4yQ1e9ejntXFoHAHkTECANH24uP/8sffihufz5Uu4DB6SXXjIXLy+pZcuc8NawoTnSIgDgqghfKDyysqQffjDDVvaSlJR7X5tNiow0v33t2pVLYQAgP91yi/TYY+Zy9Ki0YoUZxL76KucS74sXpS++MBfJDG9t25oTPkdGShERDGgEAH9B+II1srLMUQm3bTOX7dvNJeUaZ6p8fc0Ru7p2lTp1ksqXd169AFBSVaxozg324IPSmTPm6IhffGHea/v77zn9kpPNkLZihbnu5mZeBt60qblERpqj0Low0DKAkovwhYJ38qS0d6+0Z4/00085N3KfO3ft5/n5Sa1aSa1bm0ujRpK7u3NqBgBcqWxZ6Z57zMUwzC/REhLMZe1ax0vDL1/O+YJt7lyzrXRpqU4dc6lbN2epUIGrFwCUCIQv5I/kZPNegIMHzUmL9+7NCVxXu0/rr0JCzG9H27Qxl1tv5f4BACisbDapenVzGT7cvKLh++/Ne8Q2bzYH7fjpJzOkZUtLM9v/PKCHJJUpI9WuLVWteuVSrhzBDECxQfjCtRmGGayOH5eOHTP/PH7cvAfg4MGc5VqXC+amcmXz5uxGjcw/GzaUgoPzv34AgHO4uJj3eUVESPffb7adOydt3WqGrc2bzXD222+OgUySzp6VNmwwl7/y9TVHWaxUKWepWDHnz4oVJX9/AhqAIqFQhK/XXntNL774ohITE1W/fn3NmTNHTZs2vWr/uLg4Pfnkkzp48KCqV6+uadOmqXPnzvbthmFo8uTJevPNN5WcnKwWLVpo7ty5ql69ur3PmTNnNHLkSH3yySdycXFRz5499fLLL8vHx6dAj9VKtowMuZ9L/d+SIvdzqfJITZbH2bPyPHtaHmdPm38mn5XnmdPyPHNS3kknpEsXb/xFQ0KkWrXMpWZN88969aTAwPw7MABA4eTrK7VrZy7Z0tLMqyJ2785ZfvjB/IIvN+fO5Qx3fzXu7ub/K+XLO/4ZGGheKhkQYJ5dK1Mm53FAAJeyA3A6y8PX0qVLNWbMGM2bN0+RkZGaPXu2YmJitG/fPpXPZUCFjRs3ql+/fpo6daq6du2qxYsXKzY2Vtu3b1fdunUlSdOnT9crr7yiRYsWKTw8XE8++aRiYmL0008/yet/c5LcfffdOn78uOLj45WRkaHBgwdr2LBhWrx4sVOP/x/79lvpww/V4NhZuV04L9cLF+R2IU1uf6TJLS1Nbn9ckNv5c3I/lyq3i3/k/+u7uZlnscLCzG8mw8LM5ZZbzLB1jRm+AQAlUOnSUuPG5vJn58+bZ8X278/5M3s5dMi8h+xqMjLM8Ha1AHc1Xl6Sj48ZEn19HR43yXRTpncpXfbyVqZ98VKmdyllenoqy8NTmR4eyvL43+P/tWW5uyvLzV1Z7h7KcneTkf3YzU1Z7u4yXN3MM3+cqQNKJJth/PXcv3NFRkaqSZMmevXVVyVJWVlZCg0N1ciRIzV+/Pgr+vfp00dpaWlauXKlva1Zs2aKiIjQvHnzZBiGQkJCNHbsWI0bN06SlJKSoqCgIC1cuFB9+/bVnj17VLt2bX333Xdq/L8f/qtXr1bnzp31+++/KyQk5G/rTk1Nlb+/v1JSUuRnZcCYP9+81r4ApPsH6I/AYPlXqWiewapQwVyyH4eFmY8L8X1ZcUV4nq/eVf0LdP9F+b0BrldB/nsq6H9LBf2zoEjIyjIHb/r9d/Oy9z//eeyYuS0pyfzzWiGtMHFxMb/AzF5cXXMWFxfH9ey23Bab7crHf/7z7xbp6uvZ/tz+5z//blturrW9ILbBckfOZxTo/kPL+0uLFhXoa+RFXrOBpWe+0tPTtW3bNk2YMMHe5uLioujoaG3atCnX52zatEljxoxxaIuJidGK/w1te+DAASUmJio6Otq+3d/fX5GRkdq0aZP69u2rTZs2KSAgwB68JCk6OlouLi7avHmzevToccXrXrp0SZcuXbKvp/zvHqfU1NTrP/D8dI0he7NcXXW5VGldLuWjDB9fXfb1VYaPnzJ8fJXh66vLpX2V7uuv9IAySi9TRun+ZXUpoIzSA8oqwz9Axv9CVY+br/Eff1pafh9RvrpwzuK/n38gNbVg/zMpyu8NcL0K8t9TQf9bKuifBUWGt3fOAB9Xk32f8qlTOUtKitmWnOz4ODnZ/D/s/Hnz0sbz56X0dGcciSkry3w9Z74mYIGC/voo1c9Psvr3ceVkgr87r2Vp+Dp16pQyMzMVFBTk0B4UFKS9e/fm+pzExMRc+ycmJtq3Z7ddq89fL2l0c3NT2bJl7X3+aurUqXrqqaeuaA8NDb3a4VkvM1M6l2ouJ258N4PyrSBcj0FWFwAUI4OsLuAfGGR1AQBQmKWmmoPuFBLnzp2T/zXqsfyer6JiwoQJDmfcsrKydObMGd10002ycbq7SElNTVVoaKiOHDli7SWjQB7weUVRwucVRQmfV+QnwzB07ty5v719ydLwVa5cObm6uurECcfTMidOnFDwVYYdDw4Ovmb/7D9PnDihChUqOPSJiIiw90lKSnLYx+XLl3XmzJmrvq6np6c8PT0d2gICAq59gCjU/Pz8+GGLIoPPK4oSPq8oSvi8Ir9c64xXtqvfMOQEHh4eatSokRISEuxtWVlZSkhIUFRUVK7PiYqKcugvSfHx8fb+4eHhCg4OduiTmpqqzZs32/tERUUpOTlZ27Zts/f58ssvlZWVpcjIyHw7PgAAAADIZvllh2PGjNHAgQPVuHFjNW3aVLNnz1ZaWpoGDx4sSRowYIAqVqyoqVOnSpJGjRqlNm3aaObMmerSpYuWLFmirVu36o033pAk2Ww2jR49Ws8++6yqV69uH2o+JCREsbGxkqRatWqpY8eOuu+++zRv3jxlZGRoxIgR6tu3b55GOgQAAACA62V5+OrTp49OnjypSZMmKTExUREREVq9erV9wIzDhw/L5U8j+jVv3lyLFy/WE088oYkTJ6p69epasWKFfY4vSXr00UeVlpamYcOGKTk5WS1bttTq1avtc3xJ0rvvvqsRI0aoffv29kmWX3nlFecdOCzj6empyZMnX3EZKVAY8XlFUcLnFUUJn1dYwfJ5vgAAAACgJLD0ni8AAAAAKCkIXwAAAADgBIQvAAAAAHACwhcAAAAAOAHhC8XSwYMHNXToUIWHh8vb21tVq1bV5MmTlZ6e7tDv+++/V6tWreTl5aXQ0FBNnz79in3FxcWpZs2a8vLyUr169bRq1SpnHQZKuNdee01hYWHy8vJSZGSktmzZYnVJKIGmTp2qJk2ayNfXV+XLl1dsbKz27dvn0OfixYt68MEHddNNN8nHx0c9e/bUiRMnHPocPnxYXbp0UalSpVS+fHk98sgjunz5sjMPBSXMCy+8YJ+CKBufVViN8IViae/evcrKytL8+fP1448/atasWZo3b54mTpxo75OamqoOHTqoSpUq2rZtm1588UVNmTLFPmecJG3cuFH9+vXT0KFDtWPHDsXGxio2Nla7d++24rBQgixdulRjxozR5MmTtX37dtWvX18xMTFKSkqyujSUMOvWrdODDz6ob7/9VvHx8crIyFCHDh2UlpZm7/Pwww/rk08+UVxcnNatW6djx47pjjvusG/PzMxUly5dlJ6ero0bN2rRokVauHChJk2aZMUhoQT47rvvNH/+fN16660O7XxWYTkDKCGmT59uhIeH29dff/11o0yZMsalS5fsbY899phRo0YN+/qdd95pdOnSxWE/kZGRxv3331/wBaNEa9q0qfHggw/a1zMzM42QkBBj6tSpFlYFGEZSUpIhyVi3bp1hGIaRnJxsuLu7G3FxcfY+e/bsMSQZmzZtMgzDMFatWmW4uLgYiYmJ9j5z5841/Pz8HH4GA/nh3LlzRvXq1Y34+HijTZs2xqhRowzD4LOKwoEzXygxUlJSVLZsWfv6pk2b1Lp1a3l4eNjbYmJitG/fPp09e9beJzo62mE/MTEx2rRpk3OKRomUnp6ubdu2OXz2XFxcFB0dzWcPlktJSZEk+8/Tbdu2KSMjw+HzWrNmTVWuXNn+ed20aZPq1aunoKAge5+YmBilpqbqxx9/dGL1KAkefPBBdenS5Yr/v/msojBws7oAwBl+/fVXzZkzRzNmzLC3JSYmKjw83KFf9g/bxMRElSlTRomJiQ4/gLP7JCYmFnzRKLFOnTqlzMzMXD97e/futagqQMrKytLo0aPVokUL1a1bV5L589LDw0MBAQEOff/8s/JqP0uztwH5ZcmSJdq+fbu+++67K7bxWUVhwJkvFCnjx4+XzWa75vLXX06PHj2qjh07qnfv3rrvvvssqhwAir4HH3xQu3fv1pIlS6wuBbjCkSNHNGrUKL377rvy8vKyuhwgV5z5QpEyduxYDRo06Jp9br75ZvvjY8eOqV27dmrevLnDQBqSFBwcfMUIR9nrwcHB1+yTvR0oCOXKlZOrqyufPRQqI0aM0MqVK7V+/XpVqlTJ3h4cHKz09HQlJyc7nFH48+c1ODj4itE6//rzFvintm3bpqSkJDVs2NDelpmZqfXr1+vVV1/V559/zmcVluPMF4qUwMBA1axZ85pL9j1cR48eVdu2bdWoUSMtWLBALi6OH/eoqCitX79eGRkZ9rb4+HjVqFFDZcqUsfdJSEhweF58fLyioqIK+EhRknl4eKhRo0YOn72srCwlJCTw2YPTGYahESNG6MMPP9SXX355xeXajRo1kru7u8Pndd++fTp8+LD98xoVFaUffvjBYbTO+Ph4+fn5qXbt2s45EBR77du31w8//KCdO3fal8aNG+vuu++2P+azCstZPeIHUBB+//13o1q1akb79u2N33//3Th+/Lh9yZacnGwEBQUZ/fv3N3bv3m0sWbLEKFWqlDF//nx7nw0bNhhubm7GjBkzjD179hiTJ0823N3djR9++MGKw0IJsmTJEsPT09NYuHCh8dNPPxnDhg0zAgICHEbgApzh3//+t+Hv72989dVXDj9LL1y4YO8zfPhwo3LlysaXX35pbN261YiKijKioqLs2y9fvmzUrVvX6NChg7Fz505j9erVRmBgoDFhwgQrDgklyJ9HOzQMPquwHuELxdKCBQsMSbkuf7Zr1y6jZcuWhqenp1GxYkXjhRdeuGJf77//vnHLLbcYHh4eRp06dYxPP/3UWYeBEm7OnDlG5cqVDQ8PD6Np06bGt99+a3VJKIGu9rN0wYIF9j5//PGH8cADDxhlypQxSpUqZfTo0cPhyy7DMIyDBw8anTp1Mry9vY1y5coZY8eONTIyMpx8NChp/hq++KzCajbDMAxrzrkBAAAAQMnBPV8AAAAA4ASELwAAAABwAsIXAAAAADgB4QsAAAAAnIDwBQAAAABOQPgCAAAAACcgfAEAAACAExC+AAAAAMAJCF8AgCLjq6++ks1mU3JystWlOE1YWJhmz55tdRkAgHxA+AIA5NmgQYMUGxt7RXthCkVTpkxRREREvuzrWsdVWELRhQsXNGHCBFWtWlVeXl4KDAxUmzZt9NFHH1ldGgDgL9ysLgAAANy44cOHa/PmzZozZ45q166t06dPa+PGjTp9+nSBvWZ6ero8PDwKbP8AUFxx5gsAUCC++eYbtWrVSt7e3goNDdVDDz2ktLQ0+/a3335bjRs3lq+vr4KDg3XXXXcpKSnJYR+rVq3SLbfcIm9vb7Vr104HDx685msuXLhQTz31lHbt2iWbzSabzaaFCxdKkg4fPqzu3bvLx8dHfn5+uvPOO3XixIl8O97k5GTde++9CgwMlJ+fn2677Tbt2rXLvn3//v3q3r27goKC5OPjoyZNmuiLL75w2EdSUpK6desmb29vhYeH69133/3b1/344481ceJEde7cWWFhYWrUqJFGjhypIUOG2PtcunRJjz32mEJDQ+Xp6alq1arpv//9r337unXr1LRpU3l6eqpChQoaP368Ll++bN/etm1bjRgxQqNHj1a5cuUUExMjSdq9e7c6deokHx8fBQUFqX///jp16tQNv4cAUNwRvgAA+W7//v3q2LGjevbsqe+//15Lly7VN998oxEjRtj7ZGRk6JlnntGuXbu0YsUKHTx4UIMGDbJvP3LkiO644w5169ZNO3fu1L333qvx48df83X79OmjsWPHqk6dOjp+/LiOHz+uPn36KCsrS927d9eZM2e0bt06xcfH67ffflOfPn3y7Zh79+6tpKQkffbZZ9q2bZsaNmyo9u3b68yZM5Kk8+fPq3PnzkpISNCOHTvUsWNHdevWTYcPH7bvY9CgQTpy5IjWrl2rDz74QK+//voVgfSvgoODtWrVKp07d+6qfQYMGKD33ntPr7zyivbs2aP58+fLx8dHknT06FF17txZTZo00a5duzR37lz997//1bPPPuuwj0WLFsnDw0MbNmzQvHnzlJycrNtuu00NGjTQ1q1btXr1ap04cUJ33nnnjb6FAFD8GQAA5NHAgQMNV1dXo3Tp0g6Ll5eXIck4e/asYRiGMXToUGPYsGEOz/36668NFxcX448//sh13999950hyTh37pxhGIYxYcIEo3bt2g59HnvsMYfXyc3kyZON+vXrO7StWbPGcHV1NQ4fPmxv+/HHHw1JxpYtW666r7Vr1xqSrjje0qVLGzabzZg1a5b92Pz8/IyLFy86PL9q1arG/Pnzr7r/OnXqGHPmzDEMwzD27dt3RT179uwxJNlfJzfr1q0zKlWqZLi7uxuNGzc2Ro8ebXzzzTf27dn7jY+Pz/X5EydONGrUqGFkZWXZ21577TXDx8fHyMzMNAzDMNq0aWM0aNDA4XnPPPOM0aFDB4e2I0eOGJKMffv2XbVeACjJOPMFALgu7dq1086dOx2W//znPw59du3apYULF8rHx8e+xMTEKCsrSwcOHJAkbdu2Td26dVPlypXl6+urNm3aSJL9TNCePXsUGRnpsN+oqCiH9T/vf/jw4Vetec+ePQoNDVVoaKi9rXbt2goICNCePXskSXXq1LHvq1OnTg7P//rrr6845pCQEIfjPX/+vG666SaHmg4cOKD9+/dLMs98jRs3TrVq1VJAQIB8fHy0Z88eh+N1c3NTo0aN7PutWbOmAgICrnpcktS6dWv99ttvSkhIUK9evfTjjz+qVatWeuaZZyRJO3fulKurq/39ze29iYqKks1ms7e1aNFC58+f1++//25v+3Nd2ce8du1ah+OtWbOmJNmPGQDgiAE3AADXpXTp0qpWrZpD259/SZfMoHH//ffroYceuuL5lStXVlpammJiYhQTE6N3331XgYGBOnz4sGJiYpSenp7nWnbu3Gl/7Ofnd30H8herVq1SRkaGJMnb29thW3h4+BUhyM0t57/Q8+fPq0KFCvrqq6+u2G/288aNG6f4+HjNmDFD1apVk7e3t3r16nVdx3s17u7uatWqlVq1aqXHHntMzz77rJ5++mk99thjVxzLjSpdurTD+vnz59WtWzdNmzbtir4VKlTIl9cEgOKG8AUAyHcNGzbUTz/9dEVIy/bDDz/o9OnTeuGFF+xno7Zu3erQp1atWvr4448d2r799luH9dz27+HhoczMzCv2deTIER05csT+ej/99JOSk5NVu3ZtSVKVKlWu4wgdNWzYUImJiXJzc1NYWFiufTZs2KBBgwapR48ekszw8ucBRGrWrKnLly9r27ZtatKkiSRp3759NzR8f+3atXX58mVdvHhR9erVU1ZWltatW6fo6Ogr+taqVUvLli2TYRj2s18bNmyQr6+vKlWqdM1jXrZsmcLCwhyCKADg6rjsEACQ7x577DFt3LhRI0aM0M6dO/XLL7/oo48+sg+4UblyZXl4eGjOnDn67bff9PHHH9svk8s2fPhw/fLLL3rkkUe0b98+LV682D5y4bWEhYXpwIED2rlzp06dOqVLly4pOjpa9erV0913363t27dry5YtGjBggNq0aaPGjRv/4+ONjo5WVFSUYmNjtWbNGh08eFAbN27U448/bg+V1atX1/Lly7Vz507t2rVLd911l7Kysuz7qFGjhjp27Kj7779fmzdv1rZt23Tvvff+7Zmrtm3bav78+dq2bZsOHjyoVatWaeLEiWrXrp38/PwUFhamgQMHasiQIVqxYoUOHDigr776Su+//74k6YEHHtCRI0c0cuRI7d27Vx999JEmT56sMWPGyMXl6r8mPPjggzpz5oz69eun7777Tvv379fnn3+uwYMHXxF+AQAmwhcAIN/deuutWrdunX7++We1atVKDRo00KRJk+z3SQUGBmrhwoWKi4tT7dq19cILL2jGjBkO+6hcubKWLVumFStWqH79+po3b56ef/75v33tnj17qmPHjmrXrp0CAwP13nvvyWaz6aOPPlKZMmXUunVrRUdH6+abb9bSpUvz5XhtNptWrVql1q1ba/DgwbrlllvUt29fHTp0SEFBQZKkl156SWXKlFHz5s3VrVs3xcTEqGHDhg77WbBggUJCQtSmTRvdcccdGjZsmMqXL3/N146JidGiRYvUoUMH1apVSyNHjlRMTIw9XEnS3Llz1atXLz3wwAOqWbOm7rvvPvuw/xUrVtSqVau0ZcsW1a9fX8OHD9fQoUP1xBNPXPN1Q0JCtGHDBmVmZqpDhw6qV6+eRo8erYCAgGuGNgAoyWyGYRhWFwEAAAAAxR1fTQEAAACAExC+AAAAAMAJCF8AAAAA4ASELwAAAABwAsIXAAAAADgB4QsAAAAAnIDwBQAAAABOQPgCAAAAACcgfAEAAACAExC+AAAAAMAJCF8AAAAA4AT/D9vcBmb35dbMAAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ] @@ -11707,7 +11730,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -13.46\n" + "The average of 'head_to_head' is: -15.85\n" ] } ], @@ -11717,7 +11740,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 229, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11763,17 +11786,17 @@ " \n", " \n", " head_to_head\n", - " -1288.2\n", + " -1485.2\n", " 93.1\n", - " -13.8\n", - " 86.437183\n", - " 8.958303\n", - " -1.544559\n", + " -16.0\n", + " 84.368029\n", + " 8.743857\n", + " -1.824475\n", " 1.985277\n", - " 3.9\n", - " -31.6\n", - " 0.062941\n", - " 0.125882\n", + " 1.4\n", + " -33.3\n", + " 0.035661\n", + " 0.071323\n", " \n", " \n", "\n", @@ -11781,13 +11804,13 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -1288.2 93.1 -13.8 86.437183 8.958303 -1.544559 \n", + "head_to_head -1485.2 93.1 -16.0 84.368029 8.743857 -1.824475 \n", "\n", " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.985277 3.9 -31.6 0.062941 0.125882 " + "head_to_head 1.985277 1.4 -33.3 0.035661 0.071323 " ] }, - "execution_count": 150, + "execution_count": 229, "metadata": {}, "output_type": "execute_result" } @@ -11800,7 +11823,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 230, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11848,26 +11871,26 @@ " \n", " 279\n", " What will Kalshi's rank in the iPhone Top Free...\n", - " 0.05\n", + " 0.058\n", " [0.02,0.01,0.015,0.015,0.05,0.89]\n", " Not in top 50\n", - " -287.9\n", - " \n", - " \n", - " 335\n", - " How many cubic meters of water produced and su...\n", - " [0.167, 0.17296050626666667, 0.179050010833333...\n", - " [0.0346238299,0.0364286012,0.0383259676,0.0403...\n", - " 130027.0\n", - " -187.3\n", + " -273.1\n", " \n", " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " 0.15\n", + " 0.125\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", - " -170.0\n", + " -188.2\n", + " \n", + " \n", + " 47\n", + " What will be Donald Trump's net worth, accordi...\n", + " 0.17\n", + " [0.6,0.2,0.1,0.075,0.025]\n", + " 0-$6 billion, inclusive\n", + " -126.1\n", " \n", " \n", " 71\n", @@ -11878,48 +11901,34 @@ " -123.5\n", " \n", " \n", - " 87\n", - " How many movies will be new on Netflix's globa...\n", - " 0.28\n", - " [0.01,0.064,0.926]\n", - " 2 or more\n", - " -119.6\n", + " 247\n", + " Will the 500th richest person on Bloomberg's B...\n", + " 0.8\n", + " 0.333\n", + " no\n", + " -120.4\n", " \n", " \n", "\n", "" ], "text/plain": [ - " title \\\n", - "279 What will Kalshi's rank in the iPhone Top Free... \n", - "335 How many cubic meters of water produced and su... \n", - "121 How many movies will be new on Netflix's top 1... \n", - "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", - "87 How many movies will be new on Netflix's globa... \n", - "\n", - " bot_team_median \\\n", - "279 0.05 \n", - "335 [0.167, 0.17296050626666667, 0.179050010833333... \n", - "121 0.15 \n", - "71 0.16 \n", - "87 0.28 \n", - "\n", - " pro_median resolution \\\n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", - "335 [0.0346238299,0.0364286012,0.0383259676,0.0403... 130027.0 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more \n", - "71 0.55 yes \n", - "87 [0.01,0.064,0.926] 2 or more \n", - "\n", - " head_to_head \n", - "279 -287.9 \n", - "335 -187.3 \n", - "121 -170.0 \n", - "71 -123.5 \n", - "87 -119.6 " + " title bot_team_median \\\n", + "279 What will Kalshi's rank in the iPhone Top Free... 0.058 \n", + "121 How many movies will be new on Netflix's top 1... 0.125 \n", + "47 What will be Donald Trump's net worth, accordi... 0.17 \n", + "71 Will OpenAI, Anthropic, or Perplexity run an a... 0.16 \n", + "247 Will the 500th richest person on Bloomberg's B... 0.8 \n", + "\n", + " pro_median resolution head_to_head \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -273.1 \n", + "121 [0.005,0.017,0.157,0.821] 3 or more -188.2 \n", + "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -126.1 \n", + "71 0.55 yes -123.5 \n", + "247 0.333 no -120.4 " ] }, - "execution_count": 151, + "execution_count": 230, "metadata": {}, "output_type": "execute_result" } @@ -11941,7 +11950,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -11984,10 +11993,10 @@ " \n", " 85\n", " Will Elon Musk attend the Super Bowl in 2025?\n", - " 0.125\n", + " 0.1685\n", " 0.755\n", " no\n", - " 127.3\n", + " 122.2\n", " \n", " \n", " 0\n", @@ -12000,10 +12009,10 @@ " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.025806875566666665, 0.0571614027666666...\n", + " [0.0, 0.016687996933333334, 0.0361674514166666...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 531.1\n", + " 542.5\n", " \n", " \n", " 211\n", @@ -12016,7 +12025,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.95\n", + " 0.941\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12034,11 +12043,11 @@ "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "85 0.125 \n", + "85 0.1685 \n", "0 0.014926 \n", - "189 [0.0, 0.025806875566666665, 0.0571614027666666... \n", + "189 [0.0, 0.016687996933333334, 0.0361674514166666... \n", "211 0.99 \n", - "214 0.95 \n", + "214 0.941 \n", "\n", " pro_median resolution \\\n", "85 0.755 no \n", @@ -12048,14 +12057,14 @@ "214 0.95 annulled \n", "\n", " head_to_head \n", - "85 127.3 \n", + "85 122.2 \n", "0 270.3 \n", - "189 531.1 \n", + "189 542.5 \n", "211 NaN \n", "214 NaN " ] }, - "execution_count": 152, + "execution_count": 231, "metadata": {}, "output_type": "execute_result" } @@ -12068,7 +12077,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 232, "metadata": {}, "outputs": [ { @@ -12092,7 +12101,7 @@ "dtype: object" ] }, - "execution_count": 153, + "execution_count": 232, "metadata": {}, "output_type": "execute_result" } @@ -12106,7 +12115,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 233, "metadata": {}, "outputs": [ { @@ -12179,10 +12188,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.03366666666666667, 0.034105259000000006, 0....\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -79.442225\n", - " -79.442225\n", + " -75.535832\n", + " -75.535832\n", " \n", " \n", " 2\n", @@ -12197,10 +12206,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.07\n", " 0.013\n", - " -9.227528\n", - " -9.227528\n", + " -5.948545\n", + " -5.948545\n", " \n", " \n", " 3\n", @@ -12215,10 +12224,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " 0.55\n", + " 0.53625\n", " [0.16,0.44,0.4]\n", - " 22.314355\n", - " 22.314355\n", + " 19.782574\n", + " 19.782574\n", " \n", " \n", " 4\n", @@ -12233,10 +12242,10 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", + " [0.0, 0.002038679916666667, 0.0040819072666666...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 25.971582\n", - " 25.971582\n", + " 12.716305\n", + " 12.716305\n", " \n", " \n", "\n", @@ -12266,27 +12275,27 @@ "\n", " question_weight bot_team_median \\\n", "0 1.0 0.014926 \n", - "1 1.0 [0.03366666666666667, 0.034105259000000006, 0.... \n", - "2 1.0 0.1 \n", - "3 1.0 0.55 \n", - "4 1.0 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", + "1 1.0 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 1.0 0.07 \n", + "3 1.0 0.53625 \n", + "4 1.0 [0.0, 0.002038679916666667, 0.0040819072666666... \n", "\n", " pro_median head_to_head \\\n", "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -79.442225 \n", - "2 0.013 -9.227528 \n", - "3 [0.16,0.44,0.4] 22.314355 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 25.971582 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -75.535832 \n", + "2 0.013 -5.948545 \n", + "3 [0.16,0.44,0.4] 19.782574 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 12.716305 \n", "\n", " weighted_score \n", "0 270.308741 \n", - "1 -79.442225 \n", - "2 -9.227528 \n", - "3 22.314355 \n", - "4 25.971582 " + "1 -75.535832 \n", + "2 -5.948545 \n", + "3 19.782574 \n", + "4 12.716305 " ] }, - "execution_count": 154, + "execution_count": 233, "metadata": {}, "output_type": "execute_result" } @@ -12297,7 +12306,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ @@ -12309,7 +12318,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 235, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12321,7 +12330,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -12365,7 +12374,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 236, "metadata": {}, "outputs": [], "source": [ @@ -12375,7 +12384,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 237, "metadata": {}, "outputs": [ { @@ -12428,7 +12437,7 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.07\n", " 0.013\n", " \n", " \n", @@ -12444,7 +12453,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.62\n", + " 0.55\n", " 0.45\n", " \n", " \n", @@ -12460,7 +12469,7 @@ " NaN\n", " 31294\n", " 1.0\n", - " 0.85\n", + " 0.82\n", " 0.95\n", " \n", " \n", @@ -12492,7 +12501,7 @@ " NaN\n", " 31338\n", " 1.0\n", - " 0.85\n", + " 0.825\n", " 0.9\n", " \n", " \n", @@ -12515,11 +12524,11 @@ "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", "\n", " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "2 NaN NaN 31270 1.0 0.1 \n", - "5 NaN NaN 31282 1.0 0.62 \n", - "8 NaN NaN 31294 1.0 0.85 \n", + "2 NaN NaN 31270 1.0 0.07 \n", + "5 NaN NaN 31282 1.0 0.55 \n", + "8 NaN NaN 31294 1.0 0.82 \n", "10 NaN NaN 1.0 NaN \n", - "13 NaN NaN 31338 1.0 0.85 \n", + "13 NaN NaN 31338 1.0 0.825 \n", "\n", " pro_median \n", "2 0.013 \n", @@ -12529,7 +12538,7 @@ "13 0.9 " ] }, - "execution_count": 158, + "execution_count": 237, "metadata": {}, "output_type": "execute_result" } @@ -12540,7 +12549,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 238, "metadata": {}, "outputs": [ { @@ -12591,7 +12600,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 239, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12670,10 +12679,10 @@ " 100.0\n", " 31269\n", " 1.0\n", - " [0.03366666666666667, 0.034105259000000006, 0....\n", + " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -79.442225\n", - " -79.442225\n", + " -75.535832\n", + " -75.535832\n", " \n", " \n", " 2\n", @@ -12688,10 +12697,10 @@ " NaN\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.07\n", " 0.013\n", - " -9.227528\n", - " -9.227528\n", + " -5.948545\n", + " -5.948545\n", " \n", " \n", " 3\n", @@ -12706,10 +12715,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " 0.55\n", + " 0.53625\n", " [0.16,0.44,0.4]\n", - " 22.314355\n", - " 22.314355\n", + " 19.782574\n", + " 19.782574\n", " \n", " \n", " 4\n", @@ -12724,10 +12733,10 @@ " 400.0\n", " 31281\n", " 1.0\n", - " [0.0, 0.0027047194333333336, 0.0054148989, 0.0...\n", + " [0.0, 0.002038679916666667, 0.0040819072666666...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 25.971582\n", - " 25.971582\n", + " 12.716305\n", + " 12.716305\n", " \n", " \n", "\n", @@ -12757,24 +12766,24 @@ "\n", " question_weight bot_team_median \\\n", "0 1.0 0.014926 \n", - "1 1.0 [0.03366666666666667, 0.034105259000000006, 0.... \n", - "2 1.0 0.1 \n", - "3 1.0 0.55 \n", - "4 1.0 [0.0, 0.0027047194333333336, 0.0054148989, 0.0... \n", + "1 1.0 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 1.0 0.07 \n", + "3 1.0 0.53625 \n", + "4 1.0 [0.0, 0.002038679916666667, 0.0040819072666666... \n", "\n", " pro_median head_to_head \\\n", "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -79.442225 \n", - "2 0.013 -9.227528 \n", - "3 [0.16,0.44,0.4] 22.314355 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 25.971582 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -75.535832 \n", + "2 0.013 -5.948545 \n", + "3 [0.16,0.44,0.4] 19.782574 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 12.716305 \n", "\n", " weighted_score \n", "0 270.308741 \n", - "1 -79.442225 \n", - "2 -9.227528 \n", - "3 22.314355 \n", - "4 25.971582 " + "1 -75.535832 \n", + "2 -5.948545 \n", + "3 19.782574 \n", + "4 12.716305 " ] }, "metadata": {}, @@ -12832,10 +12841,10 @@ " NaN\n", " 35380\n", " 1.00\n", + " 0.92\n", " 0.95\n", - " 0.95\n", - " 0.000000\n", - " 0.000000\n", + " -3.208831\n", + " -3.208831\n", " \n", " \n", " 351\n", @@ -12850,10 +12859,10 @@ " NaN\n", " 35381\n", " 1.00\n", - " 0.575\n", + " 0.1775\n", " 0.05\n", - " -80.437282\n", - " -80.437282\n", + " -14.411350\n", + " -14.411350\n", " \n", " \n", " 355\n", @@ -12868,10 +12877,10 @@ " NaN\n", " 35385\n", " 1.00\n", - " 0.875\n", + " 0.8\n", " 0.97\n", - " -10.307219\n", - " -10.307219\n", + " -19.268434\n", + " -19.268434\n", " \n", " \n", " 361\n", @@ -12886,10 +12895,10 @@ " NaN\n", " 35386\n", " 0.85\n", - " 0.85\n", + " 0.755\n", " 0.666\n", - " -80.050570\n", - " -68.042984\n", + " -30.988278\n", + " -26.340037\n", " \n", " \n", " 364\n", @@ -12929,17 +12938,17 @@ "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", "\n", " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "342 NaN NaN 35380 1.00 0.95 \n", - "351 NaN NaN 35381 1.00 0.575 \n", - "355 NaN NaN 35385 1.00 0.875 \n", - "361 NaN NaN 35386 0.85 0.85 \n", + "342 NaN NaN 35380 1.00 0.92 \n", + "351 NaN NaN 35381 1.00 0.1775 \n", + "355 NaN NaN 35385 1.00 0.8 \n", + "361 NaN NaN 35386 0.85 0.755 \n", "364 NaN NaN 35387 0.85 0.05 \n", "\n", " pro_median head_to_head weighted_score \n", - "342 0.95 0.000000 0.000000 \n", - "351 0.05 -80.437282 -80.437282 \n", - "355 0.97 -10.307219 -10.307219 \n", - "361 0.666 -80.050570 -68.042984 \n", + "342 0.95 -3.208831 -3.208831 \n", + "351 0.05 -14.411350 -14.411350 \n", + "355 0.97 -19.268434 -19.268434 \n", + "361 0.666 -30.988278 -26.340037 \n", "364 0.03 -2.083409 -1.770897 " ] }, @@ -12953,7 +12962,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[160], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[239], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:839\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 837\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 839\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 842\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", diff --git a/functions.py b/functions.py index 2035207..38a3fb1 100644 --- a/functions.py +++ b/functions.py @@ -11,7 +11,7 @@ from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -from refactored_notebook.scoring import calculate_spot_baseline_score +from refactored_notebook.scoring import calculate_spot_baseline_score, nominal_location_to_cdf_location, calculate_spot_peer_score def extract_forecast(df): @@ -1023,7 +1023,7 @@ def scaled_location_to_unscaled_location(scaled_location, question_row): return (scaled_location - range_min) / (range_max - range_min) -def nominal_location_to_cdf_location(nominal_location, question_data): +def nominal_location_to_cdf_location_via_question_dict(nominal_location, question_data): """ Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to metaculus's "internal representation" range [0, 1] incorporating question scaling @@ -1035,28 +1035,13 @@ def nominal_location_to_cdf_location(nominal_location, question_data): Returns: float: CDF location. """ - if question_data["type"] == "date": - scaled_location = datetime.fromisoformat(nominal_location).timestamp() - else: - scaled_location = float(nominal_location) + # Unscale the value to put it into the range [0,1] range_min = question_data["range_min"] range_max = question_data["range_max"] zero_point = question_data["zero_point"] - if ~np.isnan(zero_point) and (zero_point is not None): - # logarithmically scaled question - deriv_ratio = (range_max - zero_point) / (range_min - zero_point) - unscaled_location = ( - np.log( - (scaled_location - range_min) * (deriv_ratio - 1) - + (range_max - range_min) - ) - - np.log(range_max - range_min) - ) / np.log(deriv_ratio) - else: - # linearly scaled question - unscaled_location = (scaled_location - range_min) / (range_max - range_min) - return unscaled_location + + return nominal_location_to_cdf_location(nominal_location, range_min, range_max, zero_point) def get_cdf_at(cdf, unscaled_location): @@ -1103,8 +1088,8 @@ def cdf_between(row, cdf, lower_bound, upper_bound): Returns: float: Probability between the bounds. """ - a = get_cdf_at(cdf, nominal_location_to_cdf_location(lower_bound, row)) - b = get_cdf_at(cdf, nominal_location_to_cdf_location(upper_bound, row)) + a = get_cdf_at(cdf, nominal_location_to_cdf_location_via_question_dict(lower_bound, row)) + b = get_cdf_at(cdf, nominal_location_to_cdf_location_via_question_dict(upper_bound, row)) return b - a @@ -1190,7 +1175,7 @@ def compute_bucket_forecast_value(row): # Compute forecast_value using the extracted string_location forecast_value = get_cdf_at( - row["cdf"], nominal_location_to_cdf_location(string_location, row) + row["cdf"], nominal_location_to_cdf_location_via_question_dict(string_location, row) ) # Apply logic based on comparison_type @@ -1239,143 +1224,25 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] -def calculate_peer_score_numeric(row, bot_col, pro_col='pro_median'): - """Calculate peer score for numeric questions""" - try: - # Check if bot didn't provide a forecast - if pd.isna(row[bot_col]): - return np.nan - - resolution_value = row['resolution'] - - # Get the CDF values - bot_cdf = row[bot_col] - pro_median_cdf = row[pro_col] - - # Handle special cases - if resolution_value == 'below_lower_bound': - # Use first point in CDF - if isinstance(bot_cdf, (list, np.ndarray)) and len(bot_cdf) > 0: - bot_prob = bot_cdf[0] - else: - return np.nan - - if isinstance(pro_median_cdf, (list, np.ndarray)) and len(pro_median_cdf) > 0: - pro_median_prob = pro_median_cdf[0] - else: - return np.nan - - elif resolution_value == 'above_upper_bound': - # Use (1 - last point in CDF) - if isinstance(bot_cdf, (list, np.ndarray)) and len(bot_cdf) > 0: - bot_prob = 1 - bot_cdf[-1] - else: - return np.nan - - if isinstance(pro_median_cdf, (list, np.ndarray)) and len(pro_median_cdf) > 0: - pro_median_prob = 1 - pro_median_cdf[-1] - else: - return np.nan - else: - # Convert to float if it's a numeric resolution - try: - resolution_float = float(resolution_value) - - # Convert CDF to PMF - if isinstance(bot_cdf, (list, np.ndarray)) and isinstance(pro_median_cdf, (list, np.ndarray)): - # Convert CDFs to PMFs - bot_pmf = np.diff(np.concatenate([[0], bot_cdf])) - pro_pmf = np.diff(np.concatenate([[0], pro_median_cdf])) - - # Use nominal_location_to_cdf_location to find the appropriate bucket - cdf_location = nominal_location_to_cdf_location(resolution_float, row) - - # Find the appropriate bucket index - bucket_index = min(int(cdf_location * (len(bot_pmf) - 1)), len(bot_pmf) - 1) - - # Get probabilities - bot_prob = bot_pmf[bucket_index] - pro_median_prob = pro_pmf[bucket_index] - else: - return np.nan - except: - return np.nan - - # Ensure non-zero probabilities - bot_prob = max(bot_prob, 1e-10) - pro_median_prob = max(pro_median_prob, 1e-10) - - # Calculate peer score and divide by 2 for continuous questions - return np.log(bot_prob / pro_median_prob) / 2 - - except Exception as e: - # Print the specific error for debugging - return np.nan - -def calculate_peer_score_binary(row, bot_col, pro_col='pro_median'): - """Calculate peer score for binary questions""" - if row['resolution'] == 'yes': - return np.log(row[bot_col] / row[pro_col]) - else: # resolution is 'no' - return np.log((1 - row[bot_col]) / (1 - row[pro_col])) - -def parse_cdf_string(cdf_string): - """Parse CDF string into numpy array""" - return np.array([float(x) for x in cdf_string.strip('[]').split(',')]) - -def calculate_peer_score_multiple_choice(row, bot_col, pro_col='pro_median'): - """Calculate peer score for multiple choice questions""" - # Check if bot didn't provide a forecast (NaN) - if pd.isna(row[bot_col]): - return np.nan - - # Get the resolution value and options - resolution_value = row['resolution'] +def calculate_weighted_h2h_score_between_two_forecast_columns(row, col_a, col_b): + forecast_a = row[col_a] # If string, I may need to do: [float(x) for x in bot_pmf_raw.strip('[]').split(',')] + forecast_b = row[col_b] + resolution = row['resolution'] options = row['options_parsed'] if 'options_parsed' in row else row['options'] - - # Find the index of the resolution in options array - resolution_str = str(resolution_value) - - try: - resolution_index = options.index(resolution_str) - - # Get the forecasts - bot_pmf_raw = row[bot_col] - pro_pmf_raw = row[pro_col] - - # Parse string representations of arrays if needed - if isinstance(bot_pmf_raw, str): - bot_pmf = [float(x) for x in bot_pmf_raw.strip('[]').split(',')] - else: - bot_pmf = bot_pmf_raw - - if isinstance(pro_pmf_raw, str): - pro_pmf = [float(x) for x in pro_pmf_raw.strip('[]').split(',')] - else: - pro_pmf = pro_pmf_raw - - # Get the probabilities at the correct index - bot_prob = bot_pmf[resolution_index] - pro_prob = pro_pmf[resolution_index] - - # Calculate peer score - return np.log(bot_prob / pro_prob) - except Exception as e: - # If any error occurs, return NaN - return np.nan - -def calculate_peer_score(row, bot_col, pro_col='pro_median'): - """Calculate peer score based on question type""" - if row['type'] == 'binary': - return calculate_peer_score_binary(row, bot_col, pro_col) - elif row['type'] == 'multiple_choice': - return calculate_peer_score_multiple_choice(row, bot_col, pro_col) - elif row['type'] == 'numeric': - return calculate_peer_score_numeric(row, bot_col, pro_col) - else: - # Unknown question type; return NaN - return np.nan + range_min = row['range_min'] + range_max = row['range_max'] + question_weight = row['question_weight'] + score = calculate_spot_peer_score( + forecast=forecast_a, + forecast_for_other_users=[forecast_b], + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, + question_weight=question_weight + ) + return score def calculate_all_peer_scores(df, all_bots, pro_col='pro_median'): """Calculate peer scores for all bots""" @@ -1384,10 +1251,10 @@ def calculate_all_peer_scores(df, all_bots, pro_col='pro_median'): # Calculate peer score for each bot for bot in all_bots: - df_peer[bot] = 100 * df.apply(lambda row: calculate_peer_score(row, bot, pro_col), axis=1) + df_peer[bot] = 100 * df.apply(lambda row: calculate_weighted_h2h_score_between_two_forecast_columns(row, bot, pro_col), axis=1) # Calculate peer score for bot_team_median df_peer["bot_team_median"] = 100 * df.apply( - lambda row: calculate_peer_score(row, 'bot_median', pro_col), axis=1) + lambda row: calculate_weighted_h2h_score_between_two_forecast_columns(row, 'bot_median', pro_col), axis=1) return df_peer diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 265e32d..17f548c 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,33 +1,33 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI -metac-perplexity,20.6,20.6,20.6,20.6,20.6 -metac-o1,20.2,20.2,20.2,20.2,20.2 +metac-perplexity,18.1,18.1,18.1,18.1,18.1 acm_bot,17.7,17.7,17.7,17.7,17.7 -bot_median,17.4,17.4,17.4,17.4,17.4 +bot_median,17.0,17.0,17.0,17.0,17.0 +metac-o1,16.6,16.6,16.6,16.6,16.6 +metac-claude-3-5-sonnet-20240620,14.8,14.8,14.8,14.8,14.8 manticAI,14.5,14.5,14.5,14.5,14.5 twsummerbot,14.3,14.3,14.3,14.3,14.3 jkraybill_bot,14.3,14.3,14.3,14.3,14.3 -metac-claude-3-5-sonnet-20240620,13.0,13.0,13.0,13.0,13.0 -metac-claude-3-5-sonnet-latest,12.4,12.4,12.4,12.4,12.4 -metac-deepseek-r1,12.3,12.3,12.3,12.3,12.3 -metac-Llama-3.1,12.2,12.2,12.2,12.2,12.2 -GreeneiBot2,11.8,11.8,11.8,11.8,11.8 +metac-exa,13.0,13.0,13.0,13.0,13.0 +GreeneiBot2,12.2,12.2,12.2,12.2,12.2 NextWorldLab,11.1,11.1,11.1,11.1,11.1 +metac-Llama-3.1,10.5,10.5,10.5,10.5,10.5 Grizeu_Bot,10.2,10.2,10.2,10.2,10.2 SynapseSeer,10.2,10.2,10.2,10.2,10.2 -metac-grok-2-1212,9.8,9.8,9.8,9.8,9.8 +metac-claude-3-5-sonnet-latest,10.0,10.0,10.0,10.0,10.0 mmBot,9.7,9.7,9.7,9.7,9.7 -metac-Gemini-Exp-1206,9.6,9.6,9.6,9.6,9.6 annabot,9.0,9.0,9.0,9.0,9.0 -metac-exa,8.8,8.8,8.8,8.8,8.8 VeritasAI,8.4,8.4,8.4,8.4,8.4 +metac-grok-2-1212,8.2,8.2,8.2,8.2,8.2 laylaps,7.6,7.6,7.6,7.6,7.6 +metac-Gemini-Exp-1206,7.4,7.4,7.4,7.4,7.4 metac-o1-preview,6.7,6.7,6.7,6.7,6.7 cookics_bot_TEST,6.3,6.3,6.3,6.3,6.3 +metac-deepseek-r1,5.7,5.7,5.7,5.7,5.7 MWG,5.5,5.5,5.5,5.5,5.5 ajf-bot,5.1,5.1,5.1,5.1,5.1 +metac-gpt-4o,4.8,4.8,4.8,4.8,4.8 pgodzinai,3.5,3.5,3.5,3.5,3.5 KevinTestBot,3.3,3.3,3.3,3.3,3.3 -metac-gpt-4o,3.0,3.0,3.0,3.0,3.0 InstitutPelFutur,2.7,2.7,2.7,2.7,2.7 Bot_Pepa,2.6,2.6,2.6,2.6,2.6 CumulativeBot,2.5,2.5,2.5,2.5,2.5 @@ -37,7 +37,7 @@ jonahsingerbot,2.2,2.2,2.2,2.2,2.2 bean_bot,2.1,2.1,2.1,2.1,2.1 X_bot,1.9,1.9,1.9,1.9,1.9 CatrachoCaster,1.8,1.8,1.8,1.8,1.8 -RPM_bot,0.8,0.8,0.8,0.8,0.8 +RPM_bot,1.2,1.2,1.2,1.2,1.2 4Shadower,0.6,0.6,0.6,0.6,0.6 krm-bot,0.6,0.6,0.6,0.6,0.6 andrewsiah,0.0,0.0,0.0,0.0,0.0 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 889922c..5e73739 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,33 +1,33 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -metac-perplexity,1957.5,95.0,20.6,0.0,0.0,inf,1.9847501794262088,20.6,20.6,1.0,0.000000 -metac-o1,1921.1,95.0,20.2,0.0,0.0,inf,1.9847501794262088,20.2,20.2,1.0,0.000000 +metac-perplexity,1719.7,95.0,18.1,3.570999300115835e-15,3.663767977230083e-16,4.940951081399963e+16,1.9847501794262088,18.1,18.1,1.0,0.000000 acm_bot,1680.6,95.0,17.7,3.570999300115835e-15,3.663767977230083e-16,4.828448927545706e+16,1.9847501794262088,17.7,17.7,1.0,0.000000 -bot_median,1655.0,95.0,17.4,3.570999300115835e-15,3.663767977230083e-16,4.755070072324921e+16,1.9847501794262088,17.4,17.4,1.0,0.000000 +bot_median,1610.4,95.0,17.0,3.570999300115835e-15,3.663767977230083e-16,4.626691199221798e+16,1.9847501794262088,17.0,17.0,1.0,0.000000 +metac-o1,1577.6,95.0,16.6,3.570999300115835e-15,3.663767977230083e-16,4.532462410721762e+16,1.9847501794262088,16.6,16.6,1.0,0.000000 +metac-claude-3-5-sonnet-20240620,1405.9,95.0,14.8,3.570999300115835e-15,3.663767977230083e-16,4.039353684227144e+16,1.9847501794262088,14.8,14.8,1.0,0.000000 manticAI,1378.2,95.0,14.5,0.0,0.0,inf,1.9847501794262088,14.5,14.5,1.0,0.000000 twsummerbot,1355.4,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.788325122257914e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 jkraybill_bot,1354.5,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.783286397381174e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 -metac-claude-3-5-sonnet-20240620,1235.2,95.0,13.0,1.7854996500579174e-15,1.8318839886150415e-16,7.097519447336572e+16,1.9847501794262088,13.0,13.0,1.0,0.000000 -metac-claude-3-5-sonnet-latest,1180.5,95.0,12.4,0.0,0.0,inf,1.9847501794262088,12.4,12.4,1.0,0.000000 -metac-deepseek-r1,1166.0,95.0,12.3,1.7854996500579174e-15,1.8318839886150415e-16,6.700213221693384e+16,1.9847501794262088,12.3,12.3,1.0,0.000000 -metac-Llama-3.1,1154.9,95.0,12.2,3.570999300115835e-15,3.663767977230083e-16,3.3181275591894544e+16,1.9847501794262088,12.2,12.2,1.0,0.000000 -GreeneiBot2,1119.2,95.0,11.8,1.7854996500579174e-15,1.8318839886150415e-16,6.4310595726389144e+16,1.9847501794262088,11.8,11.8,1.0,0.000000 +metac-exa,1233.6,95.0,13.0,1.7854996500579174e-15,1.8318839886150415e-16,7.088709959185136e+16,1.9847501794262088,13.0,13.0,1.0,0.000000 +GreeneiBot2,1163.2,95.0,12.2,0.0,0.0,inf,1.9847501794262088,12.2,12.2,1.0,0.000000 NextWorldLab,1050.3,95.0,11.1,1.7854996500579174e-15,1.8318839886150415e-16,6.035037516349447e+16,1.9847501794262088,11.1,11.1,1.0,0.000000 +metac-Llama-3.1,997.0,95.0,10.5,1.7854996500579174e-15,1.8318839886150415e-16,5.728815548098371e+16,1.9847501794262088,10.5,10.5,1.0,0.000000 Grizeu_Bot,966.4,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 SynapseSeer,964.7,95.0,10.2,1.7854996500579174e-15,1.8318839886150415e-16,5.5434396730578184e+16,1.9847501794262088,10.2,10.2,1.0,0.000000 -metac-grok-2-1212,932.3,95.0,9.8,1.7854996500579174e-15,1.8318839886150415e-16,5.357004504213439e+16,1.9847501794262088,9.8,9.8,1.0,0.000000 +metac-claude-3-5-sonnet-latest,949.9,95.0,10.0,0.0,0.0,inf,1.9847501794262088,10.0,10.0,1.0,0.000000 mmBot,924.8,95.0,9.7,0.0,0.0,inf,1.9847501794262088,9.7,9.7,1.0,0.000000 -metac-Gemini-Exp-1206,910.2,95.0,9.6,1.7854996500579174e-15,1.8318839886150415e-16,5.230331909359555e+16,1.9847501794262088,9.6,9.6,1.0,0.000000 annabot,854.4,95.0,9.0,1.7854996500579174e-15,1.8318839886150415e-16,4.909363317298574e+16,1.9847501794262088,9.0,9.0,1.0,0.000000 -metac-exa,836.7,95.0,8.8,1.7854996500579174e-15,1.8318839886150415e-16,4.808056144499867e+16,1.9847501794262088,8.8,8.8,1.0,0.000000 VeritasAI,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608352429717695e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 +metac-grok-2-1212,775.1,95.0,8.2,0.0,0.0,inf,1.9847501794262088,8.2,8.2,1.0,0.000000 laylaps,723.4,95.0,7.6,8.927498250289587e-16,9.159419943075207e-17,8.313179820692651e+16,1.9847501794262088,7.6,7.6,1.0,0.000000 -metac-o1-preview,640.2,95.0,6.7,8.927498250289587e-16,9.159419943075207e-17,7.357383207755715e+16,1.9847501794262088,6.7,6.7,1.0,0.000000 +metac-Gemini-Exp-1206,701.9,95.0,7.4,8.927498250289587e-16,9.159419943075207e-17,8.065986188688938e+16,1.9847501794262088,7.4,7.4,1.0,0.000000 +metac-o1-preview,633.2,95.0,6.7,8.927498250289587e-16,9.159419943075207e-17,7.277309325504542e+16,1.9847501794262088,6.7,6.7,1.0,0.000000 cookics_bot_TEST,596.4,95.0,6.3,0.0,0.0,inf,1.9847501794262088,6.3,6.3,1.0,0.000000 +metac-deepseek-r1,545.5,95.0,5.7,8.927498250289587e-16,9.159419943075207e-17,6.2687228856570984e+16,1.9847501794262088,5.7,5.7,1.0,0.000000 MWG,520.8,95.0,5.5,8.927498250289587e-16,9.159419943075207e-17,5.985647068886487e+16,1.9847501794262088,5.5,5.5,1.0,0.000000 ajf-bot,481.2,95.0,5.1,1.7854996500579174e-15,1.8318839886150415e-16,2.7648981076196796e+16,1.9847501794262088,5.1,5.1,1.0,0.000000 +metac-gpt-4o,451.6,95.0,4.8,8.927498250289587e-16,9.159419943075207e-17,5.190357943531163e+16,1.9847501794262088,4.8,4.8,1.0,0.000000 pgodzinai,336.0,95.0,3.5,8.927498250289587e-16,9.159419943075207e-17,3.8616390554277256e+16,1.9847501794262088,3.5,3.5,1.0,0.000000 KevinTestBot,314.5,95.0,3.3,8.927498250289587e-16,9.159419943075207e-17,3.614851659932975e+16,1.9847501794262088,3.3,3.3,1.0,0.000000 -metac-gpt-4o,280.3,95.0,3.0,8.927498250289587e-16,9.159419943075207e-17,3.221540864953186e+16,1.9847501794262088,3.0,3.0,1.0,0.000000 InstitutPelFutur,256.0,95.0,2.7,8.927498250289587e-16,9.159419943075207e-17,2.9416230195900824e+16,1.9847501794262088,2.7,2.7,1.0,0.000000 Bot_Pepa,246.8,95.0,2.6,0.0,0.0,inf,1.9847501794262088,2.6,2.6,1.0,0.000000 CumulativeBot,241.1,95.0,2.5,4.463749125144793e-16,4.579709971537604e-17,5.542702538240192e+16,1.9847501794262088,2.5,2.5,1.0,0.000000 @@ -37,7 +37,7 @@ jonahsingerbot,212.9,95.0,2.2,4.463749125144793e-16,4.579709971537604e-17,4.8945 bean_bot,200.0,95.0,2.1,0.0,0.0,inf,1.9847501794262088,2.1,2.1,1.0,0.000000 X_bot,181.4,95.0,1.9,0.0,0.0,inf,1.9847501794262088,1.9,1.9,1.0,0.000000 CatrachoCaster,167.5,95.0,1.8,4.463749125144793e-16,4.579709971537604e-17,3.8493725321790856e+16,1.9847501794262088,1.8,1.8,1.0,0.000000 -RPM_bot,71.4,95.0,0.8,1.1159372812861984e-16,1.144927492884401e-17,6.560692777870449e+16,1.9847501794262088,0.8,0.8,1.0,0.000000 +RPM_bot,118.6,95.0,1.2,4.463749125144793e-16,4.579709971537604e-17,2.7264857831745884e+16,1.9847501794262088,1.2,1.2,1.0,0.000000 4Shadower,61.1,95.0,0.6,2.2318745625723967e-16,2.289854985768802e-17,2.810105705323094e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 krm-bot,60.8,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,5.586128771835555e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 andrewsiah,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 080aea4..e48eae4 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -1,4 +1,6 @@ +from datetime import datetime import numpy as np +from scipy.stats.mstats import gmean from refactored_notebook.data_models import ForecastType, ResolutionType @@ -12,7 +14,57 @@ def calculate_spot_peer_score( range_max: float | None = None, question_weight: float = 1.0, ) -> float: - raise NotImplementedError("Not implemented") + forecast_for_resolution, _ = _determine_probability_for_resolution_and_baseline( + forecast, resolution, options, range_min, range_max + ) + other_user_forecasts, _ = zip( + [ + _determine_probability_for_resolution_and_baseline( + forecast, resolution, options, range_min, range_max + ) + for forecast in forecast_for_other_users + ] + ) + geometric_mean = gmean(other_user_forecasts) + peer_score = np.log(forecast_for_resolution / geometric_mean) + if isinstance( + resolution, float + ): # @Check: This doesn't account for resolution being 'above_upper_bound' or 'below_lower_bound' + peer_score /= 2 + return peer_score * question_weight + + +def nominal_location_to_cdf_location( + nominal_location: float, + range_min: float, + range_max: float, + zero_point: float | None = None, +) -> float: + """ + Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to + metaculus's "internal representation" range [0, 1] incorporating question scaling + """ + assert isinstance(zero_point, float | None) + + # TODO: Make sure to use datetime.fromisoformat(nominal_location).timestamp() if you start using date questions + scaled_location = float(nominal_location) + + # Unscale the value to put it into the range [0,1] + if zero_point is not None: + # logarithmically scaled question + deriv_ratio = (range_max - zero_point) / (range_min - zero_point) + unscaled_location = ( + np.log( + (scaled_location - range_min) * (deriv_ratio - 1) + + (range_max - range_min) + ) + - np.log(range_max - range_min) + ) / np.log(deriv_ratio) + else: + # linearly scaled question + unscaled_location = (scaled_location - range_min) / (range_max - range_min) + assert 0 <= unscaled_location <= 1 + return unscaled_location def calculate_spot_baseline_score( @@ -58,10 +110,19 @@ def _determine_probability_for_resolution_and_baseline( range_min: float | None = None, range_max: float | None = None, ) -> tuple[float, float]: + """ + Returns a 0 to 1 probability for the resolution + Also returns the baseline probability used in baseline scoring + """ + is_numeric = ( + isinstance(resolution, float) + or isinstance(resolution, int) + or resolution == "above_upper_bound" + or resolution == "below_lower_bound" + ) is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) - is_numeric = isinstance(resolution, float) or isinstance(resolution, int) if forecast is None or resolution is None: raise NotImplementedError( @@ -76,62 +137,104 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError("Forecast contains probabilities outside of 0 to 1 range") if is_binary: - if len(forecast) != 1 and len(forecast) != 2: - raise ValueError( - "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" - ) - - forecast_val = float(forecast[0]) - baseline_prob = 0.5 - if resolution: - prob_for_resolution = forecast_val - else: - prob_for_resolution = 1 - forecast_val + prob_for_resolution, baseline_prob = _binary_resolution_baseline_prob( + forecast, resolution + ) elif is_multiple_choice: if options is None: raise ValueError("Options are required for multiple choice questions") - - if len(forecast) != len(options): - raise ValueError("Forecast and options have different lengths") - - pmf = [float(p) for p in forecast] - options = [str(opt) for opt in options] - resolution_idx = options.index(str(resolution)) - prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) + prob_for_resolution, baseline_prob = _multiple_choice_resolution_baseline_prob( + forecast, resolution, options + ) elif is_numeric: if range_min is None or range_max is None: raise ValueError( "Range min and range max are required for numeric questions" ) - if len(forecast) != 201: - raise ValueError("CDF should have 201 bins") - previous_prob = 0 - for current_prob in forecast: - if current_prob < previous_prob: - raise ValueError("CDF should be in increasing order") - previous_prob = current_prob - - cdf = [float(p) for p in forecast] - pmf = [cdf[0]] + [ - cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) - ] # @Check: is this a correct conversion? - pmf.append(1 - cdf[-1]) + prob_for_resolution, baseline_prob = _numeric_resolution_baseline_prob( + forecast, resolution, range_min, range_max + ) + else: + raise ValueError("Unknown question type") - resolution = float(resolution) + assert 0 < prob_for_resolution <= 1 + assert 0 < baseline_prob <= 1 + return prob_for_resolution, baseline_prob - bin_edges = np.linspace(range_min, range_max, 200) - resolution_idx = np.searchsorted(bin_edges, resolution, side="right") - if resolution_idx >= len(pmf): - raise ValueError("Resolution is out of bounds") +def _binary_resolution_baseline_prob(forecast: list[float], resolution: bool): + if len(forecast) != 1 and len(forecast) != 2: + raise ValueError( + "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" + ) + + forecast_val = float(forecast[0]) + baseline_prob = 0.5 + if resolution: + prob_for_resolution = forecast_val + else: + prob_for_resolution = 1 - forecast_val + return prob_for_resolution, baseline_prob + - prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len( - pmf - ) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins +def _multiple_choice_resolution_baseline_prob( + forecast: list[float], resolution: str, options: list[str] +): + if options is None: + raise ValueError("Options are required for multiple choice questions") + if len(forecast) != len(options): + raise ValueError("Forecast and options have different lengths") + + pmf = [float(p) for p in forecast] + options = [str(opt) for opt in options] + resolution_idx = options.index(str(resolution)) + prob_for_resolution = pmf[resolution_idx] + baseline_prob = 1 / len(pmf) + return prob_for_resolution, baseline_prob + + +def _numeric_resolution_baseline_prob( + forecast: list[float], resolution: float | str, range_min: float, range_max: float +): + if len(forecast) != 201: + raise ValueError("CDF should have 201 bins") + + previous_prob = 0 + for current_prob in forecast: + if current_prob < previous_prob: + raise ValueError("CDF should be in increasing order") + previous_prob = current_prob + + cdf = [float(p) for p in forecast] + assert len(cdf) == 201 + pmf = [cdf[0]] + [ + cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) + ] # @Check: is this a correct conversion? + pmf.append(1 - cdf[-1]) + # pmf = np.diff(np.concatenate([[0], cdf])) + assert len(pmf) == 200 + + if resolution == "below_lower_bound": + prob_for_resolution = cdf[0] + elif resolution == "above_upper_bound": + prob_for_resolution = 1 - cdf[-1] # Grab probability of 201st bin else: - raise ValueError("Unknown question type") + resolution = float(resolution) + # bin_edges = np.linspace(range_min, range_max, 200) + # resolution_bin_idx = np.searchsorted(bin_edges, resolution, side="right") + + cdf_location = nominal_location_to_cdf_location( + resolution, range_min, range_max + ) + resolution_bin_idx = min(int(cdf_location * (len(pmf) - 1)), len(pmf) - 1) + if resolution_bin_idx >= len(pmf): + raise ValueError("Resolution is out of bounds") + + prob_for_resolution = pmf[resolution_bin_idx] + baseline_prob = 1 / len( + pmf + ) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins + # @Check: Should this be either 1, 0.9, or 0.95? return prob_for_resolution, baseline_prob diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 7080b1f..c98409c 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -156,7 +156,7 @@ def test_peer_score_zero_when_all_same( ), # Numeric ( - [[0.1] * 100 + [0.9] * 101, [0.9] * 100 + [0.1] * 101, [0.5] * 201], + [[0.1] * 100 + [0.9] * 101, [0.2] * 100 + [0.8] * 101, [0.5] * 201], 0.5, None, 0.0, From 7a65265fa78c79217ab32ce12ca8a7baa0aa3aae Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 09:39:06 -0600 Subject: [PATCH 11/26] Got all binary scoring tests passing --- refactored_notebook/scoring.py | 80 ++-- tests/test_scoring.py | 650 +++++++++++++++++++++++---------- 2 files changed, 503 insertions(+), 227 deletions(-) diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index e48eae4..dbe06af 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -17,14 +17,16 @@ def calculate_spot_peer_score( forecast_for_resolution, _ = _determine_probability_for_resolution_and_baseline( forecast, resolution, options, range_min, range_max ) - other_user_forecasts, _ = zip( - [ - _determine_probability_for_resolution_and_baseline( - forecast, resolution, options, range_min, range_max - ) - for forecast in forecast_for_other_users - ] - ) + other_user_forecasts_and_baseline_prob = [ + _determine_probability_for_resolution_and_baseline( + forecast, resolution, options, range_min, range_max + ) + for forecast in forecast_for_other_users + ] + other_user_forecasts = [ + forecast for forecast, _ in other_user_forecasts_and_baseline_prob + ] + geometric_mean = gmean(other_user_forecasts) peer_score = np.log(forecast_for_resolution / geometric_mean) if isinstance( @@ -43,6 +45,8 @@ def nominal_location_to_cdf_location( """ Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to metaculus's "internal representation" range [0, 1] incorporating question scaling + 0.8 would incidate the nomial locatoin is at cdf index 201 * 0.8 + Values higher/lower than 0 and 1 are resolutions that are above/below the upper/lower bound """ assert isinstance(zero_point, float | None) @@ -63,7 +67,6 @@ def nominal_location_to_cdf_location( else: # linearly scaled question unscaled_location = (scaled_location - range_min) / (range_max - range_min) - assert 0 <= unscaled_location <= 1 return unscaled_location @@ -115,11 +118,12 @@ def _determine_probability_for_resolution_and_baseline( Also returns the baseline probability used in baseline scoring """ + if resolution == "above_upper_bound" or resolution == "below_lower_bound": + raise ValueError("'above_upper_bound' or 'below_lower_bound' format not supported") + is_numeric = ( isinstance(resolution, float) or isinstance(resolution, int) - or resolution == "above_upper_bound" - or resolution == "below_lower_bound" ) is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) @@ -133,17 +137,16 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError("Forecast is empty") if not is_numeric and any(p <= 0 or p >= 1 for p in forecast): - # @Check: Is it valid to have a numeric forecast with 0 probability for a number? raise ValueError("Forecast contains probabilities outside of 0 to 1 range") if is_binary: - prob_for_resolution, baseline_prob = _binary_resolution_baseline_prob( + prob_for_resolution, baseline_prob = _binary_resolution_and_baseline_prob( forecast, resolution ) elif is_multiple_choice: if options is None: raise ValueError("Options are required for multiple choice questions") - prob_for_resolution, baseline_prob = _multiple_choice_resolution_baseline_prob( + prob_for_resolution, baseline_prob = _multiple_choice_resolution_and_baseline_prob( forecast, resolution, options ) elif is_numeric: @@ -151,18 +154,18 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError( "Range min and range max are required for numeric questions" ) - prob_for_resolution, baseline_prob = _numeric_resolution_baseline_prob( + prob_for_resolution, baseline_prob = _numeric_resolution_and_baseline_prob( forecast, resolution, range_min, range_max ) else: raise ValueError("Unknown question type") - assert 0 < prob_for_resolution <= 1 - assert 0 < baseline_prob <= 1 + assert 0 <= prob_for_resolution <= 1, f"Probability for resolution is {prob_for_resolution} which is not between 0 and 1" + assert 0 <= baseline_prob <= 1, f"Baseline probability is {baseline_prob} which is not between 0 and 1" return prob_for_resolution, baseline_prob -def _binary_resolution_baseline_prob(forecast: list[float], resolution: bool): +def _binary_resolution_and_baseline_prob(forecast: list[float], resolution: bool): if len(forecast) != 1 and len(forecast) != 2: raise ValueError( "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" @@ -177,7 +180,7 @@ def _binary_resolution_baseline_prob(forecast: list[float], resolution: bool): return prob_for_resolution, baseline_prob -def _multiple_choice_resolution_baseline_prob( +def _multiple_choice_resolution_and_baseline_prob( forecast: list[float], resolution: str, options: list[str] ): if options is None: @@ -194,7 +197,7 @@ def _multiple_choice_resolution_baseline_prob( return prob_for_resolution, baseline_prob -def _numeric_resolution_baseline_prob( +def _numeric_resolution_and_baseline_prob( forecast: list[float], resolution: float | str, range_min: float, range_max: float ): if len(forecast) != 201: @@ -207,31 +210,28 @@ def _numeric_resolution_baseline_prob( previous_prob = current_prob cdf = [float(p) for p in forecast] - assert len(cdf) == 201 - pmf = [cdf[0]] + [ + assert len(cdf) == 201, f"There should be 201 bins, but there are {len(cdf)}" + lower_bound_prob = cdf[0] + upper_bound_prob = 1 - cdf[-1] + pmf = [lower_bound_prob] + [ cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) - ] # @Check: is this a correct conversion? - pmf.append(1 - cdf[-1]) + ] + [upper_bound_prob] # @Check: is this a correct conversion? # pmf = np.diff(np.concatenate([[0], cdf])) - assert len(pmf) == 200 + assert len(pmf) == 202, f"There should be 202 bins, but there are {len(pmf)}" - if resolution == "below_lower_bound": - prob_for_resolution = cdf[0] - elif resolution == "above_upper_bound": - prob_for_resolution = 1 - cdf[-1] # Grab probability of 201st bin - else: - resolution = float(resolution) - # bin_edges = np.linspace(range_min, range_max, 200) - # resolution_bin_idx = np.searchsorted(bin_edges, resolution, side="right") - cdf_location = nominal_location_to_cdf_location( - resolution, range_min, range_max - ) - resolution_bin_idx = min(int(cdf_location * (len(pmf) - 1)), len(pmf) - 1) - if resolution_bin_idx >= len(pmf): - raise ValueError("Resolution is out of bounds") + resolution = float(resolution) + # bin_edges = np.linspace(range_min, range_max, 200) + # resolution_bin_idx = np.searchsorted(bin_edges, resolution, side="right") + cdf_location = nominal_location_to_cdf_location( + resolution, range_min, range_max + ) + resolution_bin_idx = min(int(cdf_location * (len(pmf) - 1)), len(pmf) - 1) + + if resolution_bin_idx >= len(pmf): + raise ValueError("Resolution is out of bounds") - prob_for_resolution = pmf[resolution_bin_idx] + prob_for_resolution = pmf[resolution_bin_idx] baseline_prob = 1 / len( pmf diff --git a/tests/test_scoring.py b/tests/test_scoring.py index c98409c..cec5050 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -7,6 +7,8 @@ calculate_spot_peer_score, ) +from dataclasses import dataclass + # TODO: # For each of Multiple Choice, Binary, and Numeric questions # - Test spot peer score @@ -20,11 +22,11 @@ ################################### HELPER FUNCTIONS ################################### -def generate_uniform_cdf(num_points: int) -> list[float]: +def generate_uniform_cdf(num_points: int = 201) -> list[float]: return [(i + 1) / num_points for i in range(num_points)] -def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[float]: +def generate_perfect_cdf(correct_index: int) -> list[float]: assert correct_index >= 0 and correct_index <= 201 length_of_cdf = 201 perfect_forecast = 0.99999 @@ -35,12 +37,343 @@ def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[ else: cdf.append(perfect_forecast) - if inverse_cdf: - cdf = [1 - c for c in cdf] - return cdf +@dataclass +class Percentile: + value: float + probability_below: float + + +def generate_cdf( + percentiles: list[Percentile], + lower_bound: float, + upper_bound: float, + open_lower_bound: bool, + open_upper_bound: bool, + zero_point: float | None = None, +) -> list[float]: + # Copied from another notebook -> definitely could be cleaned up + + percentile_values: dict[float, float] = { + percentile.probability_below * 100: percentile.value + for percentile in percentiles + } + + percentile_max = max(float(key) for key in percentile_values.keys()) + percentile_min = min(float(key) for key in percentile_values.keys()) + range_min = lower_bound + range_max = upper_bound + range_size = abs(range_max - range_min) + buffer = 1 if range_size > 100 else 0.01 * range_size + + # Adjust any values that are exactly at the bounds + for percentile, value in list(percentile_values.items()): + if not open_lower_bound and value <= range_min + buffer: + percentile_values[percentile] = range_min + buffer + if not open_upper_bound and value >= range_max - buffer: + percentile_values[percentile] = range_max - buffer + + # Set cdf values outside range + if open_upper_bound: + if range_max > percentile_values[percentile_max]: + percentile_values[int(100 - (0.5 * (100 - percentile_max)))] = range_max + else: + percentile_values[100] = range_max + + # Set cdf values outside range + if open_lower_bound: + if range_min < percentile_values[percentile_min]: + percentile_values[int(0.5 * percentile_min)] = range_min + else: + percentile_values[0] = range_min + + sorted_percentile_values = dict(sorted(percentile_values.items())) + + # Normalize percentile keys + normalized_percentile_values = {} + for key, value in sorted_percentile_values.items(): + percentile = float(key) / 100 + normalized_percentile_values[percentile] = value + + value_percentiles = { + value: key for key, value in normalized_percentile_values.items() + } + + # function for log scaled questions + def generate_cdf_locations( + range_min: float, range_max: float, zero_point: float | None + ) -> list[float]: + if zero_point is None: + scale = lambda x: range_min + (range_max - range_min) * x + else: + deriv_ratio = (range_max - zero_point) / (range_min - zero_point) + scale = lambda x: range_min + (range_max - range_min) * ( + deriv_ratio**x - 1 + ) / (deriv_ratio - 1) + return [scale(x) for x in np.linspace(0, 1, 201)] + + cdf_xaxis = generate_cdf_locations(range_min, range_max, zero_point) + + def linear_interpolation( + x_values: list[float], xy_pairs: dict[float, float] + ) -> list[float]: + # Sort the xy_pairs by x-values + sorted_pairs = sorted(xy_pairs.items()) + + # Extract sorted x and y values + known_x = [pair[0] for pair in sorted_pairs] + known_y = [pair[1] for pair in sorted_pairs] + + # Initialize the result list + y_values = [] + + for x in x_values: + # Check if x is exactly in the known x values + if x in known_x: + y_values.append(known_y[known_x.index(x)]) + else: + # Find the indices of the two nearest known x-values + i = 0 + while i < len(known_x) and known_x[i] < x: + i += 1 + # If x is outside the range of known x-values, use the nearest endpoint + if i == 0: + y_values.append(known_y[0]) + elif i == len(known_x): + y_values.append(known_y[-1]) + else: + # Perform linear interpolation + x0, x1 = known_x[i - 1], known_x[i] + y0, y1 = known_y[i - 1], known_y[i] + + # Linear interpolation formula + y = y0 + (x - x0) * (y1 - y0) / (x1 - x0) + y_values.append(y) + + return y_values + + continuous_cdf = linear_interpolation(cdf_xaxis, value_percentiles) + + percentiles = [ + Percentile(value=value, probability_below=percentile) + for value, percentile in zip(cdf_xaxis, continuous_cdf) + ] + assert len(percentiles) == 201 + + # Validate minimum spacing between consecutive values + for i in range(len(percentiles) - 1): + assert ( + abs(percentiles[i + 1].probability_below - percentiles[i].probability_below) + >= 5e-05 + ), ( + f"Percentiles at indices {i} and {i+1} are too close: " + f"{percentiles[i].probability_below} and {percentiles[i+1].probability_below} " + f"at values {percentiles[i].value} and {percentiles[i+1].value}. " + "It is possible that your prediction is mostly or completely out of the upper/lower bound range " + "Thus making this cdf mostly meaningless." + ) + + return [percentile.probability_below for percentile in percentiles] + + +################################### BASELINE SCORES ################################### + + +@pytest.mark.parametrize( + "forecast,resolution,options,range_min,range_max,question_weight,expected", + [ + # Binary: uniform forecast, should be 0 + ([0.5], True, None, None, None, 1.0, 0.0), + ([0.5], False, None, None, None, 1.0, 0.0), + ([0.5, 0.5], False, None, None, None, 1.0, 0.0), + # Multiple Choice: uniform forecast, should be 0 + ([1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None, 1.0, 0.0), + ([0.25, 0.25, 0.25, 0.25], "B", ["A", "B", "C", "D"], None, None, 1.0, 0.0), + # Numeric: uniform CDF, should be 0 + (generate_uniform_cdf(), 0.5, None, 0.0, 1.0, 1.0, 0.0), + ], +) +def test_baseline_score_is_0_with_uniform_prediction( + forecast: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, + expected: float, +): + score = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score - expected) == pytest.approx(0) + + +def test_binary_baseline_score_when_perfect_forecast(): + score = calculate_spot_baseline_score( + forecast=[0.99999999], + resolution=True, + ) + assert score == pytest.approx(100) + +@pytest.mark.parametrize( + "forecast,resolution,expected", + [ + ([0.001], True, -896.57), # Completely incorrect + ([0.999], True, 99.86), # Completely correct + ([0.001], False, 99.86), # Completely correct + ([0.4], True, -32.19), # Examples found here: https://www.metaculus.com/help/scores-faq/#:~:text=details%20for%20nerds-,Do%20all%20my%20predictions%20on%20a%20question%20count%20toward%20my%20score%3F,-Yes.%20Metaculus%20uses + ([0.7], True, 48.542), + ([0.4, 0.6], True, -32.19), + ], +) +def test_binary_baseline_examples(forecast: list[float], resolution: bool, expected: float): + score = calculate_spot_baseline_score( + forecast=forecast, + resolution=resolution, + ) + assert score == pytest.approx(expected, abs=1e-1) + + +def test_numeric_baseline_when_perfect_forecast(): + correct_index = 30 + length_of_cdf = 201 + index_to_answer_ratio = 3 + correct_answer = correct_index * index_to_answer_ratio + range_max = length_of_cdf * index_to_answer_ratio + + score = calculate_spot_baseline_score( + forecast=generate_perfect_cdf(correct_index), + resolution=correct_answer, + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(183) + + +def test_numeric_baseline_if_completly_incorrect_forecast(): + correct_index = 30 + length_of_cdf = 201 + index_to_answer_ratio = 3 + correct_answer = correct_index * index_to_answer_ratio + range_max = length_of_cdf * index_to_answer_ratio + + score = calculate_spot_baseline_score( + forecast=[0.0] * 200 + [1.0], # all probability assigned to upper bound + resolution=correct_answer, + range_min=0, + range_max=range_max, + ) + assert score == pytest.approx(-230) + + +def test_multiple_choice_perfect_forecast(): + forecast_for_answer_a = 0.999 + num_other_forecasts = 7 + other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts + score = calculate_spot_baseline_score( + forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, + resolution="A", + options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], + ) + assert score == pytest.approx(99.87) + + +def test_multiple_choice_if_completly_incorrect_forecast(): + forecast_for_answer_a = 0.001 + other_forecasts = (1 - forecast_for_answer_a) / 2 + score = calculate_spot_baseline_score( + forecast=[forecast_for_answer_a, other_forecasts, other_forecasts], + resolution="A", + options=["A", "B", "C"], + ) + assert score == pytest.approx(-232) + + +@pytest.mark.parametrize( + "forecast_closer,forecast_further,resolution,options,range_min,range_max", + [ + # Binary: closer to True + ([0.8], [0.2], True, None, None, None), + # Binary: closer to False + ([0.2], [0.8], False, None, None, None), + # Multiple Choice: closer to "A" + ([0.7, 0.2, 0.1], [0.1, 0.2, 0.7], "A", ["A", "B", "C"], None, None), + # Numeric: CDF with more mass near 0.5 vs near 0.0 + ( + generate_cdf( + [ + Percentile(value=40, probability_below=0.1), + Percentile(value=60, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + generate_cdf( + [ + Percentile(value=30, probability_below=0.1), + Percentile(value=49, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + 50, + None, + -1, + 96, + ), + ], +) +def test_baseline_score_better_when_closer( + forecast_closer: list[float], + forecast_further: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, +): + score_closer = calculate_spot_baseline_score( + forecast_closer, resolution, options, range_min, range_max, 1.0 + ) + score_further = calculate_spot_baseline_score( + forecast_further, resolution, options, range_min, range_max, 1.0 + ) + assert score_closer > score_further + + +@pytest.mark.parametrize( + "forecast,resolution,options,range_min,range_max,question_weight", + [ + # Binary + ([0.8], True, None, None, None, 2.0), + # Multiple Choice + ([0.7, 0.2, 0.1], "A", ["A", "B", "C"], None, None, 0.5), + # Numeric + ([0.1] * 50 + [0.9] * 149, 0.5, None, 0.0, 1.0, 3.0), + ], +) +def test_baseline_score_weighted( + forecast: list[float], + resolution: bool | str | None, + options: list[str] | None, + range_min: float | None, + range_max: float | None, + question_weight: float, +): + score_unweighted = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, 1.0 + ) + score_weighted = calculate_spot_baseline_score( + forecast, resolution, options, range_min, range_max, question_weight + ) + assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 + + ################################### PEER SCORES ################################### @@ -58,7 +391,7 @@ def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[ # Multiple Choice: forecast closer to resolution gets better score ( [ - [0.9, 0.1, 0.0], + [0.9, 0.09, 0.01], [0.7, 0.2, 0.1], [0.5, 0.3, 0.2], [0.3, 0.4, 0.3], @@ -72,16 +405,80 @@ def generate_perfect_cdf(correct_index: int, inverse_cdf: bool = False) -> list[ # Numeric: forecast CDFs with more mass near resolution get better score ( [ - [0.1] * 100 + [0.9] * 101, # most mass above 0.5 - [0.2] * 100 + [0.8] * 101, - [0.5] * 201, - [0.8] * 100 + [0.2] * 101, - [0.9] * 100 + [0.1] * 101, # most mass below 0.5 + generate_cdf( # Best CDF + [ + Percentile(value=40, probability_below=0.1), + Percentile(value=60, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + generate_cdf( + [ + Percentile(value=20, probability_below=0.1), + Percentile(value=50, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + generate_cdf( # worst CDF + [ + Percentile(value=10, probability_below=0.1), + Percentile(value=20, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), ], - 0.5, + 49, None, - 0.0, - 1.0, + -1, + 96, # Not even range + ), + # Numeric: forecast CDFs with more mass near upper bound get better score + ( + [ + generate_cdf( # Best CDF + [ + Percentile(value=100, probability_below=0.1), + Percentile(value=120, probability_below=0.9), + ], + lower_bound=0, + upper_bound=100, + open_lower_bound=False, + open_upper_bound=True, + ), + generate_cdf( + [ + Percentile(value=100, probability_below=0.1), + Percentile(value=120, probability_below=0.9), + ], + lower_bound=0, + upper_bound=100, + open_lower_bound=False, + open_upper_bound=True, + ), + generate_cdf( # worst CDF + [ + Percentile(value=100, probability_below=0.1), + Percentile(value=120, probability_below=0.9), + ], + lower_bound=0, + upper_bound=100, + open_lower_bound=False, + open_upper_bound=False, # No upper bound = no probability mass at upper bound + ), + ], + 120, + None, + 0, + 100, ), ], ) @@ -105,15 +502,19 @@ def test_better_forecast_means_better_peer_score( for idx, forecast in enumerate(forecasts) ] sorted_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True) - assert sorted_indices == list(range(len(scores))), "Scores should be ordered as expected (descending)" + assert len(scores) == len(set(scores)), "Scores should all be different" + assert sorted_indices == list( + range(len(scores)) + ), "Scores should be ordered as expected (descending)" @pytest.mark.parametrize( "question_type,forecast,resolution,options,range_min,range_max", [ ("binary", [0.5], True, None, None, None), - ("mc", [1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None), - ("numeric", [0.5] * 201, 0.5, None, 0.0, 1.0), + ("mc", [0.25, 0.25, 0.25, 0.25], "A", ["A", "B", "C", "D"], None, None), + ("numeric", generate_perfect_cdf(100), 100, None, 0, 100), + ("numeric", generate_uniform_cdf(), 50, None, 0, 100), ], ) def test_peer_score_zero_when_all_same( @@ -156,11 +557,42 @@ def test_peer_score_zero_when_all_same( ), # Numeric ( - [[0.1] * 100 + [0.9] * 101, [0.2] * 100 + [0.8] * 101, [0.5] * 201], - 0.5, + [ + generate_cdf( + [ + Percentile(value=30, probability_below=0.1), + Percentile(value=60, probability_below=0.9), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=True, + open_upper_bound=False, + ), + generate_cdf( + [ + Percentile(value=20, probability_below=0.4), + Percentile(value=80, probability_below=0.6), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=True, + open_upper_bound=True, + ), + generate_cdf( + [ + Percentile(value=10, probability_below=0.1), + Percentile(value=70, probability_below=0.3), + ], + lower_bound=-1, + upper_bound=96, + open_lower_bound=False, + open_upper_bound=False, + ), + ], + 50, None, - 0.0, - 1.0, + -1, + 96, ), ], ) @@ -179,7 +611,6 @@ def test_peer_score_average_zero( options, range_min, range_max, - 1.0, ) for idx, forecast in enumerate(forecasts) ] @@ -202,12 +633,16 @@ def test_peer_score_average_zero( ), # Numeric ( - [[0.1] * 100 + [0.9] * 101, [0.9] * 100 + [0.1] * 101, [0.5] * 201], - 0.5, + [ + generate_uniform_cdf(), + generate_perfect_cdf(100), + generate_perfect_cdf(101), + ], + 50, None, - 0.0, - 1.0, - 3.0, + 0, + 100, + 0.8, ), ], ) @@ -229,169 +664,10 @@ def test_peer_score_weighted( ) assert score_weighted == pytest.approx(score_unweighted * weight) + # TODO: Test the below # Best score for MC and binary is 996 # Worst score for MC and binary is -996 # Best score for numeric is 408 # Worst score for numeric is -408 # @Check: Can we even validate this (won't we need infinite other forecasters to get max score?) - -################################### BASELINE SCORES ################################### - - -@pytest.mark.parametrize( - "forecast,resolution,options,range_min,range_max,question_weight,expected", - [ - # Binary: uniform forecast, should be 0 - ([0.5], True, None, None, None, 1.0, 0.0), - ([0.5], False, None, None, None, 1.0, 0.0), - ([0.5, 0.5], False, None, None, None, 1.0, 0.0), - # Multiple Choice: uniform forecast, should be 0 - ([1 / 3, 1 / 3, 1 / 3], "A", ["A", "B", "C"], None, None, 1.0, 0.0), - ([0.25, 0.25, 0.25, 0.25], "B", ["A", "B", "C", "D"], None, None, 1.0, 0.0), - # Numeric: uniform CDF, should be 0 - (generate_uniform_cdf(201), 0.5, None, 0.0, 1.0, 1.0, 0.0), - ], -) -def test_baseline_score_is_0_with_uniform_prediction( - forecast: list[float], - resolution: bool | str | None, - options: list[str] | None, - range_min: float | None, - range_max: float | None, - question_weight: float, - expected: float, -): - score = calculate_spot_baseline_score( - forecast, resolution, options, range_min, range_max, question_weight - ) - assert abs(score - expected) == pytest.approx(0) - - -def test_binary_baseline_score_when_perfect_forecast(): - score = calculate_spot_baseline_score( - forecast=[0.99999999], - resolution=True, - ) - assert score == pytest.approx(100) - - -def test_binary_baseline_if_completly_incorrect_forecast(): - score = calculate_spot_baseline_score( - forecast=[0.0000001], - resolution=True, - ) - assert score == pytest.approx(-897) - - -def test_numeric_baseline_when_perfect_forecast(): - correct_index = 30 - length_of_cdf = 201 - index_to_answer_ratio = 3 - correct_answer = correct_index * index_to_answer_ratio - range_max = length_of_cdf * index_to_answer_ratio - - score = calculate_spot_baseline_score( - forecast=generate_perfect_cdf(correct_index), - resolution=correct_answer, - range_min=0, - range_max=range_max, - ) - assert score == pytest.approx(183) - - -def test_numeric_baseline_if_completly_incorrect_forecast(): - correct_index = 30 - length_of_cdf = 201 - index_to_answer_ratio = 3 - correct_answer = correct_index * index_to_answer_ratio - range_max = length_of_cdf * index_to_answer_ratio - - score = calculate_spot_baseline_score( - forecast=generate_perfect_cdf(correct_index), - resolution=correct_answer, - range_min=0, - range_max=range_max, - ) - assert score == pytest.approx(-230) - - -def test_multiple_choice_perfect_forecast(): - forecast_for_answer_a = 0.999999999 - num_other_forecasts = 7 - other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts - score = calculate_spot_baseline_score( - forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, - resolution="A", - options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], - ) - assert score == pytest.approx(100) - - -def test_multiple_choice_if_completly_incorrect_forecast(): - forecast_for_answer_c = 0.999999999 - other_forecasts = (1 - forecast_for_answer_c) / 2 - score = calculate_spot_baseline_score( - forecast=[other_forecasts, other_forecasts, forecast_for_answer_c], - resolution="C", - options=["A", "B", "C"], - ) - assert score == pytest.approx(-232) - - -@pytest.mark.parametrize( - "forecast_closer,forecast_further,resolution,options,range_min,range_max", - [ - # Binary: closer to True - ([0.8], [0.2], True, None, None, None), - # Binary: closer to False - ([0.2], [0.8], False, None, None, None), - # Multiple Choice: closer to "A" - ([0.7, 0.2, 0.1], [0.1, 0.2, 0.7], "A", ["A", "B", "C"], None, None), - # Numeric: CDF with more mass near 0.5 vs near 0.0 - ([0.1] * 52 + [0.9] * 149, [0.9] * 52 + [0.1] * 149, 0.5, None, 0.0, 1.0), - ], -) -def test_baseline_score_better_when_closer( - forecast_closer: list[float], - forecast_further: list[float], - resolution: bool | str | None, - options: list[str] | None, - range_min: float | None, - range_max: float | None, -): - score_closer = calculate_spot_baseline_score( - forecast_closer, resolution, options, range_min, range_max, 1.0 - ) - score_further = calculate_spot_baseline_score( - forecast_further, resolution, options, range_min, range_max, 1.0 - ) - assert score_closer > score_further - - -@pytest.mark.parametrize( - "forecast,resolution,options,range_min,range_max,question_weight", - [ - # Binary - ([0.8], True, None, None, None, 2.0), - # Multiple Choice - ([0.7, 0.2, 0.1], "A", ["A", "B", "C"], None, None, 0.5), - # Numeric - ([0.1] * 50 + [0.9] * 149, 0.5, None, 0.0, 1.0, 3.0), - ], -) -def test_baseline_score_weighted( - forecast: list[float], - resolution: bool | str | None, - options: list[str] | None, - range_min: float | None, - range_max: float | None, - question_weight: float, -): - score_unweighted = calculate_spot_baseline_score( - forecast, resolution, options, range_min, range_max, 1.0 - ) - score_weighted = calculate_spot_baseline_score( - forecast, resolution, options, range_min, range_max, question_weight - ) - assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 From 596ece8bc86fd1584abddbdddff97ba9cd34ab8a Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Sat, 3 May 2025 11:29:21 -0600 Subject: [PATCH 12/26] Got MC scoring tests passing --- refactored_notebook/scoring.py | 147 ++++++++++++++++++++------------- tests/test_scoring.py | 95 +++++++++------------ 2 files changed, 129 insertions(+), 113 deletions(-) diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index dbe06af..ba126f9 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -14,24 +14,19 @@ def calculate_spot_peer_score( range_max: float | None = None, question_weight: float = 1.0, ) -> float: - forecast_for_resolution, _ = _determine_probability_for_resolution_and_baseline( + forecast_for_resolution = _determine_probability_for_resolution( forecast, resolution, options, range_min, range_max ) - other_user_forecasts_and_baseline_prob = [ - _determine_probability_for_resolution_and_baseline( + other_user_forecasts = [ + _determine_probability_for_resolution( forecast, resolution, options, range_min, range_max ) for forecast in forecast_for_other_users ] - other_user_forecasts = [ - forecast for forecast, _ in other_user_forecasts_and_baseline_prob - ] geometric_mean = gmean(other_user_forecasts) peer_score = np.log(forecast_for_resolution / geometric_mean) - if isinstance( - resolution, float - ): # @Check: This doesn't account for resolution being 'above_upper_bound' or 'below_lower_bound' + if isinstance(resolution, float): # @Check: shouldn't other q types get a divsor? peer_score /= 2 return peer_score * question_weight @@ -83,48 +78,76 @@ def calculate_spot_baseline_score( Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ - prob_for_resolution, baseline_prob = ( - _determine_probability_for_resolution_and_baseline( - forecast, resolution, options, range_min, range_max - ) + prob_for_resolution = _determine_probability_for_resolution( + forecast, resolution, options, range_min, range_max ) - + baseline_prob = _determine_baseline(resolution, options) + divisor = _determine_divisor_for_baseline_score(resolution, options) if prob_for_resolution <= 0 or baseline_prob <= 0: raise ValueError( "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" ) - baseline_score = ( - np.log2(prob_for_resolution / baseline_prob) * 100 - ) # @Check: check correctness (also shouldn't this be natural log?) + # if resolution_bucket in [0, len(pmf) - 1]: + # baseline = 0.05 + # else: + # open_bound_count = bool(question.open_upper_bound) + bool( + # question.open_lower_bound + # ) + # baseline = (1 - 0.05 * open_bounds_count) / (len(pmf) - 2) + # forecast_score = 100 * np.log(pmf[resolution_bucket] / baseline) / 2 - if isinstance(resolution, float): - baseline_score /= 2 # Numeric scores are halved + baseline_score = np.log(prob_for_resolution / baseline_prob) / divisor * 100 weighted_score = baseline_score * question_weight return weighted_score -def _determine_probability_for_resolution_and_baseline( +def _determine_baseline( + resolution: ResolutionType, options: list[str] | None = None +) -> float: + is_binary = isinstance(resolution, bool) + is_multiple_choice = isinstance(resolution, str) + is_numeric = isinstance(resolution, float) or isinstance(resolution, int) + + if is_binary: + baseline_prob = 0.5 + elif is_multiple_choice: + if options is None: + raise ValueError("Options are required for multiple choice questions") + baseline_prob = 1 / len(options) + elif is_numeric: + baseline_prob = ( + 1 / 202 + ) # len(pmf) # ??? -> bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins + # @Check: Should this be either 1, 0.9, or 0.95 based on whether open or closed bounds + else: + raise ValueError("Unknown question type") + assert ( + 0 <= baseline_prob <= 1 + ), f"Baseline probability is {baseline_prob} which is not between 0 and 1" + return baseline_prob + + +def _determine_probability_for_resolution( forecast: ForecastType, resolution: ResolutionType, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, -) -> tuple[float, float]: +) -> float: """ Returns a 0 to 1 probability for the resolution Also returns the baseline probability used in baseline scoring """ if resolution == "above_upper_bound" or resolution == "below_lower_bound": - raise ValueError("'above_upper_bound' or 'below_lower_bound' format not supported") + raise ValueError( + "'above_upper_bound' or 'below_lower_bound' format not supported" + ) - is_numeric = ( - isinstance(resolution, float) - or isinstance(resolution, int) - ) + is_numeric = isinstance(resolution, float) or isinstance(resolution, int) is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) @@ -140,13 +163,11 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError("Forecast contains probabilities outside of 0 to 1 range") if is_binary: - prob_for_resolution, baseline_prob = _binary_resolution_and_baseline_prob( - forecast, resolution - ) + prob_for_resolution = _binary_resolution_prob(forecast, resolution) elif is_multiple_choice: if options is None: raise ValueError("Options are required for multiple choice questions") - prob_for_resolution, baseline_prob = _multiple_choice_resolution_and_baseline_prob( + prob_for_resolution = _multiple_choice_resolution_prob( forecast, resolution, options ) elif is_numeric: @@ -154,38 +175,35 @@ def _determine_probability_for_resolution_and_baseline( raise ValueError( "Range min and range max are required for numeric questions" ) - prob_for_resolution, baseline_prob = _numeric_resolution_and_baseline_prob( + prob_for_resolution = _numeric_resolution_prob( forecast, resolution, range_min, range_max ) else: raise ValueError("Unknown question type") - assert 0 <= prob_for_resolution <= 1, f"Probability for resolution is {prob_for_resolution} which is not between 0 and 1" - assert 0 <= baseline_prob <= 1, f"Baseline probability is {baseline_prob} which is not between 0 and 1" - return prob_for_resolution, baseline_prob + assert ( + 0 <= prob_for_resolution <= 1 + ), f"Probability for resolution is {prob_for_resolution} which is not between 0 and 1" + return prob_for_resolution -def _binary_resolution_and_baseline_prob(forecast: list[float], resolution: bool): +def _binary_resolution_prob(forecast: list[float], resolution: bool) -> float: if len(forecast) != 1 and len(forecast) != 2: raise ValueError( "Binary questions must have exactly one or two forecasts (for yes or 'yes and no')" ) forecast_val = float(forecast[0]) - baseline_prob = 0.5 if resolution: prob_for_resolution = forecast_val else: prob_for_resolution = 1 - forecast_val - return prob_for_resolution, baseline_prob + return prob_for_resolution -def _multiple_choice_resolution_and_baseline_prob( +def _multiple_choice_resolution_prob( forecast: list[float], resolution: str, options: list[str] -): - if options is None: - raise ValueError("Options are required for multiple choice questions") - +) -> float: if len(forecast) != len(options): raise ValueError("Forecast and options have different lengths") @@ -193,13 +211,12 @@ def _multiple_choice_resolution_and_baseline_prob( options = [str(opt) for opt in options] resolution_idx = options.index(str(resolution)) prob_for_resolution = pmf[resolution_idx] - baseline_prob = 1 / len(pmf) - return prob_for_resolution, baseline_prob + return prob_for_resolution -def _numeric_resolution_and_baseline_prob( +def _numeric_resolution_prob( forecast: list[float], resolution: float | str, range_min: float, range_max: float -): +) -> float: if len(forecast) != 201: raise ValueError("CDF should have 201 bins") @@ -213,19 +230,18 @@ def _numeric_resolution_and_baseline_prob( assert len(cdf) == 201, f"There should be 201 bins, but there are {len(cdf)}" lower_bound_prob = cdf[0] upper_bound_prob = 1 - cdf[-1] - pmf = [lower_bound_prob] + [ - cdf[i] - cdf[i - 1] for i in range(1, len(cdf)) - ] + [upper_bound_prob] # @Check: is this a correct conversion? + pmf = ( + [lower_bound_prob] + + [cdf[i] - cdf[i - 1] for i in range(1, len(cdf))] + + [upper_bound_prob] + ) # @Check: is this a correct conversion? # pmf = np.diff(np.concatenate([[0], cdf])) assert len(pmf) == 202, f"There should be 202 bins, but there are {len(pmf)}" - resolution = float(resolution) # bin_edges = np.linspace(range_min, range_max, 200) # resolution_bin_idx = np.searchsorted(bin_edges, resolution, side="right") - cdf_location = nominal_location_to_cdf_location( - resolution, range_min, range_max - ) + cdf_location = nominal_location_to_cdf_location(resolution, range_min, range_max) resolution_bin_idx = min(int(cdf_location * (len(pmf) - 1)), len(pmf) - 1) if resolution_bin_idx >= len(pmf): @@ -233,8 +249,23 @@ def _numeric_resolution_and_baseline_prob( prob_for_resolution = pmf[resolution_bin_idx] - baseline_prob = 1 / len( - pmf - ) # bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins - # @Check: Should this be either 1, 0.9, or 0.95? - return prob_for_resolution, baseline_prob + return prob_for_resolution + + +def _determine_divisor_for_baseline_score( + resolution: ResolutionType, options: list[str] | None = None +) -> float: + is_binary = isinstance(resolution, bool) + is_multiple_choice = isinstance(resolution, str) + is_numeric = isinstance(resolution, float) or isinstance(resolution, int) + + if is_binary: + return np.log(2) + elif is_multiple_choice: + if options is None: + raise ValueError("Options are required for multiple choice questions") + return np.log(len(options)) + elif is_numeric: + return 2 + else: + raise ValueError("Unknown question type") diff --git a/tests/test_scoring.py b/tests/test_scoring.py index cec5050..57fc7f1 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -1,13 +1,11 @@ +from dataclasses import dataclass + import numpy as np import pytest from refactored_notebook.data_models import ForecastType -from refactored_notebook.scoring import ( - calculate_spot_baseline_score, - calculate_spot_peer_score, -) - -from dataclasses import dataclass +from refactored_notebook.scoring import (calculate_spot_baseline_score, + calculate_spot_peer_score) # TODO: # For each of Multiple Choice, Binary, and Numeric questions @@ -25,18 +23,13 @@ def generate_uniform_cdf(num_points: int = 201) -> list[float]: return [(i + 1) / num_points for i in range(num_points)] - -def generate_perfect_cdf(correct_index: int) -> list[float]: - assert correct_index >= 0 and correct_index <= 201 - length_of_cdf = 201 - perfect_forecast = 0.99999 +def generate_cdf_with_forecast_at_index(index: int, forecast: float) -> list[float]: cdf = [] - for i in range(length_of_cdf): - if i < correct_index: - cdf.append(1 - perfect_forecast) + for i in range(201): + if i < index: + cdf.append(0.0) else: - cdf.append(perfect_forecast) - + cdf.append(forecast) return cdf @@ -163,17 +156,17 @@ def linear_interpolation( assert len(percentiles) == 201 # Validate minimum spacing between consecutive values - for i in range(len(percentiles) - 1): - assert ( - abs(percentiles[i + 1].probability_below - percentiles[i].probability_below) - >= 5e-05 - ), ( - f"Percentiles at indices {i} and {i+1} are too close: " - f"{percentiles[i].probability_below} and {percentiles[i+1].probability_below} " - f"at values {percentiles[i].value} and {percentiles[i+1].value}. " - "It is possible that your prediction is mostly or completely out of the upper/lower bound range " - "Thus making this cdf mostly meaningless." - ) + # for i in range(len(percentiles) - 1): + # assert ( + # abs(percentiles[i + 1].probability_below - percentiles[i].probability_below) + # >= 5e-05 + # ), ( + # f"Percentiles at indices {i} and {i+1} are too close: " + # f"{percentiles[i].probability_below} and {percentiles[i+1].probability_below} " + # f"at values {percentiles[i].value} and {percentiles[i+1].value}. " + # "It is possible that your prediction is mostly or completely out of the upper/lower bound range " + # "Thus making this cdf mostly meaningless." + # ) return [percentile.probability_below for percentile in percentiles] @@ -210,13 +203,6 @@ def test_baseline_score_is_0_with_uniform_prediction( assert abs(score - expected) == pytest.approx(0) -def test_binary_baseline_score_when_perfect_forecast(): - score = calculate_spot_baseline_score( - forecast=[0.99999999], - resolution=True, - ) - assert score == pytest.approx(100) - @pytest.mark.parametrize( "forecast,resolution,expected", [ @@ -237,14 +223,16 @@ def test_binary_baseline_examples(forecast: list[float], resolution: bool, expec def test_numeric_baseline_when_perfect_forecast(): - correct_index = 30 + correct_index = 31 length_of_cdf = 201 index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.59) + # As of May 3, 2025, 0.59 is max difference between 2 points on a cdf score = calculate_spot_baseline_score( - forecast=generate_perfect_cdf(correct_index), + forecast=forecast, resolution=correct_answer, range_min=0, range_max=range_max, @@ -253,14 +241,15 @@ def test_numeric_baseline_when_perfect_forecast(): def test_numeric_baseline_if_completly_incorrect_forecast(): - correct_index = 30 + correct_index = 31 length_of_cdf = 201 index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.001) score = calculate_spot_baseline_score( - forecast=[0.0] * 200 + [1.0], # all probability assigned to upper bound + forecast=forecast, resolution=correct_answer, range_min=0, range_max=range_max, @@ -268,27 +257,23 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): assert score == pytest.approx(-230) -def test_multiple_choice_perfect_forecast(): - forecast_for_answer_a = 0.999 - num_other_forecasts = 7 +@pytest.mark.parametrize( + "forecast_for_answer_a,num_total_forecasts,expected", + [ + (0.999, 8, 99.95), + (0.001, 8, -232.19), + ], +) +def test_multiple_choice_examples(forecast_for_answer_a: float, num_total_forecasts: int, expected: float): + num_other_forecasts = num_total_forecasts - 1 other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts score = calculate_spot_baseline_score( forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, resolution="A", options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], ) - assert score == pytest.approx(99.87) - + assert score == pytest.approx(expected, abs=1e-2) -def test_multiple_choice_if_completly_incorrect_forecast(): - forecast_for_answer_a = 0.001 - other_forecasts = (1 - forecast_for_answer_a) / 2 - score = calculate_spot_baseline_score( - forecast=[forecast_for_answer_a, other_forecasts, other_forecasts], - resolution="A", - options=["A", "B", "C"], - ) - assert score == pytest.approx(-232) @pytest.mark.parametrize( @@ -513,7 +498,7 @@ def test_better_forecast_means_better_peer_score( [ ("binary", [0.5], True, None, None, None), ("mc", [0.25, 0.25, 0.25, 0.25], "A", ["A", "B", "C", "D"], None, None), - ("numeric", generate_perfect_cdf(100), 100, None, 0, 100), + ("numeric", generate_cdf_with_forecast_at_index(100, 0.999), 100, None, 0, 100), ("numeric", generate_uniform_cdf(), 50, None, 0, 100), ], ) @@ -635,8 +620,8 @@ def test_peer_score_average_zero( ( [ generate_uniform_cdf(), - generate_perfect_cdf(100), - generate_perfect_cdf(101), + generate_cdf_with_forecast_at_index(100, 0.999), + generate_cdf_with_forecast_at_index(101, 0.999), ], 50, None, From dbcc56d76e78bfcb1b98c3e0131af78a4b77cc7d Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 6 May 2025 12:51:21 -0600 Subject: [PATCH 13/26] Got all scoring tests passing except numeric baseline max/min --- AI_BENCHMARKING_ANALYSIS.ipynb | 9952 +++----------------------------- functions.py | 80 +- refactored_notebook/scoring.py | 61 +- tests/test_scoring.py | 94 +- 4 files changed, 856 insertions(+), 9331 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index fe42ead..f98ede4 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -54,7 +54,16 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1495376/643149966.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" + ] + } + ], "source": [ "# @title Create df_bot_resolved_questions, df_pro_resolved_questions, df_pro_bot_resolved_questions, df_bot_question_weights\n", "\n", @@ -551,6 +560,8 @@ " options\n", " range_min\n", " range_max\n", + " open_lower_bound\n", + " open_upper_bound\n", " post_id\n", " forecast\n", " is_median\n", @@ -572,6 +583,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.568,0.366,0.041,0.024]\n", " False\n", @@ -591,6 +604,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.62,0.35,0.019,0.01]\n", " True\n", @@ -610,6 +625,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.005,0.7,0.25,0.04,0.005]\n", " False\n", @@ -629,6 +646,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.49,0.365,0.1,0.044]\n", " False\n", @@ -648,6 +667,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.56,0.36,0.059,0.02]\n", " False\n", @@ -678,19 +699,26 @@ "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", - " type options range_min range_max post_id \\\n", - "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "\n", - " forecast is_median \n", - "0 [0.001,0.568,0.366,0.041,0.024] False \n", - "1 [0.001,0.62,0.35,0.019,0.01] True \n", - "2 [0.005,0.7,0.25,0.04,0.005] False \n", - "5 [0.001,0.49,0.365,0.1,0.044] False \n", - "6 [0.001,0.56,0.36,0.059,0.02] False " + " type options range_min range_max \\\n", + "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "\n", + " open_lower_bound open_upper_bound post_id forecast \\\n", + "0 False False 31736 [0.001,0.568,0.366,0.041,0.024] \n", + "1 False False 31736 [0.001,0.62,0.35,0.019,0.01] \n", + "2 False False 31736 [0.005,0.7,0.25,0.04,0.005] \n", + "5 False False 31736 [0.001,0.49,0.365,0.1,0.044] \n", + "6 False False 31736 [0.001,0.56,0.36,0.059,0.02] \n", + "\n", + " is_median \n", + "0 False \n", + "1 True \n", + "2 False \n", + "5 False \n", + "6 False " ] }, "execution_count": 16, @@ -793,15 +821,6 @@ " \n", " \n", " \n", - " 15\n", - " bot_median\n", - " 9.993738\n", - " 3777.832847\n", - " 409\n", - " 7.260052\n", - " 1.390626\n", - " \n", - " \n", " 12\n", " metac-o1\n", " 9.674740\n", @@ -811,6 +830,15 @@ " 1.738353\n", " \n", " \n", + " 15\n", + " bot_median\n", + " 9.550728\n", + " 3610.366154\n", + " 409\n", + " 6.843423\n", + " 1.377206\n", + " \n", + " \n", " 4\n", " metac-o1-preview\n", " 8.465638\n", @@ -843,15 +871,15 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "15 bot_median 9.993738 3777.832847 409 7.260052 \n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", + "15 bot_median 9.550728 3610.366154 409 6.843423 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", - "15 1.390626 \n", "12 1.738353 \n", + "15 1.377206 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -1502,7 +1530,7 @@ " \n", " 1\n", " bot_median\n", - " 9389.288325\n", + " 9303.299412\n", " \n", " \n", " 2\n", @@ -1531,7 +1559,7 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 bot_median 9389.288325\n", + "1 bot_median 9303.299412\n", "2 metac-o1 8861.959039\n", "3 metac-o1-preview 8849.559824\n", "4 acm_bot 7605.922314\n", @@ -1697,13 +1725,13 @@ " \n", " \n", " 1\n", - " bot_median\n", - " 4077.448023\n", + " metac-o1\n", + " 3864.168122\n", " \n", " \n", " 2\n", - " metac-o1\n", - " 3864.168122\n", + " bot_median\n", + " 3821.107768\n", " \n", " \n", " 3\n", @@ -1937,8 +1965,8 @@ "text/plain": [ " bot Peer Score\n", "Rank \n", - "1 bot_median 4077.448023\n", - "2 metac-o1 3864.168122\n", + "1 metac-o1 3864.168122\n", + "2 bot_median 3821.107768\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2114,6 +2142,8 @@ " options\n", " range_min\n", " range_max\n", + " open_lower_bound\n", + " open_upper_bound\n", " post_id\n", " forecast\n", " is_median\n", @@ -2135,6 +2165,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.568,0.366,0.041,0.024]\n", " False\n", @@ -2154,6 +2186,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.62,0.35,0.019,0.01]\n", " True\n", @@ -2173,6 +2207,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.005,0.7,0.25,0.04,0.005]\n", " False\n", @@ -2192,6 +2228,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.49,0.365,0.1,0.044]\n", " False\n", @@ -2211,6 +2249,8 @@ " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " 31736\n", " [0.001,0.56,0.36,0.059,0.02]\n", " False\n", @@ -2241,19 +2281,26 @@ "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", - " type options range_min range_max post_id \\\n", - "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31736 \n", - "\n", - " forecast is_median \n", - "0 [0.001,0.568,0.366,0.041,0.024] False \n", - "1 [0.001,0.62,0.35,0.019,0.01] True \n", - "2 [0.005,0.7,0.25,0.04,0.005] False \n", - "5 [0.001,0.49,0.365,0.1,0.044] False \n", - "6 [0.001,0.56,0.36,0.059,0.02] False " + " type options range_min range_max \\\n", + "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "\n", + " open_lower_bound open_upper_bound post_id forecast \\\n", + "0 False False 31736 [0.001,0.568,0.366,0.041,0.024] \n", + "1 False False 31736 [0.001,0.62,0.35,0.019,0.01] \n", + "2 False False 31736 [0.005,0.7,0.25,0.04,0.005] \n", + "5 False False 31736 [0.001,0.49,0.365,0.1,0.044] \n", + "6 False False 31736 [0.001,0.56,0.36,0.059,0.02] \n", + "\n", + " is_median \n", + "0 False \n", + "1 True \n", + "2 False \n", + "5 False \n", + "6 False " ] }, "execution_count": 28, @@ -2331,9 +2378,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.35000000000000003,0.30000000000000004,0.250...\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.02,0.7,0.2,0.06,0.02]\n", + " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2355,8 +2402,8 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", @@ -2379,9 +2426,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.15\n", " 0.1\n", - " 0.05\n", - " 0.1\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2403,9 +2450,9 @@ " NaN\n", " [0.16,0.47,0.37]\n", " ...\n", - " [0.3,0.55,0.15]\n", + " [0.29,0.56,0.14999999999999997]\n", " [0.2,0.6,0.2]\n", - " [0.1,0.6,0.3]\n", + " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2429,7 +2476,7 @@ " ...\n", " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", @@ -2466,25 +2513,25 @@ "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... NaN NaN \n", "\n", " CatrachoCaster ... metac-o1 \\\n", - "0 NaN ... [0.45,0.3,0.15,0.05,0.05] \n", - "1 NaN ... [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", - "2 NaN ... 0.1 \n", - "3 [0.16,0.47,0.37] ... [0.3,0.55,0.15] \n", + "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", + "1 NaN ... [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", + "2 NaN ... 0.15 \n", + "3 [0.16,0.47,0.37] ... [0.29,0.56,0.14999999999999997] \n", "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", + "0 [0.02,0.7,0.2,0.06,0.02] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.1 \n", "3 [0.2,0.6,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", + "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.1 NaN \n", - "3 [0.1,0.6,0.3] NaN \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", + "2 0.15 NaN \n", + "3 [0.15,0.6,0.25] NaN \n", + "4 [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,... NaN \n", "\n", " mmBot \\\n", "0 [0.009900990099009901,0.39603960396039606,0.44... \n", @@ -2595,8 +2642,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.65\n", - " 0.15\n", + " 0.3\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2619,8 +2666,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.85\n", - " 0.9\n", + " 0.8\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2643,7 +2690,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", + " 0.85\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2667,9 +2714,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.07\n", " 0.1\n", - " 0.05\n", - " 0.1\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2693,17 +2740,17 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.65 \n", - "96 None 0.97 0.85 NaN NaN ... 0.85 \n", - "97 None 0.666 0.8 NaN NaN ... 0.8 \n", - "98 None 0.03 0.3 NaN NaN ... 0.1 \n", + "95 None 0.05 0.95 NaN NaN ... 0.3 \n", + "96 None 0.97 0.85 NaN NaN ... 0.8 \n", + "97 None 0.666 0.8 NaN NaN ... 0.85 \n", + "98 None 0.03 0.3 NaN NaN ... 0.07 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.15 NaN NaN 0.15 NaN NaN \n", - "96 0.9 NaN NaN 0.9 NaN NaN \n", + "95 0.85 NaN NaN 0.15 NaN NaN \n", + "96 0.95 NaN NaN 0.9 NaN NaN \n", "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.05 0.1 NaN 0.15 0.05 NaN \n", + "98 0.1 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -2911,9 +2958,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.02,0.7,0.2,0.07,0.01]\n", - " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.02,0.7,0.2,0.06,0.02]\n", + " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2935,9 +2982,9 @@ " NaN\n", " NaN\n", " ...\n", - " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...]\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", - " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", + " [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...]\n", + " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", + " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", " NaN\n", " [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...]\n", " [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...]\n", @@ -2959,9 +3006,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.15\n", " 0.1\n", - " 0.05\n", - " 0.1\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2983,9 +3030,9 @@ " NaN\n", " [0.16,0.47,0.37]\n", " ...\n", - " [0.3,0.55,0.15]\n", + " [0.29,0.56,0.14999999999999997]\n", " [0.2,0.6,0.2]\n", - " [0.1,0.6,0.3]\n", + " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -3008,8 +3055,8 @@ " NaN\n", " ...\n", " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...]\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...]\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...]\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...]\n", + " [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...]\n", " NaN\n", " [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", " [0.0, 0.0001141583, 0.0002446967, 0.0003862688, 0.0005272579, 0.0006650709, 0.0008243437, 0.0011074433, 0.0016696544, 0.0025699094, 0.0037138357, 0.0049708626, 0.0062610152, 0.0075426566, 0.0089765864, 0.0111726822, 0.0147311078, 0.0195212559, 0.0249547717, 0.0306181288, 0.0363105138, 0.0419407763, 0.0476011969, 0.053516341, 0.0598014349, 0.0663689162, 0.0730761187, 0.0798334547, 0.0865904866, 0.0933196582, 0.1000172031, 0.1066924089, 0.1133554776, 0.1200140176, 0.1266729489, 0.1333343989, 0.1399984689, 0.1466644317, 0.1533314439, 0.1599988203, 0.1666661444, 0.1733332523, 0.1800001372, 0.1866668598, 0.1933334943, 0.2000000995, 0.2066667101, 0.2133333393, 0.2199999878, 0.22666665, 0.2333333196, 0.2399999916, 0.2466666631, 0.2533333329, 0.2600000011, 0.2666666681, 0.2733333345, 0.2800000007, 0.286666667, 0.2933333334, 0.2999999999, 0.3066666665, 0.3133333332, 0.3199999999, 0.3266666666, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3934628939, 0.400837331, 0.40925763, 0.4186848364, 0.428718413, 0.4390353607, 0.4494419812, 0.4597974687, 0.4700329298, 0.4801500685, 0.4901790777, 0.500153105, 0.5101028922, 0.5200515519, 0.5300114112, 0.5398722838, 0.5492279015, 0.5576212737, 0.5650210292, 0.571743695, 0.5780856137, 0.5842571713, 0.5904328096, 0.5967209586, 0.603152213, 0.6097133168, ...]\n", @@ -3052,26 +3099,26 @@ "3 NaN NaN [0.16,0.47,0.37] ... \n", "4 NaN NaN NaN ... \n", "\n", - " metac-o1 \\\n", - "0 [0.45,0.3,0.15,0.05,0.05] \n", - "1 [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...] \n", - "2 0.1 \n", - "3 [0.3,0.55,0.15] \n", - "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.07,0.01] \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", - "2 0.05 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...] \n", - "\n", - " metac-perplexity \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", - "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.1 \n", - "3 [0.1,0.6,0.3] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", + " metac-o1 \\\n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...] \n", + "2 0.15 \n", + "3 [0.29,0.56,0.14999999999999997] \n", + "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.02,0.7,0.2,0.06,0.02] \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", + "2 0.1 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", + "\n", + " metac-perplexity \\\n", + "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", + "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", + "2 0.15 \n", + "3 [0.15,0.6,0.25] \n", + "4 [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3203,8 +3250,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.65\n", - " 0.15\n", + " 0.3\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3227,8 +3274,8 @@ " NaN\n", " NaN\n", " ...\n", - " 0.85\n", - " 0.9\n", + " 0.8\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3251,7 +3298,7 @@ " NaN\n", " NaN\n", " ...\n", - " 0.8\n", + " 0.85\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3275,9 +3322,9 @@ " NaN\n", " NaN\n", " ...\n", + " 0.07\n", " 0.1\n", - " 0.05\n", - " 0.1\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3301,17 +3348,17 @@ "\n", " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.65 \n", - "96 None 0.97 0.85 NaN NaN ... 0.85 \n", - "97 None 0.666 0.8 NaN NaN ... 0.8 \n", - "98 None 0.03 0.3 NaN NaN ... 0.1 \n", + "95 None 0.05 0.95 NaN NaN ... 0.3 \n", + "96 None 0.97 0.85 NaN NaN ... 0.8 \n", + "97 None 0.666 0.8 NaN NaN ... 0.85 \n", + "98 None 0.03 0.3 NaN NaN ... 0.07 \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.15 NaN NaN 0.15 NaN NaN \n", - "96 0.9 NaN NaN 0.9 NaN NaN \n", + "95 0.85 NaN NaN 0.15 NaN NaN \n", + "96 0.95 NaN NaN 0.9 NaN NaN \n", "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.05 0.1 NaN 0.15 0.05 NaN \n", + "98 0.1 0.03 NaN 0.15 0.05 NaN \n", "\n", " swingswish twsummerbot wunderplumb \n", "94 0.9 0.762 0.9 \n", @@ -3375,20 +3422,32 @@ "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'calculate_peer_score' is not defined", + "ename": "KeyError", + "evalue": "'Range_min'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Range_min'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1245\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;66;03m# options = row['options_parsed'] if 'options_parsed' in row else row['options']\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;66;03m# # Get the forecasts\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;66;03m# bot_pmf_raw = row[bot_col]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \n\u001b[1;32m 1243\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1245\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_peer_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1248\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_peer_score(row, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m'\u001b[39m, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1275\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1275\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1276\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1277\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1278\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1279\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1280\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1285\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1286\u001b[0m ),\n\u001b[1;32m 1287\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1288\u001b[0m )\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1245\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;66;03m# options = row['options_parsed'] if 'options_parsed' in row else row['options']\u001b[39;00m\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;66;03m# # Get the forecasts\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;66;03m# bot_pmf_raw = row[bot_col]\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \n\u001b[1;32m 1243\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1245\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_peer_score\u001b[49m(row, bot, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 1247\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1248\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_peer_score(row, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m'\u001b[39m, pro_col), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'calculate_peer_score' is not defined" + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1276\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[1;32m 1275\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m-> 1276\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1277\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1278\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1279\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1280\u001b[0m )\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1285\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1286\u001b[0m ),\n\u001b[1;32m 1287\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1288\u001b[0m )\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1253\u001b[0m, in \u001b[0;36mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[0;34m(row, col_a, col_b)\u001b[0m\n\u001b[1;32m 1251\u001b[0m resolution \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1252\u001b[0m options \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions_parsed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions_parsed\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m row \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m-> 1253\u001b[0m range_min \u001b[38;5;241m=\u001b[39m \u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRange_min\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 1254\u001b[0m range_max \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRange_max\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1255\u001b[0m question_weight \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestion_weight\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/series.py:1121\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m 1120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m 1124\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m 1125\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/series.py:1237\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m 1236\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1237\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m 1240\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'Range_min'" ] } ], @@ -3399,858 +3458,9 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionspro_median4ShadowerBot_PepaCatrachoCaster...metac-o1metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumb
0312683126201.0multiple_choice[0, 1, 2-3, 4-6, >6][0.001,0.62,0.35,0.019,0.01]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
331280312745-91.0multiple_choice[0-4, 5-9, >9][0.16,0.44,0.4]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
63129231286Jeff Bezos1.0multiple_choice[Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else][0.2,0.025,0.225,0.08,0.445,0.025]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
9313213137001.0multiple_choice[0, 1, 2, Greater than 2][0.336,0.364,0.2,0.1]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
133136831366≥0% and <5%1.0multiple_choice[Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%][0.05,0.45,0.45,0.05]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
\n", - "

5 rows × 53 columns

\n", - "
" - ], - "text/plain": [ - " pro_question_id bot_question_id resolution question_weight \\\n", - "0 31268 31262 0 1.0 \n", - "3 31280 31274 5-9 1.0 \n", - "6 31292 31286 Jeff Bezos 1.0 \n", - "9 31321 31370 0 1.0 \n", - "13 31368 31366 ≥0% and <5% 1.0 \n", - "\n", - " type \\\n", - "0 multiple_choice \n", - "3 multiple_choice \n", - "6 multiple_choice \n", - "9 multiple_choice \n", - "13 multiple_choice \n", - "\n", - " options \\\n", - "0 [0, 1, 2-3, 4-6, >6] \n", - "3 [0-4, 5-9, >9] \n", - "6 [Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else] \n", - "9 [0, 1, 2, Greater than 2] \n", - "13 [Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%] \n", - "\n", - " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 0.643473 2.597381 1.762901 \n", - "3 [0.16,0.44,0.4] 0.643473 2.597381 1.762901 \n", - "6 [0.2,0.025,0.225,0.08,0.445,0.025] 0.643473 2.597381 1.762901 \n", - "9 [0.336,0.364,0.2,0.1] 0.643473 2.597381 1.762901 \n", - "13 [0.05,0.45,0.45,0.05] 0.643473 2.597381 1.762901 \n", - "\n", - " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "0 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "3 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "6 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "9 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "13 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "\n", - " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", - "0 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "3 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "6 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "9 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "13 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionspro_median4ShadowerBot_PepaCatrachoCaster...metac-o1metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumb
813516935119Not in top 501.0multiple_choice[0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50][0.02,0.01,0.015,0.015,0.05,0.89]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
8235170351213 or more1.0multiple_choice[0, 1, 2, 3 or more][0.01,0.18,0.54,0.27]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
833517135123≥7.5 and ≤8.51.0multiple_choice[<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5][0.02,0.3,0.3,0.3,0.08]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
913537735334Jimmy Patronis1.0multiple_choice[Jimmy Patronis, Gay Valimont, Someone else][0.997,0.001,0.002]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
92353783533631-491.0multiple_choice[0-24, 25-30, 31-49, 50-70, >70][0.001,0.359,0.55,0.08,0.01]0.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
\n", - "

5 rows × 53 columns

\n", - "
" - ], - "text/plain": [ - " pro_question_id bot_question_id resolution question_weight \\\n", - "81 35169 35119 Not in top 50 1.0 \n", - "82 35170 35121 3 or more 1.0 \n", - "83 35171 35123 ≥7.5 and ≤8.5 1.0 \n", - "91 35377 35334 Jimmy Patronis 1.0 \n", - "92 35378 35336 31-49 1.0 \n", - "\n", - " type \\\n", - "81 multiple_choice \n", - "82 multiple_choice \n", - "83 multiple_choice \n", - "91 multiple_choice \n", - "92 multiple_choice \n", - "\n", - " options \\\n", - "81 [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50] \n", - "82 [0, 1, 2, 3 or more] \n", - "83 [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5] \n", - "91 [Jimmy Patronis, Gay Valimont, Someone else] \n", - "92 [0-24, 25-30, 31-49, 50-70, >70] \n", - "\n", - " pro_median 4Shadower Bot_Pepa CatrachoCaster \\\n", - "81 [0.02,0.01,0.015,0.015,0.05,0.89] 0.643473 2.597381 1.762901 \n", - "82 [0.01,0.18,0.54,0.27] 0.643473 2.597381 1.762901 \n", - "83 [0.02,0.3,0.3,0.3,0.08] 0.643473 2.597381 1.762901 \n", - "91 [0.997,0.001,0.002] 0.643473 2.597381 1.762901 \n", - "92 [0.001,0.359,0.55,0.08,0.01] 0.643473 2.597381 1.762901 \n", - "\n", - " ... metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot \\\n", - "81 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "82 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "83 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "91 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "92 ... 16.605891 6.665593 18.102498 -2.987997 9.735149 \n", - "\n", - " pgodzinai pianobot swingswish twsummerbot wunderplumb \n", - "81 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "82 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "83 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "91 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "92 3.537037 -2.173212 2.411469 14.267308 2.372721 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionspro_median4ShadowerBot_PepaCatrachoCaster...metac-o1metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumb
23127031264no1.0binaryNone0.0130.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
53128231276yes1.0binaryNone0.450.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
83129431288yes1.0binaryNone0.950.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
123133831334yes1.0binaryNone0.90.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
163387633751no1.0binaryNone0.0580.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
\n", - "

5 rows × 53 columns

\n", - "
" - ], - "text/plain": [ - " pro_question_id bot_question_id resolution question_weight type \\\n", - "2 31270 31264 no 1.0 binary \n", - "5 31282 31276 yes 1.0 binary \n", - "8 31294 31288 yes 1.0 binary \n", - "12 31338 31334 yes 1.0 binary \n", - "16 33876 33751 no 1.0 binary \n", - "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "2 None 0.013 0.643473 2.597381 1.762901 ... 16.605891 \n", - "5 None 0.45 0.643473 2.597381 1.762901 ... 16.605891 \n", - "8 None 0.95 0.643473 2.597381 1.762901 ... 16.605891 \n", - "12 None 0.9 0.643473 2.597381 1.762901 ... 16.605891 \n", - "16 None 0.058 0.643473 2.597381 1.762901 ... 16.605891 \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "5 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "8 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "12 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "16 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb \n", - "2 -2.173212 2.411469 14.267308 2.372721 \n", - "5 -2.173212 2.411469 14.267308 2.372721 \n", - "8 -2.173212 2.411469 14.267308 2.372721 \n", - "12 -2.173212 2.411469 14.267308 2.372721 \n", - "16 -2.173212 2.411469 14.267308 2.372721 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionspro_median4ShadowerBot_PepaCatrachoCaster...metac-o1metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumb
943538035345yes1.00binaryNone0.950.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
953538135354no1.00binaryNone0.050.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
963538535358yes1.00binaryNone0.970.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
973538635364no0.85binaryNone0.6660.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
983538735367no0.85binaryNone0.030.6434732.5973811.762901...16.6058916.66559318.102498-2.9879979.7351493.537037-2.1732122.41146914.2673082.372721
\n", - "

5 rows × 53 columns

\n", - "
" - ], - "text/plain": [ - " pro_question_id bot_question_id resolution question_weight type \\\n", - "94 35380 35345 yes 1.00 binary \n", - "95 35381 35354 no 1.00 binary \n", - "96 35385 35358 yes 1.00 binary \n", - "97 35386 35364 no 0.85 binary \n", - "98 35387 35367 no 0.85 binary \n", - "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.643473 2.597381 1.762901 ... 16.605891 \n", - "95 None 0.05 0.643473 2.597381 1.762901 ... 16.605891 \n", - "96 None 0.97 0.643473 2.597381 1.762901 ... 16.605891 \n", - "97 None 0.666 0.643473 2.597381 1.762901 ... 16.605891 \n", - "98 None 0.03 0.643473 2.597381 1.762901 ... 16.605891 \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "95 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "96 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "97 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "98 6.665593 18.102498 -2.987997 9.735149 3.537037 \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb \n", - "94 -2.173212 2.411469 14.267308 2.372721 \n", - "95 -2.173212 2.411469 14.267308 2.372721 \n", - "96 -2.173212 2.411469 14.267308 2.372721 \n", - "97 -2.173212 2.411469 14.267308 2.372721 \n", - "98 -2.173212 2.411469 14.267308 2.372721 \n", - "\n", - "[5 rows x 53 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", @@ -4259,6055 +3469,503 @@ }, { "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
botPeer Score
Rank
1metac-o13864.168122
2metac-o1-preview3162.155445
3bot_median3060.137114
4manticAI2142.538438
5metac-Gemini-Exp-12062072.216227
6acm_bot1876.466009
7twsummerbot1763.532046
8metac-perplexity1697.555196
9GreeneiBot21603.998618
10cookics_bot_TEST1140.390796
11metac-claude-3-5-sonnet-latest1134.209821
12SynapseSeer1066.533051
13CumulativeBot1030.716475
14pgodzinai926.081448
15jkraybill_bot627.932509
16metac-deepseek-r1614.572462
17question_weight378.020000
18metac-exa265.384263
19MWG215.551323
20annabot21.125670
21andrewsiah-4.170684
22cobyj-bot-15.593332
23X_bot-16.052813
24pianobot-20.745921
25CatrachoCaster-214.389722
26KevinTestBot-244.046973
27jonahsingerbot-318.088290
28krm-bot-387.131345
29ProfessorSP-406.072162
30mmBot-453.312468
31metac-grok-2-1212-492.938695
32bean_bot-494.373003
334Shadower-586.017986
34metac-claude-3-5-sonnet-20240620-647.579684
35swingswish-763.021897
36RPM_bot-905.938514
37metac-Llama-3.1-1029.014161
38InstitutPelFutur-1087.748963
39wunderplumb-1189.786803
40VeritasAI-1521.091541
41NextWorldLab-1565.096041
42Bot_Pepa-1589.575284
43laylaps-1665.296188
44minefrac1-1850.747385
45Grizeu_Bot-1898.666894
46metac-gpt-4o-2618.918368
47ajf-bot-3239.712801
\n", - "
" - ], - "text/plain": [ - " bot Peer Score\n", - "Rank \n", - "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 3060.137114\n", - "4 manticAI 2142.538438\n", - "5 metac-Gemini-Exp-1206 2072.216227\n", - "6 acm_bot 1876.466009\n", - "7 twsummerbot 1763.532046\n", - "8 metac-perplexity 1697.555196\n", - "9 GreeneiBot2 1603.998618\n", - "10 cookics_bot_TEST 1140.390796\n", - "11 metac-claude-3-5-sonnet-latest 1134.209821\n", - "12 SynapseSeer 1066.533051\n", - "13 CumulativeBot 1030.716475\n", - "14 pgodzinai 926.081448\n", - "15 jkraybill_bot 627.932509\n", - "16 metac-deepseek-r1 614.572462\n", - "17 question_weight 378.020000\n", - "18 metac-exa 265.384263\n", - "19 MWG 215.551323\n", - "20 annabot 21.125670\n", - "21 andrewsiah -4.170684\n", - "22 cobyj-bot -15.593332\n", - "23 X_bot -16.052813\n", - "24 pianobot -20.745921\n", - "25 CatrachoCaster -214.389722\n", - "26 KevinTestBot -244.046973\n", - "27 jonahsingerbot -318.088290\n", - "28 krm-bot -387.131345\n", - "29 ProfessorSP -406.072162\n", - "30 mmBot -453.312468\n", - "31 metac-grok-2-1212 -492.938695\n", - "32 bean_bot -494.373003\n", - "33 4Shadower -586.017986\n", - "34 metac-claude-3-5-sonnet-20240620 -647.579684\n", - "35 swingswish -763.021897\n", - "36 RPM_bot -905.938514\n", - "37 metac-Llama-3.1 -1029.014161\n", - "38 InstitutPelFutur -1087.748963\n", - "39 wunderplumb -1189.786803\n", - "40 VeritasAI -1521.091541\n", - "41 NextWorldLab -1565.096041\n", - "42 Bot_Pepa -1589.575284\n", - "43 laylaps -1665.296188\n", - "44 minefrac1 -1850.747385\n", - "45 Grizeu_Bot -1898.666894\n", - "46 metac-gpt-4o -2618.918368\n", - "47 ajf-bot -3239.712801" - ] - }, - "execution_count": 197, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "leaderboard" - ] - }, - { - "cell_type": "code", - "execution_count": 198, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean pro median forecast on questions that resolved yes: 74.0%\n", - "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 71.0%\n", - "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Average pro median forecast on questions that resolved yes/no vs top bot\n", - "\n", - "top_bot = leaderboard['bot'][1]\n", - "\n", - "resolved_yes = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'yes']\n", - "resolved_no = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'no']\n", - "\n", - "# Calculate the average pro median forecast for questions that resolved yes\n", - "mean_pro_median_yes = resolved_yes['pro_median'].mean().round(2) * 100\n", - "mean_pro_median_no = resolved_no['pro_median'].mean().round(2) * 100\n", - "\n", - "mean_bot_yes = resolved_yes[top_bot].mean().round(2) * 100\n", - "mean_bot_no = resolved_no[top_bot].mean().round(2) * 100\n", - "\n", - "print(f'mean pro median forecast on questions that resolved yes: {mean_pro_median_yes}%')\n", - "print(f'mean pro median forecast on questions that resolved no: {mean_pro_median_no}%')\n", - "print(f'mean {top_bot} forecast on questions that resolved yes: {mean_bot_yes}%')\n", - "print(f'mean {top_bot} forecast on questions that resolved no: {mean_bot_no}%')\n", - "\n", - "# Plot the data\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Set up the figure\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# Create x-coordinates with jitter for each group separately\n", - "x_bot_yes = np.random.normal(0, 0.04, len(resolved_yes))\n", - "x_pro_yes = np.random.normal(1, 0.04, len(resolved_yes))\n", - "x_bot_no = np.random.normal(0, 0.04, len(resolved_no))\n", - "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", - "\n", - "# Plot points for \"yes\" resolution\n", - "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", - " color='blue', alpha=0.6, label='Resolved Yes')\n", - "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", - " color='blue', alpha=0.6)\n", - "\n", - "# Plot points for \"no\" resolution\n", - "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", - " color='red', alpha=0.6, label='Resolved No')\n", - "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", - " color='red', alpha=0.6)\n", - "\n", - "# Customize the plot\n", - "plt.xticks([0, 1], ['pro_median', top_bot])\n", - "plt.ylabel('Probability (%)')\n", - "plt.title('Pro Median vs Top Bot Forecasts')\n", - "plt.legend()\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Set y-axis limits from 0 to 100\n", - "plt.ylim(0, 100)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_739597/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" - ] - } - ], - "source": [ - "bot_vs_pro_peer_for_scores = df_bot_vs_pro_peer.copy()\n", - "bot_vs_pro_peer_for_scores = bot_vs_pro_peer_for_scores.drop(['resolution', 'question_weight', 'bot_question_id', 'pro_median', 'options', 'type'], axis=1)\n", - "\n", - "total_scores = bot_vs_pro_peer_for_scores.sum(axis=0)\n", - "\n", - "df_bot_vs_pro_peer = df_bot_vs_pro_peer.drop('pro_median', axis=1)\n", - "\n", - "# First pivot to long format - each row will be a question-forecaster pair\n", - "df_long = df_bot_vs_pro_peer.melt(\n", - " id_vars=['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options'],\n", - " var_name='forecaster',\n", - " value_name='score'\n", - ")\n", - "\n", - "# Drop any rows where score is NaN\n", - "df_long = df_long.dropna(subset=['score'])\n", - "\n", - "# Cast question_weight as numeric\n", - "df_long['question_weight'] = pd.to_numeric(df_long['question_weight'], errors='coerce')\n", - "\n", - "# Group first, then do the multiplication and sum\n", - "weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n", - "\n", - "# Calculate number of questions answered by each bot\n", - "num_questions = df_long.groupby('forecaster')['bot_question_id'].nunique()\n", - "#num_weighted_questions = df_bot_vs_pro_peer.mul(df_pro_bot_forecasts['question_weight'], axis=0).apply(lambda col: col[col.notna() & col.apply(np.isreal)].count())\n", - "\n", - "# Create a new DataFrame with the results\n", - "results = pd.DataFrame({\n", - " 'Peer_vs_Pro': total_scores,\n", - " 'Count': num_questions\n", - "})\n", - "\n", - "weighted_results = pd.DataFrame({\n", - " 'W_Peer_vs_Pro': weighted_scores,\n", - " 'Count': num_questions\n", - "})\n", - "\n", - "df_bot_vs_pro_leaderboard = results.sort_values(by='Peer_vs_Pro', ascending=False)\n", - "df_bot_vs_pro_weighted_leaderboard = weighted_results.sort_values(by='W_Peer_vs_Pro', ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": {}, - "outputs": [], - "source": [ - "df_pro_baseline = df_pro_baseline.rename(columns={'question_id': 'pro_question_id'})\n", - "df_pro_baseline = df_pro_baseline[['pro_question_id', 'forecaster', 'score']]\n", - "\n", - "# Now make it wide! forecaster = columns; score = values; index = pro_question_id\n", - "df_pro_baseline_wide = df_pro_baseline.pivot(index='pro_question_id', columns='forecaster', values='score').reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": { - "cellView": "form", - "id": "tXKRpXAVHMRt" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
RankForecasterWeighted_BaselineCountWeighted Count
01pro_median4238.5616079793.10
12metac-o13010.3537889692.10
23metac-perplexity2774.0803319490.10
34bot_median2273.1150899793.10
45acm_bot2239.0586758581.25
56metac-claude-3-5-sonnet-202406202018.1102119591.50
67manticAI1865.1262607470.45
78metac-exa1826.2756819490.10
89twsummerbot1819.0641416259.40
910metac-claude-3-5-sonnet-latest1740.3151889692.10
1011metac-Llama-3.11701.1824039490.10
1112jkraybill_bot1616.0557094745.05
1213metac-Gemini-Exp-12061595.6826128177.50
1314NextWorldLab1583.0262268581.25
1415metac-o1-preview1527.6571419692.10
1516metac-deepseek-r11518.3086255552.10
1617laylaps1500.5678746865.10
1718mmBot1482.7264459793.10
1819Grizeu_Bot1399.4777185552.35
1920metac-grok-2-12121167.8671619692.10
2021VeritasAI1136.6824928278.10
2122metac-gpt-4o1045.1336789692.10
2223SynapseSeer1039.4846352826.15
2324annabot1031.9739303129.30
2425GreeneiBot2932.8835806259.35
2526MWG741.4247473028.60
2627InstitutPelFutur722.6870159591.10
2728cookics_bot_TEST714.1983722927.40
2829Bot_Pepa660.8016994745.05
2930ajf-bot484.4450303735.25
3031swingswish429.96611287.70
3132KevinTestBot331.09944498.40
3233X_bot274.53936577.00
3334CumulativeBot253.8397011110.25
3435CatrachoCaster247.2667172119.70
3536jonahsingerbot224.15439254.70
36374Shadower210.5486171514.00
3738bean_bot210.54275254.70
3839pgodzinai177.1341048177.40
3940wunderplumb112.1502452725.55
4041krm-bot65.989405109.50
4142andrewsiah0.00000000.00
4243cobyj-bot0.00000000.00
4344RPM_bot-8.69053388.00
4445ProfessorSP-217.1062982018.60
4546pianobot-217.32120454.70
4647minefrac1-299.5665065552.10
\n", - "
" - ], - "text/plain": [ - " Rank Forecaster Weighted_Baseline Count \\\n", - "0 1 pro_median 4238.561607 97 \n", - "1 2 metac-o1 3010.353788 96 \n", - "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2273.115089 97 \n", - "4 5 acm_bot 2239.058675 85 \n", - "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", - "6 7 manticAI 1865.126260 74 \n", - "7 8 metac-exa 1826.275681 94 \n", - "8 9 twsummerbot 1819.064141 62 \n", - "9 10 metac-claude-3-5-sonnet-latest 1740.315188 96 \n", - "10 11 metac-Llama-3.1 1701.182403 94 \n", - "11 12 jkraybill_bot 1616.055709 47 \n", - "12 13 metac-Gemini-Exp-1206 1595.682612 81 \n", - "13 14 NextWorldLab 1583.026226 85 \n", - "14 15 metac-o1-preview 1527.657141 96 \n", - "15 16 metac-deepseek-r1 1518.308625 55 \n", - "16 17 laylaps 1500.567874 68 \n", - "17 18 mmBot 1482.726445 97 \n", - "18 19 Grizeu_Bot 1399.477718 55 \n", - "19 20 metac-grok-2-1212 1167.867161 96 \n", - "20 21 VeritasAI 1136.682492 82 \n", - "21 22 metac-gpt-4o 1045.133678 96 \n", - "22 23 SynapseSeer 1039.484635 28 \n", - "23 24 annabot 1031.973930 31 \n", - "24 25 GreeneiBot2 932.883580 62 \n", - "25 26 MWG 741.424747 30 \n", - "26 27 InstitutPelFutur 722.687015 95 \n", - "27 28 cookics_bot_TEST 714.198372 29 \n", - "28 29 Bot_Pepa 660.801699 47 \n", - "29 30 ajf-bot 484.445030 37 \n", - "30 31 swingswish 429.966112 8 \n", - "31 32 KevinTestBot 331.099444 9 \n", - "32 33 X_bot 274.539365 7 \n", - "33 34 CumulativeBot 253.839701 11 \n", - "34 35 CatrachoCaster 247.266717 21 \n", - "35 36 jonahsingerbot 224.154392 5 \n", - "36 37 4Shadower 210.548617 15 \n", - "37 38 bean_bot 210.542752 5 \n", - "38 39 pgodzinai 177.134104 81 \n", - "39 40 wunderplumb 112.150245 27 \n", - "40 41 krm-bot 65.989405 10 \n", - "41 42 andrewsiah 0.000000 0 \n", - "42 43 cobyj-bot 0.000000 0 \n", - "43 44 RPM_bot -8.690533 8 \n", - "44 45 ProfessorSP -217.106298 20 \n", - "45 46 pianobot -217.321204 5 \n", - "46 47 minefrac1 -299.566506 55 \n", - "\n", - " Weighted Count \n", - "0 93.10 \n", - "1 92.10 \n", - "2 90.10 \n", - "3 93.10 \n", - "4 81.25 \n", - "5 91.50 \n", - "6 70.45 \n", - "7 90.10 \n", - "8 59.40 \n", - "9 92.10 \n", - "10 90.10 \n", - "11 45.05 \n", - "12 77.50 \n", - "13 81.25 \n", - "14 92.10 \n", - "15 52.10 \n", - "16 65.10 \n", - "17 93.10 \n", - "18 52.35 \n", - "19 92.10 \n", - "20 78.10 \n", - "21 92.10 \n", - "22 26.15 \n", - "23 29.30 \n", - "24 59.35 \n", - "25 28.60 \n", - "26 91.10 \n", - "27 27.40 \n", - "28 45.05 \n", - "29 35.25 \n", - "30 7.70 \n", - "31 8.40 \n", - "32 7.00 \n", - "33 10.25 \n", - "34 19.70 \n", - "35 4.70 \n", - "36 14.00 \n", - "37 4.70 \n", - "38 77.40 \n", - "39 25.55 \n", - "40 9.50 \n", - "41 0.00 \n", - "42 0.00 \n", - "43 8.00 \n", - "44 18.60 \n", - "45 4.70 \n", - "46 52.10 " - ] - }, - "execution_count": 201, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# @title Create df_pro_bot_baseline_leaderboard, df_pro_bot_baseline_weighted_leaderboard\n", - "\n", - "df_pro_bot_baseline_weights = pd.merge(\n", - " df_pro_bot_resolved_questions,\n", - " df_bot_baseline_wide,\n", - " on='bot_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "df_pro_bot_baseline_weights = pd.merge(\n", - " df_pro_bot_baseline_weights,\n", - " df_pro_baseline_wide[['pro_question_id', 'pro_median']],\n", - " on='pro_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "# Remove rows where pro_question_id is NaN (only want overlapping questions here)\n", - "df_pro_bot_baseline_weights = df_pro_bot_baseline_weights.dropna(subset=['pro_question_id'])\n", - "\n", - "# Create a list of columns to keep\n", - "forecaster_cols = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", - "df_filtered = df_pro_bot_baseline_weights[forecaster_cols]\n", - "\n", - "# Calculate the sum for each forecaster\n", - "forecaster_scores = df_filtered.sum()\n", - "forecaster_weighted_scores = df_filtered.mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", - "\n", - "question_counts = df_filtered.notna().sum()\n", - "question_weighted_counts = df_filtered.notna().mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", - "\n", - "# Create a DataFrame for the leaderboard\n", - "leaderboard = pd.DataFrame({\n", - " 'Forecaster': forecaster_scores.index,\n", - " 'Baseline': forecaster_scores.values,\n", - " 'Count': question_counts.values\n", - "})\n", - "\n", - "# Create a DataFrame for the leaderboard\n", - "weighted_leaderboard = pd.DataFrame({\n", - " 'Forecaster': forecaster_weighted_scores.index,\n", - " 'Weighted_Baseline': forecaster_weighted_scores.values,\n", - " 'Count': question_counts.values,\n", - " 'Weighted Count': question_weighted_counts.values\n", - "})\n", - "\n", - "# Sort the leaderboard by score in descending order\n", - "leaderboard = leaderboard.sort_values('Baseline', ascending=False).reset_index(drop=True)\n", - "weighted_leaderboard = weighted_leaderboard.sort_values('Weighted_Baseline', ascending=False).reset_index(drop=True)\n", - "\n", - "# Add a 'Rank' column\n", - "leaderboard['Rank'] = leaderboard.index + 1\n", - "weighted_leaderboard['Rank'] = weighted_leaderboard.index + 1\n", - "\n", - "# Reorder columns to have Rank first\n", - "leaderboard = leaderboard[['Rank', 'Forecaster', 'Baseline', 'Count']]\n", - "weighted_leaderboard = weighted_leaderboard[['Rank', 'Forecaster', 'Weighted_Baseline', 'Count', 'Weighted Count']]\n", - "\n", - "#leaderboard\n", - "weighted_leaderboard" - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
pro_median4238.693.145.562.2291686.4493987.0591051.98527758.332.71.0000000.000000
metac-o13010.492.132.757.7568596.0182995.4310541.98555044.620.71.0000000.000000
metac-perplexity2774.190.130.867.2103837.0806644.3483081.98611444.916.70.9999820.000036
bot_median2273.193.124.458.9365876.1081563.9972531.98527736.512.30.9999350.000129
acm_bot2239.181.227.655.5540546.1631694.4713431.98898539.815.30.9999870.000025
metac-claude-3-5-sonnet-202406202018.191.522.164.2193076.7135943.2852521.98578835.48.70.9992750.001450
manticAI1865.170.426.566.3530597.9053383.3489361.99348842.210.70.9993430.001314
metac-exa1826.390.120.382.2195858.6618942.3400691.98611437.53.10.9892430.021514
twsummerbot1819.159.430.654.7477997.1035174.3111002.00016344.816.40.9999680.000063
metac-claude-3-5-sonnet-latest1740.392.118.971.5459837.4551342.5346201.98555033.74.10.9935180.012963
metac-Llama-3.11701.290.118.962.1549296.5480682.8834531.98611431.95.90.9975350.004930
jkraybill_bot1616.145.035.959.7568388.9030794.0292232.01341253.817.90.9998910.000218
metac-Gemini-Exp-12061595.777.520.667.0999817.6220462.7013031.99042635.85.40.9957490.008502
NextWorldLab1583.081.219.566.4117477.3677222.6444271.98898534.14.80.9950800.009840
metac-o1-preview1527.792.116.687.1115689.0770771.8273441.98555034.6-1.40.9645390.070922
metac-deepseek-r11518.352.129.162.7649708.6955783.3513822.00537946.611.70.9992410.001519
laylaps1500.665.123.174.4573659.2282042.4977991.99634141.54.60.9924630.015074
mmBot1482.793.115.979.9905028.2901731.9210901.98527732.4-0.50.9710930.057813
Grizeu_Bot1399.552.426.760.8869058.4152223.1767552.00555543.69.90.9987400.002521
metac-grok-2-12121167.992.112.779.3224498.2654461.5341491.98555029.1-3.70.9357710.128459
VeritasAI1136.778.114.661.1249136.9166012.1042411.99009528.30.80.9806920.038617
metac-gpt-4o1045.192.111.367.7641657.0610661.6070961.98555025.4-2.70.9442530.111494
SynapseSeer1039.526.239.862.84354812.2892353.2346072.05307665.014.50.9983020.003397
annabot1032.029.335.257.68962410.6577103.3047392.04418357.013.40.9987070.002586
GreeneiBot2932.959.415.773.8321869.5837481.6401042.00014134.9-3.50.9468180.106364
MWG741.428.625.978.73589114.7227771.7608052.04656156.1-4.20.9553250.089349
InstitutPelFutur722.791.17.9100.84063310.5651670.7508541.98582928.9-13.00.7726510.454697
cookics_bot_TEST714.227.426.163.25665212.0845622.1569372.04954150.81.30.9798560.040287
Bot_Pepa660.845.014.769.73878710.3902741.4117232.01341235.6-6.30.9174720.165057
ajf-bot484.435.213.786.56822814.5807200.9425542.02873043.3-15.80.8237450.352510
swingswish430.07.755.852.06574018.7631902.9760272.367123100.311.40.9891420.021716
KevinTestBot331.18.439.476.25685526.3111141.4980972.311496100.2-21.40.9122520.175497
X_bot274.57.039.231.69380111.9791313.2740202.44691268.59.90.9915260.016949
CumulativeBot253.810.224.878.92471924.6519411.0045802.23184879.8-30.30.8296730.340653
CatrachoCaster247.319.712.675.37158416.9814400.7391372.08877748.0-22.90.7655000.469001
jonahsingerbot224.24.747.764.22018229.6225611.6100032.784843130.2-34.80.9057990.188401
bean_bot210.54.744.876.35643935.2205991.2718792.784843142.9-53.30.8612620.277476
4Shadower210.514.015.0116.14611231.0413540.4844892.14723981.7-51.60.6819500.636100
pgodzinai177.177.42.3103.63911911.7802150.1942711.99045325.7-21.20.5767600.846479
wunderplumb112.225.64.4102.06900020.1928870.2173762.05660345.9-37.10.5851440.829712
krm-bot66.09.56.968.18212422.1212020.3140092.26470957.0-43.20.6194580.761083
andrewsiah0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
cobyj-bot0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
RPM_bot-8.78.0-1.189.62555931.687420-0.0342822.36462473.8-76.00.4868050.973609
ProfessorSP-217.118.6-11.780.59407218.687303-0.6246162.09524327.5-50.80.2701180.540237
pianobot-217.34.7-46.2124.35072857.358714-0.8061302.798986114.3-206.80.2343880.468776
minefrac1-299.652.1-5.770.5819809.778562-0.5880042.00564913.9-25.40.2795600.559119
\n", - "
" - ], - "text/plain": [ - " W_score W_count W_ave W_stdev \\\n", - "pro_median 4238.6 93.1 45.5 62.229168 \n", - "metac-o1 3010.4 92.1 32.7 57.756859 \n", - "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2273.1 93.1 24.4 58.936587 \n", - "acm_bot 2239.1 81.2 27.6 55.554054 \n", - "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", - "manticAI 1865.1 70.4 26.5 66.353059 \n", - "metac-exa 1826.3 90.1 20.3 82.219585 \n", - "twsummerbot 1819.1 59.4 30.6 54.747799 \n", - "metac-claude-3-5-sonnet-latest 1740.3 92.1 18.9 71.545983 \n", - "metac-Llama-3.1 1701.2 90.1 18.9 62.154929 \n", - "jkraybill_bot 1616.1 45.0 35.9 59.756838 \n", - "metac-Gemini-Exp-1206 1595.7 77.5 20.6 67.099981 \n", - "NextWorldLab 1583.0 81.2 19.5 66.411747 \n", - "metac-o1-preview 1527.7 92.1 16.6 87.111568 \n", - "metac-deepseek-r1 1518.3 52.1 29.1 62.764970 \n", - "laylaps 1500.6 65.1 23.1 74.457365 \n", - "mmBot 1482.7 93.1 15.9 79.990502 \n", - "Grizeu_Bot 1399.5 52.4 26.7 60.886905 \n", - "metac-grok-2-1212 1167.9 92.1 12.7 79.322449 \n", - "VeritasAI 1136.7 78.1 14.6 61.124913 \n", - "metac-gpt-4o 1045.1 92.1 11.3 67.764165 \n", - "SynapseSeer 1039.5 26.2 39.8 62.843548 \n", - "annabot 1032.0 29.3 35.2 57.689624 \n", - "GreeneiBot2 932.9 59.4 15.7 73.832186 \n", - "MWG 741.4 28.6 25.9 78.735891 \n", - "InstitutPelFutur 722.7 91.1 7.9 100.840633 \n", - "cookics_bot_TEST 714.2 27.4 26.1 63.256652 \n", - "Bot_Pepa 660.8 45.0 14.7 69.738787 \n", - "ajf-bot 484.4 35.2 13.7 86.568228 \n", - "swingswish 430.0 7.7 55.8 52.065740 \n", - "KevinTestBot 331.1 8.4 39.4 76.256855 \n", - "X_bot 274.5 7.0 39.2 31.693801 \n", - "CumulativeBot 253.8 10.2 24.8 78.924719 \n", - "CatrachoCaster 247.3 19.7 12.6 75.371584 \n", - "jonahsingerbot 224.2 4.7 47.7 64.220182 \n", - "bean_bot 210.5 4.7 44.8 76.356439 \n", - "4Shadower 210.5 14.0 15.0 116.146112 \n", - "pgodzinai 177.1 77.4 2.3 103.639119 \n", - "wunderplumb 112.2 25.6 4.4 102.069000 \n", - "krm-bot 66.0 9.5 6.9 68.182124 \n", - "andrewsiah 0.0 0.0 NaN NaN \n", - "cobyj-bot 0.0 0.0 NaN NaN \n", - "RPM_bot -8.7 8.0 -1.1 89.625559 \n", - "ProfessorSP -217.1 18.6 -11.7 80.594072 \n", - "pianobot -217.3 4.7 -46.2 124.350728 \n", - "minefrac1 -299.6 52.1 -5.7 70.581980 \n", - "\n", - " std_err t_stat t_crit upper_bound \\\n", - "pro_median 6.449398 7.059105 1.985277 58.3 \n", - "metac-o1 6.018299 5.431054 1.985550 44.6 \n", - "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.108156 3.997253 1.985277 36.5 \n", - "acm_bot 6.163169 4.471343 1.988985 39.8 \n", - "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", - "manticAI 7.905338 3.348936 1.993488 42.2 \n", - "metac-exa 8.661894 2.340069 1.986114 37.5 \n", - "twsummerbot 7.103517 4.311100 2.000163 44.8 \n", - "metac-claude-3-5-sonnet-latest 7.455134 2.534620 1.985550 33.7 \n", - "metac-Llama-3.1 6.548068 2.883453 1.986114 31.9 \n", - "jkraybill_bot 8.903079 4.029223 2.013412 53.8 \n", - "metac-Gemini-Exp-1206 7.622046 2.701303 1.990426 35.8 \n", - "NextWorldLab 7.367722 2.644427 1.988985 34.1 \n", - "metac-o1-preview 9.077077 1.827344 1.985550 34.6 \n", - "metac-deepseek-r1 8.695578 3.351382 2.005379 46.6 \n", - "laylaps 9.228204 2.497799 1.996341 41.5 \n", - "mmBot 8.290173 1.921090 1.985277 32.4 \n", - "Grizeu_Bot 8.415222 3.176755 2.005555 43.6 \n", - "metac-grok-2-1212 8.265446 1.534149 1.985550 29.1 \n", - "VeritasAI 6.916601 2.104241 1.990095 28.3 \n", - "metac-gpt-4o 7.061066 1.607096 1.985550 25.4 \n", - "SynapseSeer 12.289235 3.234607 2.053076 65.0 \n", - "annabot 10.657710 3.304739 2.044183 57.0 \n", - "GreeneiBot2 9.583748 1.640104 2.000141 34.9 \n", - "MWG 14.722777 1.760805 2.046561 56.1 \n", - "InstitutPelFutur 10.565167 0.750854 1.985829 28.9 \n", - "cookics_bot_TEST 12.084562 2.156937 2.049541 50.8 \n", - "Bot_Pepa 10.390274 1.411723 2.013412 35.6 \n", - "ajf-bot 14.580720 0.942554 2.028730 43.3 \n", - "swingswish 18.763190 2.976027 2.367123 100.3 \n", - "KevinTestBot 26.311114 1.498097 2.311496 100.2 \n", - "X_bot 11.979131 3.274020 2.446912 68.5 \n", - "CumulativeBot 24.651941 1.004580 2.231848 79.8 \n", - "CatrachoCaster 16.981440 0.739137 2.088777 48.0 \n", - "jonahsingerbot 29.622561 1.610003 2.784843 130.2 \n", - "bean_bot 35.220599 1.271879 2.784843 142.9 \n", - "4Shadower 31.041354 0.484489 2.147239 81.7 \n", - "pgodzinai 11.780215 0.194271 1.990453 25.7 \n", - "wunderplumb 20.192887 0.217376 2.056603 45.9 \n", - "krm-bot 22.121202 0.314009 2.264709 57.0 \n", - "andrewsiah NaN NaN NaN NaN \n", - "cobyj-bot NaN NaN NaN NaN \n", - "RPM_bot 31.687420 -0.034282 2.364624 73.8 \n", - "ProfessorSP 18.687303 -0.624616 2.095243 27.5 \n", - "pianobot 57.358714 -0.806130 2.798986 114.3 \n", - "minefrac1 9.778562 -0.588004 2.005649 13.9 \n", - "\n", - " lower_bound cdf p_value \n", - "pro_median 32.7 1.000000 0.000000 \n", - "metac-o1 20.7 1.000000 0.000000 \n", - "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 12.3 0.999935 0.000129 \n", - "acm_bot 15.3 0.999987 0.000025 \n", - "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", - "manticAI 10.7 0.999343 0.001314 \n", - "metac-exa 3.1 0.989243 0.021514 \n", - "twsummerbot 16.4 0.999968 0.000063 \n", - "metac-claude-3-5-sonnet-latest 4.1 0.993518 0.012963 \n", - "metac-Llama-3.1 5.9 0.997535 0.004930 \n", - "jkraybill_bot 17.9 0.999891 0.000218 \n", - "metac-Gemini-Exp-1206 5.4 0.995749 0.008502 \n", - "NextWorldLab 4.8 0.995080 0.009840 \n", - "metac-o1-preview -1.4 0.964539 0.070922 \n", - "metac-deepseek-r1 11.7 0.999241 0.001519 \n", - "laylaps 4.6 0.992463 0.015074 \n", - "mmBot -0.5 0.971093 0.057813 \n", - "Grizeu_Bot 9.9 0.998740 0.002521 \n", - "metac-grok-2-1212 -3.7 0.935771 0.128459 \n", - "VeritasAI 0.8 0.980692 0.038617 \n", - "metac-gpt-4o -2.7 0.944253 0.111494 \n", - "SynapseSeer 14.5 0.998302 0.003397 \n", - "annabot 13.4 0.998707 0.002586 \n", - "GreeneiBot2 -3.5 0.946818 0.106364 \n", - "MWG -4.2 0.955325 0.089349 \n", - "InstitutPelFutur -13.0 0.772651 0.454697 \n", - "cookics_bot_TEST 1.3 0.979856 0.040287 \n", - "Bot_Pepa -6.3 0.917472 0.165057 \n", - "ajf-bot -15.8 0.823745 0.352510 \n", - "swingswish 11.4 0.989142 0.021716 \n", - "KevinTestBot -21.4 0.912252 0.175497 \n", - "X_bot 9.9 0.991526 0.016949 \n", - "CumulativeBot -30.3 0.829673 0.340653 \n", - "CatrachoCaster -22.9 0.765500 0.469001 \n", - "jonahsingerbot -34.8 0.905799 0.188401 \n", - "bean_bot -53.3 0.861262 0.277476 \n", - "4Shadower -51.6 0.681950 0.636100 \n", - "pgodzinai -21.2 0.576760 0.846479 \n", - "wunderplumb -37.1 0.585144 0.829712 \n", - "krm-bot -43.2 0.619458 0.761083 \n", - "andrewsiah NaN NaN NA \n", - "cobyj-bot NaN NaN NA \n", - "RPM_bot -76.0 0.486805 0.973609 \n", - "ProfessorSP -50.8 0.270118 0.540237 \n", - "pianobot -206.8 0.234388 0.468776 \n", - "minefrac1 -25.4 0.279560 0.559119 " - ] - }, - "execution_count": 202, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make me a list that's pro_median and all the bot forecasters\n", - "forecasters = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", - "\n", - "hey = calculate_t_test(df_pro_bot_baseline_weights, forecasters)\n", - "\n", - "hey" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "aGNedTHmU-Bm", - "outputId": "a7935679-8993-4329-d05d-fd701c4b77a8" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: divide by zero encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/functions.py:525: RuntimeWarning: invalid value encountered in scalar divide\n", - " t_statistic = (weighted_average - 0) / std_error\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
metac-perplexity1719.795.018.13.570999e-153.663768e-164.940951e+161.9847518.118.11.00.000000
acm_bot1680.695.017.73.570999e-153.663768e-164.828449e+161.9847517.717.71.00.000000
bot_median1610.495.017.03.570999e-153.663768e-164.626691e+161.9847517.017.01.00.000000
metac-o11577.695.016.63.570999e-153.663768e-164.532462e+161.9847516.616.61.00.000000
metac-claude-3-5-sonnet-202406201405.995.014.83.570999e-153.663768e-164.039354e+161.9847514.814.81.00.000000
manticAI1378.295.014.50.000000e+000.000000e+00inf1.9847514.514.51.00.000000
twsummerbot1355.495.014.31.785500e-151.831884e-167.788325e+161.9847514.314.31.00.000000
jkraybill_bot1354.595.014.31.785500e-151.831884e-167.783286e+161.9847514.314.31.00.000000
metac-exa1233.695.013.01.785500e-151.831884e-167.088710e+161.9847513.013.01.00.000000
GreeneiBot21163.295.012.20.000000e+000.000000e+00inf1.9847512.212.21.00.000000
NextWorldLab1050.395.011.11.785500e-151.831884e-166.035038e+161.9847511.111.11.00.000000
metac-Llama-3.1997.095.010.51.785500e-151.831884e-165.728816e+161.9847510.510.51.00.000000
Grizeu_Bot966.495.010.20.000000e+000.000000e+00inf1.9847510.210.21.00.000000
SynapseSeer964.795.010.21.785500e-151.831884e-165.543440e+161.9847510.210.21.00.000000
metac-claude-3-5-sonnet-latest949.995.010.00.000000e+000.000000e+00inf1.9847510.010.01.00.000000
mmBot924.895.09.70.000000e+000.000000e+00inf1.984759.79.71.00.000000
annabot854.495.09.01.785500e-151.831884e-164.909363e+161.984759.09.01.00.000000
VeritasAI802.095.08.41.785500e-151.831884e-164.608352e+161.984758.48.41.00.000000
metac-grok-2-1212775.195.08.20.000000e+000.000000e+00inf1.984758.28.21.00.000000
laylaps723.495.07.68.927498e-169.159420e-178.313180e+161.984757.67.61.00.000000
metac-Gemini-Exp-1206701.995.07.48.927498e-169.159420e-178.065986e+161.984757.47.41.00.000000
metac-o1-preview633.295.06.78.927498e-169.159420e-177.277309e+161.984756.76.71.00.000000
cookics_bot_TEST596.495.06.30.000000e+000.000000e+00inf1.984756.36.31.00.000000
metac-deepseek-r1545.595.05.78.927498e-169.159420e-176.268723e+161.984755.75.71.00.000000
MWG520.895.05.58.927498e-169.159420e-175.985647e+161.984755.55.51.00.000000
ajf-bot481.295.05.11.785500e-151.831884e-162.764898e+161.984755.15.11.00.000000
metac-gpt-4o451.695.04.88.927498e-169.159420e-175.190358e+161.984754.84.81.00.000000
pgodzinai336.095.03.58.927498e-169.159420e-173.861639e+161.984753.53.51.00.000000
KevinTestBot314.595.03.38.927498e-169.159420e-173.614852e+161.984753.33.31.00.000000
InstitutPelFutur256.095.02.78.927498e-169.159420e-172.941623e+161.984752.72.71.00.000000
Bot_Pepa246.895.02.60.000000e+000.000000e+00inf1.984752.62.61.00.000000
CumulativeBot241.195.02.54.463749e-164.579710e-175.542703e+161.984752.52.51.00.000000
swingswish229.195.02.44.463749e-164.579710e-175.265549e+161.984752.42.41.00.000000
wunderplumb225.495.02.44.463749e-164.579710e-175.180942e+161.984752.42.41.00.000000
jonahsingerbot212.995.02.24.463749e-164.579710e-174.894511e+161.984752.22.21.00.000000
bean_bot200.095.02.10.000000e+000.000000e+00inf1.984752.12.11.00.000000
X_bot181.495.01.90.000000e+000.000000e+00inf1.984751.91.91.00.000000
CatrachoCaster167.595.01.84.463749e-164.579710e-173.849373e+161.984751.81.81.00.000000
RPM_bot118.695.01.24.463749e-164.579710e-172.726486e+161.984751.21.21.00.000000
4Shadower61.195.00.62.231875e-162.289855e-172.810106e+161.984750.60.61.00.000000
krm-bot60.895.00.61.115937e-161.144927e-175.586129e+161.984750.60.61.00.000000
andrewsiah0.095.00.00.000000e+000.000000e+00NaN1.984750.00.0NaNNA
cobyj-bot0.095.00.00.000000e+000.000000e+00NaN1.984750.00.0NaNNA
pianobot-206.595.0-2.24.463749e-164.579710e-17-4.745305e+161.98475-2.2-2.20.00.000000
ProfessorSP-280.495.0-3.08.927498e-169.159420e-17-3.222942e+161.98475-3.0-3.00.00.000000
minefrac1-283.995.0-3.04.463749e-164.579710e-17-6.524424e+161.98475-3.0-3.00.00.000000
\n", - "
" - ], - "text/plain": [ - " W_score W_count W_ave W_stdev \\\n", - "metac-perplexity 1719.7 95.0 18.1 3.570999e-15 \n", - "acm_bot 1680.6 95.0 17.7 3.570999e-15 \n", - "bot_median 1610.4 95.0 17.0 3.570999e-15 \n", - "metac-o1 1577.6 95.0 16.6 3.570999e-15 \n", - "metac-claude-3-5-sonnet-20240620 1405.9 95.0 14.8 3.570999e-15 \n", - "manticAI 1378.2 95.0 14.5 0.000000e+00 \n", - "twsummerbot 1355.4 95.0 14.3 1.785500e-15 \n", - "jkraybill_bot 1354.5 95.0 14.3 1.785500e-15 \n", - "metac-exa 1233.6 95.0 13.0 1.785500e-15 \n", - "GreeneiBot2 1163.2 95.0 12.2 0.000000e+00 \n", - "NextWorldLab 1050.3 95.0 11.1 1.785500e-15 \n", - "metac-Llama-3.1 997.0 95.0 10.5 1.785500e-15 \n", - "Grizeu_Bot 966.4 95.0 10.2 0.000000e+00 \n", - "SynapseSeer 964.7 95.0 10.2 1.785500e-15 \n", - "metac-claude-3-5-sonnet-latest 949.9 95.0 10.0 0.000000e+00 \n", - "mmBot 924.8 95.0 9.7 0.000000e+00 \n", - "annabot 854.4 95.0 9.0 1.785500e-15 \n", - "VeritasAI 802.0 95.0 8.4 1.785500e-15 \n", - "metac-grok-2-1212 775.1 95.0 8.2 0.000000e+00 \n", - "laylaps 723.4 95.0 7.6 8.927498e-16 \n", - "metac-Gemini-Exp-1206 701.9 95.0 7.4 8.927498e-16 \n", - "metac-o1-preview 633.2 95.0 6.7 8.927498e-16 \n", - "cookics_bot_TEST 596.4 95.0 6.3 0.000000e+00 \n", - "metac-deepseek-r1 545.5 95.0 5.7 8.927498e-16 \n", - "MWG 520.8 95.0 5.5 8.927498e-16 \n", - "ajf-bot 481.2 95.0 5.1 1.785500e-15 \n", - "metac-gpt-4o 451.6 95.0 4.8 8.927498e-16 \n", - "pgodzinai 336.0 95.0 3.5 8.927498e-16 \n", - "KevinTestBot 314.5 95.0 3.3 8.927498e-16 \n", - "InstitutPelFutur 256.0 95.0 2.7 8.927498e-16 \n", - "Bot_Pepa 246.8 95.0 2.6 0.000000e+00 \n", - "CumulativeBot 241.1 95.0 2.5 4.463749e-16 \n", - "swingswish 229.1 95.0 2.4 4.463749e-16 \n", - "wunderplumb 225.4 95.0 2.4 4.463749e-16 \n", - "jonahsingerbot 212.9 95.0 2.2 4.463749e-16 \n", - "bean_bot 200.0 95.0 2.1 0.000000e+00 \n", - "X_bot 181.4 95.0 1.9 0.000000e+00 \n", - "CatrachoCaster 167.5 95.0 1.8 4.463749e-16 \n", - "RPM_bot 118.6 95.0 1.2 4.463749e-16 \n", - "4Shadower 61.1 95.0 0.6 2.231875e-16 \n", - "krm-bot 60.8 95.0 0.6 1.115937e-16 \n", - "andrewsiah 0.0 95.0 0.0 0.000000e+00 \n", - "cobyj-bot 0.0 95.0 0.0 0.000000e+00 \n", - "pianobot -206.5 95.0 -2.2 4.463749e-16 \n", - "ProfessorSP -280.4 95.0 -3.0 8.927498e-16 \n", - "minefrac1 -283.9 95.0 -3.0 4.463749e-16 \n", - "\n", - " std_err t_stat t_crit \\\n", - "metac-perplexity 3.663768e-16 4.940951e+16 1.98475 \n", - "acm_bot 3.663768e-16 4.828449e+16 1.98475 \n", - "bot_median 3.663768e-16 4.626691e+16 1.98475 \n", - "metac-o1 3.663768e-16 4.532462e+16 1.98475 \n", - "metac-claude-3-5-sonnet-20240620 3.663768e-16 4.039354e+16 1.98475 \n", - "manticAI 0.000000e+00 inf 1.98475 \n", - "twsummerbot 1.831884e-16 7.788325e+16 1.98475 \n", - "jkraybill_bot 1.831884e-16 7.783286e+16 1.98475 \n", - "metac-exa 1.831884e-16 7.088710e+16 1.98475 \n", - "GreeneiBot2 0.000000e+00 inf 1.98475 \n", - "NextWorldLab 1.831884e-16 6.035038e+16 1.98475 \n", - "metac-Llama-3.1 1.831884e-16 5.728816e+16 1.98475 \n", - "Grizeu_Bot 0.000000e+00 inf 1.98475 \n", - "SynapseSeer 1.831884e-16 5.543440e+16 1.98475 \n", - "metac-claude-3-5-sonnet-latest 0.000000e+00 inf 1.98475 \n", - "mmBot 0.000000e+00 inf 1.98475 \n", - "annabot 1.831884e-16 4.909363e+16 1.98475 \n", - "VeritasAI 1.831884e-16 4.608352e+16 1.98475 \n", - "metac-grok-2-1212 0.000000e+00 inf 1.98475 \n", - "laylaps 9.159420e-17 8.313180e+16 1.98475 \n", - "metac-Gemini-Exp-1206 9.159420e-17 8.065986e+16 1.98475 \n", - "metac-o1-preview 9.159420e-17 7.277309e+16 1.98475 \n", - "cookics_bot_TEST 0.000000e+00 inf 1.98475 \n", - "metac-deepseek-r1 9.159420e-17 6.268723e+16 1.98475 \n", - "MWG 9.159420e-17 5.985647e+16 1.98475 \n", - "ajf-bot 1.831884e-16 2.764898e+16 1.98475 \n", - "metac-gpt-4o 9.159420e-17 5.190358e+16 1.98475 \n", - "pgodzinai 9.159420e-17 3.861639e+16 1.98475 \n", - "KevinTestBot 9.159420e-17 3.614852e+16 1.98475 \n", - "InstitutPelFutur 9.159420e-17 2.941623e+16 1.98475 \n", - "Bot_Pepa 0.000000e+00 inf 1.98475 \n", - "CumulativeBot 4.579710e-17 5.542703e+16 1.98475 \n", - "swingswish 4.579710e-17 5.265549e+16 1.98475 \n", - "wunderplumb 4.579710e-17 5.180942e+16 1.98475 \n", - "jonahsingerbot 4.579710e-17 4.894511e+16 1.98475 \n", - "bean_bot 0.000000e+00 inf 1.98475 \n", - "X_bot 0.000000e+00 inf 1.98475 \n", - "CatrachoCaster 4.579710e-17 3.849373e+16 1.98475 \n", - "RPM_bot 4.579710e-17 2.726486e+16 1.98475 \n", - "4Shadower 2.289855e-17 2.810106e+16 1.98475 \n", - "krm-bot 1.144927e-17 5.586129e+16 1.98475 \n", - "andrewsiah 0.000000e+00 NaN 1.98475 \n", - "cobyj-bot 0.000000e+00 NaN 1.98475 \n", - "pianobot 4.579710e-17 -4.745305e+16 1.98475 \n", - "ProfessorSP 9.159420e-17 -3.222942e+16 1.98475 \n", - "minefrac1 4.579710e-17 -6.524424e+16 1.98475 \n", - "\n", - " upper_bound lower_bound cdf p_value \n", - "metac-perplexity 18.1 18.1 1.0 0.000000 \n", - "acm_bot 17.7 17.7 1.0 0.000000 \n", - "bot_median 17.0 17.0 1.0 0.000000 \n", - "metac-o1 16.6 16.6 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-20240620 14.8 14.8 1.0 0.000000 \n", - "manticAI 14.5 14.5 1.0 0.000000 \n", - "twsummerbot 14.3 14.3 1.0 0.000000 \n", - "jkraybill_bot 14.3 14.3 1.0 0.000000 \n", - "metac-exa 13.0 13.0 1.0 0.000000 \n", - "GreeneiBot2 12.2 12.2 1.0 0.000000 \n", - "NextWorldLab 11.1 11.1 1.0 0.000000 \n", - "metac-Llama-3.1 10.5 10.5 1.0 0.000000 \n", - "Grizeu_Bot 10.2 10.2 1.0 0.000000 \n", - "SynapseSeer 10.2 10.2 1.0 0.000000 \n", - "metac-claude-3-5-sonnet-latest 10.0 10.0 1.0 0.000000 \n", - "mmBot 9.7 9.7 1.0 0.000000 \n", - "annabot 9.0 9.0 1.0 0.000000 \n", - "VeritasAI 8.4 8.4 1.0 0.000000 \n", - "metac-grok-2-1212 8.2 8.2 1.0 0.000000 \n", - "laylaps 7.6 7.6 1.0 0.000000 \n", - "metac-Gemini-Exp-1206 7.4 7.4 1.0 0.000000 \n", - "metac-o1-preview 6.7 6.7 1.0 0.000000 \n", - "cookics_bot_TEST 6.3 6.3 1.0 0.000000 \n", - "metac-deepseek-r1 5.7 5.7 1.0 0.000000 \n", - "MWG 5.5 5.5 1.0 0.000000 \n", - "ajf-bot 5.1 5.1 1.0 0.000000 \n", - "metac-gpt-4o 4.8 4.8 1.0 0.000000 \n", - "pgodzinai 3.5 3.5 1.0 0.000000 \n", - "KevinTestBot 3.3 3.3 1.0 0.000000 \n", - "InstitutPelFutur 2.7 2.7 1.0 0.000000 \n", - "Bot_Pepa 2.6 2.6 1.0 0.000000 \n", - "CumulativeBot 2.5 2.5 1.0 0.000000 \n", - "swingswish 2.4 2.4 1.0 0.000000 \n", - "wunderplumb 2.4 2.4 1.0 0.000000 \n", - "jonahsingerbot 2.2 2.2 1.0 0.000000 \n", - "bean_bot 2.1 2.1 1.0 0.000000 \n", - "X_bot 1.9 1.9 1.0 0.000000 \n", - "CatrachoCaster 1.8 1.8 1.0 0.000000 \n", - "RPM_bot 1.2 1.2 1.0 0.000000 \n", - "4Shadower 0.6 0.6 1.0 0.000000 \n", - "krm-bot 0.6 0.6 1.0 0.000000 \n", - "andrewsiah 0.0 0.0 NaN NA \n", - "cobyj-bot 0.0 0.0 NaN NA \n", - "pianobot -2.2 -2.2 0.0 0.000000 \n", - "ProfessorSP -3.0 -3.0 0.0 0.000000 \n", - "minefrac1 -3.0 -3.0 0.0 0.000000 " - ] - }, - "execution_count": 203, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# @title Weighted head-to-head, T test\n", - "\n", - "\"\"\"\n", - "df_W_leaderboard: A leaderboard based on df_bot_vs_pro_peer with question\n", - "weighting and the calculations for doing a weighted T test\n", - "\"\"\"\n", - "\n", - "forecaster_weighted_scores = forecaster_weighted_scores.fillna(0)\n", - "\n", - "# Cast weights as numeric\n", - "df_bot_vs_pro_peer['question_weight'] = pd.to_numeric(df_bot_vs_pro_peer['question_weight'], errors='coerce')\n", - "\n", - "# Calculate weighted statistics for each bot\n", - "df_W_leaderboard = calculate_t_test(df_bot_vs_pro_peer, all_bots)\n", - "\n", - "df_W_leaderboard" - ] - }, - { - "cell_type": "code", - "execution_count": 204, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Write to csv\n", - "df_W_leaderboard.to_csv('notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv', index=True)" + "leaderboard" ] }, { "cell_type": "code", - "execution_count": 205, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3d_ZdL0A0qTz", - "outputId": "e30ee8fb-0faf-45ae-974e-d4af282e0252" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
RankBotW_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
01metac-o13631.1375.39.735.0711401.8102945.3442931.96598513.26.11.0000000.000000
12metac-o1-preview3121.4368.78.545.9615892.3935733.5368201.96609313.23.80.9997720.000457
23metac-Gemini-Exp-12061880.5347.15.444.8958442.4097192.2481331.96645810.20.70.9874020.025197
34SynapseSeer966.5152.06.435.6992152.8951132.1955681.97487912.10.60.9851760.029648
45manticAI2055.2315.76.555.6900633.1344982.0771541.96718712.70.30.9807010.038598
56twsummerbot1450.0241.36.045.0911402.9027092.0701531.96931311.70.30.9802470.039507
67acm_bot1738.4344.85.045.8463322.4691432.0421541.9665219.90.20.9790510.041899
78cookics_bot_TEST1143.8162.67.046.7964543.6698871.9168291.97413814.3-0.20.9714880.057024
89CumulativeBot991.4104.59.552.1803255.1044461.8585841.98213619.6-0.60.9670360.065928
910metac-claude-3-5-sonnet-latest951.3370.32.638.2630661.9883421.2919541.9660636.5-1.30.9014100.197181
1011GreeneiBot21494.7264.15.759.7283543.6750521.5398111.96859612.9-1.60.9375960.124808
1112metac-perplexity1558.4354.44.459.5883783.1652091.3891811.96637110.6-1.80.9171740.165652
1213metac-deepseek-r1516.8277.91.937.3532102.2407800.8299751.9681656.3-2.60.7963660.407268
1314pgodzinai1106.7325.43.466.6861593.6966950.9199541.96694910.7-3.90.8208600.358280
1415metac-exa599.9365.31.663.4593893.3201610.4946111.9661428.2-4.90.6894130.621173
1516MWG253.8113.42.240.6740843.8190370.5859361.9804689.8-5.30.7204540.559093
1617jkraybill_bot625.4207.43.068.5607804.7604770.6333891.97101512.4-6.40.7364100.527181
1718metac-claude-3-5-sonnet-20240620-759.5373.7-2.044.0904802.280718-0.8910111.9660142.5-6.50.1867490.373498
1819metac-grok-2-1212-550.1373.3-1.550.1642462.596293-0.5675531.9660163.6-6.60.2853400.570681
1920metac-Llama-3.1-980.9370.6-2.641.8100632.171783-1.2186111.9660621.6-6.90.1118850.223769
2021mmBot-587.4373.0-1.658.2984393.018498-0.5216711.9660174.4-7.50.3011050.602210
2122VeritasAI-1602.2330.0-4.938.7547802.133316-2.2757101.966760-0.7-9.10.0117530.023506
2223InstitutPelFutur-877.8356.0-2.564.6034773.423881-0.7201271.9663054.3-9.20.2359600.471921
2324NextWorldLab-1377.9337.6-4.151.4333882.799472-1.4581571.9666641.4-9.60.0728650.145730
2425metac-gpt-4o-2235.4373.3-6.045.4016702.349802-2.5482091.966016-1.4-10.60.0056140.011229
2526CatrachoCaster-289.481.6-3.531.9567253.538536-1.0026081.9883423.5-10.60.1595260.319052
2627laylaps-1489.1322.1-4.663.9802383.564926-1.2968551.9670502.4-11.60.0978060.195612
2728ProfessorSP-426.8128.6-3.355.1654604.863650-0.6821421.9781236.3-12.90.2481930.496385
2829krm-bot-354.7104.0-3.449.8754924.890694-0.6973341.9823276.3-13.10.2435820.487165
2930wunderplumb-986.1174.0-5.752.9658934.015334-1.4114341.9731952.3-13.60.0799560.159913
3031andrewsiah2.625.10.135.8050927.1467390.0146792.06034114.8-14.60.5057960.988409
3132annabot-190.683.8-2.359.1122286.458906-0.3522221.98640810.6-15.10.3627840.725567
3233Bot_Pepa-1490.1169.4-8.844.2657023.400530-2.5860051.973733-2.1-15.50.0052780.010555
33344Shadower-646.3115.5-5.653.8678675.012320-1.1163051.9797854.3-15.50.1333140.266629
3435minefrac1-1757.1188.2-9.344.1258493.216071-2.9021901.972106-3.0-15.70.0020750.004150
3536KevinTestBot-220.489.5-2.567.6508777.150920-0.3443101.98550511.7-16.70.3657150.731430
3637jonahsingerbot-333.464.8-5.148.0155485.964779-0.8626001.9952736.8-17.00.1957940.391588
3738bean_bot-208.867.8-3.159.9556627.281408-0.4229401.99377111.4-17.60.3368490.673697
3839Grizeu_Bot-1882.6193.2-9.756.7042374.079442-2.3885211.971774-1.7-17.80.0089420.017884
3940cobyj-bot-12.131.5-0.448.0409918.559663-0.0450462.03985017.1-17.80.4821820.964365
4041X_bot-16.17.0-2.323.9086329.036614-0.2537742.44691219.8-24.40.4040710.808142
4142ajf-bot-3208.3229.2-14.083.2955695.502524-2.5444141.969928-3.2-24.80.0058030.011607
4243pianobot-12.719.6-0.752.32348711.833775-0.0550422.09382324.1-25.40.4783470.956694
4344swingswish-777.064.8-12.073.0478929.074447-1.3214361.9952736.1-30.10.0955380.191075
4445RPM_bot-815.623.8-34.391.54540218.784720-1.8281002.0615084.4-73.10.0403390.080679
\n", - "
" - ], - "text/plain": [ - " Rank Bot W_score W_count W_ave \\\n", - "0 1 metac-o1 3631.1 375.3 9.7 \n", - "1 2 metac-o1-preview 3121.4 368.7 8.5 \n", - "2 3 metac-Gemini-Exp-1206 1880.5 347.1 5.4 \n", - "3 4 SynapseSeer 966.5 152.0 6.4 \n", - "4 5 manticAI 2055.2 315.7 6.5 \n", - "5 6 twsummerbot 1450.0 241.3 6.0 \n", - "6 7 acm_bot 1738.4 344.8 5.0 \n", - "7 8 cookics_bot_TEST 1143.8 162.6 7.0 \n", - "8 9 CumulativeBot 991.4 104.5 9.5 \n", - "9 10 metac-claude-3-5-sonnet-latest 951.3 370.3 2.6 \n", - "10 11 GreeneiBot2 1494.7 264.1 5.7 \n", - "11 12 metac-perplexity 1558.4 354.4 4.4 \n", - "12 13 metac-deepseek-r1 516.8 277.9 1.9 \n", - "13 14 pgodzinai 1106.7 325.4 3.4 \n", - "14 15 metac-exa 599.9 365.3 1.6 \n", - "15 16 MWG 253.8 113.4 2.2 \n", - "16 17 jkraybill_bot 625.4 207.4 3.0 \n", - "17 18 metac-claude-3-5-sonnet-20240620 -759.5 373.7 -2.0 \n", - "18 19 metac-grok-2-1212 -550.1 373.3 -1.5 \n", - "19 20 metac-Llama-3.1 -980.9 370.6 -2.6 \n", - "20 21 mmBot -587.4 373.0 -1.6 \n", - "21 22 VeritasAI -1602.2 330.0 -4.9 \n", - "22 23 InstitutPelFutur -877.8 356.0 -2.5 \n", - "23 24 NextWorldLab -1377.9 337.6 -4.1 \n", - "24 25 metac-gpt-4o -2235.4 373.3 -6.0 \n", - "25 26 CatrachoCaster -289.4 81.6 -3.5 \n", - "26 27 laylaps -1489.1 322.1 -4.6 \n", - "27 28 ProfessorSP -426.8 128.6 -3.3 \n", - "28 29 krm-bot -354.7 104.0 -3.4 \n", - "29 30 wunderplumb -986.1 174.0 -5.7 \n", - "30 31 andrewsiah 2.6 25.1 0.1 \n", - "31 32 annabot -190.6 83.8 -2.3 \n", - "32 33 Bot_Pepa -1490.1 169.4 -8.8 \n", - "33 34 4Shadower -646.3 115.5 -5.6 \n", - "34 35 minefrac1 -1757.1 188.2 -9.3 \n", - "35 36 KevinTestBot -220.4 89.5 -2.5 \n", - "36 37 jonahsingerbot -333.4 64.8 -5.1 \n", - "37 38 bean_bot -208.8 67.8 -3.1 \n", - "38 39 Grizeu_Bot -1882.6 193.2 -9.7 \n", - "39 40 cobyj-bot -12.1 31.5 -0.4 \n", - "40 41 X_bot -16.1 7.0 -2.3 \n", - "41 42 ajf-bot -3208.3 229.2 -14.0 \n", - "42 43 pianobot -12.7 19.6 -0.7 \n", - "43 44 swingswish -777.0 64.8 -12.0 \n", - "44 45 RPM_bot -815.6 23.8 -34.3 \n", - "\n", - " W_stdev std_err t_stat t_crit upper_bound lower_bound \\\n", - "0 35.071140 1.810294 5.344293 1.965985 13.2 6.1 \n", - "1 45.961589 2.393573 3.536820 1.966093 13.2 3.8 \n", - "2 44.895844 2.409719 2.248133 1.966458 10.2 0.7 \n", - "3 35.699215 2.895113 2.195568 1.974879 12.1 0.6 \n", - "4 55.690063 3.134498 2.077154 1.967187 12.7 0.3 \n", - "5 45.091140 2.902709 2.070153 1.969313 11.7 0.3 \n", - "6 45.846332 2.469143 2.042154 1.966521 9.9 0.2 \n", - "7 46.796454 3.669887 1.916829 1.974138 14.3 -0.2 \n", - "8 52.180325 5.104446 1.858584 1.982136 19.6 -0.6 \n", - "9 38.263066 1.988342 1.291954 1.966063 6.5 -1.3 \n", - "10 59.728354 3.675052 1.539811 1.968596 12.9 -1.6 \n", - "11 59.588378 3.165209 1.389181 1.966371 10.6 -1.8 \n", - "12 37.353210 2.240780 0.829975 1.968165 6.3 -2.6 \n", - "13 66.686159 3.696695 0.919954 1.966949 10.7 -3.9 \n", - "14 63.459389 3.320161 0.494611 1.966142 8.2 -4.9 \n", - "15 40.674084 3.819037 0.585936 1.980468 9.8 -5.3 \n", - "16 68.560780 4.760477 0.633389 1.971015 12.4 -6.4 \n", - "17 44.090480 2.280718 -0.891011 1.966014 2.5 -6.5 \n", - "18 50.164246 2.596293 -0.567553 1.966016 3.6 -6.6 \n", - "19 41.810063 2.171783 -1.218611 1.966062 1.6 -6.9 \n", - "20 58.298439 3.018498 -0.521671 1.966017 4.4 -7.5 \n", - "21 38.754780 2.133316 -2.275710 1.966760 -0.7 -9.1 \n", - "22 64.603477 3.423881 -0.720127 1.966305 4.3 -9.2 \n", - "23 51.433388 2.799472 -1.458157 1.966664 1.4 -9.6 \n", - "24 45.401670 2.349802 -2.548209 1.966016 -1.4 -10.6 \n", - "25 31.956725 3.538536 -1.002608 1.988342 3.5 -10.6 \n", - "26 63.980238 3.564926 -1.296855 1.967050 2.4 -11.6 \n", - "27 55.165460 4.863650 -0.682142 1.978123 6.3 -12.9 \n", - "28 49.875492 4.890694 -0.697334 1.982327 6.3 -13.1 \n", - "29 52.965893 4.015334 -1.411434 1.973195 2.3 -13.6 \n", - "30 35.805092 7.146739 0.014679 2.060341 14.8 -14.6 \n", - "31 59.112228 6.458906 -0.352222 1.986408 10.6 -15.1 \n", - "32 44.265702 3.400530 -2.586005 1.973733 -2.1 -15.5 \n", - "33 53.867867 5.012320 -1.116305 1.979785 4.3 -15.5 \n", - "34 44.125849 3.216071 -2.902190 1.972106 -3.0 -15.7 \n", - "35 67.650877 7.150920 -0.344310 1.985505 11.7 -16.7 \n", - "36 48.015548 5.964779 -0.862600 1.995273 6.8 -17.0 \n", - "37 59.955662 7.281408 -0.422940 1.993771 11.4 -17.6 \n", - "38 56.704237 4.079442 -2.388521 1.971774 -1.7 -17.8 \n", - "39 48.040991 8.559663 -0.045046 2.039850 17.1 -17.8 \n", - "40 23.908632 9.036614 -0.253774 2.446912 19.8 -24.4 \n", - "41 83.295569 5.502524 -2.544414 1.969928 -3.2 -24.8 \n", - "42 52.323487 11.833775 -0.055042 2.093823 24.1 -25.4 \n", - "43 73.047892 9.074447 -1.321436 1.995273 6.1 -30.1 \n", - "44 91.545402 18.784720 -1.828100 2.061508 4.4 -73.1 \n", - "\n", - " cdf p_value \n", - "0 1.000000 0.000000 \n", - "1 0.999772 0.000457 \n", - "2 0.987402 0.025197 \n", - "3 0.985176 0.029648 \n", - "4 0.980701 0.038598 \n", - "5 0.980247 0.039507 \n", - "6 0.979051 0.041899 \n", - "7 0.971488 0.057024 \n", - "8 0.967036 0.065928 \n", - "9 0.901410 0.197181 \n", - "10 0.937596 0.124808 \n", - "11 0.917174 0.165652 \n", - "12 0.796366 0.407268 \n", - "13 0.820860 0.358280 \n", - "14 0.689413 0.621173 \n", - "15 0.720454 0.559093 \n", - "16 0.736410 0.527181 \n", - "17 0.186749 0.373498 \n", - "18 0.285340 0.570681 \n", - "19 0.111885 0.223769 \n", - "20 0.301105 0.602210 \n", - "21 0.011753 0.023506 \n", - "22 0.235960 0.471921 \n", - "23 0.072865 0.145730 \n", - "24 0.005614 0.011229 \n", - "25 0.159526 0.319052 \n", - "26 0.097806 0.195612 \n", - "27 0.248193 0.496385 \n", - "28 0.243582 0.487165 \n", - "29 0.079956 0.159913 \n", - "30 0.505796 0.988409 \n", - "31 0.362784 0.725567 \n", - "32 0.005278 0.010555 \n", - "33 0.133314 0.266629 \n", - "34 0.002075 0.004150 \n", - "35 0.365715 0.731430 \n", - "36 0.195794 0.391588 \n", - "37 0.336849 0.673697 \n", - "38 0.008942 0.017884 \n", - "39 0.482182 0.964365 \n", - "40 0.404071 0.808142 \n", - "41 0.005803 0.011607 \n", - "42 0.478347 0.956694 \n", - "43 0.095538 0.191075 \n", - "44 0.040339 0.080679 " - ] - }, - "execution_count": 205, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "# @title Weighted Bot Peer, T test (to compare bots against each other, use ALL QUESTIONS)\n", + "# Average pro median forecast on questions that resolved yes/no vs top bot\n", + "\n", + "top_bot = leaderboard['bot'][1]\n", + "\n", + "resolved_yes = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'yes']\n", + "resolved_no = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'no']\n", "\n", - "df_W_bot_peer_leaderboard = pd.DataFrame()\n", + "# Calculate the average pro median forecast for questions that resolved yes\n", + "mean_pro_median_yes = resolved_yes['pro_median'].mean().round(2) * 100\n", + "mean_pro_median_no = resolved_no['pro_median'].mean().round(2) * 100\n", "\n", - "df3 = pd.DataFrame()\n", + "mean_bot_yes = resolved_yes[top_bot].mean().round(2) * 100\n", + "mean_bot_no = resolved_no[top_bot].mean().round(2) * 100\n", "\n", - "forecaster_weighted_scores = forecaster_weighted_scores.fillna(0)\n", + "print(f'mean pro median forecast on questions that resolved yes: {mean_pro_median_yes}%')\n", + "print(f'mean pro median forecast on questions that resolved no: {mean_pro_median_no}%')\n", + "print(f'mean {top_bot} forecast on questions that resolved yes: {mean_bot_yes}%')\n", + "print(f'mean {top_bot} forecast on questions that resolved no: {mean_bot_no}%')\n", "\n", - "# OMIT bot_median column for this bit\n", - "df_bot_peer_wide_b = df_bot_peer_wide.drop('bot_median', axis=1)\n", - "df_bot_peer = df_bot_peer[df_bot_peer['forecaster'] != 'bot_median']\n", + "# Plot the data\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", - "bots_for_peer = np.array(list(set(df_bot_peer['forecaster'])))\n", + "# Set up the figure\n", + "plt.figure(figsize=(10, 6))\n", "\n", - "df_W_leaderboard = calculate_t_test(df_bot_peer_wide_b, bots_for_peer)\n", + "# Create x-coordinates with jitter for each group separately\n", + "x_bot_yes = np.random.normal(0, 0.04, len(resolved_yes))\n", + "x_pro_yes = np.random.normal(1, 0.04, len(resolved_yes))\n", + "x_bot_no = np.random.normal(0, 0.04, len(resolved_no))\n", + "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", "\n", - "df_W_leaderboard_print = df_W_leaderboard.sort_values(by='lower_bound', ascending=False)\n", - "df_W_leaderboard_print['Rank'] = range(1, len(df_W_leaderboard_print) + 1)\n", + "# Plot points for \"yes\" resolution\n", + "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", + " color='blue', alpha=0.6, label='Resolved Yes')\n", + "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", + " color='blue', alpha=0.6)\n", "\n", - "# Make index into a column - Bot\n", - "df_W_leaderboard_print = df_W_leaderboard_print.reset_index()\n", - "df_W_leaderboard_print = df_W_leaderboard_print.rename(columns={'index': 'Bot'})\n", - "#df_W_leaderboard_print = df_W_leaderboard_print[['Rank', 'Bot', 'W_ave', 'W_count', 'lower_bound', 'upper_bound']]\n", - "# Make rank the first column; leave rest the same\n", - "cols = df_W_leaderboard_print.columns.tolist()\n", - "cols = ['Rank'] + cols[:-1]\n", - "df_W_leaderboard_print = df_W_leaderboard_print[cols]\n", + "# Plot points for \"no\" resolution\n", + "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", + " color='red', alpha=0.6, label='Resolved No')\n", + "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", + " color='red', alpha=0.6)\n", "\n", - "df_W_leaderboard_print" + "# Customize the plot\n", + "plt.xticks([0, 1], ['pro_median', top_bot])\n", + "plt.ylabel('Probability (%)')\n", + "plt.title('Pro Median vs Top Bot Forecasts')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Set y-axis limits from 0 to 100\n", + "plt.ylim(0, 100)\n", + "\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 206, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Write to csv\n", - "df_W_leaderboard_print.to_csv('notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bot_question_id4ShadowerBot_PepaCatrachoCasterCumulativeBotGreeneiBot2Grizeu_BotInstitutPelFuturKevinTestBotMWG...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbquestion_weight
031262NaNNaNNaNNaN-242.660874135.57527347.259183NaNNaN...-205.076095121.194882NaN-242.660874-198.879258NaNNaNNaNNaN1.0
131263NaNNaNNaNNaN-96.476789-99.090018-94.660371NaNNaN...7.9517037.951703NaN55.81904144.625993NaNNaNNaNNaN1.0
231264NaNNaNNaNNaN18.89298023.948225-86.527528NaNNaN...13.82151813.821518NaN1.30707117.305437NaNNaNNaNNaN1.0
331274NaNNaN2.076868NaN31.0945314.282464-28.806893NaN14.663415...6.44257916.621639NaN8.55905311.145899NaNNaN-9.706540NaN1.0
431275NaNNaNNaNNaN30.694891-66.461608-58.368696NaNNaN...35.698675-0.691552NaN39.41450214.411756NaNNaN-70.932651NaN1.0
\n", - "

5 rows × 48 columns

\n", - "
" - ], - "text/plain": [ - " bot_question_id 4Shadower Bot_Pepa CatrachoCaster CumulativeBot \\\n", - "0 31262 NaN NaN NaN NaN \n", - "1 31263 NaN NaN NaN NaN \n", - "2 31264 NaN NaN NaN NaN \n", - "3 31274 NaN NaN 2.076868 NaN \n", - "4 31275 NaN NaN NaN NaN \n", - "\n", - " GreeneiBot2 Grizeu_Bot InstitutPelFutur KevinTestBot MWG ... \\\n", - "0 -242.660874 135.575273 47.259183 NaN NaN ... \n", - "1 -96.476789 -99.090018 -94.660371 NaN NaN ... \n", - "2 18.892980 23.948225 -86.527528 NaN NaN ... \n", - "3 31.094531 4.282464 -28.806893 NaN 14.663415 ... \n", - "4 30.694891 -66.461608 -58.368696 NaN NaN ... \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 -205.076095 121.194882 NaN -242.660874 -198.879258 \n", - "1 7.951703 7.951703 NaN 55.819041 44.625993 \n", - "2 13.821518 13.821518 NaN 1.307071 17.305437 \n", - "3 6.442579 16.621639 NaN 8.559053 11.145899 \n", - "4 35.698675 -0.691552 NaN 39.414502 14.411756 \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb question_weight \n", - "0 NaN NaN NaN NaN 1.0 \n", - "1 NaN NaN NaN NaN 1.0 \n", - "2 NaN NaN NaN NaN 1.0 \n", - "3 NaN NaN -9.706540 NaN 1.0 \n", - "4 NaN NaN -70.932651 NaN 1.0 \n", - "\n", - "[5 rows x 48 columns]" - ] - }, - "execution_count": 207, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_bot_peer_wide.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 607 - }, - "id": "88QO8eyW6T_T", - "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title Histogram of bot\n", - "\n", - "if 'mf-bot-1' in df_bot_peer_wide.columns:\n", - " name = 'mf-bot-1'\n", - "else:\n", - " name = 'metac-o1-preview'\n", - "\n", - "scores = df_bot_peer_wide[name].dropna()\n", + "bot_vs_pro_peer_for_scores = df_bot_vs_pro_peer.copy()\n", + "bot_vs_pro_peer_for_scores = bot_vs_pro_peer_for_scores.drop(['resolution', 'question_weight', 'bot_question_id', 'pro_median', 'options', 'type'], axis=1)\n", "\n", - "# Create the histogram\n", - "plt.figure(figsize=(10, 6))\n", - "n, bins, patches = plt.hist(scores, bins=30, density=True, alpha=0.7, color='skyblue')\n", + "total_scores = bot_vs_pro_peer_for_scores.sum(axis=0)\n", "\n", - "# Fit a normal distribution to the data\n", - "mu, std = norm.fit(scores)\n", + "df_bot_vs_pro_peer = df_bot_vs_pro_peer.drop('pro_median', axis=1)\n", "\n", - "# Plot the PDF of the fitted normal distribution\n", - "xmin, xmax = plt.xlim()\n", - "x = np.linspace(xmin, xmax, 100)\n", - "p = norm.pdf(x, mu, std)\n", - "plt.plot(x, p, 'k', linewidth=2)\n", + "# First pivot to long format - each row will be a question-forecaster pair\n", + "df_long = df_bot_vs_pro_peer.melt(\n", + " id_vars=['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options'],\n", + " var_name='forecaster',\n", + " value_name='score'\n", + ")\n", "\n", - "# Customize the plot\n", - "plt.title(f\"Histogram of {name} Scores with Fitted Gaussian\", fontsize=16)\n", - "plt.xlabel(\"Score\", fontsize=14)\n", - "plt.ylabel(\"Density\", fontsize=14)\n", + "# Drop any rows where score is NaN\n", + "df_long = df_long.dropna(subset=['score'])\n", "\n", - "# Add text box with distribution parameters\n", - "textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n", - "props = dict(boxstyle='round', facecolor='white', alpha=0.5)\n", - "plt.text(0.05, 0.95, textstr, transform=plt.gca().transAxes, fontsize=14,\n", - " verticalalignment='top', bbox=props)\n", + "# Cast question_weight as numeric\n", + "df_long['question_weight'] = pd.to_numeric(df_long['question_weight'], errors='coerce')\n", "\n", - "plt.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 209, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bot_question_id4ShadowerBot_PepaCatrachoCasterCumulativeBotGreeneiBot2Grizeu_BotInstitutPelFuturKevinTestBotMWG...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbquestion_weight
031262NaNNaNNaNNaN-242.660874135.57527347.259183NaNNaN...-205.076095121.194882NaN-242.660874-198.879258NaNNaNNaNNaN1.0
131263NaNNaNNaNNaN-96.476789-99.090018-94.660371NaNNaN...7.9517037.951703NaN55.81904144.625993NaNNaNNaNNaN1.0
231264NaNNaNNaNNaN18.89298023.948225-86.527528NaNNaN...13.82151813.821518NaN1.30707117.305437NaNNaNNaNNaN1.0
331274NaNNaN2.076868NaN31.0945314.282464-28.806893NaN14.663415...6.44257916.621639NaN8.55905311.145899NaNNaN-9.706540NaN1.0
431275NaNNaNNaNNaN30.694891-66.461608-58.368696NaNNaN...35.698675-0.691552NaN39.41450214.411756NaNNaN-70.932651NaN1.0
\n", - "

5 rows × 48 columns

\n", - "
" - ], - "text/plain": [ - " bot_question_id 4Shadower Bot_Pepa CatrachoCaster CumulativeBot \\\n", - "0 31262 NaN NaN NaN NaN \n", - "1 31263 NaN NaN NaN NaN \n", - "2 31264 NaN NaN NaN NaN \n", - "3 31274 NaN NaN 2.076868 NaN \n", - "4 31275 NaN NaN NaN NaN \n", - "\n", - " GreeneiBot2 Grizeu_Bot InstitutPelFutur KevinTestBot MWG ... \\\n", - "0 -242.660874 135.575273 47.259183 NaN NaN ... \n", - "1 -96.476789 -99.090018 -94.660371 NaN NaN ... \n", - "2 18.892980 23.948225 -86.527528 NaN NaN ... \n", - "3 31.094531 4.282464 -28.806893 NaN 14.663415 ... \n", - "4 30.694891 -66.461608 -58.368696 NaN NaN ... \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 -205.076095 121.194882 NaN -242.660874 -198.879258 \n", - "1 7.951703 7.951703 NaN 55.819041 44.625993 \n", - "2 13.821518 13.821518 NaN 1.307071 17.305437 \n", - "3 6.442579 16.621639 NaN 8.559053 11.145899 \n", - "4 35.698675 -0.691552 NaN 39.414502 14.411756 \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb question_weight \n", - "0 NaN NaN NaN NaN 1.0 \n", - "1 NaN NaN NaN NaN 1.0 \n", - "2 NaN NaN NaN NaN 1.0 \n", - "3 NaN NaN -9.706540 NaN 1.0 \n", - "4 NaN NaN -70.932651 NaN 1.0 \n", - "\n", - "[5 rows x 48 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bot_question_id4ShadowerBot_PepaCatrachoCasterCumulativeBotGreeneiBot2Grizeu_BotInstitutPelFuturKevinTestBotMWG...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbquestion_weight
404356196.356385NaN6.3563858.98511614.048951-5.7402526.356385-5.74025210.822423...-5.7402526.356385NaN0.48606113.624559NaNNaN7.9416846.3563851.0
40535620-3.848478NaN2.026137-2.6463853.161815-3.84847811.301510-3.848478-23.803402...2.0261372.026137NaN7.5830468.230127NaNNaNNaN-3.8484781.0
4063562134.934257NaN-15.68138236.351904-16.055800-62.135408-96.71727734.93425732.624547...9.104719-48.411348NaN29.05964231.449931NaNNaNNaN34.9342571.0
40735622-58.153367NaNNaNNaN-14.351771-85.428443-29.09640042.884269NaN...78.87460378.874603NaN114.533049105.344243NaNNaN-1.818274-97.7260201.0
40835705-31.742288NaNNaN43.33077750.02366026.291942NaN-0.62033022.674004...-37.061593-0.620330NaN-8.60147579.739445NaNNaNNaN10.3059451.0
\n", - "

5 rows × 48 columns

\n", - "
" - ], - "text/plain": [ - " bot_question_id 4Shadower Bot_Pepa CatrachoCaster CumulativeBot \\\n", - "404 35619 6.356385 NaN 6.356385 8.985116 \n", - "405 35620 -3.848478 NaN 2.026137 -2.646385 \n", - "406 35621 34.934257 NaN -15.681382 36.351904 \n", - "407 35622 -58.153367 NaN NaN NaN \n", - "408 35705 -31.742288 NaN NaN 43.330777 \n", - "\n", - " GreeneiBot2 Grizeu_Bot InstitutPelFutur KevinTestBot MWG ... \\\n", - "404 14.048951 -5.740252 6.356385 -5.740252 10.822423 ... \n", - "405 3.161815 -3.848478 11.301510 -3.848478 -23.803402 ... \n", - "406 -16.055800 -62.135408 -96.717277 34.934257 32.624547 ... \n", - "407 -14.351771 -85.428443 -29.096400 42.884269 NaN ... \n", - "408 50.023660 26.291942 NaN -0.620330 22.674004 ... \n", - "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "404 -5.740252 6.356385 NaN 0.486061 13.624559 \n", - "405 2.026137 2.026137 NaN 7.583046 8.230127 \n", - "406 9.104719 -48.411348 NaN 29.059642 31.449931 \n", - "407 78.874603 78.874603 NaN 114.533049 105.344243 \n", - "408 -37.061593 -0.620330 NaN -8.601475 79.739445 \n", - "\n", - " pianobot swingswish twsummerbot wunderplumb question_weight \n", - "404 NaN NaN 7.941684 6.356385 1.0 \n", - "405 NaN NaN NaN -3.848478 1.0 \n", - "406 NaN NaN NaN 34.934257 1.0 \n", - "407 NaN NaN -1.818274 -97.726020 1.0 \n", - "408 NaN NaN NaN 10.305945 1.0 \n", - "\n", - "[5 rows x 48 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "# Group first, then do the multiplication and sum\n", + "weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n", + "\n", + "# Calculate number of questions answered by each bot\n", + "num_questions = df_long.groupby('forecaster')['bot_question_id'].nunique()\n", + "#num_weighted_questions = df_bot_vs_pro_peer.mul(df_pro_bot_forecasts['question_weight'], axis=0).apply(lambda col: col[col.notna() & col.apply(np.isreal)].count())\n", + "\n", + "# Create a new DataFrame with the results\n", + "results = pd.DataFrame({\n", + " 'Peer_vs_Pro': total_scores,\n", + " 'Count': num_questions\n", + "})\n", + "\n", + "weighted_results = pd.DataFrame({\n", + " 'W_Peer_vs_Pro': weighted_scores,\n", + " 'Count': num_questions\n", + "})\n", + "\n", + "df_bot_vs_pro_leaderboard = results.sort_values(by='Peer_vs_Pro', ascending=False)\n", + "df_bot_vs_pro_weighted_leaderboard = weighted_results.sort_values(by='W_Peer_vs_Pro', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [], "source": [ - "df_bot_peer_wide.shape\n", + "df_pro_baseline = df_pro_baseline.rename(columns={'question_id': 'pro_question_id'})\n", + "df_pro_baseline = df_pro_baseline[['pro_question_id', 'forecaster', 'score']]\n", "\n", - "display_head_and_tail(df_bot_peer_wide)" + "# Now make it wide! forecaster = columns; score = values; index = pro_question_id\n", + "df_pro_baseline_wide = df_pro_baseline.pivot(index='pro_question_id', columns='forecaster', values='score').reset_index()" ] }, { "cell_type": "code", - "execution_count": 210, + "execution_count": null, "metadata": { "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oxVJxrCpuXV_", - "outputId": "3df39cbc-b594-40e1-d08f-1b0e9736d6ec" + "id": "tXKRpXAVHMRt" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BOT LEADERBOARD\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2.5% CI10% CIMedian90% CI97.5% CI
metac-o16.17.49.712.013.1
metac-o1-preview3.55.08.211.112.8
manticAI0.32.15.48.610.4
metac-Gemini-Exp-12060.72.25.07.89.2
acm_bot-0.11.44.67.69.2
metac-perplexity-1.40.54.17.99.5
GreeneiBot2-1.10.73.97.28.8
twsummerbot0.11.53.96.17.4
cookics_bot_TEST0.11.13.05.16.1
pgodzinai-4.2-1.32.97.09.0
CumulativeBot-0.20.92.64.45.2
metac-claude-3-5-sonnet-latest-1.20.12.65.16.1
SynapseSeer0.10.92.44.04.7
metac-exa-5.1-2.51.85.77.9
jkraybill_bot-3.6-1.51.74.96.4
metac-deepseek-r1-2.1-0.81.23.44.4
MWG-1.6-0.80.62.22.8
pianobot-1.1-0.70.00.71.0
andrewsiah-0.9-0.5-0.00.60.9
X_bot-0.4-0.2-0.00.10.2
cobyj-bot-1.5-0.9-0.10.81.4
KevinTestBot-3.9-2.8-0.41.42.4
annabot-3.4-2.5-0.51.22.1
bean_bot-3.4-2.4-0.51.12.0
CatrachoCaster-2.2-1.7-0.70.20.7
jonahsingerbot-3.0-2.2-0.80.41.0
krm-bot-3.7-2.7-1.00.71.5
ProfessorSP-4.5-3.2-1.11.01.9
metac-grok-2-1212-6.2-4.9-1.32.03.6
mmBot-7.4-5.3-1.52.24.0
4Shadower-4.6-3.7-1.60.41.2
RPM_bot-4.9-3.7-1.9-0.6-0.0
swingswish-5.3-4.0-1.9-0.10.8
metac-claude-3-5-sonnet-20240620-6.2-4.9-2.10.82.2
InstitutPelFutur-9.1-6.5-2.41.93.6
wunderplumb-5.9-4.8-2.5-0.20.9
metac-Llama-3.1-6.9-5.2-2.80.01.5
NextWorldLab-8.6-6.9-3.7-0.51.1
Bot_Pepa-7.0-6.0-3.9-1.9-0.9
laylaps-9.6-7.6-3.9-0.21.7
VeritasAI-7.9-6.7-4.3-2.0-0.7
minefrac1-7.9-6.9-4.7-2.6-1.4
Grizeu_Bot-9.2-7.6-4.9-2.3-1.1
metac-gpt-4o-10.2-8.8-5.8-3.1-1.5
ajf-bot-15.2-12.9-8.4-4.5-2.3
\n", - "
" - ], - "text/plain": [ - " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.1 7.4 9.7 12.0 13.1\n", - "metac-o1-preview 3.5 5.0 8.2 11.1 12.8\n", - "manticAI 0.3 2.1 5.4 8.6 10.4\n", - "metac-Gemini-Exp-1206 0.7 2.2 5.0 7.8 9.2\n", - "acm_bot -0.1 1.4 4.6 7.6 9.2\n", - "metac-perplexity -1.4 0.5 4.1 7.9 9.5\n", - "GreeneiBot2 -1.1 0.7 3.9 7.2 8.8\n", - "twsummerbot 0.1 1.5 3.9 6.1 7.4\n", - "cookics_bot_TEST 0.1 1.1 3.0 5.1 6.1\n", - "pgodzinai -4.2 -1.3 2.9 7.0 9.0\n", - "CumulativeBot -0.2 0.9 2.6 4.4 5.2\n", - "metac-claude-3-5-sonnet-latest -1.2 0.1 2.6 5.1 6.1\n", - "SynapseSeer 0.1 0.9 2.4 4.0 4.7\n", - "metac-exa -5.1 -2.5 1.8 5.7 7.9\n", - "jkraybill_bot -3.6 -1.5 1.7 4.9 6.4\n", - "metac-deepseek-r1 -2.1 -0.8 1.2 3.4 4.4\n", - "MWG -1.6 -0.8 0.6 2.2 2.8\n", - "pianobot -1.1 -0.7 0.0 0.7 1.0\n", - "andrewsiah -0.9 -0.5 -0.0 0.6 0.9\n", - "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "cobyj-bot -1.5 -0.9 -0.1 0.8 1.4\n", - "KevinTestBot -3.9 -2.8 -0.4 1.4 2.4\n", - "annabot -3.4 -2.5 -0.5 1.2 2.1\n", - "bean_bot -3.4 -2.4 -0.5 1.1 2.0\n", - "CatrachoCaster -2.2 -1.7 -0.7 0.2 0.7\n", - "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", - "krm-bot -3.7 -2.7 -1.0 0.7 1.5\n", - "ProfessorSP -4.5 -3.2 -1.1 1.0 1.9\n", - "metac-grok-2-1212 -6.2 -4.9 -1.3 2.0 3.6\n", - "mmBot -7.4 -5.3 -1.5 2.2 4.0\n", - "4Shadower -4.6 -3.7 -1.6 0.4 1.2\n", - "RPM_bot -4.9 -3.7 -1.9 -0.6 -0.0\n", - "swingswish -5.3 -4.0 -1.9 -0.1 0.8\n", - "metac-claude-3-5-sonnet-20240620 -6.2 -4.9 -2.1 0.8 2.2\n", - "InstitutPelFutur -9.1 -6.5 -2.4 1.9 3.6\n", - "wunderplumb -5.9 -4.8 -2.5 -0.2 0.9\n", - "metac-Llama-3.1 -6.9 -5.2 -2.8 0.0 1.5\n", - "NextWorldLab -8.6 -6.9 -3.7 -0.5 1.1\n", - "Bot_Pepa -7.0 -6.0 -3.9 -1.9 -0.9\n", - "laylaps -9.6 -7.6 -3.9 -0.2 1.7\n", - "VeritasAI -7.9 -6.7 -4.3 -2.0 -0.7\n", - "minefrac1 -7.9 -6.9 -4.7 -2.6 -1.4\n", - "Grizeu_Bot -9.2 -7.6 -4.9 -2.3 -1.1\n", - "metac-gpt-4o -10.2 -8.8 -5.8 -3.1 -1.5\n", - "ajf-bot -15.2 -12.9 -8.4 -4.5 -2.3" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "# @title Create df_pro_bot_baseline_leaderboard, df_pro_bot_baseline_weighted_leaderboard\n", + "\n", + "df_pro_bot_baseline_weights = pd.merge(\n", + " df_pro_bot_resolved_questions,\n", + " df_bot_baseline_wide,\n", + " on='bot_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "df_pro_bot_baseline_weights = pd.merge(\n", + " df_pro_bot_baseline_weights,\n", + " df_pro_baseline_wide[['pro_question_id', 'pro_median']],\n", + " on='pro_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "# Remove rows where pro_question_id is NaN (only want overlapping questions here)\n", + "df_pro_bot_baseline_weights = df_pro_bot_baseline_weights.dropna(subset=['pro_question_id'])\n", + "\n", + "# Create a list of columns to keep\n", + "forecaster_cols = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", + "df_filtered = df_pro_bot_baseline_weights[forecaster_cols]\n", + "\n", + "# Calculate the sum for each forecaster\n", + "forecaster_scores = df_filtered.sum()\n", + "forecaster_weighted_scores = df_filtered.mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", + "\n", + "question_counts = df_filtered.notna().sum()\n", + "question_weighted_counts = df_filtered.notna().mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", + "\n", + "# Create a DataFrame for the leaderboard\n", + "leaderboard = pd.DataFrame({\n", + " 'Forecaster': forecaster_scores.index,\n", + " 'Baseline': forecaster_scores.values,\n", + " 'Count': question_counts.values\n", + "})\n", + "\n", + "# Create a DataFrame for the leaderboard\n", + "weighted_leaderboard = pd.DataFrame({\n", + " 'Forecaster': forecaster_weighted_scores.index,\n", + " 'Weighted_Baseline': forecaster_weighted_scores.values,\n", + " 'Count': question_counts.values,\n", + " 'Weighted Count': question_weighted_counts.values\n", + "})\n", + "\n", + "# Sort the leaderboard by score in descending order\n", + "leaderboard = leaderboard.sort_values('Baseline', ascending=False).reset_index(drop=True)\n", + "weighted_leaderboard = weighted_leaderboard.sort_values('Weighted_Baseline', ascending=False).reset_index(drop=True)\n", + "\n", + "# Add a 'Rank' column\n", + "leaderboard['Rank'] = leaderboard.index + 1\n", + "weighted_leaderboard['Rank'] = weighted_leaderboard.index + 1\n", + "\n", + "# Reorder columns to have Rank first\n", + "leaderboard = leaderboard[['Rank', 'Forecaster', 'Baseline', 'Count']]\n", + "weighted_leaderboard = weighted_leaderboard[['Rank', 'Forecaster', 'Weighted_Baseline', 'Count', 'Weighted Count']]\n", + "\n", + "#leaderboard\n", + "weighted_leaderboard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "# Drop 'bot_median' from all_bots list\n", - "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", - "df_bot_peer_wide_wo_median = df_bot_peer_wide.drop('bot_median', axis=1)\n", + "# make me a list that's pro_median and all the bot forecasters\n", + "forecasters = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", "\n", - "NUM = round(df_bot_peer_wide['question_weight'].sum())\n", - "ITER = 1000\n", + "hey = calculate_t_test(df_pro_bot_baseline_weights, forecasters)\n", "\n", - "result_df = weighted_bootstrap_analysis(df_bot_peer_wide_wo_median, all_bots_wo_median, NUM, ITER)\n", - "average_df = result_df / NUM\n", + "hey" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aGNedTHmU-Bm", + "outputId": "a7935679-8993-4329-d05d-fd701c4b77a8" + }, + "outputs": [], + "source": [ + "# @title Weighted head-to-head, T test\n", "\n", - "print(f'BOT LEADERBOARD\\n\\n')\n", - "df_rounded = average_df.round(1)\n", - "df_rounded" + "\"\"\"\n", + "df_W_leaderboard: A leaderboard based on df_bot_vs_pro_peer with question\n", + "weighting and the calculations for doing a weighted T test\n", + "\"\"\"\n", + "\n", + "forecaster_weighted_scores = forecaster_weighted_scores.fillna(0)\n", + "\n", + "# Cast weights as numeric\n", + "df_bot_vs_pro_peer['question_weight'] = pd.to_numeric(df_bot_vs_pro_peer['question_weight'], errors='coerce')\n", + "\n", + "# Calculate weighted statistics for each bot\n", + "df_W_leaderboard = calculate_t_test(df_bot_vs_pro_peer, all_bots)\n", + "\n", + "df_W_leaderboard" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [], + "source": [ + "# Write to csv\n", + "df_W_leaderboard.to_csv('notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv', index=True)" ] }, { "cell_type": "code", - "execution_count": 211, + "execution_count": null, "metadata": { "cellView": "form", "colab": { - "base_uri": "https://localhost:8080/", - "height": 125 + "base_uri": "https://localhost:8080/" }, - "id": "MXAev2sNXdbZ", - "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" + "id": "3d_ZdL0A0qTz", + "outputId": "e30ee8fb-0faf-45ae-974e-d4af282e0252" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "HEAD-TO-HEAD LEADERBOARD\n", - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
2.5% CI10% CIMedian90% CI97.5% CI
metac-perplexity18.118.118.118.118.1
acm_bot17.717.717.717.717.7
bot_median17.017.017.017.017.0
metac-o116.616.616.616.616.6
metac-claude-3-5-sonnet-2024062014.814.814.814.814.8
manticAI14.514.514.514.514.5
twsummerbot14.314.314.314.314.3
jkraybill_bot14.314.314.314.314.3
metac-exa13.013.013.013.013.0
GreeneiBot212.212.212.212.212.2
NextWorldLab11.111.111.111.111.1
metac-Llama-3.110.510.510.510.510.5
Grizeu_Bot10.210.210.210.210.2
SynapseSeer10.210.210.210.210.2
metac-claude-3-5-sonnet-latest10.010.010.010.010.0
mmBot9.79.79.79.79.7
annabot9.09.09.09.09.0
VeritasAI8.48.48.48.48.4
metac-grok-2-12128.28.28.28.28.2
laylaps7.67.67.67.67.6
metac-Gemini-Exp-12067.47.47.47.47.4
metac-o1-preview6.76.76.76.76.7
cookics_bot_TEST6.36.36.36.36.3
metac-deepseek-r15.75.75.75.75.7
MWG5.55.55.55.55.5
ajf-bot5.15.15.15.15.1
metac-gpt-4o4.84.84.84.84.8
pgodzinai3.53.53.53.53.5
KevinTestBot3.33.33.33.33.3
InstitutPelFutur2.72.72.72.72.7
Bot_Pepa2.62.62.62.62.6
CumulativeBot2.52.52.52.52.5
swingswish2.42.42.42.42.4
wunderplumb2.42.42.42.42.4
jonahsingerbot2.22.22.22.22.2
bean_bot2.12.12.12.12.1
X_bot1.91.91.91.91.9
CatrachoCaster1.81.81.81.81.8
RPM_bot1.21.21.21.21.2
4Shadower0.60.60.60.60.6
krm-bot0.60.60.60.60.6
andrewsiah0.00.00.00.00.0
cobyj-bot0.00.00.00.00.0
pianobot-2.2-2.2-2.2-2.2-2.2
ProfessorSP-3.0-3.0-3.0-3.0-3.0
minefrac1-3.0-3.0-3.0-3.0-3.0
\n", - "
" - ], - "text/plain": [ - " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-perplexity 18.1 18.1 18.1 18.1 18.1\n", - "acm_bot 17.7 17.7 17.7 17.7 17.7\n", - "bot_median 17.0 17.0 17.0 17.0 17.0\n", - "metac-o1 16.6 16.6 16.6 16.6 16.6\n", - "metac-claude-3-5-sonnet-20240620 14.8 14.8 14.8 14.8 14.8\n", - "manticAI 14.5 14.5 14.5 14.5 14.5\n", - "twsummerbot 14.3 14.3 14.3 14.3 14.3\n", - "jkraybill_bot 14.3 14.3 14.3 14.3 14.3\n", - "metac-exa 13.0 13.0 13.0 13.0 13.0\n", - "GreeneiBot2 12.2 12.2 12.2 12.2 12.2\n", - "NextWorldLab 11.1 11.1 11.1 11.1 11.1\n", - "metac-Llama-3.1 10.5 10.5 10.5 10.5 10.5\n", - "Grizeu_Bot 10.2 10.2 10.2 10.2 10.2\n", - "SynapseSeer 10.2 10.2 10.2 10.2 10.2\n", - "metac-claude-3-5-sonnet-latest 10.0 10.0 10.0 10.0 10.0\n", - "mmBot 9.7 9.7 9.7 9.7 9.7\n", - "annabot 9.0 9.0 9.0 9.0 9.0\n", - "VeritasAI 8.4 8.4 8.4 8.4 8.4\n", - "metac-grok-2-1212 8.2 8.2 8.2 8.2 8.2\n", - "laylaps 7.6 7.6 7.6 7.6 7.6\n", - "metac-Gemini-Exp-1206 7.4 7.4 7.4 7.4 7.4\n", - "metac-o1-preview 6.7 6.7 6.7 6.7 6.7\n", - "cookics_bot_TEST 6.3 6.3 6.3 6.3 6.3\n", - "metac-deepseek-r1 5.7 5.7 5.7 5.7 5.7\n", - "MWG 5.5 5.5 5.5 5.5 5.5\n", - "ajf-bot 5.1 5.1 5.1 5.1 5.1\n", - "metac-gpt-4o 4.8 4.8 4.8 4.8 4.8\n", - "pgodzinai 3.5 3.5 3.5 3.5 3.5\n", - "KevinTestBot 3.3 3.3 3.3 3.3 3.3\n", - "InstitutPelFutur 2.7 2.7 2.7 2.7 2.7\n", - "Bot_Pepa 2.6 2.6 2.6 2.6 2.6\n", - "CumulativeBot 2.5 2.5 2.5 2.5 2.5\n", - "swingswish 2.4 2.4 2.4 2.4 2.4\n", - "wunderplumb 2.4 2.4 2.4 2.4 2.4\n", - "jonahsingerbot 2.2 2.2 2.2 2.2 2.2\n", - "bean_bot 2.1 2.1 2.1 2.1 2.1\n", - "X_bot 1.9 1.9 1.9 1.9 1.9\n", - "CatrachoCaster 1.8 1.8 1.8 1.8 1.8\n", - "RPM_bot 1.2 1.2 1.2 1.2 1.2\n", - "4Shadower 0.6 0.6 0.6 0.6 0.6\n", - "krm-bot 0.6 0.6 0.6 0.6 0.6\n", - "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", - "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", - "pianobot -2.2 -2.2 -2.2 -2.2 -2.2\n", - "ProfessorSP -3.0 -3.0 -3.0 -3.0 -3.0\n", - "minefrac1 -3.0 -3.0 -3.0 -3.0 -3.0" - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "NUM = round(df_bot_vs_pro_peer['question_weight'].sum())\n", - "ITER = 1000\n", + "# @title Weighted Bot Peer, T test (to compare bots against each other, use ALL QUESTIONS)\n", "\n", - "result_df = weighted_bootstrap_analysis(df_bot_vs_pro_peer, all_bots, NUM, ITER)\n", - "average_df = result_df / NUM\n", + "df_W_bot_peer_leaderboard = pd.DataFrame()\n", "\n", - "print(f'\\n\\n\\nHEAD-TO-HEAD LEADERBOARD\\n\\n')\n", - "#df_rounded = result_df.round(0).astype(int)\n", - "df_rounded = average_df.round(1)\n", + "df3 = pd.DataFrame()\n", + "\n", + "forecaster_weighted_scores = forecaster_weighted_scores.fillna(0)\n", + "\n", + "# OMIT bot_median column for this bit\n", + "df_bot_peer_wide_b = df_bot_peer_wide.drop('bot_median', axis=1)\n", + "df_bot_peer = df_bot_peer[df_bot_peer['forecaster'] != 'bot_median']\n", + "\n", + "bots_for_peer = np.array(list(set(df_bot_peer['forecaster'])))\n", + "\n", + "df_W_leaderboard = calculate_t_test(df_bot_peer_wide_b, bots_for_peer)\n", + "\n", + "df_W_leaderboard_print = df_W_leaderboard.sort_values(by='lower_bound', ascending=False)\n", + "df_W_leaderboard_print['Rank'] = range(1, len(df_W_leaderboard_print) + 1)\n", + "\n", + "# Make index into a column - Bot\n", + "df_W_leaderboard_print = df_W_leaderboard_print.reset_index()\n", + "df_W_leaderboard_print = df_W_leaderboard_print.rename(columns={'index': 'Bot'})\n", + "#df_W_leaderboard_print = df_W_leaderboard_print[['Rank', 'Bot', 'W_ave', 'W_count', 'lower_bound', 'upper_bound']]\n", + "# Make rank the first column; leave rest the same\n", + "cols = df_W_leaderboard_print.columns.tolist()\n", + "cols = ['Rank'] + cols[:-1]\n", + "df_W_leaderboard_print = df_W_leaderboard_print[cols]\n", "\n", - "df_rounded" + "df_W_leaderboard_print" ] }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 206, "metadata": {}, "outputs": [], "source": [ - "# Write df_rounded (bootstrapping h2h) to csv\n", - "df_rounded.to_csv('notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv')" + "# Write to csv\n", + "df_W_leaderboard_print.to_csv('notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv', index=False)" ] }, { "cell_type": "code", - "execution_count": 213, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Weighted score for annabot: -190.5513637093994\n", - "Total score for annabot: 21.125669919166132\n", - "\n" - ] + "outputs": [], + "source": [ + "df_bot_peer_wide.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "id": "88QO8eyW6T_T", + "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" + }, + "outputs": [], "source": [ - "# @title Check specific bot records\n", - "\n", - "bot_name = 'annabot'\n", - "\n", - "df_bot = df_bot_peer_wide[['bot_question_id', 'question_weight', bot_name]]\n", - "df_bot = df_bot.dropna()\n", - "df_bot = df_bot.reset_index(drop=True)\n", - "\n", - "df_bot['weighted_score'] = df_bot[bot_name] * df_bot['question_weight']\n", + "# @title Histogram of bot\n", "\n", - "weighted_score = df_bot['weighted_score'].sum()\n", + "if 'mf-bot-1' in df_bot_peer_wide.columns:\n", + " name = 'mf-bot-1'\n", + "else:\n", + " name = 'metac-o1-preview'\n", "\n", - "print(f\"Weighted score for {bot_name}: {weighted_score}\")\n", + "scores = df_bot_peer_wide[name].dropna()\n", "\n", - "total_score = df_bot[bot_name].sum()\n", + "# Create the histogram\n", + "plt.figure(figsize=(10, 6))\n", + "n, bins, patches = plt.hist(scores, bins=30, density=True, alpha=0.7, color='skyblue')\n", "\n", - "print(f\"Total score for {bot_name}: {total_score}\\n\")\n", + "# Fit a normal distribution to the data\n", + "mu, std = norm.fit(scores)\n", "\n", - "# Create the histogram\n", - "plt.figure(figsize=(10, 6)) # Set the figure size (optional)\n", - "plt.hist(df_bot[bot_name], bins=10, edgecolor='black')\n", + "# Plot the PDF of the fitted normal distribution\n", + "xmin, xmax = plt.xlim()\n", + "x = np.linspace(xmin, xmax, 100)\n", + "p = norm.pdf(x, mu, std)\n", + "plt.plot(x, p, 'k', linewidth=2)\n", "\n", "# Customize the plot\n", - "plt.title(f'Histogram of Scores for {bot_name}')\n", - "plt.xlabel('Score')\n", - "plt.ylabel('Frequency')\n", + "plt.title(f\"Histogram of {name} Scores with Fitted Gaussian\", fontsize=16)\n", + "plt.xlabel(\"Score\", fontsize=14)\n", + "plt.ylabel(\"Density\", fontsize=14)\n", "\n", - "# Add grid lines (optional)\n", - "plt.grid(axis='y', alpha=0.75)\n", + "# Add text box with distribution parameters\n", + "textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n", + "props = dict(boxstyle='round', facecolor='white', alpha=0.5)\n", + "plt.text(0.05, 0.95, textstr, transform=plt.gca().transAxes, fontsize=14,\n", + " verticalalignment='top', bbox=props)\n", "\n", - "# Show the plot\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 214, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_bot_peer_wide.shape\n", + "\n", + "display_head_and_tail(df_bot_peer_wide)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, - "id": "I7W8JXutv2ks", - "outputId": "5e7053d3-2124-42b7-bd53-48a40a53caf2" + "id": "oxVJxrCpuXV_", + "outputId": "3df39cbc-b594-40e1-d08f-1b0e9736d6ec" }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
W_aveW_countlower_boundupper_boundp_value
metac-o1-preview12.2276.67.117.30.000004
metac-o18.4283.24.012.70.000179
pgodzinai8.7248.01.116.30.025267
GreeneiBot29.2204.81.117.30.026930
manticAI7.7245.20.514.90.035671
acm_bot5.4263.5-0.211.00.058135
metac-Gemini-Exp-12065.3269.6-0.310.80.062806
SynapseSeer6.0125.9-0.512.50.068737
metac-claude-3-5-sonnet-latest3.6278.2-0.98.20.116899
twsummerbot4.9181.9-1.811.60.152393
cookics_bot_TEST5.8135.2-1.813.40.132509
CumulativeBot8.094.2-3.018.90.153662
metac-deepseek-r10.8225.8-4.25.80.763142
MWG3.684.8-4.311.50.365354
metac-perplexity2.8264.3-4.810.30.470416
metac-grok-2-12120.1281.2-5.76.00.961620
metac-exa1.7275.2-5.89.20.654608
mmBot-0.5279.9-7.56.50.887163
InstitutPelFutur-0.1264.9-8.18.00.988352
metac-Llama-3.1-3.7280.5-8.30.90.117806
metac-claude-3-5-sonnet-20240620-3.3282.2-8.52.00.224671
VeritasAI-4.5251.9-9.40.40.072948
jkraybill_bot1.4162.4-9.712.40.808839
CatrachoCaster-2.761.9-10.65.20.493061
metac-gpt-4o-5.2281.2-10.60.10.054453
NextWorldLab-4.6256.3-10.91.80.156859
wunderplumb-5.4148.4-13.52.60.184061
4Shadower-3.7101.5-13.96.40.463979
minefrac1-7.3136.2-14.4-0.20.043444
andrewsiah0.125.1-14.614.80.988409
krm-bot-4.494.5-14.75.90.399741
ProfessorSP-4.0110.0-14.86.80.464316
laylaps-7.2257.0-15.40.90.082564
pianobot6.814.8-16.229.80.535822
cobyj-bot-0.431.5-17.817.10.964365
KevinTestBot-2.781.1-17.912.60.730388
jonahsingerbot-6.560.1-19.26.20.309592
bean_bot-4.163.1-19.611.40.600896
Bot_Pepa-12.3124.4-20.6-4.10.003751
annabot-6.354.5-23.711.10.470037
Grizeu_Bot-16.5140.9-25.8-7.10.000639
ajf-bot-16.0193.9-28.2-3.70.011119
swingswish-16.757.1-36.93.50.103364
RPM_bot-44.015.8-101.413.40.126191
\n", - "
" - ], - "text/plain": [ - " W_ave W_count lower_bound upper_bound \\\n", - "metac-o1-preview 12.2 276.6 7.1 17.3 \n", - "metac-o1 8.4 283.2 4.0 12.7 \n", - "pgodzinai 8.7 248.0 1.1 16.3 \n", - "GreeneiBot2 9.2 204.8 1.1 17.3 \n", - "manticAI 7.7 245.2 0.5 14.9 \n", - "acm_bot 5.4 263.5 -0.2 11.0 \n", - "metac-Gemini-Exp-1206 5.3 269.6 -0.3 10.8 \n", - "SynapseSeer 6.0 125.9 -0.5 12.5 \n", - "metac-claude-3-5-sonnet-latest 3.6 278.2 -0.9 8.2 \n", - "twsummerbot 4.9 181.9 -1.8 11.6 \n", - "cookics_bot_TEST 5.8 135.2 -1.8 13.4 \n", - "CumulativeBot 8.0 94.2 -3.0 18.9 \n", - "metac-deepseek-r1 0.8 225.8 -4.2 5.8 \n", - "MWG 3.6 84.8 -4.3 11.5 \n", - "metac-perplexity 2.8 264.3 -4.8 10.3 \n", - "metac-grok-2-1212 0.1 281.2 -5.7 6.0 \n", - "metac-exa 1.7 275.2 -5.8 9.2 \n", - "mmBot -0.5 279.9 -7.5 6.5 \n", - "InstitutPelFutur -0.1 264.9 -8.1 8.0 \n", - "metac-Llama-3.1 -3.7 280.5 -8.3 0.9 \n", - "metac-claude-3-5-sonnet-20240620 -3.3 282.2 -8.5 2.0 \n", - "VeritasAI -4.5 251.9 -9.4 0.4 \n", - "jkraybill_bot 1.4 162.4 -9.7 12.4 \n", - "CatrachoCaster -2.7 61.9 -10.6 5.2 \n", - "metac-gpt-4o -5.2 281.2 -10.6 0.1 \n", - "NextWorldLab -4.6 256.3 -10.9 1.8 \n", - "wunderplumb -5.4 148.4 -13.5 2.6 \n", - "4Shadower -3.7 101.5 -13.9 6.4 \n", - "minefrac1 -7.3 136.2 -14.4 -0.2 \n", - "andrewsiah 0.1 25.1 -14.6 14.8 \n", - "krm-bot -4.4 94.5 -14.7 5.9 \n", - "ProfessorSP -4.0 110.0 -14.8 6.8 \n", - "laylaps -7.2 257.0 -15.4 0.9 \n", - "pianobot 6.8 14.8 -16.2 29.8 \n", - "cobyj-bot -0.4 31.5 -17.8 17.1 \n", - "KevinTestBot -2.7 81.1 -17.9 12.6 \n", - "jonahsingerbot -6.5 60.1 -19.2 6.2 \n", - "bean_bot -4.1 63.1 -19.6 11.4 \n", - "Bot_Pepa -12.3 124.4 -20.6 -4.1 \n", - "annabot -6.3 54.5 -23.7 11.1 \n", - "Grizeu_Bot -16.5 140.9 -25.8 -7.1 \n", - "ajf-bot -16.0 193.9 -28.2 -3.7 \n", - "swingswish -16.7 57.1 -36.9 3.5 \n", - "RPM_bot -44.0 15.8 -101.4 13.4 \n", - "\n", - " p_value \n", - "metac-o1-preview 0.000004 \n", - "metac-o1 0.000179 \n", - "pgodzinai 0.025267 \n", - "GreeneiBot2 0.026930 \n", - "manticAI 0.035671 \n", - "acm_bot 0.058135 \n", - "metac-Gemini-Exp-1206 0.062806 \n", - "SynapseSeer 0.068737 \n", - "metac-claude-3-5-sonnet-latest 0.116899 \n", - "twsummerbot 0.152393 \n", - "cookics_bot_TEST 0.132509 \n", - "CumulativeBot 0.153662 \n", - "metac-deepseek-r1 0.763142 \n", - "MWG 0.365354 \n", - "metac-perplexity 0.470416 \n", - "metac-grok-2-1212 0.961620 \n", - "metac-exa 0.654608 \n", - "mmBot 0.887163 \n", - "InstitutPelFutur 0.988352 \n", - "metac-Llama-3.1 0.117806 \n", - "metac-claude-3-5-sonnet-20240620 0.224671 \n", - "VeritasAI 0.072948 \n", - "jkraybill_bot 0.808839 \n", - "CatrachoCaster 0.493061 \n", - "metac-gpt-4o 0.054453 \n", - "NextWorldLab 0.156859 \n", - "wunderplumb 0.184061 \n", - "4Shadower 0.463979 \n", - "minefrac1 0.043444 \n", - "andrewsiah 0.988409 \n", - "krm-bot 0.399741 \n", - "ProfessorSP 0.464316 \n", - "laylaps 0.082564 \n", - "pianobot 0.535822 \n", - "cobyj-bot 0.964365 \n", - "KevinTestBot 0.730388 \n", - "jonahsingerbot 0.309592 \n", - "bean_bot 0.600896 \n", - "Bot_Pepa 0.003751 \n", - "annabot 0.470037 \n", - "Grizeu_Bot 0.000639 \n", - "ajf-bot 0.011119 \n", - "swingswish 0.103364 \n", - "RPM_bot 0.126191 " - ] - }, - "execution_count": 214, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "# Drop 'bot_median' from all_bots list\n", + "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", + "df_bot_peer_wide_wo_median = df_bot_peer_wide.drop('bot_median', axis=1)\n", + "\n", + "NUM = round(df_bot_peer_wide['question_weight'].sum())\n", + "ITER = 1000\n", + "\n", + "result_df = weighted_bootstrap_analysis(df_bot_peer_wide_wo_median, all_bots_wo_median, NUM, ITER)\n", + "average_df = result_df / NUM\n", + "\n", + "print(f'BOT LEADERBOARD\\n\\n')\n", + "df_rounded = average_df.round(1)\n", + "df_rounded" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 125 + }, + "id": "MXAev2sNXdbZ", + "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" + }, + "outputs": [], + "source": [ + "NUM = round(df_bot_vs_pro_peer['question_weight'].sum())\n", + "ITER = 1000\n", + "\n", + "result_df = weighted_bootstrap_analysis(df_bot_vs_pro_peer, all_bots, NUM, ITER)\n", + "average_df = result_df / NUM\n", + "\n", + "print(f'\\n\\n\\nHEAD-TO-HEAD LEADERBOARD\\n\\n')\n", + "#df_rounded = result_df.round(0).astype(int)\n", + "df_rounded = average_df.round(1)\n", + "\n", + "df_rounded" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [], + "source": [ + "# Write df_rounded (bootstrapping h2h) to csv\n", + "df_rounded.to_csv('notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# @title Check specific bot records\n", + "\n", + "bot_name = 'annabot'\n", + "\n", + "df_bot = df_bot_peer_wide[['bot_question_id', 'question_weight', bot_name]]\n", + "df_bot = df_bot.dropna()\n", + "df_bot = df_bot.reset_index(drop=True)\n", + "\n", + "df_bot['weighted_score'] = df_bot[bot_name] * df_bot['question_weight']\n", + "\n", + "weighted_score = df_bot['weighted_score'].sum()\n", + "\n", + "print(f\"Weighted score for {bot_name}: {weighted_score}\")\n", + "\n", + "total_score = df_bot[bot_name].sum()\n", + "\n", + "print(f\"Total score for {bot_name}: {total_score}\\n\")\n", + "\n", + "# Create the histogram\n", + "plt.figure(figsize=(10, 6)) # Set the figure size (optional)\n", + "plt.hist(df_bot[bot_name], bins=10, edgecolor='black')\n", + "\n", + "# Customize the plot\n", + "plt.title(f'Histogram of Scores for {bot_name}')\n", + "plt.xlabel('Score')\n", + "plt.ylabel('Frequency')\n", + "\n", + "# Add grid lines (optional)\n", + "plt.grid(axis='y', alpha=0.75)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I7W8JXutv2ks", + "outputId": "5e7053d3-2124-42b7-bd53-48a40a53caf2" + }, + "outputs": [], "source": [ "# @title Weighted Bot Only Peer, T test\n", "\n", @@ -10333,27 +3991,9 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 10 bots:\n", - "1. metac-o1-preview\n", - "2. metac-o1\n", - "3. pgodzinai\n", - "4. GreeneiBot2\n", - "5. manticAI\n", - "6. acm_bot\n", - "7. metac-Gemini-Exp-1206\n", - "8. SynapseSeer\n", - "9. metac-claude-3-5-sonnet-latest\n", - "10. twsummerbot\n" - ] - } - ], + "outputs": [], "source": [ "# Sort the DataFrame by the lower_bound column in descending order\n", "sorted_df = df_W_bot_only_peer_leaderboard.sort_values(by='lower_bound', ascending=False)\n", @@ -10372,519 +4012,12 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": null, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.02]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.98]\n", - " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.75]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.35, 0.6]\n", - " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.8, 0.7]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.8, 0.6]\n", - " >>> Collected 2 forecasts: [0.7, 0.35]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.2, 0.35]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.7, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.5]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.3]\n", - " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", - " >>> Collected 2 forecasts: [0.02, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.25, 0.35]\n", - " >>> Collected 2 forecasts: [0.3, 0.3]\n", - " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.98, 0.98]\n", - " >>> Collected 2 forecasts: [0.4, 0.4]\n", - " >>> Collected 2 forecasts: [0.35, 0.3]\n", - " >>> Collected 2 forecasts: [0.3, 0.55]\n", - " >>> Collected 2 forecasts: [0.1, 0.02]\n", - " >>> Collected 2 forecasts: [0.85, 0.8]\n", - " >>> Collected 2 forecasts: [0.99, 0.99]\n", - " >>> Collected 2 forecasts: [0.97, 0.99]\n", - " >>> Collected 2 forecasts: [0.95, 0.15]\n", - " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.8]\n", - " >>> Collected 2 forecasts: [0.35, 0.4]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.75, 0.75]\n", - " >>> Collected 2 forecasts: [0.3, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.3]\n", - " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.03]\n", - " >>> Collected 2 forecasts: [0.85, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.95]\n", - " >>> Collected 2 forecasts: [0.9, 0.3]\n", - " >>> Collected 2 forecasts: [0.95, 0.8]\n", - " >>> Collected 2 forecasts: [0.85, 0.8]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.07]\n", - " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.82]\n", - " >>> Collected 3 forecasts: [0.8, 0.7, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.8, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.35, nan]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.35, 0.25]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.05, 0.5, 0.108]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.16]\n", - " >>> Collected 3 forecasts: [0.1, 0.15, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", - " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", - " >>> Collected 3 forecasts: [0.02, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.03]\n", - " >>> Collected 3 forecasts: [0.25, 0.35, 0.35]\n", - " >>> Collected 3 forecasts: [0.3, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", - " >>> Collected 3 forecasts: [0.98, 0.98, 0.97]\n", - " >>> Collected 3 forecasts: [0.4, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.35, 0.3, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.3, 0.55, 0.17]\n", - " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", - " >>> Collected 3 forecasts: [0.85, 0.8, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", - " >>> Collected 3 forecasts: [0.97, 0.99, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.95, 0.15, 0.4166666666666666]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.35, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.75, 0.75, 0.67]\n", - " >>> Collected 3 forecasts: [0.3, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.1, 0.15, 0.086]\n", - " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.15, 0.03, 0.02]\n", - " >>> Collected 3 forecasts: [0.85, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", - " >>> Collected 3 forecasts: [0.9, 0.3, nan]\n", - " >>> Collected 3 forecasts: [0.95, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.82, 0.794]\n", - " >>> Collected 4 forecasts: [0.8, 0.7, 0.85, 0.884]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.35, nan, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.35, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.85, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.05, 0.5, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.16, 0.652]\n", - " >>> Collected 4 forecasts: [0.1, 0.15, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", - " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", - " >>> Collected 4 forecasts: [0.02, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999]\n", - " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.98, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.3, 0.55, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.85, 0.8, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.3, 0.15, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.15, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.03, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.9, 0.3, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.8, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.35, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.35, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.85, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.5, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.16, 0.652, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.3, 0.55, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.3, 0.15, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.1, 0.15, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.03, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.3, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.8, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.35, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", - " >>> Collected 7 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27]\n", - " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", - " >>> Collected 7 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07]\n", - " >>> Collected 7 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02]\n", - " >>> Collected 7 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan]\n", - " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", - " >>> Collected 8 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", - " >>> Collected 8 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.35]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.4]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.95]\n", - " >>> Collected 9 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85]\n", - " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75, 0.638]\n", - " >>> Collected 10 forecasts: [0.8, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.35, nan, nan, nan, 0.65, 0.75, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.05, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.35, 0.289]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.4, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.3, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.3, 0.55, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.95, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.95, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" - ] - } - ], + "outputs": [], "source": [ "# @title Calculate df_bot_team_forecasts\n", "\n", @@ -10924,221 +4057,18 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
typeoptionsresolutionmetac-o1-previewmedian_forecast_5_botsmedian_forecast_8_bots
0multiple_choice[0, 1, 2-3, 4-6, >6]0[0.014083333333333333,0.6016666666666668,0.178...0.0145050.097463
1numericNaN86.82[0.05,0.0506666667,0.0513333333,0.052,0.052666...[0.037750000000000006, 0.038250620225000004, 0...[0.0402, 0.040750496180000005, 0.04130456232, ...
2binaryNaNno0.050.0630.085
3multiple_choice[0-4, 5-9, >9]5-9[0.15,0.65,0.2]0.560.56
4numericNaN119.2[0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...[0.0, 0.00207778844, 0.00416103382, 0.00624884...[0.0, 0.002104582785714286, 0.0042130633714285...
.....................
342binaryNaNyes0.90.9050.9025
351binaryNaNno0.90.30.1835
355binaryNaNyes0.950.80.8
361binaryNaNno0.850.80.755
364binaryNaNno0.050.050.046
\n", - "

99 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " type options resolution \\\n", - "0 multiple_choice [0, 1, 2-3, 4-6, >6] 0 \n", - "1 numeric NaN 86.82 \n", - "2 binary NaN no \n", - "3 multiple_choice [0-4, 5-9, >9] 5-9 \n", - "4 numeric NaN 119.2 \n", - ".. ... ... ... \n", - "342 binary NaN yes \n", - "351 binary NaN no \n", - "355 binary NaN yes \n", - "361 binary NaN no \n", - "364 binary NaN no \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.15,0.65,0.2] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", - ".. ... \n", - "342 0.9 \n", - "351 0.9 \n", - "355 0.95 \n", - "361 0.85 \n", - "364 0.05 \n", - "\n", - " median_forecast_5_bots \\\n", - "0 0.014505 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", - "2 0.063 \n", - "3 0.56 \n", - "4 [0.0, 0.00207778844, 0.00416103382, 0.00624884... \n", - ".. ... \n", - "342 0.905 \n", - "351 0.3 \n", - "355 0.8 \n", - "361 0.8 \n", - "364 0.05 \n", - "\n", - " median_forecast_8_bots \n", - "0 0.097463 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.085 \n", - "3 0.56 \n", - "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", - ".. ... \n", - "342 0.9025 \n", - "351 0.1835 \n", - "355 0.8 \n", - "361 0.755 \n", - "364 0.046 \n", - "\n", - "[99 rows x 6 columns]" - ] - }, - "execution_count": 219, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_bot_team_forecasts[['type', 'options', 'resolution', 'metac-o1-preview', 'median_forecast_5_bots', 'median_forecast_8_bots']]" ] }, { "cell_type": "code", - "execution_count": 220, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sum of weights: 95.0, Number of questions: 99\n" - ] - } - ], + "outputs": [], "source": [ "# Sanity check\n", "a = df_bot_team_forecasts['question_weight'].sum()\n", @@ -11148,7 +4078,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11156,106 +4086,7 @@ "id": "3-FedHpWV_1v", "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Bot_Team_SizeWeighted_Baseline_Score_for_Bot_Team_Median
0118.17
1224.94
2326.48
3426.48
4526.77
5626.92
6725.83
7826.50
8925.22
91025.45
\n", - "
" - ], - "text/plain": [ - " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 18.17\n", - "1 2 24.94\n", - "2 3 26.48\n", - "3 4 26.48\n", - "4 5 26.77\n", - "5 6 26.92\n", - "6 7 25.83\n", - "7 8 26.50\n", - "8 9 25.22\n", - "9 10 25.45" - ] - }, - "execution_count": 221, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# @title Calculate the baseline scores for each team size\n", "\n", @@ -11281,360 +4112,49 @@ ] }, { - "cell_type": "code", - "execution_count": 222, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['metac-o1-preview',\n", - " 'metac-o1',\n", - " 'pgodzinai',\n", - " 'GreeneiBot2',\n", - " 'manticAI',\n", - " 'acm_bot']" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Index of top bot team from weighted_scores_print?\n", - "winning_bot_team_size = weighted_scores_print.sort_values(by='Weighted_Baseline_Score_for_Bot_Team_Median', ascending=False).head(1)['Bot_Team_Size'].values[0]\n", - "top_bot_team = top_10_bots[:winning_bot_team_size]\n", - "top_bot_team" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(424, 47)" - ] - }, - "execution_count": 223, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_bot_forecasts.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge bot_team_forecasts with df_top_bot_forecasts, just get type and options columns from bot_team_forecasts, merge on bot_question_id\n", - "df_bot_forecasts = pd.merge(\n", - " df_bot_forecasts,\n", - " df_bot_team_forecasts[['bot_question_id', 'type', 'options', 'resolution']],\n", - " on='bot_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "# And make bot_question_id, type and options the first columns\n", - "df_bot_forecasts = df_bot_forecasts[['bot_question_id', 'type', 'options', 'resolution'] + [col for col in df_bot_forecasts.columns if col not in ['bot_question_id', 'type', 'options']]]" - ] - }, - { - "cell_type": "code", - "execution_count": 225, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bot_question_idquestion_weightresolutiontypeoptionsrange_minrange_maxmetac-o1-previewmetac-o1pgodzinai...median_forecast_1_botsmedian_forecast_2_botsmedian_forecast_3_botsmedian_forecast_4_botsmedian_forecast_5_botsmedian_forecast_6_botsmedian_forecast_7_botsmedian_forecast_8_botsmedian_forecast_9_botsmedian_forecast_10_bots
0312621.00multiple_choice[0, 1, 2-3, 4-6, >6]NaNNaN[0.014083333333333333,0.6016666666666668,0.178...[0.4,0.3,0.2,0.05,0.05][0.014925742574257425,0.5137871287128712,0.334......0.0140830.2070420.0149260.0145050.0145050.0149260.0974630.0974630.0149260.014926
1312631.086.82numericNaN60.0100.0[0.05,0.0506666667,0.0513333333,0.052,0.052666...[0.05,0.0506666667,0.0513333333,0.052,0.052666...[0.001,0.001060875,0.0011396,0.0012863125,0.00......[0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...[0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...[0.03366666666666667, 0.0341314028, 0.03460208...[0.037750000000000006, 0.038250620225000004, 0...[0.037750000000000006, 0.038250620225000004, 0...[0.0402, 0.040750496180000005, 0.04130456232, ...[0.0402, 0.040750496180000005, 0.04130456232, ...[0.0402, 0.040750496180000005, 0.04130456232, ...[0.041833333333333333, 0.042403191266666675, 0...[0.041833333333333333, 0.042403191266666675, 0...
2312641.0nobinaryNaNNaNNaN0.050.10.07...0.050.0750.070.0630.0630.070.0850.0850.10.1
3312741.05-9multiple_choice[0-4, 5-9, >9]NaNNaN[0.15,0.65,0.2][0.29,0.56,0.14999999999999997][0.27499999999999997,0.5125,0.21249999999999997]...0.650.6050.560.590.560.536250.560.560.536250.5125
4312751.0119.2numericNaN0.0400.0[0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...[0.0,0.0033333333,0.0066666667,0.01,0.01333333...[0.0,0.0001141583,0.0002446967,0.0003862688,0.......[0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...[0.0, 0.00366666665, 0.00733333335, 0.011, 0.0...[0.0, 0.0024824972, 0.004970454466666667, 0.00...[0.0, 0.00231641835, 0.00463693175, 0.00696020...[0.0, 0.00207778844, 0.00416103382, 0.00624884...[0.0, 0.002038679916666667, 0.0040819072666666...[0.0, 0.002104582785714286, 0.0042130633714285...[0.0, 0.002104582785714286, 0.0042130633714285...[0.0, 0.0023970654875000003, 0.0047975415625, ...[0.0, 0.002276496766666667, 0.0045560251555555...
\n", - "

5 rows × 27 columns

\n", - "
" - ], - "text/plain": [ - " bot_question_id question_weight resolution type \\\n", - "0 31262 1.0 0 multiple_choice \n", - "1 31263 1.0 86.82 numeric \n", - "2 31264 1.0 no binary \n", - "3 31274 1.0 5-9 multiple_choice \n", - "4 31275 1.0 119.2 numeric \n", - "\n", - " options range_min range_max \\\n", - "0 [0, 1, 2-3, 4-6, >6] NaN NaN \n", - "1 NaN 60.0 100.0 \n", - "2 NaN NaN NaN \n", - "3 [0-4, 5-9, >9] NaN NaN \n", - "4 NaN 0.0 400.0 \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.15,0.65,0.2] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", - "\n", - " metac-o1 \\\n", - "0 [0.4,0.3,0.2,0.05,0.05] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.29,0.56,0.14999999999999997] \n", - "4 [0.0,0.0033333333,0.0066666667,0.01,0.01333333... \n", - "\n", - " pgodzinai ... \\\n", - "0 [0.014925742574257425,0.5137871287128712,0.334... ... \n", - "1 [0.001,0.001060875,0.0011396,0.0012863125,0.00... ... \n", - "2 0.07 ... \n", - "3 [0.27499999999999997,0.5125,0.21249999999999997] ... \n", - "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... ... \n", - "\n", - " median_forecast_1_bots \\\n", - "0 0.014083 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 0.65 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", - "\n", - " median_forecast_2_bots \\\n", - "0 0.207042 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.075 \n", - "3 0.605 \n", - "4 [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0... \n", - "\n", - " median_forecast_3_bots \\\n", - "0 0.014926 \n", - "1 [0.03366666666666667, 0.0341314028, 0.03460208... \n", - "2 0.07 \n", - "3 0.56 \n", - "4 [0.0, 0.0024824972, 0.004970454466666667, 0.00... \n", - "\n", - " median_forecast_4_bots \\\n", - "0 0.014505 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", - "2 0.063 \n", - "3 0.59 \n", - "4 [0.0, 0.00231641835, 0.00463693175, 0.00696020... \n", - "\n", - " median_forecast_5_bots \\\n", - "0 0.014505 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", - "2 0.063 \n", - "3 0.56 \n", - "4 [0.0, 0.00207778844, 0.00416103382, 0.00624884... \n", - "\n", - " median_forecast_6_bots \\\n", - "0 0.014926 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.07 \n", - "3 0.53625 \n", - "4 [0.0, 0.002038679916666667, 0.0040819072666666... \n", - "\n", - " median_forecast_7_bots \\\n", - "0 0.097463 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.085 \n", - "3 0.56 \n", - "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", - "\n", - " median_forecast_8_bots \\\n", - "0 0.097463 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.085 \n", - "3 0.56 \n", - "4 [0.0, 0.002104582785714286, 0.0042130633714285... \n", - "\n", - " median_forecast_9_bots \\\n", - "0 0.014926 \n", - "1 [0.041833333333333333, 0.042403191266666675, 0... \n", - "2 0.1 \n", - "3 0.53625 \n", - "4 [0.0, 0.0023970654875000003, 0.0047975415625, ... \n", - "\n", - " median_forecast_10_bots \n", - "0 0.014926 \n", - "1 [0.041833333333333333, 0.042403191266666675, 0... \n", - "2 0.1 \n", - "3 0.5125 \n", - "4 [0.0, 0.002276496766666667, 0.0045560251555555... \n", - "\n", - "[5 rows x 27 columns]" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Index of top bot team from weighted_scores_print?\n", + "winning_bot_team_size = weighted_scores_print.sort_values(by='Weighted_Baseline_Score_for_Bot_Team_Median', ascending=False).head(1)['Bot_Team_Size'].values[0]\n", + "top_bot_team = top_10_bots[:winning_bot_team_size]\n", + "top_bot_team" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_bot_forecasts.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge bot_team_forecasts with df_top_bot_forecasts, just get type and options columns from bot_team_forecasts, merge on bot_question_id\n", + "df_bot_forecasts = pd.merge(\n", + " df_bot_forecasts,\n", + " df_bot_team_forecasts[['bot_question_id', 'type', 'options', 'resolution']],\n", + " on='bot_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "# And make bot_question_id, type and options the first columns\n", + "df_bot_forecasts = df_bot_forecasts[['bot_question_id', 'type', 'options', 'resolution'] + [col for col in df_bot_forecasts.columns if col not in ['bot_question_id', 'type', 'options']]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df_bot_team_forecasts.head()" ] @@ -11689,24 +4209,16 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Weighted Total Score: -15.6339\n" - ] - } - ], + "outputs": [], "source": [ "weighted_total_score = get_weighted_score(df_top_bot_pro_forecasts)" ] }, { "cell_type": "code", - "execution_count": 228, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11715,32 +4227,14 @@ "id": "JlU9zyqn26Rl", "outputId": "ac54d636-670b-4a8f-aea9-402679efacf9" }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The average of 'head_to_head' is: -15.85\n" - ] - } - ], + "outputs": [], "source": [ "plot_head_to_head_distribution(df_top_bot_pro_forecasts)" ] }, { "cell_type": "code", - "execution_count": 229, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11748,73 +4242,7 @@ "id": "V1qC4m2VefLe", "outputId": "2f110b55-caf6-4ea8-9dfe-b746c3e4d892" }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
head_to_head-1485.293.1-16.084.3680298.743857-1.8244751.9852771.4-33.30.0356610.071323
\n", - "
" - ], - "text/plain": [ - " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -1485.2 93.1 -16.0 84.368029 8.743857 -1.824475 \n", - "\n", - " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.985277 1.4 -33.3 0.035661 0.071323 " - ] - }, - "execution_count": 229, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_bot_team_h2h = calculate_t_test(df_top_bot_pro_forecasts, ['head_to_head'])\n", "\n", @@ -11823,7 +4251,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11831,108 +4259,7 @@ "id": "0I0myCHpl7FT", "outputId": "bcc45b9a-f328-4f0c-ef98-a7620af7e358" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 5:\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
titlebot_team_medianpro_medianresolutionhead_to_head
279What will Kalshi's rank in the iPhone Top Free...0.058[0.02,0.01,0.015,0.015,0.05,0.89]Not in top 50-273.1
121How many movies will be new on Netflix's top 1...0.125[0.005,0.017,0.157,0.821]3 or more-188.2
47What will be Donald Trump's net worth, accordi...0.17[0.6,0.2,0.1,0.075,0.025]0-$6 billion, inclusive-126.1
71Will OpenAI, Anthropic, or Perplexity run an a...0.160.55yes-123.5
247Will the 500th richest person on Bloomberg's B...0.80.333no-120.4
\n", - "
" - ], - "text/plain": [ - " title bot_team_median \\\n", - "279 What will Kalshi's rank in the iPhone Top Free... 0.058 \n", - "121 How many movies will be new on Netflix's top 1... 0.125 \n", - "47 What will be Donald Trump's net worth, accordi... 0.17 \n", - "71 Will OpenAI, Anthropic, or Perplexity run an a... 0.16 \n", - "247 Will the 500th richest person on Bloomberg's B... 0.8 \n", - "\n", - " pro_median resolution head_to_head \n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -273.1 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more -188.2 \n", - "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -126.1 \n", - "71 0.55 yes -123.5 \n", - "247 0.333 no -120.4 " - ] - }, - "execution_count": 230, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pd.set_option('display.max_colwidth', 50)\n", "\n", @@ -11952,160 +4279,18 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Bottom 5:\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
titlebot_team_medianpro_medianresolutionhead_to_head
85Will Elon Musk attend the Super Bowl in 2025?0.16850.755no122.2
0For Q1 2025, how many banks will be listed on ...0.014926[0.001,0.62,0.35,0.019,0.01]0270.3
189What will the highest rank of metac-GPT4o or m...[0.0, 0.016687996933333334, 0.0361674514166666...[0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...34.0542.5
211Will Nikola Corporation file for bankruptcy be...0.990.999annulledNaN
214Will the state of Rhode Island have any recrea...0.9410.95annulledNaN
\n", - "
" - ], - "text/plain": [ - " title \\\n", - "85 Will Elon Musk attend the Super Bowl in 2025? \n", - "0 For Q1 2025, how many banks will be listed on ... \n", - "189 What will the highest rank of metac-GPT4o or m... \n", - "211 Will Nikola Corporation file for bankruptcy be... \n", - "214 Will the state of Rhode Island have any recrea... \n", - "\n", - " bot_team_median \\\n", - "85 0.1685 \n", - "0 0.014926 \n", - "189 [0.0, 0.016687996933333334, 0.0361674514166666... \n", - "211 0.99 \n", - "214 0.941 \n", - "\n", - " pro_median resolution \\\n", - "85 0.755 no \n", - "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", - "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", - "211 0.999 annulled \n", - "214 0.95 annulled \n", - "\n", - " head_to_head \n", - "85 122.2 \n", - "0 270.3 \n", - "189 542.5 \n", - "211 NaN \n", - "214 NaN " - ] - }, - "execution_count": 231, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(\"\\nBottom 5:\")\n", "\n", - "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" - ] - }, - { - "cell_type": "code", - "execution_count": 232, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "bot_question_id Int64\n", - "title object\n", - "resolution float64\n", - "scheduled_close_time datetime64[ns]\n", - "actual_close_time datetime64[ns]\n", - "type object\n", - "options object\n", - "range_min float64\n", - "range_max float64\n", - "pro_question_id Int64\n", - "question_weight float64\n", - "bot_team_median object\n", - "pro_median object\n", - "head_to_head float64\n", - "weighted_score float64\n", - "dtype: object" - ] - }, - "execution_count": 232, - "metadata": {}, - "output_type": "execute_result" - } - ], + "df_bottom5[['title', 'bot_team_median', 'pro_median', 'resolution', 'head_to_head']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Cast df_top_bot_pro_forecasts['resolution'] as string - idk why this is necessary but it is\n", "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].astype(pd.StringDtype())\n", @@ -12115,191 +4300,9 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
031262For Q1 2025, how many banks will be listed on ...NaN2025-01-20 03:27:002025-01-20 03:27:00multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaN312681.00.014926[0.001,0.62,0.35,0.019,0.01]270.308741270.308741
131263What percentage of the vote will Alexander Luk...NaN2025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0312691.0[0.0402, 0.040750496180000005, 0.04130456232, ...[0.0013749738,0.0014499743,0.001526641,0.00160...-75.535832-75.535832
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaN312701.00.070.013-5.948545-5.948545
331274How many arms sales globally will the US State...NaN2025-01-21 11:42:002025-01-21 11:42:00multiple_choice[\"0-4\",\"5-9\",\">9\"]NaNNaN312801.00.53625[0.16,0.44,0.4]19.78257419.782574
431275How much will it rain in Brasília, Brazil in F...NaN2025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0312811.0[0.0, 0.002038679916666667, 0.0040819072666666...[0.0,0.0005044914,0.0010323506,0.0015847475,0....12.71630512.716305
\n", - "
" - ], - "text/plain": [ - " bot_question_id title \\\n", - "0 31262 For Q1 2025, how many banks will be listed on ... \n", - "1 31263 What percentage of the vote will Alexander Luk... \n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "3 31274 How many arms sales globally will the US State... \n", - "4 31275 How much will it rain in Brasília, Brazil in F... \n", - "\n", - " resolution scheduled_close_time actual_close_time type \\\n", - "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", - "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", - "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", - "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", - "\n", - " options range_min range_max pro_question_id \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", - "1 NaN 60.0 100.0 31269 \n", - "2 NaN NaN NaN 31270 \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", - "4 NaN 0.0 400.0 31281 \n", - "\n", - " question_weight bot_team_median \\\n", - "0 1.0 0.014926 \n", - "1 1.0 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 1.0 0.07 \n", - "3 1.0 0.53625 \n", - "4 1.0 [0.0, 0.002038679916666667, 0.0040819072666666... \n", - "\n", - " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -75.535832 \n", - "2 0.013 -5.948545 \n", - "3 [0.16,0.44,0.4] 19.782574 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 12.716305 \n", - "\n", - " weighted_score \n", - "0 270.308741 \n", - "1 -75.535832 \n", - "2 -5.948545 \n", - "3 19.782574 \n", - "4 12.716305 " - ] - }, - "execution_count": 233, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_top_bot_pro_forecasts.head()" ] @@ -12318,7 +4321,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12327,25 +4330,7 @@ "id": "BjNQ4IND6Ct7", "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of pro forecasts: 50\n" - ] - } - ], + "outputs": [], "source": [ "# Set up the plot\n", "plt.figure(figsize=(10, 8))\n", @@ -12368,209 +4353,34 @@ "\n", "# Show the plot\n", "plt.tight_layout()\n", - "plt.show()\n", - "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "metadata": {}, - "outputs": [], - "source": [ - "# Map resolution to 0 and 1\n", - "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxpro_question_idquestion_weightbot_team_medianpro_median
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaN312701.00.070.013
531276Will the USDA-posted recall by Pork Dynasty In...1.02025-01-21 11:42:002025-01-21 11:42:00binaryNaNNaNNaN312821.00.550.45
831288Will Eric Adams be Mayor of New York City on t...1.02025-01-22 20:19:002025-01-22 20:19:00binaryNaNNaNNaN312941.00.820.95
1031318Will the S&P 500 index go up in January 2025?1.02025-01-23 23:23:002025-01-23 23:23:00binaryNaNNaNNaN<NA>1.0NaNNaN
1331334At the end of March 2025, will Wikipedia still...1.02025-01-24 14:23:002025-01-24 14:23:00binaryNaNNaNNaN313381.00.8250.9
\n", - "
" - ], - "text/plain": [ - " bot_question_id title \\\n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "5 31276 Will the USDA-posted recall by Pork Dynasty In... \n", - "8 31288 Will Eric Adams be Mayor of New York City on t... \n", - "10 31318 Will the S&P 500 index go up in January 2025? \n", - "13 31334 At the end of March 2025, will Wikipedia still... \n", - "\n", - " resolution scheduled_close_time actual_close_time type options \\\n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary NaN \n", - "5 1.0 2025-01-21 11:42:00 2025-01-21 11:42:00 binary NaN \n", - "8 1.0 2025-01-22 20:19:00 2025-01-22 20:19:00 binary NaN \n", - "10 1.0 2025-01-23 23:23:00 2025-01-23 23:23:00 binary NaN \n", - "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", - "\n", - " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "2 NaN NaN 31270 1.0 0.07 \n", - "5 NaN NaN 31282 1.0 0.55 \n", - "8 NaN NaN 31294 1.0 0.82 \n", - "10 NaN NaN 1.0 NaN \n", - "13 NaN NaN 31338 1.0 0.825 \n", - "\n", - " pro_median \n", - "2 0.013 \n", - "5 0.45 \n", - "8 0.95 \n", - "10 NaN \n", - "13 0.9 " - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - } - ], + "plt.show()\n", + "print(f\"Number of pro forecasts: {len(df_top_bot_pro_forecasts_binary)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "# Map resolution to 0 and 1\n", + "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df_top_bot_pro_forecasts_all_binary.head()" ] }, { "cell_type": "code", - "execution_count": 238, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of pro forecasts: 50\n", - "Number of bot forecasts: 241\n" - ] - } - ], + "outputs": [], "source": [ "# Set up the plot\n", "plt.figure(figsize=(10, 8))\n", @@ -12600,7 +4410,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12608,373 +4418,7 @@ "id": "lPPgorXB7omi", "outputId": "24571b16-50b7-4e51-cd3d-420c15c7fe42" }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
031262For Q1 2025, how many banks will be listed on ...NaN2025-01-20 03:27:002025-01-20 03:27:00multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaN312681.00.014926[0.001,0.62,0.35,0.019,0.01]270.308741270.308741
131263What percentage of the vote will Alexander Luk...NaN2025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0312691.0[0.0402, 0.040750496180000005, 0.04130456232, ...[0.0013749738,0.0014499743,0.001526641,0.00160...-75.535832-75.535832
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaN312701.00.070.013-5.948545-5.948545
331274How many arms sales globally will the US State...NaN2025-01-21 11:42:002025-01-21 11:42:00multiple_choice[\"0-4\",\"5-9\",\">9\"]NaNNaN312801.00.53625[0.16,0.44,0.4]19.78257419.782574
431275How much will it rain in Brasília, Brazil in F...NaN2025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0312811.0[0.0, 0.002038679916666667, 0.0040819072666666...[0.0,0.0005044914,0.0010323506,0.0015847475,0....12.71630512.716305
\n", - "
" - ], - "text/plain": [ - " bot_question_id title \\\n", - "0 31262 For Q1 2025, how many banks will be listed on ... \n", - "1 31263 What percentage of the vote will Alexander Luk... \n", - "2 31264 Will the bubble in the Magnificent Seven pop b... \n", - "3 31274 How many arms sales globally will the US State... \n", - "4 31275 How much will it rain in Brasília, Brazil in F... \n", - "\n", - " resolution scheduled_close_time actual_close_time type \\\n", - "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", - "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", - "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", - "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", - "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", - "\n", - " options range_min range_max pro_question_id \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN 31268 \n", - "1 NaN 60.0 100.0 31269 \n", - "2 NaN NaN NaN 31270 \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN 31280 \n", - "4 NaN 0.0 400.0 31281 \n", - "\n", - " question_weight bot_team_median \\\n", - "0 1.0 0.014926 \n", - "1 1.0 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 1.0 0.07 \n", - "3 1.0 0.53625 \n", - "4 1.0 [0.0, 0.002038679916666667, 0.0040819072666666... \n", - "\n", - " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 270.308741 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -75.535832 \n", - "2 0.013 -5.948545 \n", - "3 [0.16,0.44,0.4] 19.782574 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 12.716305 \n", - "\n", - " weighted_score \n", - "0 270.308741 \n", - "1 -75.535832 \n", - "2 -5.948545 \n", - "3 19.782574 \n", - "4 12.716305 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
34235345Will the US Citizenship and Immigration Servic...1.02025-03-12 22:00:002025-03-12 22:00:00binaryNaNNaNNaN353801.000.920.95-3.208831-3.208831
35135354Will the United States impose any new tariffs ...0.02025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaN353811.000.17750.05-14.411350-14.411350
35535358Will ChatGPT rank in the top 10 global website...1.02025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaN353851.000.80.97-19.268434-19.268434
36135364Will Doge's Agency Efficiency Leaderboard have...0.02025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaN353860.850.7550.666-30.988278-26.340037
36435367Will the Project 2025 Tracker spreadsheet mark...0.02025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaN353870.850.050.03-2.083409-1.770897
\n", - "
" - ], - "text/plain": [ - " bot_question_id title \\\n", - "342 35345 Will the US Citizenship and Immigration Servic... \n", - "351 35354 Will the United States impose any new tariffs ... \n", - "355 35358 Will ChatGPT rank in the top 10 global website... \n", - "361 35364 Will Doge's Agency Efficiency Leaderboard have... \n", - "364 35367 Will the Project 2025 Tracker spreadsheet mark... \n", - "\n", - " resolution scheduled_close_time actual_close_time type options \\\n", - "342 1.0 2025-03-12 22:00:00 2025-03-12 22:00:00 binary NaN \n", - "351 0.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", - "355 1.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", - "361 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", - "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", - "\n", - " range_min range_max pro_question_id question_weight bot_team_median \\\n", - "342 NaN NaN 35380 1.00 0.92 \n", - "351 NaN NaN 35381 1.00 0.1775 \n", - "355 NaN NaN 35385 1.00 0.8 \n", - "361 NaN NaN 35386 0.85 0.755 \n", - "364 NaN NaN 35387 0.85 0.05 \n", - "\n", - " pro_median head_to_head weighted_score \n", - "342 0.95 -3.208831 -3.208831 \n", - "351 0.05 -14.411350 -14.411350 \n", - "355 0.97 -19.268434 -19.268434 \n", - "361 0.666 -30.988278 -26.340037 \n", - "364 0.03 -2.083409 -1.770897 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[239], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:839\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 829\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 830\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 837\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 839\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 842\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m'\u001b[39m: predictions, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m'\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(bins)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" - ] - } - ], + "outputs": [], "source": [ "# Calculate confidence scores for bot_team_median and pro_median\n", "display_head_and_tail(df_top_bot_pro_forecasts)\n", diff --git a/functions.py b/functions.py index 38a3fb1..dbb8331 100644 --- a/functions.py +++ b/functions.py @@ -11,7 +11,11 @@ from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -from refactored_notebook.scoring import calculate_spot_baseline_score, nominal_location_to_cdf_location, calculate_spot_peer_score +from refactored_notebook.scoring import ( + calculate_baseline_score, + calculate_peer_score, + nominal_location_to_cdf_location, +) def extract_forecast(df): @@ -428,19 +432,27 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): team_scores = {team: 0.0 for team in teams} for _, row in df_bot_team_forecasts.iterrows(): - q_type = row["type"] - resolution = row["resolution"] - options = row.get("options") - range_min = row.get("range_min") - range_max = row.get("range_max") - question_weight = row["question_weight"] + resolution = row["Resolution"] + options = row["Options"] + range_min = row["Range_min"] + range_max = row["Range_max"] + question_weight = row["Question_weight"] + open_upper_bound = row["Open_upper_bound"] + open_lower_bound = row["Open_lower_bound"] for team in teams: forecast = row[team] try: - weighted_score = calculate_spot_baseline_score( - forecast, resolution, options, range_min, range_max, question_weight + weighted_score = calculate_baseline_score( + forecast, + resolution, + options, + range_min, + range_max, + question_weight, + open_upper_bound=open_upper_bound, + open_lower_bound=open_lower_bound, ) team_scores[team] += weighted_score @@ -1041,7 +1053,9 @@ def nominal_location_to_cdf_location_via_question_dict(nominal_location, questio range_max = question_data["range_max"] zero_point = question_data["zero_point"] - return nominal_location_to_cdf_location(nominal_location, range_min, range_max, zero_point) + return nominal_location_to_cdf_location( + nominal_location, range_min, range_max, zero_point + ) def get_cdf_at(cdf, unscaled_location): @@ -1088,8 +1102,12 @@ def cdf_between(row, cdf, lower_bound, upper_bound): Returns: float: Probability between the bounds. """ - a = get_cdf_at(cdf, nominal_location_to_cdf_location_via_question_dict(lower_bound, row)) - b = get_cdf_at(cdf, nominal_location_to_cdf_location_via_question_dict(upper_bound, row)) + a = get_cdf_at( + cdf, nominal_location_to_cdf_location_via_question_dict(lower_bound, row) + ) + b = get_cdf_at( + cdf, nominal_location_to_cdf_location_via_question_dict(upper_bound, row) + ) return b - a @@ -1175,7 +1193,8 @@ def compute_bucket_forecast_value(row): # Compute forecast_value using the extracted string_location forecast_value = get_cdf_at( - row["cdf"], nominal_location_to_cdf_location_via_question_dict(string_location, row) + row["cdf"], + nominal_location_to_cdf_location_via_question_dict(string_location, row), ) # Apply logic based on comparison_type @@ -1224,37 +1243,48 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] - def calculate_weighted_h2h_score_between_two_forecast_columns(row, col_a, col_b): - forecast_a = row[col_a] # If string, I may need to do: [float(x) for x in bot_pmf_raw.strip('[]').split(',')] + forecast_a = row[ + col_a + ] # If string, I may need to do: [float(x) for x in bot_pmf_raw.strip('[]').split(',')] forecast_b = row[col_b] - resolution = row['resolution'] - options = row['options_parsed'] if 'options_parsed' in row else row['options'] - range_min = row['range_min'] - range_max = row['range_max'] - question_weight = row['question_weight'] - score = calculate_spot_peer_score( + resolution = row["resolution"] + options = row["options_parsed"] if "options_parsed" in row else row["options"] + range_min = row["range_min"] + range_max = row["range_max"] + question_weight = row["question_weight"] + score = calculate_peer_score( forecast=forecast_a, forecast_for_other_users=[forecast_b], resolution=resolution, options=options, range_min=range_min, range_max=range_max, - question_weight=question_weight + question_weight=question_weight, ) return score -def calculate_all_peer_scores(df, all_bots, pro_col='pro_median'): + +def calculate_all_peer_scores(df, all_bots, pro_col="pro_median"): """Calculate peer scores for all bots""" # Create a new DataFrame to store peer scores df_peer = df.copy() # Calculate peer score for each bot for bot in all_bots: - df_peer[bot] = 100 * df.apply(lambda row: calculate_weighted_h2h_score_between_two_forecast_columns(row, bot, pro_col), axis=1) + df_peer[bot] = 100 * df.apply( + lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( + row, bot, pro_col + ), + axis=1, + ) # Calculate peer score for bot_team_median df_peer["bot_team_median"] = 100 * df.apply( - lambda row: calculate_weighted_h2h_score_between_two_forecast_columns(row, 'bot_median', pro_col), axis=1) + lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( + row, "bot_median", pro_col + ), + axis=1, + ) return df_peer diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index ba126f9..1cd27a6 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -1,11 +1,12 @@ from datetime import datetime + import numpy as np from scipy.stats.mstats import gmean from refactored_notebook.data_models import ForecastType, ResolutionType -def calculate_spot_peer_score( +def calculate_peer_score( forecast: ForecastType, forecast_for_other_users: list[ForecastType], resolution: ResolutionType, @@ -65,38 +66,32 @@ def nominal_location_to_cdf_location( return unscaled_location -def calculate_spot_baseline_score( +def calculate_baseline_score( forecast: ForecastType, resolution: ResolutionType, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, question_weight: float = 1.0, + open_upper_bound: bool = False, + open_lower_bound: bool = False, ) -> float: """ Question type can be infered from resolution type Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ - prob_for_resolution = _determine_probability_for_resolution( forecast, resolution, options, range_min, range_max ) - baseline_prob = _determine_baseline(resolution, options) + baseline_prob = _determine_baseline( + resolution, options, range_min, range_max, open_upper_bound, open_lower_bound + ) divisor = _determine_divisor_for_baseline_score(resolution, options) if prob_for_resolution <= 0 or baseline_prob <= 0: raise ValueError( "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" ) - # if resolution_bucket in [0, len(pmf) - 1]: - # baseline = 0.05 - # else: - # open_bound_count = bool(question.open_upper_bound) + bool( - # question.open_lower_bound - # ) - # baseline = (1 - 0.05 * open_bounds_count) / (len(pmf) - 2) - # forecast_score = 100 * np.log(pmf[resolution_bucket] / baseline) / 2 - baseline_score = np.log(prob_for_resolution / baseline_prob) / divisor * 100 weighted_score = baseline_score * question_weight @@ -105,7 +100,12 @@ def calculate_spot_baseline_score( def _determine_baseline( - resolution: ResolutionType, options: list[str] | None = None + resolution: ResolutionType, + options: list[str] | None = None, + range_min: float | None = None, + range_max: float | None = None, + open_upper_bound: bool | None = None, + open_lower_bound: bool | None = None, ) -> float: is_binary = isinstance(resolution, bool) is_multiple_choice = isinstance(resolution, str) @@ -118,9 +118,34 @@ def _determine_baseline( raise ValueError("Options are required for multiple choice questions") baseline_prob = 1 / len(options) elif is_numeric: - baseline_prob = ( - 1 / 202 - ) # len(pmf) # ??? -> bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins + if open_upper_bound is None or open_lower_bound is None: + raise ValueError("Open upper bound and lower bound are required for numeric questions") + # @Check: Which version is correct? + + # Version 1: + resolved_outside_bounds = False + assert range_min is not None and range_max is not None and resolution is not None + if resolution > range_max or resolution < range_min: + resolved_outside_bounds = True + if resolved_outside_bounds: + baseline_prob = 0.05 + else: + open_bound_count = bool(open_upper_bound) + bool(open_lower_bound) + baseline_prob = (1 - 0.05 * open_bound_count) / 200 # PMF has 202 bins, 2 of which represent the bounds. So 200 is the internal bins + + # Version 2: + # open_bound_count = bool(open_upper_bound) + bool(open_lower_bound) + # if open_bound_count == 0: + # baseline_prob = 1 + # elif open_bound_count == 1: + # baseline_prob = 0.95 + # else: + # baseline_prob = 0.9 + + # Version 3: + # baseline_prob = ( + # 1 / 202 + # ) # len(pmf) # ??? -> bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins # @Check: Should this be either 1, 0.9, or 0.95 based on whether open or closed bounds else: raise ValueError("Unknown question type") @@ -145,7 +170,7 @@ def _determine_probability_for_resolution( if resolution == "above_upper_bound" or resolution == "below_lower_bound": raise ValueError( "'above_upper_bound' or 'below_lower_bound' format not supported" - ) + ) # This is an old resolution type in Q4 2024 is_numeric = isinstance(resolution, float) or isinstance(resolution, int) is_binary = isinstance(resolution, bool) diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 57fc7f1..3008279 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -4,8 +4,7 @@ import pytest from refactored_notebook.data_models import ForecastType -from refactored_notebook.scoring import (calculate_spot_baseline_score, - calculate_spot_peer_score) +from refactored_notebook.scoring import calculate_baseline_score, calculate_peer_score # TODO: # For each of Multiple Choice, Binary, and Numeric questions @@ -20,8 +19,10 @@ ################################### HELPER FUNCTIONS ################################### -def generate_uniform_cdf(num_points: int = 201) -> list[float]: - return [(i + 1) / num_points for i in range(num_points)] +def generate_uniform_cdf() -> list[float]: + num_points = 200 # cdf has 201 points, but first point is 0% if we assume closed bounds + return [0] + [(i + 1) / num_points for i in range(num_points)] + def generate_cdf_with_forecast_at_index(index: int, forecast: float) -> list[float]: cdf = [] @@ -197,7 +198,7 @@ def test_baseline_score_is_0_with_uniform_prediction( question_weight: float, expected: float, ): - score = calculate_spot_baseline_score( + score = calculate_baseline_score( forecast, resolution, options, range_min, range_max, question_weight ) assert abs(score - expected) == pytest.approx(0) @@ -206,16 +207,22 @@ def test_baseline_score_is_0_with_uniform_prediction( @pytest.mark.parametrize( "forecast,resolution,expected", [ - ([0.001], True, -896.57), # Completely incorrect - ([0.999], True, 99.86), # Completely correct - ([0.001], False, 99.86), # Completely correct - ([0.4], True, -32.19), # Examples found here: https://www.metaculus.com/help/scores-faq/#:~:text=details%20for%20nerds-,Do%20all%20my%20predictions%20on%20a%20question%20count%20toward%20my%20score%3F,-Yes.%20Metaculus%20uses + ([0.001], True, -896.57), # Completely incorrect + ([0.999], True, 99.86), # Completely correct + ([0.001], False, 99.86), # Completely correct + ( + [0.4], + True, + -32.19, + ), # Examples found here: https://www.metaculus.com/help/scores-faq/#:~:text=details%20for%20nerds-,Do%20all%20my%20predictions%20on%20a%20question%20count%20toward%20my%20score%3F,-Yes.%20Metaculus%20uses ([0.7], True, 48.542), ([0.4, 0.6], True, -32.19), ], ) -def test_binary_baseline_examples(forecast: list[float], resolution: bool, expected: float): - score = calculate_spot_baseline_score( +def test_binary_baseline_examples( + forecast: list[float], resolution: bool, expected: float +): + score = calculate_baseline_score( forecast=forecast, resolution=resolution, ) @@ -228,14 +235,16 @@ def test_numeric_baseline_when_perfect_forecast(): index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio - forecast = generate_cdf_with_forecast_at_index(correct_index, 0.59) + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.999) # As of May 3, 2025, 0.59 is max difference between 2 points on a cdf - score = calculate_spot_baseline_score( + score = calculate_baseline_score( forecast=forecast, resolution=correct_answer, range_min=0, range_max=range_max, + open_upper_bound=False, + open_lower_bound=False, ) assert score == pytest.approx(183) @@ -248,7 +257,7 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): range_max = length_of_cdf * index_to_answer_ratio forecast = generate_cdf_with_forecast_at_index(correct_index, 0.001) - score = calculate_spot_baseline_score( + score = calculate_baseline_score( forecast=forecast, resolution=correct_answer, range_min=0, @@ -264,10 +273,12 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): (0.001, 8, -232.19), ], ) -def test_multiple_choice_examples(forecast_for_answer_a: float, num_total_forecasts: int, expected: float): +def test_multiple_choice_examples( + forecast_for_answer_a: float, num_total_forecasts: int, expected: float +): num_other_forecasts = num_total_forecasts - 1 other_forecasts = (1 - forecast_for_answer_a) / num_other_forecasts - score = calculate_spot_baseline_score( + score = calculate_baseline_score( forecast=[forecast_for_answer_a] + [other_forecasts] * num_other_forecasts, resolution="A", options=["A"] + [f"B{i}" for i in range(num_other_forecasts)], @@ -275,7 +286,6 @@ def test_multiple_choice_examples(forecast_for_answer_a: float, num_total_foreca assert score == pytest.approx(expected, abs=1e-2) - @pytest.mark.parametrize( "forecast_closer,forecast_further,resolution,options,range_min,range_max", [ @@ -322,10 +332,10 @@ def test_baseline_score_better_when_closer( range_min: float | None, range_max: float | None, ): - score_closer = calculate_spot_baseline_score( + score_closer = calculate_baseline_score( forecast_closer, resolution, options, range_min, range_max, 1.0 ) - score_further = calculate_spot_baseline_score( + score_further = calculate_baseline_score( forecast_further, resolution, options, range_min, range_max, 1.0 ) assert score_closer > score_further @@ -339,7 +349,23 @@ def test_baseline_score_better_when_closer( # Multiple Choice ([0.7, 0.2, 0.1], "A", ["A", "B", "C"], None, None, 0.5), # Numeric - ([0.1] * 50 + [0.9] * 149, 0.5, None, 0.0, 1.0, 3.0), + ( + generate_cdf( + [ + Percentile(value=0.1, probability_below=0.1), + Percentile(value=0.9, probability_below=0.9), + ], + lower_bound=0.0, + upper_bound=1.0, + open_lower_bound=False, + open_upper_bound=False, + ), + 0.5, + None, + 0.0, + 1.0, + 3.0, + ), ], ) def test_baseline_score_weighted( @@ -350,10 +376,10 @@ def test_baseline_score_weighted( range_max: float | None, question_weight: float, ): - score_unweighted = calculate_spot_baseline_score( + score_unweighted = calculate_baseline_score( forecast, resolution, options, range_min, range_max, 1.0 ) - score_weighted = calculate_spot_baseline_score( + score_weighted = calculate_baseline_score( forecast, resolution, options, range_min, range_max, question_weight ) assert abs(score_weighted - score_unweighted * question_weight) < 1e-8 @@ -431,8 +457,8 @@ def test_baseline_score_weighted( [ generate_cdf( # Best CDF [ - Percentile(value=100, probability_below=0.1), - Percentile(value=120, probability_below=0.9), + Percentile(value=110, probability_below=0.1), + Percentile(value=130, probability_below=0.9), ], lower_bound=0, upper_bound=100, @@ -441,8 +467,8 @@ def test_baseline_score_weighted( ), generate_cdf( [ - Percentile(value=100, probability_below=0.1), - Percentile(value=120, probability_below=0.9), + Percentile(value=90, probability_below=0.1), + Percentile(value=140, probability_below=0.9), ], lower_bound=0, upper_bound=100, @@ -451,13 +477,13 @@ def test_baseline_score_weighted( ), generate_cdf( # worst CDF [ - Percentile(value=100, probability_below=0.1), - Percentile(value=120, probability_below=0.9), + Percentile(value=30, probability_below=0.1), + Percentile(value=110, probability_below=0.9), ], lower_bound=0, upper_bound=100, open_lower_bound=False, - open_upper_bound=False, # No upper bound = no probability mass at upper bound + open_upper_bound=True, # No upper bound = no probability mass at upper bound ), ], 120, @@ -475,7 +501,7 @@ def test_better_forecast_means_better_peer_score( range_max: float | None, ): scores = [ - calculate_spot_peer_score( + calculate_peer_score( forecast, [f for i, f in enumerate(forecasts) if i != idx], resolution, @@ -512,7 +538,7 @@ def test_peer_score_zero_when_all_same( ): forecasts = [forecast for _ in range(5)] scores = [ - calculate_spot_peer_score( + calculate_peer_score( f, [f2 for i2, f2 in enumerate(forecasts) if i2 != i], resolution, @@ -589,7 +615,7 @@ def test_peer_score_average_zero( range_max: float | None, ): scores = [ - calculate_spot_peer_score( + calculate_peer_score( forecast, [f for i, f in enumerate(forecasts) if i != idx], resolution, @@ -641,10 +667,10 @@ def test_peer_score_weighted( ): for idx, forecast in enumerate(forecasts): other_forecasts = [f for i, f in enumerate(forecasts) if i != idx] - score_unweighted = calculate_spot_peer_score( + score_unweighted = calculate_peer_score( forecast, other_forecasts, resolution, options, range_min, range_max, 1.0 ) - score_weighted = calculate_spot_peer_score( + score_weighted = calculate_peer_score( forecast, other_forecasts, resolution, options, range_min, range_max, weight ) assert score_weighted == pytest.approx(score_unweighted * weight) From ea46599b881cae8a500c79fc83e322d4788021c7 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 04:51:40 -0600 Subject: [PATCH 14/26] Fixed some dataframe row to scoring parameter conversion --- AI_BENCHMARKING_ANALYSIS.ipynb | 707 +++++++++++++-------------------- functions.py | 49 ++- refactored_notebook/scoring.py | 2 +- tests/test_scoring.py | 4 +- 4 files changed, 324 insertions(+), 438 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index f98ede4..a114c83 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -59,7 +59,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1495376/643149966.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_1914202/3462343738.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] } @@ -96,7 +96,7 @@ "df_pro_forecasts = pd.read_csv('https://data.heroku.com/dataclips/roxytxphqvznkgbygmfgzymjtfxx.csv')\n", "df_pro_questions = df_pro_forecasts.rename(columns={'question_id': 'pro_question_id', 'question_title': 'title'})\n", "\n", - "if False: # Temporary\n", + "if False: # Temporary - Only keep Binary\n", " df_bot_questions = df_bot_questions[df_bot_questions['resolution'].isin(['yes', 'no'])]\n", " df_bot_forecasts = df_bot_forecasts[df_bot_forecasts['resolution'].isin(['yes', 'no'])]\n", " df_bot_scores = df_bot_scores[df_bot_scores['resolution'].isin(['yes', 'no'])]\n", @@ -104,8 +104,8 @@ " df_pro_forecasts = df_pro_forecasts[df_pro_forecasts['resolution'].isin(['yes', 'no'])]\n", " df_pro_scores = df_pro_scores[df_pro_scores['resolution'].isin(['yes', 'no'])]\n", "\n", - "df_pro_resolved_questions = df_pro_questions[['pro_question_id', 'title', 'resolution', 'scheduled_close_time', 'actual_close_time', 'question_weight', 'type', 'options', 'range_min', 'range_max']]\n", - "df_bot_resolved_questions = df_bot_questions[['bot_question_id', 'title', 'resolution', 'scheduled_close_time', 'actual_close_time', 'question_weight', 'type', 'options', 'range_min', 'range_max']]\n", + "df_pro_resolved_questions = df_pro_questions[['pro_question_id', 'title', 'resolution', 'scheduled_close_time', 'actual_close_time', 'question_weight', 'type', 'options', 'range_min', 'range_max', 'open_upper_bound', 'open_lower_bound']]\n", + "df_bot_resolved_questions = df_bot_questions[['bot_question_id', 'title', 'resolution', 'scheduled_close_time', 'actual_close_time', 'question_weight', 'type', 'options', 'range_min', 'range_max', 'open_upper_bound', 'open_lower_bound']]\n", "\n", "df_pro_bot_resolved_questions = pd.merge(\n", " df_bot_resolved_questions,\n", @@ -198,147 +198,6 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [ - "# @title Relationships between Bot Questions, create df_bot_question_related_weights (FOR Q3 ONLY)\n", - "if 25871 in df_pro_bot_resolved_questions['bot_question_id'].values:\n", - " \"\"\"\n", - " Relationships between questions are entered as tuples. These relationships\n", - " will be used to perform logical consistency checks.\n", - "\n", - " Weights are assigned to questions based on relationships. This is a way to\n", - " deal with correlations between questions.\n", - " \"\"\"\n", - "\n", - " # Scope sensitity list of tuples where the first entry should equal the sum of the others\n", - " bot_scope_questions = [\n", - " (26019, 26017, 26018), # Starship launches\n", - " (26098, 26096, 26097), # SENSEX\n", - " (26159, 26158, 26157), # Geomagnetic storm July 28\n", - " (26194, 26195, 26196), # measles cases\n", - " (26006, 26005, 26004), # Trump lead over Biden\n", - " (26642, 26643, 26644), # spanish wikipedia\n", - " (26700, 26701, 26702), # market cap cryptocurrencies\n", - " (27261, 27262, 27263), # Geomagnetic storm Sept 11\n", - " ]\n", - "\n", - " # Sum of each tuple should logically equal 1\n", - " bot_sum_to_1_questions = [\n", - " (25952, 25953, 25954), # French PM party July 30\n", - " (25957, 25958, 25959), # Tour de France winner\n", - " (26570, 26571, 26572, 26573), # Warhammer\n", - " (26574, 26575, 26576, 26577), # H5 cases in US\n", - " (26671, 26670, 26669), # DOES NOT SUM TO EXACTLY 1 PM France Aug 31\n", - " (27748, 27747, 27746, 27749), # Speed Chess\n", - " (27488, 27489, 27490, 27491, 27492, 27493), # August CPI\n", - " (27932, 27933, 27934, 27935), # Chinese youth unemployment\n", - " (27484, 27485, 27486, 27487), # Fed rate cut Sept meeting\n", - " (28045, 28044, 28043, 28042), # Afd vote share\n", - " (28038, 28039, 28040, 28041), # Major Atlantic hurricanes\n", - " (26776, 26777, 26778, 26779), # Seattle-Tacoma-Bellevu Air Quality\n", - " ]\n", - "\n", - " # parent, child, if_yes, if_no\n", - " bot_conditional_pair = [\n", - " (26917, 26918, 26919, 26920) # israel lebanon conflict\n", - " ]\n", - "\n", - " # CDFs - Logically the probability of each successive question must not decrease\n", - " bot_increasing_questions = [\n", - " (26981, 26982, 26983, 26984, 26985, 26986), # aircraft ADIZ\n", - " (26977, 26978, 26979, 26980), # hurricane energy\n", - " (27548, 27547, 27546, 27545), # mpox CDC risk level\n", - " (28306, 28305, 28304, 28303, 28302), # Gas prices in US Sept 30\n", - " ]\n", - "\n", - " bot_repeated_questions = [\n", - " (26646, 26021), # mens 100m dash record\n", - " (26555, 27021), # USA gold silver\n", - " (26210, 26917), # israel invade lebanon\n", - " (26781, 26304), # ruto\n", - " (26100, 27136), # rfk drop out\n", - " (25956, 27158), # democrat brokered convention\n", - " (26102, 27022), # astronauts NOT EXACT REPEAT\n", - " (26022, 27085), # arrest warrants NOT EXACT REPEAT\n", - " (26235, 27281), # Buffett Indicator\n", - " (26390, 27789), # Bubble Magnificent 7\n", - " (26024, 27161), # QB Bo Nix starting for Broncos\n", - " (26302, 27282), # riots\n", - " (25955, 27157), # armed forces death US, China, Japan\n", - " (26958, 27640), # Youtube banned in Russia\n", - " (25936, 27141), # Crimean bridge attack\n", - " ]\n", - "\n", - " bot_similar_questions = [\n", - " (26915, 26916), # harris favorability\n", - " (26913, 26914), # trump favorability\n", - " (26193, 27733), # debate on Sept 10\n", - " (27886, 27968), # Taylor Swift awards\n", - " (27723, 27637), # Best Rock VMAs\n", - " (27583, 27582, 27584, 27602, 27603, 27604), # mpox Zambia, US, Angola, Russia, Japan, Mexico\n", - " (26306, 26838), # Richest people 250th > $10.2, 500th > 6.2\n", - " (27887, 27969), # Emmys Outstanding Limited or Anthology Series\n", - " (28206, 28207, 28208, 28209, 28210), # LMSYS leaderboard\n", - " (28154, 28336), # Nigeria Edo gubernatorial election\n", - " (26407, 27897), # Second Russian mobilization wave\n", - " (27539, 26215), # Nuclear weapons used\n", - " (27606, 27607, 27608, 27609, 27610), # Ukranian forces capture\n", - " (26387, 27788), # Will Tesla increase deliveries in Q3 2024\n", - " (26821, 26959), # VP debate\n", - " (26212, 26213, 26214), # number of dairy cow herds with H5N1\n", - " (26639, 26640, 26641) # Presidential debate 0, 1, or 2+\n", - " ]\n", - "\n", - " ####### CREATE QUESTION WEIGHTS #########\n", - "\n", - " # Combine both lists of tuples\n", - " all_questions = bot_scope_questions + bot_sum_to_1_questions + bot_increasing_questions + bot_similar_questions + bot_conditional_pair\n", - "\n", - " # Create an empty list to store the data\n", - " data = []\n", - "\n", - " # Process each tuple\n", - " for tuple_questions in all_questions:\n", - " # Calculate the weight for each question in the tuple\n", - " weight = np.log2(1 + len(tuple_questions))/(1 + len(tuple_questions))\n", - "\n", - " # Add each question and its weight to the data list\n", - " for question_id in tuple_questions:\n", - " data.append({'bot_question_id': question_id, 'question_weight': weight})\n", - "\n", - " # Process each tuple\n", - " for tuple_questions in bot_repeated_questions:\n", - " # 1st iteration has weight 1, 2nd has weight 1/2, 3rd weight 1/3....\n", - " count = 1\n", - "\n", - " # Add each question and its weight to the data list\n", - " for question_id in tuple_questions:\n", - " data.append({'bot_question_id': question_id, 'question_weight': 1/count})\n", - " count += 1\n", - "\n", - " # Create the DataFrame\n", - " df = pd.DataFrame(data)\n", - "\n", - " # Sort the DataFrame by bot_question_id for better readability\n", - " df_bot_question_related_weights = df.sort_values('bot_question_id').reset_index(drop=True)\n", - "\n", - "# if df_bot_question_related_weights is defined, replace the question weights in df_pro_bot_resolved_questions\n", - "if 'df_bot_question_related_weights' in locals():\n", - " df_pro_bot_resolved_questions = pd.merge(\n", - " df_pro_bot_resolved_questions,\n", - " df_bot_question_related_weights,\n", - " on='bot_question_id',\n", - " how='left'\n", - " )\n", - "\n", - " df_pro_bot_resolved_questions['question_weight'] = df_pro_bot_resolved_questions['question_weight_y'].combine_first(df_pro_bot_resolved_questions['question_weight_x'])\n", - " df_pro_bot_resolved_questions.drop(['question_weight_x', 'question_weight_y'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -355,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -363,11 +222,12 @@ "text/plain": [ "Index(['bot_question_id', 'title', 'resolution', 'scheduled_close_time',\n", " 'actual_close_time', 'type', 'options', 'range_min', 'range_max',\n", - " 'pro_question_id', 'question_weight'],\n", + " 'open_upper_bound', 'open_lower_bound', 'pro_question_id',\n", + " 'question_weight'],\n", " dtype='object')" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -378,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -413,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -428,12 +288,14 @@ "options object\n", "range_min float64\n", "range_max float64\n", + "open_upper_bound object\n", + "open_lower_bound object\n", "pro_question_id Int64\n", "question_weight float64\n", "dtype: object" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -444,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -455,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -476,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -508,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -523,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -721,7 +583,7 @@ "6 False " ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -732,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -755,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -775,7 +637,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -787,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -832,11 +694,11 @@ " \n", " 15\n", " bot_median\n", - " 9.550728\n", - " 3610.366154\n", + " 8.839589\n", + " 3341.541338\n", " 409\n", - " 6.843423\n", - " 1.377206\n", + " 6.106284\n", + " 1.390432\n", " \n", " \n", " 4\n", @@ -872,14 +734,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 9.550728 3610.366154 409 6.843423 \n", + "15 bot_median 8.839589 3341.541338 409 6.106284 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.377206 \n", + "15 1.390432 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -996,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "id": "BmAFBHIhK77X" }, @@ -1045,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1469,7 +1331,7 @@ " np.int64(35705)}" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1490,7 +1352,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1529,20 +1391,20 @@ " \n", " \n", " 1\n", - " bot_median\n", - " 9303.299412\n", - " \n", - " \n", - " 2\n", " metac-o1\n", " 8861.959039\n", " \n", " \n", - " 3\n", + " 2\n", " metac-o1-preview\n", " 8849.559824\n", " \n", " \n", + " 3\n", + " bot_median\n", + " 8784.525527\n", + " \n", + " \n", " 4\n", " acm_bot\n", " 7605.922314\n", @@ -1559,9 +1421,9 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 bot_median 9303.299412\n", - "2 metac-o1 8861.959039\n", - "3 metac-o1-preview 8849.559824\n", + "1 metac-o1 8861.959039\n", + "2 metac-o1-preview 8849.559824\n", + "3 bot_median 8784.525527\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1667,7 +1529,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1686,7 +1548,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1731,7 +1593,7 @@ " \n", " 2\n", " bot_median\n", - " 3821.107768\n", + " 3555.373483\n", " \n", " \n", " 3\n", @@ -1966,7 +1828,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3821.107768\n", + "2 bot_median 3555.373483\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2014,7 +1876,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2056,7 +1918,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -2075,7 +1937,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -2084,7 +1946,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2105,7 +1967,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2303,7 +2165,7 @@ "6 False " ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2314,7 +2176,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2347,10 +2209,10 @@ " question_weight\n", " type\n", " options\n", - " pro_median\n", - " 4Shadower\n", - " Bot_Pepa\n", - " CatrachoCaster\n", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " ...\n", " metac-o1\n", " metac-o1-preview\n", @@ -2373,14 +2235,14 @@ " 1.0\n", " multiple_choice\n", " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", - " [0.001,0.62,0.35,0.019,0.01]\n", - " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.02,0.7,0.2,0.06,0.02]\n", - " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", + " [0.5,0.3,0.15,0.04,0.01]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.25,0.3,0.25,0.15,0.05]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2397,14 +2259,14 @@ " 1.0\n", " numeric\n", " None\n", - " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 60.0\n", + " 100.0\n", + " True\n", + " True\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", - " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", @@ -2421,12 +2283,12 @@ " 1.0\n", " binary\n", " None\n", - " 0.013\n", - " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.15\n", + " 0.1\n", " 0.1\n", " 0.15\n", " NaN\n", @@ -2445,13 +2307,13 @@ " 1.0\n", " multiple_choice\n", " [\"0-4\",\"5-9\",\">9\"]\n", - " [0.16,0.44,0.4]\n", " NaN\n", " NaN\n", - " [0.16,0.47,0.37]\n", + " None\n", + " None\n", " ...\n", - " [0.29,0.56,0.14999999999999997]\n", - " [0.2,0.6,0.2]\n", + " [0.25,0.6,0.15]\n", + " [0.37,0.49000000000000005,0.13999999999999999]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -2469,14 +2331,14 @@ " 1.0\n", " numeric\n", " None\n", - " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 400.0\n", + " False\n", + " False\n", " ...\n", " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,...\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", @@ -2487,7 +2349,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -2498,40 +2360,40 @@ "3 31280 31274 5-9 1.0 \n", "4 31281 31275 119.2 1.0 \n", "\n", - " type options \\\n", - "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] \n", - "1 numeric None \n", - "2 binary None \n", - "3 multiple_choice [\"0-4\",\"5-9\",\">9\"] \n", - "4 numeric None \n", + " type options range_min range_max \\\n", + "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "1 numeric None 60.0 100.0 \n", + "2 binary None NaN NaN \n", + "3 multiple_choice [\"0-4\",\"5-9\",\">9\"] NaN NaN \n", + "4 numeric None 0.0 400.0 \n", "\n", - " pro_median 4Shadower Bot_Pepa \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] NaN NaN \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... NaN NaN \n", - "2 0.013 NaN NaN \n", - "3 [0.16,0.44,0.4] NaN NaN \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... NaN NaN \n", + " open_upper_bound open_lower_bound ... \\\n", + "0 False False ... \n", + "1 True True ... \n", + "2 False False ... \n", + "3 None None ... \n", + "4 False False ... \n", "\n", - " CatrachoCaster ... metac-o1 \\\n", - "0 NaN ... [0.4,0.35,0.2,0.04,0.01] \n", - "1 NaN ... [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", - "2 NaN ... 0.15 \n", - "3 [0.16,0.47,0.37] ... [0.29,0.56,0.14999999999999997] \n", - "4 NaN ... [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + " metac-o1 \\\n", + "0 [0.5,0.3,0.15,0.04,0.01] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.06,0.02] \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", + "0 [0.25,0.3,0.25,0.15,0.05] NaN \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... NaN \n", "2 0.15 NaN \n", "3 [0.15,0.6,0.25] NaN \n", - "4 [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,... NaN \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", "0 [0.009900990099009901,0.39603960396039606,0.44... \n", @@ -2554,7 +2416,7 @@ "3 [0.116,0.42,0.464] NaN \n", "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0... NaN \n", "\n", - "[5 rows x 53 columns]" + "[5 rows x 57 columns]" ] }, "metadata": {}, @@ -2587,10 +2449,10 @@ " question_weight\n", " type\n", " options\n", - " pro_median\n", - " 4Shadower\n", - " Bot_Pepa\n", - " CatrachoCaster\n", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " ...\n", " metac-o1\n", " metac-o1-preview\n", @@ -2613,13 +2475,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.95\n", - " 0.9\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", " 0.95\n", - " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.95\n", @@ -2637,13 +2499,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.05\n", - " 0.95\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.3\n", - " 0.85\n", + " 0.35\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2661,13 +2523,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.97\n", - " 0.85\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.8\n", - " 0.95\n", + " 0.85\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2685,12 +2547,12 @@ " 0.85\n", " binary\n", " None\n", - " 0.666\n", - " 0.8\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2709,14 +2571,14 @@ " 0.85\n", " binary\n", " None\n", - " 0.03\n", - " 0.3\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.07\n", - " 0.1\n", - " 0.03\n", + " 0.05\n", + " 0.05\n", + " 0.01\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2727,7 +2589,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -2738,28 +2600,28 @@ "97 35386 35364 no 0.85 binary \n", "98 35387 35367 no 0.85 binary \n", "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.3 \n", - "96 None 0.97 0.85 NaN NaN ... 0.8 \n", - "97 None 0.666 0.8 NaN NaN ... 0.85 \n", - "98 None 0.03 0.3 NaN NaN ... 0.07 \n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "94 None NaN NaN False False ... \n", + "95 None NaN NaN False False ... \n", + "96 None NaN NaN False False ... \n", + "97 None NaN NaN False False ... \n", + "98 None NaN NaN False False ... \n", "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", - "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.85 NaN NaN 0.15 NaN NaN \n", - "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.1 0.03 NaN 0.15 0.05 NaN \n", + " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.35 0.9 NaN NaN 0.15 NaN \n", + "96 0.85 0.9 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.05 0.05 0.01 NaN 0.15 0.05 \n", "\n", - " swingswish twsummerbot wunderplumb \n", - "94 0.9 0.762 0.9 \n", - "95 0.1 0.126 0.95 \n", - "96 0.85 0.828 0.85 \n", - "97 0.7 0.132 0.3 \n", - "98 0.2 0.27 0.2 \n", + " pianobot swingswish twsummerbot wunderplumb \n", + "94 NaN 0.9 0.762 0.9 \n", + "95 NaN 0.1 0.126 0.95 \n", + "96 NaN 0.85 0.828 0.85 \n", + "97 NaN 0.7 0.132 0.3 \n", + "98 NaN 0.2 0.27 0.2 \n", "\n", - "[5 rows x 53 columns]" + "[5 rows x 57 columns]" ] }, "metadata": {}, @@ -2797,7 +2659,11 @@ "df_bot_forecasts = df_bot_forecasts.reset_index()\n", "\n", "# One row per question, with pro_question_id and bot_question_id and resolution\n", - "df_pro_bot_resolved_questions_first = df_pro_bot_resolved_questions.groupby(['pro_question_id', 'bot_question_id']).first().reset_index()[['pro_question_id', 'bot_question_id', 'resolution', 'question_weight', 'type', 'options']]\n", + "df_pro_bot_resolved_questions_first = df_pro_bot_resolved_questions.groupby(\n", + " ['pro_question_id', 'bot_question_id']\n", + " ).first().reset_index()[\n", + " ['pro_question_id', 'bot_question_id', 'resolution', 'question_weight', 'type', 'options', 'range_min', 'range_max', 'open_upper_bound', 'open_lower_bound']\n", + " ]\n", "\n", "df2 = pd.merge(\n", " df_pro_bot_resolved_questions_first,\n", @@ -2818,14 +2684,15 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['pro_question_id', 'bot_question_id', 'resolution', 'question_weight',\n", - " 'type', 'options', 'pro_median', '4Shadower', 'Bot_Pepa',\n", + " 'type', 'options', 'range_min', 'range_max', 'open_upper_bound',\n", + " 'open_lower_bound', 'pro_median', '4Shadower', 'Bot_Pepa',\n", " 'CatrachoCaster', 'CumulativeBot', 'GreeneiBot2', 'Grizeu_Bot',\n", " 'InstitutPelFutur', 'KevinTestBot', 'MWG', 'NextWorldLab',\n", " 'ProfessorSP', 'RPM_bot', 'SynapseSeer', 'VeritasAI', 'X_bot',\n", @@ -2840,7 +2707,7 @@ " dtype='object')" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2851,7 +2718,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2861,7 +2728,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2876,7 +2743,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -2888,7 +2755,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -2897,7 +2764,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -2927,10 +2794,10 @@ " question_weight\n", " type\n", " options\n", - " pro_median\n", - " 4Shadower\n", - " Bot_Pepa\n", - " CatrachoCaster\n", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " ...\n", " metac-o1\n", " metac-o1-preview\n", @@ -2953,14 +2820,14 @@ " 1.0\n", " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", - " [0.001,0.62,0.35,0.019,0.01]\n", - " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.02,0.7,0.2,0.06,0.02]\n", - " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", + " [0.5,0.3,0.15,0.04,0.01]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.25,0.3,0.25,0.15,0.05]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2977,14 +2844,14 @@ " 1.0\n", " numeric\n", " None\n", - " [0.0013749738, 0.0014499743, 0.001526641, 0.0016050848, 0.0016854241, 0.0017677851, 0.0018523023, 0.0019391193, 0.002028389, 0.0021202748, 0.0022149507, 0.0023126022, 0.0024134273, 0.002517637, 0.0026254563, 0.0027371251, 0.0028528992, 0.0029730514, 0.0030978724, 0.0032276722, 0.0033627814, 0.0035035523, 0.0036503604, 0.003803606, 0.0039637158, 0.0041311448, 0.0043063775, 0.0044899306, 0.0046823546, 0.0048842361, 0.0050962001, 0.0053189126, 0.0055530831, 0.0057994673, 0.0060588703, 0.0063321494, 0.0066202178, 0.0069240477, 0.0072446744, 0.0075831999, 0.0079407973, 0.0083187152, 0.0087182821, 0.0091409116, 0.0095881072, 0.0100614684, 0.0105626958, 0.0110935973, 0.0116560946, 0.0122522299, 0.0128841727, 0.0135542271, 0.0142648397, 0.0150186074, 0.0158182855, 0.0166667968, 0.0175672405, 0.0185229009, 0.0195372578, 0.0206139958, 0.0217570149, 0.0229704403, 0.0242586335, 0.0256262025, 0.027078013, 0.0286191989, 0.0302551733, 0.0319916387, 0.0338345977, 0.0357903626, 0.0378655653, 0.0400671652, 0.042402458, 0.044879082, 0.0475050233, 0.0502886206, 0.0532385667, 0.0563639085, 0.0596740451, 0.0631787221, 0.0668880234, 0.0708123591, 0.0749624495, 0.0793493045, 0.0839841985, 0.0888786389, 0.0940443298, 0.0994931287, 0.1052369965, 0.1112879404, 0.1176579487, 0.1243589183, 0.1314025737, 0.1388003774, 0.1465634324, 0.1547023763, 0.1632272673, 0.1721474631, 0.1814714929, 0.1912069234, ...]\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 60.0\n", + " 100.0\n", + " True\n", + " True\n", " ...\n", - " [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...]\n", - " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", - " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...]\n", + " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", + " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", + " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...]\n", " NaN\n", " [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...]\n", " [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...]\n", @@ -3001,12 +2868,12 @@ " 1.0\n", " binary\n", " None\n", - " 0.013\n", - " NaN\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.15\n", + " 0.1\n", " 0.1\n", " 0.15\n", " NaN\n", @@ -3025,13 +2892,13 @@ " 1.0\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", - " [0.16,0.44,0.4]\n", " NaN\n", " NaN\n", - " [0.16,0.47,0.37]\n", + " None\n", + " None\n", " ...\n", - " [0.29,0.56,0.14999999999999997]\n", - " [0.2,0.6,0.2]\n", + " [0.25,0.6,0.15]\n", + " [0.37,0.49000000000000005,0.13999999999999999]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -3049,14 +2916,14 @@ " 1.0\n", " numeric\n", " None\n", - " [0.0, 0.0005044914, 0.0010323506, 0.0015847475, 0.0021629075, 0.0027681135, 0.003401708, 0.0040650959, 0.0047597462, 0.0054871954, 0.0062490491, 0.0070469847, 0.0078827545, 0.0087581873, 0.0096751916, 0.0106357578, 0.0116419606, 0.0126959618, 0.0138000124, 0.0149564548, 0.0161677252, 0.0174363555, 0.0187649755, 0.0201563143, 0.0216132019, 0.0231385708, 0.0247354566, 0.0264069992, 0.0281564425, 0.029987135, 0.0319025289, 0.0339061792, 0.0360017424, 0.0381929741, 0.0404837261, 0.0428779433, 0.045379659, 0.0479929901, 0.0507221307, 0.0535713452, 0.0565449605, 0.0596473565, 0.0628829558, 0.0662562123, 0.0697715985, 0.073433591, 0.0772466553, 0.0812152286, 0.0853437018, 0.0896363995, 0.0940975586, 0.0987313059, 0.1035416339, 0.1085323748, 0.1137071746, 0.1190694637, 0.1246224286, 0.1303689808, 0.1363117257, 0.1424529302, 0.1487944895, 0.1553378942, 0.1620841958, 0.1690339734, 0.1761872995, 0.1835437065, 0.191102154, 0.1988609968, 0.2068179538, 0.2149700792, 0.2233137345, 0.2318445639, 0.2405574718, 0.2494466036, 0.2585053305, 0.2677262387, 0.2771011237, 0.2866209903, 0.2962760595, 0.3060557827, 0.3159488636, 0.3259432898, 0.3360263733, 0.3461848008, 0.356404695, 0.3666716851, 0.3769709877, 0.3872880285, 0.3976129907, 0.4079386213, 0.4182575841, 0.4285624679, 0.4388454621, 0.4490984582, 0.459313496, 0.4694828597, 0.4795991502, 0.4896553473, 0.49964486, 0.5095615629, ...]\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 400.0\n", + " False\n", + " False\n", " ...\n", - " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...]\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...]\n", - " [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...]\n", + " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...]\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...]\n", + " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...]\n", " NaN\n", " [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", " [0.0, 0.0001141583, 0.0002446967, 0.0003862688, 0.0005272579, 0.0006650709, 0.0008243437, 0.0011074433, 0.0016696544, 0.0025699094, 0.0037138357, 0.0049708626, 0.0062610152, 0.0075426566, 0.0089765864, 0.0111726822, 0.0147311078, 0.0195212559, 0.0249547717, 0.0306181288, 0.0363105138, 0.0419407763, 0.0476011969, 0.053516341, 0.0598014349, 0.0663689162, 0.0730761187, 0.0798334547, 0.0865904866, 0.0933196582, 0.1000172031, 0.1066924089, 0.1133554776, 0.1200140176, 0.1266729489, 0.1333343989, 0.1399984689, 0.1466644317, 0.1533314439, 0.1599988203, 0.1666661444, 0.1733332523, 0.1800001372, 0.1866668598, 0.1933334943, 0.2000000995, 0.2066667101, 0.2133333393, 0.2199999878, 0.22666665, 0.2333333196, 0.2399999916, 0.2466666631, 0.2533333329, 0.2600000011, 0.2666666681, 0.2733333345, 0.2800000007, 0.286666667, 0.2933333334, 0.2999999999, 0.3066666665, 0.3133333332, 0.3199999999, 0.3266666666, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3934628939, 0.400837331, 0.40925763, 0.4186848364, 0.428718413, 0.4390353607, 0.4494419812, 0.4597974687, 0.4700329298, 0.4801500685, 0.4901790777, 0.500153105, 0.5101028922, 0.5200515519, 0.5300114112, 0.5398722838, 0.5492279015, 0.5576212737, 0.5650210292, 0.571743695, 0.5780856137, 0.5842571713, 0.5904328096, 0.5967209586, 0.603152213, 0.6097133168, ...]\n", @@ -3067,7 +2934,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -3078,47 +2945,40 @@ "3 31280 31274 5-9 1.0 \n", "4 31281 31275 119.2 1.0 \n", "\n", - " type options \\\n", - "0 multiple_choice [0, 1, 2-3, 4-6, >6] \n", - "1 numeric None \n", - "2 binary None \n", - "3 multiple_choice [0-4, 5-9, >9] \n", - "4 numeric None \n", - "\n", - " pro_median \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] \n", - "1 [0.0013749738, 0.0014499743, 0.001526641, 0.0016050848, 0.0016854241, 0.0017677851, 0.0018523023, 0.0019391193, 0.002028389, 0.0021202748, 0.0022149507, 0.0023126022, 0.0024134273, 0.002517637, 0.0026254563, 0.0027371251, 0.0028528992, 0.0029730514, 0.0030978724, 0.0032276722, 0.0033627814, 0.0035035523, 0.0036503604, 0.003803606, 0.0039637158, 0.0041311448, 0.0043063775, 0.0044899306, 0.0046823546, 0.0048842361, 0.0050962001, 0.0053189126, 0.0055530831, 0.0057994673, 0.0060588703, 0.0063321494, 0.0066202178, 0.0069240477, 0.0072446744, 0.0075831999, 0.0079407973, 0.0083187152, 0.0087182821, 0.0091409116, 0.0095881072, 0.0100614684, 0.0105626958, 0.0110935973, 0.0116560946, 0.0122522299, 0.0128841727, 0.0135542271, 0.0142648397, 0.0150186074, 0.0158182855, 0.0166667968, 0.0175672405, 0.0185229009, 0.0195372578, 0.0206139958, 0.0217570149, 0.0229704403, 0.0242586335, 0.0256262025, 0.027078013, 0.0286191989, 0.0302551733, 0.0319916387, 0.0338345977, 0.0357903626, 0.0378655653, 0.0400671652, 0.042402458, 0.044879082, 0.0475050233, 0.0502886206, 0.0532385667, 0.0563639085, 0.0596740451, 0.0631787221, 0.0668880234, 0.0708123591, 0.0749624495, 0.0793493045, 0.0839841985, 0.0888786389, 0.0940443298, 0.0994931287, 0.1052369965, 0.1112879404, 0.1176579487, 0.1243589183, 0.1314025737, 0.1388003774, 0.1465634324, 0.1547023763, 0.1632272673, 0.1721474631, 0.1814714929, 0.1912069234, ...] \n", - "2 0.013 \n", - "3 [0.16,0.44,0.4] \n", - "4 [0.0, 0.0005044914, 0.0010323506, 0.0015847475, 0.0021629075, 0.0027681135, 0.003401708, 0.0040650959, 0.0047597462, 0.0054871954, 0.0062490491, 0.0070469847, 0.0078827545, 0.0087581873, 0.0096751916, 0.0106357578, 0.0116419606, 0.0126959618, 0.0138000124, 0.0149564548, 0.0161677252, 0.0174363555, 0.0187649755, 0.0201563143, 0.0216132019, 0.0231385708, 0.0247354566, 0.0264069992, 0.0281564425, 0.029987135, 0.0319025289, 0.0339061792, 0.0360017424, 0.0381929741, 0.0404837261, 0.0428779433, 0.045379659, 0.0479929901, 0.0507221307, 0.0535713452, 0.0565449605, 0.0596473565, 0.0628829558, 0.0662562123, 0.0697715985, 0.073433591, 0.0772466553, 0.0812152286, 0.0853437018, 0.0896363995, 0.0940975586, 0.0987313059, 0.1035416339, 0.1085323748, 0.1137071746, 0.1190694637, 0.1246224286, 0.1303689808, 0.1363117257, 0.1424529302, 0.1487944895, 0.1553378942, 0.1620841958, 0.1690339734, 0.1761872995, 0.1835437065, 0.191102154, 0.1988609968, 0.2068179538, 0.2149700792, 0.2233137345, 0.2318445639, 0.2405574718, 0.2494466036, 0.2585053305, 0.2677262387, 0.2771011237, 0.2866209903, 0.2962760595, 0.3060557827, 0.3159488636, 0.3259432898, 0.3360263733, 0.3461848008, 0.356404695, 0.3666716851, 0.3769709877, 0.3872880285, 0.3976129907, 0.4079386213, 0.4182575841, 0.4285624679, 0.4388454621, 0.4490984582, 0.459313496, 0.4694828597, 0.4795991502, 0.4896553473, 0.49964486, 0.5095615629, ...] \n", + " type options range_min range_max \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] NaN NaN \n", + "1 numeric None 60.0 100.0 \n", + "2 binary None NaN NaN \n", + "3 multiple_choice [0-4, 5-9, >9] NaN NaN \n", + "4 numeric None 0.0 400.0 \n", "\n", - " 4Shadower Bot_Pepa CatrachoCaster ... \\\n", - "0 NaN NaN NaN ... \n", - "1 NaN NaN NaN ... \n", - "2 NaN NaN NaN ... \n", - "3 NaN NaN [0.16,0.47,0.37] ... \n", - "4 NaN NaN NaN ... \n", + " open_upper_bound open_lower_bound ... \\\n", + "0 False False ... \n", + "1 True True ... \n", + "2 False False ... \n", + "3 None None ... \n", + "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05, 0.0505882353, 0.0511764706, 0.0517647059, 0.0523529412, 0.0529411765, 0.0535294118, 0.0541176471, 0.0547058824, 0.0552941176, 0.0558823529, 0.0564705882, 0.0570588235, 0.0576470588, 0.0582352941, 0.0588235294, 0.0594117647, 0.06, 0.0605882353, 0.0611764706, 0.0617647059, 0.0623529412, 0.0629411765, 0.0635294118, 0.0641176471, 0.0647058824, 0.0652941176, 0.0658823529, 0.0664705882, 0.0670588235, 0.0676470588, 0.0682352941, 0.0688235294, 0.0694117647, 0.07, 0.0705882353, 0.0711764706, 0.0717647059, 0.0723529412, 0.0729411765, 0.0735294118, 0.0741176471, 0.0747058824, 0.0752941176, 0.0758823529, 0.0764705882, 0.0770588235, 0.0776470588, 0.0782352941, 0.0788235294, 0.0794117647, 0.08, 0.0805882353, 0.0811764706, 0.0817647059, 0.0823529412, 0.0829411765, 0.0835294118, 0.0841176471, 0.0847058824, 0.0852941176, 0.0858823529, 0.0864705882, 0.0870588235, 0.0876470588, 0.0882352941, 0.0888235294, 0.0894117647, 0.09, 0.0905882353, 0.0911764706, 0.0917647059, 0.0923529412, 0.0929411765, 0.0935294118, 0.0941176471, 0.0947058824, 0.0952941176, 0.0958823529, 0.0964705882, 0.0970588235, 0.0976470588, 0.0982352941, 0.0988235294, 0.0994117647, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.22, 0.24, 0.26, 0.28, ...] \n", - "2 0.15 \n", - "3 [0.29,0.56,0.14999999999999997] \n", - "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", + " metac-o1 \\\n", + "0 [0.5,0.3,0.15,0.04,0.01] \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", "\n", - " metac-o1-preview \\\n", - "0 [0.02,0.7,0.2,0.06,0.02] \n", - "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, 0.68, 0.688, 0.696, 0.704, 0.712, ...] \n", + " metac-o1-preview \\\n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", + "2 0.1 \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", "\n", - " metac-perplexity \\\n", - "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", - "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, ...] \n", - "2 0.15 \n", - "3 [0.15,0.6,0.25] \n", - "4 [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...] \n", + " metac-perplexity \\\n", + "0 [0.25,0.3,0.25,0.15,0.05] \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...] \n", + "2 0.15 \n", + "3 [0.15,0.6,0.25] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3162,7 +3022,7 @@ "3 NaN \n", "4 NaN \n", "\n", - "[5 rows x 53 columns]" + "[5 rows x 57 columns]" ] }, "metadata": {}, @@ -3195,10 +3055,10 @@ " question_weight\n", " type\n", " options\n", - " pro_median\n", - " 4Shadower\n", - " Bot_Pepa\n", - " CatrachoCaster\n", + " range_min\n", + " range_max\n", + " open_upper_bound\n", + " open_lower_bound\n", " ...\n", " metac-o1\n", " metac-o1-preview\n", @@ -3221,13 +3081,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.95\n", - " 0.9\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", " 0.95\n", - " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.95\n", @@ -3245,13 +3105,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.05\n", - " 0.95\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.3\n", - " 0.85\n", + " 0.35\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3269,13 +3129,13 @@ " 1.00\n", " binary\n", " None\n", - " 0.97\n", - " 0.85\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.8\n", - " 0.95\n", + " 0.85\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3293,12 +3153,12 @@ " 0.85\n", " binary\n", " None\n", - " 0.666\n", - " 0.8\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3317,14 +3177,14 @@ " 0.85\n", " binary\n", " None\n", - " 0.03\n", - " 0.3\n", " NaN\n", " NaN\n", + " False\n", + " False\n", " ...\n", - " 0.07\n", - " 0.1\n", - " 0.03\n", + " 0.05\n", + " 0.05\n", + " 0.01\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3335,7 +3195,7 @@ " \n", " \n", "\n", - "

5 rows × 53 columns

\n", + "

5 rows × 57 columns

\n", "" ], "text/plain": [ @@ -3346,28 +3206,28 @@ "97 35386 35364 no 0.85 binary \n", "98 35387 35367 no 0.85 binary \n", "\n", - " options pro_median 4Shadower Bot_Pepa CatrachoCaster ... metac-o1 \\\n", - "94 None 0.95 0.9 NaN NaN ... 0.95 \n", - "95 None 0.05 0.95 NaN NaN ... 0.3 \n", - "96 None 0.97 0.85 NaN NaN ... 0.8 \n", - "97 None 0.666 0.8 NaN NaN ... 0.85 \n", - "98 None 0.03 0.3 NaN NaN ... 0.07 \n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "94 None NaN NaN False False ... \n", + "95 None NaN NaN False False ... \n", + "96 None NaN NaN False False ... \n", + "97 None NaN NaN False False ... \n", + "98 None NaN NaN False False ... \n", "\n", - " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai pianobot \\\n", - "94 0.95 NaN NaN 0.95 0.95 NaN \n", - "95 0.85 NaN NaN 0.15 NaN NaN \n", - "96 0.95 NaN NaN 0.9 NaN NaN \n", - "97 0.85 0.3 NaN 0.85 0.85 NaN \n", - "98 0.1 0.03 NaN 0.15 0.05 NaN \n", + " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.35 0.9 NaN NaN 0.15 NaN \n", + "96 0.85 0.9 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.05 0.05 0.01 NaN 0.15 0.05 \n", "\n", - " swingswish twsummerbot wunderplumb \n", - "94 0.9 0.762 0.9 \n", - "95 0.1 0.126 0.95 \n", - "96 0.85 0.828 0.85 \n", - "97 0.7 0.132 0.3 \n", - "98 0.2 0.27 0.2 \n", + " pianobot swingswish twsummerbot wunderplumb \n", + "94 NaN 0.9 0.762 0.9 \n", + "95 NaN 0.1 0.126 0.95 \n", + "96 NaN 0.85 0.828 0.85 \n", + "97 NaN 0.7 0.132 0.3 \n", + "98 NaN 0.2 0.27 0.2 \n", "\n", - "[5 rows x 53 columns]" + "[5 rows x 57 columns]" ] }, "metadata": {}, @@ -3418,36 +3278,27 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'Range_min'", + "ename": "AssertionError", + "evalue": "Probability for resolution is nan which is not between 0 and 1", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Range_min'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1275\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1275\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1276\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1277\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1278\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1279\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1280\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1285\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1286\u001b[0m ),\n\u001b[1;32m 1287\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1288\u001b[0m )\n", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1310\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1310\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1318\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1320\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1321\u001b[0m ),\n\u001b[1;32m 1322\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1323\u001b[0m )\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1276\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[1;32m 1275\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m-> 1276\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1277\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1278\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1279\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1280\u001b[0m )\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1285\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1286\u001b[0m ),\n\u001b[1;32m 1287\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1288\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1253\u001b[0m, in \u001b[0;36mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[0;34m(row, col_a, col_b)\u001b[0m\n\u001b[1;32m 1251\u001b[0m resolution \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1252\u001b[0m options \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions_parsed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions_parsed\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m row \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptions\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m-> 1253\u001b[0m range_min \u001b[38;5;241m=\u001b[39m \u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRange_min\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 1254\u001b[0m range_max \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRange_max\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1255\u001b[0m question_weight \u001b[38;5;241m=\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestion_weight\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/series.py:1121\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m 1120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m 1124\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m 1125\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/series.py:1237\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m 1236\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1237\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m 1240\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'Range_min'" + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1311\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[1;32m 1310\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1314\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1315\u001b[0m )\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1318\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1320\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1321\u001b[0m ),\n\u001b[1;32m 1322\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1323\u001b[0m )\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1291\u001b[0m, in \u001b[0;36mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[0;34m(row, col_a, col_b)\u001b[0m\n\u001b[1;32m 1288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m question_weight:\n\u001b[1;32m 1289\u001b[0m question_weight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(question_weight)\n\u001b[0;32m-> 1291\u001b[0m score \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_peer_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1292\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast_a\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast_for_other_users\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mforecast_b\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1294\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1295\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m score\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:18\u001b[0m, in \u001b[0;36mcalculate_peer_score\u001b[0;34m(forecast, forecast_for_other_users, resolution, options, range_min, range_max, question_weight)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalculate_peer_score\u001b[39m(\n\u001b[1;32m 10\u001b[0m forecast: ForecastType,\n\u001b[1;32m 11\u001b[0m forecast_for_other_users: \u001b[38;5;28mlist\u001b[39m[ForecastType],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 16\u001b[0m question_weight: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m,\n\u001b[1;32m 17\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m---> 18\u001b[0m forecast_for_resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_determine_probability_for_resolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m other_user_forecasts \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 22\u001b[0m _determine_probability_for_resolution(\n\u001b[1;32m 23\u001b[0m forecast, resolution, options, range_min, range_max\n\u001b[1;32m 24\u001b[0m )\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m forecast \u001b[38;5;129;01min\u001b[39;00m forecast_for_other_users\n\u001b[1;32m 26\u001b[0m ]\n\u001b[1;32m 28\u001b[0m geometric_mean \u001b[38;5;241m=\u001b[39m gmean(other_user_forecasts)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:210\u001b[0m, in \u001b[0;36m_determine_probability_for_resolution\u001b[0;34m(forecast, resolution, options, range_min, range_max)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown question type\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[0;32m--> 210\u001b[0m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m prob_for_resolution \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 211\u001b[0m ), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProbability for resolution is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprob_for_resolution\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m which is not between 0 and 1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m prob_for_resolution\n", + "\u001b[0;31mAssertionError\u001b[0m: Probability for resolution is nan which is not between 0 and 1" ] } ], diff --git a/functions.py b/functions.py index dbb8331..806cca9 100644 --- a/functions.py +++ b/functions.py @@ -1243,16 +1243,51 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] -def calculate_weighted_h2h_score_between_two_forecast_columns(row, col_a, col_b): +def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, col_a: str, col_b: str): forecast_a = row[ col_a - ] # If string, I may need to do: [float(x) for x in bot_pmf_raw.strip('[]').split(',')] + ] + if isinstance(forecast_a, str): + forecast_a = [float(x) for x in forecast_a.strip('[]').split(',')] + forecast_b = row[col_b] - resolution = row["resolution"] + if isinstance(forecast_b, str): + forecast_b = [float(x) for x in forecast_b.strip('[]').split(',')] + options = row["options_parsed"] if "options_parsed" in row else row["options"] - range_min = row["range_min"] - range_max = row["range_max"] + resolution = row["resolution"] + question_type = row["type"] + if question_type == "binary": + if resolution == "yes": + resolution = True + elif resolution == "no": + resolution = False + + assert isinstance(forecast_a, float) + assert isinstance(forecast_b, float) + forecast_a = [forecast_a] + forecast_b = [forecast_b] + elif question_type == "multiple_choice": + resolution = resolution + elif question_type == "numeric": + if not isinstance(resolution, float): + resolution = float(resolution) + else: + raise ValueError(f"Unknown question type: {question_type}") + + + range_min = row.get("range_min") + if range_min: + range_min = float(range_min) + + range_max = row.get("range_max") + if range_max: + range_max = float(range_max) + question_weight = row["question_weight"] + if question_weight: + question_weight = float(question_weight) + score = calculate_peer_score( forecast=forecast_a, forecast_for_other_users=[forecast_b], @@ -1272,7 +1307,7 @@ def calculate_all_peer_scores(df, all_bots, pro_col="pro_median"): # Calculate peer score for each bot for bot in all_bots: - df_peer[bot] = 100 * df.apply( + df_peer[bot] = df.apply( lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( row, bot, pro_col ), @@ -1280,7 +1315,7 @@ def calculate_all_peer_scores(df, all_bots, pro_col="pro_median"): ) # Calculate peer score for bot_team_median - df_peer["bot_team_median"] = 100 * df.apply( + df_peer["bot_team_median"] = df.apply( lambda row: calculate_weighted_h2h_score_between_two_forecast_columns( row, "bot_median", pro_col ), diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 1cd27a6..596304e 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -124,7 +124,7 @@ def _determine_baseline( # Version 1: resolved_outside_bounds = False - assert range_min is not None and range_max is not None and resolution is not None + assert range_min is not None and range_max is not None and resolution is not None, f"These need to be not None: Range min: {range_min}, range max: {range_max}, resolution: {resolution}" if resolution > range_max or resolution < range_min: resolved_outside_bounds = True if resolved_outside_bounds: diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 3008279..b42c719 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -255,7 +255,7 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio - forecast = generate_cdf_with_forecast_at_index(correct_index, 0.001) + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.01/200) score = calculate_baseline_score( forecast=forecast, @@ -263,7 +263,7 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): range_min=0, range_max=range_max, ) - assert score == pytest.approx(-230) + assert score == pytest.approx(-230.25, abs=1e-1) @pytest.mark.parametrize( From 526e14e8349986ae37ef5d480e2e73917ce45240 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 06:30:16 -0600 Subject: [PATCH 15/26] Got calculate_all_peer_scores working --- AI_BENCHMARKING_ANALYSIS.ipynb | 7918 ++++++++++++++++- functions.py | 48 +- .../bootstrapped_h2h_bot_vs_pros.csv | 90 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 92 +- refactored_notebook/scoring.py | 172 +- 5 files changed, 7769 insertions(+), 551 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index a114c83..92e1549 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,21 +27,32 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 177, "metadata": { "id": "ISzIoto4hnoG" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# @title Import libraries\n", + "%load_ext autoreload\n", + "%autoreload 2\n", "from functions import *\n", "from IPython.display import display, clear_output\n", - "import pandas as pd" + "import pandas as pd\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 178, "metadata": {}, "outputs": [], "source": [ @@ -52,14 +63,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 179, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1914202/3462343738.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_1932996/3462343738.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] } @@ -131,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -149,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -175,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 182, "metadata": {}, "outputs": [ { @@ -196,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 183, "metadata": {}, "outputs": [ { @@ -214,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 184, "metadata": {}, "outputs": [ { @@ -227,7 +238,7 @@ " dtype='object')" ] }, - "execution_count": 8, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -238,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 185, "metadata": {}, "outputs": [ { @@ -273,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 186, "metadata": {}, "outputs": [ { @@ -295,7 +306,7 @@ "dtype: object" ] }, - "execution_count": 10, + "execution_count": 186, "metadata": {}, "output_type": "execute_result" } @@ -306,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 187, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 188, "metadata": {}, "outputs": [ { @@ -338,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 189, "metadata": {}, "outputs": [], "source": [ @@ -370,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 190, "metadata": {}, "outputs": [], "source": [ @@ -385,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 191, "metadata": {}, "outputs": [ { @@ -583,7 +594,7 @@ "6 False " ] }, - "execution_count": 15, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" } @@ -594,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 192, "metadata": {}, "outputs": [], "source": [ @@ -617,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 193, "metadata": {}, "outputs": [ { @@ -637,7 +648,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 17, + "execution_count": 193, "metadata": {}, "output_type": "execute_result" } @@ -649,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 194, "metadata": {}, "outputs": [ { @@ -692,15 +703,6 @@ " 1.738353\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 8.839589\n", - " 3341.541338\n", - " 409\n", - " 6.106284\n", - " 1.390432\n", - " \n", - " \n", " 4\n", " metac-o1-preview\n", " 8.465638\n", @@ -710,6 +712,15 @@ " 2.298000\n", " \n", " \n", + " 15\n", + " bot_median\n", + " 6.860987\n", + " 2593.590381\n", + " 409\n", + " 3.788648\n", + " 1.562899\n", + " \n", + " \n", " 24\n", " manticAI\n", " 6.510835\n", @@ -734,15 +745,15 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.839589 3341.541338 409 6.106284 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", + "15 bot_median 6.860987 2593.590381 409 3.788648 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.390432 \n", "4 2.298000 \n", + "15 1.562899 \n", "24 3.029040 \n", "1 2.309106 " ] @@ -858,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 195, "metadata": { "id": "BmAFBHIhK77X" }, @@ -907,7 +918,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 196, "metadata": {}, "outputs": [ { @@ -1331,7 +1342,7 @@ " np.int64(35705)}" ] }, - "execution_count": 20, + "execution_count": 196, "metadata": {}, "output_type": "execute_result" } @@ -1352,7 +1363,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 197, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1402,7 +1413,7 @@ " \n", " 3\n", " bot_median\n", - " 8784.525527\n", + " 8567.705563\n", " \n", " \n", " 4\n", @@ -1423,7 +1434,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8784.525527\n", + "3 bot_median 8567.705563\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1529,7 +1540,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 198, "metadata": {}, "outputs": [ { @@ -1548,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 199, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1592,13 +1603,13 @@ " \n", " \n", " 2\n", - " bot_median\n", - " 3555.373483\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 3\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 2974.983652\n", " \n", " \n", " 4\n", @@ -1828,8 +1839,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3555.373483\n", - "3 metac-o1-preview 3162.155445\n", + "2 metac-o1-preview 3162.155445\n", + "3 bot_median 2974.983652\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -1876,7 +1887,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 23, + "execution_count": 199, "metadata": {}, "output_type": "execute_result" } @@ -1918,7 +1929,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 200, "metadata": {}, "outputs": [], "source": [ @@ -1937,7 +1948,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ @@ -1946,7 +1957,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -1967,7 +1978,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 203, "metadata": {}, "outputs": [ { @@ -2165,7 +2176,7 @@ "6 False " ] }, - "execution_count": 27, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } @@ -2176,7 +2187,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 204, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2240,9 +2251,9 @@ " False\n", " False\n", " ...\n", - " [0.5,0.3,0.15,0.04,0.01]\n", - " [0.01,0.7,0.2,0.07,0.02]\n", - " [0.25,0.3,0.25,0.15,0.05]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2266,7 +2277,7 @@ " ...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", @@ -2290,7 +2301,7 @@ " ...\n", " 0.1\n", " 0.1\n", - " 0.15\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2313,8 +2324,8 @@ " None\n", " ...\n", " [0.25,0.6,0.15]\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", - " [0.15,0.6,0.25]\n", + " [0.7,0.25,0.05]\n", + " [0.15000000000000002,0.54,0.31000000000000005]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2375,24 +2386,24 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.5,0.3,0.15,0.04,0.01] \n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", "3 [0.25,0.6,0.15] \n", "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.2,0.07,0.02] \n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.37,0.49000000000000005,0.13999999999999999] \n", + "3 [0.7,0.25,0.05] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.25,0.3,0.25,0.15,0.05] NaN \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... NaN \n", - "2 0.15 NaN \n", - "3 [0.15,0.6,0.25] NaN \n", + "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", + "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", + "2 0.1 NaN \n", + "3 [0.15000000000000002,0.54,0.31000000000000005] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2480,7 +2491,7 @@ " False\n", " False\n", " ...\n", - " 0.95\n", + " 0.9\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2504,7 +2515,7 @@ " False\n", " False\n", " ...\n", - " 0.35\n", + " 0.65\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2576,9 +2587,9 @@ " False\n", " False\n", " ...\n", + " 0.02\n", " 0.05\n", - " 0.05\n", - " 0.01\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2608,11 +2619,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.35 0.9 NaN NaN 0.15 NaN \n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \n", + "95 0.65 0.9 NaN NaN 0.15 NaN \n", "96 0.85 0.9 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.05 0.05 0.01 NaN 0.15 0.05 \n", + "98 0.02 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -2684,7 +2695,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 205, "metadata": {}, "outputs": [ { @@ -2707,7 +2718,7 @@ " dtype='object')" ] }, - "execution_count": 29, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } @@ -2718,7 +2729,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 206, "metadata": {}, "outputs": [ { @@ -2728,7 +2739,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 30, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } @@ -2743,7 +2754,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 207, "metadata": {}, "outputs": [], "source": [ @@ -2755,7 +2766,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 208, "metadata": {}, "outputs": [], "source": [ @@ -2764,7 +2775,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 209, "metadata": {}, "outputs": [ { @@ -2825,9 +2836,9 @@ " False\n", " False\n", " ...\n", - " [0.5,0.3,0.15,0.04,0.01]\n", - " [0.01,0.7,0.2,0.07,0.02]\n", - " [0.25,0.3,0.25,0.15,0.05]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333]\n", + " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2851,7 +2862,7 @@ " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", - " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...]\n", + " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...]\n", " NaN\n", " [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...]\n", " [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...]\n", @@ -2875,7 +2886,7 @@ " ...\n", " 0.1\n", " 0.1\n", - " 0.15\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2898,8 +2909,8 @@ " None\n", " ...\n", " [0.25,0.6,0.15]\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", - " [0.15,0.6,0.25]\n", + " [0.7,0.25,0.05]\n", + " [0.15000000000000002,0.54,0.31000000000000005]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2921,8 +2932,8 @@ " False\n", " False\n", " ...\n", - " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...]\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...]\n", + " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...]\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...]\n", " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...]\n", " NaN\n", " [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", @@ -2960,25 +2971,25 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.5,0.3,0.15,0.04,0.01] \n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", "2 0.1 \n", "3 [0.25,0.6,0.15] \n", - "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", + "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.2,0.07,0.02] \n", + "0 [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", "2 0.1 \n", - "3 [0.37,0.49000000000000005,0.13999999999999999] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", + "3 [0.7,0.25,0.05] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...] \n", "\n", - " metac-perplexity \\\n", - "0 [0.25,0.3,0.25,0.15,0.05] \n", - "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...] \n", - "2 0.15 \n", - "3 [0.15,0.6,0.25] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", + " metac-perplexity \\\n", + "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", + "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...] \n", + "2 0.1 \n", + "3 [0.15000000000000002,0.54,0.31000000000000005] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3086,7 +3097,7 @@ " False\n", " False\n", " ...\n", - " 0.95\n", + " 0.9\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3110,7 +3121,7 @@ " False\n", " False\n", " ...\n", - " 0.35\n", + " 0.65\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3182,9 +3193,9 @@ " False\n", " False\n", " ...\n", + " 0.02\n", " 0.05\n", - " 0.05\n", - " 0.01\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3214,11 +3225,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.35 0.9 NaN NaN 0.15 NaN \n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \n", + "95 0.65 0.9 NaN NaN 0.15 NaN \n", "96 0.85 0.9 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.05 0.05 0.01 NaN 0.15 0.05 \n", + "98 0.02 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3278,254 +3289,2868 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 210, "metadata": {}, "outputs": [ { - "ename": "AssertionError", - "evalue": "Probability for resolution is nan which is not between 0 and 1", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_bot_vs_pro_peer \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_all_peer_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_pro_bot_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mall_bots\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention.\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1310\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores\u001b[0;34m(df, all_bots, pro_col)\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[0;32m-> 1310\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1318\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1320\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1321\u001b[0m ),\n\u001b[1;32m 1322\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1323\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1311\u001b[0m, in \u001b[0;36mcalculate_all_peer_scores..\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;66;03m# Calculate peer score for each bot\u001b[39;00m\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bot \u001b[38;5;129;01min\u001b[39;00m all_bots:\n\u001b[1;32m 1310\u001b[0m df_peer[bot] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[43mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpro_col\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1314\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1315\u001b[0m )\n\u001b[1;32m 1317\u001b[0m \u001b[38;5;66;03m# Calculate peer score for bot_team_median\u001b[39;00m\n\u001b[1;32m 1318\u001b[0m df_peer[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_team_median\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m row: calculate_weighted_h2h_score_between_two_forecast_columns(\n\u001b[1;32m 1320\u001b[0m row, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbot_median\u001b[39m\u001b[38;5;124m\"\u001b[39m, pro_col\n\u001b[1;32m 1321\u001b[0m ),\n\u001b[1;32m 1322\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1323\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:1291\u001b[0m, in \u001b[0;36mcalculate_weighted_h2h_score_between_two_forecast_columns\u001b[0;34m(row, col_a, col_b)\u001b[0m\n\u001b[1;32m 1288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m question_weight:\n\u001b[1;32m 1289\u001b[0m question_weight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(question_weight)\n\u001b[0;32m-> 1291\u001b[0m score \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_peer_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1292\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast_a\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast_for_other_users\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mforecast_b\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1294\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1295\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m score\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:18\u001b[0m, in \u001b[0;36mcalculate_peer_score\u001b[0;34m(forecast, forecast_for_other_users, resolution, options, range_min, range_max, question_weight)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalculate_peer_score\u001b[39m(\n\u001b[1;32m 10\u001b[0m forecast: ForecastType,\n\u001b[1;32m 11\u001b[0m forecast_for_other_users: \u001b[38;5;28mlist\u001b[39m[ForecastType],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 16\u001b[0m question_weight: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m,\n\u001b[1;32m 17\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m---> 18\u001b[0m forecast_for_resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_determine_probability_for_resolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m other_user_forecasts \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 22\u001b[0m _determine_probability_for_resolution(\n\u001b[1;32m 23\u001b[0m forecast, resolution, options, range_min, range_max\n\u001b[1;32m 24\u001b[0m )\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m forecast \u001b[38;5;129;01min\u001b[39;00m forecast_for_other_users\n\u001b[1;32m 26\u001b[0m ]\n\u001b[1;32m 28\u001b[0m geometric_mean \u001b[38;5;241m=\u001b[39m gmean(other_user_forecasts)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:210\u001b[0m, in \u001b[0;36m_determine_probability_for_resolution\u001b[0;34m(forecast, resolution, options, range_min, range_max)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown question type\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[0;32m--> 210\u001b[0m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m prob_for_resolution \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 211\u001b[0m ), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mProbability for resolution is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprob_for_resolution\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m which is not between 0 and 1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m prob_for_resolution\n", - "\u001b[0;31mAssertionError\u001b[0m: Probability for resolution is nan which is not between 0 and 1" + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n" ] } ], "source": [ + "from functions import *\n", "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", "# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", - "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", - "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'binary'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "leaderboard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Average pro median forecast on questions that resolved yes/no vs top bot\n", - "\n", - "top_bot = leaderboard['bot'][1]\n", - "\n", - "resolved_yes = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'yes']\n", - "resolved_no = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'no']\n", - "\n", - "# Calculate the average pro median forecast for questions that resolved yes\n", - "mean_pro_median_yes = resolved_yes['pro_median'].mean().round(2) * 100\n", - "mean_pro_median_no = resolved_no['pro_median'].mean().round(2) * 100\n", - "\n", - "mean_bot_yes = resolved_yes[top_bot].mean().round(2) * 100\n", - "mean_bot_no = resolved_no[top_bot].mean().round(2) * 100\n", - "\n", - "print(f'mean pro median forecast on questions that resolved yes: {mean_pro_median_yes}%')\n", - "print(f'mean pro median forecast on questions that resolved no: {mean_pro_median_no}%')\n", - "print(f'mean {top_bot} forecast on questions that resolved yes: {mean_bot_yes}%')\n", - "print(f'mean {top_bot} forecast on questions that resolved no: {mean_bot_no}%')\n", - "\n", - "# Plot the data\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Set up the figure\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# Create x-coordinates with jitter for each group separately\n", - "x_bot_yes = np.random.normal(0, 0.04, len(resolved_yes))\n", - "x_pro_yes = np.random.normal(1, 0.04, len(resolved_yes))\n", - "x_bot_no = np.random.normal(0, 0.04, len(resolved_no))\n", - "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", - "\n", - "# Plot points for \"yes\" resolution\n", - "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", - " color='blue', alpha=0.6, label='Resolved Yes')\n", - "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", - " color='blue', alpha=0.6)\n", - "\n", - "# Plot points for \"no\" resolution\n", - "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", - " color='red', alpha=0.6, label='Resolved No')\n", - "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", - " color='red', alpha=0.6)\n", - "\n", - "# Customize the plot\n", - "plt.xticks([0, 1], ['pro_median', top_bot])\n", - "plt.ylabel('Probability (%)')\n", - "plt.title('Pro Median vs Top Bot Forecasts')\n", - "plt.legend()\n", - "plt.grid(True, alpha=0.3)\n", - "\n", - "# Set y-axis limits from 0 to 100\n", - "plt.ylim(0, 100)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bot_vs_pro_peer_for_scores = df_bot_vs_pro_peer.copy()\n", - "bot_vs_pro_peer_for_scores = bot_vs_pro_peer_for_scores.drop(['resolution', 'question_weight', 'bot_question_id', 'pro_median', 'options', 'type'], axis=1)\n", - "\n", - "total_scores = bot_vs_pro_peer_for_scores.sum(axis=0)\n", - "\n", - "df_bot_vs_pro_peer = df_bot_vs_pro_peer.drop('pro_median', axis=1)\n", - "\n", - "# First pivot to long format - each row will be a question-forecaster pair\n", - "df_long = df_bot_vs_pro_peer.melt(\n", - " id_vars=['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options'],\n", - " var_name='forecaster',\n", - " value_name='score'\n", - ")\n", - "\n", - "# Drop any rows where score is NaN\n", - "df_long = df_long.dropna(subset=['score'])\n", - "\n", - "# Cast question_weight as numeric\n", - "df_long['question_weight'] = pd.to_numeric(df_long['question_weight'], errors='coerce')\n", - "\n", - "# Group first, then do the multiplication and sum\n", - "weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n", - "\n", - "# Calculate number of questions answered by each bot\n", - "num_questions = df_long.groupby('forecaster')['bot_question_id'].nunique()\n", - "#num_weighted_questions = df_bot_vs_pro_peer.mul(df_pro_bot_forecasts['question_weight'], axis=0).apply(lambda col: col[col.notna() & col.apply(np.isreal)].count())\n", - "\n", - "# Create a new DataFrame with the results\n", - "results = pd.DataFrame({\n", - " 'Peer_vs_Pro': total_scores,\n", - " 'Count': num_questions\n", - "})\n", - "\n", - "weighted_results = pd.DataFrame({\n", - " 'W_Peer_vs_Pro': weighted_scores,\n", - " 'Count': num_questions\n", - "})\n", - "\n", - "df_bot_vs_pro_leaderboard = results.sort_values(by='Peer_vs_Pro', ascending=False)\n", - "df_bot_vs_pro_weighted_leaderboard = weighted_results.sort_values(by='W_Peer_vs_Pro', ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 200, + "execution_count": 211, "metadata": {}, - "outputs": [], - "source": [ - "df_pro_baseline = df_pro_baseline.rename(columns={'question_id': 'pro_question_id'})\n", - "df_pro_baseline = df_pro_baseline[['pro_question_id', 'forecaster', 'score']]\n", - "\n", - "# Now make it wide! forecaster = columns; score = values; index = pro_question_id\n", - "df_pro_baseline_wide = df_pro_baseline.pivot(index='pro_question_id', columns='forecaster', values='score').reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "tXKRpXAVHMRt" - }, - "outputs": [], - "source": [ - "# @title Create df_pro_bot_baseline_leaderboard, df_pro_bot_baseline_weighted_leaderboard\n", - "\n", - "df_pro_bot_baseline_weights = pd.merge(\n", - " df_pro_bot_resolved_questions,\n", - " df_bot_baseline_wide,\n", - " on='bot_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "df_pro_bot_baseline_weights = pd.merge(\n", - " df_pro_bot_baseline_weights,\n", - " df_pro_baseline_wide[['pro_question_id', 'pro_median']],\n", - " on='pro_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "# Remove rows where pro_question_id is NaN (only want overlapping questions here)\n", - "df_pro_bot_baseline_weights = df_pro_bot_baseline_weights.dropna(subset=['pro_question_id'])\n", - "\n", - "# Create a list of columns to keep\n", - "forecaster_cols = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", - "df_filtered = df_pro_bot_baseline_weights[forecaster_cols]\n", - "\n", - "# Calculate the sum for each forecaster\n", - "forecaster_scores = df_filtered.sum()\n", - "forecaster_weighted_scores = df_filtered.mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", - "\n", - "question_counts = df_filtered.notna().sum()\n", - "question_weighted_counts = df_filtered.notna().mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", - "\n", - "# Create a DataFrame for the leaderboard\n", - "leaderboard = pd.DataFrame({\n", - " 'Forecaster': forecaster_scores.index,\n", - " 'Baseline': forecaster_scores.values,\n", - " 'Count': question_counts.values\n", - "})\n", - "\n", - "# Create a DataFrame for the leaderboard\n", - "weighted_leaderboard = pd.DataFrame({\n", - " 'Forecaster': forecaster_weighted_scores.index,\n", - " 'Weighted_Baseline': forecaster_weighted_scores.values,\n", - " 'Count': question_counts.values,\n", - " 'Weighted Count': question_weighted_counts.values\n", - "})\n", - "\n", - "# Sort the leaderboard by score in descending order\n", - "leaderboard = leaderboard.sort_values('Baseline', ascending=False).reset_index(drop=True)\n", - "weighted_leaderboard = weighted_leaderboard.sort_values('Weighted_Baseline', ascending=False).reset_index(drop=True)\n", - "\n", - "# Add a 'Rank' column\n", - "leaderboard['Rank'] = leaderboard.index + 1\n", - "weighted_leaderboard['Rank'] = weighted_leaderboard.index + 1\n", - "\n", - "# Reorder columns to have Rank first\n", - "leaderboard = leaderboard[['Rank', 'Forecaster', 'Baseline', 'Count']]\n", - "weighted_leaderboard = weighted_leaderboard[['Rank', 'Forecaster', 'Weighted_Baseline', 'Count', 'Weighted Count']]\n", - "\n", - "#leaderboard\n", + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionsrange_minrange_maxopen_upper_boundopen_lower_bound...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbbot_team_median
0312683126201.0multiple_choice[0, 1, 2-3, 4-6, >6]NaNNaNFalseFalse...2.6449925.703782NaN2.2926352.703087NaNNaNNaNNaN5.010635
331280312745-91.0multiple_choice[0-4, 5-9, >9]NaNNaNNoneNone...-0.5653140.204794NaN0.1278330.152526NaNNaN-0.046520NaN0.310155
63129231286Jeff Bezos1.0multiple_choice[Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else]NaNNaNFalseFalse...0.2475620.096331NaN-0.1845710.112526NaNNaNNaNNaN0.112526
9313213137001.0multiple_choice[0, 1, 2, Greater than 2]NaNNaNNoneNone...-0.518794-1.211941NaN-0.806476-0.494101NaNNaN-0.624154NaN-0.693147
133136831366≥0% and <5%1.0multiple_choice[Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%]NaNNaNNoneNone...0.4418330.5108260.0219790.2006710.253781NaNNaNNaNNaN-0.325422
\n", + "

5 rows × 58 columns

\n", + "
" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight \\\n", + "0 31268 31262 0 1.0 \n", + "3 31280 31274 5-9 1.0 \n", + "6 31292 31286 Jeff Bezos 1.0 \n", + "9 31321 31370 0 1.0 \n", + "13 31368 31366 ≥0% and <5% 1.0 \n", + "\n", + " type \\\n", + "0 multiple_choice \n", + "3 multiple_choice \n", + "6 multiple_choice \n", + "9 multiple_choice \n", + "13 multiple_choice \n", + "\n", + " options \\\n", + "0 [0, 1, 2-3, 4-6, >6] \n", + "3 [0-4, 5-9, >9] \n", + "6 [Larry Ellison, Elon Musk, Mark Zuckerberg, Bernard Arnault & family, Jeff Bezos, Someone else] \n", + "9 [0, 1, 2, Greater than 2] \n", + "13 [Less than -5%, ≥-5% and <0%, ≥0% and <5%, Greater than 5%] \n", + "\n", + " range_min range_max open_upper_bound open_lower_bound ... \\\n", + "0 NaN NaN False False ... \n", + "3 NaN NaN None None ... \n", + "6 NaN NaN False False ... \n", + "9 NaN NaN None None ... \n", + "13 NaN NaN None None ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "0 2.644992 5.703782 NaN 2.292635 2.703087 \n", + "3 -0.565314 0.204794 NaN 0.127833 0.152526 \n", + "6 0.247562 0.096331 NaN -0.184571 0.112526 \n", + "9 -0.518794 -1.211941 NaN -0.806476 -0.494101 \n", + "13 0.441833 0.510826 0.021979 0.200671 0.253781 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", + "0 NaN NaN NaN NaN 5.010635 \n", + "3 NaN NaN -0.046520 NaN 0.310155 \n", + "6 NaN NaN NaN NaN 0.112526 \n", + "9 NaN NaN -0.624154 NaN -0.693147 \n", + "13 NaN NaN NaN NaN -0.325422 \n", + "\n", + "[5 rows x 58 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionsrange_minrange_maxopen_upper_boundopen_lower_bound...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbbot_team_median
813516935119Not in top 501.0multiple_choice[0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50]NaNNaNFalseFalse...-2.879198-1.780586-3.007032-2.879198-3.390024NaNNaN-2.348570-2.409195-3.795489
8235170351213 or more1.0multiple_choice[0, 1, 2, 3 or more]NaNNaNNoneNone...-0.656780-0.300105-0.5232480.1053610.259511NaNNaN0.276509-0.644609-0.656780
833517135123≥7.5 and ≤8.51.0multiple_choice[<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5]NaNNaNNoneNone...-1.321756-0.265703NaN-0.182322NaNNaNNaN-0.178330-0.567984-0.693147
913537735334Jimmy Patronis1.0multiple_choice[Jimmy Patronis, Gay Valimont, Someone else]NaNNaNFalseFalse...-0.069566-0.048289NaN-0.124829-0.080377NaN-0.113529NaN-0.147818-0.124829
92353783533631-491.0multiple_choice[0-24, 25-30, 31-49, 50-70, >70]NaNNaNFalseFalse...-1.704748-1.704748NaN-1.704748-0.318454NaN-0.480973NaN-0.749237-0.480973
\n", + "

5 rows × 58 columns

\n", + "
" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight \\\n", + "81 35169 35119 Not in top 50 1.0 \n", + "82 35170 35121 3 or more 1.0 \n", + "83 35171 35123 ≥7.5 and ≤8.5 1.0 \n", + "91 35377 35334 Jimmy Patronis 1.0 \n", + "92 35378 35336 31-49 1.0 \n", + "\n", + " type \\\n", + "81 multiple_choice \n", + "82 multiple_choice \n", + "83 multiple_choice \n", + "91 multiple_choice \n", + "92 multiple_choice \n", + "\n", + " options range_min \\\n", + "81 [0-10, 11-20, 21-30, 31-40, 41-50, Not in top 50] NaN \n", + "82 [0, 1, 2, 3 or more] NaN \n", + "83 [<7.5, ≥7.5 and ≤8.5, >8.5 and <9.0, ≥9.0 and ≤9.5, >9.5] NaN \n", + "91 [Jimmy Patronis, Gay Valimont, Someone else] NaN \n", + "92 [0-24, 25-30, 31-49, 50-70, >70] NaN \n", + "\n", + " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", + "81 NaN False False ... -2.879198 \n", + "82 NaN None None ... -0.656780 \n", + "83 NaN None None ... -1.321756 \n", + "91 NaN False False ... -0.069566 \n", + "92 NaN False False ... -1.704748 \n", + "\n", + " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", + "81 -1.780586 -3.007032 -2.879198 -3.390024 NaN NaN \n", + "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", + "83 -0.265703 NaN -0.182322 NaN NaN NaN \n", + "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -1.704748 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "\n", + " twsummerbot wunderplumb bot_team_median \n", + "81 -2.348570 -2.409195 -3.795489 \n", + "82 0.276509 -0.644609 -0.656780 \n", + "83 -0.178330 -0.567984 -0.693147 \n", + "91 NaN -0.147818 -0.124829 \n", + "92 NaN -0.749237 -0.480973 \n", + "\n", + "[5 rows x 58 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionsrange_minrange_maxopen_upper_boundopen_lower_bound...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbbot_team_median
23127031264no1.0binaryNoneNaNNaNFalseFalse...-0.092275-0.092275NaN-0.210058-0.059485NaNNaNNaNNaN-0.149434
53128231276yes1.0binaryNoneNaNNaNNoneNone...-0.2513140.441833NaN0.5108260.320472NaNNaNNaNNaN0.287682
83129431288yes1.0binaryNoneNaNNaNFalseFalse...-0.054067-0.054067NaN-0.111226-0.147158NaNNaN-0.398124NaN-0.171850
123133831334yes1.0binaryNoneNaNNaNFalseFalse...-0.1823220.000000NaN0.054067-0.057158NaNNaN-0.499776NaN-0.057158
163387633751no1.0binaryNoneNaNNaNFalseFalse...0.0084570.008457NaN-0.068083NaNNaNNaN-0.076070NaN-0.076070
\n", + "

5 rows × 58 columns

\n", + "
" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight type \\\n", + "2 31270 31264 no 1.0 binary \n", + "5 31282 31276 yes 1.0 binary \n", + "8 31294 31288 yes 1.0 binary \n", + "12 31338 31334 yes 1.0 binary \n", + "16 33876 33751 no 1.0 binary \n", + "\n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "2 None NaN NaN False False ... \n", + "5 None NaN NaN None None ... \n", + "8 None NaN NaN False False ... \n", + "12 None NaN NaN False False ... \n", + "16 None NaN NaN False False ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", + "5 -0.251314 0.441833 NaN 0.510826 0.320472 \n", + "8 -0.054067 -0.054067 NaN -0.111226 -0.147158 \n", + "12 -0.182322 0.000000 NaN 0.054067 -0.057158 \n", + "16 0.008457 0.008457 NaN -0.068083 NaN \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", + "2 NaN NaN NaN NaN -0.149434 \n", + "5 NaN NaN NaN NaN 0.287682 \n", + "8 NaN NaN -0.398124 NaN -0.171850 \n", + "12 NaN NaN -0.499776 NaN -0.057158 \n", + "16 NaN NaN -0.076070 NaN -0.076070 \n", + "\n", + "[5 rows x 58 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionsrange_minrange_maxopen_upper_boundopen_lower_bound...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbbot_team_median
943538035345yes1.00binaryNoneNaNNaNFalseFalse...-0.054067NaNNaN0.0000000.000000NaN-0.054067-0.220515-0.054067-0.054067
953538135354no1.00binaryNoneNaNNaNFalseFalse...-2.251292NaNNaN-0.111226NaNNaN-0.054067-0.083382-2.944439-0.111226
963538535358yes1.00binaryNoneNaNNaNFalseFalse...-0.074901NaNNaN-0.074901NaNNaN-0.132060-0.158283-0.132060-0.132060
973538635364no0.85binaryNoneNaNNaNFalseFalse...-0.6804300.628948NaN-0.680430-0.680430NaN-0.0912550.8117930.628948-0.091255
983538735367no0.85binaryNoneNaNNaNFalseFalse...-0.0177090.000000NaN-0.112251-0.017709NaN-0.163782-0.241614-0.163782-0.112251
\n", + "

5 rows × 58 columns

\n", + "
" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight type \\\n", + "94 35380 35345 yes 1.00 binary \n", + "95 35381 35354 no 1.00 binary \n", + "96 35385 35358 yes 1.00 binary \n", + "97 35386 35364 no 0.85 binary \n", + "98 35387 35367 no 0.85 binary \n", + "\n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "94 None NaN NaN False False ... \n", + "95 None NaN NaN False False ... \n", + "96 None NaN NaN False False ... \n", + "97 None NaN NaN False False ... \n", + "98 None NaN NaN False False ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 -0.054067 NaN NaN 0.000000 0.000000 \n", + "95 -2.251292 NaN NaN -0.111226 NaN \n", + "96 -0.074901 NaN NaN -0.074901 NaN \n", + "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", + "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", + "95 NaN -0.054067 -0.083382 -2.944439 -0.111226 \n", + "96 NaN -0.132060 -0.158283 -0.132060 -0.132060 \n", + "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", + "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", + "\n", + "[5 rows x 58 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show me a few rows from each type of question in df_bot_vs_pro_peer\n", + "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'multiple_choice'])\n", + "display_head_and_tail(df_bot_vs_pro_peer[df_bot_vs_pro_peer['type'] == 'binary'])" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
botPeer Score
Rank
1metac-o13864.168122
2metac-o1-preview3162.155445
3bot_median2974.983652
4manticAI2142.538438
5metac-Gemini-Exp-12062072.216227
6acm_bot1876.466009
7twsummerbot1763.532046
8metac-perplexity1697.555196
9GreeneiBot21603.998618
10cookics_bot_TEST1140.390796
11metac-claude-3-5-sonnet-latest1134.209821
12SynapseSeer1066.533051
13CumulativeBot1030.716475
14pgodzinai926.081448
15jkraybill_bot627.932509
16metac-deepseek-r1614.572462
17question_weight378.020000
18metac-exa265.384263
19MWG215.551323
20annabot21.125670
21andrewsiah-4.170684
22cobyj-bot-15.593332
23X_bot-16.052813
24pianobot-20.745921
25CatrachoCaster-214.389722
26KevinTestBot-244.046973
27jonahsingerbot-318.088290
28krm-bot-387.131345
29ProfessorSP-406.072162
30mmBot-453.312468
31metac-grok-2-1212-492.938695
32bean_bot-494.373003
334Shadower-586.017986
34metac-claude-3-5-sonnet-20240620-647.579684
35swingswish-763.021897
36RPM_bot-905.938514
37metac-Llama-3.1-1029.014161
38InstitutPelFutur-1087.748963
39wunderplumb-1189.786803
40VeritasAI-1521.091541
41NextWorldLab-1565.096041
42Bot_Pepa-1589.575284
43laylaps-1665.296188
44minefrac1-1850.747385
45Grizeu_Bot-1898.666894
46metac-gpt-4o-2618.918368
47ajf-bot-3239.712801
\n", + "
" + ], + "text/plain": [ + " bot Peer Score\n", + "Rank \n", + "1 metac-o1 3864.168122\n", + "2 metac-o1-preview 3162.155445\n", + "3 bot_median 2974.983652\n", + "4 manticAI 2142.538438\n", + "5 metac-Gemini-Exp-1206 2072.216227\n", + "6 acm_bot 1876.466009\n", + "7 twsummerbot 1763.532046\n", + "8 metac-perplexity 1697.555196\n", + "9 GreeneiBot2 1603.998618\n", + "10 cookics_bot_TEST 1140.390796\n", + "11 metac-claude-3-5-sonnet-latest 1134.209821\n", + "12 SynapseSeer 1066.533051\n", + "13 CumulativeBot 1030.716475\n", + "14 pgodzinai 926.081448\n", + "15 jkraybill_bot 627.932509\n", + "16 metac-deepseek-r1 614.572462\n", + "17 question_weight 378.020000\n", + "18 metac-exa 265.384263\n", + "19 MWG 215.551323\n", + "20 annabot 21.125670\n", + "21 andrewsiah -4.170684\n", + "22 cobyj-bot -15.593332\n", + "23 X_bot -16.052813\n", + "24 pianobot -20.745921\n", + "25 CatrachoCaster -214.389722\n", + "26 KevinTestBot -244.046973\n", + "27 jonahsingerbot -318.088290\n", + "28 krm-bot -387.131345\n", + "29 ProfessorSP -406.072162\n", + "30 mmBot -453.312468\n", + "31 metac-grok-2-1212 -492.938695\n", + "32 bean_bot -494.373003\n", + "33 4Shadower -586.017986\n", + "34 metac-claude-3-5-sonnet-20240620 -647.579684\n", + "35 swingswish -763.021897\n", + "36 RPM_bot -905.938514\n", + "37 metac-Llama-3.1 -1029.014161\n", + "38 InstitutPelFutur -1087.748963\n", + "39 wunderplumb -1189.786803\n", + "40 VeritasAI -1521.091541\n", + "41 NextWorldLab -1565.096041\n", + "42 Bot_Pepa -1589.575284\n", + "43 laylaps -1665.296188\n", + "44 minefrac1 -1850.747385\n", + "45 Grizeu_Bot -1898.666894\n", + "46 metac-gpt-4o -2618.918368\n", + "47 ajf-bot -3239.712801" + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "leaderboard" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean pro median forecast on questions that resolved yes: 74.0%\n", + "mean pro median forecast on questions that resolved no: 22.0%\n", + "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", + "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Average pro median forecast on questions that resolved yes/no vs top bot\n", + "\n", + "top_bot = leaderboard['bot'][1]\n", + "\n", + "resolved_yes = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'yes']\n", + "resolved_no = df_pro_bot_forecasts[df_pro_bot_forecasts['resolution'] == 'no']\n", + "\n", + "# Calculate the average pro median forecast for questions that resolved yes\n", + "mean_pro_median_yes = resolved_yes['pro_median'].mean().round(2) * 100\n", + "mean_pro_median_no = resolved_no['pro_median'].mean().round(2) * 100\n", + "\n", + "mean_bot_yes = resolved_yes[top_bot].mean().round(2) * 100\n", + "mean_bot_no = resolved_no[top_bot].mean().round(2) * 100\n", + "\n", + "print(f'mean pro median forecast on questions that resolved yes: {mean_pro_median_yes}%')\n", + "print(f'mean pro median forecast on questions that resolved no: {mean_pro_median_no}%')\n", + "print(f'mean {top_bot} forecast on questions that resolved yes: {mean_bot_yes}%')\n", + "print(f'mean {top_bot} forecast on questions that resolved no: {mean_bot_no}%')\n", + "\n", + "# Plot the data\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Set up the figure\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Create x-coordinates with jitter for each group separately\n", + "x_bot_yes = np.random.normal(0, 0.04, len(resolved_yes))\n", + "x_pro_yes = np.random.normal(1, 0.04, len(resolved_yes))\n", + "x_bot_no = np.random.normal(0, 0.04, len(resolved_no))\n", + "x_pro_no = np.random.normal(1, 0.04, len(resolved_no))\n", + "\n", + "# Plot points for \"yes\" resolution\n", + "plt.scatter(x_bot_yes, resolved_yes['pro_median'] * 100,\n", + " color='blue', alpha=0.6, label='Resolved Yes')\n", + "plt.scatter(x_pro_yes, resolved_yes[top_bot] * 100,\n", + " color='blue', alpha=0.6)\n", + "\n", + "# Plot points for \"no\" resolution\n", + "plt.scatter(x_bot_no, resolved_no['pro_median'] * 100,\n", + " color='red', alpha=0.6, label='Resolved No')\n", + "plt.scatter(x_pro_no, resolved_no[top_bot] * 100,\n", + " color='red', alpha=0.6)\n", + "\n", + "# Customize the plot\n", + "plt.xticks([0, 1], ['pro_median', top_bot])\n", + "plt.ylabel('Probability (%)')\n", + "plt.title('Pro Median vs Top Bot Forecasts')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "\n", + "# Set y-axis limits from 0 to 100\n", + "plt.ylim(0, 100)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1932996/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" + ] + } + ], + "source": [ + "bot_vs_pro_peer_for_scores = df_bot_vs_pro_peer.copy()\n", + "bot_vs_pro_peer_for_scores = bot_vs_pro_peer_for_scores.drop(['resolution', 'question_weight', 'bot_question_id', 'pro_median', 'options', 'type'], axis=1)\n", + "\n", + "total_scores = bot_vs_pro_peer_for_scores.sum(axis=0)\n", + "\n", + "df_bot_vs_pro_peer = df_bot_vs_pro_peer.drop('pro_median', axis=1)\n", + "\n", + "# First pivot to long format - each row will be a question-forecaster pair\n", + "df_long = df_bot_vs_pro_peer.melt(\n", + " id_vars=['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options'],\n", + " var_name='forecaster',\n", + " value_name='score'\n", + ")\n", + "\n", + "# Drop any rows where score is NaN\n", + "df_long = df_long.dropna(subset=['score'])\n", + "\n", + "# Cast question_weight as numeric\n", + "df_long['question_weight'] = pd.to_numeric(df_long['question_weight'], errors='coerce')\n", + "\n", + "# Group first, then do the multiplication and sum\n", + "weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n", + "\n", + "# Calculate number of questions answered by each bot\n", + "num_questions = df_long.groupby('forecaster')['bot_question_id'].nunique()\n", + "#num_weighted_questions = df_bot_vs_pro_peer.mul(df_pro_bot_forecasts['question_weight'], axis=0).apply(lambda col: col[col.notna() & col.apply(np.isreal)].count())\n", + "\n", + "# Create a new DataFrame with the results\n", + "results = pd.DataFrame({\n", + " 'Peer_vs_Pro': total_scores,\n", + " 'Count': num_questions\n", + "})\n", + "\n", + "weighted_results = pd.DataFrame({\n", + " 'W_Peer_vs_Pro': weighted_scores,\n", + " 'Count': num_questions\n", + "})\n", + "\n", + "df_bot_vs_pro_leaderboard = results.sort_values(by='Peer_vs_Pro', ascending=False)\n", + "df_bot_vs_pro_weighted_leaderboard = weighted_results.sort_values(by='W_Peer_vs_Pro', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "df_pro_baseline = df_pro_baseline.rename(columns={'question_id': 'pro_question_id'})\n", + "df_pro_baseline = df_pro_baseline[['pro_question_id', 'forecaster', 'score']]\n", + "\n", + "# Now make it wide! forecaster = columns; score = values; index = pro_question_id\n", + "df_pro_baseline_wide = df_pro_baseline.pivot(index='pro_question_id', columns='forecaster', values='score').reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": { + "cellView": "form", + "id": "tXKRpXAVHMRt" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
RankForecasterWeighted_BaselineCountWeighted Count
01pro_median4238.5616079793.10
12metac-o13010.3537889692.10
23metac-perplexity2774.0803319490.10
34acm_bot2239.0586758581.25
45metac-claude-3-5-sonnet-202406202018.1102119591.50
56bot_median1970.6330699793.10
67manticAI1865.1262607470.45
78metac-exa1826.2756819490.10
89twsummerbot1819.0641416259.40
910metac-claude-3-5-sonnet-latest1740.3151889692.10
1011metac-Llama-3.11701.1824039490.10
1112jkraybill_bot1616.0557094745.05
1213metac-Gemini-Exp-12061595.6826128177.50
1314NextWorldLab1583.0262268581.25
1415metac-o1-preview1527.6571419692.10
1516metac-deepseek-r11518.3086255552.10
1617laylaps1500.5678746865.10
1718mmBot1482.7264459793.10
1819Grizeu_Bot1399.4777185552.35
1920metac-grok-2-12121167.8671619692.10
2021VeritasAI1136.6824928278.10
2122metac-gpt-4o1045.1336789692.10
2223SynapseSeer1039.4846352826.15
2324annabot1031.9739303129.30
2425GreeneiBot2932.8835806259.35
2526MWG741.4247473028.60
2627InstitutPelFutur722.6870159591.10
2728cookics_bot_TEST714.1983722927.40
2829Bot_Pepa660.8016994745.05
2930ajf-bot484.4450303735.25
3031swingswish429.96611287.70
3132KevinTestBot331.09944498.40
3233X_bot274.53936577.00
3334CumulativeBot253.8397011110.25
3435CatrachoCaster247.2667172119.70
3536jonahsingerbot224.15439254.70
36374Shadower210.5486171514.00
3738bean_bot210.54275254.70
3839pgodzinai177.1341048177.40
3940wunderplumb112.1502452725.55
4041krm-bot65.989405109.50
4142andrewsiah0.00000000.00
4243cobyj-bot0.00000000.00
4344RPM_bot-8.69053388.00
4445ProfessorSP-217.1062982018.60
4546pianobot-217.32120454.70
4647minefrac1-299.5665065552.10
\n", + "
" + ], + "text/plain": [ + " Rank Forecaster Weighted_Baseline Count \\\n", + "0 1 pro_median 4238.561607 97 \n", + "1 2 metac-o1 3010.353788 96 \n", + "2 3 metac-perplexity 2774.080331 94 \n", + "3 4 acm_bot 2239.058675 85 \n", + "4 5 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", + "5 6 bot_median 1970.633069 97 \n", + "6 7 manticAI 1865.126260 74 \n", + "7 8 metac-exa 1826.275681 94 \n", + "8 9 twsummerbot 1819.064141 62 \n", + "9 10 metac-claude-3-5-sonnet-latest 1740.315188 96 \n", + "10 11 metac-Llama-3.1 1701.182403 94 \n", + "11 12 jkraybill_bot 1616.055709 47 \n", + "12 13 metac-Gemini-Exp-1206 1595.682612 81 \n", + "13 14 NextWorldLab 1583.026226 85 \n", + "14 15 metac-o1-preview 1527.657141 96 \n", + "15 16 metac-deepseek-r1 1518.308625 55 \n", + "16 17 laylaps 1500.567874 68 \n", + "17 18 mmBot 1482.726445 97 \n", + "18 19 Grizeu_Bot 1399.477718 55 \n", + "19 20 metac-grok-2-1212 1167.867161 96 \n", + "20 21 VeritasAI 1136.682492 82 \n", + "21 22 metac-gpt-4o 1045.133678 96 \n", + "22 23 SynapseSeer 1039.484635 28 \n", + "23 24 annabot 1031.973930 31 \n", + "24 25 GreeneiBot2 932.883580 62 \n", + "25 26 MWG 741.424747 30 \n", + "26 27 InstitutPelFutur 722.687015 95 \n", + "27 28 cookics_bot_TEST 714.198372 29 \n", + "28 29 Bot_Pepa 660.801699 47 \n", + "29 30 ajf-bot 484.445030 37 \n", + "30 31 swingswish 429.966112 8 \n", + "31 32 KevinTestBot 331.099444 9 \n", + "32 33 X_bot 274.539365 7 \n", + "33 34 CumulativeBot 253.839701 11 \n", + "34 35 CatrachoCaster 247.266717 21 \n", + "35 36 jonahsingerbot 224.154392 5 \n", + "36 37 4Shadower 210.548617 15 \n", + "37 38 bean_bot 210.542752 5 \n", + "38 39 pgodzinai 177.134104 81 \n", + "39 40 wunderplumb 112.150245 27 \n", + "40 41 krm-bot 65.989405 10 \n", + "41 42 andrewsiah 0.000000 0 \n", + "42 43 cobyj-bot 0.000000 0 \n", + "43 44 RPM_bot -8.690533 8 \n", + "44 45 ProfessorSP -217.106298 20 \n", + "45 46 pianobot -217.321204 5 \n", + "46 47 minefrac1 -299.566506 55 \n", + "\n", + " Weighted Count \n", + "0 93.10 \n", + "1 92.10 \n", + "2 90.10 \n", + "3 81.25 \n", + "4 91.50 \n", + "5 93.10 \n", + "6 70.45 \n", + "7 90.10 \n", + "8 59.40 \n", + "9 92.10 \n", + "10 90.10 \n", + "11 45.05 \n", + "12 77.50 \n", + "13 81.25 \n", + "14 92.10 \n", + "15 52.10 \n", + "16 65.10 \n", + "17 93.10 \n", + "18 52.35 \n", + "19 92.10 \n", + "20 78.10 \n", + "21 92.10 \n", + "22 26.15 \n", + "23 29.30 \n", + "24 59.35 \n", + "25 28.60 \n", + "26 91.10 \n", + "27 27.40 \n", + "28 45.05 \n", + "29 35.25 \n", + "30 7.70 \n", + "31 8.40 \n", + "32 7.00 \n", + "33 10.25 \n", + "34 19.70 \n", + "35 4.70 \n", + "36 14.00 \n", + "37 4.70 \n", + "38 77.40 \n", + "39 25.55 \n", + "40 9.50 \n", + "41 0.00 \n", + "42 0.00 \n", + "43 8.00 \n", + "44 18.60 \n", + "45 4.70 \n", + "46 52.10 " + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# @title Create df_pro_bot_baseline_leaderboard, df_pro_bot_baseline_weighted_leaderboard\n", + "\n", + "df_pro_bot_baseline_weights = pd.merge(\n", + " df_pro_bot_resolved_questions,\n", + " df_bot_baseline_wide,\n", + " on='bot_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "df_pro_bot_baseline_weights = pd.merge(\n", + " df_pro_bot_baseline_weights,\n", + " df_pro_baseline_wide[['pro_question_id', 'pro_median']],\n", + " on='pro_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "# Remove rows where pro_question_id is NaN (only want overlapping questions here)\n", + "df_pro_bot_baseline_weights = df_pro_bot_baseline_weights.dropna(subset=['pro_question_id'])\n", + "\n", + "# Create a list of columns to keep\n", + "forecaster_cols = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", + "df_filtered = df_pro_bot_baseline_weights[forecaster_cols]\n", + "\n", + "# Calculate the sum for each forecaster\n", + "forecaster_scores = df_filtered.sum()\n", + "forecaster_weighted_scores = df_filtered.mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", + "\n", + "question_counts = df_filtered.notna().sum()\n", + "question_weighted_counts = df_filtered.notna().mul(df_pro_bot_baseline_weights['question_weight'], axis=0).sum()\n", + "\n", + "# Create a DataFrame for the leaderboard\n", + "leaderboard = pd.DataFrame({\n", + " 'Forecaster': forecaster_scores.index,\n", + " 'Baseline': forecaster_scores.values,\n", + " 'Count': question_counts.values\n", + "})\n", + "\n", + "# Create a DataFrame for the leaderboard\n", + "weighted_leaderboard = pd.DataFrame({\n", + " 'Forecaster': forecaster_weighted_scores.index,\n", + " 'Weighted_Baseline': forecaster_weighted_scores.values,\n", + " 'Count': question_counts.values,\n", + " 'Weighted Count': question_weighted_counts.values\n", + "})\n", + "\n", + "# Sort the leaderboard by score in descending order\n", + "leaderboard = leaderboard.sort_values('Baseline', ascending=False).reset_index(drop=True)\n", + "weighted_leaderboard = weighted_leaderboard.sort_values('Weighted_Baseline', ascending=False).reset_index(drop=True)\n", + "\n", + "# Add a 'Rank' column\n", + "leaderboard['Rank'] = leaderboard.index + 1\n", + "weighted_leaderboard['Rank'] = weighted_leaderboard.index + 1\n", + "\n", + "# Reorder columns to have Rank first\n", + "leaderboard = leaderboard[['Rank', 'Forecaster', 'Baseline', 'Count']]\n", + "weighted_leaderboard = weighted_leaderboard[['Rank', 'Forecaster', 'Weighted_Baseline', 'Count', 'Weighted Count']]\n", + "\n", + "#leaderboard\n", "weighted_leaderboard" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 217, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
pro_median4238.693.145.562.2291686.4493987.0591051.98527758.332.71.0000000.000000
metac-o13010.492.132.757.7568596.0182995.4310541.98555044.620.71.0000000.000000
metac-perplexity2774.190.130.867.2103837.0806644.3483081.98611444.916.70.9999820.000036
acm_bot2239.181.227.655.5540546.1631694.4713431.98898539.815.30.9999870.000025
metac-claude-3-5-sonnet-202406202018.191.522.164.2193076.7135943.2852521.98578835.48.70.9992750.001450
bot_median1970.693.121.265.5547436.7940583.1154931.98527734.77.70.9987760.002449
manticAI1865.170.426.566.3530597.9053383.3489361.99348842.210.70.9993430.001314
metac-exa1826.390.120.382.2195858.6618942.3400691.98611437.53.10.9892430.021514
twsummerbot1819.159.430.654.7477997.1035174.3111002.00016344.816.40.9999680.000063
metac-claude-3-5-sonnet-latest1740.392.118.971.5459837.4551342.5346201.98555033.74.10.9935180.012963
metac-Llama-3.11701.290.118.962.1549296.5480682.8834531.98611431.95.90.9975350.004930
jkraybill_bot1616.145.035.959.7568388.9030794.0292232.01341253.817.90.9998910.000218
metac-Gemini-Exp-12061595.777.520.667.0999817.6220462.7013031.99042635.85.40.9957490.008502
NextWorldLab1583.081.219.566.4117477.3677222.6444271.98898534.14.80.9950800.009840
metac-o1-preview1527.792.116.687.1115689.0770771.8273441.98555034.6-1.40.9645390.070922
metac-deepseek-r11518.352.129.162.7649708.6955783.3513822.00537946.611.70.9992410.001519
laylaps1500.665.123.174.4573659.2282042.4977991.99634141.54.60.9924630.015074
mmBot1482.793.115.979.9905028.2901731.9210901.98527732.4-0.50.9710930.057813
Grizeu_Bot1399.552.426.760.8869058.4152223.1767552.00555543.69.90.9987400.002521
metac-grok-2-12121167.992.112.779.3224498.2654461.5341491.98555029.1-3.70.9357710.128459
VeritasAI1136.778.114.661.1249136.9166012.1042411.99009528.30.80.9806920.038617
metac-gpt-4o1045.192.111.367.7641657.0610661.6070961.98555025.4-2.70.9442530.111494
SynapseSeer1039.526.239.862.84354812.2892353.2346072.05307665.014.50.9983020.003397
annabot1032.029.335.257.68962410.6577103.3047392.04418357.013.40.9987070.002586
GreeneiBot2932.959.415.773.8321869.5837481.6401042.00014134.9-3.50.9468180.106364
MWG741.428.625.978.73589114.7227771.7608052.04656156.1-4.20.9553250.089349
InstitutPelFutur722.791.17.9100.84063310.5651670.7508541.98582928.9-13.00.7726510.454697
cookics_bot_TEST714.227.426.163.25665212.0845622.1569372.04954150.81.30.9798560.040287
Bot_Pepa660.845.014.769.73878710.3902741.4117232.01341235.6-6.30.9174720.165057
ajf-bot484.435.213.786.56822814.5807200.9425542.02873043.3-15.80.8237450.352510
swingswish430.07.755.852.06574018.7631902.9760272.367123100.311.40.9891420.021716
KevinTestBot331.18.439.476.25685526.3111141.4980972.311496100.2-21.40.9122520.175497
X_bot274.57.039.231.69380111.9791313.2740202.44691268.59.90.9915260.016949
CumulativeBot253.810.224.878.92471924.6519411.0045802.23184879.8-30.30.8296730.340653
CatrachoCaster247.319.712.675.37158416.9814400.7391372.08877748.0-22.90.7655000.469001
jonahsingerbot224.24.747.764.22018229.6225611.6100032.784843130.2-34.80.9057990.188401
bean_bot210.54.744.876.35643935.2205991.2718792.784843142.9-53.30.8612620.277476
4Shadower210.514.015.0116.14611231.0413540.4844892.14723981.7-51.60.6819500.636100
pgodzinai177.177.42.3103.63911911.7802150.1942711.99045325.7-21.20.5767600.846479
wunderplumb112.225.64.4102.06900020.1928870.2173762.05660345.9-37.10.5851440.829712
krm-bot66.09.56.968.18212422.1212020.3140092.26470957.0-43.20.6194580.761083
andrewsiah0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
cobyj-bot0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
RPM_bot-8.78.0-1.189.62555931.687420-0.0342822.36462473.8-76.00.4868050.973609
ProfessorSP-217.118.6-11.780.59407218.687303-0.6246162.09524327.5-50.80.2701180.540237
pianobot-217.34.7-46.2124.35072857.358714-0.8061302.798986114.3-206.80.2343880.468776
minefrac1-299.652.1-5.770.5819809.778562-0.5880042.00564913.9-25.40.2795600.559119
\n", + "
" + ], + "text/plain": [ + " W_score W_count W_ave W_stdev \\\n", + "pro_median 4238.6 93.1 45.5 62.229168 \n", + "metac-o1 3010.4 92.1 32.7 57.756859 \n", + "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", + "acm_bot 2239.1 81.2 27.6 55.554054 \n", + "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", + "bot_median 1970.6 93.1 21.2 65.554743 \n", + "manticAI 1865.1 70.4 26.5 66.353059 \n", + "metac-exa 1826.3 90.1 20.3 82.219585 \n", + "twsummerbot 1819.1 59.4 30.6 54.747799 \n", + "metac-claude-3-5-sonnet-latest 1740.3 92.1 18.9 71.545983 \n", + "metac-Llama-3.1 1701.2 90.1 18.9 62.154929 \n", + "jkraybill_bot 1616.1 45.0 35.9 59.756838 \n", + "metac-Gemini-Exp-1206 1595.7 77.5 20.6 67.099981 \n", + "NextWorldLab 1583.0 81.2 19.5 66.411747 \n", + "metac-o1-preview 1527.7 92.1 16.6 87.111568 \n", + "metac-deepseek-r1 1518.3 52.1 29.1 62.764970 \n", + "laylaps 1500.6 65.1 23.1 74.457365 \n", + "mmBot 1482.7 93.1 15.9 79.990502 \n", + "Grizeu_Bot 1399.5 52.4 26.7 60.886905 \n", + "metac-grok-2-1212 1167.9 92.1 12.7 79.322449 \n", + "VeritasAI 1136.7 78.1 14.6 61.124913 \n", + "metac-gpt-4o 1045.1 92.1 11.3 67.764165 \n", + "SynapseSeer 1039.5 26.2 39.8 62.843548 \n", + "annabot 1032.0 29.3 35.2 57.689624 \n", + "GreeneiBot2 932.9 59.4 15.7 73.832186 \n", + "MWG 741.4 28.6 25.9 78.735891 \n", + "InstitutPelFutur 722.7 91.1 7.9 100.840633 \n", + "cookics_bot_TEST 714.2 27.4 26.1 63.256652 \n", + "Bot_Pepa 660.8 45.0 14.7 69.738787 \n", + "ajf-bot 484.4 35.2 13.7 86.568228 \n", + "swingswish 430.0 7.7 55.8 52.065740 \n", + "KevinTestBot 331.1 8.4 39.4 76.256855 \n", + "X_bot 274.5 7.0 39.2 31.693801 \n", + "CumulativeBot 253.8 10.2 24.8 78.924719 \n", + "CatrachoCaster 247.3 19.7 12.6 75.371584 \n", + "jonahsingerbot 224.2 4.7 47.7 64.220182 \n", + "bean_bot 210.5 4.7 44.8 76.356439 \n", + "4Shadower 210.5 14.0 15.0 116.146112 \n", + "pgodzinai 177.1 77.4 2.3 103.639119 \n", + "wunderplumb 112.2 25.6 4.4 102.069000 \n", + "krm-bot 66.0 9.5 6.9 68.182124 \n", + "andrewsiah 0.0 0.0 NaN NaN \n", + "cobyj-bot 0.0 0.0 NaN NaN \n", + "RPM_bot -8.7 8.0 -1.1 89.625559 \n", + "ProfessorSP -217.1 18.6 -11.7 80.594072 \n", + "pianobot -217.3 4.7 -46.2 124.350728 \n", + "minefrac1 -299.6 52.1 -5.7 70.581980 \n", + "\n", + " std_err t_stat t_crit upper_bound \\\n", + "pro_median 6.449398 7.059105 1.985277 58.3 \n", + "metac-o1 6.018299 5.431054 1.985550 44.6 \n", + "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", + "acm_bot 6.163169 4.471343 1.988985 39.8 \n", + "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", + "bot_median 6.794058 3.115493 1.985277 34.7 \n", + "manticAI 7.905338 3.348936 1.993488 42.2 \n", + "metac-exa 8.661894 2.340069 1.986114 37.5 \n", + "twsummerbot 7.103517 4.311100 2.000163 44.8 \n", + "metac-claude-3-5-sonnet-latest 7.455134 2.534620 1.985550 33.7 \n", + "metac-Llama-3.1 6.548068 2.883453 1.986114 31.9 \n", + "jkraybill_bot 8.903079 4.029223 2.013412 53.8 \n", + "metac-Gemini-Exp-1206 7.622046 2.701303 1.990426 35.8 \n", + "NextWorldLab 7.367722 2.644427 1.988985 34.1 \n", + "metac-o1-preview 9.077077 1.827344 1.985550 34.6 \n", + "metac-deepseek-r1 8.695578 3.351382 2.005379 46.6 \n", + "laylaps 9.228204 2.497799 1.996341 41.5 \n", + "mmBot 8.290173 1.921090 1.985277 32.4 \n", + "Grizeu_Bot 8.415222 3.176755 2.005555 43.6 \n", + "metac-grok-2-1212 8.265446 1.534149 1.985550 29.1 \n", + "VeritasAI 6.916601 2.104241 1.990095 28.3 \n", + "metac-gpt-4o 7.061066 1.607096 1.985550 25.4 \n", + "SynapseSeer 12.289235 3.234607 2.053076 65.0 \n", + "annabot 10.657710 3.304739 2.044183 57.0 \n", + "GreeneiBot2 9.583748 1.640104 2.000141 34.9 \n", + "MWG 14.722777 1.760805 2.046561 56.1 \n", + "InstitutPelFutur 10.565167 0.750854 1.985829 28.9 \n", + "cookics_bot_TEST 12.084562 2.156937 2.049541 50.8 \n", + "Bot_Pepa 10.390274 1.411723 2.013412 35.6 \n", + "ajf-bot 14.580720 0.942554 2.028730 43.3 \n", + "swingswish 18.763190 2.976027 2.367123 100.3 \n", + "KevinTestBot 26.311114 1.498097 2.311496 100.2 \n", + "X_bot 11.979131 3.274020 2.446912 68.5 \n", + "CumulativeBot 24.651941 1.004580 2.231848 79.8 \n", + "CatrachoCaster 16.981440 0.739137 2.088777 48.0 \n", + "jonahsingerbot 29.622561 1.610003 2.784843 130.2 \n", + "bean_bot 35.220599 1.271879 2.784843 142.9 \n", + "4Shadower 31.041354 0.484489 2.147239 81.7 \n", + "pgodzinai 11.780215 0.194271 1.990453 25.7 \n", + "wunderplumb 20.192887 0.217376 2.056603 45.9 \n", + "krm-bot 22.121202 0.314009 2.264709 57.0 \n", + "andrewsiah NaN NaN NaN NaN \n", + "cobyj-bot NaN NaN NaN NaN \n", + "RPM_bot 31.687420 -0.034282 2.364624 73.8 \n", + "ProfessorSP 18.687303 -0.624616 2.095243 27.5 \n", + "pianobot 57.358714 -0.806130 2.798986 114.3 \n", + "minefrac1 9.778562 -0.588004 2.005649 13.9 \n", + "\n", + " lower_bound cdf p_value \n", + "pro_median 32.7 1.000000 0.000000 \n", + "metac-o1 20.7 1.000000 0.000000 \n", + "metac-perplexity 16.7 0.999982 0.000036 \n", + "acm_bot 15.3 0.999987 0.000025 \n", + "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", + "bot_median 7.7 0.998776 0.002449 \n", + "manticAI 10.7 0.999343 0.001314 \n", + "metac-exa 3.1 0.989243 0.021514 \n", + "twsummerbot 16.4 0.999968 0.000063 \n", + "metac-claude-3-5-sonnet-latest 4.1 0.993518 0.012963 \n", + "metac-Llama-3.1 5.9 0.997535 0.004930 \n", + "jkraybill_bot 17.9 0.999891 0.000218 \n", + "metac-Gemini-Exp-1206 5.4 0.995749 0.008502 \n", + "NextWorldLab 4.8 0.995080 0.009840 \n", + "metac-o1-preview -1.4 0.964539 0.070922 \n", + "metac-deepseek-r1 11.7 0.999241 0.001519 \n", + "laylaps 4.6 0.992463 0.015074 \n", + "mmBot -0.5 0.971093 0.057813 \n", + "Grizeu_Bot 9.9 0.998740 0.002521 \n", + "metac-grok-2-1212 -3.7 0.935771 0.128459 \n", + "VeritasAI 0.8 0.980692 0.038617 \n", + "metac-gpt-4o -2.7 0.944253 0.111494 \n", + "SynapseSeer 14.5 0.998302 0.003397 \n", + "annabot 13.4 0.998707 0.002586 \n", + "GreeneiBot2 -3.5 0.946818 0.106364 \n", + "MWG -4.2 0.955325 0.089349 \n", + "InstitutPelFutur -13.0 0.772651 0.454697 \n", + "cookics_bot_TEST 1.3 0.979856 0.040287 \n", + "Bot_Pepa -6.3 0.917472 0.165057 \n", + "ajf-bot -15.8 0.823745 0.352510 \n", + "swingswish 11.4 0.989142 0.021716 \n", + "KevinTestBot -21.4 0.912252 0.175497 \n", + "X_bot 9.9 0.991526 0.016949 \n", + "CumulativeBot -30.3 0.829673 0.340653 \n", + "CatrachoCaster -22.9 0.765500 0.469001 \n", + "jonahsingerbot -34.8 0.905799 0.188401 \n", + "bean_bot -53.3 0.861262 0.277476 \n", + "4Shadower -51.6 0.681950 0.636100 \n", + "pgodzinai -21.2 0.576760 0.846479 \n", + "wunderplumb -37.1 0.585144 0.829712 \n", + "krm-bot -43.2 0.619458 0.761083 \n", + "andrewsiah NaN NaN NA \n", + "cobyj-bot NaN NaN NA \n", + "RPM_bot -76.0 0.486805 0.973609 \n", + "ProfessorSP -50.8 0.270118 0.540237 \n", + "pianobot -206.8 0.234388 0.468776 \n", + "minefrac1 -25.4 0.279560 0.559119 " + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# make me a list that's pro_median and all the bot forecasters\n", "forecasters = ['pro_median'] + [col for col in df_pro_bot_baseline_weights.columns if col in all_bots]\n", @@ -3537,7 +6162,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 218, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3545,7 +6170,841 @@ "id": "aGNedTHmU-Bm", "outputId": "a7935679-8993-4329-d05d-fd701c4b77a8" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
cobyj-bot0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
andrewsiah0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNA
bean_bot-0.64.7-0.10.0698490.032219-4.2651062.784843-0.0-0.20.0076740.015349
jonahsingerbot-0.64.7-0.10.0502720.023189-5.2736302.784843-0.1-0.20.0038390.007677
X_bot-0.77.0-0.10.3540680.133825-0.7471952.4469120.2-0.40.2415940.483189
RPM_bot-1.17.0-0.20.8245320.311644-0.5234062.4469120.6-0.90.3097260.619452
CumulativeBot-1.110.2-0.10.2577980.080522-1.3151322.2318480.1-0.30.1100660.220132
swingswish-1.27.7-0.20.1402750.050552-3.0749472.367123-0.0-0.30.0094760.018953
SynapseSeer-1.326.2-0.10.4525550.088498-0.5689102.0530760.1-0.20.2872310.574463
KevinTestBot-1.58.4-0.20.5894660.203385-0.8971162.3114960.3-0.70.1989520.397903
Grizeu_Bot-1.751.4-0.01.1733920.163747-0.2066162.0064470.3-0.40.4185710.837143
pianobot-2.74.7-0.60.9162040.422613-1.3843272.7989860.6-1.80.1219410.243882
CatrachoCaster-3.219.7-0.20.5209010.117361-1.3655322.0887770.1-0.40.0941440.188288
krm-bot-5.19.5-0.50.5115460.165967-3.2298462.264709-0.2-0.90.0055630.011127
annabot-6.229.3-0.20.5208690.096226-2.2117952.044183-0.0-0.40.0176100.035221
4Shadower-6.214.0-0.40.7673220.205075-2.1431942.1472390.0-0.90.0257970.051593
cookics_bot_TEST-6.927.4-0.30.7446990.142267-1.7648762.0495410.0-0.50.0445760.089152
jkraybill_bot-7.544.0-0.20.5128530.077272-2.1971332.014642-0.0-0.30.0167210.033441
twsummerbot-8.958.4-0.20.6597100.086327-1.7583912.0008550.0-0.30.0420060.084012
MWG-9.828.6-0.30.7052400.131872-2.5896252.046561-0.1-0.60.0075810.015163
ProfessorSP-10.018.6-0.50.9362770.217094-2.4844802.095243-0.1-1.00.0116440.023289
GreeneiBot2-10.458.4-0.20.8498830.111260-1.5979762.0008320.0-0.40.0577720.115544
acm_bot-10.580.2-0.10.9142650.102059-1.2877171.9893440.1-0.30.1007960.201592
ajf-bot-10.934.2-0.31.0855890.185496-1.7223952.0307780.1-0.70.0471450.094289
metac-o1-11.591.1-0.10.8882270.093060-1.3604681.9858290.1-0.30.0885380.177076
Bot_Pepa-11.544.0-0.30.7375370.111125-2.3431662.014642-0.0-0.50.0119050.023810
metac-perplexity-11.989.1-0.10.9936690.105270-1.2647311.9864050.1-0.30.1046520.209303
laylaps-12.964.1-0.20.6619050.082674-2.4404611.996907-0.0-0.40.0087440.017488
wunderplumb-13.625.6-0.50.9000510.178062-2.9840942.056603-0.2-0.90.0031740.006348
manticAI-14.669.4-0.20.6709460.080510-2.6133541.993968-0.0-0.40.0055070.011014
metac-deepseek-r1-14.652.1-0.30.7315250.101347-2.7666892.005379-0.1-0.50.0039320.007864
metac-Gemini-Exp-1206-15.276.5-0.20.9437970.107907-1.8467741.9908220.0-0.40.0343490.068698
NextWorldLab-16.980.2-0.20.9069640.101244-2.0783931.989344-0.0-0.40.0204550.040909
bot_median-17.392.1-0.20.9191220.095773-1.9639961.9855500.0-0.40.0262900.052579
minefrac1-19.251.1-0.40.8809900.123242-3.0436412.006545-0.1-0.60.0018590.003717
metac-claude-3-5-sonnet-20240620-19.590.5-0.21.0091380.106078-2.0310651.986072-0.0-0.40.0226080.045215
mmBot-21.992.1-0.20.7250100.075546-3.1501041.985550-0.1-0.40.0011040.002208
metac-grok-2-1212-22.991.1-0.31.0488290.109887-2.2835281.985829-0.0-0.50.0123750.024750
pgodzinai-23.976.4-0.30.9564520.109425-2.8586861.990849-0.1-0.50.0027490.005498
VeritasAI-24.377.1-0.30.6607030.075245-4.1859101.990482-0.2-0.50.0000380.000076
metac-claude-3-5-sonnet-latest-24.491.1-0.30.7843150.082173-3.2658271.985829-0.1-0.40.0007720.001544
metac-Llama-3.1-26.189.1-0.30.9987990.105813-2.7685651.986405-0.1-0.50.0034320.006863
metac-exa-26.689.1-0.30.8489740.089941-3.3240971.986405-0.1-0.50.0006470.001294
InstitutPelFutur-26.990.1-0.30.9737670.102587-2.9085241.986114-0.1-0.50.0022920.004584
metac-o1-preview-27.891.1-0.30.8774340.091930-3.3149741.985829-0.1-0.50.0006610.001322
metac-gpt-4o-30.591.1-0.30.9139400.095754-3.4928271.985829-0.1-0.50.0003710.000743
\n", + "
" + ], + "text/plain": [ + " W_score W_count W_ave W_stdev std_err \\\n", + "cobyj-bot 0.0 0.0 NaN NaN NaN \n", + "andrewsiah 0.0 0.0 NaN NaN NaN \n", + "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", + "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", + "RPM_bot -1.1 7.0 -0.2 0.824532 0.311644 \n", + "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", + "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", + "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", + "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", + "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", + "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", + "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", + "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", + "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", + "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", + "cookics_bot_TEST -6.9 27.4 -0.3 0.744699 0.142267 \n", + "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", + "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", + "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", + "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", + "GreeneiBot2 -10.4 58.4 -0.2 0.849883 0.111260 \n", + "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", + "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", + "metac-o1 -11.5 91.1 -0.1 0.888227 0.093060 \n", + "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", + "metac-perplexity -11.9 89.1 -0.1 0.993669 0.105270 \n", + "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", + "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", + "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", + "metac-deepseek-r1 -14.6 52.1 -0.3 0.731525 0.101347 \n", + "metac-Gemini-Exp-1206 -15.2 76.5 -0.2 0.943797 0.107907 \n", + "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", + "bot_median -17.3 92.1 -0.2 0.919122 0.095773 \n", + "minefrac1 -19.2 51.1 -0.4 0.880990 0.123242 \n", + "metac-claude-3-5-sonnet-20240620 -19.5 90.5 -0.2 1.009138 0.106078 \n", + "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", + "metac-grok-2-1212 -22.9 91.1 -0.3 1.048829 0.109887 \n", + "pgodzinai -23.9 76.4 -0.3 0.956452 0.109425 \n", + "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", + "metac-claude-3-5-sonnet-latest -24.4 91.1 -0.3 0.784315 0.082173 \n", + "metac-Llama-3.1 -26.1 89.1 -0.3 0.998799 0.105813 \n", + "metac-exa -26.6 89.1 -0.3 0.848974 0.089941 \n", + "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", + "metac-o1-preview -27.8 91.1 -0.3 0.877434 0.091930 \n", + "metac-gpt-4o -30.5 91.1 -0.3 0.913940 0.095754 \n", + "\n", + " t_stat t_crit upper_bound \\\n", + "cobyj-bot NaN NaN NaN \n", + "andrewsiah NaN NaN NaN \n", + "bean_bot -4.265106 2.784843 -0.0 \n", + "jonahsingerbot -5.273630 2.784843 -0.1 \n", + "X_bot -0.747195 2.446912 0.2 \n", + "RPM_bot -0.523406 2.446912 0.6 \n", + "CumulativeBot -1.315132 2.231848 0.1 \n", + "swingswish -3.074947 2.367123 -0.0 \n", + "SynapseSeer -0.568910 2.053076 0.1 \n", + "KevinTestBot -0.897116 2.311496 0.3 \n", + "Grizeu_Bot -0.206616 2.006447 0.3 \n", + "pianobot -1.384327 2.798986 0.6 \n", + "CatrachoCaster -1.365532 2.088777 0.1 \n", + "krm-bot -3.229846 2.264709 -0.2 \n", + "annabot -2.211795 2.044183 -0.0 \n", + "4Shadower -2.143194 2.147239 0.0 \n", + "cookics_bot_TEST -1.764876 2.049541 0.0 \n", + "jkraybill_bot -2.197133 2.014642 -0.0 \n", + "twsummerbot -1.758391 2.000855 0.0 \n", + "MWG -2.589625 2.046561 -0.1 \n", + "ProfessorSP -2.484480 2.095243 -0.1 \n", + "GreeneiBot2 -1.597976 2.000832 0.0 \n", + "acm_bot -1.287717 1.989344 0.1 \n", + "ajf-bot -1.722395 2.030778 0.1 \n", + "metac-o1 -1.360468 1.985829 0.1 \n", + "Bot_Pepa -2.343166 2.014642 -0.0 \n", + "metac-perplexity -1.264731 1.986405 0.1 \n", + "laylaps -2.440461 1.996907 -0.0 \n", + "wunderplumb -2.984094 2.056603 -0.2 \n", + "manticAI -2.613354 1.993968 -0.0 \n", + "metac-deepseek-r1 -2.766689 2.005379 -0.1 \n", + "metac-Gemini-Exp-1206 -1.846774 1.990822 0.0 \n", + "NextWorldLab -2.078393 1.989344 -0.0 \n", + "bot_median -1.963996 1.985550 0.0 \n", + "minefrac1 -3.043641 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -2.031065 1.986072 -0.0 \n", + "mmBot -3.150104 1.985550 -0.1 \n", + "metac-grok-2-1212 -2.283528 1.985829 -0.0 \n", + "pgodzinai -2.858686 1.990849 -0.1 \n", + "VeritasAI -4.185910 1.990482 -0.2 \n", + "metac-claude-3-5-sonnet-latest -3.265827 1.985829 -0.1 \n", + "metac-Llama-3.1 -2.768565 1.986405 -0.1 \n", + "metac-exa -3.324097 1.986405 -0.1 \n", + "InstitutPelFutur -2.908524 1.986114 -0.1 \n", + "metac-o1-preview -3.314974 1.985829 -0.1 \n", + "metac-gpt-4o -3.492827 1.985829 -0.1 \n", + "\n", + " lower_bound cdf p_value \n", + "cobyj-bot NaN NaN NA \n", + "andrewsiah NaN NaN NA \n", + "bean_bot -0.2 0.007674 0.015349 \n", + "jonahsingerbot -0.2 0.003839 0.007677 \n", + "X_bot -0.4 0.241594 0.483189 \n", + "RPM_bot -0.9 0.309726 0.619452 \n", + "CumulativeBot -0.3 0.110066 0.220132 \n", + "swingswish -0.3 0.009476 0.018953 \n", + "SynapseSeer -0.2 0.287231 0.574463 \n", + "KevinTestBot -0.7 0.198952 0.397903 \n", + "Grizeu_Bot -0.4 0.418571 0.837143 \n", + "pianobot -1.8 0.121941 0.243882 \n", + "CatrachoCaster -0.4 0.094144 0.188288 \n", + "krm-bot -0.9 0.005563 0.011127 \n", + "annabot -0.4 0.017610 0.035221 \n", + "4Shadower -0.9 0.025797 0.051593 \n", + "cookics_bot_TEST -0.5 0.044576 0.089152 \n", + "jkraybill_bot -0.3 0.016721 0.033441 \n", + "twsummerbot -0.3 0.042006 0.084012 \n", + "MWG -0.6 0.007581 0.015163 \n", + "ProfessorSP -1.0 0.011644 0.023289 \n", + "GreeneiBot2 -0.4 0.057772 0.115544 \n", + "acm_bot -0.3 0.100796 0.201592 \n", + "ajf-bot -0.7 0.047145 0.094289 \n", + "metac-o1 -0.3 0.088538 0.177076 \n", + "Bot_Pepa -0.5 0.011905 0.023810 \n", + "metac-perplexity -0.3 0.104652 0.209303 \n", + "laylaps -0.4 0.008744 0.017488 \n", + "wunderplumb -0.9 0.003174 0.006348 \n", + "manticAI -0.4 0.005507 0.011014 \n", + "metac-deepseek-r1 -0.5 0.003932 0.007864 \n", + "metac-Gemini-Exp-1206 -0.4 0.034349 0.068698 \n", + "NextWorldLab -0.4 0.020455 0.040909 \n", + "bot_median -0.4 0.026290 0.052579 \n", + "minefrac1 -0.6 0.001859 0.003717 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.022608 0.045215 \n", + "mmBot -0.4 0.001104 0.002208 \n", + "metac-grok-2-1212 -0.5 0.012375 0.024750 \n", + "pgodzinai -0.5 0.002749 0.005498 \n", + "VeritasAI -0.5 0.000038 0.000076 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.000772 0.001544 \n", + "metac-Llama-3.1 -0.5 0.003432 0.006863 \n", + "metac-exa -0.5 0.000647 0.001294 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 \n", + "metac-o1-preview -0.5 0.000661 0.001322 \n", + "metac-gpt-4o -0.5 0.000371 0.000743 " + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# @title Weighted head-to-head, T test\n", "\n", @@ -3567,7 +7026,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 219, "metadata": {}, "outputs": [], "source": [ @@ -3577,7 +7036,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 220, "metadata": { "cellView": "form", "colab": { @@ -3586,7 +7045,916 @@ "id": "3d_ZdL0A0qTz", "outputId": "e30ee8fb-0faf-45ae-974e-d4af282e0252" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
RankBotW_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
01metac-o13631.1375.39.735.0711401.8102945.3442931.96598513.26.11.0000000.000000
12metac-o1-preview3121.4368.78.545.9615892.3935733.5368201.96609313.23.80.9997720.000457
23metac-Gemini-Exp-12061880.5347.15.444.8958442.4097192.2481331.96645810.20.70.9874020.025197
34SynapseSeer966.5152.06.435.6992152.8951132.1955681.97487912.10.60.9851760.029648
45manticAI2055.2315.76.555.6900633.1344982.0771541.96718712.70.30.9807010.038598
56twsummerbot1450.0241.36.045.0911402.9027092.0701531.96931311.70.30.9802470.039507
67acm_bot1738.4344.85.045.8463322.4691432.0421541.9665219.90.20.9790510.041899
78cookics_bot_TEST1143.8162.67.046.7964543.6698871.9168291.97413814.3-0.20.9714880.057024
89CumulativeBot991.4104.59.552.1803255.1044461.8585841.98213619.6-0.60.9670360.065928
910metac-claude-3-5-sonnet-latest951.3370.32.638.2630661.9883421.2919541.9660636.5-1.30.9014100.197181
1011GreeneiBot21494.7264.15.759.7283543.6750521.5398111.96859612.9-1.60.9375960.124808
1112metac-perplexity1558.4354.44.459.5883783.1652091.3891811.96637110.6-1.80.9171740.165652
1213metac-deepseek-r1516.8277.91.937.3532102.2407800.8299751.9681656.3-2.60.7963660.407268
1314pgodzinai1106.7325.43.466.6861593.6966950.9199541.96694910.7-3.90.8208600.358280
1415metac-exa599.9365.31.663.4593893.3201610.4946111.9661428.2-4.90.6894130.621173
1516MWG253.8113.42.240.6740843.8190370.5859361.9804689.8-5.30.7204540.559093
1617jkraybill_bot625.4207.43.068.5607804.7604770.6333891.97101512.4-6.40.7364100.527181
1718metac-claude-3-5-sonnet-20240620-759.5373.7-2.044.0904802.280718-0.8910111.9660142.5-6.50.1867490.373498
1819metac-grok-2-1212-550.1373.3-1.550.1642462.596293-0.5675531.9660163.6-6.60.2853400.570681
1920metac-Llama-3.1-980.9370.6-2.641.8100632.171783-1.2186111.9660621.6-6.90.1118850.223769
2021mmBot-587.4373.0-1.658.2984393.018498-0.5216711.9660174.4-7.50.3011050.602210
2122VeritasAI-1602.2330.0-4.938.7547802.133316-2.2757101.966760-0.7-9.10.0117530.023506
2223InstitutPelFutur-877.8356.0-2.564.6034773.423881-0.7201271.9663054.3-9.20.2359600.471921
2324NextWorldLab-1377.9337.6-4.151.4333882.799472-1.4581571.9666641.4-9.60.0728650.145730
2425metac-gpt-4o-2235.4373.3-6.045.4016702.349802-2.5482091.966016-1.4-10.60.0056140.011229
2526CatrachoCaster-289.481.6-3.531.9567253.538536-1.0026081.9883423.5-10.60.1595260.319052
2627laylaps-1489.1322.1-4.663.9802383.564926-1.2968551.9670502.4-11.60.0978060.195612
2728ProfessorSP-426.8128.6-3.355.1654604.863650-0.6821421.9781236.3-12.90.2481930.496385
2829krm-bot-354.7104.0-3.449.8754924.890694-0.6973341.9823276.3-13.10.2435820.487165
2930wunderplumb-986.1174.0-5.752.9658934.015334-1.4114341.9731952.3-13.60.0799560.159913
3031andrewsiah2.625.10.135.8050927.1467390.0146792.06034114.8-14.60.5057960.988409
3132annabot-190.683.8-2.359.1122286.458906-0.3522221.98640810.6-15.10.3627840.725567
3233Bot_Pepa-1490.1169.4-8.844.2657023.400530-2.5860051.973733-2.1-15.50.0052780.010555
33344Shadower-646.3115.5-5.653.8678675.012320-1.1163051.9797854.3-15.50.1333140.266629
3435minefrac1-1757.1188.2-9.344.1258493.216071-2.9021901.972106-3.0-15.70.0020750.004150
3536KevinTestBot-220.489.5-2.567.6508777.150920-0.3443101.98550511.7-16.70.3657150.731430
3637jonahsingerbot-333.464.8-5.148.0155485.964779-0.8626001.9952736.8-17.00.1957940.391588
3738bean_bot-208.867.8-3.159.9556627.281408-0.4229401.99377111.4-17.60.3368490.673697
3839Grizeu_Bot-1882.6193.2-9.756.7042374.079442-2.3885211.971774-1.7-17.80.0089420.017884
3940cobyj-bot-12.131.5-0.448.0409918.559663-0.0450462.03985017.1-17.80.4821820.964365
4041X_bot-16.17.0-2.323.9086329.036614-0.2537742.44691219.8-24.40.4040710.808142
4142ajf-bot-3208.3229.2-14.083.2955695.502524-2.5444141.969928-3.2-24.80.0058030.011607
4243pianobot-12.719.6-0.752.32348711.833775-0.0550422.09382324.1-25.40.4783470.956694
4344swingswish-777.064.8-12.073.0478929.074447-1.3214361.9952736.1-30.10.0955380.191075
4445RPM_bot-815.623.8-34.391.54540218.784720-1.8281002.0615084.4-73.10.0403390.080679
\n", + "
" + ], + "text/plain": [ + " Rank Bot W_score W_count W_ave \\\n", + "0 1 metac-o1 3631.1 375.3 9.7 \n", + "1 2 metac-o1-preview 3121.4 368.7 8.5 \n", + "2 3 metac-Gemini-Exp-1206 1880.5 347.1 5.4 \n", + "3 4 SynapseSeer 966.5 152.0 6.4 \n", + "4 5 manticAI 2055.2 315.7 6.5 \n", + "5 6 twsummerbot 1450.0 241.3 6.0 \n", + "6 7 acm_bot 1738.4 344.8 5.0 \n", + "7 8 cookics_bot_TEST 1143.8 162.6 7.0 \n", + "8 9 CumulativeBot 991.4 104.5 9.5 \n", + "9 10 metac-claude-3-5-sonnet-latest 951.3 370.3 2.6 \n", + "10 11 GreeneiBot2 1494.7 264.1 5.7 \n", + "11 12 metac-perplexity 1558.4 354.4 4.4 \n", + "12 13 metac-deepseek-r1 516.8 277.9 1.9 \n", + "13 14 pgodzinai 1106.7 325.4 3.4 \n", + "14 15 metac-exa 599.9 365.3 1.6 \n", + "15 16 MWG 253.8 113.4 2.2 \n", + "16 17 jkraybill_bot 625.4 207.4 3.0 \n", + "17 18 metac-claude-3-5-sonnet-20240620 -759.5 373.7 -2.0 \n", + "18 19 metac-grok-2-1212 -550.1 373.3 -1.5 \n", + "19 20 metac-Llama-3.1 -980.9 370.6 -2.6 \n", + "20 21 mmBot -587.4 373.0 -1.6 \n", + "21 22 VeritasAI -1602.2 330.0 -4.9 \n", + "22 23 InstitutPelFutur -877.8 356.0 -2.5 \n", + "23 24 NextWorldLab -1377.9 337.6 -4.1 \n", + "24 25 metac-gpt-4o -2235.4 373.3 -6.0 \n", + "25 26 CatrachoCaster -289.4 81.6 -3.5 \n", + "26 27 laylaps -1489.1 322.1 -4.6 \n", + "27 28 ProfessorSP -426.8 128.6 -3.3 \n", + "28 29 krm-bot -354.7 104.0 -3.4 \n", + "29 30 wunderplumb -986.1 174.0 -5.7 \n", + "30 31 andrewsiah 2.6 25.1 0.1 \n", + "31 32 annabot -190.6 83.8 -2.3 \n", + "32 33 Bot_Pepa -1490.1 169.4 -8.8 \n", + "33 34 4Shadower -646.3 115.5 -5.6 \n", + "34 35 minefrac1 -1757.1 188.2 -9.3 \n", + "35 36 KevinTestBot -220.4 89.5 -2.5 \n", + "36 37 jonahsingerbot -333.4 64.8 -5.1 \n", + "37 38 bean_bot -208.8 67.8 -3.1 \n", + "38 39 Grizeu_Bot -1882.6 193.2 -9.7 \n", + "39 40 cobyj-bot -12.1 31.5 -0.4 \n", + "40 41 X_bot -16.1 7.0 -2.3 \n", + "41 42 ajf-bot -3208.3 229.2 -14.0 \n", + "42 43 pianobot -12.7 19.6 -0.7 \n", + "43 44 swingswish -777.0 64.8 -12.0 \n", + "44 45 RPM_bot -815.6 23.8 -34.3 \n", + "\n", + " W_stdev std_err t_stat t_crit upper_bound lower_bound \\\n", + "0 35.071140 1.810294 5.344293 1.965985 13.2 6.1 \n", + "1 45.961589 2.393573 3.536820 1.966093 13.2 3.8 \n", + "2 44.895844 2.409719 2.248133 1.966458 10.2 0.7 \n", + "3 35.699215 2.895113 2.195568 1.974879 12.1 0.6 \n", + "4 55.690063 3.134498 2.077154 1.967187 12.7 0.3 \n", + "5 45.091140 2.902709 2.070153 1.969313 11.7 0.3 \n", + "6 45.846332 2.469143 2.042154 1.966521 9.9 0.2 \n", + "7 46.796454 3.669887 1.916829 1.974138 14.3 -0.2 \n", + "8 52.180325 5.104446 1.858584 1.982136 19.6 -0.6 \n", + "9 38.263066 1.988342 1.291954 1.966063 6.5 -1.3 \n", + "10 59.728354 3.675052 1.539811 1.968596 12.9 -1.6 \n", + "11 59.588378 3.165209 1.389181 1.966371 10.6 -1.8 \n", + "12 37.353210 2.240780 0.829975 1.968165 6.3 -2.6 \n", + "13 66.686159 3.696695 0.919954 1.966949 10.7 -3.9 \n", + "14 63.459389 3.320161 0.494611 1.966142 8.2 -4.9 \n", + "15 40.674084 3.819037 0.585936 1.980468 9.8 -5.3 \n", + "16 68.560780 4.760477 0.633389 1.971015 12.4 -6.4 \n", + "17 44.090480 2.280718 -0.891011 1.966014 2.5 -6.5 \n", + "18 50.164246 2.596293 -0.567553 1.966016 3.6 -6.6 \n", + "19 41.810063 2.171783 -1.218611 1.966062 1.6 -6.9 \n", + "20 58.298439 3.018498 -0.521671 1.966017 4.4 -7.5 \n", + "21 38.754780 2.133316 -2.275710 1.966760 -0.7 -9.1 \n", + "22 64.603477 3.423881 -0.720127 1.966305 4.3 -9.2 \n", + "23 51.433388 2.799472 -1.458157 1.966664 1.4 -9.6 \n", + "24 45.401670 2.349802 -2.548209 1.966016 -1.4 -10.6 \n", + "25 31.956725 3.538536 -1.002608 1.988342 3.5 -10.6 \n", + "26 63.980238 3.564926 -1.296855 1.967050 2.4 -11.6 \n", + "27 55.165460 4.863650 -0.682142 1.978123 6.3 -12.9 \n", + "28 49.875492 4.890694 -0.697334 1.982327 6.3 -13.1 \n", + "29 52.965893 4.015334 -1.411434 1.973195 2.3 -13.6 \n", + "30 35.805092 7.146739 0.014679 2.060341 14.8 -14.6 \n", + "31 59.112228 6.458906 -0.352222 1.986408 10.6 -15.1 \n", + "32 44.265702 3.400530 -2.586005 1.973733 -2.1 -15.5 \n", + "33 53.867867 5.012320 -1.116305 1.979785 4.3 -15.5 \n", + "34 44.125849 3.216071 -2.902190 1.972106 -3.0 -15.7 \n", + "35 67.650877 7.150920 -0.344310 1.985505 11.7 -16.7 \n", + "36 48.015548 5.964779 -0.862600 1.995273 6.8 -17.0 \n", + "37 59.955662 7.281408 -0.422940 1.993771 11.4 -17.6 \n", + "38 56.704237 4.079442 -2.388521 1.971774 -1.7 -17.8 \n", + "39 48.040991 8.559663 -0.045046 2.039850 17.1 -17.8 \n", + "40 23.908632 9.036614 -0.253774 2.446912 19.8 -24.4 \n", + "41 83.295569 5.502524 -2.544414 1.969928 -3.2 -24.8 \n", + "42 52.323487 11.833775 -0.055042 2.093823 24.1 -25.4 \n", + "43 73.047892 9.074447 -1.321436 1.995273 6.1 -30.1 \n", + "44 91.545402 18.784720 -1.828100 2.061508 4.4 -73.1 \n", + "\n", + " cdf p_value \n", + "0 1.000000 0.000000 \n", + "1 0.999772 0.000457 \n", + "2 0.987402 0.025197 \n", + "3 0.985176 0.029648 \n", + "4 0.980701 0.038598 \n", + "5 0.980247 0.039507 \n", + "6 0.979051 0.041899 \n", + "7 0.971488 0.057024 \n", + "8 0.967036 0.065928 \n", + "9 0.901410 0.197181 \n", + "10 0.937596 0.124808 \n", + "11 0.917174 0.165652 \n", + "12 0.796366 0.407268 \n", + "13 0.820860 0.358280 \n", + "14 0.689413 0.621173 \n", + "15 0.720454 0.559093 \n", + "16 0.736410 0.527181 \n", + "17 0.186749 0.373498 \n", + "18 0.285340 0.570681 \n", + "19 0.111885 0.223769 \n", + "20 0.301105 0.602210 \n", + "21 0.011753 0.023506 \n", + "22 0.235960 0.471921 \n", + "23 0.072865 0.145730 \n", + "24 0.005614 0.011229 \n", + "25 0.159526 0.319052 \n", + "26 0.097806 0.195612 \n", + "27 0.248193 0.496385 \n", + "28 0.243582 0.487165 \n", + "29 0.079956 0.159913 \n", + "30 0.505796 0.988409 \n", + "31 0.362784 0.725567 \n", + "32 0.005278 0.010555 \n", + "33 0.133314 0.266629 \n", + "34 0.002075 0.004150 \n", + "35 0.365715 0.731430 \n", + "36 0.195794 0.391588 \n", + "37 0.336849 0.673697 \n", + "38 0.008942 0.017884 \n", + "39 0.482182 0.964365 \n", + "40 0.404071 0.808142 \n", + "41 0.005803 0.011607 \n", + "42 0.478347 0.956694 \n", + "43 0.095538 0.191075 \n", + "44 0.040339 0.080679 " + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# @title Weighted Bot Peer, T test (to compare bots against each other, use ALL QUESTIONS)\n", "\n", @@ -3621,7 +7989,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 221, "metadata": {}, "outputs": [], "source": [ @@ -3631,16 +7999,223 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 222, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_id4ShadowerBot_PepaCatrachoCasterCumulativeBotGreeneiBot2Grizeu_BotInstitutPelFuturKevinTestBotMWG...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbquestion_weight
031262NaNNaNNaNNaN-242.660874135.57527347.259183NaNNaN...-205.076095121.194882NaN-242.660874-198.879258NaNNaNNaNNaN1.0
131263NaNNaNNaNNaN-96.476789-99.090018-94.660371NaNNaN...7.9517037.951703NaN55.81904144.625993NaNNaNNaNNaN1.0
231264NaNNaNNaNNaN18.89298023.948225-86.527528NaNNaN...13.82151813.821518NaN1.30707117.305437NaNNaNNaNNaN1.0
331274NaNNaN2.076868NaN31.0945314.282464-28.806893NaN14.663415...6.44257916.621639NaN8.55905311.145899NaNNaN-9.706540NaN1.0
431275NaNNaNNaNNaN30.694891-66.461608-58.368696NaNNaN...35.698675-0.691552NaN39.41450214.411756NaNNaN-70.932651NaN1.0
\n", + "

5 rows × 48 columns

\n", + "
" + ], + "text/plain": [ + " bot_question_id 4Shadower Bot_Pepa CatrachoCaster CumulativeBot \\\n", + "0 31262 NaN NaN NaN NaN \n", + "1 31263 NaN NaN NaN NaN \n", + "2 31264 NaN NaN NaN NaN \n", + "3 31274 NaN NaN 2.076868 NaN \n", + "4 31275 NaN NaN NaN NaN \n", + "\n", + " GreeneiBot2 Grizeu_Bot InstitutPelFutur KevinTestBot MWG ... \\\n", + "0 -242.660874 135.575273 47.259183 NaN NaN ... \n", + "1 -96.476789 -99.090018 -94.660371 NaN NaN ... \n", + "2 18.892980 23.948225 -86.527528 NaN NaN ... \n", + "3 31.094531 4.282464 -28.806893 NaN 14.663415 ... \n", + "4 30.694891 -66.461608 -58.368696 NaN NaN ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "0 -205.076095 121.194882 NaN -242.660874 -198.879258 \n", + "1 7.951703 7.951703 NaN 55.819041 44.625993 \n", + "2 13.821518 13.821518 NaN 1.307071 17.305437 \n", + "3 6.442579 16.621639 NaN 8.559053 11.145899 \n", + "4 35.698675 -0.691552 NaN 39.414502 14.411756 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb question_weight \n", + "0 NaN NaN NaN NaN 1.0 \n", + "1 NaN NaN NaN NaN 1.0 \n", + "2 NaN NaN NaN NaN 1.0 \n", + "3 NaN NaN -9.706540 NaN 1.0 \n", + "4 NaN NaN -70.932651 NaN 1.0 \n", + "\n", + "[5 rows x 48 columns]" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_bot_peer_wide.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 223, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -3649,7 +8224,18 @@ "id": "88QO8eyW6T_T", "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# @title Histogram of bot\n", "\n", @@ -3691,9 +8277,420 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 224, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_id4ShadowerBot_PepaCatrachoCasterCumulativeBotGreeneiBot2Grizeu_BotInstitutPelFuturKevinTestBotMWG...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbquestion_weight
031262NaNNaNNaNNaN-242.660874135.57527347.259183NaNNaN...-205.076095121.194882NaN-242.660874-198.879258NaNNaNNaNNaN1.0
131263NaNNaNNaNNaN-96.476789-99.090018-94.660371NaNNaN...7.9517037.951703NaN55.81904144.625993NaNNaNNaNNaN1.0
231264NaNNaNNaNNaN18.89298023.948225-86.527528NaNNaN...13.82151813.821518NaN1.30707117.305437NaNNaNNaNNaN1.0
331274NaNNaN2.076868NaN31.0945314.282464-28.806893NaN14.663415...6.44257916.621639NaN8.55905311.145899NaNNaN-9.706540NaN1.0
431275NaNNaNNaNNaN30.694891-66.461608-58.368696NaNNaN...35.698675-0.691552NaN39.41450214.411756NaNNaN-70.932651NaN1.0
\n", + "

5 rows × 48 columns

\n", + "
" + ], + "text/plain": [ + " bot_question_id 4Shadower Bot_Pepa CatrachoCaster CumulativeBot \\\n", + "0 31262 NaN NaN NaN NaN \n", + "1 31263 NaN NaN NaN NaN \n", + "2 31264 NaN NaN NaN NaN \n", + "3 31274 NaN NaN 2.076868 NaN \n", + "4 31275 NaN NaN NaN NaN \n", + "\n", + " GreeneiBot2 Grizeu_Bot InstitutPelFutur KevinTestBot MWG ... \\\n", + "0 -242.660874 135.575273 47.259183 NaN NaN ... \n", + "1 -96.476789 -99.090018 -94.660371 NaN NaN ... \n", + "2 18.892980 23.948225 -86.527528 NaN NaN ... \n", + "3 31.094531 4.282464 -28.806893 NaN 14.663415 ... \n", + "4 30.694891 -66.461608 -58.368696 NaN NaN ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "0 -205.076095 121.194882 NaN -242.660874 -198.879258 \n", + "1 7.951703 7.951703 NaN 55.819041 44.625993 \n", + "2 13.821518 13.821518 NaN 1.307071 17.305437 \n", + "3 6.442579 16.621639 NaN 8.559053 11.145899 \n", + "4 35.698675 -0.691552 NaN 39.414502 14.411756 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb question_weight \n", + "0 NaN NaN NaN NaN 1.0 \n", + "1 NaN NaN NaN NaN 1.0 \n", + "2 NaN NaN NaN NaN 1.0 \n", + "3 NaN NaN -9.706540 NaN 1.0 \n", + "4 NaN NaN -70.932651 NaN 1.0 \n", + "\n", + "[5 rows x 48 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_id4ShadowerBot_PepaCatrachoCasterCumulativeBotGreeneiBot2Grizeu_BotInstitutPelFuturKevinTestBotMWG...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbquestion_weight
404356196.356385NaN6.3563858.98511614.048951-5.7402526.356385-5.74025210.822423...-5.7402526.356385NaN0.48606113.624559NaNNaN7.9416846.3563851.0
40535620-3.848478NaN2.026137-2.6463853.161815-3.84847811.301510-3.848478-23.803402...2.0261372.026137NaN7.5830468.230127NaNNaNNaN-3.8484781.0
4063562134.934257NaN-15.68138236.351904-16.055800-62.135408-96.71727734.93425732.624547...9.104719-48.411348NaN29.05964231.449931NaNNaNNaN34.9342571.0
40735622-58.153367NaNNaNNaN-14.351771-85.428443-29.09640042.884269NaN...78.87460378.874603NaN114.533049105.344243NaNNaN-1.818274-97.7260201.0
40835705-31.742288NaNNaN43.33077750.02366026.291942NaN-0.62033022.674004...-37.061593-0.620330NaN-8.60147579.739445NaNNaNNaN10.3059451.0
\n", + "

5 rows × 48 columns

\n", + "
" + ], + "text/plain": [ + " bot_question_id 4Shadower Bot_Pepa CatrachoCaster CumulativeBot \\\n", + "404 35619 6.356385 NaN 6.356385 8.985116 \n", + "405 35620 -3.848478 NaN 2.026137 -2.646385 \n", + "406 35621 34.934257 NaN -15.681382 36.351904 \n", + "407 35622 -58.153367 NaN NaN NaN \n", + "408 35705 -31.742288 NaN NaN 43.330777 \n", + "\n", + " GreeneiBot2 Grizeu_Bot InstitutPelFutur KevinTestBot MWG ... \\\n", + "404 14.048951 -5.740252 6.356385 -5.740252 10.822423 ... \n", + "405 3.161815 -3.848478 11.301510 -3.848478 -23.803402 ... \n", + "406 -16.055800 -62.135408 -96.717277 34.934257 32.624547 ... \n", + "407 -14.351771 -85.428443 -29.096400 42.884269 NaN ... \n", + "408 50.023660 26.291942 NaN -0.620330 22.674004 ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "404 -5.740252 6.356385 NaN 0.486061 13.624559 \n", + "405 2.026137 2.026137 NaN 7.583046 8.230127 \n", + "406 9.104719 -48.411348 NaN 29.059642 31.449931 \n", + "407 78.874603 78.874603 NaN 114.533049 105.344243 \n", + "408 -37.061593 -0.620330 NaN -8.601475 79.739445 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb question_weight \n", + "404 NaN NaN 7.941684 6.356385 1.0 \n", + "405 NaN NaN NaN -3.848478 1.0 \n", + "406 NaN NaN NaN 34.934257 1.0 \n", + "407 NaN NaN -1.818274 -97.726020 1.0 \n", + "408 NaN NaN NaN 10.305945 1.0 \n", + "\n", + "[5 rows x 48 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df_bot_peer_wide.shape\n", "\n", @@ -3702,7 +8699,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 225, "metadata": { "cellView": "form", "colab": { @@ -3711,7 +8708,463 @@ "id": "oxVJxrCpuXV_", "outputId": "3df39cbc-b594-40e1-d08f-1b0e9736d6ec" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BOT LEADERBOARD\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2.5% CI10% CIMedian90% CI97.5% CI
metac-o16.27.49.711.813.1
metac-o1-preview3.15.38.311.112.8
manticAI0.22.15.68.810.6
metac-Gemini-Exp-12060.61.95.28.19.4
acm_bot0.11.74.67.58.9
metac-perplexity-1.70.44.28.09.6
GreeneiBot2-1.20.74.07.18.9
twsummerbot0.21.43.86.17.3
cookics_bot_TEST0.11.03.05.16.1
pgodzinai-3.5-1.42.96.98.7
CumulativeBot-0.30.92.74.45.4
metac-claude-3-5-sonnet-latest-1.10.12.65.16.4
SynapseSeer0.41.12.64.04.9
jkraybill_bot-3.9-1.81.74.96.3
metac-exa-5.3-2.81.65.47.8
metac-deepseek-r1-1.7-0.81.33.54.6
MWG-1.5-0.70.72.22.8
andrewsiah-0.9-0.6-0.00.61.0
cobyj-bot-1.4-0.9-0.00.81.3
X_bot-0.4-0.3-0.00.10.2
pianobot-1.3-0.8-0.00.71.1
annabot-3.5-2.3-0.41.32.2
bean_bot-3.1-2.2-0.51.11.7
KevinTestBot-4.3-2.8-0.61.42.6
jonahsingerbot-3.0-2.2-0.80.41.0
CatrachoCaster-2.3-1.7-0.80.20.8
krm-bot-3.5-2.6-0.90.71.6
ProfessorSP-4.5-3.4-1.21.02.2
metac-grok-2-1212-6.6-4.9-1.61.73.5
4Shadower-4.8-3.6-1.70.31.2
mmBot-7.8-5.7-1.72.14.2
swingswish-5.2-4.0-1.9-0.20.6
RPM_bot-4.8-3.8-2.0-0.7-0.1
InstitutPelFutur-8.8-6.6-2.12.04.0
metac-claude-3-5-sonnet-20240620-6.8-5.0-2.10.92.2
wunderplumb-6.0-4.7-2.5-0.30.7
metac-Llama-3.1-6.7-5.4-2.70.01.5
NextWorldLab-8.9-6.9-3.6-0.50.9
laylaps-10.1-8.1-3.8-0.11.6
Bot_Pepa-7.2-6.0-3.9-2.0-0.9
VeritasAI-7.7-6.4-4.3-2.0-0.8
minefrac1-8.0-6.7-4.6-2.6-1.5
Grizeu_Bot-9.2-7.6-5.0-2.3-0.6
metac-gpt-4o-10.6-9.1-5.7-2.9-1.4
ajf-bot-14.6-12.4-8.3-4.4-2.0
\n", + "
" + ], + "text/plain": [ + " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", + "metac-o1 6.2 7.4 9.7 11.8 13.1\n", + "metac-o1-preview 3.1 5.3 8.3 11.1 12.8\n", + "manticAI 0.2 2.1 5.6 8.8 10.6\n", + "metac-Gemini-Exp-1206 0.6 1.9 5.2 8.1 9.4\n", + "acm_bot 0.1 1.7 4.6 7.5 8.9\n", + "metac-perplexity -1.7 0.4 4.2 8.0 9.6\n", + "GreeneiBot2 -1.2 0.7 4.0 7.1 8.9\n", + "twsummerbot 0.2 1.4 3.8 6.1 7.3\n", + "cookics_bot_TEST 0.1 1.0 3.0 5.1 6.1\n", + "pgodzinai -3.5 -1.4 2.9 6.9 8.7\n", + "CumulativeBot -0.3 0.9 2.7 4.4 5.4\n", + "metac-claude-3-5-sonnet-latest -1.1 0.1 2.6 5.1 6.4\n", + "SynapseSeer 0.4 1.1 2.6 4.0 4.9\n", + "jkraybill_bot -3.9 -1.8 1.7 4.9 6.3\n", + "metac-exa -5.3 -2.8 1.6 5.4 7.8\n", + "metac-deepseek-r1 -1.7 -0.8 1.3 3.5 4.6\n", + "MWG -1.5 -0.7 0.7 2.2 2.8\n", + "andrewsiah -0.9 -0.6 -0.0 0.6 1.0\n", + "cobyj-bot -1.4 -0.9 -0.0 0.8 1.3\n", + "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", + "annabot -3.5 -2.3 -0.4 1.3 2.2\n", + "bean_bot -3.1 -2.2 -0.5 1.1 1.7\n", + "KevinTestBot -4.3 -2.8 -0.6 1.4 2.6\n", + "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", + "CatrachoCaster -2.3 -1.7 -0.8 0.2 0.8\n", + "krm-bot -3.5 -2.6 -0.9 0.7 1.6\n", + "ProfessorSP -4.5 -3.4 -1.2 1.0 2.2\n", + "metac-grok-2-1212 -6.6 -4.9 -1.6 1.7 3.5\n", + "4Shadower -4.8 -3.6 -1.7 0.3 1.2\n", + "mmBot -7.8 -5.7 -1.7 2.1 4.2\n", + "swingswish -5.2 -4.0 -1.9 -0.2 0.6\n", + "RPM_bot -4.8 -3.8 -2.0 -0.7 -0.1\n", + "InstitutPelFutur -8.8 -6.6 -2.1 2.0 4.0\n", + "metac-claude-3-5-sonnet-20240620 -6.8 -5.0 -2.1 0.9 2.2\n", + "wunderplumb -6.0 -4.7 -2.5 -0.3 0.7\n", + "metac-Llama-3.1 -6.7 -5.4 -2.7 0.0 1.5\n", + "NextWorldLab -8.9 -6.9 -3.6 -0.5 0.9\n", + "laylaps -10.1 -8.1 -3.8 -0.1 1.6\n", + "Bot_Pepa -7.2 -6.0 -3.9 -2.0 -0.9\n", + "VeritasAI -7.7 -6.4 -4.3 -2.0 -0.8\n", + "minefrac1 -8.0 -6.7 -4.6 -2.6 -1.5\n", + "Grizeu_Bot -9.2 -7.6 -5.0 -2.3 -0.6\n", + "metac-gpt-4o -10.6 -9.1 -5.7 -2.9 -1.4\n", + "ajf-bot -14.6 -12.4 -8.3 -4.4 -2.0" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Drop 'bot_median' from all_bots list\n", "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", @@ -3730,7 +9183,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 226, "metadata": { "cellView": "form", "colab": { @@ -3740,7 +9193,475 @@ "id": "MXAev2sNXdbZ", "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "HEAD-TO-HEAD LEADERBOARD\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2.5% CI10% CIMedian90% CI97.5% CI
cobyj-bot0.00.00.00.00.0
andrewsiah0.00.00.00.00.0
X_bot-0.0-0.0-0.00.00.0
jonahsingerbot-0.0-0.0-0.0-0.0-0.0
bean_bot-0.0-0.0-0.0-0.0-0.0
RPM_bot-0.1-0.0-0.00.00.0
CumulativeBot-0.0-0.0-0.0-0.00.0
swingswish-0.0-0.0-0.0-0.0-0.0
KevinTestBot-0.1-0.0-0.00.00.0
SynapseSeer-0.1-0.0-0.00.00.0
Grizeu_Bot-0.2-0.1-0.00.10.2
pianobot-0.1-0.1-0.0-0.00.0
CatrachoCaster-0.1-0.1-0.0-0.00.0
krm-bot-0.1-0.1-0.1-0.0-0.0
4Shadower-0.1-0.1-0.1-0.0-0.0
annabot-0.1-0.1-0.1-0.0-0.0
cookics_bot_TEST-0.2-0.1-0.1-0.00.0
jkraybill_bot-0.2-0.1-0.1-0.0-0.0
twsummerbot-0.2-0.2-0.1-0.00.0
MWG-0.2-0.2-0.1-0.0-0.0
ProfessorSP-0.2-0.2-0.1-0.1-0.0
GreeneiBot2-0.2-0.2-0.1-0.00.0
ajf-bot-0.3-0.2-0.1-0.00.0
acm_bot-0.3-0.2-0.10.00.1
Bot_Pepa-0.2-0.2-0.1-0.1-0.0
metac-o1-0.3-0.2-0.1-0.00.1
metac-perplexity-0.3-0.2-0.10.00.1
laylaps-0.2-0.2-0.1-0.1-0.0
wunderplumb-0.3-0.2-0.1-0.1-0.0
manticAI-0.3-0.2-0.2-0.1-0.0
metac-deepseek-r1-0.3-0.2-0.2-0.1-0.0
metac-Gemini-Exp-1206-0.3-0.3-0.2-0.00.0
NextWorldLab-0.3-0.3-0.2-0.1-0.0
bot_median-0.4-0.3-0.2-0.10.0
minefrac1-0.3-0.3-0.2-0.1-0.1
metac-claude-3-5-sonnet-20240620-0.4-0.3-0.2-0.10.0
mmBot-0.4-0.3-0.2-0.1-0.1
metac-grok-2-1212-0.4-0.4-0.2-0.1-0.0
pgodzinai-0.4-0.4-0.2-0.1-0.1
VeritasAI-0.4-0.3-0.3-0.2-0.1
metac-claude-3-5-sonnet-latest-0.4-0.4-0.3-0.2-0.1
metac-Llama-3.1-0.5-0.4-0.3-0.1-0.1
metac-exa-0.5-0.4-0.3-0.2-0.1
InstitutPelFutur-0.5-0.4-0.3-0.2-0.1
metac-o1-preview-0.5-0.4-0.3-0.2-0.1
metac-gpt-4o-0.5-0.4-0.3-0.2-0.1
\n", + "
" + ], + "text/plain": [ + " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", + "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", + "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", + "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", + "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", + "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", + "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", + "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", + "SynapseSeer -0.1 -0.0 -0.0 0.0 0.0\n", + "Grizeu_Bot -0.2 -0.1 -0.0 0.1 0.2\n", + "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", + "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", + "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", + "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", + "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", + "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", + "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", + "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", + "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", + "ProfessorSP -0.2 -0.2 -0.1 -0.1 -0.0\n", + "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", + "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", + "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", + "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-o1 -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-perplexity -0.3 -0.2 -0.1 0.0 0.1\n", + "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\n", + "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", + "metac-deepseek-r1 -0.3 -0.2 -0.2 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.3 -0.2 -0.0 0.0\n", + "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", + "bot_median -0.4 -0.3 -0.2 -0.1 0.0\n", + "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", + "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", + "metac-grok-2-1212 -0.4 -0.4 -0.2 -0.1 -0.0\n", + "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", + "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", + "metac-claude-3-5-sonnet-latest -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.1 -0.1\n", + "metac-exa -0.5 -0.4 -0.3 -0.2 -0.1\n", + "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-o1-preview -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" + ] + }, + "execution_count": 226, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "NUM = round(df_bot_vs_pro_peer['question_weight'].sum())\n", "ITER = 1000\n", @@ -3757,7 +9678,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 227, "metadata": {}, "outputs": [], "source": [ @@ -3767,9 +9688,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 228, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighted score for annabot: -190.5513637093994\n", + "Total score for annabot: 21.125669919166132\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# @title Check specific bot records\n", "\n", @@ -3807,7 +9748,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 229, "metadata": { "cellView": "form", "colab": { @@ -3816,7 +9757,491 @@ "id": "I7W8JXutv2ks", "outputId": "5e7053d3-2124-42b7-bd53-48a40a53caf2" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
W_aveW_countlower_boundupper_boundp_value
metac-o1-preview12.2276.67.117.30.000004
metac-o18.4283.24.012.70.000179
pgodzinai8.7248.01.116.30.025267
GreeneiBot29.2204.81.117.30.026930
manticAI7.7245.20.514.90.035671
acm_bot5.4263.5-0.211.00.058135
metac-Gemini-Exp-12065.3269.6-0.310.80.062806
SynapseSeer6.0125.9-0.512.50.068737
metac-claude-3-5-sonnet-latest3.6278.2-0.98.20.116899
twsummerbot4.9181.9-1.811.60.152393
cookics_bot_TEST5.8135.2-1.813.40.132509
CumulativeBot8.094.2-3.018.90.153662
metac-deepseek-r10.8225.8-4.25.80.763142
MWG3.684.8-4.311.50.365354
metac-perplexity2.8264.3-4.810.30.470416
metac-grok-2-12120.1281.2-5.76.00.961620
metac-exa1.7275.2-5.89.20.654608
mmBot-0.5279.9-7.56.50.887163
InstitutPelFutur-0.1264.9-8.18.00.988352
metac-Llama-3.1-3.7280.5-8.30.90.117806
metac-claude-3-5-sonnet-20240620-3.3282.2-8.52.00.224671
VeritasAI-4.5251.9-9.40.40.072948
jkraybill_bot1.4162.4-9.712.40.808839
CatrachoCaster-2.761.9-10.65.20.493061
metac-gpt-4o-5.2281.2-10.60.10.054453
NextWorldLab-4.6256.3-10.91.80.156859
wunderplumb-5.4148.4-13.52.60.184061
4Shadower-3.7101.5-13.96.40.463979
minefrac1-7.3136.2-14.4-0.20.043444
andrewsiah0.125.1-14.614.80.988409
krm-bot-4.494.5-14.75.90.399741
ProfessorSP-4.0110.0-14.86.80.464316
laylaps-7.2257.0-15.40.90.082564
pianobot6.814.8-16.229.80.535822
cobyj-bot-0.431.5-17.817.10.964365
KevinTestBot-2.781.1-17.912.60.730388
jonahsingerbot-6.560.1-19.26.20.309592
bean_bot-4.163.1-19.611.40.600896
Bot_Pepa-12.3124.4-20.6-4.10.003751
annabot-6.354.5-23.711.10.470037
Grizeu_Bot-16.5140.9-25.8-7.10.000639
ajf-bot-16.0193.9-28.2-3.70.011119
swingswish-16.757.1-36.93.50.103364
RPM_bot-44.015.8-101.413.40.126191
\n", + "
" + ], + "text/plain": [ + " W_ave W_count lower_bound upper_bound \\\n", + "metac-o1-preview 12.2 276.6 7.1 17.3 \n", + "metac-o1 8.4 283.2 4.0 12.7 \n", + "pgodzinai 8.7 248.0 1.1 16.3 \n", + "GreeneiBot2 9.2 204.8 1.1 17.3 \n", + "manticAI 7.7 245.2 0.5 14.9 \n", + "acm_bot 5.4 263.5 -0.2 11.0 \n", + "metac-Gemini-Exp-1206 5.3 269.6 -0.3 10.8 \n", + "SynapseSeer 6.0 125.9 -0.5 12.5 \n", + "metac-claude-3-5-sonnet-latest 3.6 278.2 -0.9 8.2 \n", + "twsummerbot 4.9 181.9 -1.8 11.6 \n", + "cookics_bot_TEST 5.8 135.2 -1.8 13.4 \n", + "CumulativeBot 8.0 94.2 -3.0 18.9 \n", + "metac-deepseek-r1 0.8 225.8 -4.2 5.8 \n", + "MWG 3.6 84.8 -4.3 11.5 \n", + "metac-perplexity 2.8 264.3 -4.8 10.3 \n", + "metac-grok-2-1212 0.1 281.2 -5.7 6.0 \n", + "metac-exa 1.7 275.2 -5.8 9.2 \n", + "mmBot -0.5 279.9 -7.5 6.5 \n", + "InstitutPelFutur -0.1 264.9 -8.1 8.0 \n", + "metac-Llama-3.1 -3.7 280.5 -8.3 0.9 \n", + "metac-claude-3-5-sonnet-20240620 -3.3 282.2 -8.5 2.0 \n", + "VeritasAI -4.5 251.9 -9.4 0.4 \n", + "jkraybill_bot 1.4 162.4 -9.7 12.4 \n", + "CatrachoCaster -2.7 61.9 -10.6 5.2 \n", + "metac-gpt-4o -5.2 281.2 -10.6 0.1 \n", + "NextWorldLab -4.6 256.3 -10.9 1.8 \n", + "wunderplumb -5.4 148.4 -13.5 2.6 \n", + "4Shadower -3.7 101.5 -13.9 6.4 \n", + "minefrac1 -7.3 136.2 -14.4 -0.2 \n", + "andrewsiah 0.1 25.1 -14.6 14.8 \n", + "krm-bot -4.4 94.5 -14.7 5.9 \n", + "ProfessorSP -4.0 110.0 -14.8 6.8 \n", + "laylaps -7.2 257.0 -15.4 0.9 \n", + "pianobot 6.8 14.8 -16.2 29.8 \n", + "cobyj-bot -0.4 31.5 -17.8 17.1 \n", + "KevinTestBot -2.7 81.1 -17.9 12.6 \n", + "jonahsingerbot -6.5 60.1 -19.2 6.2 \n", + "bean_bot -4.1 63.1 -19.6 11.4 \n", + "Bot_Pepa -12.3 124.4 -20.6 -4.1 \n", + "annabot -6.3 54.5 -23.7 11.1 \n", + "Grizeu_Bot -16.5 140.9 -25.8 -7.1 \n", + "ajf-bot -16.0 193.9 -28.2 -3.7 \n", + "swingswish -16.7 57.1 -36.9 3.5 \n", + "RPM_bot -44.0 15.8 -101.4 13.4 \n", + "\n", + " p_value \n", + "metac-o1-preview 0.000004 \n", + "metac-o1 0.000179 \n", + "pgodzinai 0.025267 \n", + "GreeneiBot2 0.026930 \n", + "manticAI 0.035671 \n", + "acm_bot 0.058135 \n", + "metac-Gemini-Exp-1206 0.062806 \n", + "SynapseSeer 0.068737 \n", + "metac-claude-3-5-sonnet-latest 0.116899 \n", + "twsummerbot 0.152393 \n", + "cookics_bot_TEST 0.132509 \n", + "CumulativeBot 0.153662 \n", + "metac-deepseek-r1 0.763142 \n", + "MWG 0.365354 \n", + "metac-perplexity 0.470416 \n", + "metac-grok-2-1212 0.961620 \n", + "metac-exa 0.654608 \n", + "mmBot 0.887163 \n", + "InstitutPelFutur 0.988352 \n", + "metac-Llama-3.1 0.117806 \n", + "metac-claude-3-5-sonnet-20240620 0.224671 \n", + "VeritasAI 0.072948 \n", + "jkraybill_bot 0.808839 \n", + "CatrachoCaster 0.493061 \n", + "metac-gpt-4o 0.054453 \n", + "NextWorldLab 0.156859 \n", + "wunderplumb 0.184061 \n", + "4Shadower 0.463979 \n", + "minefrac1 0.043444 \n", + "andrewsiah 0.988409 \n", + "krm-bot 0.399741 \n", + "ProfessorSP 0.464316 \n", + "laylaps 0.082564 \n", + "pianobot 0.535822 \n", + "cobyj-bot 0.964365 \n", + "KevinTestBot 0.730388 \n", + "jonahsingerbot 0.309592 \n", + "bean_bot 0.600896 \n", + "Bot_Pepa 0.003751 \n", + "annabot 0.470037 \n", + "Grizeu_Bot 0.000639 \n", + "ajf-bot 0.011119 \n", + "swingswish 0.103364 \n", + "RPM_bot 0.126191 " + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# @title Weighted Bot Only Peer, T test\n", "\n", @@ -3833,7 +10258,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 230, "metadata": {}, "outputs": [], "source": [ @@ -3842,9 +10267,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 231, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 bots:\n", + "1. metac-o1-preview\n", + "2. metac-o1\n", + "3. pgodzinai\n", + "4. GreeneiBot2\n", + "5. manticAI\n", + "6. acm_bot\n", + "7. metac-Gemini-Exp-1206\n", + "8. SynapseSeer\n", + "9. metac-claude-3-5-sonnet-latest\n", + "10. twsummerbot\n" + ] + } + ], "source": [ "# Sort the DataFrame by the lower_bound column in descending order\n", "sorted_df = df_W_bot_only_peer_leaderboard.sort_values(by='lower_bound', ascending=False)\n", @@ -3863,18 +10306,525 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 237, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.97]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.97]\n", + " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.75, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", + " >>> Collected 2 forecasts: [0.85, 0.6]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.15, 0.05]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.2, 0.1]\n", + " >>> Collected 2 forecasts: [0.7, 0.85]\n", + " >>> Collected 2 forecasts: [0.15, 0.35]\n", + " >>> Collected 2 forecasts: [0.25, 0.25]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.15, 0.4]\n", + " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.1, 0.2]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.02]\n", + " >>> Collected 2 forecasts: [0.15, 0.4]\n", + " >>> Collected 2 forecasts: [0.6, 0.3]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.97, 0.98]\n", + " >>> Collected 2 forecasts: [0.4, 0.3]\n", + " >>> Collected 2 forecasts: [0.4, 0.4]\n", + " >>> Collected 2 forecasts: [0.35, 0.45]\n", + " >>> Collected 2 forecasts: [0.1, 0.02]\n", + " >>> Collected 2 forecasts: [0.6, 0.8]\n", + " >>> Collected 2 forecasts: [0.99, 0.9]\n", + " >>> Collected 2 forecasts: [0.97, 0.98]\n", + " >>> Collected 2 forecasts: [0.99, 0.25]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.9, 0.8]\n", + " >>> Collected 2 forecasts: [0.7, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.6, 0.8]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.25, 0.25]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.05]\n", + " >>> Collected 2 forecasts: [0.85, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.65]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", + " >>> Collected 2 forecasts: [0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.82]\n", + " >>> Collected 3 forecasts: [0.75, 0.85, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.1, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.35, 0.108]\n", + " >>> Collected 3 forecasts: [0.25, 0.25, 0.16]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", + " >>> Collected 3 forecasts: [0.15, 0.4, 0.15]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, 0.05]\n", + " >>> Collected 3 forecasts: [0.1, 0.2, 0.125]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", + " >>> Collected 3 forecasts: [0.05, 0.02, 0.03]\n", + " >>> Collected 3 forecasts: [0.15, 0.4, 0.35]\n", + " >>> Collected 3 forecasts: [0.6, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", + " >>> Collected 3 forecasts: [0.97, 0.98, 0.97]\n", + " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", + " >>> Collected 3 forecasts: [0.4, 0.4, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.35, 0.45, 0.17]\n", + " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.6, 0.8, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.9, 0.99]\n", + " >>> Collected 3 forecasts: [0.97, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.99, 0.25, 0.14]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.7, 0.6, 0.875]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.6, 0.8, 0.67]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.25, 0.3925]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.086]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, 0.285]\n", + " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.85, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", + " >>> Collected 3 forecasts: [0.9, 0.65, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.02, 0.05]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.82, 0.794]\n", + " >>> Collected 4 forecasts: [0.75, 0.85, 0.85, 0.884]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.1, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.85, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.15, 0.35, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.25, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", + " >>> Collected 4 forecasts: [0.15, 0.4, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.05, 0.866]\n", + " >>> Collected 4 forecasts: [0.1, 0.2, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999]\n", + " >>> Collected 4 forecasts: [0.6, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.97, 0.98, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.35, 0.45, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.6, 0.8, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.9, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.99, 0.25, 0.14, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.8, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.25, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.9, 0.65, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.02, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.82, 0.794, nan]\n", + " >>> Collected 5 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.1, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.85, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.35, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.25, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", + " >>> Collected 5 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.35, 0.45, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.6, 0.8, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.25, 0.25, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.9, 0.65, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.85, 0.6, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", + " >>> Collected 6 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88]\n", + " >>> Collected 7 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85]\n", + " >>> Collected 7 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3]\n", + " >>> Collected 7 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", + " >>> Collected 7 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85]\n", + " >>> Collected 7 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", + " >>> Collected 7 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", + " >>> Collected 8 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.15]\n", + " >>> Collected 9 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", + " >>> Collected 9 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.15, 0.289]\n", + " >>> Collected 10 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.35, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", + " >>> Collected 10 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + ] + } + ], "source": [ "# @title Calculate df_bot_team_forecasts\n", "\n", "df_bot_team_forecasts = pd.merge(\n", " df_bot_forecasts,\n", - " df_pro_bot_resolved_questions[['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options', 'range_min', 'range_max']],\n", + " df_pro_bot_resolved_questions[['bot_question_id', 'pro_question_id', 'question_weight', 'resolution', 'type', 'options', 'range_min', 'range_max', 'open_lower_bound', 'open_upper_bound']],\n", " on='bot_question_id',\n", " how='left'\n", ")\n", @@ -3882,7 +10832,7 @@ "# KEEP ONLY ROWS WHERE PRO_QUESTION_ID IS NA\n", "df_bot_team_forecasts = df_bot_team_forecasts[~df_bot_team_forecasts['pro_question_id'].isna()]\n", "\n", - "columns_to_keep = ['bot_question_id', 'question_weight', 'resolution', 'type', 'options', 'range_min', 'range_max'] + top_10_bots\n", + "columns_to_keep = ['bot_question_id', 'question_weight', 'resolution', 'type', 'options', 'range_min', 'range_max', 'open_lower_bound', 'open_upper_bound'] + top_10_bots\n", "\n", "# Filter the DataFrame to keep only the specified columns\n", "df_bot_team_forecasts = df_bot_team_forecasts[columns_to_keep]\n", @@ -3898,7 +10848,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 238, "metadata": {}, "outputs": [], "source": [ @@ -3908,18 +10858,221 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 239, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typeoptionsresolutionmetac-o1-previewmedian_forecast_5_botsmedian_forecast_8_bots
0multiple_choice[0, 1, 2-3, 4-6, >6]0[0.014083333333333333,0.6016666666666668,0.178...0.0145050.082463
1numericNaN86.82[0.05,0.0506666667,0.0513333333,0.052,0.052666...[0.037750000000000006, 0.038250620225000004, 0...[0.0402, 0.040750496180000005, 0.04130456232, ...
2binaryNaNno0.10.0850.1
3multiple_choice[0-4, 5-9, >9]5-9[0.7,0.25,0.05]0.51250.5
4numericNaN119.2[0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...[0.0, 0.0019825503600000003, 0.003970557620000...[0.0, 0.002036555585714286, 0.0040770089428571...
.....................
342binaryNaNyes0.90.90.9025
351binaryNaNno0.90.650.3585
355binaryNaNyes0.90.850.775
361binaryNaNno0.850.80.755
364binaryNaNno0.050.050.041
\n", + "

99 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " type options resolution \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] 0 \n", + "1 numeric NaN 86.82 \n", + "2 binary NaN no \n", + "3 multiple_choice [0-4, 5-9, >9] 5-9 \n", + "4 numeric NaN 119.2 \n", + ".. ... ... ... \n", + "342 binary NaN yes \n", + "351 binary NaN no \n", + "355 binary NaN yes \n", + "361 binary NaN no \n", + "364 binary NaN no \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.1 \n", + "3 [0.7,0.25,0.05] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + ".. ... \n", + "342 0.9 \n", + "351 0.9 \n", + "355 0.9 \n", + "361 0.85 \n", + "364 0.05 \n", + "\n", + " median_forecast_5_bots \\\n", + "0 0.014505 \n", + "1 [0.037750000000000006, 0.038250620225000004, 0... \n", + "2 0.085 \n", + "3 0.5125 \n", + "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", + ".. ... \n", + "342 0.9 \n", + "351 0.65 \n", + "355 0.85 \n", + "361 0.8 \n", + "364 0.05 \n", + "\n", + " median_forecast_8_bots \n", + "0 0.082463 \n", + "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", + "2 0.1 \n", + "3 0.5 \n", + "4 [0.0, 0.002036555585714286, 0.0040770089428571... \n", + ".. ... \n", + "342 0.9025 \n", + "351 0.3585 \n", + "355 0.775 \n", + "361 0.755 \n", + "364 0.041 \n", + "\n", + "[99 rows x 6 columns]" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_bot_team_forecasts[['type', 'options', 'resolution', 'metac-o1-preview', 'median_forecast_5_bots', 'median_forecast_8_bots']]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 240, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sum of weights: 95.0, Number of questions: 99\n" + ] + } + ], "source": [ "# Sanity check\n", "a = df_bot_team_forecasts['question_weight'].sum()\n", @@ -3929,7 +11082,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 241, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3937,7 +11090,22 @@ "id": "3-FedHpWV_1v", "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" }, - "outputs": [], + "outputs": [ + { + "ename": "NotImplementedError", + "evalue": "Havent decided how to handle null forecasts or anulled resolutions", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[241], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# @title Calculate the baseline scores for each team size\u001b[39;00m\n\u001b[1;32m 3\u001b[0m teams \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_1_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_2_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_3_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_9_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_10_bots\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 14\u001b[0m weighted_scores \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_weighted_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_bot_team_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mteams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Print nicely - round to 2 decimal places and first column should be just an integer (bot team size)\u001b[39;00m\n\u001b[1;32m 17\u001b[0m weighted_scores_print \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(weighted_scores)\u001b[38;5;241m.\u001b[39mreset_index()\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:448\u001b[0m, in \u001b[0;36mcalculate_weighted_scores\u001b[0;34m(df_bot_team_forecasts, teams)\u001b[0m\n\u001b[1;32m 445\u001b[0m forecast \u001b[38;5;241m=\u001b[39m row[team]\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 448\u001b[0m weighted_score \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_baseline_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 449\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 450\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43mq_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[43m \u001b[49m\u001b[43mopen_upper_bound\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopen_upper_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m \u001b[49m\u001b[43mopen_lower_bound\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopen_lower_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 458\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 459\u001b[0m team_scores[team] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m weighted_score\n\u001b[1;32m 461\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mIndexError\u001b[39;00m):\n\u001b[1;32m 462\u001b[0m \u001b[38;5;66;03m# @Check: Does skipping introduce any problems?\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:61\u001b[0m, in \u001b[0;36mcalculate_baseline_score\u001b[0;34m(forecast, resolution, q_type, options, range_min, range_max, question_weight, open_upper_bound, open_lower_bound)\u001b[0m\n\u001b[1;32m 59\u001b[0m question_type \u001b[38;5;241m=\u001b[39m _determine_question_type(q_type, resolution)\n\u001b[1;32m 60\u001b[0m resolution \u001b[38;5;241m=\u001b[39m _normalize_resolution(question_type, resolution, range_min, range_max)\n\u001b[0;32m---> 61\u001b[0m prob_for_resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_determine_probability_for_resolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m baseline_prob \u001b[38;5;241m=\u001b[39m _determine_baseline(\n\u001b[1;32m 65\u001b[0m question_type, resolution, options, range_min, range_max, open_upper_bound, open_lower_bound\n\u001b[1;32m 66\u001b[0m )\n\u001b[1;32m 67\u001b[0m divisor \u001b[38;5;241m=\u001b[39m _determine_divisor_for_baseline_score(question_type, options)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:153\u001b[0m, in \u001b[0;36m_determine_probability_for_resolution\u001b[0;34m(q_type, forecast, resolution, options, range_min, range_max)\u001b[0m\n\u001b[1;32m 150\u001b[0m resolution \u001b[38;5;241m=\u001b[39m _normalize_resolution(q_type, resolution, range_min, range_max)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m forecast \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m resolution \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 154\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHavent decided how to handle null forecasts or anulled resolutions\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 155\u001b[0m )\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(forecast) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mForecast is empty\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNotImplementedError\u001b[0m: Havent decided how to handle null forecasts or anulled resolutions" + ] + } + ], "source": [ "# @title Calculate the baseline scores for each team size\n", "\n", diff --git a/functions.py b/functions.py index 806cca9..8e894df 100644 --- a/functions.py +++ b/functions.py @@ -432,32 +432,34 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): team_scores = {team: 0.0 for team in teams} for _, row in df_bot_team_forecasts.iterrows(): - resolution = row["Resolution"] - options = row["Options"] - range_min = row["Range_min"] - range_max = row["Range_max"] - question_weight = row["Question_weight"] - open_upper_bound = row["Open_upper_bound"] - open_lower_bound = row["Open_lower_bound"] + resolution = row["resolution"] + options = row["options"] + range_min = row["range_min"] + range_max = row["range_max"] + question_weight = row["question_weight"] + open_upper_bound = row["open_upper_bound"] + open_lower_bound = row["open_lower_bound"] + question_type = row["type"] for team in teams: forecast = row[team] try: weighted_score = calculate_baseline_score( - forecast, - resolution, - options, - range_min, - range_max, - question_weight, + forecast=forecast, + resolution=resolution, + q_type=question_type, + options=options, + range_min=range_min, + range_max=range_max, + question_weight=question_weight, open_upper_bound=open_upper_bound, open_lower_bound=open_lower_bound, ) team_scores[team] += weighted_score except (ValueError, TypeError, IndexError): - # @Ben: Does skipping introduce any problems? + # @Check: Does skipping introduce any problems? continue # Be robust to bad/missing data return pd.Series(team_scores) @@ -1243,19 +1245,28 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] -def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, col_a: str, col_b: str): +def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, col_a: str, col_b: str) -> float: + question_type = row["type"] + forecast_a = row[ col_a ] if isinstance(forecast_a, str): forecast_a = [float(x) for x in forecast_a.strip('[]').split(',')] + elif isinstance(forecast_a, float) and math.isnan(forecast_a): + return np.nan forecast_b = row[col_b] if isinstance(forecast_b, str): forecast_b = [float(x) for x in forecast_b.strip('[]').split(',')] + elif isinstance(forecast_b, float) and math.isnan(forecast_b): + return np.nan options = row["options_parsed"] if "options_parsed" in row else row["options"] resolution = row["resolution"] + if resolution == "annulled" or resolution == "ambiguous": + return np.nan + question_type = row["type"] if question_type == "binary": if resolution == "yes": @@ -1270,8 +1281,12 @@ def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, co elif question_type == "multiple_choice": resolution = resolution elif question_type == "numeric": - if not isinstance(resolution, float): + if resolution == "above_upper_bound" or resolution == "below_lower_bound": + resolution = resolution + elif not isinstance(resolution, float): resolution = float(resolution) + else: + raise ValueError(f"Unknown resolution type: {resolution}") else: raise ValueError(f"Unknown question type: {question_type}") @@ -1289,6 +1304,7 @@ def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, co question_weight = float(question_weight) score = calculate_peer_score( + q_type=question_type, forecast=forecast_a, forecast_for_other_users=[forecast_b], resolution=resolution, diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 17f548c..9c027a3 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI -metac-perplexity,18.1,18.1,18.1,18.1,18.1 -acm_bot,17.7,17.7,17.7,17.7,17.7 -bot_median,17.0,17.0,17.0,17.0,17.0 -metac-o1,16.6,16.6,16.6,16.6,16.6 -metac-claude-3-5-sonnet-20240620,14.8,14.8,14.8,14.8,14.8 -manticAI,14.5,14.5,14.5,14.5,14.5 -twsummerbot,14.3,14.3,14.3,14.3,14.3 -jkraybill_bot,14.3,14.3,14.3,14.3,14.3 -metac-exa,13.0,13.0,13.0,13.0,13.0 -GreeneiBot2,12.2,12.2,12.2,12.2,12.2 -NextWorldLab,11.1,11.1,11.1,11.1,11.1 -metac-Llama-3.1,10.5,10.5,10.5,10.5,10.5 -Grizeu_Bot,10.2,10.2,10.2,10.2,10.2 -SynapseSeer,10.2,10.2,10.2,10.2,10.2 -metac-claude-3-5-sonnet-latest,10.0,10.0,10.0,10.0,10.0 -mmBot,9.7,9.7,9.7,9.7,9.7 -annabot,9.0,9.0,9.0,9.0,9.0 -VeritasAI,8.4,8.4,8.4,8.4,8.4 -metac-grok-2-1212,8.2,8.2,8.2,8.2,8.2 -laylaps,7.6,7.6,7.6,7.6,7.6 -metac-Gemini-Exp-1206,7.4,7.4,7.4,7.4,7.4 -metac-o1-preview,6.7,6.7,6.7,6.7,6.7 -cookics_bot_TEST,6.3,6.3,6.3,6.3,6.3 -metac-deepseek-r1,5.7,5.7,5.7,5.7,5.7 -MWG,5.5,5.5,5.5,5.5,5.5 -ajf-bot,5.1,5.1,5.1,5.1,5.1 -metac-gpt-4o,4.8,4.8,4.8,4.8,4.8 -pgodzinai,3.5,3.5,3.5,3.5,3.5 -KevinTestBot,3.3,3.3,3.3,3.3,3.3 -InstitutPelFutur,2.7,2.7,2.7,2.7,2.7 -Bot_Pepa,2.6,2.6,2.6,2.6,2.6 -CumulativeBot,2.5,2.5,2.5,2.5,2.5 -swingswish,2.4,2.4,2.4,2.4,2.4 -wunderplumb,2.4,2.4,2.4,2.4,2.4 -jonahsingerbot,2.2,2.2,2.2,2.2,2.2 -bean_bot,2.1,2.1,2.1,2.1,2.1 -X_bot,1.9,1.9,1.9,1.9,1.9 -CatrachoCaster,1.8,1.8,1.8,1.8,1.8 -RPM_bot,1.2,1.2,1.2,1.2,1.2 -4Shadower,0.6,0.6,0.6,0.6,0.6 -krm-bot,0.6,0.6,0.6,0.6,0.6 -andrewsiah,0.0,0.0,0.0,0.0,0.0 cobyj-bot,0.0,0.0,0.0,0.0,0.0 -pianobot,-2.2,-2.2,-2.2,-2.2,-2.2 -ProfessorSP,-3.0,-3.0,-3.0,-3.0,-3.0 -minefrac1,-3.0,-3.0,-3.0,-3.0,-3.0 +andrewsiah,0.0,0.0,0.0,0.0,0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 +jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 +bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 +CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 +swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 +KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 +SynapseSeer,-0.1,-0.0,-0.0,0.0,0.0 +Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 +pianobot,-0.1,-0.1,-0.0,-0.0,0.0 +CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 +krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 +4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 +annabot,-0.1,-0.1,-0.1,-0.0,-0.0 +cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 +jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 +twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 +MWG,-0.2,-0.2,-0.1,-0.0,-0.0 +ProfessorSP,-0.2,-0.2,-0.1,-0.1,-0.0 +GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 +ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 +acm_bot,-0.3,-0.2,-0.1,0.0,0.1 +Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-o1,-0.3,-0.2,-0.1,-0.0,0.1 +metac-perplexity,-0.3,-0.2,-0.1,0.0,0.1 +laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 +manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 +metac-deepseek-r1,-0.3,-0.2,-0.2,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.3,-0.2,-0.0,0.0 +NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 +bot_median,-0.4,-0.3,-0.2,-0.1,0.0 +minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 +metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 +mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 +metac-grok-2-1212,-0.4,-0.4,-0.2,-0.1,-0.0 +pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 +VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 +metac-claude-3-5-sonnet-latest,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.5,-0.4,-0.3,-0.1,-0.1 +metac-exa,-0.5,-0.4,-0.3,-0.2,-0.1 +InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-o1-preview,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 5e73739..49d442c 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -metac-perplexity,1719.7,95.0,18.1,3.570999300115835e-15,3.663767977230083e-16,4.940951081399963e+16,1.9847501794262088,18.1,18.1,1.0,0.000000 -acm_bot,1680.6,95.0,17.7,3.570999300115835e-15,3.663767977230083e-16,4.828448927545706e+16,1.9847501794262088,17.7,17.7,1.0,0.000000 -bot_median,1610.4,95.0,17.0,3.570999300115835e-15,3.663767977230083e-16,4.626691199221798e+16,1.9847501794262088,17.0,17.0,1.0,0.000000 -metac-o1,1577.6,95.0,16.6,3.570999300115835e-15,3.663767977230083e-16,4.532462410721762e+16,1.9847501794262088,16.6,16.6,1.0,0.000000 -metac-claude-3-5-sonnet-20240620,1405.9,95.0,14.8,3.570999300115835e-15,3.663767977230083e-16,4.039353684227144e+16,1.9847501794262088,14.8,14.8,1.0,0.000000 -manticAI,1378.2,95.0,14.5,0.0,0.0,inf,1.9847501794262088,14.5,14.5,1.0,0.000000 -twsummerbot,1355.4,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.788325122257914e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 -jkraybill_bot,1354.5,95.0,14.3,1.7854996500579174e-15,1.8318839886150415e-16,7.783286397381174e+16,1.9847501794262088,14.3,14.3,1.0,0.000000 -metac-exa,1233.6,95.0,13.0,1.7854996500579174e-15,1.8318839886150415e-16,7.088709959185136e+16,1.9847501794262088,13.0,13.0,1.0,0.000000 -GreeneiBot2,1163.2,95.0,12.2,0.0,0.0,inf,1.9847501794262088,12.2,12.2,1.0,0.000000 -NextWorldLab,1050.3,95.0,11.1,1.7854996500579174e-15,1.8318839886150415e-16,6.035037516349447e+16,1.9847501794262088,11.1,11.1,1.0,0.000000 -metac-Llama-3.1,997.0,95.0,10.5,1.7854996500579174e-15,1.8318839886150415e-16,5.728815548098371e+16,1.9847501794262088,10.5,10.5,1.0,0.000000 -Grizeu_Bot,966.4,95.0,10.2,0.0,0.0,inf,1.9847501794262088,10.2,10.2,1.0,0.000000 -SynapseSeer,964.7,95.0,10.2,1.7854996500579174e-15,1.8318839886150415e-16,5.5434396730578184e+16,1.9847501794262088,10.2,10.2,1.0,0.000000 -metac-claude-3-5-sonnet-latest,949.9,95.0,10.0,0.0,0.0,inf,1.9847501794262088,10.0,10.0,1.0,0.000000 -mmBot,924.8,95.0,9.7,0.0,0.0,inf,1.9847501794262088,9.7,9.7,1.0,0.000000 -annabot,854.4,95.0,9.0,1.7854996500579174e-15,1.8318839886150415e-16,4.909363317298574e+16,1.9847501794262088,9.0,9.0,1.0,0.000000 -VeritasAI,802.0,95.0,8.4,1.7854996500579174e-15,1.8318839886150415e-16,4.608352429717695e+16,1.9847501794262088,8.4,8.4,1.0,0.000000 -metac-grok-2-1212,775.1,95.0,8.2,0.0,0.0,inf,1.9847501794262088,8.2,8.2,1.0,0.000000 -laylaps,723.4,95.0,7.6,8.927498250289587e-16,9.159419943075207e-17,8.313179820692651e+16,1.9847501794262088,7.6,7.6,1.0,0.000000 -metac-Gemini-Exp-1206,701.9,95.0,7.4,8.927498250289587e-16,9.159419943075207e-17,8.065986188688938e+16,1.9847501794262088,7.4,7.4,1.0,0.000000 -metac-o1-preview,633.2,95.0,6.7,8.927498250289587e-16,9.159419943075207e-17,7.277309325504542e+16,1.9847501794262088,6.7,6.7,1.0,0.000000 -cookics_bot_TEST,596.4,95.0,6.3,0.0,0.0,inf,1.9847501794262088,6.3,6.3,1.0,0.000000 -metac-deepseek-r1,545.5,95.0,5.7,8.927498250289587e-16,9.159419943075207e-17,6.2687228856570984e+16,1.9847501794262088,5.7,5.7,1.0,0.000000 -MWG,520.8,95.0,5.5,8.927498250289587e-16,9.159419943075207e-17,5.985647068886487e+16,1.9847501794262088,5.5,5.5,1.0,0.000000 -ajf-bot,481.2,95.0,5.1,1.7854996500579174e-15,1.8318839886150415e-16,2.7648981076196796e+16,1.9847501794262088,5.1,5.1,1.0,0.000000 -metac-gpt-4o,451.6,95.0,4.8,8.927498250289587e-16,9.159419943075207e-17,5.190357943531163e+16,1.9847501794262088,4.8,4.8,1.0,0.000000 -pgodzinai,336.0,95.0,3.5,8.927498250289587e-16,9.159419943075207e-17,3.8616390554277256e+16,1.9847501794262088,3.5,3.5,1.0,0.000000 -KevinTestBot,314.5,95.0,3.3,8.927498250289587e-16,9.159419943075207e-17,3.614851659932975e+16,1.9847501794262088,3.3,3.3,1.0,0.000000 -InstitutPelFutur,256.0,95.0,2.7,8.927498250289587e-16,9.159419943075207e-17,2.9416230195900824e+16,1.9847501794262088,2.7,2.7,1.0,0.000000 -Bot_Pepa,246.8,95.0,2.6,0.0,0.0,inf,1.9847501794262088,2.6,2.6,1.0,0.000000 -CumulativeBot,241.1,95.0,2.5,4.463749125144793e-16,4.579709971537604e-17,5.542702538240192e+16,1.9847501794262088,2.5,2.5,1.0,0.000000 -swingswish,229.1,95.0,2.4,4.463749125144793e-16,4.579709971537604e-17,5.265549431654757e+16,1.9847501794262088,2.4,2.4,1.0,0.000000 -wunderplumb,225.4,95.0,2.4,4.463749125144793e-16,4.579709971537604e-17,5.180942325472045e+16,1.9847501794262088,2.4,2.4,1.0,0.000000 -jonahsingerbot,212.9,95.0,2.2,4.463749125144793e-16,4.579709971537604e-17,4.894510648634918e+16,1.9847501794262088,2.2,2.2,1.0,0.000000 -bean_bot,200.0,95.0,2.1,0.0,0.0,inf,1.9847501794262088,2.1,2.1,1.0,0.000000 -X_bot,181.4,95.0,1.9,0.0,0.0,inf,1.9847501794262088,1.9,1.9,1.0,0.000000 -CatrachoCaster,167.5,95.0,1.8,4.463749125144793e-16,4.579709971537604e-17,3.8493725321790856e+16,1.9847501794262088,1.8,1.8,1.0,0.000000 -RPM_bot,118.6,95.0,1.2,4.463749125144793e-16,4.579709971537604e-17,2.7264857831745884e+16,1.9847501794262088,1.2,1.2,1.0,0.000000 -4Shadower,61.1,95.0,0.6,2.2318745625723967e-16,2.289854985768802e-17,2.810105705323094e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 -krm-bot,60.8,95.0,0.6,1.1159372812861984e-16,1.144927492884401e-17,5.586128771835555e+16,1.9847501794262088,0.6,0.6,1.0,0.000000 -andrewsiah,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA -cobyj-bot,0.0,95.0,0.0,0.0,0.0,,1.9847501794262088,0.0,0.0,,NA -pianobot,-206.5,95.0,-2.2,4.463749125144793e-16,4.579709971537604e-17,-4.745304957283875e+16,1.9847501794262088,-2.2,-2.2,0.0,0.000000 -ProfessorSP,-280.4,95.0,-3.0,8.927498250289587e-16,9.159419943075207e-17,-3.2229421543642156e+16,1.9847501794262088,-3.0,-3.0,0.0,0.000000 -minefrac1,-283.9,95.0,-3.0,4.463749125144793e-16,4.579709971537604e-17,-6.524423956604449e+16,1.9847501794262088,-3.0,-3.0,0.0,0.000000 +cobyj-bot,0.0,0.0,,,,,,,,,NA +andrewsiah,0.0,0.0,,,,,,,,,NA +bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 +X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 +RPM_bot,-1.1,7.0,-0.2,0.824531966811415,0.3116437903151381,-0.5234058432057136,2.4469118511449692,0.6,-0.9,0.3097258948590483,0.619452 +CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 +swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 +SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 +KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 +Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 +pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 +CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 +krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 +annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 +4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 +cookics_bot_TEST,-6.9,27.4,-0.3,0.7446989876942366,0.14226742863646924,-1.7648756350756885,2.0495406495390753,0.0,-0.5,0.04457614500253557,0.089152 +jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 +twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 +MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 +ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 +GreeneiBot2,-10.4,58.4,-0.2,0.8498829222635632,0.11125990180982864,-1.5979756990286293,2.000831925930035,0.0,-0.4,0.05777205560013113,0.115544 +acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 +ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 +metac-o1,-11.5,91.1,-0.1,0.8882269503815736,0.09306036633541931,-1.3604682737460798,1.9858289388460384,0.1,-0.3,0.08853781411471767,0.177076 +Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 +metac-perplexity,-11.9,89.1,-0.1,0.9936685898993489,0.10526953628638332,-1.2647310023240792,1.9864049297707018,0.1,-0.3,0.10465157496376706,0.209303 +laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 +wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 +manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 +metac-deepseek-r1,-14.6,52.1,-0.3,0.7315248397695878,0.10134684096084697,-2.7666887863373426,2.0053789762011176,-0.1,-0.5,0.003932133201892011,0.007864 +metac-Gemini-Exp-1206,-15.2,76.5,-0.2,0.9437969359023713,0.1079065594460612,-1.8467741127168467,1.9908217254774627,0.0,-0.4,0.034349204246702666,0.068698 +NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 +bot_median,-17.3,92.1,-0.2,0.9191222179799003,0.09577307891459154,-1.9639956837727752,1.9855502432148115,0.0,-0.4,0.02628954496851215,0.052579 +minefrac1,-19.2,51.1,-0.4,0.8809897145082934,0.1232424683669797,-3.0436411347421197,2.0065449272360034,-0.1,-0.6,0.0018587451878251278,0.003717 +metac-claude-3-5-sonnet-20240620,-19.5,90.5,-0.2,1.0091380158423626,0.10607823314499117,-2.031064521471562,1.9860719790130024,-0.0,-0.4,0.0226076007974782,0.045215 +mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 +metac-grok-2-1212,-22.9,91.1,-0.3,1.0488287270766499,0.10988676432631847,-2.2835278472341387,1.9858289388460384,-0.0,-0.5,0.012375199205885952,0.024750 +pgodzinai,-23.9,76.4,-0.3,0.9564523461011735,0.1094250257541138,-2.858685649756527,1.9908489732268309,-0.1,-0.5,0.0027488433046459902,0.005498 +VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 +metac-claude-3-5-sonnet-latest,-24.4,91.1,-0.3,0.7843146490917536,0.08217337757580902,-3.2658265155495396,1.9858289388460384,-0.1,-0.4,0.0007722051094024979,0.001544 +metac-Llama-3.1,-26.1,89.1,-0.3,0.9987986166118539,0.10581301279218377,-2.7685645488001787,1.9864049297707018,-0.1,-0.5,0.00343170739454993,0.006863 +metac-exa,-26.6,89.1,-0.3,0.8489741653993217,0.08994056732713923,-3.324096943280282,1.9864049297707018,-0.1,-0.5,0.0006469013238867488,0.001294 +InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 +metac-o1-preview,-27.8,91.1,-0.3,0.87743376179017,0.09192955389631036,-3.31497363379348,1.9858289388460384,-0.1,-0.5,0.0006608298367709141,0.001322 +metac-gpt-4o,-30.5,91.1,-0.3,0.9139398799143879,0.09575433395355178,-3.4928274283029523,1.9858289388460384,-0.1,-0.5,0.00037140113373772884,0.000743 diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 596304e..93927be 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -1,4 +1,5 @@ -from datetime import datetime +from enum import Enum +from typing import Literal import numpy as np from scipy.stats.mstats import gmean @@ -6,21 +7,29 @@ from refactored_notebook.data_models import ForecastType, ResolutionType +class QuestionType(Enum): + BINARY = "binary" + MULTIPLE_CHOICE = "multiple_choice" + NUMERIC = "numeric" + def calculate_peer_score( forecast: ForecastType, forecast_for_other_users: list[ForecastType], resolution: ResolutionType, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, question_weight: float = 1.0, ) -> float: + question_type = _determine_question_type(q_type, resolution) + resolution = _normalize_resolution(question_type, resolution, range_min, range_max) forecast_for_resolution = _determine_probability_for_resolution( - forecast, resolution, options, range_min, range_max + question_type, forecast, resolution, options, range_min, range_max ) other_user_forecasts = [ _determine_probability_for_resolution( - forecast, resolution, options, range_min, range_max + question_type, forecast, resolution, options, range_min, range_max ) for forecast in forecast_for_other_users ] @@ -32,43 +41,10 @@ def calculate_peer_score( return peer_score * question_weight -def nominal_location_to_cdf_location( - nominal_location: float, - range_min: float, - range_max: float, - zero_point: float | None = None, -) -> float: - """ - Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to - metaculus's "internal representation" range [0, 1] incorporating question scaling - 0.8 would incidate the nomial locatoin is at cdf index 201 * 0.8 - Values higher/lower than 0 and 1 are resolutions that are above/below the upper/lower bound - """ - assert isinstance(zero_point, float | None) - - # TODO: Make sure to use datetime.fromisoformat(nominal_location).timestamp() if you start using date questions - scaled_location = float(nominal_location) - - # Unscale the value to put it into the range [0,1] - if zero_point is not None: - # logarithmically scaled question - deriv_ratio = (range_max - zero_point) / (range_min - zero_point) - unscaled_location = ( - np.log( - (scaled_location - range_min) * (deriv_ratio - 1) - + (range_max - range_min) - ) - - np.log(range_max - range_min) - ) / np.log(deriv_ratio) - else: - # linearly scaled question - unscaled_location = (scaled_location - range_min) / (range_max - range_min) - return unscaled_location - - def calculate_baseline_score( forecast: ForecastType, resolution: ResolutionType, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, @@ -80,13 +56,15 @@ def calculate_baseline_score( Question type can be infered from resolution type Scoring math: https://www.metaculus.com/help/scores-faq/#What:~:text=given%20score%20type.-,What%20is%20the%20Baseline%20score%3F,-The%20Baseline%20score """ + question_type = _determine_question_type(q_type, resolution) + resolution = _normalize_resolution(question_type, resolution, range_min, range_max) prob_for_resolution = _determine_probability_for_resolution( - forecast, resolution, options, range_min, range_max + question_type, forecast, resolution, options, range_min, range_max ) baseline_prob = _determine_baseline( - resolution, options, range_min, range_max, open_upper_bound, open_lower_bound + question_type, resolution, options, range_min, range_max, open_upper_bound, open_lower_bound ) - divisor = _determine_divisor_for_baseline_score(resolution, options) + divisor = _determine_divisor_for_baseline_score(question_type, options) if prob_for_resolution <= 0 or baseline_prob <= 0: raise ValueError( "Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue" @@ -100,6 +78,7 @@ def calculate_baseline_score( def _determine_baseline( + question_type: QuestionType, resolution: ResolutionType, options: list[str] | None = None, range_min: float | None = None, @@ -107,21 +86,22 @@ def _determine_baseline( open_upper_bound: bool | None = None, open_lower_bound: bool | None = None, ) -> float: - is_binary = isinstance(resolution, bool) - is_multiple_choice = isinstance(resolution, str) - is_numeric = isinstance(resolution, float) or isinstance(resolution, int) - - if is_binary: + resolution = _normalize_resolution(question_type, resolution, range_min, range_max) + if question_type == QuestionType.BINARY: baseline_prob = 0.5 - elif is_multiple_choice: + elif question_type == QuestionType.MULTIPLE_CHOICE: if options is None: raise ValueError("Options are required for multiple choice questions") baseline_prob = 1 / len(options) - elif is_numeric: + elif question_type == QuestionType.NUMERIC: if open_upper_bound is None or open_lower_bound is None: raise ValueError("Open upper bound and lower bound are required for numeric questions") - # @Check: Which version is correct? + if range_min is None or range_max is None: + raise ValueError("Range min and range max are required for numeric questions") + if not isinstance(resolution, float): + raise ValueError("Resolution must be a float for numeric questions") + # @Check: Which version is correct? # Version 1: resolved_outside_bounds = False assert range_min is not None and range_max is not None and resolution is not None, f"These need to be not None: Range min: {range_min}, range max: {range_max}, resolution: {resolution}" @@ -156,6 +136,7 @@ def _determine_baseline( def _determine_probability_for_resolution( + q_type: QuestionType, forecast: ForecastType, resolution: ResolutionType, options: list[str] | None = None, @@ -166,15 +147,7 @@ def _determine_probability_for_resolution( Returns a 0 to 1 probability for the resolution Also returns the baseline probability used in baseline scoring """ - - if resolution == "above_upper_bound" or resolution == "below_lower_bound": - raise ValueError( - "'above_upper_bound' or 'below_lower_bound' format not supported" - ) # This is an old resolution type in Q4 2024 - - is_numeric = isinstance(resolution, float) or isinstance(resolution, int) - is_binary = isinstance(resolution, bool) - is_multiple_choice = isinstance(resolution, str) + resolution = _normalize_resolution(q_type, resolution, range_min, range_max) if forecast is None or resolution is None: raise NotImplementedError( @@ -184,18 +157,20 @@ def _determine_probability_for_resolution( if len(forecast) == 0: raise ValueError("Forecast is empty") - if not is_numeric and any(p <= 0 or p >= 1 for p in forecast): + if not q_type == QuestionType.NUMERIC and any(p <= 0 or p >= 1 for p in forecast): raise ValueError("Forecast contains probabilities outside of 0 to 1 range") - if is_binary: + if q_type == QuestionType.BINARY: + assert isinstance(resolution, bool) prob_for_resolution = _binary_resolution_prob(forecast, resolution) - elif is_multiple_choice: + elif q_type == QuestionType.MULTIPLE_CHOICE: + assert isinstance(resolution, str) if options is None: raise ValueError("Options are required for multiple choice questions") prob_for_resolution = _multiple_choice_resolution_prob( forecast, resolution, options ) - elif is_numeric: + elif q_type == QuestionType.NUMERIC: if range_min is None or range_max is None: raise ValueError( "Range min and range max are required for numeric questions" @@ -278,19 +253,78 @@ def _numeric_resolution_prob( def _determine_divisor_for_baseline_score( - resolution: ResolutionType, options: list[str] | None = None + question_type: QuestionType, options: list[str] | None = None ) -> float: - is_binary = isinstance(resolution, bool) - is_multiple_choice = isinstance(resolution, str) - is_numeric = isinstance(resolution, float) or isinstance(resolution, int) - - if is_binary: + if question_type == QuestionType.BINARY: return np.log(2) - elif is_multiple_choice: + elif question_type == QuestionType.MULTIPLE_CHOICE: if options is None: raise ValueError("Options are required for multiple choice questions") return np.log(len(options)) - elif is_numeric: + elif question_type == QuestionType.NUMERIC: return 2 else: raise ValueError("Unknown question type") + +def nominal_location_to_cdf_location( + nominal_location: float, + range_min: float, + range_max: float, + zero_point: float | None = None, +) -> float: + """ + Takes a location in nominal format (e.g. 123, "123", or datetime in iso format) and scales it to + metaculus's "internal representation" range [0, 1] incorporating question scaling + 0.8 would incidate the nomial locatoin is at cdf index 201 * 0.8 + Values higher/lower than 0 and 1 are resolutions that are above/below the upper/lower bound + """ + assert isinstance(zero_point, float | None) + + # TODO: Make sure to use datetime.fromisoformat(nominal_location).timestamp() if you start using date questions + scaled_location = float(nominal_location) + + # Unscale the value to put it into the range [0,1] + if zero_point is not None: + # logarithmically scaled question + deriv_ratio = (range_max - zero_point) / (range_min - zero_point) + unscaled_location = ( + np.log( + (scaled_location - range_min) * (deriv_ratio - 1) + + (range_max - range_min) + ) + - np.log(range_max - range_min) + ) / np.log(deriv_ratio) + else: + # linearly scaled question + unscaled_location = (scaled_location - range_min) / (range_max - range_min) + return unscaled_location + +def _normalize_resolution(question_type: QuestionType, resolution: ResolutionType, range_min: float | None, range_max: float | None) -> ResolutionType: + if resolution == "annulled" or resolution == "ambiguous": + return None + + if question_type == QuestionType.NUMERIC: + if range_min is None or range_max is None: + raise ValueError("Range min and range max are required for numeric questions") + if resolution == "above_upper_bound": + return range_max + 0.1 + elif resolution == "below_lower_bound": + return range_min - 0.1 + else: + return resolution + else: + return resolution + + +def _determine_question_type(question_type: Literal["binary", "multiple_choice", "numeric"] | None, resolution: ResolutionType) -> QuestionType: + if question_type is None: + if isinstance(resolution, bool): + return QuestionType.BINARY + elif isinstance(resolution, float) or isinstance(resolution, int) or resolution == "above_upper_bound" or resolution == "below_lower_bound": + return QuestionType.NUMERIC + elif isinstance(resolution, str): + return QuestionType.MULTIPLE_CHOICE + else: + raise ValueError(f"Cannot infer question type from resolution. Please provide a question type. Resolution: {resolution}") + else: + return QuestionType(question_type) From 095c7ded62b1d9d0b3e70e1527ffa1bb907b08f6 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 07:20:53 -0600 Subject: [PATCH 16/26] unified peer and head to head functions --- AI_BENCHMARKING_ANALYSIS.ipynb | 4467 +++++++++++------ functions.py | 270 +- .../bootstrapped_h2h_bot_vs_pros.csv | 34 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 38 +- refactored_notebook/scoring.py | 4 +- tests/test_scoring.py | 15 +- 6 files changed, 3175 insertions(+), 1653 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 92e1549..dc8f1ff 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 277, "metadata": { "id": "ISzIoto4hnoG" }, @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 278, "metadata": {}, "outputs": [], "source": [ @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 279, "metadata": {}, "outputs": [ { @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 280, "metadata": {}, "outputs": [ { @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 281, "metadata": {}, "outputs": [ { @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 282, "metadata": {}, "outputs": [ { @@ -207,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 283, "metadata": {}, "outputs": [ { @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 284, "metadata": {}, "outputs": [ { @@ -238,7 +238,7 @@ " dtype='object')" ] }, - "execution_count": 184, + "execution_count": 284, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 285, "metadata": {}, "outputs": [ { @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 286, "metadata": {}, "outputs": [ { @@ -306,7 +306,7 @@ "dtype: object" ] }, - "execution_count": 186, + "execution_count": 286, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 287, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 288, "metadata": {}, "outputs": [ { @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 289, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 290, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 291, "metadata": {}, "outputs": [ { @@ -445,7 +445,7 @@ " 0\n", " 31268\n", " Jgalt\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 101465\n", " 1\n", @@ -466,7 +466,7 @@ " 1\n", " 31268\n", " MaciekK\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 117580\n", " 1\n", @@ -487,7 +487,7 @@ " 2\n", " 31268\n", " OpenSystem\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 120160\n", " 1\n", @@ -508,7 +508,7 @@ " 5\n", " 31268\n", " darkives\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 103907\n", " 1\n", @@ -529,7 +529,7 @@ " 6\n", " 31268\n", " datscilly\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 103777\n", " 1\n", @@ -551,12 +551,19 @@ "" ], "text/plain": [ - " question_id forecaster question_title \\\n", - "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", - "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", - "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", - "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", - "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster \\\n", + "0 31268 Jgalt \n", + "1 31268 MaciekK \n", + "2 31268 OpenSystem \n", + "5 31268 darkives \n", + "6 31268 datscilly \n", + "\n", + " question_title \\\n", + "0 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "1 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "2 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "5 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "6 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", @@ -594,7 +601,7 @@ "6 False " ] }, - "execution_count": 191, + "execution_count": 291, "metadata": {}, "output_type": "execute_result" } @@ -605,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 292, "metadata": {}, "outputs": [], "source": [ @@ -628,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 293, "metadata": {}, "outputs": [ { @@ -648,7 +655,7 @@ " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 193, + "execution_count": 293, "metadata": {}, "output_type": "execute_result" } @@ -660,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 294, "metadata": {}, "outputs": [ { @@ -703,6 +710,15 @@ " 1.738353\n", " \n", " \n", + " 15\n", + " bot_median\n", + " 8.520428\n", + " 3220.892206\n", + " 409\n", + " 5.620668\n", + " 1.475108\n", + " \n", + " \n", " 4\n", " metac-o1-preview\n", " 8.465638\n", @@ -712,15 +728,6 @@ " 2.298000\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 6.860987\n", - " 2593.590381\n", - " 409\n", - " 3.788648\n", - " 1.562899\n", - " \n", - " \n", " 24\n", " manticAI\n", " 6.510835\n", @@ -745,15 +752,15 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", + "15 bot_median 8.520428 3220.892206 409 5.620668 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", - "15 bot_median 6.860987 2593.590381 409 3.788648 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", + "15 1.475108 \n", "4 2.298000 \n", - "15 1.562899 \n", "24 3.029040 \n", "1 2.309106 " ] @@ -869,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 295, "metadata": { "id": "BmAFBHIhK77X" }, @@ -918,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 296, "metadata": {}, "outputs": [ { @@ -1342,7 +1349,7 @@ " np.int64(35705)}" ] }, - "execution_count": 196, + "execution_count": 296, "metadata": {}, "output_type": "execute_result" } @@ -1363,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 297, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1413,7 +1420,7 @@ " \n", " 3\n", " bot_median\n", - " 8567.705563\n", + " 8766.210698\n", " \n", " \n", " 4\n", @@ -1434,7 +1441,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8567.705563\n", + "3 bot_median 8766.210698\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1540,7 +1547,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 298, "metadata": {}, "outputs": [ { @@ -1559,7 +1566,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 299, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1603,13 +1610,13 @@ " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3504.379897\n", " \n", " \n", " 3\n", - " bot_median\n", - " 2974.983652\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 4\n", @@ -1839,8 +1846,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 2974.983652\n", + "2 bot_median 3504.379897\n", + "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -1887,7 +1894,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 199, + "execution_count": 299, "metadata": {}, "output_type": "execute_result" } @@ -1929,7 +1936,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 300, "metadata": {}, "outputs": [], "source": [ @@ -1948,7 +1955,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 301, "metadata": {}, "outputs": [], "source": [ @@ -1957,7 +1964,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 302, "metadata": {}, "outputs": [ { @@ -1978,7 +1985,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 303, "metadata": {}, "outputs": [ { @@ -2027,7 +2034,7 @@ " 0\n", " 31268\n", " Jgalt\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 101465\n", " 1\n", @@ -2048,7 +2055,7 @@ " 1\n", " 31268\n", " MaciekK\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 117580\n", " 1\n", @@ -2069,7 +2076,7 @@ " 2\n", " 31268\n", " OpenSystem\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 120160\n", " 1\n", @@ -2090,7 +2097,7 @@ " 5\n", " 31268\n", " darkives\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 103907\n", " 1\n", @@ -2111,7 +2118,7 @@ " 6\n", " 31268\n", " datscilly\n", - " For Q1 2025, how many banks will be listed on ...\n", + " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", " 2025-01-17 19:06:22.013528+00\n", " 103777\n", " 1\n", @@ -2133,12 +2140,19 @@ "" ], "text/plain": [ - " question_id forecaster question_title \\\n", - "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", - "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", - "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", - "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", - "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster \\\n", + "0 31268 Jgalt \n", + "1 31268 MaciekK \n", + "2 31268 OpenSystem \n", + "5 31268 darkives \n", + "6 31268 datscilly \n", + "\n", + " question_title \\\n", + "0 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "1 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "2 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "5 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + "6 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", @@ -2176,7 +2190,7 @@ "6 False " ] }, - "execution_count": 203, + "execution_count": 303, "metadata": {}, "output_type": "execute_result" } @@ -2187,7 +2201,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 304, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2251,12 +2265,12 @@ " False\n", " False\n", " ...\n", - " [0.25,0.3,0.3,0.1,0.05]\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", - " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", + " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", - " [0.009900990099009901,0.39603960396039606,0.44...\n", - " [0.014925742574257425,0.5137871287128712,0.334...\n", + " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", + " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", " NaN\n", " NaN\n", " NaN\n", @@ -2275,12 +2289,12 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.1028571429,0.1057142857,0.1085714286,0.1114285714,0.1142857143,0.1171428571,0.12,0.1228571429,0.1257142857,0.1285714286,0.1314285714,0.1342857143,0.1371428571,0.14,0.1428571429,0.1457142857,0.1485714286,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.1685714286,0.1714285714,0.1742857143,0.1771428571,0.18,0.1828571429,0.1857142857,0.1885714286,0.1914285714,0.1942857143,0.1971428571,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95]\n", " NaN\n", - " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", - " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", + " [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911]\n", + " [0.001,0.001060875,0.0011396,0.0012863125,0.0015459984,0.0019048369,0.0023147701,0.0027425688,0.0031719899,0.0035935463,0.0040047171,0.0044081612,0.0048073678,0.0052048637,0.0056023079,0.0060005117,0.0063995798,0.0067992898,0.0071993689,0.0075995902,0.007999808,0.0083999595,0.0088000381,0.0092000616,0.0096525538,0.0103347221,0.0114180238,0.0128617561,0.0144931539,0.0161909912,0.0178965175,0.0195748423,0.0212159342,0.0228289888,0.0244265464,0.0260177161,0.0276085304,0.0292020038,0.0307985773,0.0323974755,0.0339977246,0.0355985069,0.0371992898,0.0387998404,0.0404001295,0.0420002192,0.0436001942,0.0452001261,0.0468000593,0.0484758458,0.0504834257,0.0530704368,0.056178071,0.0595567722,0.0630314345,0.0665171977,0.0699636664,0.0733563529,0.0767085411,0.0800383523,0.0833589543,0.0866790344,0.0900028852,0.0933311337,0.0967326953,0.1004442449,0.1047006189,0.1094577119,0.1144907128,0.1196353715,0.1248049846,0.1299418958,0.1350232879,0.1400570021,0.1452540043,0.1513017567,0.1589133116,0.1680377058,0.1780770546,0.1885468618,0.1991553484,0.2096896812,0.2200450325,0.2302229342,0.2402681458,0.2502302229,0.2601553402,0.27007834,0.2800179047,0.2899799302,0.2999629146,0.3099614863,0.3199691186,0.3299801956,0.3403173669,0.3521487483,0.3668129253,0.3844513624,0.4041888551,0.4247935739,0.4442765262,0.4605082419,0.4728869633,0.4822309604,0.4895341295,0.4956449952,0.5013686886,0.5073076754,0.5137610388,0.5206987551,0.5276657564,0.5340334461,0.5395220756,0.5442306919,0.5484901071,0.5530599502,0.5588761244,0.5663266439,0.5752119583,0.585204242,0.5959735276,0.6071500854,0.6184053116,0.6295209059,0.6403758638,0.650921239,0.6611693012,0.671174569,0.681009388,0.6907471485,0.7004527783,0.7101763721,0.7199504677,0.7297911321,0.7397010124,0.7496729757,0.7596938994,0.7697481465,0.7798202777,0.7898968803,0.7999675731,0.8100253018,0.8200662214,0.8300893951,0.8400025166,0.8494453768,0.8579165269,0.8651653723,0.8712540566,0.8763468591,0.8806505608,0.8844338485,0.8879756773,0.8915092577,0.8952099002,0.8991948145,0.9035195392,0.9081838533,0.9131467515,0.9183416751,0.9236898731,0.9291127196,0.9345414554,0.9399230919,0.9451659123,0.9500324455,0.9542146638,0.9575690762,0.9601504006,0.9620795658,0.9635039422,0.9646063832,0.965571997,0.9665531773,0.9676621061,0.9689711529,0.9705116418,0.9722785871,0.9742409577,0.9763519694,0.9785580215,0.9808067315,0.9830531373,0.9852633275,0.987415817,0.9895011861,0.9915203598,0.9934820158,0.9953894047,0.9970771779,0.998127745,0.99846,0.99852,0.99858,0.99864,0.9987,0.99876,0.99882,0.99888,0.99894,0.99899]\n", " NaN\n", " NaN\n", " NaN\n", @@ -2299,9 +2313,9 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.15\n", + " 0.05\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2324,8 +2338,8 @@ " None\n", " ...\n", " [0.25,0.6,0.15]\n", - " [0.7,0.25,0.05]\n", - " [0.15000000000000002,0.54,0.31000000000000005]\n", + " [0.15,0.65,0.2]\n", + " [0.15,0.45,0.4]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2347,15 +2361,15 @@ " False\n", " False\n", " ...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0.0114285714,0.0142857143,0.0171428571,0.02,0.0228571429,0.0257142857,0.0285714286,0.0314285714,0.0342857143,0.0371428571,0.04,0.0428571429,0.0457142857,0.0485714286,0.0514285714,0.0542857143,0.0571428571,0.06,0.0628571429,0.0657142857,0.0685714286,0.0714285714,0.0742857143,0.0771428571,0.08,0.0828571429,0.0857142857,0.0885714286,0.0914285714,0.0942857143,0.0971428571,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.9015384615,0.9030769231,0.9046153846,0.9061538462,0.9076923077,0.9092307692,0.9107692308,0.9123076923,0.9138461538,0.9153846154,0.9169230769,0.9184615385,0.92,0.9215384615,0.9230769231,0.9246153846,0.9261538462,0.9276923077,0.9292307692,0.9307692308,0.9323076923,0.9338461538,0.9353846154,0.9369230769,0.9384615385,0.94,0.9415384615,0.9430769231,0.9446153846,0.9461538462,0.9476923077,0.9492307692,0.9507692308,0.9523076923,0.9538461538,0.9553846154,0.9569230769,0.9584615385,0.96,0.9615384615,0.9630769231,0.9646153846,0.9661538462,0.9676923077,0.9692307692,0.9707692308,0.9723076923,0.9738461538,0.9753846154,0.9769230769,0.9784615385,0.98,0.9815384615,0.9830769231,0.9846153846,0.9861538462,0.9876923077,0.9892307692,0.9907692308,0.9923076923,0.9938461538,0.9953846154,0.9969230769,0.9984615385,1.0]\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0]\n", + " [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.034,0.036,0.038,0.04,0.042,0.044,0.046,0.048,0.05,0.052,0.054,0.056,0.058,0.06,0.062,0.064,0.066,0.068,0.07,0.072,0.074,0.076,0.078,0.08,0.082,0.084,0.086,0.088,0.09,0.092,0.094,0.096,0.098,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", " NaN\n", - " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", - " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", + " [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0]\n", + " [0.0,0.0001141583,0.0002446967,0.0003862688,0.0005272579,0.0006650709,0.0008243437,0.0011074433,0.0016696544,0.0025699094,0.0037138357,0.0049708626,0.0062610152,0.0075426566,0.0089765864,0.0111726822,0.0147311078,0.0195212559,0.0249547717,0.0306181288,0.0363105138,0.0419407763,0.0476011969,0.053516341,0.0598014349,0.0663689162,0.0730761187,0.0798334547,0.0865904866,0.0933196582,0.1000172031,0.1066924089,0.1133554776,0.1200140176,0.1266729489,0.1333343989,0.1399984689,0.1466644317,0.1533314439,0.1599988203,0.1666661444,0.1733332523,0.1800001372,0.1866668598,0.1933334943,0.2000000995,0.2066667101,0.2133333393,0.2199999878,0.22666665,0.2333333196,0.2399999916,0.2466666631,0.2533333329,0.2600000011,0.2666666681,0.2733333345,0.2800000007,0.286666667,0.2933333334,0.2999999999,0.3066666665,0.3133333332,0.3199999999,0.3266666666,0.3333333333,0.34,0.3466666667,0.3533333333,0.36,0.3666666667,0.3733333333,0.38,0.3866666667,0.3934628939,0.400837331,0.40925763,0.4186848364,0.428718413,0.4390353607,0.4494419812,0.4597974687,0.4700329298,0.4801500685,0.4901790777,0.500153105,0.5101028922,0.5200515519,0.5300114112,0.5398722838,0.5492279015,0.5576212737,0.5650210292,0.571743695,0.5780856137,0.5842571713,0.5904328096,0.5967209586,0.603152213,0.6097133168,0.6163738459,0.6230958146,0.6298433017,0.6365902337,0.6433215069,0.6500308134,0.656718392,0.6633885674,0.6700472479,0.6767001542,0.6833518918,0.6900055659,0.6966627826,0.7033239321,0.7099885835,0.7166558627,0.723324761,0.7299943545,0.7366639271,0.7433330133,0.7500013847,0.7566690034,0.7633359628,0.770002427,0.7766685825,0.7833346018,0.7900006228,0.7966667394,0.8033330023,0.8099994258,0.8166659972,0.8233326871,0.8299994586,0.8366662749,0.8433331037,0.8499999207,0.8566667097,0.8633334627,0.8700001785,0.8766668606,0.8833335157,0.8899751517,0.8964699017,0.9025861327,0.9081211655,0.9130226546,0.9173491712,0.921198292,0.9246959323,0.9279877368,0.9312103051,0.934472912,0.9378540969,0.9414005467,0.9450901244,0.9487670554,0.9522009139,0.9552513327,0.9578998205,0.9601715711,0.96211589,0.9638162438,0.9653702301,0.9668664828,0.9683781475,0.9699605983,0.9716476808,0.9734519305,0.9753688047,0.9773815283,0.9794657325,0.9815941718,0.9837408125,0.9858836701,0.9879773814,0.9898993305,0.9914888717,0.9926681205,0.9934599632,0.9939261174,0.9941560479,0.9942611072,0.9943265488,0.9943865488,0.9944537386,0.9945561009,0.9947328687,0.9950042368,0.9953660612,0.9958058993,0.9963078442,0.9968511117,0.9974139813,0.9979781729,0.9985251814,0.999027536,0.9994498435,0.999736686,0.9998734993,0.99994,1.0]\n", " NaN\n", " NaN\n", - " [0.0,0.001311947,0.0026238939,0.0039358409,0.0...\n", + " [0.0,0.001311947,0.0026238939,0.0039358409,0.0052477878,0.0065597348,0.0078716817,0.0091836287,0.0104955756,0.0118075226,0.0131194695,0.0144314165,0.0157433634,0.0170553104,0.0183672573,0.0196792043,0.0209911512,0.0223030982,0.0236150451,0.0249269921,0.026238939,0.027550886,0.0288628329,0.0301747799,0.0314867268,0.0327986738,0.0341106207,0.0354225677,0.0367345146,0.0380464616,0.0393584085,0.0406703555,0.0419823024,0.0432942494,0.0446061963,0.0459181433,0.0472300902,0.0485420372,0.0498539841,0.0511659311,0.052477878,0.053789825,0.0551017719,0.0564137189,0.0577256658,0.0590376128,0.0603495597,0.0616615067,0.0629734536,0.0642854006,0.0655973475,0.0669092945,0.0682212414,0.0695331884,0.0708451353,0.0721570823,0.0734690292,0.0747809762,0.0760929231,0.0774048701,0.078716817,0.080028764,0.0813407109,0.0826526579,0.0839646048,0.0852765518,0.0865884987,0.0879004457,0.0902457862,0.0933094828,0.0978079399,0.1023063969,0.1068048539,0.111303311,0.115801768,0.120300225,0.124798682,0.1292971391,0.1338199508,0.1388055027,0.1440933779,0.1496807808,0.1571177226,0.1652387403,0.1753118263,0.1904276903,0.2058197291,0.2212117678,0.237030829,0.2551785571,0.273870758,0.2925629589,0.3115548313,0.3307464845,0.3499926649,0.3692260274,0.3884136416,0.407661417,0.4269091924,0.4457073638,0.464050886,0.4823944081,0.5007379302,0.5190814523,0.5374249745,0.5538739661,0.5696118391,0.5853388804,0.6010659216,0.6161284786,0.6273538036,0.6382421632,0.6486483242,0.6588094975,0.668725683,0.6786418685,0.688558054,0.6984742395,0.708390425,0.7183066106,0.7278808508,0.7373411092,0.7468013677,0.7561442929,0.7645842622,0.7730242316,0.7814642009,0.7899041702,0.7983441395,0.8067841088,0.8152111577,0.8229940495,0.8307769414,0.8385598332,0.8447944123,0.8509124517,0.8563824526,0.8610823306,0.8657454654,0.8704086002,0.8750717351,0.8797348699,0.8843980047,0.8890611396,0.8934873987,0.8970573375,0.9006272763,0.9041972151,0.9077671539,0.9103291006,0.9126390493,0.914948998,0.9172589467,0.9195688953,0.921878844,0.9236671785,0.9253634634,0.9270597483,0.9287560333,0.9304523182,0.9321486031,0.933844888,0.935541173,0.9372374579,0.9389337428,0.9406300277,0.9423263126,0.9440225976,0.9457188825,0.9474151674,0.9491114523,0.9508077373,0.9525040222,0.9542003071,0.955896592,0.9575928769,0.9592891619,0.9609854468,0.9626817317,0.9643780166,0.9660743016,0.9677705865,0.9694668714,0.9711631563,0.9728594412,0.9745557262,0.9762520111,0.977948296,0.9796445809,0.9813408659,0.9830371508,0.9847334357,0.9864297206,0.9881260055,0.9898222905,0.9915185754,0.9932148603,0.9949111452,0.9966074302,0.9983037151,1.0]\n", " NaN\n", " \n", " \n", @@ -2385,47 +2399,68 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.25,0.3,0.3,0.1,0.05] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + " metac-o1 \\\n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", + "2 0.15 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.0114285714,0.0142857143,0.0171428571,0.02,0.0228571429,0.0257142857,0.0285714286,0.0314285714,0.0342857143,0.0371428571,0.04,0.0428571429,0.0457142857,0.0485714286,0.0514285714,0.0542857143,0.0571428571,0.06,0.0628571429,0.0657142857,0.0685714286,0.0714285714,0.0742857143,0.0771428571,0.08,0.0828571429,0.0857142857,0.0885714286,0.0914285714,0.0942857143,0.0971428571,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.9015384615,0.9030769231,0.9046153846,0.9061538462,0.9076923077,0.9092307692,0.9107692308,0.9123076923,0.9138461538,0.9153846154,0.9169230769,0.9184615385,0.92,0.9215384615,0.9230769231,0.9246153846,0.9261538462,0.9276923077,0.9292307692,0.9307692308,0.9323076923,0.9338461538,0.9353846154,0.9369230769,0.9384615385,0.94,0.9415384615,0.9430769231,0.9446153846,0.9461538462,0.9476923077,0.9492307692,0.9507692308,0.9523076923,0.9538461538,0.9553846154,0.9569230769,0.9584615385,0.96,0.9615384615,0.9630769231,0.9646153846,0.9661538462,0.9676923077,0.9692307692,0.9707692308,0.9723076923,0.9738461538,0.9753846154,0.9769230769,0.9784615385,0.98,0.9815384615,0.9830769231,0.9846153846,0.9861538462,0.9876923077,0.9892307692,0.9907692308,0.9923076923,0.9938461538,0.9953846154,0.9969230769,0.9984615385,1.0] \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0] \n", + "\n", + " metac-perplexity \\\n", + "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.1028571429,0.1057142857,0.1085714286,0.1114285714,0.1142857143,0.1171428571,0.12,0.1228571429,0.1257142857,0.1285714286,0.1314285714,0.1342857143,0.1371428571,0.14,0.1428571429,0.1457142857,0.1485714286,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.1685714286,0.1714285714,0.1742857143,0.1771428571,0.18,0.1828571429,0.1857142857,0.1885714286,0.1914285714,0.1942857143,0.1971428571,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95] \n", + "2 0.15 \n", + "3 [0.15,0.45,0.4] \n", + "4 [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.034,0.036,0.038,0.04,0.042,0.044,0.046,0.048,0.05,0.052,0.054,0.056,0.058,0.06,0.062,0.064,0.066,0.068,0.07,0.072,0.074,0.076,0.078,0.08,0.082,0.084,0.086,0.088,0.09,0.092,0.094,0.096,0.098,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", "\n", - " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.7,0.25,0.05] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", - "\n", - " metac-perplexity minefrac1 \\\n", - "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.1 NaN \n", - "3 [0.15000000000000002,0.54,0.31000000000000005] NaN \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", - "\n", - " mmBot \\\n", - "0 [0.009900990099009901,0.39603960396039606,0.44... \n", - "1 [0.0215944348,0.0218024136,0.0220262706,0.0222... \n", - "2 0.2 \n", - "3 [0.25,0.5,0.25] \n", - "4 [0.0,0.0006552097,0.0013605064,0.0021151815,0.... \n", - "\n", - " pgodzinai pianobot swingswish \\\n", - "0 [0.014925742574257425,0.5137871287128712,0.334... NaN NaN \n", - "1 [0.001,0.001060875,0.0011396,0.0012863125,0.00... NaN NaN \n", - "2 0.07 NaN NaN \n", - "3 [0.27499999999999997,0.5125,0.21249999999999997] NaN NaN \n", - "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... NaN NaN \n", - "\n", - " twsummerbot wunderplumb \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 [0.116,0.42,0.464] NaN \n", - "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0... NaN \n", + " minefrac1 \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + " mmBot \\\n", + "0 [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297] \n", + "1 [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911] \n", + "2 0.2 \n", + "3 [0.25,0.5,0.25] \n", + "4 [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0] \n", + "\n", + " pgodzinai \\\n", + "0 [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965] \n", + "1 [0.001,0.001060875,0.0011396,0.0012863125,0.0015459984,0.0019048369,0.0023147701,0.0027425688,0.0031719899,0.0035935463,0.0040047171,0.0044081612,0.0048073678,0.0052048637,0.0056023079,0.0060005117,0.0063995798,0.0067992898,0.0071993689,0.0075995902,0.007999808,0.0083999595,0.0088000381,0.0092000616,0.0096525538,0.0103347221,0.0114180238,0.0128617561,0.0144931539,0.0161909912,0.0178965175,0.0195748423,0.0212159342,0.0228289888,0.0244265464,0.0260177161,0.0276085304,0.0292020038,0.0307985773,0.0323974755,0.0339977246,0.0355985069,0.0371992898,0.0387998404,0.0404001295,0.0420002192,0.0436001942,0.0452001261,0.0468000593,0.0484758458,0.0504834257,0.0530704368,0.056178071,0.0595567722,0.0630314345,0.0665171977,0.0699636664,0.0733563529,0.0767085411,0.0800383523,0.0833589543,0.0866790344,0.0900028852,0.0933311337,0.0967326953,0.1004442449,0.1047006189,0.1094577119,0.1144907128,0.1196353715,0.1248049846,0.1299418958,0.1350232879,0.1400570021,0.1452540043,0.1513017567,0.1589133116,0.1680377058,0.1780770546,0.1885468618,0.1991553484,0.2096896812,0.2200450325,0.2302229342,0.2402681458,0.2502302229,0.2601553402,0.27007834,0.2800179047,0.2899799302,0.2999629146,0.3099614863,0.3199691186,0.3299801956,0.3403173669,0.3521487483,0.3668129253,0.3844513624,0.4041888551,0.4247935739,0.4442765262,0.4605082419,0.4728869633,0.4822309604,0.4895341295,0.4956449952,0.5013686886,0.5073076754,0.5137610388,0.5206987551,0.5276657564,0.5340334461,0.5395220756,0.5442306919,0.5484901071,0.5530599502,0.5588761244,0.5663266439,0.5752119583,0.585204242,0.5959735276,0.6071500854,0.6184053116,0.6295209059,0.6403758638,0.650921239,0.6611693012,0.671174569,0.681009388,0.6907471485,0.7004527783,0.7101763721,0.7199504677,0.7297911321,0.7397010124,0.7496729757,0.7596938994,0.7697481465,0.7798202777,0.7898968803,0.7999675731,0.8100253018,0.8200662214,0.8300893951,0.8400025166,0.8494453768,0.8579165269,0.8651653723,0.8712540566,0.8763468591,0.8806505608,0.8844338485,0.8879756773,0.8915092577,0.8952099002,0.8991948145,0.9035195392,0.9081838533,0.9131467515,0.9183416751,0.9236898731,0.9291127196,0.9345414554,0.9399230919,0.9451659123,0.9500324455,0.9542146638,0.9575690762,0.9601504006,0.9620795658,0.9635039422,0.9646063832,0.965571997,0.9665531773,0.9676621061,0.9689711529,0.9705116418,0.9722785871,0.9742409577,0.9763519694,0.9785580215,0.9808067315,0.9830531373,0.9852633275,0.987415817,0.9895011861,0.9915203598,0.9934820158,0.9953894047,0.9970771779,0.998127745,0.99846,0.99852,0.99858,0.99864,0.9987,0.99876,0.99882,0.99888,0.99894,0.99899] \n", + "2 0.07 \n", + "3 [0.27499999999999997,0.5125,0.21249999999999997] \n", + "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.0005272579,0.0006650709,0.0008243437,0.0011074433,0.0016696544,0.0025699094,0.0037138357,0.0049708626,0.0062610152,0.0075426566,0.0089765864,0.0111726822,0.0147311078,0.0195212559,0.0249547717,0.0306181288,0.0363105138,0.0419407763,0.0476011969,0.053516341,0.0598014349,0.0663689162,0.0730761187,0.0798334547,0.0865904866,0.0933196582,0.1000172031,0.1066924089,0.1133554776,0.1200140176,0.1266729489,0.1333343989,0.1399984689,0.1466644317,0.1533314439,0.1599988203,0.1666661444,0.1733332523,0.1800001372,0.1866668598,0.1933334943,0.2000000995,0.2066667101,0.2133333393,0.2199999878,0.22666665,0.2333333196,0.2399999916,0.2466666631,0.2533333329,0.2600000011,0.2666666681,0.2733333345,0.2800000007,0.286666667,0.2933333334,0.2999999999,0.3066666665,0.3133333332,0.3199999999,0.3266666666,0.3333333333,0.34,0.3466666667,0.3533333333,0.36,0.3666666667,0.3733333333,0.38,0.3866666667,0.3934628939,0.400837331,0.40925763,0.4186848364,0.428718413,0.4390353607,0.4494419812,0.4597974687,0.4700329298,0.4801500685,0.4901790777,0.500153105,0.5101028922,0.5200515519,0.5300114112,0.5398722838,0.5492279015,0.5576212737,0.5650210292,0.571743695,0.5780856137,0.5842571713,0.5904328096,0.5967209586,0.603152213,0.6097133168,0.6163738459,0.6230958146,0.6298433017,0.6365902337,0.6433215069,0.6500308134,0.656718392,0.6633885674,0.6700472479,0.6767001542,0.6833518918,0.6900055659,0.6966627826,0.7033239321,0.7099885835,0.7166558627,0.723324761,0.7299943545,0.7366639271,0.7433330133,0.7500013847,0.7566690034,0.7633359628,0.770002427,0.7766685825,0.7833346018,0.7900006228,0.7966667394,0.8033330023,0.8099994258,0.8166659972,0.8233326871,0.8299994586,0.8366662749,0.8433331037,0.8499999207,0.8566667097,0.8633334627,0.8700001785,0.8766668606,0.8833335157,0.8899751517,0.8964699017,0.9025861327,0.9081211655,0.9130226546,0.9173491712,0.921198292,0.9246959323,0.9279877368,0.9312103051,0.934472912,0.9378540969,0.9414005467,0.9450901244,0.9487670554,0.9522009139,0.9552513327,0.9578998205,0.9601715711,0.96211589,0.9638162438,0.9653702301,0.9668664828,0.9683781475,0.9699605983,0.9716476808,0.9734519305,0.9753688047,0.9773815283,0.9794657325,0.9815941718,0.9837408125,0.9858836701,0.9879773814,0.9898993305,0.9914888717,0.9926681205,0.9934599632,0.9939261174,0.9941560479,0.9942611072,0.9943265488,0.9943865488,0.9944537386,0.9945561009,0.9947328687,0.9950042368,0.9953660612,0.9958058993,0.9963078442,0.9968511117,0.9974139813,0.9979781729,0.9985251814,0.999027536,0.9994498435,0.999736686,0.9998734993,0.99994,1.0] \n", + "\n", + " pianobot swingswish \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " twsummerbot \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 [0.116,0.42,0.464] \n", + "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0052477878,0.0065597348,0.0078716817,0.0091836287,0.0104955756,0.0118075226,0.0131194695,0.0144314165,0.0157433634,0.0170553104,0.0183672573,0.0196792043,0.0209911512,0.0223030982,0.0236150451,0.0249269921,0.026238939,0.027550886,0.0288628329,0.0301747799,0.0314867268,0.0327986738,0.0341106207,0.0354225677,0.0367345146,0.0380464616,0.0393584085,0.0406703555,0.0419823024,0.0432942494,0.0446061963,0.0459181433,0.0472300902,0.0485420372,0.0498539841,0.0511659311,0.052477878,0.053789825,0.0551017719,0.0564137189,0.0577256658,0.0590376128,0.0603495597,0.0616615067,0.0629734536,0.0642854006,0.0655973475,0.0669092945,0.0682212414,0.0695331884,0.0708451353,0.0721570823,0.0734690292,0.0747809762,0.0760929231,0.0774048701,0.078716817,0.080028764,0.0813407109,0.0826526579,0.0839646048,0.0852765518,0.0865884987,0.0879004457,0.0902457862,0.0933094828,0.0978079399,0.1023063969,0.1068048539,0.111303311,0.115801768,0.120300225,0.124798682,0.1292971391,0.1338199508,0.1388055027,0.1440933779,0.1496807808,0.1571177226,0.1652387403,0.1753118263,0.1904276903,0.2058197291,0.2212117678,0.237030829,0.2551785571,0.273870758,0.2925629589,0.3115548313,0.3307464845,0.3499926649,0.3692260274,0.3884136416,0.407661417,0.4269091924,0.4457073638,0.464050886,0.4823944081,0.5007379302,0.5190814523,0.5374249745,0.5538739661,0.5696118391,0.5853388804,0.6010659216,0.6161284786,0.6273538036,0.6382421632,0.6486483242,0.6588094975,0.668725683,0.6786418685,0.688558054,0.6984742395,0.708390425,0.7183066106,0.7278808508,0.7373411092,0.7468013677,0.7561442929,0.7645842622,0.7730242316,0.7814642009,0.7899041702,0.7983441395,0.8067841088,0.8152111577,0.8229940495,0.8307769414,0.8385598332,0.8447944123,0.8509124517,0.8563824526,0.8610823306,0.8657454654,0.8704086002,0.8750717351,0.8797348699,0.8843980047,0.8890611396,0.8934873987,0.8970573375,0.9006272763,0.9041972151,0.9077671539,0.9103291006,0.9126390493,0.914948998,0.9172589467,0.9195688953,0.921878844,0.9236671785,0.9253634634,0.9270597483,0.9287560333,0.9304523182,0.9321486031,0.933844888,0.935541173,0.9372374579,0.9389337428,0.9406300277,0.9423263126,0.9440225976,0.9457188825,0.9474151674,0.9491114523,0.9508077373,0.9525040222,0.9542003071,0.955896592,0.9575928769,0.9592891619,0.9609854468,0.9626817317,0.9643780166,0.9660743016,0.9677705865,0.9694668714,0.9711631563,0.9728594412,0.9745557262,0.9762520111,0.977948296,0.9796445809,0.9813408659,0.9830371508,0.9847334357,0.9864297206,0.9881260055,0.9898222905,0.9915185754,0.9932148603,0.9949111452,0.9966074302,0.9983037151,1.0] \n", + "\n", + " wunderplumb \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", "\n", "[5 rows x 57 columns]" ] @@ -2491,7 +2526,7 @@ " False\n", " False\n", " ...\n", - " 0.9\n", + " 0.95\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2515,8 +2550,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.9\n", + " 0.35\n", + " 0.4\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2563,7 +2598,7 @@ " False\n", " False\n", " ...\n", - " 0.8\n", + " 0.85\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2587,9 +2622,9 @@ " False\n", " False\n", " ...\n", - " 0.02\n", + " 0.1\n", + " 0.05\n", " 0.05\n", - " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2619,11 +2654,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.65 0.9 NaN NaN 0.15 NaN \n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.35 0.4 NaN NaN 0.15 NaN \n", "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.02 0.05 0.03 NaN 0.15 0.05 \n", + "97 0.85 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.05 0.05 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -2695,7 +2730,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 305, "metadata": {}, "outputs": [ { @@ -2718,7 +2753,7 @@ " dtype='object')" ] }, - "execution_count": 205, + "execution_count": 305, "metadata": {}, "output_type": "execute_result" } @@ -2729,7 +2764,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 306, "metadata": {}, "outputs": [ { @@ -2739,7 +2774,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 206, + "execution_count": 306, "metadata": {}, "output_type": "execute_result" } @@ -2754,7 +2789,7 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 307, "metadata": {}, "outputs": [], "source": [ @@ -2766,7 +2801,7 @@ }, { "cell_type": "code", - "execution_count": 208, + "execution_count": 308, "metadata": {}, "outputs": [], "source": [ @@ -2775,7 +2810,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 309, "metadata": {}, "outputs": [ { @@ -2836,9 +2871,9 @@ " False\n", " False\n", " ...\n", - " [0.25,0.3,0.3,0.1,0.05]\n", - " [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333]\n", - " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", + " [0.4,0.35,0.2,0.04,0.01]\n", + " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", + " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2860,9 +2895,9 @@ " True\n", " True\n", " ...\n", + " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...]\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", - " [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...]\n", + " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...]\n", " NaN\n", " [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...]\n", " [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...]\n", @@ -2884,9 +2919,9 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.15\n", + " 0.05\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2909,8 +2944,8 @@ " None\n", " ...\n", " [0.25,0.6,0.15]\n", - " [0.7,0.25,0.05]\n", - " [0.15000000000000002,0.54,0.31000000000000005]\n", + " [0.15,0.65,0.2]\n", + " [0.15,0.45,0.4]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2932,9 +2967,9 @@ " False\n", " False\n", " ...\n", - " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...]\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...]\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...]\n", + " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...]\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...]\n", + " [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...]\n", " NaN\n", " [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", " [0.0, 0.0001141583, 0.0002446967, 0.0003862688, 0.0005272579, 0.0006650709, 0.0008243437, 0.0011074433, 0.0016696544, 0.0025699094, 0.0037138357, 0.0049708626, 0.0062610152, 0.0075426566, 0.0089765864, 0.0111726822, 0.0147311078, 0.0195212559, 0.0249547717, 0.0306181288, 0.0363105138, 0.0419407763, 0.0476011969, 0.053516341, 0.0598014349, 0.0663689162, 0.0730761187, 0.0798334547, 0.0865904866, 0.0933196582, 0.1000172031, 0.1066924089, 0.1133554776, 0.1200140176, 0.1266729489, 0.1333343989, 0.1399984689, 0.1466644317, 0.1533314439, 0.1599988203, 0.1666661444, 0.1733332523, 0.1800001372, 0.1866668598, 0.1933334943, 0.2000000995, 0.2066667101, 0.2133333393, 0.2199999878, 0.22666665, 0.2333333196, 0.2399999916, 0.2466666631, 0.2533333329, 0.2600000011, 0.2666666681, 0.2733333345, 0.2800000007, 0.286666667, 0.2933333334, 0.2999999999, 0.3066666665, 0.3133333332, 0.3199999999, 0.3266666666, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3934628939, 0.400837331, 0.40925763, 0.4186848364, 0.428718413, 0.4390353607, 0.4494419812, 0.4597974687, 0.4700329298, 0.4801500685, 0.4901790777, 0.500153105, 0.5101028922, 0.5200515519, 0.5300114112, 0.5398722838, 0.5492279015, 0.5576212737, 0.5650210292, 0.571743695, 0.5780856137, 0.5842571713, 0.5904328096, 0.5967209586, 0.603152213, 0.6097133168, ...]\n", @@ -2970,26 +3005,26 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.25,0.3,0.3,0.1,0.05] \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", - "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.608, 0.616, 0.624, 0.632, 0.64, 0.648, 0.656, 0.664, 0.672, ...] \n", + " metac-o1 \\\n", + "0 [0.4,0.35,0.2,0.04,0.01] \n", + "1 [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...] \n", + "2 0.15 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", "\n", " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333] \n", + "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", - "2 0.1 \n", - "3 [0.7,0.25,0.05] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, ...] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", "\n", - " metac-perplexity \\\n", - "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", - "1 [0.05, 0.0508333333, 0.0516666667, 0.0525, 0.0533333333, 0.0541666667, 0.055, 0.0558333333, 0.0566666667, 0.0575, 0.0583333333, 0.0591666667, 0.06, 0.0608333333, 0.0616666667, 0.0625, 0.0633333333, 0.0641666667, 0.065, 0.0658333333, 0.0666666667, 0.0675, 0.0683333333, 0.0691666667, 0.07, 0.0708333333, 0.0716666667, 0.0725, 0.0733333333, 0.0741666667, 0.075, 0.0758333333, 0.0766666667, 0.0775, 0.0783333333, 0.0791666667, 0.08, 0.0808333333, 0.0816666667, 0.0825, 0.0833333333, 0.0841666667, 0.085, 0.0858333333, 0.0866666667, 0.0875, 0.0883333333, 0.0891666667, 0.09, 0.0908333333, 0.0916666667, 0.0925, 0.0933333333, 0.0941666667, 0.095, 0.0958333333, 0.0966666667, 0.0975, 0.0983333333, 0.0991666667, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, ...] \n", - "2 0.1 \n", - "3 [0.15000000000000002,0.54,0.31000000000000005] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02, 0.0225, 0.025, 0.0275, 0.03, 0.0325, 0.035, 0.0375, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.105, 0.11, 0.115, 0.12, 0.125, 0.13, 0.135, 0.14, 0.145, 0.15, 0.155, 0.16, 0.165, 0.17, 0.175, 0.18, 0.185, 0.19, 0.195, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.4133333333, 0.4266666667, 0.44, 0.4533333333, 0.4666666667, 0.48, 0.4933333333, 0.5066666667, 0.52, 0.5333333333, 0.5466666667, 0.56, 0.5733333333, 0.5866666667, 0.6, 0.608, 0.616, 0.624, 0.632, ...] \n", + " metac-perplexity \\\n", + "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...] \n", + "2 0.15 \n", + "3 [0.15,0.45,0.4] \n", + "4 [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3097,7 +3132,7 @@ " False\n", " False\n", " ...\n", - " 0.9\n", + " 0.95\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3121,8 +3156,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.9\n", + " 0.35\n", + " 0.4\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3169,7 +3204,7 @@ " False\n", " False\n", " ...\n", - " 0.8\n", + " 0.85\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3193,9 +3228,9 @@ " False\n", " False\n", " ...\n", - " 0.02\n", + " 0.1\n", + " 0.05\n", " 0.05\n", - " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3225,11 +3260,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.65 0.9 NaN NaN 0.15 NaN \n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.35 0.4 NaN NaN 0.15 NaN \n", "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.02 0.05 0.03 NaN 0.15 0.05 \n", + "97 0.85 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.05 0.05 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3289,7 +3324,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 310, "metadata": {}, "outputs": [ { @@ -3363,7 +3398,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 311, "metadata": {}, "outputs": [ { @@ -3424,8 +3459,8 @@ " False\n", " False\n", " ...\n", - " 2.644992\n", - " 5.703782\n", + " 2.343407\n", + " 5.857933\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -3448,8 +3483,8 @@ " None\n", " None\n", " ...\n", - " -0.565314\n", - " 0.204794\n", + " 0.390198\n", + " 0.022473\n", " NaN\n", " 0.127833\n", " 0.152526\n", @@ -3472,7 +3507,7 @@ " False\n", " False\n", " ...\n", - " 0.247562\n", + " 0.298855\n", " 0.096331\n", " NaN\n", " -0.184571\n", @@ -3481,7 +3516,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.112526\n", + " -0.106610\n", " \n", " \n", " 9\n", @@ -3521,7 +3556,7 @@ " None\n", " ...\n", " 0.441833\n", - " 0.510826\n", + " 0.287682\n", " 0.021979\n", " 0.200671\n", " 0.253781\n", @@ -3529,7 +3564,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.325422\n", + " -0.062598\n", " \n", " \n", "\n", @@ -3566,18 +3601,18 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.644992 5.703782 NaN 2.292635 2.703087 \n", - "3 -0.565314 0.204794 NaN 0.127833 0.152526 \n", - "6 0.247562 0.096331 NaN -0.184571 0.112526 \n", + "0 2.343407 5.857933 NaN 2.292635 2.703087 \n", + "3 0.390198 0.022473 NaN 0.127833 0.152526 \n", + "6 0.298855 0.096331 NaN -0.184571 0.112526 \n", "9 -0.518794 -1.211941 NaN -0.806476 -0.494101 \n", - "13 0.441833 0.510826 0.021979 0.200671 0.253781 \n", + "13 0.441833 0.287682 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "0 NaN NaN NaN NaN 5.010635 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN 0.112526 \n", + "6 NaN NaN NaN NaN -0.106610 \n", "9 NaN NaN -0.624154 NaN -0.693147 \n", - "13 NaN NaN NaN NaN -0.325422 \n", + "13 NaN NaN NaN NaN -0.062598 \n", "\n", "[5 rows x 58 columns]" ] @@ -3643,7 +3678,7 @@ " False\n", " False\n", " ...\n", - " -2.879198\n", + " -3.795489\n", " -1.780586\n", " -3.007032\n", " -2.879198\n", @@ -3652,7 +3687,7 @@ " NaN\n", " -2.348570\n", " -2.409195\n", - " -3.795489\n", + " -3.390024\n", " \n", " \n", " 82\n", @@ -3667,8 +3702,8 @@ " None\n", " None\n", " ...\n", - " -0.656780\n", - " -0.300105\n", + " -0.993252\n", + " -0.186776\n", " -0.523248\n", " 0.105361\n", " 0.259511\n", @@ -3676,7 +3711,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " -0.656780\n", + " 0.276509\n", " \n", " \n", " 83\n", @@ -3691,8 +3726,8 @@ " None\n", " None\n", " ...\n", - " -1.321756\n", - " -0.265703\n", + " -0.693147\n", + " -0.182322\n", " NaN\n", " -0.182322\n", " NaN\n", @@ -3716,7 +3751,7 @@ " False\n", " ...\n", " -0.069566\n", - " -0.048289\n", + " -0.102356\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -3739,8 +3774,8 @@ " False\n", " False\n", " ...\n", - " -1.704748\n", - " -1.704748\n", + " -0.606136\n", + " -4.007333\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -3748,7 +3783,7 @@ " -0.480973\n", " NaN\n", " -0.749237\n", - " -0.480973\n", + " -0.200671\n", " \n", " \n", "\n", @@ -3778,25 +3813,25 @@ "92 [0-24, 25-30, 31-49, 50-70, >70] NaN \n", "\n", " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", - "81 NaN False False ... -2.879198 \n", - "82 NaN None None ... -0.656780 \n", - "83 NaN None None ... -1.321756 \n", + "81 NaN False False ... -3.795489 \n", + "82 NaN None None ... -0.993252 \n", + "83 NaN None None ... -0.693147 \n", "91 NaN False False ... -0.069566 \n", - "92 NaN False False ... -1.704748 \n", + "92 NaN False False ... -0.606136 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", "81 -1.780586 -3.007032 -2.879198 -3.390024 NaN NaN \n", - "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", - "83 -0.265703 NaN -0.182322 NaN NaN NaN \n", - "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -1.704748 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "82 -0.186776 -0.523248 0.105361 0.259511 NaN NaN \n", + "83 -0.182322 NaN -0.182322 NaN NaN NaN \n", + "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -4.007333 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -3.795489 \n", - "82 0.276509 -0.644609 -0.656780 \n", + "81 -2.348570 -2.409195 -3.390024 \n", + "82 0.276509 -0.644609 0.276509 \n", "83 -0.178330 -0.567984 -0.693147 \n", "91 NaN -0.147818 -0.124829 \n", - "92 NaN -0.749237 -0.480973 \n", + "92 NaN -0.749237 -0.200671 \n", "\n", "[5 rows x 58 columns]" ] @@ -3862,8 +3897,8 @@ " False\n", " False\n", " ...\n", - " -0.092275\n", - " -0.092275\n", + " -0.038208\n", + " -0.149434\n", " NaN\n", " -0.210058\n", " -0.059485\n", @@ -3871,7 +3906,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.149434\n", + " -0.179287\n", " \n", " \n", " 5\n", @@ -3886,8 +3921,8 @@ " None\n", " None\n", " ...\n", - " -0.251314\n", - " 0.441833\n", + " -0.810930\n", + " 0.200671\n", " NaN\n", " 0.510826\n", " 0.320472\n", @@ -3911,7 +3946,7 @@ " False\n", " ...\n", " -0.054067\n", - " -0.054067\n", + " 0.000000\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -3958,8 +3993,8 @@ " False\n", " False\n", " ...\n", - " 0.008457\n", - " 0.008457\n", + " -0.045611\n", + " 0.039547\n", " NaN\n", " -0.068083\n", " NaN\n", @@ -3967,7 +4002,7 @@ " NaN\n", " -0.076070\n", " NaN\n", - " -0.076070\n", + " -0.096728\n", " \n", " \n", "\n", @@ -3990,18 +4025,18 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", - "5 -0.251314 0.441833 NaN 0.510826 0.320472 \n", - "8 -0.054067 -0.054067 NaN -0.111226 -0.147158 \n", + "2 -0.038208 -0.149434 NaN -0.210058 -0.059485 \n", + "5 -0.810930 0.200671 NaN 0.510826 0.320472 \n", + "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", "12 -0.182322 0.000000 NaN 0.054067 -0.057158 \n", - "16 0.008457 0.008457 NaN -0.068083 NaN \n", + "16 -0.045611 0.039547 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "2 NaN NaN NaN NaN -0.149434 \n", + "2 NaN NaN NaN NaN -0.179287 \n", "5 NaN NaN NaN NaN 0.287682 \n", "8 NaN NaN -0.398124 NaN -0.171850 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", - "16 NaN NaN -0.076070 NaN -0.076070 \n", + "16 NaN NaN -0.076070 NaN -0.096728 \n", "\n", "[5 rows x 58 columns]" ] @@ -4091,7 +4126,7 @@ " False\n", " False\n", " ...\n", - " -2.251292\n", + " -0.459532\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4164,7 +4199,7 @@ " False\n", " ...\n", " -0.017709\n", - " 0.000000\n", + " -0.017709\n", " NaN\n", " -0.112251\n", " -0.017709\n", @@ -4196,10 +4231,10 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -2.251292 NaN NaN -0.111226 NaN \n", + "95 -0.459532 NaN NaN -0.111226 NaN \n", "96 -0.074901 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.017709 -0.017709 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", @@ -4223,7 +4258,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 312, "metadata": {}, "outputs": [ { @@ -4264,13 +4299,13 @@ " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 3162.155445\n", + " bot_median\n", + " 3504.379897\n", " \n", " \n", " 3\n", - " bot_median\n", - " 2974.983652\n", + " metac-o1-preview\n", + " 3162.155445\n", " \n", " \n", " 4\n", @@ -4500,8 +4535,8 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 metac-o1-preview 3162.155445\n", - "3 bot_median 2974.983652\n", + "2 bot_median 3504.379897\n", + "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", "6 acm_bot 1876.466009\n", @@ -4548,7 +4583,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 212, + "execution_count": 312, "metadata": {}, "output_type": "execute_result" } @@ -4559,7 +4594,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 313, "metadata": {}, "outputs": [ { @@ -4568,13 +4603,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", - "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" + "mean metac-o1 forecast on questions that resolved yes: 69.0%\n", + "mean metac-o1 forecast on questions that resolved no: 30.0%\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -4643,7 +4678,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 314, "metadata": {}, "outputs": [ { @@ -4700,7 +4735,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 315, "metadata": {}, "outputs": [], "source": [ @@ -4713,7 +4748,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 316, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4775,28 +4810,28 @@ " \n", " 3\n", " 4\n", + " bot_median\n", + " 2456.727963\n", + " 97\n", + " 93.10\n", + " \n", + " \n", + " 4\n", + " 5\n", " acm_bot\n", " 2239.058675\n", " 85\n", " 81.25\n", " \n", " \n", - " 4\n", - " 5\n", + " 5\n", + " 6\n", " metac-claude-3-5-sonnet-20240620\n", " 2018.110211\n", " 95\n", " 91.50\n", " \n", " \n", - " 5\n", - " 6\n", - " bot_median\n", - " 1970.633069\n", - " 97\n", - " 93.10\n", - " \n", - " \n", " 6\n", " 7\n", " manticAI\n", @@ -5133,9 +5168,9 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 acm_bot 2239.058675 85 \n", - "4 5 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", - "5 6 bot_median 1970.633069 97 \n", + "3 4 bot_median 2456.727963 97 \n", + "4 5 acm_bot 2239.058675 85 \n", + "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", "8 9 twsummerbot 1819.064141 62 \n", @@ -5182,9 +5217,9 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 81.25 \n", - "4 91.50 \n", - "5 93.10 \n", + "3 93.10 \n", + "4 81.25 \n", + "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", "8 59.40 \n", @@ -5228,7 +5263,7 @@ "46 52.10 " ] }, - "execution_count": 216, + "execution_count": 316, "metadata": {}, "output_type": "execute_result" } @@ -5297,7 +5332,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 317, "metadata": {}, "outputs": [ { @@ -5378,6 +5413,20 @@ " 0.000036\n", " \n", " \n", + " bot_median\n", + " 2456.7\n", + " 93.1\n", + " 26.4\n", + " 58.198995\n", + " 6.031713\n", + " 4.374886\n", + " 1.985277\n", + " 38.4\n", + " 14.4\n", + " 0.999984\n", + " 0.000032\n", + " \n", + " \n", " acm_bot\n", " 2239.1\n", " 81.2\n", @@ -5406,20 +5455,6 @@ " 0.001450\n", " \n", " \n", - " bot_median\n", - " 1970.6\n", - " 93.1\n", - " 21.2\n", - " 65.554743\n", - " 6.794058\n", - " 3.115493\n", - " 1.985277\n", - " 34.7\n", - " 7.7\n", - " 0.998776\n", - " 0.002449\n", - " \n", - " \n", " manticAI\n", " 1865.1\n", " 70.4\n", @@ -6002,9 +6037,9 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", + "bot_median 2456.7 93.1 26.4 58.198995 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", - "bot_median 1970.6 93.1 21.2 65.554743 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", "twsummerbot 1819.1 59.4 30.6 54.747799 \n", @@ -6051,9 +6086,9 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", + "bot_median 6.031713 4.374886 1.985277 38.4 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", - "bot_median 6.794058 3.115493 1.985277 34.7 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", "twsummerbot 7.103517 4.311100 2.000163 44.8 \n", @@ -6100,9 +6135,9 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", + "bot_median 14.4 0.999984 0.000032 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", - "bot_median 7.7 0.998776 0.002449 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", "twsummerbot 16.4 0.999968 0.000063 \n", @@ -6146,7 +6181,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 217, + "execution_count": 317, "metadata": {}, "output_type": "execute_result" } @@ -6162,7 +6197,7 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 318, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -6235,18 +6270,18 @@ " NA\n", " \n", " \n", - " bean_bot\n", - " -0.6\n", - " 4.7\n", + " RPM_bot\n", + " -0.5\n", + " 7.0\n", " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", + " 0.840163\n", + " 0.317552\n", + " -0.229115\n", + " 2.446912\n", + " 0.7\n", + " -0.8\n", + " 0.413195\n", + " 0.826390\n", " \n", " \n", " jonahsingerbot\n", @@ -6263,6 +6298,20 @@ " 0.007677\n", " \n", " \n", + " bean_bot\n", + " -0.6\n", + " 4.7\n", + " -0.1\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", + " \n", + " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6277,20 +6326,6 @@ " 0.483189\n", " \n", " \n", - " RPM_bot\n", - " -1.1\n", - " 7.0\n", - " -0.2\n", - " 0.824532\n", - " 0.311644\n", - " -0.523406\n", - " 2.446912\n", - " 0.6\n", - " -0.9\n", - " 0.309726\n", - " 0.619452\n", - " \n", - " \n", " CumulativeBot\n", " -1.1\n", " 10.2\n", @@ -6432,17 +6467,17 @@ " \n", " \n", " cookics_bot_TEST\n", - " -6.9\n", + " -6.5\n", " 27.4\n", - " -0.3\n", - " 0.744699\n", - " 0.142267\n", - " -1.764876\n", + " -0.2\n", + " 0.747831\n", + " 0.142866\n", + " -1.667933\n", " 2.049541\n", - " 0.0\n", + " 0.1\n", " -0.5\n", - " 0.044576\n", - " 0.089152\n", + " 0.053575\n", + " 0.107149\n", " \n", " \n", " jkraybill_bot\n", @@ -6501,18 +6536,18 @@ " 0.023289\n", " \n", " \n", - " GreeneiBot2\n", + " metac-o1\n", " -10.4\n", - " 58.4\n", - " -0.2\n", - " 0.849883\n", - " 0.111260\n", - " -1.597976\n", - " 2.000832\n", - " 0.0\n", - " -0.4\n", - " 0.057772\n", - " 0.115544\n", + " 91.1\n", + " -0.1\n", + " 0.931550\n", + " 0.097599\n", + " -1.171004\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.122342\n", + " 0.244685\n", " \n", " \n", " acm_bot\n", @@ -6529,6 +6564,20 @@ " 0.201592\n", " \n", " \n", + " GreeneiBot2\n", + " -10.6\n", + " 58.4\n", + " -0.2\n", + " 0.849331\n", + " 0.111188\n", + " -1.638406\n", + " 2.000832\n", + " 0.0\n", + " -0.4\n", + " 0.053406\n", + " 0.106813\n", + " \n", + " \n", " ajf-bot\n", " -10.9\n", " 34.2\n", @@ -6543,18 +6592,18 @@ " 0.094289\n", " \n", " \n", - " metac-o1\n", - " -11.5\n", - " 91.1\n", + " bot_median\n", + " -11.1\n", + " 92.1\n", " -0.1\n", - " 0.888227\n", - " 0.093060\n", - " -1.360468\n", - " 1.985829\n", + " 0.834391\n", + " 0.086944\n", + " -1.391942\n", + " 1.985550\n", " 0.1\n", " -0.3\n", - " 0.088538\n", - " 0.177076\n", + " 0.083665\n", + " 0.167329\n", " \n", " \n", " Bot_Pepa\n", @@ -6571,20 +6620,6 @@ " 0.023810\n", " \n", " \n", - " metac-perplexity\n", - " -11.9\n", - " 89.1\n", - " -0.1\n", - " 0.993669\n", - " 0.105270\n", - " -1.264731\n", - " 1.986405\n", - " 0.1\n", - " -0.3\n", - " 0.104652\n", - " 0.209303\n", - " \n", - " \n", " laylaps\n", " -12.9\n", " 64.1\n", @@ -6613,6 +6648,20 @@ " 0.006348\n", " \n", " \n", + " metac-deepseek-r1\n", + " -14.1\n", + " 52.1\n", + " -0.3\n", + " 0.817209\n", + " 0.113218\n", + " -2.393750\n", + " 2.005379\n", + " -0.0\n", + " -0.5\n", + " 0.010193\n", + " 0.020386\n", + " \n", + " \n", " manticAI\n", " -14.6\n", " 69.4\n", @@ -6627,39 +6676,39 @@ " 0.011014\n", " \n", " \n", - " metac-deepseek-r1\n", - " -14.6\n", - " 52.1\n", - " -0.3\n", - " 0.731525\n", - " 0.101347\n", - " -2.766689\n", - " 2.005379\n", - " -0.1\n", - " -0.5\n", - " 0.003932\n", - " 0.007864\n", - " \n", - " \n", " metac-Gemini-Exp-1206\n", - " -15.2\n", + " -14.6\n", " 76.5\n", " -0.2\n", - " 0.943797\n", - " 0.107907\n", - " -1.846774\n", + " 0.936930\n", + " 0.107121\n", + " -1.780658\n", " 1.990822\n", " 0.0\n", " -0.4\n", - " 0.034349\n", - " 0.068698\n", + " 0.039496\n", + " 0.078991\n", " \n", " \n", - " NextWorldLab\n", - " -16.9\n", - " 80.2\n", + " metac-perplexity\n", + " -16.1\n", + " 89.1\n", " -0.2\n", - " 0.906964\n", + " 1.069491\n", + " 0.113302\n", + " -1.599489\n", + " 1.986405\n", + " 0.0\n", + " -0.4\n", + " 0.056646\n", + " 0.113292\n", + " \n", + " \n", + " NextWorldLab\n", + " -16.9\n", + " 80.2\n", + " -0.2\n", + " 0.906964\n", " 0.101244\n", " -2.078393\n", " 1.989344\n", @@ -6669,46 +6718,60 @@ " 0.040909\n", " \n", " \n", - " bot_median\n", - " -17.3\n", - " 92.1\n", - " -0.2\n", - " 0.919122\n", - " 0.095773\n", - " -1.963996\n", - " 1.985550\n", - " 0.0\n", - " -0.4\n", - " 0.026290\n", - " 0.052579\n", - " \n", - " \n", " minefrac1\n", - " -19.2\n", + " -18.5\n", " 51.1\n", " -0.4\n", - " 0.880990\n", - " 0.123242\n", - " -3.043641\n", + " 0.878223\n", + " 0.122855\n", + " -2.945421\n", " 2.006545\n", " -0.1\n", " -0.6\n", - " 0.001859\n", - " 0.003717\n", + " 0.002441\n", + " 0.004882\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -19.5\n", + " -20.8\n", " 90.5\n", " -0.2\n", - " 1.009138\n", - " 0.106078\n", - " -2.031065\n", + " 0.985458\n", + " 0.103589\n", + " -2.217659\n", " 1.986072\n", " -0.0\n", " -0.4\n", - " 0.022608\n", - " 0.045215\n", + " 0.014555\n", + " 0.029110\n", + " \n", + " \n", + " metac-Llama-3.1\n", + " -21.0\n", + " 89.1\n", + " -0.2\n", + " 1.131903\n", + " 0.119914\n", + " -1.966710\n", + " 1.986405\n", + " 0.0\n", + " -0.5\n", + " 0.026182\n", + " 0.052364\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-latest\n", + " -21.7\n", + " 91.1\n", + " -0.2\n", + " 0.867992\n", + " 0.090940\n", + " -2.614756\n", + " 1.985829\n", + " -0.1\n", + " -0.4\n", + " 0.005233\n", + " 0.010466\n", " \n", " \n", " mmBot\n", @@ -6725,32 +6788,18 @@ " 0.002208\n", " \n", " \n", - " metac-grok-2-1212\n", - " -22.9\n", - " 91.1\n", - " -0.3\n", - " 1.048829\n", - " 0.109887\n", - " -2.283528\n", - " 1.985829\n", - " -0.0\n", - " -0.5\n", - " 0.012375\n", - " 0.024750\n", - " \n", - " \n", " pgodzinai\n", - " -23.9\n", + " -23.5\n", " 76.4\n", " -0.3\n", - " 0.956452\n", - " 0.109425\n", - " -2.858686\n", + " 0.973567\n", + " 0.111383\n", + " -2.763550\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.002749\n", - " 0.005498\n", + " 0.003591\n", + " 0.007181\n", " \n", " \n", " VeritasAI\n", @@ -6767,88 +6816,74 @@ " 0.000076\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -24.4\n", - " 91.1\n", - " -0.3\n", - " 0.784315\n", - " 0.082173\n", - " -3.265827\n", - " 1.985829\n", - " -0.1\n", - " -0.4\n", - " 0.000772\n", - " 0.001544\n", - " \n", - " \n", - " metac-Llama-3.1\n", - " -26.1\n", + " metac-exa\n", + " -24.7\n", " 89.1\n", " -0.3\n", - " 0.998799\n", - " 0.105813\n", - " -2.768565\n", + " 0.812195\n", + " 0.086044\n", + " -3.219787\n", " 1.986405\n", " -0.1\n", - " -0.5\n", - " 0.003432\n", - " 0.006863\n", + " -0.4\n", + " 0.000899\n", + " 0.001797\n", " \n", " \n", - " metac-exa\n", - " -26.6\n", - " 89.1\n", + " metac-o1-preview\n", + " -25.5\n", + " 91.1\n", " -0.3\n", - " 0.848974\n", - " 0.089941\n", - " -3.324097\n", - " 1.986405\n", + " 0.849888\n", + " 0.089044\n", + " -3.149214\n", + " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000647\n", - " 0.001294\n", + " 0.001111\n", + " 0.002221\n", " \n", " \n", " InstitutPelFutur\n", " -26.9\n", " 90.1\n", " -0.3\n", - " 0.973767\n", - " 0.102587\n", - " -2.908524\n", + " 0.973971\n", + " 0.102609\n", + " -2.904302\n", " 1.986114\n", " -0.1\n", " -0.5\n", - " 0.002292\n", - " 0.004584\n", + " 0.002320\n", + " 0.004640\n", " \n", " \n", - " metac-o1-preview\n", - " -27.8\n", + " metac-grok-2-1212\n", + " -27.9\n", " 91.1\n", " -0.3\n", - " 0.877434\n", - " 0.091930\n", - " -3.314974\n", + " 1.005409\n", + " 0.105338\n", + " -2.903858\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000661\n", - " 0.001322\n", + " 0.002318\n", + " 0.004635\n", " \n", " \n", " metac-gpt-4o\n", - " -30.5\n", + " -28.8\n", " 91.1\n", " -0.3\n", - " 0.913940\n", - " 0.095754\n", - " -3.492827\n", + " 0.819883\n", + " 0.085900\n", + " -3.676519\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000371\n", - " 0.000743\n", + " 0.000201\n", + " 0.000401\n", " \n", " \n", "\n", @@ -6858,10 +6893,10 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "RPM_bot -0.5 7.0 -0.1 0.840163 0.317552 \n", "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", + "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", - "RPM_bot -1.1 7.0 -0.2 0.824532 0.311644 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", @@ -6872,44 +6907,44 @@ "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.9 27.4 -0.3 0.744699 0.142267 \n", + "cookics_bot_TEST -6.5 27.4 -0.2 0.747831 0.142866 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", - "GreeneiBot2 -10.4 58.4 -0.2 0.849883 0.111260 \n", + "metac-o1 -10.4 91.1 -0.1 0.931550 0.097599 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", + "GreeneiBot2 -10.6 58.4 -0.2 0.849331 0.111188 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", - "metac-o1 -11.5 91.1 -0.1 0.888227 0.093060 \n", + "bot_median -11.1 92.1 -0.1 0.834391 0.086944 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", - "metac-perplexity -11.9 89.1 -0.1 0.993669 0.105270 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", + "metac-deepseek-r1 -14.1 52.1 -0.3 0.817209 0.113218 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-deepseek-r1 -14.6 52.1 -0.3 0.731525 0.101347 \n", - "metac-Gemini-Exp-1206 -15.2 76.5 -0.2 0.943797 0.107907 \n", + "metac-Gemini-Exp-1206 -14.6 76.5 -0.2 0.936930 0.107121 \n", + "metac-perplexity -16.1 89.1 -0.2 1.069491 0.113302 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "bot_median -17.3 92.1 -0.2 0.919122 0.095773 \n", - "minefrac1 -19.2 51.1 -0.4 0.880990 0.123242 \n", - "metac-claude-3-5-sonnet-20240620 -19.5 90.5 -0.2 1.009138 0.106078 \n", + "minefrac1 -18.5 51.1 -0.4 0.878223 0.122855 \n", + "metac-claude-3-5-sonnet-20240620 -20.8 90.5 -0.2 0.985458 0.103589 \n", + "metac-Llama-3.1 -21.0 89.1 -0.2 1.131903 0.119914 \n", + "metac-claude-3-5-sonnet-latest -21.7 91.1 -0.2 0.867992 0.090940 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "metac-grok-2-1212 -22.9 91.1 -0.3 1.048829 0.109887 \n", - "pgodzinai -23.9 76.4 -0.3 0.956452 0.109425 \n", + "pgodzinai -23.5 76.4 -0.3 0.973567 0.111383 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-claude-3-5-sonnet-latest -24.4 91.1 -0.3 0.784315 0.082173 \n", - "metac-Llama-3.1 -26.1 89.1 -0.3 0.998799 0.105813 \n", - "metac-exa -26.6 89.1 -0.3 0.848974 0.089941 \n", - "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", - "metac-o1-preview -27.8 91.1 -0.3 0.877434 0.091930 \n", - "metac-gpt-4o -30.5 91.1 -0.3 0.913940 0.095754 \n", + "metac-exa -24.7 89.1 -0.3 0.812195 0.086044 \n", + "metac-o1-preview -25.5 91.1 -0.3 0.849888 0.089044 \n", + "InstitutPelFutur -26.9 90.1 -0.3 0.973971 0.102609 \n", + "metac-grok-2-1212 -27.9 91.1 -0.3 1.005409 0.105338 \n", + "metac-gpt-4o -28.8 91.1 -0.3 0.819883 0.085900 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "bean_bot -4.265106 2.784843 -0.0 \n", + "RPM_bot -0.229115 2.446912 0.7 \n", "jonahsingerbot -5.273630 2.784843 -0.1 \n", + "bean_bot -4.265106 2.784843 -0.0 \n", "X_bot -0.747195 2.446912 0.2 \n", - "RPM_bot -0.523406 2.446912 0.6 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", @@ -6920,44 +6955,44 @@ "krm-bot -3.229846 2.264709 -0.2 \n", "annabot -2.211795 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.764876 2.049541 0.0 \n", + "cookics_bot_TEST -1.667933 2.049541 0.1 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", "MWG -2.589625 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", - "GreeneiBot2 -1.597976 2.000832 0.0 \n", + "metac-o1 -1.171004 1.985829 0.1 \n", "acm_bot -1.287717 1.989344 0.1 \n", + "GreeneiBot2 -1.638406 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", - "metac-o1 -1.360468 1.985829 0.1 \n", + "bot_median -1.391942 1.985550 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", - "metac-perplexity -1.264731 1.986405 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", + "metac-deepseek-r1 -2.393750 2.005379 -0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-deepseek-r1 -2.766689 2.005379 -0.1 \n", - "metac-Gemini-Exp-1206 -1.846774 1.990822 0.0 \n", + "metac-Gemini-Exp-1206 -1.780658 1.990822 0.0 \n", + "metac-perplexity -1.599489 1.986405 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "bot_median -1.963996 1.985550 0.0 \n", - "minefrac1 -3.043641 2.006545 -0.1 \n", - "metac-claude-3-5-sonnet-20240620 -2.031065 1.986072 -0.0 \n", + "minefrac1 -2.945421 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -2.217659 1.986072 -0.0 \n", + "metac-Llama-3.1 -1.966710 1.986405 0.0 \n", + "metac-claude-3-5-sonnet-latest -2.614756 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "metac-grok-2-1212 -2.283528 1.985829 -0.0 \n", - "pgodzinai -2.858686 1.990849 -0.1 \n", + "pgodzinai -2.763550 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-claude-3-5-sonnet-latest -3.265827 1.985829 -0.1 \n", - "metac-Llama-3.1 -2.768565 1.986405 -0.1 \n", - "metac-exa -3.324097 1.986405 -0.1 \n", - "InstitutPelFutur -2.908524 1.986114 -0.1 \n", - "metac-o1-preview -3.314974 1.985829 -0.1 \n", - "metac-gpt-4o -3.492827 1.985829 -0.1 \n", + "metac-exa -3.219787 1.986405 -0.1 \n", + "metac-o1-preview -3.149214 1.985829 -0.1 \n", + "InstitutPelFutur -2.904302 1.986114 -0.1 \n", + "metac-grok-2-1212 -2.903858 1.985829 -0.1 \n", + "metac-gpt-4o -3.676519 1.985829 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "bean_bot -0.2 0.007674 0.015349 \n", + "RPM_bot -0.8 0.413195 0.826390 \n", "jonahsingerbot -0.2 0.003839 0.007677 \n", + "bean_bot -0.2 0.007674 0.015349 \n", "X_bot -0.4 0.241594 0.483189 \n", - "RPM_bot -0.9 0.309726 0.619452 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", @@ -6968,39 +7003,39 @@ "krm-bot -0.9 0.005563 0.011127 \n", "annabot -0.4 0.017610 0.035221 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.044576 0.089152 \n", + "cookics_bot_TEST -0.5 0.053575 0.107149 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", "MWG -0.6 0.007581 0.015163 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", - "GreeneiBot2 -0.4 0.057772 0.115544 \n", + "metac-o1 -0.3 0.122342 0.244685 \n", "acm_bot -0.3 0.100796 0.201592 \n", + "GreeneiBot2 -0.4 0.053406 0.106813 \n", "ajf-bot -0.7 0.047145 0.094289 \n", - "metac-o1 -0.3 0.088538 0.177076 \n", + "bot_median -0.3 0.083665 0.167329 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", - "metac-perplexity -0.3 0.104652 0.209303 \n", "laylaps -0.4 0.008744 0.017488 \n", "wunderplumb -0.9 0.003174 0.006348 \n", + "metac-deepseek-r1 -0.5 0.010193 0.020386 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-deepseek-r1 -0.5 0.003932 0.007864 \n", - "metac-Gemini-Exp-1206 -0.4 0.034349 0.068698 \n", + "metac-Gemini-Exp-1206 -0.4 0.039496 0.078991 \n", + "metac-perplexity -0.4 0.056646 0.113292 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "bot_median -0.4 0.026290 0.052579 \n", - "minefrac1 -0.6 0.001859 0.003717 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.022608 0.045215 \n", + "minefrac1 -0.6 0.002441 0.004882 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.014555 0.029110 \n", + "metac-Llama-3.1 -0.5 0.026182 0.052364 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.005233 0.010466 \n", "mmBot -0.4 0.001104 0.002208 \n", - "metac-grok-2-1212 -0.5 0.012375 0.024750 \n", - "pgodzinai -0.5 0.002749 0.005498 \n", + "pgodzinai -0.5 0.003591 0.007181 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.000772 0.001544 \n", - "metac-Llama-3.1 -0.5 0.003432 0.006863 \n", - "metac-exa -0.5 0.000647 0.001294 \n", - "InstitutPelFutur -0.5 0.002292 0.004584 \n", - "metac-o1-preview -0.5 0.000661 0.001322 \n", - "metac-gpt-4o -0.5 0.000371 0.000743 " + "metac-exa -0.4 0.000899 0.001797 \n", + "metac-o1-preview -0.5 0.001111 0.002221 \n", + "InstitutPelFutur -0.5 0.002320 0.004640 \n", + "metac-grok-2-1212 -0.5 0.002318 0.004635 \n", + "metac-gpt-4o -0.5 0.000201 0.000401 " ] }, - "execution_count": 218, + "execution_count": 318, "metadata": {}, "output_type": "execute_result" } @@ -7026,7 +7061,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 319, "metadata": {}, "outputs": [], "source": [ @@ -7036,7 +7071,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 320, "metadata": { "cellView": "form", "colab": { @@ -7950,7 +7985,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 220, + "execution_count": 320, "metadata": {}, "output_type": "execute_result" } @@ -7989,7 +8024,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 321, "metadata": {}, "outputs": [], "source": [ @@ -7999,7 +8034,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 322, "metadata": {}, "outputs": [ { @@ -8204,7 +8239,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 222, + "execution_count": 322, "metadata": {}, "output_type": "execute_result" } @@ -8215,7 +8250,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 323, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -8277,7 +8312,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 324, "metadata": {}, "outputs": [ { @@ -8699,7 +8734,7 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 325, "metadata": { "cellView": "form", "colab": { @@ -8749,160 +8784,160 @@ " \n", " \n", " metac-o1\n", - " 6.2\n", - " 7.4\n", - " 9.7\n", - " 11.8\n", + " 6.1\n", + " 7.2\n", + " 9.6\n", + " 11.9\n", " 13.1\n", " \n", " \n", " metac-o1-preview\n", - " 3.1\n", + " 3.7\n", " 5.3\n", " 8.3\n", - " 11.1\n", - " 12.8\n", + " 11.3\n", + " 12.7\n", " \n", " \n", " manticAI\n", - " 0.2\n", - " 2.1\n", - " 5.6\n", - " 8.8\n", + " 0.0\n", + " 2.2\n", + " 5.7\n", + " 8.9\n", " 10.6\n", " \n", " \n", " metac-Gemini-Exp-1206\n", " 0.6\n", - " 1.9\n", - " 5.2\n", - " 8.1\n", - " 9.4\n", + " 2.2\n", + " 4.9\n", + " 7.8\n", + " 9.3\n", " \n", " \n", " acm_bot\n", " 0.1\n", " 1.7\n", - " 4.6\n", - " 7.5\n", - " 8.9\n", + " 4.7\n", + " 7.6\n", + " 8.8\n", " \n", " \n", " metac-perplexity\n", - " -1.7\n", - " 0.4\n", + " -1.6\n", + " 0.2\n", " 4.2\n", - " 8.0\n", - " 9.6\n", + " 7.9\n", + " 9.5\n", " \n", " \n", " GreeneiBot2\n", - " -1.2\n", - " 0.7\n", + " -1.4\n", + " 0.6\n", " 4.0\n", - " 7.1\n", - " 8.9\n", + " 7.3\n", + " 9.0\n", " \n", " \n", " twsummerbot\n", - " 0.2\n", - " 1.4\n", - " 3.8\n", - " 6.1\n", - " 7.3\n", + " 0.3\n", + " 1.6\n", + " 3.7\n", + " 6.2\n", + " 7.4\n", + " \n", + " \n", + " pgodzinai\n", + " -3.8\n", + " -1.0\n", + " 3.1\n", + " 7.1\n", + " 9.4\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.1\n", + " -0.3\n", " 1.0\n", - " 3.0\n", - " 5.1\n", + " 3.1\n", + " 5.0\n", " 6.1\n", " \n", " \n", - " pgodzinai\n", - " -3.5\n", - " -1.4\n", - " 2.9\n", - " 6.9\n", - " 8.7\n", - " \n", - " \n", " CumulativeBot\n", - " -0.3\n", - " 0.9\n", - " 2.7\n", + " -0.2\n", + " 0.8\n", + " 2.6\n", " 4.4\n", " 5.4\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -1.1\n", - " 0.1\n", - " 2.6\n", - " 5.1\n", - " 6.4\n", - " \n", - " \n", " SynapseSeer\n", " 0.4\n", " 1.1\n", - " 2.6\n", - " 4.0\n", + " 2.5\n", + " 4.1\n", + " 4.9\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-latest\n", + " -1.4\n", + " 0.1\n", + " 2.4\n", " 4.9\n", + " 6.1\n", " \n", " \n", " jkraybill_bot\n", - " -3.9\n", - " -1.8\n", - " 1.7\n", + " -3.4\n", + " -1.7\n", + " 1.8\n", " 4.9\n", - " 6.3\n", + " 6.2\n", " \n", " \n", " metac-exa\n", - " -5.3\n", - " -2.8\n", + " -4.6\n", + " -2.3\n", " 1.6\n", - " 5.4\n", - " 7.8\n", + " 5.5\n", + " 7.7\n", " \n", " \n", " metac-deepseek-r1\n", - " -1.7\n", + " -2.0\n", " -0.8\n", " 1.3\n", - " 3.5\n", - " 4.6\n", + " 3.4\n", + " 4.4\n", " \n", " \n", " MWG\n", - " -1.5\n", - " -0.7\n", + " -1.7\n", + " -0.8\n", " 0.7\n", - " 2.2\n", - " 2.8\n", + " 2.1\n", + " 2.9\n", " \n", " \n", " andrewsiah\n", " -0.9\n", " -0.6\n", - " -0.0\n", + " 0.0\n", " 0.6\n", - " 1.0\n", + " 0.9\n", " \n", " \n", " cobyj-bot\n", " -1.4\n", " -0.9\n", " -0.0\n", - " 0.8\n", - " 1.3\n", + " 0.9\n", + " 1.4\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.0\n", " 0.1\n", " 0.2\n", @@ -8918,194 +8953,194 @@ " \n", " annabot\n", " -3.5\n", - " -2.3\n", - " -0.4\n", - " 1.3\n", - " 2.2\n", + " -2.4\n", + " -0.5\n", + " 1.1\n", + " 2.0\n", " \n", " \n", " bean_bot\n", - " -3.1\n", - " -2.2\n", + " -2.9\n", + " -2.1\n", " -0.5\n", - " 1.1\n", - " 1.7\n", + " 1.3\n", + " 2.1\n", " \n", " \n", " KevinTestBot\n", - " -4.3\n", - " -2.8\n", - " -0.6\n", - " 1.4\n", + " -4.0\n", + " -2.6\n", + " -0.5\n", + " 1.5\n", " 2.6\n", " \n", " \n", - " jonahsingerbot\n", - " -3.0\n", + " CatrachoCaster\n", " -2.2\n", + " -1.7\n", " -0.8\n", - " 0.4\n", - " 1.0\n", + " 0.2\n", + " 0.7\n", " \n", " \n", - " CatrachoCaster\n", + " jonahsingerbot\n", + " -2.8\n", " -2.3\n", - " -1.7\n", " -0.8\n", - " 0.2\n", - " 0.8\n", + " 0.5\n", + " 1.2\n", " \n", " \n", " krm-bot\n", - " -3.5\n", - " -2.6\n", - " -0.9\n", - " 0.7\n", + " -3.4\n", + " -2.5\n", + " -1.0\n", + " 0.8\n", " 1.6\n", " \n", " \n", " ProfessorSP\n", " -4.5\n", - " -3.4\n", - " -1.2\n", - " 1.0\n", - " 2.2\n", + " -3.3\n", + " -1.0\n", + " 1.0\n", + " 1.9\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.6\n", + " -6.4\n", " -4.9\n", " -1.6\n", - " 1.7\n", - " 3.5\n", + " 1.8\n", + " 3.1\n", " \n", " \n", - " 4Shadower\n", - " -4.8\n", - " -3.6\n", - " -1.7\n", - " 0.3\n", - " 1.2\n", + " mmBot\n", + " -7.3\n", + " -5.5\n", + " -1.6\n", + " 2.2\n", + " 3.9\n", " \n", " \n", - " mmBot\n", - " -7.8\n", - " -5.7\n", + " 4Shadower\n", + " -5.0\n", + " -3.8\n", " -1.7\n", - " 2.1\n", - " 4.2\n", + " 0.2\n", + " 1.2\n", " \n", " \n", " swingswish\n", - " -5.2\n", - " -4.0\n", - " -1.9\n", - " -0.2\n", - " 0.6\n", + " -5.4\n", + " -4.2\n", + " -2.0\n", + " -0.1\n", + " 0.9\n", " \n", " \n", " RPM_bot\n", - " -4.8\n", - " -3.8\n", + " -4.9\n", + " -3.9\n", " -2.0\n", " -0.7\n", " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", - " -8.8\n", - " -6.6\n", - " -2.1\n", - " 2.0\n", - " 4.0\n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.5\n", + " -4.8\n", + " -2.0\n", + " 0.8\n", + " 2.7\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.8\n", - " -5.0\n", - " -2.1\n", - " 0.9\n", - " 2.2\n", + " InstitutPelFutur\n", + " -9.2\n", + " -6.7\n", + " -2.2\n", + " 1.6\n", + " 4.0\n", " \n", " \n", " wunderplumb\n", - " -6.0\n", - " -4.7\n", - " -2.5\n", - " -0.3\n", + " -6.5\n", + " -5.1\n", + " -2.6\n", + " -0.2\n", " 0.7\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.7\n", - " -5.4\n", + " -6.9\n", + " -5.3\n", " -2.7\n", - " 0.0\n", - " 1.5\n", + " -0.1\n", + " 1.4\n", " \n", " \n", " NextWorldLab\n", - " -8.9\n", - " -6.9\n", + " -8.6\n", + " -6.7\n", " -3.6\n", - " -0.5\n", - " 0.9\n", + " -0.6\n", + " 1.0\n", " \n", " \n", - " laylaps\n", - " -10.1\n", - " -8.1\n", + " Bot_Pepa\n", + " -7.0\n", + " -5.9\n", " -3.8\n", - " -0.1\n", - " 1.6\n", + " -1.9\n", + " -1.0\n", " \n", " \n", - " Bot_Pepa\n", - " -7.2\n", - " -6.0\n", - " -3.9\n", - " -2.0\n", - " -0.9\n", + " laylaps\n", + " -9.7\n", + " -7.7\n", + " -4.0\n", + " -0.1\n", + " 2.2\n", " \n", " \n", " VeritasAI\n", " -7.7\n", - " -6.4\n", - " -4.3\n", - " -2.0\n", - " -0.8\n", + " -6.6\n", + " -4.2\n", + " -1.8\n", + " -0.5\n", " \n", " \n", " minefrac1\n", - " -8.0\n", - " -6.7\n", + " -7.9\n", + " -6.8\n", " -4.6\n", - " -2.6\n", - " -1.5\n", + " -2.5\n", + " -1.7\n", " \n", " \n", " Grizeu_Bot\n", - " -9.2\n", + " -9.0\n", " -7.6\n", " -5.0\n", - " -2.3\n", + " -2.2\n", " -0.6\n", " \n", " \n", " metac-gpt-4o\n", " -10.6\n", - " -9.1\n", - " -5.7\n", + " -8.9\n", + " -6.0\n", " -2.9\n", - " -1.4\n", + " -1.6\n", " \n", " \n", " ajf-bot\n", " -14.6\n", - " -12.4\n", - " -8.3\n", + " -12.6\n", + " -8.5\n", " -4.4\n", - " -2.0\n", + " -2.4\n", " \n", " \n", "\n", @@ -9113,54 +9148,54 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.2 7.4 9.7 11.8 13.1\n", - "metac-o1-preview 3.1 5.3 8.3 11.1 12.8\n", - "manticAI 0.2 2.1 5.6 8.8 10.6\n", - "metac-Gemini-Exp-1206 0.6 1.9 5.2 8.1 9.4\n", - "acm_bot 0.1 1.7 4.6 7.5 8.9\n", - "metac-perplexity -1.7 0.4 4.2 8.0 9.6\n", - "GreeneiBot2 -1.2 0.7 4.0 7.1 8.9\n", - "twsummerbot 0.2 1.4 3.8 6.1 7.3\n", - "cookics_bot_TEST 0.1 1.0 3.0 5.1 6.1\n", - "pgodzinai -3.5 -1.4 2.9 6.9 8.7\n", - "CumulativeBot -0.3 0.9 2.7 4.4 5.4\n", - "metac-claude-3-5-sonnet-latest -1.1 0.1 2.6 5.1 6.4\n", - "SynapseSeer 0.4 1.1 2.6 4.0 4.9\n", - "jkraybill_bot -3.9 -1.8 1.7 4.9 6.3\n", - "metac-exa -5.3 -2.8 1.6 5.4 7.8\n", - "metac-deepseek-r1 -1.7 -0.8 1.3 3.5 4.6\n", - "MWG -1.5 -0.7 0.7 2.2 2.8\n", - "andrewsiah -0.9 -0.6 -0.0 0.6 1.0\n", - "cobyj-bot -1.4 -0.9 -0.0 0.8 1.3\n", - "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "metac-o1 6.1 7.2 9.6 11.9 13.1\n", + "metac-o1-preview 3.7 5.3 8.3 11.3 12.7\n", + "manticAI 0.0 2.2 5.7 8.9 10.6\n", + "metac-Gemini-Exp-1206 0.6 2.2 4.9 7.8 9.3\n", + "acm_bot 0.1 1.7 4.7 7.6 8.8\n", + "metac-perplexity -1.6 0.2 4.2 7.9 9.5\n", + "GreeneiBot2 -1.4 0.6 4.0 7.3 9.0\n", + "twsummerbot 0.3 1.6 3.7 6.2 7.4\n", + "pgodzinai -3.8 -1.0 3.1 7.1 9.4\n", + "cookics_bot_TEST -0.3 1.0 3.1 5.0 6.1\n", + "CumulativeBot -0.2 0.8 2.6 4.4 5.4\n", + "SynapseSeer 0.4 1.1 2.5 4.1 4.9\n", + "metac-claude-3-5-sonnet-latest -1.4 0.1 2.4 4.9 6.1\n", + "jkraybill_bot -3.4 -1.7 1.8 4.9 6.2\n", + "metac-exa -4.6 -2.3 1.6 5.5 7.7\n", + "metac-deepseek-r1 -2.0 -0.8 1.3 3.4 4.4\n", + "MWG -1.7 -0.8 0.7 2.1 2.9\n", + "andrewsiah -0.9 -0.6 0.0 0.6 0.9\n", + "cobyj-bot -1.4 -0.9 -0.0 0.9 1.4\n", + "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", - "annabot -3.5 -2.3 -0.4 1.3 2.2\n", - "bean_bot -3.1 -2.2 -0.5 1.1 1.7\n", - "KevinTestBot -4.3 -2.8 -0.6 1.4 2.6\n", - "jonahsingerbot -3.0 -2.2 -0.8 0.4 1.0\n", - "CatrachoCaster -2.3 -1.7 -0.8 0.2 0.8\n", - "krm-bot -3.5 -2.6 -0.9 0.7 1.6\n", - "ProfessorSP -4.5 -3.4 -1.2 1.0 2.2\n", - "metac-grok-2-1212 -6.6 -4.9 -1.6 1.7 3.5\n", - "4Shadower -4.8 -3.6 -1.7 0.3 1.2\n", - "mmBot -7.8 -5.7 -1.7 2.1 4.2\n", - "swingswish -5.2 -4.0 -1.9 -0.2 0.6\n", - "RPM_bot -4.8 -3.8 -2.0 -0.7 -0.1\n", - "InstitutPelFutur -8.8 -6.6 -2.1 2.0 4.0\n", - "metac-claude-3-5-sonnet-20240620 -6.8 -5.0 -2.1 0.9 2.2\n", - "wunderplumb -6.0 -4.7 -2.5 -0.3 0.7\n", - "metac-Llama-3.1 -6.7 -5.4 -2.7 0.0 1.5\n", - "NextWorldLab -8.9 -6.9 -3.6 -0.5 0.9\n", - "laylaps -10.1 -8.1 -3.8 -0.1 1.6\n", - "Bot_Pepa -7.2 -6.0 -3.9 -2.0 -0.9\n", - "VeritasAI -7.7 -6.4 -4.3 -2.0 -0.8\n", - "minefrac1 -8.0 -6.7 -4.6 -2.6 -1.5\n", - "Grizeu_Bot -9.2 -7.6 -5.0 -2.3 -0.6\n", - "metac-gpt-4o -10.6 -9.1 -5.7 -2.9 -1.4\n", - "ajf-bot -14.6 -12.4 -8.3 -4.4 -2.0" + "annabot -3.5 -2.4 -0.5 1.1 2.0\n", + "bean_bot -2.9 -2.1 -0.5 1.3 2.1\n", + "KevinTestBot -4.0 -2.6 -0.5 1.5 2.6\n", + "CatrachoCaster -2.2 -1.7 -0.8 0.2 0.7\n", + "jonahsingerbot -2.8 -2.3 -0.8 0.5 1.2\n", + "krm-bot -3.4 -2.5 -1.0 0.8 1.6\n", + "ProfessorSP -4.5 -3.3 -1.0 1.0 1.9\n", + "metac-grok-2-1212 -6.4 -4.9 -1.6 1.8 3.1\n", + "mmBot -7.3 -5.5 -1.6 2.2 3.9\n", + "4Shadower -5.0 -3.8 -1.7 0.2 1.2\n", + "swingswish -5.4 -4.2 -2.0 -0.1 0.9\n", + "RPM_bot -4.9 -3.9 -2.0 -0.7 -0.1\n", + "metac-claude-3-5-sonnet-20240620 -6.5 -4.8 -2.0 0.8 2.7\n", + "InstitutPelFutur -9.2 -6.7 -2.2 1.6 4.0\n", + "wunderplumb -6.5 -5.1 -2.6 -0.2 0.7\n", + "metac-Llama-3.1 -6.9 -5.3 -2.7 -0.1 1.4\n", + "NextWorldLab -8.6 -6.7 -3.6 -0.6 1.0\n", + "Bot_Pepa -7.0 -5.9 -3.8 -1.9 -1.0\n", + "laylaps -9.7 -7.7 -4.0 -0.1 2.2\n", + "VeritasAI -7.7 -6.6 -4.2 -1.8 -0.5\n", + "minefrac1 -7.9 -6.8 -4.6 -2.5 -1.7\n", + "Grizeu_Bot -9.0 -7.6 -5.0 -2.2 -0.6\n", + "metac-gpt-4o -10.6 -8.9 -6.0 -2.9 -1.6\n", + "ajf-bot -14.6 -12.6 -8.5 -4.4 -2.4" ] }, - "execution_count": 225, + "execution_count": 325, "metadata": {}, "output_type": "execute_result" } @@ -9183,7 +9218,7 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 326, "metadata": { "cellView": "form", "colab": { @@ -9252,6 +9287,14 @@ " 0.0\n", " \n", " \n", + " RPM_bot\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", " X_bot\n", " -0.0\n", " -0.0\n", @@ -9276,14 +9319,6 @@ " -0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", - " -0.0\n", - " -0.0\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", " CumulativeBot\n", " -0.0\n", " -0.0\n", @@ -9392,7 +9427,7 @@ " -0.2\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " -0.0\n", " \n", " \n", @@ -9405,19 +9440,19 @@ " \n", " \n", " GreeneiBot2\n", - " -0.2\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", " 0.0\n", " \n", " \n", - " ajf-bot\n", + " metac-o1\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", " 0.0\n", + " 0.1\n", " \n", " \n", " acm_bot\n", @@ -9428,31 +9463,23 @@ " 0.1\n", " \n", " \n", - " Bot_Pepa\n", - " -0.2\n", - " -0.2\n", - " -0.1\n", - " -0.1\n", - " -0.0\n", - " \n", - " \n", - " metac-o1\n", + " ajf-bot\n", " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", - " 0.1\n", + " 0.0\n", " \n", " \n", - " metac-perplexity\n", + " bot_median\n", " -0.3\n", " -0.2\n", " -0.1\n", - " 0.0\n", + " -0.0\n", " 0.1\n", " \n", " \n", - " laylaps\n", + " Bot_Pepa\n", " -0.2\n", " -0.2\n", " -0.1\n", @@ -9465,20 +9492,28 @@ " -0.2\n", " -0.1\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", - " manticAI\n", - " -0.3\n", + " laylaps\n", " -0.2\n", " -0.2\n", " -0.1\n", + " -0.1\n", " -0.0\n", " \n", " \n", " metac-deepseek-r1\n", " -0.3\n", " -0.2\n", + " -0.1\n", + " -0.1\n", + " -0.0\n", + " \n", + " \n", + " manticAI\n", + " -0.3\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.0\n", @@ -9492,16 +9527,16 @@ " 0.0\n", " \n", " \n", - " NextWorldLab\n", - " -0.3\n", + " metac-perplexity\n", + " -0.4\n", " -0.3\n", " -0.2\n", - " -0.1\n", " -0.0\n", + " 0.0\n", " \n", " \n", - " bot_median\n", - " -0.4\n", + " NextWorldLab\n", + " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -9521,23 +9556,31 @@ " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", + " \n", + " \n", + " metac-Llama-3.1\n", + " -0.4\n", + " -0.4\n", + " -0.2\n", + " -0.1\n", " 0.0\n", " \n", " \n", - " mmBot\n", + " metac-claude-3-5-sonnet-latest\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " \n", " \n", - " metac-grok-2-1212\n", - " -0.4\n", + " mmBot\n", " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", " pgodzinai\n", @@ -9551,12 +9594,12 @@ " VeritasAI\n", " -0.4\n", " -0.3\n", - " -0.3\n", + " -0.2\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " metac-exa\n", " -0.4\n", " -0.4\n", " -0.3\n", @@ -9564,16 +9607,8 @@ " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", - " -0.5\n", + " metac-o1-preview\n", " -0.4\n", - " -0.3\n", - " -0.1\n", - " -0.1\n", - " \n", - " \n", - " metac-exa\n", - " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9588,7 +9623,7 @@ " -0.1\n", " \n", " \n", - " metac-o1-preview\n", + " metac-grok-2-1212\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9611,10 +9646,10 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", @@ -9628,36 +9663,36 @@ "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", - "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", + "MWG -0.2 -0.2 -0.1 -0.1 -0.0\n", "ProfessorSP -0.2 -0.2 -0.1 -0.1 -0.0\n", - "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", - "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", + "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", + "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", + "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", + "bot_median -0.3 -0.2 -0.1 -0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-o1 -0.3 -0.2 -0.1 -0.0 0.1\n", - "metac-perplexity -0.3 -0.2 -0.1 0.0 0.1\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", - "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\n", + "metac-deepseek-r1 -0.3 -0.2 -0.1 -0.1 -0.0\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "metac-deepseek-r1 -0.3 -0.2 -0.2 -0.1 -0.0\n", "metac-Gemini-Exp-1206 -0.3 -0.3 -0.2 -0.0 0.0\n", - "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", - "bot_median -0.4 -0.3 -0.2 -0.1 0.0\n", + "metac-perplexity -0.4 -0.3 -0.2 -0.0 0.0\n", + "NextWorldLab -0.3 -0.3 -0.2 -0.1 0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 -0.0\n", + "metac-Llama-3.1 -0.4 -0.4 -0.2 -0.1 0.0\n", + "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.0\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "metac-grok-2-1212 -0.4 -0.4 -0.2 -0.1 -0.0\n", "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", - "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", - "metac-claude-3-5-sonnet-latest -0.4 -0.4 -0.3 -0.2 -0.1\n", - "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.1 -0.1\n", - "metac-exa -0.5 -0.4 -0.3 -0.2 -0.1\n", + "VeritasAI -0.4 -0.3 -0.2 -0.2 -0.1\n", + "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-o1-preview -0.4 -0.4 -0.3 -0.2 -0.1\n", "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-o1-preview -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" ] }, - "execution_count": 226, + "execution_count": 326, "metadata": {}, "output_type": "execute_result" } @@ -9678,7 +9713,7 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 327, "metadata": {}, "outputs": [], "source": [ @@ -9688,7 +9723,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 328, "metadata": {}, "outputs": [ { @@ -9748,7 +9783,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 329, "metadata": { "cellView": "form", "colab": { @@ -10237,7 +10272,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 229, + "execution_count": 329, "metadata": {}, "output_type": "execute_result" } @@ -10258,7 +10293,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 330, "metadata": {}, "outputs": [], "source": [ @@ -10267,7 +10302,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 331, "metadata": {}, "outputs": [ { @@ -10306,7 +10341,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 332, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" @@ -10316,15 +10351,15 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.8]\n", + " >>> Collected 1 forecasts: [0.75]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.7]\n", @@ -10337,485 +10372,485 @@ " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.98]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.01]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.97]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.99]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.75]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.02]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.4]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.35, 0.6]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.75, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.7, 0.4]\n", - " >>> Collected 2 forecasts: [0.85, 0.6]\n", + " >>> Collected 2 forecasts: [0.05, 0.15]\n", + " >>> Collected 2 forecasts: [0.2, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.8]\n", + " >>> Collected 2 forecasts: [0.75, 0.7]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.8, 0.6]\n", + " >>> Collected 2 forecasts: [0.75, 0.35]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.15, 0.05]\n", - " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.2, 0.1]\n", - " >>> Collected 2 forecasts: [0.7, 0.85]\n", - " >>> Collected 2 forecasts: [0.15, 0.35]\n", - " >>> Collected 2 forecasts: [0.25, 0.25]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.2, 0.35]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.7, 0.8]\n", + " >>> Collected 2 forecasts: [0.15, 0.5]\n", + " >>> Collected 2 forecasts: [0.25, 0.1]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.15, 0.4]\n", + " >>> Collected 2 forecasts: [0.15, 0.3]\n", " >>> Collected 2 forecasts: [0.95, 0.9]\n", - " >>> Collected 2 forecasts: [0.1, 0.2]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.02]\n", " >>> Collected 2 forecasts: [0.15, 0.4]\n", - " >>> Collected 2 forecasts: [0.6, 0.3]\n", - " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.97, 0.98]\n", - " >>> Collected 2 forecasts: [0.4, 0.3]\n", - " >>> Collected 2 forecasts: [0.4, 0.4]\n", - " >>> Collected 2 forecasts: [0.35, 0.45]\n", - " >>> Collected 2 forecasts: [0.1, 0.02]\n", - " >>> Collected 2 forecasts: [0.6, 0.8]\n", - " >>> Collected 2 forecasts: [0.99, 0.9]\n", - " >>> Collected 2 forecasts: [0.97, 0.98]\n", + " >>> Collected 2 forecasts: [0.25, 0.4]\n", + " >>> Collected 2 forecasts: [0.15, 0.25]\n", + " >>> Collected 2 forecasts: [0.98, 0.96]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", + " >>> Collected 2 forecasts: [0.35, 0.4]\n", + " >>> Collected 2 forecasts: [0.65, 0.6]\n", + " >>> Collected 2 forecasts: [0.01, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.7]\n", + " >>> Collected 2 forecasts: [0.99, 0.7]\n", + " >>> Collected 2 forecasts: [0.2, 0.98]\n", " >>> Collected 2 forecasts: [0.99, 0.25]\n", " >>> Collected 2 forecasts: [0.9, 0.85]\n", " >>> Collected 2 forecasts: [0.9, 0.8]\n", - " >>> Collected 2 forecasts: [0.7, 0.6]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.6, 0.4]\n", + " >>> Collected 2 forecasts: [0.85, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.15]\n", + " >>> Collected 2 forecasts: [0.25, 0.5]\n", + " >>> Collected 2 forecasts: [0.75, 0.75]\n", " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.6, 0.8]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.25, 0.25]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.05]\n", - " >>> Collected 2 forecasts: [0.85, 0.9]\n", + " >>> Collected 2 forecasts: [0.25, 0.3]\n", + " >>> Collected 2 forecasts: [0.02, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.1, 0.03]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.65]\n", + " >>> Collected 2 forecasts: [0.9, 0.95]\n", + " >>> Collected 2 forecasts: [0.4, 0.35]\n", " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.85, 0.8]\n", - " >>> Collected 2 forecasts: [0.05, 0.02]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", - " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.82]\n", - " >>> Collected 3 forecasts: [0.75, 0.85, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.6, nan]\n", + " >>> Collected 2 forecasts: [0.85, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 3 forecasts: [0.05, 0.15, 0.07]\n", + " >>> Collected 3 forecasts: [0.2, 0.6, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.8, 0.82]\n", + " >>> Collected 3 forecasts: [0.75, 0.7, 0.85]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.8, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.75, 0.35, nan]\n", " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, 0.25]\n", - " >>> Collected 3 forecasts: [0.2, 0.1, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.35, 0.108]\n", - " >>> Collected 3 forecasts: [0.25, 0.25, 0.16]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.35, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.5, 0.108]\n", + " >>> Collected 3 forecasts: [0.25, 0.1, 0.16]\n", " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.4, 0.15]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.9, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.2, 0.125]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.05, 0.02, 0.03]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.03]\n", " >>> Collected 3 forecasts: [0.15, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.6, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", - " >>> Collected 3 forecasts: [0.97, 0.98, 0.97]\n", - " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", - " >>> Collected 3 forecasts: [0.4, 0.4, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.35, 0.45, 0.17]\n", - " >>> Collected 3 forecasts: [0.1, 0.02, 0.12]\n", - " >>> Collected 3 forecasts: [0.6, 0.8, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.9, 0.99]\n", - " >>> Collected 3 forecasts: [0.97, 0.98, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.99, 0.25, 0.14]\n", + " >>> Collected 3 forecasts: [0.25, 0.4, 0.35]\n", + " >>> Collected 3 forecasts: [0.15, 0.25, 0.115]\n", + " >>> Collected 3 forecasts: [0.98, 0.96, 0.97]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", + " >>> Collected 3 forecasts: [0.35, 0.4, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.65, 0.6, 0.17]\n", + " >>> Collected 3 forecasts: [0.01, 0.05, 0.12]\n", + " >>> Collected 3 forecasts: [0.1, 0.7, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", + " >>> Collected 3 forecasts: [0.2, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.99, 0.25, 0.4166666666666666]\n", " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.7, 0.6, 0.875]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, 0.16]\n", - " >>> Collected 3 forecasts: [0.6, 0.8, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.25, 0.3925]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.086]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", - " >>> Collected 3 forecasts: [0.85, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", - " >>> Collected 3 forecasts: [0.9, 0.65, nan]\n", + " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", + " >>> Collected 3 forecasts: [0.85, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.05, 0.15, 0.026]\n", + " >>> Collected 3 forecasts: [0.25, 0.5, 0.16]\n", + " >>> Collected 3 forecasts: [0.75, 0.75, 0.67]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.02, 0.05, 0.086]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.285]\n", + " >>> Collected 3 forecasts: [0.1, 0.03, 0.02]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", + " >>> Collected 3 forecasts: [0.4, 0.35, nan]\n", " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.02, 0.05]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.82, 0.794]\n", - " >>> Collected 4 forecasts: [0.75, 0.85, 0.85, 0.884]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.6, nan, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.6, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.75, 0.7, 0.85, 0.884]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.35, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.1, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.85, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.15, 0.35, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.25, 0.25, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.35, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.15, 0.5, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.1, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.4, 0.15, 0.12]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.05, 0.866]\n", - " >>> Collected 4 forecasts: [0.1, 0.2, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.05, 0.918]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999]\n", - " >>> Collected 4 forecasts: [0.6, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.97, 0.98, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.35, 0.45, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.1, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.6, 0.8, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.9, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.25, 0.14, 0.2]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.15, 0.4, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.25, 0.4, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.15, 0.25, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.96, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.65, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.01, 0.05, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.1, 0.7, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2]\n", " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.8, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", - " >>> Collected 4 forecasts: [0.25, 0.25, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, nan, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.25, 0.5, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.02, 0.05, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.4, 0.35, nan, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.02, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.75, 0.35, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.1, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.85, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.35, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.25, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.35, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.5, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.1, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.35, 0.45, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.65, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336]\n", " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.6, 0.8, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.25, 0.25, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.25, 0.5, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.02, 0.05, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.4, 0.35, nan, nan, 0.05]\n", " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.85, 0.6, nan, nan, nan, 0.65]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.75, 0.35, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325]\n", " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055]\n", " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88]\n", - " >>> Collected 7 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", + " >>> Collected 7 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95]\n", + " >>> Collected 7 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85]\n", - " >>> Collected 7 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3]\n", - " >>> Collected 7 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3]\n", + " >>> Collected 7 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85]\n", - " >>> Collected 7 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", - " >>> Collected 7 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", + " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", + " >>> Collected 7 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3]\n", + " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65]\n", " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", - " >>> Collected 7 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", - " >>> Collected 8 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan]\n", + " >>> Collected 8 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", - " >>> Collected 8 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124]\n", + " >>> Collected 8 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765]\n", + " >>> Collected 8 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95]\n", + " >>> Collected 8 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", + " >>> Collected 8 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8]\n", - " >>> Collected 9 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.75]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.15]\n", - " >>> Collected 9 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.35]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", - " >>> Collected 9 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", - " >>> Collected 9 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", + " >>> Collected 9 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.8]\n", + " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.25]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8]\n", + " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85]\n", " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8, 0.638]\n", - " >>> Collected 10 forecasts: [0.75, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.85, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.75, nan]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", + " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.25, nan, nan, 0.225, 0.15, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.1, nan, 0.242, nan, 0.275, 0.85, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.15, 0.35, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.25, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.15, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.4, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.2, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.15, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.15, 0.289]\n", - " >>> Collected 10 forecasts: [0.6, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.35, 0.55, 0.35, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.4, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.35, 0.45, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", - " >>> Collected 10 forecasts: [0.6, 0.8, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.25, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.15, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.75, nan]\n", - " >>> Collected 10 forecasts: [0.7, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.6, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.8, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.25, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.85, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35, 0.293]\n", + " >>> Collected 10 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", + " >>> Collected 10 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.8, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85, 0.126]\n", " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.02, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -10848,7 +10883,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 333, "metadata": {}, "outputs": [], "source": [ @@ -10858,7 +10893,7 @@ }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 334, "metadata": {}, "outputs": [ { @@ -10896,9 +10931,9 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", - " 0.014505\n", - " 0.082463\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", + " 0.012671\n", + " 0.097463\n", " \n", " \n", " 1\n", @@ -10906,26 +10941,26 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.037750000000000006, 0.038250620225000004, 0...\n", - " [0.0402, 0.040750496180000005, 0.04130456232, ...\n", + " [0.037750000000000006, 0.03822284245, 0.038700...\n", + " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", " \n", " \n", " 2\n", " binary\n", " NaN\n", " no\n", - " 0.1\n", - " 0.085\n", - " 0.1\n", + " 0.05\n", + " 0.063\n", + " 0.11\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.7,0.25,0.05]\n", + " [0.15,0.65,0.2]\n", + " 0.6\n", " 0.5125\n", - " 0.5\n", " \n", " \n", " 4\n", @@ -10934,7 +10969,7 @@ " 119.2\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0, 0.0019825503600000003, 0.003970557620000...\n", - " [0.0, 0.002036555585714286, 0.0040770089428571...\n", + " [0.0, 0.0020603651142857148, 0.004124627985714...\n", " \n", " \n", " ...\n", @@ -10951,17 +10986,17 @@ " NaN\n", " yes\n", " 0.9\n", - " 0.9\n", - " 0.9025\n", + " 0.905\n", + " 0.92\n", " \n", " \n", " 351\n", " binary\n", " NaN\n", " no\n", - " 0.9\n", - " 0.65\n", - " 0.3585\n", + " 0.4\n", + " 0.35\n", + " 0.2085\n", " \n", " \n", " 355\n", @@ -10978,8 +11013,8 @@ " NaN\n", " no\n", " 0.85\n", - " 0.8\n", - " 0.755\n", + " 0.85\n", + " 0.78\n", " \n", " \n", " 364\n", @@ -10988,7 +11023,7 @@ " no\n", " 0.05\n", " 0.05\n", - " 0.041\n", + " 0.046\n", " \n", " \n", "\n", @@ -11010,48 +11045,48 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.7,0.25,0.05] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", "342 0.9 \n", - "351 0.9 \n", + "351 0.4 \n", "355 0.9 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 0.014505 \n", - "1 [0.037750000000000006, 0.038250620225000004, 0... \n", - "2 0.085 \n", - "3 0.5125 \n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 0.063 \n", + "3 0.6 \n", "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", ".. ... \n", - "342 0.9 \n", - "351 0.65 \n", + "342 0.905 \n", + "351 0.35 \n", "355 0.85 \n", - "361 0.8 \n", + "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 0.082463 \n", - "1 [0.0402, 0.040750496180000005, 0.04130456232, ... \n", - "2 0.1 \n", - "3 0.5 \n", - "4 [0.0, 0.002036555585714286, 0.0040770089428571... \n", + "0 0.097463 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.11 \n", + "3 0.5125 \n", + "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", ".. ... \n", - "342 0.9025 \n", - "351 0.3585 \n", + "342 0.92 \n", + "351 0.2085 \n", "355 0.775 \n", - "361 0.755 \n", - "364 0.041 \n", + "361 0.78 \n", + "364 0.046 \n", "\n", "[99 rows x 6 columns]" ] }, - "execution_count": 239, + "execution_count": 334, "metadata": {}, "output_type": "execute_result" } @@ -11062,7 +11097,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 335, "metadata": {}, "outputs": [ { @@ -11082,7 +11117,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 336, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11092,18 +11127,102 @@ }, "outputs": [ { - "ename": "NotImplementedError", - "evalue": "Havent decided how to handle null forecasts or anulled resolutions", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[241], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# @title Calculate the baseline scores for each team size\u001b[39;00m\n\u001b[1;32m 3\u001b[0m teams \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_1_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_2_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_3_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_9_bots\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian_forecast_10_bots\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 14\u001b[0m weighted_scores \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_weighted_scores\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_bot_team_forecasts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mteams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Print nicely - round to 2 decimal places and first column should be just an integer (bot team size)\u001b[39;00m\n\u001b[1;32m 17\u001b[0m weighted_scores_print \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(weighted_scores)\u001b[38;5;241m.\u001b[39mreset_index()\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:448\u001b[0m, in \u001b[0;36mcalculate_weighted_scores\u001b[0;34m(df_bot_team_forecasts, teams)\u001b[0m\n\u001b[1;32m 445\u001b[0m forecast \u001b[38;5;241m=\u001b[39m row[team]\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 448\u001b[0m weighted_score \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_baseline_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 449\u001b[0m \u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 450\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43mq_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrange_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 455\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquestion_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[43m \u001b[49m\u001b[43mopen_upper_bound\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopen_upper_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m \u001b[49m\u001b[43mopen_lower_bound\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopen_lower_bound\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 458\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 459\u001b[0m team_scores[team] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m weighted_score\n\u001b[1;32m 461\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mIndexError\u001b[39;00m):\n\u001b[1;32m 462\u001b[0m \u001b[38;5;66;03m# @Check: Does skipping introduce any problems?\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:61\u001b[0m, in \u001b[0;36mcalculate_baseline_score\u001b[0;34m(forecast, resolution, q_type, options, range_min, range_max, question_weight, open_upper_bound, open_lower_bound)\u001b[0m\n\u001b[1;32m 59\u001b[0m question_type \u001b[38;5;241m=\u001b[39m _determine_question_type(q_type, resolution)\n\u001b[1;32m 60\u001b[0m resolution \u001b[38;5;241m=\u001b[39m _normalize_resolution(question_type, resolution, range_min, range_max)\n\u001b[0;32m---> 61\u001b[0m prob_for_resolution \u001b[38;5;241m=\u001b[39m \u001b[43m_determine_probability_for_resolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mquestion_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforecast\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrange_max\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m baseline_prob \u001b[38;5;241m=\u001b[39m _determine_baseline(\n\u001b[1;32m 65\u001b[0m question_type, resolution, options, range_min, range_max, open_upper_bound, open_lower_bound\n\u001b[1;32m 66\u001b[0m )\n\u001b[1;32m 67\u001b[0m divisor \u001b[38;5;241m=\u001b[39m _determine_divisor_for_baseline_score(question_type, options)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:153\u001b[0m, in \u001b[0;36m_determine_probability_for_resolution\u001b[0;34m(q_type, forecast, resolution, options, range_min, range_max)\u001b[0m\n\u001b[1;32m 150\u001b[0m resolution \u001b[38;5;241m=\u001b[39m _normalize_resolution(q_type, resolution, range_min, range_max)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m forecast \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m resolution \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 154\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHavent decided how to handle null forecasts or anulled resolutions\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 155\u001b[0m )\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(forecast) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mForecast is empty\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mNotImplementedError\u001b[0m: Havent decided how to handle null forecasts or anulled resolutions" - ] + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Bot_Team_SizeWeighted_Baseline_Score_for_Bot_Team_Median
0160.20
1259.19
2319.30
3417.74
456.91
567.07
6716.34
7816.34
8921.85
91021.85
\n", + "
" + ], + "text/plain": [ + " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", + "0 1 60.20\n", + "1 2 59.19\n", + "2 3 19.30\n", + "3 4 17.74\n", + "4 5 6.91\n", + "5 6 7.07\n", + "6 7 16.34\n", + "7 8 16.34\n", + "8 9 21.85\n", + "9 10 21.85" + ] + }, + "execution_count": 336, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -11132,9 +11251,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 337, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['metac-o1-preview']" + ] + }, + "execution_count": 337, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Index of top bot team from weighted_scores_print?\n", "winning_bot_team_size = weighted_scores_print.sort_values(by='Weighted_Baseline_Score_for_Bot_Team_Median', ascending=False).head(1)['Bot_Team_Size'].values[0]\n", @@ -11144,16 +11274,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 338, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(424, 47)" + ] + }, + "execution_count": 338, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_bot_forecasts.shape" ] }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 339, "metadata": {}, "outputs": [], "source": [ @@ -11171,106 +11312,468 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_bot_team_forecasts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Z3TTBVWoZVzU", - "outputId": "0eb32f2c-09c6-4a15-e81a-bee353b1bccf" - }, - "outputs": [], - "source": [ - "# @title Weighted team-vs-pro\n", - "\n", - "# We have our top bot team members.\n", - "# Calculate their median forecast on the pro_bot questions.\n", - "# Create df with bot_question_id, forecasts, resolution, weights\n", - "# Calculate the head-to-head score\n", - "\n", - "df_top_bot_forecasts = df_bot_team_forecasts[['bot_question_id', f'median_forecast_{len(top_bot_team)}_bots']]\n", - "df_top_bot_forecasts = df_top_bot_forecasts.rename(columns={f'median_forecast_{len(top_bot_team)}_bots': 'bot_team_median'})\n", - "\n", - "df_pro_median = df_pro_forecasts[['pro_question_id', 'pro_median']]\n", - "\n", - "df_top_bot_pro_forecasts = pd.merge(\n", - " df_pro_bot_resolved_questions,\n", - " df_top_bot_forecasts[['bot_question_id', 'bot_team_median']],\n", - " on='bot_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "df_top_bot_pro_forecasts = pd.merge(\n", - " df_top_bot_pro_forecasts,\n", - " df_pro_median,\n", - " on='pro_question_id',\n", - " how='left'\n", - ")\n", - "\n", - "# Copy with union (not just overlapping questions)\n", - "df_top_bot_pro_forecasts_all = df_top_bot_pro_forecasts.copy()\n", - "\n", - "# Filter to only those rows where pro_median is not NA\n", - "df_top_bot_pro_forecasts = df_top_bot_pro_forecasts.dropna(subset=['pro_median'])\n", - "\n", - "# Add the head_to_head column\n", - "df_top_bot_pro_forecasts['head_to_head'] = df_top_bot_pro_forecasts.apply(calculate_head_to_head, args=('bot_team_median', 'pro_median'), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 340, "metadata": {}, - "outputs": [], - "source": [ - "weighted_total_score = get_weighted_score(df_top_bot_pro_forecasts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 582 - }, - "id": "JlU9zyqn26Rl", - "outputId": "ac54d636-670b-4a8f-aea9-402679efacf9" - }, - "outputs": [], - "source": [ - "plot_head_to_head_distribution(df_top_bot_pro_forecasts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "V1qC4m2VefLe", - "outputId": "2f110b55-caf6-4ea8-9dfe-b746c3e4d892" - }, - "outputs": [], - "source": [ - "df_bot_team_h2h = calculate_t_test(df_top_bot_pro_forecasts, ['head_to_head'])\n", - "\n", - "df_bot_team_h2h" - ] - }, - { + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idquestion_weightresolutiontypeoptionsrange_minrange_maxopen_lower_boundopen_upper_boundmetac-o1-preview...median_forecast_1_botsmedian_forecast_2_botsmedian_forecast_3_botsmedian_forecast_4_botsmedian_forecast_5_botsmedian_forecast_6_botsmedian_forecast_7_botsmedian_forecast_8_botsmedian_forecast_9_botsmedian_forecast_10_bots
0312621.00multiple_choice[0, 1, 2-3, 4-6, >6]NaNNaNFalseFalse[0.010416666666666666,0.20833333333333334,0.04......0.0104170.2052080.0149260.0126710.0126710.0149260.0974630.0974630.0484750.048475
1312631.086.82numericNaN60.0100.0TrueTrue[0.05,0.0506666667,0.0513333333,0.052,0.052666......[0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...[0.05, 0.05061111115, 0.0512222222, 0.05183333...[0.03366666666666667, 0.03409436576666667, 0.0...[0.037750000000000006, 0.03822284245, 0.038700...[0.037750000000000006, 0.03822284245, 0.038700...[0.0402, 0.040728273960000005, 0.04126011788, ...[0.0402, 0.040728273960000005, 0.04126011788, ...[0.0402, 0.040728273960000005, 0.04126011788, ...[0.041833333333333333, 0.04238467275, 0.042938...[0.041833333333333333, 0.04238467275, 0.042938...
2312641.0nobinaryNaNNaNNaNFalseFalse0.05...0.050.10.070.0630.0630.070.110.110.150.15
3312741.05-9multiple_choice[0-4, 5-9, >9]NaNNaNNaNNaN[0.15,0.65,0.2]...0.650.6250.60.610.60.556250.51250.51250.556250.5125
4312751.0119.2numericNaN0.0400.0FalseFalse[0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,......[0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...[0.0, 0.00342857145, 0.00685714285, 0.01028571...[0.0, 0.0023237670666666666, 0.004652994133333...[0.0, 0.00219737075, 0.0043988365, 0.006603060...[0.0, 0.0019825503600000003, 0.003970557620000...[0.0, 0.0019593148500000003, 0.0039231771, 0.0...[0.0, 0.0020603651142857148, 0.004124627985714...[0.0, 0.0020603651142857148, 0.004124627985714...[0.0, 0.0022194861375, 0.004442382825, 0.00666...[0.0, 0.002118648455555556, 0.0042403284999999...
\n", + "

5 rows × 29 columns

\n", + "
" + ], + "text/plain": [ + " bot_question_id question_weight resolution type \\\n", + "0 31262 1.0 0 multiple_choice \n", + "1 31263 1.0 86.82 numeric \n", + "2 31264 1.0 no binary \n", + "3 31274 1.0 5-9 multiple_choice \n", + "4 31275 1.0 119.2 numeric \n", + "\n", + " options range_min range_max open_lower_bound \\\n", + "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [0-4, 5-9, >9] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", + "\n", + " open_upper_bound metac-o1-preview ... \\\n", + "0 False [0.010416666666666666,0.20833333333333334,0.04... ... \n", + "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", + "2 False 0.05 ... \n", + "3 NaN [0.15,0.65,0.2] ... \n", + "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", + "\n", + " median_forecast_1_bots \\\n", + "0 0.010417 \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.05 \n", + "3 0.65 \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " median_forecast_2_bots \\\n", + "0 0.205208 \n", + "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", + "2 0.1 \n", + "3 0.625 \n", + "4 [0.0, 0.00342857145, 0.00685714285, 0.01028571... \n", + "\n", + " median_forecast_3_bots \\\n", + "0 0.014926 \n", + "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", + "2 0.07 \n", + "3 0.6 \n", + "4 [0.0, 0.0023237670666666666, 0.004652994133333... \n", + "\n", + " median_forecast_4_bots \\\n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 0.063 \n", + "3 0.61 \n", + "4 [0.0, 0.00219737075, 0.0043988365, 0.006603060... \n", + "\n", + " median_forecast_5_bots \\\n", + "0 0.012671 \n", + "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", + "2 0.063 \n", + "3 0.6 \n", + "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", + "\n", + " median_forecast_6_bots \\\n", + "0 0.014926 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.07 \n", + "3 0.55625 \n", + "4 [0.0, 0.0019593148500000003, 0.0039231771, 0.0... \n", + "\n", + " median_forecast_7_bots \\\n", + "0 0.097463 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.11 \n", + "3 0.5125 \n", + "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", + "\n", + " median_forecast_8_bots \\\n", + "0 0.097463 \n", + "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", + "2 0.11 \n", + "3 0.5125 \n", + "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", + "\n", + " median_forecast_9_bots \\\n", + "0 0.048475 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", + "2 0.15 \n", + "3 0.55625 \n", + "4 [0.0, 0.0022194861375, 0.004442382825, 0.00666... \n", + "\n", + " median_forecast_10_bots \n", + "0 0.048475 \n", + "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", + "2 0.15 \n", + "3 0.5125 \n", + "4 [0.0, 0.002118648455555556, 0.0042403284999999... \n", + "\n", + "[5 rows x 29 columns]" + ] + }, + "execution_count": 340, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_bot_team_forecasts.head()" + ] + }, + { "cell_type": "code", - "execution_count": null, + "execution_count": 341, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z3TTBVWoZVzU", + "outputId": "0eb32f2c-09c6-4a15-e81a-bee353b1bccf" + }, + "outputs": [], + "source": [ + "# @title Weighted team-vs-pro\n", + "\n", + "# We have our top bot team members.\n", + "# Calculate their median forecast on the pro_bot questions.\n", + "# Create df with bot_question_id, forecasts, resolution, weights\n", + "# Calculate the head-to-head score\n", + "\n", + "df_top_bot_forecasts = df_bot_team_forecasts[['bot_question_id', f'median_forecast_{len(top_bot_team)}_bots']]\n", + "df_top_bot_forecasts = df_top_bot_forecasts.rename(columns={f'median_forecast_{len(top_bot_team)}_bots': 'bot_team_median'})\n", + "\n", + "df_pro_median = df_pro_forecasts[['pro_question_id', 'pro_median']]\n", + "\n", + "df_top_bot_pro_forecasts = pd.merge(\n", + " df_pro_bot_resolved_questions,\n", + " df_top_bot_forecasts[['bot_question_id', 'bot_team_median']],\n", + " on='bot_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "df_top_bot_pro_forecasts = pd.merge(\n", + " df_top_bot_pro_forecasts,\n", + " df_pro_median,\n", + " on='pro_question_id',\n", + " how='left'\n", + ")\n", + "\n", + "# Copy with union (not just overlapping questions)\n", + "df_top_bot_pro_forecasts_all = df_top_bot_pro_forecasts.copy()\n", + "\n", + "# Filter to only those rows where pro_median is not NA\n", + "df_top_bot_pro_forecasts = df_top_bot_pro_forecasts.dropna(subset=['pro_median'])\n", + "\n", + "# Add the head_to_head column\n", + "df_top_bot_pro_forecasts['head_to_head'] = df_top_bot_pro_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'pro_median'), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighted Total Score: -30.1253\n" + ] + } + ], + "source": [ + "weighted_total_score = get_weighted_score(df_top_bot_pro_forecasts)" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 582 + }, + "id": "JlU9zyqn26Rl", + "outputId": "ac54d636-670b-4a8f-aea9-402679efacf9" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The average of 'head_to_head' is: -31.65\n" + ] + } + ], + "source": [ + "plot_head_to_head_distribution(df_top_bot_pro_forecasts)" + ] + }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "V1qC4m2VefLe", + "outputId": "2f110b55-caf6-4ea8-9dfe-b746c3e4d892" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
W_scoreW_countW_aveW_stdevstd_errt_statt_critupper_boundlower_boundcdfp_value
head_to_head-2861.992.1-31.1105.68264411.012194-2.8217741.98555-9.2-52.90.0029310.005863
\n", + "
" + ], + "text/plain": [ + " W_score W_count W_ave W_stdev std_err t_stat \\\n", + "head_to_head -2861.9 92.1 -31.1 105.682644 11.012194 -2.821774 \n", + "\n", + " t_crit upper_bound lower_bound cdf p_value \n", + "head_to_head 1.98555 -9.2 -52.9 0.002931 0.005863 " + ] + }, + "execution_count": 344, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_bot_team_h2h = calculate_t_test(df_top_bot_pro_forecasts, ['head_to_head'])\n", + "\n", + "df_bot_team_h2h" + ] + }, + { + "cell_type": "code", + "execution_count": 345, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11278,7 +11781,122 @@ "id": "0I0myCHpl7FT", "outputId": "bcc45b9a-f328-4f0c-ef98-a7620af7e358" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 5:\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
titlebot_team_medianpro_medianresolutionhead_to_head
228Will Donald Trump grant executive clemency to ...0.990.125no-447.2
279What will Kalshi's rank in the iPhone Top Free...0.02[0.02,0.01,0.015,0.015,0.05,0.89]Not in top 50-379.5
12What will be the monthly cargo volumes at the ...[0.16, 0.1627, 0.1654, 0.1681, 0.1708, 0.1735,...[0.001714054,0.0017985406,0.0018846914,0.00197...720283.0-274.3
291How many registered Syrian refugees will be in...[0.05, 0.05125, 0.0525, 0.05375, 0.055, 0.0562...[0.001,0.00105,0.0011,0.00115,0.0012,0.00125,0...2807615.0-243.6
208Will the Trump administration impose new tarif...0.10.8yes-207.9
\n", + "
" + ], + "text/plain": [ + " title \\\n", + "228 Will Donald Trump grant executive clemency to ... \n", + "279 What will Kalshi's rank in the iPhone Top Free... \n", + "12 What will be the monthly cargo volumes at the ... \n", + "291 How many registered Syrian refugees will be in... \n", + "208 Will the Trump administration impose new tarif... \n", + "\n", + " bot_team_median \\\n", + "228 0.99 \n", + "279 0.02 \n", + "12 [0.16, 0.1627, 0.1654, 0.1681, 0.1708, 0.1735,... \n", + "291 [0.05, 0.05125, 0.0525, 0.05375, 0.055, 0.0562... \n", + "208 0.1 \n", + "\n", + " pro_median resolution \\\n", + "228 0.125 no \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", + "12 [0.001714054,0.0017985406,0.0018846914,0.00197... 720283.0 \n", + "291 [0.001,0.00105,0.0011,0.00115,0.0012,0.00125,0... 2807615.0 \n", + "208 0.8 yes \n", + "\n", + " head_to_head \n", + "228 -447.2 \n", + "279 -379.5 \n", + "12 -274.3 \n", + "291 -243.6 \n", + "208 -207.9 " + ] + }, + "execution_count": 345, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.set_option('display.max_colwidth', 50)\n", "\n", @@ -11296,9 +11914,125 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 346, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Bottom 5:\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
titlebot_team_medianpro_medianresolutionhead_to_head
0For Q1 2025, how many banks will be listed on ...0.010417[0.001,0.62,0.35,0.019,0.01]0234.3
189What will the highest rank of metac-GPT4o or m...[0.0, 0.0030510204, 0.0061020408, 0.0091530612...[0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...34.0401.1
123Which party will win the 2nd highest number of...NaN[0.03,0.9,0.06,0.009,0.001]Alternative for GermanyNaN
211Will Nikola Corporation file for bankruptcy be...0.990.999annulledNaN
214Will the state of Rhode Island have any recrea...0.20.95annulledNaN
\n", + "
" + ], + "text/plain": [ + " title \\\n", + "0 For Q1 2025, how many banks will be listed on ... \n", + "189 What will the highest rank of metac-GPT4o or m... \n", + "123 Which party will win the 2nd highest number of... \n", + "211 Will Nikola Corporation file for bankruptcy be... \n", + "214 Will the state of Rhode Island have any recrea... \n", + "\n", + " bot_team_median \\\n", + "0 0.010417 \n", + "189 [0.0, 0.0030510204, 0.0061020408, 0.0091530612... \n", + "123 NaN \n", + "211 0.99 \n", + "214 0.2 \n", + "\n", + " pro_median \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] \n", + "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... \n", + "123 [0.03,0.9,0.06,0.009,0.001] \n", + "211 0.999 \n", + "214 0.95 \n", + "\n", + " resolution head_to_head \n", + "0 0 234.3 \n", + "189 34.0 401.1 \n", + "123 Alternative for Germany NaN \n", + "211 annulled NaN \n", + "214 annulled NaN " + ] + }, + "execution_count": 346, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(\"\\nBottom 5:\")\n", "\n", @@ -11307,9 +12041,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 347, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "bot_question_id Int64\n", + "title object\n", + "resolution float64\n", + "scheduled_close_time datetime64[ns]\n", + "actual_close_time datetime64[ns]\n", + "type object\n", + "options object\n", + "range_min float64\n", + "range_max float64\n", + "open_upper_bound object\n", + "open_lower_bound object\n", + "pro_question_id Int64\n", + "question_weight float64\n", + "bot_team_median object\n", + "pro_median object\n", + "head_to_head float64\n", + "weighted_score float64\n", + "dtype: object" + ] + }, + "execution_count": 347, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Cast df_top_bot_pro_forecasts['resolution'] as string - idk why this is necessary but it is\n", "df_top_bot_pro_forecasts['resolution'] = df_top_bot_pro_forecasts['resolution'].astype(pd.StringDtype())\n", @@ -11319,16 +12081,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 348, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
031262For Q1 2025, how many banks will be listed on ...NaN2025-01-20 03:27:002025-01-20 03:27:00multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse312681.00.010417[0.001,0.62,0.35,0.019,0.01]234.340709234.340709
131263What percentage of the vote will Alexander Luk...NaN2025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0TrueTrue312691.0[0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...[0.0013749738,0.0014499743,0.001526641,0.00160...-101.083204-101.083204
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaNFalseFalse312701.00.050.013-3.820805-3.820805
331274How many arms sales globally will the US State...NaN2025-01-21 11:42:002025-01-21 11:42:00multiple_choice[\"0-4\",\"5-9\",\">9\"]NaNNaNNaNNaN312801.00.65[0.16,0.44,0.4]39.01976439.019764
431275How much will it rain in Brasília, Brazil in F...NaN2025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0FalseFalse312811.0[0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...[0.0,0.0005044914,0.0010323506,0.0015847475,0....45.54604145.546041
\n", + "
" + ], + "text/plain": [ + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", + "\n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", + "\n", + " options range_min range_max open_upper_bound \\\n", + "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", + "\n", + " open_lower_bound pro_question_id question_weight \\\n", + "0 False 31268 1.0 \n", + "1 True 31269 1.0 \n", + "2 False 31270 1.0 \n", + "3 NaN 31280 1.0 \n", + "4 False 31281 1.0 \n", + "\n", + " bot_team_median \\\n", + "0 0.010417 \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.05 \n", + "3 0.65 \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " pro_median head_to_head \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 234.340709 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -101.083204 \n", + "2 0.013 -3.820805 \n", + "3 [0.16,0.44,0.4] 39.019764 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 45.546041 \n", + "\n", + " weighted_score \n", + "0 234.340709 \n", + "1 -101.083204 \n", + "2 -3.820805 \n", + "3 39.019764 \n", + "4 45.546041 " + ] + }, + "execution_count": 348, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_top_bot_pro_forecasts.head()" ] }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 349, "metadata": {}, "outputs": [], "source": [ @@ -11340,7 +12303,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 350, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11349,7 +12312,25 @@ "id": "BjNQ4IND6Ct7", "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of pro forecasts: 50\n" + ] + } + ], "source": [ "# Set up the plot\n", "plt.figure(figsize=(10, 8))\n", @@ -11378,7 +12359,7 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 351, "metadata": {}, "outputs": [], "source": [ @@ -11388,18 +12369,205 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 352, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_median
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaNFalseFalse312701.00.050.013
531276Will the USDA-posted recall by Pork Dynasty In...1.02025-01-21 11:42:002025-01-21 11:42:00binaryNaNNaNNaNNaNNaN312821.00.20.45
831288Will Eric Adams be Mayor of New York City on t...1.02025-01-22 20:19:002025-01-22 20:19:00binaryNaNNaNNaNFalseFalse312941.00.90.95
1031318Will the S&P 500 index go up in January 2025?1.02025-01-23 23:23:002025-01-23 23:23:00binaryNaNNaNNaNNaNNaN<NA>1.0NaNNaN
1331334At the end of March 2025, will Wikipedia still...1.02025-01-24 14:23:002025-01-24 14:23:00binaryNaNNaNNaNFalseFalse313381.00.750.9
\n", + "
" + ], + "text/plain": [ + " bot_question_id title \\\n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "5 31276 Will the USDA-posted recall by Pork Dynasty In... \n", + "8 31288 Will Eric Adams be Mayor of New York City on t... \n", + "10 31318 Will the S&P 500 index go up in January 2025? \n", + "13 31334 At the end of March 2025, will Wikipedia still... \n", + "\n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary NaN \n", + "5 1.0 2025-01-21 11:42:00 2025-01-21 11:42:00 binary NaN \n", + "8 1.0 2025-01-22 20:19:00 2025-01-22 20:19:00 binary NaN \n", + "10 1.0 2025-01-23 23:23:00 2025-01-23 23:23:00 binary NaN \n", + "13 1.0 2025-01-24 14:23:00 2025-01-24 14:23:00 binary NaN \n", + "\n", + " range_min range_max open_upper_bound open_lower_bound pro_question_id \\\n", + "2 NaN NaN False False 31270 \n", + "5 NaN NaN NaN NaN 31282 \n", + "8 NaN NaN False False 31294 \n", + "10 NaN NaN NaN NaN \n", + "13 NaN NaN False False 31338 \n", + "\n", + " question_weight bot_team_median pro_median \n", + "2 1.0 0.05 0.013 \n", + "5 1.0 0.2 0.45 \n", + "8 1.0 0.9 0.95 \n", + "10 1.0 NaN NaN \n", + "13 1.0 0.75 0.9 " + ] + }, + "execution_count": 352, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_top_bot_pro_forecasts_all_binary.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 353, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of pro forecasts: 50\n", + "Number of bot forecasts: 241\n" + ] + } + ], "source": [ "# Set up the plot\n", "plt.figure(figsize=(10, 8))\n", @@ -11429,7 +12597,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 354, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11437,7 +12605,404 @@ "id": "lPPgorXB7omi", "outputId": "24571b16-50b7-4e51-cd3d-420c15c7fe42" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
031262For Q1 2025, how many banks will be listed on ...NaN2025-01-20 03:27:002025-01-20 03:27:00multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse312681.00.010417[0.001,0.62,0.35,0.019,0.01]234.340709234.340709
131263What percentage of the vote will Alexander Luk...NaN2025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0TrueTrue312691.0[0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...[0.0013749738,0.0014499743,0.001526641,0.00160...-101.083204-101.083204
231264Will the bubble in the Magnificent Seven pop b...0.02025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaNFalseFalse312701.00.050.013-3.820805-3.820805
331274How many arms sales globally will the US State...NaN2025-01-21 11:42:002025-01-21 11:42:00multiple_choice[\"0-4\",\"5-9\",\">9\"]NaNNaNNaNNaN312801.00.65[0.16,0.44,0.4]39.01976439.019764
431275How much will it rain in Brasília, Brazil in F...NaN2025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0FalseFalse312811.0[0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...[0.0,0.0005044914,0.0010323506,0.0015847475,0....45.54604145.546041
\n", + "
" + ], + "text/plain": [ + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", + "\n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 NaN 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 0.0 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", + "\n", + " options range_min range_max open_upper_bound \\\n", + "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", + "\n", + " open_lower_bound pro_question_id question_weight \\\n", + "0 False 31268 1.0 \n", + "1 True 31269 1.0 \n", + "2 False 31270 1.0 \n", + "3 NaN 31280 1.0 \n", + "4 False 31281 1.0 \n", + "\n", + " bot_team_median \\\n", + "0 0.010417 \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.05 \n", + "3 0.65 \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " pro_median head_to_head \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 234.340709 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -101.083204 \n", + "2 0.013 -3.820805 \n", + "3 [0.16,0.44,0.4] 39.019764 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 45.546041 \n", + "\n", + " weighted_score \n", + "0 234.340709 \n", + "1 -101.083204 \n", + "2 -3.820805 \n", + "3 39.019764 \n", + "4 45.546041 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_medianhead_to_headweighted_score
34235345Will the US Citizenship and Immigration Servic...1.02025-03-12 22:00:002025-03-12 22:00:00binaryNaNNaNNaNFalseFalse353801.000.90.95-5.406722-5.406722
35135354Will the United States impose any new tariffs ...0.02025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353811.000.40.05-45.953233-45.953233
35535358Will ChatGPT rank in the top 10 global website...1.02025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353851.000.90.97-7.490131-7.490131
36135364Will Doge's Agency Efficiency Leaderboard have...0.02025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaNFalseFalse353860.850.850.666-80.050570-68.042984
36435367Will the Project 2025 Tracker spreadsheet mark...0.02025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaNFalseFalse353870.850.050.03-2.083409-1.770897
\n", + "
" + ], + "text/plain": [ + " bot_question_id title \\\n", + "342 35345 Will the US Citizenship and Immigration Servic... \n", + "351 35354 Will the United States impose any new tariffs ... \n", + "355 35358 Will ChatGPT rank in the top 10 global website... \n", + "361 35364 Will Doge's Agency Efficiency Leaderboard have... \n", + "364 35367 Will the Project 2025 Tracker spreadsheet mark... \n", + "\n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "342 1.0 2025-03-12 22:00:00 2025-03-12 22:00:00 binary NaN \n", + "351 0.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "355 1.0 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "361 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "364 0.0 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "\n", + " range_min range_max open_upper_bound open_lower_bound pro_question_id \\\n", + "342 NaN NaN False False 35380 \n", + "351 NaN NaN False False 35381 \n", + "355 NaN NaN False False 35385 \n", + "361 NaN NaN False False 35386 \n", + "364 NaN NaN False False 35387 \n", + "\n", + " question_weight bot_team_median pro_median head_to_head weighted_score \n", + "342 1.00 0.9 0.95 -5.406722 -5.406722 \n", + "351 1.00 0.4 0.05 -45.953233 -45.953233 \n", + "355 1.00 0.9 0.97 -7.490131 -7.490131 \n", + "361 0.85 0.85 0.666 -80.050570 -68.042984 \n", + "364 0.85 0.05 0.03 -2.083409 -1.770897 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[354], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:853\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 843\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 844\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 852\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 853\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 856\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 857\u001b[0m bins\n\u001b[1;32m 858\u001b[0m )\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], "source": [ "# Calculate confidence scores for bot_team_median and pro_median\n", "display_head_and_tail(df_top_bot_pro_forecasts)\n", @@ -12310,9 +13875,9 @@ "\n", "# Recommend paying attention to the bot team h2h scores vs CP graph (further down) rather than pgodzinai (he was selected as the bot \"team\" vs the PROS)\n", "\n", - "df_top_bot_pro_cp_forecasts['head_to_head_bot_vs_cp'] = df_top_bot_pro_cp_forecasts.apply(calculate_head_to_head, args=('bot_team_median', 'forecast_values'), axis=1)\n", - "df_top_bot_pro_cp_forecasts['head_to_head_cp_vs_pro'] = df_top_bot_pro_cp_forecasts.apply(calculate_head_to_head, args=('forecast_values', 'pro_median'), axis=1)\n", - "df_top_bot_pro_cp_forecasts['head_to_head_bot_vs_pro'] = df_top_bot_pro_cp_forecasts.apply(calculate_head_to_head, args=('bot_team_median', 'pro_median'), axis=1)\n", + "df_top_bot_pro_cp_forecasts['head_to_head_bot_vs_cp'] = df_top_bot_pro_cp_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'forecast_values'), axis=1)\n", + "df_top_bot_pro_cp_forecasts['head_to_head_cp_vs_pro'] = df_top_bot_pro_cp_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('forecast_values', 'pro_median'), axis=1)\n", + "df_top_bot_pro_cp_forecasts['head_to_head_bot_vs_pro'] = df_top_bot_pro_cp_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'pro_median'), axis=1)\n", "\n", "plot_head_to_head_distribution(df_top_bot_pro_cp_forecasts, 'head_to_head_bot_vs_cp', ('pgodzinai', 'CP'))\n", "plot_head_to_head_distribution(df_top_bot_pro_cp_forecasts, 'head_to_head_cp_vs_pro', ('CP', 'Pro median'))\n", @@ -12464,7 +14029,7 @@ "df_top_bot_cp_forecasts = df_top_bot_cp_forecasts.dropna(subset=['forecast_values'])\n", "\n", "# Add the head_to_head column\n", - "df_top_bot_cp_forecasts['head_to_head'] = df_top_bot_cp_forecasts.apply(calculate_head_to_head, args=('bot_team_median', 'forecast_values'), axis=1)\n", + "df_top_bot_cp_forecasts['head_to_head'] = df_top_bot_cp_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'forecast_values'), axis=1)\n", "\n", "display_head_and_tail(df_top_bot_cp_forecasts)" ] diff --git a/functions.py b/functions.py index 8e894df..0d6593b 100644 --- a/functions.py +++ b/functions.py @@ -11,11 +11,9 @@ from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -from refactored_notebook.scoring import ( - calculate_baseline_score, - calculate_peer_score, - nominal_location_to_cdf_location, -) +from refactored_notebook.scoring import (calculate_baseline_score, + calculate_peer_score, + nominal_location_to_cdf_location) def extract_forecast(df): @@ -193,7 +191,7 @@ def make_wide(df_bot_peer, df_pro_bot_resolved_questions): df_pivoted = df_pivoted[cols] all_columns = df_pivoted.columns.tolist() - ## Remove 'question_id' and 'bot_median' from the list if they exist + # Remove 'question_id' and 'bot_median' from the list if they exist all_columns = [col for col in all_columns if col not in ["bot_question_id"]] new_column_order = ["bot_question_id"] + all_columns df_pivoted = df_pivoted[new_column_order] @@ -432,17 +430,21 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): team_scores = {team: 0.0 for team in teams} for _, row in df_bot_team_forecasts.iterrows(): - resolution = row["resolution"] - options = row["options"] - range_min = row["range_min"] - range_max = row["range_max"] - question_weight = row["question_weight"] - open_upper_bound = row["open_upper_bound"] - open_lower_bound = row["open_lower_bound"] - question_type = row["type"] - for team in teams: - forecast = row[team] + # @Check: that the conversion is corret + cleaned_row = _prepare_new_row_for_scoring(row, [team]) + if _is_unscorable(cleaned_row, [team]): + continue + + forecast = cleaned_row[team] + resolution = cleaned_row["resolution"] + options = cleaned_row["options"] + range_min = cleaned_row["range_min"] + range_max = cleaned_row["range_max"] + question_weight = cleaned_row["question_weight"] + open_upper_bound = cleaned_row["open_upper_bound"] + open_lower_bound = cleaned_row["open_lower_bound"] + question_type = cleaned_row["type"] try: weighted_score = calculate_baseline_score( @@ -576,114 +578,6 @@ def calculate_t_test(df_input, bot_list, weight_col="question_weight"): return df_W_leaderboard -def calculate_head_to_head(row, a, b): - """ - @Check:... - - Calculates the head-to-head score for two forecasters. - Positive if 'a' did better than 'b', negative if 'b' did better than 'a'. - - Args: - row (pandas.Series): Row containing 'resolution', 'type', and forecast columns. - a (str): Column name for first forecaster. - b (str): Column name for second forecaster. - - Returns: - float: Head-to-head score. - """ - q_type = row["type"] - resolution = row["resolution"] - options = row["options"] - range_min = row.get("range_min") - range_max = row.get("range_max") - - forecast_a = row[a] - forecast_b = row[b] - - if q_type == "binary": - if (resolution == "yes") or (resolution == 1): - return 100 * np.log(forecast_a / forecast_b) - elif (resolution == "no") or (resolution == 0): - return 100 * np.log((1 - forecast_a) / (1 - forecast_b)) - else: - return np.nan - - elif q_type == "multiple_choice": - # Parse forecast_a if it's a string - if isinstance(forecast_a, str): - forecast_a = ast.literal_eval(forecast_a) - options = ( - ast.literal_eval(row["options"]) - if isinstance(row["options"], str) - else row["options"] - ) - resolution_idx = options.index(str(row["resolution"])) - forecast_a = forecast_a[resolution_idx] - - # Parse forecast_b if it's a string - if isinstance(forecast_b, str): - forecast_b = ast.literal_eval(forecast_b) - options = ( - ast.literal_eval(row["options"]) - if isinstance(row["options"], str) - else row["options"] - ) - resolution_idx = options.index(str(row["resolution"])) - forecast_b = forecast_b[resolution_idx] - - # Now both are floats with the prob assigned to the correct bin - return 100 * np.log(forecast_a / forecast_b) - - elif q_type == "numeric": - # Ensure both forecasts are Python lists - if isinstance(forecast_a, str): - forecast_a = ast.literal_eval(forecast_a) - elif isinstance(forecast_a, np.ndarray): - forecast_a = forecast_a.tolist() - - if isinstance(forecast_b, str): - forecast_b = ast.literal_eval(forecast_b) - elif isinstance(forecast_b, np.ndarray): - forecast_b = forecast_b.tolist() - - if not forecast_a or not forecast_b: - return np.nan - - cdf_a = forecast_a - cdf_b = forecast_b - - pmf_a = [cdf_a[0]] + [cdf_a[i] - cdf_a[i - 1] for i in range(1, len(cdf_a))] - pmf_a.append(1 - cdf_a[-1]) - - pmf_b = [cdf_b[0]] + [cdf_b[i] - cdf_b[i - 1] for i in range(1, len(cdf_b))] - pmf_b.append(1 - cdf_b[-1]) - - bin_edges = np.linspace(range_min, range_max, 200) - - if resolution == "below_lower_bound": - resolution_idx = 0 - elif resolution == "above_upper_bound": - resolution_idx = len(pmf_a) - 1 # i.e., 200 - else: - try: - resolution_val = float(resolution) - resolution_idx = np.searchsorted( - bin_edges, resolution_val, side="right" - ) - except ValueError: - print(f"Bad resolution value: {resolution}") - return np.nan - - p_a = pmf_a[resolution_idx] - p_b = pmf_b[resolution_idx] - - if p_a <= 0 or p_b <= 0: - print(f"Invalid PMF values: p_a={p_a}, p_b={p_b}") - return np.nan - - return 100 * np.log(p_a / p_b) - - def plot_head_to_head_distribution( df_forecasts, col="head_to_head", vs=("Bot Team", "Pros") ): @@ -1079,7 +973,8 @@ def get_cdf_at(cdf, unscaled_location): if index_scaled_location.is_integer(): return cdf[int(index_scaled_location)] # linear interpolation step - left_index = int(index_scaled_location) # This is the floor, which is what we want + # This is the floor, which is what we want + left_index = int(index_scaled_location) right_index = left_index + 1 left_value = cdf[left_index] right_value = cdf[right_index] @@ -1245,39 +1140,86 @@ def parse_options_array(options_str): return [p.strip().strip("\"'") for p in cleaned.split(",")] -def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, col_a: str, col_b: str) -> float: - question_type = row["type"] +def calculate_weighted_h2h_score_between_two_forecast_columns( + row: pd.Series, col_a: str, col_b: str +) -> float: + """ + Calculates the head-to-head score for two forecasters. + Positive if 'a' did better than 'b', negative if 'b' did better than 'a'. - forecast_a = row[ - col_a - ] - if isinstance(forecast_a, str): - forecast_a = [float(x) for x in forecast_a.strip('[]').split(',')] - elif isinstance(forecast_a, float) and math.isnan(forecast_a): - return np.nan + Args: + row (pandas.Series): Row containing 'resolution', 'type', and forecast columns. + a (str): Column name for first forecaster. + b (str): Column name for second forecaster. - forecast_b = row[col_b] - if isinstance(forecast_b, str): - forecast_b = [float(x) for x in forecast_b.strip('[]').split(',')] - elif isinstance(forecast_b, float) and math.isnan(forecast_b): + Returns: + float: Head-to-head score. + """ + # @Check: that the row conversion is corret + + cleaned_row = _prepare_new_row_for_scoring(row, [col_a, col_b]) + if _is_unscorable(cleaned_row, [col_a, col_b]): return np.nan - options = row["options_parsed"] if "options_parsed" in row else row["options"] + question_type = cleaned_row["type"] + forecast_a = cleaned_row[col_a] + forecast_b = cleaned_row[col_b] + resolution = cleaned_row["resolution"] + options = cleaned_row["options"] + range_min = cleaned_row["range_min"] + range_max = cleaned_row["range_max"] + question_weight = cleaned_row["question_weight"] + + score = calculate_peer_score( + q_type=question_type, + forecast=forecast_a, + forecast_for_other_users=[forecast_b], + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, + question_weight=question_weight, + ) + return score + + +def _is_unscorable(row: pd.Series, forecast_columns_to_check_null: list[str]): + is_unscorable = False + for col in forecast_columns_to_check_null: + forecast = row[col] + if forecast is None: + is_unscorable = True + elif isinstance(forecast, float) and math.isnan(forecast): + is_unscorable = True resolution = row["resolution"] if resolution == "annulled" or resolution == "ambiguous": - return np.nan + is_unscorable = True + return is_unscorable + - question_type = row["type"] +def _prepare_new_row_for_scoring( + original_row: pd.Series, forecast_columns: list[str] +) -> pd.Series: + new_row = original_row.copy() + question_type = original_row["type"] + + options = ( + original_row["options_parsed"] + if "options_parsed" in new_row + else new_row["options"] + ) + if isinstance(options, str): + options = options.strip("[]").split(",") + new_row["options"] = options + + resolution = original_row["resolution"] + question_type = original_row["type"] if question_type == "binary": if resolution == "yes": resolution = True elif resolution == "no": resolution = False - assert isinstance(forecast_a, float) - assert isinstance(forecast_b, float) - forecast_a = [forecast_a] - forecast_b = [forecast_b] elif question_type == "multiple_choice": resolution = resolution elif question_type == "numeric": @@ -1289,31 +1231,37 @@ def calculate_weighted_h2h_score_between_two_forecast_columns(row: pd.Series, co raise ValueError(f"Unknown resolution type: {resolution}") else: raise ValueError(f"Unknown question type: {question_type}") + new_row["resolution"] = resolution - - range_min = row.get("range_min") + range_min = original_row.get("range_min") if range_min: range_min = float(range_min) + new_row["range_min"] = range_min - range_max = row.get("range_max") + range_max = original_row.get("range_max") if range_max: range_max = float(range_max) + new_row["range_max"] = range_max - question_weight = row["question_weight"] + question_weight = original_row["question_weight"] if question_weight: question_weight = float(question_weight) - - score = calculate_peer_score( - q_type=question_type, - forecast=forecast_a, - forecast_for_other_users=[forecast_b], - resolution=resolution, - options=options, - range_min=range_min, - range_max=range_max, - question_weight=question_weight, - ) - return score + new_row["question_weight"] = question_weight + + for col in forecast_columns: + forecast = original_row[col] + if isinstance(forecast, float) and math.isnan(forecast): + forecast = forecast + elif question_type == "binary": + if isinstance(forecast, str): + forecast = [float(forecast)] + forecast = [forecast] + elif isinstance(forecast, str): + forecast = [float(x) for x in forecast.strip("[]").split(",")] + + new_row[col] = forecast + + return new_row def calculate_all_peer_scores(df, all_bots, pro_col="pro_median"): diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 9c027a3..4ece3f3 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,10 +1,10 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 X_bot,-0.0,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 @@ -18,30 +18,30 @@ annabot,-0.1,-0.1,-0.1,-0.0,-0.0 cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 -MWG,-0.2,-0.2,-0.1,-0.0,-0.0 +MWG,-0.2,-0.2,-0.1,-0.1,-0.0 ProfessorSP,-0.2,-0.2,-0.1,-0.1,-0.0 -GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 -ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 +GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 +metac-o1,-0.3,-0.2,-0.1,0.0,0.1 acm_bot,-0.3,-0.2,-0.1,0.0,0.1 +ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 +bot_median,-0.3,-0.2,-0.1,-0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-o1,-0.3,-0.2,-0.1,-0.0,0.1 -metac-perplexity,-0.3,-0.2,-0.1,0.0,0.1 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 -wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 +metac-deepseek-r1,-0.3,-0.2,-0.1,-0.1,-0.0 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -metac-deepseek-r1,-0.3,-0.2,-0.2,-0.1,-0.0 metac-Gemini-Exp-1206,-0.3,-0.3,-0.2,-0.0,0.0 -NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 -bot_median,-0.4,-0.3,-0.2,-0.1,0.0 +metac-perplexity,-0.4,-0.3,-0.2,-0.0,0.0 +NextWorldLab,-0.3,-0.3,-0.2,-0.1,0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 +metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,-0.0 +metac-Llama-3.1,-0.4,-0.4,-0.2,-0.1,0.0 +metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.0 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -metac-grok-2-1212,-0.4,-0.4,-0.2,-0.1,-0.0 pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 -VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 -metac-claude-3-5-sonnet-latest,-0.4,-0.4,-0.3,-0.2,-0.1 -metac-Llama-3.1,-0.5,-0.4,-0.3,-0.1,-0.1 -metac-exa,-0.5,-0.4,-0.3,-0.2,-0.1 +VeritasAI,-0.4,-0.3,-0.2,-0.2,-0.1 +metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-o1-preview,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-o1-preview,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 49d442c..8c1e7a0 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,10 +1,10 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +RPM_bot,-0.5,7.0,-0.1,0.8401626602195374,0.31755163711190787,-0.22911491175620202,2.4469118511449692,0.7,-0.8,0.4131948210081994,0.826390 jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 +bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 -RPM_bot,-1.1,7.0,-0.2,0.824531966811415,0.3116437903151381,-0.5234058432057136,2.4469118511449692,0.6,-0.9,0.3097258948590483,0.619452 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 @@ -15,33 +15,33 @@ CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.36553170 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.9,27.4,-0.3,0.7446989876942366,0.14226742863646924,-1.7648756350756885,2.0495406495390753,0.0,-0.5,0.04457614500253557,0.089152 +cookics_bot_TEST,-6.5,27.4,-0.2,0.7478313737485887,0.14286584023204454,-1.6679327769704273,2.0495406495390753,0.1,-0.5,0.053574616968489516,0.107149 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 -GreeneiBot2,-10.4,58.4,-0.2,0.8498829222635632,0.11125990180982864,-1.5979756990286293,2.000831925930035,0.0,-0.4,0.05777205560013113,0.115544 +metac-o1,-10.4,91.1,-0.1,0.9315503207588304,0.09759939627192438,-1.1710037539243623,1.9858289388460384,0.1,-0.3,0.12234246603454144,0.244685 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 +GreeneiBot2,-10.6,58.4,-0.2,0.84933087242601,0.11118763184285871,-1.638405629664946,2.000831925930035,0.0,-0.4,0.053406273914708285,0.106813 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 -metac-o1,-11.5,91.1,-0.1,0.8882269503815736,0.09306036633541931,-1.3604682737460798,1.9858289388460384,0.1,-0.3,0.08853781411471767,0.177076 +bot_median,-11.1,92.1,-0.1,0.8343911715991652,0.08694405375037174,-1.3919418427248071,1.9855502432148115,0.1,-0.3,0.08366450804542999,0.167329 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 -metac-perplexity,-11.9,89.1,-0.1,0.9936685898993489,0.10526953628638332,-1.2647310023240792,1.9864049297707018,0.1,-0.3,0.10465157496376706,0.209303 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 +metac-deepseek-r1,-14.1,52.1,-0.3,0.8172087173883323,0.11321764813763505,-2.3937504961816116,2.0053789762011176,-0.0,-0.5,0.01019302014325762,0.020386 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-deepseek-r1,-14.6,52.1,-0.3,0.7315248397695878,0.10134684096084697,-2.7666887863373426,2.0053789762011176,-0.1,-0.5,0.003932133201892011,0.007864 -metac-Gemini-Exp-1206,-15.2,76.5,-0.2,0.9437969359023713,0.1079065594460612,-1.8467741127168467,1.9908217254774627,0.0,-0.4,0.034349204246702666,0.068698 +metac-Gemini-Exp-1206,-14.6,76.5,-0.2,0.9369300827202118,0.1071214557093134,-1.7806582480922164,1.9908217254774627,0.0,-0.4,0.03949550680306326,0.078991 +metac-perplexity,-16.1,89.1,-0.2,1.0694909108673796,0.11330217478335987,-1.5994893543452755,1.9864049297707018,0.0,-0.4,0.05664610517795549,0.113292 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -bot_median,-17.3,92.1,-0.2,0.9191222179799003,0.09577307891459154,-1.9639956837727752,1.9855502432148115,0.0,-0.4,0.02628954496851215,0.052579 -minefrac1,-19.2,51.1,-0.4,0.8809897145082934,0.1232424683669797,-3.0436411347421197,2.0065449272360034,-0.1,-0.6,0.0018587451878251278,0.003717 -metac-claude-3-5-sonnet-20240620,-19.5,90.5,-0.2,1.0091380158423626,0.10607823314499117,-2.031064521471562,1.9860719790130024,-0.0,-0.4,0.0226076007974782,0.045215 +minefrac1,-18.5,51.1,-0.4,0.8782230217189723,0.1228554331463025,-2.94542136244705,2.0065449272360034,-0.1,-0.6,0.002440792164293176,0.004882 +metac-claude-3-5-sonnet-20240620,-20.8,90.5,-0.2,0.9854576682401628,0.10358901026916505,-2.2176587156495677,1.9860719790130024,-0.0,-0.4,0.01455504948064986,0.029110 +metac-Llama-3.1,-21.0,89.1,-0.2,1.131903405632652,0.11991417243449026,-1.9667104273244107,1.9864049297707018,0.0,-0.5,0.026181998267921627,0.052364 +metac-claude-3-5-sonnet-latest,-21.7,91.1,-0.2,0.8679924761244506,0.0909403815880937,-2.6147562800776485,1.9858289388460384,-0.1,-0.4,0.005233245635108678,0.010466 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -metac-grok-2-1212,-22.9,91.1,-0.3,1.0488287270766499,0.10988676432631847,-2.2835278472341387,1.9858289388460384,-0.0,-0.5,0.012375199205885952,0.024750 -pgodzinai,-23.9,76.4,-0.3,0.9564523461011735,0.1094250257541138,-2.858685649756527,1.9908489732268309,-0.1,-0.5,0.0027488433046459902,0.005498 +pgodzinai,-23.5,76.4,-0.3,0.9735671748298226,0.11138308522466013,-2.763549748735371,1.9908489732268309,-0.1,-0.5,0.003590727855444895,0.007181 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-claude-3-5-sonnet-latest,-24.4,91.1,-0.3,0.7843146490917536,0.08217337757580902,-3.2658265155495396,1.9858289388460384,-0.1,-0.4,0.0007722051094024979,0.001544 -metac-Llama-3.1,-26.1,89.1,-0.3,0.9987986166118539,0.10581301279218377,-2.7685645488001787,1.9864049297707018,-0.1,-0.5,0.00343170739454993,0.006863 -metac-exa,-26.6,89.1,-0.3,0.8489741653993217,0.08994056732713923,-3.324096943280282,1.9864049297707018,-0.1,-0.5,0.0006469013238867488,0.001294 -InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 -metac-o1-preview,-27.8,91.1,-0.3,0.87743376179017,0.09192955389631036,-3.31497363379348,1.9858289388460384,-0.1,-0.5,0.0006608298367709141,0.001322 -metac-gpt-4o,-30.5,91.1,-0.3,0.9139398799143879,0.09575433395355178,-3.4928274283029523,1.9858289388460384,-0.1,-0.5,0.00037140113373772884,0.000743 +metac-exa,-24.7,89.1,-0.3,0.8121952445686516,0.08604419787326485,-3.2197865951234235,1.9864049297707018,-0.1,-0.4,0.0008985159820669422,0.001797 +metac-o1-preview,-25.5,91.1,-0.3,0.8498877252707713,0.08904352994884641,-3.1492143531875287,1.9858289388460384,-0.1,-0.5,0.0011106007145197491,0.002221 +InstitutPelFutur,-26.9,90.1,-0.3,0.9739711690022733,0.10260858670161008,-2.9043019887843187,1.9861137662360124,-0.1,-0.5,0.0023202343180469525,0.004640 +metac-grok-2-1212,-27.9,91.1,-0.3,1.0054085980592369,0.10533759689680534,-2.9038578245582283,1.9858289388460384,-0.1,-0.5,0.0023176059032990978,0.004635 +metac-gpt-4o,-28.8,91.1,-0.3,0.8198830654548765,0.08589991374463501,-3.67651905388223,1.9858289388460384,-0.1,-0.5,0.0002007468680573961,0.000401 diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 93927be..9e33cc3 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -16,11 +16,11 @@ def calculate_peer_score( forecast: ForecastType, forecast_for_other_users: list[ForecastType], resolution: ResolutionType, - q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, question_weight: float = 1.0, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, ) -> float: question_type = _determine_question_type(q_type, resolution) resolution = _normalize_resolution(question_type, resolution, range_min, range_max) @@ -44,13 +44,13 @@ def calculate_peer_score( def calculate_baseline_score( forecast: ForecastType, resolution: ResolutionType, - q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, options: list[str] | None = None, range_min: float | None = None, range_max: float | None = None, question_weight: float = 1.0, open_upper_bound: bool = False, open_lower_bound: bool = False, + q_type: Literal["binary", "multiple_choice", "numeric"] | None = None, ) -> float: """ Question type can be infered from resolution type diff --git a/tests/test_scoring.py b/tests/test_scoring.py index b42c719..3b31bf9 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -235,7 +235,7 @@ def test_numeric_baseline_when_perfect_forecast(): index_to_answer_ratio = 3 correct_answer = correct_index * index_to_answer_ratio range_max = length_of_cdf * index_to_answer_ratio - forecast = generate_cdf_with_forecast_at_index(correct_index, 0.999) + forecast = generate_cdf_with_forecast_at_index(correct_index, 0.59) # As of May 3, 2025, 0.59 is max difference between 2 points on a cdf score = calculate_baseline_score( @@ -333,10 +333,18 @@ def test_baseline_score_better_when_closer( range_max: float | None, ): score_closer = calculate_baseline_score( - forecast_closer, resolution, options, range_min, range_max, 1.0 + forecast=forecast_closer, + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, ) score_further = calculate_baseline_score( - forecast_further, resolution, options, range_min, range_max, 1.0 + forecast=forecast_further, + resolution=resolution, + options=options, + range_min=range_min, + range_max=range_max, ) assert score_closer > score_further @@ -512,6 +520,7 @@ def test_better_forecast_means_better_peer_score( ) for idx, forecast in enumerate(forecasts) ] + assert scores[1] > 0, "The first score should be positive" sorted_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True) assert len(scores) == len(set(scores)), "Scores should all be different" assert sorted_indices == list( From 80ff1982f0f29085f4773293f24beeb45607c7e8 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 08:08:40 -0600 Subject: [PATCH 17/26] Small touchups --- AI_BENCHMARKING_ANALYSIS.ipynb | 4 +--- functions.py | 26 ++++++++++++-------------- refactored_notebook/scoring.py | 6 ++---- tests/test_scoring.py | 3 +-- 4 files changed, 16 insertions(+), 23 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index dc8f1ff..10d1981 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -3391,9 +3391,7 @@ } ], "source": [ - "from functions import *\n", - "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)\n", - "# @Check: -> This wasn't implemented when I saw it, so I'm not sure the correct intention." + "df_bot_vs_pro_peer = calculate_all_peer_scores(df_pro_bot_forecasts, all_bots)" ] }, { diff --git a/functions.py b/functions.py index 0d6593b..99b0a94 100644 --- a/functions.py +++ b/functions.py @@ -11,9 +11,11 @@ from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -from refactored_notebook.scoring import (calculate_baseline_score, - calculate_peer_score, - nominal_location_to_cdf_location) +from refactored_notebook.scoring import ( + calculate_baseline_score, + calculate_peer_score, + nominal_location_to_cdf_location, +) def extract_forecast(df): @@ -348,8 +350,6 @@ def get_median_forecast_multiple_choice(row, forecasts): def get_median_forecast(row, bots): """ - @Check: - Calculates the median forecast for a given set of bots, handling different question types properly. Args: @@ -378,18 +378,18 @@ def get_median_forecast(row, bots): return np.nan if q_type == "numeric": - forecasts = [f for f in forecasts if isinstance(f, list)] + numeric_forecasts: list[list[float]] = [f for f in forecasts if isinstance(f, list)] - if not forecasts: + if not numeric_forecasts: return np.nan - cdfs_array = np.array(forecasts, dtype=float) - mean_cdf = np.mean(cdfs_array, axis=0) + cdfs_array = np.array(numeric_forecasts, dtype=float) + median_cdf = np.median(cdfs_array, axis=0) - return mean_cdf + return median_cdf elif q_type == "binary": - probs = [] + probs: list[float] = [] for f in forecasts: try: val = float(f) @@ -416,8 +416,6 @@ def get_median_forecast(row, bots): def calculate_weighted_scores(df_bot_team_forecasts, teams): """ - @Check: - Calculates weighted scores for each team based on their forecasts and question weights. Args: @@ -431,7 +429,7 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): for _, row in df_bot_team_forecasts.iterrows(): for team in teams: - # @Check: that the conversion is corret + # @Check: that the row conversion is corret cleaned_row = _prepare_new_row_for_scoring(row, [team]) if _is_unscorable(cleaned_row, [team]): continue diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index 9e33cc3..a79a02b 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -125,8 +125,7 @@ def _determine_baseline( # Version 3: # baseline_prob = ( # 1 / 202 - # ) # len(pmf) # ??? -> bins = 201 because of extra appended bin # @Check: This comment seems off since its the cdf that has 201 bins - # @Check: Should this be either 1, 0.9, or 0.95 based on whether open or closed bounds + # ) # len(pmf) # bins = 201 because of extra appended bin else: raise ValueError("Unknown question type") assert ( @@ -234,8 +233,7 @@ def _numeric_resolution_prob( [lower_bound_prob] + [cdf[i] - cdf[i - 1] for i in range(1, len(cdf))] + [upper_bound_prob] - ) # @Check: is this a correct conversion? - # pmf = np.diff(np.concatenate([[0], cdf])) + ) assert len(pmf) == 202, f"There should be 202 bins, but there are {len(pmf)}" resolution = float(resolution) diff --git a/tests/test_scoring.py b/tests/test_scoring.py index 3b31bf9..b9d91de 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -685,9 +685,8 @@ def test_peer_score_weighted( assert score_weighted == pytest.approx(score_unweighted * weight) -# TODO: Test the below +# TODO: Test the below for peer scores # Best score for MC and binary is 996 # Worst score for MC and binary is -996 # Best score for numeric is 408 # Worst score for numeric is -408 -# @Check: Can we even validate this (won't we need infinite other forecasters to get max score?) From 984b81d42618298438700de4984ffd81e2e0883a Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 08:14:38 -0600 Subject: [PATCH 18/26] Small touchup --- functions.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/functions.py b/functions.py index 99b0a94..62b8f3b 100644 --- a/functions.py +++ b/functions.py @@ -458,7 +458,8 @@ def calculate_weighted_scores(df_bot_team_forecasts, teams): ) team_scores[team] += weighted_score - except (ValueError, TypeError, IndexError): + except (ValueError, TypeError, IndexError) as e: + print(f" >>> Error calculating baseline score for question {row.get('bot_question_id')} — skipping: {e}") # @Check: Does skipping introduce any problems? continue # Be robust to bad/missing data From ac44cfe1646d547d5c92e0c8dc6d0b3bb397e792 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Wed, 7 May 2025 15:46:06 -0600 Subject: [PATCH 19/26] Updated resolution types for numeric tests --- tests/test_scoring.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_scoring.py b/tests/test_scoring.py index b9d91de..ca437bf 100644 --- a/tests/test_scoring.py +++ b/tests/test_scoring.py @@ -240,7 +240,7 @@ def test_numeric_baseline_when_perfect_forecast(): score = calculate_baseline_score( forecast=forecast, - resolution=correct_answer, + resolution=float(correct_answer), range_min=0, range_max=range_max, open_upper_bound=False, @@ -259,7 +259,7 @@ def test_numeric_baseline_if_completly_incorrect_forecast(): score = calculate_baseline_score( forecast=forecast, - resolution=correct_answer, + resolution=float(correct_answer), range_min=0, range_max=range_max, ) @@ -317,7 +317,7 @@ def test_multiple_choice_examples( open_lower_bound=False, open_upper_bound=False, ), - 50, + 50.0, None, -1, 96, @@ -327,7 +327,7 @@ def test_multiple_choice_examples( def test_baseline_score_better_when_closer( forecast_closer: list[float], forecast_further: list[float], - resolution: bool | str | None, + resolution: bool | str | float | None, options: list[str] | None, range_min: float | None, range_max: float | None, From b96d18355c023165aebe45a8971632a4112d6e4f Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 20 May 2025 20:04:02 -0600 Subject: [PATCH 20/26] Fixed option parsing problem, and provided median MC question better --- AI_BENCHMARKING_ANALYSIS.ipynb | 4503 ++++++++++------- functions.py | 33 +- .../bootstrapped_h2h_bot_vs_pros.csv | 46 +- ...ghted_bot_ONLY_peer_leaderboard_t_test.csv | 2 +- .../weighted_bot_peer_leaderboard_t_test.csv | 2 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 42 +- refactored_notebook/scoring.py | 9 +- 7 files changed, 2662 insertions(+), 1975 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 10d1981..d830bc0 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -27,20 +27,11 @@ }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 1, "metadata": { "id": "ISzIoto4hnoG" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# @title Import libraries\n", "%load_ext autoreload\n", @@ -52,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -63,16 +54,350 @@ }, { "cell_type": "code", - "execution_count": 279, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1932996/3462343738.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_3762618/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timequestion_weight_xtypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weight_y
031262For Q1 2025, how many banks will be listed on ...02025-01-20 03:27:00+002025-01-20 03:27:00+001.0multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse31268.01.0
131262For Q1 2025, how many banks will be listed on ...02025-01-20 03:27:00+002025-01-20 03:27:00+001.0multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse31268.01.0
231262For Q1 2025, how many banks will be listed on ...02025-01-20 03:27:00+002025-01-20 03:27:00+001.0multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse31268.01.0
331262For Q1 2025, how many banks will be listed on ...02025-01-20 03:27:00+002025-01-20 03:27:00+001.0multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse31268.01.0
431262For Q1 2025, how many banks will be listed on ...02025-01-20 03:27:00+002025-01-20 03:27:00+001.0multiple_choice[\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]NaNNaNFalseFalse31268.01.0
\n", + "
" + ], + "text/plain": [ + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31262 For Q1 2025, how many banks will be listed on ... \n", + "2 31262 For Q1 2025, how many banks will be listed on ... \n", + "3 31262 For Q1 2025, how many banks will be listed on ... \n", + "4 31262 For Q1 2025, how many banks will be listed on ... \n", + "\n", + " resolution scheduled_close_time actual_close_time \\\n", + "0 0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 \n", + "1 0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 \n", + "2 0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 \n", + "3 0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 \n", + "4 0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 \n", + "\n", + " question_weight_x type options range_min \\\n", + "0 1.0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN \n", + "1 1.0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN \n", + "2 1.0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN \n", + "3 1.0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN \n", + "4 1.0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN \n", + "\n", + " range_max open_upper_bound open_lower_bound pro_question_id \\\n", + "0 NaN False False 31268.0 \n", + "1 NaN False False 31268.0 \n", + "2 NaN False False 31268.0 \n", + "3 NaN False False 31268.0 \n", + "4 NaN False False 31268.0 \n", + "\n", + " question_weight_y \n", + "0 1.0 \n", + "1 1.0 \n", + "2 1.0 \n", + "3 1.0 \n", + "4 1.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timequestion_weight_xtypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weight_y
23691635705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
23691735705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
23691835705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
23691935705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
23692035705Which podcast will be ranked higher on Spotify...Candace2025-03-20 20:00:00+002025-03-20 20:00:00+001.0multiple_choice[\"Call Her Daddy\",\"Candace\"]NaNNaNFalseFalseNaNNaN
\n", + "
" + ], + "text/plain": [ + " bot_question_id title \\\n", + "236916 35705 Which podcast will be ranked higher on Spotify... \n", + "236917 35705 Which podcast will be ranked higher on Spotify... \n", + "236918 35705 Which podcast will be ranked higher on Spotify... \n", + "236919 35705 Which podcast will be ranked higher on Spotify... \n", + "236920 35705 Which podcast will be ranked higher on Spotify... \n", + "\n", + " resolution scheduled_close_time actual_close_time \\\n", + "236916 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "236917 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "236918 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "236919 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "236920 Candace 2025-03-20 20:00:00+00 2025-03-20 20:00:00+00 \n", + "\n", + " question_weight_x type options \\\n", + "236916 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "236917 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "236918 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "236919 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "236920 1.0 multiple_choice [\"Call Her Daddy\",\"Candace\"] \n", + "\n", + " range_min range_max open_upper_bound open_lower_bound \\\n", + "236916 NaN NaN False False \n", + "236917 NaN NaN False False \n", + "236918 NaN NaN False False \n", + "236919 NaN NaN False False \n", + "236920 NaN NaN False False \n", + "\n", + " pro_question_id question_weight_y \n", + "236916 NaN NaN \n", + "236917 NaN NaN \n", + "236918 NaN NaN \n", + "236919 NaN NaN \n", + "236920 NaN NaN " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -94,7 +419,7 @@ "This is done by matching the title and scheduled_close_time.\n", "\n", "We remove early closers from the analysis. I do this by comparing actual close time to scheduled\n", - "close time in a later cell!\n", + "close time in a later cell! @Check: Do we want to do this now that tournament is closed? Are we still doing this?\n", "\n", "df_pro_bot_resolved_questions: Has pro_question_id, bot_question_id, title, resolution, scheduled_close_time, question_weight\n", "\"\"\"\n", @@ -124,6 +449,7 @@ " on=['title', 'scheduled_close_time'],\n", " how='left'\n", ")\n", + "display_head_and_tail(df_pro_bot_resolved_questions)\n", "\n", "df_pro_bot_resolved_questions['question_weight'] = df_pro_bot_resolved_questions['question_weight_x'].combine_first(df_pro_bot_resolved_questions['question_weight_y'])\n", "df_pro_bot_resolved_questions.drop(['question_weight_x', 'question_weight_y'], axis=1, inplace=True)\n", @@ -134,6 +460,7 @@ "# Cast both question ids to int64\n", "df_pro_bot_resolved_questions['pro_question_id'] = df_pro_bot_resolved_questions['pro_question_id'].astype('Int64')\n", "df_pro_bot_resolved_questions['bot_question_id'] = df_pro_bot_resolved_questions['bot_question_id'].astype('Int64')\n", + "df_pro_bot_resolved_questions['options'] = df_pro_bot_resolved_questions['options'].apply(parse_options_array)\n", "\n", "# Remove df_bot_resolved_questions and df_pro_resolved_questions to make sure you only ever use df_pro_bot_resolved_questions\n", "del df_bot_resolved_questions\n", @@ -142,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -160,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -186,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 282, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -207,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 283, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -225,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 284, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -238,7 +565,7 @@ " dtype='object')" ] }, - "execution_count": 284, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 285, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -284,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 286, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -306,7 +633,7 @@ "dtype: object" ] }, - "execution_count": 286, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 287, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 288, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -349,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 289, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 290, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 291, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -445,7 +772,7 @@ " 0\n", " 31268\n", " Jgalt\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 101465\n", " 1\n", @@ -466,7 +793,7 @@ " 1\n", " 31268\n", " MaciekK\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 117580\n", " 1\n", @@ -487,7 +814,7 @@ " 2\n", " 31268\n", " OpenSystem\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 120160\n", " 1\n", @@ -508,7 +835,7 @@ " 5\n", " 31268\n", " darkives\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 103907\n", " 1\n", @@ -529,7 +856,7 @@ " 6\n", " 31268\n", " datscilly\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 103777\n", " 1\n", @@ -551,19 +878,12 @@ "" ], "text/plain": [ - " question_id forecaster \\\n", - "0 31268 Jgalt \n", - "1 31268 MaciekK \n", - "2 31268 OpenSystem \n", - "5 31268 darkives \n", - "6 31268 datscilly \n", - "\n", - " question_title \\\n", - "0 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "1 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "2 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "5 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "6 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + " question_id forecaster question_title \\\n", + "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", + "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", + "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", + "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", + "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", @@ -601,7 +921,7 @@ "6 False " ] }, - "execution_count": 291, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -612,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -635,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -648,14 +968,14 @@ " 'metac-grok-2-1212', 'metac-gpt-4o', 'bot_median', 'pgodzinai',\n", " 'metac-exa', 'jkraybill_bot', 'VeritasAI', 'MWG', 'twsummerbot',\n", " 'CatrachoCaster', 'X_bot', 'manticAI', 'annabot', 'minefrac1',\n", - " 'metac-deepseek-r1', 'Bot_Pepa', 'laylaps', 'ajf-bot',\n", + " 'metac-deepseek-r1+asknews', 'Bot_Pepa', 'laylaps', 'ajf-bot',\n", " 'SynapseSeer', 'RPM_bot', 'cookics_bot_TEST', 'ProfessorSP',\n", " 'wunderplumb', 'CumulativeBot', 'pianobot', 'krm-bot',\n", " 'KevinTestBot', '4Shadower', 'swingswish', 'jonahsingerbot',\n", " 'bean_bot', 'andrewsiah', 'cobyj-bot'], dtype=object)" ] }, - "execution_count": 293, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -667,7 +987,7 @@ }, { "cell_type": "code", - "execution_count": 294, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -712,11 +1032,11 @@ " \n", " 15\n", " bot_median\n", - " 8.520428\n", - " 3220.892206\n", + " 8.388094\n", + " 3170.867318\n", " 409\n", - " 5.620668\n", - " 1.475108\n", + " 5.494976\n", + " 1.471729\n", " \n", " \n", " 4\n", @@ -752,14 +1072,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.520428 3220.892206 409 5.620668 \n", + "15 bot_median 8.388094 3170.867318 409 5.494976 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.475108 \n", + "15 1.471729 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -876,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 19, "metadata": { "id": "BmAFBHIhK77X" }, @@ -925,7 +1245,7 @@ }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1349,7 +1669,7 @@ " np.int64(35705)}" ] }, - "execution_count": 296, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1370,7 +1690,7 @@ }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 21, "metadata": { "cellView": "form", "id": "XceLWcgCPNw-" @@ -1409,18 +1729,18 @@ " \n", " \n", " 1\n", - " metac-o1\n", - " 8861.959039\n", + " bot_median\n", + " 8997.290873\n", " \n", " \n", " 2\n", - " metac-o1-preview\n", - " 8849.559824\n", + " metac-o1\n", + " 8861.959039\n", " \n", " \n", " 3\n", - " bot_median\n", - " 8766.210698\n", + " metac-o1-preview\n", + " 8849.559824\n", " \n", " \n", " 4\n", @@ -1439,9 +1759,9 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 metac-o1 8861.959039\n", - "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8766.210698\n", + "1 bot_median 8997.290873\n", + "2 metac-o1 8861.959039\n", + "3 metac-o1-preview 8849.559824\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1547,7 +1867,7 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1566,7 +1886,7 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 23, "metadata": { "cellView": "form", "id": "iRDMoH7hTBEq" @@ -1611,7 +1931,7 @@ " \n", " 2\n", " bot_median\n", - " 3504.379897\n", + " 3538.184052\n", " \n", " \n", " 3\n", @@ -1680,7 +2000,7 @@ " \n", " \n", " 16\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 614.572462\n", " \n", " \n", @@ -1846,7 +2166,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3504.379897\n", + "2 bot_median 3538.184052\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -1860,7 +2180,7 @@ "13 CumulativeBot 1030.716475\n", "14 pgodzinai 926.081448\n", "15 jkraybill_bot 627.932509\n", - "16 metac-deepseek-r1 614.572462\n", + "16 metac-deepseek-r1+asknews 614.572462\n", "17 question_weight 378.020000\n", "18 metac-exa 265.384263\n", "19 MWG 215.551323\n", @@ -1894,7 +2214,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 299, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1936,7 +2256,7 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1955,7 +2275,7 @@ }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1964,7 +2284,7 @@ }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1985,7 +2305,7 @@ }, { "cell_type": "code", - "execution_count": 303, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2034,7 +2354,7 @@ " 0\n", " 31268\n", " Jgalt\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 101465\n", " 1\n", @@ -2055,7 +2375,7 @@ " 1\n", " 31268\n", " MaciekK\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 117580\n", " 1\n", @@ -2076,7 +2396,7 @@ " 2\n", " 31268\n", " OpenSystem\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 120160\n", " 1\n", @@ -2097,7 +2417,7 @@ " 5\n", " 31268\n", " darkives\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 103907\n", " 1\n", @@ -2118,7 +2438,7 @@ " 6\n", " 31268\n", " datscilly\n", - " For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List?\n", + " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", " 103777\n", " 1\n", @@ -2140,19 +2460,12 @@ "" ], "text/plain": [ - " question_id forecaster \\\n", - "0 31268 Jgalt \n", - "1 31268 MaciekK \n", - "2 31268 OpenSystem \n", - "5 31268 darkives \n", - "6 31268 datscilly \n", - "\n", - " question_title \\\n", - "0 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "1 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "2 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "5 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", - "6 For Q1 2025, how many banks will be listed on the FDIC's Failed Bank List? \n", + " question_id forecaster question_title \\\n", + "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", + "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", + "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", + "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", + "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", @@ -2190,7 +2503,7 @@ "6 False " ] }, - "execution_count": 303, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2201,7 +2514,7 @@ }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 28, "metadata": { "cellView": "form", "id": "Yfq0_lDKAMl7" @@ -2259,18 +2572,18 @@ " 0\n", " 1.0\n", " multiple_choice\n", - " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", + " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", " False\n", " False\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", - " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", + " [0.5,0.3,0.15,0.04,0.01]\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", - " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", - " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", + " [0.009900990099009901,0.39603960396039606,0.44...\n", + " [0.014925742574257425,0.5137871287128712,0.334...\n", " NaN\n", " NaN\n", " NaN\n", @@ -2289,12 +2602,12 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.1028571429,0.1057142857,0.1085714286,0.1114285714,0.1142857143,0.1171428571,0.12,0.1228571429,0.1257142857,0.1285714286,0.1314285714,0.1342857143,0.1371428571,0.14,0.1428571429,0.1457142857,0.1485714286,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.1685714286,0.1714285714,0.1742857143,0.1771428571,0.18,0.1828571429,0.1857142857,0.1885714286,0.1914285714,0.1942857143,0.1971428571,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95]\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", - " [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911]\n", - " [0.001,0.001060875,0.0011396,0.0012863125,0.0015459984,0.0019048369,0.0023147701,0.0027425688,0.0031719899,0.0035935463,0.0040047171,0.0044081612,0.0048073678,0.0052048637,0.0056023079,0.0060005117,0.0063995798,0.0067992898,0.0071993689,0.0075995902,0.007999808,0.0083999595,0.0088000381,0.0092000616,0.0096525538,0.0103347221,0.0114180238,0.0128617561,0.0144931539,0.0161909912,0.0178965175,0.0195748423,0.0212159342,0.0228289888,0.0244265464,0.0260177161,0.0276085304,0.0292020038,0.0307985773,0.0323974755,0.0339977246,0.0355985069,0.0371992898,0.0387998404,0.0404001295,0.0420002192,0.0436001942,0.0452001261,0.0468000593,0.0484758458,0.0504834257,0.0530704368,0.056178071,0.0595567722,0.0630314345,0.0665171977,0.0699636664,0.0733563529,0.0767085411,0.0800383523,0.0833589543,0.0866790344,0.0900028852,0.0933311337,0.0967326953,0.1004442449,0.1047006189,0.1094577119,0.1144907128,0.1196353715,0.1248049846,0.1299418958,0.1350232879,0.1400570021,0.1452540043,0.1513017567,0.1589133116,0.1680377058,0.1780770546,0.1885468618,0.1991553484,0.2096896812,0.2200450325,0.2302229342,0.2402681458,0.2502302229,0.2601553402,0.27007834,0.2800179047,0.2899799302,0.2999629146,0.3099614863,0.3199691186,0.3299801956,0.3403173669,0.3521487483,0.3668129253,0.3844513624,0.4041888551,0.4247935739,0.4442765262,0.4605082419,0.4728869633,0.4822309604,0.4895341295,0.4956449952,0.5013686886,0.5073076754,0.5137610388,0.5206987551,0.5276657564,0.5340334461,0.5395220756,0.5442306919,0.5484901071,0.5530599502,0.5588761244,0.5663266439,0.5752119583,0.585204242,0.5959735276,0.6071500854,0.6184053116,0.6295209059,0.6403758638,0.650921239,0.6611693012,0.671174569,0.681009388,0.6907471485,0.7004527783,0.7101763721,0.7199504677,0.7297911321,0.7397010124,0.7496729757,0.7596938994,0.7697481465,0.7798202777,0.7898968803,0.7999675731,0.8100253018,0.8200662214,0.8300893951,0.8400025166,0.8494453768,0.8579165269,0.8651653723,0.8712540566,0.8763468591,0.8806505608,0.8844338485,0.8879756773,0.8915092577,0.8952099002,0.8991948145,0.9035195392,0.9081838533,0.9131467515,0.9183416751,0.9236898731,0.9291127196,0.9345414554,0.9399230919,0.9451659123,0.9500324455,0.9542146638,0.9575690762,0.9601504006,0.9620795658,0.9635039422,0.9646063832,0.965571997,0.9665531773,0.9676621061,0.9689711529,0.9705116418,0.9722785871,0.9742409577,0.9763519694,0.9785580215,0.9808067315,0.9830531373,0.9852633275,0.987415817,0.9895011861,0.9915203598,0.9934820158,0.9953894047,0.9970771779,0.998127745,0.99846,0.99852,0.99858,0.99864,0.9987,0.99876,0.99882,0.99888,0.99894,0.99899]\n", + " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", + " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", " NaN\n", " NaN\n", " NaN\n", @@ -2313,9 +2626,9 @@ " False\n", " False\n", " ...\n", - " 0.15\n", - " 0.05\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2331,15 +2644,15 @@ " 5-9\n", " 1.0\n", " multiple_choice\n", - " [\"0-4\",\"5-9\",\">9\"]\n", + " [0-4, 5-9, >9]\n", " NaN\n", " NaN\n", " None\n", " None\n", " ...\n", " [0.25,0.6,0.15]\n", - " [0.15,0.65,0.2]\n", - " [0.15,0.45,0.4]\n", + " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2361,15 +2674,15 @@ " False\n", " False\n", " ...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0.0114285714,0.0142857143,0.0171428571,0.02,0.0228571429,0.0257142857,0.0285714286,0.0314285714,0.0342857143,0.0371428571,0.04,0.0428571429,0.0457142857,0.0485714286,0.0514285714,0.0542857143,0.0571428571,0.06,0.0628571429,0.0657142857,0.0685714286,0.0714285714,0.0742857143,0.0771428571,0.08,0.0828571429,0.0857142857,0.0885714286,0.0914285714,0.0942857143,0.0971428571,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.9015384615,0.9030769231,0.9046153846,0.9061538462,0.9076923077,0.9092307692,0.9107692308,0.9123076923,0.9138461538,0.9153846154,0.9169230769,0.9184615385,0.92,0.9215384615,0.9230769231,0.9246153846,0.9261538462,0.9276923077,0.9292307692,0.9307692308,0.9323076923,0.9338461538,0.9353846154,0.9369230769,0.9384615385,0.94,0.9415384615,0.9430769231,0.9446153846,0.9461538462,0.9476923077,0.9492307692,0.9507692308,0.9523076923,0.9538461538,0.9553846154,0.9569230769,0.9584615385,0.96,0.9615384615,0.9630769231,0.9646153846,0.9661538462,0.9676923077,0.9692307692,0.9707692308,0.9723076923,0.9738461538,0.9753846154,0.9769230769,0.9784615385,0.98,0.9815384615,0.9830769231,0.9846153846,0.9861538462,0.9876923077,0.9892307692,0.9907692308,0.9923076923,0.9938461538,0.9953846154,0.9969230769,0.9984615385,1.0]\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0]\n", - " [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.034,0.036,0.038,0.04,0.042,0.044,0.046,0.048,0.05,0.052,0.054,0.056,0.058,0.06,0.062,0.064,0.066,0.068,0.07,0.072,0.074,0.076,0.078,0.08,0.082,0.084,0.086,0.088,0.09,0.092,0.094,0.096,0.098,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", - " [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0]\n", - " [0.0,0.0001141583,0.0002446967,0.0003862688,0.0005272579,0.0006650709,0.0008243437,0.0011074433,0.0016696544,0.0025699094,0.0037138357,0.0049708626,0.0062610152,0.0075426566,0.0089765864,0.0111726822,0.0147311078,0.0195212559,0.0249547717,0.0306181288,0.0363105138,0.0419407763,0.0476011969,0.053516341,0.0598014349,0.0663689162,0.0730761187,0.0798334547,0.0865904866,0.0933196582,0.1000172031,0.1066924089,0.1133554776,0.1200140176,0.1266729489,0.1333343989,0.1399984689,0.1466644317,0.1533314439,0.1599988203,0.1666661444,0.1733332523,0.1800001372,0.1866668598,0.1933334943,0.2000000995,0.2066667101,0.2133333393,0.2199999878,0.22666665,0.2333333196,0.2399999916,0.2466666631,0.2533333329,0.2600000011,0.2666666681,0.2733333345,0.2800000007,0.286666667,0.2933333334,0.2999999999,0.3066666665,0.3133333332,0.3199999999,0.3266666666,0.3333333333,0.34,0.3466666667,0.3533333333,0.36,0.3666666667,0.3733333333,0.38,0.3866666667,0.3934628939,0.400837331,0.40925763,0.4186848364,0.428718413,0.4390353607,0.4494419812,0.4597974687,0.4700329298,0.4801500685,0.4901790777,0.500153105,0.5101028922,0.5200515519,0.5300114112,0.5398722838,0.5492279015,0.5576212737,0.5650210292,0.571743695,0.5780856137,0.5842571713,0.5904328096,0.5967209586,0.603152213,0.6097133168,0.6163738459,0.6230958146,0.6298433017,0.6365902337,0.6433215069,0.6500308134,0.656718392,0.6633885674,0.6700472479,0.6767001542,0.6833518918,0.6900055659,0.6966627826,0.7033239321,0.7099885835,0.7166558627,0.723324761,0.7299943545,0.7366639271,0.7433330133,0.7500013847,0.7566690034,0.7633359628,0.770002427,0.7766685825,0.7833346018,0.7900006228,0.7966667394,0.8033330023,0.8099994258,0.8166659972,0.8233326871,0.8299994586,0.8366662749,0.8433331037,0.8499999207,0.8566667097,0.8633334627,0.8700001785,0.8766668606,0.8833335157,0.8899751517,0.8964699017,0.9025861327,0.9081211655,0.9130226546,0.9173491712,0.921198292,0.9246959323,0.9279877368,0.9312103051,0.934472912,0.9378540969,0.9414005467,0.9450901244,0.9487670554,0.9522009139,0.9552513327,0.9578998205,0.9601715711,0.96211589,0.9638162438,0.9653702301,0.9668664828,0.9683781475,0.9699605983,0.9716476808,0.9734519305,0.9753688047,0.9773815283,0.9794657325,0.9815941718,0.9837408125,0.9858836701,0.9879773814,0.9898993305,0.9914888717,0.9926681205,0.9934599632,0.9939261174,0.9941560479,0.9942611072,0.9943265488,0.9943865488,0.9944537386,0.9945561009,0.9947328687,0.9950042368,0.9953660612,0.9958058993,0.9963078442,0.9968511117,0.9974139813,0.9979781729,0.9985251814,0.999027536,0.9994498435,0.999736686,0.9998734993,0.99994,1.0]\n", + " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", + " [0.0,0.0001141583,0.0002446967,0.0003862688,0....\n", " NaN\n", " NaN\n", - " [0.0,0.001311947,0.0026238939,0.0039358409,0.0052477878,0.0065597348,0.0078716817,0.0091836287,0.0104955756,0.0118075226,0.0131194695,0.0144314165,0.0157433634,0.0170553104,0.0183672573,0.0196792043,0.0209911512,0.0223030982,0.0236150451,0.0249269921,0.026238939,0.027550886,0.0288628329,0.0301747799,0.0314867268,0.0327986738,0.0341106207,0.0354225677,0.0367345146,0.0380464616,0.0393584085,0.0406703555,0.0419823024,0.0432942494,0.0446061963,0.0459181433,0.0472300902,0.0485420372,0.0498539841,0.0511659311,0.052477878,0.053789825,0.0551017719,0.0564137189,0.0577256658,0.0590376128,0.0603495597,0.0616615067,0.0629734536,0.0642854006,0.0655973475,0.0669092945,0.0682212414,0.0695331884,0.0708451353,0.0721570823,0.0734690292,0.0747809762,0.0760929231,0.0774048701,0.078716817,0.080028764,0.0813407109,0.0826526579,0.0839646048,0.0852765518,0.0865884987,0.0879004457,0.0902457862,0.0933094828,0.0978079399,0.1023063969,0.1068048539,0.111303311,0.115801768,0.120300225,0.124798682,0.1292971391,0.1338199508,0.1388055027,0.1440933779,0.1496807808,0.1571177226,0.1652387403,0.1753118263,0.1904276903,0.2058197291,0.2212117678,0.237030829,0.2551785571,0.273870758,0.2925629589,0.3115548313,0.3307464845,0.3499926649,0.3692260274,0.3884136416,0.407661417,0.4269091924,0.4457073638,0.464050886,0.4823944081,0.5007379302,0.5190814523,0.5374249745,0.5538739661,0.5696118391,0.5853388804,0.6010659216,0.6161284786,0.6273538036,0.6382421632,0.6486483242,0.6588094975,0.668725683,0.6786418685,0.688558054,0.6984742395,0.708390425,0.7183066106,0.7278808508,0.7373411092,0.7468013677,0.7561442929,0.7645842622,0.7730242316,0.7814642009,0.7899041702,0.7983441395,0.8067841088,0.8152111577,0.8229940495,0.8307769414,0.8385598332,0.8447944123,0.8509124517,0.8563824526,0.8610823306,0.8657454654,0.8704086002,0.8750717351,0.8797348699,0.8843980047,0.8890611396,0.8934873987,0.8970573375,0.9006272763,0.9041972151,0.9077671539,0.9103291006,0.9126390493,0.914948998,0.9172589467,0.9195688953,0.921878844,0.9236671785,0.9253634634,0.9270597483,0.9287560333,0.9304523182,0.9321486031,0.933844888,0.935541173,0.9372374579,0.9389337428,0.9406300277,0.9423263126,0.9440225976,0.9457188825,0.9474151674,0.9491114523,0.9508077373,0.9525040222,0.9542003071,0.955896592,0.9575928769,0.9592891619,0.9609854468,0.9626817317,0.9643780166,0.9660743016,0.9677705865,0.9694668714,0.9711631563,0.9728594412,0.9745557262,0.9762520111,0.977948296,0.9796445809,0.9813408659,0.9830371508,0.9847334357,0.9864297206,0.9881260055,0.9898222905,0.9915185754,0.9932148603,0.9949111452,0.9966074302,0.9983037151,1.0]\n", + " [0.0,0.001311947,0.0026238939,0.0039358409,0.0...\n", " NaN\n", " \n", " \n", @@ -2385,12 +2698,12 @@ "3 31280 31274 5-9 1.0 \n", "4 31281 31275 119.2 1.0 \n", "\n", - " type options range_min range_max \\\n", - "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "1 numeric None 60.0 100.0 \n", - "2 binary None NaN NaN \n", - "3 multiple_choice [\"0-4\",\"5-9\",\">9\"] NaN NaN \n", - "4 numeric None 0.0 400.0 \n", + " type options range_min range_max \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] NaN NaN \n", + "1 numeric None 60.0 100.0 \n", + "2 binary None NaN NaN \n", + "3 multiple_choice [0-4, 5-9, >9] NaN NaN \n", + "4 numeric None 0.0 400.0 \n", "\n", " open_upper_bound open_lower_bound ... \\\n", "0 False False ... \n", @@ -2399,68 +2712,47 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", - "2 0.15 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.0114285714,0.0142857143,0.0171428571,0.02,0.0228571429,0.0257142857,0.0285714286,0.0314285714,0.0342857143,0.0371428571,0.04,0.0428571429,0.0457142857,0.0485714286,0.0514285714,0.0542857143,0.0571428571,0.06,0.0628571429,0.0657142857,0.0685714286,0.0714285714,0.0742857143,0.0771428571,0.08,0.0828571429,0.0857142857,0.0885714286,0.0914285714,0.0942857143,0.0971428571,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.9015384615,0.9030769231,0.9046153846,0.9061538462,0.9076923077,0.9092307692,0.9107692308,0.9123076923,0.9138461538,0.9153846154,0.9169230769,0.9184615385,0.92,0.9215384615,0.9230769231,0.9246153846,0.9261538462,0.9276923077,0.9292307692,0.9307692308,0.9323076923,0.9338461538,0.9353846154,0.9369230769,0.9384615385,0.94,0.9415384615,0.9430769231,0.9446153846,0.9461538462,0.9476923077,0.9492307692,0.9507692308,0.9523076923,0.9538461538,0.9553846154,0.9569230769,0.9584615385,0.96,0.9615384615,0.9630769231,0.9646153846,0.9661538462,0.9676923077,0.9692307692,0.9707692308,0.9723076923,0.9738461538,0.9753846154,0.9769230769,0.9784615385,0.98,0.9815384615,0.9830769231,0.9846153846,0.9861538462,0.9876923077,0.9892307692,0.9907692308,0.9923076923,0.9938461538,0.9953846154,0.9969230769,0.9984615385,1.0] \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", - "2 0.05 \n", - "3 [0.15,0.65,0.2] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0] \n", - "\n", - " metac-perplexity \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.1028571429,0.1057142857,0.1085714286,0.1114285714,0.1142857143,0.1171428571,0.12,0.1228571429,0.1257142857,0.1285714286,0.1314285714,0.1342857143,0.1371428571,0.14,0.1428571429,0.1457142857,0.1485714286,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.1685714286,0.1714285714,0.1742857143,0.1771428571,0.18,0.1828571429,0.1857142857,0.1885714286,0.1914285714,0.1942857143,0.1971428571,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95] \n", - "2 0.15 \n", - "3 [0.15,0.45,0.4] \n", - "4 [0.0,0.002,0.004,0.006,0.008,0.01,0.012,0.014,0.016,0.018,0.02,0.022,0.024,0.026,0.028,0.03,0.032,0.034,0.036,0.038,0.04,0.042,0.044,0.046,0.048,0.05,0.052,0.054,0.056,0.058,0.06,0.062,0.064,0.066,0.068,0.07,0.072,0.074,0.076,0.078,0.08,0.082,0.084,0.086,0.088,0.09,0.092,0.094,0.096,0.098,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", - "\n", - " minefrac1 \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - " mmBot \\\n", - "0 [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297] \n", - "1 [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911] \n", - "2 0.2 \n", - "3 [0.25,0.5,0.25] \n", - "4 [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0] \n", - "\n", - " pgodzinai \\\n", - "0 [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965] \n", - "1 [0.001,0.001060875,0.0011396,0.0012863125,0.0015459984,0.0019048369,0.0023147701,0.0027425688,0.0031719899,0.0035935463,0.0040047171,0.0044081612,0.0048073678,0.0052048637,0.0056023079,0.0060005117,0.0063995798,0.0067992898,0.0071993689,0.0075995902,0.007999808,0.0083999595,0.0088000381,0.0092000616,0.0096525538,0.0103347221,0.0114180238,0.0128617561,0.0144931539,0.0161909912,0.0178965175,0.0195748423,0.0212159342,0.0228289888,0.0244265464,0.0260177161,0.0276085304,0.0292020038,0.0307985773,0.0323974755,0.0339977246,0.0355985069,0.0371992898,0.0387998404,0.0404001295,0.0420002192,0.0436001942,0.0452001261,0.0468000593,0.0484758458,0.0504834257,0.0530704368,0.056178071,0.0595567722,0.0630314345,0.0665171977,0.0699636664,0.0733563529,0.0767085411,0.0800383523,0.0833589543,0.0866790344,0.0900028852,0.0933311337,0.0967326953,0.1004442449,0.1047006189,0.1094577119,0.1144907128,0.1196353715,0.1248049846,0.1299418958,0.1350232879,0.1400570021,0.1452540043,0.1513017567,0.1589133116,0.1680377058,0.1780770546,0.1885468618,0.1991553484,0.2096896812,0.2200450325,0.2302229342,0.2402681458,0.2502302229,0.2601553402,0.27007834,0.2800179047,0.2899799302,0.2999629146,0.3099614863,0.3199691186,0.3299801956,0.3403173669,0.3521487483,0.3668129253,0.3844513624,0.4041888551,0.4247935739,0.4442765262,0.4605082419,0.4728869633,0.4822309604,0.4895341295,0.4956449952,0.5013686886,0.5073076754,0.5137610388,0.5206987551,0.5276657564,0.5340334461,0.5395220756,0.5442306919,0.5484901071,0.5530599502,0.5588761244,0.5663266439,0.5752119583,0.585204242,0.5959735276,0.6071500854,0.6184053116,0.6295209059,0.6403758638,0.650921239,0.6611693012,0.671174569,0.681009388,0.6907471485,0.7004527783,0.7101763721,0.7199504677,0.7297911321,0.7397010124,0.7496729757,0.7596938994,0.7697481465,0.7798202777,0.7898968803,0.7999675731,0.8100253018,0.8200662214,0.8300893951,0.8400025166,0.8494453768,0.8579165269,0.8651653723,0.8712540566,0.8763468591,0.8806505608,0.8844338485,0.8879756773,0.8915092577,0.8952099002,0.8991948145,0.9035195392,0.9081838533,0.9131467515,0.9183416751,0.9236898731,0.9291127196,0.9345414554,0.9399230919,0.9451659123,0.9500324455,0.9542146638,0.9575690762,0.9601504006,0.9620795658,0.9635039422,0.9646063832,0.965571997,0.9665531773,0.9676621061,0.9689711529,0.9705116418,0.9722785871,0.9742409577,0.9763519694,0.9785580215,0.9808067315,0.9830531373,0.9852633275,0.987415817,0.9895011861,0.9915203598,0.9934820158,0.9953894047,0.9970771779,0.998127745,0.99846,0.99852,0.99858,0.99864,0.9987,0.99876,0.99882,0.99888,0.99894,0.99899] \n", - "2 0.07 \n", - "3 [0.27499999999999997,0.5125,0.21249999999999997] \n", - "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.0005272579,0.0006650709,0.0008243437,0.0011074433,0.0016696544,0.0025699094,0.0037138357,0.0049708626,0.0062610152,0.0075426566,0.0089765864,0.0111726822,0.0147311078,0.0195212559,0.0249547717,0.0306181288,0.0363105138,0.0419407763,0.0476011969,0.053516341,0.0598014349,0.0663689162,0.0730761187,0.0798334547,0.0865904866,0.0933196582,0.1000172031,0.1066924089,0.1133554776,0.1200140176,0.1266729489,0.1333343989,0.1399984689,0.1466644317,0.1533314439,0.1599988203,0.1666661444,0.1733332523,0.1800001372,0.1866668598,0.1933334943,0.2000000995,0.2066667101,0.2133333393,0.2199999878,0.22666665,0.2333333196,0.2399999916,0.2466666631,0.2533333329,0.2600000011,0.2666666681,0.2733333345,0.2800000007,0.286666667,0.2933333334,0.2999999999,0.3066666665,0.3133333332,0.3199999999,0.3266666666,0.3333333333,0.34,0.3466666667,0.3533333333,0.36,0.3666666667,0.3733333333,0.38,0.3866666667,0.3934628939,0.400837331,0.40925763,0.4186848364,0.428718413,0.4390353607,0.4494419812,0.4597974687,0.4700329298,0.4801500685,0.4901790777,0.500153105,0.5101028922,0.5200515519,0.5300114112,0.5398722838,0.5492279015,0.5576212737,0.5650210292,0.571743695,0.5780856137,0.5842571713,0.5904328096,0.5967209586,0.603152213,0.6097133168,0.6163738459,0.6230958146,0.6298433017,0.6365902337,0.6433215069,0.6500308134,0.656718392,0.6633885674,0.6700472479,0.6767001542,0.6833518918,0.6900055659,0.6966627826,0.7033239321,0.7099885835,0.7166558627,0.723324761,0.7299943545,0.7366639271,0.7433330133,0.7500013847,0.7566690034,0.7633359628,0.770002427,0.7766685825,0.7833346018,0.7900006228,0.7966667394,0.8033330023,0.8099994258,0.8166659972,0.8233326871,0.8299994586,0.8366662749,0.8433331037,0.8499999207,0.8566667097,0.8633334627,0.8700001785,0.8766668606,0.8833335157,0.8899751517,0.8964699017,0.9025861327,0.9081211655,0.9130226546,0.9173491712,0.921198292,0.9246959323,0.9279877368,0.9312103051,0.934472912,0.9378540969,0.9414005467,0.9450901244,0.9487670554,0.9522009139,0.9552513327,0.9578998205,0.9601715711,0.96211589,0.9638162438,0.9653702301,0.9668664828,0.9683781475,0.9699605983,0.9716476808,0.9734519305,0.9753688047,0.9773815283,0.9794657325,0.9815941718,0.9837408125,0.9858836701,0.9879773814,0.9898993305,0.9914888717,0.9926681205,0.9934599632,0.9939261174,0.9941560479,0.9942611072,0.9943265488,0.9943865488,0.9944537386,0.9945561009,0.9947328687,0.9950042368,0.9953660612,0.9958058993,0.9963078442,0.9968511117,0.9974139813,0.9979781729,0.9985251814,0.999027536,0.9994498435,0.999736686,0.9998734993,0.99994,1.0] \n", - "\n", - " pianobot swingswish \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " twsummerbot \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 [0.116,0.42,0.464] \n", - "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0052477878,0.0065597348,0.0078716817,0.0091836287,0.0104955756,0.0118075226,0.0131194695,0.0144314165,0.0157433634,0.0170553104,0.0183672573,0.0196792043,0.0209911512,0.0223030982,0.0236150451,0.0249269921,0.026238939,0.027550886,0.0288628329,0.0301747799,0.0314867268,0.0327986738,0.0341106207,0.0354225677,0.0367345146,0.0380464616,0.0393584085,0.0406703555,0.0419823024,0.0432942494,0.0446061963,0.0459181433,0.0472300902,0.0485420372,0.0498539841,0.0511659311,0.052477878,0.053789825,0.0551017719,0.0564137189,0.0577256658,0.0590376128,0.0603495597,0.0616615067,0.0629734536,0.0642854006,0.0655973475,0.0669092945,0.0682212414,0.0695331884,0.0708451353,0.0721570823,0.0734690292,0.0747809762,0.0760929231,0.0774048701,0.078716817,0.080028764,0.0813407109,0.0826526579,0.0839646048,0.0852765518,0.0865884987,0.0879004457,0.0902457862,0.0933094828,0.0978079399,0.1023063969,0.1068048539,0.111303311,0.115801768,0.120300225,0.124798682,0.1292971391,0.1338199508,0.1388055027,0.1440933779,0.1496807808,0.1571177226,0.1652387403,0.1753118263,0.1904276903,0.2058197291,0.2212117678,0.237030829,0.2551785571,0.273870758,0.2925629589,0.3115548313,0.3307464845,0.3499926649,0.3692260274,0.3884136416,0.407661417,0.4269091924,0.4457073638,0.464050886,0.4823944081,0.5007379302,0.5190814523,0.5374249745,0.5538739661,0.5696118391,0.5853388804,0.6010659216,0.6161284786,0.6273538036,0.6382421632,0.6486483242,0.6588094975,0.668725683,0.6786418685,0.688558054,0.6984742395,0.708390425,0.7183066106,0.7278808508,0.7373411092,0.7468013677,0.7561442929,0.7645842622,0.7730242316,0.7814642009,0.7899041702,0.7983441395,0.8067841088,0.8152111577,0.8229940495,0.8307769414,0.8385598332,0.8447944123,0.8509124517,0.8563824526,0.8610823306,0.8657454654,0.8704086002,0.8750717351,0.8797348699,0.8843980047,0.8890611396,0.8934873987,0.8970573375,0.9006272763,0.9041972151,0.9077671539,0.9103291006,0.9126390493,0.914948998,0.9172589467,0.9195688953,0.921878844,0.9236671785,0.9253634634,0.9270597483,0.9287560333,0.9304523182,0.9321486031,0.933844888,0.935541173,0.9372374579,0.9389337428,0.9406300277,0.9423263126,0.9440225976,0.9457188825,0.9474151674,0.9491114523,0.9508077373,0.9525040222,0.9542003071,0.955896592,0.9575928769,0.9592891619,0.9609854468,0.9626817317,0.9643780166,0.9660743016,0.9677705865,0.9694668714,0.9711631563,0.9728594412,0.9745557262,0.9762520111,0.977948296,0.9796445809,0.9813408659,0.9830371508,0.9847334357,0.9864297206,0.9881260055,0.9898222905,0.9915185754,0.9932148603,0.9949111452,0.9966074302,0.9983037151,1.0] \n", + " metac-o1 \\\n", + "0 [0.5,0.3,0.15,0.04,0.01] \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", - " wunderplumb \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", + " metac-o1-preview \\\n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.1 \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "\n", + " metac-perplexity minefrac1 \\\n", + "0 [0.3,0.4,0.2,0.07,0.03] NaN \n", + "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", + "2 0.1 NaN \n", + "3 [0.15,0.6,0.25] NaN \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", + "\n", + " mmBot \\\n", + "0 [0.009900990099009901,0.39603960396039606,0.44... \n", + "1 [0.0215944348,0.0218024136,0.0220262706,0.0222... \n", + "2 0.2 \n", + "3 [0.25,0.5,0.25] \n", + "4 [0.0,0.0006552097,0.0013605064,0.0021151815,0.... \n", + "\n", + " pgodzinai pianobot swingswish \\\n", + "0 [0.014925742574257425,0.5137871287128712,0.334... NaN NaN \n", + "1 [0.001,0.001060875,0.0011396,0.0012863125,0.00... NaN NaN \n", + "2 0.07 NaN NaN \n", + "3 [0.27499999999999997,0.5125,0.21249999999999997] NaN NaN \n", + "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.... NaN NaN \n", + "\n", + " twsummerbot wunderplumb \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 [0.116,0.42,0.464] NaN \n", + "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0... NaN \n", "\n", "[5 rows x 57 columns]" ] @@ -2526,8 +2818,8 @@ " False\n", " False\n", " ...\n", - " 0.95\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.95\n", @@ -2550,8 +2842,8 @@ " False\n", " False\n", " ...\n", - " 0.35\n", - " 0.4\n", + " 0.65\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2574,8 +2866,8 @@ " False\n", " False\n", " ...\n", - " 0.85\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2598,7 +2890,7 @@ " False\n", " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2622,7 +2914,7 @@ " False\n", " False\n", " ...\n", - " 0.1\n", + " 0.05\n", " 0.05\n", " 0.05\n", " NaN\n", @@ -2654,11 +2946,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.35 0.4 NaN NaN 0.15 NaN \n", - "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.85 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.05 0.05 NaN 0.15 0.05 \n", + "94 0.9 0.95 NaN NaN 0.95 0.95 \n", + "95 0.65 0.9 NaN NaN 0.15 NaN \n", + "96 0.9 0.95 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -2730,7 +3022,7 @@ }, { "cell_type": "code", - "execution_count": 305, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2746,14 +3038,14 @@ " 'cobyj-bot', 'cookics_bot_TEST', 'jkraybill_bot', 'jonahsingerbot',\n", " 'krm-bot', 'laylaps', 'manticAI', 'metac-Gemini-Exp-1206',\n", " 'metac-Llama-3.1', 'metac-claude-3-5-sonnet-20240620',\n", - " 'metac-claude-3-5-sonnet-latest', 'metac-deepseek-r1', 'metac-exa',\n", - " 'metac-gpt-4o', 'metac-grok-2-1212', 'metac-o1', 'metac-o1-preview',\n", - " 'metac-perplexity', 'minefrac1', 'mmBot', 'pgodzinai', 'pianobot',\n", - " 'swingswish', 'twsummerbot', 'wunderplumb'],\n", + " 'metac-claude-3-5-sonnet-latest', 'metac-deepseek-r1+asknews',\n", + " 'metac-exa', 'metac-gpt-4o', 'metac-grok-2-1212', 'metac-o1',\n", + " 'metac-o1-preview', 'metac-perplexity', 'minefrac1', 'mmBot',\n", + " 'pgodzinai', 'pianobot', 'swingswish', 'twsummerbot', 'wunderplumb'],\n", " dtype='object')" ] }, - "execution_count": 305, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2764,7 +3056,7 @@ }, { "cell_type": "code", - "execution_count": 306, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2774,7 +3066,7 @@ "Name: GreeneiBot2, dtype: object" ] }, - "execution_count": 306, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2789,7 +3081,7 @@ }, { "cell_type": "code", - "execution_count": 307, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -2801,18 +3093,17 @@ }, { "cell_type": "code", - "execution_count": 308, - "metadata": {}, - "outputs": [], - "source": [ - "df_pro_bot_forecasts['options'] = df_pro_bot_forecasts['options'].apply(parse_options_array)" - ] - }, - { - "cell_type": "code", - "execution_count": 309, + "execution_count": 32, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3762618/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" + ] + }, { "data": { "text/html": [ @@ -2871,9 +3162,9 @@ " False\n", " False\n", " ...\n", - " [0.4,0.35,0.2,0.04,0.01]\n", - " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", - " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", + " [0.5,0.3,0.15,0.04,0.01]\n", + " [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333]\n", + " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -2895,12 +3186,12 @@ " True\n", " True\n", " ...\n", - " [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...]\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...]\n", - " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...]\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.44,0.48,0.52,0.56,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", + " [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", " NaN\n", - " [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...]\n", - " [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...]\n", + " [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911]\n", + " [0.001,0.001060875,0.0011396,0.0012863125,0.0015459984,0.0019048369,0.0023147701,0.0027425688,0.0031719899,0.0035935463,0.0040047171,0.0044081612,0.0048073678,0.0052048637,0.0056023079,0.0060005117,0.0063995798,0.0067992898,0.0071993689,0.0075995902,0.007999808,0.0083999595,0.0088000381,0.0092000616,0.0096525538,0.0103347221,0.0114180238,0.0128617561,0.0144931539,0.0161909912,0.0178965175,0.0195748423,0.0212159342,0.0228289888,0.0244265464,0.0260177161,0.0276085304,0.0292020038,0.0307985773,0.0323974755,0.0339977246,0.0355985069,0.0371992898,0.0387998404,0.0404001295,0.0420002192,0.0436001942,0.0452001261,0.0468000593,0.0484758458,0.0504834257,0.0530704368,0.056178071,0.0595567722,0.0630314345,0.0665171977,0.0699636664,0.0733563529,0.0767085411,0.0800383523,0.0833589543,0.0866790344,0.0900028852,0.0933311337,0.0967326953,0.1004442449,0.1047006189,0.1094577119,0.1144907128,0.1196353715,0.1248049846,0.1299418958,0.1350232879,0.1400570021,0.1452540043,0.1513017567,0.1589133116,0.1680377058,0.1780770546,0.1885468618,0.1991553484,0.2096896812,0.2200450325,0.2302229342,0.2402681458,0.2502302229,0.2601553402,0.27007834,0.2800179047,0.2899799302,0.2999629146,0.3099614863,0.3199691186,0.3299801956,0.3403173669,0.3521487483,0.3668129253,0.3844513624,0.4041888551,0.4247935739,0.4442765262,0.4605082419,0.4728869633,0.4822309604,0.4895341295,0.4956449952,0.5013686886,0.5073076754,0.5137610388,0.5206987551,0.5276657564,0.5340334461,0.5395220756,0.5442306919,0.5484901071,0.5530599502,0.5588761244,0.5663266439,0.5752119583,0.585204242,0.5959735276,0.6071500854,0.6184053116,0.6295209059,0.6403758638,0.650921239,0.6611693012,0.671174569,0.681009388,0.6907471485,0.7004527783,0.7101763721,0.7199504677,0.7297911321,0.7397010124,0.7496729757,0.7596938994,0.7697481465,0.7798202777,0.7898968803,0.7999675731,0.8100253018,0.8200662214,0.8300893951,0.8400025166,0.8494453768,0.8579165269,0.8651653723,0.8712540566,0.8763468591,0.8806505608,0.8844338485,0.8879756773,0.8915092577,0.8952099002,0.8991948145,0.9035195392,0.9081838533,0.9131467515,0.9183416751,0.9236898731,0.9291127196,0.9345414554,0.9399230919,0.9451659123,0.9500324455,0.9542146638,0.9575690762,0.9601504006,0.9620795658,0.9635039422,0.9646063832,0.965571997,0.9665531773,0.9676621061,0.9689711529,0.9705116418,0.9722785871,0.9742409577,0.9763519694,0.9785580215,0.9808067315,0.9830531373,0.9852633275,0.987415817,0.9895011861,0.9915203598,0.9934820158,0.9953894047,0.9970771779,0.998127745,0.99846,0.99852,0.99858,0.99864,0.9987,0.99876,0.99882,0.99888,0.99894,0.99899]\n", " NaN\n", " NaN\n", " NaN\n", @@ -2919,9 +3210,9 @@ " False\n", " False\n", " ...\n", - " 0.15\n", - " 0.05\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2944,8 +3235,8 @@ " None\n", " ...\n", " [0.25,0.6,0.15]\n", - " [0.15,0.65,0.2]\n", - " [0.15,0.45,0.4]\n", + " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2967,15 +3258,15 @@ " False\n", " False\n", " ...\n", - " [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...]\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...]\n", - " [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...]\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.6066666667,0.6133333333,0.62,0.6266666667,0.6333333333,0.64,0.6466666667,0.6533333333,0.66,0.6666666667,0.6733333333,0.68,0.6866666667,0.6933333333,0.7,0.7066666667,0.7133333333,0.72,0.7266666667,0.7333333333,0.74,0.7466666667,0.7533333333,0.76,0.7666666667,0.7733333333,0.78,0.7866666667,0.7933333333,0.8,0.8028571429,0.8057142857,0.8085714286,0.8114285714,0.8142857143,0.8171428571,0.82,0.8228571429,0.8257142857,0.8285714286,0.8314285714,0.8342857143,0.8371428571,0.84,0.8428571429,0.8457142857,0.8485714286,0.8514285714,0.8542857143,0.8571428571,0.86,0.8628571429,0.8657142857,0.8685714286,0.8714285714,0.8742857143,0.8771428571,0.88,0.8828571429,0.8857142857,0.8885714286,0.8914285714,0.8942857143,0.8971428571,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0]\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4066666667,0.4133333333,0.42,0.4266666667,0.4333333333,0.44,0.4466666667,0.4533333333,0.46,0.4666666667,0.4733333333,0.48,0.4866666667,0.4933333333,0.5,0.5066666667,0.5133333333,0.52,0.5266666667,0.5333333333,0.54,0.5466666667,0.5533333333,0.56,0.5666666667,0.5733333333,0.58,0.5866666667,0.5933333333,0.6,0.6066666667,0.6133333333,0.62,0.6266666667,0.6333333333,0.64,0.6466666667,0.6533333333,0.66,0.6666666667,0.6733333333,0.68,0.6866666667,0.6933333333,0.7,0.7066666667,0.7133333333,0.72,0.7266666667,0.7333333333,0.74,0.7466666667,0.7533333333,0.76,0.7666666667,0.7733333333,0.78,0.7866666667,0.7933333333,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.9028571429,0.9057142857,0.9085714286,0.9114285714,0.9142857143,0.9171428571,0.92,0.9228571429,0.9257142857,0.9285714286,0.9314285714,0.9342857143,0.9371428571,0.94,0.9428571429,0.9457142857,0.9485714286,0.9514285714,0.9542857143,0.9571428571,0.96,0.9628571429,0.9657142857,0.9685714286,0.9714285714,0.9742857143,0.9771428571,0.98,0.9828571429,0.9857142857,0.9885714286,0.9914285714,0.9942857143,0.9971428571,1.0]\n", " NaN\n", - " [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...]\n", - " [0.0, 0.0001141583, 0.0002446967, 0.0003862688, 0.0005272579, 0.0006650709, 0.0008243437, 0.0011074433, 0.0016696544, 0.0025699094, 0.0037138357, 0.0049708626, 0.0062610152, 0.0075426566, 0.0089765864, 0.0111726822, 0.0147311078, 0.0195212559, 0.0249547717, 0.0306181288, 0.0363105138, 0.0419407763, 0.0476011969, 0.053516341, 0.0598014349, 0.0663689162, 0.0730761187, 0.0798334547, 0.0865904866, 0.0933196582, 0.1000172031, 0.1066924089, 0.1133554776, 0.1200140176, 0.1266729489, 0.1333343989, 0.1399984689, 0.1466644317, 0.1533314439, 0.1599988203, 0.1666661444, 0.1733332523, 0.1800001372, 0.1866668598, 0.1933334943, 0.2000000995, 0.2066667101, 0.2133333393, 0.2199999878, 0.22666665, 0.2333333196, 0.2399999916, 0.2466666631, 0.2533333329, 0.2600000011, 0.2666666681, 0.2733333345, 0.2800000007, 0.286666667, 0.2933333334, 0.2999999999, 0.3066666665, 0.3133333332, 0.3199999999, 0.3266666666, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3934628939, 0.400837331, 0.40925763, 0.4186848364, 0.428718413, 0.4390353607, 0.4494419812, 0.4597974687, 0.4700329298, 0.4801500685, 0.4901790777, 0.500153105, 0.5101028922, 0.5200515519, 0.5300114112, 0.5398722838, 0.5492279015, 0.5576212737, 0.5650210292, 0.571743695, 0.5780856137, 0.5842571713, 0.5904328096, 0.5967209586, 0.603152213, 0.6097133168, ...]\n", + " [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0]\n", + " [0.0,0.0001141583,0.0002446967,0.0003862688,0.0005272579,0.0006650709,0.0008243437,0.0011074433,0.0016696544,0.0025699094,0.0037138357,0.0049708626,0.0062610152,0.0075426566,0.0089765864,0.0111726822,0.0147311078,0.0195212559,0.0249547717,0.0306181288,0.0363105138,0.0419407763,0.0476011969,0.053516341,0.0598014349,0.0663689162,0.0730761187,0.0798334547,0.0865904866,0.0933196582,0.1000172031,0.1066924089,0.1133554776,0.1200140176,0.1266729489,0.1333343989,0.1399984689,0.1466644317,0.1533314439,0.1599988203,0.1666661444,0.1733332523,0.1800001372,0.1866668598,0.1933334943,0.2000000995,0.2066667101,0.2133333393,0.2199999878,0.22666665,0.2333333196,0.2399999916,0.2466666631,0.2533333329,0.2600000011,0.2666666681,0.2733333345,0.2800000007,0.286666667,0.2933333334,0.2999999999,0.3066666665,0.3133333332,0.3199999999,0.3266666666,0.3333333333,0.34,0.3466666667,0.3533333333,0.36,0.3666666667,0.3733333333,0.38,0.3866666667,0.3934628939,0.400837331,0.40925763,0.4186848364,0.428718413,0.4390353607,0.4494419812,0.4597974687,0.4700329298,0.4801500685,0.4901790777,0.500153105,0.5101028922,0.5200515519,0.5300114112,0.5398722838,0.5492279015,0.5576212737,0.5650210292,0.571743695,0.5780856137,0.5842571713,0.5904328096,0.5967209586,0.603152213,0.6097133168,0.6163738459,0.6230958146,0.6298433017,0.6365902337,0.6433215069,0.6500308134,0.656718392,0.6633885674,0.6700472479,0.6767001542,0.6833518918,0.6900055659,0.6966627826,0.7033239321,0.7099885835,0.7166558627,0.723324761,0.7299943545,0.7366639271,0.7433330133,0.7500013847,0.7566690034,0.7633359628,0.770002427,0.7766685825,0.7833346018,0.7900006228,0.7966667394,0.8033330023,0.8099994258,0.8166659972,0.8233326871,0.8299994586,0.8366662749,0.8433331037,0.8499999207,0.8566667097,0.8633334627,0.8700001785,0.8766668606,0.8833335157,0.8899751517,0.8964699017,0.9025861327,0.9081211655,0.9130226546,0.9173491712,0.921198292,0.9246959323,0.9279877368,0.9312103051,0.934472912,0.9378540969,0.9414005467,0.9450901244,0.9487670554,0.9522009139,0.9552513327,0.9578998205,0.9601715711,0.96211589,0.9638162438,0.9653702301,0.9668664828,0.9683781475,0.9699605983,0.9716476808,0.9734519305,0.9753688047,0.9773815283,0.9794657325,0.9815941718,0.9837408125,0.9858836701,0.9879773814,0.9898993305,0.9914888717,0.9926681205,0.9934599632,0.9939261174,0.9941560479,0.9942611072,0.9943265488,0.9943865488,0.9944537386,0.9945561009,0.9947328687,0.9950042368,0.9953660612,0.9958058993,0.9963078442,0.9968511117,0.9974139813,0.9979781729,0.9985251814,0.999027536,0.9994498435,0.999736686,0.9998734993,0.99994,1.0]\n", " NaN\n", " NaN\n", - " [0.0, 0.001311947, 0.0026238939, 0.0039358409, 0.0052477878, 0.0065597348, 0.0078716817, 0.0091836287, 0.0104955756, 0.0118075226, 0.0131194695, 0.0144314165, 0.0157433634, 0.0170553104, 0.0183672573, 0.0196792043, 0.0209911512, 0.0223030982, 0.0236150451, 0.0249269921, 0.026238939, 0.027550886, 0.0288628329, 0.0301747799, 0.0314867268, 0.0327986738, 0.0341106207, 0.0354225677, 0.0367345146, 0.0380464616, 0.0393584085, 0.0406703555, 0.0419823024, 0.0432942494, 0.0446061963, 0.0459181433, 0.0472300902, 0.0485420372, 0.0498539841, 0.0511659311, 0.052477878, 0.053789825, 0.0551017719, 0.0564137189, 0.0577256658, 0.0590376128, 0.0603495597, 0.0616615067, 0.0629734536, 0.0642854006, 0.0655973475, 0.0669092945, 0.0682212414, 0.0695331884, 0.0708451353, 0.0721570823, 0.0734690292, 0.0747809762, 0.0760929231, 0.0774048701, 0.078716817, 0.080028764, 0.0813407109, 0.0826526579, 0.0839646048, 0.0852765518, 0.0865884987, 0.0879004457, 0.0902457862, 0.0933094828, 0.0978079399, 0.1023063969, 0.1068048539, 0.111303311, 0.115801768, 0.120300225, 0.124798682, 0.1292971391, 0.1338199508, 0.1388055027, 0.1440933779, 0.1496807808, 0.1571177226, 0.1652387403, 0.1753118263, 0.1904276903, 0.2058197291, 0.2212117678, 0.237030829, 0.2551785571, 0.273870758, 0.2925629589, 0.3115548313, 0.3307464845, 0.3499926649, 0.3692260274, 0.3884136416, 0.407661417, 0.4269091924, 0.4457073638, ...]\n", + " [0.0,0.001311947,0.0026238939,0.0039358409,0.0052477878,0.0065597348,0.0078716817,0.0091836287,0.0104955756,0.0118075226,0.0131194695,0.0144314165,0.0157433634,0.0170553104,0.0183672573,0.0196792043,0.0209911512,0.0223030982,0.0236150451,0.0249269921,0.026238939,0.027550886,0.0288628329,0.0301747799,0.0314867268,0.0327986738,0.0341106207,0.0354225677,0.0367345146,0.0380464616,0.0393584085,0.0406703555,0.0419823024,0.0432942494,0.0446061963,0.0459181433,0.0472300902,0.0485420372,0.0498539841,0.0511659311,0.052477878,0.053789825,0.0551017719,0.0564137189,0.0577256658,0.0590376128,0.0603495597,0.0616615067,0.0629734536,0.0642854006,0.0655973475,0.0669092945,0.0682212414,0.0695331884,0.0708451353,0.0721570823,0.0734690292,0.0747809762,0.0760929231,0.0774048701,0.078716817,0.080028764,0.0813407109,0.0826526579,0.0839646048,0.0852765518,0.0865884987,0.0879004457,0.0902457862,0.0933094828,0.0978079399,0.1023063969,0.1068048539,0.111303311,0.115801768,0.120300225,0.124798682,0.1292971391,0.1338199508,0.1388055027,0.1440933779,0.1496807808,0.1571177226,0.1652387403,0.1753118263,0.1904276903,0.2058197291,0.2212117678,0.237030829,0.2551785571,0.273870758,0.2925629589,0.3115548313,0.3307464845,0.3499926649,0.3692260274,0.3884136416,0.407661417,0.4269091924,0.4457073638,0.464050886,0.4823944081,0.5007379302,0.5190814523,0.5374249745,0.5538739661,0.5696118391,0.5853388804,0.6010659216,0.6161284786,0.6273538036,0.6382421632,0.6486483242,0.6588094975,0.668725683,0.6786418685,0.688558054,0.6984742395,0.708390425,0.7183066106,0.7278808508,0.7373411092,0.7468013677,0.7561442929,0.7645842622,0.7730242316,0.7814642009,0.7899041702,0.7983441395,0.8067841088,0.8152111577,0.8229940495,0.8307769414,0.8385598332,0.8447944123,0.8509124517,0.8563824526,0.8610823306,0.8657454654,0.8704086002,0.8750717351,0.8797348699,0.8843980047,0.8890611396,0.8934873987,0.8970573375,0.9006272763,0.9041972151,0.9077671539,0.9103291006,0.9126390493,0.914948998,0.9172589467,0.9195688953,0.921878844,0.9236671785,0.9253634634,0.9270597483,0.9287560333,0.9304523182,0.9321486031,0.933844888,0.935541173,0.9372374579,0.9389337428,0.9406300277,0.9423263126,0.9440225976,0.9457188825,0.9474151674,0.9491114523,0.9508077373,0.9525040222,0.9542003071,0.955896592,0.9575928769,0.9592891619,0.9609854468,0.9626817317,0.9643780166,0.9660743016,0.9677705865,0.9694668714,0.9711631563,0.9728594412,0.9745557262,0.9762520111,0.977948296,0.9796445809,0.9813408659,0.9830371508,0.9847334357,0.9864297206,0.9881260055,0.9898222905,0.9915185754,0.9932148603,0.9949111452,0.9966074302,0.9983037151,1.0]\n", " NaN\n", " \n", " \n", @@ -3005,26 +3296,26 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.35,0.2,0.04,0.01] \n", - "1 [0.05, 0.0505555556, 0.0511111111, 0.0516666667, 0.0522222222, 0.0527777778, 0.0533333333, 0.0538888889, 0.0544444444, 0.055, 0.0555555556, 0.0561111111, 0.0566666667, 0.0572222222, 0.0577777778, 0.0583333333, 0.0588888889, 0.0594444444, 0.06, 0.0605555556, 0.0611111111, 0.0616666667, 0.0622222222, 0.0627777778, 0.0633333333, 0.0638888889, 0.0644444444, 0.065, 0.0655555556, 0.0661111111, 0.0666666667, 0.0672222222, 0.0677777778, 0.0683333333, 0.0688888889, 0.0694444444, 0.07, 0.0705555556, 0.0711111111, 0.0716666667, 0.0722222222, 0.0727777778, 0.0733333333, 0.0738888889, 0.0744444444, 0.075, 0.0755555556, 0.0761111111, 0.0766666667, 0.0772222222, 0.0777777778, 0.0783333333, 0.0788888889, 0.0794444444, 0.08, 0.0805555556, 0.0811111111, 0.0816666667, 0.0822222222, 0.0827777778, 0.0833333333, 0.0838888889, 0.0844444444, 0.085, 0.0855555556, 0.0861111111, 0.0866666667, 0.0872222222, 0.0877777778, 0.0883333333, 0.0888888889, 0.0894444444, 0.09, 0.0905555556, 0.0911111111, 0.0916666667, 0.0922222222, 0.0927777778, 0.0933333333, 0.0938888889, 0.0944444444, 0.095, 0.0955555556, 0.0961111111, 0.0966666667, 0.0972222222, 0.0977777778, 0.0983333333, 0.0988888889, 0.0994444444, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, ...] \n", - "2 0.15 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0, 0.0028571429, 0.0057142857, 0.0085714286, 0.0114285714, 0.0142857143, 0.0171428571, 0.02, 0.0228571429, 0.0257142857, 0.0285714286, 0.0314285714, 0.0342857143, 0.0371428571, 0.04, 0.0428571429, 0.0457142857, 0.0485714286, 0.0514285714, 0.0542857143, 0.0571428571, 0.06, 0.0628571429, 0.0657142857, 0.0685714286, 0.0714285714, 0.0742857143, 0.0771428571, 0.08, 0.0828571429, 0.0857142857, 0.0885714286, 0.0914285714, 0.0942857143, 0.0971428571, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.0526666667, 0.0533333333, 0.054, 0.0546666667, 0.0553333333, 0.056, 0.0566666667, 0.0573333333, 0.058, 0.0586666667, 0.0593333333, 0.06, 0.0606666667, 0.0613333333, 0.062, 0.0626666667, 0.0633333333, 0.064, 0.0646666667, 0.0653333333, 0.066, 0.0666666667, 0.0673333333, 0.068, 0.0686666667, 0.0693333333, 0.07, 0.0706666667, 0.0713333333, 0.072, 0.0726666667, 0.0733333333, 0.074, 0.0746666667, 0.0753333333, 0.076, 0.0766666667, 0.0773333333, 0.078, 0.0786666667, 0.0793333333, 0.08, 0.0806666667, 0.0813333333, 0.082, 0.0826666667, 0.0833333333, 0.084, 0.0846666667, 0.0853333333, 0.086, 0.0866666667, 0.0873333333, 0.088, 0.0886666667, 0.0893333333, 0.09, 0.0906666667, 0.0913333333, 0.092, 0.0926666667, 0.0933333333, 0.094, 0.0946666667, 0.0953333333, 0.096, 0.0966666667, 0.0973333333, 0.098, 0.0986666667, 0.0993333333, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, ...] \n", - "2 0.05 \n", - "3 [0.15,0.65,0.2] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024, 0.028, 0.032, 0.036, 0.04, 0.044, 0.048, 0.052, 0.056, 0.06, 0.064, 0.068, 0.072, 0.076, 0.08, 0.084, 0.088, 0.092, 0.096, 0.1, 0.104, 0.108, 0.112, 0.116, 0.12, 0.124, 0.128, 0.132, 0.136, 0.14, 0.144, 0.148, 0.152, 0.156, 0.16, 0.164, 0.168, 0.172, 0.176, 0.18, 0.184, 0.188, 0.192, 0.196, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, ...] \n", - "\n", - " metac-perplexity \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", - "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.1028571429, 0.1057142857, 0.1085714286, 0.1114285714, 0.1142857143, 0.1171428571, 0.12, 0.1228571429, 0.1257142857, 0.1285714286, 0.1314285714, 0.1342857143, 0.1371428571, 0.14, 0.1428571429, 0.1457142857, 0.1485714286, 0.1514285714, 0.1542857143, 0.1571428571, 0.16, 0.1628571429, 0.1657142857, 0.1685714286, 0.1714285714, 0.1742857143, 0.1771428571, 0.18, 0.1828571429, 0.1857142857, 0.1885714286, 0.1914285714, 0.1942857143, 0.1971428571, 0.2, 0.2133333333, 0.2266666667, 0.24, 0.2533333333, 0.2666666667, 0.28, 0.2933333333, 0.3066666667, 0.32, 0.3333333333, 0.3466666667, 0.36, 0.3733333333, 0.3866666667, ...] \n", - "2 0.15 \n", - "3 [0.15,0.45,0.4] \n", - "4 [0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016, 0.018, 0.02, 0.022, 0.024, 0.026, 0.028, 0.03, 0.032, 0.034, 0.036, 0.038, 0.04, 0.042, 0.044, 0.046, 0.048, 0.05, 0.052, 0.054, 0.056, 0.058, 0.06, 0.062, 0.064, 0.066, 0.068, 0.07, 0.072, 0.074, 0.076, 0.078, 0.08, 0.082, 0.084, 0.086, 0.088, 0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.1066666667, 0.1133333333, 0.12, 0.1266666667, 0.1333333333, 0.14, 0.1466666667, 0.1533333333, 0.16, 0.1666666667, 0.1733333333, 0.18, 0.1866666667, 0.1933333333, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, ...] \n", + " metac-o1 \\\n", + "0 [0.5,0.3,0.15,0.04,0.01] \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.44,0.48,0.52,0.56,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.6066666667,0.6133333333,0.62,0.6266666667,0.6333333333,0.64,0.6466666667,0.6533333333,0.66,0.6666666667,0.6733333333,0.68,0.6866666667,0.6933333333,0.7,0.7066666667,0.7133333333,0.72,0.7266666667,0.7333333333,0.74,0.7466666667,0.7533333333,0.76,0.7666666667,0.7733333333,0.78,0.7866666667,0.7933333333,0.8,0.8028571429,0.8057142857,0.8085714286,0.8114285714,0.8142857143,0.8171428571,0.82,0.8228571429,0.8257142857,0.8285714286,0.8314285714,0.8342857143,0.8371428571,0.84,0.8428571429,0.8457142857,0.8485714286,0.8514285714,0.8542857143,0.8571428571,0.86,0.8628571429,0.8657142857,0.8685714286,0.8714285714,0.8742857143,0.8771428571,0.88,0.8828571429,0.8857142857,0.8885714286,0.8914285714,0.8942857143,0.8971428571,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.1 \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0] \n", + "\n", + " metac-perplexity \\\n", + "0 [0.3,0.4,0.2,0.07,0.03] \n", + "1 [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.1 \n", + "3 [0.15,0.6,0.25] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4066666667,0.4133333333,0.42,0.4266666667,0.4333333333,0.44,0.4466666667,0.4533333333,0.46,0.4666666667,0.4733333333,0.48,0.4866666667,0.4933333333,0.5,0.5066666667,0.5133333333,0.52,0.5266666667,0.5333333333,0.54,0.5466666667,0.5533333333,0.56,0.5666666667,0.5733333333,0.58,0.5866666667,0.5933333333,0.6,0.6066666667,0.6133333333,0.62,0.6266666667,0.6333333333,0.64,0.6466666667,0.6533333333,0.66,0.6666666667,0.6733333333,0.68,0.6866666667,0.6933333333,0.7,0.7066666667,0.7133333333,0.72,0.7266666667,0.7333333333,0.74,0.7466666667,0.7533333333,0.76,0.7666666667,0.7733333333,0.78,0.7866666667,0.7933333333,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.9028571429,0.9057142857,0.9085714286,0.9114285714,0.9142857143,0.9171428571,0.92,0.9228571429,0.9257142857,0.9285714286,0.9314285714,0.9342857143,0.9371428571,0.94,0.9428571429,0.9457142857,0.9485714286,0.9514285714,0.9542857143,0.9571428571,0.96,0.9628571429,0.9657142857,0.9685714286,0.9714285714,0.9742857143,0.9771428571,0.98,0.9828571429,0.9857142857,0.9885714286,0.9914285714,0.9942857143,0.9971428571,1.0] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3033,19 +3324,19 @@ "3 NaN \n", "4 NaN \n", "\n", - " mmBot \\\n", - "0 [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297] \n", - "1 [0.0215944348, 0.0218024136, 0.0220262706, 0.0222657692, 0.0225205234, 0.0227900084, 0.0230735761, 0.0233704727, 0.0236798595, 0.0240008339, 0.0243324518, 0.0246737484, 0.0250237592, 0.0253815375, 0.0257461704, 0.0261167925, 0.0264925953, 0.0268728349, 0.0272568365, 0.0276439961, 0.0280337803, 0.0284257242, 0.0288194274, 0.0292145496, 0.0296108048, 0.0300079559, 0.0304058088, 0.0308042061, 0.031203022, 0.0316021576, 0.0320015358, 0.0324010988, 0.0328008038, 0.033200622, 0.0336005361, 0.0340005406, 0.0344006419, 0.0348008594, 0.0352012288, 0.0356018064, 0.0360026751, 0.0364039532, 0.0368058059, 0.0372084598, 0.0376122217, 0.0380175022, 0.0384248443, 0.0388349581, 0.0392487619, 0.0396674303, 0.040092449, 0.0405256766, 0.040969412, 0.0414264662, 0.0419002382, 0.0423947905, 0.0429149226, 0.0434662384, 0.0440552034, 0.0446891875, 0.0453764888, 0.0461263346, 0.0469488546, 0.047855024, 0.0488565752, 0.0499658763, 0.0511957788, 0.0525594355, 0.0540700958, 0.0557408822, 0.0575845575, 0.0596132911, 0.061838434, 0.0642703126, 0.0669180506, 0.0697894271, 0.0728907793, 0.0762269529, 0.0798013046, 0.0836157568, 0.0876709009, 0.091966147, 0.096499911, 0.1012698318, 0.1062730078, 0.1115062433, 0.116966291, 0.1226500836, 0.1285549408, 0.1346787459, 0.1410200827, 0.1475783286, 0.1543537019, 0.1613472593, 0.1685608481, 0.1759970129, 0.1836588644, 0.1915499147, 0.1996738871, 0.208034508, ...] \n", - "2 0.2 \n", - "3 [0.25,0.5,0.25] \n", - "4 [0.0, 0.0006552097, 0.0013605064, 0.0021151815, 0.0029180701, 0.0037675922, 0.0046618077, 0.0055984833, 0.0065751692, 0.0075892831, 0.0086381998, 0.0097193446, 0.0108302867, 0.0119688337, 0.0131331257, 0.014321727, 0.0155337159, 0.0167687729, 0.0180272663, 0.0193103356, 0.020619972, 0.0219590952, 0.0233316264, 0.024742554, 0.0261979914, 0.0277052245, 0.0292727448, 0.030910267, 0.0326287265, 0.034440256, 0.0363581376, 0.0383967303, 0.0405713707, 0.042898249, 0.0453942605, 0.0480768342, 0.0509637431, 0.0540728987, 0.0574221344, 0.0610289827, 0.0649104508, 0.069082799, 0.0735613277, 0.0783601755, 0.0834921337, 0.0889684789, 0.0947988278, 0.1009910149, 0.1075509944, 0.1144827695, 0.1217883466, 0.1294677162, 0.1375188601, 0.1459377845, 0.1547185775, 0.1638534906, 0.173333043, 0.183146147, 0.1932802518, 0.2037215056, 0.2144549309, 0.2254646117, 0.2367338883, 0.2482455564, 0.2599820665, 0.2719257181, 0.2840588463, 0.2963639938, 0.308824066, 0.3214224646, 0.3341431959, 0.3469709515, 0.3598911602, 0.3728900098, 0.3859544391, 0.3990721017, 0.4122313044, 0.4254209242, 0.4386303077, 0.4518491587, 0.4650674199, 0.4782751541, 0.4914624335, 0.5046192399, 0.5177353826, 0.5308004395, 0.5438037232, 0.5567342756, 0.5695808913, 0.5823321691, 0.5949765903, 0.6075026181, 0.6198988152, 0.6321539735, 0.6442572471, 0.6561982838, 0.6679673464, 0.679555418, 0.6909542849, 0.7021565932, ...] \n", + " mmBot \\\n", + "0 [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297] \n", + "1 [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911] \n", + "2 0.2 \n", + "3 [0.25,0.5,0.25] \n", + "4 [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0] \n", "\n", - " pgodzinai \\\n", - "0 [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965] \n", - "1 [0.001, 0.001060875, 0.0011396, 0.0012863125, 0.0015459984, 0.0019048369, 0.0023147701, 0.0027425688, 0.0031719899, 0.0035935463, 0.0040047171, 0.0044081612, 0.0048073678, 0.0052048637, 0.0056023079, 0.0060005117, 0.0063995798, 0.0067992898, 0.0071993689, 0.0075995902, 0.007999808, 0.0083999595, 0.0088000381, 0.0092000616, 0.0096525538, 0.0103347221, 0.0114180238, 0.0128617561, 0.0144931539, 0.0161909912, 0.0178965175, 0.0195748423, 0.0212159342, 0.0228289888, 0.0244265464, 0.0260177161, 0.0276085304, 0.0292020038, 0.0307985773, 0.0323974755, 0.0339977246, 0.0355985069, 0.0371992898, 0.0387998404, 0.0404001295, 0.0420002192, 0.0436001942, 0.0452001261, 0.0468000593, 0.0484758458, 0.0504834257, 0.0530704368, 0.056178071, 0.0595567722, 0.0630314345, 0.0665171977, 0.0699636664, 0.0733563529, 0.0767085411, 0.0800383523, 0.0833589543, 0.0866790344, 0.0900028852, 0.0933311337, 0.0967326953, 0.1004442449, 0.1047006189, 0.1094577119, 0.1144907128, 0.1196353715, 0.1248049846, 0.1299418958, 0.1350232879, 0.1400570021, 0.1452540043, 0.1513017567, 0.1589133116, 0.1680377058, 0.1780770546, 0.1885468618, 0.1991553484, 0.2096896812, 0.2200450325, 0.2302229342, 0.2402681458, 0.2502302229, 0.2601553402, 0.27007834, 0.2800179047, 0.2899799302, 0.2999629146, 0.3099614863, 0.3199691186, 0.3299801956, 0.3403173669, 0.3521487483, 0.3668129253, 0.3844513624, 0.4041888551, 0.4247935739, ...] \n", - "2 0.07 \n", - "3 [0.27499999999999997,0.5125,0.21249999999999997] \n", - "4 [0.0, 0.0001141583, 0.0002446967, 0.0003862688, 0.0005272579, 0.0006650709, 0.0008243437, 0.0011074433, 0.0016696544, 0.0025699094, 0.0037138357, 0.0049708626, 0.0062610152, 0.0075426566, 0.0089765864, 0.0111726822, 0.0147311078, 0.0195212559, 0.0249547717, 0.0306181288, 0.0363105138, 0.0419407763, 0.0476011969, 0.053516341, 0.0598014349, 0.0663689162, 0.0730761187, 0.0798334547, 0.0865904866, 0.0933196582, 0.1000172031, 0.1066924089, 0.1133554776, 0.1200140176, 0.1266729489, 0.1333343989, 0.1399984689, 0.1466644317, 0.1533314439, 0.1599988203, 0.1666661444, 0.1733332523, 0.1800001372, 0.1866668598, 0.1933334943, 0.2000000995, 0.2066667101, 0.2133333393, 0.2199999878, 0.22666665, 0.2333333196, 0.2399999916, 0.2466666631, 0.2533333329, 0.2600000011, 0.2666666681, 0.2733333345, 0.2800000007, 0.286666667, 0.2933333334, 0.2999999999, 0.3066666665, 0.3133333332, 0.3199999999, 0.3266666666, 0.3333333333, 0.34, 0.3466666667, 0.3533333333, 0.36, 0.3666666667, 0.3733333333, 0.38, 0.3866666667, 0.3934628939, 0.400837331, 0.40925763, 0.4186848364, 0.428718413, 0.4390353607, 0.4494419812, 0.4597974687, 0.4700329298, 0.4801500685, 0.4901790777, 0.500153105, 0.5101028922, 0.5200515519, 0.5300114112, 0.5398722838, 0.5492279015, 0.5576212737, 0.5650210292, 0.571743695, 0.5780856137, 0.5842571713, 0.5904328096, 0.5967209586, 0.603152213, 0.6097133168, ...] \n", + " pgodzinai \\\n", + "0 [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965] \n", + "1 [0.001,0.001060875,0.0011396,0.0012863125,0.0015459984,0.0019048369,0.0023147701,0.0027425688,0.0031719899,0.0035935463,0.0040047171,0.0044081612,0.0048073678,0.0052048637,0.0056023079,0.0060005117,0.0063995798,0.0067992898,0.0071993689,0.0075995902,0.007999808,0.0083999595,0.0088000381,0.0092000616,0.0096525538,0.0103347221,0.0114180238,0.0128617561,0.0144931539,0.0161909912,0.0178965175,0.0195748423,0.0212159342,0.0228289888,0.0244265464,0.0260177161,0.0276085304,0.0292020038,0.0307985773,0.0323974755,0.0339977246,0.0355985069,0.0371992898,0.0387998404,0.0404001295,0.0420002192,0.0436001942,0.0452001261,0.0468000593,0.0484758458,0.0504834257,0.0530704368,0.056178071,0.0595567722,0.0630314345,0.0665171977,0.0699636664,0.0733563529,0.0767085411,0.0800383523,0.0833589543,0.0866790344,0.0900028852,0.0933311337,0.0967326953,0.1004442449,0.1047006189,0.1094577119,0.1144907128,0.1196353715,0.1248049846,0.1299418958,0.1350232879,0.1400570021,0.1452540043,0.1513017567,0.1589133116,0.1680377058,0.1780770546,0.1885468618,0.1991553484,0.2096896812,0.2200450325,0.2302229342,0.2402681458,0.2502302229,0.2601553402,0.27007834,0.2800179047,0.2899799302,0.2999629146,0.3099614863,0.3199691186,0.3299801956,0.3403173669,0.3521487483,0.3668129253,0.3844513624,0.4041888551,0.4247935739,0.4442765262,0.4605082419,0.4728869633,0.4822309604,0.4895341295,0.4956449952,0.5013686886,0.5073076754,0.5137610388,0.5206987551,0.5276657564,0.5340334461,0.5395220756,0.5442306919,0.5484901071,0.5530599502,0.5588761244,0.5663266439,0.5752119583,0.585204242,0.5959735276,0.6071500854,0.6184053116,0.6295209059,0.6403758638,0.650921239,0.6611693012,0.671174569,0.681009388,0.6907471485,0.7004527783,0.7101763721,0.7199504677,0.7297911321,0.7397010124,0.7496729757,0.7596938994,0.7697481465,0.7798202777,0.7898968803,0.7999675731,0.8100253018,0.8200662214,0.8300893951,0.8400025166,0.8494453768,0.8579165269,0.8651653723,0.8712540566,0.8763468591,0.8806505608,0.8844338485,0.8879756773,0.8915092577,0.8952099002,0.8991948145,0.9035195392,0.9081838533,0.9131467515,0.9183416751,0.9236898731,0.9291127196,0.9345414554,0.9399230919,0.9451659123,0.9500324455,0.9542146638,0.9575690762,0.9601504006,0.9620795658,0.9635039422,0.9646063832,0.965571997,0.9665531773,0.9676621061,0.9689711529,0.9705116418,0.9722785871,0.9742409577,0.9763519694,0.9785580215,0.9808067315,0.9830531373,0.9852633275,0.987415817,0.9895011861,0.9915203598,0.9934820158,0.9953894047,0.9970771779,0.998127745,0.99846,0.99852,0.99858,0.99864,0.9987,0.99876,0.99882,0.99888,0.99894,0.99899] \n", + "2 0.07 \n", + "3 [0.27499999999999997,0.5125,0.21249999999999997] \n", + "4 [0.0,0.0001141583,0.0002446967,0.0003862688,0.0005272579,0.0006650709,0.0008243437,0.0011074433,0.0016696544,0.0025699094,0.0037138357,0.0049708626,0.0062610152,0.0075426566,0.0089765864,0.0111726822,0.0147311078,0.0195212559,0.0249547717,0.0306181288,0.0363105138,0.0419407763,0.0476011969,0.053516341,0.0598014349,0.0663689162,0.0730761187,0.0798334547,0.0865904866,0.0933196582,0.1000172031,0.1066924089,0.1133554776,0.1200140176,0.1266729489,0.1333343989,0.1399984689,0.1466644317,0.1533314439,0.1599988203,0.1666661444,0.1733332523,0.1800001372,0.1866668598,0.1933334943,0.2000000995,0.2066667101,0.2133333393,0.2199999878,0.22666665,0.2333333196,0.2399999916,0.2466666631,0.2533333329,0.2600000011,0.2666666681,0.2733333345,0.2800000007,0.286666667,0.2933333334,0.2999999999,0.3066666665,0.3133333332,0.3199999999,0.3266666666,0.3333333333,0.34,0.3466666667,0.3533333333,0.36,0.3666666667,0.3733333333,0.38,0.3866666667,0.3934628939,0.400837331,0.40925763,0.4186848364,0.428718413,0.4390353607,0.4494419812,0.4597974687,0.4700329298,0.4801500685,0.4901790777,0.500153105,0.5101028922,0.5200515519,0.5300114112,0.5398722838,0.5492279015,0.5576212737,0.5650210292,0.571743695,0.5780856137,0.5842571713,0.5904328096,0.5967209586,0.603152213,0.6097133168,0.6163738459,0.6230958146,0.6298433017,0.6365902337,0.6433215069,0.6500308134,0.656718392,0.6633885674,0.6700472479,0.6767001542,0.6833518918,0.6900055659,0.6966627826,0.7033239321,0.7099885835,0.7166558627,0.723324761,0.7299943545,0.7366639271,0.7433330133,0.7500013847,0.7566690034,0.7633359628,0.770002427,0.7766685825,0.7833346018,0.7900006228,0.7966667394,0.8033330023,0.8099994258,0.8166659972,0.8233326871,0.8299994586,0.8366662749,0.8433331037,0.8499999207,0.8566667097,0.8633334627,0.8700001785,0.8766668606,0.8833335157,0.8899751517,0.8964699017,0.9025861327,0.9081211655,0.9130226546,0.9173491712,0.921198292,0.9246959323,0.9279877368,0.9312103051,0.934472912,0.9378540969,0.9414005467,0.9450901244,0.9487670554,0.9522009139,0.9552513327,0.9578998205,0.9601715711,0.96211589,0.9638162438,0.9653702301,0.9668664828,0.9683781475,0.9699605983,0.9716476808,0.9734519305,0.9753688047,0.9773815283,0.9794657325,0.9815941718,0.9837408125,0.9858836701,0.9879773814,0.9898993305,0.9914888717,0.9926681205,0.9934599632,0.9939261174,0.9941560479,0.9942611072,0.9943265488,0.9943865488,0.9944537386,0.9945561009,0.9947328687,0.9950042368,0.9953660612,0.9958058993,0.9963078442,0.9968511117,0.9974139813,0.9979781729,0.9985251814,0.999027536,0.9994498435,0.999736686,0.9998734993,0.99994,1.0] \n", "\n", " pianobot swingswish \\\n", "0 NaN NaN \n", @@ -3054,12 +3345,12 @@ "3 NaN NaN \n", "4 NaN NaN \n", "\n", - " twsummerbot \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 [0.116,0.42,0.464] \n", - "4 [0.0, 0.001311947, 0.0026238939, 0.0039358409, 0.0052477878, 0.0065597348, 0.0078716817, 0.0091836287, 0.0104955756, 0.0118075226, 0.0131194695, 0.0144314165, 0.0157433634, 0.0170553104, 0.0183672573, 0.0196792043, 0.0209911512, 0.0223030982, 0.0236150451, 0.0249269921, 0.026238939, 0.027550886, 0.0288628329, 0.0301747799, 0.0314867268, 0.0327986738, 0.0341106207, 0.0354225677, 0.0367345146, 0.0380464616, 0.0393584085, 0.0406703555, 0.0419823024, 0.0432942494, 0.0446061963, 0.0459181433, 0.0472300902, 0.0485420372, 0.0498539841, 0.0511659311, 0.052477878, 0.053789825, 0.0551017719, 0.0564137189, 0.0577256658, 0.0590376128, 0.0603495597, 0.0616615067, 0.0629734536, 0.0642854006, 0.0655973475, 0.0669092945, 0.0682212414, 0.0695331884, 0.0708451353, 0.0721570823, 0.0734690292, 0.0747809762, 0.0760929231, 0.0774048701, 0.078716817, 0.080028764, 0.0813407109, 0.0826526579, 0.0839646048, 0.0852765518, 0.0865884987, 0.0879004457, 0.0902457862, 0.0933094828, 0.0978079399, 0.1023063969, 0.1068048539, 0.111303311, 0.115801768, 0.120300225, 0.124798682, 0.1292971391, 0.1338199508, 0.1388055027, 0.1440933779, 0.1496807808, 0.1571177226, 0.1652387403, 0.1753118263, 0.1904276903, 0.2058197291, 0.2212117678, 0.237030829, 0.2551785571, 0.273870758, 0.2925629589, 0.3115548313, 0.3307464845, 0.3499926649, 0.3692260274, 0.3884136416, 0.407661417, 0.4269091924, 0.4457073638, ...] \n", + " twsummerbot \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 [0.116,0.42,0.464] \n", + "4 [0.0,0.001311947,0.0026238939,0.0039358409,0.0052477878,0.0065597348,0.0078716817,0.0091836287,0.0104955756,0.0118075226,0.0131194695,0.0144314165,0.0157433634,0.0170553104,0.0183672573,0.0196792043,0.0209911512,0.0223030982,0.0236150451,0.0249269921,0.026238939,0.027550886,0.0288628329,0.0301747799,0.0314867268,0.0327986738,0.0341106207,0.0354225677,0.0367345146,0.0380464616,0.0393584085,0.0406703555,0.0419823024,0.0432942494,0.0446061963,0.0459181433,0.0472300902,0.0485420372,0.0498539841,0.0511659311,0.052477878,0.053789825,0.0551017719,0.0564137189,0.0577256658,0.0590376128,0.0603495597,0.0616615067,0.0629734536,0.0642854006,0.0655973475,0.0669092945,0.0682212414,0.0695331884,0.0708451353,0.0721570823,0.0734690292,0.0747809762,0.0760929231,0.0774048701,0.078716817,0.080028764,0.0813407109,0.0826526579,0.0839646048,0.0852765518,0.0865884987,0.0879004457,0.0902457862,0.0933094828,0.0978079399,0.1023063969,0.1068048539,0.111303311,0.115801768,0.120300225,0.124798682,0.1292971391,0.1338199508,0.1388055027,0.1440933779,0.1496807808,0.1571177226,0.1652387403,0.1753118263,0.1904276903,0.2058197291,0.2212117678,0.237030829,0.2551785571,0.273870758,0.2925629589,0.3115548313,0.3307464845,0.3499926649,0.3692260274,0.3884136416,0.407661417,0.4269091924,0.4457073638,0.464050886,0.4823944081,0.5007379302,0.5190814523,0.5374249745,0.5538739661,0.5696118391,0.5853388804,0.6010659216,0.6161284786,0.6273538036,0.6382421632,0.6486483242,0.6588094975,0.668725683,0.6786418685,0.688558054,0.6984742395,0.708390425,0.7183066106,0.7278808508,0.7373411092,0.7468013677,0.7561442929,0.7645842622,0.7730242316,0.7814642009,0.7899041702,0.7983441395,0.8067841088,0.8152111577,0.8229940495,0.8307769414,0.8385598332,0.8447944123,0.8509124517,0.8563824526,0.8610823306,0.8657454654,0.8704086002,0.8750717351,0.8797348699,0.8843980047,0.8890611396,0.8934873987,0.8970573375,0.9006272763,0.9041972151,0.9077671539,0.9103291006,0.9126390493,0.914948998,0.9172589467,0.9195688953,0.921878844,0.9236671785,0.9253634634,0.9270597483,0.9287560333,0.9304523182,0.9321486031,0.933844888,0.935541173,0.9372374579,0.9389337428,0.9406300277,0.9423263126,0.9440225976,0.9457188825,0.9474151674,0.9491114523,0.9508077373,0.9525040222,0.9542003071,0.955896592,0.9575928769,0.9592891619,0.9609854468,0.9626817317,0.9643780166,0.9660743016,0.9677705865,0.9694668714,0.9711631563,0.9728594412,0.9745557262,0.9762520111,0.977948296,0.9796445809,0.9813408659,0.9830371508,0.9847334357,0.9864297206,0.9881260055,0.9898222905,0.9915185754,0.9932148603,0.9949111452,0.9966074302,0.9983037151,1.0] \n", "\n", " wunderplumb \n", "0 NaN \n", @@ -3132,8 +3423,8 @@ " False\n", " False\n", " ...\n", - " 0.95\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.95\n", @@ -3156,8 +3447,8 @@ " False\n", " False\n", " ...\n", - " 0.35\n", - " 0.4\n", + " 0.65\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3180,8 +3471,8 @@ " False\n", " False\n", " ...\n", - " 0.85\n", " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3204,7 +3495,7 @@ " False\n", " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3228,7 +3519,7 @@ " False\n", " False\n", " ...\n", - " 0.1\n", + " 0.05\n", " 0.05\n", " 0.05\n", " NaN\n", @@ -3260,11 +3551,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.35 0.4 NaN NaN 0.15 NaN \n", - "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.85 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.05 0.05 NaN 0.15 0.05 \n", + "94 0.9 0.95 NaN NaN 0.95 0.95 \n", + "95 0.65 0.9 NaN NaN 0.15 NaN \n", + "96 0.9 0.95 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3280,6 +3571,21 @@ "output_type": "display_data" } ], + "source": [ + "multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n", + "if len(multiple_choice_rows_with_empty_options) > 0:\n", + " display_head_and_tail(multiple_choice_rows_with_empty_options)\n", + " raise ValueError(\"Multiple choice questions with empty options found\")\n", + "\n", + "df_pro_bot_forecasts['options'] = df_pro_bot_forecasts['options'].apply(parse_options_array) # @Check: TODO: Refactor/move this (and other times parse_options_array is used) to one central area at beginning cell data normalization should happen together and be availabe at all times in notebook\n", + "display_head_and_tail(df_pro_bot_forecasts)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], "source": [ "# Simple function to parse CDF strings for numeric questions\n", "def parse_numeric_forecasts(df):\n", @@ -3318,13 +3624,12 @@ " return df\n", "\n", "# Now parse the numeric forecasts\n", - "df_pro_bot_forecasts = parse_numeric_forecasts(df_pro_bot_forecasts)\n", - "display_head_and_tail(df_pro_bot_forecasts)" + "df_pro_bot_forecasts = parse_numeric_forecasts(df_pro_bot_forecasts)\n" ] }, { "cell_type": "code", - "execution_count": 310, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -3396,7 +3701,7 @@ }, { "cell_type": "code", - "execution_count": 311, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -3457,8 +3762,8 @@ " False\n", " False\n", " ...\n", - " 2.343407\n", - " 5.857933\n", + " 2.644992\n", + " 5.703782\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -3466,7 +3771,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 5.010635\n", + " 4.656813\n", " \n", " \n", " 3\n", @@ -3481,8 +3786,8 @@ " None\n", " None\n", " ...\n", - " 0.390198\n", - " 0.022473\n", + " 0.107631\n", + " 0.310155\n", " NaN\n", " 0.127833\n", " 0.152526\n", @@ -3506,7 +3811,7 @@ " False\n", " ...\n", " 0.298855\n", - " 0.096331\n", + " -0.106610\n", " NaN\n", " -0.184571\n", " 0.112526\n", @@ -3514,7 +3819,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.106610\n", + " -0.576613\n", " \n", " \n", " 9\n", @@ -3529,7 +3834,7 @@ " None\n", " None\n", " ...\n", - " -0.518794\n", + " -0.423484\n", " -1.211941\n", " NaN\n", " -0.806476\n", @@ -3553,7 +3858,7 @@ " None\n", " None\n", " ...\n", - " 0.441833\n", + " 0.575364\n", " 0.287682\n", " 0.021979\n", " 0.200671\n", @@ -3599,16 +3904,16 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.343407 5.857933 NaN 2.292635 2.703087 \n", - "3 0.390198 0.022473 NaN 0.127833 0.152526 \n", - "6 0.298855 0.096331 NaN -0.184571 0.112526 \n", - "9 -0.518794 -1.211941 NaN -0.806476 -0.494101 \n", - "13 0.441833 0.287682 0.021979 0.200671 0.253781 \n", + "0 2.644992 5.703782 NaN 2.292635 2.703087 \n", + "3 0.107631 0.310155 NaN 0.127833 0.152526 \n", + "6 0.298855 -0.106610 NaN -0.184571 0.112526 \n", + "9 -0.423484 -1.211941 NaN -0.806476 -0.494101 \n", + "13 0.575364 0.287682 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "0 NaN NaN NaN NaN 5.010635 \n", + "0 NaN NaN NaN NaN 4.656813 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN -0.106610 \n", + "6 NaN NaN NaN NaN -0.576613 \n", "9 NaN NaN -0.624154 NaN -0.693147 \n", "13 NaN NaN NaN NaN -0.062598 \n", "\n", @@ -3676,16 +3981,16 @@ " False\n", " False\n", " ...\n", - " -3.795489\n", + " -2.879198\n", " -1.780586\n", " -3.007032\n", " -2.879198\n", - " -3.390024\n", + " -3.795489\n", " NaN\n", " NaN\n", " -2.348570\n", " -2.409195\n", - " -3.390024\n", + " -2.879198\n", " \n", " \n", " 82\n", @@ -3701,7 +4006,7 @@ " None\n", " ...\n", " -0.993252\n", - " -0.186776\n", + " 0.000000\n", " -0.523248\n", " 0.105361\n", " 0.259511\n", @@ -3709,7 +4014,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " 0.276509\n", + " -0.993252\n", " \n", " \n", " 83\n", @@ -3725,7 +4030,7 @@ " None\n", " ...\n", " -0.693147\n", - " -0.182322\n", + " -0.693147\n", " NaN\n", " -0.182322\n", " NaN\n", @@ -3748,8 +4053,8 @@ " False\n", " False\n", " ...\n", - " -0.069566\n", - " -0.102356\n", + " -0.048289\n", + " -0.048289\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -3757,7 +4062,7 @@ " -0.113529\n", " NaN\n", " -0.147818\n", - " -0.124829\n", + " -0.121048\n", " \n", " \n", " 92\n", @@ -3772,8 +4077,8 @@ " False\n", " False\n", " ...\n", - " -0.606136\n", - " -4.007333\n", + " -1.704748\n", + " -1.011601\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -3781,7 +4086,7 @@ " -0.480973\n", " NaN\n", " -0.749237\n", - " -0.200671\n", + " -0.318454\n", " \n", " \n", "\n", @@ -3811,25 +4116,25 @@ "92 [0-24, 25-30, 31-49, 50-70, >70] NaN \n", "\n", " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", - "81 NaN False False ... -3.795489 \n", + "81 NaN False False ... -2.879198 \n", "82 NaN None None ... -0.993252 \n", "83 NaN None None ... -0.693147 \n", - "91 NaN False False ... -0.069566 \n", - "92 NaN False False ... -0.606136 \n", + "91 NaN False False ... -0.048289 \n", + "92 NaN False False ... -1.704748 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -1.780586 -3.007032 -2.879198 -3.390024 NaN NaN \n", - "82 -0.186776 -0.523248 0.105361 0.259511 NaN NaN \n", - "83 -0.182322 NaN -0.182322 NaN NaN NaN \n", - "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -4.007333 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "81 -1.780586 -3.007032 -2.879198 -3.795489 NaN NaN \n", + "82 0.000000 -0.523248 0.105361 0.259511 NaN NaN \n", + "83 -0.693147 NaN -0.182322 NaN NaN NaN \n", + "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -1.011601 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -3.390024 \n", - "82 0.276509 -0.644609 0.276509 \n", + "81 -2.348570 -2.409195 -2.879198 \n", + "82 0.276509 -0.644609 -0.993252 \n", "83 -0.178330 -0.567984 -0.693147 \n", - "91 NaN -0.147818 -0.124829 \n", - "92 NaN -0.749237 -0.200671 \n", + "91 NaN -0.147818 -0.121048 \n", + "92 NaN -0.749237 -0.318454 \n", "\n", "[5 rows x 58 columns]" ] @@ -3895,8 +4200,8 @@ " False\n", " False\n", " ...\n", - " -0.038208\n", - " -0.149434\n", + " -0.092275\n", + " -0.092275\n", " NaN\n", " -0.210058\n", " -0.059485\n", @@ -3904,7 +4209,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.179287\n", + " -0.149434\n", " \n", " \n", " 5\n", @@ -3928,7 +4233,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.287682\n", + " 0.320472\n", " \n", " \n", " 8\n", @@ -3944,7 +4249,7 @@ " False\n", " ...\n", " -0.054067\n", - " 0.000000\n", + " -0.054067\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -3952,7 +4257,7 @@ " NaN\n", " -0.398124\n", " NaN\n", - " -0.171850\n", + " -0.179379\n", " \n", " \n", " 12\n", @@ -3967,7 +4272,7 @@ " False\n", " False\n", " ...\n", - " -0.182322\n", + " -0.057158\n", " 0.000000\n", " NaN\n", " 0.054067\n", @@ -3992,7 +4297,7 @@ " False\n", " ...\n", " -0.045611\n", - " 0.039547\n", + " -0.045611\n", " NaN\n", " -0.068083\n", " NaN\n", @@ -4023,16 +4328,16 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.038208 -0.149434 NaN -0.210058 -0.059485 \n", + "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", "5 -0.810930 0.200671 NaN 0.510826 0.320472 \n", - "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", - "12 -0.182322 0.000000 NaN 0.054067 -0.057158 \n", - "16 -0.045611 0.039547 NaN -0.068083 NaN \n", + "8 -0.054067 -0.054067 NaN -0.111226 -0.147158 \n", + "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", + "16 -0.045611 -0.045611 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "2 NaN NaN NaN NaN -0.179287 \n", - "5 NaN NaN NaN NaN 0.287682 \n", - "8 NaN NaN -0.398124 NaN -0.171850 \n", + "2 NaN NaN NaN NaN -0.149434 \n", + "5 NaN NaN NaN NaN 0.320472 \n", + "8 NaN NaN -0.398124 NaN -0.179379 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", "16 NaN NaN -0.076070 NaN -0.096728 \n", "\n", @@ -4100,7 +4405,7 @@ " False\n", " False\n", " ...\n", - " -0.054067\n", + " 0.000000\n", " NaN\n", " NaN\n", " 0.000000\n", @@ -4124,7 +4429,7 @@ " False\n", " False\n", " ...\n", - " -0.459532\n", + " -2.251292\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4148,7 +4453,7 @@ " False\n", " False\n", " ...\n", - " -0.074901\n", + " -0.020834\n", " NaN\n", " NaN\n", " -0.074901\n", @@ -4228,9 +4533,9 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -0.459532 NaN NaN -0.111226 NaN \n", - "96 -0.074901 NaN NaN -0.074901 NaN \n", + "94 0.000000 NaN NaN 0.000000 0.000000 \n", + "95 -2.251292 NaN NaN -0.111226 NaN \n", + "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", "98 -0.017709 -0.017709 NaN -0.112251 -0.017709 \n", "\n", @@ -4256,7 +4561,7 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -4298,7 +4603,7 @@ " \n", " 2\n", " bot_median\n", - " 3504.379897\n", + " 3538.184052\n", " \n", " \n", " 3\n", @@ -4367,7 +4672,7 @@ " \n", " \n", " 16\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 614.572462\n", " \n", " \n", @@ -4533,7 +4838,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3504.379897\n", + "2 bot_median 3538.184052\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4547,7 +4852,7 @@ "13 CumulativeBot 1030.716475\n", "14 pgodzinai 926.081448\n", "15 jkraybill_bot 627.932509\n", - "16 metac-deepseek-r1 614.572462\n", + "16 metac-deepseek-r1+asknews 614.572462\n", "17 question_weight 378.020000\n", "18 metac-exa 265.384263\n", "19 MWG 215.551323\n", @@ -4581,7 +4886,7 @@ "47 ajf-bot -3239.712801" ] }, - "execution_count": 312, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -4592,7 +4897,7 @@ }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -4601,13 +4906,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 69.0%\n", - "mean metac-o1 forecast on questions that resolved no: 30.0%\n" + "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", + "mean metac-o1 forecast on questions that resolved no: 27.0%\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -4676,14 +4981,14 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1932996/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_3762618/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -4733,7 +5038,7 @@ }, { "cell_type": "code", - "execution_count": 315, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -4746,7 +5051,7 @@ }, { "cell_type": "code", - "execution_count": 316, + "execution_count": 40, "metadata": { "cellView": "form", "id": "tXKRpXAVHMRt" @@ -4809,7 +5114,7 @@ " 3\n", " 4\n", " bot_median\n", - " 2456.727963\n", + " 2475.479525\n", " 97\n", " 93.10\n", " \n", @@ -4904,7 +5209,7 @@ " \n", " 15\n", " 16\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 1518.308625\n", " 55\n", " 52.10\n", @@ -5166,7 +5471,7 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2456.727963 97 \n", + "3 4 bot_median 2475.479525 97 \n", "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", @@ -5178,7 +5483,7 @@ "12 13 metac-Gemini-Exp-1206 1595.682612 81 \n", "13 14 NextWorldLab 1583.026226 85 \n", "14 15 metac-o1-preview 1527.657141 96 \n", - "15 16 metac-deepseek-r1 1518.308625 55 \n", + "15 16 metac-deepseek-r1+asknews 1518.308625 55 \n", "16 17 laylaps 1500.567874 68 \n", "17 18 mmBot 1482.726445 97 \n", "18 19 Grizeu_Bot 1399.477718 55 \n", @@ -5261,7 +5566,7 @@ "46 52.10 " ] }, - "execution_count": 316, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -5330,7 +5635,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -5412,17 +5717,17 @@ " \n", " \n", " bot_median\n", - " 2456.7\n", + " 2475.5\n", " 93.1\n", - " 26.4\n", - " 58.198995\n", - " 6.031713\n", - " 4.374886\n", + " 26.6\n", + " 57.595415\n", + " 5.969158\n", + " 4.454476\n", " 1.985277\n", " 38.4\n", - " 14.4\n", - " 0.999984\n", - " 0.000032\n", + " 14.7\n", + " 0.999988\n", + " 0.000024\n", " \n", " \n", " acm_bot\n", @@ -5579,7 +5884,7 @@ " 0.070922\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 1518.3\n", " 52.1\n", " 29.1\n", @@ -6035,7 +6340,7 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2456.7 93.1 26.4 58.198995 \n", + "bot_median 2475.5 93.1 26.6 57.595415 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", @@ -6047,7 +6352,7 @@ "metac-Gemini-Exp-1206 1595.7 77.5 20.6 67.099981 \n", "NextWorldLab 1583.0 81.2 19.5 66.411747 \n", "metac-o1-preview 1527.7 92.1 16.6 87.111568 \n", - "metac-deepseek-r1 1518.3 52.1 29.1 62.764970 \n", + "metac-deepseek-r1+asknews 1518.3 52.1 29.1 62.764970 \n", "laylaps 1500.6 65.1 23.1 74.457365 \n", "mmBot 1482.7 93.1 15.9 79.990502 \n", "Grizeu_Bot 1399.5 52.4 26.7 60.886905 \n", @@ -6084,7 +6389,7 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.031713 4.374886 1.985277 38.4 \n", + "bot_median 5.969158 4.454476 1.985277 38.4 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", @@ -6096,7 +6401,7 @@ "metac-Gemini-Exp-1206 7.622046 2.701303 1.990426 35.8 \n", "NextWorldLab 7.367722 2.644427 1.988985 34.1 \n", "metac-o1-preview 9.077077 1.827344 1.985550 34.6 \n", - "metac-deepseek-r1 8.695578 3.351382 2.005379 46.6 \n", + "metac-deepseek-r1+asknews 8.695578 3.351382 2.005379 46.6 \n", "laylaps 9.228204 2.497799 1.996341 41.5 \n", "mmBot 8.290173 1.921090 1.985277 32.4 \n", "Grizeu_Bot 8.415222 3.176755 2.005555 43.6 \n", @@ -6133,7 +6438,7 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 14.4 0.999984 0.000032 \n", + "bot_median 14.7 0.999988 0.000024 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", @@ -6145,7 +6450,7 @@ "metac-Gemini-Exp-1206 5.4 0.995749 0.008502 \n", "NextWorldLab 4.8 0.995080 0.009840 \n", "metac-o1-preview -1.4 0.964539 0.070922 \n", - "metac-deepseek-r1 11.7 0.999241 0.001519 \n", + "metac-deepseek-r1+asknews 11.7 0.999241 0.001519 \n", "laylaps 4.6 0.992463 0.015074 \n", "mmBot -0.5 0.971093 0.057813 \n", "Grizeu_Bot 9.9 0.998740 0.002521 \n", @@ -6179,7 +6484,7 @@ "minefrac1 -25.4 0.279560 0.559119 " ] }, - "execution_count": 317, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -6195,7 +6500,7 @@ }, { "cell_type": "code", - "execution_count": 318, + "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -6268,18 +6573,18 @@ " NA\n", " \n", " \n", - " RPM_bot\n", - " -0.5\n", - " 7.0\n", + " bean_bot\n", + " -0.6\n", + " 4.7\n", " -0.1\n", - " 0.840163\n", - " 0.317552\n", - " -0.229115\n", - " 2.446912\n", - " 0.7\n", - " -0.8\n", - " 0.413195\n", - " 0.826390\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", " \n", " \n", " jonahsingerbot\n", @@ -6296,20 +6601,6 @@ " 0.007677\n", " \n", " \n", - " bean_bot\n", - " -0.6\n", - " 4.7\n", - " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", - " \n", - " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6352,6 +6643,20 @@ " 0.018953\n", " \n", " \n", + " RPM_bot\n", + " -1.3\n", + " 7.0\n", + " -0.2\n", + " 0.826978\n", + " 0.312568\n", + " -0.610596\n", + " 2.446912\n", + " 0.6\n", + " -1.0\n", + " 0.281933\n", + " 0.563865\n", + " \n", + " \n", " SynapseSeer\n", " -1.3\n", " 26.2\n", @@ -6436,18 +6741,32 @@ " 0.011127\n", " \n", " \n", + " metac-o1\n", + " -5.3\n", + " 91.1\n", + " -0.1\n", + " 0.908473\n", + " 0.095182\n", + " -0.611363\n", + " 1.985829\n", + " 0.1\n", + " -0.2\n", + " 0.271249\n", + " 0.542499\n", + " \n", + " \n", " annabot\n", - " -6.2\n", + " -5.9\n", " 29.3\n", " -0.2\n", - " 0.520869\n", - " 0.096226\n", - " -2.211795\n", + " 0.517575\n", + " 0.095618\n", + " -2.112203\n", " 2.044183\n", " -0.0\n", " -0.4\n", - " 0.017610\n", - " 0.035221\n", + " 0.021811\n", + " 0.043621\n", " \n", " \n", " 4Shadower\n", @@ -6465,17 +6784,17 @@ " \n", " \n", " cookics_bot_TEST\n", - " -6.5\n", + " -6.8\n", " 27.4\n", " -0.2\n", - " 0.747831\n", - " 0.142866\n", - " -1.667933\n", + " 0.747290\n", + " 0.142762\n", + " -1.737830\n", " 2.049541\n", - " 0.1\n", + " 0.0\n", " -0.5\n", - " 0.053575\n", - " 0.107149\n", + " 0.046947\n", + " 0.093894\n", " \n", " \n", " jkraybill_bot\n", @@ -6507,17 +6826,17 @@ " \n", " \n", " MWG\n", - " -9.8\n", + " -9.6\n", " 28.6\n", " -0.3\n", - " 0.705240\n", - " 0.131872\n", - " -2.589625\n", + " 0.711160\n", + " 0.132979\n", + " -2.535384\n", " 2.046561\n", " -0.1\n", " -0.6\n", - " 0.007581\n", - " 0.015163\n", + " 0.008595\n", + " 0.017191\n", " \n", " \n", " ProfessorSP\n", @@ -6534,20 +6853,6 @@ " 0.023289\n", " \n", " \n", - " metac-o1\n", - " -10.4\n", - " 91.1\n", - " -0.1\n", - " 0.931550\n", - " 0.097599\n", - " -1.171004\n", - " 1.985829\n", - " 0.1\n", - " -0.3\n", - " 0.122342\n", - " 0.244685\n", - " \n", - " \n", " acm_bot\n", " -10.5\n", " 80.2\n", @@ -6568,12 +6873,12 @@ " -0.2\n", " 0.849331\n", " 0.111188\n", - " -1.638406\n", + " -1.638794\n", " 2.000832\n", " 0.0\n", " -0.4\n", - " 0.053406\n", - " 0.106813\n", + " 0.053366\n", + " 0.106731\n", " \n", " \n", " ajf-bot\n", @@ -6590,20 +6895,6 @@ " 0.094289\n", " \n", " \n", - " bot_median\n", - " -11.1\n", - " 92.1\n", - " -0.1\n", - " 0.834391\n", - " 0.086944\n", - " -1.391942\n", - " 1.985550\n", - " 0.1\n", - " -0.3\n", - " 0.083665\n", - " 0.167329\n", - " \n", - " \n", " Bot_Pepa\n", " -11.5\n", " 44.0\n", @@ -6618,6 +6909,20 @@ " 0.023810\n", " \n", " \n", + " metac-deepseek-r1+asknews\n", + " -11.7\n", + " 52.1\n", + " -0.2\n", + " 0.669031\n", + " 0.092689\n", + " -2.432744\n", + " 2.005379\n", + " -0.0\n", + " -0.4\n", + " 0.009262\n", + " 0.018524\n", + " \n", + " \n", " laylaps\n", " -12.9\n", " 64.1\n", @@ -6646,60 +6951,46 @@ " 0.006348\n", " \n", " \n", - " metac-deepseek-r1\n", - " -14.1\n", - " 52.1\n", - " -0.3\n", - " 0.817209\n", - " 0.113218\n", - " -2.393750\n", - " 2.005379\n", - " -0.0\n", - " -0.5\n", - " 0.010193\n", - " 0.020386\n", - " \n", - " \n", - " manticAI\n", - " -14.6\n", - " 69.4\n", + " metac-perplexity\n", + " -13.6\n", + " 89.1\n", " -0.2\n", - " 0.670946\n", - " 0.080510\n", - " -2.613354\n", - " 1.993968\n", - " -0.0\n", + " 0.953801\n", + " 0.101046\n", + " -1.515249\n", + " 1.986405\n", + " 0.0\n", " -0.4\n", - " 0.005507\n", - " 0.011014\n", + " 0.066645\n", + " 0.133289\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " -14.6\n", + " -13.9\n", " 76.5\n", " -0.2\n", - " 0.936930\n", - " 0.107121\n", - " -1.780658\n", + " 0.960843\n", + " 0.109855\n", + " -1.650953\n", " 1.990822\n", " 0.0\n", " -0.4\n", - " 0.039496\n", - " 0.078991\n", + " 0.051451\n", + " 0.102902\n", " \n", " \n", - " metac-perplexity\n", - " -16.1\n", - " 89.1\n", + " manticAI\n", + " -14.6\n", + " 69.4\n", " -0.2\n", - " 1.069491\n", - " 0.113302\n", - " -1.599489\n", - " 1.986405\n", - " 0.0\n", + " 0.670946\n", + " 0.080510\n", + " -2.613354\n", + " 1.993968\n", + " -0.0\n", " -0.4\n", - " 0.056646\n", - " 0.113292\n", + " 0.005507\n", + " 0.011014\n", " \n", " \n", " NextWorldLab\n", @@ -6716,60 +7007,74 @@ " 0.040909\n", " \n", " \n", - " minefrac1\n", - " -18.5\n", - " 51.1\n", + " metac-claude-3-5-sonnet-latest\n", + " -17.7\n", + " 91.1\n", + " -0.2\n", + " 0.822269\n", + " 0.086150\n", + " -2.253410\n", + " 1.985829\n", + " -0.0\n", " -0.4\n", - " 0.878223\n", - " 0.122855\n", - " -2.945421\n", - " 2.006545\n", - " -0.1\n", - " -0.6\n", - " 0.002441\n", - " 0.004882\n", + " 0.013330\n", + " 0.026660\n", + " \n", + " \n", + " bot_median\n", + " -17.9\n", + " 92.1\n", + " -0.2\n", + " 0.829829\n", + " 0.086469\n", + " -2.248076\n", + " 1.985550\n", + " -0.0\n", + " -0.4\n", + " 0.013492\n", + " 0.026984\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -20.8\n", + " -18.2\n", " 90.5\n", " -0.2\n", - " 0.985458\n", - " 0.103589\n", - " -2.217659\n", + " 0.988222\n", + " 0.103880\n", + " -1.930829\n", " 1.986072\n", - " -0.0\n", + " 0.0\n", + " -0.4\n", + " 0.028335\n", + " 0.056670\n", + " \n", + " \n", + " minefrac1\n", + " -18.8\n", + " 51.1\n", " -0.4\n", - " 0.014555\n", - " 0.029110\n", + " 0.874752\n", + " 0.122370\n", + " -3.013581\n", + " 2.006545\n", + " -0.1\n", + " -0.6\n", + " 0.002021\n", + " 0.004043\n", " \n", " \n", " metac-Llama-3.1\n", - " -21.0\n", + " -21.3\n", " 89.1\n", " -0.2\n", - " 1.131903\n", - " 0.119914\n", - " -1.966710\n", + " 0.912804\n", + " 0.096703\n", + " -2.471743\n", " 1.986405\n", - " 0.0\n", - " -0.5\n", - " 0.026182\n", - " 0.052364\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-latest\n", - " -21.7\n", - " 91.1\n", - " -0.2\n", - " 0.867992\n", - " 0.090940\n", - " -2.614756\n", - " 1.985829\n", - " -0.1\n", + " -0.0\n", " -0.4\n", - " 0.005233\n", - " 0.010466\n", + " 0.007684\n", + " 0.015368\n", " \n", " \n", " mmBot\n", @@ -6786,18 +7091,32 @@ " 0.002208\n", " \n", " \n", + " metac-exa\n", + " -22.4\n", + " 89.1\n", + " -0.3\n", + " 0.812802\n", + " 0.086108\n", + " -2.923729\n", + " 1.986405\n", + " -0.1\n", + " -0.4\n", + " 0.002198\n", + " 0.004396\n", + " \n", + " \n", " pgodzinai\n", - " -23.5\n", + " -23.9\n", " 76.4\n", " -0.3\n", - " 0.973567\n", - " 0.111383\n", - " -2.763550\n", + " 0.991479\n", + " 0.113432\n", + " -2.755452\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.003591\n", - " 0.007181\n", + " 0.003672\n", + " 0.007345\n", " \n", " \n", " VeritasAI\n", @@ -6814,74 +7133,60 @@ " 0.000076\n", " \n", " \n", - " metac-exa\n", - " -24.7\n", - " 89.1\n", - " -0.3\n", - " 0.812195\n", - " 0.086044\n", - " -3.219787\n", - " 1.986405\n", - " -0.1\n", - " -0.4\n", - " 0.000899\n", - " 0.001797\n", - " \n", - " \n", - " metac-o1-preview\n", - " -25.5\n", + " metac-grok-2-1212\n", + " -24.5\n", " 91.1\n", " -0.3\n", - " 0.849888\n", - " 0.089044\n", - " -3.149214\n", + " 1.013996\n", + " 0.106237\n", + " -2.526844\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.001111\n", - " 0.002221\n", + " 0.006627\n", + " 0.013254\n", " \n", " \n", - " InstitutPelFutur\n", - " -26.9\n", - " 90.1\n", + " metac-gpt-4o\n", + " -26.0\n", + " 91.1\n", " -0.3\n", - " 0.973971\n", - " 0.102609\n", - " -2.904302\n", - " 1.986114\n", + " 0.851645\n", + " 0.089228\n", + " -3.193010\n", + " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.002320\n", - " 0.004640\n", + " 0.000970\n", + " 0.001940\n", " \n", " \n", - " metac-grok-2-1212\n", - " -27.9\n", + " metac-o1-preview\n", + " -26.2\n", " 91.1\n", " -0.3\n", - " 1.005409\n", - " 0.105338\n", - " -2.903858\n", + " 0.914333\n", + " 0.095796\n", + " -2.997048\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.002318\n", - " 0.004635\n", + " 0.001761\n", + " 0.003522\n", " \n", " \n", - " metac-gpt-4o\n", - " -28.8\n", - " 91.1\n", + " InstitutPelFutur\n", + " -26.9\n", + " 90.1\n", " -0.3\n", - " 0.819883\n", - " 0.085900\n", - " -3.676519\n", - " 1.985829\n", + " 0.973767\n", + " 0.102587\n", + " -2.908524\n", + " 1.986114\n", " -0.1\n", " -0.5\n", - " 0.000201\n", - " 0.000401\n", + " 0.002292\n", + " 0.004584\n", " \n", " \n", "\n", @@ -6891,149 +7196,149 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "RPM_bot -0.5 7.0 -0.1 0.840163 0.317552 \n", - "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", + "RPM_bot -1.3 7.0 -0.2 0.826978 0.312568 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", - "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", + "metac-o1 -5.3 91.1 -0.1 0.908473 0.095182 \n", + "annabot -5.9 29.3 -0.2 0.517575 0.095618 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.5 27.4 -0.2 0.747831 0.142866 \n", + "cookics_bot_TEST -6.8 27.4 -0.2 0.747290 0.142762 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", - "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", + "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", - "metac-o1 -10.4 91.1 -0.1 0.931550 0.097599 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", "GreeneiBot2 -10.6 58.4 -0.2 0.849331 0.111188 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", - "bot_median -11.1 92.1 -0.1 0.834391 0.086944 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", + "metac-deepseek-r1+asknews -11.7 52.1 -0.2 0.669031 0.092689 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", - "metac-deepseek-r1 -14.1 52.1 -0.3 0.817209 0.113218 \n", + "metac-perplexity -13.6 89.1 -0.2 0.953801 0.101046 \n", + "metac-Gemini-Exp-1206 -13.9 76.5 -0.2 0.960843 0.109855 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-Gemini-Exp-1206 -14.6 76.5 -0.2 0.936930 0.107121 \n", - "metac-perplexity -16.1 89.1 -0.2 1.069491 0.113302 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "minefrac1 -18.5 51.1 -0.4 0.878223 0.122855 \n", - "metac-claude-3-5-sonnet-20240620 -20.8 90.5 -0.2 0.985458 0.103589 \n", - "metac-Llama-3.1 -21.0 89.1 -0.2 1.131903 0.119914 \n", - "metac-claude-3-5-sonnet-latest -21.7 91.1 -0.2 0.867992 0.090940 \n", + "metac-claude-3-5-sonnet-latest -17.7 91.1 -0.2 0.822269 0.086150 \n", + "bot_median -17.9 92.1 -0.2 0.829829 0.086469 \n", + "metac-claude-3-5-sonnet-20240620 -18.2 90.5 -0.2 0.988222 0.103880 \n", + "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", + "metac-Llama-3.1 -21.3 89.1 -0.2 0.912804 0.096703 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "pgodzinai -23.5 76.4 -0.3 0.973567 0.111383 \n", + "metac-exa -22.4 89.1 -0.3 0.812802 0.086108 \n", + "pgodzinai -23.9 76.4 -0.3 0.991479 0.113432 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-exa -24.7 89.1 -0.3 0.812195 0.086044 \n", - "metac-o1-preview -25.5 91.1 -0.3 0.849888 0.089044 \n", - "InstitutPelFutur -26.9 90.1 -0.3 0.973971 0.102609 \n", - "metac-grok-2-1212 -27.9 91.1 -0.3 1.005409 0.105338 \n", - "metac-gpt-4o -28.8 91.1 -0.3 0.819883 0.085900 \n", + "metac-grok-2-1212 -24.5 91.1 -0.3 1.013996 0.106237 \n", + "metac-gpt-4o -26.0 91.1 -0.3 0.851645 0.089228 \n", + "metac-o1-preview -26.2 91.1 -0.3 0.914333 0.095796 \n", + "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "RPM_bot -0.229115 2.446912 0.7 \n", - "jonahsingerbot -5.273630 2.784843 -0.1 \n", "bean_bot -4.265106 2.784843 -0.0 \n", + "jonahsingerbot -5.273630 2.784843 -0.1 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", + "RPM_bot -0.610596 2.446912 0.6 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", "CatrachoCaster -1.365532 2.088777 0.1 \n", "krm-bot -3.229846 2.264709 -0.2 \n", - "annabot -2.211795 2.044183 -0.0 \n", + "metac-o1 -0.611363 1.985829 0.1 \n", + "annabot -2.112203 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.667933 2.049541 0.1 \n", + "cookics_bot_TEST -1.737830 2.049541 0.0 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", - "MWG -2.589625 2.046561 -0.1 \n", + "MWG -2.535384 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", - "metac-o1 -1.171004 1.985829 0.1 \n", "acm_bot -1.287717 1.989344 0.1 \n", - "GreeneiBot2 -1.638406 2.000832 0.0 \n", + "GreeneiBot2 -1.638794 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", - "bot_median -1.391942 1.985550 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", + "metac-deepseek-r1+asknews -2.432744 2.005379 -0.0 \n", "laylaps -2.440461 1.996907 -0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", - "metac-deepseek-r1 -2.393750 2.005379 -0.0 \n", + "metac-perplexity -1.515249 1.986405 0.0 \n", + "metac-Gemini-Exp-1206 -1.650953 1.990822 0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-Gemini-Exp-1206 -1.780658 1.990822 0.0 \n", - "metac-perplexity -1.599489 1.986405 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "minefrac1 -2.945421 2.006545 -0.1 \n", - "metac-claude-3-5-sonnet-20240620 -2.217659 1.986072 -0.0 \n", - "metac-Llama-3.1 -1.966710 1.986405 0.0 \n", - "metac-claude-3-5-sonnet-latest -2.614756 1.985829 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.253410 1.985829 -0.0 \n", + "bot_median -2.248076 1.985550 -0.0 \n", + "metac-claude-3-5-sonnet-20240620 -1.930829 1.986072 0.0 \n", + "minefrac1 -3.013581 2.006545 -0.1 \n", + "metac-Llama-3.1 -2.471743 1.986405 -0.0 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "pgodzinai -2.763550 1.990849 -0.1 \n", + "metac-exa -2.923729 1.986405 -0.1 \n", + "pgodzinai -2.755452 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-exa -3.219787 1.986405 -0.1 \n", - "metac-o1-preview -3.149214 1.985829 -0.1 \n", - "InstitutPelFutur -2.904302 1.986114 -0.1 \n", - "metac-grok-2-1212 -2.903858 1.985829 -0.1 \n", - "metac-gpt-4o -3.676519 1.985829 -0.1 \n", + "metac-grok-2-1212 -2.526844 1.985829 -0.1 \n", + "metac-gpt-4o -3.193010 1.985829 -0.1 \n", + "metac-o1-preview -2.997048 1.985829 -0.1 \n", + "InstitutPelFutur -2.908524 1.986114 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "RPM_bot -0.8 0.413195 0.826390 \n", - "jonahsingerbot -0.2 0.003839 0.007677 \n", "bean_bot -0.2 0.007674 0.015349 \n", + "jonahsingerbot -0.2 0.003839 0.007677 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", + "RPM_bot -1.0 0.281933 0.563865 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", "CatrachoCaster -0.4 0.094144 0.188288 \n", "krm-bot -0.9 0.005563 0.011127 \n", - "annabot -0.4 0.017610 0.035221 \n", + "metac-o1 -0.2 0.271249 0.542499 \n", + "annabot -0.4 0.021811 0.043621 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.053575 0.107149 \n", + "cookics_bot_TEST -0.5 0.046947 0.093894 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", - "MWG -0.6 0.007581 0.015163 \n", + "MWG -0.6 0.008595 0.017191 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", - "metac-o1 -0.3 0.122342 0.244685 \n", "acm_bot -0.3 0.100796 0.201592 \n", - "GreeneiBot2 -0.4 0.053406 0.106813 \n", + "GreeneiBot2 -0.4 0.053366 0.106731 \n", "ajf-bot -0.7 0.047145 0.094289 \n", - "bot_median -0.3 0.083665 0.167329 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", + "metac-deepseek-r1+asknews -0.4 0.009262 0.018524 \n", "laylaps -0.4 0.008744 0.017488 \n", "wunderplumb -0.9 0.003174 0.006348 \n", - "metac-deepseek-r1 -0.5 0.010193 0.020386 \n", + "metac-perplexity -0.4 0.066645 0.133289 \n", + "metac-Gemini-Exp-1206 -0.4 0.051451 0.102902 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-Gemini-Exp-1206 -0.4 0.039496 0.078991 \n", - "metac-perplexity -0.4 0.056646 0.113292 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "minefrac1 -0.6 0.002441 0.004882 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.014555 0.029110 \n", - "metac-Llama-3.1 -0.5 0.026182 0.052364 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.005233 0.010466 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.013330 0.026660 \n", + "bot_median -0.4 0.013492 0.026984 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.028335 0.056670 \n", + "minefrac1 -0.6 0.002021 0.004043 \n", + "metac-Llama-3.1 -0.4 0.007684 0.015368 \n", "mmBot -0.4 0.001104 0.002208 \n", - "pgodzinai -0.5 0.003591 0.007181 \n", + "metac-exa -0.4 0.002198 0.004396 \n", + "pgodzinai -0.5 0.003672 0.007345 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-exa -0.4 0.000899 0.001797 \n", - "metac-o1-preview -0.5 0.001111 0.002221 \n", - "InstitutPelFutur -0.5 0.002320 0.004640 \n", - "metac-grok-2-1212 -0.5 0.002318 0.004635 \n", - "metac-gpt-4o -0.5 0.000201 0.000401 " + "metac-grok-2-1212 -0.5 0.006627 0.013254 \n", + "metac-gpt-4o -0.5 0.000970 0.001940 \n", + "metac-o1-preview -0.5 0.001761 0.003522 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 " ] }, - "execution_count": 318, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -7059,7 +7364,7 @@ }, { "cell_type": "code", - "execution_count": 319, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -7069,7 +7374,7 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 44, "metadata": { "cellView": "form", "colab": { @@ -7311,7 +7616,7 @@ " \n", " 12\n", " 13\n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 516.8\n", " 277.9\n", " 1.9\n", @@ -7854,7 +8159,7 @@ "9 10 metac-claude-3-5-sonnet-latest 951.3 370.3 2.6 \n", "10 11 GreeneiBot2 1494.7 264.1 5.7 \n", "11 12 metac-perplexity 1558.4 354.4 4.4 \n", - "12 13 metac-deepseek-r1 516.8 277.9 1.9 \n", + "12 13 metac-deepseek-r1+asknews 516.8 277.9 1.9 \n", "13 14 pgodzinai 1106.7 325.4 3.4 \n", "14 15 metac-exa 599.9 365.3 1.6 \n", "15 16 MWG 253.8 113.4 2.2 \n", @@ -7983,7 +8288,7 @@ "44 0.040339 0.080679 " ] }, - "execution_count": 320, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -8022,7 +8327,7 @@ }, { "cell_type": "code", - "execution_count": 321, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -8032,7 +8337,7 @@ }, { "cell_type": "code", - "execution_count": 322, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -8237,7 +8542,7 @@ "[5 rows x 48 columns]" ] }, - "execution_count": 322, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -8248,7 +8553,7 @@ }, { "cell_type": "code", - "execution_count": 323, + "execution_count": 47, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -8310,7 +8615,7 @@ }, { "cell_type": "code", - "execution_count": 324, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -8732,7 +9037,7 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 49, "metadata": { "cellView": "form", "colab": { @@ -8782,139 +9087,139 @@ " \n", " \n", " metac-o1\n", - " 6.1\n", + " 6.0\n", " 7.2\n", - " 9.6\n", - " 11.9\n", - " 13.1\n", + " 9.5\n", + " 11.8\n", + " 12.8\n", " \n", " \n", " metac-o1-preview\n", - " 3.7\n", - " 5.3\n", - " 8.3\n", - " 11.3\n", - " 12.7\n", + " 3.8\n", + " 5.2\n", + " 8.2\n", + " 11.1\n", + " 12.6\n", " \n", " \n", " manticAI\n", - " 0.0\n", + " 0.5\n", " 2.2\n", - " 5.7\n", + " 5.6\n", " 8.9\n", - " 10.6\n", + " 10.5\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.6\n", - " 2.2\n", - " 4.9\n", - " 7.8\n", - " 9.3\n", + " 0.7\n", + " 2.1\n", + " 4.8\n", + " 7.5\n", + " 8.9\n", " \n", " \n", " acm_bot\n", " 0.1\n", - " 1.7\n", - " 4.7\n", + " 1.8\n", + " 4.6\n", " 7.6\n", - " 8.8\n", + " 8.9\n", " \n", " \n", " metac-perplexity\n", - " -1.6\n", - " 0.2\n", + " -1.5\n", + " 0.5\n", " 4.2\n", - " 7.9\n", - " 9.5\n", + " 7.7\n", + " 9.3\n", " \n", " \n", " GreeneiBot2\n", - " -1.4\n", - " 0.6\n", - " 4.0\n", - " 7.3\n", - " 9.0\n", + " -1.2\n", + " 0.7\n", + " 4.1\n", + " 7.4\n", + " 9.7\n", " \n", " \n", " twsummerbot\n", " 0.3\n", - " 1.6\n", - " 3.7\n", - " 6.2\n", - " 7.4\n", + " 1.5\n", + " 3.8\n", + " 6.1\n", + " 7.5\n", " \n", " \n", " pgodzinai\n", - " -3.8\n", + " -2.9\n", " -1.0\n", " 3.1\n", - " 7.1\n", + " 7.2\n", " 9.4\n", " \n", " \n", " cookics_bot_TEST\n", - " -0.3\n", - " 1.0\n", - " 3.1\n", + " -0.0\n", + " 1.1\n", + " 3.0\n", " 5.0\n", " 6.1\n", " \n", " \n", " CumulativeBot\n", - " -0.2\n", + " -0.1\n", " 0.8\n", - " 2.6\n", - " 4.4\n", + " 2.7\n", + " 4.5\n", " 5.4\n", " \n", " \n", " SynapseSeer\n", " 0.4\n", - " 1.1\n", + " 1.2\n", " 2.5\n", - " 4.1\n", - " 4.9\n", + " 4.0\n", + " 4.8\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.4\n", - " 0.1\n", - " 2.4\n", + " -1.3\n", + " -0.1\n", + " 2.5\n", " 4.9\n", - " 6.1\n", + " 6.3\n", + " \n", + " \n", + " metac-exa\n", + " -5.0\n", + " -2.6\n", + " 2.0\n", + " 5.8\n", + " 7.8\n", " \n", " \n", " jkraybill_bot\n", - " -3.4\n", + " -4.3\n", " -1.7\n", - " 1.8\n", + " 1.7\n", " 4.9\n", - " 6.2\n", - " \n", - " \n", - " metac-exa\n", - " -4.6\n", - " -2.3\n", - " 1.6\n", - " 5.5\n", - " 7.7\n", + " 6.6\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " -2.0\n", " -0.8\n", " 1.3\n", - " 3.4\n", - " 4.4\n", + " 3.3\n", + " 4.5\n", " \n", " \n", " MWG\n", - " -1.7\n", - " -0.8\n", - " 0.7\n", - " 2.1\n", - " 2.9\n", + " -1.5\n", + " -0.7\n", + " 0.8\n", + " 2.2\n", + " 2.8\n", " \n", " \n", " andrewsiah\n", @@ -8925,17 +9230,9 @@ " 0.9\n", " \n", " \n", - " cobyj-bot\n", - " -1.4\n", - " -0.9\n", - " -0.0\n", - " 0.9\n", - " 1.4\n", - " \n", - " \n", " X_bot\n", " -0.4\n", - " -0.2\n", + " -0.3\n", " -0.0\n", " 0.1\n", " 0.2\n", @@ -8943,202 +9240,210 @@ " \n", " pianobot\n", " -1.3\n", - " -0.8\n", + " -0.9\n", " -0.0\n", " 0.7\n", " 1.1\n", " \n", " \n", + " cobyj-bot\n", + " -1.3\n", + " -0.9\n", + " -0.1\n", + " 0.8\n", + " 1.4\n", + " \n", + " \n", " annabot\n", - " -3.5\n", - " -2.4\n", - " -0.5\n", - " 1.1\n", + " -3.9\n", + " -2.5\n", + " -0.4\n", + " 1.3\n", " 2.0\n", " \n", " \n", - " bean_bot\n", - " -2.9\n", - " -2.1\n", + " KevinTestBot\n", + " -4.0\n", + " -2.7\n", " -0.5\n", - " 1.3\n", - " 2.1\n", + " 1.6\n", + " 2.7\n", " \n", " \n", - " KevinTestBot\n", - " -4.0\n", - " -2.6\n", + " bean_bot\n", + " -3.3\n", + " -2.2\n", " -0.5\n", - " 1.5\n", - " 2.6\n", + " 0.9\n", + " 1.7\n", " \n", " \n", " CatrachoCaster\n", - " -2.2\n", - " -1.7\n", - " -0.8\n", + " -2.3\n", + " -1.8\n", + " -0.7\n", " 0.2\n", - " 0.7\n", + " 0.6\n", " \n", " \n", " jonahsingerbot\n", - " -2.8\n", - " -2.3\n", - " -0.8\n", - " 0.5\n", - " 1.2\n", + " -2.9\n", + " -2.2\n", + " -0.9\n", + " 0.4\n", + " 0.9\n", " \n", " \n", " krm-bot\n", - " -3.4\n", - " -2.5\n", - " -1.0\n", - " 0.8\n", - " 1.6\n", + " -3.6\n", + " -2.6\n", + " -0.9\n", + " 0.7\n", + " 1.7\n", " \n", " \n", " ProfessorSP\n", - " -4.5\n", - " -3.3\n", - " -1.0\n", + " -4.2\n", + " -3.2\n", + " -1.1\n", " 1.0\n", - " 1.9\n", + " 2.1\n", " \n", " \n", - " metac-grok-2-1212\n", - " -6.4\n", - " -4.9\n", - " -1.6\n", - " 1.8\n", - " 3.1\n", + " mmBot\n", + " -7.0\n", + " -5.2\n", + " -1.2\n", + " 2.3\n", + " 4.4\n", " \n", " \n", - " mmBot\n", - " -7.3\n", - " -5.5\n", - " -1.6\n", - " 2.2\n", - " 3.9\n", + " metac-grok-2-1212\n", + " -6.6\n", + " -5.0\n", + " -1.5\n", + " 1.7\n", + " 3.7\n", " \n", " \n", " 4Shadower\n", - " -5.0\n", - " -3.8\n", + " -4.6\n", + " -3.6\n", " -1.7\n", " 0.2\n", " 1.2\n", " \n", " \n", " swingswish\n", - " -5.4\n", - " -4.2\n", - " -2.0\n", - " -0.1\n", - " 0.9\n", + " -5.3\n", + " -3.9\n", + " -1.9\n", + " -0.2\n", + " 0.6\n", + " \n", + " \n", + " InstitutPelFutur\n", + " -8.7\n", + " -6.6\n", + " -2.1\n", + " 1.7\n", + " 4.0\n", " \n", " \n", " RPM_bot\n", - " -4.9\n", - " -3.9\n", - " -2.0\n", + " -4.6\n", + " -3.7\n", + " -2.1\n", " -0.7\n", - " -0.1\n", + " -0.0\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -6.5\n", - " -4.8\n", - " -2.0\n", - " 0.8\n", - " 2.7\n", - " \n", - " \n", - " InstitutPelFutur\n", - " -9.2\n", - " -6.7\n", + " -6.6\n", + " -5.0\n", " -2.2\n", - " 1.6\n", - " 4.0\n", + " 0.7\n", + " 2.4\n", " \n", " \n", " wunderplumb\n", - " -6.5\n", - " -5.1\n", + " -6.4\n", + " -5.0\n", " -2.6\n", - " -0.2\n", - " 0.7\n", + " -0.4\n", + " 0.8\n", " \n", " \n", " metac-Llama-3.1\n", " -6.9\n", - " -5.3\n", - " -2.7\n", - " -0.1\n", - " 1.4\n", + " -5.5\n", + " -2.8\n", + " -0.0\n", + " 1.7\n", " \n", " \n", " NextWorldLab\n", - " -8.6\n", - " -6.7\n", + " -8.8\n", + " -6.8\n", " -3.6\n", - " -0.6\n", - " 1.0\n", + " -0.4\n", + " 1.8\n", " \n", " \n", - " Bot_Pepa\n", - " -7.0\n", - " -5.9\n", + " laylaps\n", + " -9.6\n", + " -7.8\n", " -3.8\n", - " -1.9\n", - " -1.0\n", + " -0.2\n", + " 1.4\n", " \n", " \n", - " laylaps\n", - " -9.7\n", - " -7.7\n", - " -4.0\n", - " -0.1\n", - " 2.2\n", + " Bot_Pepa\n", + " -7.1\n", + " -6.0\n", + " -3.9\n", + " -2.1\n", + " -1.2\n", " \n", " \n", " VeritasAI\n", - " -7.7\n", - " -6.6\n", - " -4.2\n", - " -1.8\n", - " -0.5\n", + " -7.5\n", + " -6.5\n", + " -4.3\n", + " -1.9\n", + " -0.8\n", " \n", " \n", " minefrac1\n", - " -7.9\n", - " -6.8\n", + " -7.6\n", + " -6.7\n", " -4.6\n", " -2.5\n", - " -1.7\n", + " -1.6\n", " \n", " \n", " Grizeu_Bot\n", - " -9.0\n", - " -7.6\n", + " -9.2\n", + " -7.9\n", " -5.0\n", - " -2.2\n", - " -0.6\n", + " -2.5\n", + " -1.2\n", " \n", " \n", " metac-gpt-4o\n", - " -10.6\n", - " -8.9\n", + " -10.7\n", + " -9.0\n", " -6.0\n", - " -2.9\n", - " -1.6\n", + " -3.2\n", + " -1.8\n", " \n", " \n", " ajf-bot\n", - " -14.6\n", - " -12.6\n", + " -15.7\n", + " -12.9\n", " -8.5\n", - " -4.4\n", - " -2.4\n", + " -4.2\n", + " -2.0\n", " \n", " \n", "\n", @@ -9146,54 +9451,54 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.1 7.2 9.6 11.9 13.1\n", - "metac-o1-preview 3.7 5.3 8.3 11.3 12.7\n", - "manticAI 0.0 2.2 5.7 8.9 10.6\n", - "metac-Gemini-Exp-1206 0.6 2.2 4.9 7.8 9.3\n", - "acm_bot 0.1 1.7 4.7 7.6 8.8\n", - "metac-perplexity -1.6 0.2 4.2 7.9 9.5\n", - "GreeneiBot2 -1.4 0.6 4.0 7.3 9.0\n", - "twsummerbot 0.3 1.6 3.7 6.2 7.4\n", - "pgodzinai -3.8 -1.0 3.1 7.1 9.4\n", - "cookics_bot_TEST -0.3 1.0 3.1 5.0 6.1\n", - "CumulativeBot -0.2 0.8 2.6 4.4 5.4\n", - "SynapseSeer 0.4 1.1 2.5 4.1 4.9\n", - "metac-claude-3-5-sonnet-latest -1.4 0.1 2.4 4.9 6.1\n", - "jkraybill_bot -3.4 -1.7 1.8 4.9 6.2\n", - "metac-exa -4.6 -2.3 1.6 5.5 7.7\n", - "metac-deepseek-r1 -2.0 -0.8 1.3 3.4 4.4\n", - "MWG -1.7 -0.8 0.7 2.1 2.9\n", + "metac-o1 6.0 7.2 9.5 11.8 12.8\n", + "metac-o1-preview 3.8 5.2 8.2 11.1 12.6\n", + "manticAI 0.5 2.2 5.6 8.9 10.5\n", + "metac-Gemini-Exp-1206 0.7 2.1 4.8 7.5 8.9\n", + "acm_bot 0.1 1.8 4.6 7.6 8.9\n", + "metac-perplexity -1.5 0.5 4.2 7.7 9.3\n", + "GreeneiBot2 -1.2 0.7 4.1 7.4 9.7\n", + "twsummerbot 0.3 1.5 3.8 6.1 7.5\n", + "pgodzinai -2.9 -1.0 3.1 7.2 9.4\n", + "cookics_bot_TEST -0.0 1.1 3.0 5.0 6.1\n", + "CumulativeBot -0.1 0.8 2.7 4.5 5.4\n", + "SynapseSeer 0.4 1.2 2.5 4.0 4.8\n", + "metac-claude-3-5-sonnet-latest -1.3 -0.1 2.5 4.9 6.3\n", + "metac-exa -5.0 -2.6 2.0 5.8 7.8\n", + "jkraybill_bot -4.3 -1.7 1.7 4.9 6.6\n", + "metac-deepseek-r1+asknews -2.0 -0.8 1.3 3.3 4.5\n", + "MWG -1.5 -0.7 0.8 2.2 2.8\n", "andrewsiah -0.9 -0.6 0.0 0.6 0.9\n", - "cobyj-bot -1.4 -0.9 -0.0 0.9 1.4\n", - "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", - "annabot -3.5 -2.4 -0.5 1.1 2.0\n", - "bean_bot -2.9 -2.1 -0.5 1.3 2.1\n", - "KevinTestBot -4.0 -2.6 -0.5 1.5 2.6\n", - "CatrachoCaster -2.2 -1.7 -0.8 0.2 0.7\n", - "jonahsingerbot -2.8 -2.3 -0.8 0.5 1.2\n", - "krm-bot -3.4 -2.5 -1.0 0.8 1.6\n", - "ProfessorSP -4.5 -3.3 -1.0 1.0 1.9\n", - "metac-grok-2-1212 -6.4 -4.9 -1.6 1.8 3.1\n", - "mmBot -7.3 -5.5 -1.6 2.2 3.9\n", - "4Shadower -5.0 -3.8 -1.7 0.2 1.2\n", - "swingswish -5.4 -4.2 -2.0 -0.1 0.9\n", - "RPM_bot -4.9 -3.9 -2.0 -0.7 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -6.5 -4.8 -2.0 0.8 2.7\n", - "InstitutPelFutur -9.2 -6.7 -2.2 1.6 4.0\n", - "wunderplumb -6.5 -5.1 -2.6 -0.2 0.7\n", - "metac-Llama-3.1 -6.9 -5.3 -2.7 -0.1 1.4\n", - "NextWorldLab -8.6 -6.7 -3.6 -0.6 1.0\n", - "Bot_Pepa -7.0 -5.9 -3.8 -1.9 -1.0\n", - "laylaps -9.7 -7.7 -4.0 -0.1 2.2\n", - "VeritasAI -7.7 -6.6 -4.2 -1.8 -0.5\n", - "minefrac1 -7.9 -6.8 -4.6 -2.5 -1.7\n", - "Grizeu_Bot -9.0 -7.6 -5.0 -2.2 -0.6\n", - "metac-gpt-4o -10.6 -8.9 -6.0 -2.9 -1.6\n", - "ajf-bot -14.6 -12.6 -8.5 -4.4 -2.4" + "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "pianobot -1.3 -0.9 -0.0 0.7 1.1\n", + "cobyj-bot -1.3 -0.9 -0.1 0.8 1.4\n", + "annabot -3.9 -2.5 -0.4 1.3 2.0\n", + "KevinTestBot -4.0 -2.7 -0.5 1.6 2.7\n", + "bean_bot -3.3 -2.2 -0.5 0.9 1.7\n", + "CatrachoCaster -2.3 -1.8 -0.7 0.2 0.6\n", + "jonahsingerbot -2.9 -2.2 -0.9 0.4 0.9\n", + "krm-bot -3.6 -2.6 -0.9 0.7 1.7\n", + "ProfessorSP -4.2 -3.2 -1.1 1.0 2.1\n", + "mmBot -7.0 -5.2 -1.2 2.3 4.4\n", + "metac-grok-2-1212 -6.6 -5.0 -1.5 1.7 3.7\n", + "4Shadower -4.6 -3.6 -1.7 0.2 1.2\n", + "swingswish -5.3 -3.9 -1.9 -0.2 0.6\n", + "InstitutPelFutur -8.7 -6.6 -2.1 1.7 4.0\n", + "RPM_bot -4.6 -3.7 -2.1 -0.7 -0.0\n", + "metac-claude-3-5-sonnet-20240620 -6.6 -5.0 -2.2 0.7 2.4\n", + "wunderplumb -6.4 -5.0 -2.6 -0.4 0.8\n", + "metac-Llama-3.1 -6.9 -5.5 -2.8 -0.0 1.7\n", + "NextWorldLab -8.8 -6.8 -3.6 -0.4 1.8\n", + "laylaps -9.6 -7.8 -3.8 -0.2 1.4\n", + "Bot_Pepa -7.1 -6.0 -3.9 -2.1 -1.2\n", + "VeritasAI -7.5 -6.5 -4.3 -1.9 -0.8\n", + "minefrac1 -7.6 -6.7 -4.6 -2.5 -1.6\n", + "Grizeu_Bot -9.2 -7.9 -5.0 -2.5 -1.2\n", + "metac-gpt-4o -10.7 -9.0 -6.0 -3.2 -1.8\n", + "ajf-bot -15.7 -12.9 -8.5 -4.2 -2.0" ] }, - "execution_count": 325, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -9216,7 +9521,7 @@ }, { "cell_type": "code", - "execution_count": 326, + "execution_count": 50, "metadata": { "cellView": "form", "colab": { @@ -9285,12 +9590,12 @@ " 0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", + " jonahsingerbot\n", + " -0.0\n", + " -0.0\n", + " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " X_bot\n", @@ -9301,7 +9606,7 @@ " 0.0\n", " \n", " \n", - " jonahsingerbot\n", + " bean_bot\n", " -0.0\n", " -0.0\n", " -0.0\n", @@ -9309,12 +9614,12 @@ " -0.0\n", " \n", " \n", - " bean_bot\n", - " -0.0\n", - " -0.0\n", - " -0.0\n", + " RPM_bot\n", + " -0.1\n", " -0.0\n", " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " CumulativeBot\n", @@ -9381,8 +9686,16 @@ " -0.0\n", " \n", " \n", - " 4Shadower\n", + " metac-o1\n", + " -0.2\n", + " -0.2\n", " -0.1\n", + " 0.1\n", + " 0.1\n", + " \n", + " \n", + " 4Shadower\n", + " -0.2\n", " -0.1\n", " -0.1\n", " -0.0\n", @@ -9402,11 +9715,11 @@ " -0.1\n", " -0.1\n", " -0.0\n", - " 0.0\n", + " -0.0\n", " \n", " \n", " jkraybill_bot\n", - " -0.2\n", + " -0.1\n", " -0.1\n", " -0.1\n", " -0.0\n", @@ -9425,7 +9738,7 @@ " -0.2\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " -0.0\n", " \n", " \n", @@ -9433,51 +9746,51 @@ " -0.2\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " -0.0\n", " \n", " \n", " GreeneiBot2\n", - " -0.3\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.0\n", " 0.0\n", " \n", " \n", - " metac-o1\n", + " ajf-bot\n", " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", " 0.0\n", - " 0.1\n", " \n", " \n", " acm_bot\n", " -0.3\n", " -0.2\n", " -0.1\n", - " 0.0\n", + " -0.0\n", " 0.1\n", " \n", " \n", - " ajf-bot\n", - " -0.3\n", + " Bot_Pepa\n", " -0.2\n", + " -0.2\n", + " -0.1\n", " -0.1\n", " -0.0\n", - " 0.0\n", " \n", " \n", - " bot_median\n", - " -0.3\n", + " metac-deepseek-r1+asknews\n", + " -0.2\n", " -0.2\n", " -0.1\n", + " -0.1\n", " -0.0\n", - " 0.1\n", " \n", " \n", - " Bot_Pepa\n", + " laylaps\n", " -0.2\n", " -0.2\n", " -0.1\n", @@ -9493,20 +9806,20 @@ " -0.1\n", " \n", " \n", - " laylaps\n", - " -0.2\n", - " -0.2\n", - " -0.1\n", + " metac-perplexity\n", + " -0.3\n", + " -0.3\n", " -0.1\n", " -0.0\n", + " 0.1\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-Gemini-Exp-1206\n", + " -0.3\n", " -0.3\n", - " -0.2\n", - " -0.1\n", " -0.1\n", " -0.0\n", + " 0.0\n", " \n", " \n", " manticAI\n", @@ -9517,63 +9830,63 @@ " -0.0\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", + " NextWorldLab\n", " -0.3\n", " -0.3\n", " -0.2\n", + " -0.1\n", " -0.0\n", - " 0.0\n", " \n", " \n", - " metac-perplexity\n", - " -0.4\n", + " metac-claude-3-5-sonnet-latest\n", + " -0.3\n", " -0.3\n", " -0.2\n", + " -0.1\n", " -0.0\n", - " 0.0\n", " \n", " \n", - " NextWorldLab\n", - " -0.3\n", + " metac-claude-3-5-sonnet-20240620\n", + " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " 0.0\n", " \n", " \n", - " minefrac1\n", + " bot_median\n", " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -0.4\n", + " minefrac1\n", + " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", " metac-Llama-3.1\n", " -0.4\n", - " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", - " 0.0\n", + " -0.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " mmBot\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", - " mmBot\n", + " metac-exa\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9582,7 +9895,7 @@ " \n", " \n", " pgodzinai\n", - " -0.4\n", + " -0.5\n", " -0.4\n", " -0.2\n", " -0.1\n", @@ -9597,15 +9910,15 @@ " -0.1\n", " \n", " \n", - " metac-exa\n", - " -0.4\n", + " metac-grok-2-1212\n", + " -0.5\n", " -0.4\n", " -0.3\n", - " -0.2\n", + " -0.1\n", " -0.1\n", " \n", " \n", - " metac-o1-preview\n", + " metac-gpt-4o\n", " -0.4\n", " -0.4\n", " -0.3\n", @@ -9613,23 +9926,15 @@ " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", - " -0.5\n", + " metac-o1-preview\n", " -0.4\n", - " -0.3\n", - " -0.2\n", - " -0.1\n", - " \n", - " \n", - " metac-grok-2-1212\n", - " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", + " InstitutPelFutur\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9644,10 +9949,10 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", - "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", @@ -9656,41 +9961,41 @@ "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", + "metac-o1 -0.2 -0.2 -0.1 0.1 0.1\n", + "4Shadower -0.2 -0.1 -0.1 -0.0 -0.0\n", "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", - "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", + "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 -0.0\n", + "jkraybill_bot -0.1 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", - "MWG -0.2 -0.2 -0.1 -0.1 -0.0\n", - "ProfessorSP -0.2 -0.2 -0.1 -0.1 -0.0\n", - "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", - "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", - "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", + "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", + "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", + "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", - "bot_median -0.3 -0.2 -0.1 -0.0 0.1\n", + "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", + "metac-deepseek-r1+asknews -0.2 -0.2 -0.1 -0.1 -0.0\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-deepseek-r1 -0.3 -0.2 -0.1 -0.1 -0.0\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", + "metac-perplexity -0.3 -0.3 -0.1 -0.0 0.1\n", + "metac-Gemini-Exp-1206 -0.3 -0.3 -0.1 -0.0 0.0\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "metac-Gemini-Exp-1206 -0.3 -0.3 -0.2 -0.0 0.0\n", - "metac-perplexity -0.4 -0.3 -0.2 -0.0 0.0\n", - "NextWorldLab -0.3 -0.3 -0.2 -0.1 0.0\n", + "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", + "metac-claude-3-5-sonnet-latest -0.3 -0.3 -0.2 -0.1 -0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", + "bot_median -0.3 -0.3 -0.2 -0.1 -0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 -0.0\n", - "metac-Llama-3.1 -0.4 -0.4 -0.2 -0.1 0.0\n", - "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.0\n", + "metac-Llama-3.1 -0.4 -0.3 -0.2 -0.1 -0.0\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", + "metac-exa -0.4 -0.3 -0.2 -0.1 -0.1\n", + "pgodzinai -0.5 -0.4 -0.2 -0.1 -0.1\n", "VeritasAI -0.4 -0.3 -0.2 -0.2 -0.1\n", - "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.1 -0.1\n", + "metac-gpt-4o -0.4 -0.4 -0.3 -0.2 -0.1\n", "metac-o1-preview -0.4 -0.4 -0.3 -0.2 -0.1\n", - "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" + "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1" ] }, - "execution_count": 326, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -9711,7 +10016,7 @@ }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -9721,7 +10026,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -9781,7 +10086,7 @@ }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 53, "metadata": { "cellView": "form", "colab": { @@ -9917,7 +10222,7 @@ " 0.153662\n", " \n", " \n", - " metac-deepseek-r1\n", + " metac-deepseek-r1+asknews\n", " 0.8\n", " 225.8\n", " -4.2\n", @@ -10190,7 +10495,7 @@ "twsummerbot 4.9 181.9 -1.8 11.6 \n", "cookics_bot_TEST 5.8 135.2 -1.8 13.4 \n", "CumulativeBot 8.0 94.2 -3.0 18.9 \n", - "metac-deepseek-r1 0.8 225.8 -4.2 5.8 \n", + "metac-deepseek-r1+asknews 0.8 225.8 -4.2 5.8 \n", "MWG 3.6 84.8 -4.3 11.5 \n", "metac-perplexity 2.8 264.3 -4.8 10.3 \n", "metac-grok-2-1212 0.1 281.2 -5.7 6.0 \n", @@ -10236,7 +10541,7 @@ "twsummerbot 0.152393 \n", "cookics_bot_TEST 0.132509 \n", "CumulativeBot 0.153662 \n", - "metac-deepseek-r1 0.763142 \n", + "metac-deepseek-r1+asknews 0.763142 \n", "MWG 0.365354 \n", "metac-perplexity 0.470416 \n", "metac-grok-2-1212 0.961620 \n", @@ -10270,7 +10575,7 @@ "RPM_bot 0.126191 " ] }, - "execution_count": 329, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -10291,7 +10596,7 @@ }, { "cell_type": "code", - "execution_count": 330, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -10300,7 +10605,7 @@ }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -10339,7 +10644,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 56, "metadata": { "cellView": "form", "id": "x6e1kZl12qFZ" @@ -10349,506 +10654,506 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.75]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.98]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.4]\n", " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.98]\n", + " >>> Collected 1 forecasts: [0.97]\n", " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.65]\n", - " >>> Collected 1 forecasts: [0.01]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.97]\n", " >>> Collected 1 forecasts: [0.99]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.75]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.02]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.8]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.15]\n", - " >>> Collected 2 forecasts: [0.2, 0.6]\n", - " >>> Collected 2 forecasts: [0.9, 0.8]\n", - " >>> Collected 2 forecasts: [0.75, 0.7]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.7]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.85, 0.75]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.8, 0.6]\n", - " >>> Collected 2 forecasts: [0.75, 0.35]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.8, 0.4]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.2, 0.35]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.7, 0.8]\n", - " >>> Collected 2 forecasts: [0.15, 0.5]\n", - " >>> Collected 2 forecasts: [0.25, 0.1]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.25, 0.2]\n", + " >>> Collected 2 forecasts: [0.25, 0.15]\n", + " >>> Collected 2 forecasts: [0.2, 0.9]\n", + " >>> Collected 2 forecasts: [0.25, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.2]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.15, 0.3]\n", - " >>> Collected 2 forecasts: [0.95, 0.9]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.98, 0.95]\n", + " >>> Collected 2 forecasts: [0.1, 0.35]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.15, 0.4]\n", - " >>> Collected 2 forecasts: [0.25, 0.4]\n", - " >>> Collected 2 forecasts: [0.15, 0.25]\n", - " >>> Collected 2 forecasts: [0.98, 0.96]\n", + " >>> Collected 2 forecasts: [0.1, 0.35]\n", + " >>> Collected 2 forecasts: [0.4, 0.3]\n", + " >>> Collected 2 forecasts: [0.15, 0.2]\n", + " >>> Collected 2 forecasts: [0.97, 0.98]\n", " >>> Collected 2 forecasts: [0.7, 0.4]\n", - " >>> Collected 2 forecasts: [0.35, 0.4]\n", - " >>> Collected 2 forecasts: [0.65, 0.6]\n", - " >>> Collected 2 forecasts: [0.01, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.7]\n", - " >>> Collected 2 forecasts: [0.99, 0.7]\n", - " >>> Collected 2 forecasts: [0.2, 0.98]\n", - " >>> Collected 2 forecasts: [0.99, 0.25]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.9, 0.8]\n", - " >>> Collected 2 forecasts: [0.6, 0.4]\n", - " >>> Collected 2 forecasts: [0.85, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.15]\n", - " >>> Collected 2 forecasts: [0.25, 0.5]\n", - " >>> Collected 2 forecasts: [0.75, 0.75]\n", - " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.02, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.03]\n", + " >>> Collected 2 forecasts: [0.3, 0.25]\n", + " >>> Collected 2 forecasts: [0.85, 0.6]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.7, 0.7]\n", + " >>> Collected 2 forecasts: [0.99, 0.99]\n", + " >>> Collected 2 forecasts: [0.97, 0.98]\n", + " >>> Collected 2 forecasts: [0.99, 0.15]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.95]\n", - " >>> Collected 2 forecasts: [0.4, 0.35]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.85, 0.85]\n", + " >>> Collected 2 forecasts: [0.9, 0.65]\n", + " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.8, 0.85]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.07]\n", - " >>> Collected 3 forecasts: [0.2, 0.6, 0.62]\n", - " >>> Collected 3 forecasts: [0.9, 0.8, 0.82]\n", - " >>> Collected 3 forecasts: [0.75, 0.7, 0.85]\n", + " >>> Collected 2 forecasts: [0.2, 0.3]\n", + " >>> Collected 2 forecasts: [0.65, 0.85]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.15, 0.25]\n", + " >>> Collected 2 forecasts: [0.02, 0.05]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.8, 0.9]\n", + " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.65]\n", + " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.2, 0.7, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.82]\n", + " >>> Collected 3 forecasts: [0.85, 0.75, 0.85]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.8, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.75, 0.35, nan]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.8, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.35, 0.25]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.5, 0.108]\n", - " >>> Collected 3 forecasts: [0.25, 0.1, 0.16]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.2, 0.25]\n", + " >>> Collected 3 forecasts: [0.25, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.108]\n", + " >>> Collected 3 forecasts: [0.1, 0.2, 0.16]\n", " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", - " >>> Collected 3 forecasts: [0.95, 0.9, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.15]\n", + " >>> Collected 3 forecasts: [0.98, 0.95, 0.05]\n", + " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.03]\n", - " >>> Collected 3 forecasts: [0.15, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.25, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.15, 0.25, 0.115]\n", - " >>> Collected 3 forecasts: [0.98, 0.96, 0.97]\n", + " >>> Collected 3 forecasts: [0.1, 0.35, 0.35]\n", + " >>> Collected 3 forecasts: [0.4, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.15, 0.2, 0.115]\n", + " >>> Collected 3 forecasts: [0.97, 0.98, 0.97]\n", " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.35, 0.4, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.65, 0.6, 0.17]\n", - " >>> Collected 3 forecasts: [0.01, 0.05, 0.12]\n", - " >>> Collected 3 forecasts: [0.1, 0.7, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", - " >>> Collected 3 forecasts: [0.2, 0.98, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.99, 0.25, 0.4166666666666666]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.85, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.026]\n", - " >>> Collected 3 forecasts: [0.25, 0.5, 0.16]\n", - " >>> Collected 3 forecasts: [0.75, 0.75, 0.67]\n", + " >>> Collected 3 forecasts: [0.3, 0.25, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.85, 0.6, 0.17]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, 0.12]\n", + " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", + " >>> Collected 3 forecasts: [0.97, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.99, 0.15, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.65, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.875]\n", + " >>> Collected 3 forecasts: [0.8, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.2, 0.3, 0.16]\n", + " >>> Collected 3 forecasts: [0.65, 0.85, 0.67]\n", " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.15, 0.25, 0.3925]\n", " >>> Collected 3 forecasts: [0.02, 0.05, 0.086]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.285]\n", - " >>> Collected 3 forecasts: [0.1, 0.03, 0.02]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", - " >>> Collected 3 forecasts: [0.4, 0.35, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999]\n", - " >>> Collected 4 forecasts: [0.75, 0.7, 0.85, 0.884]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, 0.285]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, 0.95]\n", + " >>> Collected 3 forecasts: [0.9, 0.65, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.82, 0.794]\n", + " >>> Collected 4 forecasts: [0.85, 0.75, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.35, nan, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.35, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.15, 0.5, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.25, 0.1, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.2, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.2, 0.9, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.1, 0.2, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.12]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.98, 0.95, 0.05, 0.918]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.15, 0.4, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.25, 0.4, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.15, 0.25, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.96, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.15, 0.2, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.97, 0.98, 0.97, 0.932]\n", " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.65, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.01, 0.05, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.1, 0.7, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.85, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.25, 0.5, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.75, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.85, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.8, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.3, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.65, 0.85, 0.67, nan]\n", " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.25, 0.3925, nan]\n", " >>> Collected 4 forecasts: [0.02, 0.05, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.03, 0.02, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.4, 0.35, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.9, 0.65, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.75, 0.35, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.35, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.5, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.1, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.2, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.9, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.2, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475]\n", " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.65, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.25, 0.5, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.75, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.85, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.2, 0.3, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.65, 0.85, 0.67, nan, 0.76]\n", " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.3925, nan, 0.38]\n", " >>> Collected 5 forecasts: [0.02, 0.05, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.1, 0.03, 0.02, nan, 0.098]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.4, 0.35, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.9, 0.65, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.75, 0.35, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.8, 0.4, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725]\n", " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675]\n", " >>> Collected 6 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", - " >>> Collected 7 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95]\n", - " >>> Collected 7 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", + " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", - " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3]\n", - " >>> Collected 7 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3]\n", + " >>> Collected 7 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18]\n", " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15]\n", - " >>> Collected 7 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15]\n", - " >>> Collected 7 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", - " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", - " >>> Collected 7 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3]\n", - " >>> Collected 7 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", + " >>> Collected 7 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", + " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", + " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2]\n", + " >>> Collected 7 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9]\n", + " >>> Collected 7 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan]\n", - " >>> Collected 8 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124]\n", - " >>> Collected 8 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765]\n", - " >>> Collected 8 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", + " >>> Collected 8 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", - " >>> Collected 8 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", + " >>> Collected 8 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", + " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615]\n", + " >>> Collected 8 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", + " >>> Collected 8 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan, 0.8]\n", - " >>> Collected 9 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", - " >>> Collected 9 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.8]\n", - " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.98]\n", - " >>> Collected 9 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.25]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.4]\n", + " >>> Collected 9 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", + " >>> Collected 9 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35]\n", - " >>> Collected 9 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8]\n", - " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.82, 0.7959999999999999, nan, 0.75, 0.95, nan, 0.8, 0.638]\n", - " >>> Collected 10 forecasts: [0.75, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.75, 0.35, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.35, 0.25, nan, nan, 0.225, 0.18, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.15, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.1, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.12, 0.05, 0.15, 0.2, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.9, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", + " >>> Collected 10 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.15, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.25, 0.4, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35, 0.293]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.01, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", - " >>> Collected 10 forecasts: [0.1, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.8, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.2, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.99, 0.95, 0.98, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.25, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.75, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.5, 0.16, nan, 0.05, 0.225, 0.3, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.75, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85, 0.955]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.4, 0.126]\n", + " >>> Collected 10 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.35, 0.574]\n", - " >>> Collected 10 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.03, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.4, 0.35, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.85, 0.126]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -10881,7 +11186,7 @@ }, { "cell_type": "code", - "execution_count": 333, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -10891,7 +11196,7 @@ }, { "cell_type": "code", - "execution_count": 334, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -10929,36 +11234,36 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " 0.012671\n", - " 0.097463\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", " \n", " \n", " 1\n", " numeric\n", " NaN\n", " 86.82\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", " \n", " 2\n", " binary\n", " NaN\n", " no\n", - " 0.05\n", - " 0.063\n", - " 0.11\n", + " 0.1\n", + " 0.085\n", + " 0.1\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.15,0.65,0.2]\n", - " 0.6\n", - " 0.5125\n", + " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.49000000000000005, 0.0001]\n", " \n", " \n", " 4\n", @@ -10966,8 +11271,8 @@ " NaN\n", " 119.2\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0, 0.0019825503600000003, 0.003970557620000...\n", - " [0.0, 0.0020603651142857148, 0.004124627985714...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " \n", " \n", " ...\n", @@ -10983,7 +11288,7 @@ " binary\n", " NaN\n", " yes\n", - " 0.9\n", + " 0.95\n", " 0.905\n", " 0.92\n", " \n", @@ -10992,17 +11297,17 @@ " binary\n", " NaN\n", " no\n", - " 0.4\n", - " 0.35\n", - " 0.2085\n", + " 0.9\n", + " 0.65\n", + " 0.3585\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", + " 0.95\n", " 0.9\n", - " 0.85\n", " 0.775\n", " \n", " \n", @@ -11011,8 +11316,8 @@ " NaN\n", " no\n", " 0.85\n", - " 0.85\n", - " 0.78\n", + " 0.8\n", + " 0.709\n", " \n", " \n", " 364\n", @@ -11043,48 +11348,48 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.15,0.65,0.2] \n", + "0 [0.014083333333333333,0.6016666666666668,0.178... \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.1 \n", + "3 [0.37,0.49000000000000005,0.13999999999999999] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", - "342 0.9 \n", - "351 0.4 \n", - "355 0.9 \n", + "342 0.95 \n", + "351 0.9 \n", + "355 0.95 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.063 \n", - "3 0.6 \n", - "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", "342 0.905 \n", - "351 0.35 \n", - "355 0.85 \n", - "361 0.85 \n", + "351 0.65 \n", + "355 0.9 \n", + "361 0.8 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 0.097463 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.11 \n", - "3 0.5125 \n", - "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", "342 0.92 \n", - "351 0.2085 \n", + "351 0.3585 \n", "355 0.775 \n", - "361 0.78 \n", + "361 0.709 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" ] }, - "execution_count": 334, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -11095,7 +11400,7 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -11115,7 +11420,7 @@ }, { "cell_type": "code", - "execution_count": 336, + "execution_count": 60, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11124,6 +11429,22 @@ "outputId": "7327c204-c501-4dfb-bdfb-176606c96dc4" }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n", + " >>> Error calculating baseline score for question 34454 — skipping: Probability for resolution or baseline probability is less than or equal to 0 which could cause a log(0) issue\n" + ] + }, { "data": { "text/html": [ @@ -11153,52 +11474,52 @@ " \n", " 0\n", " 1\n", - " 60.20\n", + " 636.97\n", " \n", " \n", " 1\n", " 2\n", - " 59.19\n", + " 2444.36\n", " \n", " \n", " 2\n", " 3\n", - " 19.30\n", + " 2419.66\n", " \n", " \n", " 3\n", " 4\n", - " 17.74\n", + " 2491.70\n", " \n", " \n", " 4\n", " 5\n", - " 6.91\n", + " 2645.79\n", " \n", " \n", " 5\n", " 6\n", - " 7.07\n", + " 2517.08\n", " \n", " \n", " 6\n", " 7\n", - " 16.34\n", + " 2392.69\n", " \n", " \n", " 7\n", " 8\n", - " 16.34\n", + " 2484.64\n", " \n", " \n", " 8\n", " 9\n", - " 21.85\n", + " 2381.71\n", " \n", " \n", " 9\n", " 10\n", - " 21.85\n", + " 2419.31\n", " \n", " \n", "\n", @@ -11206,19 +11527,19 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 60.20\n", - "1 2 59.19\n", - "2 3 19.30\n", - "3 4 17.74\n", - "4 5 6.91\n", - "5 6 7.07\n", - "6 7 16.34\n", - "7 8 16.34\n", - "8 9 21.85\n", - "9 10 21.85" + "0 1 636.97\n", + "1 2 2444.36\n", + "2 3 2419.66\n", + "3 4 2491.70\n", + "4 5 2645.79\n", + "5 6 2517.08\n", + "6 7 2392.69\n", + "7 8 2484.64\n", + "8 9 2381.71\n", + "9 10 2419.31" ] }, - "execution_count": 336, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -11249,16 +11570,16 @@ }, { "cell_type": "code", - "execution_count": 337, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['metac-o1-preview']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2', 'manticAI']" ] }, - "execution_count": 337, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -11272,7 +11593,7 @@ }, { "cell_type": "code", - "execution_count": 338, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -11281,7 +11602,7 @@ "(424, 47)" ] }, - "execution_count": 338, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -11292,7 +11613,7 @@ }, { "cell_type": "code", - "execution_count": 339, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -11310,7 +11631,7 @@ }, { "cell_type": "code", - "execution_count": 340, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -11369,235 +11690,590 @@ " NaN\n", " False\n", " False\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " ...\n", - " 0.010417\n", - " 0.205208\n", - " 0.014926\n", - " 0.012671\n", - " 0.012671\n", - " 0.014926\n", - " 0.097463\n", - " 0.097463\n", - " 0.048475\n", - " 0.048475\n", - " \n", - " \n", - " 1\n", - " 31263\n", - " 1.0\n", - " 86.82\n", - " numeric\n", - " NaN\n", - " 60.0\n", - " 100.0\n", - " True\n", - " True\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " ...\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", - " [0.03366666666666667, 0.03409436576666667, 0.0...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.037750000000000006, 0.03822284245, 0.038700...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.0402, 0.040728273960000005, 0.04126011788, ...\n", - " [0.041833333333333333, 0.04238467275, 0.042938...\n", - " [0.041833333333333333, 0.04238467275, 0.042938...\n", + " [0.014083333333333333,0.6016666666666668,0.178...\n", + " ...\n", + " [0.014083333333333333, 0.0001, 0.0001, 0.0001,...\n", + " [0.25704166666666667, 0.0001, 0.0001, 0.0001, ...\n", + " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.05, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.05, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " \n", + " \n", + " 1\n", + " 31263\n", + " 1.0\n", + " 86.82\n", + " numeric\n", + " NaN\n", + " 60.0\n", + " 100.0\n", + " True\n", + " True\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " ...\n", + " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05...\n", + " [0.05, 0.05079411765, 0.0515882353, 0.05238235...\n", + " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", + " [0.05, 0.050679136250000006, 0.051358272499999...\n", + " [0.05, 0.050679136250000006, 0.051358272499999...\n", + " \n", + " \n", + " 2\n", + " 31264\n", + " 1.0\n", + " no\n", + " binary\n", + " NaN\n", + " NaN\n", + " NaN\n", + " False\n", + " False\n", + " 0.1\n", + " ...\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.085\n", + " 0.085\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " \n", + " \n", + " 3\n", + " 31274\n", + " 1.0\n", + " 5-9\n", + " multiple_choice\n", + " [0-4, 5-9, >9]\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " [0.37,0.49000000000000005,0.13999999999999999]\n", + " ...\n", + " [0.0001, 0.49000000000000005, 0.0001]\n", + " [0.0001, 0.545, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.5562499999999999, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.50125, 0.0001]\n", + " [0.0001, 0.49000000000000005, 0.0001]\n", + " [0.0001, 0.49000000000000005, 0.0001]\n", + " [0.0001, 0.50125, 0.0001]\n", + " [0.0001, 0.49000000000000005, 0.0001]\n", + " \n", + " \n", + " 4\n", + " 31275\n", + " 1.0\n", + " 119.2\n", + " numeric\n", + " NaN\n", + " 0.0\n", + " 400.0\n", + " False\n", + " False\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " ...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0029090909, 0.0058181818, 0.0087272727...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", + " [0.0, 0.00183065955, 0.00366131905, 0.00549197...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", + " [0.0, 0.00217156865, 0.00434313725, 0.00651470...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", + " \n", + " \n", + "\n", + "

5 rows × 29 columns

\n", + "" + ], + "text/plain": [ + " bot_question_id question_weight resolution type \\\n", + "0 31262 1.0 0 multiple_choice \n", + "1 31263 1.0 86.82 numeric \n", + "2 31264 1.0 no binary \n", + "3 31274 1.0 5-9 multiple_choice \n", + "4 31275 1.0 119.2 numeric \n", + "\n", + " options range_min range_max open_lower_bound \\\n", + "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [0-4, 5-9, >9] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", + "\n", + " open_upper_bound metac-o1-preview ... \\\n", + "0 False [0.014083333333333333,0.6016666666666668,0.178... ... \n", + "1 True [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... ... \n", + "2 False 0.1 ... \n", + "3 NaN [0.37,0.49000000000000005,0.13999999999999999] ... \n", + "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", + "\n", + " median_forecast_1_bots \\\n", + "0 [0.014083333333333333, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05... \n", + "2 0.1 \n", + "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " median_forecast_2_bots \\\n", + "0 [0.25704166666666667, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.05079411765, 0.0515882353, 0.05238235... \n", + "2 0.1 \n", + "3 [0.0001, 0.545, 0.0001] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " median_forecast_3_bots \\\n", + "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", + "2 0.1 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "\n", + " median_forecast_4_bots \\\n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5562499999999999, 0.0001] \n", + "4 [0.0, 0.0029090909, 0.0058181818, 0.0087272727... \n", + "\n", + " median_forecast_5_bots \\\n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", + "\n", + " median_forecast_6_bots \\\n", + "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.50125, 0.0001] \n", + "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", + "\n", + " median_forecast_7_bots \\\n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", + "\n", + " median_forecast_8_bots \\\n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", + "\n", + " median_forecast_9_bots \\\n", + "0 [0.05, 0.0001, 0.0001, 0.0001, 0.0001] \n", + "1 [0.05, 0.050679136250000006, 0.051358272499999... \n", + "2 0.1 \n", + "3 [0.0001, 0.50125, 0.0001] \n", + "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", + "\n", + " median_forecast_10_bots \n", + "0 [0.05, 0.0001, 0.0001, 0.0001, 0.0001] \n", + "1 [0.05, 0.050679136250000006, 0.051358272499999... \n", + "2 0.1 \n", + "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", + "\n", + "[5 rows x 29 columns]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_bot_team_forecasts.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z3TTBVWoZVzU", + "outputId": "0eb32f2c-09c6-4a15-e81a-bee353b1bccf" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_median
031262For Q1 2025, how many banks will be listed on ...02025-01-20 03:27:002025-01-20 03:27:00multiple_choice[0, 1, 2-3, 4-6, >6]NaNNaNFalseFalse312681.0[0.014504537953795379, 0.0001, 0.0001, 0.0001,...[0.001,0.62,0.35,0.019,0.01]
131263What percentage of the vote will Alexander Luk...86.822025-01-20 03:27:002025-01-20 03:27:00numericNaN60.0100.0TrueTrue312691.0[0.05, 0.0505982539, 0.0511965078, 0.051794761...[0.0013749738,0.0014499743,0.001526641,0.00160...
231264Will the bubble in the Magnificent Seven pop b...no2025-01-20 03:27:002025-01-20 03:27:00binaryNaNNaNNaNFalseFalse312701.00.0850.013
331274How many arms sales globally will the US State...5-92025-01-21 11:42:002025-01-21 11:42:00multiple_choice[0-4, 5-9, >9]NaNNaNNaNNaN312801.0[0.0001, 0.5125, 0.0001][0.16,0.44,0.4]
431275How much will it rain in Brasília, Brazil in F...119.22025-01-21 11:42:002025-01-21 11:42:00numericNaN0.0400.0FalseFalse312811.0[0.0, 0.0018181818, 0.0036363636, 0.0054545455...[0.0,0.0005044914,0.0010323506,0.0015847475,0....
\n", + "
" + ], + "text/plain": [ + " bot_question_id title \\\n", + "0 31262 For Q1 2025, how many banks will be listed on ... \n", + "1 31263 What percentage of the vote will Alexander Luk... \n", + "2 31264 Will the bubble in the Magnificent Seven pop b... \n", + "3 31274 How many arms sales globally will the US State... \n", + "4 31275 How much will it rain in Brasília, Brazil in F... \n", + "\n", + " resolution scheduled_close_time actual_close_time type \\\n", + "0 0 2025-01-20 03:27:00 2025-01-20 03:27:00 multiple_choice \n", + "1 86.82 2025-01-20 03:27:00 2025-01-20 03:27:00 numeric \n", + "2 no 2025-01-20 03:27:00 2025-01-20 03:27:00 binary \n", + "3 5-9 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", + "4 119.2 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", + "\n", + " options range_min range_max open_upper_bound \\\n", + "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [0-4, 5-9, >9] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", + "\n", + " open_lower_bound pro_question_id question_weight \\\n", + "0 False 31268 1.0 \n", + "1 True 31269 1.0 \n", + "2 False 31270 1.0 \n", + "3 NaN 31280 1.0 \n", + "4 False 31281 1.0 \n", + "\n", + " bot_team_median \\\n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", + "\n", + " pro_median \n", + "0 [0.001,0.62,0.35,0.019,0.01] \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... \n", + "2 0.013 \n", + "3 [0.16,0.44,0.4] \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
bot_question_idtitleresolutionscheduled_close_timeactual_close_timetypeoptionsrange_minrange_maxopen_upper_boundopen_lower_boundpro_question_idquestion_weightbot_team_medianpro_median
34235345Will the US Citizenship and Immigration Servic...yes2025-03-12 22:00:002025-03-12 22:00:00binaryNaNNaNNaNFalseFalse353801.000.9050.95
2312641.035135354Will the United States impose any new tariffs ...no2025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353811.000.650.05...0.050.10.070.0630.0630.070.110.110.150.15
3312741.05-9multiple_choice[0-4, 5-9, >9]35535358Will ChatGPT rank in the top 10 global website...yes2025-03-13 03:00:002025-03-13 03:00:00binaryNaNNaNNaNFalseFalse353851.000.90.97
36135364Will Doge's Agency Efficiency Leaderboard have...no2025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaN[0.15,0.65,0.2]...0.650.6250.60.610.60.556250.51250.51250.556250.5125FalseFalse353860.850.80.666
4312751.0119.2numeric36435367Will the Project 2025 Tracker spreadsheet mark...no2025-03-14 23:00:002025-03-14 23:00:00binaryNaNNaNNaN0.0400.0FalseFalse[0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,......[0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...[0.0, 0.00342857145, 0.00685714285, 0.01028571...[0.0, 0.0023237670666666666, 0.004652994133333...[0.0, 0.00219737075, 0.0043988365, 0.006603060...[0.0, 0.0019825503600000003, 0.003970557620000...[0.0, 0.0019593148500000003, 0.0039231771, 0.0...[0.0, 0.0020603651142857148, 0.004124627985714...[0.0, 0.0020603651142857148, 0.004124627985714...[0.0, 0.0022194861375, 0.004442382825, 0.00666...[0.0, 0.002118648455555556, 0.0042403284999999...353870.850.050.03
\n", - "

5 rows × 29 columns

\n", "
" ], "text/plain": [ - " bot_question_id question_weight resolution type \\\n", - "0 31262 1.0 0 multiple_choice \n", - "1 31263 1.0 86.82 numeric \n", - "2 31264 1.0 no binary \n", - "3 31274 1.0 5-9 multiple_choice \n", - "4 31275 1.0 119.2 numeric \n", - "\n", - " options range_min range_max open_lower_bound \\\n", - "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", - "1 NaN 60.0 100.0 True \n", - "2 NaN NaN NaN False \n", - "3 [0-4, 5-9, >9] NaN NaN NaN \n", - "4 NaN 0.0 400.0 False \n", - "\n", - " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.010416666666666666,0.20833333333333334,0.04... ... \n", - "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", - "2 False 0.05 ... \n", - "3 NaN [0.15,0.65,0.2] ... \n", - "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", - "\n", - " median_forecast_1_bots \\\n", - "0 0.010417 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 0.65 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", - "\n", - " median_forecast_2_bots \\\n", - "0 0.205208 \n", - "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", - "2 0.1 \n", - "3 0.625 \n", - "4 [0.0, 0.00342857145, 0.00685714285, 0.01028571... \n", - "\n", - " median_forecast_3_bots \\\n", - "0 0.014926 \n", - "1 [0.03366666666666667, 0.03409436576666667, 0.0... \n", - "2 0.07 \n", - "3 0.6 \n", - "4 [0.0, 0.0023237670666666666, 0.004652994133333... \n", - "\n", - " median_forecast_4_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.063 \n", - "3 0.61 \n", - "4 [0.0, 0.00219737075, 0.0043988365, 0.006603060... \n", - "\n", - " median_forecast_5_bots \\\n", - "0 0.012671 \n", - "1 [0.037750000000000006, 0.03822284245, 0.038700... \n", - "2 0.063 \n", - "3 0.6 \n", - "4 [0.0, 0.0019825503600000003, 0.003970557620000... \n", - "\n", - " median_forecast_6_bots \\\n", - "0 0.014926 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.07 \n", - "3 0.55625 \n", - "4 [0.0, 0.0019593148500000003, 0.0039231771, 0.0... \n", - "\n", - " median_forecast_7_bots \\\n", - "0 0.097463 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.11 \n", - "3 0.5125 \n", - "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", - "\n", - " median_forecast_8_bots \\\n", - "0 0.097463 \n", - "1 [0.0402, 0.040728273960000005, 0.04126011788, ... \n", - "2 0.11 \n", - "3 0.5125 \n", - "4 [0.0, 0.0020603651142857148, 0.004124627985714... \n", + " bot_question_id title \\\n", + "342 35345 Will the US Citizenship and Immigration Servic... \n", + "351 35354 Will the United States impose any new tariffs ... \n", + "355 35358 Will ChatGPT rank in the top 10 global website... \n", + "361 35364 Will Doge's Agency Efficiency Leaderboard have... \n", + "364 35367 Will the Project 2025 Tracker spreadsheet mark... \n", "\n", - " median_forecast_9_bots \\\n", - "0 0.048475 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", - "3 0.55625 \n", - "4 [0.0, 0.0022194861375, 0.004442382825, 0.00666... \n", + " resolution scheduled_close_time actual_close_time type options \\\n", + "342 yes 2025-03-12 22:00:00 2025-03-12 22:00:00 binary NaN \n", + "351 no 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "355 yes 2025-03-13 03:00:00 2025-03-13 03:00:00 binary NaN \n", + "361 no 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", + "364 no 2025-03-14 23:00:00 2025-03-14 23:00:00 binary NaN \n", "\n", - " median_forecast_10_bots \n", - "0 0.048475 \n", - "1 [0.041833333333333333, 0.04238467275, 0.042938... \n", - "2 0.15 \n", - "3 0.5125 \n", - "4 [0.0, 0.002118648455555556, 0.0042403284999999... \n", + " range_min range_max open_upper_bound open_lower_bound pro_question_id \\\n", + "342 NaN NaN False False 35380 \n", + "351 NaN NaN False False 35381 \n", + "355 NaN NaN False False 35385 \n", + "361 NaN NaN False False 35386 \n", + "364 NaN NaN False False 35387 \n", "\n", - "[5 rows x 29 columns]" + " question_weight bot_team_median pro_median \n", + "342 1.00 0.905 0.95 \n", + "351 1.00 0.65 0.05 \n", + "355 1.00 0.9 0.97 \n", + "361 0.85 0.8 0.666 \n", + "364 0.85 0.05 0.03 " ] }, - "execution_count": 340, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + " peer_score = np.log(forecast_for_resolution / geometric_mean)\n" + ] } ], - "source": [ - "df_bot_team_forecasts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 341, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Z3TTBVWoZVzU", - "outputId": "0eb32f2c-09c6-4a15-e81a-bee353b1bccf" - }, - "outputs": [], "source": [ "# @title Weighted team-vs-pro\n", "\n", @@ -11631,20 +12307,22 @@ "# Filter to only those rows where pro_median is not NA\n", "df_top_bot_pro_forecasts = df_top_bot_pro_forecasts.dropna(subset=['pro_median'])\n", "\n", + "display_head_and_tail(df_top_bot_pro_forecasts)\n", + "\n", "# Add the head_to_head column\n", "df_top_bot_pro_forecasts['head_to_head'] = df_top_bot_pro_forecasts.apply(calculate_weighted_h2h_score_between_two_forecast_columns, args=('bot_team_median', 'pro_median'), axis=1)" ] }, { "cell_type": "code", - "execution_count": 342, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -30.1253\n" + "Weighted Total Score: -0.1175\n" ] } ], @@ -11654,7 +12332,7 @@ }, { "cell_type": "code", - "execution_count": 343, + "execution_count": 67, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -11666,7 +12344,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -11678,7 +12356,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -31.65\n" + "The average of 'head_to_head' is: -0.12\n" ] } ], @@ -11688,7 +12366,7 @@ }, { "cell_type": "code", - "execution_count": 344, + "execution_count": 68, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11734,31 +12412,31 @@ " \n", " \n", " head_to_head\n", - " -2861.9\n", + " -11.2\n", " 92.1\n", - " -31.1\n", - " 105.682644\n", - " 11.012194\n", - " -2.821774\n", + " -0.1\n", + " 0.671397\n", + " 0.06996\n", + " -1.732732\n", " 1.98555\n", - " -9.2\n", - " -52.9\n", - " 0.002931\n", - " 0.005863\n", + " 0.0\n", + " -0.3\n", + " 0.043264\n", + " 0.086527\n", " \n", " \n", "\n", "" ], "text/plain": [ - " W_score W_count W_ave W_stdev std_err t_stat \\\n", - "head_to_head -2861.9 92.1 -31.1 105.682644 11.012194 -2.821774 \n", + " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", + "head_to_head -11.2 92.1 -0.1 0.671397 0.06996 -1.732732 1.98555 \n", "\n", - " t_crit upper_bound lower_bound cdf p_value \n", - "head_to_head 1.98555 -9.2 -52.9 0.002931 0.005863 " + " upper_bound lower_bound cdf p_value \n", + "head_to_head 0.0 -0.3 0.043264 0.086527 " ] }, - "execution_count": 344, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -11771,7 +12449,7 @@ }, { "cell_type": "code", - "execution_count": 345, + "execution_count": 69, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -11817,44 +12495,44 @@ " \n", " \n", " \n", - " 228\n", - " Will Donald Trump grant executive clemency to ...\n", - " 0.99\n", - " 0.125\n", - " no\n", - " -447.2\n", - " \n", - " \n", " 279\n", " What will Kalshi's rank in the iPhone Top Free...\n", - " 0.02\n", + " [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05]\n", " [0.02,0.01,0.015,0.015,0.05,0.89]\n", " Not in top 50\n", - " -379.5\n", + " -2.9\n", " \n", " \n", - " 12\n", - " What will be the monthly cargo volumes at the ...\n", - " [0.16, 0.1627, 0.1654, 0.1681, 0.1708, 0.1735,...\n", - " [0.001714054,0.0017985406,0.0018846914,0.00197...\n", - " 720283.0\n", - " -274.3\n", + " 121\n", + " How many movies will be new on Netflix's top 1...\n", + " [0.0001, 0.0001, 0.0001, 0.125]\n", + " [0.005,0.017,0.157,0.821]\n", + " 3 or more\n", + " -1.9\n", " \n", " \n", - " 291\n", - " How many registered Syrian refugees will be in...\n", - " [0.05, 0.05125, 0.0525, 0.05375, 0.055, 0.0562...\n", - " [0.001,0.00105,0.0011,0.00115,0.0012,0.00125,0...\n", - " 2807615.0\n", - " -243.6\n", + " 232\n", + " How many movies will be new on Netflix's top 1...\n", + " [0.0001, 0.0001, 0.0001, 0.2963039014373716]\n", + " [0.002,0.008,0.09,0.9]\n", + " 3 or more\n", + " -1.1\n", " \n", " \n", - " 208\n", - " Will the Trump administration impose new tarif...\n", - " 0.1\n", - " 0.8\n", - " yes\n", - " -207.9\n", + " 247\n", + " Will the 500th richest person on Bloomberg's B...\n", + " 0.766667\n", + " 0.333\n", + " no\n", + " -1.1\n", + " \n", + " \n", + " 87\n", + " How many movies will be new on Netflix's globa...\n", + " [0.0001, 0.0001, 0.335]\n", + " [0.01,0.064,0.926]\n", + " 2 or more\n", + " -1.0\n", " \n", " \n", "\n", @@ -11862,35 +12540,28 @@ ], "text/plain": [ " title \\\n", - "228 Will Donald Trump grant executive clemency to ... \n", "279 What will Kalshi's rank in the iPhone Top Free... \n", - "12 What will be the monthly cargo volumes at the ... \n", - "291 How many registered Syrian refugees will be in... \n", - "208 Will the Trump administration impose new tarif... \n", - "\n", - " bot_team_median \\\n", - "228 0.99 \n", - "279 0.02 \n", - "12 [0.16, 0.1627, 0.1654, 0.1681, 0.1708, 0.1735,... \n", - "291 [0.05, 0.05125, 0.0525, 0.05375, 0.055, 0.0562... \n", - "208 0.1 \n", - "\n", - " pro_median resolution \\\n", - "228 0.125 no \n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 \n", - "12 [0.001714054,0.0017985406,0.0018846914,0.00197... 720283.0 \n", - "291 [0.001,0.00105,0.0011,0.00115,0.0012,0.00125,0... 2807615.0 \n", - "208 0.8 yes \n", - "\n", - " head_to_head \n", - "228 -447.2 \n", - "279 -379.5 \n", - "12 -274.3 \n", - "291 -243.6 \n", - "208 -207.9 " + "121 How many movies will be new on Netflix's top 1... \n", + "232 How many movies will be new on Netflix's top 1... \n", + "247 Will the 500th richest person on Bloomberg's B... \n", + "87 How many movies will be new on Netflix's globa... \n", + "\n", + " bot_team_median \\\n", + "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", + "121 [0.0001, 0.0001, 0.0001, 0.125] \n", + "232 [0.0001, 0.0001, 0.0001, 0.2963039014373716] \n", + "247 0.766667 \n", + "87 [0.0001, 0.0001, 0.335] \n", + "\n", + " pro_median resolution head_to_head \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", + "121 [0.005,0.017,0.157,0.821] 3 or more -1.9 \n", + "232 [0.002,0.008,0.09,0.9] 3 or more -1.1 \n", + "247 0.333 no -1.1 \n", + "87 [0.01,0.064,0.926] 2 or more -1.0 " ] }, - "execution_count": 345, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -11912,7 +12583,7 @@ }, { "cell_type": "code", - "execution_count": 346, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -11955,25 +12626,25 @@ " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " 0.010417\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", - " 234.3\n", + " 2.7\n", " \n", " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.0030510204, 0.0061020408, 0.0091530612...\n", + " [0.0, 0.0106785714, 0.0213571429, 0.0320357143...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 401.1\n", + " 2.8\n", " \n", " \n", - " 123\n", - " Which party will win the 2nd highest number of...\n", - " NaN\n", - " [0.03,0.9,0.06,0.009,0.001]\n", - " Alternative for Germany\n", + " 151\n", + " How many earthquakes of magnitude ≥ 4 will hap...\n", + " [0.0, 0.0035714286, 0.0071428571, 0.0107142857...\n", + " [0.0,0.0158237002,0.0235315723,0.0279864362,0....\n", + " 0.0\n", " NaN\n", " \n", " \n", @@ -11987,7 +12658,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.2\n", + " 0.954\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12000,33 +12671,33 @@ " title \\\n", "0 For Q1 2025, how many banks will be listed on ... \n", "189 What will the highest rank of metac-GPT4o or m... \n", - "123 Which party will win the 2nd highest number of... \n", + "151 How many earthquakes of magnitude ≥ 4 will hap... \n", "211 Will Nikola Corporation file for bankruptcy be... \n", "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "0 0.010417 \n", - "189 [0.0, 0.0030510204, 0.0061020408, 0.0091530612... \n", - "123 NaN \n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "189 [0.0, 0.0106785714, 0.0213571429, 0.0320357143... \n", + "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", "211 0.99 \n", - "214 0.2 \n", - "\n", - " pro_median \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] \n", - "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... \n", - "123 [0.03,0.9,0.06,0.009,0.001] \n", - "211 0.999 \n", - "214 0.95 \n", - "\n", - " resolution head_to_head \n", - "0 0 234.3 \n", - "189 34.0 401.1 \n", - "123 Alternative for Germany NaN \n", - "211 annulled NaN \n", - "214 annulled NaN " + "214 0.954 \n", + "\n", + " pro_median resolution \\\n", + "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", + "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", + "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... 0.0 \n", + "211 0.999 annulled \n", + "214 0.95 annulled \n", + "\n", + " head_to_head \n", + "0 2.7 \n", + "189 2.8 \n", + "151 NaN \n", + "211 NaN \n", + "214 NaN " ] }, - "execution_count": 346, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -12039,7 +12710,7 @@ }, { "cell_type": "code", - "execution_count": 347, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -12065,7 +12736,7 @@ "dtype: object" ] }, - "execution_count": 347, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -12079,7 +12750,7 @@ }, { "cell_type": "code", - "execution_count": 348, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -12131,17 +12802,17 @@ " 2025-01-20 03:27:00\n", " 2025-01-20 03:27:00\n", " multiple_choice\n", - " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", + " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", " False\n", " False\n", " 31268\n", " 1.0\n", - " 0.010417\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 234.340709\n", - " 234.340709\n", + " 2.674462\n", + " 2.674462\n", " \n", " \n", " 1\n", @@ -12158,10 +12829,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -101.083204\n", - " -101.083204\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -12178,10 +12849,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.05\n", + " 0.085\n", " 0.013\n", - " -3.820805\n", - " -3.820805\n", + " -0.075746\n", + " -0.075746\n", " \n", " \n", " 3\n", @@ -12191,17 +12862,17 @@ " 2025-01-21 11:42:00\n", " 2025-01-21 11:42:00\n", " multiple_choice\n", - " [\"0-4\",\"5-9\",\">9\"]\n", + " [0-4, 5-9, >9]\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " 31280\n", " 1.0\n", - " 0.65\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 39.019764\n", - " 39.019764\n", + " 0.152526\n", + " 0.152526\n", " \n", " \n", " 4\n", @@ -12218,10 +12889,10 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 45.546041\n", - " 45.546041\n", + " 0.243782\n", + " 0.243782\n", " \n", " \n", "\n", @@ -12242,12 +12913,12 @@ "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", "\n", - " options range_min range_max open_upper_bound \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN False \n", - "1 NaN 60.0 100.0 True \n", - "2 NaN NaN NaN False \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN NaN \n", - "4 NaN 0.0 400.0 False \n", + " options range_min range_max open_upper_bound \\\n", + "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [0-4, 5-9, >9] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", "\n", " open_lower_bound pro_question_id question_weight \\\n", "0 False 31268 1.0 \n", @@ -12257,28 +12928,28 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 0.010417 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 0.65 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 234.340709 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -101.083204 \n", - "2 0.013 -3.820805 \n", - "3 [0.16,0.44,0.4] 39.019764 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 45.546041 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.674462 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", + "2 0.013 -0.075746 \n", + "3 [0.16,0.44,0.4] 0.152526 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.243782 \n", "\n", " weighted_score \n", - "0 234.340709 \n", - "1 -101.083204 \n", - "2 -3.820805 \n", - "3 39.019764 \n", - "4 45.546041 " + "0 2.674462 \n", + "1 -0.158842 \n", + "2 -0.075746 \n", + "3 0.152526 \n", + "4 0.243782 " ] }, - "execution_count": 348, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -12289,7 +12960,7 @@ }, { "cell_type": "code", - "execution_count": 349, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -12301,7 +12972,7 @@ }, { "cell_type": "code", - "execution_count": 350, + "execution_count": 74, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12313,7 +12984,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -12357,7 +13028,7 @@ }, { "cell_type": "code", - "execution_count": 351, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -12367,7 +13038,7 @@ }, { "cell_type": "code", - "execution_count": 352, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -12424,7 +13095,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.05\n", + " 0.085\n", " 0.013\n", " \n", " \n", @@ -12442,7 +13113,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.2\n", + " 0.62\n", " 0.45\n", " \n", " \n", @@ -12460,7 +13131,7 @@ " False\n", " 31294\n", " 1.0\n", - " 0.9\n", + " 0.86\n", " 0.95\n", " \n", " \n", @@ -12496,7 +13167,7 @@ " False\n", " 31338\n", " 1.0\n", - " 0.75\n", + " 0.85\n", " 0.9\n", " \n", " \n", @@ -12526,14 +13197,14 @@ "13 NaN NaN False False 31338 \n", "\n", " question_weight bot_team_median pro_median \n", - "2 1.0 0.05 0.013 \n", - "5 1.0 0.2 0.45 \n", - "8 1.0 0.9 0.95 \n", + "2 1.0 0.085 0.013 \n", + "5 1.0 0.62 0.45 \n", + "8 1.0 0.86 0.95 \n", "10 1.0 NaN NaN \n", - "13 1.0 0.75 0.9 " + "13 1.0 0.85 0.9 " ] }, - "execution_count": 352, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -12544,7 +13215,7 @@ }, { "cell_type": "code", - "execution_count": 353, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -12595,7 +13266,7 @@ }, { "cell_type": "code", - "execution_count": 354, + "execution_count": 78, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -12653,17 +13324,17 @@ " 2025-01-20 03:27:00\n", " 2025-01-20 03:27:00\n", " multiple_choice\n", - " [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"]\n", + " [0, 1, 2-3, 4-6, >6]\n", " NaN\n", " NaN\n", " False\n", " False\n", " 31268\n", " 1.0\n", - " 0.010417\n", + " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 234.340709\n", - " 234.340709\n", + " 2.674462\n", + " 2.674462\n", " \n", " \n", " 1\n", @@ -12680,10 +13351,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -101.083204\n", - " -101.083204\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -12700,10 +13371,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.05\n", + " 0.085\n", " 0.013\n", - " -3.820805\n", - " -3.820805\n", + " -0.075746\n", + " -0.075746\n", " \n", " \n", " 3\n", @@ -12713,17 +13384,17 @@ " 2025-01-21 11:42:00\n", " 2025-01-21 11:42:00\n", " multiple_choice\n", - " [\"0-4\",\"5-9\",\">9\"]\n", + " [0-4, 5-9, >9]\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " 31280\n", " 1.0\n", - " 0.65\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 39.019764\n", - " 39.019764\n", + " 0.152526\n", + " 0.152526\n", " \n", " \n", " 4\n", @@ -12740,10 +13411,10 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 45.546041\n", - " 45.546041\n", + " 0.243782\n", + " 0.243782\n", " \n", " \n", "\n", @@ -12764,12 +13435,12 @@ "3 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 multiple_choice \n", "4 NaN 2025-01-21 11:42:00 2025-01-21 11:42:00 numeric \n", "\n", - " options range_min range_max open_upper_bound \\\n", - "0 [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN False \n", - "1 NaN 60.0 100.0 True \n", - "2 NaN NaN NaN False \n", - "3 [\"0-4\",\"5-9\",\">9\"] NaN NaN NaN \n", - "4 NaN 0.0 400.0 False \n", + " options range_min range_max open_upper_bound \\\n", + "0 [0, 1, 2-3, 4-6, >6] NaN NaN False \n", + "1 NaN 60.0 100.0 True \n", + "2 NaN NaN NaN False \n", + "3 [0-4, 5-9, >9] NaN NaN NaN \n", + "4 NaN 0.0 400.0 False \n", "\n", " open_lower_bound pro_question_id question_weight \\\n", "0 False 31268 1.0 \n", @@ -12779,25 +13450,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 0.010417 \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 0.65 \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.085 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 234.340709 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -101.083204 \n", - "2 0.013 -3.820805 \n", - "3 [0.16,0.44,0.4] 39.019764 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 45.546041 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.674462 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", + "2 0.013 -0.075746 \n", + "3 [0.16,0.44,0.4] 0.152526 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.243782 \n", "\n", " weighted_score \n", - "0 234.340709 \n", - "1 -101.083204 \n", - "2 -3.820805 \n", - "3 39.019764 \n", - "4 45.546041 " + "0 2.674462 \n", + "1 -0.158842 \n", + "2 -0.075746 \n", + "3 0.152526 \n", + "4 0.243782 " ] }, "metadata": {}, @@ -12859,10 +13530,10 @@ " False\n", " 35380\n", " 1.00\n", - " 0.9\n", + " 0.905\n", " 0.95\n", - " -5.406722\n", - " -5.406722\n", + " -0.048527\n", + " -0.048527\n", " \n", " \n", " 351\n", @@ -12879,10 +13550,10 @@ " False\n", " 35381\n", " 1.00\n", - " 0.4\n", + " 0.65\n", " 0.05\n", - " -45.953233\n", - " -45.953233\n", + " -0.998529\n", + " -0.998529\n", " \n", " \n", " 355\n", @@ -12901,8 +13572,8 @@ " 1.00\n", " 0.9\n", " 0.97\n", - " -7.490131\n", - " -7.490131\n", + " -0.074901\n", + " -0.074901\n", " \n", " \n", " 361\n", @@ -12919,10 +13590,10 @@ " False\n", " 35386\n", " 0.85\n", - " 0.85\n", + " 0.8\n", " 0.666\n", - " -80.050570\n", - " -68.042984\n", + " -0.435900\n", + " -0.370515\n", " \n", " \n", " 364\n", @@ -12941,8 +13612,8 @@ " 0.85\n", " 0.05\n", " 0.03\n", - " -2.083409\n", - " -1.770897\n", + " -0.017709\n", + " -0.015053\n", " \n", " \n", "\n", @@ -12971,11 +13642,11 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median head_to_head weighted_score \n", - "342 1.00 0.9 0.95 -5.406722 -5.406722 \n", - "351 1.00 0.4 0.05 -45.953233 -45.953233 \n", - "355 1.00 0.9 0.97 -7.490131 -7.490131 \n", - "361 0.85 0.85 0.666 -80.050570 -68.042984 \n", - "364 0.85 0.05 0.03 -2.083409 -1.770897 " + "342 1.00 0.905 0.95 -0.048527 -0.048527 \n", + "351 1.00 0.65 0.05 -0.998529 -0.998529 \n", + "355 1.00 0.9 0.97 -0.074901 -0.074901 \n", + "361 0.85 0.8 0.666 -0.435900 -0.370515 \n", + "364 0.85 0.05 0.03 -0.017709 -0.015053 " ] }, "metadata": {}, @@ -12983,13 +13654,13 @@ }, { "ename": "ValueError", - "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "evalue": "operands could not be broadcast together with shapes (201,) (5,) ", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[354], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:853\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 843\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 844\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 852\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 853\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 856\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 857\u001b[0m bins\n\u001b[1;32m 858\u001b[0m )\n", + "Cell \u001b[0;32mIn[78], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:750\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 740\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 748\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 749\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 750\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 752\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 753\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 754\u001b[0m bins\n\u001b[1;32m 755\u001b[0m )\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", @@ -12997,7 +13668,7 @@ "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (201,) (5,) " ] } ], diff --git a/functions.py b/functions.py index 62b8f3b..0373237 100644 --- a/functions.py +++ b/functions.py @@ -10,7 +10,7 @@ from scipy import stats from scipy.optimize import minimize_scalar from scipy.stats import binom, norm - +import re from refactored_notebook.scoring import ( calculate_baseline_score, calculate_peer_score, @@ -345,7 +345,14 @@ def get_median_forecast_multiple_choice(row, forecasts): # print(f"NO PROBS collected for multiple-choice question {row.get('bot_question_id')} — returning np.nan") return np.nan - return np.nanmedian(probs) + median_forecast = [] # NOTE: This forecast will not add to 1, but we only need the median for the resolution + for i, _ in enumerate(options): + if i == resolution_idx: + median_forecast.append(np.nanmedian(probs)) + else: + median_forecast.append(0.0001) # this is filler @Check: This won't screw anything up right? Perviously we were just returning the probability of resolution + + return median_forecast def get_median_forecast(row, bots): @@ -1106,7 +1113,7 @@ def compute_bucket_forecast_value(row): return df -def parse_options_array(options_str): +def parse_options_array(options_str: str) -> list[str]: """ Parse options string that looks like an array into an actual array. @@ -1119,24 +1126,30 @@ def parse_options_array(options_str): if not isinstance(options_str, str): return options_str # Already parsed or None + if options_str == "[]": + return [] # This can happen for numeric/binary questions with no options + + options = [] try: # First try using eval (safer than literal_eval for this specific case) - options_array = eval(options_str) - return options_array + options = eval(options_str) except: # If that fails, try custom parsing # Strip brackets and split by comma cleaned = options_str.strip("[]") # Split by comma, but respect quotes - import re # Match items in quotes with commas inside parts = re.findall(r'"([^"]*)"', cleaned) if parts: - return parts - - # Simple fallback: just split by comma and strip quotes - return [p.strip().strip("\"'") for p in cleaned.split(",")] + options = parts + else: + # Simple fallback: just split by comma and strip quotes + options = [p for p in cleaned.split(",")] + stripped_options = [p.strip("\"' ") for p in options] + if len(stripped_options) == 0: + raise ValueError(f"No options found in {options_str}") + return stripped_options def calculate_weighted_h2h_score_between_two_forecast_columns( diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 4ece3f3..c42ccb5 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,10 +1,10 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 -X_bot,-0.0,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 @@ -13,35 +13,35 @@ Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 pianobot,-0.1,-0.1,-0.0,-0.0,0.0 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 -4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 +metac-o1,-0.2,-0.2,-0.1,0.1,0.1 +4Shadower,-0.2,-0.1,-0.1,-0.0,-0.0 annabot,-0.1,-0.1,-0.1,-0.0,-0.0 -cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 -jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 +cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,-0.0 +jkraybill_bot,-0.1,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 -MWG,-0.2,-0.2,-0.1,-0.1,-0.0 -ProfessorSP,-0.2,-0.2,-0.1,-0.1,-0.0 -GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 -metac-o1,-0.3,-0.2,-0.1,0.0,0.1 -acm_bot,-0.3,-0.2,-0.1,0.0,0.1 +MWG,-0.2,-0.2,-0.1,-0.0,-0.0 +ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 +GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 -bot_median,-0.3,-0.2,-0.1,-0.0,0.1 +acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 +metac-deepseek-r1+asknews,-0.2,-0.2,-0.1,-0.1,-0.0 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-deepseek-r1,-0.3,-0.2,-0.1,-0.1,-0.0 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 +metac-perplexity,-0.3,-0.3,-0.1,-0.0,0.1 +metac-Gemini-Exp-1206,-0.3,-0.3,-0.1,-0.0,0.0 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -metac-Gemini-Exp-1206,-0.3,-0.3,-0.2,-0.0,0.0 -metac-perplexity,-0.4,-0.3,-0.2,-0.0,0.0 -NextWorldLab,-0.3,-0.3,-0.2,-0.1,0.0 +NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 +metac-claude-3-5-sonnet-latest,-0.3,-0.3,-0.2,-0.1,-0.0 +metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 +bot_median,-0.3,-0.3,-0.2,-0.1,-0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,-0.0 -metac-Llama-3.1,-0.4,-0.4,-0.2,-0.1,0.0 -metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.0 +metac-Llama-3.1,-0.4,-0.3,-0.2,-0.1,-0.0 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 +metac-exa,-0.4,-0.3,-0.2,-0.1,-0.1 +pgodzinai,-0.5,-0.4,-0.2,-0.1,-0.1 VeritasAI,-0.4,-0.3,-0.2,-0.2,-0.1 -metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-grok-2-1212,-0.5,-0.4,-0.3,-0.1,-0.1 +metac-gpt-4o,-0.4,-0.4,-0.3,-0.2,-0.1 metac-o1-preview,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv index 76b7626..029f529 100644 --- a/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv +++ b/notebook_outputs/weighted_bot_ONLY_peer_leaderboard_t_test.csv @@ -15,7 +15,7 @@ metac-perplexity,734.7,264.3,2.8,62.518732274252,3.8454321257670965,0.7228462253 metac-exa,470.9,275.2,1.7,63.38280444669259,3.8205989842983494,0.4478599398298826,1.9681111912388756,9.2,-5.8,0.6726960546336258,0.654608 MWG,307.0,84.8,3.6,36.6252501807067,3.976544679654517,0.9101477753110279,1.987508353566517,11.5,-4.3,0.8173229386375491,0.365354 jkraybill_bot,219.6,162.4,1.4,71.12529221576798,5.5817601187391634,0.24232123347298368,1.9740758524924067,12.4,-9.7,0.5955805198867354,0.808839 -metac-deepseek-r1,172.5,225.8,0.8,38.0431452483966,2.5318249833740962,0.3017230896257882,1.9700645882216863,5.8,-4.2,0.6184289375422699,0.763142 +metac-deepseek-r1+asknews,172.5,225.8,0.8,38.0431452483966,2.5318249833740962,0.3017230896257882,1.9700645882216863,5.8,-4.2,0.6184289375422699,0.763142 pianobot,101.0,14.8,6.8,41.27615494222523,10.711147680523258,0.6349321054235654,2.1450947126002333,29.8,-16.2,0.7320891967624292,0.535822 metac-grok-2-1212,40.0,281.2,0.1,49.508070078167286,2.952248394236147,0.04816426739476925,1.967947383995502,6.0,-5.7,0.5191901814794234,0.961620 andrewsiah,2.6,25.1,0.1,35.80509173037023,7.1467391327710805,0.014679458541325375,2.0603406998894913,14.8,-14.6,0.5057956215530941,0.988409 diff --git a/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv b/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv index 3a4a494..b32fa6b 100644 --- a/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv +++ b/notebook_outputs/weighted_bot_peer_leaderboard_t_test.csv @@ -11,7 +11,7 @@ Rank,Bot,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_b 10,metac-claude-3-5-sonnet-latest,951.3,370.3,2.6,38.26306555715613,1.988342419831904,1.2919544880180496,1.966062599368744,6.5,-1.3,0.9014096170572055,0.197181 11,GreeneiBot2,1494.7,264.1,5.7,59.728354485253575,3.675051787269948,1.539810539883174,1.9685962808273842,12.9,-1.6,0.9375959149496895,0.124808 12,metac-perplexity,1558.4,354.4,4.4,59.58837847152926,3.1652094732771676,1.389181319604283,1.9663705248092669,10.6,-1.8,0.9171738658225362,0.165652 -13,metac-deepseek-r1,516.8,277.9,1.9,37.353209862667065,2.2407803261049724,0.8299752665727909,1.968164543586558,6.3,-2.6,0.7963661024103902,0.407268 +13,metac-deepseek-r1+asknews,516.8,277.9,1.9,37.353209862667065,2.2407803261049724,0.8299752665727909,1.968164543586558,6.3,-2.6,0.7963661024103902,0.407268 14,pgodzinai,1106.7,325.4,3.4,66.68615909814488,3.6966946914459644,0.9199538936245306,1.966948755554642,10.7,-3.9,0.8208598109837832,0.358280 15,metac-exa,599.9,365.3,1.6,63.45938884307718,3.3201611290993176,0.4946106204656042,1.9661417524889626,8.2,-4.9,0.6894134359021193,0.621173 16,MWG,253.8,113.4,2.2,40.6740836146038,3.819036516963852,0.5859361127584735,1.980468444487731,9.8,-5.3,0.7204535666937473,0.559093 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 8c1e7a0..746b52f 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -RPM_bot,-0.5,7.0,-0.1,0.8401626602195374,0.31755163711190787,-0.22911491175620202,2.4469118511449692,0.7,-0.8,0.4131948210081994,0.826390 -jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 +RPM_bot,-1.3,7.0,-0.2,0.8269776545743774,0.3125681734016113,-0.610595609477049,2.4469118511449692,0.6,-1.0,0.2819326101745987,0.563865 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 -annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 +metac-o1,-5.3,91.1,-0.1,0.9084726497398434,0.09518152714706545,-0.6113627344286646,1.9858289388460384,0.1,-0.2,0.27124945946442813,0.542499 +annabot,-5.9,29.3,-0.2,0.5175750572467731,0.09561797207152893,-2.1122028342259047,2.0441825433909937,-0.0,-0.4,0.021810527148697016,0.043621 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.5,27.4,-0.2,0.7478313737485887,0.14286584023204454,-1.6679327769704273,2.0495406495390753,0.1,-0.5,0.053574616968489516,0.107149 +cookics_bot_TEST,-6.8,27.4,-0.2,0.7472901092218875,0.14276243695944935,-1.737830063646217,2.0495406495390753,0.0,-0.5,0.04694721167123542,0.093894 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 -MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 +MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 -metac-o1,-10.4,91.1,-0.1,0.9315503207588304,0.09759939627192438,-1.1710037539243623,1.9858289388460384,0.1,-0.3,0.12234246603454144,0.244685 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 -GreeneiBot2,-10.6,58.4,-0.2,0.84933087242601,0.11118763184285871,-1.638405629664946,2.000831925930035,0.0,-0.4,0.053406273914708285,0.106813 +GreeneiBot2,-10.6,58.4,-0.2,0.8493306622643327,0.11118760433016613,-1.638793797628407,2.000831925930035,0.0,-0.4,0.05336569544684546,0.106731 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 -bot_median,-11.1,92.1,-0.1,0.8343911715991652,0.08694405375037174,-1.3919418427248071,1.9855502432148115,0.1,-0.3,0.08366450804542999,0.167329 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 +metac-deepseek-r1+asknews,-11.7,52.1,-0.2,0.6690305553273252,0.09268876407541017,-2.4327442879372825,2.0053789762011176,-0.0,-0.4,0.009262209683005887,0.018524 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 -metac-deepseek-r1,-14.1,52.1,-0.3,0.8172087173883323,0.11321764813763505,-2.3937504961816116,2.0053789762011176,-0.0,-0.5,0.01019302014325762,0.020386 +metac-perplexity,-13.6,89.1,-0.2,0.953800697354561,0.10104592028043681,-1.5152493493302568,1.9864049297707018,0.0,-0.4,0.06664452341402785,0.133289 +metac-Gemini-Exp-1206,-13.9,76.5,-0.2,0.9608427574536519,0.10985544896515206,-1.6509533909374279,1.9908217254774627,0.0,-0.4,0.051451032994077626,0.102902 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-Gemini-Exp-1206,-14.6,76.5,-0.2,0.9369300827202118,0.1071214557093134,-1.7806582480922164,1.9908217254774627,0.0,-0.4,0.03949550680306326,0.078991 -metac-perplexity,-16.1,89.1,-0.2,1.0694909108673796,0.11330217478335987,-1.5994893543452755,1.9864049297707018,0.0,-0.4,0.05664610517795549,0.113292 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -minefrac1,-18.5,51.1,-0.4,0.8782230217189723,0.1228554331463025,-2.94542136244705,2.0065449272360034,-0.1,-0.6,0.002440792164293176,0.004882 -metac-claude-3-5-sonnet-20240620,-20.8,90.5,-0.2,0.9854576682401628,0.10358901026916505,-2.2176587156495677,1.9860719790130024,-0.0,-0.4,0.01455504948064986,0.029110 -metac-Llama-3.1,-21.0,89.1,-0.2,1.131903405632652,0.11991417243449026,-1.9667104273244107,1.9864049297707018,0.0,-0.5,0.026181998267921627,0.052364 -metac-claude-3-5-sonnet-latest,-21.7,91.1,-0.2,0.8679924761244506,0.0909403815880937,-2.6147562800776485,1.9858289388460384,-0.1,-0.4,0.005233245635108678,0.010466 +metac-claude-3-5-sonnet-latest,-17.7,91.1,-0.2,0.822268712940962,0.08614986025763702,-2.253410401302691,1.9858289388460384,-0.0,-0.4,0.013329842987401584,0.026660 +bot_median,-17.9,92.1,-0.2,0.8298286106445787,0.0864686321994526,-2.248076238150116,1.9855502432148115,-0.0,-0.4,0.013491943459249906,0.026984 +metac-claude-3-5-sonnet-20240620,-18.2,90.5,-0.2,0.9882219785580354,0.10387958811855824,-1.9308293392916587,1.9860719790130024,0.0,-0.4,0.028334774283890096,0.056670 +minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 +metac-Llama-3.1,-21.3,89.1,-0.2,0.9128041314903421,0.0967027322983173,-2.471742593789836,1.9864049297707018,-0.0,-0.4,0.007684177160478823,0.015368 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -pgodzinai,-23.5,76.4,-0.3,0.9735671748298226,0.11138308522466013,-2.763549748735371,1.9908489732268309,-0.1,-0.5,0.003590727855444895,0.007181 +metac-exa,-22.4,89.1,-0.3,0.8128016858276886,0.08610844443471673,-2.92372894610568,1.9864049297707018,-0.1,-0.4,0.002197830440677215,0.004396 +pgodzinai,-23.9,76.4,-0.3,0.9914794382114891,0.11343237695345683,-2.755452219862641,1.9908489732268309,-0.1,-0.5,0.00367232305294701,0.007345 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-exa,-24.7,89.1,-0.3,0.8121952445686516,0.08604419787326485,-3.2197865951234235,1.9864049297707018,-0.1,-0.4,0.0008985159820669422,0.001797 -metac-o1-preview,-25.5,91.1,-0.3,0.8498877252707713,0.08904352994884641,-3.1492143531875287,1.9858289388460384,-0.1,-0.5,0.0011106007145197491,0.002221 -InstitutPelFutur,-26.9,90.1,-0.3,0.9739711690022733,0.10260858670161008,-2.9043019887843187,1.9861137662360124,-0.1,-0.5,0.0023202343180469525,0.004640 -metac-grok-2-1212,-27.9,91.1,-0.3,1.0054085980592369,0.10533759689680534,-2.9038578245582283,1.9858289388460384,-0.1,-0.5,0.0023176059032990978,0.004635 -metac-gpt-4o,-28.8,91.1,-0.3,0.8198830654548765,0.08589991374463501,-3.67651905388223,1.9858289388460384,-0.1,-0.5,0.0002007468680573961,0.000401 +metac-grok-2-1212,-24.5,91.1,-0.3,1.0139958650854732,0.10623729287533687,-2.5268442158424125,1.9858289388460384,-0.1,-0.5,0.006626896274566267,0.013254 +metac-gpt-4o,-26.0,91.1,-0.3,0.8516451147774127,0.08922765328715744,-3.193010060382893,1.9858289388460384,-0.1,-0.5,0.0009699028149533728,0.001940 +metac-o1-preview,-26.2,91.1,-0.3,0.9143330864911109,0.09579553057346926,-2.9970476132039527,1.9858289388460384,-0.1,-0.5,0.0017609124521279873,0.003522 +InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 diff --git a/refactored_notebook/scoring.py b/refactored_notebook/scoring.py index a79a02b..eec131c 100644 --- a/refactored_notebook/scoring.py +++ b/refactored_notebook/scoring.py @@ -153,8 +153,11 @@ def _determine_probability_for_resolution( "Havent decided how to handle null forecasts or anulled resolutions" ) - if len(forecast) == 0: - raise ValueError("Forecast is empty") + try: + if len(forecast) == 0: + raise ValueError("Forecast is empty") + except Exception as e: + raise ValueError(f"Error encountered for question of type {q_type} with resolution {resolution} and forecast {forecast}: {e}") if not q_type == QuestionType.NUMERIC and any(p <= 0 or p >= 1 for p in forecast): raise ValueError("Forecast contains probabilities outside of 0 to 1 range") @@ -207,7 +210,7 @@ def _multiple_choice_resolution_prob( raise ValueError("Forecast and options have different lengths") pmf = [float(p) for p in forecast] - options = [str(opt) for opt in options] + options = [str(opt) for opt in options] # @Check: TODO: For whatever reason, options had " and ' surrounding them, and were not parsed at this point. This is the easier way to handle it, but should be dealt with earlier in the pipeline. resolution_idx = options.index(str(resolution)) prob_for_resolution = pmf[resolution_idx] return prob_for_resolution From 819c3a7510aebc36f7cd274e9c2ef795cb2a8ecb Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 20 May 2025 21:07:09 -0600 Subject: [PATCH 21/26] Moved community prediction comparison files to archived --- .../df_top_bot_pro_cp_forecasts.csv | 44 ----------------- notebook_outputs/weighted_baseline_bot_cp.csv | 48 ------------------- 2 files changed, 92 deletions(-) delete mode 100644 notebook_outputs/df_top_bot_pro_cp_forecasts.csv delete mode 100644 notebook_outputs/weighted_baseline_bot_cp.csv diff --git a/notebook_outputs/df_top_bot_pro_cp_forecasts.csv b/notebook_outputs/df_top_bot_pro_cp_forecasts.csv deleted file mode 100644 index b32298b..0000000 --- a/notebook_outputs/df_top_bot_pro_cp_forecasts.csv +++ /dev/null @@ -1,44 +0,0 @@ -bot_question_id,cp_post_id,cp_question_id,title,resolution,cp_reveal_time,community_prediction,bot_team_median,pgodzinai,pro_median -28959,28972.0,28907.0,Will the Liberal Democratic Party win a majority of seats in the 2024 Japanese general election?,0,2024-10-23 14:30:00+00:00,0.6,0.5375,0.57,0.5 -28960,28974.0,28909.0,Will the Botswana Democratic Party win the 2024 general election?,0,2024-10-23 14:30:00+00:00,0.55,0.685,0.67,0.75 -29108,28854.0,28811.0,"Will any more United Kingdom MPs be suspended from their party, resign, or change allegiance before 2025?",1,2024-10-26 14:30:00+00:00,0.82,0.52,0.49,0.85 -29109,28841.0,28801.0,Will Eric Adams be Mayor of New York City on the 1st of January 2025?,1,2024-10-26 14:30:00+00:00,0.75,0.6999999999999998,0.6999999999999998,0.81 -29110,28657.0,28657.0,"Will China officially announce export restrictions on any additional metals before January 1, 2025?",1,2024-10-26 14:30:00+00:00,0.45,0.3,0.25,0.4 -29111,28650.0,28650.0,"Will the US Steel/Nippon Steel merger collapse before January 1, 2025?",0,2024-10-26 14:30:00+00:00,0.6,0.6399999999999999,0.6399999999999999,0.63 -29112,28658.0,28658.0,"Will the Russian government officially ban YouTube before January 1, 2025?",0,2024-10-26 14:30:00+00:00,0.18,0.31000000000000005,0.31000000000000005,0.12 -29163,29140.0,29050.0,"Will the lowest COVID-19 hospitalization rate from October 5, 2024, to January 4, 2025, be below 2.0?",1,2024-10-29 14:30:00+00:00,0.31411009579259686,0.26,0.37,0.37 -29348,29242.0,29145.0,[Short fuse] Will California's Proposition 33 (allowing rent control) pass in the 2024 general election?,0,2024-11-02 14:30:00+00:00,0.25,0.39,0.39,0.12 -29349,29077.0,28997.0,"Will it rain more than 100mm in Brasília, Brazil in December 2024?",1,2024-11-02 14:30:00+00:00,0.9583453266740091,0.82625,0.88,0.952 -29350,29077.0,28997.0,"Will it rain more than 150mm in Brasília, Brazil in December 2024?",1,2024-11-02 14:30:00+00:00,0.870125541541337,0.72,0.72,0.85 -29351,29077.0,28997.0,"Will it rain more than 200mm in Brasília, Brazil in December 2024?",1,2024-11-02 14:30:00+00:00,0.7274736790938451,0.51,0.51,0.6 -29353,29028.0,28953.0,Will the US State Department approve more than 20 arms sales globally in the fourth quarter of 2024?,1,2024-11-02 14:30:00+00:00,0.9611013007377774,0.575,0.67,0.995 -29354,29028.0,28953.0,Will the US State Department approve more than 25 arms sales globally in the fourth quarter of 2024?,1,2024-11-02 14:30:00+00:00,0.8831983617655779,0.37,0.37,0.95 -29355,28783.0,28771.0,Will there be a white Christmas in at least 4 of these 9 large European cities in 2024?,0,2024-11-02 14:30:00+00:00,0.15,0.22999999999999998,0.22999999999999998,0.10999999999999999 -29414,29145.0,29055.0,"Will the XEC COVID-19 variant account for at least 50% of the variants monitored in the US before January 5, 2025?",0,2024-11-05 15:30:00+00:00,0.37,0.273,0.15999999999999992,0.32000000000000006 -29461,29507.0,29365.0,"Will the weekly total number of influenza hospitalizations for the United States for the week of Nov 23, 2024 be more than 2,000?",1,2024-11-06 15:30:00+00:00,0.5399754435659441,0.58,0.58,0.66 -29463,29141.0,29051.0,"Will the CDC report more than 80% of the tested influenza sequences as influenza A during the 2024-25 season through the week ending Dec 21, 2024?",1,2024-11-06 15:30:00+00:00,0.4480539957586608,0.7263,0.78,0.8599999999999999 -29635,28834.0,28798.0,Will the 2024 World Chess Champion be decided in the first 10 games?,0,2024-11-09 15:30:00+00:00,0.1,0.18999999999999995,0.18999999999999995,0.07699999999999996 -29642,29608.0,29480.0,"Will Elon Musk’s net worth be less than or equal to the highest other net worth on the Forbes Real-Time Billionaires list as of January 1, 2025?",0,2024-11-09 15:30:00+00:00,0.025423118381141845,0.18999999999999995,0.12,0.040000000000000036 -29643,29608.0,29480.0,"Will Elon Musk’s net worth differ from the highest other net worth on the Forbes Real-Time Billionaires list by greater than zero and less than $40 billion as of January 1, 2025?",0,2024-11-09 15:30:00+00:00,0.17136511413304625,0.24,0.24,0.15000000000000002 -29644,29608.0,29480.0,"Will Elon Musk’s net worth differ from the highest other net worth on the Forbes Real-Time Billionaires list by greater than or equal to $40 billion and less than or equal to $70 billion as of January 1, 2025?",0,2024-11-09 15:30:00+00:00,0.4568016904397837,0.3400000000000001,0.3400000000000001,0.35 -29645,29608.0,29480.0,"Will Elon Musk’s net worth differ from the highest other net worth on the Forbes Real-Time Billionaires list by greater than $70 billion and less than $100 billion as of January 1, 2025?",0,2024-11-09 15:30:00+00:00,0.27419647031085126,0.19999999999999996,0.19999999999999996,0.30000000000000016 -29646,29608.0,29480.0,"Will Elon Musk’s net worth differ from the highest other net worth on the Forbes Real-Time Billionaires list by greater than or equal to $100 billion as of January 1, 2025?",1,2024-11-09 15:30:00+00:00,0.07221360673517696,0.19999999999999996,0.1,0.15 -29940,29900.0,29741.0,Will Bitcoin dominance exceed 63.0% on any single day in November or December 2024?,0,2024-11-16 15:30:00+00:00,0.43,0.30000000000000016,0.30000000000000016,0.4 -29943,29903.0,29744.0,Will the International Criminal Court issue any warrants in November or December 2024?,1,2024-11-16 15:30:00+00:00,0.19,0.17999999999999997,0.17999999999999997,0.20999999999999996 -29944,29902.0,29743.0,Will Astro Bot win the Game of the Year 2024 award?,1,2024-11-16 15:30:00+00:00,0.38,0.29000000000000004,0.29000000000000004,0.45 -29945,29901.0,29742.0,"Will North Korea deploy military personnel to Ukraine before January 1, 2025?",0,2024-11-16 15:30:00+00:00,0.15,0.44999999999999996,0.44999999999999996,0.14000000000000012 -30194,30297.0,30101.0,Will Nvidia have the largest market cap in the world at the end of 2024?,0,2024-11-23 15:30:00+00:00,0.75,0.56,0.030000000000000138,0.72 -30196,30252.0,30060.0,"Will at least one of Andrea Bocelli's concerts at Madison Square Garden on December 18 or 19, 2024 sell out?",0,2024-11-23 15:30:00+00:00,0.65,0.54,0.36,0.75 -30197,30295.0,30100.0,Will Bluesky reach 30 million users before 1 January 2025?,0,2024-11-23 15:30:00+00:00,0.7,0.742,0.81,0.44999999999999996 -30435,30516.0,30300.0,"Will any more of Trump's announced Cabinet picks drop out before January 1, 2025?",0,2024-12-04 15:30:00+00:00,0.72,0.19999999999999996,0.19999999999999996,0.8 -30440,30434.0,30235.0,"Will be Donald Trump's net favorability rating on December 27, 2024 be greater than -4?",1,2024-12-04 15:30:00+00:00,0.6187329492907317,0.20999999999999996,0.20999999999999996,0.75 -30441,30434.0,30235.0,"Will be Donald Trump's net favorability rating on December 27, 2024 be greater than or equal to -6 and less than or equal to -4?",0,2024-12-04 15:30:00+00:00,0.2703026033342055,0.38,0.38,0.17999999999999994 -30442,30434.0,30235.0,"Will be Donald Trump's net favorability rating on December 27, 2024 be less than -6?",0,2024-12-04 15:30:00+00:00,0.11096444737506272,0.4099999999999999,0.4099999999999999,0.07999999999999996 -30576,30517.0,30301.0,"Will New Delhi experience a ""Hazardous"" air quality index for at least one third of the last two weeks of December 2024?",1,2024-12-07 15:30:00+00:00,0.5,0.30999999999999994,0.19999999999999996,0.6 -30579,30477.0,30270.0,"Will Joe Biden sign 4 or more executive orders after Election Day and before January 1, 2025?",1,2024-12-07 15:30:00+00:00,0.25510245901639345,0.185,0.16000000000000003,0.1 -30723,30809.0,30563.0,"Before Jan 1, 2025, will Ontario Premier Doug Ford call an early provincial election scheduled for 2025?",0,2024-12-12 15:30:00+00:00,0.79,0.06999999999999995,0.05999999999999994,0.040000000000000036 -30787,30922.0,30654.0,"Will Blue Origin launch its New Glenn rocket before January 1, 2025?",0,2024-12-14 15:30:00+00:00,0.25,0.12500000000000006,0.14000000000000012,0.30000000000000016 -30791,28860.0,28815.0,"Will US airline passenger volume be for the week of Christmas through New Years Eve 2024 be greater than 19 million, according to the TSA?",0,2024-12-14 15:30:00+00:00,0.0798111936708924,0.26499999999999996,0.12,0.09999999999999998 -30792,28860.0,28815.0,"Will US airline passenger volume be for the week of Christmas through New Years Eve 2024 be greater than or equal to 18 million and less than or equal to 19 million, according to the TSA?",1,2024-12-14 15:30:00+00:00,0.2944184317995511,0.395,0.34,0.4 -30793,28860.0,28815.0,"Will US airline passenger volume be for the week of Christmas through New Years Eve 2024 be greater than 17 million and less than 18 million, according to the TSA?",0,2024-12-14 15:30:00+00:00,0.4338699030478743,0.42999999999999994,0.4099999999999999,0.38 -30794,28860.0,28815.0,"Will US airline passenger volume be for the week of Christmas through New Years Eve 2024 be less than or equal to 17 million, according to the TSA?",0,2024-12-14 15:30:00+00:00,0.19190047148168224,0.185,0.13,0.050000000000000044 diff --git a/notebook_outputs/weighted_baseline_bot_cp.csv b/notebook_outputs/weighted_baseline_bot_cp.csv deleted file mode 100644 index cb81d7f..0000000 --- a/notebook_outputs/weighted_baseline_bot_cp.csv +++ /dev/null @@ -1,48 +0,0 @@ -Rank,Forecaster,Weighted_Baseline,Count,Weighted Count -1,pro_median,620.6,43,32.1 -2,cp_baseline_score,541.2,43,32.1 -3,estr.ai,370.4,32,23.2 -4,manticAI,313.0,41,30.1 -5,mf-bot-4,247.2,43,32.1 -6,archipelago,239.0,43,32.1 -7,pgodzinai,173.6,43,32.1 -8,Cassie,154.8,42,31.1 -9,VeritasAI,111.9,31,23.1 -10,histerio,89.8,43,32.1 -11,silicoqr,76.5,4,3.5 -12,bot_median,69.0,43,32.1 -13,lookahead,44.4,1,1.0 -14,gnosis-ai,42.6,41,30.1 -15,tombot61,35.7,18,13.5 -16,Jay_Bailey_Bot,24.5,39,28.6 -17,hlb-bot,0.0,0,0.0 -18,karamazov,-56.2,5,4.5 -19,MWG,-71.0,37,27.6 -20,RyansAGI,-90.1,43,32.1 -21,SaraBase,-97.7,41,31.2 -22,RonanMcGovern,-108.6,28,21.2 -23,annabot,-112.6,30,23.2 -24,mmBot,-115.3,43,32.1 -25,ProfessorSP,-129.8,12,7.9 -26,tombot37,-157.3,3,3.0 -27,HunchexBot,-183.7,2,2.0 -28,twsummerbot,-185.3,43,32.1 -29,Panshul42,-186.5,10,9.5 -30,lostandfound,-190.4,7,6.5 -31,acm_bot,-212.7,36,27.6 -32,mf-bot-1,-215.6,43,32.1 -33,jkraybill_bot,-276.0,21,15.9 -34,GreeneiBot2,-285.1,43,32.1 -35,bestworldbot,-287.9,38,27.6 -36,biak_bot,-321.1,3,2.1 -37,predictomatic,-332.4,1,1.0 -38,Bot_Pepa,-364.8,33,25.4 -39,Unwrapped80T,-501.6,38,27.6 -40,SynapseSeer,-522.1,43,32.1 -41,SeidrBot,-542.8,20,14.7 -42,Grizeu_Bot,-553.7,23,15.5 -43,mf-bot-5,-567.3,36,25.6 -44,HSeldon,-641.3,41,30.6 -45,mf-bot-3,-664.0,41,30.7 -46,InstitutPelFutur,-790.9,43,32.1 -47,000_bot,-967.6,26,18.7 From 0f53d31b12a2160b027212b845e039cc2ab85f77 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 20 May 2025 21:25:43 -0600 Subject: [PATCH 22/26] Moved another cp comparison csv --- .../weighted_t_test_h2h_bot_vs_cp.csv | 46 ------------------- 1 file changed, 46 deletions(-) delete mode 100644 notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv deleted file mode 100644 index 43e438b..0000000 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_cp.csv +++ /dev/null @@ -1,46 +0,0 @@ -,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value -MWG,934.1,27.6,33.8,67.99956179064671,12.944316148637085,2.61482274028625,2.0371123352756833,60.2,7.5,0.9927401446377133,0.014520 -pgodzinai,775.1,32.1,24.1,61.62265361380467,10.87706083884435,2.2202577866730513,2.0255910818367266,46.2,2.1,0.983089016447948,0.033822 -manticAI,674.8,30.1,22.4,63.569938340783196,11.587607962998844,1.9348638085688206,2.0292684480061114,45.9,-1.1,0.9686090668459504,0.062782 -Jay_Bailey_Bot,291.0,28.6,10.2,60.29444868257388,11.280695842052237,0.9031116606239613,2.0331720805198046,33.1,-12.7,0.8128547160526456,0.374291 -GreeneiBot2,276.2,32.1,8.6,71.49803120629352,12.62017114943309,0.6817671393950533,2.0255910818367266,34.2,-17.0,0.7497835645920188,0.500433 -mf-bot-5,235.6,25.6,9.2,86.99925860400279,17.20542775018099,0.5356516817886486,2.0402225316476676,44.3,-25.9,0.7014921991751082,0.597016 -Panshul42,182.2,9.5,19.1,74.90155249758483,24.26515714560076,0.787892540486301,2.2706573707273647,74.2,-36.0,0.7739494782536727,0.452101 -mf-bot-4,158.2,32.1,4.9,59.70976252026739,10.539414996247475,0.4675283601407147,2.0255910818367266,26.3,-16.4,0.6783090963298364,0.643382 -tombot61,152.9,13.5,11.3,68.98129564147818,18.754683284490017,0.6024420597510902,2.128697124216934,51.2,-28.6,0.7211864660418732,0.557627 -histerio,82.5,32.1,2.6,57.8817323117301,10.21674801215234,0.2515218792132334,2.0255910818367266,23.3,-18.1,0.5984672717402144,0.803065 -twsummerbot,79.6,32.1,2.5,72.52929207502656,12.802199779362391,0.1936770812699229,2.0255910818367266,28.4,-23.5,0.5761558355035294,0.847688 -mf-bot-3,64.8,30.7,2.1,74.0248237893511,13.367523027554315,0.15815432223488393,2.0288819531064624,29.2,-25.0,0.5622965645838617,0.875407 -estr.ai,59.2,23.2,2.5,65.16188395731811,13.51717226531655,0.18840918637357723,2.0504274174371617,30.3,-25.2,0.5738676988236109,0.852265 -bot_median,56.2,32.1,1.8,54.96058096140207,9.70113339490268,0.18045782950944683,2.0255910818367266,21.4,-17.9,0.5710179544838705,0.857964 -silicoqr,38.4,3.5,10.9,69.96745904576092,37.2488912364119,0.2920084104978259,3.3176021457598317,134.5,-112.7,0.6037214959454129,0.792557 -ProfessorSP,24.1,7.9,3.1,110.09982526875474,39.27730411233198,0.07809917155148716,2.243078372893419,91.2,-85.0,0.5300104946327199,0.939979 -hlb-bot,0.0,0.0,,,,,,,,,NA -mmBot,-4.9,32.1,-0.2,67.78772450377606,11.965262128685994,-0.012740456030611613,2.0255910818367266,24.1,-24.4,0.49495812577689857,0.989916 -Cassie,-13.0,31.1,-0.4,69.70635619812525,12.500190334337788,-0.033497292525897754,2.0273826818596135,24.9,-25.8,0.48674959938803974,0.973499 -VeritasAI,-42.4,23.1,-1.8,52.244414568961474,10.870932037536209,-0.16889537973672442,2.053537595217514,20.5,-24.2,0.4337077415061645,0.867415 -tombot37,-45.9,3.0,-15.3,12.089475322396334,6.979861831746861,-2.1930227352176725,4.302652729696142,14.7,-45.3,0.07979583710109377,0.159592 -lookahead,-76.2,1.0,-76.2,,,,,,,,NA -HunchexBot,-94.4,2.0,-47.2,,,,,,,,NA -RonanMcGovern,-178.9,21.2,-8.4,81.71995095851909,17.732178207011486,-0.4749294362734798,2.063719587272493,28.2,-45.0,0.31996108624133784,0.639922 -Bot_Pepa,-194.3,25.4,-7.6,85.14895327774806,16.89044500774321,-0.45258529475458426,2.045833904482552,26.9,-42.2,0.327421771808115,0.654844 -karamazov,-204.8,4.5,-45.2,46.41033523122176,21.809570340819626,-2.0737271663542223,2.837547169466547,16.7,-107.1,0.058030388543722666,0.116061 -mf-bot-1,-206.7,32.1,-6.4,81.60628646751783,14.404386872111331,-0.4470780625943071,2.0255910818367266,22.7,-35.6,0.32895862372063667,0.657917 -annabot,-214.0,23.2,-9.2,58.56304138030242,12.148309266762793,-0.7581987590125773,2.055599690487606,15.8,-34.2,0.22814618493778038,0.456292 -Unwrapped80T,-235.8,27.6,-8.6,70.15955153331606,13.362342488617267,-0.6400587815732534,2.0353912666365304,18.6,-35.8,0.2638080372174302,0.527616 -SynapseSeer,-244.8,32.1,-7.6,103.7946767306766,18.320876290449746,-0.4163764833971151,2.0255910818367266,29.5,-44.7,0.3399972579405359,0.679995 -gnosis-ai,-250.8,30.1,-8.3,86.53950293341585,15.774528959419731,-0.5282555131865916,2.0292684480061114,23.7,-40.3,0.3006641207476347,0.601328 -predictomatic,-253.5,1.0,-253.5,,,,,,,,NA -jkraybill_bot,-258.5,15.9,-16.3,70.61924708539081,17.736868314338615,-0.919398824301456,2.1025540890848893,21.0,-53.6,0.1862891672899884,0.372578 -acm_bot,-270.1,27.6,-9.8,52.68384970687823,10.028835312684553,-0.9760332291957625,2.0389004828343844,10.7,-30.2,0.16892131594648319,0.337843 -bestworldbot,-275.0,27.6,-10.0,62.17578628244504,11.841782517816986,-0.8425178646712551,2.0353912666365304,14.1,-34.1,0.20350950435289517,0.407019 -SaraBase,-314.7,31.2,-10.1,70.37879095425852,12.606351847835478,-0.8009236725439546,2.0286230770271323,15.5,-35.7,0.21471946565995076,0.429439 -lostandfound,-362.0,6.5,-55.4,63.51590603516997,24.858930817953187,-2.230570251193899,2.4688679926149897,5.9,-116.8,0.03549949616419362,0.070999 -HSeldon,-461.1,30.6,-15.1,66.86815008284378,12.094401321796376,-1.2471163641036698,2.0290817308585303,9.5,-39.6,0.11107157947358574,0.222143 -Grizeu_Bot,-472.2,15.5,-30.4,53.515395518128614,13.575462350964026,-2.2385161306381507,2.0929658760040315,-2.0,-58.8,0.020648371334677226,0.041297 -archipelago,-507.3,32.1,-15.8,67.3896199917363,11.894992402477628,-1.3287659013850484,2.0255910818367266,8.3,-39.9,0.0967969300523642,0.193594 -RyansAGI,-564.0,32.1,-17.6,61.73847125597879,10.897503897788782,-1.6125035887585866,2.0255910818367266,4.5,-39.6,0.058478384073676865,0.116957 -SeidrBot,-614.5,14.7,-41.9,79.96138285219106,20.87095954869434,-2.0057724501259644,2.1130789897414655,2.2,-86.0,0.03253867679548821,0.065077 -000_bot,-680.2,18.7,-36.4,89.32476849218054,20.668227155305534,-1.7620818346858242,2.074120815909775,6.4,-79.3,0.047668670198786844,0.095337 -InstitutPelFutur,-849.3,32.1,-26.5,65.84096176145067,11.621637575939939,-2.2767909784039224,2.0255910818367266,-2.9,-50.0,0.014915920141661794,0.029832 -biak_bot,-1159.7,2.1,-563.9,54.9778914515305,38.33656206398873,-14.709494335976174,5.068057066389925,-369.6,-758.2,0.018776916060069822,0.037554 From d2fb58de83080abb37606794cd5476a33e2ba722 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Tue, 20 May 2025 22:14:41 -0600 Subject: [PATCH 23/26] Moved discrimination chart above failing cell --- AI_BENCHMARKING_ANALYSIS.ipynb | 2885 +++++++++-------- functions.py | 2 +- .../bootstrapped_h2h_bot_vs_pros.csv | 44 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 40 +- 4 files changed, 1498 insertions(+), 1473 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index d830bc0..a2b1b4e 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -61,7 +61,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3762618/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_3873332/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] }, @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1032,11 +1032,11 @@ " \n", " 15\n", " bot_median\n", - " 8.388094\n", - " 3170.867318\n", + " 9.060773\n", + " 3425.153221\n", " 409\n", - " 5.494976\n", - " 1.471729\n", + " 6.048852\n", + " 1.532164\n", " \n", " \n", " 4\n", @@ -1072,14 +1072,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.388094 3170.867318 409 5.494976 \n", + "15 bot_median 9.060773 3425.153221 409 6.048852 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.471729 \n", + "15 1.532164 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -1729,20 +1729,20 @@ " \n", " \n", " 1\n", - " bot_median\n", - " 8997.290873\n", - " \n", - " \n", - " 2\n", " metac-o1\n", " 8861.959039\n", " \n", " \n", - " 3\n", + " 2\n", " metac-o1-preview\n", " 8849.559824\n", " \n", " \n", + " 3\n", + " bot_median\n", + " 8602.129306\n", + " \n", + " \n", " 4\n", " acm_bot\n", " 7605.922314\n", @@ -1759,9 +1759,9 @@ "text/plain": [ " Bot Baseline_Score\n", "Rank \n", - "1 bot_median 8997.290873\n", - "2 metac-o1 8861.959039\n", - "3 metac-o1-preview 8849.559824\n", + "1 metac-o1 8861.959039\n", + "2 metac-o1-preview 8849.559824\n", + "3 bot_median 8602.129306\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1931,7 +1931,7 @@ " \n", " 2\n", " bot_median\n", - " 3538.184052\n", + " 3398.202830\n", " \n", " \n", " 3\n", @@ -2166,7 +2166,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3538.184052\n", + "2 bot_median 3398.202830\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2578,8 +2578,8 @@ " False\n", " False\n", " ...\n", - " [0.5,0.3,0.15,0.04,0.01]\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", @@ -2603,7 +2603,7 @@ " True\n", " ...\n", " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", @@ -2626,9 +2626,9 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.15\n", + " 0.05\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2650,8 +2650,8 @@ " None\n", " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.45,0.45,0.1]\n", + " [0.2,0.6,0.2]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -2674,8 +2674,8 @@ " False\n", " False\n", " ...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2713,23 +2713,23 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.5,0.3,0.15,0.04,0.01] \n", + "0 [0.45,0.3,0.15,0.05,0.05] \n", "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", - "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "2 0.15 \n", + "3 [0.45,0.45,0.1] \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", "\n", " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", - "2 0.1 \n", - "3 [0.37,0.49000000000000005,0.13999999999999999] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.05 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", "\n", " metac-perplexity minefrac1 \\\n", "0 [0.3,0.4,0.2,0.07,0.03] NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.1 NaN \n", + "2 0.15 NaN \n", "3 [0.15,0.6,0.25] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", @@ -2818,8 +2818,8 @@ " False\n", " False\n", " ...\n", - " 0.9\n", " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.95\n", @@ -2842,8 +2842,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.9\n", + " 0.4\n", + " 0.15\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2867,7 +2867,7 @@ " False\n", " ...\n", " 0.9\n", - " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2946,9 +2946,9 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.9 0.95 NaN NaN 0.95 0.95 \n", - "95 0.65 0.9 NaN NaN 0.15 NaN \n", - "96 0.9 0.95 NaN NaN 0.9 NaN \n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.4 0.15 NaN NaN 0.15 NaN \n", + "96 0.9 0.9 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", "\n", @@ -3100,7 +3100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3762618/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/tmp/ipykernel_3873332/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" ] }, @@ -3162,8 +3162,8 @@ " False\n", " False\n", " ...\n", - " [0.5,0.3,0.15,0.04,0.01]\n", - " [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333]\n", + " [0.45,0.3,0.15,0.05,0.05]\n", + " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", @@ -3186,8 +3186,8 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.44,0.48,0.52,0.56,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.9008333333,0.9016666667,0.9025,0.9033333333,0.9041666667,0.905,0.9058333333,0.9066666667,0.9075,0.9083333333,0.9091666667,0.91,0.9108333333,0.9116666667,0.9125,0.9133333333,0.9141666667,0.915,0.9158333333,0.9166666667,0.9175,0.9183333333,0.9191666667,0.92,0.9208333333,0.9216666667,0.9225,0.9233333333,0.9241666667,0.925,0.9258333333,0.9266666667,0.9275,0.9283333333,0.9291666667,0.93,0.9308333333,0.9316666667,0.9325,0.9333333333,0.9341666667,0.935,0.9358333333,0.9366666667,0.9375,0.9383333333,0.9391666667,0.94,0.9408333333,0.9416666667,0.9425,0.9433333333,0.9441666667,0.945,0.9458333333,0.9466666667,0.9475,0.9483333333,0.9491666667,0.95]\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911]\n", @@ -3210,9 +3210,9 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.15\n", + " 0.05\n", + " 0.15\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -3234,8 +3234,8 @@ " None\n", " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.45,0.45,0.1]\n", + " [0.2,0.6,0.2]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -3258,9 +3258,9 @@ " False\n", " False\n", " ...\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.6066666667,0.6133333333,0.62,0.6266666667,0.6333333333,0.64,0.6466666667,0.6533333333,0.66,0.6666666667,0.6733333333,0.68,0.6866666667,0.6933333333,0.7,0.7066666667,0.7133333333,0.72,0.7266666667,0.7333333333,0.74,0.7466666667,0.7533333333,0.76,0.7666666667,0.7733333333,0.78,0.7866666667,0.7933333333,0.8,0.8028571429,0.8057142857,0.8085714286,0.8114285714,0.8142857143,0.8171428571,0.82,0.8228571429,0.8257142857,0.8285714286,0.8314285714,0.8342857143,0.8371428571,0.84,0.8428571429,0.8457142857,0.8485714286,0.8514285714,0.8542857143,0.8571428571,0.86,0.8628571429,0.8657142857,0.8685714286,0.8714285714,0.8742857143,0.8771428571,0.88,0.8828571429,0.8857142857,0.8885714286,0.8914285714,0.8942857143,0.8971428571,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0]\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4066666667,0.4133333333,0.42,0.4266666667,0.4333333333,0.44,0.4466666667,0.4533333333,0.46,0.4666666667,0.4733333333,0.48,0.4866666667,0.4933333333,0.5,0.5066666667,0.5133333333,0.52,0.5266666667,0.5333333333,0.54,0.5466666667,0.5533333333,0.56,0.5666666667,0.5733333333,0.58,0.5866666667,0.5933333333,0.6,0.6066666667,0.6133333333,0.62,0.6266666667,0.6333333333,0.64,0.6466666667,0.6533333333,0.66,0.6666666667,0.6733333333,0.68,0.6866666667,0.6933333333,0.7,0.7066666667,0.7133333333,0.72,0.7266666667,0.7333333333,0.74,0.7466666667,0.7533333333,0.76,0.7666666667,0.7733333333,0.78,0.7866666667,0.7933333333,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.9028571429,0.9057142857,0.9085714286,0.9114285714,0.9142857143,0.9171428571,0.92,0.9228571429,0.9257142857,0.9285714286,0.9314285714,0.9342857143,0.9371428571,0.94,0.9428571429,0.9457142857,0.9485714286,0.9514285714,0.9542857143,0.9571428571,0.96,0.9628571429,0.9657142857,0.9685714286,0.9714285714,0.9742857143,0.9771428571,0.98,0.9828571429,0.9857142857,0.9885714286,0.9914285714,0.9942857143,0.9971428571,1.0]\n", + " [0.0,0.0028571429,0.0057142857,0.0085714286,0.0114285714,0.0142857143,0.0171428571,0.02,0.0228571429,0.0257142857,0.0285714286,0.0314285714,0.0342857143,0.0371428571,0.04,0.0428571429,0.0457142857,0.0485714286,0.0514285714,0.0542857143,0.0571428571,0.06,0.0628571429,0.0657142857,0.0685714286,0.0714285714,0.0742857143,0.0771428571,0.08,0.0828571429,0.0857142857,0.0885714286,0.0914285714,0.0942857143,0.0971428571,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9018181818,0.9036363636,0.9054545455,0.9072727273,0.9090909091,0.9109090909,0.9127272727,0.9145454545,0.9163636364,0.9181818182,0.92,0.9218181818,0.9236363636,0.9254545455,0.9272727273,0.9290909091,0.9309090909,0.9327272727,0.9345454545,0.9363636364,0.9381818182,0.94,0.9418181818,0.9436363636,0.9454545455,0.9472727273,0.9490909091,0.9509090909,0.9527272727,0.9545454545,0.9563636364,0.9581818182,0.96,0.9618181818,0.9636363636,0.9654545455,0.9672727273,0.9690909091,0.9709090909,0.9727272727,0.9745454545,0.9763636364,0.9781818182,0.98,0.9818181818,0.9836363636,0.9854545455,0.9872727273,0.9890909091,0.9909090909,0.9927272727,0.9945454545,0.9963636364,0.9981818182,1.0]\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.9011764706,0.9023529412,0.9035294118,0.9047058824,0.9058823529,0.9070588235,0.9082352941,0.9094117647,0.9105882353,0.9117647059,0.9129411765,0.9141176471,0.9152941176,0.9164705882,0.9176470588,0.9188235294,0.92,0.9211764706,0.9223529412,0.9235294118,0.9247058824,0.9258823529,0.9270588235,0.9282352941,0.9294117647,0.9305882353,0.9317647059,0.9329411765,0.9341176471,0.9352941176,0.9364705882,0.9376470588,0.9388235294,0.94,0.9411764706,0.9423529412,0.9435294118,0.9447058824,0.9458823529,0.9470588235,0.9482352941,0.9494117647,0.9505882353,0.9517647059,0.9529411765,0.9541176471,0.9552941176,0.9564705882,0.9576470588,0.9588235294,0.96,0.9611764706,0.9623529412,0.9635294118,0.9647058824,0.9658823529,0.9670588235,0.9682352941,0.9694117647,0.9705882353,0.9717647059,0.9729411765,0.9741176471,0.9752941176,0.9764705882,0.9776470588,0.9788235294,0.98,0.9811764706,0.9823529412,0.9835294118,0.9847058824,0.9858823529,0.9870588235,0.9882352941,0.9894117647,0.9905882353,0.9917647059,0.9929411765,0.9941176471,0.9952941176,0.9964705882,0.9976470588,0.9988235294,1.0]\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0]\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0.0005272579,0.0006650709,0.0008243437,0.0011074433,0.0016696544,0.0025699094,0.0037138357,0.0049708626,0.0062610152,0.0075426566,0.0089765864,0.0111726822,0.0147311078,0.0195212559,0.0249547717,0.0306181288,0.0363105138,0.0419407763,0.0476011969,0.053516341,0.0598014349,0.0663689162,0.0730761187,0.0798334547,0.0865904866,0.0933196582,0.1000172031,0.1066924089,0.1133554776,0.1200140176,0.1266729489,0.1333343989,0.1399984689,0.1466644317,0.1533314439,0.1599988203,0.1666661444,0.1733332523,0.1800001372,0.1866668598,0.1933334943,0.2000000995,0.2066667101,0.2133333393,0.2199999878,0.22666665,0.2333333196,0.2399999916,0.2466666631,0.2533333329,0.2600000011,0.2666666681,0.2733333345,0.2800000007,0.286666667,0.2933333334,0.2999999999,0.3066666665,0.3133333332,0.3199999999,0.3266666666,0.3333333333,0.34,0.3466666667,0.3533333333,0.36,0.3666666667,0.3733333333,0.38,0.3866666667,0.3934628939,0.400837331,0.40925763,0.4186848364,0.428718413,0.4390353607,0.4494419812,0.4597974687,0.4700329298,0.4801500685,0.4901790777,0.500153105,0.5101028922,0.5200515519,0.5300114112,0.5398722838,0.5492279015,0.5576212737,0.5650210292,0.571743695,0.5780856137,0.5842571713,0.5904328096,0.5967209586,0.603152213,0.6097133168,0.6163738459,0.6230958146,0.6298433017,0.6365902337,0.6433215069,0.6500308134,0.656718392,0.6633885674,0.6700472479,0.6767001542,0.6833518918,0.6900055659,0.6966627826,0.7033239321,0.7099885835,0.7166558627,0.723324761,0.7299943545,0.7366639271,0.7433330133,0.7500013847,0.7566690034,0.7633359628,0.770002427,0.7766685825,0.7833346018,0.7900006228,0.7966667394,0.8033330023,0.8099994258,0.8166659972,0.8233326871,0.8299994586,0.8366662749,0.8433331037,0.8499999207,0.8566667097,0.8633334627,0.8700001785,0.8766668606,0.8833335157,0.8899751517,0.8964699017,0.9025861327,0.9081211655,0.9130226546,0.9173491712,0.921198292,0.9246959323,0.9279877368,0.9312103051,0.934472912,0.9378540969,0.9414005467,0.9450901244,0.9487670554,0.9522009139,0.9552513327,0.9578998205,0.9601715711,0.96211589,0.9638162438,0.9653702301,0.9668664828,0.9683781475,0.9699605983,0.9716476808,0.9734519305,0.9753688047,0.9773815283,0.9794657325,0.9815941718,0.9837408125,0.9858836701,0.9879773814,0.9898993305,0.9914888717,0.9926681205,0.9934599632,0.9939261174,0.9941560479,0.9942611072,0.9943265488,0.9943865488,0.9944537386,0.9945561009,0.9947328687,0.9950042368,0.9953660612,0.9958058993,0.9963078442,0.9968511117,0.9974139813,0.9979781729,0.9985251814,0.999027536,0.9994498435,0.999736686,0.9998734993,0.99994,1.0]\n", @@ -3296,26 +3296,26 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.5,0.3,0.15,0.04,0.01] \n", - "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.44,0.48,0.52,0.56,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", - "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.6066666667,0.6133333333,0.62,0.6266666667,0.6333333333,0.64,0.6466666667,0.6533333333,0.66,0.6666666667,0.6733333333,0.68,0.6866666667,0.6933333333,0.7,0.7066666667,0.7133333333,0.72,0.7266666667,0.7333333333,0.74,0.7466666667,0.7533333333,0.76,0.7666666667,0.7733333333,0.78,0.7866666667,0.7933333333,0.8,0.8028571429,0.8057142857,0.8085714286,0.8114285714,0.8142857143,0.8171428571,0.82,0.8228571429,0.8257142857,0.8285714286,0.8314285714,0.8342857143,0.8371428571,0.84,0.8428571429,0.8457142857,0.8485714286,0.8514285714,0.8542857143,0.8571428571,0.86,0.8628571429,0.8657142857,0.8685714286,0.8714285714,0.8742857143,0.8771428571,0.88,0.8828571429,0.8857142857,0.8885714286,0.8914285714,0.8942857143,0.8971428571,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.17833333333333332,0.04808333333333334,0.15783333333333333] \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", - "2 0.1 \n", - "3 [0.37,0.49000000000000005,0.13999999999999999] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0] \n", - "\n", - " metac-perplexity \\\n", - "0 [0.3,0.4,0.2,0.07,0.03] \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", - "2 0.1 \n", - "3 [0.15,0.6,0.25] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4066666667,0.4133333333,0.42,0.4266666667,0.4333333333,0.44,0.4466666667,0.4533333333,0.46,0.4666666667,0.4733333333,0.48,0.4866666667,0.4933333333,0.5,0.5066666667,0.5133333333,0.52,0.5266666667,0.5333333333,0.54,0.5466666667,0.5533333333,0.56,0.5666666667,0.5733333333,0.58,0.5866666667,0.5933333333,0.6,0.6066666667,0.6133333333,0.62,0.6266666667,0.6333333333,0.64,0.6466666667,0.6533333333,0.66,0.6666666667,0.6733333333,0.68,0.6866666667,0.6933333333,0.7,0.7066666667,0.7133333333,0.72,0.7266666667,0.7333333333,0.74,0.7466666667,0.7533333333,0.76,0.7666666667,0.7733333333,0.78,0.7866666667,0.7933333333,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.9028571429,0.9057142857,0.9085714286,0.9114285714,0.9142857143,0.9171428571,0.92,0.9228571429,0.9257142857,0.9285714286,0.9314285714,0.9342857143,0.9371428571,0.94,0.9428571429,0.9457142857,0.9485714286,0.9514285714,0.9542857143,0.9571428571,0.96,0.9628571429,0.9657142857,0.9685714286,0.9714285714,0.9742857143,0.9771428571,0.98,0.9828571429,0.9857142857,0.9885714286,0.9914285714,0.9942857143,0.9971428571,1.0] \n", + " metac-o1 \\\n", + "0 [0.45,0.3,0.15,0.05,0.05] \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", + "2 0.15 \n", + "3 [0.45,0.45,0.1] \n", + "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.0114285714,0.0142857143,0.0171428571,0.02,0.0228571429,0.0257142857,0.0285714286,0.0314285714,0.0342857143,0.0371428571,0.04,0.0428571429,0.0457142857,0.0485714286,0.0514285714,0.0542857143,0.0571428571,0.06,0.0628571429,0.0657142857,0.0685714286,0.0714285714,0.0742857143,0.0771428571,0.08,0.0828571429,0.0857142857,0.0885714286,0.0914285714,0.0942857143,0.0971428571,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9018181818,0.9036363636,0.9054545455,0.9072727273,0.9090909091,0.9109090909,0.9127272727,0.9145454545,0.9163636364,0.9181818182,0.92,0.9218181818,0.9236363636,0.9254545455,0.9272727273,0.9290909091,0.9309090909,0.9327272727,0.9345454545,0.9363636364,0.9381818182,0.94,0.9418181818,0.9436363636,0.9454545455,0.9472727273,0.9490909091,0.9509090909,0.9527272727,0.9545454545,0.9563636364,0.9581818182,0.96,0.9618181818,0.9636363636,0.9654545455,0.9672727273,0.9690909091,0.9709090909,0.9727272727,0.9745454545,0.9763636364,0.9781818182,0.98,0.9818181818,0.9836363636,0.9854545455,0.9872727273,0.9890909091,0.9909090909,0.9927272727,0.9945454545,0.9963636364,0.9981818182,1.0] \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.9008333333,0.9016666667,0.9025,0.9033333333,0.9041666667,0.905,0.9058333333,0.9066666667,0.9075,0.9083333333,0.9091666667,0.91,0.9108333333,0.9116666667,0.9125,0.9133333333,0.9141666667,0.915,0.9158333333,0.9166666667,0.9175,0.9183333333,0.9191666667,0.92,0.9208333333,0.9216666667,0.9225,0.9233333333,0.9241666667,0.925,0.9258333333,0.9266666667,0.9275,0.9283333333,0.9291666667,0.93,0.9308333333,0.9316666667,0.9325,0.9333333333,0.9341666667,0.935,0.9358333333,0.9366666667,0.9375,0.9383333333,0.9391666667,0.94,0.9408333333,0.9416666667,0.9425,0.9433333333,0.9441666667,0.945,0.9458333333,0.9466666667,0.9475,0.9483333333,0.9491666667,0.95] \n", + "2 0.05 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.9011764706,0.9023529412,0.9035294118,0.9047058824,0.9058823529,0.9070588235,0.9082352941,0.9094117647,0.9105882353,0.9117647059,0.9129411765,0.9141176471,0.9152941176,0.9164705882,0.9176470588,0.9188235294,0.92,0.9211764706,0.9223529412,0.9235294118,0.9247058824,0.9258823529,0.9270588235,0.9282352941,0.9294117647,0.9305882353,0.9317647059,0.9329411765,0.9341176471,0.9352941176,0.9364705882,0.9376470588,0.9388235294,0.94,0.9411764706,0.9423529412,0.9435294118,0.9447058824,0.9458823529,0.9470588235,0.9482352941,0.9494117647,0.9505882353,0.9517647059,0.9529411765,0.9541176471,0.9552941176,0.9564705882,0.9576470588,0.9588235294,0.96,0.9611764706,0.9623529412,0.9635294118,0.9647058824,0.9658823529,0.9670588235,0.9682352941,0.9694117647,0.9705882353,0.9717647059,0.9729411765,0.9741176471,0.9752941176,0.9764705882,0.9776470588,0.9788235294,0.98,0.9811764706,0.9823529412,0.9835294118,0.9847058824,0.9858823529,0.9870588235,0.9882352941,0.9894117647,0.9905882353,0.9917647059,0.9929411765,0.9941176471,0.9952941176,0.9964705882,0.9976470588,0.9988235294,1.0] \n", + "\n", + " metac-perplexity \\\n", + "0 [0.3,0.4,0.2,0.07,0.03] \n", + "1 [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.15 \n", + "3 [0.15,0.6,0.25] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3423,8 +3423,8 @@ " False\n", " False\n", " ...\n", - " 0.9\n", " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.95\n", @@ -3447,8 +3447,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.9\n", + " 0.4\n", + " 0.15\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3472,7 +3472,7 @@ " False\n", " ...\n", " 0.9\n", - " 0.95\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3551,9 +3551,9 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.9 0.95 NaN NaN 0.95 0.95 \n", - "95 0.65 0.9 NaN NaN 0.15 NaN \n", - "96 0.9 0.95 NaN NaN 0.9 NaN \n", + "94 0.95 0.9 NaN NaN 0.95 0.95 \n", + "95 0.4 0.15 NaN NaN 0.15 NaN \n", + "96 0.9 0.9 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", "\n", @@ -3762,7 +3762,7 @@ " False\n", " False\n", " ...\n", - " 2.644992\n", + " 2.343407\n", " 5.703782\n", " NaN\n", " 2.292635\n", @@ -3786,7 +3786,7 @@ " None\n", " None\n", " ...\n", - " 0.107631\n", + " 0.310155\n", " 0.310155\n", " NaN\n", " 0.127833\n", @@ -3810,8 +3810,8 @@ " False\n", " False\n", " ...\n", - " 0.298855\n", - " -0.106610\n", + " 0.116534\n", + " 0.211844\n", " NaN\n", " -0.184571\n", " 0.112526\n", @@ -3819,7 +3819,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.576613\n", + " -0.704447\n", " \n", " \n", " 9\n", @@ -3843,7 +3843,7 @@ " NaN\n", " -0.624154\n", " NaN\n", - " -0.693147\n", + " -0.518794\n", " \n", " \n", " 13\n", @@ -3858,7 +3858,7 @@ " None\n", " None\n", " ...\n", - " 0.575364\n", + " 0.330943\n", " 0.287682\n", " 0.021979\n", " 0.200671\n", @@ -3904,17 +3904,17 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.644992 5.703782 NaN 2.292635 2.703087 \n", - "3 0.107631 0.310155 NaN 0.127833 0.152526 \n", - "6 0.298855 -0.106610 NaN -0.184571 0.112526 \n", + "0 2.343407 5.703782 NaN 2.292635 2.703087 \n", + "3 0.310155 0.310155 NaN 0.127833 0.152526 \n", + "6 0.116534 0.211844 NaN -0.184571 0.112526 \n", "9 -0.423484 -1.211941 NaN -0.806476 -0.494101 \n", - "13 0.575364 0.287682 0.021979 0.200671 0.253781 \n", + "13 0.330943 0.287682 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "0 NaN NaN NaN NaN 4.656813 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN -0.576613 \n", - "9 NaN NaN -0.624154 NaN -0.693147 \n", + "6 NaN NaN NaN NaN -0.704447 \n", + "9 NaN NaN -0.624154 NaN -0.518794 \n", "13 NaN NaN NaN NaN -0.062598 \n", "\n", "[5 rows x 58 columns]" @@ -3982,10 +3982,10 @@ " False\n", " ...\n", " -2.879198\n", - " -1.780586\n", + " -0.933288\n", " -3.007032\n", " -2.879198\n", - " -3.795489\n", + " -3.390024\n", " NaN\n", " NaN\n", " -2.348570\n", @@ -4006,7 +4006,7 @@ " None\n", " ...\n", " -0.993252\n", - " 0.000000\n", + " -0.300105\n", " -0.523248\n", " 0.105361\n", " 0.259511\n", @@ -4014,7 +4014,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " -0.993252\n", + " -0.941958\n", " \n", " \n", " 83\n", @@ -4053,7 +4053,7 @@ " False\n", " False\n", " ...\n", - " -0.048289\n", + " -0.037817\n", " -0.048289\n", " NaN\n", " -0.124829\n", @@ -4077,8 +4077,8 @@ " False\n", " False\n", " ...\n", - " -1.704748\n", - " -1.011601\n", + " -1.299283\n", + " -2.908721\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -4119,19 +4119,19 @@ "81 NaN False False ... -2.879198 \n", "82 NaN None None ... -0.993252 \n", "83 NaN None None ... -0.693147 \n", - "91 NaN False False ... -0.048289 \n", - "92 NaN False False ... -1.704748 \n", + "91 NaN False False ... -0.037817 \n", + "92 NaN False False ... -1.299283 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -1.780586 -3.007032 -2.879198 -3.795489 NaN NaN \n", - "82 0.000000 -0.523248 0.105361 0.259511 NaN NaN \n", + "81 -0.933288 -3.007032 -2.879198 -3.390024 NaN NaN \n", + "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", "83 -0.693147 NaN -0.182322 NaN NaN NaN \n", "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -1.011601 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "92 -2.908721 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", "81 -2.348570 -2.409195 -2.879198 \n", - "82 0.276509 -0.644609 -0.993252 \n", + "82 0.276509 -0.644609 -0.941958 \n", "83 -0.178330 -0.567984 -0.693147 \n", "91 NaN -0.147818 -0.121048 \n", "92 NaN -0.749237 -0.318454 \n", @@ -4200,8 +4200,8 @@ " False\n", " False\n", " ...\n", - " -0.092275\n", - " -0.092275\n", + " -0.038208\n", + " -0.149434\n", " NaN\n", " -0.210058\n", " -0.059485\n", @@ -4233,7 +4233,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.320472\n", + " 0.367725\n", " \n", " \n", " 8\n", @@ -4248,7 +4248,7 @@ " False\n", " False\n", " ...\n", - " -0.054067\n", + " 0.000000\n", " -0.054067\n", " NaN\n", " -0.111226\n", @@ -4257,7 +4257,7 @@ " NaN\n", " -0.398124\n", " NaN\n", - " -0.179379\n", + " -0.171850\n", " \n", " \n", " 12\n", @@ -4297,7 +4297,7 @@ " False\n", " ...\n", " -0.045611\n", - " -0.045611\n", + " 0.008457\n", " NaN\n", " -0.068083\n", " NaN\n", @@ -4328,16 +4328,16 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", + "2 -0.038208 -0.149434 NaN -0.210058 -0.059485 \n", "5 -0.810930 0.200671 NaN 0.510826 0.320472 \n", - "8 -0.054067 -0.054067 NaN -0.111226 -0.147158 \n", + "8 0.000000 -0.054067 NaN -0.111226 -0.147158 \n", "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", - "16 -0.045611 -0.045611 NaN -0.068083 NaN \n", + "16 -0.045611 0.008457 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "2 NaN NaN NaN NaN -0.149434 \n", - "5 NaN NaN NaN NaN 0.320472 \n", - "8 NaN NaN -0.398124 NaN -0.179379 \n", + "5 NaN NaN NaN NaN 0.367725 \n", + "8 NaN NaN -0.398124 NaN -0.171850 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", "16 NaN NaN -0.076070 NaN -0.096728 \n", "\n", @@ -4405,7 +4405,7 @@ " False\n", " False\n", " ...\n", - " 0.000000\n", + " -0.054067\n", " NaN\n", " NaN\n", " 0.000000\n", @@ -4429,7 +4429,7 @@ " False\n", " False\n", " ...\n", - " -2.251292\n", + " -0.111226\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4453,7 +4453,7 @@ " False\n", " False\n", " ...\n", - " -0.020834\n", + " -0.074901\n", " NaN\n", " NaN\n", " -0.074901\n", @@ -4533,9 +4533,9 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.000000 NaN NaN 0.000000 0.000000 \n", - "95 -2.251292 NaN NaN -0.111226 NaN \n", - "96 -0.020834 NaN NaN -0.074901 NaN \n", + "94 -0.054067 NaN NaN 0.000000 0.000000 \n", + "95 -0.111226 NaN NaN -0.111226 NaN \n", + "96 -0.074901 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", "98 -0.017709 -0.017709 NaN -0.112251 -0.017709 \n", "\n", @@ -4603,7 +4603,7 @@ " \n", " 2\n", " bot_median\n", - " 3538.184052\n", + " 3398.202830\n", " \n", " \n", " 3\n", @@ -4838,7 +4838,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3538.184052\n", + "2 bot_median 3398.202830\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4906,13 +4906,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", - "mean metac-o1 forecast on questions that resolved no: 27.0%\n" + "mean metac-o1 forecast on questions that resolved yes: 74.0%\n", + "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1IAAAIQCAYAAACLwV/UAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAg1xJREFUeJzt3Xl4U2Xax/Ff0tIVGpZSoBbZytIiQll0EBEVBEFRBlxQlOI+DuCCuKACAiqDCzrKgDozsjj6Ou4LozKKOzIMlsrIUJYisihQCpJAawsl5/3jmYamDZB0S5p+P9fVK+ack3PukzZy33k2m2VZlgAAAAAAfrMHOwAAAAAAqGsopAAAAAAgQBRSAAAAABAgCikAAAAACBCFFAAAAAAEiEIKAAAAAAJEIQUAAAAAAaKQAgAAAIAAUUgBAAAAQIAopACgHvrxxx9ls9m0aNEiz7aHHnpINpsteEEBAFCHUEgBqNcWLVokm83m+YmJiVGnTp00YcIE7dmzp8avP27cONlsNiUkJOjXX3+tsH/z5s2e2J544okajyfcnHvuuV6/3+P9PPTQQzUeS9u2bSv8rXXs2FF333239u/fX6lzfvPNN3rooYd04MABv44v/Xvz9fPRRx9VKoa65NFHH9U777wT7DAAhInIYAcAAKFg5syZateunYqKivT1119rwYIF+uCDD7Ru3TrFxcXV6LUjIyNVWFio999/X1dccYXXvpdfflkxMTEqKiqq0Rgk6cEHH9R9991X49epTQ888IBuvPFGz/PVq1frmWee0f3336+0tDTP9tNPP71W4unRo4fuuusuSVJRUZGysrL09NNP64svvtC///3vgM/3zTffaMaMGRo3bpwaN27s12uio6P1l7/8pcL27t27B3z9uubRRx/VZZddphEjRgQ7FABhgEIKACQNHTpUvXv3liTdeOONatasmebOnat3331XV111lc/XFBQUKD4+vsrXjo6OVr9+/fR///d/FQqpV155RRdddJHefPPNKl/nZCIjIxUZGV7/LFxwwQVez2NiYvTMM8/oggsu0Lnnnlvr8Zxyyim65pprPM9vvPFGNWzYUE888YQ2b96sjh071ngMkZGRXjFUp8LCwhr/4gEAQgVd+wDAh/PPP1+StHXrVkmmS1TDhg21ZcsWDRs2TI0aNdKYMWMkmYLqrrvuUuvWrRUdHa3OnTvriSeekGVZfl/v6quv1ocffujVRWv16tXavHmzrr76ap+vOXDggO644w7PdVNTUzVnzhy53e4Kx40bN04Oh0ONGzdWZmamz65gvsZILVy4UOeff76SkpIUHR2t9PR0LViwoMJr27Ztq4svvlhff/21zjjjDMXExKh9+/ZasmTJCe/7yJEjatq0qa677roK+1wul2JiYjR58mTPtmeffVZdu3ZVXFycmjRpot69e+uVV1454TX8MX/+fHXt2lXR0dFKTk7W+PHjK7xH5557rk477TRlZWXprLPOUmxsrNq1a6fnnnuuStdu2bKlJFUoYj/99FP1799f8fHxaty4sS699FLl5OR49j/00EO6++67JUnt2rXzdNH78ccfqxSPFPj7cc455yguLk7333+/JKm4uFjTp09XamqqoqOj1bp1a91zzz0qLi6ucK2//e1vOuOMMzy/03POOUf//Oc/PfvfffddXXTRRUpOTlZ0dLQ6dOigWbNm6ejRo17n2bx5s0aNGqWWLVsqJiZGKSkpGj16tJxOpyTJZrOpoKBAixcv9rxX48aNkyQdPHhQd9xxh9q2bavo6GglJSXpggsu0Jo1a6r8XgIIX+H11SMAVJMtW7ZIkpo1a+bZVlJSoiFDhujss8/WE088obi4OFmWpUsuuUSfffaZbrjhBvXo0UPLli3T3XffrZ9++klPPfWUX9cbOXKkfve73+mtt97S9ddfL8m0RnXp0kU9e/ascHxhYaEGDBign376SbfccotOPfVUffPNN5oyZYp27dqlp59+WpJkWZYuvfRSff311/rd736ntLQ0vf3228rMzPQrrgULFqhr16665JJLFBkZqffff1+///3v5Xa7NX78eK9jc3Nzddlll+mGG25QZmamXnzxRY0bN069evVS165dfZ6/QYMG+u1vf6u33npLzz//vKKiojz73nnnHRUXF2v06NGSpD//+c+67bbbdNlll+n2229XUVGR/vOf/2jVqlXHLTb98dBDD2nGjBkaNGiQbr31Vm3cuFELFizQ6tWrtWLFCjVo0MBz7C+//KJhw4bpiiuu0FVXXaXXXntNt956q6Kiojy/txM5cuSI8vPzJZmufdnZ2Zo7d67OOecctWvXznPcJ598oqFDh6p9+/Z66KGH9Ouvv+rZZ59Vv379tGbNGrVt21YjR47Upk2b9H//93966qmnlJiYKElq3rz5SeMojaFUgwYN5HA4An4/9u3bp6FDh2r06NG65ppr1KJFC7ndbl1yySX6+uuvdfPNNystLU3ff/+9nnrqKW3atMlrjNKMGTP00EMP6ayzztLMmTMVFRWlVatW6dNPP9XgwYMlmXGMDRs21KRJk9SwYUN9+umnmjZtmlwulx5//HFJ0uHDhzVkyBAVFxdr4sSJatmypX766SctXbpUBw4ckMPh0EsvvaQbb7xRZ5xxhm6++WZJUocOHSRJv/vd7/TGG29owoQJSk9P1759+/T1118rJyfH5+cPACRJFgDUYwsXLrQkWZ988om1d+9ea8eOHdarr75qNWvWzIqNjbV27txpWZZlZWZmWpKs++67z+v177zzjiXJevjhh722X3bZZZbNZrNyc3NPeP3MzEwrPj7e85qBAwdalmVZR48etVq2bGnNmDHD2rp1qyXJevzxxz2vmzVrlhUfH29t2rTJ63z33XefFRERYW3fvt0rvscee8xzTElJidW/f39LkrVw4ULP9unTp1vl/1koLCysEPOQIUOs9u3be21r06aNJcn68ssvPdvy8vKs6Oho66677jrhe7Bs2TJLkvX+++97bR82bJjXdS699FKra9euJzzXybz++uuWJOuzzz7zxBgVFWUNHjzYOnr0qOe4efPmWZKsF1980bNtwIABliTrySef9GwrLi62evToYSUlJVmHDx8+4bVL36PyP/369bPy8/O9ji095759+zzb1q5da9ntdmvs2LGebY8//rglydq6datf91/6d1z+Z8CAAZV+P5577jmva7z00kuW3W63vvrqK6/tzz33nCXJWrFihWVZlrV582bLbrdbv/3tb72uZVmW5Xa7Pf/t62/wlltuseLi4qyioiLLsiwrOzvbkmS9/vrrJ7z/+Ph4KzMzs8J2h8NhjR8//oSvBYDy6NoHAJIGDRqk5s2bq3Xr1ho9erQaNmyot99+W6eccorXcbfeeqvX8w8++EARERG67bbbvLbfddddsixLH374od8xXH311fr888+1e/duffrpp9q9e/dxW1pef/119e/fX02aNFF+fr7nZ9CgQTp69Ki+/PJLT3yRkZFecUdERGjixIl+xRQbG+v5b6fTqfz8fA0YMEA//PCDp8tUqfT0dPXv39/zvHnz5urcubN++OGHE17j/PPPV2Jiov7+9797tv3yyy/6+OOPdeWVV3q2NW7cWDt37tTq1av9it0fn3zyiQ4fPqw77rhDdvuxfxJvuukmJSQk6B//+IfX8ZGRkbrllls8z6OionTLLbcoLy9PWVlZJ73emWeeqY8//lgff/yxli5dqkceeUT//e9/dckll3hmbdy1a5e+++47jRs3Tk2bNvW89vTTT9cFF1ygDz74oEr3HBMT44mh9OfJJ5+s1PsRHR1doVvm66+/rrS0NHXp0sXrb7O0u+xnn30mybQ4ut1uTZs2zetakry6mJb9Gzx48KDy8/PVv39/FRYWasOGDZLkaU1btmyZCgsLA35PGjdurFWrVunnn38O+LUA6i+69gGApD/96U/q1KmTIiMj1aJFC3Xu3LlCchcZGamUlBSvbdu2bVNycrIaNWrktb10Rrht27b5HUPp2Ku///3v+u6779SnTx+lpqb6HPOyefNm/ec//zluN668vDzP9Vu1aqWGDRt67e/cubNfMa1YsULTp0/XypUrKySoTqfTk8BK0qmnnlrh9U2aNNEvv/xywmtERkZq1KhReuWVV1RcXKzo6Gi99dZbOnLkiFchde+99+qTTz7RGWecodTUVA0ePFhXX321+vXr59e9+FL6+yn/fkRFRal9+/YVfn/JyckVJhjp1KmTJLM2129+85sTXi8xMVGDBg3yPL/ooovUuXNnXXbZZfrLX/6iiRMnHjcmyfxdLVu2rEoTnURERHjFUFag78cpp5zi1R1TMn+bOTk5J/3b3LJli+x2u9LT008Y73//+189+OCD+vTTT+Vyubz2lRbz7dq106RJkzR37ly9/PLL6t+/vy655BJdc801Xn+jx/PYY48pMzNTrVu3Vq9evTRs2DCNHTtW7du3P+lrAdRfFFIAIOmMM87wzNp3PNHR0RWKq+oUHR2tkSNHavHixfrhhx9OuLaR2+3WBRdcoHvuucfn/tLkviq2bNmigQMHqkuXLpo7d65at26tqKgoffDBB3rqqacqTGoRERHh8zyWH5NujB49Ws8//7w+/PBDjRgxQq+99pq6dOniNSV3WlqaNm7cqKVLl+qjjz7Sm2++qfnz52vatGmaMWNG1W42iAYOHChJ+vLLL/1uKQwVZVuLSrndbnXr1k1z5871+ZrWrVv7ff4DBw5owIABSkhI0MyZM9WhQwfFxMRozZo1uvfee73+Bp988kmNGzdO7777rv75z3/qtttu0+zZs/Wvf/2rwhcg5V1xxRXq37+/3n77bf3zn//U448/rjlz5uitt97S0KFD/Y4XQP1CIQUAVdCmTRt98sknOnjwoFerVGmXozZt2gR0vquvvlovvvii7Ha7Z5IFXzp06KBDhw4dt2WhbHzLly/XoUOHvFqlNm7ceNJY3n//fRUXF+u9997zam0q7ZpVnc455xy1atVKf//733X22Wfr008/1QMPPFDhuPj4eF155ZW68sordfjwYY0cOVKPPPKIpkyZopiYmICvW/r72bhxo1frw+HDh7V169YK7+/PP/9coTVo06ZNkszMhZVRUlIiSTp06FCFmMrbsGGDEhMTPdcvP8tiVQX6fvjSoUMHrV27VgMHDjxhfB06dJDb7db69evVo0cPn8d8/vnn2rdvn9566y2dc845nu2ls2mW161bN3Xr1k0PPvigvvnmG/Xr10/PPfecHn74YUknfr9atWql3//+9/r973+vvLw89ezZU4888giFFIDjYowUAFTBsGHDdPToUc2bN89r+1NPPSWbzRZwEnbeeedp1qxZmjdvnmdabF+uuOIKrVy5UsuWLauw78CBA57kfNiwYSopKfGasvzo0aN69tlnTxpLaQtT2RYlp9OphQsX+n0//rLb7brsssv0/vvv66WXXlJJSYlXtz7JzBBXVlRUlNLT02VZlo4cOVKp6w4aNEhRUVF65plnvO7zr3/9q5xOpy666CKv40tKSvT88897nh8+fFjPP/+8mjdvrl69elUqhvfff1/SsQVxW7VqpR49emjx4sVeU46vW7dO//znPzVs2DDPttKCytd09pUR6PvhyxVXXKGffvpJf/7znyvs+/XXX1VQUCBJGjFihOx2u2bOnFmhdbP02r7+Bg8fPqz58+d7He9yuTx/86W6desmu93uNeV6fHx8hffq6NGjFcb7JSUlKTk52ed07QBQihYpAKiC4cOH67zzztMDDzygH3/8Ud27d9c///lPvfvuu7rjjjs80yv7y26368EHHzzpcXfffbfee+89XXzxxZ4pxgsKCvT999/rjTfe0I8//qjExEQNHz5c/fr103333acff/xR6enpeuuttyokjr4MHjxYUVFRGj58uG655RYdOnRIf/7zn5WUlKRdu3YFdF/+uPLKK/Xss89q+vTp6tatm2ecWdl4WrZsqX79+qlFixbKycnRvHnzdNFFF1UYo+av5s2ba8qUKZoxY4YuvPBCXXLJJdq4caPmz5+vPn36VFi4Njk5WXPmzNGPP/6oTp06ecazvfDCC17Tgh/PTz/9pL/97W+STEGwdu1aPf/880pMTPTq1vf4449r6NCh6tu3r2644QbP9OcOh8Ory2dp8fbAAw9o9OjRatCggYYPH17p8VOBvh++XHvttXrttdf0u9/9Tp999pn69euno0ePasOGDXrttde0bNky9e7dW6mpqXrggQc0a9Ys9e/fXyNHjlR0dLRWr16t5ORkzZ49W2eddZaaNGmizMxM3XbbbbLZbHrppZcqdBf99NNPNWHCBF1++eXq1KmTSkpK9NJLLykiIkKjRo3yer8++eQTzZ07V8nJyWrXrp06d+6slJQUXXbZZerevbsaNmyoTz75RKtXr/ZMwgEAPgVtvkAACAGl05+vXr36hMeVnaa8vIMHD1p33nmnlZycbDVo0MDq2LGj9fjjj3tN4VyZ85byNf156XWnTJlipaamWlFRUVZiYqJ11llnWU888YTXVNz79u2zrr32WishIcFyOBzWtdde65ku+mTTn7/33nvW6aefbsXExFht27a15syZY7344osVptxu06aNddFFF1WIfcCAAZ6ptU/G7XZbrVu39jmdvGVZ1vPPP2+dc845VrNmzazo6GirQ4cO1t133205nU6/zm9ZFac/LzVv3jyrS5cuVoMGDawWLVpYt956q/XLL79UuJeuXbta3377rdW3b18rJibGatOmjTVv3jy/rl1++nO73W4lJSVZV111lc9p8j/55BOrX79+VmxsrJWQkGANHz7cWr9+fYXjZs2aZZ1yyimW3W4/6VTo/vy9WVZg74cvhw8ftubMmWN17drVio6Otpo0aWL16tXLmjFjRoXf14svvmhlZGR4jhswYID18ccfe/avWLHC+s1vfmPFxsZaycnJ1j333OOZMr/09/jDDz9Y119/vdWhQwcrJibGatq0qXXeeedZn3zyide1NmzYYJ1zzjlWbGysJcnKzMy0iouLrbvvvtvq3r271ahRIys+Pt7q3r27NX/+/JO+TwDqN5tl+TEKGACAeu7cc89Vfn6+1q1bF+xQAAAhgDFSAAAAABAgCikAAAAACBCFFAAAAAAEKKiF1Jdffqnhw4crOTlZNptN77zzjtd+y7I0bdo0tWrVSrGxsRo0aJA2b97sdcz+/fs1ZswYJSQkqHHjxrrhhhs8a3EAAFBdPv/8c8ZHAQA8glpIFRQUqHv37vrTn/7kc/9jjz2mZ555Rs8995xWrVql+Ph4DRkyREVFRZ5jxowZo//+97/6+OOPtXTpUn355Ze6+eaba+sWAAAAANRDITNrn81m09tvv60RI0ZIMq1RycnJuuuuuzR58mRJZiHIFi1aaNGiRRo9erRycnKUnp6u1atXq3fv3pKkjz76SMOGDdPOnTuVnJwcrNsBAAAAEMZCdkHerVu3avfu3Ro0aJBnm8Ph0JlnnqmVK1dq9OjRWrlypRo3buwpoiSzKrvdbteqVav029/+1ue5i4uLvVYrd7vd2r9/v5o1ayabzVZzNwUAAAAgpFmWpYMHDyo5OVl2+/E78IVsIbV7925JUosWLby2t2jRwrNv9+7dSkpK8tofGRmppk2beo7xZfbs2ZoxY0Y1RwwAAAAgXOzYsUMpKSnH3R+yhVRNmjJliiZNmuR57nQ6deqpp2rbtm1KSEgIYmTBsWWLdO+9UpMmUqNGFfcfPCj98os0Z47UoYN/53S73crPz1diYuIJK3kAAAD4h/yqdrhcLrVp00aNfCXGZYRsIdWyZUtJ0p49e9SqVSvP9j179qhHjx6eY/Ly8rxeV1JSov3793te70t0dLSio6MrbG/cuHG9LKTcbunIEcnhkCIiKu5PSJDy8sxxjRv7e063Dh8+rMaNG/NBBwAAqAbkV7Wj9L092ZCfkP0NtGvXTi1bttTy5cs921wul1atWqW+fftKkvr27asDBw4oKyvLc8ynn34qt9utM888s9ZjrqscDikmRioo8L2/sNDsdzhqNy4AAAAgVAW1RerQoUPKzc31PN+6dau+++47NW3aVKeeeqruuOMOPfzww+rYsaPatWunqVOnKjk52TOzX1pami688ELddNNNeu6553TkyBFNmDBBo0ePZsa+AKSmSmlpUlaWeSxbfFuWtHOn1Lu3Oa42uN1Sbq7kdJriLTVVOtGXLoEeDwAAEEzkLuEhqIXUt99+q/POO8/zvHTcUmZmphYtWqR77rlHBQUFuvnmm3XgwAGdffbZ+uijjxQTE+N5zcsvv6wJEyZo4MCBstvtGjVqlJ555plav5e6zG6XMjOlbduknBwpJUWKizMtUTt3SomJ0tixtfMBz86WFi82cRQVmZawtDQTX0ZG1Y8HAAAIJnKX8BEy60gFk8vlksPhkNPprJdjpEr5+mCnp5siKtAPttvtVl5enpKSkvzuw5udLc2cKeXnm2IuPt50Nywt5qZN844j0OMBAACCqaq5S2XyKwTO39ogZCebQO3LyJC6dw9OU7PbbYq4/Hzv7oUJCeZ5To60ZImJz24P/HgAAIBgIncJP/ya4MVulzp1kvr0MY+19UHOzT3WrbD8BCk2m9m+fr05rjLHAwAABBO5S/ihkEK1cbulTZuk1aulzZvNc385naY7YXy87/1xcWa/01m54wEAAIKJ3CX80LUP1aL8+KrYWKlfP+mii6SePU/++rJTsPvqilp+CvZAjwcAAAgmcpfwQ4sUqqx04GRWltS0qdSxo3nMzZUeftjsP5nSKdh37jRTrpdVOgV7evqxKdgDPR4AACCYyF3CD4UUqqT8wMmEBCkiwjy2bm22L1ly8m5+pVOwJyaaVi2XSyopMY85ORWnYA/0eAAAgGAidwk//KpQJdU5cDIjw0z72auXtH+/ec3+/WYxYF/TgQZ6PAAAQDCRu4QXxkihSvwZOPnTT/4PnAx0CvZgTtkOAAAQKHKX8EEhhSqpiYGTpVOw19TxAAAAwUTuEh6ofVElDJwEAABAfUQhhSo50cDJHTsYOAkAAIDwRNe+esbtrv4+uaUDJ0vXkfr5Z7OO1NlnS8OG+T9w8mSx1UTsAAAA4Yi8qeZRSNUj5RfNjYkx3fIyM6s+S0z5gZMJCVKjRlLLltUTW03GDgAAEE7Im2oHhVQ9Ubpobn6+mZI8Pt5MEJGVJW3bVj1TbpYdOOl2S3l51RPbFVdIr71Ws7EDAACEg9rI+WDQwFcPnGjR3LQ0/xfNDUZse/dKjz1mHkMtdgAAgFASyjlfOKKQqgeqc9Hc2o7N4TCTVjRuHHqxAwAAhJJQzvnCEYVUPeDPorlFRf4vmludThZbRIR05Ih59CWYsQMAAISSUM75whGFVD1QdtFcXyqzaG51OVlsR49KDRqYR1+CGTsAAEAoCeWcLxxRSNUDobxo7sliczql1q2lAwdCL3YAAIBQEso5XziikKoHTrRobk5OcBfNPVlszZtL99xjHkMtdgAAgFASyjlfOLJZVvl6tf5xuVxyOBxyOp1KSEgIdjg1xteaAunp5gNV3dNgut1u5eXlKSkpSXY/Pq0ni602YwcAAAhF/uZX5E1V429tQCGl+lNISbW3ynWghZQ/sbFCNwAAqM8Cya/ImyrP39qABXnrmbKL5oaak8UWyrEDAACEEvKmmkddCgAAAAABopACAAAAgABRSAEAAABAgCikAAAAACBAFFIAAAAAECAKKQAAAAAIEIUUAAAAAASIQgoAAAAAAkQhBQAAAAABopACAAAAgABRSAEAAABAgCKDHQDCg9st5eZKTqfkcEjt2wc7IgAAgOpVPt9JTZXsPpol/D2uKnFs2iStW2een3aa1KlTJa5R04GG5qWrDYUUqiw7W1q8WMrJkYqKpJgYKT1duuwyKSkp2NEBAABUna98Jy1NysyUMjICP66ytmyR7rpL+vpryeUy2xISpH79pLvvDuAaNR1oaF66WlFIoUqys6WZM6X8fCklRYqPlwoKpKwsqaREio2VevYMdpQAAACVd6J8Z9s2ado0UwD4e1xlffed9Ne/Sv/8p3nepIlks5mCatkyadcuae5cP65R04GG5qWrXR1rQEMocbvNtwn5+eZbhIQEKSLCPKalmQ/13/5mjgMAAKiLTpbv5OdLS5aYL5D9Oa6yeZHbbV7/889SgwZSs2ZSdLQUFSUlJpptmzaZGE54DX9vqAYSuCBeukZQSKHScnNNk2xKivk2pCybzXyo1683xwEAANRFJ8t3UlJMvrN8uX/HVTYvys01rTZut2nFKX+Nhg3Nvm+/Pck1/L2hGkjggnjpGkEhhUpzOk2/1vh43/ujo81+p7N24wIAAKguJ8t34uLM/t27/TuusnmR0ykVFpr/jvQxOCcyUrIs003uhNfw94ZqIIEL4qVrBIUUKs3hMIMDCwp87y8uNvsdjtqNCwAAoLqcLN8pLDT7W7b077jK5kUOhyk0JNONsLySEtOqEx9/kmv4e0M1kMAF8dI1gkIKlZaaavqz7txpvgEpy7JMP9f0dHMcAABAXXSyfGfnTpPvDBzo33GVzYtSU6VevcwU4QUFFa9x6JDZ17v3Sa7h7w3VQAIXxEvXCAopVJrdbqapTEw0/V1dLvNtiMtlnickSNdcU/fWBAAAACh1snwnMVEaO9Z0rfPnuMrmRXa7eX1ysnTkiLRvn+n9U1xsvrw+csSsJZWZeZJr+HtDNZDABfHSNcJmWeXrwfrH5XLJ4XDI6XQqISEh2OHUOb7WAuja1a1Ro/LUt2+S7HXl0wAAAHAcx1s3c+zYk68j5eu4ynC73Vq5Mk/z5yfp66/tXutInX22NHlyFdeRqq5AQ/fSfvG3NqCQEoVUdSi/OnX79m7l5+cpKYlCCgAAhIfy+U5qqu/WE3+PC/z6buXl5SkxMUm5uXatW2e2n3aaaY0K+Bo1FWhoX/qk/K0NWJAX1cJuNx/gUnVl/n8AAAB/lc93qnpcVeLo0sX8VPlENRloaF662oRI3QcAAAAAdQeFFAAAAAAEiEIKAAAAAAJEIQUAAAAAAaKQAgAAAIAAUUgBAAAAQIAopAAAAAAgQBRSAAAAABAgCikAAAAACBCFFAAAAAAEiEIKAAAAAAIUGewAcIzbLeXmSk6n5HBIqamSnVIXAAAgJIRcrhZyAdUvFFIhIjtbWrxYysmRioqkmBgpLU3KzJQyMoIdHQAAQP0WcrlayAVU/1BIhYDsbGnmTCk/X0pJkeLjpYICKStL2rZNmjaNzwMAAECwhFyu9t130qxZIRRQ/UTbX5C53ebLhPx88yVCQoIUEWEe09LM9iVLzHEAAACoXSGXq7nd0ksvhVBA9ReFVJDl5poW2ZQUyWbz3mezme3r15vjAAAAULtCLlfbtSvEAqq/KKSCzOk03Vrj433vj4sz+53O2o0LAAAAIZirFRaGWED1F4VUkDkcZmxgQYHv/YWFZr/DUbtxAQAAIARztbi4EAuo/qKQCrLUVNOddedOybK891mW2Z6ebo4DAABA7Qq5XK1VqxALqP6ikAoyu93MUpmYaLq7ulxSSYl5zMkx28eOZUkAAACAYAi5XM1ul669NoQCqr9sllW+lK1/XC6XHA6HnE6nEhISghKDr6UA0tPN56Auzl7pdruVl5enpKQk2fkgAwCAOi4UcjWv/Grt2uAHFKb8rQ1YRypEZGRI3buzODUAAEAoCrlcLeQCqn8opEKI3S516hTsKAAAAOBLyOVqIRdQ/ULJCgAAAAABopACAAAAgABRSAEAAABAgCikAAAAACBAFFIAAAAAECAKKQAAAAAIEIUUAAAAAASIQgoAAAAAAkQhBQAAAAABopACAAAAgABRSAEAAABAgCikAAAAACBAIV1IHT16VFOnTlW7du0UGxurDh06aNasWbIsy3OMZVmaNm2aWrVqpdjYWA0aNEibN28OYtR1h9stbdokrV5tHt3uYEcEAACAkEYC6REZ7ABOZM6cOVqwYIEWL16srl276ttvv9V1110nh8Oh2267TZL02GOP6ZlnntHixYvVrl07TZ06VUOGDNH69esVExMT5DsIXdnZ0uLFUk6OVFQkxcRIaWlSZqaUkRHs6AAAABBySCC9hHQh9c033+jSSy/VRRddJElq27at/u///k///ve/JZnWqKeffloPPvigLr30UknSkiVL1KJFC73zzjsaPXp00GIPZdnZ0syZUn6+lJIixcdLBQVSVpa0bZs0bVq9/CwAAADgeEggKwjprn1nnXWWli9frk2bNkmS1q5dq6+//lpDhw6VJG3dulW7d+/WoEGDPK9xOBw688wztXLlyqDEHOrcbvNFQn6++QIhIUGKiDCPaWlm+5Il9bqVFgAAAGWRQPoU0i1S9913n1wul7p06aKIiAgdPXpUjzzyiMaMGSNJ2r17tySpRYsWXq9r0aKFZ58vxcXFKi4u9jx3uVySJLfbLXeY/wFs3ixt2CC1bi3Zy5XRNpvZnpNjjuvYsfLXcbvdsiwr7N9PAACA2hK0/Kq2EsgQ4e/7G9KF1GuvvaaXX35Zr7zyirp27arvvvtOd9xxh5KTk5WZmVnp886ePVszZsyosH3v3r0qKiqqSsghLy9PatVKSk6u+DmQpKZNpehoc5zDUfnruN1uOZ1OWZYlu68LAQAAICBBy69qK4EMEQcPHvTruJAupO6++27dd999nrFO3bp107Zt2zR79mxlZmaqZcuWkqQ9e/aoVatWntft2bNHPXr0OO55p0yZokmTJnmeu1wutW7dWs2bN1dCQkLN3EyIcDqlXbuk4mLTGlueyyXt3y8lJZmfynK73bLZbGrevDmFFAAAQDUIWn5VWwlkiPB3wrqQLqQKCwsr/JFERER4mtvatWunli1bavny5Z7CyeVyadWqVbr11luPe97o6GhFR0dX2G6328M+6e/YUerSxYwLTEszrbGlLEvasUPq3dscV9W3wmaz1Yv3FAAAoLYEJb+qzQQyBPj73oZ0ITV8+HA98sgjOvXUU9W1a1dlZ2dr7ty5uv766yWZP6Q77rhDDz/8sDp27OiZ/jw5OVkjRowIbvAhym43M1Ru22a6sqakSHFxUmGhtHOnlJgojR0bFp8BAAAAVAcSSJ9sVtnVbUPMwYMHNXXqVL399tvKy8tTcnKyrrrqKk2bNk1RUVGSzBTo06dP1wsvvKADBw7o7LPP1vz589WpUye/r+NyueRwOOR0OsO+a18pX8sApKebz0B1zFzpdruVl5enpKQkWqQAAACqQdDzq5pOIEOEv7VBSBdStaU+FlKSmaEyN9d0e3U4pNTU6vsiIegfdAAAgDATEvlVTSaQIcLf2iCku/ahZtntUgANdwAAAKjvSCA9wqt8BAAAAIBaQCEFAAAAAAGikAIAAACAAFFIAQAAAECAKKQAAAAAIEAUUgAAAAAQIAopAAAAAAgQhRQAAAAABIgFeVF9yq50nZAgNWoU7IgAAACAGkEhheqRnS0tXizl5EhFRVJsrNSvn3TRRVLPnsGODgAAAKhWdO1D1WVnSzNnSllZUtOmUseO5jE3V3r4YbMfAAAACCMUUqgat9u0ROXnS2lppktfRIR5bN3abF+yxBwHAACASnG7pc2bj/2QWgUfXftQNbm5pjtfSopks3nvs9nM9vXrzXGdOgUnRgAAgDqsdATFhg1Sq1bSrl1Sly5SZqaUkRHs6OovWqRQNU6nGRMVH+97f1yc2e901m5cAAAAYaD8CIrkZPOYlWW2M4IieCikUDUOhxQTIxUU+N5fWGj2Oxy1GxcAAEAd52sEhd1uHtPSGEERbBRSqJrUVPNJ3rlTsizvfZZltqenm+MAAADgt0BGUKD2UUihaux200E3MdF80l0uqaTEPO7YYbaPHWuOAwAAgN8YQRHayG5RdRkZ0rRpUq9e0v795muR/fvNNOgPPsgoSAAAgEpgBEVoY9Y+VI+MDKl7d1NEOZ2m826jRlLLlsGODAAAoE4qHUGRlWUey3bvKx1B0bs3IyiChUIK1cduPzbFudst5eUFNx4AAIA6rHQExbZtZgRF69Zmxj5GUIQG3nYAAAAgRJUfQbFrl3ns3dtsZwRF8NAiBQAAAISw0hEUmzebDj9JSWYoOi1RwUUhBQAAAIQ4u90UTw6HKaQoooKPXwEAAAAABIhCCgAAAAACRCEFAAAAAAGikAIAAACAAFFIAQAAAECAKKQAAAAAIEAUUgAAAAAQIAopAAAAAAgQhRQAAAAABIhCCgAAAAACRCEFAAAAAAGKDHYAgIfbLeXmSk6n5HBIqamSnVofAAD4FkqpQyjFgtpBIYXQkJ0tLV4s5eRIRUVSTIyUliZlZkoZGcGODgAAhJhQSh1CKRbUHgopBF92tjRzppSfL6WkSPHxUkGBlJUlbdsmTZvG/4UAAIBHKKUOoRQLahcNjggut9t8hZOfb766SUiQIiLMY1qa2b5kiTkOAADUe6GUOoRSLKh9FFIIrtxc0w6ekiLZbN77bDazff16cxwAAKj3Qil1CKVYUPsopBBcTqfpTBwf73t/XJzZ73TWblwAACAkhVLqEEqxoPZRSCG4HA4zIrOgwPf+wkKz3+Go3bgAAEBICqXUIZRiQe2jkEJwpaaaTsQ7d0qW5b3Pssz29HRzHAAAqPdCKXUIpVhQ+yikEFx2u5kbNDHRdDJ2uaSSEvOYk2O2jx3LQgwAAEBSaKUOoRQLap/NssrXz/WPy+WSw+GQ0+lUQkJCsMMJC263W3l5eUpKSpLdn/97+FqAIT3d/N+HOUMBAEA5oZQ61FYsAedXqBR/awPWkUJoyMiQundnSXAAAOCXUEodQikW1B4KKYQOu13q1CnYUQAAgDoilFKHUIoFtYM6GQAAAAACRCEFAAAAAAGikAIAAACAADFGCtXD7fYeYdm+fbAjAgAAAGoMhRSq7nhzfl52mZSUFOzoAAAAgGpHIYWqyc6WZs6U8vOllBQpPl4qKJCyssyKdLGxUs+ewY4SAAAAqFaMkULlud2mJSo/X0pLkxISpIgI85iWZpb1/tvfzHEAAABAGKGQQuXl5prufCkpks3mvc9mkxITpfXrzXEAAABAGKGQQuU5nWZMVHy87/3R0Wa/01m7cQEAAAA1jEIKledwmIklCgp87y8uNvsdjtqNCwAAAKhhFFKovNRUMxZq507Jsrz3WZYZO5Webo4DAAAAwgiz9qHy7HYpM1Patu3YWKm4OKmwUPrpJ6l7d+maa8xxNan8GlapqTV/TQAAAF/CPS8J9/sLAIUUqiYjQ5o27dg6Uj//bLrz9e4tjRol9ehRs9f3tYZVWpop8DIyavbaAAAAZYV7XhLu9xcgCilUXUaGaX0q++1E+/ama19NOtEaVtu2mQKvHn6oAQBAEIR7XhLu91cJ9bMdDtXPbpc6dZL69DGPtdGd70RrWOXnS0uWsIYVAACoeeGel4T7/VUShRTqppOtYZWSwhpWAACgdoR7XhLu91dJFFKom062hlVcHGtYAQCA2hHueUm4318lUUihbjrZGlaFhaxhBQAAake45yXhfn+VRCGFuulka1jt3MkaVgAAoHaEe14S7vdXSRRSqJtK17BKTDR9dl0uqaTEPObkmO1jx9bbdQ0AAEAtCve8JNzvr5JsllW+rKx/XC6XHA6HnE6nEhISgh1OWHC73crLy1NSUpLsNfmh8rWeQXq6+TDXsyk4AQBAkNVwXlJr+dXx1JO8y9/agHWkULf5WsOqHq+wDQAAgijc85Jwv78AUUih7itdwwoAACDYwj0vCff7C0D9LB8BAAAAoAoopAAAAAAgQBRSAAAAABAgCikAAAAACBCFFAAAAAAEiEIKAAAAAAJEIQUAAAAAAaKQAgAAAIAAUUgBAAAAQIAopAAAAAAgQBRSAAAAABCgyGAHANQKt1vKzZWcTsnhkFJTJTvfIwAAUFfwT3no4HdhVKmQKi4uVnR0dHXF4tNPP/2ke++9Vx9++KEKCwuVmpqqhQsXqnfv3pIky7I0ffp0/fnPf9aBAwfUr18/LViwQB07dqzRuFCHZGdLixdLOTlSUZEUEyOlpUmZmVJGRrCjAwAAJ8E/5aGD38UxAdWOH374oTIzM9W+fXs1aNBAcXFxSkhI0IABA/TII4/o559/rtbgfvnlF/Xr108NGjTQhx9+qPXr1+vJJ59UkyZNPMc89thjeuaZZ/Tcc89p1apVio+P15AhQ1RUVFStsaCOys6WZs6UsrKkpk2ljh3NY1aW2Z6dHewIAQDACfBPeejgd+HNZlmWdbKD3n77bd177706ePCghg0bpjPOOEPJycmKjY3V/v37tW7dOn311VdauXKlxo0bp1mzZql58+ZVDu6+++7TihUr9NVXX/ncb1mWkpOTddddd2ny5MmSJKfTqRYtWmjRokUaPXq0X9dxuVxyOBxyOp1KSEioctyQ3G638vLylJSUJHuw2nrdbmnSJPPpTkuTbLZj+yzLfJXSu7f05JP1sz0aAIAQxz/l3oKZX9Wn34W/tYFfXfsee+wxPfXUUxo6dKjPX9oVV1whyXTDe/bZZ/W3v/1Nd955ZyVDP+a9997TkCFDdPnll+uLL77QKaecot///ve66aabJElbt27V7t27NWjQIM9rHA6HzjzzTK1cufK4hVRxcbGKi4s9z10ulyTzx+l2u6scN8x7aVlWcN/PzZulDRuk1q0rfqJtNrM9J8ccR1dQAABCDv+UewtmflWffhf+vr9+FVIrV67062SnnHKK/vCHP/h1rD9++OEHLViwQJMmTdL999+v1atX67bbblNUVJQyMzO1e/duSVKLFi28XteiRQvPPl9mz56tGTNmVNi+d+9eugRWE7fbLafTKcuygtcilZcntWolJSf7/mqkaVMpOtoc53DUfnwAAOCE+KfcWzDzq/r0uzh48KBfx1V51r6CggIdPXq0RrrEud1u9e7dW48++qgkKSMjQ+vWrdNzzz2nzMzMSp93ypQpmjRpkue5y+VS69at1bx5c7r2VRO32y2bzabmzZsHr5ByOqVdu6TiYsnX79Xlkvbvl5KSzA8AAAgp/FPuLZj5VX36XcTExPh1XKULqfXr12vs2LFas2aNbDab0tPTvWbTqw6tWrVSenq617a0tDS9+eabkqSWLVtKkvbs2aNWrVp5jtmzZ4969Ohx3PNGR0f7nG3QbrcHL+kPQzabLbjvaceOUpcux+/Mu2OH6czbsWPd78wLAEAY4p/yioKVX9Wn34W/722lb/OWW27RhAkTdOjQIe3bt08jR46sUiuRL/369dPGjRu9tm3atElt2rSRJLVr104tW7bU8uXLPftdLpdWrVqlvn37VmssqIPsdjMXZ2Ki6bTrckklJeYxJ8dsHzu27n/aAQAIU/xTHjr4XVTk961eeuml+umnnzzP9+7dq0suuURxcXFq3Lixhg0bpj179lRrcHfeeaf+9a9/6dFHH1Vubq5eeeUVvfDCCxo/frwkU5Hfcccdevjhh/Xee+/p+++/19ixY5WcnKwRI0ZUayyoozIypGnTpF69THtzbq557N3bbK9vCx4AAFDH8E956OB34c3vrn3XXHONzj//fI0fP14TJ07UhAkT1LVrVw0YMEBHjhzRp59+qrvuuqtag+vTp4/efvttTZkyRTNnzlS7du309NNPa8yYMZ5j7rnnHhUUFOjmm2/WgQMHdPbZZ+ujjz7yu28j6oGMDKl7d5bgBgCgjuKf8tDB7+IYv9aRKuV0OnXvvfcqOztbzz33nCIjI/X555/r6NGj6tevn/r06VOTsdYY1pGqfiGxjhQAAEAYIb+qHdW6jlQph8Oh5557Tl9//bUyMzN1wQUXaNasWYqLi6tywAAAAABQVwRUyu7fv19ZWVnq1q2bsrKylJCQoIyMDH3wwQc1FR8AAAAAhBy/C6lXXnlFKSkpuuiii9SmTRt9+OGHmj59ut5991099thjuuKKK6p9sgkAAAAACEV+F1JTpkzRiy++qN27d2v58uWaOnWqJKlLly76/PPPdcEFFzDlOAAAAIB6we9C6tChQ+rcubMkqUOHDiosLPTaf9NNN+lf//pX9UYHAAAAACHI78kmMjMzddFFF+ncc8/Vt99+q2uvvbbCMUlJSdUaHAAAAACEIr8Lqblz5+q8887Thg0bNG7cOA0ePLgm4wIAAACAkBXQ9OfDhw/X8OHDayoWAAAAAKgT/Boj9eqrr/p9wh07dmjFihWVDggAAAAAQp1fhdSCBQuUlpamxx57TDk5ORX2O51OffDBB7r66qvVs2dP7du3r9oDBQAAAIBQ4VfXvi+++ELvvfeenn32WU2ZMkXx8fFq0aKFYmJi9Msvv2j37t1KTEzUuHHjtG7dOrVo0aKm4wYAAACAoPF7jNQll1yiSy65RPn5+fr666+1bds2/frrr0pMTFRGRoYyMjJkt/s9mzrg4XZLubmS0yk5HFJqqsSfEgAAAEJZQJNNSFJiYqJGjBhRA6GgPsrOlhYvlnJypKIiKSZGSkuTMjOljIxgRwcAAAD4FnAhBVSX7Gxp5kwpP19KSZHi46WCAikrS9q2TZo2jWIKAAAAoYkOVAgKt9u0ROXnmxaohAQpIsI8pqWZ7UuWmOMAAACAUEOLFIIiN9d050tJkWw27302m9m+fr05rlOn4MQIAADqJrfbrcOHDwc7jGrndrt15MgRFRUVMTdBFTRo0EARERFVPg+FFILC6TRjouLjfe+Pi5N+/tkcBwAA4K/Dhw9r69atcodhtxbLsuR2u3Xw4EHZyn8TjYA0btxYLVu2rNL7GHAh9dlnn+m8886r9AUByczOFxNjxkQlJFTcX1ho9jsctR8bAAComyzL0q5duxQREaHWrVuHXauNZVkqKSlRZGQkhVQlWZalwsJC5eXlSZJatWpV6XMFXEhdeOGFSklJ0XXXXafMzEy1bt260hdH/ZWaasZCZWWZx7L/L7AsaedOqXdvcxwAAIA/SkpKVFhYqOTkZMXFxQU7nGpHIVU9YmNjJUl5eXlKSkqqdDe/gMv0n376SRMmTNAbb7yh9u3ba8iQIXrttdfCsh8qao7dbqY4T0w0Y6VcLqmkxDzm5JjtY8eynhQAAPDf0aNHJUlRUVFBjgShrrTQPnLkSKXPEXCampiYqDvvvFPfffedVq1apU6dOun3v/+9kpOTddttt2nt2rWVDgb1S0aGmeK8Vy9p/34zscT+/aYliqnPAQBAZdFag5Opjr+RKk020bNnT7Vs2VLNmjXTH/7wB7344ouaP3+++vbtq+eee05du3atcoAIbxkZUvfupohyOs2YqNRUWqIAAAAQ2iqVrh45ckRvvPGGhg0bpjZt2mjZsmWaN2+e9uzZo9zcXLVp00aXX355dceKMGW3mynO+/QxjxRRAAAAtWvcuHEaMWJEjV/HZrPpnXfeqfHr1IaAU9aJEyeqVatWuuWWW9SpUydlZ2dr5cqVuvHGGxUfH6+2bdvqiSee0IYNG2oiXgAAACCsjBs3TjabTTabTQ0aNFC7du10zz33qKioKNih1RrLsjRo0CANGTKkwr758+ercePG2rlzZxAiO76Au/atX79ezz77rEaOHKno6GifxyQmJuqzzz6rcnAAAABAbXO7a3/YwYUXXqiFCxfqyJEjysrKUmZmpmw2m+bMmVOzFw4RNptNCxcuVLdu3fT888/rlltukSRt3bpV99xzjxYsWKCUlJQgR+kt4D+J6dOn6/LLL69QRJWUlOjLL7+UJEVGRmrAgAHVEyEAAABQS7KzpUmTpIkTpcmTzeOkSWZ7TYqOjlbLli3VunVrjRgxQoMGDdLHH3/s2e92uzV79mx16tRJcXFx6t69u9544w3P/l9++UVjxoxR8+bNFRsbq44dO2rhwoWe/d9//73OP/98xcbGqlmzZrr55pt16NAhn7G88MILSk5OrrCo8aWXXqrrr7/e8/zdd99Vz549FRMTo/bt22vGjBkqKSnx7N+8ebPOOeccxcTEKD093et+fGndurX++Mc/avLkydq6dassy9INN9ygwYMH69prr9W6des0dOhQNWzYUC1atNC1116r/Px8z+vfeOMNdevWzXOPgwYNUkFBwUne+coLuJA677zztH///grbnU4nC/UCAACgzsrOlmbONOtcNm0qdexoHrOyzPaaLqZKrVu3Tt98843XNO6zZ8/WSy+9pHnz5mndunW68847dc011+iLL76QJE2dOlXr16/Xhx9+qJycHC1YsECJiYmSpIKCAg0ZMkRNmjTR6tWr9frrr+uTTz7RhAkTfF7/8ssv1759+7x6mO3fv18fffSRxowZI0n66quvNHbsWN1+++1av369nn/+eS1atEiPPPKIJFP4jRw5UlFRUVq1apWee+453XvvvSe998zMTA0cOFDXX3+9516ff/55HThwQOeff74yMjL07bff6qOPPtKePXt0xRVXSJJ27dqlq666Stdff71ycnL0+eefa+TIkbIsqxK/Af8E3LXPsiyf0wXu27dP8fHx1RIUAAAAUJvcbmnxYik/X0pLk0rT3YQE8zwnR1qyxMw2XBPd/JYuXaqGDRuqpKRExcXFstvtmjdvniSpuLhYjz76qD7++GP16dNHkZGR6tChg77++ms9//zzGjBggLZv366MjAz17t1bktS2bVvPuV955RUVFRVpyZIlnnx93rx5Gj58uObMmaMWLVp4xdKkSRMNHTpUr7zyigYOHCjJtPYkJiZ6Gk5mzJih++67T5mZmZKk9u3ba9asWbrnnns0ffp0ffLJJ9qwYYOWLVum5ORkSdKjjz6qoUOHnvS9eOGFF9S1a1d9+eWXevPNN9W8eXM9/PDDysjI0KOPPuo57sUXX1Tr1q21adMmHTp0SCUlJRo5cqTatGkjSerWrVvAv4dA+F1IjRw5UpLpvzhu3Divrn1Hjx7Vf/7zH5111lnVHyEAAABQw3JzTbGUknKsiCpls5nt69eb4zp1qv7rn3feeVqwYIEKCgr01FNPKTIyUqNGjfpfbLkqLCzU4MGDvV5z+PBhZfxv4c1bb71Vo0aN0po1azR48GCNGDHCk5vn5OSoe/fuXo0e/fr1k9vt1saNGysUUpI0ZswY3XTTTZo/f76io6P18ssva/To0bL/r4pcu3atVqxY4WmBkkxNUFRUpMLCQuXk5Kh169aeIkqS+vbt69d7kZSUpFtuuUXvvPOOZybBtWvX6rPPPlPDhg0rHL9lyxYNHjxYAwcOVLdu3TRkyBANHjxYl112mZo0aeLXNSvD70LK4XBIMi1SjRo1UmxsrGdfVFSUfvOb3+imm26q/ggBAACAGuZ0SkVF0vE6WMXFST//bI6rCfHx8UpNTZVkWlq6d++uv/71r7rhhhs8Y5mWLl2qFi1aKDIy0tNDrLRxY+jQodq2bZs++OADffzxxxo4cKDGjx+vJ554olLxDB8+XJZl6R//+If69Omjr776Sk899ZRn/6FDhzRjxgxPY0tZMTExlbpmWZGRkYqMPFaqHDp0yNOCVl6rVq0UERGhjz/+WN98843++c9/6tlnn9UDDzygVatWqV27dlWOx2eM/h5YOlitbdu2mjx5Mt34AAAAEDYcDikmRiooMN35yissNPv/17ZQo+x2u+6//35NmjRJV199tdLT0xUdHa3t27erX79+XoVUWc2bN1dmZqYyMzPVv39/3X333XriiSeUlpamRYsWqaCgwJPDr1ixQna7XZ07d/YZQ0xMjEaOHKmXX35Zubm56ty5s3r27OnZ37NnT23cuNFT/JWXlpamHTt2aNeuXWrVqpUk6V//+lel35OePXvqzTffVNu2bb0KrLJsNpv69eunfv36adq0aWrTpo3efvttTZo0qdLXPZFKzdpHEQUAAIBwkppqxkLt3CmVn5/Assz29HRzXG24/PLLFRERoT/96U9q1KiRJk+erEmTJmnJkiXasmWL1qxZo2effVaLFy+WJE2bNk3vvvuucnNz9d///ldLly5VWlqaJNNNLyYmRpmZmVq3bp0+++wzTZw4Uddee63Pbn2lxowZo3/84x968cUXPZNMlJo2bZqWLFmiGTNm6L///a9ycnL06quv6sEHH5QkDRo0SJ06dVJmZqbWrl2rr776Sg888ECl34/x48dr//79uuqqq7R69Wpt2bJFy5Yt03XXXaejR49q1apVevTRR/Xtt99q+/bteuutt7R3717Pe1AT/GqR6tmzp5YvX64mTZooIyPDZwVcas2aNdUWHAAAAFAb7HYpM1Patu3YWKm4ONMStXOnlJgojR1b8+tJlYqMjNSECRP02GOP6dZbb9WsWbOUmJjoed64cWP17NlT999/vyQz1GbKlCn68ccfFRsbq/79++vVV1+VJMXFxWnZsmW6/fbb1adPH8XFxWnUqFGaO3fuCWM4//zz1bRpU23cuFFXX321174hQ4Zo6dKlmjlzpubMmaMGDRqoS5cuuvHGGyWZVrW3335bN9xwg8444wy1bdtWzzzzjC688MJKvR/JyclasWKF7r33Xg0ePFjFxcVq06aNLrzwQtntdiUkJOjLL7/U008/LZfLpTZt2ujJJ5/0a3KLyrJZfswJOGPGDN19992Ki4vTjBkzTnjs9OnTqy242uJyueRwOOR0OpXgqy0XAXO73crLy1NSUpJnUCIAAEBNKioq0tatW9WuXbtKj9PJzjaz9+XkmDFTMTGmJWrsWOl/8zoEjWVZKikpOW7XPvjvRH8r/tYGfrVIlS2O6mKhFNaCsfR2oOpCjAAAADLFUvfupC44uYDXkUII8fWVSVqaaZcO9lcmpb77ziy6EMoxAgAAlGG318wU5wgvfhVSTZo08bv5cP/+/VUKCH4qXXo7P9904o2PN9PMZGWZzr3TpgW/UNmyRXrySWnv3srFSEsWAAAAQpRfhdTTTz9dw2EgIMFeetvfGD/9tPIx1oXWNgAAANRbfhVSmZmZNR0HAhHspbf9sWWLtGNH5WKsC61tAAAAqNf8KqRcLpdnxgqXy3XCY5n1rhYEe+ltfzid0pEjgcdYF1rbAAAAUO/5PUZq165dSkpKUuPGjX2Ol7IsSzabTUePHq32IFFOKC29fTwOh9SggXTokNSoUcX9x4uxLrS2AQAAoN7zq5D69NNP1bRpU0nSZ599VqMBwQ+lS29nZXm32kjHlt7u3bv2lt72pUMHqXVr6eOPpS5d/I+xLrS2AQAAoN7zq5AaMGCAz/9GkITa0tvHi/H88814p0BirAutbQAAAKj3KpVp//LLL3riiSd0ww036IYbbtCTTz7JtOe1LSPDTLrQq5e0f7/p6rZ/v2nlCZXJGDp0kB58MLAYS1vbdu40LVdllbZkpacHt7UNAAAgzIwbN04jRoyo8evYbDa98847NX6d2hBwIfXll1+qbdu2euaZZ/TLL7/ol19+0TPPPKN27drpyy+/rIkYcTwZGdLcudKzz0pPPGEen3wyNIqoUj16BBZjaWtbYqJpyXK5pJIS85iTExqtbQAAANVo3LhxstlsstlsatCggdq1a6d77rlHRUVFwQ6t1pW+F3/4wx+8tr/zzjt+r2tbW/zq2lfW+PHjdeWVV2rBggWKiIiQJB09elS///3vNX78eH3//ffVHiROoC4svR1ojKWtbaXrSP38s+nO17u3KaJCqVAEAADhx+02PWmcTjOcIDW1xr/EvfDCC7Vw4UIdOXJEWVlZyszMlM1m05w5c2r0uqEoJiZGc+bM0S233KImTZoEO5zjCvgvIjc3V3fddZeniJKkiIgITZo0Sbm5udUaHOqxutDaBgAAwk92tjRpkjRxojR5snmcNMlsr0HR0dFq2bKlWrdurREjRmjQoEH6+OOPPfvdbrdmz56tTp06KS4uTt27d9cbb7zh2f/LL79ozJgxat68uWJjY9WxY0ctXLjQs//777/X+eefr9jYWDVr1kw333yzDh065DOWF154QcnJyXK73V7bL730Ul1//fWe5++++6569uypmJgYtW/fXjNmzFBJSYln/+bNm3XOOecoJiZG6enpXvdzIoMGDVLLli01e/bsEx735ptvqmvXroqOjlbbtm315JNP+nX+6hJwIdWzZ0/l5ORU2J6Tk6Pu3btXS1CApGMtWX36mEe68wEAgJqUnS3NnGlmRm7aVOrY0TxmZZntNVxMlVq3bp2++eYbRUVFebbNnj1bL730kubNm6d169bpzjvv1DXXXKMvvvhCkjR16lStX79eH374oXJycrRgwQIlJiZKkgoKCjRkyBA1adJEq1ev1uuvv65PPvlEEyZM8Hn9yy+/XPv27fOarXv//v366KOPNGbMGEnSV199pbFjx+r222/X+vXr9fzzz2vRokV65JFHJJnCb+TIkYqKitKqVav03HPP6d577/Xr/iMiIvToo4/q2Wef1c6dO30ek5WVpSuuuEKjR4/W999/r4ceekhTp07VokWL/LpGdfCra99//vMfz3/fdtttuv3225Wbm6vf/OY3kqR//etf+tOf/lShLyMAAABQJ7jdZlhBfr738jIJCeZ5To60ZInUvXuNfLm7dOlSNWzYUCUlJSouLpbdbte8efMkScXFxXr00Uf18ccfq0+fPoqMjFSHDh309ddf6/nnn9eAAQO0fft2ZWRkqHfv3pKktm3bes79yiuvqKioSEuWLFH8/5aYmTdvnoYPH645c+aoRYsWXrE0adJEQ4cO1SuvvKKBAwdKkt544w0lJibqvPPOkyTNmDFD9913nzIzMyVJ7du316xZs3TPPfdo+vTp+uSTT7RhwwYtW7ZMycnJkqRHH31UQ4cO9ev9+O1vf6sePXpo+vTp+utf/1ph/9y5czVw4EBNnTpVktSpUyetX79ejz/+uMaNG+fXNarKr0KqR48estlsssrMonbPPfdUOO7qq6/WlVdeWX3RAQAAALUhN/fYki3lJzWw2cz29evNcTUwPv28887TggULVFBQoKeeekqRkZEaNWrU/0LLVWFhoQYPHuz1msOHDyvjf8Mebr31Vo0aNUpr1qzR4MGDNWLECJ111lmSjvUciy+zTme/fv3kdru1cePGCoWUJI0ZM0Y33XST5s+fr+joaL388ssaPXq07P8rIteuXasVK1Z4WqAkM29CUVGRCgsLlZOTo9atW3uKKEnq27dvQO/JnDlzdP7552vy5MkV9uXk5OjSSy/12tavXz89/fTTOnr0qNcwpJriVyG1devWmo4DAAAACB6nUyoqksoUG17i4swEWE5njVw+Pj5eqf9b3uXFF19U9+7d9de//lU33HCDZyzT0qVL1aJFC0VGRnpmsIuOjpYkDR06VNu2bdMHH3ygjz/+WAMHDtT48eP1xBNPVCqe4cOHy7Is/eMf/1CfPn301Vdf6amnnvLsP3TokGbMmKGRI0dWeG1MTEylrlneOeecoyFDhmjKlCm11soUCL8KqTZt2tR0HAAAAEDwOBxmluCCAtOdr7zCQrPf4ajxUOx2u+6//35NmjRJV199tdLT0xUdHa3t27erX79+XoVUWc2bN1dmZqYyMzPVv39/3X333XriiSeUlpamRYsWqaCgwNMqtWLFCtntdnXu3NlnDDExMRo5cqRefvll5ebmqnPnzurZs6dnf8+ePbVx40ZP8VdeWlqaduzYoV27dqlVq1aSzHCgQP3hD39Qjx49KsSZlpamFStWeG1bsWKFOnXqVCutUVIlpj8vtX79em3fvl2HDx/22n7JJZdUOSgAAACgVqWmmrFQWVneY6QkybKknTvNUizHKRyq2+WXX667775bf/rTnzR58mRNnjxZkyZN0pEjRzRgwAC5XC6tWLFCCQkJyszM1LRp09SrVy917dpVxcXFWrp0qdLS0iSZbnrTp09XZmamHnroIe3du1cTJ07Utdde67NbX6kxY8bo4osv1n//+19dc801XvumTZumiy++WKeeeqouu+wy2e12rV27VuvWrdPDDz+sQYMGqVOnTsrMzNTjjz8ul8ulBx54IOD3oVu3bhozZoyeeeYZr+133XWX+vTpo1mzZunKK6/UypUrNW/ePM2fPz/ga1RWwIXUDz/8oN/+9rf6/vvvvcZNlVbFR48erd4IAQAAgJpmt0uZmdK2bcfGSsXFmZaonTulxESznmUtzSIcGRmpCRMm6LHHHtOtt96qWbNmKTEx0fO8cePG6tmzp+6//35JUlRUlKZMmaIff/xRsbGx6t+/v1599VVJUlxcnJYtW6bbb79dffr0UVxcnEaNGqW5c+eeMIbzzz9fTZs21caNG3X11Vd77RsyZIiWLl2qmTNnas6cOWrQoIG6dOmiG2+8UZJpVXv77bd1ww036IwzzlDbtm31zDPP6MILLwz4vZg5c6b+/ve/e23r2bOnXnvtNU2bNk2zZs1Sq1atNHPmzFrtAmizys4g4Yfhw4crIiJCf/nLX9SuXTv9+9//1r59+3TXXXfpiSeeUP/+/Wsq1hrjcrnkcDjkdDqV4KspFwFzu93Ky8tTUlKSZ1AiAABATSoqKtLWrVvVrl27yo/Tyc42s/fl5JgxUzExUnq6KaKCvJ6lZVkqKSk5btc++O9Efyv+1gYBt0itXLlSn376qRITE2W322W323X22Wdr9uzZuu2225RdS/PrAwAAANUuI8NMcZ6bayaWcDhMdz6+GEY5ARdSR48eVaNGjSRJiYmJ+vnnn9W5c2e1adNGGzdurPYAAQAAgFplt9fIFOcILwEXUqeddprWrl2rdu3a6cwzz9Rjjz2mqKgovfDCC2rfvn1NxIgQ4nbzBQ0AAAAQcCH14IMPqqCgQJIZ+HXxxRerf//+atasWYVBYAgvvroMp6WZcZlB7jIMAAAA1KqAC6khQ4Z4/js1NVUbNmzQ/v371aRJEwa9hbHsbGnmTCk/30xiEx9vllnIyjKT20ybRjEFAABCQ4BzqaEeqo6/kSp1ytqxY4d27Nihpk2bUkSFMbfbtETl55sWqIQEKSLCPKalme1LlpjjAAAAgqV0Idby65wC5RUWFkqSGjRoUOlzBNwiVVJSohkzZuiZZ57RoUOHJEkNGzbUxIkTNX369CoFg9CUm3tsOYXy9bLNZravX2+OY1wmAAAIlsjISMXFxWnv3r1q0KBB2C3BwvTnVWdZlgoLC5WXl6fGjRt7iu/KCLiQmjhxot566y099thj6tu3ryQzJfpDDz2kffv2acGCBZUOBqHJ6TRjouLjfe+Pi5N+/tkcBwAAECw2m02tWrXS1q1btW3btmCHU+0sy5Lb7ZbdbqeQqqLGjRurZcuWVTpHwIXUK6+8oldffVVDhw71bDv99NPVunVrXXXVVRRSYcjhMBNLFBSY7nzlFRaa/Q5H7ccGAABQVlRUlDp27BiW3fvcbrf27dunZs2ahV1rW21q0KBBlVqiSgVcSEVHR6tt27YVtrdr105RUVFVDgihJzXVjIXKyjKPZb8AsSxp506pd29zHAAAQLDZ7XbFxMQEO4xq53a71aBBA8XExFBIhYCAfwMTJkzQrFmzVFxc7NlWXFysRx55RBMmTKjW4BAa7HYzxXliohkr5XJJJSXmMSfHbB87lvWkAAAAUH/41SI1cuRIr+effPKJUlJS1L17d0nS2rVrdfjwYQ0cOLD6I0RIyMgwU5yXriP188+mO1/v3qaIYupzAAAA1Cd+FVKOcoNfRo0a5fW8devW1RcRQlZGhtS9u5mdz+k0Y6JSU2mJAgAAQP3jVyG1cOHCmo4DdYTdzhTnAAAAQMCTTZTau3evNm7cKEnq3LmzmjdvXm1BAQAAAEAoC7hTVkFBga6//nq1atVK55xzjs455xwlJyfrhhtu8KwQDAAAAADhLOBCatKkSfriiy/0/vvv68CBAzpw4IDeffddffHFF7rrrrtqIkYAAAAACCkBd+1788039cYbb+jcc8/1bBs2bJhiY2N1xRVXsCAvAAAAgLAXcItUYWGhWrRoUWF7UlISXfsAAAAA1AsBF1J9+/bV9OnTVVRU5Nn266+/asaMGerbt2+1BgcAAAAAoSjgrn1PP/20LrzwwgoL8sbExGjZsmXVHiAAAAAAhJqAC6lu3bpp8+bNevnll7VhwwZJ0lVXXaUxY8YoNja22gMEAAAAgFATUCF15MgRdenSRUuXLtVNN91UUzEBAAAAQEgLaIxUgwYNvMZGAQAAAEB9FPBkE+PHj9ecOXNUUlJSE/EAAAAAQMgLeIzU6tWrtXz5cv3zn/9Ut27dFB8f77X/rbfeqrbgAAAAgDrH7ZZycyWnU3I4pNRUyR5w+0XNC4U4QyGGSgq4kGrcuLFGjRpVE7Gc1B/+8AdNmTJFt99+u55++mlJUlFRke666y69+uqrKi4u1pAhQzR//nyfa10BAAAANSo7W1q8WMrJkYqKpJgYKS1NysyUMjKCHd0xoRBnKMRQBQEXUgsXLqyJOE5q9erVev7553X66ad7bb/zzjv1j3/8Q6+//rocDocmTJigkSNHasWKFUGJEwAAAPVUdrY0c6aUny+lpEjx8VJBgZSVJW3bJk2bFhoFQijEGQoxVJHf7WZut1tz5sxRv3791KdPH91333369ddfazI2j0OHDmnMmDH685//rCZNmni2O51O/fWvf9XcuXN1/vnnq1evXlq4cKG++eYb/etf/6qV2AAAAAC53aZ1JT/ftKokJEgREeYxLc1sX7LEHFff4wyFGKqB3y1SjzzyiB566CENGjRIsbGx+uMf/6i8vDy9+OKLNRmfJDPBxUUXXaRBgwbp4Ycf9mzPysrSkSNHNGjQIM+2Ll266NRTT9XKlSv1m9/8xuf5iouLVVxc7HnucrkkmWLRHeK/sLrC7XbLsizeTwAAUD9s3ixt2CC1bl1xjI/NZrbn5JjjOnas1CWqJb+qhTjrRAwn4O/763chtWTJEs2fP1+33HKLJOmTTz7RRRddpL/85S+y1+CAsFdffVVr1qzR6tWrK+zbvXu3oqKi1LhxY6/tLVq00O7du497ztmzZ2vGjBkVtu/du5fp3auJ2+2W0+mUZVk1+vcBAAAQEvLypFatpORk35MlNG0qRUeb4xyOSl2iWvKrWoizTsRwAgcPHvTrOL8Lqe3bt2vYsGGe54MGDZLNZtPPP/+slJSUwCP0w44dO3T77bfr448/VkxMTLWdd8qUKZo0aZLnucvlUuvWrdW8eXMlJCRU23XqM7fbLZvNpubNm1NIAQCA8Od0Srt2ScXFpotaeS6XtH+/lJRkfiqhWvKrWoizTsRwAv7WHX4XUiUlJRVO2qBBAx05ciSwyAKQlZWlvLw89ezZ07Pt6NGj+vLLLzVv3jwtW7ZMhw8f1oEDB7xapfbs2aOWLVse97zR0dGKjo6usN1ut5P0VyObzcZ7CgAA6oeOHaUuXcxkCWlppotaKcuSduyQevc2x1UhN6pyflVLcYZ8DCfg73vrdyFlWZbGjRvnVYAUFRXpd7/7nddaUtW5jtTAgQP1/fffe2277rrr1KVLF917771q3bq1GjRooOXLl3umZN+4caO2b9+uvn37VlscAAAAwAnZ7Wba7m3bzPielBQpLk4qLJR27pQSE6WxY4O/RlIoxBkKMVQDm2VZlj8HXnfddX6dsKanRz/33HPVo0cPzzpSt956qz744AMtWrRICQkJmjhxoiTpm2++8fucLpdLDodDTqeTrn3VxO12Ky8vT0lJSbRIAQCA+sPX2kjp6aYwqOJ03tWaX9VgnHUqBh/8rQ38bpEK1vpRJ/PUU0/Jbrdr1KhRXgvyAgAAALUuI0Pq3l3KzTVjgRwOKTU19FpXQiHOUIihCvxukQpntEhVP1qkAAAAqhf5Ve3wtzbgNwAAAAAAAaKQAgAAAIAAUUgBAAAAQIAopAAAAAAgQBRSAAAAABAgCikAAAAACBCFFAAAAAAEiEIKAAAAAAJEIQUAAAAAAaKQAgAAAIAAUUgBAAAAQIAigx0A6ie3W8rNlZxOyeGQUlMlO2U9AACoy0hwAlaX3zIKKdS67Gxp8WIpJ0cqKpJiYqS0NCkzU8rICHZ0AAAAlUCCE7C6/pZRSKFWZWdLM2dK+flSSooUHy8VFEhZWdK2bdK0aXXjgwMAAOBBghOwcHjL6kjDGcKB222+dcjPN982JCRIERHmMS3NbF+yxBwHAABQJ5DgBCxc3jIKKdSa3FzTdJuSItls3vtsNrN9/XpzHAAAQJ1AghOwcHnLKKRQa5xO0/81Pt73/rg4s9/prN24AAAAKo0EJ2Dh8pZRSKHWOBxmEGFBge/9hYVmv8NRu3EBAABUGglOwMLlLaOQQq1JTTX9XnfulCzLe59lme3p6eY4AACAOoEEJ2Dh8pZRSKF6uN3Spk3S6tXm0cfoQLvdTGeZmGj6xbpcUkmJeczJMdvHjq07awcAAACQ4AQuXN4ym2WVrwPrH5fLJYfDIafTqYSEhGCHU/f4WATAnZ6uvMsuU1LfvrKX+xT4WjMgPd18YEJ9mksAAACfaiHBcbvdysvLU1JSUoX8qi4K1ZzQ39qAdaRQNSdaBKCkRIqNlXr29HpJRobUvXvdXcUaAACgAhKcgNX1t4xCCpVXfhGA0vkrExLMj8sl/e1vUo8eFT4RdrvUqVPthwwAAFBjSHACVpffsjpS7yEknWwRgMTEurEIAAAAABAgCilU3skWAYiOrhuLAAAAAAABopBC5Z1sEYDi4rqxCAAAAAAQIAopVN7JFgHIz68biwAAAAAAAaKQQuWdbBGAhATpmmvqztQrAAAAgJ/IcFE1GRnStGlSr17S/v1mYon9+6XevaXRo82MfQAAAECYYfpzVJ2vRQDatzdd+wAAAIAwRCGF6lF+EQC3O3ixAAAAADWMrn0AAAAAECAKKQAAAAAIEIUUAAAAAASIQgoAAAAAAsRkEwgJbrf3pH+pqSw/BQAAKikcEgtf9+DvcXXtXusoCikEXXa2tHixWcO3qEiKiZHS0sxavxkZwY4OAADUKeGQWBzvHsaOlZKTT35cXbrXOoxCCkGVnS3NnGmWnEpJkeLjpYICKStL2rbNrPXL/wcAAIBfwiGxONE9bN8u3XWXlJQUHvdax9Huh6Bxu82XKPn55suThAQpIsI8pqWZ7UuWsCQVAADwQzgkFv7cw2efSSUldf9ewwCFFIImN9e0RKekSDab9z6bzWxfv94cBwAAcELhkFj4cw/bt0ufflr37zUMUEghaJxO0503Pt73/rg4s9/prN24AABAHRQOiYU/93DkiLRnT92/1zBAIYWqcbulTZuk1avNYwBNyA6HGRNZUOB7f2Gh2e9wVFOsAAAgfIVDYuHPPTRoILVoUffvNQxQSKHysrOlSZOkiROlyZPN46RJZrsfUlNNN96dOyXL8t5nWWZ7evrxZ/sEAADwCIfEwp97OPVU6fzz6/69hgEKKVRO6UwxWVlS06ZSx47mMSvLbP/uu5Oewm43s3MmJppuvi6XGTvpcpnniYlmlk+WQgAAACcVDomFP/dw3nlSZGTdv9cwYLOs8mVs/eNyueRwOOR0OpWQkBDscEKf221anrKyzLchZQc5WpaUkyN3nz7KmzxZSS1byn6SD7GvJRDS083nn1k7AQBAQMIhsTjOPbivvVZ5yclKSkoy+VU43GsI8rc2YB0pBM7fWXF27ZJatjzp6TIypO7dWZQbAABUg3BILI53D5KUl3fy4+rSvdZhFFIInD8zyuzaZQY6+slulzp1qqb4AABA/RYOiYWve/A1qVc43GsdRbmKwPk7K05cXO3GBQAAANQSCikEzt9ZcVq1Ck58AAAAQA2jkELg/JlR5ppr6J8LAACAsEWmi8rJyJCmTZN69ZL27zeDHPfvl3r3Ntt79Ah2hAAAAECNYbIJVN6JZorxNRgSAAAACBMUUqgaZooBAABAPUTXPgAAAAAIEIUUAAAAAASIQgoAAAAAAkQhBQAAAAABopACAAAAgAAxax9Cm9vte3r1mnodAABAXVJTOQ+51ElRSCF0ZWdLixdLOTlSUZEUEyOlpUmZmWYNq+p+HQAAQF1SUzkPuZRfKKQQmrKzpZkzpfx8KSVFio+XCgqkrCxp2zZp2jTfH+TKvg4AAKAuqamch1zKb7TPIfS43eZbkPx88+1HQoIUEWEe09LM9iVLzHHV8ToAAIC6pKZyHnKpgFBIIfTk5pqm5JQUyWbz3mezme3r15vjquN1AAAAdcmWLTWT85BLBYRCCqHH6TT9cePjfe+PizP7nc7qeR0AAEBdUlM5D7lUQCikEHocDjOosaDA9/7CQrPf4aie1wEAANQlNZXzkEsFhEIKoSc11fTD3blTsizvfZZltqenm+Oq43UAAAB1SYcONZPzkEsFhEIKocduN9NrJiaafroul1RSYh5zcsz2sWMrrmVQ2dcBAADUJTWV85BLBcRmWeXLzfrH5XLJ4XDI6XQqISEh2OGEBbfbrby8PCUlJcle2Q+brzUM0tPNBzjQdaT8eR0AAEAIq5Bf1VTOU89zKX9rAwopUUjVhGoppMyJKreqNqtxAwCAMOMzv6qpnKce51L+1gYsyIvQZrdLnTrV3usAAADqkprKecilTqp+lJUAAAAAUI0opAAAAAAgQBRSAAAAABAgCikAAAAACBCFFAAAAAAEiEIKAAAAAAJEIQUAAAAAAaKQAgAAAIAAUUgBAAAAQIAopAAAAAAgQBRSAAAAABCgkC6kZs+erT59+qhRo0ZKSkrSiBEjtHHjRq9jioqKNH78eDVr1kwNGzbUqFGjtGfPniBFHIbcbmnTJmn1avPodgc7IgAAgLqtbH61YYP58ZFrVUsa5nab87/xhvnZsIF8rppEBjuAE/niiy80fvx49enTRyUlJbr//vs1ePBgrV+/XvHx8ZKkO++8U//4xz/0+uuvy+FwaMKECRo5cqRWrFgR5OjDQHa2tHixlJMjFRVJMTFSWpqUmSllZAQ7OgAAgLqnbH6Vn29+JCkx0fz8L9fKVkaFNCw9XbrsMikpKYBrPf64tGKF5HKZbQkJUr9+0t13k89Vkc2yLCvYQfhr7969SkpK0hdffKFzzjlHTqdTzZs31yuvvKLLLrtMkrRhwwalpaVp5cqV+s1vfuPXeV0ulxwOh5xOpxISEmryFuqO7Gxp5kzz4U5JkeLjpYICaedO8yGfNu2EHz632628vDwlJSXJbg/phk8AAIDaUTa/io83rUMFBWZfXJwpogoKdCAyUTM1TatLMrzSsJ9+cqt79zxdd12SevY8SX6VnS1NmiT95z+S3S41aiTZbKagcrul00+X5s6lmPLB39qgTmW4TqdTktS0aVNJUlZWlo4cOaJBgwZ5junSpYtOPfVUrVy5MigxhgW323xTkp9vPtAJCVJEhHlMSzPblyyhWRgAAMBfZfOrLl2kn36SiovNF9TNmpn//vlnWZ27aN+mfJ25aYnSu7grpGEul/S3v50kDXO7pUWLTH/ABg3M+aOjpagoc70GDcy+xYvJ56ogpLv2leV2u3XHHXeoX79+Ou200yRJu3fvVlRUlBo3bux1bIsWLbR79+7jnqu4uFjFxcWe567/NXW63W65+WOSNm8235C0bm2+wSjLZjPbc3LMcR07+jyF2+2WZVm8nwAAAJJ3fnXokKmIGjU6lms1aiQ5nXLtPqgf1VqdlaN1Bzdrb+NjuZbN5lZioqX1690nSsPMtdasOXbeiAjv/QkJpokrK+uE+Vx95W/+WmcKqfHjx2vdunX6+uuvq3yu2bNna8aMGRW27927V0VFRVU+f52Xlye1aiUlJ1cspCSpaVPzrUZenuRw+DyF2+2W0+mUZVl07QMAACibXx08KHXubPrs2Wxmv9stFRbqYFJzFXZtJIei1bx5ntT4WK5ls7nVsKFTv/xiKS/Pfrw0zFzrlFOk2FjTZbB8LmZZppBq0uSE+Vx9dfDgQb+OqxOF1IQJE7R06VJ9+eWXSklJ8Wxv2bKlDh8+rAMHDni1Su3Zs0ctW7Y87vmmTJmiSZMmeZ67XC61bt1azZs3Z4yUJDmd0q5dponZ1/vhckn795uRjscZ7eh2u2Wz2dS8eXMKKQAAgLL5lWVJGzeaL6YbNDD7Dx+WDh9WTFRTbf/vPkVqv/Y2TdLeI8dyLZvNrYICm3btaq6kJPvxJ51wOk3XwR9+MIVUVJT3/iNHTCHVvv0J87n6KiYmxq/jQrqQsixLEydO1Ntvv63PP/9c7dq189rfq1cvNWjQQMuXL9eoUaMkSRs3btT27dvVt2/f4543Ojpa0dHRFbbb7XaSfsk073bpYpp709KOfVMimQ/+jh1S797muBO8XzabjfcUAABA8s6vunQxX1bn50uNG5v86uBBKSlJjpaN1FYb9K16K69RR8k6lkdZlpSfb1Naml0dO9qPn4Z17Cj17GkKqYMHzTXK5nMul+nu16vXSfO5+sjf3DWkC6nx48frlVde0bvvvqtGjRp5xj05HA7FxsbK4XDohhtu0KRJk9S0aVMlJCRo4sSJ6tu3r98z9sEHu91Mcb5tmxkLlZJivs0oLDw2a9/YsXzoAAAA/FU2v9qwwXS9c7mOTX8eFyclJ8u2cYOadUrUKo3V+g12rzTsp5+k7t2la645SRpmt0vjxpkZ+/7zH2nfPjNWSjKFldt9bEkb8rlKC+npz21lK+cyFi5cqHHjxkkyC/Ledddd+r//+z8VFxdryJAhmj9//gm79pXH9OfH4WsdqfR0U0SdZKpMpj8HAADw4WTrSP0v1/K1jlTXrm6NGpWnvn39zK+Ot47U2WdLkycz9flx+FsbhHQhVVsopE7A7ZZyc01fW4dDSk3165sLCikAAIDjKJtflW0pKpdrlU/D2rd3Kz8/wPzK7TZTna9bZ56fdprUqRMtUSfgb20Q0l37EALsdvNhAwAAQPXwM78qf1ilVpWx282YrC5dKvFinAilKAAAAAAEiEIKAAAAAAJE1z7UrkqOuQIAAABCCYUUao+vWQBLp95k1hgAAADUIRRSqB3Z2dLMmWaKz5QUKT7erKidlWXWU5g2jWIKAAAAdQZ9qlDz3G7TEpWfb1qgEhLMatoJCeZ5fr60ZEklp6IBAAAAah+FFGpebq7pzpeSIpVfZNlmM9vXrzfHAQAAAHUAhRRqntNpxkTFx/veHxdn9judtRsXAAAAUEkUUqh5DoeZWKKgwPf+wkKz3+Go3bgAAACASqKQQs1LTTVjoXbulCzLe59lme3p6eY4AAAAoA6gkELNs9vNFOeJiWaslMsllZSYx5wcs33sWNaTAgAAQJ3B9OeoHRkZZorz0nWkfv7ZdOfr3dsUUUx9DgAAQoHbbSbAcjrNsIPU1PD5svd49xbO91yDKKRQezIypO7d+aACAIDQlJ197EvfoiLzpW9amulZU9e/9D3evZ15prRqVXjecw2jkELtstulTp2CHQUAAIC37Gxp5kyzvmVKipltuKBAysqStm0zPWvqamFxvHv74gvptdekFi2kLl3C655rAU0BCDq3W9q0SVq92jyyLi8AAKhVbrdprcnPN60xCQlSRIR5TEsz25csqZtJyvHurVEj6cgR6dAhM3a9UaPwuedaQosUgiqcW9ABAEAdkZtrkpGUFMlm895ns5nt69eb4+paz5rj3ZvLZYZaNGkiHThg/rtxY7Ovrt9zLaFFCkFT2sqclSU1bSp17Gges7LM9uzsYEcIAADqBafTfKMbH+97f1yc2e901m5c1eF493b4sGmJio42j4cPe++vy/dcSyikEBTh3IIOAADqGIfDdIspKPC9v7DQ7Hc4ajeu6nC8e4uKkiIjpeJi8xgV5b2/Lt9zLaGQQlAE0oIOAABQo1JTzTe5O3dKluW9z7LM9vR0c1xdc7x7S0gwRdIvv5gufWULprp+z7WEQgpBEc4t6AAAoI6x280A7cRE802vy2W6u7lc5nlioln3si4u2XK8ezt4UGrQQGrY0LRIHTwYPvdcS5hsAkFRtpU5IaHiflqTAQBArcrIMNN9l86C9fPPJhnp3dsUFHV5Fqzj3du550pnnHFsHalwuudaQCGFoChtZc7KMo9lu/eVtib37k1rMgAAqEUZGVL37mZsgdNpvtFNTQ2PVpkT3duVV4bnPdcwCinUDLdb+uknaft20++23AeytJV527ZjY6Xi4kxL1M6dtCYDAIAgsdvDd7rv491bON9zDaKQQvXLzjZT7hUUmBV2o6N9Lg4Vzi3oAAAACG8UUqhepYtD7dsn9eljFoc6dMj04du2zVRO5YqpcG1BBwAAQPiikEL1Kbs4VHq66atXOptEWpppdlqyxFRO5br50ZoMAACAuoTv/VF9WBwKAAAA9QSFFKoPi0MBAACgnqCQQvUpuziULywOBQAAgDBBIYXqU7o41M6dZjGoskoXh0pPZ3EoAAAA1HlMNoHqU35xqIYNpZIS00LF4lAAAAAII2S0qF6li0P16iUdPGgmlti/3ywOVW7qcwAAAKCuokUK1S8jQ+rWTVq7Vjp6VGrcmMWhAAAAEFYopFAz7HbplFOkpCQKKAAAAIQdCqn6wu023eycTjNrHi1EAAAAoaUm8zV/z03O6DcKqfogO1tavNhMAFFUZKYgT0szE0MwZgkAACD4ajJf8/fc5IwBoZAKd9nZ0syZUn6+lJJiFsstKJCysszsekwAAQAAEFw1ma/5e25yxoDRThfO3G7zrUJ+vvk2ISFBiogwj2lpZvuSJeY4AAAA1L6azNf8PXdJCTljJVBIhbPcXNM0m5Ii2Wze+2w2s339enMcAAAAal9N5mv+nnv5cnLGSqCQCmdOp+nfGh/ve39cnNnvdNZuXAAAADBqMl/z99y7d5MzVgKFVDhzOMwgwYIC3/sLC81+h6N24wIAAIBRk/mav+du2ZKcsRIopMJZaqrp17pzp2RZ3vssy2xPTzfHAQAAoPbVZL7m77kHDiRnrAQKqXBmt5vpKhMTTb9Xl8sMJnS5zPPERGnsWNYGAAAACJaazNf8PXdkJDljJdgsq3zZWf+4XC45HA45nU4lJCQEO5zq52tNgPR084GooWks3W638vLylJSUJPvxPnQsDAcAAGD4ka/5lV9V8twBHRfm/K0NKKRUDwopqdaLkZN+0FkYDgAAwNtJ8rVKF1J+nDvg48KYv7UBC/LWF3a71KlTsKMwWBgOAACgoprM1/w9dyjljCGufpWXCD4WhgMAAEAYoJBC7WJhOAAAAIQBCinULhaGAwAAQBigkELtYmE4AAAAhAEKKdQuFoYDAABAGKCQQu1iYTgAAACEAbJQ1L6MDDN1ea9e0v79ZsKI/ful3r29pzT39zgAAACglrGOFIKz8FpGhtS9+8mv6+9xAAAAQC2ikKrvsrPNek05OWYWvJgYMzYpM9O/Fp+qFGEsDAcAAIA6ikKqPsvOlmbONIvbpqSYqcYLCqSsLGnbtpN3nztREda9e+3dBwAAAFDL6B9VX7ndpgjKzzfFT0KCFBFhHtPSzPYlS8xxvpQWYVlZUtOmUseO5jEry2z/7rtavR0AAACgNtEiVV/l5pqWpJQUyWbz3mezme3r15vjSrvVlXbj++UXaf58ae9eMwV56etLi7CcHOlvf5MmT/Z97WCMyQIAAKhNbre0aZO0bp1ZuqVhQ/Olc5MmJ899yr5Wkk47zbxmyxYpL8/kUB07Vi1/Kp+PtW8v/fDDyfMz8jgPCqn6yuk03fHi433vj4uTfv7ZHCd5d+Pbv1/aulVq1kzat89MRV6qbBG2a5dZWLesqo7JAgAACHXZ2dLjj0srVpgvoA8fNjlSo0ZShw7SmWceP/cp+1qXy2yLiTFfWDdrJnXubHKsLl0qnz+Vz8cOHz6Wl0VFHT8/I4/zQiFVXzkc5o+/oMB8MMsrLDT7HY6KY6liYqQffzRF1po1Us+e3sVUXJz5gBcWep+zqmOyAAAAQl12tjRpkvSf/5jWm6NHzXa32+ROP/xgihBfuU/Z19rtpgWrqMjkTvn55vW9eh0bTlGZ/Kl8Pvbrr9K335qiLSHBLDMTG1vx/ORxFdTPdjiYZti0NGnnTtPcXJZlme3p6aaZt/xYqthYU0zFx5sP3+bN3ucoLcLi4o5tq+qYLAAAgFDndkuLFplueQ0amG2WZXKn2FhTHB06JJWUmCESZXOf8q9NTDStQ7/+al4XGWkKl127TMtWZfKn8vlYo0amu2BJiZScbIq+LVsqnr+khDzOBwqp+spuN82wiYmmedblMh8Sl8s8T0yUxo4135qUH0uVkCA1bmwKpvh402Rd2gWwbBHWqtWx6wUyJgsAAKAuys01LTRutymCjhw5VlDZbOa/LcsUUQ6Hd+5T9rUNG5pthw+bnwYNjr3W5TI/lcmfyudjTqd04IDJ52w283jgQMXzL19OHucDhVR9VDqAsaTEFFM9e5pxT7m55rF372PNs77GUtlsZgKK0q6BxcXm25KyRdg113gPPPRnTFZR0bGCDAAAoK5xOo8NbbDbTc5VtvCw200xVFJiWnTK5j5Op8mrLMu0Pknm9W63eV1pXnX0qCmupMDzp/L52OHDJpbS60VGmuflz797N3mcD4yRqm98DRLs0kW69VbplFMqzr5yvLFUiYmmAFu3zkw4sWuX6a/bu7dpyere3cwqUyqQMVkAAAB1kcNxbGhDaQFkWceKqdLCKjLSFERlcx+H41jLUEmJaYEqLaDKdpmLiDCtXVLg+VP5fCwq6ljxFBV1rKgqf/6WLcnjfKBFqj453tpPa9aY4ioy0rQ0lW1JOtFYqmbNpObNpYsvlubNk559VnrySd8DDf0dk5WaWv33DQAAUBtSU81kEHb7sS55R46YfZZl/ttmM/mT0+md+5R97aFDZltU1LEugqWvTUgwP5XJn8rnYw6HGa5R2hJWUGCelz//wIHkcT5QSNUXlZ3s4WRjqZo3l26/3UzjWb4IC+Q8pWOy6uk6BAAAIAzY7dK4cSYnKi2gbDYzBOLXX4+Nf4qMNDlU2dyn/Gvz883widhY87qSEtNi1aqVdPBg5fKn8vnYwYNmOvbISLPsTUSEeV7+/JGR5HE+2CyrfFlZ/7hcLjkcDjmdTiX4aq4MB5s2SRMnmhYoX/focpnxUc8+e2wB3rJ8dQlMTzcfGh8tUG63W3l5eUpKSpK97Ieq/Hmio80sMYMGSWec4XsxOImF3wAAQN1xvHWkEhJMrvOb3xw3hzrROlLuZs2U17mzknbtkj0t7fjn8Ce+k60j5SvPCzAfrKv8rQ0opFRPCqnVq6XJk013voiIivtLSkyx8sQTUp8+vs8RwErWxy2kyp7n3/+WPv7YjK8qLvb9IW7WzLxm3z4WfgMAAHVH6eRe69aZ7m8NG5ovtJs0OfmXwmVfK0mnnSalpsq9Zcux/Kpjx6p9sVw+r/P1Zbav8weQD9ZV/tYGTDZRX1THZA92u+/WqkDZ7SaON9888WJwRUXSsmXmNX36mCKwni/8BgAA6gi73Uzo1aVL9b22Y0eTqyUlVb148ZXX+ZPnVVc+GAbCq3zE8YXSZA/HWwzu6FHTza+kxDz/6adj6yb89JP54Nbzhd8AAAAQGiik6otQmuyh/GJwLlfFxeD27jU/8fGmKbx0cTipXi/8BgAAgNBA1776JCPDdIcrHST488+mO1/p2k+11U3ueIvBlT53u81+m800X9tsxxb+dTrN8RERpjtg6cJv9aC/LgAAAEIHhVR9k5FhFssNZtFxvMXgCgrMT1GRKaxsNrOSdkKCKZT++19TPJWUmPNERZkuf5GRFWeQYUIKAAAA1CAKqfoo2IMES8drZWUdW9MqOtqM07LZzJituDjz+OuvZiKMqCizr2FDs2//fnMfTz99bAXwlBTTqsWEFAAAAKhh9H2q70qn11y92jzWxuQNvsZrud2mcDp61HTbK11V22Yz20pKTEuWZZmWtIYNzbpTmzebuLt08X+RYQAAAKCKaJGqz3wtqlZbXeLKjtf69luzWF2jRmZfRIQZDxUZKbVoYSadcLtNK1RMjJnys2NHs7+0SDp40Hvq9vITUjBNJwAAAKoRhVR9lZ0tzZx5bB2nYHSJKx2v9dZb0iOPmKIpJsZMJhEdbX6Ki6VvvjGtUqedZoqo0gko8vKOTeV++HDF88fFmQk1mJACAAAA1YxCqj4qv46TzWa2l3aJy8kxXeK6d6/5QsNuNy1L+/ebiSUk87xxY9OKFBVljrHbTaFVttWpdNxU6X+XV3aR4WC2vgEAACDs8HV8fVR+HaeyanuNpuxsadEiMwaqpOTYxBP5+dKaNaalqbSQKu36Vyoh4fj73G4zfqppU2ndOmnGDNPa1rSp6RbYtKl5PnOmiQEAAAAIAC1S9VH5dZzKK98lrqaUtozt2yedeaYpaJxOE1dCgmml+ve/TeFjs0kbNpgiLy7OtDbt3Hls7FPZfT/9JH3/vXTkiLnGnXeaIu3MM815peC0vgEAACBsUEjVR+XXcSqvbJe4mlS2ZSwhQerZ08zAd+CAiSEy0vzccYfUps3xFxKWju3btEnasUNq0MCcr1EjadcuU0hlZ5ttiYnmNUxIAQAAgEoKm0LqT3/6kx5//HHt3r1b3bt317PPPqszzjgj2GGFpvLrOJXt3mdZpqWnd29zXE0q3zKWmCg1a2a2Hz5sZu/Ly5NOOeXkCwl3726KqBkzzP1kZJh9e/ea/U2bmtdt3myuUXrPtdX6BgAAgLASFn2Z/v73v2vSpEmaPn261qxZo+7du2vIkCHKy8sLdmihydc6TiUl5jEnx2wfO7bmu7qVbRkrZbOZiSaSkkyrUmzssZax0oWE+/Qxj2XjKx0rtX+/6QpYui8qyrRqHT1qCrZffvEummqr9Q0AAABhJSwKqblz5+qmm27Sddddp/T0dD333HOKi4vTiy++GOzQQlfpOk69epniIzfXPPbuXTtTn0vHWsZ27jw2jXmp0pax9HT/W8Z8jf1KSDCFWUGBKahKSo5NlV6ZawAAAAAKg659hw8fVlZWlqZMmeLZZrfbNWjQIK1cudLna4qLi1VcXOx57vxfC8WBAwfkLl3gtT5o184UTVu3Husu166dac05cKBKp3a73XK5XIqKipL9RC1bI0dKW7aYmfVOOcW0QP36q5kwolkz6be/NS1l/rDbTSuW0+k9i1/79tKhQ6ZQjIw0BdT+/ZW7BgAAQJD4nV+hSlz/ywut8l/0l1PnC6n8/HwdPXpULVq08NreokULbdiwwedrZs+erRkzZlTY3qZNmxqJEVWwdGn1n/Pdd2v+GgAAAKjTDh48KMcJhn/U+UKqMqZMmaJJkyZ5nrvdbu3fv1/NmjWTrfy6SqgUl8ul1q1ba8eOHUrwNTMgAAAAAkJ+VTssy9LBgweVnJx8wuPqfCGVmJioiIgI7dmzx2v7nj171LJlS5+viY6OVnR0tNe2xo0b11SI9VpCQgIfdAAAgGpEflXzTtQSVarOd66MiopSr169tHz5cs82t9ut5cuXq2/fvkGMDAAAAEC4qvMtUpI0adIkZWZmqnfv3jrjjDP09NNPq6CgQNddd12wQwMAAAAQhsKikLryyiu1d+9eTZs2Tbt371aPHj300UcfVZiAArUnOjpa06dPr9CFEgAAAJVDfhVabNbJ5vUDAAAAAHip82OkAAAAAKC2UUgBAAAAQIAopAAAAAAgQBRSCFlt27bV008/7Xlus9n0zjvvBC0eAAAAoBSFFOqMXbt2aejQocEOAwAAoNqde+65uuOOO4IdhiSpqKhI48aNU7du3RQZGakRI0YEO6SQRCFVjxw+fDjYIVRJy5Ytme4TAACghh09elSxsbG67bbbNGjQoGCHE7IopOqwc889VxMmTNCECRPkcDiUmJioqVOnqnRG+7Zt22rWrFkaO3asEhISdPPNN0uS3nzzTXXt2lXR0dFq27atnnzySb+v2bZtWz388MMaO3asGjZsqDZt2ui9997T3r17demll6phw4Y6/fTT9e2333q97uuvv1b//v0VGxur1q1b67bbblNBQYFnf15enoYPH67Y2Fi1a9dOL7/8coVrl+/ad++996pTp06Ki4tT+/btNXXqVB05csSz/6GHHlKPHj300ksvqW3btnI4HBo9erQOHjzo9/0CAACUd+6552rixIm644471KRJE7Vo0UJ//vOfVVBQoOuuu06NGjVSamqqPvzwQ89r1q1bp6FDh6phw4Zq0aKFrr32WuXn50uSxo0bpy+++EJ//OMfZbPZZLPZ9OOPP+ro0aO64YYb1K5dO8XGxqpz58764x//WCGeF1980ZPbtWrVShMmTDhh/L/88ovGjh2rJk2aKC4uTkOHDtXmzZs9++Pj47VgwQLddNNNatmyZTW9a+GHQqqOW7x4sSIjI/Xvf/9bf/zjHzV37lz95S9/8ex/4okn1L17d2VnZ2vq1KnKysrSFVdcodGjR+v777/XQw89pKlTp2rRokV+X/Opp55Sv379lJ2drYsuukjXXnutxo4dq2uuuUZr1qxRhw4dNHbsWE9Bt2XLFl144YUaNWqU/vOf/+jvf/+7vv76a68P+bhx47Rjxw599tlneuONNzR//nzl5eWdMI5GjRpp0aJFWr9+vf74xz/qz3/+s5566imvY7Zs2aJ33nlHS5cu1dKlS/XFF1/oD3/4g9/3CgAA4MvixYuVmJiof//735o4caJuvfVWXX755TrrrLO0Zs0aDR48WNdee60KCwt14MABnX/++crIyNC3336rjz76SHv27NEVV1whSfrjH/+ovn376qabbtKuXbu0a9cutW7dWm63WykpKXr99de1fv16TZs2Tffff79ee+01TxwLFizQ+PHjdfPNN+v777/Xe++9p9TU1BPGPm7cOH377bd67733tHLlSlmWpWHDhnl9IQ0/WKizBgwYYKWlpVlut9uz7d5777XS0tIsy7KsNm3aWCNGjPB6zdVXX21dcMEFXtvuvvtuKz093a9rtmnTxrrmmms8z3ft2mVJsqZOnerZtnLlSkuStWvXLsuyLOuGG26wbr75Zq/zfPXVV5bdbrd+/fVXa+PGjZYk69///rdnf05OjiXJeuqppzzbJFlvv/32cWN7/PHHrV69enmeT58+3YqLi7NcLpfXvZ555pl+3SsAAIAvAwYMsM4++2zP85KSEis+Pt669tprPdtKc6SVK1das2bNsgYPHux1jh07dliSrI0bN3rOefvtt5/02uPHj7dGjRrleZ6cnGw98MADfse+adMmS5K1YsUKz7b8/HwrNjbWeu211yocn5mZaV166aV+n78+oUWqjvvNb34jm83med63b19t3rxZR48elST17t3b6/icnBz169fPa1u/fv28XnMyp59+uue/W7RoIUnq1q1bhW2lLUpr167VokWL1LBhQ8/PkCFD5Ha7tXXrVuXk5CgyMlK9evXynKNLly5q3LjxCeP4+9//rn79+qlly5Zq2LChHnzwQW3fvt3rmLZt26pRo0ae561atTppSxcAAMDJlM2HIiIi1KxZs+PmQ2vXrtVnn33mlQt16dJFkuk9cyJ/+tOf1KtXLzVv3lwNGzbUCy+84Ml38vLy9PPPP2vgwIE+X/u73/3O65qSPHnXmWee6TmuWbNm6ty5s3JycirxTtRfkcEOADUrPj6+2s/ZoEEDz3+XFnG+trndbknSoUOHdMstt+i2226rcK5TTz1VmzZtCjiGlStXasyYMZoxY4aGDBkih8OhV199tcJ4r7JxlcZWGhcAAEBl+coxjpcPHTp0SMOHD9ecOXMqnKdVq1bHvcarr76qyZMn68knn1Tfvn3VqFEjPf7441q1apUkKTY29oQxzpw5U5MnT/b7nhAYCqk6rvSDVOpf//qXOnbsqIiICJ/Hp6WlacWKFV7bVqxYoU6dOh33NVXVs2dPrV+//rj9dbt06aKSkhJlZWWpT58+kqSNGzfqwIEDxz3nN998ozZt2uiBBx7wbNu2bVu1xg0AAFAdevbsqTfffFNt27ZVZKTv9DsqKqpC76AVK1borLPO0u9//3vPtrItWI0aNVLbtm21fPlynXfeeRXOmZSUpKSkJK9taWlpKikp0apVq3TWWWdJkvbt26eNGzcqPT290vdYH9G1r47bvn27Jk2apI0bN+r//u//9Oyzz+r2228/7vF33XWXli9frlmzZmnTpk1avHix5s2bV6PfVtx777365ptvNGHCBH333XfavHmz3n33Xc9kE507d9aFF16oW265RatWrVJWVpZuvPHGE37L0rFjR23fvl2vvvqqtmzZomeeeUZvv/12jd0DAABAZY0fP1779+/XVVddpdWrV2vLli1atmyZrrvuOk/x1LZtW61atUo//vij8vPz5Xa71bFjR3377bdatmyZNm3apKlTp2r16tVe537ooYf05JNP6plnntHmzZu1Zs0aPfvss8eNpWPHjrr00kt100036euvv9batWt1zTXX6JRTTtGll17qOW79+vX67rvvtH//fjmdTn333Xf67rvvauT9qasopOq4sWPH6tdff9UZZ5yh8ePH6/bbb/dMc+5Lz5499dprr+nVV1/VaaedpmnTpmnmzJkaN25cjcV4+umn64svvtCmTZvUv39/ZWRkaNq0aUpOTvYcs3DhQiUnJ2vAgAEaOXKkbr755grfoJR1ySWX6M4779SECRPUo0cPffPNN5o6dWqN3QMAAEBlJScna8WKFTp69KgGDx6sbt266Y477lDjxo1lt5t0fPLkyYqIiFB6erqaN2+u7du365ZbbtHIkSN15ZVX6swzz9S+ffu8WqckKTMzU08//bTmz5+vrl276uKLL/aaytyXhQsXqlevXrr44ovVt29fWZalDz74wKtr4rBhw5SRkaH3339fn3/+uTIyMpSRkVH9b04dZrOs/81RjTrn3HPPVY8ePfT0008HOxQAAACgXqFFCgAAAAACRCEFj6+++spriszyPwAAAAAMuvbB49dff9VPP/103P0nWyUbAAAAqC8opAAAAAAgQHTtAwAAAIAAUUgBAAAAQIAopAAAAAAgQBRSAAAAABAgCikAAAAACBCFFAAAAAAEiEIKAAAAAAJEIQUAAAAAAfp/yMJsnveYEokAAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ "
" ] @@ -4988,7 +4988,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3762618/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_3873332/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -5114,7 +5114,7 @@ " 3\n", " 4\n", " bot_median\n", - " 2475.479525\n", + " 2477.274734\n", " 97\n", " 93.10\n", " \n", @@ -5471,7 +5471,7 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2475.479525 97 \n", + "3 4 bot_median 2477.274734 97 \n", "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", @@ -5717,17 +5717,17 @@ " \n", " \n", " bot_median\n", - " 2475.5\n", + " 2477.3\n", " 93.1\n", " 26.6\n", - " 57.595415\n", - " 5.969158\n", - " 4.454476\n", + " 58.467357\n", + " 6.059526\n", + " 4.391227\n", " 1.985277\n", - " 38.4\n", - " 14.7\n", - " 0.999988\n", - " 0.000024\n", + " 38.6\n", + " 14.6\n", + " 0.999985\n", + " 0.000030\n", " \n", " \n", " acm_bot\n", @@ -6340,7 +6340,7 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2475.5 93.1 26.6 57.595415 \n", + "bot_median 2477.3 93.1 26.6 58.467357 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", @@ -6389,7 +6389,7 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 5.969158 4.454476 1.985277 38.4 \n", + "bot_median 6.059526 4.391227 1.985277 38.6 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", @@ -6438,7 +6438,7 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 14.7 0.999988 0.000024 \n", + "bot_median 14.6 0.999985 0.000030 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", @@ -6573,18 +6573,18 @@ " NA\n", " \n", " \n", - " bean_bot\n", + " RPM_bot\n", " -0.6\n", - " 4.7\n", + " 7.0\n", " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", + " 0.820675\n", + " 0.310186\n", + " -0.269729\n", + " 2.446912\n", + " 0.7\n", + " -0.8\n", + " 0.398203\n", + " 0.796405\n", " \n", " \n", " jonahsingerbot\n", @@ -6601,6 +6601,20 @@ " 0.007677\n", " \n", " \n", + " bean_bot\n", + " -0.6\n", + " 4.7\n", + " -0.1\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", + " \n", + " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6643,20 +6657,6 @@ " 0.018953\n", " \n", " \n", - " RPM_bot\n", - " -1.3\n", - " 7.0\n", - " -0.2\n", - " 0.826978\n", - " 0.312568\n", - " -0.610596\n", - " 2.446912\n", - " 0.6\n", - " -1.0\n", - " 0.281933\n", - " 0.563865\n", - " \n", - " \n", " SynapseSeer\n", " -1.3\n", " 26.2\n", @@ -6741,32 +6741,18 @@ " 0.011127\n", " \n", " \n", - " metac-o1\n", - " -5.3\n", - " 91.1\n", - " -0.1\n", - " 0.908473\n", - " 0.095182\n", - " -0.611363\n", - " 1.985829\n", - " 0.1\n", - " -0.2\n", - " 0.271249\n", - " 0.542499\n", - " \n", - " \n", " annabot\n", - " -5.9\n", + " -6.2\n", " 29.3\n", " -0.2\n", - " 0.517575\n", - " 0.095618\n", - " -2.112203\n", + " 0.520869\n", + " 0.096226\n", + " -2.211795\n", " 2.044183\n", " -0.0\n", " -0.4\n", - " 0.021811\n", - " 0.043621\n", + " 0.017610\n", + " 0.035221\n", " \n", " \n", " 4Shadower\n", @@ -6784,17 +6770,17 @@ " \n", " \n", " cookics_bot_TEST\n", - " -6.8\n", + " -6.6\n", " 27.4\n", " -0.2\n", - " 0.747290\n", - " 0.142762\n", - " -1.737830\n", + " 0.747093\n", + " 0.142725\n", + " -1.683660\n", " 2.049541\n", - " 0.0\n", + " 0.1\n", " -0.5\n", - " 0.046947\n", - " 0.093894\n", + " 0.052019\n", + " 0.104037\n", " \n", " \n", " jkraybill_bot\n", @@ -6825,18 +6811,32 @@ " 0.084012\n", " \n", " \n", + " metac-o1\n", + " -9.3\n", + " 91.1\n", + " -0.1\n", + " 0.901141\n", + " 0.094413\n", + " -1.081897\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.141093\n", + " 0.282185\n", + " \n", + " \n", " MWG\n", - " -9.6\n", + " -9.8\n", " 28.6\n", " -0.3\n", - " 0.711160\n", - " 0.132979\n", - " -2.535384\n", + " 0.705240\n", + " 0.131872\n", + " -2.589625\n", " 2.046561\n", " -0.1\n", " -0.6\n", - " 0.008595\n", - " 0.017191\n", + " 0.007581\n", + " 0.015163\n", " \n", " \n", " ProfessorSP\n", @@ -6853,6 +6853,20 @@ " 0.023289\n", " \n", " \n", + " GreeneiBot2\n", + " -10.4\n", + " 58.4\n", + " -0.2\n", + " 0.849317\n", + " 0.111186\n", + " -1.601352\n", + " 2.000832\n", + " 0.0\n", + " -0.4\n", + " 0.057397\n", + " 0.114793\n", + " \n", + " \n", " acm_bot\n", " -10.5\n", " 80.2\n", @@ -6867,20 +6881,6 @@ " 0.201592\n", " \n", " \n", - " GreeneiBot2\n", - " -10.6\n", - " 58.4\n", - " -0.2\n", - " 0.849331\n", - " 0.111188\n", - " -1.638794\n", - " 2.000832\n", - " 0.0\n", - " -0.4\n", - " 0.053366\n", - " 0.106731\n", - " \n", - " \n", " ajf-bot\n", " -10.9\n", " 34.2\n", @@ -6909,18 +6909,32 @@ " 0.023810\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -11.7\n", - " 52.1\n", + " metac-perplexity\n", + " -12.3\n", + " 89.1\n", + " -0.1\n", + " 0.992894\n", + " 0.105187\n", + " -1.316799\n", + " 1.986405\n", + " 0.1\n", + " -0.3\n", + " 0.095661\n", + " 0.191321\n", + " \n", + " \n", + " metac-Gemini-Exp-1206\n", + " -12.6\n", + " 76.5\n", " -0.2\n", - " 0.669031\n", - " 0.092689\n", - " -2.432744\n", - " 2.005379\n", - " -0.0\n", + " 1.007464\n", + " 0.115186\n", + " -1.431098\n", + " 1.990822\n", + " 0.1\n", " -0.4\n", - " 0.009262\n", - " 0.018524\n", + " 0.078264\n", + " 0.156528\n", " \n", " \n", " laylaps\n", @@ -6951,32 +6965,18 @@ " 0.006348\n", " \n", " \n", - " metac-perplexity\n", - " -13.6\n", - " 89.1\n", - " -0.2\n", - " 0.953801\n", - " 0.101046\n", - " -1.515249\n", - " 1.986405\n", - " 0.0\n", - " -0.4\n", - " 0.066645\n", - " 0.133289\n", - " \n", - " \n", - " metac-Gemini-Exp-1206\n", - " -13.9\n", - " 76.5\n", + " bot_median\n", + " -14.4\n", + " 92.1\n", " -0.2\n", - " 0.960843\n", - " 0.109855\n", - " -1.650953\n", - " 1.990822\n", + " 0.806477\n", + " 0.084035\n", + " -1.864964\n", + " 1.985550\n", " 0.0\n", - " -0.4\n", - " 0.051451\n", - " 0.102902\n", + " -0.3\n", + " 0.032703\n", + " 0.065406\n", " \n", " \n", " manticAI\n", @@ -6993,6 +6993,20 @@ " 0.011014\n", " \n", " \n", + " metac-deepseek-r1+asknews\n", + " -15.8\n", + " 52.1\n", + " -0.3\n", + " 0.772503\n", + " 0.107024\n", + " -2.827984\n", + " 2.005379\n", + " -0.1\n", + " -0.5\n", + " 0.003337\n", + " 0.006674\n", + " \n", + " \n", " NextWorldLab\n", " -16.9\n", " 80.2\n", @@ -7007,74 +7021,46 @@ " 0.040909\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -17.7\n", - " 91.1\n", - " -0.2\n", - " 0.822269\n", - " 0.086150\n", - " -2.253410\n", - " 1.985829\n", - " -0.0\n", - " -0.4\n", - " 0.013330\n", - " 0.026660\n", - " \n", - " \n", - " bot_median\n", - " -17.9\n", - " 92.1\n", - " -0.2\n", - " 0.829829\n", - " 0.086469\n", - " -2.248076\n", - " 1.985550\n", - " -0.0\n", + " minefrac1\n", + " -19.4\n", + " 51.1\n", " -0.4\n", - " 0.013492\n", - " 0.026984\n", + " 0.878544\n", + " 0.122900\n", + " -3.095343\n", + " 2.006545\n", + " -0.1\n", + " -0.6\n", + " 0.001607\n", + " 0.003215\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -18.2\n", + " -20.5\n", " 90.5\n", " -0.2\n", - " 0.988222\n", - " 0.103880\n", - " -1.930829\n", + " 1.002602\n", + " 0.105391\n", + " -2.144815\n", " 1.986072\n", - " 0.0\n", - " -0.4\n", - " 0.028335\n", - " 0.056670\n", - " \n", - " \n", - " minefrac1\n", - " -18.8\n", - " 51.1\n", + " -0.0\n", " -0.4\n", - " 0.874752\n", - " 0.122370\n", - " -3.013581\n", - " 2.006545\n", - " -0.1\n", - " -0.6\n", - " 0.002021\n", - " 0.004043\n", + " 0.017338\n", + " 0.034677\n", " \n", " \n", - " metac-Llama-3.1\n", - " -21.3\n", - " 89.1\n", + " metac-o1-preview\n", + " -21.8\n", + " 91.1\n", " -0.2\n", - " 0.912804\n", - " 0.096703\n", - " -2.471743\n", - " 1.986405\n", - " -0.0\n", + " 0.778395\n", + " 0.081553\n", + " -2.928718\n", + " 1.985829\n", + " -0.1\n", " -0.4\n", - " 0.007684\n", - " 0.015368\n", + " 0.002155\n", + " 0.004310\n", " \n", " \n", " mmBot\n", @@ -7091,32 +7077,32 @@ " 0.002208\n", " \n", " \n", - " metac-exa\n", - " -22.4\n", - " 89.1\n", - " -0.3\n", - " 0.812802\n", - " 0.086108\n", - " -2.923729\n", - " 1.986405\n", + " metac-claude-3-5-sonnet-latest\n", + " -22.6\n", + " 91.1\n", + " -0.2\n", + " 0.807536\n", + " 0.084606\n", + " -2.930813\n", + " 1.985829\n", " -0.1\n", " -0.4\n", - " 0.002198\n", - " 0.004396\n", + " 0.002142\n", + " 0.004284\n", " \n", " \n", " pgodzinai\n", - " -23.9\n", + " -23.4\n", " 76.4\n", " -0.3\n", - " 0.991479\n", - " 0.113432\n", - " -2.755452\n", + " 0.973824\n", + " 0.111413\n", + " -2.746500\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.003672\n", - " 0.007345\n", + " 0.003765\n", + " 0.007529\n", " \n", " \n", " VeritasAI\n", @@ -7133,60 +7119,74 @@ " 0.000076\n", " \n", " \n", - " metac-grok-2-1212\n", - " -24.5\n", - " 91.1\n", + " metac-exa\n", + " -24.9\n", + " 89.1\n", " -0.3\n", - " 1.013996\n", - " 0.106237\n", - " -2.526844\n", - " 1.985829\n", + " 0.829710\n", + " 0.087900\n", + " -3.180190\n", + " 1.986405\n", " -0.1\n", " -0.5\n", - " 0.006627\n", - " 0.013254\n", + " 0.001016\n", + " 0.002032\n", " \n", " \n", - " metac-gpt-4o\n", - " -26.0\n", + " InstitutPelFutur\n", + " -26.9\n", + " 90.1\n", + " -0.3\n", + " 0.973767\n", + " 0.102587\n", + " -2.908524\n", + " 1.986114\n", + " -0.1\n", + " -0.5\n", + " 0.002292\n", + " 0.004584\n", + " \n", + " \n", + " metac-grok-2-1212\n", + " -28.0\n", " 91.1\n", " -0.3\n", - " 0.851645\n", - " 0.089228\n", - " -3.193010\n", + " 1.005364\n", + " 0.105333\n", + " -2.923031\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000970\n", - " 0.001940\n", + " 0.002191\n", + " 0.004383\n", " \n", " \n", - " metac-o1-preview\n", - " -26.2\n", + " metac-gpt-4o\n", + " -28.0\n", " 91.1\n", " -0.3\n", - " 0.914333\n", - " 0.095796\n", - " -2.997048\n", + " 0.864425\n", + " 0.090567\n", + " -3.393460\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.001761\n", - " 0.003522\n", + " 0.000514\n", + " 0.001027\n", " \n", " \n", - " InstitutPelFutur\n", - " -26.9\n", - " 90.1\n", - " -0.3\n", - " 0.973767\n", - " 0.102587\n", - " -2.908524\n", - " 1.986114\n", + " metac-Llama-3.1\n", + " -28.2\n", + " 89.1\n", + " -0.3\n", + " 0.906064\n", + " 0.095989\n", + " -3.291937\n", + " 1.986405\n", " -0.1\n", " -0.5\n", - " 0.002292\n", - " 0.004584\n", + " 0.000716\n", + " 0.001433\n", " \n", " \n", "\n", @@ -7196,146 +7196,146 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "RPM_bot -0.6 7.0 -0.1 0.820675 0.310186 \n", "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", + "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", - "RPM_bot -1.3 7.0 -0.2 0.826978 0.312568 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", - "metac-o1 -5.3 91.1 -0.1 0.908473 0.095182 \n", - "annabot -5.9 29.3 -0.2 0.517575 0.095618 \n", + "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.8 27.4 -0.2 0.747290 0.142762 \n", + "cookics_bot_TEST -6.6 27.4 -0.2 0.747093 0.142725 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", - "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", + "metac-o1 -9.3 91.1 -0.1 0.901141 0.094413 \n", + "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", + "GreeneiBot2 -10.4 58.4 -0.2 0.849317 0.111186 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", - "GreeneiBot2 -10.6 58.4 -0.2 0.849331 0.111188 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", - "metac-deepseek-r1+asknews -11.7 52.1 -0.2 0.669031 0.092689 \n", + "metac-perplexity -12.3 89.1 -0.1 0.992894 0.105187 \n", + "metac-Gemini-Exp-1206 -12.6 76.5 -0.2 1.007464 0.115186 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", - "metac-perplexity -13.6 89.1 -0.2 0.953801 0.101046 \n", - "metac-Gemini-Exp-1206 -13.9 76.5 -0.2 0.960843 0.109855 \n", + "bot_median -14.4 92.1 -0.2 0.806477 0.084035 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", + "metac-deepseek-r1+asknews -15.8 52.1 -0.3 0.772503 0.107024 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "metac-claude-3-5-sonnet-latest -17.7 91.1 -0.2 0.822269 0.086150 \n", - "bot_median -17.9 92.1 -0.2 0.829829 0.086469 \n", - "metac-claude-3-5-sonnet-20240620 -18.2 90.5 -0.2 0.988222 0.103880 \n", - "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", - "metac-Llama-3.1 -21.3 89.1 -0.2 0.912804 0.096703 \n", + "minefrac1 -19.4 51.1 -0.4 0.878544 0.122900 \n", + "metac-claude-3-5-sonnet-20240620 -20.5 90.5 -0.2 1.002602 0.105391 \n", + "metac-o1-preview -21.8 91.1 -0.2 0.778395 0.081553 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "metac-exa -22.4 89.1 -0.3 0.812802 0.086108 \n", - "pgodzinai -23.9 76.4 -0.3 0.991479 0.113432 \n", + "metac-claude-3-5-sonnet-latest -22.6 91.1 -0.2 0.807536 0.084606 \n", + "pgodzinai -23.4 76.4 -0.3 0.973824 0.111413 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-grok-2-1212 -24.5 91.1 -0.3 1.013996 0.106237 \n", - "metac-gpt-4o -26.0 91.1 -0.3 0.851645 0.089228 \n", - "metac-o1-preview -26.2 91.1 -0.3 0.914333 0.095796 \n", + "metac-exa -24.9 89.1 -0.3 0.829710 0.087900 \n", "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", + "metac-grok-2-1212 -28.0 91.1 -0.3 1.005364 0.105333 \n", + "metac-gpt-4o -28.0 91.1 -0.3 0.864425 0.090567 \n", + "metac-Llama-3.1 -28.2 89.1 -0.3 0.906064 0.095989 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "bean_bot -4.265106 2.784843 -0.0 \n", + "RPM_bot -0.269729 2.446912 0.7 \n", "jonahsingerbot -5.273630 2.784843 -0.1 \n", + "bean_bot -4.265106 2.784843 -0.0 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", - "RPM_bot -0.610596 2.446912 0.6 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", "CatrachoCaster -1.365532 2.088777 0.1 \n", "krm-bot -3.229846 2.264709 -0.2 \n", - "metac-o1 -0.611363 1.985829 0.1 \n", - "annabot -2.112203 2.044183 -0.0 \n", + "annabot -2.211795 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.737830 2.049541 0.0 \n", + "cookics_bot_TEST -1.683660 2.049541 0.1 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", - "MWG -2.535384 2.046561 -0.1 \n", + "metac-o1 -1.081897 1.985829 0.1 \n", + "MWG -2.589625 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", + "GreeneiBot2 -1.601352 2.000832 0.0 \n", "acm_bot -1.287717 1.989344 0.1 \n", - "GreeneiBot2 -1.638794 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", - "metac-deepseek-r1+asknews -2.432744 2.005379 -0.0 \n", + "metac-perplexity -1.316799 1.986405 0.1 \n", + "metac-Gemini-Exp-1206 -1.431098 1.990822 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", - "metac-perplexity -1.515249 1.986405 0.0 \n", - "metac-Gemini-Exp-1206 -1.650953 1.990822 0.0 \n", + "bot_median -1.864964 1.985550 0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", + "metac-deepseek-r1+asknews -2.827984 2.005379 -0.1 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "metac-claude-3-5-sonnet-latest -2.253410 1.985829 -0.0 \n", - "bot_median -2.248076 1.985550 -0.0 \n", - "metac-claude-3-5-sonnet-20240620 -1.930829 1.986072 0.0 \n", - "minefrac1 -3.013581 2.006545 -0.1 \n", - "metac-Llama-3.1 -2.471743 1.986405 -0.0 \n", + "minefrac1 -3.095343 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -2.144815 1.986072 -0.0 \n", + "metac-o1-preview -2.928718 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "metac-exa -2.923729 1.986405 -0.1 \n", - "pgodzinai -2.755452 1.990849 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.930813 1.985829 -0.1 \n", + "pgodzinai -2.746500 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-grok-2-1212 -2.526844 1.985829 -0.1 \n", - "metac-gpt-4o -3.193010 1.985829 -0.1 \n", - "metac-o1-preview -2.997048 1.985829 -0.1 \n", + "metac-exa -3.180190 1.986405 -0.1 \n", "InstitutPelFutur -2.908524 1.986114 -0.1 \n", + "metac-grok-2-1212 -2.923031 1.985829 -0.1 \n", + "metac-gpt-4o -3.393460 1.985829 -0.1 \n", + "metac-Llama-3.1 -3.291937 1.986405 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "bean_bot -0.2 0.007674 0.015349 \n", + "RPM_bot -0.8 0.398203 0.796405 \n", "jonahsingerbot -0.2 0.003839 0.007677 \n", + "bean_bot -0.2 0.007674 0.015349 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", - "RPM_bot -1.0 0.281933 0.563865 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", "CatrachoCaster -0.4 0.094144 0.188288 \n", "krm-bot -0.9 0.005563 0.011127 \n", - "metac-o1 -0.2 0.271249 0.542499 \n", - "annabot -0.4 0.021811 0.043621 \n", + "annabot -0.4 0.017610 0.035221 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.046947 0.093894 \n", + "cookics_bot_TEST -0.5 0.052019 0.104037 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", - "MWG -0.6 0.008595 0.017191 \n", + "metac-o1 -0.3 0.141093 0.282185 \n", + "MWG -0.6 0.007581 0.015163 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", + "GreeneiBot2 -0.4 0.057397 0.114793 \n", "acm_bot -0.3 0.100796 0.201592 \n", - "GreeneiBot2 -0.4 0.053366 0.106731 \n", "ajf-bot -0.7 0.047145 0.094289 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", - "metac-deepseek-r1+asknews -0.4 0.009262 0.018524 \n", + "metac-perplexity -0.3 0.095661 0.191321 \n", + "metac-Gemini-Exp-1206 -0.4 0.078264 0.156528 \n", "laylaps -0.4 0.008744 0.017488 \n", "wunderplumb -0.9 0.003174 0.006348 \n", - "metac-perplexity -0.4 0.066645 0.133289 \n", - "metac-Gemini-Exp-1206 -0.4 0.051451 0.102902 \n", + "bot_median -0.3 0.032703 0.065406 \n", "manticAI -0.4 0.005507 0.011014 \n", + "metac-deepseek-r1+asknews -0.5 0.003337 0.006674 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.013330 0.026660 \n", - "bot_median -0.4 0.013492 0.026984 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.028335 0.056670 \n", - "minefrac1 -0.6 0.002021 0.004043 \n", - "metac-Llama-3.1 -0.4 0.007684 0.015368 \n", + "minefrac1 -0.6 0.001607 0.003215 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.017338 0.034677 \n", + "metac-o1-preview -0.4 0.002155 0.004310 \n", "mmBot -0.4 0.001104 0.002208 \n", - "metac-exa -0.4 0.002198 0.004396 \n", - "pgodzinai -0.5 0.003672 0.007345 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.002142 0.004284 \n", + "pgodzinai -0.5 0.003765 0.007529 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-grok-2-1212 -0.5 0.006627 0.013254 \n", - "metac-gpt-4o -0.5 0.000970 0.001940 \n", - "metac-o1-preview -0.5 0.001761 0.003522 \n", - "InstitutPelFutur -0.5 0.002292 0.004584 " + "metac-exa -0.5 0.001016 0.002032 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 \n", + "metac-grok-2-1212 -0.5 0.002191 0.004383 \n", + "metac-gpt-4o -0.5 0.000514 0.001027 \n", + "metac-Llama-3.1 -0.5 0.000716 0.001433 " ] }, "execution_count": 42, @@ -9087,363 +9087,363 @@ " \n", " \n", " metac-o1\n", - " 6.0\n", - " 7.2\n", - " 9.5\n", + " 6.1\n", + " 7.4\n", + " 9.7\n", " 11.8\n", - " 12.8\n", + " 13.2\n", " \n", " \n", " metac-o1-preview\n", - " 3.8\n", - " 5.2\n", - " 8.2\n", - " 11.1\n", - " 12.6\n", + " 3.9\n", + " 5.4\n", + " 8.3\n", + " 11.4\n", + " 12.9\n", " \n", " \n", " manticAI\n", - " 0.5\n", - " 2.2\n", - " 5.6\n", - " 8.9\n", - " 10.5\n", + " 0.3\n", + " 2.0\n", + " 5.4\n", + " 8.8\n", + " 10.6\n", " \n", " \n", " metac-Gemini-Exp-1206\n", " 0.7\n", - " 2.1\n", - " 4.8\n", - " 7.5\n", - " 8.9\n", + " 2.2\n", + " 5.0\n", + " 7.8\n", + " 9.2\n", " \n", " \n", " acm_bot\n", - " 0.1\n", - " 1.8\n", - " 4.6\n", - " 7.6\n", - " 8.9\n", + " 0.6\n", + " 1.9\n", + " 4.7\n", + " 7.5\n", + " 8.7\n", " \n", " \n", " metac-perplexity\n", - " -1.5\n", - " 0.5\n", - " 4.2\n", - " 7.7\n", - " 9.3\n", + " -1.9\n", + " 0.3\n", + " 4.3\n", + " 7.9\n", + " 9.8\n", " \n", " \n", " GreeneiBot2\n", - " -1.2\n", + " -1.4\n", " 0.7\n", - " 4.1\n", - " 7.4\n", - " 9.7\n", + " 3.9\n", + " 7.0\n", + " 8.6\n", " \n", " \n", " twsummerbot\n", - " 0.3\n", - " 1.5\n", - " 3.8\n", - " 6.1\n", + " 0.1\n", + " 1.4\n", + " 3.9\n", + " 6.3\n", " 7.5\n", " \n", " \n", - " pgodzinai\n", - " -2.9\n", - " -1.0\n", - " 3.1\n", - " 7.2\n", - " 9.4\n", - " \n", - " \n", " cookics_bot_TEST\n", " -0.0\n", " 1.1\n", - " 3.0\n", + " 3.1\n", " 5.0\n", - " 6.1\n", + " 5.8\n", + " \n", + " \n", + " pgodzinai\n", + " -3.4\n", + " -1.1\n", + " 3.1\n", + " 7.3\n", + " 9.5\n", " \n", " \n", " CumulativeBot\n", - " -0.1\n", - " 0.8\n", + " 0.1\n", + " 0.9\n", " 2.7\n", " 4.5\n", - " 5.4\n", + " 5.3\n", " \n", " \n", " SynapseSeer\n", - " 0.4\n", - " 1.2\n", + " 0.1\n", + " 0.9\n", " 2.5\n", - " 4.0\n", + " 4.1\n", " 4.8\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.3\n", - " -0.1\n", - " 2.5\n", - " 4.9\n", - " 6.3\n", + " -1.6\n", + " -0.2\n", + " 2.4\n", + " 5.0\n", + " 6.2\n", + " \n", + " \n", + " jkraybill_bot\n", + " -3.9\n", + " -1.7\n", + " 1.9\n", + " 5.0\n", + " 7.0\n", " \n", " \n", " metac-exa\n", - " -5.0\n", + " -4.8\n", " -2.6\n", - " 2.0\n", + " 1.5\n", " 5.8\n", - " 7.8\n", - " \n", - " \n", - " jkraybill_bot\n", - " -4.3\n", - " -1.7\n", - " 1.7\n", - " 4.9\n", - " 6.6\n", + " 7.6\n", " \n", " \n", " metac-deepseek-r1+asknews\n", - " -2.0\n", + " -1.8\n", " -0.8\n", " 1.3\n", - " 3.3\n", + " 3.5\n", " 4.5\n", " \n", " \n", " MWG\n", " -1.5\n", " -0.7\n", - " 0.8\n", + " 0.7\n", " 2.2\n", - " 2.8\n", + " 3.0\n", + " \n", + " \n", + " pianobot\n", + " -1.2\n", + " -0.8\n", + " 0.0\n", + " 0.7\n", + " 1.1\n", " \n", " \n", " andrewsiah\n", " -0.9\n", - " -0.6\n", - " 0.0\n", + " -0.5\n", + " -0.0\n", " 0.6\n", - " 0.9\n", + " 1.0\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", - " pianobot\n", - " -1.3\n", - " -0.9\n", - " -0.0\n", - " 0.7\n", - " 1.1\n", - " \n", - " \n", " cobyj-bot\n", - " -1.3\n", + " -1.4\n", " -0.9\n", " -0.1\n", " 0.8\n", - " 1.4\n", + " 1.3\n", " \n", " \n", " annabot\n", - " -3.9\n", + " -3.4\n", " -2.5\n", " -0.4\n", - " 1.3\n", - " 2.0\n", + " 1.2\n", + " 2.1\n", " \n", " \n", " KevinTestBot\n", - " -4.0\n", - " -2.7\n", + " -3.9\n", + " -2.8\n", " -0.5\n", " 1.6\n", - " 2.7\n", + " 2.6\n", " \n", " \n", " bean_bot\n", - " -3.3\n", + " -3.2\n", " -2.2\n", " -0.5\n", - " 0.9\n", - " 1.7\n", + " 1.0\n", + " 1.9\n", " \n", " \n", " CatrachoCaster\n", " -2.3\n", " -1.8\n", - " -0.7\n", + " -0.8\n", " 0.2\n", - " 0.6\n", + " 0.8\n", " \n", " \n", " jonahsingerbot\n", - " -2.9\n", + " -3.0\n", " -2.2\n", " -0.9\n", - " 0.4\n", - " 0.9\n", + " 0.3\n", + " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.6\n", + " -3.5\n", " -2.6\n", " -0.9\n", - " 0.7\n", - " 1.7\n", + " 0.8\n", + " 1.6\n", " \n", " \n", " ProfessorSP\n", - " -4.2\n", - " -3.2\n", - " -1.1\n", + " -4.4\n", + " -3.3\n", + " -1.0\n", " 1.0\n", - " 2.1\n", + " 2.0\n", " \n", " \n", " mmBot\n", - " -7.0\n", - " -5.2\n", - " -1.2\n", - " 2.3\n", - " 4.4\n", + " -7.3\n", + " -5.5\n", + " -1.5\n", + " 2.4\n", + " 4.2\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.6\n", - " -5.0\n", + " -6.3\n", + " -4.7\n", " -1.5\n", - " 1.7\n", + " 2.0\n", " 3.7\n", " \n", " \n", " 4Shadower\n", - " -4.6\n", - " -3.6\n", - " -1.7\n", + " -4.9\n", + " -3.7\n", + " -1.6\n", " 0.2\n", " 1.2\n", " \n", " \n", " swingswish\n", - " -5.3\n", + " -5.4\n", + " -4.2\n", + " -2.0\n", + " -0.1\n", + " 0.7\n", + " \n", + " \n", + " RPM_bot\n", + " -4.9\n", " -3.9\n", - " -1.9\n", + " -2.1\n", + " -0.8\n", " -0.2\n", - " 0.6\n", + " \n", + " \n", + " metac-claude-3-5-sonnet-20240620\n", + " -6.7\n", + " -5.0\n", + " -2.2\n", + " 0.8\n", + " 2.5\n", " \n", " \n", " InstitutPelFutur\n", " -8.7\n", " -6.6\n", - " -2.1\n", - " 1.7\n", - " 4.0\n", - " \n", - " \n", - " RPM_bot\n", - " -4.6\n", - " -3.7\n", - " -2.1\n", - " -0.7\n", - " -0.0\n", + " -2.5\n", + " 1.6\n", + " 3.3\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -6.6\n", - " -5.0\n", - " -2.2\n", - " 0.7\n", - " 2.4\n", + " metac-Llama-3.1\n", + " -6.7\n", + " -5.3\n", + " -2.6\n", + " 0.3\n", + " 1.7\n", " \n", " \n", " wunderplumb\n", - " -6.4\n", + " -6.2\n", " -5.0\n", " -2.6\n", - " -0.4\n", - " 0.8\n", - " \n", - " \n", - " metac-Llama-3.1\n", - " -6.9\n", - " -5.5\n", - " -2.8\n", - " -0.0\n", - " 1.7\n", + " -0.2\n", + " 1.3\n", " \n", " \n", " NextWorldLab\n", - " -8.8\n", - " -6.8\n", - " -3.6\n", - " -0.4\n", - " 1.8\n", + " -8.3\n", + " -6.7\n", + " -3.7\n", + " -0.6\n", + " 0.9\n", " \n", " \n", - " laylaps\n", - " -9.6\n", - " -7.8\n", - " -3.8\n", - " -0.2\n", - " 1.4\n", + " Bot_Pepa\n", + " -6.9\n", + " -5.7\n", + " -3.9\n", + " -2.0\n", + " -1.1\n", " \n", " \n", - " Bot_Pepa\n", - " -7.1\n", - " -6.0\n", + " laylaps\n", + " -10.1\n", + " -8.1\n", " -3.9\n", - " -2.1\n", - " -1.2\n", + " -0.5\n", + " 1.3\n", " \n", " \n", " VeritasAI\n", - " -7.5\n", + " -7.8\n", " -6.5\n", - " -4.3\n", - " -1.9\n", - " -0.8\n", + " -4.2\n", + " -1.8\n", + " -0.5\n", " \n", " \n", " minefrac1\n", - " -7.6\n", - " -6.7\n", + " -8.0\n", + " -6.8\n", " -4.6\n", " -2.5\n", - " -1.6\n", + " -1.5\n", " \n", " \n", " Grizeu_Bot\n", - " -9.2\n", - " -7.9\n", - " -5.0\n", - " -2.5\n", - " -1.2\n", + " -9.4\n", + " -7.7\n", + " -4.9\n", + " -2.4\n", + " -1.1\n", " \n", " \n", " metac-gpt-4o\n", - " -10.7\n", + " -10.6\n", " -9.0\n", - " -6.0\n", - " -3.2\n", - " -1.8\n", + " -5.9\n", + " -2.9\n", + " -1.3\n", " \n", " \n", " ajf-bot\n", - " -15.7\n", - " -12.9\n", - " -8.5\n", + " -15.4\n", + " -12.8\n", + " -8.3\n", " -4.2\n", - " -2.0\n", + " -2.1\n", " \n", " \n", "\n", @@ -9451,51 +9451,51 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.0 7.2 9.5 11.8 12.8\n", - "metac-o1-preview 3.8 5.2 8.2 11.1 12.6\n", - "manticAI 0.5 2.2 5.6 8.9 10.5\n", - "metac-Gemini-Exp-1206 0.7 2.1 4.8 7.5 8.9\n", - "acm_bot 0.1 1.8 4.6 7.6 8.9\n", - "metac-perplexity -1.5 0.5 4.2 7.7 9.3\n", - "GreeneiBot2 -1.2 0.7 4.1 7.4 9.7\n", - "twsummerbot 0.3 1.5 3.8 6.1 7.5\n", - "pgodzinai -2.9 -1.0 3.1 7.2 9.4\n", - "cookics_bot_TEST -0.0 1.1 3.0 5.0 6.1\n", - "CumulativeBot -0.1 0.8 2.7 4.5 5.4\n", - "SynapseSeer 0.4 1.2 2.5 4.0 4.8\n", - "metac-claude-3-5-sonnet-latest -1.3 -0.1 2.5 4.9 6.3\n", - "metac-exa -5.0 -2.6 2.0 5.8 7.8\n", - "jkraybill_bot -4.3 -1.7 1.7 4.9 6.6\n", - "metac-deepseek-r1+asknews -2.0 -0.8 1.3 3.3 4.5\n", - "MWG -1.5 -0.7 0.8 2.2 2.8\n", - "andrewsiah -0.9 -0.6 0.0 0.6 0.9\n", - "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", - "pianobot -1.3 -0.9 -0.0 0.7 1.1\n", - "cobyj-bot -1.3 -0.9 -0.1 0.8 1.4\n", - "annabot -3.9 -2.5 -0.4 1.3 2.0\n", - "KevinTestBot -4.0 -2.7 -0.5 1.6 2.7\n", - "bean_bot -3.3 -2.2 -0.5 0.9 1.7\n", - "CatrachoCaster -2.3 -1.8 -0.7 0.2 0.6\n", - "jonahsingerbot -2.9 -2.2 -0.9 0.4 0.9\n", - "krm-bot -3.6 -2.6 -0.9 0.7 1.7\n", - "ProfessorSP -4.2 -3.2 -1.1 1.0 2.1\n", - "mmBot -7.0 -5.2 -1.2 2.3 4.4\n", - "metac-grok-2-1212 -6.6 -5.0 -1.5 1.7 3.7\n", - "4Shadower -4.6 -3.6 -1.7 0.2 1.2\n", - "swingswish -5.3 -3.9 -1.9 -0.2 0.6\n", - "InstitutPelFutur -8.7 -6.6 -2.1 1.7 4.0\n", - "RPM_bot -4.6 -3.7 -2.1 -0.7 -0.0\n", - "metac-claude-3-5-sonnet-20240620 -6.6 -5.0 -2.2 0.7 2.4\n", - "wunderplumb -6.4 -5.0 -2.6 -0.4 0.8\n", - "metac-Llama-3.1 -6.9 -5.5 -2.8 -0.0 1.7\n", - "NextWorldLab -8.8 -6.8 -3.6 -0.4 1.8\n", - "laylaps -9.6 -7.8 -3.8 -0.2 1.4\n", - "Bot_Pepa -7.1 -6.0 -3.9 -2.1 -1.2\n", - "VeritasAI -7.5 -6.5 -4.3 -1.9 -0.8\n", - "minefrac1 -7.6 -6.7 -4.6 -2.5 -1.6\n", - "Grizeu_Bot -9.2 -7.9 -5.0 -2.5 -1.2\n", - "metac-gpt-4o -10.7 -9.0 -6.0 -3.2 -1.8\n", - "ajf-bot -15.7 -12.9 -8.5 -4.2 -2.0" + "metac-o1 6.1 7.4 9.7 11.8 13.2\n", + "metac-o1-preview 3.9 5.4 8.3 11.4 12.9\n", + "manticAI 0.3 2.0 5.4 8.8 10.6\n", + "metac-Gemini-Exp-1206 0.7 2.2 5.0 7.8 9.2\n", + "acm_bot 0.6 1.9 4.7 7.5 8.7\n", + "metac-perplexity -1.9 0.3 4.3 7.9 9.8\n", + "GreeneiBot2 -1.4 0.7 3.9 7.0 8.6\n", + "twsummerbot 0.1 1.4 3.9 6.3 7.5\n", + "cookics_bot_TEST -0.0 1.1 3.1 5.0 5.8\n", + "pgodzinai -3.4 -1.1 3.1 7.3 9.5\n", + "CumulativeBot 0.1 0.9 2.7 4.5 5.3\n", + "SynapseSeer 0.1 0.9 2.5 4.1 4.8\n", + "metac-claude-3-5-sonnet-latest -1.6 -0.2 2.4 5.0 6.2\n", + "jkraybill_bot -3.9 -1.7 1.9 5.0 7.0\n", + "metac-exa -4.8 -2.6 1.5 5.8 7.6\n", + "metac-deepseek-r1+asknews -1.8 -0.8 1.3 3.5 4.5\n", + "MWG -1.5 -0.7 0.7 2.2 3.0\n", + "pianobot -1.2 -0.8 0.0 0.7 1.1\n", + "andrewsiah -0.9 -0.5 -0.0 0.6 1.0\n", + "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", + "cobyj-bot -1.4 -0.9 -0.1 0.8 1.3\n", + "annabot -3.4 -2.5 -0.4 1.2 2.1\n", + "KevinTestBot -3.9 -2.8 -0.5 1.6 2.6\n", + "bean_bot -3.2 -2.2 -0.5 1.0 1.9\n", + "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", + "jonahsingerbot -3.0 -2.2 -0.9 0.3 1.0\n", + "krm-bot -3.5 -2.6 -0.9 0.8 1.6\n", + "ProfessorSP -4.4 -3.3 -1.0 1.0 2.0\n", + "mmBot -7.3 -5.5 -1.5 2.4 4.2\n", + "metac-grok-2-1212 -6.3 -4.7 -1.5 2.0 3.7\n", + "4Shadower -4.9 -3.7 -1.6 0.2 1.2\n", + "swingswish -5.4 -4.2 -2.0 -0.1 0.7\n", + "RPM_bot -4.9 -3.9 -2.1 -0.8 -0.2\n", + "metac-claude-3-5-sonnet-20240620 -6.7 -5.0 -2.2 0.8 2.5\n", + "InstitutPelFutur -8.7 -6.6 -2.5 1.6 3.3\n", + "metac-Llama-3.1 -6.7 -5.3 -2.6 0.3 1.7\n", + "wunderplumb -6.2 -5.0 -2.6 -0.2 1.3\n", + "NextWorldLab -8.3 -6.7 -3.7 -0.6 0.9\n", + "Bot_Pepa -6.9 -5.7 -3.9 -2.0 -1.1\n", + "laylaps -10.1 -8.1 -3.9 -0.5 1.3\n", + "VeritasAI -7.8 -6.5 -4.2 -1.8 -0.5\n", + "minefrac1 -8.0 -6.8 -4.6 -2.5 -1.5\n", + "Grizeu_Bot -9.4 -7.7 -4.9 -2.4 -1.1\n", + "metac-gpt-4o -10.6 -9.0 -5.9 -2.9 -1.3\n", + "ajf-bot -15.4 -12.8 -8.3 -4.2 -2.1" ] }, "execution_count": 49, @@ -9590,20 +9590,20 @@ " 0.0\n", " \n", " \n", - " jonahsingerbot\n", - " -0.0\n", - " -0.0\n", - " -0.0\n", + " RPM_bot\n", + " -0.1\n", " -0.0\n", " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " X_bot\n", + " jonahsingerbot\n", + " -0.0\n", + " -0.0\n", " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " bean_bot\n", @@ -9614,8 +9614,8 @@ " -0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", + " X_bot\n", + " -0.0\n", " -0.0\n", " -0.0\n", " 0.0\n", @@ -9686,16 +9686,8 @@ " -0.0\n", " \n", " \n", - " metac-o1\n", - " -0.2\n", - " -0.2\n", - " -0.1\n", - " 0.1\n", - " 0.1\n", - " \n", - " \n", " 4Shadower\n", - " -0.2\n", + " -0.1\n", " -0.1\n", " -0.1\n", " -0.0\n", @@ -9715,11 +9707,11 @@ " -0.1\n", " -0.1\n", " -0.0\n", - " -0.0\n", + " 0.0\n", " \n", " \n", " jkraybill_bot\n", - " -0.1\n", + " -0.2\n", " -0.1\n", " -0.1\n", " -0.0\n", @@ -9738,16 +9730,16 @@ " -0.2\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " -0.0\n", " \n", " \n", - " ProfessorSP\n", - " -0.2\n", + " metac-o1\n", + " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", - " -0.0\n", + " 0.0\n", + " 0.1\n", " \n", " \n", " GreeneiBot2\n", @@ -9758,6 +9750,14 @@ " 0.0\n", " \n", " \n", + " ProfessorSP\n", + " -0.2\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " \n", + " \n", " ajf-bot\n", " -0.3\n", " -0.2\n", @@ -9770,7 +9770,7 @@ " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " 0.0\n", " 0.1\n", " \n", " \n", @@ -9782,12 +9782,12 @@ " -0.0\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -0.2\n", - " -0.2\n", - " -0.1\n", + " metac-perplexity\n", + " -0.3\n", + " -0.3\n", " -0.1\n", - " -0.0\n", + " 0.0\n", + " 0.1\n", " \n", " \n", " laylaps\n", @@ -9798,26 +9798,26 @@ " -0.0\n", " \n", " \n", - " wunderplumb\n", + " metac-Gemini-Exp-1206\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", - " -0.1\n", + " -0.0\n", + " 0.1\n", " \n", " \n", - " metac-perplexity\n", - " -0.3\n", + " wunderplumb\n", " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", " -0.1\n", - " -0.0\n", - " 0.1\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", + " bot_median\n", " -0.3\n", " -0.3\n", - " -0.1\n", + " -0.2\n", " -0.0\n", " 0.0\n", " \n", @@ -9830,15 +9830,15 @@ " -0.0\n", " \n", " \n", - " NextWorldLab\n", + " metac-deepseek-r1+asknews\n", " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " NextWorldLab\n", " -0.3\n", " -0.3\n", " -0.2\n", @@ -9846,36 +9846,28 @@ " -0.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -0.4\n", - " -0.3\n", - " -0.2\n", - " -0.1\n", - " 0.0\n", - " \n", - " \n", - " bot_median\n", + " minefrac1\n", " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", - " minefrac1\n", - " -0.3\n", + " metac-claude-3-5-sonnet-20240620\n", + " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " 0.0\n", " \n", " \n", - " metac-Llama-3.1\n", + " metac-o1-preview\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " \n", " \n", " mmBot\n", @@ -9886,7 +9878,7 @@ " -0.1\n", " \n", " \n", - " metac-exa\n", + " metac-claude-3-5-sonnet-latest\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9895,7 +9887,7 @@ " \n", " \n", " pgodzinai\n", - " -0.5\n", + " -0.4\n", " -0.4\n", " -0.2\n", " -0.1\n", @@ -9905,36 +9897,44 @@ " VeritasAI\n", " -0.4\n", " -0.3\n", + " -0.3\n", " -0.2\n", + " -0.1\n", + " \n", + " \n", + " metac-exa\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-grok-2-1212\n", + " InstitutPelFutur\n", " -0.5\n", " -0.4\n", " -0.3\n", - " -0.1\n", + " -0.2\n", " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", - " -0.4\n", + " metac-grok-2-1212\n", + " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-o1-preview\n", - " -0.4\n", + " metac-gpt-4o\n", + " -0.5\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", + " metac-Llama-3.1\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9949,10 +9949,10 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", @@ -9961,38 +9961,38 @@ "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "metac-o1 -0.2 -0.2 -0.1 0.1 0.1\n", - "4Shadower -0.2 -0.1 -0.1 -0.0 -0.0\n", + "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 -0.0\n", - "jkraybill_bot -0.1 -0.1 -0.1 -0.0 -0.0\n", + "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", + "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", - "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", - "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", + "MWG -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", + "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", - "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", + "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-deepseek-r1+asknews -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-perplexity -0.3 -0.3 -0.1 0.0 0.1\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.2 -0.1 -0.0 0.1\n", "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", - "metac-perplexity -0.3 -0.3 -0.1 -0.0 0.1\n", - "metac-Gemini-Exp-1206 -0.3 -0.3 -0.1 -0.0 0.0\n", + "bot_median -0.3 -0.3 -0.2 -0.0 0.0\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", + "metac-deepseek-r1+asknews -0.3 -0.3 -0.2 -0.1 -0.1\n", "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", - "metac-claude-3-5-sonnet-latest -0.3 -0.3 -0.2 -0.1 -0.0\n", - "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", - "bot_median -0.3 -0.3 -0.2 -0.1 -0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-Llama-3.1 -0.4 -0.3 -0.2 -0.1 -0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", + "metac-o1-preview -0.4 -0.3 -0.2 -0.1 -0.1\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "metac-exa -0.4 -0.3 -0.2 -0.1 -0.1\n", - "pgodzinai -0.5 -0.4 -0.2 -0.1 -0.1\n", - "VeritasAI -0.4 -0.3 -0.2 -0.2 -0.1\n", - "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.1 -0.1\n", - "metac-gpt-4o -0.4 -0.4 -0.3 -0.2 -0.1\n", - "metac-o1-preview -0.4 -0.4 -0.3 -0.2 -0.1\n", - "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1" + "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.1\n", + "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", + "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", + "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.2 -0.1" ] }, "execution_count": 50, @@ -10654,505 +10654,505 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.8]\n", + " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.98]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.98]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.35]\n", " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.65]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.02]\n", " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.05, 0.15]\n", " >>> Collected 2 forecasts: [0.2, 0.7]\n", - " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.85, 0.75]\n", + " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.85, 0.7]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.8, 0.4]\n", + " >>> Collected 2 forecasts: [0.7, 0.6]\n", " >>> Collected 2 forecasts: [0.7, 0.4]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.25, 0.2]\n", - " >>> Collected 2 forecasts: [0.25, 0.15]\n", - " >>> Collected 2 forecasts: [0.2, 0.9]\n", + " >>> Collected 2 forecasts: [0.15, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.35]\n", + " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.6, 0.9]\n", + " >>> Collected 2 forecasts: [0.25, 0.5]\n", " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.1, 0.2]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.98, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.35]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.35]\n", - " >>> Collected 2 forecasts: [0.4, 0.3]\n", - " >>> Collected 2 forecasts: [0.15, 0.2]\n", - " >>> Collected 2 forecasts: [0.97, 0.98]\n", - " >>> Collected 2 forecasts: [0.7, 0.4]\n", - " >>> Collected 2 forecasts: [0.3, 0.25]\n", - " >>> Collected 2 forecasts: [0.85, 0.6]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.15, 0.3]\n", + " >>> Collected 2 forecasts: [0.95, 0.95]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.1, 0.4]\n", + " >>> Collected 2 forecasts: [0.25, 0.3]\n", + " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.98, 0.97]\n", + " >>> Collected 2 forecasts: [0.4, 0.4]\n", + " >>> Collected 2 forecasts: [0.35, 0.4]\n", + " >>> Collected 2 forecasts: [0.65, 0.6]\n", + " >>> Collected 2 forecasts: [0.25, 0.02]\n", " >>> Collected 2 forecasts: [0.7, 0.7]\n", - " >>> Collected 2 forecasts: [0.99, 0.99]\n", - " >>> Collected 2 forecasts: [0.97, 0.98]\n", - " >>> Collected 2 forecasts: [0.99, 0.15]\n", + " >>> Collected 2 forecasts: [0.99, 0.7]\n", + " >>> Collected 2 forecasts: [0.95, 0.98]\n", + " >>> Collected 2 forecasts: [0.95, 0.15]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.65]\n", - " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.7]\n", + " >>> Collected 2 forecasts: [0.35, 0.4]\n", " >>> Collected 2 forecasts: [0.8, 0.85]\n", " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.2, 0.3]\n", - " >>> Collected 2 forecasts: [0.65, 0.85]\n", - " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.15, 0.25]\n", - " >>> Collected 2 forecasts: [0.02, 0.05]\n", + " >>> Collected 2 forecasts: [0.3, 0.3]\n", + " >>> Collected 2 forecasts: [0.6, 0.85]\n", " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.2, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.02]\n", + " >>> Collected 2 forecasts: [0.1, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.1]\n", " >>> Collected 2 forecasts: [0.8, 0.9]\n", - " >>> Collected 2 forecasts: [0.95, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.65]\n", - " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.95]\n", + " >>> Collected 2 forecasts: [0.15, 0.4]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", " >>> Collected 2 forecasts: [0.85, 0.8]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.05, 0.15, 0.07]\n", " >>> Collected 3 forecasts: [0.2, 0.7, 0.62]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.82]\n", - " >>> Collected 3 forecasts: [0.85, 0.75, 0.85]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, 0.82]\n", + " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.8, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.2, 0.25]\n", - " >>> Collected 3 forecasts: [0.25, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.108]\n", - " >>> Collected 3 forecasts: [0.1, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.15, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.1, 0.35, 0.25]\n", + " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.6, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.5, 0.108]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, 0.15]\n", - " >>> Collected 3 forecasts: [0.98, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", + " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.03]\n", - " >>> Collected 3 forecasts: [0.1, 0.35, 0.35]\n", - " >>> Collected 3 forecasts: [0.4, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.15, 0.2, 0.115]\n", - " >>> Collected 3 forecasts: [0.97, 0.98, 0.97]\n", - " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.3, 0.25, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.85, 0.6, 0.17]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, 0.12]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.03]\n", + " >>> Collected 3 forecasts: [0.1, 0.4, 0.35]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.15, 0.15, 0.115]\n", + " >>> Collected 3 forecasts: [0.98, 0.97, 0.97]\n", + " >>> Collected 3 forecasts: [0.4, 0.4, 0.285]\n", + " >>> Collected 3 forecasts: [0.35, 0.4, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.65, 0.6, 0.17]\n", + " >>> Collected 3 forecasts: [0.25, 0.02, 0.12]\n", " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", - " >>> Collected 3 forecasts: [0.97, 0.98, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.99, 0.15, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", + " >>> Collected 3 forecasts: [0.95, 0.98, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.95, 0.15, 0.14]\n", " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.65, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.35, 0.6, 0.875]\n", + " >>> Collected 3 forecasts: [0.9, 0.7, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.35, 0.4, 0.875]\n", " >>> Collected 3 forecasts: [0.8, 0.85, 0.84]\n", " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.2, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.65, 0.85, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.25, 0.3925]\n", - " >>> Collected 3 forecasts: [0.02, 0.05, 0.086]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.3, 0.3, 0.16]\n", + " >>> Collected 3 forecasts: [0.6, 0.85, 0.67]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.1, 0.02, 0.086]\n", + " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", + " >>> Collected 3 forecasts: [0.15, 0.1, 0.02]\n", " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.95, 0.9, 0.95]\n", - " >>> Collected 3 forecasts: [0.9, 0.65, nan]\n", - " >>> Collected 3 forecasts: [0.95, 0.9, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", + " >>> Collected 3 forecasts: [0.15, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999]\n", " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.82, 0.794]\n", - " >>> Collected 4 forecasts: [0.85, 0.75, 0.85, 0.884]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.82, 0.794]\n", + " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.8, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.25, 0.2, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.25, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.2, 0.9, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.1, 0.2, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.6, 0.9, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.25, 0.5, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, 0.15, 0.12]\n", - " >>> Collected 4 forecasts: [0.98, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.15, 0.2, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.97, 0.98, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.85, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.65, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.25, 0.02, 0.12, 0.29]\n", " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.95, 0.15, 0.14, 0.2]\n", " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.9, 0.7, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999]\n", " >>> Collected 4 forecasts: [0.8, 0.85, 0.84, 0.86]\n", " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.65, 0.85, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.25, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.02, 0.05, 0.086, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.3, 0.3, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.85, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.02, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.1, 0.02, nan]\n", " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.15, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan]\n", " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.82, 0.794, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.8, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.2, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.9, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.2, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.9, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.5, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05]\n", - " >>> Collected 5 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.85, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.65, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06]\n", " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336]\n", " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", " >>> Collected 5 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999]\n", " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.2, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.65, 0.85, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.15, 0.25, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.02, 0.05, 0.086, nan, 0.12]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.3, 0.3, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.6, 0.85, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.2, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.02, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", + " >>> Collected 5 forecasts: [0.15, 0.1, 0.02, nan, 0.098]\n", " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.15, 0.4, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.744]\n", " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175]\n", " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.8, 0.4, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.7]\n", " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05]\n", " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325]\n", " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05]\n", " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8]\n", " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", - " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", + " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88]\n", + " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18]\n", - " >>> Collected 7 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", - " >>> Collected 7 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3]\n", - " >>> Collected 7 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1]\n", + " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", + " >>> Collected 7 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2]\n", + " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", - " >>> Collected 7 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", - " >>> Collected 7 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", + " >>> Collected 7 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2]\n", " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", + " >>> Collected 7 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2]\n", - " >>> Collected 7 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9]\n", - " >>> Collected 7 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2]\n", - " >>> Collected 7 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", + " >>> Collected 7 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6]\n", + " >>> Collected 7 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", + " >>> Collected 7 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", + " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", + " >>> Collected 7 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", - " >>> Collected 8 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", - " >>> Collected 8 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", + " >>> Collected 8 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", + " >>> Collected 8 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615]\n", - " >>> Collected 8 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", - " >>> Collected 8 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", - " >>> Collected 8 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", + " >>> Collected 8 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223]\n", + " >>> Collected 8 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", + " >>> Collected 8 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", + " >>> Collected 8 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4]\n", - " >>> Collected 9 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.4]\n", - " >>> Collected 9 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", + " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.8]\n", + " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", + " >>> Collected 9 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35]\n", " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35]\n", " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58, 0.2]\n", + " >>> Collected 9 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.95]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.85, nan, 0.75, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.75, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.75, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.8, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.25, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", - " >>> Collected 10 forecasts: [0.2, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.108, 0.264, nan, 0.2, 0.3, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.98, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.1, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4, 0.293]\n", - " >>> Collected 10 forecasts: [0.15, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.97, 0.98, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.85, 0.955]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.4, 0.126]\n", - " >>> Collected 10 forecasts: [0.3, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.97, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35, 0.293]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", + " >>> Collected 10 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.8, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35, 0.408]\n", " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35, nan]\n", " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.2, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.3, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.65, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.15, 0.25, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.02, 0.05, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.75, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58, 0.2, 0.574]\n", + " >>> Collected 10 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65, 0.126]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.95, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } @@ -11234,16 +11234,16 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", " numeric\n", " NaN\n", " 86.82\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", @@ -11252,25 +11252,25 @@ " binary\n", " NaN\n", " no\n", - " 0.1\n", - " 0.085\n", - " 0.1\n", + " 0.05\n", + " 0.063\n", + " 0.11\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.2,0.6,0.2]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", " numeric\n", " NaN\n", " 119.2\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " \n", @@ -11288,27 +11288,27 @@ " binary\n", " NaN\n", " yes\n", - " 0.95\n", + " 0.9\n", " 0.905\n", - " 0.92\n", + " 0.9025\n", " \n", " \n", " 351\n", " binary\n", " NaN\n", " no\n", - " 0.9\n", - " 0.65\n", - " 0.3585\n", + " 0.15\n", + " 0.15\n", + " 0.1085\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", - " 0.95\n", " 0.9\n", - " 0.775\n", + " 0.9\n", + " 0.825\n", " \n", " \n", " 361\n", @@ -11317,7 +11317,7 @@ " no\n", " 0.85\n", " 0.8\n", - " 0.709\n", + " 0.755\n", " \n", " \n", " 364\n", @@ -11348,42 +11348,42 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.014083333333333333,0.6016666666666668,0.178... \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", - "2 0.1 \n", - "3 [0.37,0.49000000000000005,0.13999999999999999] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", + "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", + "2 0.05 \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", ".. ... \n", - "342 0.95 \n", - "351 0.9 \n", - "355 0.95 \n", + "342 0.9 \n", + "351 0.15 \n", + "355 0.9 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", "342 0.905 \n", - "351 0.65 \n", + "351 0.15 \n", "355 0.9 \n", "361 0.8 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "2 0.11 \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", - "342 0.92 \n", - "351 0.3585 \n", - "355 0.775 \n", - "361 0.709 \n", + "342 0.9025 \n", + "351 0.1085 \n", + "355 0.825 \n", + "361 0.755 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -11474,52 +11474,52 @@ " \n", " 0\n", " 1\n", - " 636.97\n", + " 1326.64\n", " \n", " \n", " 1\n", " 2\n", - " 2444.36\n", + " 2492.14\n", " \n", " \n", " 2\n", " 3\n", - " 2419.66\n", + " 2545.30\n", " \n", " \n", " 3\n", " 4\n", - " 2491.70\n", + " 2613.88\n", " \n", " \n", " 4\n", " 5\n", - " 2645.79\n", + " 2743.23\n", " \n", " \n", " 5\n", " 6\n", - " 2517.08\n", + " 2513.69\n", " \n", " \n", " 6\n", " 7\n", - " 2392.69\n", + " 2611.87\n", " \n", " \n", " 7\n", " 8\n", - " 2484.64\n", + " 2685.15\n", " \n", " \n", " 8\n", " 9\n", - " 2381.71\n", + " 2381.69\n", " \n", " \n", " 9\n", " 10\n", - " 2419.31\n", + " 2215.95\n", " \n", " \n", "\n", @@ -11527,16 +11527,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 636.97\n", - "1 2 2444.36\n", - "2 3 2419.66\n", - "3 4 2491.70\n", - "4 5 2645.79\n", - "5 6 2517.08\n", - "6 7 2392.69\n", - "7 8 2484.64\n", - "8 9 2381.71\n", - "9 10 2419.31" + "0 1 1326.64\n", + "1 2 2492.14\n", + "2 3 2545.30\n", + "3 4 2613.88\n", + "4 5 2743.23\n", + "5 6 2513.69\n", + "6 7 2611.87\n", + "7 8 2685.15\n", + "8 9 2381.69\n", + "9 10 2215.95" ] }, "execution_count": 60, @@ -11690,18 +11690,18 @@ " NaN\n", " False\n", " False\n", - " [0.014083333333333333,0.6016666666666668,0.178...\n", + " [0.010416666666666666,0.20833333333333334,0.04...\n", " ...\n", - " [0.014083333333333333, 0.0001, 0.0001, 0.0001,...\n", - " [0.25704166666666667, 0.0001, 0.0001, 0.0001, ...\n", + " [0.010416666666666666, 0.0001, 0.0001, 0.0001,...\n", + " [0.23020833333333335, 0.0001, 0.0001, 0.0001, ...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", - " [0.05, 0.0001, 0.0001, 0.0001, 0.0001]\n", - " [0.05, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", + " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", @@ -11714,18 +11714,18 @@ " 100.0\n", " True\n", " True\n", - " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " ...\n", - " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05...\n", - " [0.05, 0.05079411765, 0.0515882353, 0.05238235...\n", + " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", + " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", - " [0.05, 0.050679136250000006, 0.051358272499999...\n", - " [0.05, 0.050679136250000006, 0.051358272499999...\n", + " [0.05, 0.0506374696, 0.051274939150000004, 0.0...\n", + " [0.05, 0.0506374696, 0.051274939150000004, 0.0...\n", " \n", " \n", " 2\n", @@ -11738,18 +11738,18 @@ " NaN\n", " False\n", " False\n", - " 0.1\n", + " 0.05\n", " ...\n", + " 0.05\n", " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.085\n", - " 0.085\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.07\n", + " 0.063\n", + " 0.063\n", + " 0.07\n", + " 0.11\n", + " 0.11\n", + " 0.15\n", + " 0.15\n", " \n", " \n", " 3\n", @@ -11762,18 +11762,18 @@ " NaN\n", " NaN\n", " NaN\n", - " [0.37,0.49000000000000005,0.13999999999999999]\n", + " [0.2,0.6,0.2]\n", " ...\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", - " [0.0001, 0.545, 0.0001]\n", + " [0.0001, 0.6, 0.0001]\n", + " [0.0001, 0.525, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.5562499999999999, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.50125, 0.0001]\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", - " [0.0001, 0.50125, 0.0001]\n", - " [0.0001, 0.49000000000000005, 0.0001]\n", + " [0.0001, 0.48124999999999996, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.48124999999999996, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", @@ -11786,12 +11786,12 @@ " 400.0\n", " False\n", " False\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " ...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.0029090909, 0.0058181818, 0.0087272727...\n", + " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", + " [0.0, 0.00267857145, 0.00535714285, 0.00803571...\n", + " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", + " [0.0, 0.0021590909, 0.0043181818, 0.0064772727...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0, 0.00183065955, 0.00366131905, 0.00549197...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", @@ -11820,80 +11820,80 @@ "4 NaN 0.0 400.0 False \n", "\n", " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.014083333333333333,0.6016666666666668,0.178... ... \n", - "1 True [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... ... \n", - "2 False 0.1 ... \n", - "3 NaN [0.37,0.49000000000000005,0.13999999999999999] ... \n", - "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", + "0 False [0.010416666666666666,0.20833333333333334,0.04... ... \n", + "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", + "2 False 0.05 ... \n", + "3 NaN [0.2,0.6,0.2] ... \n", + "4 False [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... ... \n", "\n", " median_forecast_1_bots \\\n", - "0 [0.014083333333333333, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "0 [0.010416666666666666, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", + "2 0.05 \n", + "3 [0.0001, 0.6, 0.0001] \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", "\n", " median_forecast_2_bots \\\n", - "0 [0.25704166666666667, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.05079411765, 0.0515882353, 0.05238235... \n", + "0 [0.23020833333333335, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", "2 0.1 \n", - "3 [0.0001, 0.545, 0.0001] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "3 [0.0001, 0.525, 0.0001] \n", + "4 [0.0, 0.00267857145, 0.00535714285, 0.00803571... \n", "\n", " median_forecast_3_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", - "2 0.1 \n", + "2 0.07 \n", "3 [0.0001, 0.5125, 0.0001] \n", - "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", + "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", "\n", " median_forecast_4_bots \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5562499999999999, 0.0001] \n", - "4 [0.0, 0.0029090909, 0.0058181818, 0.0087272727... \n", + "4 [0.0, 0.0021590909, 0.0043181818, 0.0064772727... \n", "\n", " median_forecast_5_bots \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " median_forecast_6_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.50125, 0.0001] \n", + "2 0.07 \n", + "3 [0.0001, 0.48124999999999996, 0.0001] \n", "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", "\n", " median_forecast_7_bots \\\n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "2 0.11 \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_8_bots \\\n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "2 0.11 \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_9_bots \\\n", - "0 [0.05, 0.0001, 0.0001, 0.0001, 0.0001] \n", - "1 [0.05, 0.050679136250000006, 0.051358272499999... \n", - "2 0.1 \n", - "3 [0.0001, 0.50125, 0.0001] \n", + "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", + "2 0.15 \n", + "3 [0.0001, 0.48124999999999996, 0.0001] \n", "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", "\n", " median_forecast_10_bots \n", - "0 [0.05, 0.0001, 0.0001, 0.0001, 0.0001] \n", - "1 [0.05, 0.050679136250000006, 0.051358272499999... \n", - "2 0.1 \n", - "3 [0.0001, 0.49000000000000005, 0.0001] \n", + "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", + "2 0.15 \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", "[5 rows x 29 columns]" @@ -11973,7 +11973,7 @@ " False\n", " 31268\n", " 1.0\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " \n", " \n", @@ -12009,7 +12009,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.063\n", " 0.013\n", " \n", " \n", @@ -12082,9 +12082,9 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", @@ -12171,7 +12171,7 @@ " False\n", " 35381\n", " 1.00\n", - " 0.65\n", + " 0.15\n", " 0.05\n", " \n", " \n", @@ -12256,7 +12256,7 @@ "\n", " question_weight bot_team_median pro_median \n", "342 1.00 0.905 0.95 \n", - "351 1.00 0.65 0.05 \n", + "351 1.00 0.15 0.05 \n", "355 1.00 0.9 0.97 \n", "361 0.85 0.8 0.666 \n", "364 0.85 0.05 0.03 " @@ -12315,14 +12315,14 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -0.1175\n" + "Weighted Total Score: -0.1182\n" ] } ], @@ -12344,7 +12344,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -12415,25 +12415,25 @@ " -11.2\n", " 92.1\n", " -0.1\n", - " 0.671397\n", - " 0.06996\n", - " -1.732732\n", + " 0.640747\n", + " 0.066766\n", + " -1.826475\n", " 1.98555\n", " 0.0\n", " -0.3\n", - " 0.043264\n", - " 0.086527\n", + " 0.035527\n", + " 0.071054\n", " \n", " \n", "\n", "" ], "text/plain": [ - " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", - "head_to_head -11.2 92.1 -0.1 0.671397 0.06996 -1.732732 1.98555 \n", + " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", + "head_to_head -11.2 92.1 -0.1 0.640747 0.066766 -1.826475 1.98555 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "head_to_head 0.0 -0.3 0.043264 0.086527 " + "head_to_head 0.0 -0.3 0.035527 0.071054 " ] }, "execution_count": 68, @@ -12527,12 +12527,12 @@ " -1.1\n", " \n", " \n", - " 87\n", - " How many movies will be new on Netflix's globa...\n", - " [0.0001, 0.0001, 0.335]\n", - " [0.01,0.064,0.926]\n", - " 2 or more\n", - " -1.0\n", + " 245\n", + " Will Nebraska have 1.7 million or more residen...\n", + " 0.9\n", + " 0.7\n", + " no\n", + " -0.9\n", " \n", " \n", "\n", @@ -12544,21 +12544,21 @@ "121 How many movies will be new on Netflix's top 1... \n", "232 How many movies will be new on Netflix's top 1... \n", "247 Will the 500th richest person on Bloomberg's B... \n", - "87 How many movies will be new on Netflix's globa... \n", + "245 Will Nebraska have 1.7 million or more residen... \n", "\n", " bot_team_median \\\n", "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", "121 [0.0001, 0.0001, 0.0001, 0.125] \n", "232 [0.0001, 0.0001, 0.0001, 0.2963039014373716] \n", "247 0.766667 \n", - "87 [0.0001, 0.0001, 0.335] \n", + "245 0.9 \n", "\n", " pro_median resolution head_to_head \n", "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", "121 [0.005,0.017,0.157,0.821] 3 or more -1.9 \n", "232 [0.002,0.008,0.09,0.9] 3 or more -1.1 \n", "247 0.333 no -1.1 \n", - "87 [0.01,0.064,0.926] 2 or more -1.0 " + "245 0.7 no -0.9 " ] }, "execution_count": 69, @@ -12624,20 +12624,20 @@ " \n", " \n", " \n", - " 0\n", - " For Q1 2025, how many banks will be listed on ...\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", - " [0.001,0.62,0.35,0.019,0.01]\n", - " 0\n", - " 2.7\n", - " \n", - " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.0106785714, 0.0213571429, 0.0320357143...\n", + " [0.0, 0.0030510204, 0.0061020408, 0.0102928751...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 2.8\n", + " 2.5\n", + " \n", + " \n", + " 0\n", + " For Q1 2025, how many banks will be listed on ...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.001,0.62,0.35,0.019,0.01]\n", + " 0\n", + " 2.5\n", " \n", " \n", " 151\n", @@ -12658,7 +12658,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.954\n", + " 0.95\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12669,29 +12669,29 @@ ], "text/plain": [ " title \\\n", - "0 For Q1 2025, how many banks will be listed on ... \n", "189 What will the highest rank of metac-GPT4o or m... \n", + "0 For Q1 2025, how many banks will be listed on ... \n", "151 How many earthquakes of magnitude ≥ 4 will hap... \n", "211 Will Nikola Corporation file for bankruptcy be... \n", "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", - "189 [0.0, 0.0106785714, 0.0213571429, 0.0320357143... \n", + "189 [0.0, 0.0030510204, 0.0061020408, 0.0102928751... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", "211 0.99 \n", - "214 0.954 \n", + "214 0.95 \n", "\n", " pro_median resolution \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... 0.0 \n", "211 0.999 annulled \n", "214 0.95 annulled \n", "\n", " head_to_head \n", - "0 2.7 \n", - "189 2.8 \n", + "189 2.5 \n", + "0 2.5 \n", "151 NaN \n", "211 NaN \n", "214 NaN " @@ -12809,10 +12809,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.674462\n", - " 2.674462\n", + " 2.539332\n", + " 2.539332\n", " \n", " \n", " 1\n", @@ -12831,8 +12831,8 @@ " 1.0\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.158842\n", - " -0.158842\n", + " -0.250003\n", + " -0.250003\n", " \n", " \n", " 2\n", @@ -12849,10 +12849,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.063\n", " 0.013\n", - " -0.075746\n", - " -0.075746\n", + " -0.051987\n", + " -0.051987\n", " \n", " \n", " 3\n", @@ -12891,8 +12891,8 @@ " 1.0\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 0.243782\n", - " 0.243782\n", + " 0.387623\n", + " 0.387623\n", " \n", " \n", "\n", @@ -12928,25 +12928,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.674462 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", - "2 0.013 -0.075746 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.539332 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", + "2 0.013 -0.051987 \n", "3 [0.16,0.44,0.4] 0.152526 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.243782 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", "\n", " weighted_score \n", - "0 2.674462 \n", - "1 -0.158842 \n", - "2 -0.075746 \n", + "0 2.539332 \n", + "1 -0.250003 \n", + "2 -0.051987 \n", "3 0.152526 \n", - "4 0.243782 " + "4 0.387623 " ] }, "execution_count": 72, @@ -12984,7 +12984,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -13095,7 +13095,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.063\n", " 0.013\n", " \n", " \n", @@ -13197,7 +13197,7 @@ "13 NaN NaN False False 31338 \n", "\n", " question_weight bot_team_median pro_median \n", - "2 1.0 0.085 0.013 \n", + "2 1.0 0.063 0.013 \n", "5 1.0 0.62 0.45 \n", "8 1.0 0.86 0.95 \n", "10 1.0 NaN NaN \n", @@ -13267,13 +13267,120 @@ { "cell_type": "code", "execution_count": 78, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "N26JZjCV9_jc", + "outputId": "eacb7626-54d0-47c7-8f21-48e95e709564" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Call the function with your DataFrame and column names\n", + "create_discrimination_histogram(df_top_bot_pro_forecasts,\n", + " 'bot_team_median',\n", + " 'pro_median',\n", + " 'resolution')" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4dkNBotk_4e3", + "outputId": "d393a72e-997a-4025-ca7b-6f5328436286" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bot average forecast difference (1 - 0): 0.4365\n", + "Pro average forecast difference (1 - 0): 0.5238\n", + "Difference between pro and bot differences: 0.0873\n" + ] + } + ], + "source": [ + "# Calculate average forecasts for resolved 1 and 0 for bots\n", + "bot_avg_1 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 1]['bot_team_median'].mean()\n", + "bot_avg_0 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 0]['bot_team_median'].mean()\n", + "\n", + "# Calculate average forecasts for resolved 1 and 0 for pros\n", + "pro_avg_1 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 1]['pro_median'].mean()\n", + "pro_avg_0 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 0]['pro_median'].mean()\n", + "\n", + "# Calculate the differences\n", + "bot_difference = bot_avg_1 - bot_avg_0\n", + "pro_difference = pro_avg_1 - pro_avg_0\n", + "\n", + "print(f\"Bot average forecast difference (1 - 0): {bot_difference:.4f}\")\n", + "print(f\"Pro average forecast difference (1 - 0): {pro_difference:.4f}\")\n", + "\n", + "# Calculate the difference between pro and bot differences\n", + "pro_bot_difference = pro_difference - bot_difference\n", + "print(f\"Difference between pro and bot differences: {pro_bot_difference:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "lPPgorXB7omi", - "outputId": "24571b16-50b7-4e51-cd3d-420c15c7fe42" + "id": "bGnXswWOx_yw", + "outputId": "35a0e2a8-5831-43cf-a006-f8e0262666ec" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighted number of 1 resolutions: 14.5\n", + "Weighted number of 0 resolutions: 31.35\n", + "Average 1 resolutions: 0.31624863685932386\n" + ] + } + ], + "source": [ + "# Calculate weighted number of 1 resolutions\n", + "weighted_ones = np.sum(\n", + " df_top_bot_pro_forecasts['resolution'] *\n", + " df_top_bot_pro_forecasts['question_weight']\n", + ")\n", + "\n", + "# Calculate weighted number of 0 resolutions\n", + "weighted_zeros = np.sum(\n", + " (1 - df_top_bot_pro_forecasts['resolution']) *\n", + " df_top_bot_pro_forecasts['question_weight']\n", + ")\n", + "\n", + "print(f\"Weighted number of 1 resolutions: {weighted_ones}\")\n", + "print(f\"Weighted number of 0 resolutions: {weighted_zeros}\")\n", + "\n", + "print(f\"Average 1 resolutions: {weighted_ones / (weighted_zeros + weighted_ones)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, "outputs": [ { "data": { @@ -13331,10 +13438,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.014504537953795379, 0.0001, 0.0001, 0.0001,...\n", + " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.674462\n", - " 2.674462\n", + " 2.539332\n", + " 2.539332\n", " \n", " \n", " 1\n", @@ -13353,8 +13460,8 @@ " 1.0\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.158842\n", - " -0.158842\n", + " -0.250003\n", + " -0.250003\n", " \n", " \n", " 2\n", @@ -13371,10 +13478,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.063\n", " 0.013\n", - " -0.075746\n", - " -0.075746\n", + " -0.051987\n", + " -0.051987\n", " \n", " \n", " 3\n", @@ -13413,8 +13520,8 @@ " 1.0\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 0.243782\n", - " 0.243782\n", + " 0.387623\n", + " 0.387623\n", " \n", " \n", "\n", @@ -13450,25 +13557,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.014504537953795379, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.674462 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", - "2 0.013 -0.075746 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.539332 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", + "2 0.013 -0.051987 \n", "3 [0.16,0.44,0.4] 0.152526 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.243782 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", "\n", " weighted_score \n", - "0 2.674462 \n", - "1 -0.158842 \n", - "2 -0.075746 \n", + "0 2.539332 \n", + "1 -0.250003 \n", + "2 -0.051987 \n", "3 0.152526 \n", - "4 0.243782 " + "4 0.387623 " ] }, "metadata": {}, @@ -13550,10 +13657,10 @@ " False\n", " 35381\n", " 1.00\n", - " 0.65\n", + " 0.15\n", " 0.05\n", - " -0.998529\n", - " -0.998529\n", + " -0.111226\n", + " -0.111226\n", " \n", " \n", " 355\n", @@ -13643,7 +13750,7 @@ "\n", " question_weight bot_team_median pro_median head_to_head weighted_score \n", "342 1.00 0.905 0.95 -0.048527 -0.048527 \n", - "351 1.00 0.65 0.05 -0.998529 -0.998529 \n", + "351 1.00 0.15 0.05 -0.111226 -0.111226 \n", "355 1.00 0.9 0.97 -0.074901 -0.074901 \n", "361 0.85 0.8 0.666 -0.435900 -0.370515 \n", "364 0.85 0.05 0.03 -0.017709 -0.015053 " @@ -13659,7 +13766,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[81], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:750\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 740\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 748\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 749\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 750\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 752\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 753\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 754\u001b[0m bins\n\u001b[1;32m 755\u001b[0m )\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", @@ -13685,88 +13792,6 @@ "print(f\"Pro team is {interpret_confidence(pro_confidence)}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "N26JZjCV9_jc", - "outputId": "eacb7626-54d0-47c7-8f21-48e95e709564" - }, - "outputs": [], - "source": [ - "# Call the function with your DataFrame and column names\n", - "create_discrimination_histogram(df_top_bot_pro_forecasts,\n", - " 'bot_team_median',\n", - " 'pro_median',\n", - " 'resolution')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4dkNBotk_4e3", - "outputId": "d393a72e-997a-4025-ca7b-6f5328436286" - }, - "outputs": [], - "source": [ - "# Calculate average forecasts for resolved 1 and 0 for bots\n", - "bot_avg_1 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 1]['bot_team_median'].mean()\n", - "bot_avg_0 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 0]['bot_team_median'].mean()\n", - "\n", - "# Calculate average forecasts for resolved 1 and 0 for pros\n", - "pro_avg_1 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 1]['pro_median'].mean()\n", - "pro_avg_0 = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['resolution'] == 0]['pro_median'].mean()\n", - "\n", - "# Calculate the differences\n", - "bot_difference = bot_avg_1 - bot_avg_0\n", - "pro_difference = pro_avg_1 - pro_avg_0\n", - "\n", - "print(f\"Bot average forecast difference (1 - 0): {bot_difference:.4f}\")\n", - "print(f\"Pro average forecast difference (1 - 0): {pro_difference:.4f}\")\n", - "\n", - "# Calculate the difference between pro and bot differences\n", - "pro_bot_difference = pro_difference - bot_difference\n", - "print(f\"Difference between pro and bot differences: {pro_bot_difference:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bGnXswWOx_yw", - "outputId": "35a0e2a8-5831-43cf-a006-f8e0262666ec" - }, - "outputs": [], - "source": [ - "# Calculate weighted number of 1 resolutions\n", - "weighted_ones = np.sum(\n", - " df_top_bot_pro_forecasts['resolution'] *\n", - " df_top_bot_pro_forecasts['question_weight']\n", - ")\n", - "\n", - "# Calculate weighted number of 0 resolutions\n", - "weighted_zeros = np.sum(\n", - " (1 - df_top_bot_pro_forecasts['resolution']) *\n", - " df_top_bot_pro_forecasts['question_weight']\n", - ")\n", - "\n", - "print(f\"Weighted number of 1 resolutions: {weighted_ones}\")\n", - "print(f\"Weighted number of 0 resolutions: {weighted_zeros}\")\n", - "\n", - "print(f\"Average 1 resolutions: {weighted_ones / (weighted_zeros + weighted_ones)}\")" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/functions.py b/functions.py index 0373237..8de9f69 100644 --- a/functions.py +++ b/functions.py @@ -421,7 +421,7 @@ def get_median_forecast(row, bots): raise ValueError(f"Unknown question type: {q_type}") -def calculate_weighted_scores(df_bot_team_forecasts, teams): +def calculate_weighted_scores(df_bot_team_forecasts: pd.DataFrame, teams: list[str]) -> pd.Series: """ Calculates weighted scores for each team based on their forecasts and question weights. diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index c42ccb5..6d552fc 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,10 +1,10 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 -X_bot,-0.0,-0.0,-0.0,0.0,0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 @@ -13,35 +13,35 @@ Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 pianobot,-0.1,-0.1,-0.0,-0.0,0.0 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 -metac-o1,-0.2,-0.2,-0.1,0.1,0.1 -4Shadower,-0.2,-0.1,-0.1,-0.0,-0.0 +4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 annabot,-0.1,-0.1,-0.1,-0.0,-0.0 -cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,-0.0 -jkraybill_bot,-0.1,-0.1,-0.1,-0.0,-0.0 +cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 +jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 -MWG,-0.2,-0.2,-0.1,-0.0,-0.0 -ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 +MWG,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-o1,-0.3,-0.2,-0.1,0.0,0.1 GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 +ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 -acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 +acm_bot,-0.3,-0.2,-0.1,0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-deepseek-r1+asknews,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-perplexity,-0.3,-0.3,-0.1,0.0,0.1 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.2,-0.1,-0.0,0.1 wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 -metac-perplexity,-0.3,-0.3,-0.1,-0.0,0.1 -metac-Gemini-Exp-1206,-0.3,-0.3,-0.1,-0.0,0.0 +bot_median,-0.3,-0.3,-0.2,-0.0,0.0 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 +metac-deepseek-r1+asknews,-0.3,-0.3,-0.2,-0.1,-0.1 NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 -metac-claude-3-5-sonnet-latest,-0.3,-0.3,-0.2,-0.1,-0.0 -metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 -bot_median,-0.3,-0.3,-0.2,-0.1,-0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-Llama-3.1,-0.4,-0.3,-0.2,-0.1,-0.0 +metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 +metac-o1-preview,-0.4,-0.3,-0.2,-0.1,-0.1 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -metac-exa,-0.4,-0.3,-0.2,-0.1,-0.1 -pgodzinai,-0.5,-0.4,-0.2,-0.1,-0.1 -VeritasAI,-0.4,-0.3,-0.2,-0.2,-0.1 -metac-grok-2-1212,-0.5,-0.4,-0.3,-0.1,-0.1 -metac-gpt-4o,-0.4,-0.4,-0.3,-0.2,-0.1 -metac-o1-preview,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.1 +pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 +VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 +metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 746b52f..4d49be6 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +RPM_bot,-0.6,7.0,-0.1,0.8206747298542999,0.31018589178137035,-0.2697293560809546,2.4469118511449692,0.7,-0.8,0.3982026167089623,0.796405 jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 +bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 -RPM_bot,-1.3,7.0,-0.2,0.8269776545743774,0.3125681734016113,-0.610595609477049,2.4469118511449692,0.6,-1.0,0.2819326101745987,0.563865 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 -metac-o1,-5.3,91.1,-0.1,0.9084726497398434,0.09518152714706545,-0.6113627344286646,1.9858289388460384,0.1,-0.2,0.27124945946442813,0.542499 -annabot,-5.9,29.3,-0.2,0.5175750572467731,0.09561797207152893,-2.1122028342259047,2.0441825433909937,-0.0,-0.4,0.021810527148697016,0.043621 +annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.8,27.4,-0.2,0.7472901092218875,0.14276243695944935,-1.737830063646217,2.0495406495390753,0.0,-0.5,0.04694721167123542,0.093894 +cookics_bot_TEST,-6.6,27.4,-0.2,0.7470933569588007,0.14272484937169871,-1.6836598504701996,2.0495406495390753,0.1,-0.5,0.05201867599309354,0.104037 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 -MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 +metac-o1,-9.3,91.1,-0.1,0.9011413735401934,0.09441342249931468,-1.0818974297140194,1.9858289388460384,0.1,-0.3,0.14109261555912994,0.282185 +MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 +GreeneiBot2,-10.4,58.4,-0.2,0.8493165305196299,0.11118575431472652,-1.6013523121813948,2.000831925930035,0.0,-0.4,0.05739674059552304,0.114793 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 -GreeneiBot2,-10.6,58.4,-0.2,0.8493306622643327,0.11118760433016613,-1.638793797628407,2.000831925930035,0.0,-0.4,0.05336569544684546,0.106731 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 -metac-deepseek-r1+asknews,-11.7,52.1,-0.2,0.6690305553273252,0.09268876407541017,-2.4327442879372825,2.0053789762011176,-0.0,-0.4,0.009262209683005887,0.018524 +metac-perplexity,-12.3,89.1,-0.1,0.9928936435472672,0.1051874382468964,-1.3167986298410923,1.9864049297707018,0.1,-0.3,0.09566061681542057,0.191321 +metac-Gemini-Exp-1206,-12.6,76.5,-0.2,1.0074640479435764,0.11518577253617869,-1.4310981247048116,1.9908217254774627,0.1,-0.4,0.0782642072080301,0.156528 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 -metac-perplexity,-13.6,89.1,-0.2,0.953800697354561,0.10104592028043681,-1.5152493493302568,1.9864049297707018,0.0,-0.4,0.06664452341402785,0.133289 -metac-Gemini-Exp-1206,-13.9,76.5,-0.2,0.9608427574536519,0.10985544896515206,-1.6509533909374279,1.9908217254774627,0.0,-0.4,0.051451032994077626,0.102902 +bot_median,-14.4,92.1,-0.2,0.8064767886698918,0.08403535853352312,-1.8649643315938071,1.9855502432148115,0.0,-0.3,0.03270280660214449,0.065406 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 +metac-deepseek-r1+asknews,-15.8,52.1,-0.3,0.7725034544186158,0.1070240960803573,-2.8279843345318105,2.0053789762011176,-0.1,-0.5,0.0033369803575435406,0.006674 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -metac-claude-3-5-sonnet-latest,-17.7,91.1,-0.2,0.822268712940962,0.08614986025763702,-2.253410401302691,1.9858289388460384,-0.0,-0.4,0.013329842987401584,0.026660 -bot_median,-17.9,92.1,-0.2,0.8298286106445787,0.0864686321994526,-2.248076238150116,1.9855502432148115,-0.0,-0.4,0.013491943459249906,0.026984 -metac-claude-3-5-sonnet-20240620,-18.2,90.5,-0.2,0.9882219785580354,0.10387958811855824,-1.9308293392916587,1.9860719790130024,0.0,-0.4,0.028334774283890096,0.056670 -minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 -metac-Llama-3.1,-21.3,89.1,-0.2,0.9128041314903421,0.0967027322983173,-2.471742593789836,1.9864049297707018,-0.0,-0.4,0.007684177160478823,0.015368 +minefrac1,-19.4,51.1,-0.4,0.8785436286688769,0.12290028314991908,-3.0953430020106336,2.0065449272360034,-0.1,-0.6,0.0016073014389962144,0.003215 +metac-claude-3-5-sonnet-20240620,-20.5,90.5,-0.2,1.0026017690668347,0.10539115813794282,-2.144815075299298,1.9860719790130024,-0.0,-0.4,0.017338365150828438,0.034677 +metac-o1-preview,-21.8,91.1,-0.2,0.7783952357785447,0.08155319511998359,-2.9287175025862417,1.9858289388460384,-0.1,-0.4,0.0021550719003434007,0.004310 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -metac-exa,-22.4,89.1,-0.3,0.8128016858276886,0.08610844443471673,-2.92372894610568,1.9864049297707018,-0.1,-0.4,0.002197830440677215,0.004396 -pgodzinai,-23.9,76.4,-0.3,0.9914794382114891,0.11343237695345683,-2.755452219862641,1.9908489732268309,-0.1,-0.5,0.00367232305294701,0.007345 +metac-claude-3-5-sonnet-latest,-22.6,91.1,-0.2,0.8075357879826596,0.08460627796346898,-2.930812576746788,1.9858289388460384,-0.1,-0.4,0.002141865770272775,0.004284 +pgodzinai,-23.4,76.4,-0.3,0.9738243593913162,0.11141250898777778,-2.746500218115244,1.9908489732268309,-0.1,-0.5,0.00376450038951266,0.007529 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-grok-2-1212,-24.5,91.1,-0.3,1.0139958650854732,0.10623729287533687,-2.5268442158424125,1.9858289388460384,-0.1,-0.5,0.006626896274566267,0.013254 -metac-gpt-4o,-26.0,91.1,-0.3,0.8516451147774127,0.08922765328715744,-3.193010060382893,1.9858289388460384,-0.1,-0.5,0.0009699028149533728,0.001940 -metac-o1-preview,-26.2,91.1,-0.3,0.9143330864911109,0.09579553057346926,-2.9970476132039527,1.9858289388460384,-0.1,-0.5,0.0017609124521279873,0.003522 +metac-exa,-24.9,89.1,-0.3,0.8297104160130679,0.08789976017509527,-3.180189674479708,1.9864049297707018,-0.1,-0.5,0.0010160377455861174,0.002032 InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 +metac-grok-2-1212,-28.0,91.1,-0.3,1.0053639878633573,0.10533292304496032,-2.9230309952832156,1.9858289388460384,-0.1,-0.5,0.0021912955912464513,0.004383 +metac-gpt-4o,-28.0,91.1,-0.3,0.8644250725107907,0.09056662138298972,-3.3934602737720856,1.9858289388460384,-0.1,-0.5,0.0005136910361772879,0.001027 +metac-Llama-3.1,-28.2,89.1,-0.3,0.9060643910911743,0.0959887222614469,-3.291936866376594,1.9864049297707018,-0.1,-0.5,0.0007163844167320878,0.001433 From 64f26576c83a170d14c7244d8d7e8c150c2027f3 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Thu, 22 May 2025 08:27:00 -0600 Subject: [PATCH 24/26] Debugging calibration curve --- AI_BENCHMARKING_ANALYSIS.ipynb | 3241 ++++++++++------- functions.py | 126 +- .../bootstrapped_h2h_bot_vs_pros.csv | 32 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 38 +- 4 files changed, 1956 insertions(+), 1481 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index a2b1b4e..942c3c1 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -61,7 +61,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3873332/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_691899/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] }, @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1032,11 +1032,11 @@ " \n", " 15\n", " bot_median\n", - " 9.060773\n", - " 3425.153221\n", + " 8.319299\n", + " 3144.861339\n", " 409\n", - " 6.048852\n", - " 1.532164\n", + " 5.304507\n", + " 1.533625\n", " \n", " \n", " 4\n", @@ -1072,14 +1072,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 9.060773 3425.153221 409 6.048852 \n", + "15 bot_median 8.319299 3144.861339 409 5.304507 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.532164 \n", + "15 1.533625 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -1740,7 +1740,7 @@ " \n", " 3\n", " bot_median\n", - " 8602.129306\n", + " 8575.707679\n", " \n", " \n", " 4\n", @@ -1761,7 +1761,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8602.129306\n", + "3 bot_median 8575.707679\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1931,7 +1931,7 @@ " \n", " 2\n", " bot_median\n", - " 3398.202830\n", + " 3328.161138\n", " \n", " \n", " 3\n", @@ -2166,7 +2166,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3398.202830\n", + "2 bot_median 3328.161138\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2578,9 +2578,9 @@ " False\n", " False\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " [0.3,0.4,0.2,0.07,0.03]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.35000000000000003,0.30000000000000004,0.250...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2602,7 +2602,7 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", " NaN\n", @@ -2626,9 +2626,9 @@ " False\n", " False\n", " ...\n", - " 0.15\n", - " 0.05\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -2650,8 +2650,8 @@ " None\n", " None\n", " ...\n", - " [0.45,0.45,0.1]\n", - " [0.2,0.6,0.2]\n", + " [0.25,0.6,0.15]\n", + " [0.6,0.35,0.05]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -2674,8 +2674,8 @@ " False\n", " False\n", " ...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0....\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0....\n", @@ -2713,23 +2713,23 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.45,0.3,0.15,0.05,0.05] \n", - "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", - "2 0.15 \n", - "3 [0.45,0.45,0.1] \n", - "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.... \n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", + "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "2 0.1 \n", + "3 [0.6,0.35,0.05] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.3,0.4,0.2,0.07,0.03] NaN \n", + "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", - "2 0.15 NaN \n", + "2 0.1 NaN \n", "3 [0.15,0.6,0.25] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", @@ -2818,7 +2818,7 @@ " False\n", " False\n", " ...\n", - " 0.95\n", + " 0.9\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2842,8 +2842,8 @@ " False\n", " False\n", " ...\n", - " 0.4\n", - " 0.15\n", + " 0.65\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2866,7 +2866,7 @@ " False\n", " False\n", " ...\n", - " 0.9\n", + " 0.85\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2890,7 +2890,7 @@ " False\n", " False\n", " ...\n", - " 0.8\n", + " 0.7\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2914,9 +2914,9 @@ " False\n", " False\n", " ...\n", + " 0.1\n", " 0.05\n", - " 0.05\n", - " 0.05\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -2946,11 +2946,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.4 0.15 NaN NaN 0.15 NaN \n", - "96 0.9 0.9 NaN NaN 0.9 NaN \n", - "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \n", + "95 0.65 0.85 NaN NaN 0.15 NaN \n", + "96 0.85 0.9 NaN NaN 0.9 NaN \n", + "97 0.7 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3100,7 +3100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3873332/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/tmp/ipykernel_691899/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" ] }, @@ -3162,9 +3162,9 @@ " False\n", " False\n", " ...\n", - " [0.45,0.3,0.15,0.05,0.05]\n", - " [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666]\n", - " [0.3,0.4,0.2,0.07,0.03]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -3186,9 +3186,9 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.9008333333,0.9016666667,0.9025,0.9033333333,0.9041666667,0.905,0.9058333333,0.9066666667,0.9075,0.9083333333,0.9091666667,0.91,0.9108333333,0.9116666667,0.9125,0.9133333333,0.9141666667,0.915,0.9158333333,0.9166666667,0.9175,0.9183333333,0.9191666667,0.92,0.9208333333,0.9216666667,0.9225,0.9233333333,0.9241666667,0.925,0.9258333333,0.9266666667,0.9275,0.9283333333,0.9291666667,0.93,0.9308333333,0.9316666667,0.9325,0.9333333333,0.9341666667,0.935,0.9358333333,0.9366666667,0.9375,0.9383333333,0.9391666667,0.94,0.9408333333,0.9416666667,0.9425,0.9433333333,0.9441666667,0.945,0.9458333333,0.9466666667,0.9475,0.9483333333,0.9491666667,0.95]\n", - " [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", + " [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", + " [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95]\n", + " [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911]\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.0015459984,0.0019048369,0.0023147701,0.0027425688,0.0031719899,0.0035935463,0.0040047171,0.0044081612,0.0048073678,0.0052048637,0.0056023079,0.0060005117,0.0063995798,0.0067992898,0.0071993689,0.0075995902,0.007999808,0.0083999595,0.0088000381,0.0092000616,0.0096525538,0.0103347221,0.0114180238,0.0128617561,0.0144931539,0.0161909912,0.0178965175,0.0195748423,0.0212159342,0.0228289888,0.0244265464,0.0260177161,0.0276085304,0.0292020038,0.0307985773,0.0323974755,0.0339977246,0.0355985069,0.0371992898,0.0387998404,0.0404001295,0.0420002192,0.0436001942,0.0452001261,0.0468000593,0.0484758458,0.0504834257,0.0530704368,0.056178071,0.0595567722,0.0630314345,0.0665171977,0.0699636664,0.0733563529,0.0767085411,0.0800383523,0.0833589543,0.0866790344,0.0900028852,0.0933311337,0.0967326953,0.1004442449,0.1047006189,0.1094577119,0.1144907128,0.1196353715,0.1248049846,0.1299418958,0.1350232879,0.1400570021,0.1452540043,0.1513017567,0.1589133116,0.1680377058,0.1780770546,0.1885468618,0.1991553484,0.2096896812,0.2200450325,0.2302229342,0.2402681458,0.2502302229,0.2601553402,0.27007834,0.2800179047,0.2899799302,0.2999629146,0.3099614863,0.3199691186,0.3299801956,0.3403173669,0.3521487483,0.3668129253,0.3844513624,0.4041888551,0.4247935739,0.4442765262,0.4605082419,0.4728869633,0.4822309604,0.4895341295,0.4956449952,0.5013686886,0.5073076754,0.5137610388,0.5206987551,0.5276657564,0.5340334461,0.5395220756,0.5442306919,0.5484901071,0.5530599502,0.5588761244,0.5663266439,0.5752119583,0.585204242,0.5959735276,0.6071500854,0.6184053116,0.6295209059,0.6403758638,0.650921239,0.6611693012,0.671174569,0.681009388,0.6907471485,0.7004527783,0.7101763721,0.7199504677,0.7297911321,0.7397010124,0.7496729757,0.7596938994,0.7697481465,0.7798202777,0.7898968803,0.7999675731,0.8100253018,0.8200662214,0.8300893951,0.8400025166,0.8494453768,0.8579165269,0.8651653723,0.8712540566,0.8763468591,0.8806505608,0.8844338485,0.8879756773,0.8915092577,0.8952099002,0.8991948145,0.9035195392,0.9081838533,0.9131467515,0.9183416751,0.9236898731,0.9291127196,0.9345414554,0.9399230919,0.9451659123,0.9500324455,0.9542146638,0.9575690762,0.9601504006,0.9620795658,0.9635039422,0.9646063832,0.965571997,0.9665531773,0.9676621061,0.9689711529,0.9705116418,0.9722785871,0.9742409577,0.9763519694,0.9785580215,0.9808067315,0.9830531373,0.9852633275,0.987415817,0.9895011861,0.9915203598,0.9934820158,0.9953894047,0.9970771779,0.998127745,0.99846,0.99852,0.99858,0.99864,0.9987,0.99876,0.99882,0.99888,0.99894,0.99899]\n", @@ -3210,9 +3210,9 @@ " False\n", " False\n", " ...\n", - " 0.15\n", - " 0.05\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " NaN\n", " 0.2\n", " 0.07\n", @@ -3234,8 +3234,8 @@ " None\n", " None\n", " ...\n", - " [0.45,0.45,0.1]\n", - " [0.2,0.6,0.2]\n", + " [0.25,0.6,0.15]\n", + " [0.6,0.35,0.05]\n", " [0.15,0.6,0.25]\n", " NaN\n", " [0.25,0.5,0.25]\n", @@ -3258,8 +3258,8 @@ " False\n", " False\n", " ...\n", - " [0.0,0.0028571429,0.0057142857,0.0085714286,0.0114285714,0.0142857143,0.0171428571,0.02,0.0228571429,0.0257142857,0.0285714286,0.0314285714,0.0342857143,0.0371428571,0.04,0.0428571429,0.0457142857,0.0485714286,0.0514285714,0.0542857143,0.0571428571,0.06,0.0628571429,0.0657142857,0.0685714286,0.0714285714,0.0742857143,0.0771428571,0.08,0.0828571429,0.0857142857,0.0885714286,0.0914285714,0.0942857143,0.0971428571,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9018181818,0.9036363636,0.9054545455,0.9072727273,0.9090909091,0.9109090909,0.9127272727,0.9145454545,0.9163636364,0.9181818182,0.92,0.9218181818,0.9236363636,0.9254545455,0.9272727273,0.9290909091,0.9309090909,0.9327272727,0.9345454545,0.9363636364,0.9381818182,0.94,0.9418181818,0.9436363636,0.9454545455,0.9472727273,0.9490909091,0.9509090909,0.9527272727,0.9545454545,0.9563636364,0.9581818182,0.96,0.9618181818,0.9636363636,0.9654545455,0.9672727273,0.9690909091,0.9709090909,0.9727272727,0.9745454545,0.9763636364,0.9781818182,0.98,0.9818181818,0.9836363636,0.9854545455,0.9872727273,0.9890909091,0.9909090909,0.9927272727,0.9945454545,0.9963636364,0.9981818182,1.0]\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.9011764706,0.9023529412,0.9035294118,0.9047058824,0.9058823529,0.9070588235,0.9082352941,0.9094117647,0.9105882353,0.9117647059,0.9129411765,0.9141176471,0.9152941176,0.9164705882,0.9176470588,0.9188235294,0.92,0.9211764706,0.9223529412,0.9235294118,0.9247058824,0.9258823529,0.9270588235,0.9282352941,0.9294117647,0.9305882353,0.9317647059,0.9329411765,0.9341176471,0.9352941176,0.9364705882,0.9376470588,0.9388235294,0.94,0.9411764706,0.9423529412,0.9435294118,0.9447058824,0.9458823529,0.9470588235,0.9482352941,0.9494117647,0.9505882353,0.9517647059,0.9529411765,0.9541176471,0.9552941176,0.9564705882,0.9576470588,0.9588235294,0.96,0.9611764706,0.9623529412,0.9635294118,0.9647058824,0.9658823529,0.9670588235,0.9682352941,0.9694117647,0.9705882353,0.9717647059,0.9729411765,0.9741176471,0.9752941176,0.9764705882,0.9776470588,0.9788235294,0.98,0.9811764706,0.9823529412,0.9835294118,0.9847058824,0.9858823529,0.9870588235,0.9882352941,0.9894117647,0.9905882353,0.9917647059,0.9929411765,0.9941176471,0.9952941176,0.9964705882,0.9976470588,0.9988235294,1.0]\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9018181818,0.9036363636,0.9054545455,0.9072727273,0.9090909091,0.9109090909,0.9127272727,0.9145454545,0.9163636364,0.9181818182,0.92,0.9218181818,0.9236363636,0.9254545455,0.9272727273,0.9290909091,0.9309090909,0.9327272727,0.9345454545,0.9363636364,0.9381818182,0.94,0.9418181818,0.9436363636,0.9454545455,0.9472727273,0.9490909091,0.9509090909,0.9527272727,0.9545454545,0.9563636364,0.9581818182,0.96,0.9618181818,0.9636363636,0.9654545455,0.9672727273,0.9690909091,0.9709090909,0.9727272727,0.9745454545,0.9763636364,0.9781818182,0.98,0.9818181818,0.9836363636,0.9854545455,0.9872727273,0.9890909091,0.9909090909,0.9927272727,0.9945454545,0.9963636364,0.9981818182,1.0]\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0]\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0]\n", @@ -3296,26 +3296,26 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.45,0.3,0.15,0.05,0.05] \n", - "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", - "2 0.15 \n", - "3 [0.45,0.45,0.1] \n", - "4 [0.0,0.0028571429,0.0057142857,0.0085714286,0.0114285714,0.0142857143,0.0171428571,0.02,0.0228571429,0.0257142857,0.0285714286,0.0314285714,0.0342857143,0.0371428571,0.04,0.0428571429,0.0457142857,0.0485714286,0.0514285714,0.0542857143,0.0571428571,0.06,0.0628571429,0.0657142857,0.0685714286,0.0714285714,0.0742857143,0.0771428571,0.08,0.0828571429,0.0857142857,0.0885714286,0.0914285714,0.0942857143,0.0971428571,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9018181818,0.9036363636,0.9054545455,0.9072727273,0.9090909091,0.9109090909,0.9127272727,0.9145454545,0.9163636364,0.9181818182,0.92,0.9218181818,0.9236363636,0.9254545455,0.9272727273,0.9290909091,0.9309090909,0.9327272727,0.9345454545,0.9363636364,0.9381818182,0.94,0.9418181818,0.9436363636,0.9454545455,0.9472727273,0.9490909091,0.9509090909,0.9527272727,0.9545454545,0.9563636364,0.9581818182,0.96,0.9618181818,0.9636363636,0.9654545455,0.9672727273,0.9690909091,0.9709090909,0.9727272727,0.9745454545,0.9763636364,0.9781818182,0.98,0.9818181818,0.9836363636,0.9854545455,0.9872727273,0.9890909091,0.9909090909,0.9927272727,0.9945454545,0.9963636364,0.9981818182,1.0] \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.041666666666666664,0.010416666666666666,0.7291666666666666] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.9008333333,0.9016666667,0.9025,0.9033333333,0.9041666667,0.905,0.9058333333,0.9066666667,0.9075,0.9083333333,0.9091666667,0.91,0.9108333333,0.9116666667,0.9125,0.9133333333,0.9141666667,0.915,0.9158333333,0.9166666667,0.9175,0.9183333333,0.9191666667,0.92,0.9208333333,0.9216666667,0.9225,0.9233333333,0.9241666667,0.925,0.9258333333,0.9266666667,0.9275,0.9283333333,0.9291666667,0.93,0.9308333333,0.9316666667,0.9325,0.9333333333,0.9341666667,0.935,0.9358333333,0.9366666667,0.9375,0.9383333333,0.9391666667,0.94,0.9408333333,0.9416666667,0.9425,0.9433333333,0.9441666667,0.945,0.9458333333,0.9466666667,0.9475,0.9483333333,0.9491666667,0.95] \n", - "2 0.05 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.9011764706,0.9023529412,0.9035294118,0.9047058824,0.9058823529,0.9070588235,0.9082352941,0.9094117647,0.9105882353,0.9117647059,0.9129411765,0.9141176471,0.9152941176,0.9164705882,0.9176470588,0.9188235294,0.92,0.9211764706,0.9223529412,0.9235294118,0.9247058824,0.9258823529,0.9270588235,0.9282352941,0.9294117647,0.9305882353,0.9317647059,0.9329411765,0.9341176471,0.9352941176,0.9364705882,0.9376470588,0.9388235294,0.94,0.9411764706,0.9423529412,0.9435294118,0.9447058824,0.9458823529,0.9470588235,0.9482352941,0.9494117647,0.9505882353,0.9517647059,0.9529411765,0.9541176471,0.9552941176,0.9564705882,0.9576470588,0.9588235294,0.96,0.9611764706,0.9623529412,0.9635294118,0.9647058824,0.9658823529,0.9670588235,0.9682352941,0.9694117647,0.9705882353,0.9717647059,0.9729411765,0.9741176471,0.9752941176,0.9764705882,0.9776470588,0.9788235294,0.98,0.9811764706,0.9823529412,0.9835294118,0.9847058824,0.9858823529,0.9870588235,0.9882352941,0.9894117647,0.9905882353,0.9917647059,0.9929411765,0.9941176471,0.9952941176,0.9964705882,0.9976470588,0.9988235294,1.0] \n", - "\n", - " metac-perplexity \\\n", - "0 [0.3,0.4,0.2,0.07,0.03] \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1514285714,0.1542857143,0.1571428571,0.16,0.1628571429,0.1657142857,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", - "2 0.15 \n", - "3 [0.15,0.6,0.25] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", + " metac-o1 \\\n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", + "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", + "2 0.1 \n", + "3 [0.25,0.6,0.15] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9018181818,0.9036363636,0.9054545455,0.9072727273,0.9090909091,0.9109090909,0.9127272727,0.9145454545,0.9163636364,0.9181818182,0.92,0.9218181818,0.9236363636,0.9254545455,0.9272727273,0.9290909091,0.9309090909,0.9327272727,0.9345454545,0.9363636364,0.9381818182,0.94,0.9418181818,0.9436363636,0.9454545455,0.9472727273,0.9490909091,0.9509090909,0.9527272727,0.9545454545,0.9563636364,0.9581818182,0.96,0.9618181818,0.9636363636,0.9654545455,0.9672727273,0.9690909091,0.9709090909,0.9727272727,0.9745454545,0.9763636364,0.9781818182,0.98,0.9818181818,0.9836363636,0.9854545455,0.9872727273,0.9890909091,0.9909090909,0.9927272727,0.9945454545,0.9963636364,0.9981818182,1.0] \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95] \n", + "2 0.1 \n", + "3 [0.6,0.35,0.05] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0] \n", + "\n", + " metac-perplexity \\\n", + "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", + "1 [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.1 \n", + "3 [0.15,0.6,0.25] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3423,7 +3423,7 @@ " False\n", " False\n", " ...\n", - " 0.95\n", + " 0.9\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3447,8 +3447,8 @@ " False\n", " False\n", " ...\n", - " 0.4\n", - " 0.15\n", + " 0.65\n", + " 0.85\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3471,7 +3471,7 @@ " False\n", " False\n", " ...\n", - " 0.9\n", + " 0.85\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3495,7 +3495,7 @@ " False\n", " False\n", " ...\n", - " 0.8\n", + " 0.7\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3519,9 +3519,9 @@ " False\n", " False\n", " ...\n", + " 0.1\n", " 0.05\n", - " 0.05\n", - " 0.05\n", + " 0.03\n", " NaN\n", " 0.15\n", " 0.05\n", @@ -3551,11 +3551,11 @@ "98 None NaN NaN False False ... \n", "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "94 0.95 0.9 NaN NaN 0.95 0.95 \n", - "95 0.4 0.15 NaN NaN 0.15 NaN \n", - "96 0.9 0.9 NaN NaN 0.9 NaN \n", - "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.05 0.05 0.05 NaN 0.15 0.05 \n", + "94 0.9 0.9 NaN NaN 0.95 0.95 \n", + "95 0.65 0.85 NaN NaN 0.15 NaN \n", + "96 0.85 0.9 NaN NaN 0.9 NaN \n", + "97 0.7 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3762,8 +3762,8 @@ " False\n", " False\n", " ...\n", - " 2.343407\n", - " 5.703782\n", + " 2.302585\n", + " 5.857933\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -3786,7 +3786,7 @@ " None\n", " None\n", " ...\n", - " 0.310155\n", + " -0.228842\n", " 0.310155\n", " NaN\n", " 0.127833\n", @@ -3811,15 +3811,15 @@ " False\n", " ...\n", " 0.116534\n", - " 0.211844\n", + " -0.106610\n", " NaN\n", " -0.184571\n", - " 0.112526\n", + " 0.111521\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " -0.704447\n", + " 0.298855\n", " \n", " \n", " 9\n", @@ -3834,8 +3834,8 @@ " None\n", " None\n", " ...\n", - " -0.423484\n", - " -1.211941\n", + " -0.518794\n", + " -0.806476\n", " NaN\n", " -0.806476\n", " -0.494101\n", @@ -3843,7 +3843,7 @@ " NaN\n", " -0.624154\n", " NaN\n", - " -0.518794\n", + " -0.693147\n", " \n", " \n", " 13\n", @@ -3858,8 +3858,8 @@ " None\n", " None\n", " ...\n", - " 0.330943\n", - " 0.287682\n", + " -2.145931\n", + " 0.510826\n", " 0.021979\n", " 0.200671\n", " 0.253781\n", @@ -3904,17 +3904,17 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.343407 5.703782 NaN 2.292635 2.703087 \n", - "3 0.310155 0.310155 NaN 0.127833 0.152526 \n", - "6 0.116534 0.211844 NaN -0.184571 0.112526 \n", - "9 -0.423484 -1.211941 NaN -0.806476 -0.494101 \n", - "13 0.330943 0.287682 0.021979 0.200671 0.253781 \n", + "0 2.302585 5.857933 NaN 2.292635 2.703087 \n", + "3 -0.228842 0.310155 NaN 0.127833 0.152526 \n", + "6 0.116534 -0.106610 NaN -0.184571 0.111521 \n", + "9 -0.518794 -0.806476 NaN -0.806476 -0.494101 \n", + "13 -2.145931 0.510826 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "0 NaN NaN NaN NaN 4.656813 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN -0.704447 \n", - "9 NaN NaN -0.624154 NaN -0.518794 \n", + "6 NaN NaN NaN NaN 0.298855 \n", + "9 NaN NaN -0.624154 NaN -0.693147 \n", "13 NaN NaN NaN NaN -0.062598 \n", "\n", "[5 rows x 58 columns]" @@ -3982,15 +3982,15 @@ " False\n", " ...\n", " -2.879198\n", - " -0.933288\n", + " -2.879198\n", " -3.007032\n", " -2.879198\n", - " -3.390024\n", + " -3.795489\n", " NaN\n", " NaN\n", " -2.348570\n", " -2.409195\n", - " -2.879198\n", + " -2.348570\n", " \n", " \n", " 82\n", @@ -4005,7 +4005,7 @@ " None\n", " None\n", " ...\n", - " -0.993252\n", + " -0.587787\n", " -0.300105\n", " -0.523248\n", " 0.105361\n", @@ -4014,7 +4014,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " -0.941958\n", + " -0.498556\n", " \n", " \n", " 83\n", @@ -4029,7 +4029,7 @@ " None\n", " None\n", " ...\n", - " -0.693147\n", + " -0.899761\n", " -0.693147\n", " NaN\n", " -0.182322\n", @@ -4053,8 +4053,8 @@ " False\n", " False\n", " ...\n", - " -0.037817\n", - " -0.048289\n", + " -0.054625\n", + " -0.102356\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -4078,7 +4078,7 @@ " False\n", " ...\n", " -1.299283\n", - " -2.908721\n", + " -1.704748\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -4117,21 +4117,21 @@ "\n", " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", "81 NaN False False ... -2.879198 \n", - "82 NaN None None ... -0.993252 \n", - "83 NaN None None ... -0.693147 \n", - "91 NaN False False ... -0.037817 \n", + "82 NaN None None ... -0.587787 \n", + "83 NaN None None ... -0.899761 \n", + "91 NaN False False ... -0.054625 \n", "92 NaN False False ... -1.299283 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -0.933288 -3.007032 -2.879198 -3.390024 NaN NaN \n", + "81 -2.879198 -3.007032 -2.879198 -3.795489 NaN NaN \n", "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", "83 -0.693147 NaN -0.182322 NaN NaN NaN \n", - "91 -0.048289 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -2.908721 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -1.704748 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -2.879198 \n", - "82 0.276509 -0.644609 -0.941958 \n", + "81 -2.348570 -2.409195 -2.348570 \n", + "82 0.276509 -0.644609 -0.498556 \n", "83 -0.178330 -0.567984 -0.693147 \n", "91 NaN -0.147818 -0.121048 \n", "92 NaN -0.749237 -0.318454 \n", @@ -4200,8 +4200,8 @@ " False\n", " False\n", " ...\n", - " -0.038208\n", - " -0.149434\n", + " -0.092275\n", + " -0.092275\n", " NaN\n", " -0.210058\n", " -0.059485\n", @@ -4224,8 +4224,8 @@ " None\n", " None\n", " ...\n", - " -0.810930\n", - " 0.200671\n", + " -0.251314\n", + " 0.287682\n", " NaN\n", " 0.510826\n", " 0.320472\n", @@ -4233,7 +4233,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.367725\n", + " 0.287682\n", " \n", " \n", " 8\n", @@ -4248,8 +4248,8 @@ " False\n", " False\n", " ...\n", - " 0.000000\n", " -0.054067\n", + " 0.000000\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -4273,7 +4273,7 @@ " False\n", " ...\n", " -0.057158\n", - " 0.000000\n", + " -0.057158\n", " NaN\n", " 0.054067\n", " -0.057158\n", @@ -4305,7 +4305,7 @@ " NaN\n", " -0.076070\n", " NaN\n", - " -0.096728\n", + " -0.076070\n", " \n", " \n", "\n", @@ -4328,18 +4328,18 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.038208 -0.149434 NaN -0.210058 -0.059485 \n", - "5 -0.810930 0.200671 NaN 0.510826 0.320472 \n", - "8 0.000000 -0.054067 NaN -0.111226 -0.147158 \n", - "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", + "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", + "5 -0.251314 0.287682 NaN 0.510826 0.320472 \n", + "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", + "12 -0.057158 -0.057158 NaN 0.054067 -0.057158 \n", "16 -0.045611 0.008457 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "2 NaN NaN NaN NaN -0.149434 \n", - "5 NaN NaN NaN NaN 0.367725 \n", + "5 NaN NaN NaN NaN 0.287682 \n", "8 NaN NaN -0.398124 NaN -0.171850 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", - "16 NaN NaN -0.076070 NaN -0.096728 \n", + "16 NaN NaN -0.076070 NaN -0.076070 \n", "\n", "[5 rows x 58 columns]" ] @@ -4429,7 +4429,7 @@ " False\n", " False\n", " ...\n", - " -0.111226\n", + " -1.845827\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4502,7 +4502,7 @@ " False\n", " ...\n", " -0.017709\n", - " -0.017709\n", + " 0.000000\n", " NaN\n", " -0.112251\n", " -0.017709\n", @@ -4534,10 +4534,10 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -0.111226 NaN NaN -0.111226 NaN \n", + "95 -1.845827 NaN NaN -0.111226 NaN \n", "96 -0.074901 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.017709 -0.017709 NaN -0.112251 -0.017709 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", @@ -4603,7 +4603,7 @@ " \n", " 2\n", " bot_median\n", - " 3398.202830\n", + " 3328.161138\n", " \n", " \n", " 3\n", @@ -4838,7 +4838,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3398.202830\n", + "2 bot_median 3328.161138\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4906,13 +4906,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 74.0%\n", + "mean metac-o1 forecast on questions that resolved yes: 70.0%\n", "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -4988,7 +4988,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3873332/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_691899/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -5114,7 +5114,7 @@ " 3\n", " 4\n", " bot_median\n", - " 2477.274734\n", + " 2437.335374\n", " 97\n", " 93.10\n", " \n", @@ -5471,7 +5471,7 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2477.274734 97 \n", + "3 4 bot_median 2437.335374 97 \n", "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", @@ -5717,17 +5717,17 @@ " \n", " \n", " bot_median\n", - " 2477.3\n", + " 2437.3\n", " 93.1\n", - " 26.6\n", - " 58.467357\n", - " 6.059526\n", - " 4.391227\n", + " 26.2\n", + " 60.692389\n", + " 6.290127\n", + " 4.162040\n", " 1.985277\n", - " 38.6\n", - " 14.6\n", - " 0.999985\n", - " 0.000030\n", + " 38.7\n", + " 13.7\n", + " 0.999965\n", + " 0.000071\n", " \n", " \n", " acm_bot\n", @@ -6340,7 +6340,7 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2477.3 93.1 26.6 58.467357 \n", + "bot_median 2437.3 93.1 26.2 60.692389 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", @@ -6389,7 +6389,7 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.059526 4.391227 1.985277 38.6 \n", + "bot_median 6.290127 4.162040 1.985277 38.7 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", @@ -6438,7 +6438,7 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 14.6 0.999985 0.000030 \n", + "bot_median 13.7 0.999965 0.000071 \n", "acm_bot 15.3 0.999987 0.000025 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", @@ -6573,18 +6573,18 @@ " NA\n", " \n", " \n", - " RPM_bot\n", + " bean_bot\n", " -0.6\n", - " 7.0\n", + " 4.7\n", " -0.1\n", - " 0.820675\n", - " 0.310186\n", - " -0.269729\n", - " 2.446912\n", - " 0.7\n", - " -0.8\n", - " 0.398203\n", - " 0.796405\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", " \n", " \n", " jonahsingerbot\n", @@ -6601,20 +6601,6 @@ " 0.007677\n", " \n", " \n", - " bean_bot\n", - " -0.6\n", - " 4.7\n", - " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", - " \n", - " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6671,6 +6657,20 @@ " 0.574463\n", " \n", " \n", + " RPM_bot\n", + " -1.4\n", + " 7.0\n", + " -0.2\n", + " 0.819543\n", + " 0.309758\n", + " -0.650313\n", + " 2.446912\n", + " 0.6\n", + " -1.0\n", + " 0.269789\n", + " 0.539577\n", + " \n", + " \n", " KevinTestBot\n", " -1.5\n", " 8.4\n", @@ -6773,14 +6773,14 @@ " -6.6\n", " 27.4\n", " -0.2\n", - " 0.747093\n", - " 0.142725\n", - " -1.683660\n", + " 0.745283\n", + " 0.142379\n", + " -1.694619\n", " 2.049541\n", " 0.1\n", " -0.5\n", - " 0.052019\n", - " 0.104037\n", + " 0.050957\n", + " 0.101914\n", " \n", " \n", " jkraybill_bot\n", @@ -6811,32 +6811,18 @@ " 0.084012\n", " \n", " \n", - " metac-o1\n", - " -9.3\n", - " 91.1\n", - " -0.1\n", - " 0.901141\n", - " 0.094413\n", - " -1.081897\n", - " 1.985829\n", - " 0.1\n", - " -0.3\n", - " 0.141093\n", - " 0.282185\n", - " \n", - " \n", " MWG\n", - " -9.8\n", + " -9.6\n", " 28.6\n", " -0.3\n", - " 0.705240\n", - " 0.131872\n", - " -2.589625\n", + " 0.711160\n", + " 0.132979\n", + " -2.535384\n", " 2.046561\n", " -0.1\n", " -0.6\n", - " 0.007581\n", - " 0.015163\n", + " 0.008595\n", + " 0.017191\n", " \n", " \n", " ProfessorSP\n", @@ -6853,20 +6839,6 @@ " 0.023289\n", " \n", " \n", - " GreeneiBot2\n", - " -10.4\n", - " 58.4\n", - " -0.2\n", - " 0.849317\n", - " 0.111186\n", - " -1.601352\n", - " 2.000832\n", - " 0.0\n", - " -0.4\n", - " 0.057397\n", - " 0.114793\n", - " \n", - " \n", " acm_bot\n", " -10.5\n", " 80.2\n", @@ -6881,6 +6853,20 @@ " 0.201592\n", " \n", " \n", + " GreeneiBot2\n", + " -10.7\n", + " 58.4\n", + " -0.2\n", + " 0.849274\n", + " 0.111180\n", + " -1.642777\n", + " 2.000832\n", + " 0.0\n", + " -0.4\n", + " 0.052951\n", + " 0.105902\n", + " \n", + " \n", " ajf-bot\n", " -10.9\n", " 34.2\n", @@ -6895,6 +6881,20 @@ " 0.094289\n", " \n", " \n", + " metac-o1\n", + " -11.3\n", + " 91.1\n", + " -0.1\n", + " 0.885302\n", + " 0.092754\n", + " -1.342987\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.091325\n", + " 0.182650\n", + " \n", + " \n", " Bot_Pepa\n", " -11.5\n", " 44.0\n", @@ -6909,34 +6909,6 @@ " 0.023810\n", " \n", " \n", - " metac-perplexity\n", - " -12.3\n", - " 89.1\n", - " -0.1\n", - " 0.992894\n", - " 0.105187\n", - " -1.316799\n", - " 1.986405\n", - " 0.1\n", - " -0.3\n", - " 0.095661\n", - " 0.191321\n", - " \n", - " \n", - " metac-Gemini-Exp-1206\n", - " -12.6\n", - " 76.5\n", - " -0.2\n", - " 1.007464\n", - " 0.115186\n", - " -1.431098\n", - " 1.990822\n", - " 0.1\n", - " -0.4\n", - " 0.078264\n", - " 0.156528\n", - " \n", - " \n", " laylaps\n", " -12.9\n", " 64.1\n", @@ -6951,6 +6923,20 @@ " 0.017488\n", " \n", " \n", + " metac-deepseek-r1+asknews\n", + " -13.3\n", + " 52.1\n", + " -0.3\n", + " 0.780892\n", + " 0.108186\n", + " -2.366308\n", + " 2.005379\n", + " -0.0\n", + " -0.5\n", + " 0.010898\n", + " 0.021795\n", + " \n", + " \n", " wunderplumb\n", " -13.6\n", " 25.6\n", @@ -6965,18 +6951,32 @@ " 0.006348\n", " \n", " \n", + " metac-Gemini-Exp-1206\n", + " -13.7\n", + " 76.5\n", + " -0.2\n", + " 0.956701\n", + " 0.109382\n", + " -1.640002\n", + " 1.990822\n", + " 0.0\n", + " -0.4\n", + " 0.052582\n", + " 0.105165\n", + " \n", + " \n", " bot_median\n", - " -14.4\n", + " -14.2\n", " 92.1\n", " -0.2\n", - " 0.806477\n", - " 0.084035\n", - " -1.864964\n", + " 0.806056\n", + " 0.083992\n", + " -1.829889\n", " 1.985550\n", " 0.0\n", " -0.3\n", - " 0.032703\n", - " 0.065406\n", + " 0.035269\n", + " 0.070537\n", " \n", " \n", " manticAI\n", @@ -6993,18 +6993,32 @@ " 0.011014\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -15.8\n", - " 52.1\n", - " -0.3\n", - " 0.772503\n", - " 0.107024\n", - " -2.827984\n", - " 2.005379\n", - " -0.1\n", - " -0.5\n", - " 0.003337\n", - " 0.006674\n", + " metac-claude-3-5-sonnet-20240620\n", + " -15.7\n", + " 90.5\n", + " -0.2\n", + " 0.957721\n", + " 0.100673\n", + " -1.726279\n", + " 1.986072\n", + " 0.0\n", + " -0.4\n", + " 0.043874\n", + " 0.087748\n", + " \n", + " \n", + " metac-perplexity\n", + " -16.1\n", + " 89.1\n", + " -0.2\n", + " 1.040224\n", + " 0.110202\n", + " -1.638549\n", + " 1.986405\n", + " 0.0\n", + " -0.4\n", + " 0.052437\n", + " 0.104874\n", " \n", " \n", " NextWorldLab\n", @@ -7022,45 +7036,31 @@ " \n", " \n", " minefrac1\n", - " -19.4\n", + " -18.8\n", " 51.1\n", " -0.4\n", - " 0.878544\n", - " 0.122900\n", - " -3.095343\n", + " 0.874752\n", + " 0.122370\n", + " -3.013581\n", " 2.006545\n", " -0.1\n", " -0.6\n", - " 0.001607\n", - " 0.003215\n", - " \n", - " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -20.5\n", - " 90.5\n", - " -0.2\n", - " 1.002602\n", - " 0.105391\n", - " -2.144815\n", - " 1.986072\n", - " -0.0\n", - " -0.4\n", - " 0.017338\n", - " 0.034677\n", + " 0.002021\n", + " 0.004043\n", " \n", " \n", - " metac-o1-preview\n", - " -21.8\n", + " metac-claude-3-5-sonnet-latest\n", + " -21.9\n", " 91.1\n", " -0.2\n", - " 0.778395\n", - " 0.081553\n", - " -2.928718\n", + " 0.826778\n", + " 0.086622\n", + " -2.778813\n", " 1.985829\n", " -0.1\n", " -0.4\n", - " 0.002155\n", - " 0.004310\n", + " 0.003320\n", + " 0.006640\n", " \n", " \n", " mmBot\n", @@ -7077,32 +7077,32 @@ " 0.002208\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -22.6\n", - " 91.1\n", - " -0.2\n", - " 0.807536\n", - " 0.084606\n", - " -2.930813\n", - " 1.985829\n", - " -0.1\n", - " -0.4\n", - " 0.002142\n", - " 0.004284\n", - " \n", - " \n", " pgodzinai\n", - " -23.4\n", + " -23.5\n", " 76.4\n", " -0.3\n", - " 0.973824\n", - " 0.111413\n", - " -2.746500\n", + " 1.001063\n", + " 0.114529\n", + " -2.684830\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.003765\n", - " 0.007529\n", + " 0.004459\n", + " 0.008918\n", + " \n", + " \n", + " metac-exa\n", + " -24.1\n", + " 89.1\n", + " -0.3\n", + " 0.823877\n", + " 0.087282\n", + " -3.103268\n", + " 1.986405\n", + " -0.1\n", + " -0.4\n", + " 0.001286\n", + " 0.002573\n", " \n", " \n", " VeritasAI\n", @@ -7119,18 +7119,18 @@ " 0.000076\n", " \n", " \n", - " metac-exa\n", - " -24.9\n", + " metac-Llama-3.1\n", + " -26.6\n", " 89.1\n", " -0.3\n", - " 0.829710\n", - " 0.087900\n", - " -3.180190\n", + " 0.890468\n", + " 0.094336\n", + " -3.169730\n", " 1.986405\n", " -0.1\n", " -0.5\n", - " 0.001016\n", - " 0.002032\n", + " 0.001049\n", + " 0.002099\n", " \n", " \n", " InstitutPelFutur\n", @@ -7147,46 +7147,46 @@ " 0.004584\n", " \n", " \n", - " metac-grok-2-1212\n", - " -28.0\n", + " metac-o1-preview\n", + " -27.3\n", " 91.1\n", " -0.3\n", - " 1.005364\n", - " 0.105333\n", - " -2.923031\n", + " 0.839685\n", + " 0.087975\n", + " -3.407500\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.002191\n", - " 0.004383\n", + " 0.000491\n", + " 0.000982\n", " \n", " \n", - " metac-gpt-4o\n", - " -28.0\n", + " metac-grok-2-1212\n", + " -28.3\n", " 91.1\n", " -0.3\n", - " 0.864425\n", - " 0.090567\n", - " -3.393460\n", + " 1.037474\n", + " 0.108697\n", + " -2.862896\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000514\n", - " 0.001027\n", + " 0.002610\n", + " 0.005220\n", " \n", " \n", - " metac-Llama-3.1\n", - " -28.2\n", - " 89.1\n", + " metac-gpt-4o\n", + " -28.7\n", + " 91.1\n", " -0.3\n", - " 0.906064\n", - " 0.095989\n", - " -3.291937\n", - " 1.986405\n", + " 0.893717\n", + " 0.093636\n", + " -3.366630\n", + " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.000716\n", - " 0.001433\n", + " 0.000560\n", + " 0.001120\n", " \n", " \n", "\n", @@ -7196,13 +7196,13 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "RPM_bot -0.6 7.0 -0.1 0.820675 0.310186 \n", - "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", + "RPM_bot -1.4 7.0 -0.2 0.819543 0.309758 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", @@ -7210,47 +7210,47 @@ "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.6 27.4 -0.2 0.747093 0.142725 \n", + "cookics_bot_TEST -6.6 27.4 -0.2 0.745283 0.142379 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", - "metac-o1 -9.3 91.1 -0.1 0.901141 0.094413 \n", - "MWG -9.8 28.6 -0.3 0.705240 0.131872 \n", + "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", - "GreeneiBot2 -10.4 58.4 -0.2 0.849317 0.111186 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", + "GreeneiBot2 -10.7 58.4 -0.2 0.849274 0.111180 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", + "metac-o1 -11.3 91.1 -0.1 0.885302 0.092754 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", - "metac-perplexity -12.3 89.1 -0.1 0.992894 0.105187 \n", - "metac-Gemini-Exp-1206 -12.6 76.5 -0.2 1.007464 0.115186 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", + "metac-deepseek-r1+asknews -13.3 52.1 -0.3 0.780892 0.108186 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", - "bot_median -14.4 92.1 -0.2 0.806477 0.084035 \n", + "metac-Gemini-Exp-1206 -13.7 76.5 -0.2 0.956701 0.109382 \n", + "bot_median -14.2 92.1 -0.2 0.806056 0.083992 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-deepseek-r1+asknews -15.8 52.1 -0.3 0.772503 0.107024 \n", + "metac-claude-3-5-sonnet-20240620 -15.7 90.5 -0.2 0.957721 0.100673 \n", + "metac-perplexity -16.1 89.1 -0.2 1.040224 0.110202 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "minefrac1 -19.4 51.1 -0.4 0.878544 0.122900 \n", - "metac-claude-3-5-sonnet-20240620 -20.5 90.5 -0.2 1.002602 0.105391 \n", - "metac-o1-preview -21.8 91.1 -0.2 0.778395 0.081553 \n", + "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", + "metac-claude-3-5-sonnet-latest -21.9 91.1 -0.2 0.826778 0.086622 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "metac-claude-3-5-sonnet-latest -22.6 91.1 -0.2 0.807536 0.084606 \n", - "pgodzinai -23.4 76.4 -0.3 0.973824 0.111413 \n", + "pgodzinai -23.5 76.4 -0.3 1.001063 0.114529 \n", + "metac-exa -24.1 89.1 -0.3 0.823877 0.087282 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-exa -24.9 89.1 -0.3 0.829710 0.087900 \n", + "metac-Llama-3.1 -26.6 89.1 -0.3 0.890468 0.094336 \n", "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", - "metac-grok-2-1212 -28.0 91.1 -0.3 1.005364 0.105333 \n", - "metac-gpt-4o -28.0 91.1 -0.3 0.864425 0.090567 \n", - "metac-Llama-3.1 -28.2 89.1 -0.3 0.906064 0.095989 \n", + "metac-o1-preview -27.3 91.1 -0.3 0.839685 0.087975 \n", + "metac-grok-2-1212 -28.3 91.1 -0.3 1.037474 0.108697 \n", + "metac-gpt-4o -28.7 91.1 -0.3 0.893717 0.093636 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "RPM_bot -0.269729 2.446912 0.7 \n", - "jonahsingerbot -5.273630 2.784843 -0.1 \n", "bean_bot -4.265106 2.784843 -0.0 \n", + "jonahsingerbot -5.273630 2.784843 -0.1 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", + "RPM_bot -0.650313 2.446912 0.6 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", @@ -7258,47 +7258,47 @@ "krm-bot -3.229846 2.264709 -0.2 \n", "annabot -2.211795 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.683660 2.049541 0.1 \n", + "cookics_bot_TEST -1.694619 2.049541 0.1 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", - "metac-o1 -1.081897 1.985829 0.1 \n", - "MWG -2.589625 2.046561 -0.1 \n", + "MWG -2.535384 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", - "GreeneiBot2 -1.601352 2.000832 0.0 \n", "acm_bot -1.287717 1.989344 0.1 \n", + "GreeneiBot2 -1.642777 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", + "metac-o1 -1.342987 1.985829 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", - "metac-perplexity -1.316799 1.986405 0.1 \n", - "metac-Gemini-Exp-1206 -1.431098 1.990822 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", + "metac-deepseek-r1+asknews -2.366308 2.005379 -0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", - "bot_median -1.864964 1.985550 0.0 \n", + "metac-Gemini-Exp-1206 -1.640002 1.990822 0.0 \n", + "bot_median -1.829889 1.985550 0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-deepseek-r1+asknews -2.827984 2.005379 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -1.726279 1.986072 0.0 \n", + "metac-perplexity -1.638549 1.986405 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "minefrac1 -3.095343 2.006545 -0.1 \n", - "metac-claude-3-5-sonnet-20240620 -2.144815 1.986072 -0.0 \n", - "metac-o1-preview -2.928718 1.985829 -0.1 \n", + "minefrac1 -3.013581 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.778813 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "metac-claude-3-5-sonnet-latest -2.930813 1.985829 -0.1 \n", - "pgodzinai -2.746500 1.990849 -0.1 \n", + "pgodzinai -2.684830 1.990849 -0.1 \n", + "metac-exa -3.103268 1.986405 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-exa -3.180190 1.986405 -0.1 \n", + "metac-Llama-3.1 -3.169730 1.986405 -0.1 \n", "InstitutPelFutur -2.908524 1.986114 -0.1 \n", - "metac-grok-2-1212 -2.923031 1.985829 -0.1 \n", - "metac-gpt-4o -3.393460 1.985829 -0.1 \n", - "metac-Llama-3.1 -3.291937 1.986405 -0.1 \n", + "metac-o1-preview -3.407500 1.985829 -0.1 \n", + "metac-grok-2-1212 -2.862896 1.985829 -0.1 \n", + "metac-gpt-4o -3.366630 1.985829 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "RPM_bot -0.8 0.398203 0.796405 \n", - "jonahsingerbot -0.2 0.003839 0.007677 \n", "bean_bot -0.2 0.007674 0.015349 \n", + "jonahsingerbot -0.2 0.003839 0.007677 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", + "RPM_bot -1.0 0.269789 0.539577 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", @@ -7306,36 +7306,36 @@ "krm-bot -0.9 0.005563 0.011127 \n", "annabot -0.4 0.017610 0.035221 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.052019 0.104037 \n", + "cookics_bot_TEST -0.5 0.050957 0.101914 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", - "metac-o1 -0.3 0.141093 0.282185 \n", - "MWG -0.6 0.007581 0.015163 \n", + "MWG -0.6 0.008595 0.017191 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", - "GreeneiBot2 -0.4 0.057397 0.114793 \n", "acm_bot -0.3 0.100796 0.201592 \n", + "GreeneiBot2 -0.4 0.052951 0.105902 \n", "ajf-bot -0.7 0.047145 0.094289 \n", + "metac-o1 -0.3 0.091325 0.182650 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", - "metac-perplexity -0.3 0.095661 0.191321 \n", - "metac-Gemini-Exp-1206 -0.4 0.078264 0.156528 \n", "laylaps -0.4 0.008744 0.017488 \n", + "metac-deepseek-r1+asknews -0.5 0.010898 0.021795 \n", "wunderplumb -0.9 0.003174 0.006348 \n", - "bot_median -0.3 0.032703 0.065406 \n", + "metac-Gemini-Exp-1206 -0.4 0.052582 0.105165 \n", + "bot_median -0.3 0.035269 0.070537 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-deepseek-r1+asknews -0.5 0.003337 0.006674 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.043874 0.087748 \n", + "metac-perplexity -0.4 0.052437 0.104874 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "minefrac1 -0.6 0.001607 0.003215 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.017338 0.034677 \n", - "metac-o1-preview -0.4 0.002155 0.004310 \n", + "minefrac1 -0.6 0.002021 0.004043 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.003320 0.006640 \n", "mmBot -0.4 0.001104 0.002208 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.002142 0.004284 \n", - "pgodzinai -0.5 0.003765 0.007529 \n", + "pgodzinai -0.5 0.004459 0.008918 \n", + "metac-exa -0.4 0.001286 0.002573 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-exa -0.5 0.001016 0.002032 \n", + "metac-Llama-3.1 -0.5 0.001049 0.002099 \n", "InstitutPelFutur -0.5 0.002292 0.004584 \n", - "metac-grok-2-1212 -0.5 0.002191 0.004383 \n", - "metac-gpt-4o -0.5 0.000514 0.001027 \n", - "metac-Llama-3.1 -0.5 0.000716 0.001433 " + "metac-o1-preview -0.5 0.000491 0.000982 \n", + "metac-grok-2-1212 -0.5 0.002610 0.005220 \n", + "metac-gpt-4o -0.5 0.000560 0.001120 " ] }, "execution_count": 42, @@ -9087,197 +9087,197 @@ " \n", " \n", " metac-o1\n", - " 6.1\n", - " 7.4\n", - " 9.7\n", - " 11.8\n", - " 13.2\n", + " 5.9\n", + " 7.3\n", + " 9.6\n", + " 11.9\n", + " 12.9\n", " \n", " \n", " metac-o1-preview\n", - " 3.9\n", - " 5.4\n", + " 3.8\n", + " 5.3\n", " 8.3\n", - " 11.4\n", - " 12.9\n", + " 11.3\n", + " 13.2\n", " \n", " \n", " manticAI\n", " 0.3\n", - " 2.0\n", + " 2.1\n", " 5.4\n", " 8.8\n", - " 10.6\n", + " 10.7\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.7\n", - " 2.2\n", - " 5.0\n", - " 7.8\n", - " 9.2\n", + " 0.5\n", + " 2.1\n", + " 5.1\n", + " 8.0\n", + " 9.6\n", " \n", " \n", " acm_bot\n", - " 0.6\n", - " 1.9\n", - " 4.7\n", - " 7.5\n", - " 8.7\n", + " 0.2\n", + " 1.4\n", + " 4.4\n", + " 7.4\n", + " 9.1\n", " \n", " \n", " metac-perplexity\n", - " -1.9\n", - " 0.3\n", - " 4.3\n", - " 7.9\n", - " 9.8\n", + " -1.8\n", + " 0.1\n", + " 4.2\n", + " 7.6\n", + " 9.9\n", " \n", " \n", " GreeneiBot2\n", - " -1.4\n", + " -1.1\n", " 0.7\n", - " 3.9\n", - " 7.0\n", - " 8.6\n", + " 4.0\n", + " 7.2\n", + " 9.4\n", " \n", " \n", " twsummerbot\n", " 0.1\n", - " 1.4\n", + " 1.5\n", " 3.9\n", " 6.3\n", - " 7.5\n", + " 7.4\n", " \n", " \n", " cookics_bot_TEST\n", - " -0.0\n", + " -0.2\n", " 1.1\n", " 3.1\n", " 5.0\n", - " 5.8\n", + " 6.3\n", " \n", " \n", " pgodzinai\n", - " -3.4\n", + " -3.5\n", " -1.1\n", " 3.1\n", - " 7.3\n", - " 9.5\n", + " 6.9\n", + " 8.9\n", " \n", " \n", " CumulativeBot\n", - " 0.1\n", - " 0.9\n", + " -0.2\n", + " 0.8\n", " 2.7\n", - " 4.5\n", - " 5.3\n", + " 4.6\n", + " 5.6\n", " \n", " \n", " SynapseSeer\n", - " 0.1\n", - " 0.9\n", + " 0.3\n", + " 1.0\n", " 2.5\n", " 4.1\n", - " 4.8\n", + " 4.9\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.6\n", - " -0.2\n", - " 2.4\n", - " 5.0\n", + " -1.1\n", + " -0.0\n", + " 2.5\n", + " 4.9\n", " 6.2\n", " \n", " \n", - " jkraybill_bot\n", - " -3.9\n", - " -1.7\n", - " 1.9\n", - " 5.0\n", - " 7.0\n", + " metac-exa\n", + " -5.1\n", + " -2.2\n", + " 1.7\n", + " 5.6\n", + " 7.8\n", " \n", " \n", - " metac-exa\n", - " -4.8\n", - " -2.6\n", - " 1.5\n", - " 5.8\n", - " 7.6\n", + " jkraybill_bot\n", + " -4.4\n", + " -1.7\n", + " 1.7\n", + " 4.8\n", + " 6.5\n", " \n", " \n", " metac-deepseek-r1+asknews\n", - " -1.8\n", + " -2.0\n", " -0.8\n", " 1.3\n", - " 3.5\n", - " 4.5\n", + " 3.4\n", + " 4.6\n", " \n", " \n", " MWG\n", - " -1.5\n", - " -0.7\n", - " 0.7\n", + " -1.6\n", + " -0.8\n", + " 0.6\n", " 2.2\n", " 3.0\n", " \n", " \n", - " pianobot\n", - " -1.2\n", - " -0.8\n", + " andrewsiah\n", + " -0.9\n", + " -0.6\n", " 0.0\n", - " 0.7\n", - " 1.1\n", + " 0.6\n", + " 1.0\n", " \n", " \n", - " andrewsiah\n", - " -0.9\n", - " -0.5\n", + " cobyj-bot\n", + " -1.4\n", + " -1.0\n", " -0.0\n", - " 0.6\n", + " 0.9\n", + " 1.4\n", + " \n", + " \n", + " pianobot\n", + " -1.3\n", + " -0.8\n", + " -0.0\n", + " 0.7\n", " 1.0\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.2\n", + " -0.3\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", - " cobyj-bot\n", - " -1.4\n", - " -0.9\n", - " -0.1\n", - " 0.8\n", - " 1.3\n", - " \n", - " \n", " annabot\n", " -3.4\n", - " -2.5\n", + " -2.4\n", " -0.4\n", " 1.2\n", " 2.1\n", " \n", " \n", - " KevinTestBot\n", - " -3.9\n", - " -2.8\n", - " -0.5\n", - " 1.6\n", - " 2.6\n", - " \n", - " \n", " bean_bot\n", - " -3.2\n", + " -3.3\n", " -2.2\n", " -0.5\n", " 1.0\n", " 1.9\n", " \n", " \n", + " KevinTestBot\n", + " -4.1\n", + " -2.7\n", + " -0.5\n", + " 1.6\n", + " 2.5\n", + " \n", + " \n", " CatrachoCaster\n", " -2.3\n", " -1.8\n", @@ -9288,250 +9288,667 @@ " \n", " jonahsingerbot\n", " -3.0\n", - " -2.2\n", + " -2.3\n", " -0.9\n", - " 0.3\n", - " 1.0\n", + " 0.5\n", + " 1.1\n", " \n", " \n", " krm-bot\n", - " -3.5\n", - " -2.6\n", - " -0.9\n", - " 0.8\n", - " 1.6\n", + " -3.6\n", + " -2.7\n", + " -1.0\n", + " 0.7\n", + " 1.5\n", " \n", " \n", " ProfessorSP\n", - " -4.4\n", + " -4.5\n", " -3.3\n", - " -1.0\n", + " -1.1\n", " 1.0\n", - " 2.0\n", + " 2.1\n", " \n", " \n", - " mmBot\n", - " -7.3\n", - " -5.5\n", - " -1.5\n", - " 2.4\n", - " 4.2\n", + " metac-grok-2-1212\n", + " -6.5\n", + " -4.6\n", + " -1.4\n", + " 1.9\n", + " 3.5\n", " \n", " \n", - " metac-grok-2-1212\n", - " -6.3\n", - " -4.7\n", + " mmBot\n", + " -6.9\n", + " -5.2\n", " -1.5\n", - " 2.0\n", - " 3.7\n", + " 2.3\n", + " 4.3\n", " \n", " \n", " 4Shadower\n", - " -4.9\n", + " -4.8\n", " -3.7\n", - " -1.6\n", - " 0.2\n", - " 1.2\n", + " -1.7\n", + " 0.3\n", + " 1.4\n", " \n", " \n", " swingswish\n", - " -5.4\n", + " -5.3\n", " -4.2\n", " -2.0\n", - " -0.1\n", - " 0.7\n", - " \n", - " \n", - " RPM_bot\n", - " -4.9\n", - " -3.9\n", - " -2.1\n", - " -0.8\n", " -0.2\n", + " 0.7\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -6.7\n", - " -5.0\n", - " -2.2\n", + " -6.4\n", + " -4.8\n", + " -2.0\n", " 0.8\n", - " 2.5\n", + " 2.4\n", + " \n", + " \n", + " RPM_bot\n", + " -4.9\n", + " -3.8\n", + " -2.0\n", + " -0.7\n", + " -0.1\n", " \n", " \n", " InstitutPelFutur\n", - " -8.7\n", - " -6.6\n", - " -2.5\n", + " -8.9\n", + " -6.4\n", + " -2.2\n", " 1.6\n", - " 3.3\n", + " 4.0\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.7\n", - " -5.3\n", + " -6.9\n", + " -5.1\n", " -2.6\n", - " 0.3\n", - " 1.7\n", + " 0.1\n", + " 1.6\n", " \n", " \n", " wunderplumb\n", - " -6.2\n", + " -6.1\n", " -5.0\n", - " -2.6\n", - " -0.2\n", - " 1.3\n", + " -2.7\n", + " -0.1\n", + " 0.9\n", " \n", " \n", " NextWorldLab\n", - " -8.3\n", - " -6.7\n", - " -3.7\n", - " -0.6\n", - " 0.9\n", + " -8.7\n", + " -6.9\n", + " -3.6\n", + " -0.2\n", + " 1.4\n", " \n", " \n", " Bot_Pepa\n", - " -6.9\n", - " -5.7\n", - " -3.9\n", + " -6.8\n", + " -5.9\n", + " -3.8\n", " -2.0\n", - " -1.1\n", + " -0.9\n", " \n", " \n", " laylaps\n", - " -10.1\n", - " -8.1\n", - " -3.9\n", - " -0.5\n", - " 1.3\n", + " -10.2\n", + " -8.0\n", + " -3.8\n", + " -0.1\n", + " 1.9\n", " \n", " \n", " VeritasAI\n", - " -7.8\n", - " -6.5\n", + " -8.0\n", + " -6.6\n", " -4.2\n", - " -1.8\n", - " -0.5\n", + " -1.9\n", + " -0.7\n", " \n", " \n", " minefrac1\n", - " -8.0\n", - " -6.8\n", - " -4.6\n", - " -2.5\n", + " -7.8\n", + " -6.9\n", + " -4.7\n", + " -2.6\n", + " -1.6\n", + " \n", + " \n", + " Grizeu_Bot\n", + " -9.1\n", + " -7.6\n", + " -4.9\n", + " -2.3\n", + " -0.9\n", + " \n", + " \n", + " metac-gpt-4o\n", + " -10.7\n", + " -9.1\n", + " -6.1\n", + " -3.0\n", " -1.5\n", " \n", " \n", - " Grizeu_Bot\n", - " -9.4\n", - " -7.7\n", - " -4.9\n", - " -2.4\n", - " -1.1\n", + " ajf-bot\n", + " -15.3\n", + " -12.9\n", + " -8.4\n", + " -4.3\n", + " -2.4\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", + "metac-o1 5.9 7.3 9.6 11.9 12.9\n", + "metac-o1-preview 3.8 5.3 8.3 11.3 13.2\n", + "manticAI 0.3 2.1 5.4 8.8 10.7\n", + "metac-Gemini-Exp-1206 0.5 2.1 5.1 8.0 9.6\n", + "acm_bot 0.2 1.4 4.4 7.4 9.1\n", + "metac-perplexity -1.8 0.1 4.2 7.6 9.9\n", + "GreeneiBot2 -1.1 0.7 4.0 7.2 9.4\n", + "twsummerbot 0.1 1.5 3.9 6.3 7.4\n", + "cookics_bot_TEST -0.2 1.1 3.1 5.0 6.3\n", + "pgodzinai -3.5 -1.1 3.1 6.9 8.9\n", + "CumulativeBot -0.2 0.8 2.7 4.6 5.6\n", + "SynapseSeer 0.3 1.0 2.5 4.1 4.9\n", + "metac-claude-3-5-sonnet-latest -1.1 -0.0 2.5 4.9 6.2\n", + "metac-exa -5.1 -2.2 1.7 5.6 7.8\n", + "jkraybill_bot -4.4 -1.7 1.7 4.8 6.5\n", + "metac-deepseek-r1+asknews -2.0 -0.8 1.3 3.4 4.6\n", + "MWG -1.6 -0.8 0.6 2.2 3.0\n", + "andrewsiah -0.9 -0.6 0.0 0.6 1.0\n", + "cobyj-bot -1.4 -1.0 -0.0 0.9 1.4\n", + "pianobot -1.3 -0.8 -0.0 0.7 1.0\n", + "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", + "annabot -3.4 -2.4 -0.4 1.2 2.1\n", + "bean_bot -3.3 -2.2 -0.5 1.0 1.9\n", + "KevinTestBot -4.1 -2.7 -0.5 1.6 2.5\n", + "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", + "jonahsingerbot -3.0 -2.3 -0.9 0.5 1.1\n", + "krm-bot -3.6 -2.7 -1.0 0.7 1.5\n", + "ProfessorSP -4.5 -3.3 -1.1 1.0 2.1\n", + "metac-grok-2-1212 -6.5 -4.6 -1.4 1.9 3.5\n", + "mmBot -6.9 -5.2 -1.5 2.3 4.3\n", + "4Shadower -4.8 -3.7 -1.7 0.3 1.4\n", + "swingswish -5.3 -4.2 -2.0 -0.2 0.7\n", + "metac-claude-3-5-sonnet-20240620 -6.4 -4.8 -2.0 0.8 2.4\n", + "RPM_bot -4.9 -3.8 -2.0 -0.7 -0.1\n", + "InstitutPelFutur -8.9 -6.4 -2.2 1.6 4.0\n", + "metac-Llama-3.1 -6.9 -5.1 -2.6 0.1 1.6\n", + "wunderplumb -6.1 -5.0 -2.7 -0.1 0.9\n", + "NextWorldLab -8.7 -6.9 -3.6 -0.2 1.4\n", + "Bot_Pepa -6.8 -5.9 -3.8 -2.0 -0.9\n", + "laylaps -10.2 -8.0 -3.8 -0.1 1.9\n", + "VeritasAI -8.0 -6.6 -4.2 -1.9 -0.7\n", + "minefrac1 -7.8 -6.9 -4.7 -2.6 -1.6\n", + "Grizeu_Bot -9.1 -7.6 -4.9 -2.3 -0.9\n", + "metac-gpt-4o -10.7 -9.1 -6.1 -3.0 -1.5\n", + "ajf-bot -15.3 -12.9 -8.4 -4.3 -2.4" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop 'bot_median' from all_bots list\n", + "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", + "df_bot_peer_wide_wo_median = df_bot_peer_wide.drop('bot_median', axis=1)\n", + "\n", + "NUM = round(df_bot_peer_wide['question_weight'].sum())\n", + "ITER = 1000\n", + "\n", + "result_df = weighted_bootstrap_analysis(df_bot_peer_wide_wo_median, all_bots_wo_median, NUM, ITER)\n", + "average_df = result_df / NUM\n", + "\n", + "print(f'BOT LEADERBOARD\\n\\n')\n", + "df_rounded = average_df.round(1)\n", + "df_rounded" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 125 + }, + "id": "MXAev2sNXdbZ", + "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionsrange_minrange_maxopen_upper_boundopen_lower_bound...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbbot_team_median
0312683126201.0multiple_choice[0, 1, 2-3, 4-6, >6]NaNNaNFalseFalse...2.3025855.857933NaN2.2926352.703087NaNNaNNaNNaN4.656813
1312693126386.821.0numericNone60.0100.0TrueTrue...-0.270414-0.505416NaN-0.050442-0.163369NaNNaNNaNNaN-1.478371
23127031264no1.0binaryNoneNaNNaNFalseFalse...-0.092275-0.092275NaN-0.210058-0.059485NaNNaNNaNNaN-0.149434
331280312745-91.0multiple_choice[0-4, 5-9, >9]NaNNaNNoneNone...-0.2288420.310155NaN0.1278330.152526NaNNaN-0.046520NaN0.310155
43128131275119.21.0numericNone0.0400.0FalseFalse...0.243782-0.102791NaN0.2653720.041050NaNNaN-0.771754NaN0.184891
\n", + "

5 rows × 57 columns

\n", + "
" + ], + "text/plain": [ + " pro_question_id bot_question_id resolution question_weight \\\n", + "0 31268 31262 0 1.0 \n", + "1 31269 31263 86.82 1.0 \n", + "2 31270 31264 no 1.0 \n", + "3 31280 31274 5-9 1.0 \n", + "4 31281 31275 119.2 1.0 \n", + "\n", + " type options range_min range_max \\\n", + "0 multiple_choice [0, 1, 2-3, 4-6, >6] NaN NaN \n", + "1 numeric None 60.0 100.0 \n", + "2 binary None NaN NaN \n", + "3 multiple_choice [0-4, 5-9, >9] NaN NaN \n", + "4 numeric None 0.0 400.0 \n", + "\n", + " open_upper_bound open_lower_bound ... metac-o1-preview metac-perplexity \\\n", + "0 False False ... 2.302585 5.857933 \n", + "1 True True ... -0.270414 -0.505416 \n", + "2 False False ... -0.092275 -0.092275 \n", + "3 None None ... -0.228842 0.310155 \n", + "4 False False ... 0.243782 -0.102791 \n", + "\n", + " minefrac1 mmBot pgodzinai pianobot swingswish twsummerbot \\\n", + "0 NaN 2.292635 2.703087 NaN NaN NaN \n", + "1 NaN -0.050442 -0.163369 NaN NaN NaN \n", + "2 NaN -0.210058 -0.059485 NaN NaN NaN \n", + "3 NaN 0.127833 0.152526 NaN NaN -0.046520 \n", + "4 NaN 0.265372 0.041050 NaN NaN -0.771754 \n", + "\n", + " wunderplumb bot_team_median \n", + "0 NaN 4.656813 \n", + "1 NaN -1.478371 \n", + "2 NaN -0.149434 \n", + "3 NaN 0.310155 \n", + "4 NaN 0.184891 \n", + "\n", + "[5 rows x 57 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
pro_question_idbot_question_idresolutionquestion_weighttypeoptionsrange_minrange_maxopen_upper_boundopen_lower_bound...metac-o1-previewmetac-perplexityminefrac1mmBotpgodzinaipianobotswingswishtwsummerbotwunderplumbbot_team_median
943538035345yes1.00binaryNoneNaNNaNFalseFalse...-0.054067NaNNaN0.0000000.000000NaN-0.054067-0.220515-0.054067-0.054067
953538135354no1.00binaryNoneNaNNaNFalseFalse...-1.845827NaNNaN-0.111226NaNNaN-0.054067-0.083382-2.944439-0.111226
963538535358yes1.00binaryNoneNaNNaNFalseFalse...-0.074901NaNNaN-0.074901NaNNaN-0.132060-0.158283-0.132060-0.132060
metac-gpt-4o-10.6-9.0-5.9-2.9-1.3973538635364no0.85binaryNoneNaNNaNFalseFalse...-0.6804300.628948NaN-0.680430-0.680430NaN-0.0912550.8117930.628948-0.091255
ajf-bot-15.4-12.8-8.3-4.2-2.1983538735367no0.85binaryNoneNaNNaNFalseFalse...-0.0177090.000000NaN-0.112251-0.017709NaN-0.163782-0.241614-0.163782-0.112251
\n", + "

5 rows × 57 columns

\n", "
" ], "text/plain": [ - " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.1 7.4 9.7 11.8 13.2\n", - "metac-o1-preview 3.9 5.4 8.3 11.4 12.9\n", - "manticAI 0.3 2.0 5.4 8.8 10.6\n", - "metac-Gemini-Exp-1206 0.7 2.2 5.0 7.8 9.2\n", - "acm_bot 0.6 1.9 4.7 7.5 8.7\n", - "metac-perplexity -1.9 0.3 4.3 7.9 9.8\n", - "GreeneiBot2 -1.4 0.7 3.9 7.0 8.6\n", - "twsummerbot 0.1 1.4 3.9 6.3 7.5\n", - "cookics_bot_TEST -0.0 1.1 3.1 5.0 5.8\n", - "pgodzinai -3.4 -1.1 3.1 7.3 9.5\n", - "CumulativeBot 0.1 0.9 2.7 4.5 5.3\n", - "SynapseSeer 0.1 0.9 2.5 4.1 4.8\n", - "metac-claude-3-5-sonnet-latest -1.6 -0.2 2.4 5.0 6.2\n", - "jkraybill_bot -3.9 -1.7 1.9 5.0 7.0\n", - "metac-exa -4.8 -2.6 1.5 5.8 7.6\n", - "metac-deepseek-r1+asknews -1.8 -0.8 1.3 3.5 4.5\n", - "MWG -1.5 -0.7 0.7 2.2 3.0\n", - "pianobot -1.2 -0.8 0.0 0.7 1.1\n", - "andrewsiah -0.9 -0.5 -0.0 0.6 1.0\n", - "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", - "cobyj-bot -1.4 -0.9 -0.1 0.8 1.3\n", - "annabot -3.4 -2.5 -0.4 1.2 2.1\n", - "KevinTestBot -3.9 -2.8 -0.5 1.6 2.6\n", - "bean_bot -3.2 -2.2 -0.5 1.0 1.9\n", - "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.2 -0.9 0.3 1.0\n", - "krm-bot -3.5 -2.6 -0.9 0.8 1.6\n", - "ProfessorSP -4.4 -3.3 -1.0 1.0 2.0\n", - "mmBot -7.3 -5.5 -1.5 2.4 4.2\n", - "metac-grok-2-1212 -6.3 -4.7 -1.5 2.0 3.7\n", - "4Shadower -4.9 -3.7 -1.6 0.2 1.2\n", - "swingswish -5.4 -4.2 -2.0 -0.1 0.7\n", - "RPM_bot -4.9 -3.9 -2.1 -0.8 -0.2\n", - "metac-claude-3-5-sonnet-20240620 -6.7 -5.0 -2.2 0.8 2.5\n", - "InstitutPelFutur -8.7 -6.6 -2.5 1.6 3.3\n", - "metac-Llama-3.1 -6.7 -5.3 -2.6 0.3 1.7\n", - "wunderplumb -6.2 -5.0 -2.6 -0.2 1.3\n", - "NextWorldLab -8.3 -6.7 -3.7 -0.6 0.9\n", - "Bot_Pepa -6.9 -5.7 -3.9 -2.0 -1.1\n", - "laylaps -10.1 -8.1 -3.9 -0.5 1.3\n", - "VeritasAI -7.8 -6.5 -4.2 -1.8 -0.5\n", - "minefrac1 -8.0 -6.8 -4.6 -2.5 -1.5\n", - "Grizeu_Bot -9.4 -7.7 -4.9 -2.4 -1.1\n", - "metac-gpt-4o -10.6 -9.0 -5.9 -2.9 -1.3\n", - "ajf-bot -15.4 -12.8 -8.3 -4.2 -2.1" + " pro_question_id bot_question_id resolution question_weight type \\\n", + "94 35380 35345 yes 1.00 binary \n", + "95 35381 35354 no 1.00 binary \n", + "96 35385 35358 yes 1.00 binary \n", + "97 35386 35364 no 0.85 binary \n", + "98 35387 35367 no 0.85 binary \n", + "\n", + " options range_min range_max open_upper_bound open_lower_bound ... \\\n", + "94 None NaN NaN False False ... \n", + "95 None NaN NaN False False ... \n", + "96 None NaN NaN False False ... \n", + "97 None NaN NaN False False ... \n", + "98 None NaN NaN False False ... \n", + "\n", + " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", + "94 -0.054067 NaN NaN 0.000000 0.000000 \n", + "95 -1.845827 NaN NaN -0.111226 NaN \n", + "96 -0.074901 NaN NaN -0.074901 NaN \n", + "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "\n", + " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", + "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", + "95 NaN -0.054067 -0.083382 -2.944439 -0.111226 \n", + "96 NaN -0.132060 -0.158283 -0.132060 -0.132060 \n", + "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", + "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", + "\n", + "[5 rows x 57 columns]" ] }, - "execution_count": 49, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Drop 'bot_median' from all_bots list\n", - "all_bots_wo_median = np.delete(all_bots, np.where(all_bots == 'bot_median')[0][0])\n", - "df_bot_peer_wide_wo_median = df_bot_peer_wide.drop('bot_median', axis=1)\n", - "\n", - "NUM = round(df_bot_peer_wide['question_weight'].sum())\n", - "ITER = 1000\n", - "\n", - "result_df = weighted_bootstrap_analysis(df_bot_peer_wide_wo_median, all_bots_wo_median, NUM, ITER)\n", - "average_df = result_df / NUM\n", - "\n", - "print(f'BOT LEADERBOARD\\n\\n')\n", - "df_rounded = average_df.round(1)\n", - "df_rounded" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 125 + "output_type": "display_data" }, - "id": "MXAev2sNXdbZ", - "outputId": "eebb723f-5494-4b89-cf0d-efa5b1626cb7" - }, - "outputs": [ { "name": "stdout", "output_type": "stream", @@ -9590,14 +10007,6 @@ " 0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", - " -0.0\n", - " -0.0\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", " jonahsingerbot\n", " -0.0\n", " -0.0\n", @@ -9606,20 +10015,20 @@ " -0.0\n", " \n", " \n", - " bean_bot\n", - " -0.0\n", - " -0.0\n", + " X_bot\n", " -0.0\n", " -0.0\n", " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", - " X_bot\n", + " bean_bot\n", + " -0.0\n", + " -0.0\n", " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " CumulativeBot\n", @@ -9638,7 +10047,7 @@ " -0.0\n", " \n", " \n", - " KevinTestBot\n", + " RPM_bot\n", " -0.1\n", " -0.0\n", " -0.0\n", @@ -9646,7 +10055,7 @@ " 0.0\n", " \n", " \n", - " SynapseSeer\n", + " KevinTestBot\n", " -0.1\n", " -0.0\n", " -0.0\n", @@ -9654,12 +10063,12 @@ " 0.0\n", " \n", " \n", - " Grizeu_Bot\n", - " -0.2\n", + " SynapseSeer\n", " -0.1\n", " -0.0\n", - " 0.1\n", - " 0.2\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " pianobot\n", @@ -9670,6 +10079,14 @@ " 0.0\n", " \n", " \n", + " Grizeu_Bot\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 0.1\n", + " 0.2\n", + " \n", + " \n", " CatrachoCaster\n", " -0.1\n", " -0.1\n", @@ -9730,64 +10147,72 @@ " -0.2\n", " -0.2\n", " -0.1\n", + " -0.0\n", + " -0.0\n", + " \n", + " \n", + " ProfessorSP\n", + " -0.2\n", + " -0.2\n", " -0.1\n", " -0.0\n", + " -0.0\n", " \n", " \n", - " metac-o1\n", + " GreeneiBot2\n", " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", " 0.0\n", - " 0.1\n", " \n", " \n", - " GreeneiBot2\n", - " -0.2\n", + " ajf-bot\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", " 0.0\n", " \n", " \n", - " ProfessorSP\n", + " Bot_Pepa\n", " -0.2\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " -0.1\n", " -0.0\n", " \n", " \n", - " ajf-bot\n", + " acm_bot\n", " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", - " 0.0\n", + " 0.1\n", " \n", " \n", - " acm_bot\n", + " metac-o1\n", " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", " 0.0\n", - " 0.1\n", " \n", " \n", - " Bot_Pepa\n", - " -0.2\n", + " metac-deepseek-r1+asknews\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", " -0.0\n", " \n", " \n", - " metac-perplexity\n", - " -0.3\n", + " wunderplumb\n", " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.1\n", " -0.1\n", - " 0.0\n", - " 0.1\n", " \n", " \n", " laylaps\n", @@ -9801,41 +10226,41 @@ " metac-Gemini-Exp-1206\n", " -0.3\n", " -0.2\n", - " -0.1\n", + " -0.2\n", " -0.0\n", - " 0.1\n", + " 0.0\n", " \n", " \n", - " wunderplumb\n", + " manticAI\n", " -0.3\n", " -0.2\n", + " -0.2\n", " -0.1\n", - " -0.1\n", - " -0.1\n", + " -0.0\n", " \n", " \n", " bot_median\n", " -0.3\n", - " -0.3\n", " -0.2\n", - " -0.0\n", + " -0.2\n", + " -0.1\n", " 0.0\n", " \n", " \n", - " manticAI\n", + " metac-claude-3-5-sonnet-20240620\n", + " -0.3\n", " -0.3\n", - " -0.2\n", " -0.2\n", " -0.1\n", - " -0.0\n", + " 0.0\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -0.3\n", + " metac-perplexity\n", + " -0.4\n", " -0.3\n", " -0.2\n", - " -0.1\n", - " -0.1\n", + " -0.0\n", + " 0.0\n", " \n", " \n", " NextWorldLab\n", @@ -9854,15 +10279,7 @@ " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -0.4\n", - " -0.3\n", - " -0.2\n", - " -0.1\n", - " 0.0\n", - " \n", - " \n", - " metac-o1-preview\n", + " mmBot\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9870,7 +10287,7 @@ " -0.1\n", " \n", " \n", - " mmBot\n", + " metac-claude-3-5-sonnet-latest\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -9878,20 +10295,20 @@ " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " pgodzinai\n", + " -0.4\n", " -0.4\n", - " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", " \n", " \n", - " pgodzinai\n", + " metac-exa\n", " -0.4\n", " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", " \n", " \n", " VeritasAI\n", @@ -9902,7 +10319,7 @@ " -0.1\n", " \n", " \n", - " metac-exa\n", + " metac-Llama-3.1\n", " -0.4\n", " -0.4\n", " -0.3\n", @@ -9910,7 +10327,7 @@ " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", + " metac-o1-preview\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9918,7 +10335,7 @@ " -0.1\n", " \n", " \n", - " metac-grok-2-1212\n", + " InstitutPelFutur\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9926,7 +10343,7 @@ " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", + " metac-grok-2-1212\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9934,7 +10351,7 @@ " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", + " metac-gpt-4o\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -9949,16 +10366,16 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", + "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", "SynapseSeer -0.1 -0.0 -0.0 0.0 0.0\n", - "Grizeu_Bot -0.2 -0.1 -0.0 0.1 0.2\n", "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", + "Grizeu_Bot -0.2 -0.1 -0.0 0.1 0.2\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", @@ -9966,33 +10383,33 @@ "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", - "MWG -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", - "GreeneiBot2 -0.2 -0.2 -0.1 -0.0 0.0\n", + "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", + "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", - "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-perplexity -0.3 -0.3 -0.1 0.0 0.1\n", - "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-Gemini-Exp-1206 -0.3 -0.2 -0.1 -0.0 0.1\n", + "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-o1 -0.3 -0.2 -0.1 -0.0 0.0\n", + "metac-deepseek-r1+asknews -0.3 -0.2 -0.1 -0.1 -0.0\n", "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", - "bot_median -0.3 -0.3 -0.2 -0.0 0.0\n", + "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.2 -0.2 -0.0 0.0\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "metac-deepseek-r1+asknews -0.3 -0.3 -0.2 -0.1 -0.1\n", + "bot_median -0.3 -0.2 -0.2 -0.1 0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.3 -0.3 -0.2 -0.1 0.0\n", + "metac-perplexity -0.4 -0.3 -0.2 -0.0 0.0\n", "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-claude-3-5-sonnet-20240620 -0.4 -0.3 -0.2 -0.1 0.0\n", - "metac-o1-preview -0.4 -0.3 -0.2 -0.1 -0.1\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.1\n", "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", - "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", + "metac-Llama-3.1 -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-o1-preview -0.5 -0.4 -0.3 -0.2 -0.1\n", "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.2 -0.1" + "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" ] }, "execution_count": 50, @@ -10004,6 +10421,7 @@ "NUM = round(df_bot_vs_pro_peer['question_weight'].sum())\n", "ITER = 1000\n", "\n", + "display_head_and_tail(df_bot_vs_pro_peer)\n", "result_df = weighted_bootstrap_analysis(df_bot_vs_pro_peer, all_bots, NUM, ITER)\n", "average_df = result_df / NUM\n", "\n", @@ -10654,506 +11072,506 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.65]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.2]\n", " >>> Collected 1 forecasts: [0.98]\n", " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.65]\n", - " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.01]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.95]\n", + " >>> Collected 1 forecasts: [0.97]\n", + " >>> Collected 1 forecasts: [0.99]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.35]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.15]\n", - " >>> Collected 2 forecasts: [0.2, 0.7]\n", - " >>> Collected 2 forecasts: [0.95, 0.9]\n", - " >>> Collected 2 forecasts: [0.85, 0.7]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.85]\n", " >>> Collected 2 forecasts: [0.1, 0.05]\n", " >>> Collected 2 forecasts: [0.7, 0.6]\n", - " >>> Collected 2 forecasts: [0.7, 0.4]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.15, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.35]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", - " >>> Collected 2 forecasts: [0.6, 0.9]\n", - " >>> Collected 2 forecasts: [0.25, 0.5]\n", - " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.7, 0.6]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.2, 0.25]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", + " >>> Collected 2 forecasts: [0.7, 0.8]\n", + " >>> Collected 2 forecasts: [0.65, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.2]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", " >>> Collected 2 forecasts: [0.15, 0.3]\n", " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.35]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.4]\n", - " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.3, 0.3]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", " >>> Collected 2 forecasts: [0.98, 0.97]\n", " >>> Collected 2 forecasts: [0.4, 0.4]\n", - " >>> Collected 2 forecasts: [0.35, 0.4]\n", - " >>> Collected 2 forecasts: [0.65, 0.6]\n", - " >>> Collected 2 forecasts: [0.25, 0.02]\n", + " >>> Collected 2 forecasts: [0.4, 0.25]\n", + " >>> Collected 2 forecasts: [0.85, 0.6]\n", + " >>> Collected 2 forecasts: [0.01, 0.02]\n", " >>> Collected 2 forecasts: [0.7, 0.7]\n", - " >>> Collected 2 forecasts: [0.99, 0.7]\n", - " >>> Collected 2 forecasts: [0.95, 0.98]\n", - " >>> Collected 2 forecasts: [0.95, 0.15]\n", - " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.7]\n", - " >>> Collected 2 forecasts: [0.35, 0.4]\n", + " >>> Collected 2 forecasts: [0.99, 0.9]\n", + " >>> Collected 2 forecasts: [0.97, 0.99]\n", + " >>> Collected 2 forecasts: [0.99, 0.1]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.9, 0.8]\n", + " >>> Collected 2 forecasts: [0.6, 0.4]\n", " >>> Collected 2 forecasts: [0.8, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", - " >>> Collected 2 forecasts: [0.3, 0.3]\n", - " >>> Collected 2 forecasts: [0.6, 0.85]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.2, 0.3]\n", - " >>> Collected 2 forecasts: [0.1, 0.02]\n", + " >>> Collected 2 forecasts: [0.05, 0.15]\n", + " >>> Collected 2 forecasts: [0.3, 0.2]\n", + " >>> Collected 2 forecasts: [0.75, 0.7]\n", + " >>> Collected 2 forecasts: [0.15, 0.2]\n", + " >>> Collected 2 forecasts: [0.25, 0.3]\n", + " >>> Collected 2 forecasts: [0.05, 0.15]\n", " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.1]\n", + " >>> Collected 2 forecasts: [0.15, 0.05]\n", " >>> Collected 2 forecasts: [0.8, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.95]\n", - " >>> Collected 2 forecasts: [0.15, 0.4]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.85, 0.8]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.07]\n", - " >>> Collected 3 forecasts: [0.2, 0.7, 0.62]\n", - " >>> Collected 3 forecasts: [0.95, 0.9, 0.82]\n", - " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.65]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.7]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.82]\n", + " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.35, 0.25]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.6, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.5, 0.108]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.95]\n", + " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.2, 0.25, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", + " >>> Collected 3 forecasts: [0.65, 0.3, 0.108]\n", + " >>> Collected 3 forecasts: [0.1, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.95]\n", " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.125]\n", + " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.03]\n", - " >>> Collected 3 forecasts: [0.1, 0.4, 0.35]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, 0.115]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.03]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.3, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, 0.115]\n", " >>> Collected 3 forecasts: [0.98, 0.97, 0.97]\n", " >>> Collected 3 forecasts: [0.4, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.35, 0.4, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.65, 0.6, 0.17]\n", - " >>> Collected 3 forecasts: [0.25, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.4, 0.25, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.85, 0.6, 0.17]\n", + " >>> Collected 3 forecasts: [0.01, 0.02, 0.12]\n", " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", - " >>> Collected 3 forecasts: [0.95, 0.98, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.95, 0.15, 0.14]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.7, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.35, 0.4, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.9, 0.99]\n", + " >>> Collected 3 forecasts: [0.97, 0.99, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.99, 0.1, 0.14]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", " >>> Collected 3 forecasts: [0.8, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.3, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.6, 0.85, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.1, 0.02, 0.086]\n", + " >>> Collected 3 forecasts: [0.05, 0.15, 0.026]\n", + " >>> Collected 3 forecasts: [0.3, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.75, 0.7, 0.67]\n", + " >>> Collected 3 forecasts: [0.15, 0.2, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.05, 0.15, 0.086]\n", " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.15, 0.1, 0.02]\n", + " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.95, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.2, 0.7, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.95, 0.9, 0.82, 0.794]\n", - " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.884]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", + " >>> Collected 3 forecasts: [0.85, 0.65, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.6, 0.9, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.5, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, 0.652]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.95, 0.052]\n", + " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.25, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.65, 0.3, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.1, 0.2, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.95, 0.052]\n", " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", - " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", + " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.65, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.25, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.85, 0.6, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\n", " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.95, 0.15, 0.14, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.7, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.99, 0.9, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.99, 0.1, 0.14, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", " >>> Collected 4 forecasts: [0.8, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.3, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.85, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.02, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.3, 0.2, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.7, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.2, nan, nan]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.15, 0.086, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.1, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.95, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.15, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.95, 0.9, 0.82, 0.794, nan]\n", - " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", + " >>> Collected 4 forecasts: [0.85, 0.65, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.9, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.5, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, 0.652, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999]\n", + " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.25, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.65, 0.3, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.2, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999]\n", " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.65, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.85, 0.6, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", " >>> Collected 5 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.6, 0.85, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.2, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.1, 0.02, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.3, 0.2, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.7, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.15, 0.2, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.05, 0.15, 0.086, nan, 0.12]\n", " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.1, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", - " >>> Collected 5 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.15, 0.4, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75]\n", - " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", + " >>> Collected 5 forecasts: [0.85, 0.65, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125]\n", + " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125]\n", " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", " >>> Collected 6 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1]\n", " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28]\n", - " >>> Collected 7 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88]\n", - " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", + " >>> Collected 6 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85]\n", " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2]\n", - " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15]\n", + " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", + " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65]\n", + " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27]\n", - " >>> Collected 7 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02]\n", - " >>> Collected 7 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65]\n", - " >>> Collected 7 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3]\n", - " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6]\n", - " >>> Collected 7 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03]\n", - " >>> Collected 7 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75]\n", - " >>> Collected 7 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9]\n", - " >>> Collected 7 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", + " >>> Collected 7 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", + " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75]\n", + " >>> Collected 7 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38]\n", + " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65]\n", + " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", + " >>> Collected 7 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9]\n", + " >>> Collected 7 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75]\n", + " >>> Collected 7 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3]\n", + " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765]\n", + " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513]\n", - " >>> Collected 8 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847]\n", - " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223]\n", - " >>> Collected 8 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58]\n", - " >>> Collected 8 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785]\n", - " >>> Collected 8 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 8 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", + " >>> Collected 8 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847]\n", + " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", + " >>> Collected 8 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", + " >>> Collected 8 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", + " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.65]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", - " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.8]\n", - " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95]\n", - " >>> Collected 9 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58, 0.2]\n", - " >>> Collected 9 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.95]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.07, 0.0559999999999999, nan, 0.175, 0.28, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.65, nan, 0.75, nan]\n", - " >>> Collected 10 forecasts: [0.95, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.75, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", + " >>> Collected 9 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.65]\n", + " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.75]\n", + " >>> Collected 9 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", + " >>> Collected 9 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.95]\n", + " >>> Collected 9 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.9]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.75, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.25, nan, nan, 0.225, 0.15, nan, 0.2, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.6, 0.9, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.5, 0.108, 0.264, nan, 0.2, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.18, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.2, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.95, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", + " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.3339999999999999, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.35, 0.293]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.65, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.27, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.65, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.25, 0.155]\n", - " >>> Collected 10 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.65, 0.85, 0.8, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.95, 0.98, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.95, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.95, 0.15, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.35, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.02, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.7, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.3, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.9, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.85, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, nan, 0.2, 0.2, 0.35, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.2, 0.3, 0.3925, nan, 0.38, 0.675, 0.6, 0.58, 0.2, 0.574]\n", - " >>> Collected 10 forecasts: [0.1, 0.02, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.03, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.1, 0.02, nan, 0.098, 0.05, 0.02, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.75, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.95, 0.95, 0.905, 0.78, 0.935, 0.9, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.15, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.65, 0.126]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, nan, nan, 0.744, 0.8, 0.85, 0.7240000000000001, 0.95, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.65, 0.155]\n", + " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.75, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35, 0.088]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.95, 0.762]\n", + " >>> Collected 10 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15, 0.126]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.9, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -11234,9 +11652,9 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", - " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", " \n", " \n", " 1\n", @@ -11244,7 +11662,7 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", " \n", @@ -11252,25 +11670,25 @@ " binary\n", " NaN\n", " no\n", - " 0.05\n", - " 0.063\n", - " 0.11\n", + " 0.1\n", + " 0.085\n", + " 0.1\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.2,0.6,0.2]\n", + " [0.6,0.35,0.05]\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", " numeric\n", " NaN\n", " 119.2\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " \n", @@ -11289,7 +11707,7 @@ " NaN\n", " yes\n", " 0.9\n", - " 0.905\n", + " 0.9\n", " 0.9025\n", " \n", " \n", @@ -11297,9 +11715,9 @@ " binary\n", " NaN\n", " no\n", - " 0.15\n", - " 0.15\n", - " 0.1085\n", + " 0.85\n", + " 0.65\n", + " 0.3585\n", " \n", " \n", " 355\n", @@ -11307,8 +11725,8 @@ " NaN\n", " yes\n", " 0.9\n", - " 0.9\n", - " 0.825\n", + " 0.85\n", + " 0.772\n", " \n", " \n", " 361\n", @@ -11316,8 +11734,8 @@ " NaN\n", " no\n", " 0.85\n", - " 0.8\n", - " 0.755\n", + " 0.71\n", + " 0.709\n", " \n", " \n", " 364\n", @@ -11348,42 +11766,42 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.010416666666666666,0.20833333333333334,0.04... \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.05 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "2 0.1 \n", + "3 [0.6,0.35,0.05] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", "342 0.9 \n", - "351 0.15 \n", + "351 0.85 \n", "355 0.9 \n", "361 0.85 \n", "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", - "342 0.905 \n", - "351 0.15 \n", - "355 0.9 \n", - "361 0.8 \n", + "342 0.9 \n", + "351 0.65 \n", + "355 0.85 \n", + "361 0.71 \n", "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.11 \n", - "3 [0.0001, 0.45, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", "342 0.9025 \n", - "351 0.1085 \n", - "355 0.825 \n", - "361 0.755 \n", + "351 0.3585 \n", + "355 0.772 \n", + "361 0.709 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -11474,52 +11892,52 @@ " \n", " 0\n", " 1\n", - " 1326.64\n", + " 702.66\n", " \n", " \n", " 1\n", " 2\n", - " 2492.14\n", + " 2127.15\n", " \n", " \n", " 2\n", " 3\n", - " 2545.30\n", + " 2378.31\n", " \n", " \n", " 3\n", " 4\n", - " 2613.88\n", + " 2447.50\n", " \n", " \n", " 4\n", " 5\n", - " 2743.23\n", + " 2613.58\n", " \n", " \n", " 5\n", " 6\n", - " 2513.69\n", + " 2565.78\n", " \n", " \n", " 6\n", " 7\n", - " 2611.87\n", + " 2492.12\n", " \n", " \n", " 7\n", " 8\n", - " 2685.15\n", + " 2572.02\n", " \n", " \n", " 8\n", " 9\n", - " 2381.69\n", + " 2483.55\n", " \n", " \n", " 9\n", " 10\n", - " 2215.95\n", + " 2418.82\n", " \n", " \n", "\n", @@ -11527,16 +11945,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 1326.64\n", - "1 2 2492.14\n", - "2 3 2545.30\n", - "3 4 2613.88\n", - "4 5 2743.23\n", - "5 6 2513.69\n", - "6 7 2611.87\n", - "7 8 2685.15\n", - "8 9 2381.69\n", - "9 10 2215.95" + "0 1 702.66\n", + "1 2 2127.15\n", + "2 3 2378.31\n", + "3 4 2447.50\n", + "4 5 2613.58\n", + "5 6 2565.78\n", + "6 7 2492.12\n", + "7 8 2572.02\n", + "8 9 2483.55\n", + "9 10 2418.82" ] }, "execution_count": 60, @@ -11690,18 +12108,18 @@ " NaN\n", " False\n", " False\n", - " [0.010416666666666666,0.20833333333333334,0.04...\n", + " [0.01,0.7,0.2,0.07,0.02]\n", " ...\n", - " [0.010416666666666666, 0.0001, 0.0001, 0.0001,...\n", - " [0.23020833333333335, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.13, 0.0001, 0.0001, 0.0001, 0.0001]\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", - " [0.22757702085998072, 0.0001, 0.0001, 0.0001, ...\n", - " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", - " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.04847475882512753, 0.0001, 0.0001, 0.0001, ...\n", + " [0.04847475882512753, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", @@ -11717,10 +12135,10 @@ " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", - " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", + " [0.05, 0.0505555556, 0.0511111111, 0.051666666...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", @@ -11738,18 +12156,18 @@ " NaN\n", " False\n", " False\n", - " 0.05\n", + " 0.1\n", " ...\n", - " 0.05\n", " 0.1\n", - " 0.07\n", - " 0.063\n", - " 0.063\n", - " 0.07\n", - " 0.11\n", - " 0.11\n", - " 0.15\n", - " 0.15\n", + " 0.1\n", + " 0.1\n", + " 0.085\n", + " 0.085\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", + " 0.1\n", " \n", " \n", " 3\n", @@ -11762,18 +12180,18 @@ " NaN\n", " NaN\n", " NaN\n", - " [0.2,0.6,0.2]\n", + " [0.6,0.35,0.05]\n", " ...\n", - " [0.0001, 0.6, 0.0001]\n", - " [0.0001, 0.525, 0.0001]\n", + " [0.0001, 0.35, 0.0001]\n", + " [0.0001, 0.475, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.5562499999999999, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.48124999999999996, 0.0001]\n", - " [0.0001, 0.45, 0.0001]\n", - " [0.0001, 0.45, 0.0001]\n", - " [0.0001, 0.48124999999999996, 0.0001]\n", - " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.47324999999999995, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.5048350576136786, 0.0001]\n", + " [0.0001, 0.49717011522735727, 0.0001]\n", " \n", " \n", " 4\n", @@ -11786,10 +12204,10 @@ " 400.0\n", " False\n", " False\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " ...\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", - " [0.0, 0.00267857145, 0.00535714285, 0.00803571...\n", + " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", + " [0.0, 0.0032500000000000003, 0.006500000000000...\n", " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", " [0.0, 0.0021590909, 0.0043181818, 0.0064772727...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", @@ -11820,80 +12238,80 @@ "4 NaN 0.0 400.0 False \n", "\n", " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.010416666666666666,0.20833333333333334,0.04... ... \n", + "0 False [0.01,0.7,0.2,0.07,0.02] ... \n", "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", - "2 False 0.05 ... \n", - "3 NaN [0.2,0.6,0.2] ... \n", - "4 False [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... ... \n", + "2 False 0.1 ... \n", + "3 NaN [0.6,0.35,0.05] ... \n", + "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", "\n", " median_forecast_1_bots \\\n", - "0 [0.010416666666666666, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.01, 0.0001, 0.0001, 0.0001, 0.0001] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.05 \n", - "3 [0.0001, 0.6, 0.0001] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", + "2 0.1 \n", + "3 [0.0001, 0.35, 0.0001] \n", + "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 [0.23020833333333335, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", + "0 [0.13, 0.0001, 0.0001, 0.0001, 0.0001] \n", + "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", "2 0.1 \n", - "3 [0.0001, 0.525, 0.0001] \n", - "4 [0.0, 0.00267857145, 0.00535714285, 0.00803571... \n", + "3 [0.0001, 0.475, 0.0001] \n", + "4 [0.0, 0.0032500000000000003, 0.006500000000000... \n", "\n", " median_forecast_3_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", - "2 0.07 \n", + "1 [0.05, 0.0505555556, 0.0511111111, 0.051666666... \n", + "2 0.1 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", "\n", " median_forecast_4_bots \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5562499999999999, 0.0001] \n", "4 [0.0, 0.0021590909, 0.0043181818, 0.0064772727... \n", "\n", " median_forecast_5_bots \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " median_forecast_6_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.07 \n", - "3 [0.0001, 0.48124999999999996, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.47324999999999995, 0.0001] \n", "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", "\n", " median_forecast_7_bots \\\n", - "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.11 \n", - "3 [0.0001, 0.45, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_8_bots \\\n", - "0 [0.22757702085998072, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.11 \n", - "3 [0.0001, 0.45, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_9_bots \\\n", - "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.04847475882512753, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", - "2 0.15 \n", - "3 [0.0001, 0.48124999999999996, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.5048350576136786, 0.0001] \n", "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", "\n", " median_forecast_10_bots \n", - "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.04847475882512753, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", - "2 0.15 \n", - "3 [0.0001, 0.45, 0.0001] \n", + "2 0.1 \n", + "3 [0.0001, 0.49717011522735727, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", "[5 rows x 29 columns]" @@ -11973,7 +12391,7 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " \n", " \n", @@ -11991,7 +12409,7 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", " \n", " \n", @@ -12009,7 +12427,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.063\n", + " 0.085\n", " 0.013\n", " \n", " \n", @@ -12082,9 +12500,9 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", @@ -12153,7 +12571,7 @@ " False\n", " 35380\n", " 1.00\n", - " 0.905\n", + " 0.9\n", " 0.95\n", " \n", " \n", @@ -12171,7 +12589,7 @@ " False\n", " 35381\n", " 1.00\n", - " 0.15\n", + " 0.65\n", " 0.05\n", " \n", " \n", @@ -12189,7 +12607,7 @@ " False\n", " 35385\n", " 1.00\n", - " 0.9\n", + " 0.85\n", " 0.97\n", " \n", " \n", @@ -12207,7 +12625,7 @@ " False\n", " 35386\n", " 0.85\n", - " 0.8\n", + " 0.71\n", " 0.666\n", " \n", " \n", @@ -12255,10 +12673,10 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median \n", - "342 1.00 0.905 0.95 \n", - "351 1.00 0.15 0.05 \n", - "355 1.00 0.9 0.97 \n", - "361 0.85 0.8 0.666 \n", + "342 1.00 0.9 0.95 \n", + "351 1.00 0.65 0.05 \n", + "355 1.00 0.85 0.97 \n", + "361 0.85 0.71 0.666 \n", "364 0.85 0.05 0.03 " ] }, @@ -12315,14 +12733,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -0.1182\n" + "Weighted Total Score: -0.1240\n" ] } ], @@ -12344,7 +12762,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAIjCAYAAADvBuGTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAdxxJREFUeJzt3XlYFWX/x/HPAWURWVxYE8V9F8wF96VIXNNKU1sU08p2H7LSntK2J7PFtDItK7HFUtO00kwll9x3SzNzx1TADRBUVJjfH/Pj4BE39OCAvl/XNZfMPfeZ853DEfice+Yem2EYhgAAAAAA18TF6gIAAAAA4EZAuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAWGrPnj2y2WyKi4uzupRCidcHAIoOwhWAQi0uLk42m81hCQgIUNu2bfXLL79c9X7ffPNNzZw587L92rRpk+f5L7S88sorV13L9RIWFqbOnTtfcNuiRYtks9n0/fffX+eq8mfy5MkaPXp0gez7Rnh99uzZo379+qly5cry8PBQUFCQWrVqpeHDh1tdWpEUFhaW52dPy5Yt9cMPP1hdGoBCqpjVBQDAlXjttddUsWJFGYahpKQkxcXFqWPHjvrpp58u+gfxpbz55pvq3r27unXrdsl+//3vfzVgwAD7+po1a/TBBx/oxRdfVM2aNe3t9erVy3cNyL/Jkydr8+bNGjRokNWlFDo7duxQo0aN5OnpqYceekhhYWE6ePCg1q9fr5EjR+rVV1+1usQiKSIiQs8++6wk6cCBA/rkk0909913a9y4cRo4cKDF1QEobAhXAIqEDh06qGHDhvb1/v37KzAwUN9+++1Vhasrdccddzise3h46IMPPtAdd9yhNm3aFNjzAvn1/vvvKz09XRs3blSFChUctiUnJ1/XWjIyMuTl5XVdn7Og3HLLLXrggQfs63369FGVKlX0/vvvXzRcnT17VtnZ2XJzc7teZQIoJDgtEECR5OfnJ09PTxUr5vgZUUZGhp599lmFhobK3d1d1atX17vvvivDMOx9bDabMjIyNGnSJPvpPjExMddUzy+//KKWLVvKy8tL3t7e6tSpk7Zs2eLQ548//lBMTIwqVapkP2XroYce0pEjRxz6vfLKK7LZbPrnn3/0wAMPyNfXV/7+/nr55ZdlGIb27dunrl27ysfHR0FBQXrvvfeuqfZL2b9/vx566CEFBgbK3d1dtWvX1hdffOHQ5/Tp0xo2bJgaNGggX19feXl5qWXLllq4cGGe/aWkpCgmJka+vr7y8/NT3759lZKSckW1tGnTRrNnz9bevXvt37ewsDD79uTkZHvo9vDwUHh4uCZNmnQth39Zhen12blzp8qVK5cnWElSQEBAnrZffvlFrVu3lre3t3x8fNSoUSNNnjzZoc+0adPUoEEDeXp6qmzZsnrggQe0f/9+hz4xMTEqWbKkdu7cqY4dO8rb21v333+/JCk7O1ujR49W7dq15eHhocDAQD366KM6duyYwz7Wrl2r6OholS1bVp6enqpYsaIeeuihSx5v586dValSpQtua9q0qcOHMfPnz1eLFi3k5+enkiVLqnr16nrxxRcvuf+LCQoKUs2aNbV7925JudfEvfvuuxo9erQqV64sd3d3/fXXX5Kk3377zf6zwc/PT127dtXWrVsd9nn8+HENGjRIYWFhcnd3V0BAgO644w6tX7/+qmoEYB1GrgAUCampqTp8+LAMw1BycrI+/PBDpaenO3yibBiG7rzzTi1cuFD9+/dXRESEfv31Vz333HPav3+/3n//fUnSV199pQEDBqhx48Z65JFHJEmVK1e+6tq++uor9e3bV9HR0Ro5cqROnDihcePGqUWLFtqwYYM9AMyfP1+7du1Sv379FBQUpC1btujTTz/Vli1btHLlStlsNof99uzZUzVr1tRbb72l2bNn64033lDp0qX1ySef6LbbbtPIkSP1zTffaPDgwWrUqJFatWp12VrPnDmjw4cP52lPTU3N05aUlKQmTZrIZrPpySeflL+/v3755Rf1799faWlp9lPz0tLS9Nlnn6l37956+OGHdfz4cX3++eeKjo7W6tWrFRERIcn8/nTt2lVLly7VwIEDVbNmTf3www/q27fvFb3O//3vf5Wamqp///3X/r0sWbKkJOnkyZNq06aNduzYoSeffFIVK1bUtGnTFBMTo5SUFD3zzDNX9BxF+fWpUKGCFixYoN9++0233XbbJfvGxcXpoYceUu3atTV06FD5+flpw4YNmjt3ru677z57n379+qlRo0YaMWKEkpKSNGbMGC1btkwbNmyQn5+ffX9nz55VdHS0WrRooXfffVclSpSQJD366KP2/Tz99NPavXu3PvroI23YsEHLli1T8eLFlZycrHbt2snf319DhgyRn5+f9uzZoxkzZlzyGHr27Kk+ffpozZo1atSokb197969Wrlypd555x1J0pYtW9S5c2fVq1dPr732mtzd3bVjxw4tW7bsil7X8505c0b79u1TmTJlHNonTpyoU6dO6ZFHHpG7u7tKly6tBQsWqEOHDqpUqZJeeeUVnTx5Uh9++KGaN2+u9evX2382DBw4UN9//72efPJJ1apVS0eOHNHSpUu1detW3XrrrVdVJwCLGABQiE2cONGQlGdxd3c34uLiHPrOnDnTkGS88cYbDu3du3c3bDabsWPHDnubl5eX0bdv33zXM23aNEOSsXDhQsMwDOP48eOGn5+f8fDDDzv0S0xMNHx9fR3aT5w4kWd/3377rSHJWLJkib1t+PDhhiTjkUcesbedPXvWKFeunGGz2Yy33nrL3n7s2DHD09Pzio6lQoUKF3wtz12mTZtm79+/f38jODjYOHz4sMN+evXqZfj6+tqP5+zZs0ZmZqZDn2PHjhmBgYHGQw89ZG/L+f68/fbbDsfVsmVLQ5IxceLEyx5Dp06djAoVKuRpHz16tCHJ+Prrr+1tp0+fNpo2bWqULFnSSEtLu+y+i/rrs3nzZsPT09OQZERERBjPPPOMMXPmTCMjI8OhX0pKiuHt7W1ERkYaJ0+edNiWnZ1tGIb52gUEBBh16tRx6PPzzz8bkoxhw4bZ2/r27WtIMoYMGeKwr99//92QZHzzzTcO7XPnznVo/+GHHwxJxpo1ay55fOdLTU013N3djWeffdah/e233zZsNpuxd+9ewzAM4/333zckGYcOHcrX/g3DfE+0a9fOOHTokHHo0CFj06ZNRq9evQxJxlNPPWUYhmHs3r3bkGT4+PgYycnJDo+PiIgwAgICjCNHjtjbNm3aZLi4uBh9+vSxt/n6+hpPPPFEvusDUPhwWiCAImHs2LGaP3++5s+fr6+//lpt27bVgAEDHD7dnjNnjlxdXfX00087PPbZZ5+VYRjXNLvgxcyfP18pKSnq3bu3Dh8+bF9cXV0VGRnpcOqXp6en/etTp07p8OHDatKkiSRd8PSfcyfScHV1VcOGDWUYhvr3729v9/PzU/Xq1bVr164rqjcyMtL+Op67vPvuuw79DMPQ9OnT1aVLFxmG4XBs0dHRSk1Ntdfs6upqv7YkOztbR48e1dmzZ9WwYUOH45ozZ46KFSumxx57zOG4nnrqqSuq/VLmzJmjoKAg9e7d295WvHhxPf3000pPT9fixYuvaD9F+fWpXbu2Nm7cqAceeEB79uzRmDFj1K1bNwUGBmrChAn2fvPnz9fx48c1ZMgQeXh4OOwjZ/R07dq1Sk5O1uOPP+7Qp1OnTqpRo4Zmz56d5/nPrVsyTyn09fXVHXfc4fD6NGjQQCVLlrT/38gZAfv555915syZKzpWSfLx8VGHDh00depUh9N+p0yZoiZNmqh8+fIO+581a5ays7OveP855s2bJ39/f/n7+ys8PFzTpk3Tgw8+qJEjRzr0u+eee+Tv729fP3jwoDZu3KiYmBiVLl3a3l6vXj3dcccdmjNnjr3Nz89Pq1at0oEDB/JdH4DChdMCARQJjRs3driGonfv3qpfv76efPJJde7cWW5ubtq7d69CQkLk7e3t8NicWf327t3r9Lq2b98uSRc9DcvHx8f+9dGjR/Xqq6/qu+++yzPBwIVOO8v54zCHr6+vPDw8VLZs2Tzt51+3dTFly5ZVVFRUnvbzr107dOiQUlJS9Omnn+rTTz+94L7OPYZJkybpvffe099//+3wB3LFihXtX+/du1fBwcH2U/lyVK9e3WH95MmTeV6PoKCgSx7X3r17VbVqVbm4OH5meP73PjU1VSdPnrRvd3Nzc/jDtyi8PpdSrVo1ffXVV8rKytJff/2ln3/+WW+//bYeeeQRVaxYUVFRUdq5c6ckqU6dOhfdT87rdaHnrlGjhpYuXerQVqxYMZUrV86hbfv27UpNTb3g9V5S7uvTunVr3XPPPXr11Vf1/vvvq02bNurWrZvuu+8+ubu7X/J4e/bsqZkzZ2rFihVq1qyZdu7cqXXr1jlM19+zZ0999tlnGjBggIYMGaLbb79dd999t7p3757n/XIhkZGReuONN2Sz2VSiRAnVrFnT4ZTIHOd+L6VLv4Y1a9bUr7/+ap/44+2331bfvn0VGhqqBg0aqGPHjurTp89FrykDUHgRrgAUSS4uLmrbtq3GjBmj7du3q3bt2pbUkfNJ+FdffXXBAHDuH+X33nuvli9frueee04REREqWbKksrOz1b59+wt+ou7q6npFbZIcPrl3hpx6HnjggYte85Mz/fzXX3+tmJgYdevWTc8995wCAgLk6uqqESNG2P+Qz48pU6aoX79+Dm3OOr5nnnnGYZKL1q1ba9GiRfnej5Wvz5VwdXVV3bp1VbduXTVt2lRt27bVN998c8Hg6Azu7u55gkp2drYCAgL0zTffXPAxOaM8OfcPW7lypX766Sf9+uuveuihh/Tee+9p5cqVecLmubp06aISJUpo6tSpatasmaZOnSoXFxf16NHD3sfT01NLlizRwoULNXv2bM2dO1dTpkzRbbfdpnnz5l30/1SOiwXu8507Mp1f9957r/3+WfPmzdM777yjkSNHasaMGerQocNV7xfA9Ue4AlBknT17VpKUnp4uKfeC/uPHjzuMXv3999/27TnOnzziauVMhBEQEHDJP8COHTum+Ph4vfrqqxo2bJi9PWfkq7Dx9/eXt7e3srKyLvuH5ffff69KlSppxowZDq/r+TeurVChguLj45Wenu7wB/O2bdsc+kVHR2v+/PkXfK6Lfd8qVKigP/74Q9nZ2Q5/5J//vX/++ecdJkEpVarUJY/tYqx8ffIrZ8T34MGDknLfs5s3b1aVKlUu+Jic12vbtm15RmW3bdt2wRkJz1e5cmUtWLBAzZs3v6Lg0aRJEzVp0kT/+9//NHnyZN1///367rvvHE6PPZ+Xl5c6d+6sadOmadSoUZoyZYpatmypkJAQh34uLi66/fbbdfvtt2vUqFF688039d///lcLFy4ssMB57mt4vr///ltly5Z1mK4+ODhYjz/+uB5//HElJyfr1ltv1f/+9z/CFVDEcM0VgCLpzJkzmjdvntzc3OynfnXs2FFZWVn66KOPHPq+//77stlsDn+keHl5XfEU15cSHR0tHx8fvfnmmxe8XuTQoUOSckeczh+BOff0pcLE1dVV99xzj6ZPn67Nmzfn2Z5zXDl9JcdjW7VqlVasWOHwmI4dO+rs2bMaN26cvS0rK0sffvihQ7/g4GBFRUU5LDm8vLwueAplx44dlZiYqClTptjbzp49qw8//FAlS5ZU69atJUm1atVy2G+DBg2u6PU4n5Wvz8X8/vvvF3wP5lzbk3N6Wrt27eTt7a0RI0bo1KlTDn1zamzYsKECAgI0fvx4ZWZm2rf/8ssv2rp1qzp16nTZeu69915lZWXp9ddfz7Pt7Nmz9v9/x44dy/P/ImcGxXOf+2J69uypAwcO6LPPPtOmTZvUs2dPh+1Hjx7N85j87P9qBQcHKyIiQpMmTXL4WbN582bNmzdPHTt2lGR+j89/TwcEBCgkJKRA6wNQMBi5AlAk/PLLL/ZRiOTkZE2ePFnbt2/XkCFD7Nc1denSRW3bttV///tf7dmzR+Hh4Zo3b55mzZqlQYMGOUy33qBBAy1YsECjRo1SSEiIKlasqMjIyHzX5ePjo3HjxunBBx/Urbfeql69esnf318JCQmaPXu2mjdvro8++kg+Pj5q1aqV3n77bZ05c0a33HKL5s2bZ79XTmH01ltvaeHChYqMjNTDDz+sWrVq6ejRo1q/fr0WLFhg/6O1c+fOmjFjhu666y516tRJu3fv1vjx41WrVi37qKJkfn+aN2+uIUOGaM+ePapVq5ZmzJhxwbB0MQ0aNNCUKVMUGxurRo0aqWTJkurSpYseeeQRffLJJ4qJidG6desUFham77//XsuWLdPo0aPzXId3I74+I0eO1Lp163T33XfbT0lcv369vvzyS5UuXdo+NbyPj4/ef/99DRgwQI0aNdJ9992nUqVKadOmTTpx4oQmTZqk4sWLa+TIkerXr59at26t3r1726diDwsL03/+85/L1tO6dWs9+uijGjFihDZu3Kh27dqpePHi2r59u6ZNm6YxY8aoe/fumjRpkj7++GPdddddqly5so4fP64JEybIx8fHHkAuJefeWoMHD7aH3nO99tprWrJkiTp16qQKFSooOTlZH3/8scqVK6cWLVpc0Wt7td555x116NBBTZs2Vf/+/e1Tsfv6+uqVV16RZN7jqly5curevbvCw8NVsmRJLViwQGvWrCnQe9gBKCBWTFEIAFfqQlOxe3h4GBEREca4cePsU0fnOH78uPGf//zHCAkJMYoXL25UrVrVeOedd/L0+/vvv41WrVrZp66+0mnZz5+KPcfChQuN6Ohow9fX1/Dw8DAqV65sxMTEGGvXrrX3+ffff4277rrL8PPzM3x9fY0ePXoYBw4cMCQZw4cPt/fLmYr9/Kmj+/bta3h5eeWpqXXr1kbt2rUvW3uFChWMTp06XXDbwoUL80w1bhiGkZSUZDzxxBNGaGioUbx4cSMoKMi4/fbbjU8//dTeJzs723jzzTeNChUqGO7u7kb9+vWNn3/+2ejbt2+eadOPHDliPPjgg4aPj4/h6+trPPjgg8aGDRuueCr29PR047777jP8/PwMSQ77T0pKMvr162eULVvWcHNzM+rWrXtF+8xR1F+fZcuWGU888YRRp04dw9fX1yhevLhRvnx5IyYmxti5c2ee/j/++KPRrFkzw9PT0/Dx8TEaN25sfPvttw59pkyZYtSvX99wd3c3Spcubdx///3Gv//+69DnYu/LHJ9++qnRoEEDw9PT0/D29jbq1q1rPP/888aBAwcMwzCM9evXG7179zbKly9vuLu7GwEBAUbnzp0d/u9czv33329IMqKiovJsi4+PN7p27WqEhIQYbm5uRkhIiNG7d2/jn3/+uex+L/WeyJEzFfs777xzwe0LFiwwmjdvbn+du3TpYvz111/27ZmZmcZzzz1nhIeHG97e3oaXl5cRHh5ufPzxx5etD0DhYzMMJ18FDQAAAAA3Ia65AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4ATcRvoDs7GwdOHBA3t7estlsVpcDAAAAwCKGYej48eMKCQmRi8ulx6YIVxdw4MABhYaGWl0GAAAAgEJi3759Kleu3CX7EK4uwNvbW5L5Avr4+FhcDQAAAACrpKWlKTQ01J4RLoVwdQE5pwL6+PgQrgAAAABc0eVCTGgBAAAAAE5AuAIAAAAAJyBcAQAAAIATcM0VAAAACiXDMHT27FllZWVZXQpuYK6uripWrJhTbsFEuAIAAEChc/r0aR08eFAnTpywuhTcBEqUKKHg4GC5ubld034IVwAAAChUsrOztXv3brm6uiokJERubm5OGVUAzmcYhk6fPq1Dhw5p9+7dqlq16mVvFHwphCsAAAAUKqdPn1Z2drZCQ0NVokQJq8vBDc7T01PFixfX3r17dfr0aXl4eFz1vpjQAgAAAIXStYwgAPnhrPca71gAAAAAcALCFQAAAAA4AeEKAAAAKGBt2rTRoEGDrtvzxcXFyc/P77o9X0G63q/dtSBcAQAAAE4QExMjm82WZ9mxY4dmzJih119/3d43LCxMo0ePdni8FYFo4cKF6ty5s/z9/eXh4aHKlSurZ8+eWrJkyXWt41LOf+0KM8IVAAAA4CTt27fXwYMHHZaKFSuqdOnS8vb2tro8Bx9//LFuv/12lSlTRlOmTNG2bdv0ww8/qFmzZvrPf/5jdXl2hfG1uxjCFQAAAOAk7u7uCgoKclhcXV0dTm1r06aN9u7dq//85z/20a1FixapX79+Sk1Ntbe98sorkqTMzEwNHjxYt9xyi7y8vBQZGalFixY5PG9cXJzKly+vEiVK6K677tKRI0cuWWdCQoIGDRqkQYMGadKkSbrttttUoUIF1atXT88884zWrl1r73vkyBH17t1bt9xyi0qUKKG6devq22+/ddjfhUbiIiIi7MdgGIZeeeUVlS9fXu7u7goJCdHTTz9t7/vxxx+ratWq8vDwUGBgoLp3727fdv5pgV999ZUaNmwob29vBQUF6b777lNycrJ9+6JFi2Sz2RQfH6+GDRuqRIkSatasmbZt23bJ18QZuM8VAAAACr+GDaXExOv/vEFB0jlBwxlmzJih8PBwPfLII3r44YclmaMzo0eP1rBhw+whoGTJkpKkJ598Un/99Ze+++47hYSE6IcfflD79u31559/qmrVqlq1apX69++vESNGqFu3bpo7d66GDx9+yRqmT5+uM2fO6Pnnn7/g9nNv2nzq1Ck1aNBAL7zwgnx8fDR79mw9+OCDqly5sho3bnxFxzx9+nS9//77+u6771S7dm0lJiZq06ZNkqS1a9fq6aef1ldffaVmzZrp6NGj+v333y+6rzNnzuj1119X9erVlZycrNjYWMXExGjOnDkO/f773//qvffek7+/vwYOHKiHHnpIy5Ytu6J6rxbhCgAAAIVfYqK0f7/VVVzWzz//bA9FktShQwdNmzbNoU/p0qXl6upqH3nJ4evrK5vN5tCWkJCgiRMnKiEhQSEhIZKkwYMHa+7cuZo4caLefPNNjRkzRu3bt7cHpWrVqmn58uWaO3fuRev8559/5OPj4/Bc06dPV9++fe3rK1asUN26dXXLLbdo8ODB9vannnpKv/76q6ZOnXrF4SohIUFBQUGKiopS8eLFVb58eftjExIS5OXlpc6dO8vb21sVKlRQ/fr1L7qvhx56yP51pUqV9MEHH6hRo0ZKT093eO3/97//qXXr1pKkIUOGqFOnTjp16tQ13ST4cghXAAAAKPzOCQGF+Xnbtm2rcePG2de9vLyu6en//PNPZWVlqVq1ag7tmZmZKlOmjCRp69atuuuuuxy2N23a9JLhSnIcnZKk6Ohobdy4Ufv371ebNm2UlZUlScrKytKbb76pqVOnav/+/Tp9+rQyMzNVokSJKz6OHj16aPTo0apUqZLat2+vjh07qkuXLipWrJjuuOMOVahQwb6tffv2uuuuuy66/3Xr1umVV17Rpk2bdOzYMWVnZ0syQ1qtWrXs/erVq2f/Ojg4WJKUnJys8uXLX3Hd+UW4AgAAQOHn5FPzCoqXl5eqVKnitP2lp6fL1dVV69atk6urq8O2c0dp8qtq1apKTU1VYmKiffSqZMmSqlKliooVc4wI77zzjsaMGaPRo0erbt268vLy0qBBg3T69Gl7HxcXFxmG4fC4M2fO2L8ODQ3Vtm3btGDBAs2fP1+PP/643nnnHS1evFje3t5av369Fi1apHnz5mnYsGF65ZVXtGbNmjyzJ2ZkZCg6OlrR0dH65ptv5O/vr4SEBEVHRzvUI0nFixe3f50TJHOCWEFhQgsAAADgOnNzc7OPDF2qrX79+srKylJycrKqVKnisOSEopo1a2rVqlUOj1u5cuUln7979+4qXry4Ro4cedlaly1bpq5du+qBBx5QeHi4KlWqpH/++cehj7+/vw4ePGhfT0tL0+7dux36eHp6qkuXLvrggw+0aNEirVixQn/++ackqVixYoqKitLbb7+tP/74Q3v27NFvv/2Wp5a///5bR44c0VtvvaWWLVuqRo0aDpNZWI2RKwAALmHaztQC23ePyr4Ftm8AhVtYWJiWLFmiXr16yd3dXWXLllVYWJjS09MVHx+v8PBwlShRQtWqVdP999+vPn366L333lP9+vV16NAhxcfHq169eurUqZOefvppNW/eXO+++666du2qX3/99bKnBJYvX17vvfeennnmGR09elQxMTGqWLGijh49qq+//lqS7CNlVatW1ffff6/ly5erVKlSGjVqlJKSkhxOwbvtttsUFxenLl26yM/PT8OGDXMYaYuLi1NWVpYiIyNVokQJff311/L09FSFChX0888/a9euXWrVqpVKlSqlOXPmKDs7W9WrV79g3W5ubvrwww81cOBAbd68uVDdA4uRKwAAAOA6e+2117Rnzx5VrlxZ/v7+kqRmzZpp4MCB6tmzp/z9/fX2229LkiZOnKg+ffro2WefVfXq1dWtWzetWbPGfu1QkyZNNGHCBI0ZM0bh4eGaN2+eXnrppcvW8NRTT2nevHk6dOiQunfvrqpVq6pjx47avXu35s6dq7p160qSXnrpJd16662Kjo5WmzZtFBQUpG7dujnsa+jQoWrdurU6d+6sTp06qVu3bqpcubJ9u5+fnyZMmKDmzZurXr16WrBggX766SeVKVNGfn5+mjFjhm677TbVrFlT48eP17fffqvatWvnqdnf319xcXGaNm2aatWqpbfeekvvvvvuVX0PCoLNOP/kSCgtLU2+vr5KTU2Vj4+P1eUAACzEyBVw/Z06dUq7d+9WxYoVC3RmNyDHpd5z+ckGjFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAol5l3D9eKs9xrhCgAAAIVK8eLFJUknTpywuBLcLHLeaznvvavFTYQBAABQqLi6usrPz0/JycmSpBIlSshms1lcFW5EhmHoxIkTSk5Olp+fn8ONj68G4QoAAACFTlBQkCTZAxZQkPz8/OzvuWtBuAIAAEChY7PZFBwcrICAAJ05c8bqcnADK168+DWPWOUgXAEAAKDQcnV1ddofvkBBs3RCixEjRqhRo0by9vZWQECAunXrpm3btl32cdOmTVONGjXk4eGhunXras6cOQ7bDcPQsGHDFBwcLE9PT0VFRWn79u0FdRgAAAAAYG24Wrx4sZ544gmtXLlS8+fP15kzZ9SuXTtlZGRc9DHLly9X79691b9/f23YsEHdunVTt27dtHnzZnuft99+Wx988IHGjx+vVatWycvLS9HR0Tp16tT1OCwAAAAANyGbUYhuIHDo0CEFBARo8eLFatWq1QX79OzZUxkZGfr555/tbU2aNFFERITGjx8vwzAUEhKiZ599VoMHD5YkpaamKjAwUHFxcerVq9dl60hLS5Ovr69SU1Pl4+PjnIMDABRJ03amFti+e1T2LbB9AwCcIz/ZoFDd5yo11fwFVrp06Yv2WbFihaKiohzaoqOjtWLFCknS7t27lZiY6NDH19dXkZGR9j7ny8zMVFpamsMCAAAAAPlRaMJVdna2Bg0apObNm6tOnToX7ZeYmKjAwECHtsDAQCUmJtq357RdrM/5RowYIV9fX/sSGhp6LYcCAAAA4CZUaMLVE088oc2bN+u777677s89dOhQpaam2pd9+/Zd9xoAAAAAFG2FYir2J598Uj///LOWLFmicuXKXbJvUFCQkpKSHNqSkpLsN/3K+TcpKUnBwcEOfSIiIi64T3d3d7m7u1/DEQAAAAC42Vk6cmUYhp588kn98MMP+u2331SxYsXLPqZp06aKj493aJs/f76aNm0qSapYsaKCgoIc+qSlpWnVqlX2PgAAAADgbJaOXD3xxBOaPHmyZs2aJW9vb/s1Ub6+vvL09JQk9enTR7fccotGjBghSXrmmWfUunVrvffee+rUqZO+++47rV27Vp9++qkk827egwYN0htvvKGqVauqYsWKevnllxUSEqJu3bpZcpwAAAAAbnyWhqtx48ZJktq0aePQPnHiRMXExEiSEhIS5OKSO8DWrFkzTZ48WS+99JJefPFFVa1aVTNnznSYBOP5559XRkaGHnnkEaWkpKhFixaaO3euPDw8CvyYAAAAANycCtV9rgoL7nMFAMjBfa4A4OZWZO9zBQAAAABFFeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJzA0nC1ZMkSdenSRSEhIbLZbJo5c+Yl+8fExMhms+VZateube/zyiuv5Nleo0aNAj4SAAAAADc7S8NVRkaGwsPDNXbs2CvqP2bMGB08eNC+7Nu3T6VLl1aPHj0c+tWuXduh39KlSwuifAAAAACwK2blk3fo0EEdOnS44v6+vr7y9fW1r8+cOVPHjh1Tv379HPoVK1ZMQUFBTqsTAAAAAC6nSF9z9fnnnysqKkoVKlRwaN++fbtCQkJUqVIl3X///UpISLjkfjIzM5WWluawAAAAAEB+FNlwdeDAAf3yyy8aMGCAQ3tkZKTi4uI0d+5cjRs3Trt371bLli11/Pjxi+5rxIgR9lExX19fhYaGFnT5AAAAAG4wRTZcTZo0SX5+furWrZtDe4cOHdSjRw/Vq1dP0dHRmjNnjlJSUjR16tSL7mvo0KFKTU21L/v27Svg6gEAAADcaCy95upqGYahL774Qg8++KDc3Nwu2dfPz0/VqlXTjh07LtrH3d1d7u7uzi4TAAAAwE2kSI5cLV68WDt27FD//v0v2zc9PV07d+5UcHDwdagMAAAAwM3K0nCVnp6ujRs3auPGjZKk3bt3a+PGjfYJKIYOHao+ffrkedznn3+uyMhI1alTJ8+2wYMHa/HixdqzZ4+WL1+uu+66S66ururdu3eBHgsAAACAm5ulpwWuXbtWbdu2ta/HxsZKkvr27au4uDgdPHgwz0x/qampmj59usaMGXPBff7777/q3bu3jhw5In9/f7Vo0UIrV66Uv79/wR0IAAAAgJuezTAMw+oiCpu0tDT5+voqNTVVPj4+VpcDALDQtJ2pBbbvHpV9L98JAGCp/GSDInnNFQAAAAAUNoQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACS8PVkiVL1KVLF4WEhMhms2nmzJmX7L9o0SLZbLY8S2JiokO/sWPHKiwsTB4eHoqMjNTq1asL8CgAAAAAwOJwlZGRofDwcI0dOzZfj9u2bZsOHjxoXwICAuzbpkyZotjYWA0fPlzr169XeHi4oqOjlZyc7OzyAQAAAMCumJVP3qFDB3Xo0CHfjwsICJCfn98Ft40aNUoPP/yw+vXrJ0kaP368Zs+erS+++EJDhgy5lnIBAAAA4KKK5DVXERERCg4O1h133KFly5bZ20+fPq1169YpKirK3ubi4qKoqCitWLHiovvLzMxUWlqawwIAAAAA+VGkwlVwcLDGjx+v6dOna/r06QoNDVWbNm20fv16SdLhw4eVlZWlwMBAh8cFBgbmuS7rXCNGjJCvr699CQ0NLdDjAAAAAHDjsfS0wPyqXr26qlevbl9v1qyZdu7cqffff19fffXVVe936NChio2Nta+npaURsAAAAADkS5EKVxfSuHFjLV26VJJUtmxZubq6KikpyaFPUlKSgoKCLroPd3d3ubu7F2idAAAAAG5sReq0wAvZuHGjgoODJUlubm5q0KCB4uPj7duzs7MVHx+vpk2bWlUiAAAAgJuApSNX6enp2rFjh3199+7d2rhxo0qXLq3y5ctr6NCh2r9/v7788ktJ0ujRo1WxYkXVrl1bp06d0meffabffvtN8+bNs+8jNjZWffv2VcOGDdW4cWONHj1aGRkZ9tkDAQAAAKAgWBqu1q5dq7Zt29rXc6576tu3r+Li4nTw4EElJCTYt58+fVrPPvus9u/frxIlSqhevXpasGCBwz569uypQ4cOadiwYUpMTFRERITmzp2bZ5ILAAAAAHAmm2EYhtVFFDZpaWny9fVVamqqfHx8rC4HAGChaTtTC2zfPSr7Fti+AQDOkZ9sUOSvuQIAAACAwoBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOYGm4WrJkibp06aKQkBDZbDbNnDnzkv1nzJihO+64Q/7+/vLx8VHTpk3166+/OvR55ZVXZLPZHJYaNWoU4FEAAAAAgMXhKiMjQ+Hh4Ro7duwV9V+yZInuuOMOzZkzR+vWrVPbtm3VpUsXbdiwwaFf7dq1dfDgQfuydOnSgigfAAAAAOyKWfnkHTp0UIcOHa64/+jRox3W33zzTc2aNUs//fST6tevb28vVqyYgoKCnFUmAAAAAFxWkb7mKjs7W8ePH1fp0qUd2rdv366QkBBVqlRJ999/vxISEi65n8zMTKWlpTksAAAAAJAfRTpcvfvuu0pPT9e9995rb4uMjFRcXJzmzp2rcePGaffu3WrZsqWOHz9+0f2MGDFCvr6+9iU0NPR6lA8AAADgBlJkw9XkyZP16quvaurUqQoICLC3d+jQQT169FC9evUUHR2tOXPmKCUlRVOnTr3ovoYOHarU1FT7sm/fvutxCAAAAABuIJZec3W1vvvuOw0YMEDTpk1TVFTUJfv6+fmpWrVq2rFjx0X7uLu7y93d3dllAgAAALiJFLmRq2+//Vb9+vXTt99+q06dOl22f3p6unbu3Kng4ODrUB0AAACAm5WlI1fp6ekOI0q7d+/Wxo0bVbp0aZUvX15Dhw7V/v379eWXX0oyTwXs27evxowZo8jISCUmJkqSPD095evrK0kaPHiwunTpogoVKujAgQMaPny4XF1d1bt37+t/gAAAAABuGpaOXK1du1b169e3T6MeGxur+vXra9iwYZKkgwcPOsz09+mnn+rs2bN64oknFBwcbF+eeeYZe59///1XvXv3VvXq1XXvvfeqTJkyWrlypfz9/a/vwQEAAAC4qdgMwzCsLqKwSUtLk6+vr1JTU+Xj42N1OQAAC03bmVpg++5R2bfA9g0AcI78ZIMid80VAAAAABRGhCsAAAAAcALCFQAAAAA4wVWFq127djm7DgAAAAAo0q4qXFWpUkVt27bV119/rVOnTjm7JgAAAAAocq4qXK1fv1716tVTbGysgoKC9Oijj2r16tXOrg0AAAAAioyrClcREREaM2aMDhw4oC+++EIHDx5UixYtVKdOHY0aNUqHDh1ydp0AAAAAUKhd04QWxYoV0913361p06Zp5MiR2rFjhwYPHqzQ0FD16dNHBw8edFadAAAAAFCoXVO4Wrt2rR5//HEFBwdr1KhRGjx4sHbu3Kn58+frwIED6tq1q7PqBAAAAIBCrdjVPGjUqFGaOHGitm3bpo4dO+rLL79Ux44d5eJiZrWKFSsqLi5OYWFhzqwVAAAAAAqtqwpX48aN00MPPaSYmBgFBwdfsE9AQIA+//zzayoOAAAAAIqKqwpX8+fPV/ny5e0jVTkMw9C+fftUvnx5ubm5qW/fvk4pEgAAAAAKu6u65qpy5co6fPhwnvajR4+qYsWK11wUAAAAABQ1VxWuDMO4YHt6ero8PDyuqSAAAAAAKIrydVpgbGysJMlms2nYsGEqUaKEfVtWVpZWrVqliIgIpxYIAAAAAEVBvsLVhg0bJJkjV3/++afc3Nzs29zc3BQeHq7Bgwc7t0IAAAAAKALyFa4WLlwoSerXr5/GjBkjHx+fAikKAAAAAIqaq5otcOLEic6uAwAAAACKtCsOV3fffbfi4uLk4+Oju++++5J9Z8yYcc2FAQAAAEBRcsXhytfXVzabzf41AAAAACDXFYerc08F5LRAAAAAAHB0Vfe5OnnypE6cOGFf37t3r0aPHq158+Y5rTAAAAAAKEquKlx17dpVX375pSQpJSVFjRs31nvvvaeuXbtq3LhxTi0QAAAAAIqCqwpX69evV8uWLSVJ33//vYKCgrR37159+eWX+uCDD5xaIAAAAAAUBVcVrk6cOCFvb29J0rx583T33XfLxcVFTZo00d69e51aIAAAAAAUBVcVrqpUqaKZM2dq3759+vXXX9WuXTtJUnJyMjcWBgAAAHBTuqpwNWzYMA0ePFhhYWGKjIxU06ZNJZmjWPXr13dqgQAAAABQFFzxVOzn6t69u1q0aKGDBw8qPDzc3n777bfrrrvuclpxAAAAAFBUXFW4kqSgoCAFBQU5tDVu3PiaCwIAAACAouiqwlVGRobeeustxcfHKzk5WdnZ2Q7bd+3a5ZTiAAAAAKCouKpwNWDAAC1evFgPPviggoODZbPZnF0XAAAAABQpVxWufvnlF82ePVvNmzd3dj0AAAAAUCRd1WyBpUqVUunSpZ1dCwAAAAAUWVcVrl5//XUNGzZMJ06ccHY9AAAAAFAkXdVpge+995527typwMBAhYWFqXjx4g7b169f75TiAAAAAKCouKpw1a1bNyeXAQAAAABF21WFq+HDhzu7DgAAAAAo0q7qmitJSklJ0WeffaahQ4fq6NGjkszTAffv3++04gAAAACgqLiqkas//vhDUVFR8vX11Z49e/Twww+rdOnSmjFjhhISEvTll186u04AAAAAKNSuauQqNjZWMTEx2r59uzw8POztHTt21JIlS5xWHAAAAAAUFVcVrtasWaNHH300T/stt9yixMTEay4KAAAAAIqaqwpX7u7uSktLy9P+zz//yN/f/5qLAgAAAICi5qrC1Z133qnXXntNZ86ckSTZbDYlJCTohRde0D333HPF+1myZIm6dOmikJAQ2Ww2zZw587KPWbRokW699Va5u7urSpUqiouLy9Nn7NixCgsLk4eHhyIjI7V69eorrgkAAAAArsZVhav33ntP6enp8vf318mTJ9W6dWtVqVJF3t7e+t///nfF+8nIyFB4eLjGjh17Rf13796tTp06qW3bttq4caMGDRqkAQMG6Ndff7X3mTJlimJjYzV8+HCtX79e4eHhio6OVnJycr6PEwAAAACulM0wDONqH7xs2TJt2rRJ6enpuvXWWxUVFXX1hdhs+uGHHy55g+IXXnhBs2fP1ubNm+1tvXr1UkpKiubOnStJioyMVKNGjfTRRx9JkrKzsxUaGqqnnnpKQ4YMueB+MzMzlZmZaV9PS0tTaGioUlNT5ePjc9XHBAAo+qbtTC2wffeo7Ftg+wYAOEdaWpp8fX2vKBvkeyr27OxsxcXFacaMGdqzZ49sNpsqVqyooKAgGYYhm8121YVfzooVK/IEuOjoaA0aNEiSdPr0aa1bt05Dhw61b3dxcVFUVJRWrFhx0f2OGDFCr776aoHUDAAAAODmkK/TAg3D0J133qkBAwZo//79qlu3rmrXrq29e/cqJiZGd911V0HVKUlKTExUYGCgQ1tgYKDS0tJ08uRJHT58WFlZWRfsc6lZDIcOHarU1FT7sm/fvgKpHwAAAMCNK18jV3FxcVqyZIni4+PVtm1bh22//fabunXrpi+//FJ9+vRxapEFzd3dXe7u7laXAQAAAKAIy9fI1bfffqsXX3wxT7CSpNtuu01DhgzRN99847TizhcUFKSkpCSHtqSkJPn4+MjT01Nly5aVq6vrBfsEBQUVWF0AAAAAkK9w9ccff6h9+/YX3d6hQwdt2rTpmou6mKZNmyo+Pt6hbf78+WratKkkyc3NTQ0aNHDok52drfj4eHsfAAAAACgI+QpXR48ezXM907kCAwN17NixK95fenq6Nm7cqI0bN0oyp1rfuHGjEhISJJnXQp17iuHAgQO1a9cuPf/88/r777/18ccfa+rUqfrPf/5j7xMbG6sJEyZo0qRJ2rp1qx577DFlZGSoX79++TlUAAAAAMiXfF1zlZWVpWLFLv4QV1dXnT179or3t3btWodTDGNjYyVJffv2VVxcnA4ePGgPWpJUsWJFzZ49W//5z380ZswYlStXTp999pmio6PtfXr27KlDhw5p2LBhSkxMVEREhObOnXvJUAgAAAAA1ypf97lycXFRhw4dLjr5Q2ZmpubOnausrCynFWiF/MxlDwC4sXGfKwC4uRXYfa769u172T5FbaZAAAAAAHCGfIWriRMnFlQdAAAAAFCk5WtCCwAAAADAhRGuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOUCjC1dixYxUWFiYPDw9FRkZq9erVF+3bpk0b2Wy2PEunTp3sfWJiYvJsb9++/fU4FAAAAAA3qWJWFzBlyhTFxsZq/PjxioyM1OjRoxUdHa1t27YpICAgT/8ZM2bo9OnT9vUjR44oPDxcPXr0cOjXvn17TZw40b7u7u5ecAcBAAAA4KZn+cjVqFGj9PDDD6tfv36qVauWxo8frxIlSuiLL764YP/SpUsrKCjIvsyfP18lSpTIE67c3d0d+pUqVep6HA4AAACAm5Sl4er06dNat26doqKi7G0uLi6KiorSihUrrmgfn3/+uXr16iUvLy+H9kWLFikgIEDVq1fXY489piNHjlx0H5mZmUpLS3NYAAAAACA/LA1Xhw8fVlZWlgIDAx3aAwMDlZiYeNnHr169Wps3b9aAAQMc2tu3b68vv/xS8fHxGjlypBYvXqwOHTooKyvrgvsZMWKEfH197UtoaOjVHxQAAACAm5Ll11xdi88//1x169ZV48aNHdp79epl/7pu3bqqV6+eKleurEWLFun222/Ps5+hQ4cqNjbWvp6WlkbAAgAAAJAvlo5clS1bVq6urkpKSnJoT0pKUlBQ0CUfm5GRoe+++079+/e/7PNUqlRJZcuW1Y4dOy643d3dXT4+Pg4LAAAAAOSHpeHKzc1NDRo0UHx8vL0tOztb8fHxatq06SUfO23aNGVmZuqBBx647PP8+++/OnLkiIKDg6+5ZgAAAAC4EMtnC4yNjdWECRM0adIkbd26VY899pgyMjLUr18/SVKfPn00dOjQPI/7/PPP1a1bN5UpU8ahPT09Xc8995xWrlypPXv2KD4+Xl27dlWVKlUUHR19XY4JAAAAwM3H8muuevbsqUOHDmnYsGFKTExURESE5s6da5/kIiEhQS4ujhlw27ZtWrp0qebNm5dnf66urvrjjz80adIkpaSkKCQkRO3atdPrr7/Ova4AAAAAFBibYRiG1UUUNmlpafL19VVqairXXwHATW7aztQC23ePyr4Ftm8AgHPkJxtYflogAAAAANwICFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnMDyqdgBALgWBTmbHwAA+cHIFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJygmNUFAABQaGVnyzPpoLz+3Suvf/eqeFqqDBdXGa6u5r8uNvvXZ728lF6hktLDKivb3cPqygEAFiBcAQBgGPLesU2BKxbLZ/vf8tpnhqkS+/fJ9czp/O3KZtOJW0J1vGJVHa9URcfDqiitSnUdqd9I2R6eBXQAAIDCgHAFALgpeSQnKmDZIgUuX6TA5YvlmXTQKfu1GYa8/k2Q178JCvo93t5+1sNTyU1bKbH1HTrYOkonQsOc8nwAgMKDcAUAuGn4/POXwqZPVtCSePlu33rJvmc9SyijXAVlhP7/Uq6CMkuVkc3IlrKzZcvKku2cf91Sj8l79w5z2bVDxdPTHPZX7NRJhSz8VSELf5UkpVWuJnXtLHXsKLVuLRXjVzIAFHU2wzAMq4sobNLS0uTr66vU1FT5+PhYXQ4A4BKm7Uy95HbXkydUbs4PqvTdJJXdsPqCfc56ltChRs2U1LyNjtRvrIwKFZVZuqxks11dUYYh9yOH5L1ru7x37VCZjWsUtGSBPJMTL9w/NFQaOFAaMEAKCLi65wQAFIj8ZAPC1QUQrgCg6LhYuPLZtkWVvotThZlT5HbccRTJcHHR0bq3Kql5GyU1b6OjEY2U7e5esIUahny3/qngRfMVvHi+ymxYLVt2tmMfNzfp3nulJ56QIiOvPtwBAJyGcHWNCFcAUHQ4hCvDUNCiear58XsXHKVKqV5Lu3rGKOHOHjrjV+o6VplX8ZRj6vbPCmnyZGn2bOn8X8e33mqGrPvukzyYfRAArEK4ukaEKwAoOnLClf/K31XnvdfzhKqzHp7a1/lu7eoZo6MRDQvVaFCPyr7mF7t2SePHS59/Lh096tipXDnptdekPn0kV9frXyQA3OQIV9eIcAUARceCGb+p7qjXFbhskUN7SvVa2tWrnxK69tAZHz9Larsce7jKcfKkNGWK9NFH0rp1jttq15beekvq1KlQBUQAuNHlJxu4XKeaAABwrs2bpbvuUtQ9tzsEq9SqNbXs4680/+dl2vngw4U2WF2Qp6cUEyOtWSOtXCl17py7bcsWqUsXqU0bcxsAoNAhXAEAipaUFOmxx6R69aSZM+3N6aFhWvXuJ5r381IdaNelaI/u2GzmhBY//SQtXiw1aZK7bckSqWlT6Z57pH/+sa5GAEAehCsAQNFgGNL06VKtWub1Sf9/VvvJwGCte22U5v66Wgndet541yW1aiUtX24ee7Vque0zZpgBc8QI6cwZ6+oDANgRrgAAhd++fVK3blL37tLBg2abl5f09tuaE79eu+57SIabm6UlFiibTbr7bvNUyPHjpaAgsz0zU3rxRXOUa8MGa2sEABCuAACFWFaWOblDrVrSjz/mtnfuLP31l/Tcc8r28LSuvuuteHHp0UelHTukwYMll///Nb5hg9SokfTf/0qnTllbIwDcxAhXAIDCacsWqUUL6amnpPR0sy0wUJo61Qxa5ctbW5+VvLykd94xJ7aoU8dsy8qS3nxTioiQli2ztDwAuFkRrgAAhYthSJ98IjVs6Dgr3sMPS1u3Sj16FO3JKpypUSNzyvZXXzVHtSRp2zapZUtp0CBGsQDgOiNcAQAKj5QU6d57pYEDc4NB9ermjHmffiqVKmVpeYWSm5s0bJi0fr3UuLHZZhjSmDFSs2bmKYQAgOuCcAUAKBxWrDBPafv++9y2xx83rydq1cqysoqMOnXMWQXffVdydzfbNmyQbr3VvDExAKDAEa4AANbKzjanE2/ZUtq712zz8zOnGh871ryxLq6Mq6v07LPS6tW507YfPy716mWOBp48aW19AHCDI1wBAKyTmChFR5vTiWdlmW3Nm0sbN0p33WVpaUVavXrS2rXS/ffntn3yiXkz4m3brKsLAG5whCsAgDVWrjRPWVuwwFy32aSXXpIWLZIqVLC0tBuCt7f01VfSZ59JHh5m2x9/SA0aSJMnW1sbANygCFcAgOtv0iSpdevcGwIHB5sh6/XXpWLFrK3tRmKzSf37S2vWSDVqmG0ZGeaI1uDBuaOFAACnIFwBAK6fs2el2FgpJkY6fdpsa9XKPA3wttusrOzGVqeOeZpg3765be+9J915p5Saal1dAHCDIVwBAK6PY8ekjh2l99/PbRs4UJo/XwoIsK6um4WXlxQXJ338sTnxhSTNmSM1bcp07QDgJIQrAEDB27rVvAfT/PnmerFi0rhx5uLmZm1tN5vHHpPmzcu9Z9jWrVJkpLRwobV1AcANgHAFAChYP/9s/vGeMzpStqwUH2+OWsEat91mTtdes6a5fvSo1K6dNH68tXUBQBFXKK4aHjt2rN555x0lJiYqPDxcH374oRrn3GX+PHFxcerXr59Dm7u7u06dOmVfNwxDw4cP14QJE5SSkqLmzZtr3Lhxqlq1aoEeBwDgPB9/LD31lHkvK0kKD5dmzWI2wOtg2s7LXEtl81exyXPVZNAABS+eb14P99hj2rF0nTa+/JaMy0ws0qOyrxOrBYAbg+UjV1OmTFFsbKyGDx+u9evXKzw8XNHR0UpOTr7oY3x8fHTw4EH7sjfnppP/7+2339YHH3yg8ePHa9WqVfLy8lJ0dLRDAAMAFKDsbGnIEOmJJ3KDVY8e0rJlBKtC5Ky3r5Z++p229X/S3lblm8/U7LH75Xoiw8LKAKBosjxcjRo1Sg8//LD69eunWrVqafz48SpRooS++OKLiz7GZrMpKCjIvgQGBtq3GYah0aNH66WXXlLXrl1Vr149ffnllzpw4IBmzpx5HY4IAG5ymZnSgw9KI0fmtg0ZIk2ZYk6qgMLF1VV/DH1Dq0eOVXbx4pKkkIW/qvWDXeV25LDFxQFA0WJpuDp9+rTWrVunqKgoe5uLi4uioqK0YsWKiz4uPT1dFSpUUGhoqLp27aotW7bYt+3evVuJiYkO+/T19VVkZORF95mZmam0tDSHBQBwFVJSpA4dcm9S6+IijR0rjRhh3nMJhdbee+7Xki+m60xJH0lSmU1rdVvPaHkl7LG2MAAoQiwNV4cPH1ZWVpbDyJMkBQYGKjEx8YKPqV69ur744gvNmjVLX3/9tbKzs9WsWTP9+++/kmR/XH72OWLECPn6+tqX0NDQaz00ALj57NsntWyZO+ucp6c0Y4b0+OPW1oUrdqhpKy38bo5OBgZLkrz37NRt97aT3+aN1hYGAEWE5acF5lfTpk3Vp08fRUREqHXr1poxY4b8/f31ySefXPU+hw4dqtTUVPuyb98+J1YMADeBP/8075e0ebO5Xras9NtvUteu1taFfEutUUfx0+YprXJ1SZLH4WS1ub+zAn//zeLKAKDwszRclS1bVq6urkpKSnJoT0pKUlBQ0BXto3jx4qpfv752/P8UvzmPy88+3d3d5ePj47AAAK7Q0qXmiNX+/eZ65crS8uVSkybW1oWrdjIkVL9NmavDDczvYfGMdLV4+F5V+OFbiysDgMLN0nDl5uamBg0aKD4+3t6WnZ2t+Ph4NW3a9Ir2kZWVpT///FPBweYpDBUrVlRQUJDDPtPS0rRq1aor3icA4Ar98ot5f6TU/5/2u1EjM1hx64si74xfKS2e9IP+je4iSXI5e1aNn3tM1T770OLKAKDwsvy0wNjYWE2YMEGTJk3S1q1b9dhjjykjI8N+L6s+ffpo6NCh9v6vvfaa5s2bp127dmn9+vV64IEHtHfvXg0YMECSOZPgoEGD9MYbb+jHH3/Un3/+qT59+igkJETdunWz4hAB4MY0ZYp0553SyZPmenS0eb1VQIC1dcFpsj08teKDOO144GF7W/hbL6v26P9JhmFhZQBQOFl+E+GePXvq0KFDGjZsmBITExUREaG5c+faJ6RISEiQi0tuBjx27JgefvhhJSYmqlSpUmrQoIGWL1+uWrVq2fs8//zzysjI0COPPKKUlBS1aNFCc+fOlYeHx3U/PgC4IX36qTRwYO4f2D16SF9/Lbm5WVsXnM/VVRuGv61T/gGq8/7/JEm1PnpHsp2SRo82Z4QEAEiSbIbBR0/nS0tLk6+vr1JTU7n+CgDON3Kked+qHAMGSOPHS66ulpQzbWeqJc/rDD0q+xbYvgvidany5Seq/9oLuQ0xMdKECVIxyz+rBYACk59swMdNAIArYxjS0KGOweq558xRLIuCFa6vHX0e1eq3P5aRM1oVFyf16mXeOBoAYP1pgQCAIiA7W3riCXOEKsebb5pB6wpuDlyUR5cKUlF8XfbefZ/OepVUs0H9pTNnpOnTpePHzXuaeXlZXR4AWIqRKwDApZ09a57+lROsbDbp44/NUawrCFa48eyPvlP6+WfzRtGSNG+eOaFJSoqldQGA1QhXAICLO3NGuv9+6auvzHVXV3Piisces7YuWK9dO2n+fCnn+oNly6SoKOnoUWvrAgALEa4AABeWmWnOAjh1qrlevLj0/ffSffdZWxcKj+bNpUWLJH9/c33dOqltW+nQIUvLAgCrEK4AAHmdPCnddZc0a5a57u4uzZwpcb9AnK9+fTNgBQWZ63/8IbVpIx08aGVVAGAJwhUAwFFGhtS5s/TLL+a6p6c0e7bUsaO1daHwqlVLWrJEKlfOXP/rL6l1a+nff62tCwCuM8IVACBXWprUvr3022/mesmS0q+/Srffbm1dKPyqVjUDVoUK5vr27VKrVtKePZaWBQDXE+EKAGBKSTEnKVi61Fz39TUnLGjZ0tKyUIRUrGgGrMqVzfXdu80RrJ07ra0LAK4TwhUAwJzhLSpKWrXKXC9d2hy9atLE2rpQ9JQvbwas6tXN9YQEcwTr77+trQsArgPCFQDc7I4cMYPVunXmur+/OUHBrbdaWhaKsJAQafFiqU4dc/3AAXOSi7/+srQsAChohCsAuJkdOiTddpu0YYO5HhhoBqu6dS0tCzeAwEBp4UIpIsJcT0oyp2nfvNnSsgCgIBGuAOBmlZxsBqs//jDXg4PNYFWrlqVl4QZStqwUH587CpqcbAasnPccANxgilldAADAAomJ5gyA/3+a1onAEC3+6ielFw+WdqZaXBxuKKVLSwsWSNHR0po10uHDZqhfsCB3VAsAbhCMXAHAzeb8619CQ7Vo8mylh1W2tCzcwEqVkubNkyIjzfUjR8yAtX69tXUBgJMRrgDgZrJ/vxmstm0z1ytUkBYvVkaFipaWhZuAn58ZsJo1M9ePHTNHT9eutbQsAHAmwhUA3Cz27TPvObR9u7lesaJ5jVVFghWuEx8fae5cqUULcz0lxfEWAABQxBGuAOBmkJBgjljl3My1UiUzWIWFWVgUbkre3tIvv5j3vpKk1FTz5tUrVlhbFwA4AeEKAG50e/aYI1a7dpnrVaqY9yAqX97SsnATK1lSmjPHnDlQktLSzIC1dKm1dQHANSJcAcCNbNcuM1jt2WOuV6tmBqty5SwtC5CXl/Tzz+ZpgZKUni61by8tWWJtXQBwDQhXAHCj2rHDPBUwIcFcr1HDPBUwJMTKqoBcJUpIP/5ojlpJUkaG1KGD+T4FgCKIcAUAN6Lt281gtW+fuV6rlrRwoXmjYKAw8fSUZs0yQ5UknTghdexo3nwYAIoYwhUA3Gi2bTNPBdy/31yvU8cMVkFB1tYFXIyHh/TDD1KnTub6yZNS587m1O0AUIQQrgDgRrJlixmsDh401+vVk377TQoIsLYu4HLc3aXp06WuXc31U6ekO+80ZxYEgCKCcAUAN4pNm8xTAZOSzPWICPPUKn9/K6sCrpy7uzR1qnT33eZ6ZqbUrZv000+WlgUAV4pwBQA3gnXrzGmtDx821xs2NINV2bLW1gXkl5ub9N13Uo8e5vrp02bYmj7d2roA4AoQrgCgqFu1Srr9dunYMXO9SRNpwQKpdGlr6wKuVvHi0uTJ0n33metnz0o9e5ptAFCIEa4AoChbulS64w4pNdVcb9nSnATA19fauoBrVayY9OWXUkyMuZ6VJT3wgBQXZ2VVAHBJhCsAKKoWLTJvunr8uLl+223mxf/e3paWBTiNq6v0+efSo4+a64Yh9esnffqptXUBwEUQrgCgKJo/37wXUEaGuR4dLf38s+TlZW1dgLO5uEjjxklPP53b9uij0ocfWlcTAFwE4QoAippZs8x7AJ08aa537izNnGnejBW4Edls0ujR0nPP5bY9/bT0zjuWlQQAF0K4AoCiZPJk6Z57zBnUpNxZ1Dw8rK0LKGg2mzRypPTyy7ltzz8vDR9uni4IAIUA4QoAiooJE8wL+rOyzPUHH5SmTDGnrgZuBjab9Npr0htv5La99poUG0vAAlAoEK4AoCgYNUp65JHcPyAHDjRnTStWzNKyAEv897/maYI5Ro+WHn4494MHALAI4QoACjPDMD+Zf/bZ3LbBg6WPPzYv9AduVs88Y84kmPP/4PPPzfti5ZwyCwAW4DczABRWhpF7TUmO116T3n7bPD0KuNk99JD07be5I7hTp0p33ZU72QsAXGeEKwAojLKyzFP/3n03t23UKPNifoIVkOvee80ZNHMmdZkzR+rQIff+bwBwHRGuAKCwycyUevbMvVGqzWZ+/Z//WFsXUFh17CjNnSuVLGmuL14s3X67dPiwtXUBuOkQrgCgMElLMz91nz7dXC9WTPrmG/NifQAX17q1FB8vlSplrq9ZI7VoIe3da21dAG4qhCsAKCySkqQ2baSFC831EiWkn36Seve2tCygyGjcWFqyRAoONte3bZOaNZP+/NPaugDcNAhXAFAY7N5tfsq+YYO5Xrq09NtvUvv21tYFFDV16kjLl0vVqpnrBw5ILVtKv/9ubV0AbgqEKwCw2h9/mJ+u79hhrpcrJy1dKkVGWlsXUFSFhZn/hxo1MtdTU6V27cyJLwCgABWKcDV27FiFhYXJw8NDkZGRWr169UX7TpgwQS1btlSpUqVUqlQpRUVF5ekfExMjm83msLTn018AhdHvv0utWkmJieZ6jRrmp+41a1pbF1DU+fubo7/t2pnrp05Jd98tffaZtXUBuKFZHq6mTJmi2NhYDR8+XOvXr1d4eLiio6OVnJx8wf6LFi1S7969tXDhQq1YsUKhoaFq166d9u/f79Cvffv2OnjwoH359ttvr8fhAMCVmzJFuuMO81N1yRypWrpUCg21ti7gRlGypHnd4v33m+vZ2ebkMG+8Yd5HDgCczPJwNWrUKD388MPq16+fatWqpfHjx6tEiRL64osvLtj/m2++0eOPP66IiAjVqFFDn332mbKzsxUfH+/Qz93dXUFBQfalVM7sQQBgNcOQRo6UevUyp12XpOhoacECqUwZa2sDbjRubtKXXzreyuDll6VHH5XOnLGuLgA3JEvD1enTp7Vu3TpFRUXZ21xcXBQVFaUVK1Zc0T5OnDihM2fOqHTp0g7tixYtUkBAgKpXr67HHntMR44cueg+MjMzlZaW5rAAQIE4e1Z67DFpyJDctn79zE/Xc+7RA8C5XFyk996T3n47t23CBKlTp9yRYwBwAkvD1eHDh5WVlaXAwECH9sDAQCXmXH9wGS+88IJCQkIcAlr79u315ZdfKj4+XiNHjtTixYvVoUMHZWVlXXAfI0aMkK+vr30J5ZQcAAXh+HGpSxfpk09y2954Q/r8c6l4cevqAm4GNpv03HPmfePc3My2+fOl5s25FxYApylmdQHX4q233tJ3332nRYsWycPDw97eq1cv+9d169ZVvXr1VLlyZS1atEi33357nv0MHTpUsbGx9vW0tDQCFgDn2r/f/JR80yZz3c1N+uKL3GtBAFwf990nlS8vdesmHTkibdkiNWlijh43bGh1dQCKOEtHrsqWLStXV1clJSU5tCclJSkoKOiSj3333Xf11ltvad68eapXr94l+1aqVElly5bVjpxpjs/j7u4uHx8fhwUAnOaPP8zJKnKCValS0rx5BCvAKi1aSCtWSFWrmuuJieasnTNnWloWgKLP0nDl5uamBg0aOExGkTM5RdOmTS/6uLfffluvv/665s6dq4ZX8CnTv//+qyNHjig4547tAHC9zJplnnaUM6NpxYrmVOutW1tbF3Czq1rVDFgtWpjrJ0+aU7W//z4zCQK4apbPFhgbG6sJEyZo0qRJ2rp1qx577DFlZGSoX79+kqQ+ffpo6NCh9v4jR47Uyy+/rC+++EJhYWFKTExUYmKi0tPTJUnp6el67rnntHLlSu3Zs0fx8fHq2rWrqlSpoujoaEuOEcBNyDDM66m6dZP+/+eTGjeWVq4072UFwHplypizdOaMIhuGFBtrTteeM5MnAOSD5ddc9ezZU4cOHdKwYcOUmJioiIgIzZ071z7JRUJCglxccjPguHHjdPr0aXXv3t1hP8OHD9crr7wiV1dX/fHHH5o0aZJSUlIUEhKidu3a6fXXX5e7u/t1PTYAN6mMDHMGwGnTctt69jSvsSpRwrq6AOTl7i599ZVUpYr06qtm2+efm9diTZ8uhYRYWx+AIsVmGIx9ny8tLU2+vr5KTU3l+isA+bN3r9S1a+71VTab9L//mVOv22zW1nYJ03YyHTXyp0dlX6tLcL5vv5X69zdPEZSk4GBpxgxzwgsAN638ZAPLTwsEgBvGkiXmbGM5wcrbW/rxR2no0EIdrAD8v969pWXLzNkEJengQfP6yM8/t7YuAEUG4QoArpVhSOPHS7ffLh0+bLZVqSKtWiV17mxtbQDyp359ae3a3ElnTp+WBgyQnnxSOnPG2toAFHqEKwC4FhkZUt++0mOPSWfPmm3t2kmrV0s1a1pbG4Cr4+9v3mD4qady28aOlaKipPNuHwMA5yJcAcDV+usvcwbAr77KbXv2WWn2bPNeVgCKruLFpQ8+MCeicXMz25YskSIipN9+s7Q0AIUX4QoArsbXX0uNGpkBS5JKlpS++056912pmOUTsQJwln79zFCVM2tgYqI5gvXqq1JWlrW1ASh0CFcAkB8nT0qPPCI9+KB04oTZVreutG6dOd06gBtPZKS0YYN0xx3mumFIr7xingKcmGhpaQAKF8IVAFyp7dulpk2lCRNy2/r3NyeuqFbNuroAFLyAAGnuXPPWCjn33/ztN/M0wfh4S0sDUHgQrgDgcgxDmjRJatAgd5p1T08pLk767DPzawA3PhcX6cUXpYULc08TTEoyR7SGD+c0QQCEKwC4pMOHpe7dpZgY6fhxs61GDXM2wL59LS0NgEVatZI2bpSio811w5Bee81s37nT0tIAWMtmGIZhdRGFTX7uwgzgBvbLL9JDDzleU9G3r/TRR+YEFueZtjO1QMvpUdm3wPZd0LUD+VGQ73Wnys6WRo6UXnrJ/FqSvLzMiW0efZSbhwM3iPxkA0auAOB8GRnS449LHTvmBqsyZaTvvzdPBbxAsAJwE3JxkYYONWcTrFTJbMvIMO9717GjdOCAtfUBuO4IVwBwrtWrpVtvlcaNy23r0EH680/pnnusqwtA4dW8uXk95qOP5rbNnSvVqWPeogHATYNwBQCSOa36kCFSs2bSP/+YbZ6e0scfmzcFDg62tj4AhVvJktL48dKcObk/L44dk3r3lnr1ko4csbY+ANcF4QoA5s8371U1cmTubF+NGpkXrD/2GNdNALhyOSPd5973bsoUcyKcr74yJ78AcMMiXAG4eR0+LPXpY94IdNcus83NzZz1a9ky7l0F4OqUKWOeDvjtt1KpUmZbzs+bqKjc0XEANxzCFYCbj2FIX38t1axpfpKco1Ur6Y8/pJdflooXt64+ADeGXr2kLVuke+/NbfvtN6lePen116XMTOtqA1AgCFcAbi7//GPem+bBB81PkiXJz0+aMMG8MWj16paWB+AGExxsnhY4e7ZUoYLZlpkpDRsmhYdLixdbWx8ApyJcAbg5HDsmxcZKtWub11jluPdeaetWacAAc1plACgIHTuao1jPPy+5uppt27ZJbdqYH/bs22dpeQCcg78kANzYzp41Z/yrWlV6/31zXZJCQ6WffjI/UQ4KsrZGADcHLy9z4pz166XIyNz2r782r/F8+WUpPd26+gBcM8IVgBvXvHnmaTdPPJE7DbKnpzR8uDla1bmztfUBuDnVqyctX27eT690abPt1CnpjTfMD4I+/zx35lIARQrhCsCNZ8sWqUsX89qqv/7Kbb/vPvM0nFdeMT9BBgCruLhIAwdKO3aYpyznTKKTmGiepnzrrdKCBdbWCCDfbIbBDRfOl5aWJl9fX6WmpsrHx8fqcgBcqZzgNGWK471kGjeWRo+WmjYt0KeftjO1QPcP3Cx6VPa1uoTrb8cO6YUXpBkzHNujo82fa02aWFIWcCkF/XuvsPwsyE82YOQKQNG3Y4fUt69Uq5Z5b5mcYHXLLeZU6ytWFHiwAoBrUqWKNH26tGSJ1LBhbvuvv5o/vzp0kFatsq4+AFeEcAWg6Nqzxzx9pkYN6csvpexss71sWemdd8xp1x94gFkAARQdLVuaIeqrr3KnbpekuXPN0auOHQlZQCHGXxwAip7Nm6WHHjJn1zr3wu/SpaURI6Tdu6XBg6USJaytEwCuhouL+cHQP/+Y9+A7N2T98ktuyFq+3LoaAVwQ4QpA0WAY5sXd7dtLdetKEydKZ86Y23x9pddeM0PVkCFSyZLW1goAzuDmZo7OXyxkNW9uBq0pU3JvMwHAUoQrAIXb6dPmKX8REdIdd5jXH+Tw9ZVeeskMVS+/LDEBDYAb0aVC1qpVUq9eUqVK5unQKSmWlQmAcAWgsNq/37znS8WK5mQVf/yRuy0szJz9b98+6fXXpVKlrKoSAK6fc0PWxInm/bJy7NsnPf+8VK6c9NRT0vbt1tUJ3MQIVwAKj7NnpZ9+ku68Uypf3hyNOnAgd3vjxtLUqeYfDc88I3l7W1crAFjFzU2KiZE2bpTi4x1viJ6RIX30kXlNaps25sj/iRMWFQrcfAhXAKy3Z48ZpCpUMIPVTz/lzvxns0ldu0q//y6tXCn16CEVK2ZpuQBQKNhs0m23mT8zt22THn/ccSKfxYvNkf/gYPOGxWvWON4DEIDTEa4AWOPIEfPagdtvN68VeOMNx1GqcuWkYcOkXbukmTOlFi3MPyQAAHlVqyaNHWueHvj221L16rnb0tKkTz4xR//Dw6VRo6R//7WuVuAGRrgCcP2kpkqTJpk3wwwKkh55RPrtt9xPUl1dzVGqn382R7NefdW8vgoAcGVKl5aee07aulVaulTq10/y8srd/uef0rPPSqGh5myDY8aY17gCcArOrQFQsA4fNm9++f335tTBp0/n7VOlivkHQEyMFBJy3UsEgBuOzWaGp5wANWWK9MUX0ooVuX2WLzeXQYPMfj16SPfcY545AOCqEK4AOJdhmDP7zZ5tLitX5l4/da7y5aWePc0phOvX55Q/ACgo3t7mLIMDBkh//20GrWnTpC1bcvssW2YugwaZt77o0MG8UXGTJlznCuSDzTC4svF8aWlp8vX1VWpqqnwKwX1zpu1MLdD996jsW6D7x4UV5Pf1un9Pjx2TliwxR6Zmz774ufzBwdK995qhqkkTSwJVQf9/AlD4FeTPyCL1O/uvv8yQNXWq+fUFnPbxVVKL23SwdZQSW0Up0z/wqp+OvzduPEXq/X4N8pMN+CgCQP4dO2bO3rdokbls3HjxGahq1ZI6dTKnCm7e3LyuCgBgvVq1pOHDzWXLFm355CsFL/xVpTdvtHdxS0tV6JwfFDrnB0lSWuVqOtS4uQ41aqbDjZrpZPAtFhUPFE6EKwCXZhjmjH2rVpnL779fOky5u0tt25qBqlMn8ybAAIDCrXZt/fXMUP31zFC5H0pS0O/xClq8QEG/x8stLXd0wmfnP/LZ+Y8qfztRkpQeGqZDjc2gdSS8oY5XqsqHaLipEa4AOEpONu+Fsnp17nL06MX722zm+flt2pih6rbbHGemAgAUKZn+gdp7933ae/d9sp09q9Kb1ipo8QIFLl+sUps3yOXsWXvfkvv2qOS+Pao4fbIk6YxXSaXUqqejdevrWL1bdbTurcooH8Z1tbhpEK6Am9XJk+Y59n/8YU7Nm7MkJV36ceeGqTZtpJYtpVKlrkPBAIDrzShWTEcaNNGRBk20JfYluZ7IUJkNa+S/Zpn8Vy1T6U3r5Ho6096/eEa6/Ncsl/+a5fa2075+SqleW2oUIdWpYy61a/O7AzckwhVwI8vONu9fsn279M8/uf/+84+0Y8eFZ/E7n7+/FBlp3nwyZ+EXIgDclLJKeCm5eRslN28jSXLJPKXSm9apzPrVKv3nBpX6c728DjhOauSWmqKA1cuk1cscd3bLLWbQql7dvCVH1armv2FhzFCIIot3LlCUGYbcDyerxIF/5bV/n0oc2KcS+xOklETzJrw7dpgjVFeqbFmpXj1zZConUFWowOkcAIALynb30OHGzXW4cXN7m/uRQyr15waV/mO9Sv25QaW2bJJncmLeB+/fby6//urYXqyYGbCqVjX/LV/ecQkJIXyh0OKdCRRCLpmn5H7ksNyPHZH7kcPyOJQoz+Qk89+kRHkcyv363NMxrpinp1SzplS3rhmm6tY1l8BAghQA4JpklvFXYpt2SmzTzt5WPOWYup3YJ23enLv8+ac5++z5zp41PxzcsePCT+DiYo56hYSYt/gICsr9N+frgADzA8MSJfi9huuqUISrsWPH6p133lFiYqLCw8P14YcfqnHjxhftP23aNL388svas2ePqlatqpEjR6pjx4727YZhaPjw4ZowYYJSUlLUvHlzjRs3TlWrVr0ehwOYp9ulp0vHj+cuaWnmL5GUFCklRXV2Jap4WqqKH0+VW8oxM0gdPSz3Y0dV7ETGtddQvLhUqZL5yV+1auaS83VIiPnLCQCA6+CMXympQZh5nW4Ow5ASE80QtX173n8zLvK7MDtb2rfPXC7Hw0MqU8YMWjlL6dKSn5/k62v+e+7i42PedNnb25yciREy5JPl75gpU6YoNjZW48ePV2RkpEaPHq3o6Ght27ZNAQEBefovX75cvXv31ogRI9S5c2dNnjxZ3bp10/r161WnTh1J0ttvv60PPvhAkyZNUsWKFfXyyy8rOjpaf/31lzw8PK73IeJ6MQzz066sLPPfs2elM2dy/z13OXtWOn06d8nMdFw/deriy8mT5g/8EyfM5dyvcwLVxX4hnKPmNR5upl8pnfIP0omQcjpxS6gyQkJ14hZzua1ZbfPTO6bDBQAUVjabOcoUHOwYuiTzd3pSkpSQkHfZt0/au1c6dOjyz3HqVO7ph1fD01MqWdIMWyVLmiNhOYunp+PXnp7m7Ug8PMzl3K/d3C68FC+euxQrlvfrYsXM3+XFijECV0TYDONiN6u5PiIjI9WoUSN99NFHkqTs7GyFhobqqaee0pAhQ/L079mzpzIyMvTzzz/b25o0aaKIiAiNHz9ehmEoJCREzz77rAYPHixJSk1NVWBgoOLi4tSrV6/L1pSfuzAXqMRE6fHH9W/GmYt2sZ377Tv/W3ne+sX6hpQo5th2qX/P//pyS3b2hdezsx2/PrftQktWVt4lpz0nUF3J5AyFmOHioky/0jpdqowyS5dWZumyyixVRpmly+qUf6BOBQTqpH+QTgUE6pR/oLLdL/5BQWG5o3lhVNB3kwdQ+BXkz8iC/hlD7ec4c8YMYImJ0sGDuf8ePCgdPmwuR47kfn3m4n9PFQk2m2PYcnU1z0K52L+XWmy2vF/bbLnL+evnL/9fT9KpLEmSIcd2STJsF2m70HH9P4ftNptu8SoujRplnoljofxkA0tHrk6fPq1169Zp6NCh9jYXFxdFRUVpxYoVF3zMihUrFBsb69AWHR2tmTNnSpJ2796txMRERUVF2bf7+voqMjJSK1asuGC4yszMVGZm7nUrqanmD5e0tLSrPjanSEqSfvhBBR3vLD7KG4O7e+6nVyVLOn7KlfO1t3fuaQi+vlp6srjO+PjqTEkfnfH20WlfX8nlCkeackbYLiItjU+3LubEcd7xwM2uIH9GFvTPGGo/j4+PuVSrdul+hmGeXXLkiHl6fmqq45KWZranpZlnn6Sn5y4ZGblnpWRexXXOzmIYuWfgFBKeBbz/NEkaPNg8ndNCOZngSsakLA1Xhw8fVlZWlgIDAx3aAwMD9ffff1/wMYmJiRfsn5iYaN+e03axPucbMWKEXn311TztoaGhV3YgQGamuVzowlwLxFhdAAAUYjFWF3ANYqwu4BrEWF0AiqbzTxm10PHjx+Xre+kRWMuvuSoMhg4d6jAalp2draNHj6pMmTKyXeP5rWlpaQoNDdW+ffusPcUQluO9AIn3AXLxXoDE+wC5eC8UXoZh6Pjx4woJCblsX0vDVdmyZeXq6qqkpCSH9qSkJAUFBV3wMUFBQZfsn/NvUlKSgoODHfpERERccJ/u7u5yd3d3aPPz88vPoVyWj48P/1EgifcCTLwPkIP3AiTeB8jFe6FwutyIVQ5L52J2c3NTgwYNFB8fb2/Lzs5WfHy8mjZtesHHNG3a1KG/JM2fP9/ev2LFigoKCnLok5aWplWrVl10nwAAAABwrSw/LTA2NlZ9+/ZVw4YN1bhxY40ePVoZGRnq16+fJKlPnz665ZZbNGLECEnSM888o9atW+u9995Tp06d9N1332nt2rX69NNPJUk2m02DBg3SG2+8oapVq9qnYg8JCVG3bt2sOkwAAAAANzjLw1XPnj116NAhDRs2TImJiYqIiNDcuXPtE1IkJCTI5ZybnTZr1kyTJ0/WSy+9pBdffFFVq1bVzJkz7fe4kqTnn39eGRkZeuSRR5SSkqIWLVpo7ty5ltzjyt3dXcOHD89z2iFuPrwXIPE+QC7eC5B4HyAX74Ubg+X3uQIAAACAG4Gl11wBAAAAwI2CcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrq6zO++8U+XLl5eHh4eCg4P14IMP6sCBA1aXhetoz5496t+/vypWrChPT09VrlxZw4cP1+nTp60uDRb43//+p2bNmqlEiRJOv3k5Cq+xY8cqLCxMHh4eioyM1OrVq60uCdfZkiVL1KVLF4WEhMhms2nmzJlWlwQLjBgxQo0aNZK3t7cCAgLUrVs3bdu2zeqycA0IV9dZ27ZtNXXqVG3btk3Tp0/Xzp071b17d6vLwnX0999/Kzs7W5988om2bNmi999/X+PHj9eLL75odWmwwOnTp9WjRw899thjVpeC62TKlCmKjY3V8OHDtX79eoWHhys6OlrJyclWl4brKCMjQ+Hh4Ro7dqzVpcBCixcv1hNPPKGVK1dq/vz5OnPmjNq1a6eMjAyrS8NVYip2i/3444/q1q2bMjMzVbx4cavLgUXeeecdjRs3Trt27bK6FFgkLi5OgwYNUkpKitWloIBFRkaqUaNG+uijjyRJ2dnZCg0N1VNPPaUhQ4ZYXB2sYLPZ9MMPP6hbt25WlwKLHTp0SAEBAVq8eLFatWpldTm4CoxcWejo0aP65ptv1KxZM4LVTS41NVWlS5e2ugwABez06dNat26doqKi7G0uLi6KiorSihUrLKwMQGGQmpoqSfxNUIQRrizwwgsvyMvLS2XKlFFCQoJmzZpldUmw0I4dO/Thhx/q0UcftboUAAXs8OHDysrKUmBgoEN7YGCgEhMTLaoKQGGQnZ2tQYMGqXnz5qpTp47V5eAqEa6cYMiQIbLZbJdc/v77b3v/5557Ths2bNC8efPk6uqqPn36iLMzi778vg8kaf/+/Wrfvr169Oihhx9+2KLK4WxX814AANzcnnjiCW3evFnfffed1aXgGhSzuoAbwbPPPquYmJhL9qlUqZL967Jly6ps2bKqVq2aatasqdDQUK1cuVJNmzYt4EpRkPL7Pjhw4IDatm2rZs2a6dNPPy3g6nA95fe9gJtH2bJl5erqqqSkJIf2pKQkBQUFWVQVAKs9+eST+vnnn7VkyRKVK1fO6nJwDQhXTuDv7y9/f/+remx2drYkKTMz05klwQL5eR/s379fbdu2VYMGDTRx4kS5uDCIfCO5lp8JuLG5ubmpQYMGio+Pt09ekJ2drfj4eD355JPWFgfgujMMQ0899ZR++OEHLVq0SBUrVrS6JFwjwtV1tGrVKq1Zs0YtWrRQqVKltHPnTr388suqXLkyo1Y3kf3796tNmzaqUKGC3n33XR06dMi+jU+ubz4JCQk6evSoEhISlJWVpY0bN0qSqlSpopIlS1pbHApEbGys+vbtq4YNG6px48YaPXq0MjIy1K9fP6tLw3WUnp6uHTt22Nd3796tjRs3qnTp0ipfvryFleF6euKJJzR58mTNmjVL3t7e9msvfX195enpaXF1uBpMxX4d/fnnn3rmmWe0adMmZWRkKDg4WO3bt9dLL72kW265xerycJ3ExcVd9I8o/jvefGJiYjRp0qQ87QsXLlSbNm2uf0G4Lj766CO98847SkxMVEREhD744ANFRkZaXRauo0WLFqlt27Z52vv27au4uLjrXxAsYbPZLtg+ceLEy55ejsKJcAUAAAAATsCFHgAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwCAQmPRokWy2WxKSUmxupTrJiwsTKNHj7a6DACAExCuAAB2MTEx6tatW572whR6XnnlFUVERDhlX5c6rsISek6cOKGhQ4eqcuXK8vDwkL+/v1q3bq1Zs2ZZXRoA4DzFrC4AAABc3MCBA7Vq1Sp9+OGHqlWrlo4cOaLly5fryJEjBfacp0+flpubW4HtHwBuVIxcAQCuytKlS9WyZUt5enoqNDRUTz/9tDIyMuzbv/rqKzVs2FDe3t4KCgrSfffdp+TkZId9zJkzR9WqVZOnp6fatm2rPXv2XPI54+Li9Oqrr2rTpk2y2Wyy2WyKi4uTJCUkJKhr164qWbKkfHx8dO+99yopKclpx5uSkqIBAwbI399fPj4+uu2227Rp0yb79p07d6pr164KDAxUyZIl1ahRIy1YsMBhH8nJyerSpYs8PT1VsWJFffPNN5d93h9//FEvvviiOnbsqLCwMDVo0EBPPfWUHnroIXufzMxMvfDCCwoNDZW7u7uqVKmizz//3L598eLFaty4sdzd3RUcHKwhQ4bo7Nmz9u1t2rTRk08+qUGDBqls2bKKjo6WJG3evFkdOnRQyZIlFRgYqAcffFCHDx++6tcQAG50hCsAQL7t3LlT7du31z333KM//vhDU6ZM0dKlS/Xkk0/a+5w5c0avv/66Nm3apJkzZ2rPnj2KiYmxb9+3b5/uvvtudenSRRs3btSAAQM0ZMiQSz5vz5499eyzz6p27do6ePCgDh48qJ49eyo7O1tdu3bV0aNHtXjxYs2fP1+7du1Sz549nXbMPXr0UHJysn755RetW7dOt956q26//XYdPXpUkpSenq6OHTsqPj5eGzZsUPv27dWlSxclJCTY9xETE6N9+/Zp4cKF+v777/Xxxx/nCZznCwoK0pw5c3T8+PGL9unTp4++/fZbffDBB9q6das++eQTlSxZUpK0f/9+dezYUY0aNdKmTZs0btw4ff7553rjjTcc9jFp0iS5ublp2bJlGj9+vFJSUnTbbbepfv36Wrt2rebOnaukpCTde++9V/sSAsCNzwAA4P/17dvXcHV1Nby8vBwWDw8PQ5Jx7NgxwzAMo3///sYjjzzi8Njff//dcHFxMU6ePHnBfa9Zs8aQZBw/ftwwDMMYOnSoUatWLYc+L7zwgsPzXMjw4cON8PBwh7Z58+YZrq6uRkJCgr1ty5YthiRj9erVF93XwoULDUl5jtfLy8uw2WzG+++/bz82Hx8f49SpUw6Pr1y5svHJJ59cdP+1a9c2PvzwQ8MwDGPbtm156tm6dashyf48F7J48WKjXLlyRvHixY2GDRsagwYNMpYuXWrfnrPf+fPnX/DxL774olG9enUjOzvb3jZ27FijZMmSRlZWlmEYhtG6dWujfv36Do97/fXXjXbt2jm07du3z5BkbNu27aL1AsDNjJErAICDtm3bauPGjQ7LZ5995tBn06ZNiouLU8mSJe1LdHS0srOztXv3bknSunXr1KVLF5UvX17e3t5q3bq1JNlHcrZu3arIyEiH/TZt2tRh/dz9Dxw48KI1b926VaGhoQoNDbW31apVS35+ftq6daskqXbt2vZ9dejQweHxv//+e55jDgkJcTje9PR0lSlTxqGm3bt3a+fOnZLMkavBgwerZs2a8vPzU8mSJbV161aH4y1WrJgaNGhg32+NGjXk5+d30eOSpFatWmnXrl2Kj49X9+7dtWXLFrVs2VKvv/66JGnjxo1ydXW1v74Xem2aNm0qm81mb2vevLnS09P177//2tvOrSvnmBcuXOhwvDVq1JAk+zEDABwxoQUAwIGXl5eqVKni0HbuH+GSGSQeffRRPf3003keX758eWVkZCg6OlrR0dH65ptv5O/vr4SEBEVHR+v06dNXXMvGjRvtX/v4+OTvQM4zZ84cnTlzRpLk6enpsK1ixYp5Qk6xYrm/ItPT0xUcHKxFixbl2W/O4wYPHqz58+fr3XffVZUqVeTp6anu3bvn63gvpnjx4mrZsqVatmypF154QW+88YZee+01vfDCC3mO5Wp5eXk5rKenp6tLly4aOXJknr7BwcFOeU4AuNEQrgAA+Xbrrbfqr7/+yhPCcvz55586cuSI3nrrLfto0tq1ax361KxZUz/++KND28qVKx3WL7R/Nzc3ZWVl5dnXvn37tG/fPvvz/fXXX0pJSVGtWrUkSRUqVMjHETq69dZblZiYqGLFiiksLOyCfZYtW6aYmBjdddddksxwcu4EHTVq1NDZs2e1bt06NWrUSJK0bdu2q5revlatWjp79qxOnTqlunXrKjs7W4sXL1ZUVFSevjVr1tT06dNlGIZ99GrZsmXy9vZWuXLlLnnM06dPV1hYmEPQBABcHKcFAgDy7YUXXtDy5cv15JNPauPGjdq+fbtmzZpln9CifPnycnNz04cffqhdu3bpxx9/tJ/GlmPgwIHavn27nnvuOW3btk2TJ0+2z/x3KWFhYdq9e7c2btyow4cPKzMzU1FRUapbt67uv/9+rV+/XqtXr1afPn3UunVrNWzY8JqPNyoqSk2bNlW3bt00b9487dmzR8uXL9d///tfe2isWrWqZsyYoY0bN2rTpk267777lJ2dbd9H9erV1b59ez366KNatWqV1q1bpwEDBlx25KlNmzb65JNPtG7dOu3Zs0dz5szRiy++qLZt28rHx0dhYWHq27evHnroIc2cOVO7d+/WokWLNHXqVEnS448/rn379umpp57S33//rVmzZmn48OGKjY2Vi8vF/wx44okndPToUfXu3Vtr1qzRzp079euvv6pfv355wi0AwES4AgDkW7169bR48WL9888/atmyperXr69hw4bZr1Py9/dXXFycpk2bplq1aumtt97Su+++67CP8uXLa/r06Zo5c6bCw8M1fvx4vfnmm5d97nvuuUft27dX27Zt5e/vr2+//VY2m02zZs1SqVKl1KpVK0VFRalSpUqaMmWKU47XZrNpzpw5atWqlfr166dq1aqpV69e2rt3rwIDAyVJo0aNUqlSpdSsWTN16dJF0dHRuvXWWx32M3HiRIWEhKh169a6++679cgjjyggIOCSzx0dHa1JkyapXbt2qlmzpp566ilFR0fbw5MkjRs3Tt27d9fjjz+uGjVq6OGHH7ZPi3/LLbdozpw5Wr16tcLDwzVw4ED1799fL7300iWfNyQkRMuWLVNWVpbatWununXratCgQfLz87tkKAOAm5nNMAzD6iIAAAAAoKjjoycAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJzg/wBfbIGv2xbgXgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -12356,7 +12774,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -0.12\n" + "The average of 'head_to_head' is: -0.13\n" ] } ], @@ -12412,17 +12830,17 @@ " \n", " \n", " head_to_head\n", - " -11.2\n", + " -11.8\n", " 92.1\n", " -0.1\n", - " 0.640747\n", - " 0.066766\n", - " -1.826475\n", + " 0.643536\n", + " 0.067057\n", + " -1.907958\n", " 1.98555\n", " 0.0\n", " -0.3\n", - " 0.035527\n", - " 0.071054\n", + " 0.029773\n", + " 0.059546\n", " \n", " \n", "\n", @@ -12430,10 +12848,10 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", - "head_to_head -11.2 92.1 -0.1 0.640747 0.066766 -1.826475 1.98555 \n", + "head_to_head -11.8 92.1 -0.1 0.643536 0.067057 -1.907958 1.98555 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "head_to_head 0.0 -0.3 0.035527 0.071054 " + "head_to_head 0.0 -0.3 0.029773 0.059546 " ] }, "execution_count": 68, @@ -12505,34 +12923,34 @@ " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.125]\n", + " [0.0001, 0.0001, 0.0001, 0.13]\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", - " -1.9\n", - " \n", - " \n", - " 232\n", - " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.2963039014373716]\n", - " [0.002,0.008,0.09,0.9]\n", - " 3 or more\n", - " -1.1\n", + " -1.8\n", " \n", " \n", " 247\n", " Will the 500th richest person on Bloomberg's B...\n", - " 0.766667\n", + " 0.8\n", " 0.333\n", " no\n", - " -1.1\n", + " -1.2\n", " \n", " \n", - " 245\n", - " Will Nebraska have 1.7 million or more residen...\n", - " 0.9\n", - " 0.7\n", - " no\n", - " -0.9\n", + " 232\n", + " How many movies will be new on Netflix's top 1...\n", + " [0.0001, 0.0001, 0.0001, 0.32130390143737164]\n", + " [0.002,0.008,0.09,0.9]\n", + " 3 or more\n", + " -1.0\n", + " \n", + " \n", + " 71\n", + " Will OpenAI, Anthropic, or Perplexity run an a...\n", + " 0.18\n", + " 0.55\n", + " yes\n", + " -1.0\n", " \n", " \n", "\n", @@ -12542,23 +12960,23 @@ " title \\\n", "279 What will Kalshi's rank in the iPhone Top Free... \n", "121 How many movies will be new on Netflix's top 1... \n", - "232 How many movies will be new on Netflix's top 1... \n", "247 Will the 500th richest person on Bloomberg's B... \n", - "245 Will Nebraska have 1.7 million or more residen... \n", + "232 How many movies will be new on Netflix's top 1... \n", + "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", "\n", " bot_team_median \\\n", "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", - "121 [0.0001, 0.0001, 0.0001, 0.125] \n", - "232 [0.0001, 0.0001, 0.0001, 0.2963039014373716] \n", - "247 0.766667 \n", - "245 0.9 \n", + "121 [0.0001, 0.0001, 0.0001, 0.13] \n", + "247 0.8 \n", + "232 [0.0001, 0.0001, 0.0001, 0.32130390143737164] \n", + "71 0.18 \n", "\n", " pro_median resolution head_to_head \n", "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more -1.9 \n", - "232 [0.002,0.008,0.09,0.9] 3 or more -1.1 \n", - "247 0.333 no -1.1 \n", - "245 0.7 no -0.9 " + "121 [0.005,0.017,0.157,0.821] 3 or more -1.8 \n", + "247 0.333 no -1.2 \n", + "232 [0.002,0.008,0.09,0.9] 3 or more -1.0 \n", + "71 0.55 yes -1.0 " ] }, "execution_count": 69, @@ -12634,7 +13052,7 @@ " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", " 2.5\n", @@ -12658,7 +13076,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.95\n", + " 0.954\n", " 0.95\n", " annulled\n", " NaN\n", @@ -12677,10 +13095,10 @@ "\n", " bot_team_median \\\n", "189 [0.0, 0.0030510204, 0.0061020408, 0.0102928751... \n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", "211 0.99 \n", - "214 0.95 \n", + "214 0.954 \n", "\n", " pro_median resolution \\\n", "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", @@ -12809,10 +13227,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.539332\n", - " 2.539332\n", + " 2.522754\n", + " 2.522754\n", " \n", " \n", " 1\n", @@ -12829,10 +13247,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.250003\n", - " -0.250003\n", + " -0.161101\n", + " -0.161101\n", " \n", " \n", " 2\n", @@ -12849,10 +13267,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.063\n", + " 0.085\n", " 0.013\n", - " -0.051987\n", - " -0.051987\n", + " -0.075746\n", + " -0.075746\n", " \n", " \n", " 3\n", @@ -12928,23 +13346,23 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.063 \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.539332 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", - "2 0.013 -0.051987 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.522754 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.161101 \n", + "2 0.013 -0.075746 \n", "3 [0.16,0.44,0.4] 0.152526 \n", "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", "\n", " weighted_score \n", - "0 2.539332 \n", - "1 -0.250003 \n", - "2 -0.051987 \n", + "0 2.522754 \n", + "1 -0.161101 \n", + "2 -0.075746 \n", "3 0.152526 \n", "4 0.387623 " ] @@ -12960,19 +13378,32 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 91, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows in calibration df: 48\n" + ] + } + ], "source": [ "# Make binary-only df_top_bot_pro_forecasts for calibration curves etc\n", - "df_top_bot_pro_forecasts_binary = df_top_bot_pro_forecasts[df_top_bot_pro_forecasts['type'] == 'binary'].copy()\n", + "df_top_bot_pro_forecasts_binary = df_top_bot_pro_forecasts[\n", + " (df_top_bot_pro_forecasts['type'] == 'binary') &\n", + " (df_top_bot_pro_forecasts['resolution'].notna())\n", + "].copy()\n", + "print(f\"Rows in calibration df: {len(df_top_bot_pro_forecasts_binary)}\")\n", + "\n", "\n", "df_top_bot_pro_forecasts_all_binary = df_top_bot_pro_forecasts_all[df_top_bot_pro_forecasts_all['type'] == 'binary'].copy()" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 92, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -12982,9 +13413,25 @@ "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Count: 10\n", + "Count: 11\n", + "Count: 11\n", + "Count: 11\n", + "Count: 11\n", + "Count: 10\n", + "Count: 9\n", + "Count: 10\n", + "Count: 9\n", + "Count: 10\n" + ] + }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -12996,7 +13443,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of pro forecasts: 50\n" + "Number of pro forecasts: 48\n" ] } ], @@ -13038,7 +13485,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -13131,7 +13578,7 @@ " False\n", " 31294\n", " 1.0\n", - " 0.86\n", + " 0.81\n", " 0.95\n", " \n", " \n", @@ -13199,7 +13646,7 @@ " question_weight bot_team_median pro_median \n", "2 1.0 0.063 0.013 \n", "5 1.0 0.62 0.45 \n", - "8 1.0 0.86 0.95 \n", + "8 1.0 0.81 0.95 \n", "10 1.0 NaN NaN \n", "13 1.0 0.85 0.9 " ] @@ -13215,7 +13662,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -13266,7 +13713,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13278,7 +13725,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -13297,7 +13744,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13310,9 +13757,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bot average forecast difference (1 - 0): 0.4365\n", + "Bot average forecast difference (1 - 0): 0.4288\n", "Pro average forecast difference (1 - 0): 0.5238\n", - "Difference between pro and bot differences: 0.0873\n" + "Difference between pro and bot differences: 0.0950\n" ] } ], @@ -13339,7 +13786,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13379,7 +13826,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -13438,10 +13885,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012671204620462045, 0.0001, 0.0001, 0.0001,...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.539332\n", - " 2.539332\n", + " 2.522754\n", + " 2.522754\n", " \n", " \n", " 1\n", @@ -13460,8 +13907,8 @@ " 1.0\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.250003\n", - " -0.250003\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -13557,22 +14004,22 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012671204620462045, 0.0001, 0.0001, 0.0001,... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.539332 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.522754 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", "2 0.013 -0.051987 \n", "3 [0.16,0.44,0.4] 0.152526 \n", "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", "\n", " weighted_score \n", - "0 2.539332 \n", - "1 -0.250003 \n", + "0 2.522754 \n", + "1 -0.158842 \n", "2 -0.051987 \n", "3 0.152526 \n", "4 0.387623 " @@ -13637,10 +14084,10 @@ " False\n", " 35380\n", " 1.00\n", - " 0.905\n", + " 0.9\n", " 0.95\n", - " -0.048527\n", - " -0.048527\n", + " -0.054067\n", + " -0.054067\n", " \n", " \n", " 351\n", @@ -13657,10 +14104,10 @@ " False\n", " 35381\n", " 1.00\n", - " 0.15\n", + " 0.3\n", " 0.05\n", - " -0.111226\n", - " -0.111226\n", + " -0.305382\n", + " -0.305382\n", " \n", " \n", " 355\n", @@ -13677,10 +14124,10 @@ " False\n", " 35385\n", " 1.00\n", - " 0.9\n", + " 0.85\n", " 0.97\n", - " -0.074901\n", - " -0.074901\n", + " -0.132060\n", + " -0.132060\n", " \n", " \n", " 361\n", @@ -13749,9 +14196,9 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median head_to_head weighted_score \n", - "342 1.00 0.905 0.95 -0.048527 -0.048527 \n", - "351 1.00 0.15 0.05 -0.111226 -0.111226 \n", - "355 1.00 0.9 0.97 -0.074901 -0.074901 \n", + "342 1.00 0.9 0.95 -0.054067 -0.054067 \n", + "351 1.00 0.3 0.05 -0.305382 -0.305382 \n", + "355 1.00 0.85 0.97 -0.132060 -0.132060 \n", "361 0.85 0.8 0.666 -0.435900 -0.370515 \n", "364 0.85 0.05 0.03 -0.017709 -0.015053 " ] diff --git a/functions.py b/functions.py index 8de9f69..11257c4 100644 --- a/functions.py +++ b/functions.py @@ -10,7 +10,7 @@ from scipy import stats from scipy.optimize import minimize_scalar from scipy.stats import binom, norm -import re + from refactored_notebook.scoring import ( calculate_baseline_score, calculate_peer_score, @@ -266,7 +266,7 @@ def calc_weighted_std_dev2(df3, bot, weighted_score, weighted_count, weight_col) ) -def weighted_bootstrap_analysis(df_bot_peer_wide, bots, NUM, ITER): +def weighted_bootstrap_analysis(df_bot_peer_wide: pd.DataFrame, bots: list[str], NUM: int, ITER: int): """ Performs weighted bootstrap analysis to calculate confidence intervals and medians. @@ -281,7 +281,7 @@ def weighted_bootstrap_analysis(df_bot_peer_wide, bots, NUM, ITER): """ # Function to perform a single bootstrap iteration - def single_bootstrap(df): + def single_bootstrap(df: pd.DataFrame): # Weighted sampling of questions sampled_df = df.sample(n=NUM, weights="question_weight", replace=True) # Calculate total weighted score for each bot @@ -632,7 +632,80 @@ def plot_head_to_head_distribution( print(f"The average of 'head_to_head' is: {mean:.2f}") -def calculate_calibration_curve(forecasts, resolutions, weights): +def plot_calibration_curve(df: pd.DataFrame, column_name: str, label: str, color: str): + """ + Plots a calibration curve with confidence intervals. + + Args: + df (pandas.DataFrame): DataFrame with forecast and resolution data. + column_name (str): Column name for forecast probabilities. + label (str): Label for the plot. + color (str): Color for the plot. + + Returns: + None + """ + _assert_calibration_dataframe_matches_assumptions(df) + # Filter to binary questions in case the DataFrame has other types (0 or 1 INT or 'yes'/'no' STR) + df = df[df["resolution"].isin(["yes", "no", 1, 0])] + + y_true = df["resolution"] + y_pred = df[column_name] + weights = [1.0 for _ in y_true] + calibration_curve = _calculate_calibration_curve(y_pred, y_true, weights)[ + "calibration_curve" + ] + prob_true = [item["average_resolution"] for item in calibration_curve] + bin_center = [ + (item["bin_lower"] + item["bin_upper"]) / 2 for item in calibration_curve + ] + ci_lower = [item["lower_confidence_interval"] for item in calibration_curve] + ci_upper = [item["upper_confidence_interval"] for item in calibration_curve] + + plt.plot(bin_center, prob_true, marker="o", linewidth=2, label=label, color=color) + plt.fill_between(bin_center, ci_lower, ci_upper, alpha=0.2, color=color) + for x, y in zip(bin_center, prob_true): + if x is None or y is None: + continue + plt.annotate( + f"({x:.2f}, {y:.2f})", + (x, y), + textcoords="offset points", + xytext=(0, 10), + ha="center", + color=color, + fontsize=8, + ) + +def _assert_calibration_dataframe_matches_assumptions(df: pd.DataFrame): + # 1. Only binary questions + assert (df['type'] == 'binary').all(), "DataFrame contains non-binary questions." + + # 2. Only valid resolutions (0, 1, 'yes', 'no') + valid_resolutions = {0, 1} + assert set(df['resolution'].unique()).issubset(valid_resolutions), ( + f"DataFrame contains invalid resolutions: {set(df['resolution'].unique()) - valid_resolutions}" + ) + + # 3. Each question_id appears only once (if grouped by question) + if 'question_id' in df.columns: + assert df['question_id'].is_unique, "Each question_id should appear only once." + + # 4. No missing values in key columns + for col in ['resolution', 'type']: + assert df[col].notnull().all(), f"Missing values found in column: {col}" + + # 5. Probabilities are between 0 and 1 for forecast columns + prob_cols = [col for col in df.columns if 'prob' in col or 'median' in col or 'forecast' in col] + for col in prob_cols: + if df[col].dtype.kind in {'f', 'i'}: + assert ((df[col] >= 0) & (df[col] <= 1)).all(), f"Column {col} contains values outside [0, 1]" + + # 6. DataFrame is not empty + assert not df.empty, "DataFrame is empty after filtering." + + +def _calculate_calibration_curve(forecasts: list[float], resolutions: list[int], weights: list[float]) -> dict: """ Calculates a calibration curve for forecasts. @@ -690,51 +763,6 @@ def calculate_calibration_curve(forecasts, resolutions, weights): } -def plot_calibration_curve(df, column_name, label, color): - """ - Plots a calibration curve with confidence intervals. - - Args: - df (pandas.DataFrame): DataFrame with forecast and resolution data. - column_name (str): Column name for forecast probabilities. - label (str): Label for the plot. - color (str): Color for the plot. - - Returns: - None - """ - # Filter to binary questions in case the DataFrame has other types (0 or 1 INT or 'yes'/'no' STR) - df = df[df["resolution"].isin(["yes", "no", 1, 0])] - - y_true = df["resolution"] - y_pred = df[column_name] - weights = [1.0 for _ in y_true] - calibration_curve = calculate_calibration_curve(y_pred, y_true, weights)[ - "calibration_curve" - ] - prob_true = [item["average_resolution"] for item in calibration_curve] - bin_center = [ - (item["bin_lower"] + item["bin_upper"]) / 2 for item in calibration_curve - ] - ci_lower = [item["lower_confidence_interval"] for item in calibration_curve] - ci_upper = [item["upper_confidence_interval"] for item in calibration_curve] - - plt.plot(bin_center, prob_true, marker="o", linewidth=2, label=label, color=color) - plt.fill_between(bin_center, ci_lower, ci_upper, alpha=0.2, color=color) - for x, y in zip(bin_center, prob_true): - if x is None or y is None: - continue - plt.annotate( - f"({x:.2f}, {y:.2f})", - (x, y), - textcoords="offset points", - xytext=(0, 10), - ha="center", - color=color, - fontsize=8, - ) - - def calculate_confidence(predictions, outcomes): """ Calculates over- or under-confidence for a set of predictions. diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 6d552fc..930eefb 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,16 +1,16 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 -bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 X_bot,-0.0,-0.0,-0.0,0.0,0.0 +bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 SynapseSeer,-0.1,-0.0,-0.0,0.0,0.0 -Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 pianobot,-0.1,-0.1,-0.0,-0.0,0.0 +Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 @@ -18,30 +18,30 @@ annabot,-0.1,-0.1,-0.1,-0.0,-0.0 cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 -MWG,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-o1,-0.3,-0.2,-0.1,0.0,0.1 -GreeneiBot2,-0.2,-0.2,-0.1,-0.0,0.0 +MWG,-0.2,-0.2,-0.1,-0.0,-0.0 ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 +GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 -acm_bot,-0.3,-0.2,-0.1,0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-perplexity,-0.3,-0.3,-0.1,0.0,0.1 -laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-Gemini-Exp-1206,-0.3,-0.2,-0.1,-0.0,0.1 +acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 +metac-o1,-0.3,-0.2,-0.1,-0.0,0.0 +metac-deepseek-r1+asknews,-0.3,-0.2,-0.1,-0.1,-0.0 wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 -bot_median,-0.3,-0.3,-0.2,-0.0,0.0 +laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.2,-0.2,-0.0,0.0 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -metac-deepseek-r1+asknews,-0.3,-0.3,-0.2,-0.1,-0.1 +bot_median,-0.3,-0.2,-0.2,-0.1,0.0 +metac-claude-3-5-sonnet-20240620,-0.3,-0.3,-0.2,-0.1,0.0 +metac-perplexity,-0.4,-0.3,-0.2,-0.0,0.0 NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-claude-3-5-sonnet-20240620,-0.4,-0.3,-0.2,-0.1,0.0 -metac-o1-preview,-0.4,-0.3,-0.2,-0.1,-0.1 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.1 pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 -VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 +VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-o1-preview,-0.5,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-Llama-3.1,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 4d49be6..477882c 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,13 +1,13 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -RPM_bot,-0.6,7.0,-0.1,0.8206747298542999,0.31018589178137035,-0.2697293560809546,2.4469118511449692,0.7,-0.8,0.3982026167089623,0.796405 -jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 +RPM_bot,-1.4,7.0,-0.2,0.8195427278689026,0.3097580352475143,-0.650312775083108,2.4469118511449692,0.6,-1.0,0.26978865902437565,0.539577 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 @@ -15,33 +15,33 @@ CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.36553170 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.6,27.4,-0.2,0.7470933569588007,0.14272484937169871,-1.6836598504701996,2.0495406495390753,0.1,-0.5,0.05201867599309354,0.104037 +cookics_bot_TEST,-6.6,27.4,-0.2,0.7452828646172052,0.14237897258891655,-1.694618782556622,2.0495406495390753,0.1,-0.5,0.05095705221638959,0.101914 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 -metac-o1,-9.3,91.1,-0.1,0.9011413735401934,0.09441342249931468,-1.0818974297140194,1.9858289388460384,0.1,-0.3,0.14109261555912994,0.282185 -MWG,-9.8,28.6,-0.3,0.7052396109620804,0.1318723303007465,-2.5896247567648802,2.0465614134207835,-0.1,-0.6,0.00758134121398338,0.015163 +MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 -GreeneiBot2,-10.4,58.4,-0.2,0.8493165305196299,0.11118575431472652,-1.6013523121813948,2.000831925930035,0.0,-0.4,0.05739674059552304,0.114793 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 +GreeneiBot2,-10.7,58.4,-0.2,0.8492744520587402,0.11118024573783404,-1.6427768404571312,2.000831925930035,0.0,-0.4,0.05295076167168595,0.105902 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 +metac-o1,-11.3,91.1,-0.1,0.885301596604543,0.09275387429075187,-1.342986841449772,1.9858289388460384,0.1,-0.3,0.09132478421461744,0.182650 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 -metac-perplexity,-12.3,89.1,-0.1,0.9928936435472672,0.1051874382468964,-1.3167986298410923,1.9864049297707018,0.1,-0.3,0.09566061681542057,0.191321 -metac-Gemini-Exp-1206,-12.6,76.5,-0.2,1.0074640479435764,0.11518577253617869,-1.4310981247048116,1.9908217254774627,0.1,-0.4,0.0782642072080301,0.156528 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 +metac-deepseek-r1+asknews,-13.3,52.1,-0.3,0.7808915178330472,0.10818619432038376,-2.3663082727832094,2.0053789762011176,-0.0,-0.5,0.010897575637344883,0.021795 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 -bot_median,-14.4,92.1,-0.2,0.8064767886698918,0.08403535853352312,-1.8649643315938071,1.9855502432148115,0.0,-0.3,0.03270280660214449,0.065406 +metac-Gemini-Exp-1206,-13.7,76.5,-0.2,0.9567011955687134,0.10938193429612067,-1.6400021546672607,1.9908217254774627,0.0,-0.4,0.05258248904380755,0.105165 +bot_median,-14.2,92.1,-0.2,0.8060563380929024,0.08399154733464013,-1.8298886724683292,1.9855502432148115,0.0,-0.3,0.03526855952035323,0.070537 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-deepseek-r1+asknews,-15.8,52.1,-0.3,0.7725034544186158,0.1070240960803573,-2.8279843345318105,2.0053789762011176,-0.1,-0.5,0.0033369803575435406,0.006674 +metac-claude-3-5-sonnet-20240620,-15.7,90.5,-0.2,0.9577206882239262,0.10067336366115942,-1.726279013247091,1.9860719790130024,0.0,-0.4,0.043873862980955504,0.087748 +metac-perplexity,-16.1,89.1,-0.2,1.04022365857026,0.11020159365499146,-1.6385490214880174,1.9864049297707018,0.0,-0.4,0.052436941119456015,0.104874 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -minefrac1,-19.4,51.1,-0.4,0.8785436286688769,0.12290028314991908,-3.0953430020106336,2.0065449272360034,-0.1,-0.6,0.0016073014389962144,0.003215 -metac-claude-3-5-sonnet-20240620,-20.5,90.5,-0.2,1.0026017690668347,0.10539115813794282,-2.144815075299298,1.9860719790130024,-0.0,-0.4,0.017338365150828438,0.034677 -metac-o1-preview,-21.8,91.1,-0.2,0.7783952357785447,0.08155319511998359,-2.9287175025862417,1.9858289388460384,-0.1,-0.4,0.0021550719003434007,0.004310 +minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 +metac-claude-3-5-sonnet-latest,-21.9,91.1,-0.2,0.8267775869528969,0.08662225919479004,-2.7788128175615063,1.9858289388460384,-0.1,-0.4,0.0033198064428072906,0.006640 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -metac-claude-3-5-sonnet-latest,-22.6,91.1,-0.2,0.8075357879826596,0.08460627796346898,-2.930812576746788,1.9858289388460384,-0.1,-0.4,0.002141865770272775,0.004284 -pgodzinai,-23.4,76.4,-0.3,0.9738243593913162,0.11141250898777778,-2.746500218115244,1.9908489732268309,-0.1,-0.5,0.00376450038951266,0.007529 +pgodzinai,-23.5,76.4,-0.3,1.0010628527586396,0.11452878848708839,-2.684829528603297,1.9908489732268309,-0.1,-0.5,0.004459201995123589,0.008918 +metac-exa,-24.1,89.1,-0.3,0.8238773759897631,0.08728180623689599,-3.103267575628089,1.9864049297707018,-0.1,-0.4,0.0012863793448356026,0.002573 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-exa,-24.9,89.1,-0.3,0.8297104160130679,0.08789976017509527,-3.180189674479708,1.9864049297707018,-0.1,-0.5,0.0010160377455861174,0.002032 +metac-Llama-3.1,-26.6,89.1,-0.3,0.8904683193506574,0.09433646993436098,-3.1697302934806575,1.9864049297707018,-0.1,-0.5,0.001049393935170647,0.002099 InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 -metac-grok-2-1212,-28.0,91.1,-0.3,1.0053639878633573,0.10533292304496032,-2.9230309952832156,1.9858289388460384,-0.1,-0.5,0.0021912955912464513,0.004383 -metac-gpt-4o,-28.0,91.1,-0.3,0.8644250725107907,0.09056662138298972,-3.3934602737720856,1.9858289388460384,-0.1,-0.5,0.0005136910361772879,0.001027 -metac-Llama-3.1,-28.2,89.1,-0.3,0.9060643910911743,0.0959887222614469,-3.291936866376594,1.9864049297707018,-0.1,-0.5,0.0007163844167320878,0.001433 +metac-o1-preview,-27.3,91.1,-0.3,0.8396846352431687,0.0879745426868476,-3.4074998848675455,1.9858289388460384,-0.1,-0.5,0.0004908622706364246,0.000982 +metac-grok-2-1212,-28.3,91.1,-0.3,1.0374739049385253,0.10869710901649764,-2.862896131089403,1.9858289388460384,-0.1,-0.5,0.00261020744989918,0.005220 +metac-gpt-4o,-28.7,91.1,-0.3,0.8937174262561063,0.09363560861558237,-3.3666300493101518,1.9858289388460384,-0.1,-0.5,0.0005601224288125974,0.001120 From a98b04c5dd524b748b7d8846b21b9c1d4048a636 Mon Sep 17 00:00:00 2001 From: Ben Wilson Date: Thu, 22 May 2025 08:37:15 -0600 Subject: [PATCH 25/26] Fixed second calibration graph --- AI_BENCHMARKING_ANALYSIS.ipynb | 2626 ++++++++--------- .../bootstrapped_h2h_bot_vs_pros.csv | 38 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 38 +- 3 files changed, 1348 insertions(+), 1354 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index 942c3c1..bb7044b 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -61,7 +61,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_691899/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_1441081/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] }, @@ -1032,11 +1032,11 @@ " \n", " 15\n", " bot_median\n", - " 8.319299\n", - " 3144.861339\n", + " 8.546230\n", + " 3230.645695\n", " 409\n", - " 5.304507\n", - " 1.533625\n", + " 5.546573\n", + " 1.525925\n", " \n", " \n", " 4\n", @@ -1072,14 +1072,14 @@ "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.319299 3144.861339 409 5.304507 \n", + "15 bot_median 8.546230 3230.645695 409 5.546573 \n", "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", "24 manticAI 6.510835 2055.210309 337 0.552564 \n", "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", "12 1.738353 \n", - "15 1.533625 \n", + "15 1.525925 \n", "4 2.298000 \n", "24 3.029040 \n", "1 2.309106 " @@ -1740,7 +1740,7 @@ " \n", " 3\n", " bot_median\n", - " 8575.707679\n", + " 8674.761163\n", " \n", " \n", " 4\n", @@ -1761,7 +1761,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8575.707679\n", + "3 bot_median 8674.761163\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1931,7 +1931,7 @@ " \n", " 2\n", " bot_median\n", - " 3328.161138\n", + " 3544.710382\n", " \n", " \n", " 3\n", @@ -2166,7 +2166,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3328.161138\n", + "2 bot_median 3544.710382\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2578,9 +2578,9 @@ " False\n", " False\n", " ...\n", - " [0.25,0.3,0.3,0.1,0.05]\n", - " [0.01,0.7,0.2,0.07,0.02]\n", - " [0.35000000000000003,0.30000000000000004,0.250...\n", + " [0.4,0.31,0.2,0.05600000000000001,0.034]\n", + " [0.01,0.7,0.25,0.03,0.01]\n", + " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2602,9 +2602,9 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0...\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05,0.0508333333,0.0516666667,0.0525,0.05333...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.00...\n", @@ -2650,9 +2650,9 @@ " None\n", " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.6,0.35,0.05]\n", - " [0.15,0.6,0.25]\n", + " [0.45,0.45,0.1]\n", + " [0.2,0.6,0.2]\n", + " [0.1,0.6,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2674,7 +2674,7 @@ " False\n", " False\n", " ...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", + " [0.0,0.0033333333,0.0066666667,0.01,0.01333333...\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0...\n", " NaN\n", @@ -2713,24 +2713,24 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.25,0.3,0.3,0.1,0.05] \n", - "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0... \n", + "0 [0.4,0.31,0.2,0.05600000000000001,0.034] \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... \n", + "3 [0.45,0.45,0.1] \n", + "4 [0.0,0.0033333333,0.0066666667,0.01,0.01333333... \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.2,0.07,0.02] \n", + "0 [0.01,0.7,0.25,0.03,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.6,0.35,0.05] \n", + "3 [0.2,0.6,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.250... NaN \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.05333... NaN \n", + "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... NaN \n", "2 0.1 NaN \n", - "3 [0.15,0.6,0.25] NaN \n", + "3 [0.1,0.6,0.3] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2842,8 +2842,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.85\n", + " 0.3\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -2867,7 +2867,7 @@ " False\n", " ...\n", " 0.85\n", - " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -2890,7 +2890,7 @@ " False\n", " False\n", " ...\n", - " 0.7\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -2915,7 +2915,7 @@ " False\n", " ...\n", " 0.1\n", - " 0.05\n", + " 0.1\n", " 0.03\n", " NaN\n", " 0.15\n", @@ -2947,10 +2947,10 @@ "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.65 0.85 NaN NaN 0.15 NaN \n", - "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.7 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.05 0.03 NaN 0.15 0.05 \n", + "95 0.3 0.9 NaN NaN 0.15 NaN \n", + "96 0.85 0.95 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.1 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3100,7 +3100,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_691899/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/tmp/ipykernel_1441081/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" ] }, @@ -3162,9 +3162,9 @@ " False\n", " False\n", " ...\n", - " [0.25,0.3,0.3,0.1,0.05]\n", - " [0.01,0.7,0.2,0.07,0.02]\n", - " [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782]\n", + " [0.4,0.31,0.2,0.05600000000000001,0.034]\n", + " [0.01,0.7,0.25,0.03,0.01]\n", + " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -3186,9 +3186,9 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.44,0.48,0.52,0.56,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007142857,0.9014285714,0.9021428571,0.9028571429,0.9035714286,0.9042857143,0.905,0.9057142857,0.9064285714,0.9071428571,0.9078571429,0.9085714286,0.9092857143,0.91,0.9107142857,0.9114285714,0.9121428571,0.9128571429,0.9135714286,0.9142857143,0.915,0.9157142857,0.9164285714,0.9171428571,0.9178571429,0.9185714286,0.9192857143,0.92,0.9207142857,0.9214285714,0.9221428571,0.9228571429,0.9235714286,0.9242857143,0.925,0.9257142857,0.9264285714,0.9271428571,0.9278571429,0.9285714286,0.9292857143,0.93,0.9307142857,0.9314285714,0.9321428571,0.9328571429,0.9335714286,0.9342857143,0.935,0.9357142857,0.9364285714,0.9371428571,0.9378571429,0.9385714286,0.9392857143,0.94,0.9407142857,0.9414285714,0.9421428571,0.9428571429,0.9435714286,0.9442857143,0.945,0.9457142857,0.9464285714,0.9471428571,0.9478571429,0.9485714286,0.9492857143,0.95]\n", " [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95]\n", - " [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911]\n", " [0.001,0.001060875,0.0011396,0.0012863125,0.0015459984,0.0019048369,0.0023147701,0.0027425688,0.0031719899,0.0035935463,0.0040047171,0.0044081612,0.0048073678,0.0052048637,0.0056023079,0.0060005117,0.0063995798,0.0067992898,0.0071993689,0.0075995902,0.007999808,0.0083999595,0.0088000381,0.0092000616,0.0096525538,0.0103347221,0.0114180238,0.0128617561,0.0144931539,0.0161909912,0.0178965175,0.0195748423,0.0212159342,0.0228289888,0.0244265464,0.0260177161,0.0276085304,0.0292020038,0.0307985773,0.0323974755,0.0339977246,0.0355985069,0.0371992898,0.0387998404,0.0404001295,0.0420002192,0.0436001942,0.0452001261,0.0468000593,0.0484758458,0.0504834257,0.0530704368,0.056178071,0.0595567722,0.0630314345,0.0665171977,0.0699636664,0.0733563529,0.0767085411,0.0800383523,0.0833589543,0.0866790344,0.0900028852,0.0933311337,0.0967326953,0.1004442449,0.1047006189,0.1094577119,0.1144907128,0.1196353715,0.1248049846,0.1299418958,0.1350232879,0.1400570021,0.1452540043,0.1513017567,0.1589133116,0.1680377058,0.1780770546,0.1885468618,0.1991553484,0.2096896812,0.2200450325,0.2302229342,0.2402681458,0.2502302229,0.2601553402,0.27007834,0.2800179047,0.2899799302,0.2999629146,0.3099614863,0.3199691186,0.3299801956,0.3403173669,0.3521487483,0.3668129253,0.3844513624,0.4041888551,0.4247935739,0.4442765262,0.4605082419,0.4728869633,0.4822309604,0.4895341295,0.4956449952,0.5013686886,0.5073076754,0.5137610388,0.5206987551,0.5276657564,0.5340334461,0.5395220756,0.5442306919,0.5484901071,0.5530599502,0.5588761244,0.5663266439,0.5752119583,0.585204242,0.5959735276,0.6071500854,0.6184053116,0.6295209059,0.6403758638,0.650921239,0.6611693012,0.671174569,0.681009388,0.6907471485,0.7004527783,0.7101763721,0.7199504677,0.7297911321,0.7397010124,0.7496729757,0.7596938994,0.7697481465,0.7798202777,0.7898968803,0.7999675731,0.8100253018,0.8200662214,0.8300893951,0.8400025166,0.8494453768,0.8579165269,0.8651653723,0.8712540566,0.8763468591,0.8806505608,0.8844338485,0.8879756773,0.8915092577,0.8952099002,0.8991948145,0.9035195392,0.9081838533,0.9131467515,0.9183416751,0.9236898731,0.9291127196,0.9345414554,0.9399230919,0.9451659123,0.9500324455,0.9542146638,0.9575690762,0.9601504006,0.9620795658,0.9635039422,0.9646063832,0.965571997,0.9665531773,0.9676621061,0.9689711529,0.9705116418,0.9722785871,0.9742409577,0.9763519694,0.9785580215,0.9808067315,0.9830531373,0.9852633275,0.987415817,0.9895011861,0.9915203598,0.9934820158,0.9953894047,0.9970771779,0.998127745,0.99846,0.99852,0.99858,0.99864,0.9987,0.99876,0.99882,0.99888,0.99894,0.99899]\n", @@ -3234,9 +3234,9 @@ " None\n", " None\n", " ...\n", - " [0.25,0.6,0.15]\n", - " [0.6,0.35,0.05]\n", - " [0.15,0.6,0.25]\n", + " [0.45,0.45,0.1]\n", + " [0.2,0.6,0.2]\n", + " [0.1,0.6,0.3]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -3258,8 +3258,8 @@ " False\n", " False\n", " ...\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9018181818,0.9036363636,0.9054545455,0.9072727273,0.9090909091,0.9109090909,0.9127272727,0.9145454545,0.9163636364,0.9181818182,0.92,0.9218181818,0.9236363636,0.9254545455,0.9272727273,0.9290909091,0.9309090909,0.9327272727,0.9345454545,0.9363636364,0.9381818182,0.94,0.9418181818,0.9436363636,0.9454545455,0.9472727273,0.9490909091,0.9509090909,0.9527272727,0.9545454545,0.9563636364,0.9581818182,0.96,0.9618181818,0.9636363636,0.9654545455,0.9672727273,0.9690909091,0.9709090909,0.9727272727,0.9745454545,0.9763636364,0.9781818182,0.98,0.9818181818,0.9836363636,0.9854545455,0.9872727273,0.9890909091,0.9909090909,0.9927272727,0.9945454545,0.9963636364,0.9981818182,1.0]\n", - " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0]\n", + " [0.0,0.0033333333,0.0066666667,0.01,0.0133333333,0.0166666667,0.02,0.0233333333,0.0266666667,0.03,0.0333333333,0.0366666667,0.04,0.0433333333,0.0466666667,0.05,0.0533333333,0.0566666667,0.06,0.0633333333,0.0666666667,0.07,0.0733333333,0.0766666667,0.08,0.0833333333,0.0866666667,0.09,0.0933333333,0.0966666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.6057142857,0.6114285714,0.6171428571,0.6228571429,0.6285714286,0.6342857143,0.64,0.6457142857,0.6514285714,0.6571428571,0.6628571429,0.6685714286,0.6742857143,0.68,0.6857142857,0.6914285714,0.6971428571,0.7028571429,0.7085714286,0.7142857143,0.72,0.7257142857,0.7314285714,0.7371428571,0.7428571429,0.7485714286,0.7542857143,0.76,0.7657142857,0.7714285714,0.7771428571,0.7828571429,0.7885714286,0.7942857143,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", + " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9013333333,0.9026666667,0.904,0.9053333333,0.9066666667,0.908,0.9093333333,0.9106666667,0.912,0.9133333333,0.9146666667,0.916,0.9173333333,0.9186666667,0.92,0.9213333333,0.9226666667,0.924,0.9253333333,0.9266666667,0.928,0.9293333333,0.9306666667,0.932,0.9333333333,0.9346666667,0.936,0.9373333333,0.9386666667,0.94,0.9413333333,0.9426666667,0.944,0.9453333333,0.9466666667,0.948,0.9493333333,0.9506666667,0.952,0.9533333333,0.9546666667,0.956,0.9573333333,0.9586666667,0.96,0.9613333333,0.9626666667,0.964,0.9653333333,0.9666666667,0.968,0.9693333333,0.9706666667,0.972,0.9733333333,0.9746666667,0.976,0.9773333333,0.9786666667,0.98,0.9813333333,0.9826666667,0.984,0.9853333333,0.9866666667,0.988,0.9893333333,0.9906666667,0.992,0.9933333333,0.9946666667,0.996,0.9973333333,0.9986666667,1.0]\n", " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0]\n", @@ -3297,25 +3297,25 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.25,0.3,0.3,0.1,0.05] \n", - "1 [0.05,0.0505555556,0.0511111111,0.0516666667,0.0522222222,0.0527777778,0.0533333333,0.0538888889,0.0544444444,0.055,0.0555555556,0.0561111111,0.0566666667,0.0572222222,0.0577777778,0.0583333333,0.0588888889,0.0594444444,0.06,0.0605555556,0.0611111111,0.0616666667,0.0622222222,0.0627777778,0.0633333333,0.0638888889,0.0644444444,0.065,0.0655555556,0.0661111111,0.0666666667,0.0672222222,0.0677777778,0.0683333333,0.0688888889,0.0694444444,0.07,0.0705555556,0.0711111111,0.0716666667,0.0722222222,0.0727777778,0.0733333333,0.0738888889,0.0744444444,0.075,0.0755555556,0.0761111111,0.0766666667,0.0772222222,0.0777777778,0.0783333333,0.0788888889,0.0794444444,0.08,0.0805555556,0.0811111111,0.0816666667,0.0822222222,0.0827777778,0.0833333333,0.0838888889,0.0844444444,0.085,0.0855555556,0.0861111111,0.0866666667,0.0872222222,0.0877777778,0.0883333333,0.0888888889,0.0894444444,0.09,0.0905555556,0.0911111111,0.0916666667,0.0922222222,0.0927777778,0.0933333333,0.0938888889,0.0944444444,0.095,0.0955555556,0.0961111111,0.0966666667,0.0972222222,0.0977777778,0.0983333333,0.0988888889,0.0994444444,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.24,0.28,0.32,0.36,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", + "0 [0.4,0.31,0.2,0.05600000000000001,0.034] \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.44,0.48,0.52,0.56,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007142857,0.9014285714,0.9021428571,0.9028571429,0.9035714286,0.9042857143,0.905,0.9057142857,0.9064285714,0.9071428571,0.9078571429,0.9085714286,0.9092857143,0.91,0.9107142857,0.9114285714,0.9121428571,0.9128571429,0.9135714286,0.9142857143,0.915,0.9157142857,0.9164285714,0.9171428571,0.9178571429,0.9185714286,0.9192857143,0.92,0.9207142857,0.9214285714,0.9221428571,0.9228571429,0.9235714286,0.9242857143,0.925,0.9257142857,0.9264285714,0.9271428571,0.9278571429,0.9285714286,0.9292857143,0.93,0.9307142857,0.9314285714,0.9321428571,0.9328571429,0.9335714286,0.9342857143,0.935,0.9357142857,0.9364285714,0.9371428571,0.9378571429,0.9385714286,0.9392857143,0.94,0.9407142857,0.9414285714,0.9421428571,0.9428571429,0.9435714286,0.9442857143,0.945,0.9457142857,0.9464285714,0.9471428571,0.9478571429,0.9485714286,0.9492857143,0.95] \n", "2 0.1 \n", - "3 [0.25,0.6,0.15] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.2133333333,0.2266666667,0.24,0.2533333333,0.2666666667,0.28,0.2933333333,0.3066666667,0.32,0.3333333333,0.3466666667,0.36,0.3733333333,0.3866666667,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9018181818,0.9036363636,0.9054545455,0.9072727273,0.9090909091,0.9109090909,0.9127272727,0.9145454545,0.9163636364,0.9181818182,0.92,0.9218181818,0.9236363636,0.9254545455,0.9272727273,0.9290909091,0.9309090909,0.9327272727,0.9345454545,0.9363636364,0.9381818182,0.94,0.9418181818,0.9436363636,0.9454545455,0.9472727273,0.9490909091,0.9509090909,0.9527272727,0.9545454545,0.9563636364,0.9581818182,0.96,0.9618181818,0.9636363636,0.9654545455,0.9672727273,0.9690909091,0.9709090909,0.9727272727,0.9745454545,0.9763636364,0.9781818182,0.98,0.9818181818,0.9836363636,0.9854545455,0.9872727273,0.9890909091,0.9909090909,0.9927272727,0.9945454545,0.9963636364,0.9981818182,1.0] \n", + "3 [0.45,0.45,0.1] \n", + "4 [0.0,0.0033333333,0.0066666667,0.01,0.0133333333,0.0166666667,0.02,0.0233333333,0.0266666667,0.03,0.0333333333,0.0366666667,0.04,0.0433333333,0.0466666667,0.05,0.0533333333,0.0566666667,0.06,0.0633333333,0.0666666667,0.07,0.0733333333,0.0766666667,0.08,0.0833333333,0.0866666667,0.09,0.0933333333,0.0966666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.6057142857,0.6114285714,0.6171428571,0.6228571429,0.6285714286,0.6342857143,0.64,0.6457142857,0.6514285714,0.6571428571,0.6628571429,0.6685714286,0.6742857143,0.68,0.6857142857,0.6914285714,0.6971428571,0.7028571429,0.7085714286,0.7142857143,0.72,0.7257142857,0.7314285714,0.7371428571,0.7428571429,0.7485714286,0.7542857143,0.76,0.7657142857,0.7714285714,0.7771428571,0.7828571429,0.7885714286,0.7942857143,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.2,0.07,0.02] \n", + "0 [0.01,0.7,0.25,0.03,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95] \n", "2 0.1 \n", - "3 [0.6,0.35,0.05] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9014285714,0.9028571429,0.9042857143,0.9057142857,0.9071428571,0.9085714286,0.91,0.9114285714,0.9128571429,0.9142857143,0.9157142857,0.9171428571,0.9185714286,0.92,0.9214285714,0.9228571429,0.9242857143,0.9257142857,0.9271428571,0.9285714286,0.93,0.9314285714,0.9328571429,0.9342857143,0.9357142857,0.9371428571,0.9385714286,0.94,0.9414285714,0.9428571429,0.9442857143,0.9457142857,0.9471428571,0.9485714286,0.95,0.9514285714,0.9528571429,0.9542857143,0.9557142857,0.9571428571,0.9585714286,0.96,0.9614285714,0.9628571429,0.9642857143,0.9657142857,0.9671428571,0.9685714286,0.97,0.9714285714,0.9728571429,0.9742857143,0.9757142857,0.9771428571,0.9785714286,0.98,0.9814285714,0.9828571429,0.9842857143,0.9857142857,0.9871428571,0.9885714286,0.99,0.9914285714,0.9928571429,0.9942857143,0.9957142857,0.9971428571,0.9985714286,1.0] \n", + "3 [0.2,0.6,0.2] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9013333333,0.9026666667,0.904,0.9053333333,0.9066666667,0.908,0.9093333333,0.9106666667,0.912,0.9133333333,0.9146666667,0.916,0.9173333333,0.9186666667,0.92,0.9213333333,0.9226666667,0.924,0.9253333333,0.9266666667,0.928,0.9293333333,0.9306666667,0.932,0.9333333333,0.9346666667,0.936,0.9373333333,0.9386666667,0.94,0.9413333333,0.9426666667,0.944,0.9453333333,0.9466666667,0.948,0.9493333333,0.9506666667,0.952,0.9533333333,0.9546666667,0.956,0.9573333333,0.9586666667,0.96,0.9613333333,0.9626666667,0.964,0.9653333333,0.9666666667,0.968,0.9693333333,0.9706666667,0.972,0.9733333333,0.9746666667,0.976,0.9773333333,0.9786666667,0.98,0.9813333333,0.9826666667,0.984,0.9853333333,0.9866666667,0.988,0.9893333333,0.9906666667,0.992,0.9933333333,0.9946666667,0.996,0.9973333333,0.9986666667,1.0] \n", "\n", - " metac-perplexity \\\n", - "0 [0.35000000000000003,0.30000000000000004,0.25000000000000006,0.08000000000000002,0.019999999999999782] \n", - "1 [0.05,0.0508333333,0.0516666667,0.0525,0.0533333333,0.0541666667,0.055,0.0558333333,0.0566666667,0.0575,0.0583333333,0.0591666667,0.06,0.0608333333,0.0616666667,0.0625,0.0633333333,0.0641666667,0.065,0.0658333333,0.0666666667,0.0675,0.0683333333,0.0691666667,0.07,0.0708333333,0.0716666667,0.0725,0.0733333333,0.0741666667,0.075,0.0758333333,0.0766666667,0.0775,0.0783333333,0.0791666667,0.08,0.0808333333,0.0816666667,0.0825,0.0833333333,0.0841666667,0.085,0.0858333333,0.0866666667,0.0875,0.0883333333,0.0891666667,0.09,0.0908333333,0.0916666667,0.0925,0.0933333333,0.0941666667,0.095,0.0958333333,0.0966666667,0.0975,0.0983333333,0.0991666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", - "2 0.1 \n", - "3 [0.15,0.6,0.25] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", + " metac-perplexity \\\n", + "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.1 \n", + "3 [0.1,0.6,0.3] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3447,8 +3447,8 @@ " False\n", " False\n", " ...\n", - " 0.65\n", - " 0.85\n", + " 0.3\n", + " 0.9\n", " NaN\n", " NaN\n", " 0.15\n", @@ -3472,7 +3472,7 @@ " False\n", " ...\n", " 0.85\n", - " 0.9\n", + " 0.95\n", " NaN\n", " NaN\n", " 0.9\n", @@ -3495,7 +3495,7 @@ " False\n", " False\n", " ...\n", - " 0.7\n", + " 0.8\n", " 0.85\n", " 0.3\n", " NaN\n", @@ -3520,7 +3520,7 @@ " False\n", " ...\n", " 0.1\n", - " 0.05\n", + " 0.1\n", " 0.03\n", " NaN\n", " 0.15\n", @@ -3552,10 +3552,10 @@ "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.65 0.85 NaN NaN 0.15 NaN \n", - "96 0.85 0.9 NaN NaN 0.9 NaN \n", - "97 0.7 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.05 0.03 NaN 0.15 0.05 \n", + "95 0.3 0.9 NaN NaN 0.15 NaN \n", + "96 0.85 0.95 NaN NaN 0.9 NaN \n", + "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", + "98 0.1 0.1 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3763,7 +3763,7 @@ " False\n", " ...\n", " 2.302585\n", - " 5.857933\n", + " 5.703782\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -3771,7 +3771,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 4.656813\n", + " 5.521275\n", " \n", " \n", " 3\n", @@ -3786,7 +3786,7 @@ " None\n", " None\n", " ...\n", - " -0.228842\n", + " 0.310155\n", " 0.310155\n", " NaN\n", " 0.127833\n", @@ -3819,7 +3819,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.298855\n", + " 0.111521\n", " \n", " \n", " 9\n", @@ -3843,7 +3843,7 @@ " NaN\n", " -0.624154\n", " NaN\n", - " -0.693147\n", + " -0.518794\n", " \n", " \n", " 13\n", @@ -3858,7 +3858,7 @@ " None\n", " None\n", " ...\n", - " -2.145931\n", + " 0.441833\n", " 0.510826\n", " 0.021979\n", " 0.200671\n", @@ -3867,7 +3867,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -0.062598\n", + " 0.158111\n", " \n", " \n", "\n", @@ -3904,18 +3904,18 @@ "13 NaN NaN None None ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "0 2.302585 5.857933 NaN 2.292635 2.703087 \n", - "3 -0.228842 0.310155 NaN 0.127833 0.152526 \n", + "0 2.302585 5.703782 NaN 2.292635 2.703087 \n", + "3 0.310155 0.310155 NaN 0.127833 0.152526 \n", "6 0.116534 -0.106610 NaN -0.184571 0.111521 \n", "9 -0.518794 -0.806476 NaN -0.806476 -0.494101 \n", - "13 -2.145931 0.510826 0.021979 0.200671 0.253781 \n", + "13 0.441833 0.510826 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "0 NaN NaN NaN NaN 4.656813 \n", + "0 NaN NaN NaN NaN 5.521275 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN 0.298855 \n", - "9 NaN NaN -0.624154 NaN -0.693147 \n", - "13 NaN NaN NaN NaN -0.062598 \n", + "6 NaN NaN NaN NaN 0.111521 \n", + "9 NaN NaN -0.624154 NaN -0.518794 \n", + "13 NaN NaN NaN NaN 0.158111 \n", "\n", "[5 rows x 58 columns]" ] @@ -3982,7 +3982,7 @@ " False\n", " ...\n", " -2.879198\n", - " -2.879198\n", + " -0.933288\n", " -3.007032\n", " -2.879198\n", " -3.795489\n", @@ -3990,7 +3990,7 @@ " NaN\n", " -2.348570\n", " -2.409195\n", - " -2.348570\n", + " -2.879198\n", " \n", " \n", " 82\n", @@ -4005,7 +4005,7 @@ " None\n", " None\n", " ...\n", - " -0.587787\n", + " -0.076961\n", " -0.300105\n", " -0.523248\n", " 0.105361\n", @@ -4014,7 +4014,7 @@ " NaN\n", " 0.276509\n", " -0.644609\n", - " -0.498556\n", + " -0.587787\n", " \n", " \n", " 83\n", @@ -4029,8 +4029,8 @@ " None\n", " None\n", " ...\n", - " -0.899761\n", " -0.693147\n", + " -0.182322\n", " NaN\n", " -0.182322\n", " NaN\n", @@ -4053,8 +4053,8 @@ " False\n", " False\n", " ...\n", - " -0.054625\n", - " -0.102356\n", + " -0.069566\n", + " -0.080377\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -4062,7 +4062,7 @@ " -0.113529\n", " NaN\n", " -0.147818\n", - " -0.121048\n", + " -0.124829\n", " \n", " \n", " 92\n", @@ -4077,8 +4077,8 @@ " False\n", " False\n", " ...\n", - " -1.299283\n", - " -1.704748\n", + " -0.788457\n", + " -1.011601\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -4117,23 +4117,23 @@ "\n", " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", "81 NaN False False ... -2.879198 \n", - "82 NaN None None ... -0.587787 \n", - "83 NaN None None ... -0.899761 \n", - "91 NaN False False ... -0.054625 \n", - "92 NaN False False ... -1.299283 \n", + "82 NaN None None ... -0.076961 \n", + "83 NaN None None ... -0.693147 \n", + "91 NaN False False ... -0.069566 \n", + "92 NaN False False ... -0.788457 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -2.879198 -3.007032 -2.879198 -3.795489 NaN NaN \n", + "81 -0.933288 -3.007032 -2.879198 -3.795489 NaN NaN \n", "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", - "83 -0.693147 NaN -0.182322 NaN NaN NaN \n", - "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -1.704748 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "83 -0.182322 NaN -0.182322 NaN NaN NaN \n", + "91 -0.080377 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -1.011601 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -2.348570 \n", - "82 0.276509 -0.644609 -0.498556 \n", + "81 -2.348570 -2.409195 -2.879198 \n", + "82 0.276509 -0.644609 -0.587787 \n", "83 -0.178330 -0.567984 -0.693147 \n", - "91 NaN -0.147818 -0.121048 \n", + "91 NaN -0.147818 -0.124829 \n", "92 NaN -0.749237 -0.318454 \n", "\n", "[5 rows x 58 columns]" @@ -4225,7 +4225,7 @@ " None\n", " ...\n", " -0.251314\n", - " 0.287682\n", + " 0.200671\n", " NaN\n", " 0.510826\n", " 0.320472\n", @@ -4248,8 +4248,8 @@ " False\n", " False\n", " ...\n", + " -0.111226\n", " -0.054067\n", - " 0.000000\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -4257,7 +4257,7 @@ " NaN\n", " -0.398124\n", " NaN\n", - " -0.171850\n", + " -0.147158\n", " \n", " \n", " 12\n", @@ -4273,7 +4273,7 @@ " False\n", " ...\n", " -0.057158\n", - " -0.057158\n", + " 0.000000\n", " NaN\n", " 0.054067\n", " -0.057158\n", @@ -4296,7 +4296,7 @@ " False\n", " False\n", " ...\n", - " -0.045611\n", + " 0.008457\n", " 0.008457\n", " NaN\n", " -0.068083\n", @@ -4305,7 +4305,7 @@ " NaN\n", " -0.076070\n", " NaN\n", - " -0.076070\n", + " -0.096728\n", " \n", " \n", "\n", @@ -4329,17 +4329,17 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", - "5 -0.251314 0.287682 NaN 0.510826 0.320472 \n", - "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", - "12 -0.057158 -0.057158 NaN 0.054067 -0.057158 \n", - "16 -0.045611 0.008457 NaN -0.068083 NaN \n", + "5 -0.251314 0.200671 NaN 0.510826 0.320472 \n", + "8 -0.111226 -0.054067 NaN -0.111226 -0.147158 \n", + "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", + "16 0.008457 0.008457 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "2 NaN NaN NaN NaN -0.149434 \n", "5 NaN NaN NaN NaN 0.287682 \n", - "8 NaN NaN -0.398124 NaN -0.171850 \n", + "8 NaN NaN -0.398124 NaN -0.147158 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", - "16 NaN NaN -0.076070 NaN -0.076070 \n", + "16 NaN NaN -0.076070 NaN -0.096728 \n", "\n", "[5 rows x 58 columns]" ] @@ -4429,7 +4429,7 @@ " False\n", " False\n", " ...\n", - " -1.845827\n", + " -2.251292\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -4453,7 +4453,7 @@ " False\n", " False\n", " ...\n", - " -0.074901\n", + " -0.020834\n", " NaN\n", " NaN\n", " -0.074901\n", @@ -4501,7 +4501,7 @@ " False\n", " False\n", " ...\n", - " -0.017709\n", + " -0.063666\n", " 0.000000\n", " NaN\n", " -0.112251\n", @@ -4534,10 +4534,10 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -1.845827 NaN NaN -0.111226 NaN \n", - "96 -0.074901 NaN NaN -0.074901 NaN \n", + "95 -2.251292 NaN NaN -0.111226 NaN \n", + "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.063666 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", @@ -4603,7 +4603,7 @@ " \n", " 2\n", " bot_median\n", - " 3328.161138\n", + " 3544.710382\n", " \n", " \n", " 3\n", @@ -4838,7 +4838,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3328.161138\n", + "2 bot_median 3544.710382\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4906,13 +4906,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 70.0%\n", - "mean metac-o1 forecast on questions that resolved no: 28.000000000000004%\n" + "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", + "mean metac-o1 forecast on questions that resolved no: 27.0%\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -4988,7 +4988,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_691899/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_1441081/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -5113,20 +5113,20 @@ " \n", " 3\n", " 4\n", - " bot_median\n", - " 2437.335374\n", - " 97\n", - " 93.10\n", - " \n", - " \n", - " 4\n", - " 5\n", " acm_bot\n", " 2239.058675\n", " 85\n", " 81.25\n", " \n", " \n", + " 4\n", + " 5\n", + " bot_median\n", + " 2138.701789\n", + " 97\n", + " 93.10\n", + " \n", + " \n", " 5\n", " 6\n", " metac-claude-3-5-sonnet-20240620\n", @@ -5471,8 +5471,8 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 bot_median 2437.335374 97 \n", - "4 5 acm_bot 2239.058675 85 \n", + "3 4 acm_bot 2239.058675 85 \n", + "4 5 bot_median 2138.701789 97 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", @@ -5520,8 +5520,8 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 93.10 \n", - "4 81.25 \n", + "3 81.25 \n", + "4 93.10 \n", "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", @@ -5716,20 +5716,6 @@ " 0.000036\n", " \n", " \n", - " bot_median\n", - " 2437.3\n", - " 93.1\n", - " 26.2\n", - " 60.692389\n", - " 6.290127\n", - " 4.162040\n", - " 1.985277\n", - " 38.7\n", - " 13.7\n", - " 0.999965\n", - " 0.000071\n", - " \n", - " \n", " acm_bot\n", " 2239.1\n", " 81.2\n", @@ -5744,6 +5730,20 @@ " 0.000025\n", " \n", " \n", + " bot_median\n", + " 2138.7\n", + " 93.1\n", + " 23.0\n", + " 64.275382\n", + " 6.661466\n", + " 3.448504\n", + " 1.985277\n", + " 36.2\n", + " 9.7\n", + " 0.999574\n", + " 0.000852\n", + " \n", + " \n", " metac-claude-3-5-sonnet-20240620\n", " 2018.1\n", " 91.5\n", @@ -6340,8 +6340,8 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", - "bot_median 2437.3 93.1 26.2 60.692389 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", + "bot_median 2138.7 93.1 23.0 64.275382 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", @@ -6389,8 +6389,8 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", - "bot_median 6.290127 4.162040 1.985277 38.7 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", + "bot_median 6.661466 3.448504 1.985277 36.2 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", @@ -6438,8 +6438,8 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", - "bot_median 13.7 0.999965 0.000071 \n", "acm_bot 15.3 0.999987 0.000025 \n", + "bot_median 9.7 0.999574 0.000852 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", @@ -6573,18 +6573,18 @@ " NA\n", " \n", " \n", - " bean_bot\n", + " RPM_bot\n", " -0.6\n", - " 4.7\n", + " 7.0\n", " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", + " 0.820675\n", + " 0.310186\n", + " -0.269729\n", + " 2.446912\n", + " 0.7\n", + " -0.8\n", + " 0.398203\n", + " 0.796405\n", " \n", " \n", " jonahsingerbot\n", @@ -6601,6 +6601,20 @@ " 0.007677\n", " \n", " \n", + " bean_bot\n", + " -0.6\n", + " 4.7\n", + " -0.1\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", + " \n", + " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6657,20 +6671,6 @@ " 0.574463\n", " \n", " \n", - " RPM_bot\n", - " -1.4\n", - " 7.0\n", - " -0.2\n", - " 0.819543\n", - " 0.309758\n", - " -0.650313\n", - " 2.446912\n", - " 0.6\n", - " -1.0\n", - " 0.269789\n", - " 0.539577\n", - " \n", - " \n", " KevinTestBot\n", " -1.5\n", " 8.4\n", @@ -6742,17 +6742,17 @@ " \n", " \n", " annabot\n", - " -6.2\n", + " -5.9\n", " 29.3\n", " -0.2\n", - " 0.520869\n", - " 0.096226\n", - " -2.211795\n", + " 0.517575\n", + " 0.095618\n", + " -2.112203\n", " 2.044183\n", " -0.0\n", " -0.4\n", - " 0.017610\n", - " 0.035221\n", + " 0.021811\n", + " 0.043621\n", " \n", " \n", " 4Shadower\n", @@ -6773,14 +6773,14 @@ " -6.6\n", " 27.4\n", " -0.2\n", - " 0.745283\n", - " 0.142379\n", - " -1.694619\n", + " 0.747093\n", + " 0.142725\n", + " -1.683660\n", " 2.049541\n", " 0.1\n", " -0.5\n", - " 0.050957\n", - " 0.101914\n", + " 0.052019\n", + " 0.104037\n", " \n", " \n", " jkraybill_bot\n", @@ -6857,14 +6857,14 @@ " -10.7\n", " 58.4\n", " -0.2\n", - " 0.849274\n", - " 0.111180\n", - " -1.642777\n", + " 0.848714\n", + " 0.111107\n", + " -1.647027\n", " 2.000832\n", " 0.0\n", " -0.4\n", - " 0.052951\n", - " 0.105902\n", + " 0.052511\n", + " 0.105022\n", " \n", " \n", " ajf-bot\n", @@ -6881,20 +6881,6 @@ " 0.094289\n", " \n", " \n", - " metac-o1\n", - " -11.3\n", - " 91.1\n", - " -0.1\n", - " 0.885302\n", - " 0.092754\n", - " -1.342987\n", - " 1.985829\n", - " 0.1\n", - " -0.3\n", - " 0.091325\n", - " 0.182650\n", - " \n", - " \n", " Bot_Pepa\n", " -11.5\n", " 44.0\n", @@ -6909,6 +6895,48 @@ " 0.023810\n", " \n", " \n", + " metac-perplexity\n", + " -12.0\n", + " 89.1\n", + " -0.1\n", + " 1.000845\n", + " 0.106030\n", + " -1.269604\n", + " 1.986405\n", + " 0.1\n", + " -0.3\n", + " 0.103785\n", + " 0.207569\n", + " \n", + " \n", + " bot_median\n", + " -12.2\n", + " 92.1\n", + " -0.1\n", + " 0.875909\n", + " 0.091270\n", + " -1.448706\n", + " 1.985550\n", + " 0.0\n", + " -0.3\n", + " 0.075426\n", + " 0.150853\n", + " \n", + " \n", + " metac-o1\n", + " -12.4\n", + " 91.1\n", + " -0.1\n", + " 0.941303\n", + " 0.098621\n", + " -1.375036\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.086265\n", + " 0.172530\n", + " \n", + " \n", " laylaps\n", " -12.9\n", " 64.1\n", @@ -6924,17 +6952,31 @@ " \n", " \n", " metac-deepseek-r1+asknews\n", - " -13.3\n", + " -13.4\n", " 52.1\n", " -0.3\n", - " 0.780892\n", - " 0.108186\n", - " -2.366308\n", + " 0.686642\n", + " 0.095129\n", + " -2.702394\n", " 2.005379\n", - " -0.0\n", - " -0.5\n", - " 0.010898\n", - " 0.021795\n", + " -0.1\n", + " -0.4\n", + " 0.004660\n", + " 0.009321\n", + " \n", + " \n", + " metac-Gemini-Exp-1206\n", + " -13.5\n", + " 76.5\n", + " -0.2\n", + " 1.006606\n", + " 0.115088\n", + " -1.527727\n", + " 1.990822\n", + " 0.1\n", + " -0.4\n", + " 0.065380\n", + " 0.130759\n", " \n", " \n", " wunderplumb\n", @@ -6951,34 +6993,6 @@ " 0.006348\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " -13.7\n", - " 76.5\n", - " -0.2\n", - " 0.956701\n", - " 0.109382\n", - " -1.640002\n", - " 1.990822\n", - " 0.0\n", - " -0.4\n", - " 0.052582\n", - " 0.105165\n", - " \n", - " \n", - " bot_median\n", - " -14.2\n", - " 92.1\n", - " -0.2\n", - " 0.806056\n", - " 0.083992\n", - " -1.829889\n", - " 1.985550\n", - " 0.0\n", - " -0.3\n", - " 0.035269\n", - " 0.070537\n", - " \n", - " \n", " manticAI\n", " -14.6\n", " 69.4\n", @@ -6994,31 +7008,17 @@ " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -15.7\n", + " -14.7\n", " 90.5\n", " -0.2\n", - " 0.957721\n", - " 0.100673\n", - " -1.726279\n", + " 0.942980\n", + " 0.099124\n", + " -1.642585\n", " 1.986072\n", " 0.0\n", " -0.4\n", - " 0.043874\n", - " 0.087748\n", - " \n", - " \n", - " metac-perplexity\n", - " -16.1\n", - " 89.1\n", - " -0.2\n", - " 1.040224\n", - " 0.110202\n", - " -1.638549\n", - " 1.986405\n", - " 0.0\n", - " -0.4\n", - " 0.052437\n", - " 0.104874\n", + " 0.051989\n", + " 0.103978\n", " \n", " \n", " NextWorldLab\n", @@ -7035,32 +7035,46 @@ " 0.040909\n", " \n", " \n", + " metac-claude-3-5-sonnet-latest\n", + " -18.9\n", + " 91.1\n", + " -0.2\n", + " 0.731708\n", + " 0.076662\n", + " -2.699995\n", + " 1.985829\n", + " -0.1\n", + " -0.4\n", + " 0.004141\n", + " 0.008282\n", + " \n", + " \n", " minefrac1\n", - " -18.8\n", + " -19.2\n", " 51.1\n", " -0.4\n", - " 0.874752\n", - " 0.122370\n", - " -3.013581\n", + " 0.880990\n", + " 0.123242\n", + " -3.043641\n", " 2.006545\n", " -0.1\n", " -0.6\n", - " 0.002021\n", - " 0.004043\n", + " 0.001859\n", + " 0.003717\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -21.9\n", + " metac-o1-preview\n", + " -20.9\n", " 91.1\n", " -0.2\n", - " 0.826778\n", - " 0.086622\n", - " -2.778813\n", + " 0.802181\n", + " 0.084045\n", + " -2.728807\n", " 1.985829\n", " -0.1\n", " -0.4\n", - " 0.003320\n", - " 0.006640\n", + " 0.003821\n", + " 0.007643\n", " \n", " \n", " mmBot\n", @@ -7077,32 +7091,46 @@ " 0.002208\n", " \n", " \n", - " pgodzinai\n", + " metac-Llama-3.1\n", + " -23.2\n", + " 89.1\n", + " -0.3\n", + " 1.031278\n", + " 0.109254\n", + " -2.379606\n", + " 1.986405\n", + " -0.0\n", + " -0.5\n", + " 0.009745\n", + " 0.019489\n", + " \n", + " \n", + " metac-grok-2-1212\n", " -23.5\n", - " 76.4\n", + " 91.1\n", " -0.3\n", - " 1.001063\n", - " 0.114529\n", - " -2.684830\n", - " 1.990849\n", - " -0.1\n", + " 1.068006\n", + " 0.111896\n", + " -2.303421\n", + " 1.985829\n", + " -0.0\n", " -0.5\n", - " 0.004459\n", - " 0.008918\n", + " 0.011778\n", + " 0.023556\n", " \n", " \n", - " metac-exa\n", - " -24.1\n", - " 89.1\n", + " pgodzinai\n", + " -24.0\n", + " 76.4\n", " -0.3\n", - " 0.823877\n", - " 0.087282\n", - " -3.103268\n", - " 1.986405\n", + " 0.976590\n", + " 0.111729\n", + " -2.811085\n", + " 1.990849\n", " -0.1\n", - " -0.4\n", - " 0.001286\n", - " 0.002573\n", + " -0.5\n", + " 0.003144\n", + " 0.006289\n", " \n", " \n", " VeritasAI\n", @@ -7119,18 +7147,32 @@ " 0.000076\n", " \n", " \n", - " metac-Llama-3.1\n", - " -26.6\n", + " metac-exa\n", + " -26.2\n", " 89.1\n", " -0.3\n", - " 0.890468\n", - " 0.094336\n", - " -3.169730\n", + " 0.830275\n", + " 0.087960\n", + " -3.341545\n", " 1.986405\n", " -0.1\n", " -0.5\n", - " 0.001049\n", - " 0.002099\n", + " 0.000612\n", + " 0.001224\n", + " \n", + " \n", + " metac-gpt-4o\n", + " -26.6\n", + " 91.1\n", + " -0.3\n", + " 0.879087\n", + " 0.092103\n", + " -3.165570\n", + " 1.985829\n", + " -0.1\n", + " -0.5\n", + " 0.001056\n", + " 0.002112\n", " \n", " \n", " InstitutPelFutur\n", @@ -7146,48 +7188,6 @@ " 0.002292\n", " 0.004584\n", " \n", - " \n", - " metac-o1-preview\n", - " -27.3\n", - " 91.1\n", - " -0.3\n", - " 0.839685\n", - " 0.087975\n", - " -3.407500\n", - " 1.985829\n", - " -0.1\n", - " -0.5\n", - " 0.000491\n", - " 0.000982\n", - " \n", - " \n", - " metac-grok-2-1212\n", - " -28.3\n", - " 91.1\n", - " -0.3\n", - " 1.037474\n", - " 0.108697\n", - " -2.862896\n", - " 1.985829\n", - " -0.1\n", - " -0.5\n", - " 0.002610\n", - " 0.005220\n", - " \n", - " \n", - " metac-gpt-4o\n", - " -28.7\n", - " 91.1\n", - " -0.3\n", - " 0.893717\n", - " 0.093636\n", - " -3.366630\n", - " 1.985829\n", - " -0.1\n", - " -0.5\n", - " 0.000560\n", - " 0.001120\n", - " \n", " \n", "\n", "" @@ -7196,146 +7196,146 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "RPM_bot -0.6 7.0 -0.1 0.820675 0.310186 \n", "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", + "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", - "RPM_bot -1.4 7.0 -0.2 0.819543 0.309758 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", - "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", + "annabot -5.9 29.3 -0.2 0.517575 0.095618 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.6 27.4 -0.2 0.745283 0.142379 \n", + "cookics_bot_TEST -6.6 27.4 -0.2 0.747093 0.142725 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", - "GreeneiBot2 -10.7 58.4 -0.2 0.849274 0.111180 \n", + "GreeneiBot2 -10.7 58.4 -0.2 0.848714 0.111107 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", - "metac-o1 -11.3 91.1 -0.1 0.885302 0.092754 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", + "metac-perplexity -12.0 89.1 -0.1 1.000845 0.106030 \n", + "bot_median -12.2 92.1 -0.1 0.875909 0.091270 \n", + "metac-o1 -12.4 91.1 -0.1 0.941303 0.098621 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", - "metac-deepseek-r1+asknews -13.3 52.1 -0.3 0.780892 0.108186 \n", + "metac-deepseek-r1+asknews -13.4 52.1 -0.3 0.686642 0.095129 \n", + "metac-Gemini-Exp-1206 -13.5 76.5 -0.2 1.006606 0.115088 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", - "metac-Gemini-Exp-1206 -13.7 76.5 -0.2 0.956701 0.109382 \n", - "bot_median -14.2 92.1 -0.2 0.806056 0.083992 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-claude-3-5-sonnet-20240620 -15.7 90.5 -0.2 0.957721 0.100673 \n", - "metac-perplexity -16.1 89.1 -0.2 1.040224 0.110202 \n", + "metac-claude-3-5-sonnet-20240620 -14.7 90.5 -0.2 0.942980 0.099124 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", - "metac-claude-3-5-sonnet-latest -21.9 91.1 -0.2 0.826778 0.086622 \n", + "metac-claude-3-5-sonnet-latest -18.9 91.1 -0.2 0.731708 0.076662 \n", + "minefrac1 -19.2 51.1 -0.4 0.880990 0.123242 \n", + "metac-o1-preview -20.9 91.1 -0.2 0.802181 0.084045 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "pgodzinai -23.5 76.4 -0.3 1.001063 0.114529 \n", - "metac-exa -24.1 89.1 -0.3 0.823877 0.087282 \n", + "metac-Llama-3.1 -23.2 89.1 -0.3 1.031278 0.109254 \n", + "metac-grok-2-1212 -23.5 91.1 -0.3 1.068006 0.111896 \n", + "pgodzinai -24.0 76.4 -0.3 0.976590 0.111729 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-Llama-3.1 -26.6 89.1 -0.3 0.890468 0.094336 \n", + "metac-exa -26.2 89.1 -0.3 0.830275 0.087960 \n", + "metac-gpt-4o -26.6 91.1 -0.3 0.879087 0.092103 \n", "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", - "metac-o1-preview -27.3 91.1 -0.3 0.839685 0.087975 \n", - "metac-grok-2-1212 -28.3 91.1 -0.3 1.037474 0.108697 \n", - "metac-gpt-4o -28.7 91.1 -0.3 0.893717 0.093636 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "bean_bot -4.265106 2.784843 -0.0 \n", + "RPM_bot -0.269729 2.446912 0.7 \n", "jonahsingerbot -5.273630 2.784843 -0.1 \n", + "bean_bot -4.265106 2.784843 -0.0 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", - "RPM_bot -0.650313 2.446912 0.6 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", "CatrachoCaster -1.365532 2.088777 0.1 \n", "krm-bot -3.229846 2.264709 -0.2 \n", - "annabot -2.211795 2.044183 -0.0 \n", + "annabot -2.112203 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.694619 2.049541 0.1 \n", + "cookics_bot_TEST -1.683660 2.049541 0.1 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", "MWG -2.535384 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", "acm_bot -1.287717 1.989344 0.1 \n", - "GreeneiBot2 -1.642777 2.000832 0.0 \n", + "GreeneiBot2 -1.647027 2.000832 0.0 \n", "ajf-bot -1.722395 2.030778 0.1 \n", - "metac-o1 -1.342987 1.985829 0.1 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", + "metac-perplexity -1.269604 1.986405 0.1 \n", + "bot_median -1.448706 1.985550 0.0 \n", + "metac-o1 -1.375036 1.985829 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", - "metac-deepseek-r1+asknews -2.366308 2.005379 -0.0 \n", + "metac-deepseek-r1+asknews -2.702394 2.005379 -0.1 \n", + "metac-Gemini-Exp-1206 -1.527727 1.990822 0.1 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", - "metac-Gemini-Exp-1206 -1.640002 1.990822 0.0 \n", - "bot_median -1.829889 1.985550 0.0 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-claude-3-5-sonnet-20240620 -1.726279 1.986072 0.0 \n", - "metac-perplexity -1.638549 1.986405 0.0 \n", + "metac-claude-3-5-sonnet-20240620 -1.642585 1.986072 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "minefrac1 -3.013581 2.006545 -0.1 \n", - "metac-claude-3-5-sonnet-latest -2.778813 1.985829 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.699995 1.985829 -0.1 \n", + "minefrac1 -3.043641 2.006545 -0.1 \n", + "metac-o1-preview -2.728807 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "pgodzinai -2.684830 1.990849 -0.1 \n", - "metac-exa -3.103268 1.986405 -0.1 \n", + "metac-Llama-3.1 -2.379606 1.986405 -0.0 \n", + "metac-grok-2-1212 -2.303421 1.985829 -0.0 \n", + "pgodzinai -2.811085 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-Llama-3.1 -3.169730 1.986405 -0.1 \n", + "metac-exa -3.341545 1.986405 -0.1 \n", + "metac-gpt-4o -3.165570 1.985829 -0.1 \n", "InstitutPelFutur -2.908524 1.986114 -0.1 \n", - "metac-o1-preview -3.407500 1.985829 -0.1 \n", - "metac-grok-2-1212 -2.862896 1.985829 -0.1 \n", - "metac-gpt-4o -3.366630 1.985829 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "bean_bot -0.2 0.007674 0.015349 \n", + "RPM_bot -0.8 0.398203 0.796405 \n", "jonahsingerbot -0.2 0.003839 0.007677 \n", + "bean_bot -0.2 0.007674 0.015349 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", - "RPM_bot -1.0 0.269789 0.539577 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", "CatrachoCaster -0.4 0.094144 0.188288 \n", "krm-bot -0.9 0.005563 0.011127 \n", - "annabot -0.4 0.017610 0.035221 \n", + "annabot -0.4 0.021811 0.043621 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.050957 0.101914 \n", + "cookics_bot_TEST -0.5 0.052019 0.104037 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", "MWG -0.6 0.008595 0.017191 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", "acm_bot -0.3 0.100796 0.201592 \n", - "GreeneiBot2 -0.4 0.052951 0.105902 \n", + "GreeneiBot2 -0.4 0.052511 0.105022 \n", "ajf-bot -0.7 0.047145 0.094289 \n", - "metac-o1 -0.3 0.091325 0.182650 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", + "metac-perplexity -0.3 0.103785 0.207569 \n", + "bot_median -0.3 0.075426 0.150853 \n", + "metac-o1 -0.3 0.086265 0.172530 \n", "laylaps -0.4 0.008744 0.017488 \n", - "metac-deepseek-r1+asknews -0.5 0.010898 0.021795 \n", + "metac-deepseek-r1+asknews -0.4 0.004660 0.009321 \n", + "metac-Gemini-Exp-1206 -0.4 0.065380 0.130759 \n", "wunderplumb -0.9 0.003174 0.006348 \n", - "metac-Gemini-Exp-1206 -0.4 0.052582 0.105165 \n", - "bot_median -0.3 0.035269 0.070537 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.043874 0.087748 \n", - "metac-perplexity -0.4 0.052437 0.104874 \n", + "metac-claude-3-5-sonnet-20240620 -0.4 0.051989 0.103978 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "minefrac1 -0.6 0.002021 0.004043 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.003320 0.006640 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.004141 0.008282 \n", + "minefrac1 -0.6 0.001859 0.003717 \n", + "metac-o1-preview -0.4 0.003821 0.007643 \n", "mmBot -0.4 0.001104 0.002208 \n", - "pgodzinai -0.5 0.004459 0.008918 \n", - "metac-exa -0.4 0.001286 0.002573 \n", + "metac-Llama-3.1 -0.5 0.009745 0.019489 \n", + "metac-grok-2-1212 -0.5 0.011778 0.023556 \n", + "pgodzinai -0.5 0.003144 0.006289 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-Llama-3.1 -0.5 0.001049 0.002099 \n", - "InstitutPelFutur -0.5 0.002292 0.004584 \n", - "metac-o1-preview -0.5 0.000491 0.000982 \n", - "metac-grok-2-1212 -0.5 0.002610 0.005220 \n", - "metac-gpt-4o -0.5 0.000560 0.001120 " + "metac-exa -0.5 0.000612 0.001224 \n", + "metac-gpt-4o -0.5 0.001056 0.002112 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 " ] }, "execution_count": 42, @@ -9087,139 +9087,139 @@ " \n", " \n", " metac-o1\n", - " 5.9\n", - " 7.3\n", - " 9.6\n", - " 11.9\n", - " 12.9\n", + " 6.0\n", + " 7.6\n", + " 9.7\n", + " 12.0\n", + " 13.2\n", " \n", " \n", " metac-o1-preview\n", - " 3.8\n", - " 5.3\n", + " 3.4\n", + " 5.2\n", " 8.3\n", - " 11.3\n", - " 13.2\n", + " 11.2\n", + " 12.5\n", " \n", " \n", " manticAI\n", - " 0.3\n", - " 2.1\n", - " 5.4\n", - " 8.8\n", - " 10.7\n", + " 0.2\n", + " 2.2\n", + " 5.3\n", + " 8.6\n", + " 10.3\n", " \n", " \n", " metac-Gemini-Exp-1206\n", - " 0.5\n", + " 0.4\n", " 2.1\n", - " 5.1\n", - " 8.0\n", - " 9.6\n", + " 4.9\n", + " 7.7\n", + " 9.1\n", " \n", " \n", " acm_bot\n", - " 0.2\n", - " 1.4\n", - " 4.4\n", + " -0.0\n", + " 1.3\n", + " 4.7\n", " 7.4\n", - " 9.1\n", + " 8.8\n", " \n", " \n", " metac-perplexity\n", - " -1.8\n", - " 0.1\n", - " 4.2\n", - " 7.6\n", - " 9.9\n", + " -2.0\n", + " 0.6\n", + " 4.3\n", + " 8.2\n", + " 9.8\n", " \n", " \n", " GreeneiBot2\n", - " -1.1\n", + " -1.5\n", " 0.7\n", " 4.0\n", - " 7.2\n", - " 9.4\n", + " 7.0\n", + " 8.8\n", " \n", " \n", " twsummerbot\n", - " 0.1\n", - " 1.5\n", - " 3.9\n", - " 6.3\n", - " 7.4\n", + " 0.2\n", + " 1.6\n", + " 3.7\n", + " 6.2\n", + " 7.3\n", " \n", " \n", " cookics_bot_TEST\n", - " -0.2\n", + " 0.0\n", " 1.1\n", " 3.1\n", - " 5.0\n", - " 6.3\n", + " 5.1\n", + " 6.2\n", " \n", " \n", " pgodzinai\n", - " -3.5\n", + " -3.6\n", " -1.1\n", " 3.1\n", - " 6.9\n", - " 8.9\n", + " 6.5\n", + " 9.0\n", " \n", " \n", " CumulativeBot\n", - " -0.2\n", - " 0.8\n", - " 2.7\n", - " 4.6\n", - " 5.6\n", + " -0.1\n", + " 0.9\n", + " 2.6\n", + " 4.5\n", + " 5.4\n", " \n", " \n", " SynapseSeer\n", " 0.3\n", - " 1.0\n", - " 2.5\n", + " 1.1\n", + " 2.6\n", " 4.1\n", " 4.9\n", " \n", " \n", " metac-claude-3-5-sonnet-latest\n", - " -1.1\n", - " -0.0\n", - " 2.5\n", - " 4.9\n", - " 6.2\n", - " \n", - " \n", - " metac-exa\n", - " -5.1\n", - " -2.2\n", - " 1.7\n", - " 5.6\n", - " 7.8\n", + " -1.4\n", + " -0.2\n", + " 2.6\n", + " 5.1\n", + " 6.3\n", " \n", " \n", " jkraybill_bot\n", - " -4.4\n", + " -3.6\n", " -1.7\n", - " 1.7\n", - " 4.8\n", + " 1.8\n", + " 5.1\n", " 6.5\n", " \n", " \n", + " metac-exa\n", + " -4.8\n", + " -2.7\n", + " 1.8\n", + " 5.6\n", + " 7.3\n", + " \n", + " \n", " metac-deepseek-r1+asknews\n", - " -2.0\n", + " -2.1\n", " -0.8\n", " 1.3\n", - " 3.4\n", - " 4.6\n", + " 3.3\n", + " 4.5\n", " \n", " \n", " MWG\n", - " -1.6\n", + " -1.7\n", " -0.8\n", - " 0.6\n", - " 2.2\n", - " 3.0\n", + " 0.7\n", + " 2.0\n", + " 2.9\n", " \n", " \n", " andrewsiah\n", @@ -9230,20 +9230,12 @@ " 1.0\n", " \n", " \n", - " cobyj-bot\n", - " -1.4\n", - " -1.0\n", - " -0.0\n", - " 0.9\n", - " 1.4\n", - " \n", - " \n", " pianobot\n", " -1.3\n", " -0.8\n", " -0.0\n", " 0.7\n", - " 1.0\n", + " 1.1\n", " \n", " \n", " X_bot\n", @@ -9254,12 +9246,28 @@ " 0.2\n", " \n", " \n", + " cobyj-bot\n", + " -1.5\n", + " -0.9\n", + " -0.1\n", + " 0.8\n", + " 1.3\n", + " \n", + " \n", " annabot\n", - " -3.4\n", - " -2.4\n", - " -0.4\n", + " -3.2\n", + " -2.1\n", + " -0.3\n", " 1.2\n", - " 2.1\n", + " 2.0\n", + " \n", + " \n", + " KevinTestBot\n", + " -4.0\n", + " -2.6\n", + " -0.4\n", + " 1.6\n", + " 2.6\n", " \n", " \n", " bean_bot\n", @@ -9270,14 +9278,6 @@ " 1.9\n", " \n", " \n", - " KevinTestBot\n", - " -4.1\n", - " -2.7\n", - " -0.5\n", - " 1.6\n", - " 2.5\n", - " \n", - " \n", " CatrachoCaster\n", " -2.3\n", " -1.8\n", @@ -9290,96 +9290,96 @@ " -3.0\n", " -2.3\n", " -0.9\n", - " 0.5\n", - " 1.1\n", + " 0.4\n", + " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.6\n", + " -3.8\n", " -2.7\n", " -1.0\n", " 0.7\n", - " 1.5\n", + " 1.6\n", " \n", " \n", " ProfessorSP\n", - " -4.5\n", + " -4.6\n", " -3.3\n", " -1.1\n", - " 1.0\n", - " 2.1\n", + " 0.9\n", + " 1.9\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.5\n", - " -4.6\n", - " -1.4\n", - " 1.9\n", - " 3.5\n", + " -6.7\n", + " -4.8\n", + " -1.3\n", + " 1.7\n", + " 3.4\n", " \n", " \n", " mmBot\n", - " -6.9\n", - " -5.2\n", - " -1.5\n", - " 2.3\n", - " 4.3\n", + " -7.2\n", + " -5.5\n", + " -1.6\n", + " 2.4\n", + " 4.5\n", " \n", " \n", " 4Shadower\n", " -4.8\n", " -3.7\n", - " -1.7\n", - " 0.3\n", - " 1.4\n", + " -1.6\n", + " 0.2\n", + " 1.1\n", " \n", " \n", " swingswish\n", " -5.3\n", - " -4.2\n", + " -3.9\n", " -2.0\n", - " -0.2\n", - " 0.7\n", + " -0.1\n", + " 0.8\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -6.4\n", - " -4.8\n", - " -2.0\n", + " -6.6\n", + " -5.0\n", + " -2.1\n", " 0.8\n", " 2.4\n", " \n", " \n", " RPM_bot\n", - " -4.9\n", + " -4.7\n", " -3.8\n", - " -2.0\n", + " -2.1\n", " -0.7\n", " -0.1\n", " \n", " \n", " InstitutPelFutur\n", - " -8.9\n", + " -9.3\n", " -6.4\n", " -2.2\n", - " 1.6\n", - " 4.0\n", + " 1.8\n", + " 4.2\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.9\n", - " -5.1\n", + " -6.7\n", + " -5.4\n", " -2.6\n", " 0.1\n", - " 1.6\n", + " 1.4\n", " \n", " \n", " wunderplumb\n", - " -6.1\n", - " -5.0\n", - " -2.7\n", - " -0.1\n", - " 0.9\n", + " -6.3\n", + " -4.9\n", + " -2.6\n", + " -0.4\n", + " 0.7\n", " \n", " \n", " NextWorldLab\n", @@ -9387,63 +9387,63 @@ " -6.9\n", " -3.6\n", " -0.2\n", - " 1.4\n", - " \n", - " \n", - " Bot_Pepa\n", - " -6.8\n", - " -5.9\n", - " -3.8\n", - " -2.0\n", - " -0.9\n", + " 1.1\n", " \n", " \n", " laylaps\n", - " -10.2\n", - " -8.0\n", + " -10.4\n", + " -7.7\n", " -3.8\n", " -0.1\n", - " 1.9\n", + " 1.6\n", + " \n", + " \n", + " Bot_Pepa\n", + " -7.0\n", + " -5.8\n", + " -3.9\n", + " -2.0\n", + " -1.1\n", " \n", " \n", " VeritasAI\n", - " -8.0\n", - " -6.6\n", - " -4.2\n", - " -1.9\n", - " -0.7\n", + " -8.1\n", + " -6.8\n", + " -4.3\n", + " -1.7\n", + " -0.9\n", " \n", " \n", " minefrac1\n", " -7.8\n", - " -6.9\n", - " -4.7\n", + " -6.8\n", + " -4.6\n", " -2.6\n", - " -1.6\n", + " -1.5\n", " \n", " \n", " Grizeu_Bot\n", - " -9.1\n", - " -7.6\n", + " -9.4\n", + " -7.8\n", " -4.9\n", - " -2.3\n", + " -2.2\n", " -0.9\n", " \n", " \n", " metac-gpt-4o\n", - " -10.7\n", - " -9.1\n", - " -6.1\n", - " -3.0\n", - " -1.5\n", + " -10.3\n", + " -8.9\n", + " -5.9\n", + " -3.1\n", + " -1.6\n", " \n", " \n", " ajf-bot\n", - " -15.3\n", + " -14.8\n", " -12.9\n", - " -8.4\n", - " -4.3\n", - " -2.4\n", + " -8.3\n", + " -4.4\n", + " -2.1\n", " \n", " \n", "\n", @@ -9451,51 +9451,51 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 5.9 7.3 9.6 11.9 12.9\n", - "metac-o1-preview 3.8 5.3 8.3 11.3 13.2\n", - "manticAI 0.3 2.1 5.4 8.8 10.7\n", - "metac-Gemini-Exp-1206 0.5 2.1 5.1 8.0 9.6\n", - "acm_bot 0.2 1.4 4.4 7.4 9.1\n", - "metac-perplexity -1.8 0.1 4.2 7.6 9.9\n", - "GreeneiBot2 -1.1 0.7 4.0 7.2 9.4\n", - "twsummerbot 0.1 1.5 3.9 6.3 7.4\n", - "cookics_bot_TEST -0.2 1.1 3.1 5.0 6.3\n", - "pgodzinai -3.5 -1.1 3.1 6.9 8.9\n", - "CumulativeBot -0.2 0.8 2.7 4.6 5.6\n", - "SynapseSeer 0.3 1.0 2.5 4.1 4.9\n", - "metac-claude-3-5-sonnet-latest -1.1 -0.0 2.5 4.9 6.2\n", - "metac-exa -5.1 -2.2 1.7 5.6 7.8\n", - "jkraybill_bot -4.4 -1.7 1.7 4.8 6.5\n", - "metac-deepseek-r1+asknews -2.0 -0.8 1.3 3.4 4.6\n", - "MWG -1.6 -0.8 0.6 2.2 3.0\n", + "metac-o1 6.0 7.6 9.7 12.0 13.2\n", + "metac-o1-preview 3.4 5.2 8.3 11.2 12.5\n", + "manticAI 0.2 2.2 5.3 8.6 10.3\n", + "metac-Gemini-Exp-1206 0.4 2.1 4.9 7.7 9.1\n", + "acm_bot -0.0 1.3 4.7 7.4 8.8\n", + "metac-perplexity -2.0 0.6 4.3 8.2 9.8\n", + "GreeneiBot2 -1.5 0.7 4.0 7.0 8.8\n", + "twsummerbot 0.2 1.6 3.7 6.2 7.3\n", + "cookics_bot_TEST 0.0 1.1 3.1 5.1 6.2\n", + "pgodzinai -3.6 -1.1 3.1 6.5 9.0\n", + "CumulativeBot -0.1 0.9 2.6 4.5 5.4\n", + "SynapseSeer 0.3 1.1 2.6 4.1 4.9\n", + "metac-claude-3-5-sonnet-latest -1.4 -0.2 2.6 5.1 6.3\n", + "jkraybill_bot -3.6 -1.7 1.8 5.1 6.5\n", + "metac-exa -4.8 -2.7 1.8 5.6 7.3\n", + "metac-deepseek-r1+asknews -2.1 -0.8 1.3 3.3 4.5\n", + "MWG -1.7 -0.8 0.7 2.0 2.9\n", "andrewsiah -0.9 -0.6 0.0 0.6 1.0\n", - "cobyj-bot -1.4 -1.0 -0.0 0.9 1.4\n", - "pianobot -1.3 -0.8 -0.0 0.7 1.0\n", + "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", - "annabot -3.4 -2.4 -0.4 1.2 2.1\n", + "cobyj-bot -1.5 -0.9 -0.1 0.8 1.3\n", + "annabot -3.2 -2.1 -0.3 1.2 2.0\n", + "KevinTestBot -4.0 -2.6 -0.4 1.6 2.6\n", "bean_bot -3.3 -2.2 -0.5 1.0 1.9\n", - "KevinTestBot -4.1 -2.7 -0.5 1.6 2.5\n", "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.3 -0.9 0.5 1.1\n", - "krm-bot -3.6 -2.7 -1.0 0.7 1.5\n", - "ProfessorSP -4.5 -3.3 -1.1 1.0 2.1\n", - "metac-grok-2-1212 -6.5 -4.6 -1.4 1.9 3.5\n", - "mmBot -6.9 -5.2 -1.5 2.3 4.3\n", - "4Shadower -4.8 -3.7 -1.7 0.3 1.4\n", - "swingswish -5.3 -4.2 -2.0 -0.2 0.7\n", - "metac-claude-3-5-sonnet-20240620 -6.4 -4.8 -2.0 0.8 2.4\n", - "RPM_bot -4.9 -3.8 -2.0 -0.7 -0.1\n", - "InstitutPelFutur -8.9 -6.4 -2.2 1.6 4.0\n", - "metac-Llama-3.1 -6.9 -5.1 -2.6 0.1 1.6\n", - "wunderplumb -6.1 -5.0 -2.7 -0.1 0.9\n", - "NextWorldLab -8.7 -6.9 -3.6 -0.2 1.4\n", - "Bot_Pepa -6.8 -5.9 -3.8 -2.0 -0.9\n", - "laylaps -10.2 -8.0 -3.8 -0.1 1.9\n", - "VeritasAI -8.0 -6.6 -4.2 -1.9 -0.7\n", - "minefrac1 -7.8 -6.9 -4.7 -2.6 -1.6\n", - "Grizeu_Bot -9.1 -7.6 -4.9 -2.3 -0.9\n", - "metac-gpt-4o -10.7 -9.1 -6.1 -3.0 -1.5\n", - "ajf-bot -15.3 -12.9 -8.4 -4.3 -2.4" + "jonahsingerbot -3.0 -2.3 -0.9 0.4 1.0\n", + "krm-bot -3.8 -2.7 -1.0 0.7 1.6\n", + "ProfessorSP -4.6 -3.3 -1.1 0.9 1.9\n", + "metac-grok-2-1212 -6.7 -4.8 -1.3 1.7 3.4\n", + "mmBot -7.2 -5.5 -1.6 2.4 4.5\n", + "4Shadower -4.8 -3.7 -1.6 0.2 1.1\n", + "swingswish -5.3 -3.9 -2.0 -0.1 0.8\n", + "metac-claude-3-5-sonnet-20240620 -6.6 -5.0 -2.1 0.8 2.4\n", + "RPM_bot -4.7 -3.8 -2.1 -0.7 -0.1\n", + "InstitutPelFutur -9.3 -6.4 -2.2 1.8 4.2\n", + "metac-Llama-3.1 -6.7 -5.4 -2.6 0.1 1.4\n", + "wunderplumb -6.3 -4.9 -2.6 -0.4 0.7\n", + "NextWorldLab -8.7 -6.9 -3.6 -0.2 1.1\n", + "laylaps -10.4 -7.7 -3.8 -0.1 1.6\n", + "Bot_Pepa -7.0 -5.8 -3.9 -2.0 -1.1\n", + "VeritasAI -8.1 -6.8 -4.3 -1.7 -0.9\n", + "minefrac1 -7.8 -6.8 -4.6 -2.6 -1.5\n", + "Grizeu_Bot -9.4 -7.8 -4.9 -2.2 -0.9\n", + "metac-gpt-4o -10.3 -8.9 -5.9 -3.1 -1.6\n", + "ajf-bot -14.8 -12.9 -8.3 -4.4 -2.1" ] }, "execution_count": 49, @@ -9591,7 +9591,7 @@ " False\n", " ...\n", " 2.302585\n", - " 5.857933\n", + " 5.703782\n", " NaN\n", " 2.292635\n", " 2.703087\n", @@ -9599,7 +9599,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 4.656813\n", + " 5.521275\n", " \n", " \n", " 1\n", @@ -9615,7 +9615,7 @@ " True\n", " ...\n", " -0.270414\n", - " -0.505416\n", + " -0.616988\n", " NaN\n", " -0.050442\n", " -0.163369\n", @@ -9623,7 +9623,7 @@ " NaN\n", " NaN\n", " NaN\n", - " -1.478371\n", + " -1.512868\n", " \n", " \n", " 2\n", @@ -9662,7 +9662,7 @@ " None\n", " None\n", " ...\n", - " -0.228842\n", + " 0.310155\n", " 0.310155\n", " NaN\n", " 0.127833\n", @@ -9718,10 +9718,10 @@ "4 numeric None 0.0 400.0 \n", "\n", " open_upper_bound open_lower_bound ... metac-o1-preview metac-perplexity \\\n", - "0 False False ... 2.302585 5.857933 \n", - "1 True True ... -0.270414 -0.505416 \n", + "0 False False ... 2.302585 5.703782 \n", + "1 True True ... -0.270414 -0.616988 \n", "2 False False ... -0.092275 -0.092275 \n", - "3 None None ... -0.228842 0.310155 \n", + "3 None None ... 0.310155 0.310155 \n", "4 False False ... 0.243782 -0.102791 \n", "\n", " minefrac1 mmBot pgodzinai pianobot swingswish twsummerbot \\\n", @@ -9732,8 +9732,8 @@ "4 NaN 0.265372 0.041050 NaN NaN -0.771754 \n", "\n", " wunderplumb bot_team_median \n", - "0 NaN 4.656813 \n", - "1 NaN -1.478371 \n", + "0 NaN 5.521275 \n", + "1 NaN -1.512868 \n", "2 NaN -0.149434 \n", "3 NaN 0.310155 \n", "4 NaN 0.184891 \n", @@ -9826,7 +9826,7 @@ " False\n", " False\n", " ...\n", - " -1.845827\n", + " -2.251292\n", " NaN\n", " NaN\n", " -0.111226\n", @@ -9850,7 +9850,7 @@ " False\n", " False\n", " ...\n", - " -0.074901\n", + " -0.020834\n", " NaN\n", " NaN\n", " -0.074901\n", @@ -9898,7 +9898,7 @@ " False\n", " False\n", " ...\n", - " -0.017709\n", + " -0.063666\n", " 0.000000\n", " NaN\n", " -0.112251\n", @@ -9931,10 +9931,10 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 -0.054067 NaN NaN 0.000000 0.000000 \n", - "95 -1.845827 NaN NaN -0.111226 NaN \n", - "96 -0.074901 NaN NaN -0.074901 NaN \n", + "95 -2.251292 NaN NaN -0.111226 NaN \n", + "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.063666 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", @@ -10007,6 +10007,14 @@ " 0.0\n", " \n", " \n", + " RPM_bot\n", + " -0.1\n", + " -0.0\n", + " -0.0\n", + " 0.0\n", + " 0.0\n", + " \n", + " \n", " jonahsingerbot\n", " -0.0\n", " -0.0\n", @@ -10015,27 +10023,27 @@ " -0.0\n", " \n", " \n", - " X_bot\n", + " bean_bot\n", " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " bean_bot\n", " -0.0\n", " -0.0\n", + " \n", + " \n", + " X_bot\n", " -0.0\n", " -0.0\n", " -0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " CumulativeBot\n", " -0.0\n", " -0.0\n", " -0.0\n", - " -0.0\n", + " 0.0\n", " 0.0\n", " \n", " \n", @@ -10047,7 +10055,7 @@ " -0.0\n", " \n", " \n", - " RPM_bot\n", + " KevinTestBot\n", " -0.1\n", " -0.0\n", " -0.0\n", @@ -10055,7 +10063,7 @@ " 0.0\n", " \n", " \n", - " KevinTestBot\n", + " SynapseSeer\n", " -0.1\n", " -0.0\n", " -0.0\n", @@ -10063,12 +10071,12 @@ " 0.0\n", " \n", " \n", - " SynapseSeer\n", + " Grizeu_Bot\n", + " -0.2\n", " -0.1\n", " -0.0\n", - " -0.0\n", - " 0.0\n", - " 0.0\n", + " 0.1\n", + " 0.2\n", " \n", " \n", " pianobot\n", @@ -10079,14 +10087,6 @@ " 0.0\n", " \n", " \n", - " Grizeu_Bot\n", - " -0.2\n", - " -0.1\n", - " -0.0\n", - " 0.1\n", - " 0.2\n", - " \n", - " \n", " CatrachoCaster\n", " -0.1\n", " -0.1\n", @@ -10103,7 +10103,7 @@ " -0.0\n", " \n", " \n", - " 4Shadower\n", + " annabot\n", " -0.1\n", " -0.1\n", " -0.1\n", @@ -10111,7 +10111,7 @@ " -0.0\n", " \n", " \n", - " annabot\n", + " 4Shadower\n", " -0.1\n", " -0.1\n", " -0.1\n", @@ -10159,7 +10159,7 @@ " -0.0\n", " \n", " \n", - " GreeneiBot2\n", + " ajf-bot\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -10167,7 +10167,7 @@ " 0.0\n", " \n", " \n", - " ajf-bot\n", + " GreeneiBot2\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -10175,6 +10175,14 @@ " 0.0\n", " \n", " \n", + " acm_bot\n", + " -0.3\n", + " -0.2\n", + " -0.1\n", + " -0.0\n", + " 0.1\n", + " \n", + " \n", " Bot_Pepa\n", " -0.2\n", " -0.2\n", @@ -10183,7 +10191,15 @@ " -0.0\n", " \n", " \n", - " acm_bot\n", + " metac-perplexity\n", + " -0.3\n", + " -0.3\n", + " -0.1\n", + " -0.0\n", + " 0.1\n", + " \n", + " \n", + " bot_median\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -10193,10 +10209,10 @@ " \n", " metac-o1\n", " -0.3\n", - " -0.2\n", + " -0.3\n", " -0.1\n", " -0.0\n", - " 0.0\n", + " 0.1\n", " \n", " \n", " metac-deepseek-r1+asknews\n", @@ -10207,16 +10223,16 @@ " -0.0\n", " \n", " \n", - " wunderplumb\n", - " -0.3\n", + " laylaps\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.1\n", - " -0.1\n", + " -0.0\n", " \n", " \n", - " laylaps\n", - " -0.2\n", + " wunderplumb\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", @@ -10225,40 +10241,24 @@ " \n", " metac-Gemini-Exp-1206\n", " -0.3\n", - " -0.2\n", - " -0.2\n", - " -0.0\n", - " 0.0\n", - " \n", - " \n", - " manticAI\n", " -0.3\n", - " -0.2\n", - " -0.2\n", " -0.1\n", " -0.0\n", + " 0.1\n", " \n", " \n", - " bot_median\n", + " manticAI\n", " -0.3\n", " -0.2\n", " -0.2\n", " -0.1\n", - " 0.0\n", + " -0.0\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", " -0.3\n", " -0.3\n", " -0.2\n", - " -0.1\n", - " 0.0\n", - " \n", - " \n", - " metac-perplexity\n", - " -0.4\n", - " -0.3\n", - " -0.2\n", " -0.0\n", " 0.0\n", " \n", @@ -10271,7 +10271,7 @@ " -0.0\n", " \n", " \n", - " minefrac1\n", + " metac-claude-3-5-sonnet-latest\n", " -0.3\n", " -0.3\n", " -0.2\n", @@ -10279,15 +10279,15 @@ " -0.1\n", " \n", " \n", - " mmBot\n", - " -0.4\n", + " minefrac1\n", + " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " metac-o1-preview\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -10295,55 +10295,55 @@ " -0.1\n", " \n", " \n", - " pgodzinai\n", - " -0.4\n", + " mmBot\n", " -0.4\n", + " -0.3\n", " -0.2\n", " -0.1\n", " -0.1\n", " \n", " \n", - " metac-exa\n", + " metac-Llama-3.1\n", " -0.4\n", " -0.4\n", - " -0.3\n", " -0.2\n", " -0.1\n", + " -0.0\n", " \n", " \n", - " VeritasAI\n", + " pgodzinai\n", + " -0.4\n", " -0.4\n", " -0.3\n", - " -0.3\n", - " -0.2\n", + " -0.1\n", " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", - " -0.4\n", + " metac-grok-2-1212\n", + " -0.5\n", " -0.4\n", " -0.3\n", - " -0.2\n", " -0.1\n", + " -0.0\n", " \n", " \n", - " metac-o1-preview\n", - " -0.5\n", + " VeritasAI\n", " -0.4\n", " -0.3\n", + " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " InstitutPelFutur\n", - " -0.5\n", + " metac-exa\n", + " -0.4\n", " -0.4\n", " -0.3\n", " -0.2\n", " -0.1\n", " \n", " \n", - " metac-grok-2-1212\n", + " InstitutPelFutur\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -10366,49 +10366,49 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", + "CumulativeBot -0.0 -0.0 -0.0 0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", "SynapseSeer -0.1 -0.0 -0.0 0.0 0.0\n", - "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "Grizeu_Bot -0.2 -0.1 -0.0 0.1 0.2\n", + "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", + "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", - "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", - "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", + "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", - "metac-o1 -0.3 -0.2 -0.1 -0.0 0.0\n", + "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-perplexity -0.3 -0.3 -0.1 -0.0 0.1\n", + "bot_median -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-o1 -0.3 -0.3 -0.1 -0.0 0.1\n", "metac-deepseek-r1+asknews -0.3 -0.2 -0.1 -0.1 -0.0\n", - "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.1\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-Gemini-Exp-1206 -0.3 -0.2 -0.2 -0.0 0.0\n", + "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.3 -0.1 -0.0 0.1\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "bot_median -0.3 -0.2 -0.2 -0.1 0.0\n", - "metac-claude-3-5-sonnet-20240620 -0.3 -0.3 -0.2 -0.1 0.0\n", - "metac-perplexity -0.4 -0.3 -0.2 -0.0 0.0\n", + "metac-claude-3-5-sonnet-20240620 -0.3 -0.3 -0.2 -0.0 0.0\n", "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", + "metac-claude-3-5-sonnet-latest -0.3 -0.3 -0.2 -0.1 -0.1\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", + "metac-o1-preview -0.4 -0.3 -0.2 -0.1 -0.1\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.1\n", - "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", - "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", + "metac-Llama-3.1 -0.4 -0.4 -0.2 -0.1 -0.0\n", + "pgodzinai -0.4 -0.4 -0.3 -0.1 -0.1\n", + "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.1 -0.0\n", "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", - "metac-Llama-3.1 -0.4 -0.4 -0.3 -0.2 -0.1\n", - "metac-o1-preview -0.5 -0.4 -0.3 -0.2 -0.1\n", + "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.2 -0.1\n", "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" ] }, @@ -11023,7 +11023,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -11074,32 +11074,32 @@ "text": [ " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.65]\n", " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.98]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.4]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.97]\n", " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.01]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.7]\n", " >>> Collected 1 forecasts: [0.99]\n", " >>> Collected 1 forecasts: [0.97]\n", @@ -11108,470 +11108,470 @@ " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.6]\n", " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.75]\n", - " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.35, 0.6]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.9]\n", " >>> Collected 2 forecasts: [0.85, 0.85]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.7, 0.6]\n", - " >>> Collected 2 forecasts: [0.7, 0.6]\n", - " >>> Collected 2 forecasts: [0.05, 0.05]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.6, 0.4]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", " >>> Collected 2 forecasts: [0.2, 0.25]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.7, 0.8]\n", - " >>> Collected 2 forecasts: [0.65, 0.3]\n", - " >>> Collected 2 forecasts: [0.1, 0.2]\n", + " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.6, 0.85]\n", + " >>> Collected 2 forecasts: [0.25, 0.65]\n", + " >>> Collected 2 forecasts: [0.25, 0.2]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.15, 0.3]\n", + " >>> Collected 2 forecasts: [0.15, 0.2]\n", " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.35]\n", + " >>> Collected 2 forecasts: [0.1, 0.25]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", - " >>> Collected 2 forecasts: [0.3, 0.3]\n", + " >>> Collected 2 forecasts: [0.05, 0.02]\n", + " >>> Collected 2 forecasts: [0.25, 0.35]\n", + " >>> Collected 2 forecasts: [0.4, 0.3]\n", " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.98, 0.97]\n", - " >>> Collected 2 forecasts: [0.4, 0.4]\n", - " >>> Collected 2 forecasts: [0.4, 0.25]\n", - " >>> Collected 2 forecasts: [0.85, 0.6]\n", - " >>> Collected 2 forecasts: [0.01, 0.02]\n", - " >>> Collected 2 forecasts: [0.7, 0.7]\n", - " >>> Collected 2 forecasts: [0.99, 0.9]\n", + " >>> Collected 2 forecasts: [0.97, 0.96]\n", + " >>> Collected 2 forecasts: [0.4, 0.3]\n", + " >>> Collected 2 forecasts: [0.3, 0.4]\n", + " >>> Collected 2 forecasts: [0.65, 0.7]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.7, 0.75]\n", + " >>> Collected 2 forecasts: [0.99, 0.7]\n", " >>> Collected 2 forecasts: [0.97, 0.99]\n", - " >>> Collected 2 forecasts: [0.99, 0.1]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.9, 0.8]\n", + " >>> Collected 2 forecasts: [0.99, 0.15]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.65]\n", " >>> Collected 2 forecasts: [0.6, 0.4]\n", - " >>> Collected 2 forecasts: [0.8, 0.85]\n", - " >>> Collected 2 forecasts: [0.05, 0.15]\n", - " >>> Collected 2 forecasts: [0.3, 0.2]\n", - " >>> Collected 2 forecasts: [0.75, 0.7]\n", - " >>> Collected 2 forecasts: [0.15, 0.2]\n", + " >>> Collected 2 forecasts: [0.8, 0.9]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.05, 0.15]\n", + " >>> Collected 2 forecasts: [0.65, 0.75]\n", + " >>> Collected 2 forecasts: [0.2, 0.2]\n", + " >>> Collected 2 forecasts: [0.1, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.15, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.05]\n", " >>> Collected 2 forecasts: [0.8, 0.9]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.85, 0.65]\n", - " >>> Collected 2 forecasts: [0.9, 0.85]\n", - " >>> Collected 2 forecasts: [0.85, 0.7]\n", - " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.9, 0.3]\n", + " >>> Collected 2 forecasts: [0.95, 0.85]\n", + " >>> Collected 2 forecasts: [0.85, 0.8]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.82]\n", + " >>> Collected 3 forecasts: [0.85, 0.9, 0.82]\n", " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.6, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", " >>> Collected 3 forecasts: [0.2, 0.25, 0.25]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.8, nan]\n", - " >>> Collected 3 forecasts: [0.65, 0.3, 0.108]\n", - " >>> Collected 3 forecasts: [0.1, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.6, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.25, 0.65, 0.108]\n", + " >>> Collected 3 forecasts: [0.25, 0.2, 0.16]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.3, 0.15]\n", + " >>> Collected 3 forecasts: [0.15, 0.2, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.35, 0.125]\n", + " >>> Collected 3 forecasts: [0.1, 0.25, 0.125]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.03]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.3, 0.3, 0.35]\n", + " >>> Collected 3 forecasts: [0.05, 0.02, 0.03]\n", + " >>> Collected 3 forecasts: [0.25, 0.35, 0.35]\n", + " >>> Collected 3 forecasts: [0.4, 0.3, 0.35]\n", " >>> Collected 3 forecasts: [0.2, 0.15, 0.115]\n", - " >>> Collected 3 forecasts: [0.98, 0.97, 0.97]\n", - " >>> Collected 3 forecasts: [0.4, 0.4, 0.285]\n", - " >>> Collected 3 forecasts: [0.4, 0.25, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.85, 0.6, 0.17]\n", - " >>> Collected 3 forecasts: [0.01, 0.02, 0.12]\n", - " >>> Collected 3 forecasts: [0.7, 0.7, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.9, 0.99]\n", + " >>> Collected 3 forecasts: [0.97, 0.96, 0.97]\n", + " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", + " >>> Collected 3 forecasts: [0.3, 0.4, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.65, 0.7, 0.17]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, 0.12]\n", + " >>> Collected 3 forecasts: [0.7, 0.75, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", " >>> Collected 3 forecasts: [0.97, 0.99, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.99, 0.1, 0.14]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.8, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.99, 0.15, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.65, 0.7666666666666667]\n", " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.8, 0.85, 0.84]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.026]\n", - " >>> Collected 3 forecasts: [0.3, 0.2, 0.16]\n", - " >>> Collected 3 forecasts: [0.75, 0.7, 0.67]\n", - " >>> Collected 3 forecasts: [0.15, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.05, 0.15, 0.086]\n", + " >>> Collected 3 forecasts: [0.8, 0.9, 0.84]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", + " >>> Collected 3 forecasts: [0.65, 0.75, 0.67]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", + " >>> Collected 3 forecasts: [0.1, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.086]\n", " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.15, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.1, 0.05, 0.02]\n", " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", - " >>> Collected 3 forecasts: [0.85, 0.65, nan]\n", - " >>> Collected 3 forecasts: [0.9, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.85, 0.7, 0.85]\n", - " >>> Collected 3 forecasts: [0.05, 0.1, 0.05]\n", + " >>> Collected 3 forecasts: [0.9, 0.3, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.05]\n", " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999]\n", + " >>> Collected 4 forecasts: [0.85, 0.9, 0.82, 0.794]\n", " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.6, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", " >>> Collected 4 forecasts: [0.2, 0.25, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", - " >>> Collected 4 forecasts: [0.7, 0.8, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.65, 0.3, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.1, 0.2, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.6, 0.85, nan, 0.936]\n", + " >>> Collected 4 forecasts: [0.25, 0.65, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.2, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.3, 0.15, 0.144]\n", - " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.918]\n", - " >>> Collected 4 forecasts: [0.1, 0.35, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.15, 0.2, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\n", + " >>> Collected 4 forecasts: [0.1, 0.25, 0.125, 0.212]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.3, 0.3, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.35, 0.5]\n", " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.4, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.85, 0.6, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.01, 0.02, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.7, 0.7, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.9, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.97, 0.96, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.65, 0.7, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.7, 0.75, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.1, 0.14, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.8, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.8, 0.85, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.3, 0.2, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.75, 0.7, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.15, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.8, 0.9, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.65, 0.75, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.086, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.05, 0.02, nan]\n", " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.85, 0.65, nan, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.85, nan, nan]\n", - " >>> Collected 4 forecasts: [0.85, 0.7, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.05, 0.1, 0.05, 0.02]\n", + " >>> Collected 4 forecasts: [0.9, 0.3, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.85, nan, nan]\n", + " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.05, 0.02]\n", " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.85, 0.9, 0.82, 0.794, nan]\n", " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", " >>> Collected 5 forecasts: [0.2, 0.25, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.8, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.65, 0.3, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.2, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.6, 0.85, nan, 0.936, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.65, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.2, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", + " >>> Collected 5 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375]\n", " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.85, 0.6, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.65, 0.7, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.3, 0.2, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.75, 0.7, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.15, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.05, 0.15, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.65, 0.75, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.086, nan, 0.12]\n", " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.15, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.1, 0.05, 0.02, nan, 0.098]\n", " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.85, 0.65, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.9, 0.85, nan, nan, 0.744]\n", - " >>> Collected 5 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052]\n", + " >>> Collected 5 forecasts: [0.9, 0.3, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.85, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052]\n", " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75]\n", + " >>> Collected 6 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75]\n", " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.6, 0.4, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", " >>> Collected 6 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", - " >>> Collected 6 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85]\n", + " >>> Collected 6 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15]\n", - " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85]\n", + " >>> Collected 6 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1]\n", " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05]\n", " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8]\n", - " >>> Collected 6 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25]\n", + " >>> Collected 6 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15]\n", " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92]\n", + " >>> Collected 7 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92]\n", " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", - " >>> Collected 7 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27]\n", + " >>> Collected 7 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9]\n", + " >>> Collected 7 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15]\n", " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65]\n", - " >>> Collected 7 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05]\n", + " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", - " >>> Collected 7 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15]\n", - " >>> Collected 7 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75]\n", - " >>> Collected 7 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65]\n", + " >>> Collected 7 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", + " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38]\n", - " >>> Collected 7 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65]\n", - " >>> Collected 7 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1]\n", - " >>> Collected 7 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9]\n", - " >>> Collected 7 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75]\n", - " >>> Collected 7 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05]\n", + " >>> Collected 7 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9]\n", + " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27]\n", + " >>> Collected 7 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", + " >>> Collected 7 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2]\n", " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", - " >>> Collected 7 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3]\n", - " >>> Collected 7 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9]\n", - " >>> Collected 7 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92]\n", + " >>> Collected 7 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan]\n", " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765]\n", - " >>> Collected 8 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", + " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", + " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", - " >>> Collected 8 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513]\n", + " >>> Collected 8 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847]\n", - " >>> Collected 8 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615]\n", - " >>> Collected 8 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", - " >>> Collected 8 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85]\n", - " >>> Collected 8 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan]\n", + " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847]\n", + " >>> Collected 8 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", + " >>> Collected 8 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", + " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", - " >>> Collected 8 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708]\n", - " >>> Collected 8 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.35]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", + " >>> Collected 9 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", - " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2]\n", - " >>> Collected 9 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", + " >>> Collected 9 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.65]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", - " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.65]\n", - " >>> Collected 9 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", - " >>> Collected 9 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.75]\n", - " >>> Collected 9 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", + " >>> Collected 9 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", + " >>> Collected 9 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", - " >>> Collected 9 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25]\n", - " >>> Collected 9 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.4]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.2]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15]\n", " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.95]\n", - " >>> Collected 9 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15]\n", - " >>> Collected 9 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.9]\n", - " >>> Collected 9 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.25, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.82, 0.7959999999999999, nan, 0.75, 0.92, nan, 0.8, 0.638]\n", + " >>> Collected 9 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15]\n", + " >>> Collected 9 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", + " >>> Collected 10 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan, 0.85, 0.638]\n", " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.05, 0.127]\n", - " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.2, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.2, 0.281]\n", - " >>> Collected 10 forecasts: [0.7, 0.8, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.65, 0.3, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.2, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.3, 0.15, 0.144, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", - " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.918, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.35, 0.125, 0.212, 0.085, 0.725, 0.27, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.25, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", + " >>> Collected 10 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", + " >>> Collected 10 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", + " >>> Collected 10 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.03, 0.072, 0.1, 0.075, 0.1, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.35, 0.226, 0.1149999999999999, 0.275, 0.65, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.3, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.65, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", - " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.4, 0.25, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.85, 0.6, 0.17, 0.236, nan, 0.3, 0.15, 0.6485000000000001, 0.65, 0.155]\n", - " >>> Collected 10 forecasts: [0.01, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.7, 0.875, 0.92, 0.6599999999999999, 0.75, 0.75, 0.85, 0.75, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.9, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", + " >>> Collected 10 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", + " >>> Collected 10 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.1, 0.14, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.8, 0.7666666666666667, nan, nan, nan, 0.85, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.65, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.8, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.026, 0.0559999999999999, 0.05, 0.085, 0.1, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.3, 0.2, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.75, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.35, 0.088]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.25, 0.574]\n", - " >>> Collected 10 forecasts: [0.05, 0.15, 0.086, nan, 0.12, 0.1, 0.05, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.4, 0.408]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.2, 0.574]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15, nan]\n", " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.8, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.95, 0.762]\n", - " >>> Collected 10 forecasts: [0.85, 0.65, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15, 0.126]\n", - " >>> Collected 10 forecasts: [0.9, 0.85, nan, nan, 0.744, 0.8, 0.3, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.7, 0.85, 0.71, 0.55, 0.475, 0.9, 0.708, 0.9, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -11652,9 +11652,9 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.01,0.7,0.25,0.03,0.01]\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", @@ -11662,7 +11662,7 @@ " NaN\n", " 86.82\n", " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", " \n", @@ -11679,9 +11679,9 @@ " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.6,0.35,0.05]\n", - " [0.0001, 0.5125, 0.0001]\n", + " [0.2,0.6,0.2]\n", " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", @@ -11715,18 +11715,18 @@ " binary\n", " NaN\n", " no\n", - " 0.85\n", - " 0.65\n", - " 0.3585\n", + " 0.9\n", + " 0.3\n", + " 0.1835\n", " \n", " \n", " 355\n", " binary\n", " NaN\n", " yes\n", - " 0.9\n", + " 0.95\n", " 0.85\n", - " 0.772\n", + " 0.775\n", " \n", " \n", " 361\n", @@ -11734,16 +11734,16 @@ " NaN\n", " no\n", " 0.85\n", - " 0.71\n", - " 0.709\n", + " 0.8\n", + " 0.755\n", " \n", " \n", " 364\n", " binary\n", " NaN\n", " no\n", - " 0.05\n", - " 0.05\n", + " 0.1\n", + " 0.052\n", " 0.046\n", " \n", " \n", @@ -11766,42 +11766,42 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.2,0.07,0.02] \n", + "0 [0.01,0.7,0.25,0.03,0.01] \n", "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", "2 0.1 \n", - "3 [0.6,0.35,0.05] \n", + "3 [0.2,0.6,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", "342 0.9 \n", - "351 0.85 \n", - "355 0.9 \n", + "351 0.9 \n", + "355 0.95 \n", "361 0.85 \n", - "364 0.05 \n", + "364 0.1 \n", "\n", " median_forecast_5_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", "342 0.9 \n", - "351 0.65 \n", + "351 0.3 \n", "355 0.85 \n", - "361 0.71 \n", - "364 0.05 \n", + "361 0.8 \n", + "364 0.052 \n", "\n", " median_forecast_8_bots \n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", "2 0.1 \n", - "3 [0.0001, 0.5125, 0.0001] \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", "342 0.9025 \n", - "351 0.3585 \n", - "355 0.772 \n", - "361 0.709 \n", + "351 0.1835 \n", + "355 0.775 \n", + "361 0.755 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -11892,52 +11892,52 @@ " \n", " 0\n", " 1\n", - " 702.66\n", + " 1399.41\n", " \n", " \n", " 1\n", " 2\n", - " 2127.15\n", + " 2492.32\n", " \n", " \n", " 2\n", " 3\n", - " 2378.31\n", + " 2451.57\n", " \n", " \n", " 3\n", " 4\n", - " 2447.50\n", + " 2407.46\n", " \n", " \n", " 4\n", " 5\n", - " 2613.58\n", + " 2500.43\n", " \n", " \n", " 5\n", " 6\n", - " 2565.78\n", + " 2492.29\n", " \n", " \n", " 6\n", " 7\n", - " 2492.12\n", + " 2620.65\n", " \n", " \n", " 7\n", " 8\n", - " 2572.02\n", + " 2688.63\n", " \n", " \n", " 8\n", " 9\n", - " 2483.55\n", + " 2505.22\n", " \n", " \n", " 9\n", " 10\n", - " 2418.82\n", + " 2396.81\n", " \n", " \n", "\n", @@ -11945,16 +11945,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 702.66\n", - "1 2 2127.15\n", - "2 3 2378.31\n", - "3 4 2447.50\n", - "4 5 2613.58\n", - "5 6 2565.78\n", - "6 7 2492.12\n", - "7 8 2572.02\n", - "8 9 2483.55\n", - "9 10 2418.82" + "0 1 1399.41\n", + "1 2 2492.32\n", + "2 3 2451.57\n", + "3 4 2407.46\n", + "4 5 2500.43\n", + "5 6 2492.29\n", + "6 7 2620.65\n", + "7 8 2688.63\n", + "8 9 2505.22\n", + "9 10 2396.81" ] }, "execution_count": 60, @@ -11994,7 +11994,14 @@ { "data": { "text/plain": [ - "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2', 'manticAI']" + "['metac-o1-preview',\n", + " 'metac-o1',\n", + " 'pgodzinai',\n", + " 'GreeneiBot2',\n", + " 'manticAI',\n", + " 'acm_bot',\n", + " 'metac-Gemini-Exp-1206',\n", + " 'SynapseSeer']" ] }, "execution_count": 61, @@ -12011,7 +12018,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -12108,18 +12115,18 @@ " NaN\n", " False\n", " False\n", - " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.01,0.7,0.25,0.03,0.01]\n", " ...\n", " [0.01, 0.0001, 0.0001, 0.0001, 0.0001]\n", - " [0.13, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.20500000000000002, 0.0001, 0.0001, 0.0001, ...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", - " [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0...\n", - " [0.04847475882512753, 0.0001, 0.0001, 0.0001, ...\n", - " [0.04847475882512753, 0.0001, 0.0001, 0.0001, ...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", + " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", " \n", " \n", " 1\n", @@ -12135,10 +12142,10 @@ " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", " ...\n", " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.05061111115, 0.0512222222, 0.05183333...\n", - " [0.05, 0.0505555556, 0.0511111111, 0.051666666...\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", + " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", @@ -12180,18 +12187,18 @@ " NaN\n", " NaN\n", " NaN\n", - " [0.6,0.35,0.05]\n", + " [0.2,0.6,0.2]\n", " ...\n", - " [0.0001, 0.35, 0.0001]\n", - " [0.0001, 0.475, 0.0001]\n", + " [0.0001, 0.6, 0.0001]\n", + " [0.0001, 0.525, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.5562499999999999, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.47324999999999995, 0.0001]\n", - " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.5048350576136786, 0.0001]\n", - " [0.0001, 0.49717011522735727, 0.0001]\n", + " [0.0001, 0.48124999999999996, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.442, 0.0001]\n", + " [0.0001, 0.434, 0.0001]\n", " \n", " \n", " 4\n", @@ -12207,9 +12214,9 @@ " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,...\n", " ...\n", " [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,...\n", - " [0.0, 0.0032500000000000003, 0.006500000000000...\n", - " [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0...\n", - " [0.0, 0.0021590909, 0.0043181818, 0.0064772727...\n", + " [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0...\n", + " [0.0, 0.0033333333, 0.0066666667, 0.01, 0.0133...\n", + " [0.0, 0.00257575755, 0.00515151515, 0.00772727...\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0, 0.00183065955, 0.00366131905, 0.00549197...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", @@ -12238,43 +12245,43 @@ "4 NaN 0.0 400.0 False \n", "\n", " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.01,0.7,0.2,0.07,0.02] ... \n", + "0 False [0.01,0.7,0.25,0.03,0.01] ... \n", "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", "2 False 0.1 ... \n", - "3 NaN [0.6,0.35,0.05] ... \n", + "3 NaN [0.2,0.6,0.2] ... \n", "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", "\n", " median_forecast_1_bots \\\n", "0 [0.01, 0.0001, 0.0001, 0.0001, 0.0001] \n", "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", "2 0.1 \n", - "3 [0.0001, 0.35, 0.0001] \n", + "3 [0.0001, 0.6, 0.0001] \n", "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 [0.13, 0.0001, 0.0001, 0.0001, 0.0001] \n", - "1 [0.05, 0.05061111115, 0.0512222222, 0.05183333... \n", + "0 [0.20500000000000002, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", "2 0.1 \n", - "3 [0.0001, 0.475, 0.0001] \n", - "4 [0.0, 0.0032500000000000003, 0.006500000000000... \n", + "3 [0.0001, 0.525, 0.0001] \n", + "4 [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0... \n", "\n", " median_forecast_3_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.0505555556, 0.0511111111, 0.051666666... \n", + "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", "2 0.1 \n", "3 [0.0001, 0.5125, 0.0001] \n", - "4 [0.0, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.0... \n", + "4 [0.0, 0.0033333333, 0.0066666667, 0.01, 0.0133... \n", "\n", " median_forecast_4_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", "2 0.085 \n", "3 [0.0001, 0.5562499999999999, 0.0001] \n", - "4 [0.0, 0.0021590909, 0.0043181818, 0.0064772727... \n", + "4 [0.0, 0.00257575755, 0.00515151515, 0.00772727... \n", "\n", " median_forecast_5_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", "2 0.085 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", @@ -12283,35 +12290,35 @@ "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", "2 0.1 \n", - "3 [0.0001, 0.47324999999999995, 0.0001] \n", + "3 [0.0001, 0.48124999999999996, 0.0001] \n", "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", "\n", " median_forecast_7_bots \\\n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", "2 0.1 \n", - "3 [0.0001, 0.5125, 0.0001] \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_8_bots \\\n", - "0 [0.0824628712871287, 0.0001, 0.0001, 0.0001, 0... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", "2 0.1 \n", - "3 [0.0001, 0.5125, 0.0001] \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_9_bots \\\n", - "0 [0.04847475882512753, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", "2 0.1 \n", - "3 [0.0001, 0.5048350576136786, 0.0001] \n", + "3 [0.0001, 0.442, 0.0001] \n", "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", "\n", " median_forecast_10_bots \n", - "0 [0.04847475882512753, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", "2 0.1 \n", - "3 [0.0001, 0.49717011522735727, 0.0001] \n", + "3 [0.0001, 0.434, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", "[5 rows x 29 columns]" @@ -12391,7 +12398,7 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " \n", " \n", @@ -12409,7 +12416,7 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", " \n", " \n", @@ -12427,7 +12434,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", " \n", " \n", @@ -12445,7 +12452,7 @@ " NaN\n", " 31280\n", " 1.0\n", - " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " [0.16,0.44,0.4]\n", " \n", " \n", @@ -12463,7 +12470,7 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", " \n", " \n", @@ -12500,11 +12507,11 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", - "2 0.085 \n", - "3 [0.0001, 0.5125, 0.0001] \n", - "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.45, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " pro_median \n", "0 [0.001,0.62,0.35,0.019,0.01] \n", @@ -12571,7 +12578,7 @@ " False\n", " 35380\n", " 1.00\n", - " 0.9\n", + " 0.9025\n", " 0.95\n", " \n", " \n", @@ -12589,7 +12596,7 @@ " False\n", " 35381\n", " 1.00\n", - " 0.65\n", + " 0.1835\n", " 0.05\n", " \n", " \n", @@ -12607,7 +12614,7 @@ " False\n", " 35385\n", " 1.00\n", - " 0.85\n", + " 0.775\n", " 0.97\n", " \n", " \n", @@ -12625,7 +12632,7 @@ " False\n", " 35386\n", " 0.85\n", - " 0.71\n", + " 0.755\n", " 0.666\n", " \n", " \n", @@ -12643,7 +12650,7 @@ " False\n", " 35387\n", " 0.85\n", - " 0.05\n", + " 0.046\n", " 0.03\n", " \n", " \n", @@ -12673,11 +12680,11 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median \n", - "342 1.00 0.9 0.95 \n", - "351 1.00 0.65 0.05 \n", - "355 1.00 0.85 0.97 \n", - "361 0.85 0.71 0.666 \n", - "364 0.85 0.05 0.03 " + "342 1.00 0.9025 0.95 \n", + "351 1.00 0.1835 0.05 \n", + "355 1.00 0.775 0.97 \n", + "361 0.85 0.755 0.666 \n", + "364 0.85 0.046 0.03 " ] }, "metadata": {}, @@ -12740,7 +12747,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -0.1240\n" + "Weighted Total Score: -0.1115\n" ] } ], @@ -12762,7 +12769,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAIjCAYAAADvBuGTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAdxxJREFUeJzt3XlYFWX/x/HPAWURWVxYE8V9F8wF96VIXNNKU1sU08p2H7LSntK2J7PFtDItK7HFUtO00kwll9x3SzNzx1TADRBUVJjfH/Pj4BE39OCAvl/XNZfMPfeZ853DEfice+Yem2EYhgAAAAAA18TF6gIAAAAA4EZAuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAWGrPnj2y2WyKi4uzupRCidcHAIoOwhWAQi0uLk42m81hCQgIUNu2bfXLL79c9X7ffPNNzZw587L92rRpk+f5L7S88sorV13L9RIWFqbOnTtfcNuiRYtks9n0/fffX+eq8mfy5MkaPXp0gez7Rnh99uzZo379+qly5cry8PBQUFCQWrVqpeHDh1tdWpEUFhaW52dPy5Yt9cMPP1hdGoBCqpjVBQDAlXjttddUsWJFGYahpKQkxcXFqWPHjvrpp58u+gfxpbz55pvq3r27unXrdsl+//3vfzVgwAD7+po1a/TBBx/oxRdfVM2aNe3t9erVy3cNyL/Jkydr8+bNGjRokNWlFDo7duxQo0aN5OnpqYceekhhYWE6ePCg1q9fr5EjR+rVV1+1usQiKSIiQs8++6wk6cCBA/rkk0909913a9y4cRo4cKDF1QEobAhXAIqEDh06qGHDhvb1/v37KzAwUN9+++1Vhasrdccddzise3h46IMPPtAdd9yhNm3aFNjzAvn1/vvvKz09XRs3blSFChUctiUnJ1/XWjIyMuTl5XVdn7Og3HLLLXrggQfs63369FGVKlX0/vvvXzRcnT17VtnZ2XJzc7teZQIoJDgtEECR5OfnJ09PTxUr5vgZUUZGhp599lmFhobK3d1d1atX17vvvivDMOx9bDabMjIyNGnSJPvpPjExMddUzy+//KKWLVvKy8tL3t7e6tSpk7Zs2eLQ548//lBMTIwqVapkP2XroYce0pEjRxz6vfLKK7LZbPrnn3/0wAMPyNfXV/7+/nr55ZdlGIb27dunrl27ysfHR0FBQXrvvfeuqfZL2b9/vx566CEFBgbK3d1dtWvX1hdffOHQ5/Tp0xo2bJgaNGggX19feXl5qWXLllq4cGGe/aWkpCgmJka+vr7y8/NT3759lZKSckW1tGnTRrNnz9bevXvt37ewsDD79uTkZHvo9vDwUHh4uCZNmnQth39Zhen12blzp8qVK5cnWElSQEBAnrZffvlFrVu3lre3t3x8fNSoUSNNnjzZoc+0adPUoEEDeXp6qmzZsnrggQe0f/9+hz4xMTEqWbKkdu7cqY4dO8rb21v333+/JCk7O1ujR49W7dq15eHhocDAQD366KM6duyYwz7Wrl2r6OholS1bVp6enqpYsaIeeuihSx5v586dValSpQtua9q0qcOHMfPnz1eLFi3k5+enkiVLqnr16nrxxRcvuf+LCQoKUs2aNbV7925JudfEvfvuuxo9erQqV64sd3d3/fXXX5Kk3377zf6zwc/PT127dtXWrVsd9nn8+HENGjRIYWFhcnd3V0BAgO644w6tX7/+qmoEYB1GrgAUCampqTp8+LAMw1BycrI+/PBDpaenO3yibBiG7rzzTi1cuFD9+/dXRESEfv31Vz333HPav3+/3n//fUnSV199pQEDBqhx48Z65JFHJEmVK1e+6tq++uor9e3bV9HR0Ro5cqROnDihcePGqUWLFtqwYYM9AMyfP1+7du1Sv379FBQUpC1btujTTz/Vli1btHLlStlsNof99uzZUzVr1tRbb72l2bNn64033lDp0qX1ySef6LbbbtPIkSP1zTffaPDgwWrUqJFatWp12VrPnDmjw4cP52lPTU3N05aUlKQmTZrIZrPpySeflL+/v3755Rf1799faWlp9lPz0tLS9Nlnn6l37956+OGHdfz4cX3++eeKjo7W6tWrFRERIcn8/nTt2lVLly7VwIEDVbNmTf3www/q27fvFb3O//3vf5Wamqp///3X/r0sWbKkJOnkyZNq06aNduzYoSeffFIVK1bUtGnTFBMTo5SUFD3zzDNX9BxF+fWpUKGCFixYoN9++0233XbbJfvGxcXpoYceUu3atTV06FD5+flpw4YNmjt3ru677z57n379+qlRo0YaMWKEkpKSNGbMGC1btkwbNmyQn5+ffX9nz55VdHS0WrRooXfffVclSpSQJD366KP2/Tz99NPavXu3PvroI23YsEHLli1T8eLFlZycrHbt2snf319DhgyRn5+f9uzZoxkzZlzyGHr27Kk+ffpozZo1atSokb197969Wrlypd555x1J0pYtW9S5c2fVq1dPr732mtzd3bVjxw4tW7bsil7X8505c0b79u1TmTJlHNonTpyoU6dO6ZFHHpG7u7tKly6tBQsWqEOHDqpUqZJeeeUVnTx5Uh9++KGaN2+u9evX2382DBw4UN9//72efPJJ1apVS0eOHNHSpUu1detW3XrrrVdVJwCLGABQiE2cONGQlGdxd3c34uLiHPrOnDnTkGS88cYbDu3du3c3bDabsWPHDnubl5eX0bdv33zXM23aNEOSsXDhQsMwDOP48eOGn5+f8fDDDzv0S0xMNHx9fR3aT5w4kWd/3377rSHJWLJkib1t+PDhhiTjkUcesbedPXvWKFeunGGz2Yy33nrL3n7s2DHD09Pzio6lQoUKF3wtz12mTZtm79+/f38jODjYOHz4sMN+evXqZfj6+tqP5+zZs0ZmZqZDn2PHjhmBgYHGQw89ZG/L+f68/fbbDsfVsmVLQ5IxceLEyx5Dp06djAoVKuRpHz16tCHJ+Prrr+1tp0+fNpo2bWqULFnSSEtLu+y+i/rrs3nzZsPT09OQZERERBjPPPOMMXPmTCMjI8OhX0pKiuHt7W1ERkYaJ0+edNiWnZ1tGIb52gUEBBh16tRx6PPzzz8bkoxhw4bZ2/r27WtIMoYMGeKwr99//92QZHzzzTcO7XPnznVo/+GHHwxJxpo1ay55fOdLTU013N3djWeffdah/e233zZsNpuxd+9ewzAM4/333zckGYcOHcrX/g3DfE+0a9fOOHTokHHo0CFj06ZNRq9evQxJxlNPPWUYhmHs3r3bkGT4+PgYycnJDo+PiIgwAgICjCNHjtjbNm3aZLi4uBh9+vSxt/n6+hpPPPFEvusDUPhwWiCAImHs2LGaP3++5s+fr6+//lpt27bVgAEDHD7dnjNnjlxdXfX00087PPbZZ5+VYRjXNLvgxcyfP18pKSnq3bu3Dh8+bF9cXV0VGRnpcOqXp6en/etTp07p8OHDatKkiSRd8PSfcyfScHV1VcOGDWUYhvr3729v9/PzU/Xq1bVr164rqjcyMtL+Op67vPvuuw79DMPQ9OnT1aVLFxmG4XBs0dHRSk1Ntdfs6upqv7YkOztbR48e1dmzZ9WwYUOH45ozZ46KFSumxx57zOG4nnrqqSuq/VLmzJmjoKAg9e7d295WvHhxPf3000pPT9fixYuvaD9F+fWpXbu2Nm7cqAceeEB79uzRmDFj1K1bNwUGBmrChAn2fvPnz9fx48c1ZMgQeXh4OOwjZ/R07dq1Sk5O1uOPP+7Qp1OnTqpRo4Zmz56d5/nPrVsyTyn09fXVHXfc4fD6NGjQQCVLlrT/38gZAfv555915syZKzpWSfLx8VGHDh00depUh9N+p0yZoiZNmqh8+fIO+581a5ays7OveP855s2bJ39/f/n7+ys8PFzTpk3Tgw8+qJEjRzr0u+eee+Tv729fP3jwoDZu3KiYmBiVLl3a3l6vXj3dcccdmjNnjr3Nz89Pq1at0oEDB/JdH4DChdMCARQJjRs3driGonfv3qpfv76efPJJde7cWW5ubtq7d69CQkLk7e3t8NicWf327t3r9Lq2b98uSRc9DcvHx8f+9dGjR/Xqq6/qu+++yzPBwIVOO8v54zCHr6+vPDw8VLZs2Tzt51+3dTFly5ZVVFRUnvbzr107dOiQUlJS9Omnn+rTTz+94L7OPYZJkybpvffe099//+3wB3LFihXtX+/du1fBwcH2U/lyVK9e3WH95MmTeV6PoKCgSx7X3r17VbVqVbm4OH5meP73PjU1VSdPnrRvd3Nzc/jDtyi8PpdSrVo1ffXVV8rKytJff/2ln3/+WW+//bYeeeQRVaxYUVFRUdq5c6ckqU6dOhfdT87rdaHnrlGjhpYuXerQVqxYMZUrV86hbfv27UpNTb3g9V5S7uvTunVr3XPPPXr11Vf1/vvvq02bNurWrZvuu+8+ubu7X/J4e/bsqZkzZ2rFihVq1qyZdu7cqXXr1jlM19+zZ0999tlnGjBggIYMGaLbb79dd999t7p3757n/XIhkZGReuONN2Sz2VSiRAnVrFnT4ZTIHOd+L6VLv4Y1a9bUr7/+ap/44+2331bfvn0VGhqqBg0aqGPHjurTp89FrykDUHgRrgAUSS4uLmrbtq3GjBmj7du3q3bt2pbUkfNJ+FdffXXBAHDuH+X33nuvli9frueee04REREqWbKksrOz1b59+wt+ou7q6npFbZIcPrl3hpx6HnjggYte85Mz/fzXX3+tmJgYdevWTc8995wCAgLk6uqqESNG2P+Qz48pU6aoX79+Dm3OOr5nnnnGYZKL1q1ba9GiRfnej5Wvz5VwdXVV3bp1VbduXTVt2lRt27bVN998c8Hg6Azu7u55gkp2drYCAgL0zTffXPAxOaM8OfcPW7lypX766Sf9+uuveuihh/Tee+9p5cqVecLmubp06aISJUpo6tSpatasmaZOnSoXFxf16NHD3sfT01NLlizRwoULNXv2bM2dO1dTpkzRbbfdpnnz5l30/1SOiwXu8507Mp1f9957r/3+WfPmzdM777yjkSNHasaMGerQocNV7xfA9Ue4AlBknT17VpKUnp4uKfeC/uPHjzuMXv3999/27TnOnzziauVMhBEQEHDJP8COHTum+Ph4vfrqqxo2bJi9PWfkq7Dx9/eXt7e3srKyLvuH5ffff69KlSppxowZDq/r+TeurVChguLj45Wenu7wB/O2bdsc+kVHR2v+/PkXfK6Lfd8qVKigP/74Q9nZ2Q5/5J//vX/++ecdJkEpVarUJY/tYqx8ffIrZ8T34MGDknLfs5s3b1aVKlUu+Jic12vbtm15RmW3bdt2wRkJz1e5cmUtWLBAzZs3v6Lg0aRJEzVp0kT/+9//NHnyZN1///367rvvHE6PPZ+Xl5c6d+6sadOmadSoUZoyZYpatmypkJAQh34uLi66/fbbdfvtt2vUqFF688039d///lcLFy4ssMB57mt4vr///ltly5Z1mK4+ODhYjz/+uB5//HElJyfr1ltv1f/+9z/CFVDEcM0VgCLpzJkzmjdvntzc3OynfnXs2FFZWVn66KOPHPq+//77stlsDn+keHl5XfEU15cSHR0tHx8fvfnmmxe8XuTQoUOSckeczh+BOff0pcLE1dVV99xzj6ZPn67Nmzfn2Z5zXDl9JcdjW7VqlVasWOHwmI4dO+rs2bMaN26cvS0rK0sffvihQ7/g4GBFRUU5LDm8vLwueAplx44dlZiYqClTptjbzp49qw8//FAlS5ZU69atJUm1atVy2G+DBg2u6PU4n5Wvz8X8/vvvF3wP5lzbk3N6Wrt27eTt7a0RI0bo1KlTDn1zamzYsKECAgI0fvx4ZWZm2rf/8ssv2rp1qzp16nTZeu69915lZWXp9ddfz7Pt7Nmz9v9/x44dy/P/ImcGxXOf+2J69uypAwcO6LPPPtOmTZvUs2dPh+1Hjx7N85j87P9qBQcHKyIiQpMmTXL4WbN582bNmzdPHTt2lGR+j89/TwcEBCgkJKRA6wNQMBi5AlAk/PLLL/ZRiOTkZE2ePFnbt2/XkCFD7Nc1denSRW3bttV///tf7dmzR+Hh4Zo3b55mzZqlQYMGOUy33qBBAy1YsECjRo1SSEiIKlasqMjIyHzX5ePjo3HjxunBBx/Urbfeql69esnf318JCQmaPXu2mjdvro8++kg+Pj5q1aqV3n77bZ05c0a33HKL5s2bZ79XTmH01ltvaeHChYqMjNTDDz+sWrVq6ejRo1q/fr0WLFhg/6O1c+fOmjFjhu666y516tRJu3fv1vjx41WrVi37qKJkfn+aN2+uIUOGaM+ePapVq5ZmzJhxwbB0MQ0aNNCUKVMUGxurRo0aqWTJkurSpYseeeQRffLJJ4qJidG6desUFham77//XsuWLdPo0aPzXId3I74+I0eO1Lp163T33XfbT0lcv369vvzyS5UuXdo+NbyPj4/ef/99DRgwQI0aNdJ9992nUqVKadOmTTpx4oQmTZqk4sWLa+TIkerXr59at26t3r1726diDwsL03/+85/L1tO6dWs9+uijGjFihDZu3Kh27dqpePHi2r59u6ZNm6YxY8aoe/fumjRpkj7++GPdddddqly5so4fP64JEybIx8fHHkAuJefeWoMHD7aH3nO99tprWrJkiTp16qQKFSooOTlZH3/8scqVK6cWLVpc0Wt7td555x116NBBTZs2Vf/+/e1Tsfv6+uqVV16RZN7jqly5curevbvCw8NVsmRJLViwQGvWrCnQe9gBKCBWTFEIAFfqQlOxe3h4GBEREca4cePsU0fnOH78uPGf//zHCAkJMYoXL25UrVrVeOedd/L0+/vvv41WrVrZp66+0mnZz5+KPcfChQuN6Ohow9fX1/Dw8DAqV65sxMTEGGvXrrX3+ffff4277rrL8PPzM3x9fY0ePXoYBw4cMCQZw4cPt/fLmYr9/Kmj+/bta3h5eeWpqXXr1kbt2rUvW3uFChWMTp06XXDbwoUL80w1bhiGkZSUZDzxxBNGaGioUbx4cSMoKMi4/fbbjU8//dTeJzs723jzzTeNChUqGO7u7kb9+vWNn3/+2ejbt2+eadOPHDliPPjgg4aPj4/h6+trPPjgg8aGDRuueCr29PR047777jP8/PwMSQ77T0pKMvr162eULVvWcHNzM+rWrXtF+8xR1F+fZcuWGU888YRRp04dw9fX1yhevLhRvnx5IyYmxti5c2ee/j/++KPRrFkzw9PT0/Dx8TEaN25sfPvttw59pkyZYtSvX99wd3c3Spcubdx///3Gv//+69DnYu/LHJ9++qnRoEEDw9PT0/D29jbq1q1rPP/888aBAwcMwzCM9evXG7179zbKly9vuLu7GwEBAUbnzp0d/u9czv33329IMqKiovJsi4+PN7p27WqEhIQYbm5uRkhIiNG7d2/jn3/+uex+L/WeyJEzFfs777xzwe0LFiwwmjdvbn+du3TpYvz111/27ZmZmcZzzz1nhIeHG97e3oaXl5cRHh5ufPzxx5etD0DhYzMMJ18FDQAAAAA3Ia65AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4ATcRvoDs7GwdOHBA3t7estlsVpcDAAAAwCKGYej48eMKCQmRi8ulx6YIVxdw4MABhYaGWl0GAAAAgEJi3759Kleu3CX7EK4uwNvbW5L5Avr4+FhcDQAAAACrpKWlKTQ01J4RLoVwdQE5pwL6+PgQrgAAAABc0eVCTGgBAAAAAE5AuAIAAAAAJyBcAQAAAIATcM0VAAAACiXDMHT27FllZWVZXQpuYK6uripWrJhTbsFEuAIAAEChc/r0aR08eFAnTpywuhTcBEqUKKHg4GC5ubld034IVwAAAChUsrOztXv3brm6uiokJERubm5OGVUAzmcYhk6fPq1Dhw5p9+7dqlq16mVvFHwphCsAAAAUKqdPn1Z2drZCQ0NVokQJq8vBDc7T01PFixfX3r17dfr0aXl4eFz1vpjQAgAAAIXStYwgAPnhrPca71gAAAAAcALCFQAAAAA4AeEKAAAAKGBt2rTRoEGDrtvzxcXFyc/P77o9X0G63q/dtSBcAQAAAE4QExMjm82WZ9mxY4dmzJih119/3d43LCxMo0ePdni8FYFo4cKF6ty5s/z9/eXh4aHKlSurZ8+eWrJkyXWt41LOf+0KM8IVAAAA4CTt27fXwYMHHZaKFSuqdOnS8vb2tro8Bx9//LFuv/12lSlTRlOmTNG2bdv0ww8/qFmzZvrPf/5jdXl2hfG1uxjCFQAAAOAk7u7uCgoKclhcXV0dTm1r06aN9u7dq//85z/20a1FixapX79+Sk1Ntbe98sorkqTMzEwNHjxYt9xyi7y8vBQZGalFixY5PG9cXJzKly+vEiVK6K677tKRI0cuWWdCQoIGDRqkQYMGadKkSbrttttUoUIF1atXT88884zWrl1r73vkyBH17t1bt9xyi0qUKKG6devq22+/ddjfhUbiIiIi7MdgGIZeeeUVlS9fXu7u7goJCdHTTz9t7/vxxx+ratWq8vDwUGBgoLp3727fdv5pgV999ZUaNmwob29vBQUF6b777lNycrJ9+6JFi2Sz2RQfH6+GDRuqRIkSatasmbZt23bJ18QZuM8VAAAACr+GDaXExOv/vEFB0jlBwxlmzJih8PBwPfLII3r44YclmaMzo0eP1rBhw+whoGTJkpKkJ598Un/99Ze+++47hYSE6IcfflD79u31559/qmrVqlq1apX69++vESNGqFu3bpo7d66GDx9+yRqmT5+uM2fO6Pnnn7/g9nNv2nzq1Ck1aNBAL7zwgnx8fDR79mw9+OCDqly5sho3bnxFxzx9+nS9//77+u6771S7dm0lJiZq06ZNkqS1a9fq6aef1ldffaVmzZrp6NGj+v333y+6rzNnzuj1119X9erVlZycrNjYWMXExGjOnDkO/f773//qvffek7+/vwYOHKiHHnpIy5Ytu6J6rxbhCgAAAIVfYqK0f7/VVVzWzz//bA9FktShQwdNmzbNoU/p0qXl6upqH3nJ4evrK5vN5tCWkJCgiRMnKiEhQSEhIZKkwYMHa+7cuZo4caLefPNNjRkzRu3bt7cHpWrVqmn58uWaO3fuRev8559/5OPj4/Bc06dPV9++fe3rK1asUN26dXXLLbdo8ODB9vannnpKv/76q6ZOnXrF4SohIUFBQUGKiopS8eLFVb58eftjExIS5OXlpc6dO8vb21sVKlRQ/fr1L7qvhx56yP51pUqV9MEHH6hRo0ZKT093eO3/97//qXXr1pKkIUOGqFOnTjp16tQ13ST4cghXAAAAKPzOCQGF+Xnbtm2rcePG2de9vLyu6en//PNPZWVlqVq1ag7tmZmZKlOmjCRp69atuuuuuxy2N23a9JLhSnIcnZKk6Ohobdy4Ufv371ebNm2UlZUlScrKytKbb76pqVOnav/+/Tp9+rQyMzNVokSJKz6OHj16aPTo0apUqZLat2+vjh07qkuXLipWrJjuuOMOVahQwb6tffv2uuuuuy66/3Xr1umVV17Rpk2bdOzYMWVnZ0syQ1qtWrXs/erVq2f/Ojg4WJKUnJys8uXLX3Hd+UW4AgAAQOHn5FPzCoqXl5eqVKnitP2lp6fL1dVV69atk6urq8O2c0dp8qtq1apKTU1VYmKiffSqZMmSqlKliooVc4wI77zzjsaMGaPRo0erbt268vLy0qBBg3T69Gl7HxcXFxmG4fC4M2fO2L8ODQ3Vtm3btGDBAs2fP1+PP/643nnnHS1evFje3t5av369Fi1apHnz5mnYsGF65ZVXtGbNmjyzJ2ZkZCg6OlrR0dH65ptv5O/vr4SEBEVHRzvUI0nFixe3f50TJHOCWEFhQgsAAADgOnNzc7OPDF2qrX79+srKylJycrKqVKnisOSEopo1a2rVqlUOj1u5cuUln7979+4qXry4Ro4cedlaly1bpq5du+qBBx5QeHi4KlWqpH/++cehj7+/vw4ePGhfT0tL0+7dux36eHp6qkuXLvrggw+0aNEirVixQn/++ackqVixYoqKitLbb7+tP/74Q3v27NFvv/2Wp5a///5bR44c0VtvvaWWLVuqRo0aDpNZWI2RKwAALmHaztQC23ePyr4Ftm8AhVtYWJiWLFmiXr16yd3dXWXLllVYWJjS09MVHx+v8PBwlShRQtWqVdP999+vPn366L333lP9+vV16NAhxcfHq169eurUqZOefvppNW/eXO+++666du2qX3/99bKnBJYvX17vvfeennnmGR09elQxMTGqWLGijh49qq+//lqS7CNlVatW1ffff6/ly5erVKlSGjVqlJKSkhxOwbvtttsUFxenLl26yM/PT8OGDXMYaYuLi1NWVpYiIyNVokQJff311/L09FSFChX0888/a9euXWrVqpVKlSqlOXPmKDs7W9WrV79g3W5ubvrwww81cOBAbd68uVDdA4uRKwAAAOA6e+2117Rnzx5VrlxZ/v7+kqRmzZpp4MCB6tmzp/z9/fX2229LkiZOnKg+ffro2WefVfXq1dWtWzetWbPGfu1QkyZNNGHCBI0ZM0bh4eGaN2+eXnrppcvW8NRTT2nevHk6dOiQunfvrqpVq6pjx47avXu35s6dq7p160qSXnrpJd16662Kjo5WmzZtFBQUpG7dujnsa+jQoWrdurU6d+6sTp06qVu3bqpcubJ9u5+fnyZMmKDmzZurXr16WrBggX766SeVKVNGfn5+mjFjhm677TbVrFlT48eP17fffqvatWvnqdnf319xcXGaNm2aatWqpbfeekvvvvvuVX0PCoLNOP/kSCgtLU2+vr5KTU2Vj4+P1eUAACzEyBVw/Z06dUq7d+9WxYoVC3RmNyDHpd5z+ckGjFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAol5l3D9eKs9xrhCgAAAIVK8eLFJUknTpywuBLcLHLeaznvvavFTYQBAABQqLi6usrPz0/JycmSpBIlSshms1lcFW5EhmHoxIkTSk5Olp+fn8ONj68G4QoAAACFTlBQkCTZAxZQkPz8/OzvuWtBuAIAAEChY7PZFBwcrICAAJ05c8bqcnADK168+DWPWOUgXAEAAKDQcnV1ddofvkBBs3RCixEjRqhRo0by9vZWQECAunXrpm3btl32cdOmTVONGjXk4eGhunXras6cOQ7bDcPQsGHDFBwcLE9PT0VFRWn79u0FdRgAAAAAYG24Wrx4sZ544gmtXLlS8+fP15kzZ9SuXTtlZGRc9DHLly9X79691b9/f23YsEHdunVTt27dtHnzZnuft99+Wx988IHGjx+vVatWycvLS9HR0Tp16tT1OCwAAAAANyGbUYhuIHDo0CEFBARo8eLFatWq1QX79OzZUxkZGfr555/tbU2aNFFERITGjx8vwzAUEhKiZ599VoMHD5YkpaamKjAwUHFxcerVq9dl60hLS5Ovr69SU1Pl4+PjnIMDABRJ03amFti+e1T2LbB9AwCcIz/ZoFDd5yo11fwFVrp06Yv2WbFihaKiohzaoqOjtWLFCknS7t27lZiY6NDH19dXkZGR9j7ny8zMVFpamsMCAAAAAPlRaMJVdna2Bg0apObNm6tOnToX7ZeYmKjAwECHtsDAQCUmJtq357RdrM/5RowYIV9fX/sSGhp6LYcCAAAA4CZUaMLVE088oc2bN+u777677s89dOhQpaam2pd9+/Zd9xoAAAAAFG2FYir2J598Uj///LOWLFmicuXKXbJvUFCQkpKSHNqSkpLsN/3K+TcpKUnBwcEOfSIiIi64T3d3d7m7u1/DEQAAAAC42Vk6cmUYhp588kn98MMP+u2331SxYsXLPqZp06aKj493aJs/f76aNm0qSapYsaKCgoIc+qSlpWnVqlX2PgAAAADgbJaOXD3xxBOaPHmyZs2aJW9vb/s1Ub6+vvL09JQk9enTR7fccotGjBghSXrmmWfUunVrvffee+rUqZO+++47rV27Vp9++qkk827egwYN0htvvKGqVauqYsWKevnllxUSEqJu3bpZcpwAAAAAbnyWhqtx48ZJktq0aePQPnHiRMXExEiSEhIS5OKSO8DWrFkzTZ48WS+99JJefPFFVa1aVTNnznSYBOP5559XRkaGHnnkEaWkpKhFixaaO3euPDw8CvyYAAAAANycCtV9rgoL7nMFAMjBfa4A4OZWZO9zBQAAAABFFeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJzA0nC1ZMkSdenSRSEhIbLZbJo5c+Yl+8fExMhms+VZateube/zyiuv5Nleo0aNAj4SAAAAADc7S8NVRkaGwsPDNXbs2CvqP2bMGB08eNC+7Nu3T6VLl1aPHj0c+tWuXduh39KlSwuifAAAAACwK2blk3fo0EEdOnS44v6+vr7y9fW1r8+cOVPHjh1Tv379HPoVK1ZMQUFBTqsTAAAAAC6nSF9z9fnnnysqKkoVKlRwaN++fbtCQkJUqVIl3X///UpISLjkfjIzM5WWluawAAAAAEB+FNlwdeDAAf3yyy8aMGCAQ3tkZKTi4uI0d+5cjRs3Trt371bLli11/Pjxi+5rxIgR9lExX19fhYaGFnT5AAAAAG4wRTZcTZo0SX5+furWrZtDe4cOHdSjRw/Vq1dP0dHRmjNnjlJSUjR16tSL7mvo0KFKTU21L/v27Svg6gEAAADcaCy95upqGYahL774Qg8++KDc3Nwu2dfPz0/VqlXTjh07LtrH3d1d7u7uzi4TAAAAwE2kSI5cLV68WDt27FD//v0v2zc9PV07d+5UcHDwdagMAAAAwM3K0nCVnp6ujRs3auPGjZKk3bt3a+PGjfYJKIYOHao+ffrkedznn3+uyMhI1alTJ8+2wYMHa/HixdqzZ4+WL1+uu+66S66ururdu3eBHgsAAACAm5ulpwWuXbtWbdu2ta/HxsZKkvr27au4uDgdPHgwz0x/qampmj59usaMGXPBff7777/q3bu3jhw5In9/f7Vo0UIrV66Uv79/wR0IAAAAgJuezTAMw+oiCpu0tDT5+voqNTVVPj4+VpcDALDQtJ2pBbbvHpV9L98JAGCp/GSDInnNFQAAAAAUNoQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwAAAADgBIQrAAAAAHACS8PVkiVL1KVLF4WEhMhms2nmzJmX7L9o0SLZbLY8S2JiokO/sWPHKiwsTB4eHoqMjNTq1asL8CgAAAAAwOJwlZGRofDwcI0dOzZfj9u2bZsOHjxoXwICAuzbpkyZotjYWA0fPlzr169XeHi4oqOjlZyc7OzyAQAAAMCumJVP3qFDB3Xo0CHfjwsICJCfn98Ft40aNUoPP/yw+vXrJ0kaP368Zs+erS+++EJDhgy5lnIBAAAA4KKK5DVXERERCg4O1h133KFly5bZ20+fPq1169YpKirK3ubi4qKoqCitWLHiovvLzMxUWlqawwIAAAAA+VGkwlVwcLDGjx+v6dOna/r06QoNDVWbNm20fv16SdLhw4eVlZWlwMBAh8cFBgbmuS7rXCNGjJCvr699CQ0NLdDjAAAAAHDjsfS0wPyqXr26qlevbl9v1qyZdu7cqffff19fffXVVe936NChio2Nta+npaURsAAAAADkS5EKVxfSuHFjLV26VJJUtmxZubq6KikpyaFPUlKSgoKCLroPd3d3ubu7F2idAAAAAG5sReq0wAvZuHGjgoODJUlubm5q0KCB4uPj7duzs7MVHx+vpk2bWlUiAAAAgJuApSNX6enp2rFjh3199+7d2rhxo0qXLq3y5ctr6NCh2r9/v7788ktJ0ujRo1WxYkXVrl1bp06d0meffabffvtN8+bNs+8jNjZWffv2VcOGDdW4cWONHj1aGRkZ9tkDAQAAAKAgWBqu1q5dq7Zt29rXc6576tu3r+Li4nTw4EElJCTYt58+fVrPPvus9u/frxIlSqhevXpasGCBwz569uypQ4cOadiwYUpMTFRERITmzp2bZ5ILAAAAAHAmm2EYhtVFFDZpaWny9fVVamqqfHx8rC4HAGChaTtTC2zfPSr7Fti+AQDOkZ9sUOSvuQIAAACAwoBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOYGm4WrJkibp06aKQkBDZbDbNnDnzkv1nzJihO+64Q/7+/vLx8VHTpk3166+/OvR55ZVXZLPZHJYaNWoU4FEAAAAAgMXhKiMjQ+Hh4Ro7duwV9V+yZInuuOMOzZkzR+vWrVPbtm3VpUsXbdiwwaFf7dq1dfDgQfuydOnSgigfAAAAAOyKWfnkHTp0UIcOHa64/+jRox3W33zzTc2aNUs//fST6tevb28vVqyYgoKCnFUmAAAAAFxWkb7mKjs7W8ePH1fp0qUd2rdv366QkBBVqlRJ999/vxISEi65n8zMTKWlpTksAAAAAJAfRTpcvfvuu0pPT9e9995rb4uMjFRcXJzmzp2rcePGaffu3WrZsqWOHz9+0f2MGDFCvr6+9iU0NPR6lA8AAADgBlJkw9XkyZP16quvaurUqQoICLC3d+jQQT169FC9evUUHR2tOXPmKCUlRVOnTr3ovoYOHarU1FT7sm/fvutxCAAAAABuIJZec3W1vvvuOw0YMEDTpk1TVFTUJfv6+fmpWrVq2rFjx0X7uLu7y93d3dllAgAAALiJFLmRq2+//Vb9+vXTt99+q06dOl22f3p6unbu3Kng4ODrUB0AAACAm5WlI1fp6ekOI0q7d+/Wxo0bVbp0aZUvX15Dhw7V/v379eWXX0oyTwXs27evxowZo8jISCUmJkqSPD095evrK0kaPHiwunTpogoVKujAgQMaPny4XF1d1bt37+t/gAAAAABuGpaOXK1du1b169e3T6MeGxur+vXra9iwYZKkgwcPOsz09+mnn+rs2bN64oknFBwcbF+eeeYZe59///1XvXv3VvXq1XXvvfeqTJkyWrlypfz9/a/vwQEAAAC4qdgMwzCsLqKwSUtLk6+vr1JTU+Xj42N1OQAAC03bmVpg++5R2bfA9g0AcI78ZIMid80VAAAAABRGhCsAAAAAcALCFQAAAAA4wVWFq127djm7DgAAAAAo0q4qXFWpUkVt27bV119/rVOnTjm7JgAAAAAocq4qXK1fv1716tVTbGysgoKC9Oijj2r16tXOrg0AAAAAioyrClcREREaM2aMDhw4oC+++EIHDx5UixYtVKdOHY0aNUqHDh1ydp0AAAAAUKhd04QWxYoV0913361p06Zp5MiR2rFjhwYPHqzQ0FD16dNHBw8edFadAAAAAFCoXVO4Wrt2rR5//HEFBwdr1KhRGjx4sHbu3Kn58+frwIED6tq1q7PqBAAAAIBCrdjVPGjUqFGaOHGitm3bpo4dO+rLL79Ux44d5eJiZrWKFSsqLi5OYWFhzqwVAAAAAAqtqwpX48aN00MPPaSYmBgFBwdfsE9AQIA+//zzayoOAAAAAIqKqwpX8+fPV/ny5e0jVTkMw9C+fftUvnx5ubm5qW/fvk4pEgAAAAAKu6u65qpy5co6fPhwnvajR4+qYsWK11wUAAAAABQ1VxWuDMO4YHt6ero8PDyuqSAAAAAAKIrydVpgbGysJMlms2nYsGEqUaKEfVtWVpZWrVqliIgIpxYIAAAAAEVBvsLVhg0bJJkjV3/++afc3Nzs29zc3BQeHq7Bgwc7t0IAAAAAKALyFa4WLlwoSerXr5/GjBkjHx+fAikKAAAAAIqaq5otcOLEic6uAwAAAACKtCsOV3fffbfi4uLk4+Oju++++5J9Z8yYcc2FAQAAAEBRcsXhytfXVzabzf41AAAAACDXFYerc08F5LRAAAAAAHB0Vfe5OnnypE6cOGFf37t3r0aPHq158+Y5rTAAAAAAKEquKlx17dpVX375pSQpJSVFjRs31nvvvaeuXbtq3LhxTi0QAAAAAIqCqwpX69evV8uWLSVJ33//vYKCgrR37159+eWX+uCDD5xaIAAAAAAUBVcVrk6cOCFvb29J0rx583T33XfLxcVFTZo00d69e51aIAAAAAAUBVcVrqpUqaKZM2dq3759+vXXX9WuXTtJUnJyMjcWBgAAAHBTuqpwNWzYMA0ePFhhYWGKjIxU06ZNJZmjWPXr13dqgQAAAABQFFzxVOzn6t69u1q0aKGDBw8qPDzc3n777bfrrrvuclpxAAAAAFBUXFW4kqSgoCAFBQU5tDVu3PiaCwIAAACAouiqwlVGRobeeustxcfHKzk5WdnZ2Q7bd+3a5ZTiAAAAAKCouKpwNWDAAC1evFgPPviggoODZbPZnF0XAAAAABQpVxWufvnlF82ePVvNmzd3dj0AAAAAUCRd1WyBpUqVUunSpZ1dCwAAAAAUWVcVrl5//XUNGzZMJ06ccHY9AAAAAFAkXdVpge+995527typwMBAhYWFqXjx4g7b169f75TiAAAAAKCouKpw1a1bNyeXAQAAAABF21WFq+HDhzu7DgAAAAAo0q7qmitJSklJ0WeffaahQ4fq6NGjkszTAffv3++04gAAAACgqLiqkas//vhDUVFR8vX11Z49e/Twww+rdOnSmjFjhhISEvTll186u04AAAAAKNSuauQqNjZWMTEx2r59uzw8POztHTt21JIlS5xWHAAAAAAUFVcVrtasWaNHH300T/stt9yixMTEay4KAAAAAIqaqwpX7u7uSktLy9P+zz//yN/f/5qLAgAAAICi5qrC1Z133qnXXntNZ86ckSTZbDYlJCTohRde0D333HPF+1myZIm6dOmikJAQ2Ww2zZw587KPWbRokW699Va5u7urSpUqiouLy9Nn7NixCgsLk4eHhyIjI7V69eorrgkAAAAArsZVhav33ntP6enp8vf318mTJ9W6dWtVqVJF3t7e+t///nfF+8nIyFB4eLjGjh17Rf13796tTp06qW3bttq4caMGDRqkAQMG6Ndff7X3mTJlimJjYzV8+HCtX79e4eHhio6OVnJycr6PEwAAAACulM0wDONqH7xs2TJt2rRJ6enpuvXWWxUVFXX1hdhs+uGHHy55g+IXXnhBs2fP1ubNm+1tvXr1UkpKiubOnStJioyMVKNGjfTRRx9JkrKzsxUaGqqnnnpKQ4YMueB+MzMzlZmZaV9PS0tTaGioUlNT5ePjc9XHBAAo+qbtTC2wffeo7Ftg+wYAOEdaWpp8fX2vKBvkeyr27OxsxcXFacaMGdqzZ49sNpsqVqyooKAgGYYhm8121YVfzooVK/IEuOjoaA0aNEiSdPr0aa1bt05Dhw61b3dxcVFUVJRWrFhx0f2OGDFCr776aoHUDAAAAODmkK/TAg3D0J133qkBAwZo//79qlu3rmrXrq29e/cqJiZGd911V0HVKUlKTExUYGCgQ1tgYKDS0tJ08uRJHT58WFlZWRfsc6lZDIcOHarU1FT7sm/fvgKpHwAAAMCNK18jV3FxcVqyZIni4+PVtm1bh22//fabunXrpi+//FJ9+vRxapEFzd3dXe7u7laXAQAAAKAIy9fI1bfffqsXX3wxT7CSpNtuu01DhgzRN99847TizhcUFKSkpCSHtqSkJPn4+MjT01Nly5aVq6vrBfsEBQUVWF0AAAAAkK9w9ccff6h9+/YX3d6hQwdt2rTpmou6mKZNmyo+Pt6hbf78+WratKkkyc3NTQ0aNHDok52drfj4eHsfAAAAACgI+QpXR48ezXM907kCAwN17NixK95fenq6Nm7cqI0bN0oyp1rfuHGjEhISJJnXQp17iuHAgQO1a9cuPf/88/r777/18ccfa+rUqfrPf/5j7xMbG6sJEyZo0qRJ2rp1qx577DFlZGSoX79++TlUAAAAAMiXfF1zlZWVpWLFLv4QV1dXnT179or3t3btWodTDGNjYyVJffv2VVxcnA4ePGgPWpJUsWJFzZ49W//5z380ZswYlStXTp999pmio6PtfXr27KlDhw5p2LBhSkxMVEREhObOnXvJUAgAAAAA1ypf97lycXFRhw4dLjr5Q2ZmpubOnausrCynFWiF/MxlDwC4sXGfKwC4uRXYfa769u172T5FbaZAAAAAAHCGfIWriRMnFlQdAAAAAFCk5WtCCwAAAADAhRGuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOQLgCAAAAACcgXAEAAACAExCuAAAAAMAJCFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnIBwBQAAAABOUCjC1dixYxUWFiYPDw9FRkZq9erVF+3bpk0b2Wy2PEunTp3sfWJiYvJsb9++/fU4FAAAAAA3qWJWFzBlyhTFxsZq/PjxioyM1OjRoxUdHa1t27YpICAgT/8ZM2bo9OnT9vUjR44oPDxcPXr0cOjXvn17TZw40b7u7u5ecAcBAAAA4KZn+cjVqFGj9PDDD6tfv36qVauWxo8frxIlSuiLL764YP/SpUsrKCjIvsyfP18lSpTIE67c3d0d+pUqVep6HA4AAACAm5Sl4er06dNat26doqKi7G0uLi6KiorSihUrrmgfn3/+uXr16iUvLy+H9kWLFikgIEDVq1fXY489piNHjlx0H5mZmUpLS3NYAAAAACA/LA1Xhw8fVlZWlgIDAx3aAwMDlZiYeNnHr169Wps3b9aAAQMc2tu3b68vv/xS8fHxGjlypBYvXqwOHTooKyvrgvsZMWKEfH197UtoaOjVHxQAAACAm5Ll11xdi88//1x169ZV48aNHdp79epl/7pu3bqqV6+eKleurEWLFun222/Ps5+hQ4cqNjbWvp6WlkbAAgAAAJAvlo5clS1bVq6urkpKSnJoT0pKUlBQ0CUfm5GRoe+++079+/e/7PNUqlRJZcuW1Y4dOy643d3dXT4+Pg4LAAAAAOSHpeHKzc1NDRo0UHx8vL0tOztb8fHxatq06SUfO23aNGVmZuqBBx647PP8+++/OnLkiIKDg6+5ZgAAAAC4EMtnC4yNjdWECRM0adIkbd26VY899pgyMjLUr18/SVKfPn00dOjQPI/7/PPP1a1bN5UpU8ahPT09Xc8995xWrlypPXv2KD4+Xl27dlWVKlUUHR19XY4JAAAAwM3H8muuevbsqUOHDmnYsGFKTExURESE5s6da5/kIiEhQS4ujhlw27ZtWrp0qebNm5dnf66urvrjjz80adIkpaSkKCQkRO3atdPrr7/Ova4AAAAAFBibYRiG1UUUNmlpafL19VVqairXXwHATW7aztQC23ePyr4Ftm8AgHPkJxtYflogAAAAANwICFcAAAAA4ASEKwAAAABwAsIVAAAAADgB4QoAAAAAnMDyqdgBALgWBTmbHwAA+cHIFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJygmNUFAABQaGVnyzPpoLz+3Suvf/eqeFqqDBdXGa6u5r8uNvvXZ728lF6hktLDKivb3cPqygEAFiBcAQBgGPLesU2BKxbLZ/vf8tpnhqkS+/fJ9czp/O3KZtOJW0J1vGJVHa9URcfDqiitSnUdqd9I2R6eBXQAAIDCgHAFALgpeSQnKmDZIgUuX6TA5YvlmXTQKfu1GYa8/k2Q178JCvo93t5+1sNTyU1bKbH1HTrYOkonQsOc8nwAgMKDcAUAuGn4/POXwqZPVtCSePlu33rJvmc9SyijXAVlhP7/Uq6CMkuVkc3IlrKzZcvKku2cf91Sj8l79w5z2bVDxdPTHPZX7NRJhSz8VSELf5UkpVWuJnXtLHXsKLVuLRXjVzIAFHU2wzAMq4sobNLS0uTr66vU1FT5+PhYXQ4A4BKm7Uy95HbXkydUbs4PqvTdJJXdsPqCfc56ltChRs2U1LyNjtRvrIwKFZVZuqxks11dUYYh9yOH5L1ru7x37VCZjWsUtGSBPJMTL9w/NFQaOFAaMEAKCLi65wQAFIj8ZAPC1QUQrgCg6LhYuPLZtkWVvotThZlT5HbccRTJcHHR0bq3Kql5GyU1b6OjEY2U7e5esIUahny3/qngRfMVvHi+ymxYLVt2tmMfNzfp3nulJ56QIiOvPtwBAJyGcHWNCFcAUHQ4hCvDUNCiear58XsXHKVKqV5Lu3rGKOHOHjrjV+o6VplX8ZRj6vbPCmnyZGn2bOn8X8e33mqGrPvukzyYfRAArEK4ukaEKwAoOnLClf/K31XnvdfzhKqzHp7a1/lu7eoZo6MRDQvVaFCPyr7mF7t2SePHS59/Lh096tipXDnptdekPn0kV9frXyQA3OQIV9eIcAUARceCGb+p7qjXFbhskUN7SvVa2tWrnxK69tAZHz9Larsce7jKcfKkNGWK9NFH0rp1jttq15beekvq1KlQBUQAuNHlJxu4XKeaAABwrs2bpbvuUtQ9tzsEq9SqNbXs4680/+dl2vngw4U2WF2Qp6cUEyOtWSOtXCl17py7bcsWqUsXqU0bcxsAoNAhXAEAipaUFOmxx6R69aSZM+3N6aFhWvXuJ5r381IdaNelaI/u2GzmhBY//SQtXiw1aZK7bckSqWlT6Z57pH/+sa5GAEAehCsAQNFgGNL06VKtWub1Sf9/VvvJwGCte22U5v66Wgndet541yW1aiUtX24ee7Vque0zZpgBc8QI6cwZ6+oDANgRrgAAhd++fVK3blL37tLBg2abl5f09tuaE79eu+57SIabm6UlFiibTbr7bvNUyPHjpaAgsz0zU3rxRXOUa8MGa2sEABCuAACFWFaWOblDrVrSjz/mtnfuLP31l/Tcc8r28LSuvuuteHHp0UelHTukwYMll///Nb5hg9SokfTf/0qnTllbIwDcxAhXAIDCacsWqUUL6amnpPR0sy0wUJo61Qxa5ctbW5+VvLykd94xJ7aoU8dsy8qS3nxTioiQli2ztDwAuFkRrgAAhYthSJ98IjVs6Dgr3sMPS1u3Sj16FO3JKpypUSNzyvZXXzVHtSRp2zapZUtp0CBGsQDgOiNcAQAKj5QU6d57pYEDc4NB9ermjHmffiqVKmVpeYWSm5s0bJi0fr3UuLHZZhjSmDFSs2bmKYQAgOuCcAUAKBxWrDBPafv++9y2xx83rydq1cqysoqMOnXMWQXffVdydzfbNmyQbr3VvDExAKDAEa4AANbKzjanE2/ZUtq712zz8zOnGh871ryxLq6Mq6v07LPS6tW507YfPy716mWOBp48aW19AHCDI1wBAKyTmChFR5vTiWdlmW3Nm0sbN0p33WVpaUVavXrS2rXS/ffntn3yiXkz4m3brKsLAG5whCsAgDVWrjRPWVuwwFy32aSXXpIWLZIqVLC0tBuCt7f01VfSZ59JHh5m2x9/SA0aSJMnW1sbANygCFcAgOtv0iSpdevcGwIHB5sh6/XXpWLFrK3tRmKzSf37S2vWSDVqmG0ZGeaI1uDBuaOFAACnIFwBAK6fs2el2FgpJkY6fdpsa9XKPA3wttusrOzGVqeOeZpg3765be+9J915p5Saal1dAHCDIVwBAK6PY8ekjh2l99/PbRs4UJo/XwoIsK6um4WXlxQXJ338sTnxhSTNmSM1bcp07QDgJIQrAEDB27rVvAfT/PnmerFi0rhx5uLmZm1tN5vHHpPmzcu9Z9jWrVJkpLRwobV1AcANgHAFAChYP/9s/vGeMzpStqwUH2+OWsEat91mTtdes6a5fvSo1K6dNH68tXUBQBFXKK4aHjt2rN555x0lJiYqPDxcH374oRrn3GX+PHFxcerXr59Dm7u7u06dOmVfNwxDw4cP14QJE5SSkqLmzZtr3Lhxqlq1aoEeBwDgPB9/LD31lHkvK0kKD5dmzWI2wOtg2s7LXEtl81exyXPVZNAABS+eb14P99hj2rF0nTa+/JaMy0ws0qOyrxOrBYAbg+UjV1OmTFFsbKyGDx+u9evXKzw8XNHR0UpOTr7oY3x8fHTw4EH7sjfnppP/7+2339YHH3yg8ePHa9WqVfLy8lJ0dLRDAAMAFKDsbGnIEOmJJ3KDVY8e0rJlBKtC5Ky3r5Z++p229X/S3lblm8/U7LH75Xoiw8LKAKBosjxcjRo1Sg8//LD69eunWrVqafz48SpRooS++OKLiz7GZrMpKCjIvgQGBtq3GYah0aNH66WXXlLXrl1Vr149ffnllzpw4IBmzpx5HY4IAG5ymZnSgw9KI0fmtg0ZIk2ZYk6qgMLF1VV/DH1Dq0eOVXbx4pKkkIW/qvWDXeV25LDFxQFA0WJpuDp9+rTWrVunqKgoe5uLi4uioqK0YsWKiz4uPT1dFSpUUGhoqLp27aotW7bYt+3evVuJiYkO+/T19VVkZORF95mZmam0tDSHBQBwFVJSpA4dcm9S6+IijR0rjRhh3nMJhdbee+7Xki+m60xJH0lSmU1rdVvPaHkl7LG2MAAoQiwNV4cPH1ZWVpbDyJMkBQYGKjEx8YKPqV69ur744gvNmjVLX3/9tbKzs9WsWTP9+++/kmR/XH72OWLECPn6+tqX0NDQaz00ALj57NsntWyZO+ucp6c0Y4b0+OPW1oUrdqhpKy38bo5OBgZLkrz37NRt97aT3+aN1hYGAEWE5acF5lfTpk3Vp08fRUREqHXr1poxY4b8/f31ySefXPU+hw4dqtTUVPuyb98+J1YMADeBP/8075e0ebO5Xras9NtvUteu1taFfEutUUfx0+YprXJ1SZLH4WS1ub+zAn//zeLKAKDwszRclS1bVq6urkpKSnJoT0pKUlBQ0BXto3jx4qpfv752/P8UvzmPy88+3d3d5ePj47AAAK7Q0qXmiNX+/eZ65crS8uVSkybW1oWrdjIkVL9NmavDDczvYfGMdLV4+F5V+OFbiysDgMLN0nDl5uamBg0aKD4+3t6WnZ2t+Ph4NW3a9Ir2kZWVpT///FPBweYpDBUrVlRQUJDDPtPS0rRq1aor3icA4Ar98ot5f6TU/5/2u1EjM1hx64si74xfKS2e9IP+je4iSXI5e1aNn3tM1T770OLKAKDwsvy0wNjYWE2YMEGTJk3S1q1b9dhjjykjI8N+L6s+ffpo6NCh9v6vvfaa5s2bp127dmn9+vV64IEHtHfvXg0YMECSOZPgoEGD9MYbb+jHH3/Un3/+qT59+igkJETdunWz4hAB4MY0ZYp0553SyZPmenS0eb1VQIC1dcFpsj08teKDOO144GF7W/hbL6v26P9JhmFhZQBQOFl+E+GePXvq0KFDGjZsmBITExUREaG5c+faJ6RISEiQi0tuBjx27JgefvhhJSYmqlSpUmrQoIGWL1+uWrVq2fs8//zzysjI0COPPKKUlBS1aNFCc+fOlYeHx3U/PgC4IX36qTRwYO4f2D16SF9/Lbm5WVsXnM/VVRuGv61T/gGq8/7/JEm1PnpHsp2SRo82Z4QEAEiSbIbBR0/nS0tLk6+vr1JTU7n+CgDON3Kked+qHAMGSOPHS66ulpQzbWeqJc/rDD0q+xbYvgvidany5Seq/9oLuQ0xMdKECVIxyz+rBYACk59swMdNAIArYxjS0KGOweq558xRLIuCFa6vHX0e1eq3P5aRM1oVFyf16mXeOBoAYP1pgQCAIiA7W3riCXOEKsebb5pB6wpuDlyUR5cKUlF8XfbefZ/OepVUs0H9pTNnpOnTpePHzXuaeXlZXR4AWIqRKwDApZ09a57+lROsbDbp44/NUawrCFa48eyPvlP6+WfzRtGSNG+eOaFJSoqldQGA1QhXAICLO3NGuv9+6auvzHVXV3Piisces7YuWK9dO2n+fCnn+oNly6SoKOnoUWvrAgALEa4AABeWmWnOAjh1qrlevLj0/ffSffdZWxcKj+bNpUWLJH9/c33dOqltW+nQIUvLAgCrEK4AAHmdPCnddZc0a5a57u4uzZwpcb9AnK9+fTNgBQWZ63/8IbVpIx08aGVVAGAJwhUAwFFGhtS5s/TLL+a6p6c0e7bUsaO1daHwqlVLWrJEKlfOXP/rL6l1a+nff62tCwCuM8IVACBXWprUvr3022/mesmS0q+/Srffbm1dKPyqVjUDVoUK5vr27VKrVtKePZaWBQDXE+EKAGBKSTEnKVi61Fz39TUnLGjZ0tKyUIRUrGgGrMqVzfXdu80RrJ07ra0LAK4TwhUAwJzhLSpKWrXKXC9d2hy9atLE2rpQ9JQvbwas6tXN9YQEcwTr77+trQsArgPCFQDc7I4cMYPVunXmur+/OUHBrbdaWhaKsJAQafFiqU4dc/3AAXOSi7/+srQsAChohCsAuJkdOiTddpu0YYO5HhhoBqu6dS0tCzeAwEBp4UIpIsJcT0oyp2nfvNnSsgCgIBGuAOBmlZxsBqs//jDXg4PNYFWrlqVl4QZStqwUH587CpqcbAasnPccANxgilldAADAAomJ5gyA/3+a1onAEC3+6ielFw+WdqZaXBxuKKVLSwsWSNHR0po10uHDZqhfsCB3VAsAbhCMXAHAzeb8619CQ7Vo8mylh1W2tCzcwEqVkubNkyIjzfUjR8yAtX69tXUBgJMRrgDgZrJ/vxmstm0z1ytUkBYvVkaFipaWhZuAn58ZsJo1M9ePHTNHT9eutbQsAHAmwhUA3Cz27TPvObR9u7lesaJ5jVVFghWuEx8fae5cqUULcz0lxfEWAABQxBGuAOBmkJBgjljl3My1UiUzWIWFWVgUbkre3tIvv5j3vpKk1FTz5tUrVlhbFwA4AeEKAG50e/aYI1a7dpnrVaqY9yAqX97SsnATK1lSmjPHnDlQktLSzIC1dKm1dQHANSJcAcCNbNcuM1jt2WOuV6tmBqty5SwtC5CXl/Tzz+ZpgZKUni61by8tWWJtXQBwDQhXAHCj2rHDPBUwIcFcr1HDPBUwJMTKqoBcJUpIP/5ojlpJUkaG1KGD+T4FgCKIcAUAN6Lt281gtW+fuV6rlrRwoXmjYKAw8fSUZs0yQ5UknTghdexo3nwYAIoYwhUA3Gi2bTNPBdy/31yvU8cMVkFB1tYFXIyHh/TDD1KnTub6yZNS587m1O0AUIQQrgDgRrJlixmsDh401+vVk377TQoIsLYu4HLc3aXp06WuXc31U6ekO+80ZxYEgCKCcAUAN4pNm8xTAZOSzPWICPPUKn9/K6sCrpy7uzR1qnT33eZ6ZqbUrZv000+WlgUAV4pwBQA3gnXrzGmtDx821xs2NINV2bLW1gXkl5ub9N13Uo8e5vrp02bYmj7d2roA4AoQrgCgqFu1Srr9dunYMXO9SRNpwQKpdGlr6wKuVvHi0uTJ0n33metnz0o9e5ptAFCIEa4AoChbulS64w4pNdVcb9nSnATA19fauoBrVayY9OWXUkyMuZ6VJT3wgBQXZ2VVAHBJhCsAKKoWLTJvunr8uLl+223mxf/e3paWBTiNq6v0+efSo4+a64Yh9esnffqptXUBwEUQrgCgKJo/37wXUEaGuR4dLf38s+TlZW1dgLO5uEjjxklPP53b9uij0ocfWlcTAFwE4QoAippZs8x7AJ08aa537izNnGnejBW4Edls0ujR0nPP5bY9/bT0zjuWlQQAF0K4AoCiZPJk6Z57zBnUpNxZ1Dw8rK0LKGg2mzRypPTyy7ltzz8vDR9uni4IAIUA4QoAiooJE8wL+rOyzPUHH5SmTDGnrgZuBjab9Npr0htv5La99poUG0vAAlAoEK4AoCgYNUp65JHcPyAHDjRnTStWzNKyAEv897/maYI5Ro+WHn4494MHALAI4QoACjPDMD+Zf/bZ3LbBg6WPPzYv9AduVs88Y84kmPP/4PPPzfti5ZwyCwAW4DczABRWhpF7TUmO116T3n7bPD0KuNk99JD07be5I7hTp0p33ZU72QsAXGeEKwAojLKyzFP/3n03t23UKPNifoIVkOvee80ZNHMmdZkzR+rQIff+bwBwHRGuAKCwycyUevbMvVGqzWZ+/Z//WFsXUFh17CjNnSuVLGmuL14s3X67dPiwtXUBuOkQrgCgMElLMz91nz7dXC9WTPrmG/NifQAX17q1FB8vlSplrq9ZI7VoIe3da21dAG4qhCsAKCySkqQ2baSFC831EiWkn36Seve2tCygyGjcWFqyRAoONte3bZOaNZP+/NPaugDcNAhXAFAY7N5tfsq+YYO5Xrq09NtvUvv21tYFFDV16kjLl0vVqpnrBw5ILVtKv/9ubV0AbgqEKwCw2h9/mJ+u79hhrpcrJy1dKkVGWlsXUFSFhZn/hxo1MtdTU6V27cyJLwCgABWKcDV27FiFhYXJw8NDkZGRWr169UX7TpgwQS1btlSpUqVUqlQpRUVF5ekfExMjm83msLTn018AhdHvv0utWkmJieZ6jRrmp+41a1pbF1DU+fubo7/t2pnrp05Jd98tffaZtXUBuKFZHq6mTJmi2NhYDR8+XOvXr1d4eLiio6OVnJx8wf6LFi1S7969tXDhQq1YsUKhoaFq166d9u/f79Cvffv2OnjwoH359ttvr8fhAMCVmzJFuuMO81N1yRypWrpUCg21ti7gRlGypHnd4v33m+vZ2ebkMG+8Yd5HDgCczPJwNWrUKD388MPq16+fatWqpfHjx6tEiRL64osvLtj/m2++0eOPP66IiAjVqFFDn332mbKzsxUfH+/Qz93dXUFBQfalVM7sQQBgNcOQRo6UevUyp12XpOhoacECqUwZa2sDbjRubtKXXzreyuDll6VHH5XOnLGuLgA3JEvD1enTp7Vu3TpFRUXZ21xcXBQVFaUVK1Zc0T5OnDihM2fOqHTp0g7tixYtUkBAgKpXr67HHntMR44cueg+MjMzlZaW5rAAQIE4e1Z67DFpyJDctn79zE/Xc+7RA8C5XFyk996T3n47t23CBKlTp9yRYwBwAkvD1eHDh5WVlaXAwECH9sDAQCXmXH9wGS+88IJCQkIcAlr79u315ZdfKj4+XiNHjtTixYvVoUMHZWVlXXAfI0aMkK+vr30J5ZQcAAXh+HGpSxfpk09y2954Q/r8c6l4cevqAm4GNpv03HPmfePc3My2+fOl5s25FxYApylmdQHX4q233tJ3332nRYsWycPDw97eq1cv+9d169ZVvXr1VLlyZS1atEi33357nv0MHTpUsbGx9vW0tDQCFgDn2r/f/JR80yZz3c1N+uKL3GtBAFwf990nlS8vdesmHTkibdkiNWlijh43bGh1dQCKOEtHrsqWLStXV1clJSU5tCclJSkoKOiSj3333Xf11ltvad68eapXr94l+1aqVElly5bVjpxpjs/j7u4uHx8fhwUAnOaPP8zJKnKCValS0rx5BCvAKi1aSCtWSFWrmuuJieasnTNnWloWgKLP0nDl5uamBg0aOExGkTM5RdOmTS/6uLfffluvv/665s6dq4ZX8CnTv//+qyNHjig4547tAHC9zJplnnaUM6NpxYrmVOutW1tbF3Czq1rVDFgtWpjrJ0+aU7W//z4zCQK4apbPFhgbG6sJEyZo0qRJ2rp1qx577DFlZGSoX79+kqQ+ffpo6NCh9v4jR47Uyy+/rC+++EJhYWFKTExUYmKi0tPTJUnp6el67rnntHLlSu3Zs0fx8fHq2rWrqlSpoujoaEuOEcBNyDDM66m6dZP+/+eTGjeWVq4072UFwHplypizdOaMIhuGFBtrTteeM5MnAOSD5ddc9ezZU4cOHdKwYcOUmJioiIgIzZ071z7JRUJCglxccjPguHHjdPr0aXXv3t1hP8OHD9crr7wiV1dX/fHHH5o0aZJSUlIUEhKidu3a6fXXX5e7u/t1PTYAN6mMDHMGwGnTctt69jSvsSpRwrq6AOTl7i599ZVUpYr06qtm2+efm9diTZ8uhYRYWx+AIsVmGIx9ny8tLU2+vr5KTU3l+isA+bN3r9S1a+71VTab9L//mVOv22zW1nYJ03YyHTXyp0dlX6tLcL5vv5X69zdPEZSk4GBpxgxzwgsAN638ZAPLTwsEgBvGkiXmbGM5wcrbW/rxR2no0EIdrAD8v969pWXLzNkEJengQfP6yM8/t7YuAEUG4QoArpVhSOPHS7ffLh0+bLZVqSKtWiV17mxtbQDyp359ae3a3ElnTp+WBgyQnnxSOnPG2toAFHqEKwC4FhkZUt++0mOPSWfPmm3t2kmrV0s1a1pbG4Cr4+9v3mD4qady28aOlaKipPNuHwMA5yJcAcDV+usvcwbAr77KbXv2WWn2bPNeVgCKruLFpQ8+MCeicXMz25YskSIipN9+s7Q0AIUX4QoArsbXX0uNGpkBS5JKlpS++056912pmOUTsQJwln79zFCVM2tgYqI5gvXqq1JWlrW1ASh0CFcAkB8nT0qPPCI9+KB04oTZVreutG6dOd06gBtPZKS0YYN0xx3mumFIr7xingKcmGhpaQAKF8IVAFyp7dulpk2lCRNy2/r3NyeuqFbNuroAFLyAAGnuXPPWCjn33/ztN/M0wfh4S0sDUHgQrgDgcgxDmjRJatAgd5p1T08pLk767DPzawA3PhcX6cUXpYULc08TTEoyR7SGD+c0QQCEKwC4pMOHpe7dpZgY6fhxs61GDXM2wL59LS0NgEVatZI2bpSio811w5Bee81s37nT0tIAWMtmGIZhdRGFTX7uwgzgBvbLL9JDDzleU9G3r/TRR+YEFueZtjO1QMvpUdm3wPZd0LUD+VGQ73Wnys6WRo6UXnrJ/FqSvLzMiW0efZSbhwM3iPxkA0auAOB8GRnS449LHTvmBqsyZaTvvzdPBbxAsAJwE3JxkYYONWcTrFTJbMvIMO9717GjdOCAtfUBuO4IVwBwrtWrpVtvlcaNy23r0EH680/pnnusqwtA4dW8uXk95qOP5rbNnSvVqWPeogHATYNwBQCSOa36kCFSs2bSP/+YbZ6e0scfmzcFDg62tj4AhVvJktL48dKcObk/L44dk3r3lnr1ko4csbY+ANcF4QoA5s8371U1cmTubF+NGpkXrD/2GNdNALhyOSPd5973bsoUcyKcr74yJ78AcMMiXAG4eR0+LPXpY94IdNcus83NzZz1a9ky7l0F4OqUKWOeDvjtt1KpUmZbzs+bqKjc0XEANxzCFYCbj2FIX38t1axpfpKco1Ur6Y8/pJdflooXt64+ADeGXr2kLVuke+/NbfvtN6lePen116XMTOtqA1AgCFcAbi7//GPem+bBB81PkiXJz0+aMMG8MWj16paWB+AGExxsnhY4e7ZUoYLZlpkpDRsmhYdLixdbWx8ApyJcAbg5HDsmxcZKtWub11jluPdeaetWacAAc1plACgIHTuao1jPPy+5uppt27ZJbdqYH/bs22dpeQCcg78kANzYzp41Z/yrWlV6/31zXZJCQ6WffjI/UQ4KsrZGADcHLy9z4pz166XIyNz2r782r/F8+WUpPd26+gBcM8IVgBvXvHnmaTdPPJE7DbKnpzR8uDla1bmztfUBuDnVqyctX27eT690abPt1CnpjTfMD4I+/zx35lIARQrhCsCNZ8sWqUsX89qqv/7Kbb/vPvM0nFdeMT9BBgCruLhIAwdKO3aYpyznTKKTmGiepnzrrdKCBdbWCCDfbIbBDRfOl5aWJl9fX6WmpsrHx8fqcgBcqZzgNGWK471kGjeWRo+WmjYt0KeftjO1QPcP3Cx6VPa1uoTrb8cO6YUXpBkzHNujo82fa02aWFIWcCkF/XuvsPwsyE82YOQKQNG3Y4fUt69Uq5Z5b5mcYHXLLeZU6ytWFHiwAoBrUqWKNH26tGSJ1LBhbvuvv5o/vzp0kFatsq4+AFeEcAWg6Nqzxzx9pkYN6csvpexss71sWemdd8xp1x94gFkAARQdLVuaIeqrr3KnbpekuXPN0auOHQlZQCHGXxwAip7Nm6WHHjJn1zr3wu/SpaURI6Tdu6XBg6USJaytEwCuhouL+cHQP/+Y9+A7N2T98ktuyFq+3LoaAVwQ4QpA0WAY5sXd7dtLdetKEydKZ86Y23x9pddeM0PVkCFSyZLW1goAzuDmZo7OXyxkNW9uBq0pU3JvMwHAUoQrAIXb6dPmKX8REdIdd5jXH+Tw9ZVeeskMVS+/LDEBDYAb0aVC1qpVUq9eUqVK5unQKSmWlQmAcAWgsNq/37znS8WK5mQVf/yRuy0szJz9b98+6fXXpVKlrKoSAK6fc0PWxInm/bJy7NsnPf+8VK6c9NRT0vbt1tUJ3MQIVwAKj7NnpZ9+ku68Uypf3hyNOnAgd3vjxtLUqeYfDc88I3l7W1crAFjFzU2KiZE2bpTi4x1viJ6RIX30kXlNaps25sj/iRMWFQrcfAhXAKy3Z48ZpCpUMIPVTz/lzvxns0ldu0q//y6tXCn16CEVK2ZpuQBQKNhs0m23mT8zt22THn/ccSKfxYvNkf/gYPOGxWvWON4DEIDTEa4AWOPIEfPagdtvN68VeOMNx1GqcuWkYcOkXbukmTOlFi3MPyQAAHlVqyaNHWueHvj221L16rnb0tKkTz4xR//Dw6VRo6R//7WuVuAGRrgCcP2kpkqTJpk3wwwKkh55RPrtt9xPUl1dzVGqn382R7NefdW8vgoAcGVKl5aee07aulVaulTq10/y8srd/uef0rPPSqGh5myDY8aY17gCcArOrQFQsA4fNm9++f335tTBp0/n7VOlivkHQEyMFBJy3UsEgBuOzWaGp5wANWWK9MUX0ooVuX2WLzeXQYPMfj16SPfcY545AOCqEK4AOJdhmDP7zZ5tLitX5l4/da7y5aWePc0phOvX55Q/ACgo3t7mLIMDBkh//20GrWnTpC1bcvssW2YugwaZt77o0MG8UXGTJlznCuSDzTC4svF8aWlp8vX1VWpqqnwKwX1zpu1MLdD996jsW6D7x4UV5Pf1un9Pjx2TliwxR6Zmz774ufzBwdK995qhqkkTSwJVQf9/AlD4FeTPyCL1O/uvv8yQNXWq+fUFnPbxVVKL23SwdZQSW0Up0z/wqp+OvzduPEXq/X4N8pMN+CgCQP4dO2bO3rdokbls3HjxGahq1ZI6dTKnCm7e3LyuCgBgvVq1pOHDzWXLFm355CsFL/xVpTdvtHdxS0tV6JwfFDrnB0lSWuVqOtS4uQ41aqbDjZrpZPAtFhUPFE6EKwCXZhjmjH2rVpnL779fOky5u0tt25qBqlMn8ybAAIDCrXZt/fXMUP31zFC5H0pS0O/xClq8QEG/x8stLXd0wmfnP/LZ+Y8qfztRkpQeGqZDjc2gdSS8oY5XqsqHaLipEa4AOEpONu+Fsnp17nL06MX722zm+flt2pih6rbbHGemAgAUKZn+gdp7933ae/d9sp09q9Kb1ipo8QIFLl+sUps3yOXsWXvfkvv2qOS+Pao4fbIk6YxXSaXUqqejdevrWL1bdbTurcooH8Z1tbhpEK6Am9XJk+Y59n/8YU7Nm7MkJV36ceeGqTZtpJYtpVKlrkPBAIDrzShWTEcaNNGRBk20JfYluZ7IUJkNa+S/Zpn8Vy1T6U3r5Ho6096/eEa6/Ncsl/+a5fa2075+SqleW2oUIdWpYy61a/O7AzckwhVwI8vONu9fsn279M8/uf/+84+0Y8eFZ/E7n7+/FBlp3nwyZ+EXIgDclLJKeCm5eRslN28jSXLJPKXSm9apzPrVKv3nBpX6c728DjhOauSWmqKA1cuk1cscd3bLLWbQql7dvCVH1armv2FhzFCIIot3LlCUGYbcDyerxIF/5bV/n0oc2KcS+xOklETzJrw7dpgjVFeqbFmpXj1zZConUFWowOkcAIALynb30OHGzXW4cXN7m/uRQyr15waV/mO9Sv25QaW2bJJncmLeB+/fby6//urYXqyYGbCqVjX/LV/ecQkJIXyh0OKdCRRCLpmn5H7ksNyPHZH7kcPyOJQoz+Qk89+kRHkcyv363NMxrpinp1SzplS3rhmm6tY1l8BAghQA4JpklvFXYpt2SmzTzt5WPOWYup3YJ23enLv8+ac5++z5zp41PxzcsePCT+DiYo56hYSYt/gICsr9N+frgADzA8MSJfi9huuqUISrsWPH6p133lFiYqLCw8P14YcfqnHjxhftP23aNL388svas2ePqlatqpEjR6pjx4727YZhaPjw4ZowYYJSUlLUvHlzjRs3TlWrVr0ehwOYp9ulp0vHj+cuaWnmL5GUFCklRXV2Jap4WqqKH0+VW8oxM0gdPSz3Y0dV7ETGtddQvLhUqZL5yV+1auaS83VIiPnLCQCA6+CMXympQZh5nW4Ow5ASE80QtX173n8zLvK7MDtb2rfPXC7Hw0MqU8YMWjlL6dKSn5/k62v+e+7i42PedNnb25yciREy5JPl75gpU6YoNjZW48ePV2RkpEaPHq3o6Ght27ZNAQEBefovX75cvXv31ogRI9S5c2dNnjxZ3bp10/r161WnTh1J0ttvv60PPvhAkyZNUsWKFfXyyy8rOjpaf/31lzw8PK73IeJ6MQzz066sLPPfs2elM2dy/z13OXtWOn06d8nMdFw/deriy8mT5g/8EyfM5dyvcwLVxX4hnKPmNR5upl8pnfIP0omQcjpxS6gyQkJ14hZzua1ZbfPTO6bDBQAUVjabOcoUHOwYuiTzd3pSkpSQkHfZt0/au1c6dOjyz3HqVO7ph1fD01MqWdIMWyVLmiNhOYunp+PXnp7m7Ug8PMzl3K/d3C68FC+euxQrlvfrYsXM3+XFijECV0TYDONiN6u5PiIjI9WoUSN99NFHkqTs7GyFhobqqaee0pAhQ/L079mzpzIyMvTzzz/b25o0aaKIiAiNHz9ehmEoJCREzz77rAYPHixJSk1NVWBgoOLi4tSrV6/L1pSfuzAXqMRE6fHH9W/GmYt2sZ377Tv/W3ne+sX6hpQo5th2qX/P//pyS3b2hdezsx2/PrftQktWVt4lpz0nUF3J5AyFmOHioky/0jpdqowyS5dWZumyyixVRpmly+qUf6BOBQTqpH+QTgUE6pR/oLLdL/5BQWG5o3lhVNB3kwdQ+BXkz8iC/hlD7ec4c8YMYImJ0sGDuf8ePCgdPmwuR47kfn3m4n9PFQk2m2PYcnU1z0K52L+XWmy2vF/bbLnL+evnL/9fT9KpLEmSIcd2STJsF2m70HH9P4ftNptu8SoujRplnoljofxkA0tHrk6fPq1169Zp6NCh9jYXFxdFRUVpxYoVF3zMihUrFBsb69AWHR2tmTNnSpJ2796txMRERUVF2bf7+voqMjJSK1asuGC4yszMVGZm7nUrqanmD5e0tLSrPjanSEqSfvhBBR3vLD7KG4O7e+6nVyVLOn7KlfO1t3fuaQi+vlp6srjO+PjqTEkfnfH20WlfX8nlCkeackbYLiItjU+3LubEcd7xwM2uIH9GFvTPGGo/j4+PuVSrdul+hmGeXXLkiHl6fmqq45KWZranpZlnn6Sn5y4ZGblnpWRexXXOzmIYuWfgFBKeBbz/NEkaPNg8ndNCOZngSsakLA1Xhw8fVlZWlgIDAx3aAwMD9ffff1/wMYmJiRfsn5iYaN+e03axPucbMWKEXn311TztoaGhV3YgQGamuVzowlwLxFhdAAAUYjFWF3ANYqwu4BrEWF0AiqbzTxm10PHjx+Xre+kRWMuvuSoMhg4d6jAalp2draNHj6pMmTKyXeP5rWlpaQoNDdW+ffusPcUQluO9AIn3AXLxXoDE+wC5eC8UXoZh6Pjx4woJCblsX0vDVdmyZeXq6qqkpCSH9qSkJAUFBV3wMUFBQZfsn/NvUlKSgoODHfpERERccJ/u7u5yd3d3aPPz88vPoVyWj48P/1EgifcCTLwPkIP3AiTeB8jFe6FwutyIVQ5L52J2c3NTgwYNFB8fb2/Lzs5WfHy8mjZtesHHNG3a1KG/JM2fP9/ev2LFigoKCnLok5aWplWrVl10nwAAAABwrSw/LTA2NlZ9+/ZVw4YN1bhxY40ePVoZGRnq16+fJKlPnz665ZZbNGLECEnSM888o9atW+u9995Tp06d9N1332nt2rX69NNPJUk2m02DBg3SG2+8oapVq9qnYg8JCVG3bt2sOkwAAAAANzjLw1XPnj116NAhDRs2TImJiYqIiNDcuXPtE1IkJCTI5ZybnTZr1kyTJ0/WSy+9pBdffFFVq1bVzJkz7fe4kqTnn39eGRkZeuSRR5SSkqIWLVpo7ty5ltzjyt3dXcOHD89z2iFuPrwXIPE+QC7eC5B4HyAX74Ubg+X3uQIAAACAG4Gl11wBAAAAwI2CcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrq6zO++8U+XLl5eHh4eCg4P14IMP6sCBA1aXhetoz5496t+/vypWrChPT09VrlxZw4cP1+nTp60uDRb43//+p2bNmqlEiRJOv3k5Cq+xY8cqLCxMHh4eioyM1OrVq60uCdfZkiVL1KVLF4WEhMhms2nmzJlWlwQLjBgxQo0aNZK3t7cCAgLUrVs3bdu2zeqycA0IV9dZ27ZtNXXqVG3btk3Tp0/Xzp071b17d6vLwnX0999/Kzs7W5988om2bNmi999/X+PHj9eLL75odWmwwOnTp9WjRw899thjVpeC62TKlCmKjY3V8OHDtX79eoWHhys6OlrJyclWl4brKCMjQ+Hh4Ro7dqzVpcBCixcv1hNPPKGVK1dq/vz5OnPmjNq1a6eMjAyrS8NVYip2i/3444/q1q2bMjMzVbx4cavLgUXeeecdjRs3Trt27bK6FFgkLi5OgwYNUkpKitWloIBFRkaqUaNG+uijjyRJ2dnZCg0N1VNPPaUhQ4ZYXB2sYLPZ9MMPP6hbt25WlwKLHTp0SAEBAVq8eLFatWpldTm4CoxcWejo0aP65ptv1KxZM4LVTS41NVWlS5e2ugwABez06dNat26doqKi7G0uLi6KiorSihUrLKwMQGGQmpoqSfxNUIQRrizwwgsvyMvLS2XKlFFCQoJmzZpldUmw0I4dO/Thhx/q0UcftboUAAXs8OHDysrKUmBgoEN7YGCgEhMTLaoKQGGQnZ2tQYMGqXnz5qpTp47V5eAqEa6cYMiQIbLZbJdc/v77b3v/5557Ths2bNC8efPk6uqqPn36iLMzi778vg8kaf/+/Wrfvr169Oihhx9+2KLK4WxX814AANzcnnjiCW3evFnfffed1aXgGhSzuoAbwbPPPquYmJhL9qlUqZL967Jly6ps2bKqVq2aatasqdDQUK1cuVJNmzYt4EpRkPL7Pjhw4IDatm2rZs2a6dNPPy3g6nA95fe9gJtH2bJl5erqqqSkJIf2pKQkBQUFWVQVAKs9+eST+vnnn7VkyRKVK1fO6nJwDQhXTuDv7y9/f/+remx2drYkKTMz05klwQL5eR/s379fbdu2VYMGDTRx4kS5uDCIfCO5lp8JuLG5ubmpQYMGio+Pt09ekJ2drfj4eD355JPWFgfgujMMQ0899ZR++OEHLVq0SBUrVrS6JFwjwtV1tGrVKq1Zs0YtWrRQqVKltHPnTr388suqXLkyo1Y3kf3796tNmzaqUKGC3n33XR06dMi+jU+ubz4JCQk6evSoEhISlJWVpY0bN0qSqlSpopIlS1pbHApEbGys+vbtq4YNG6px48YaPXq0MjIy1K9fP6tLw3WUnp6uHTt22Nd3796tjRs3qnTp0ipfvryFleF6euKJJzR58mTNmjVL3t7e9msvfX195enpaXF1uBpMxX4d/fnnn3rmmWe0adMmZWRkKDg4WO3bt9dLL72kW265xerycJ3ExcVd9I8o/jvefGJiYjRp0qQ87QsXLlSbNm2uf0G4Lj766CO98847SkxMVEREhD744ANFRkZaXRauo0WLFqlt27Z52vv27au4uLjrXxAsYbPZLtg+ceLEy55ejsKJcAUAAAAATsCFHgAAAADgBIQrAAAAAHACwhUAAAAAOAHhCgAAAACcgHAFAAAAAE5AuAIAAAAAJyBcAQAAAIATEK4AAAAAwAkIVwCAQmPRokWy2WxKSUmxupTrJiwsTKNHj7a6DACAExCuAAB2MTEx6tatW572whR6XnnlFUVERDhlX5c6rsISek6cOKGhQ4eqcuXK8vDwkL+/v1q3bq1Zs2ZZXRoA4DzFrC4AAABc3MCBA7Vq1Sp9+OGHqlWrlo4cOaLly5fryJEjBfacp0+flpubW4HtHwBuVIxcAQCuytKlS9WyZUt5enoqNDRUTz/9tDIyMuzbv/rqKzVs2FDe3t4KCgrSfffdp+TkZId9zJkzR9WqVZOnp6fatm2rPXv2XPI54+Li9Oqrr2rTpk2y2Wyy2WyKi4uTJCUkJKhr164qWbKkfHx8dO+99yopKclpx5uSkqIBAwbI399fPj4+uu2227Rp0yb79p07d6pr164KDAxUyZIl1ahRIy1YsMBhH8nJyerSpYs8PT1VsWJFffPNN5d93h9//FEvvviiOnbsqLCwMDVo0EBPPfWUHnroIXufzMxMvfDCCwoNDZW7u7uqVKmizz//3L598eLFaty4sdzd3RUcHKwhQ4bo7Nmz9u1t2rTRk08+qUGDBqls2bKKjo6WJG3evFkdOnRQyZIlFRgYqAcffFCHDx++6tcQAG50hCsAQL7t3LlT7du31z333KM//vhDU6ZM0dKlS/Xkk0/a+5w5c0avv/66Nm3apJkzZ2rPnj2KiYmxb9+3b5/uvvtudenSRRs3btSAAQM0ZMiQSz5vz5499eyzz6p27do6ePCgDh48qJ49eyo7O1tdu3bV0aNHtXjxYs2fP1+7du1Sz549nXbMPXr0UHJysn755RetW7dOt956q26//XYdPXpUkpSenq6OHTsqPj5eGzZsUPv27dWlSxclJCTY9xETE6N9+/Zp4cKF+v777/Xxxx/nCZznCwoK0pw5c3T8+PGL9unTp4++/fZbffDBB9q6das++eQTlSxZUpK0f/9+dezYUY0aNdKmTZs0btw4ff7553rjjTcc9jFp0iS5ublp2bJlGj9+vFJSUnTbbbepfv36Wrt2rebOnaukpCTde++9V/sSAsCNzwAA4P/17dvXcHV1Nby8vBwWDw8PQ5Jx7NgxwzAMo3///sYjjzzi8Njff//dcHFxMU6ePHnBfa9Zs8aQZBw/ftwwDMMYOnSoUatWLYc+L7zwgsPzXMjw4cON8PBwh7Z58+YZrq6uRkJCgr1ty5YthiRj9erVF93XwoULDUl5jtfLy8uw2WzG+++/bz82Hx8f49SpUw6Pr1y5svHJJ59cdP+1a9c2PvzwQ8MwDGPbtm156tm6dashyf48F7J48WKjXLlyRvHixY2GDRsagwYNMpYuXWrfnrPf+fPnX/DxL774olG9enUjOzvb3jZ27FijZMmSRlZWlmEYhtG6dWujfv36Do97/fXXjXbt2jm07du3z5BkbNu27aL1AsDNjJErAICDtm3bauPGjQ7LZ5995tBn06ZNiouLU8mSJe1LdHS0srOztXv3bknSunXr1KVLF5UvX17e3t5q3bq1JNlHcrZu3arIyEiH/TZt2tRh/dz9Dxw48KI1b926VaGhoQoNDbW31apVS35+ftq6daskqXbt2vZ9dejQweHxv//+e55jDgkJcTje9PR0lSlTxqGm3bt3a+fOnZLMkavBgwerZs2a8vPzU8mSJbV161aH4y1WrJgaNGhg32+NGjXk5+d30eOSpFatWmnXrl2Kj49X9+7dtWXLFrVs2VKvv/66JGnjxo1ydXW1v74Xem2aNm0qm81mb2vevLnS09P177//2tvOrSvnmBcuXOhwvDVq1JAk+zEDABwxoQUAwIGXl5eqVKni0HbuH+GSGSQeffRRPf3003keX758eWVkZCg6OlrR0dH65ptv5O/vr4SEBEVHR+v06dNXXMvGjRvtX/v4+OTvQM4zZ84cnTlzRpLk6enpsK1ixYp5Qk6xYrm/ItPT0xUcHKxFixbl2W/O4wYPHqz58+fr3XffVZUqVeTp6anu3bvn63gvpnjx4mrZsqVatmypF154QW+88YZee+01vfDCC3mO5Wp5eXk5rKenp6tLly4aOXJknr7BwcFOeU4AuNEQrgAA+Xbrrbfqr7/+yhPCcvz55586cuSI3nrrLfto0tq1ax361KxZUz/++KND28qVKx3WL7R/Nzc3ZWVl5dnXvn37tG/fPvvz/fXXX0pJSVGtWrUkSRUqVMjHETq69dZblZiYqGLFiiksLOyCfZYtW6aYmBjdddddksxwcu4EHTVq1NDZs2e1bt06NWrUSJK0bdu2q5revlatWjp79qxOnTqlunXrKjs7W4sXL1ZUVFSevjVr1tT06dNlGIZ99GrZsmXy9vZWuXLlLnnM06dPV1hYmEPQBABcHKcFAgDy7YUXXtDy5cv15JNPauPGjdq+fbtmzZpln9CifPnycnNz04cffqhdu3bpxx9/tJ/GlmPgwIHavn27nnvuOW3btk2TJ0+2z/x3KWFhYdq9e7c2btyow4cPKzMzU1FRUapbt67uv/9+rV+/XqtXr1afPn3UunVrNWzY8JqPNyoqSk2bNlW3bt00b9487dmzR8uXL9d///tfe2isWrWqZsyYoY0bN2rTpk267777lJ2dbd9H9erV1b59ez366KNatWqV1q1bpwEDBlx25KlNmzb65JNPtG7dOu3Zs0dz5szRiy++qLZt28rHx0dhYWHq27evHnroIc2cOVO7d+/WokWLNHXqVEnS448/rn379umpp57S33//rVmzZmn48OGKjY2Vi8vF/wx44okndPToUfXu3Vtr1qzRzp079euvv6pfv355wi0AwES4AgDkW7169bR48WL9888/atmyperXr69hw4bZr1Py9/dXXFycpk2bplq1aumtt97Su+++67CP8uXLa/r06Zo5c6bCw8M1fvx4vfnmm5d97nvuuUft27dX27Zt5e/vr2+//VY2m02zZs1SqVKl1KpVK0VFRalSpUqaMmWKU47XZrNpzpw5atWqlfr166dq1aqpV69e2rt3rwIDAyVJo0aNUqlSpdSsWTN16dJF0dHRuvXWWx32M3HiRIWEhKh169a6++679cgjjyggIOCSzx0dHa1JkyapXbt2qlmzpp566ilFR0fbw5MkjRs3Tt27d9fjjz+uGjVq6OGHH7ZPi3/LLbdozpw5Wr16tcLDwzVw4ED1799fL7300iWfNyQkRMuWLVNWVpbatWununXratCgQfLz87tkKAOAm5nNMAzD6iIAAAAAoKjjoycAAAAAcALCFQAAAAA4AeEKAAAAAJyAcAUAAAAATkC4AgAAAAAnIFwBAAAAgBMQrgAAAADACQhXAAAAAOAEhCsAAAAAcALCFQAAAAA4AeEKAAAAAJzg/wBfbIGv2xbgXgAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ "
" ] @@ -12774,7 +12781,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -0.13\n" + "The average of 'head_to_head' is: -0.12\n" ] } ], @@ -12830,17 +12837,17 @@ " \n", " \n", " head_to_head\n", - " -11.8\n", + " -10.6\n", " 92.1\n", " -0.1\n", - " 0.643536\n", - " 0.067057\n", - " -1.907958\n", + " 0.846125\n", + " 0.088167\n", + " -1.304254\n", " 1.98555\n", - " 0.0\n", + " 0.1\n", " -0.3\n", - " 0.029773\n", - " 0.059546\n", + " 0.097716\n", + " 0.195433\n", " \n", " \n", "\n", @@ -12848,10 +12855,10 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", - "head_to_head -11.8 92.1 -0.1 0.643536 0.067057 -1.907958 1.98555 \n", + "head_to_head -10.6 92.1 -0.1 0.846125 0.088167 -1.304254 1.98555 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "head_to_head 0.0 -0.3 0.029773 0.059546 " + "head_to_head 0.1 -0.3 0.097716 0.195433 " ] }, "execution_count": 68, @@ -12923,7 +12930,7 @@ " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.13]\n", + " [0.0001, 0.0001, 0.0001, 0.14]\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", " -1.8\n", @@ -12931,26 +12938,26 @@ " \n", " 247\n", " Will the 500th richest person on Bloomberg's B...\n", - " 0.8\n", + " 0.833333\n", " 0.333\n", " no\n", - " -1.2\n", + " -1.4\n", " \n", " \n", " 232\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.32130390143737164]\n", + " [0.0001, 0.0001, 0.0001, 0.27130390143737165]\n", " [0.002,0.008,0.09,0.9]\n", " 3 or more\n", - " -1.0\n", + " -1.2\n", " \n", " \n", - " 71\n", - " Will OpenAI, Anthropic, or Perplexity run an a...\n", - " 0.18\n", - " 0.55\n", - " yes\n", - " -1.0\n", + " 47\n", + " What will be Donald Trump's net worth, accordi...\n", + " [0.185, 0.0001, 0.0001, 0.0001, 0.0001]\n", + " [0.6,0.2,0.1,0.075,0.025]\n", + " 0-$6 billion, inclusive\n", + " -1.2\n", " \n", " \n", "\n", @@ -12962,21 +12969,21 @@ "121 How many movies will be new on Netflix's top 1... \n", "247 Will the 500th richest person on Bloomberg's B... \n", "232 How many movies will be new on Netflix's top 1... \n", - "71 Will OpenAI, Anthropic, or Perplexity run an a... \n", + "47 What will be Donald Trump's net worth, accordi... \n", "\n", " bot_team_median \\\n", "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", - "121 [0.0001, 0.0001, 0.0001, 0.13] \n", - "247 0.8 \n", - "232 [0.0001, 0.0001, 0.0001, 0.32130390143737164] \n", - "71 0.18 \n", - "\n", - " pro_median resolution head_to_head \n", - "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more -1.8 \n", - "247 0.333 no -1.2 \n", - "232 [0.002,0.008,0.09,0.9] 3 or more -1.0 \n", - "71 0.55 yes -1.0 " + "121 [0.0001, 0.0001, 0.0001, 0.14] \n", + "247 0.833333 \n", + "232 [0.0001, 0.0001, 0.0001, 0.27130390143737165] \n", + "47 [0.185, 0.0001, 0.0001, 0.0001, 0.0001] \n", + "\n", + " pro_median resolution head_to_head \n", + "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", + "121 [0.005,0.017,0.157,0.821] 3 or more -1.8 \n", + "247 0.333 no -1.4 \n", + "232 [0.002,0.008,0.09,0.9] 3 or more -1.2 \n", + "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -1.2 " ] }, "execution_count": 69, @@ -13044,23 +13051,23 @@ " \n", " 189\n", " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.0030510204, 0.0061020408, 0.0102928751...\n", + " [0.0, 0.0106785714, 0.0213571429, 0.0320357143...\n", " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", " 34.0\n", - " 2.5\n", + " 2.9\n", " \n", " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", - " 2.5\n", + " 5.3\n", " \n", " \n", " 151\n", " How many earthquakes of magnitude ≥ 4 will hap...\n", - " [0.0, 0.0035714286, 0.0071428571, 0.0107142857...\n", + " [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0...\n", " [0.0,0.0158237002,0.0235315723,0.0279864362,0....\n", " 0.0\n", " NaN\n", @@ -13076,7 +13083,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.954\n", + " 0.952\n", " 0.95\n", " annulled\n", " NaN\n", @@ -13094,11 +13101,11 @@ "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "189 [0.0, 0.0030510204, 0.0061020408, 0.0102928751... \n", - "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", + "189 [0.0, 0.0106785714, 0.0213571429, 0.0320357143... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "151 [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0... \n", "211 0.99 \n", - "214 0.954 \n", + "214 0.952 \n", "\n", " pro_median resolution \\\n", "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", @@ -13108,8 +13115,8 @@ "214 0.95 annulled \n", "\n", " head_to_head \n", - "189 2.5 \n", - "0 2.5 \n", + "189 2.9 \n", + "0 5.3 \n", "151 NaN \n", "211 NaN \n", "214 NaN " @@ -13227,10 +13234,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", + " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 2.522754\n", - " 2.522754\n", + " 5.334952\n", + " 5.334952\n", " \n", " \n", " 1\n", @@ -13247,10 +13254,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.05058191405, 0.05116382805, 0.0517457...\n", + " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.161101\n", - " -0.161101\n", + " -0.250003\n", + " -0.250003\n", " \n", " \n", " 2\n", @@ -13267,10 +13274,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.085\n", + " 0.1\n", " 0.013\n", - " -0.075746\n", - " -0.075746\n", + " -0.092275\n", + " -0.092275\n", " \n", " \n", " 3\n", @@ -13287,10 +13294,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " [0.0001, 0.5125, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 0.152526\n", - " 0.152526\n", + " 0.022473\n", + " 0.022473\n", " \n", " \n", " 4\n", @@ -13307,10 +13314,10 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", + " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 0.387623\n", - " 0.387623\n", + " -0.102791\n", + " -0.102791\n", " \n", " \n", "\n", @@ -13346,25 +13353,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", - "1 [0.05, 0.05058191405, 0.05116382805, 0.0517457... \n", - "2 0.085 \n", - "3 [0.0001, 0.5125, 0.0001] \n", - "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", + "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", + "2 0.1 \n", + "3 [0.0001, 0.45, 0.0001] \n", + "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 2.522754 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.161101 \n", - "2 0.013 -0.075746 \n", - "3 [0.16,0.44,0.4] 0.152526 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 5.334952 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", + "2 0.013 -0.092275 \n", + "3 [0.16,0.44,0.4] 0.022473 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... -0.102791 \n", "\n", " weighted_score \n", - "0 2.522754 \n", - "1 -0.161101 \n", - "2 -0.075746 \n", - "3 0.152526 \n", - "4 0.387623 " + "0 5.334952 \n", + "1 -0.250003 \n", + "2 -0.092275 \n", + "3 0.022473 \n", + "4 -0.102791 " ] }, "execution_count": 72, @@ -13378,7 +13385,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -13403,7 +13410,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 93, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13413,25 +13420,9 @@ "outputId": "c0ec1316-ef4e-4bd1-875d-148b65ba0114" }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Count: 10\n", - "Count: 11\n", - "Count: 11\n", - "Count: 11\n", - "Count: 11\n", - "Count: 10\n", - "Count: 9\n", - "Count: 10\n", - "Count: 9\n", - "Count: 10\n" - ] - }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAMWCAYAAADs4eXxAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Xd8U1X/wPFP0nRvuhcUWbK3iMwCCqLIUhSULU5c6M/18DyAC1FB3AMeQEUFcSAI6KPspcyyhAItbaGUDrp3k5zfHyGxoWkp0DalfN+8+qK59+beb5KbNN97zvkejVJKIYQQQgghhBBCiGqntXcAQgghhBBCCCFEfSVJtxBCCCGEEEIIUUMk6RZCCCGEEEIIIWqIJN1CCCGEEEIIIUQNkaRbCCGEEEIIIYSoIZJ0CyGEEEIIIYQQNUSSbiGEEEIIIYQQooZI0i2EEEIIIYQQQtQQSbqFEEIIIYQQQogaIkm3EKLOmzBhAhqNhvj4eMuy+Ph4NBoNEyZMsNq2b9++aDSa2g3wMth6LEKIus/WZ8uSJUvQaDQsWbLEPkGJOs/WZ/6mTZvQaDTMnDnTbnEJIWqXJN1CiGqxd+9eJk+eTLNmzXB3d8fV1ZUmTZowduxYfv/9d3uHV2uu1S/hBQUFvPfee0RFRREQEICjoyMNGjSgZ8+evPnmm6Slpdk7xFplfh3L/mi1Wnx8fOjVqxeLFy++6mNUdOGoMhfHdKmfa9nMmTMtj+O5556rcLsXXnjBsp0kMbXLnFCW/fHy8qJr1668++67lJaW1npMZd+7d999d4XbffLJJ5btLuc9KIQQV0Jn7wCEENc2o9HIc889x7vvvotOp6Nfv37cddddODo6EhcXx5o1a1i6dCmvvPIK//73v6vtuGFhYRw9ehRvb+9q22dtmD17Ni+++CJhYWH2DsXiwIEDDB06lISEBBo1asRdd91FUFAQOTk5/Pnnn7z00kvMnj2bs2fP4u7ubu9wa1X//v3p2bMnAHq9ntOnT/Pzzz8zadIk/v77b95+++1ajWfGjBnlls2fP5/s7Gyb6+oDnU7H0qVLefPNN9HprL+26PV6vvzyS3Q6HXq9vtZjGz58ODfffDMhISG1fuy6ZPLkyYSHh6OU4vTp0/z4449MmzaNDRs2sHr1arvEpNPpWL16Nenp6fj7+5db/9///tdu581NN93E0aNHbcYlhKifJOkWQlyV6dOn8+6779KhQwe+//57mjRpYrW+sLCQDz/8kPPnz1frcR0dHbnxxhurdZ+1ISQkpE59QT9z5gy33XYb6enpzJ07l6eeegoHBwerbfbv38/UqVPt0mplbwMGDODFF1+0WhYfH0+bNm344IMPeOWVV3B1da21eGy15C5ZsoTs7Ox628p7++23s3r1an755ReGDRtmtW7t2rWcO3eOu+66i1WrVtV6bN7e3tfchb+a8OCDD3LzzTdbbr/22mt07NiRX375hU2bNtG3b99aj8l83ixdupSnn37aat3BgwfZu3ev3c4bNze3a/LvlxDiykn3ciHEFTt58iRvvfUWfn5+/Prrr+USbgBXV1f+7//+j1mzZlmWHT9+nOeff55OnTrh5+eHi4sLzZs358UXXyQvL69Kx75U19yioiJefPFFGjZsiIuLCy1btuSDDz5AKWW1Xdnu4KtXr6ZHjx54enoSGRkJQElJCR988AEDBw4kIiICZ2dnAgMDGTFiBPv377fa14QJE5g4cSIAEydOtNnNt7Ix3YsXL6Zbt254eHjg4eFBt27dbHZTLzsecM+ePdx66614enri7e3N8OHDL2u8+L/+9S9SU1N5+eWXmTZtWrmEG6Bjx45s3rwZLy+vcse/WEWvS2RkJJGRkWRlZTF16lQiIiLQ6XQsWbKE/v37o9VqSUhIsBnjk08+iUajKTdMYcuWLQwZMgR/f3+cnZ1p1qwZ06dPp6CgoMqP/0pERkbSokULiouLyc3NLbd+9erVREVF4e3tjaurK+3bt2fevHlWLWpLliyhcePGAHzxxRdW58qmTZuqJc6SkhLmzZtHp06dcHd3x9PTk169etlMMi73PWke31xcXMzLL79Mw4YNcXV1pXPnzvzxxx8AZGdn8/jjjxMaGoqLiwvdu3dn165dl/04RowYgY+PD4sWLSq3btGiRfj6+jJ8+PAK75+amsozzzxD06ZNcXZ2xt/fn5EjR3L48GGb22/bto0+ffrg7u6On58f9957L6dPn7a5bUXDSX766SdGjx5N06ZNcXNzw9vbm169evHDDz+U20fZ98zJkycZPnw4vr6+uLu7M2DAAA4cOFDJs/OPyZMno9Fo2LJli8318+bNQ6PRsGDBAsuyjRs3cvvttxMaGoqzszNBQUH06tWLzz//vErHrEhoaCgjRowAYPfu3cA/wwU2bdrEkiVL6NSpE25ublYJeUJCApMnTyYsLAwnJyfCw8OZPHkyiYmJlx3DLbfcwo033mhzKMiiRYtwcHBg/PjxFd4/NzeXGTNm0Lp1a1xdXfHx8WHgwIFs27bN5vZHjhzhzjvvtHwWDx48uMJzrKLP0I0bNzJp0iRatGhh+TvQpUuXCl8PjUZD3759SUlJYfz48fj7++Pq6srNN99cbZ8jQojqIS3dQogrtmTJEgwGAw8//DBBQUGVbuvs7Gz5/ccff+S///0vUVFR9O3bF6PRyJ9//smcOXPYvHkzW7ZswdHR8apiGzVqFPv372fkyJEA/PDDDzz55JPEx8czd+7cctuvWLGC//3vf9x555089thj5OTkAJCRkcHTTz9Nr169GDx4ML6+vsTFxbFq1SrWrVvHli1b6Nq1KwDDhg0jKyuLn3/+maFDh9KhQ4cqx/vkk0/ywQcfEBYWxuTJky0xT5w4kf379/Pee++Vu8/u3bt56623iIqK4uGHH2b//v2sXLmSQ4cOcfjwYVxcXCo9ZkFBAcuWLcPV1bXSMbNAuW69V6K4uJh+/fqRl5fHXXfdhU6nIygoiLFjx7Jhwwa+/vprXn75Zav76PV6li1bRmhoKP3797cs/+STT3j88cfx8fFhyJAhBAYGsmfPHl5//XU2btzIxo0bcXJysmzft29fNm/ezMaNG6+61S0hIYGYmBjCw8MJDAy0Wjdv3jyeffZZGjRowJgxY3B3d2fVqlU8++yzbN26lR9//BGNRkOHDh146qmneO+992jfvr1VC675gs/VKC4uZtCgQWzatIkOHTowefJkSktLWbNmDUOHDuWDDz5g6tSplu2v9D157733cujQIe666y4KCwv5+uuvufPOO9m+fTsPPfQQJSUl3HPPPaSlpbF8+XIGDRrEqVOnLqt12MXFhdGjR7NgwQJSUlIsnzUpKSmsWbOGhx56qMJzPTY2lr59+1p6dAwbNozU1FR++OEHfvvtN9avX0+3bt0s269fv57bb78drVbLvffeS2hoKOvXr6dHjx74+vpWOeaXXnoJJycnevbsSUhICGlpaaxatYq7776b999/nyeeeKLcfeLj47n55ptp3bo1kyZNIjY2lp9//pmoqCiOHj16yc/YsWPHsmjRIpYuXUrv3r3Lrf/qq69wdnbmnnvuAWDNmjUMGTIEHx8fhg4daonzwIEDfPXVVzz00ENVfryVubi2wNtvv83GjRsZOnQot912m+VC3/Hjx+nZsydpaWkMGTKE1q1bc/jwYRYtWsTq1avZtm0bzZs3v6xjT5w4kRdeeIG9e/fSuXNnwHQx6uuvv2bgwIGEhobavF9GRga9e/fmyJEj9OjRg0ceeYScnBzL67FixQqr9+zhw4fp0aMHeXl5jBgxgmbNmrFr1y569OhB+/btqxzvnDlzOHnyJDfffDPDhw8nKyuLX3/9lYcffpiYmBibf7uysrLo2bMn3t7ejB07ltTUVJYvX87AgQPZu3cvbdq0uaznTAhRQ5QQQlyhvn37KkD98ccfl3W/M2fOqOLi4nLLZ82apQC1dOlSq+Xjx49XgDp16pRl2alTpxSgxo8fb7Vtnz59FKBatGihsrKyLMuzsrJUixYtlEajUbt377YsX7x4sQKUVqtVv//+e7mYioqK1JkzZ8otP3z4sPLw8FADBgywWm7e3+LFi20+dluPZfPmzQpQLVu2tIo5IyNDNW/eXAFqy5YtluUbN25UgALUsmXLrPY/duxYBahvv/3W5vHL2rRpkwJUz549L7ltWebjz5gxo9y6il6XRo0aKUANHDhQFRQUWK3LyclRrq6uqlWrVuX2t3r1agWo5557zrLsyJEjSqfTqfbt26v09HSr7WfPnq0A9c4771gtN58XGzdurNJjNL+O/fv3VzNmzFAzZsxQ//rXv9T48eOVr6+vCgwMLHfenzx5Uul0OhUYGKgSExMty4uKilTPnj0VoL788stLPleXy/zclvXyyy8rQP373/9WRqPRsjwnJ0d16dJFOTk5qaSkJMvyy31Pmp/Pnj17qry8PMvy5cuXK0D5+Pioe+65R5WWllrWzZkzRwFq7ty5VXpcM2bMsJzLe/bsUYB66623LOvfeustBai9e/eqb7/91uY5ecsttygHBwf166+/Wi2PiYlRnp6eqm3btpZlBoNB3XDDDUqj0aitW7dalhuNRjVmzBjLe66sit7vsbGx5R5Pbm6uatu2rfL29lb5+fmW5ebzAFBvvvmm1X2mT5+uADV79uzKn6wLcTZs2FD5+vqqoqIiq3WHDh1SgLr77rsty0aMGKEAFR0dXW5fF7+vKmL+PNu5c6fV8uTkZBUUFKQAtXnzZqXUP6+nu7u7OnjwYLl9RUVFKUB99tlnVss/+ugjBah+/fpVKSbzazJ79myVnJysdDqdeuyxxyzrv/vuOwWoH374Qe3cudPme9D8ei9YsMBqeUpKioqIiFABAQGqsLDQstz8frj4ffLSSy9ZXtuyn/kVfYbGxcWVezylpaXq1ltvVQ4ODiohIcFqnXnfjz32mDIYDJblCxcuVIB6+OGHK32uhBC1R5JuIcQVu/HGGxWgjh07Vi37O3/+vALUhAkTrJZfSdJ98ZcfpZT66quvFKCmTp1qWWb+gjZ8+PDLjnfIkCHKyclJlZSUlNvf5STdkyZNUoBavnx5ue2//vprBahJkyZZlpm/sPXu3bvc9uZ106ZNu2T8y5YtU4C67777LrmtrWNcSdJ94MABm/scPXq0JYEqa9SoUeUSgyeffLLchQgzg8GgAgICVOfOna2WJyQkqKNHj1olO5Uxv462fnQ6nZo6dapKSUmxus8rr7yiADVnzpxy+9u+fXu5xKGmkm6DwaB8fX1VkyZNrBJus1WrVilAffDBB5fcd0XvSfP7zJxQlT22o6OjAsolCImJiQpQ48aNq9LjKpt0K6VUu3btVMuWLS3rW7Zsqdq3b6+UUjaT7n379pV775Q1bdo0BahDhw4ppf65+DVkyJBy28bHxysHB4cqJ90VmTt3rgLUpk2bLMvM50Hjxo2tEqey60aMGFGl/ZuTvB9++MFq+fPPP68AtXLlSssyc9IdExNTpX3bYv48mzx5spoxY4b6z3/+oyZNmqR8fHwUoIYOHWrZ1vx6PvPMM+X2k5CQoADVqlWrcueswWCw/K0pezGrImWTbqWUuuuuu5Svr68lSR40aJAKCAhQJSUlNpPutLQ05eDgUGGS//777ytArV692ir2du3alds2NzfX8lxUJemuyA8//KAAtWTJEqvl5osYubm5VstLS0uVTqdTnTp1qtL+hRA1T7qXCyFqnVKKxYsXs2TJEg4fPkx2djZGo9Gy/uzZs1d9jF69elW47OKx2GCqJluR6Oho3nrrLbZt28a5c+fKFRRLT0+/quJo5nhsdXuOioqyxHAxc3fJssLDwwFTl8O6xsXFhbZt29pcN3bsWL799lu++uorOnXqBEBOTg6rV6+mbdu2Vl00//zzTwBL9+CLOTo6cuzYMatlDRs2vKKYzdXmwVSpPzk5mZUrV/Lss8+ydu1a9u3bZ+kqXdnr2L17d1xcXGy+jtUtJiaGzMxMQkNDrWopmJmnfyv7HF3pe/LiIRRarZbAwEAKCgrKPefm98iVvr8nTZrE008/zc6dOwE4evSozWEXZubzJCUlxWb9AfPjP3bsGG3atLGMnbb12dGoUSMiIiKqXC8hNTWVN998k3Xr1pGQkEBhYaHVelvPQYcOHdBqrUvtXO77eezYscyePZuvvvrKMqbaaDTyzTff4Ofnx+DBgy3b3nffffz444/cfPPNjBkzhv79+9OrV68rqqj93//+1/K7h4cHLVu25P777+fxxx8vt62tz1rz+6JPnz7luqNrtVp69+7NsWPHiI6OJiIi4rJimzRpEqtWreKnn36id+/e/O9//+Opp56qcAjT7t27MRgMFBcX2zxvTpw4AZjOmzvvvNNy3phnOSjLw8ODDh06VHl8dW5uLu+88w4rV64kNjaW/Px8q/W2zpvmzZvj4eFhtcw8dKcu/h0Q4nolSbcQ4ooFBwdz7NgxkpKSaNGiRZXv9+STT/Lhhx8SERHBXXfdRUhIiGXM96xZsyguLr7q2GyNfzQvy87OrtL2ADt27KBfv34A3HbbbTRr1gwPDw80Gg0rV67kwIEDVx1vTk4OWq2WgIAAm3FpNBrLGPOyzIXNyjKPvTYYDJc8bnBwMABJSUmXG/IVCQwMrHDu6Ntuu42goCCWLVvGO++8g4ODA99//z2FhYWMHTvWatuMjAwAXn/99RqPuSytVktYWBiPP/44ycnJvP7663z44Yf861//ArC8RrbOJY1GQ1BQUK081+bn58iRIxw5cqTC7cp+ob/S92RF52Bl5+aVVsF/4IEHeP755y0F1ZycnLj//vsr3N78PKxZs4Y1a9ZUuJ35eTB/Llw8Tt8sKCioSkl3RkYGXbt2JTExkR49ejBgwAB8fHxwcHAgOjqan3/+2ebzebXvZ4CWLVvSuXNn1q5dS2ZmJr6+vmzatIkzZ87w2GOPWSWa99xzDytXrmTevHl8+umnfPTRR2g0GqKiopg7d+5l1aTYuXOnVfXyyth6f1T23oF/LtjY+hy8lDvuuIOgoCAWLVpEXFwcRqORSZMmVbi9+bzZvn0727dvr3C7yzlvqqKkpIS+ffuyb98+OnbsyNixY/Hz80On0xEfH88XX3xR5fMGTOdOVc8bIUTNk6RbCHHFevTowaZNm1i/fr0lMb2U1NRUPvroI9q1a8fOnTtxc3OzrDt37pzNlrkrkZKSUq6lLSUlBcBmEaeKksHXX3+d4uJitm7dWq4l488//6xyZeHKeHl5YTQaSUtLK/fFLTU1FaVUhV+srkbXrl1xcnJiz5495OTkVPkY5tY4W/Pb2rqgYVbRcwzg4ODA6NGjmT9/Pn/88QcDBw7kq6++QqvVMmbMGKttzXHm5OTg6elZpZirm7n4lrkyc9m4UlJSaNSokdX2SilSUlJq5HW8mPkYI0eO5Pvvv7/k9rX1nrxafn5+DB06lOXLlwOmwoV+fn4Vbm9+Hi4uGlcR8+dCamqqzfXmz49L+e9//0tiYiKvvvoq06dPt1r35ptv8vPPP1dpP1dq7NixPP3003z33Xc8/PDDfPXVV5blFxs6dChDhw4lNzeX7du3WwrqDRo0iGPHjuHj41Pt8dn6HCj73rHl3LlzVttdDp1Ox7hx45g7dy5HjhzhpptuqrS4mPkYzz77LO+8884l919d583PP//Mvn37mDx5MgsXLrRat2zZMr744osq7UcIUTfJlGFCiCs2YcIEHBwc+Pzzzy1dVitivkIfFxeHUooBAwZYfbkH2Lp1a7XFZmtf5mUdO3as8n5iY2Np0KBBuYS7oKCAffv2ldveXIn3cloYzPHY6oJoXnY5rU5V5ebmxn333UdhYaHNqrhl6fV6S3djcxVnW622trruV5U5KVi6dCmnT59m8+bNREVFERYWZrWdOeE1dx+2h8zMTACrLtiVvY5//fUXRUVFVq/jlZwrVdGyZUu8vLzYs2dPlVqVa+s9WR0mTZpEbm4uubm5lbZWwj/nibk7+qWYhzDYeswJCQkVTht2sdjYWMCU0F6sNp7P0aNHo9PpWLp0KYWFhfz44480bdq00pZoT09PBg0axOeff86ECRNISUnhr7/+qvFYzczviy1btpSb1lEpZZkG7Uo/BydNmmQZHnKp86Zr165oNJrLPm9sTSWWl5dX5SEl9j5vhBA1S5JuIcQVa9q0Kc8//zzp6encfvvtnDp1qtw2RUVFzJs3zzI2ztwCuGPHDquE5cyZM7z00kvVFturr75q1eqanZ3Na6+9hkajqXRu1os1atSIzMxMq266BoOB5557zuaFhgYNGgBU+Qs6YIln1qxZVt0ns7OzLa2MlxPz5Xj99dcJCAjg9ddf5/3337d6TcwOHjxI3759LbG1aNECT09PVq1aZemKCaYWnddee+2KY+nUqROtWrXip59+4rPPPkMpZbN17rHHHkOn0/HEE0/YnL83KyurXPKfmJjIsWPHqmUO76KiIj7++GMAq6mZxowZg06nY968eVZjL0tKSnjhhRcArOYv9/X1RaPRXNa5UhU6nY5HH32UhIQEnnvuOZuJ9+HDhy0tc7X1nqwOt912GytXrmTlypXceuutlW5700030a1bN7799ltL63hZRqORzZs3W2737NmTxo0b88svv1glUEopXn755SpfHDE/nxcnYd988w1r166t0j6uRmBgILfddhvbt29n/vz55OTk8MADD5TbbsuWLTYfk/m8uNSUg9WpYcOGREVFceTIkXLzsX/++eccPXqUfv36XfZ4brMbb7yRdevW8dNPP1U6JAFMw25GjRrFjh07ePvtt8tdBADTRTTzZ0nDhg3p3bs3Bw8e5Ouvv7ba7o033qjyuOqKzpvNmzdbza0uhLg2SfdyIcRVee211ygqKuLdd9+lRYsW9OvXjzZt2uDo6MipU6f4448/OH/+vCUZCwkJYeTIkfzwww906dKF/v37k5KSwi+//EL//v0tV/uvVvPmzWnTpo3VPN1nzpxh2rRpdOnSpcr7eeKJJ/jf//5Hz549GTVqFC4uLmzatImkpCT69u1brlWze/fuuLq6Mn/+fDIzMy3jtC/uZlpW7969eeKJJ/jggw8sMSulLDE/+eSTNufdrQ7h4eH873//Y9iwYTz11FO8++679O/fn6CgIHJycti1axe7d+/Gy8vLMh7UycmJJ554gjfeeINOnTpZuqeuXr2aPn36XNVrOHbsWF566SXeeust3NzcLK9fWW3atOHjjz/m0UcfpUWLFgwePJgmTZqQm5tLXFwcmzdvZsKECXz66aeW+4wbN+6K5un+448/KCoqAkxJ2rlz51i3bh1nzpyhQ4cOPPbYY5ZtmzRpwpw5c3j22Wdp164do0aNwt3dndWrVxMTE8PQoUOtkh8PDw+6du3Kli1bGDt2LM2aNUOr1TJ27Nhy3dMv16xZs9i3bx/vv/8+a9asoXfv3gQGBpKUlMShQ4c4cOAAO3fuJDAwsNbek9VBq9XabAmsyLfffktUVBT33Xcf8+fPp1OnTri6upKYmMjOnTtJS0uzvL5arZbPP/+cwYMHM2DAAMs83Rs2bCA5OZl27dpx8ODBSx5z7NixzJkzhyeeeIKNGzfSqFEjDhw4wPr16xkxYgQ//vjjFT/+qho7dixr165lxowZADaT7ieffJKzZ8/Ss2dPIiMj0Wg0bNu2jV27dnHzzTfbLAxWkz755BN69uzJlClTWL16Na1ateLIkSOsWrWKgIAAPvnkk6va/6BBg6q87ccff0xMTAzPP/88X331Fd27d8fHx4fTp0+zZ88eTpw4QXJysqVnyEcffUSPHj0YN24cK1eutMzTvXv3bnr16lWlluohQ4YQGRnJW2+9xeHDh2nTpg0xMTH88ssvDB8+vEpDRYQQdZh9iqYLIeqb3bt3q0mTJqmmTZsqV1dX5ezsrCIjI9WYMWPKzX+dm5urnn32WRUZGamcnZ1Vs2bN1KuvvqpKSkoUoPr06WO1/ZVMGVZYWKief/55FRERoZycnFSLFi3U+++/X246mqpM+fP999+rTp06KTc3N+Xv769GjRqlYmNjbcallFJr1qxRXbt2Va6uruXm9q3oPkoptWjRItW1a1fl5uam3NzcVNeuXdWiRYvKbXclU3ZdSn5+vpo/f77q06eP8vf3VzqdTvn4+Kju3bur119/vdy8vQaDQc2cOdPy/DZv3ly99957Ki4ursIpwxo1anTJOBITE5VWq1WAGj16dKXb7tq1S913330qNDRUOTo6Kn9/f9WpUyf14osvqqNHj1pte6XzdF/84+7urjp06KBee+21Cqcf+/nnn1WfPn2Up6encnZ2Vm3btlVz5861mrPaLCYmRg0ePFj5+PgojUZzWTGa2ZqnWyml9Hq9+uyzz1SPHj2Ul5eXcnZ2Vg0bNlSDBg1Sn3zyidX82pf7njQ/nxXFU9FrbWtfFbl4yrDKVDRPt1Km+e6nT5+u2rRpo1xdXZWHh4dq1qyZGjNmjPrxxx/Lbb9lyxbVu3dv5erqqho0aKDuuecelZCQYPMxV/T5ER0drW677Tbl6+urPD09VZ8+fdQff/xhc/tLvWcv5zkzKygoUF5eXgpQ3bt3t7nNsmXL1KhRo1STJk2Um5ub8vb2Vu3bt1dz5swpNwVVRSqap9sW8+tZ2fkdHx+vJk6cqEJCQpROp1MhISFq4sSJKj4+vkrxKFV+yrDKVDRPt1Km5/Ctt95SnTt3Vu7u7srV1VU1btxYDRs2TH355Zfl3s+HDh1SgwcPVh4eHsrT01Pdfvvt6tChQzY/8yubp3vkyJEqICDA8jdg2bJlFW5f2blR1c9cIUTt0Chlo9+MEEIIIYQQQgghrpqM6RZCCCGEEEIIIWqIJN1CCCGEEEIIIUQNkaRbCCGEEEIIIYSoIZJ0CyGEEEIIIYQQNUSSbiGEEEIIIYQQooZI0i2EEEIIIYQQQtQQSbqFEOI6pJSic+fO3HbbbbV63CVLlqDRaFiyZEmtHrcumjlzJhqNhk2bNtk7FGEHEyZMQKPREB8fb+9QbOrVqxfdunWzdxhCCFEvSNIthBDXoS+//JJ9+/bxyiuv2DsUcQ1avnw5Go0GjUbDsmXLbG5z9uxZnnrqKVq1aoW7uztBQUH07NmTr776CoPBUMsR175r/QLTzJkz2bVrV4WvrxBCiKqTpFsIIa4zRqORmTNn0qtXL26++WZ7hyOuMefOnePxxx/H3d29wm3i4uJo3749H3zwAY0aNWLq1KmMGDGC2NhYxo0bx4MPPliLEddNs2fP5ujRo4SFhdk7FJv69+9Pp06dmDFjBkope4cjRO255x7YudP0u9EITzwBTZpA06bw4YcV32/tWujUCTp0gDZt4Isv/lm3ezf06AHt25vWb9hQtVgWLYK2bUGng/nzK9/2r79M+2/eHPr1g6SkS68rKoLOnSE7u2rxiCsmSbcQQlxn1q1bR3x8POPGjbN3KOIa9NBDD+Hp6ckjjzxS4TbvvPMO6enpvPvuu6xbt445c+bwySefcPToURo2bMiSJUtISEioxajrnpCQEG688UYcHR3tHUqFHnjgAY4fP86GqiYIQlzrdu2CjAzo3t10e+lS+PtvOH7ctO7tt+HIkfL3UwoeeACWLIHoaPjlF3j4YcjNNa0bPhxmzYIDB+C772DCBCgsvHQ8nTubth8zpvLtjEa4/35TYn78OAweDE8/fel1Li4wdizMnVuFJ0dcDUm6hRDiOrN48WI0Gg0jR460uT4hIYHJkycTFhaGk5MT4eHhTJ48mcTExHLb9u3bF41GQ2lpKTNnziQyMhJnZ2eaN2/Oxx9/fMlYsrOzcXd3p3Xr1jbXG41GIiMj8fX1pbAqX1AuPL5u3brh4eGBh4cH3bp1K9fFd+vWrWg0GiZNmmRzH6mpqTg6OtKjRw+r5bm5ucyYMYPWrVvj6uqKj48PAwcOZNu2beX2YX5uioqKmD59Ok2aNMHR0ZGZM2dWGv+iRYsYOnQokZGRuLi40KBBAwYOHMjGjRvLbbtp0yY0Gg0zZ85k27Zt9O3bF09PT3x8fBg5ciQnT56s/Mm6TEuWLGH16tUsXLgQDw+PCreLi4sDYPDgwVbLfXx86NmzJwDp6elVPu7ChQtp06YNLi4uRERE8Pzzz1NUVIRGo6Fv375W20ZGRhIZGWlzP+bX5GJKKRYtWkSPHj3w8vLCzc2NLl26sGjRonLbFhUVMXfuXNq3b4+3tzfu7u5ERkYyatQoDhw4AJjGa0+cOBGAiRMnWrrilz12ZWO6q3IOg/Xrv2fPHm699VY8PT3x9vZm+PDhNve9b98+7r77bho2bIizszMBAQF07dqV119/vdy299xzD8A120VeiMv22WfWCe7y5TBlCjg4QIMGcO+98O23tu+r0UBWlun3nBzw8wNnZzh/HtLSYMAA07rmzcHHB9atu3Q87dtDy5agvUTKtnevqTU8Ksp0++GHYfVqU0t2ZesA7rsPFiwwXRwQNUaSbiGEuI4opdi4cSMtWrTA19e33Prjx4/TtWtXFi1aROfOnXn22Wfp2LEjixYtokuXLhw/ftzmfkePHs2iRYsYOHAgkydPJiMjg8cff5wFCxZUGo+3tzf33Xcff//9Nzt27Ci3/vfffychIYH7778fV1fXSz6+J598kkmTJpGUlMTkyZOZPHkySUlJTJw4kaeeesqyXc+ePYmMjOSHH36gyPzFo4xvv/0WvV7P2LFjLcsyMjLo3r07r7zyCr6+vjzyyCOMHDmSvXv3EhUVxcqVK23GNHLkSJYsWUJUVBRPPfUUjRs3rvQxPP7446SkpDBgwACeeeYZ7rzzTnbu3MmAAQP4+eefbd7nzz//pH///nh7e/PEE0/Qp08ffvrpJ2655RZLAmxmHms8YcKESuO42OnTp3n66ad56KGH6N+/f6XbtmnTBoC1a9daLc/KymL79u0EBwfTqlWrKh331VdfZcqUKaSnpzNlyhTuueceli9fbkkIr5ZSivvvv5/JkyeTlpbGmDFjePDBB8nPz2fy5Mk899xzVtuPHz/esmzixIlMnTqVW265ha1bt7J7924Ahg0bxtChQwEYOnQoM2bMsPxcSlXP4bJ2795N7969cXJy4uGHH6ZLly6sXLmSAQMGWJ3f0dHR3HLLLaxbt46ePXsybdo07r77btzc3Pj888/L7Tc8PJyIiAjWr19ftSdTiGvdpk1QtoBgYiI0avTP7chI07KLaTSmBH3ECNP2PXuaupc7OYG/P4SEmFqswdTVPCYGqrOI4sVxenqClxecPVv5OoDgYHB1td2CL6qPEkIIcd04cuSIAtT9999vc31UVJQC1GeffWa1/KOPPlKA6tevn9XyPn36KEB169ZNZWdnW5YfO3ZM6XQ61aJFC6vtFy9erAC1ePFiy7K//vpLAWrChAnl4rn77rsVoKKjoy/52DZv3qwA1bJlS5WVlWVZnpGRoZo3b64AtWXLFsvy6dOnK0AtX7683L46d+6snJyc1Pnz5y3LxowZowC1YMECq21TUlJURESECggIUIWFheWemw4dOljtx2zGjBkKUBs3brRaHhcXV27bs2fPqtDQUNWsWTOr5Rs3blSAAtSnn35qte7TTz9VgLrzzjutlptfg/Hjx5c7TkWMRqO69dZbVUREhMrJybGK/9tvvy23/blz51Tz5s2VRqNRgwYNUs8//7x65JFHVHBwsLrhhhvUzp07q3TcEydOKJ1Op8LCwlRKSopleXZ2tmrRooUCVJ8+fazu06hRI9WoUSOb+zO/JmV9/vnnClATJ05UJSUlluXFxcVqyJAhClB79uxRSimVlZWlNBqN6ty5s9Lr9Vb70ev1KjMz03Lb1rle1vjx4xWgTp06ZVl2uedw2dd/2bJlVvsfO3Zsuddn2rRpClArV64sF096errNOIcPH64Am+elEPWOk5NSqan/3G7TRqkdO/65/dFHSo0dW/5+paVK9emj1ObNptu7dikVHKxUWprpdnS0UgMHKtWhg1L3369Uv35Kvfde1eMaP16pd9+teP333yt1223WywIClIqNrXydWffuSq1bV/V4xGWTlm4hhLiOnDlzBoCgoKBy6xITE9m4cSOtWrViypQpVuseeeQRbrzxRjZs2MDp06fL3Xf27Nl4eXlZbrdo0YIePXoQExNDbm5upTHddNNNdOzYkRUrVpCTk2NZnpaWxqpVq+jatSvt27e/5GP74kLRmpkzZ+Lt7W1Z7uvra2lhLNtN1tyKvXTpUqv9HD16lL179zJ48GAaNGgAmLpCL1++nH79+pUrAhYYGMj//d//kZaWxh9//FEurlmzZln2UxW2WsJDQkIYOXIkJ06csDkWunnz5uVesylTptCsWTPWrFlDWlqaZfnw4cM5evQos2fPrnJMn376Kb///jsLFizA09PzktsHBQWxc+dOBg0axK+//spbb73Fp59+SnZ2NuPGjavS6wnwzTffoNfrmTZtGoGBgZblXl5eTJ8+vcrxV+bDDz/E3d2djz76yGp8tZOTk6XL9bcXupNqNBqUUri4uKC9qLung4MDPj4+VxXL5Z7DZr179+bee++1WmYeOmFufS/LVq8RPz8/mzGZPyvMnx1C1Gtubv90uwZo2BDKfubGx5uWXSw62tRy3Lu36XbXrhAeDvv3m263bw+//mq6vXSpadsKhlVdkYvjzM01FUcLDa18nVlRkam1W9QYnb0DEEIIUXvOnz8PYDM5iI6OBqBPnz7lxr1qtVp69+7NsWPHiI6OJiIiwmp9586dy+0vPDwcMHUpvlSi9vDDD/PII4/wzTffWAp0ffnll5SUlJRLJiuy/8KXm4vH+AJEXRjLZn6MYEpUb7rpJn799VfS09Px9/cH/knCy3Yt3717NwaDgeLiYptjsk+cOAHAsWPHuPPOO63W3XTTTVWK3ywuLo7Zs2ezYcMGkpKSKC4utlp/9uxZGpXtKgj06NGjXBKo1Wrp0aMHJ06c4MCBAwy4MJ7Q29vbKqGrSjz/93//x6RJkxg4cGCV7nPy5EmGDBmCh4cHW7dupUOHDmRlZbF06VKmT5/Ob7/9xtatW3FwcKh0P+Yx0r169Sq3ztayy1VQUMChQ4cIDQ1lzpw55daXlpYCptcVTMn+4MGDWbt2LZ06deKee+6hb9++dO3atVoKol3uOWx2qfef2ahRo5g/fz7Dhw/n3nvv5dZbb6V3796VVlAve+FJiHqvXTtT12/z37h77jGNd77nHlOiuny5qUjaxSIiIDkZjh41jcE+eRJiY6FFC9P65GRTF3Mw7c/d3VRFHEwV0ZOS4DIuhJbTuTOUlsLGjaax2599BkOGmAqlVbYOwGAwxdq27ZUfX1ySJN1CCHEdMbdw2RrHbG5lttUKDqbW1rLblVW2ldtMpzP9ianKnMxjxozhueeeY+HChZak+7///S8eHh6MHj36kvc3x6XVagkICCi3LigoCI1GUy72sWPHsmvXLpYvX87jjz+OUoqvv/4aX19f7rjjDst2GRkZAGzfvp3t27dXGEN+fr7NY1fVyZMnuemmm8jJySEqKoohQ4bg5eWFVqtl06ZNbN68uVwSXtkxzMuzr2I6mMmTJ+Pj48O8efOqfJ8JEyaQkJBAXFwcwcHBAHh4ePDiiy+SkpLC/PnzWbZsGffff3+l+zHHXbaV2+xynteKZGZmopQiKSmJWbNmVbhd2dd1xYoVvPHGG3zzzTf861//Akzn/8SJE3njjTdwc3O74niu5Bw2H/9itt5/3bp1Y9OmTZb4Fy9eDEDXrl2ZM2eOJbEvy1zA8GoelxDXjLvvht9++6fo2dixpjHYzZqZxm1Pm/ZPcrpqleln4UIICoLPP4dRo0xFz4xGUzJtbhX//HP4+mtTsbKWLeGnn0z7A1N19BtusB3PkiUwfTpkZsLKlfDOO6YiaB07wqefmlrMX3nFdMylS01F0oqKTK3YX31l2kdl6wC2bTO1zF9Gjyxx+aR7uRBCXEfMX+bNSWRZ5i/uKSkpNu977tw5q+2qk6enJ/fffz979+4lOjqa7du3c/ToUe67775Kq2SX5eXlhdFotOpKbZaamopSqlzs9913H46OjpbW7S1btpCQkMCoUaNwdna22jfAs88+i1Kqwh9bhbJsVcuuyLvvvktmZiZLlizh999/Z/78+bzyyivMnDmTG2+8scL7VfSamZdfTsv2xfbv309SUhI+Pj5WVbjNSero0aPRaDTMvzCHbG5uLtu3b6dly5aWhLssc2JnbtWtjDnu1NTUcusqesxarRa9Xm9z3cUXH8yva+fOnSt9XctWjndzc+O1114jLi6OuLg4/vvf/9KiRQvee+89nnnmmUs+pspcyTl8uXr16sW6devIzMxk48aNTJs2jUOHDnHHHXeUK7oH/3xW2LoQIES9M3GiKek2X2hzcICPPoK4OFNrcNlihnfdZUq4zUaPhkOHTNOCHTpkXQV9xgzTdF0nTpgS9bK9xQ4eNCXEtkyYAGfOmOLJyjL93rGjad0jj5gSbrPu3U37On7cVBCu7DEqW/fJJ/DCC1V/jsQVkaRbCCGuI61bt0ar1RITE1NuXYcOHQBT4qkumjpEKcWWLVustqtuD1/40rFgwQIWXvgiU9Wu5QAdL3wR2bRpU7l15mUXx+7v78+gQYP4888/OXnypCX5fuCBB6y269q1KxqNhp07d1Y5nisRGxsLYKl8baaUqrSFffv27RiNRqtlRqORHTt2oNFoqjyG2pZx48ZZqmiX/TE/31FRUUyePNlSsbykpASouDuyOaEse1GjIua4t27dWm6drWVgGv+cmppaLvHOz8+3DAMw8/T0pGXLlhw9etSqG3ZVNW7cmEmTJrF582Y8PDxYtWqVZZ2563xVenqYXck5fKVcXV3p27cvc+fO5eWXX6awsJDff/+93HYxMTE4OjpWetFHiHrDwwPefRdOnaq9Y27bZqoobg9FRdCnD9x6q32Ofx2RpFsIIa4jPj4+tGvXjj179pRL0ho2bEhUVBRHjhwpNz/x559/ztGjR+nXr1+58dzVpWPHjnTt2pWvv/6aFStW0K5du8saDz1+/HjAVLisbBfc7OxsS6useZuyzGO3Fy5cyIoVK2jcuHG5+bmDg4MZNWoUO3bs4O233y53UQLgr7/+oqCgoMrx2mIeq33xvN9vvvkmhw8frvB+x48fLzc924IFCzh+/Dh33HGHVStldnY2x44dIzk5uUoxvf/++yxcuLDcz1133QXAQw89xMKFCy1jxv38/GjRogWJiYmWiydmWVlZvPPOOwA2uzJfbMyYMTg4ODBv3jyr1u6cnBxee+01m/fp2rUrpaWlfP3115ZlSileeuklm93/n3zySQoKCpgyZYrN9adOnbLMd52WlmbzdcjMzKS4uBgX8xhJ/hkLbavwYEWu9Byuqp07d9ocWmLuNVA2fjBdQNm/fz9dunSR7uXi+tG/P1y4iFjvubjAo4/aO4rrgozpFkKI68zw4cOZMWMGf/75J7fccovVuk8++YSePXsyZcoUVq9eTatWrThy5AirVq0iICCATz75pEZje+SRR5g8eTJwea3cYKrg/MQTT/DBBx/Qpk0bRo4ciVKKH374gTNnzvDkk0/S21xZtowhQ4bg7e3NvHnzKC0t5cknn7TZJfzjjz8mJiaG559/nq+++oru3bvj4+PD6dOn2bNnDydOnCA5OfmqkpNHHnmExYsXM3LkSEaNGoWfnx9//vkn+/bt44477mDNmjU27zdw4ECefPJJ1q5dS+vWrTly5AirV6/G39+f9957z2rbn376iYkTJzJ+/HiblbCrw7vvvstdd93FlClTWLZsGR07diQzM5NVq1aRlpbGyJEjLUl6ZZo2bcp//vMfZsyYQbt27Rg1ahQ6nY4ffviBdu3a2eyxMXXqVBYvXsyDDz7I77//TkBAAFu3biUrK4v27dtbirOZPfzww/z555988cUXbN++nQEDBhAaGkpKSgrHjh3jr7/+4ptvviEyMpKkpCQ6duxI+/btadeuHWFhYZw/f56ff/6Z0tJSqzm9u3fvjqurK/PnzyczM9Ny4aOyqutXeg5X1Zw5c9i4cSO9e/emcePGuLi4sG/fPtavX88NN9zA8OHDrbbfunUrxcXFDBs27IqPKYQQApmnWwghrjdJSUlKp9OpRx991Ob6+Ph4NXHiRBUSEqJ0Op0KCQlREydOVPHx8eW2tTXvsZmteYgvNXdxfn6+cnZ2Vq6urlZzHl+ORYsWqa5duyo3Nzfl5uamunbtqhYtWlTpfR588EHLfMcxMTEVbldQUKDeeust1blzZ+Xu7q5cXV1V48aN1bBhw9SXX36pSktLLdtW9twoVfE83Rs3blQ9evRQnp6eysfHRw0ePFjt3bvX5vbmeZpnzJihtm7dqvr06aPc3d2Vl5eXGj58uDpx4kS5417JPN2VxW9rnm6llNq1a5e65557LOeRh4eH6tq1q/rggw/KzXF9KQsWLFCtWrVSTk5OKjw8XD333HOqoKDA5jzdSim1YcMG1a1bN+Xs7Kz8/PzU2LFjVUpKSqWvyfLly9WAAQOUr6+vcnR0VGFhYapv375q7ty5Ku3CXLuZmZlq5syZqnfv3iokJEQ5OTmp0NBQNWjQILXOxhy3a9asUV27dlWurq6W88vM1vvDrKrncNnX/2KnTp0q9zr/+uuvaty4capFixbK09NTeXh4qFatWqmXX37Z8hjLmjBhgnJyclKpZectFkIIcdk0StnoIyeEEKJeGzt2LGvWrCEhIaFK8y7Xlj179tC1a1fGjh3Ll19+ae9w6rxNmzYRFRXFjBkzbE5lVt9pNBr69Oljcwy0uDqZmZk0atSIu+++u9xwEyGEEJdHxnQLIcR16LXXXqOwsJAPPvjA3qFYefvttwF4VMaYCWFX8+bNw2Aw8Oqrr9o7FCGEuObJmG4hhLgONWrUiC+++KLCaZdqU2JiIt988w1Hjhzhu+++Y+DAgXTv3t3eYQlxXWvQoAFffvklYWFh9g5FCCGueZJ0CyHEdWrUqFH2DgGAuLg4XnrpJTw8PBgyZAiff/65vUMS4rp3tXOOCyGE+EedGtO9ZcsW3n77bfbu3UtycjI//fTTJStmbtq0iWnTpnHkyBEiIiKYPn06EyZMqJV4hRBCCCGEEEKIytSpMd35+fm0b9+ejz76qErbnzp1ijvuuIOoqCiio6N5+umnefDBB/ntt99qOFIhhBBCCCGEEOLS6lRLd1kajeaSLd0vvPACa9as4fDhw5Zl9913H1lZWfz666+1EKUQQgghhBBCCFGxa3pM986dOxkwYIDVsoEDB/L0009XeJ/i4mKKi4stt41GIxkZGfj5+aHRaGoqVCGEEEIIIYQQdZxSitzcXEJDQ9Fqq6dj+DWddJ87d46goCCrZUFBQeTk5FBYWIirq2u5+8yePZtZs2bVVohCCCGEEEIIIa4xp0+fJjw8vFr2dU0n3VfipZdeYtq0aZbb2dnZNGzYkFOnTuHj42O/wISoJkajkfT0dPz9/avt6pwQ9iTntKhvrvqcNhrh7Fk4fhyKisDJybL4fAYkJUFxCTg6VHPgQtikKPZSOOdoAOk1WhUe8Ye58dtLNwIaV62CXr1qISKh1+v5/PPPyc3NRavV8sYbb+Dp6Vlt+7+mk+7g4OByc8ympKTg5eVls5UbwNnZGWdn53LLfXx8JOkW9YLRaKSkpAQfHx9JUES9IOe0qG+u6pzOz4cTJyA+Hry8oFEjAAoKIDERkvPBLQSCvKo/biFsUSjyHYpwN7igkaS7SvRNG+K87hOcslJtP2MaDYSHw+DB4CBXz2rL0KFD2bNnD/369eONN96o1qHH1/S3l+7du7N+/XqrZb///jvdu3e3U0RCCCGEEDVAKUhOht27IS4OAgPB1xejEVJS4PBhSD4Hfg1MubgQog7TOhA7/Dnb68yJ3vz5knDXsPT0dBITEy23W7duzbhx46q1hdusTiXdeXl5REdHEx0dDZimBIuOjrY8GS+99BLjxo2zbP/II48QFxfH888/z7Fjx/j444/57rvveOaZZ+wRvhBCCCFE9Ssuhr//NiXcRUXQsCE4O1NYZGr0PnoUDEYICgRHR3sHK4SoiuymncDBRqfj8HD4/nsYMaL2g7qOHDp0iAULFrB8+XJyc3Mty2uqsHad6l6+Z88eoqKiLLfNY6/Hjx/PkiVLSE5Otroa0bhxY9asWcMzzzzDe++9R3h4OAsXLmTgwIG1HrsQQgghRLVLT4djxyA1FQICwM0NpUyLE+IhNxd8fcHGyDkhRB0WtOsXNAa96Ub//tChA/TpI13Ka1hpaSm//vor+/btAyA0NLRWjlunku6+fftS2bThS5YssXmf/fv312BUJgaDgdLS0ho/jhBXy2g0UlpaSlFR0SXHCjo6OuIgH+xCCFH36PWmcdvHj5sqpIWHg4MDxcWQeBrOJplatYOC/umNKoS4RihF8I4f/7n9yCOmgog9e0rCXYPS09P5/vvvLTXBevfuTZ8+fWqlXkydSrrrIqUU586dIysry96hCFElSimMRiO5ublV6iLj4+NDcHCwzFMvhBB1RXY2xMSYypD7+oKnJ0pBxnlTHp6dDQ0aSOu2ENcq92N7cUu70Hu3SxfTkJFz5+wbVD136NAhfvnlF0pKSnB3d2fEiBHccMMNtXZ8SbovwZxwBwYG4ubmJomJqPOUUuj1enQ6XaXnq1KKgoICUlNTAQgJCamtEIUQQthiNMKZM6aEu6AAQkNBp6OkxLQ4KcnUqh0UBFLIX4hrV4NNP/xzY+RI+wVyHYmNjaWkpITIyEhGjBhRI8XSKiNJdyUMBoMl4fbz87N3OEJUSVWTbsAytV5qaiqBgYHS1VwIIeyloMDUlTwhAdzdTd3JgcxMiE+AzAxTo7eLi53jFEJcFYfsDLz2bgTA6NsAbd++9g3oOjF48GCCg4O56aab7DL9qCTdlTCP4XZzc7NzJELUHPP5XVpaKkm3EELUNqVM3UpjYkwZdlAQODtTWmpq2T59xrSZtG4LUT/4bF2F9kIBNf2gITg5OoLBYOeo6p+DBw9y4sQJRowYgUajwcnJiZtvvtlu8UjSXQXSpVzUZ3J+CyGEnRQXw6lTEBtrqooWEQEaDVlZkJhoqlDu7Q1y7V+IesJoxGfjT5abpUOG42THcOqj0tJS1q1bZym03bx5c9q2bWvnqCTpFkIIIYSofTk5cPKk1VRgej0kJ5sSbqMRAgOlkLEQ9Yn7kV04pSYBkNniZhxDw+0cUf2Snp7OihUrLPWK+vTpQ+vWre0clYl0VBKXbebMmQQFBaHRaFi5cmWNHaem938pmzZtQqPRWCrXL1myBB8fH8v6mTNn0qFDB7vEdjkufhxCCCHsSK83tWyfOAEZGaax225u5ObC0WOmxc7O4O8vCbcQ9Y3Phn8KqCX3kAJq1engwYN8/vnnpKam4u7uztixY+nbt69dxm/bUjeiENVuwoQJaDQayxiGpk2b8sorr6DX669qv0ePHmXWrFl89tlnJCcnc/vtt191rNdK8nrvvfdy/PjxWjmWJMpCCFEP5eTA/v1w+LBpTt7QUAw4cOaMadH5dAgINNVRE0LUL7rMNDz3bQGg1MefjNa97BxR/bFx40Z++uknSktLiYyM5OGHH67V6cCqQrqX12ODBg1i8eLFFBcXs3btWh5//HEcHR156aWXLntfBoMBjUZDbGwsAEOHDr3uxgK7urpaqn1fqZKSEpycZPSOEEJcV4xGU1W0Y8dMVcpDQkCvJy8PTieaeph7eECZzlRCiHrGe/PPaIymgmmZvYaiHCQNqy7Nmzdn+/bt9OzZk969e9eZ1u2y6l5Eoto4OzsTHBxMo0aNePTRRxkwYACrVq0CoLi4mOeee46wsDDc3d3p1q0bmzZtstzX3JV61apVtGrVCmdnZyZNmsSQIUMA0Gq1Vkn3woULadmyJS4uLtx44418/PHHVrGcOXOG0aNH06BBA9zd3enSpQt//fUXS5YsYdasWRw4cMDSMr9kyZJyj6Vfv35MnTrVallaWhpOTk6sX7++wudg9erVdO3aFRcXF/z9/Rk+fLhl3VdffUWXLl3w9PQkODiYMWPGWMaA2HJx93Kzzz77jIiICNzc3Bg1ahTZ2dmWdRMmTGDYsGG8/vrrhIaG0qJFi0seOz4+nqioKAB8fX3RaDRMmDABAKPRyOzZs2ncuDGurq60b9+e77//3iqetWvX0qpVK9zc3IiKiiI+Pr7CxySEEKKGFRTAwYOwb5/pdng4Rq2OjEw4cgTS0kxdyT087BumEKIGGQ34bloJgNJoyegzvPLtxSVlZGRYfg8LC+Opp56qU93JLyaXWK5QSUlJheu0Wi06na5K22o0GhwdHS+5bXW0jrq6unL+/HkApk6dyt9//82yZcsIDQ3lp59+YtCgQRw6dIhmzZoBUFBQwJw5c1i4cCF+fn6EhITQt29fJk6cSHJysmW/X3/9Nf/5z3/48MMP6dixI/v372fKlCm4u7szfvx48vLy6NOnD2FhYaxatYrg4GD27duH0Wjk3nvv5fDhw/z666/88ccfAHh7e5eL/cEHH2Tq1KnMnTsXZ2dnAJYuXUpYWBj9+vWz+XjXrFnD8OHD+de//sWXX35JSUkJa9eutawvLS3l1VdfpUWLFqSmpjJt2jQmTJhgtc2lnDx5ku+++47Vq1eTk5PD5MmTeeyxx/j6668t26xfvx4vLy9+//33Kh07IiKCH374gZEjRxITE4OXl5elhX327NksXbqUTz/9lGbNmrFlyxYeeOABAgIC6NOnD6dPn2bkyJE8+uijPPzww+zdu5dnn322yo9HCCFENVEKUlJMrdtlpgIrKICERDiTA14a02IhRP3mcXAnjufPAZDX/hZK/YIh+xJ3EjaZq5MfPHiQBx98kODgYAA8PT3tHFnlJOm+QrNnz65wXbNmzRgzZozl9jvvvGOZ8/tijRo1srRiArz33nsUFBSU227GjBlXHKtSivXr1/Pbb7/xxBNPkJiYyOLFi0lMTCQ0NBSA5557jl9//ZXFixfzxhtvAKaT+uOPP6Z9+/aWfZlbes0nuDm2uXPnMmLECAAaN27M33//zWeffcb48eP55ptvSEtLY/fu3TRo0ACApk2bWu7v4eGBTqez2ufFRowYwdSpU/n5558ZNWoUYGp5No9dt+X111/nvvvuY9asWZZlZR/LpEmTLL/fcMMNvP/++3Tt2pW8vDw8qtjkUFRUxJdffklYWBgAH3zwAXfccQdz5861PB53d3cWLlxodeHkUsc2P0+BgYGW57y4uJg33niDP/74g+7du1vuu23bNj777DP69OnDJ598QpMmTXjrrbfQ6XTceOONHDp0iDlz5lTp8QghhKgGxcWmYmmxsaDTQUQERqUhLQUSEiCvALyCwMvecQohakXZAmpZUSPsGMm17eLq5KdPn640f6hLJOmux3755Rc8PDwoLS3FaDQyZswYZs6cyaZNmzAYDDRv3txq++LiYvz8/Cy3nZycaNeuXaXHyM/PJzY2lsmTJzNlyhTLcr1eb2mxjo6OpmPHjpZE8kq4uLgwduxYFi1axKhRo9i3bx+HDx+2dJe3JTo62iqmi+3du5eZM2dy4MABMjMzMRqNACQmJtKqVasqxdWwYUNLwg3QvXt3jEYjMTExlg+Btm3bluupcCXHPnnyJAUFBdx6661Wy0tKSujYsSNgKnR30003Wa03J+hCCCFqwfnzEBMD585ZpgIrLILEBNN0YM4uEBQIBQ6Awd7BCiFqmu78OTyitwNQ6hdEXoceYLRzUNeggwcP8ssvv1BaWoq7uzsjRoyoc8XSKiNJ9xWqrBjZxWMJnnvuuQq3vbiV9qmnnrq6wMqIiorik08+wcnJidDQUEuX97y8PBwcHNi7dy8OF81HUraF19XV9ZLF0vLy8gBYsGAB3bp1s1pn3vfVFh8ze/DBB+nQoQNnzpxh8eLF9OvXj0aNGlW4fWXHzc/PZ+DAgQwcOJCvv/6agIAAEhMTGThwYKXDAa6E+0VlaK/02Obnes2aNVaJPmDpci+EEMJO9HpTM/bx46bfw8NRWgfS0yA+AfJywdfXNB2YsnesQoha47PpZzTKlGVn9RkGWgdJui+DuTv5/v37AVOP2hEjRlS5V2pdIUn3FbqcMdY1te2luLu7W3XjNuvYsSMGg4HU1FR69bq66QqCgoIIDQ0lLi6O+++/3+Y27dq1Y+HChWRkZNhs7XZycsJguPTl/rZt29KlSxcWLFjAN998w4cffljp9u3atWP9+vVMnDix3Lpjx45x/vx53nzzTSIiIgDYs2fPJWO4WGJiImfPnrV00//zzz/RarWWgmm2VOXY5vOg7PNiLmiXmJhInz59bO67ZcuW5Vr///zzz8t+XEIIIS5DTo6pdfvMGVMJci8viorg9Bk4m2SaHSwoCK6zST+EEAY9PptXAqC0DmT1GWrfeK5B0dHRloS7T58+dbY6+aVI0n0dat68Offffz/jxo1j7ty5dOzYkbS0NNavX0+7du244447Lmt/s2bN4sknn8Tb25tBgwZRXFzMnj17yMzMZNq0aYwePZo33niDYcOGMXv2bEJCQti/fz+hoaF0796dyMhITp06RXR0NOHh4Xh6elbYcmsuqObu7m5VidyWGTNm0L9/f5o0acJ9992HXq9n7dq1vPDCCzRs2BAnJyc++OADHnnkEQ4fPsyrr756WY8bTN3ex48fzzvvvENOTg5PPvkko0aNqnR8SVWO3ahRIzQaDb/88guDBw/G1dUVT09PnnvuOZ555hmMRiM9e/YkOzub7du34+Xlxfjx43nkkUeYO3cuL774IlOmTGHfvn02q8ELIYSoBhdPBRYainLQcT7d1OidnQ0NGphat4UQ1x+P6G04ZqYBkNexF/oGgXaO6NrTuXNnzpw5Q4cOHWjcuLG9w7li195lAlEtFi9ezLhx43j22Wdp0aIFw4YNY/fu3TRs2PCy9/Xggw+ycOFCFi9eTNu2benTpw9LliyxvDGcnJz43//+R2BgIIMHD6Zt27a8+eablu7nI0eOZNCgQURFRREQEMC3335b4bFGjx6NTqdj9OjRuLi4VBpX3759WbFiBatWraJDhw7069ePXbt2ARAQEMCSJUtYsWIFrVq14s033+Sdd9657MfetGlTRowYweDBg7ntttto165duenSLlaVY4eFhTFr1ixefPFFgoKCLNOlvfrqq/z73/9m9uzZtGzZkkGDBrFmzRrLc92wYUO+//57y2P+9NNPLYXxhBBCVCMbU4GVGHWcOgV//w2FhZaC5UKI65Tvhh8tv2f2kwJqVVFaWsqmTZssRai1Wi3Dhw+/phNuAI1S6roeWpSTk4O3tzeZmZnl5mAuKiri1KlTNG7c+JIJnqgd8fHxNGnShN27d9OpUyd7h1MnKaXQ6/XodLpLjskHOc9F3Wc0GklNTSUwMPCa7FIm6hnzVGAxMZCRAYGB4OJCZqZp7HZmhmnsdmUfpwpFvkMR7gYXNEifc3Ftk/PZNse0szR5digapSjxDyV27kq48DdMr4esbOjYATw8AIPBVHyxVy/TB8h1Ki0tjRUrVpCWlkbnzp2588477RJHVlYWvr6+ZGdn4+VVPfNMSPdycU0oLS3l/PnzTJ8+nZtvvlkSbiGEELWvpAROnrSaCqxUryEp3jR+G0yt23JtSAjhs+knNBfaNrOihssHwyUcOHCANWvWWKqTt27d2t4hVStJusU1Yfv27URFRdG8eXO+//57e4cjhBDiemNjKrCsLEhMhPR08PYGNzd7BymEqBP0enw2mwrbKgcHsnoPsXNAdVdpaSlr164lOjoauHark1+KJN3imtC3b1+u85EQQggh7ME8FdiJE5apwEqVA+dOmxJug8HUw/yiGTiFENcxz32b0GWfByC3cxQGH387R1Q3nT9/nuXLl5OWZio217dvX3r16lUvh5JJ0i2EEEIIYYt5KrDTp03jLAMCyMkx5eBpaeDlBe7u9g5SCFHXSAG1qtHpdOTl5eHu7s7IkSOv+WJplZGkWwghhBCiLPNUYDExkJ8PYWHo0XHujCn/LimBgEDQSeu2EOIijimncT9imi2nJCiCgpZd7BxR3WI0Gi0t2d7e3tx33300aNCg3nUnv1j9a7sXQgghhLhShYVw6JBpKjClIDyc3CIdMTGmHuY6nak7uSTcQghbrFq5o0ZIAbUy0tLS+Oyzz4iJibEsa9iwYb1PuEFauoUQQgghTAl2aiocO2YqmhYUhMHRhdRk01RgxUXgHyDJthCiYprSEry3rgbAqHMkWwqoWZStTr5+/XqaN29epalt6wtJuoUQQghxfSspgbg403RgWi00bEh+gYbEU3AuBdzdTFOBCSFEZTz3bECXmwVAbtd+GDx97BpPXXBxdfIbbriB4cOHX1cJN0jSLYQQQojrWUaGaex2cjL4+2N0dSc1xVSZPD8f/PzA0dHeQQohrgU+VgXURtoxkrohLS2NFStWkJaWhkajoU+fPvW2OvmlXH+PWIgaFB8fj0ajsVzN27RpExqNhqysLLvGJYQQ4iIGA8TGwq5dpom2w8Mp1Lpz4oSph7nBCMHBknALIarGKekU7sf2AVAc2pjCFh3tHJF9ZWdns2DBAtLS0vDw8GDcuHH06dPnuky4QZLuWmMwwKZN8O23pv8Nhpo93oQJE9BoNJYfPz8/Bg0axMGDBy97P8OGDat0m7LHsfUzc+bMK38g1WjmzJloNBoGDRpUbt3bb7+NRqOhb9++1XrMW265heTkZLy9vat1v0IIIa5Cbi5ER8PBg+DkhAoJJS3DgcOH4exZ0+xgPvKxLYS4DD4byxZQGw7XWffpi3l7e9OuXTtuuOEGHn74YSIjI+0dkl1J9/Ja8OOP8NRTcObMP8vCw+G992BEDU7dN2jQIBYvXgzAuXPnmD59OnfeeSeJiYnVepzk5GTL78uXL+c///mPVVXCulSRMCQkhI0bN3LmzBnCw8MtyxctWkTDhg2r/XhOTk4EBwdX+36FEEJcAaPRlFUfOwZ5eRASQpHBkdMnTYudnExjt6/z78pCiMukKSnCZ9saAIyOzmT3vMPOEdlHWloarq6ulu/+gwYNQqvVXret22XJM1DDfvwR7r7bOuEG0/Sfd99tWl9TnJ2dCQ4OJjg4mA4dOvDiiy9y+vRp0tLSLNscOnSIfv364erqip+fHw899BB5eXmAqWX4iy++4Oeff7a0Wm/atKnccczHCA4OxtvbG41GY7Vs2bJltGzZEhcXF2688UY+/vhjq/u/8MILNG/eHDc3N2644Qb+/e9/U1paalk/c+ZMOnToYEmMPTw8eOyxxzAYDLz11lsEBwcTGBjI66+/fsnnJDAwkNtuu40vvvjCsmzHjh2kp6dzxx3lPyAXLlxYaey7du2iY8eOuLi40KVLF/bv32+1/uLu5efPn2f06NGEhYXh5uZG27Zt+fbbb63u07dvX5588kmef/55GjRoQHBwcJ3pLSCEENeswkI4fBj27gWjERUeQXq2I0eOmObe9vYGHx9JuIUQl89r1x845OcAkNNtAEaP66+rzIEDB1iwYAE//vgjRqMRAJ1OJwn3BdLSXYMMBlMLt1Ll1yll+sP+9NMwdCg41PAUJHl5eSxdupSmTZvi5+cHQH5+PgMHDqR79+7s3r2b1NRUHnzwQaZOncqSJUt47rnnOHr0KDk5OZYW8wYNGlzWcb/++mv+85//8OGHH9KxY0f279/PlClTcHd3Z/z48QB4enqyZMkSQkNDOXToEFOmTMHT05Pnn3/esp/Y2FjWrVvHr7/+SmxsLHfffTdxcXE0b96czZs3s2PHDiZNmsSAAQPo1q1bpTFNmjSJ559/nn/961+AqZX7/vvvv+zY8/LyuPPOO7n11ltZunQpp06d4qmnnqr02EVFRXTu3JkXXngBLy8v1qxZw9ixY2nSpAk33XSTZbsvvviCadOm8ddff7Fz504mTJhAjx49uPXWW6v83AshhLggJcVqKrBijQtn4kwXwB0cTGO3JdkWQlypsgXUsqJqsBtrHVRSUsK6dess9Yw0Gg0lJSW4uLjYN7A6RpLuK9ClC5w7d+ntiotNtVkqopTp6npwMDg7X3p/wcGwZ0/V4/zll18s3Tvy8/MJCQnhl19+sVxx+uabbygqKuLLL7/E3d0dgA8//JAhQ4YwZ84cgoKCcHV1pbi4+Iq7SM+YMYO5c+cy4kI/+saNG/P333/z2WefWZLu6dOnW7aPjIzkueeeY9myZVZJt9FoZNGiRXh6etKqVSuioqKIiYlh7dq1aLVaWrRowZw5c9i4ceMlk+4777yTRx55hC1bttC5c2e+++47tm3bxqJFiy4r9m+++Qaj0ch///tfXFxcaN26NWfOnOHRRx+t8NhhYWE899xzlttPPPEEv/32G999951V0t2uXTtmzJgBQLNmzfjwww9Zv369JN1CCHE5Lp4KLCKCjCwtCQmQmWkauy3fC4UQV8P59EncTphqJhVFNKWwWTs7R1R7Lq5O3rdvX3r27Cmt2zZI0n0Fzp0zXR2vLpUl5lcjKiqKTz75BIDMzEw+/vhjbr/9dnbt2kWjRo04evQo7du3tyTcAD169MBoNBITE0PQVU5Kmp+fT2xsLJMnT2bKlCmW5Xq93qqw2PLly3n//feJjY0lLy8PvV6Pl5eX1b4iIyPx9PS03A4KCsLBwcHqTR0UFERqauol43J0dOSBBx5g8eLFltbydu2sPyCrEvvRo0dp166d1ZW87t27V3psg8HAG2+8wXfffUdSUhIlJSUUFxfj5uZmtd3F8YSEhFTpsQkhhLjgoqnASp3cOZP4z3CvoCBTHi6EEFfDZ8MPlt+zokZcN91moqOjWbt2LaWlpXh4eDBy5MjrvlhaZSTpvgJVbfS9VEu3mb9/1Vu6L4e7uztNmza13F64cCHe3t4sWLCA11577fJ2dgXMY8MXLFhQrvXZ4UJ/+p07d3L//fcza9YsBg4ciLe3N8uWLWPu3LlW2zteNGeLRqOxucw8huRSJk2aRLdu3Th8+DCTJk26otivxNtvv817773H/Pnzadu2Le7u7jz99NOUlJRYbXc1j00IIa5rBgMkJMDx41BaCuHhZOY6kHjS1LvcxwdcXe0dpBCiPtAUFeK9fS0ARicXsnsMtnNEtUOv17Nt2zZKS0u54YYbGDFihFUjnihPku4rUNUu3gYDREaaWsVtjevWaExVzE+dqvkx3abjadBqtRQWFgLQsmVLlixZQn5+vuWNsn37dkt3bTBV3zZc4fxmQUFBhIaGEhcXZ3PMNJiKmDVq1MgyvhogISHhio53OVq3bk3r1q05ePAgY8aMKbe+KrG3bNmSr776iqKiIktr959//lnpcbdv387QoUN54IEHAFO3+ePHj9OqVaurfERCCCHIzTUl24mJ4O1NqW8AyUmmm0YjBAbWzt9bIcT1wevP33AozAcgp/tAjG51Z8aemqTT6bjnnns4fvw4PXv2RHOdtO5fDelYVYMcHEzTgkH5nibm2/Pn19wXgOLiYs6dO8e5c+c4evQoTzzxBHl5eQwZMgSA+++/HxcXF8aPH8/hw4fZuHEjTzzxBGPHjrV0LY+MjOTgwYPExMSQnp5uVVW8KmbNmsXs2bN5//33OX78OIcOHWLx4sXMmzcPMI1XTkxMZNmyZcTGxvL+++/z008/Ve8TUYENGzaQnJyMj4/PFcU+ZswYNBoNU6ZM4e+//2bt2rW88847lR6zWbNm/P777+zYsYOjR4/y8MMPk5KSUt0PTQghri9KmfqN79plKpYSEkKOxptjRyE21jRuOyBAEm4hRPXyLTs3d7/6XUAtOjqaXbt2WW4HBQXRq1cvSbirSJLuGjZiBHz/PYSFWS8PDzctr8l5un/99VdCQkIICQmhW7du7N69mxUrVtC3b18A3Nzc+O2338jIyKBr167cfffd9O/fnw8//NCyjylTptCiRQu6dOlCQEAA27dvv6wYHnzwQRYuXMjixYtp27Ytffr0YcmSJTRu3BiAu+66i2eeeYapU6fSoUMHduzYwb///e9qew4q4+7uXmHCXZXYPTw8WL16NYcOHaJjx47861//Ys6cOZUec/r06XTq1ImBAwfSt29fgoODGTZsWDU+KiGEuM4UFsKhQ6apwAwG9CERnE4xTQWWkQH+ASC9HoUQ1c0l/hiucX8DUBh5I0WN62evxZKSElauXMnPP//Mb7/9Jo1FV0ijlK2Oz9ePnJwcvL29yczMLJeAFRUVcerUKRo3bnzVZe8NBti61VTPJSQEevWSK+6iZiil0Ov16HS6Kl19rM7zXIiaYDQaSU1NJTAwUCqiCmsXTQWWq3chMcG02NMTPOpoT0+FIt+hCHeDCxqklUhc267X8zl40ev4bjT1zkye+DJZl9HSrddDVjZ07HDhc8pgMFVq7tXLNK1CHZGamsr3339vVZ38emjdzsrKwtfXl+zs7HLFna+UjOmuJQ4OcKGBWQghhBBXwzwVWGwsaDQYQiNITdcSnwDFRRAQCDq5sC2EqCHawny8dv4GgMHFjZzuA+0cUfWLjo5mzZo16PV6qU5eDSTpFkIIIcS146KpwPJxJ/GkqZHI3d00FZgQQtQkr52/4lBUAEDOLbdjdK1fY1hWr17Nvn37AKQ6eTWRpFsIIYQQdZ95KrATJ6C4GGNIGKkZOhISoKDQNP2mTr7VCCFqmlL4lpmbuz4WUPP397+uupPXBvnzJIQQQoi67aKpwArd/UmIM7Vuu7hAsLRuCyFqiUvcEVwSjgNQeENrihu1sHNE1aOwsBBXV1cAbr75Zho3bkxwcLCdo6o/JOkWQgghRN2kFCQlmbqT5+aigkNIy3IkPgby86FBA3BysneQQojrie+GMtOE9R9px0iqR0lJCWvXruXMmTNMmTIFZ2dnNBqNJNzVTJJuIYQQQtQ9hYWmruSnToGrK0X+4ZxO0JCUBM7OprHb0uNRCFGbtPm5eP15oYCamwc53W6zc0RXJzU1lRUrVpCeno5GoyE+Pp4WLepHy31dI0m3EEIIIeqW1FQ4ehQyMlABgaTnu5D4N2Rnm1q3nZ3tHaAQ4nrkvWMt2pJiALJ7DEY5X5tTrSqliI6OZu3atej1ejw9PRk5ciSNGjWyd2j1liTdQgghhKgbSktNU4GdPAlAcUA4Z85qOXPGVCQtOFhat4UQdqIUPmW6lmdFXZsF1MzdyQ8cOABAkyZNGD58uFQnr2GSdAshhBDC/jIz4dgx01Rgfn5klHiQcMy02LcBuEjrthDCjlxPHMDlTCwABc3bUxzR1M4RXZnffvuNAwcOoNFoiIqKomfPnlKdvBZo7R2AEFXRt29fnn76acvtyMhI5s+fb7d4hBBCVBODwTRue9cuSEujNDCMU+keHPkb8vJNY7cl4RZC2FvZVu7MftduAbWoqChCQkIYP368TAdWiyTpri0GA2zaBN9+a/rfYKjRw02YMAGNRoNGo8HJyYmmTZvyyiuvoNfrq/U48fHxaDQaHBwcSEpKslqXnJyMTqezFGaoTrt37+ahhx6q1n0KIYSoZXl5EB0NBw6ATkemexhHYnScigN3N/D3A618UxFC2JlDbhZeu/4AQO/hTW7X/naOqOpKSkosXckBPDw8mDJliozfrmXyp6w2/PgjREZCVBSMGWP6PzLStLwGDRo0iOTkZE6cOMGzzz7LzJkzefvtt21uW1JSclXHCgsL48svv7Ra9sUXXxAWFnZV+61IQEAAbm5uNbJvIYQQNcw8Fdhff8Hp05T6B5OQ7cORw6YpuQMD4cJ0sUIIYXfe29agLTV9V87ueQfK6drofpOamsqCBQtYuXIlhw8ftiyX1u3aJ0l3TfvxR7j7bjhzxnp5UpJpeQ0m3s7OzgQHB9OoUSMeffRRBgwYwKpVqwBTS/iwYcN4/fXXCQ0NtUwPcOjQIfr164erqyt+fn489NBD5OXlXfJY48ePZ/HixVbLFi9ezPjx48tte/jwYW6//XY8PDwICgpi7NixpKenW9bn5+czbtw4PDw8CAkJYe7cueX2cXH38nnz5tG2bVvc3d2JiIjgscces4p7yZIl+Pj48Ntvv9GyZUs8PDwsFyWEEELUoqIiOHwY9uwBvZ5sz3COnnAkNtaUaPv7g4ODvYMUQogLlMJnY5kCav3qfgE1pRT79+9nwYIFpKen4+npiaenp73Duq5J0l2TDAZ46inTFf2LmZc9/XSNdzU3c3V1tWrRXr9+PTExMfz+++/88ssv5OfnM3DgQHx9fdm9ezcrVqzgjz/+YOrUqZfc91133UVmZibbtm0DYNu2bWRmZjJkyBCr7bKysujXrx8dO3Zkz549/Prrr6SkpDBq1CjLNv/3f//H5s2b+fnnn/nf//7Hpk2b2LdvX6XH12q1vP/++xw5coQvvviCDRs28Pzzz1ttU1BQwDvvvMNXX33Fli1bSExM5LnnnrvkYxNCCFFNUlNh9244cQK9jz+ni/w58reGrCwICATpwCSEqGvcju3FOTkBgPyWnSkJibRvQJdQUlLCypUrWbVqFXq9niZNmvDwww9Ld3I7k+rlV6JLFzh37tLbFRdDmRbccpSC06dNc6BUZdLR4GBTy8BlUkqxfv16fvvtN5544gnLcnd3dxYuXIiTkxMACxYsoKioiC+//NIybcCHH37IkCFDmDNnDkFBQRUew9HRkQceeIBFixbRs2dPFi1axAMPPICjo6PVdh9++CEdO3bkjTfesCxbtGgRERERHD9+nNDQUP773/+ydOlS+vc3jZf54osvCA8Pr/QxXlxk7bXXXuORRx7h448/tiwvLS3l008/pUmTJgBMnTqVV155pdL9CiGEqAYXTQWW6xNBfLyW9DTw8ARfXzvHJ4QQFbiWpglLTU1lxYoVpKenS3XyOkaS7itx7pype3h1qSwxvwq//PILHh4elJaWYjQaGTNmDDNnzrSsb9u2rSXhBjh69Cjt27e3mqevR48eGI1GYmJiKk26ASZNmsQtt9zCG2+8wYoVK9i5c2e5wm0HDhxg48aNeHh4lLt/bGwshYWFlJSU0K1bN8vyBg0aWLq/V+SPP/5g9uzZHDt2jJycHPR6PUVFRRQUFFjGfru5uVkSboCQkBBSU1Mr3a8QQoirlJkJMTFw9iwGHz9S8j1I+Nt0Xdo/AHTSlVwIUUc5ZGfgtXsDAHpPX3K7RNk5osplZmZaupOPHDlSWrfrEEm6r0RwcNW2u1RLt5m/f9Vbui9DVFQUn3zyCU5OToSGhqLTWb/cZZPr6tC2bVtuvPFGRo8eTcuWLWnTpg3R0dFW2+Tl5Vlazi8WEhLCyQutIJcjPj6eO++8k0cffZTXX3+dBg0asG3bNiZPnkxJSYkl6b641V2j0aBsdf0XQghx9QwGU2+umBgoLibfJ4zEszrOpZgqk1/iOq4QQtid99bVaAymBqSs3kNQjk6XuEftU0pZWrJbtGjBXXfdRfPmzav9e764OpJ0X4mqdvE2GExVypOSbI/r1mggPNw0P2kNVI1xd3enadOmVd6+ZcuWLFmyhPz8fMsbdfv27Wi12ku2NJtNmjSJxx57jE8++cTm+k6dOvHDDz8QGRlZ7iIAQJMmTXB0dOSvv/6iYcOGgOmq3fHjx+nTp4/Nfe7duxej0cjcuXPRXphb5rvvvqtSvEIIIWpAXp4p2T59GqO7JykO/iQeg4JC0zRgNj7+hRCibjEa8d34k+VmVtRwOwZjW0pKCmvWrGHkyJF4e3sD0LFjRztHJWyRQmo1ycEB3nvP9PvFYynMt+fPrzNlWu+//35cXFwYP348hw8fZuPGjTzxxBOMHTv2kl3LzaZMmUJaWhoPPvigzfWPP/44GRkZjB49mt27dxMbG8tvv/3GxIkTMRgMeHh4MHnyZP7v//6PDRs2cPjwYSZMmGBJpm1p2rQppaWlfPDBB8TFxfHVV1/x6aefXtFzIIQQ4ipcNBVYgWcQx1N9iDlmWhUcJAm3EOLa4Pb3bpxSTbMP5bXpRmlQhJ0j+odSin379rFw4UJOnz7Nb7/9Zu+QxCVI0l3TRoyA77+Hi+erDg83LR9RdwoyuLm58dtvv5GRkUHXrl25++676d+/Px9++GGV96HT6fD397fZig0QGhrK9u3bMRgM3HbbbbRt25ann34aHx8fS2L99ttv06tXL4YMGcKAAQPo2bMnnTt3rvCY7du3Z968ecyZM4c2bdrw9ddfM3v27Mt78EIIIa6OeSqwvXtRpXpSncI5fNyJ5GRTobQLjTBCCHFN8N3wg+X3ujRNmLk6+erVq9Hr9TRt2pQ77rjD3mGJS9Co63xQa05ODt7e3mRmZuLj42O1rqioiFOnTtG4cWNcXFyu7kAGA2zdCsnJEBICvXrVmRZuUb8opdDr9eh0uipVq6zW81yIGmA0GklNTSUwMLDSXi/CjlJTTd3J09Io9A7idKoLZ8+aypV4e5fv7HW9UyjyHYpwN7igQZ4ccW2rj+ezLiudpk/fgcZgQO/tx4n5a6q1m45eD1nZ0LEDeHhgyhPOnTPlB5VM55CSksKKFSs4f/48Go2Gfv360aNHD6lOXs2ysrLw9fUlOzsbLy+vatmndPKqLQ4O0LevvaMQQgghqk+ZqcCUgnTXCBJOasnJgQYNqlYjVAgh6hrvzT+jMRgAyOoztE6Mi0lISGDp0qXo9XqpTn4Nsv8ZJIQQQohrT1YWHDsGZ89S7OnH6QwPzp41fTcNDpbWbSHENcpowGfTSgCURkNmHSmgFhoaSoMGDfDy8mLYsGFSnfwaI0m3EEIIIaqu7FRgRUWcdw0j/pSO7GzwbQAu0rothLiGuR/6E6f0ZADy292C3j/EbrGcP38eX19ftFotjo6OjBs3Djc3N+lOfg2SpFsIIYQQVZOXBydOQHw8Ja5eJJWGc+YUoDHNuy1D7oUQ17qyBdQy7VRATSnF/v37WbduHb169aJ3794A0rp9DZOkWwghhBCVU8pUCPToUcjNJdM5mPgkJzIzwMcHXF3tHaAQQlw9XUYKHvu3AVDqG0he+x61HkNJSQlr1qzh4MGDACQlJaGUktbta5wk3VVgNBrtHYIQNUbObyFEpYqKTK3bp05R6uBMkj6c0wkaUBAYKBNxCCHqD59NP6NRpu9FWX2HgUPtpkop6ems+PprS3Xy/v37c8stt0jCXQ9I0l0JJycntFotZ8+eJSAgACcnJznpRZ1X1SnDlFKUlJSQlpaGVqvFycmpFqMUQlwT0tJMxdLS0sh2CSIhxYX0dNM0YG5u9g5OCCGqkUFfpoCalqy+Q2vt0Eop9qens275cvQGA56entx99900bNiw1mIQNUuS7kpotVoaN25McnIyZ8+etXc4QlSJUgqj0YhWq63SRSI3NzcaNmwo8x8LIf5RWgqnTsGJE+j1kEwEp2O16PUQEAg6ad0WQtQzHge245iZCkBex57oGwTV2rGzCwtZe+YMBqVo1qwZw4YNw02ubNYrknRfgpOTEw0bNkSv12O4MF+fEHWZ0Wjk/Pnz+Pn5XTKRdnBwuGSLuBDiOmOeCiw5mVzHBsRneZCeBh6e4Otr7+CEEKJm+G740fJ7Zr+RtXpsHzc3bg8Pp6hhQ27p31++l9VDknRXgUajwdHREUdHR3uHIsQlGY1GHB0dcXFxkdZrIUTVGQxw5gwcO4ahoIhzhJIYr6O4GPwDpHVbCFF/Oaadxf3gDgBK/EPIb3tzjR5PKcXZ4tME53jSzMN0NbOzvz907gyScNdLknQLIYQQ17v8fDh+HOLjydN6kZgXTkoKuLubpgITQoj6zGfTSjRKARcKqGlr7ipjqVHPnvSDnC48y9m/XXk0oDcu0khS70nSLYQQQlyvzFOBHTuGMTObFIdgEs44UVQE/v6gk28JQoj6Tq/HZ/PPACgHB7L71FwBtaziHHam7CW3NB8NGtqHNsJZpwOZSabekz+nQgghxPWoqAhiYyE2lgKDM4mFESSnaHBzldZtIcT1w3P/ZnTZ5wHI7dQHvY9/tR9DKcWp3NPsTz+MQRlxdXChpWsnbmrYQHqTXyck6RZCCCGuN2lpEBOD8VwKadogElJcyS8AvwYg5UuEENcTnzIF1LKiRlT7/g1GA7vTDpKYlwRAiFsgnRp0oDBPpmq9nkjSLYQQQlwvykwFVlQECUUNSU7R4uwCQYFSv0cIcX1xTDmNx+G/ACgJDCe/9U3VfgytRkupsRQNGto2aEELnyYYDBoKq/1Ioi6TpFsIIYS4HmRlQUwM6kwS5zV+xKd7kJtrmgbM2dnewQkhRO3z2fiT5ffMqOFQTQXNlFIoFFqNFo1Gw02BHcgtzcPfpUG17F9ceyTpFkIIIeozoxFOn4aYGIqzCknUh3H2nA5HR9PYbWndFkJcjzSlJfhsWQ2ActCR3fuuatlvqVHP3rSDaDAl2xqNBmcHJ5wdJOG+nknSLYQQQtRXF6YCU/EJZOo9OZUVTnY2NGggrdtCiOub556N6HIzAcjp2g+Dl+9V7zOrOIcdKXvJu1Cd/EafJng7e131fsW1T5JuIYQQor4xTwUWE0NJWhZn9MEkpTqh0Zhat2VKWCHE9c5nY5kCav2uroCaUoq43ET2px/BeKE6effgTpJwCwtJuoUQQoj6pLgYTp6EuDiyCpw4lRtBZqYGX19wcbF3cEIIYX9OZ+NxP7oXgOKQRhTc2PmK92XuTp6YdxYwVSe/KbADzg5SnVz8Q5JuIYQQor5IT4djxyg9m8rZ0kAS010Bad0WQoiyrFq5o0ZccXELpRRbk3eRXpRxoTr5jbTwuQGNFMsQF5GkWwghhLjW6fWWqcCys4wkFEaQnqHF2xvc3OwdnBBC1B2akmK8t60BwOjoRFavO698XxoNrX2bsTvtIDcHdZTq5KJCknQLIYQQ17LsbDh2DH1iEudK/Eg474HBAIGB4OBg7+CEEKJu8dy9Hl1eNgA5Nw3A6OF9WfcvNerJLsmxJNhBbgHc3rAvDhr5wBUVk6RbCCGEuBaVmQosL62QU0VhpGXo8PICd3d7ByeEEHWT74YfLL9fbgE1c3XyIn0xt0b0wtPR9GErCbe4FEm6hRBCiGvNhanADHEJpOR7kJATTnExBASCTr77CSGETU5nYnE7fgCAorAbKGzWvkr3U0oRl5PI/vOm6uRuOhdKDaXgWJPRivpEkm4hhBDiWqEUnDsHx46RfzaLhMIgUrKc8fAAHx97ByeEEHWb74YyBdT6j6xSAbVSYyl70g5xWqqTi6sgSbcQQghxLbgwFZjhRCzpOU6cyomgqFiDvz/o5K+5EEJUSlNchPf2CwXUnJzJvmXwJe+TWZzNzpR95JXmm6qT+91IC2+pTi4un/yZFkIIIeq6C1OBFSamklgYyNksV9xcTVOBCSGEuDSvv/6HQ0EeADk334bR3fOS90nITSKvNB83nQs3B3XG38W3psMU9ZQk3UIIIURdpddDfDzGmOOcTzNyKj+C/CItfg3AUcYSCiFElfmu/6eAWma/kVW6T1u/G9Fo4EafptKdXFwVSbqFEEKIuujCVGCFsUmcyW9AUo4nzi4QFFilYYhCCCEucE6IwTXuCABFjZpTdENrm9tlFmdzPOsUXQPbodVocdBoae/XqjZDFfWUJN1CCCFEXWI0wpkzqKPHyDhbyKnCMHILdPj6grOzvYMTQohrT9kCapn9yhdQU0oRm5NI9IXq5J5O7rTybVbbYYp6TJJuIYQQoq64MBVY8fEEknI8OJ0bjpOTaey2tG4LIcTl0xbm47VjHQAGFzdyug+yWm+rOnkTr0a1Hqeo3yTpFkIIIeztwlRg6lgMWXEZnCoKJqvAmQYNpHVbCCGuhtfO33AoKgAgp/tAjK7ulnWm6uR7ySstQIOGdn430lyqk4saIEm3EEIIYU8XpgIrORZLcroTCfkN0TpoCAoCrdbewQkhxDVMKXw32C6gdjrvLH+lRmNURtx0rnQP6oSfVCcXNUSSbiGEEMJeLkwFln0ilfiCAM4XuOHrCy4u9g5MCCGufS6n/sYlIQaAwhtaURx5o2Wdt5MXWjQEuwXRNbC9VCcXNUqSbiGEEKK2XZgKrPTvE5w7ayC+MBy0DtK6LYQQ1ciqgFrUCIoNJZbk2svJg/7hPfFy9JDu5KLGSdIthBBC1KbsbIiJIedYEom5vqQWeOLtDW5u9g5MCCHqD21BHl47fwPA4OpOdKsb2Zewnt4hNxHg6geAt5OnPUMU1xFJuoUQQojacGEqsNIjMaTFFxBfFIoeHYGB4OBg7+CEEKJ+8d6+Fm1JEQAxHbuyO/ckAAl5SZakW4jaIkm3EEIIUdMKCuD4cXKPJHD6vDvnisPx8gJf90vfVQghxGVSCp+N/3Qt39y+5YXq5C1p7t3YjoGJ65Uk3UIIIURNuTAVmP7vGNJPZBJfGESR0ZmAQNBJ67YQQtQIlxMHcTltatlOjIggLyySflKdXNiRJN1CCCFETSguhthY8g7GkpTiyNmSCDw8NQR52DswIYSo31x+/8bye9wt/bg1vJdUJxd2JUm3EEIIUd3On8dw5Bjnj6ZwKj+AQuWGv7RuCyFEjdPmZRO0dxsAJW7uBN36IEjCLexMkm4hhBCiulyYCqzgwAnOJuo5rQ/H3cOBICmQK4QQNUYpRXzuGcLcgwnatgZtaTEAub3uAmdXO0cnhCTdQgghRPXIycF4NIbzB8+QmONLjvLEzx8cHe0dmBBC1F+lxlL2pB7kdH4yyfnneKDM3NxZUSPsGJkQ/5CkWwghhLgaRiMkJVG4/xjJsQUklobi7K4j2NvegQkhRP2WWZzNznN7ydMXoEFD86QUnJPjAci/sRMlYVKpXNQNknQLIYQQV6qgAHX8OBn7EkhMdyNLG04Df3CS4YNCCFFjlFLE5iQQnf43Roy46VzpHtSJtr+9Y9kmq5+0cou6Q5JuIYQQ4nIpBSkpFB04xrm/M0ksCcLR3Zkgb9Bo7B2cEELUXyWGUvakHeRMfjIAoW5BdA1sj1tBAZ67NwCg9/Qht0s/e4YphBVJuoUQQojLUVyMOhlL1t5YTifrSNdE0MBfg7OzvQMTQoj6T6E4X5SJBg3t/FrS3LsxGo0G7y3L0epLAcjuNQTlKF2ORN0hSbcQQghRVefPU3wwhpSD50gsDEDj5kawr7RuCyFETVJKobnwQevs4MQtwZ0B8HPxNW1gNOK78Z8CapnStVzUMZJ0CyGEEJei10NCApm7jnM2QU+KJhxffwdcXOwdmBBC1G/m7uQhboE09ooAyiTbF7gd3YNTymkA8lvfRGlQRK3HKURlJOkWQgghKpOTQ+nhGFL2nuZ0ni96Ny+CfEGrtXdgQghRv2UWZ7Pj3F7y9QWkFKYR5h6Mk0P5eRh9N0grt6jbJOkWQgghbLkwFVjWX8dIPlnAOcLw9tfh42rvwIQQon5TSnEyJ4EDF1Unt5VwO2Sl47l3IwB6bz9yO/Wt5WiFuDRJuoUQQoiLFRRQ+vdx0nYncPq8G8Xu4QQ0AAcHewcmhBD1m63q5DcFtsfJwXZhNJ8tq9EYDABk9b4LdJLeiLpHzkohhBDC7MJUYLl7Yjh7OINzKhD3ABcC3O0dmBBC1H96o4E/kraSV1qA9kJ18mYXqpPbZDTis/EnAJRGQ1bfYbUXrBCXQZJuIYQQAqCkBH3MSdJ2xnI2VUeeq2kqMJ20bgshRK3QaR2IcA8lMS+Jm4M6lSuYdjH3w3/ilH4WgPy23SkNDKuNMIW4bJJ0CyGEEOfPk7s3hnPR5zhbEoCbvxuBHvYOSggh6r8SQyl6pcdNZyqY0bpBc1r4NLE5fvtiVgXUoqSAmqi7JOkWQghx/dLrMcQlkL7zBEkJenJcw/GLcJDWbSGEqAUZxVnsPLcPJwdH+oXdgoPGAa1Gi5PDpaeH0GWk4rF/KwClvgHkdexZ0+EKccUk6RZCCHF9yskhf18M53af5myhL84BAQR52jsoIYSo/0zVyeM5kH4UI0bAlUJ9ER6OVS+g4bP5ZzTGCwXU+gwDB0lrRN0lZ6cQQojri9GI8XQS6dtjOHsynyyXMPwa6qTgrRBC1AJTdfIDnMk/B0CYexBdAyquTm6TQY/PppUAKI2WrL5DayBSIaqPfMUQQghx/SgspODAcZJ3xHMuxw2HgHCCvO0dlBBCXB/M3cnz9VWsTl4BjwM7cMxIASCvQw/0fsE1Ea4Q1UaSbiGEEPWfUqhzKaRvjyH58HkynYPwaeiC02U0rAghhLhySimi04+Qry/AXedaperkFfHZ+E8Btax+I6srRCFqjCTdQggh6reSEor+jiN560nOpWohsCEBPhous2FFCCHEVdBoNNwU2IEjGcfp6N/68rqTl6FLT8bjwHYASv2CyWvXvTrDFKJGSNIthBCi3lLnMzi/I4Zz0cmcxx/vSHecne0dlRBCXB8yirJIL8qguc8NAHg4utMtqONV7dN300o0SgGQ2XcYaGW6CVH3SdIthBCi/jEYKI6JJ3nrCVJOl6L3DyfAz0Fat4UQohb8U538b4wovJ28CHLzv/od6/V4b/7ZdAytA9l9pICauDZcehK8WvbRRx8RGRmJi4sL3bp1Y9euXZVuP3/+fFq0aIGrqysRERE888wzFBUV1VK0Qggh6pzcXM6v38/Jnw6SeM4Jp8ahNPCXhFsIIWpDiaGUHSl72Z9+BCOKMPdgfJ2rp2KlZ/RWHLPSAcjt1Bu9b0C17FeImlanWrqXL1/OtGnT+PTTT+nWrRvz589n4MCBxMTEEBgYWG77b775hhdffJFFixZxyy23cPz4cSZMmIBGo2HevHl2eARCCCHsxmik+NRZUjYfIzUujyLfEPwCHNHWucvLQghRP2UUZbEzpWx18lY084687OrkFfHZ8IPldymgJq4ldSrpnjdvHlOmTGHixIkAfPrpp6xZs4ZFixbx4osvltt+x44d9OjRgzFjxgAQGRnJ6NGj+euvv2o1biGEEHZWWEjmrhMk7zzF+QJX3BtF4Odq76CEEOL6EZudQPSF1m13nSvdgzrTwMWn2vbvmHoGj0N/AlASGEZ+65uqbd9C1LQ6c/2/pKSEvXv3MmDAAMsyrVbLgAED2Llzp8373HLLLezdu9fSBT0uLo61a9cyePDgWolZCCGE/ZWeSeH0j7uI+99JMrX++Dbxw1USbiGEqFUOGgdLd/Jbw3tXa8IN4LPxJ8vvWVHDkW5M4lpSZ1q609PTMRgMBAUFWS0PCgri2LFjNu8zZswY0tPT6dmzJ0op9Ho9jzzyCC+//HKFxykuLqa4uNhyOycnBwCj0YjRaKyGRyKEfRmNRpRScj6LeqPCc7qkhOzoUyRvPUlGlhaXiHB8PbSAQtklUiGqRpX5J8S1zKAMaDVaFIpGXmG46JwJcvVHo9FU7/mtL8Vny2oAlIOOzF5Drun3j7rwY1SmH5T650e+v9ldTXyHrjNJ95XYtGkTb7zxBh9//DHdunXj5MmTPPXUU7z66qv8+9//tnmf2bNnM2vWrHLL09LSKCkpqemQhahxRqOR7OxslFJo5SqwqAdsndOGrFwyDyeRHZ9BsYsXbq1cUNoS8u0cqxBVoVAUO5QCoEEq/Ilrj1KKhKwzxGedpnvDzigH03ns6elJAcWXuPfl8929AV1OBgCZXXuT08ANuHYLJxs0UOoK5/VQUMQ/iXZGBkg+YnfZ2dnVvs86k3T7+/vj4OBASkqK1fKUlBSCg4Nt3uff//43Y8eO5cEHHwSgbdu25Ofn89BDD/Gvf/3LZsLx0ksvMW3aNMvtnJwcIiIiCAgIwMfHp/oekBB2YjQa0Wg0BAQESNIt6gWrc1opcg4ncnbzSbJTS3GMCMPX08HUZGCwd6RCVI25hc7d4CJJt7jmlBhK2ZN2gKR803f2lMw0IvzDavR8Dvl9teX3nKh7cDe41MhxaoteDyWF4KcDDxfAcOEPWIMGIPmI3Tk5OVX7PutM0u3k5ETnzp1Zv349w4YNA0xftNavX8/UqVNt3qegoKBcUuHg4ACYrsDZ4uzsjLOzc7nlWq1WEhRRb2g0GjmnRb2i0WhQufkkbz1J6p5E8h298W4RiM7B3pEJcWU0Zf4Jca0wVSffS76+EC0a2vu3oolXIwoorrHz2Sk5AfejewAoDm5IYcsu1/z7RnPhR6sx/aDR/PMj393sria+P9eZpBtg2rRpjB8/ni5dunDTTTcxf/588vPzLdXMx40bR1hYGLNnzwZgyJAhzJs3j44dO1q6l//73/9myJAhluRbCCHENU4pCs+kE7v7HFmJeTiEhuDn62jvqIQQ4rqhlOJEdjwHz/99oTq5G92DOtHAxafGx1ZbFVDrN8KUmApxjalTSfe9995LWloa//nPfzh37hwdOnTg119/tRRXS0xMtLryMH36dDQaDdOnTycpKYmAgACGDBnC66+/bq+HIIQQohoZ8wtJ3nqc5KPxGLJd8GkRga5O/eUSQoj673j2KQ6c/xuAMPdguga0x8mh5i9+akqK8d5q6lpudHQiu+edNX5MIWqCRlXUD/s6kZOTg7e3N5mZmTKmW9QLRqOR1NRUAgMDpXu5uKblx6WQtOEYGbEZFEd6E+Dudc13KRQCTGO68x2KZEy3uGaUGEpZn7Sdpt6NaOoViaZMa3NNns9e29cR9qmpOHL2Lbdz9tFXq3X/9qLXQ1Y2dOwAHh6YxnSfOwe9eoGvr73Du+5lZWXh6+tLdnY2Xl5e1bJPaS8QQghRp6jiElL/jOPstlgKCzW4NQnDwb1ECqUJIUQtUUqRXJBKiFsgGo0GJwdHBkb0Rqup3Yv5vht/tPye2W9krR5biOokSbcQQog6o+BMBkkbYsj4OxmNnx8+DT1Aoyi1d2BCCHGdKDGUsjvtAEn55+jk34am3pEAtZ5wO52JxS1mPwDFYTdQ2Lx9rR5fiOokSbcQQgi7U3oDaXsSSN5ygvzMYtxuCMPZzfQn6roeAyWEELXIujq5Fuw4/MG3TAG1zCgpoCaubZJ0CyGEsKuitFzObDhO5oFElJc3Pq385buVEELUIlN18lMcPH/0n+rkwZ1o4Oxjl3g0xUV4b1sDgNHJmeyeg+0ShxDVRZJuIYQQ9qEUadFJJG2IoSA1F7cbQnB2l6nAhBCiNpUYSi50J08BINw9mC61VJ28Il5//Y5DQS4AOd1uw+hePcWshLAXSbqFEELUuuKsQs5sOsH5PacwOrvg3SocrYM0bwshRG3LKcnjbH4qWrS0929FU69GVtXJ7cHHqoDaCDtGIkT1kKRbCCFErUr/O5WzG46Sn3gel4ZBuPi42DskIYS4bvm7NqBTQBt8nb3t1p28LOeE47idPARAUcPmFDVpY+eIhLh6knQLIYSoFaX5JZzeEkf6n7EowLNlBA6OMpe8EELUphJDCfvSD9PKtxleTp4ANPFqZOeo/mE1TZgUUBP1hCTdQgghalxmXCZn1h8j70QyLmF+uPh52DskIYS47pwvyuTPlH3k6wvJLc1nQFhPu3clL0tTVIDX9nUAGJ1dyekxyM4RCVE9JOkWQghRY/TFBs5sTyBt5wlUYTEeN4ahc5I/PUIIUZtsVSfvHNC2TiXcAN47f8OhKB+A7O6DMLrKBVpRP8g3HyGEEDUi60wep/+IIe/YaZwDPHFt5G/vkIQQ4rpTYihhV+oBzhaYq5OH0CWgnV2rk1ekbAG1LCmgJuoRSbqFEEJUK4NekbT7LCmbj2HIzsWzWTAOznXvy50QQtR3+aUFbDy7kwJ9YZ2qTm6LS9zfuJ46CkBh41YUNW5p54iEqD6SdAshhKg2OSmFJG48Se6BOFy8XfBoFS5FcIQQwk5cdS646VzRoKF7cKc6UZ28ItLKLeozSbqFEEJcNaMRzkancnbjMVRaOp6Ng3Bwl6nAhBCitpUYSnDQ6nDQaNFqtHQP6oSDxqFOdic30xbm4b3zNwAMLu5k33ybnSMSonpJ0i2EEOKq5GWWkrgpjuy9J3FxBZdWEaCVqcCEEKK2nS/KZGfKPsLcg+no3xowtXbXdV7bf0VbXAhAdo/BKBc3O0ckRPWSpFsIIcQVMRoh+e9MkjbGoM6cxbORHw5eUmlWCCFqm1KK4xeqkysUyfkptGnQAkftNfBVXyl8N/xguSldy0V9dA28E4UQQtQ1BbkG4reeJmtXDC6aYlxahqHRyZ8UIYSobRVVJ78mEm7AJfYwLqdPAFDQtC3FDZvZOSIhqt+18W4UQghRJygF507mkbTxOKVxiXiGeKLzk6nAhBDCHszdyc3VyTv4t6JJHa1OXhHfDWULqI20YyRC1BxJuoUQQlRJYYEifsdZMnYew7U0F5/mQeDkZO+whBDiuqQ36tmavIsSYykeOje6B3fG19nb3mFdFm1+Dl5//g8Ag5snOd0G2DkiIWqGJN1CCCEqpRSkJhaRuP4EpcdP4R3ojEOATAUmhBD2pNPq6BzQltN5yXQJaFenq5NXxHvbWrSlxQBk97wD5VT3i74JcSUk6RZCCFGh4mKI35VK+o4YXHLT8GkShMZVvhQJIYQ9nC/KxKAMBLqahvVEeIQS7h5yTXUnt7iogFqmFFAT9Zgk3UIIIWxKO1tKwqY4io+cxNsLHJrLVGBCCGEPZauTOzk4clt4b8tUYNdkwg24Ho/G+ewpAApadKQk7AY7RyREzZGkWwghhJWSEkg4kEXKlmO4ZJylQUM/cJepwIQQwh6KDSXsLlOdPNDFD53Wwc5RXb2yBdQyo+zTyv30+zcw4fYUOjTLx2iEN5ZGsPWAN6AYNyiV+29Ns3m/zdFevP99GEYFBoOGSXecY1ivDAAOxbnx5tIICoq0oIEXxpzh5ta5l4yloNiByQ+6s/uA6fr2G2/A3XeX3+7QIRg79p/bWVmQkwMZpsNz221w7pxpH56e8P770LGjaV2vXvDll9C48WU8SaJaSNIthBDC4nyqgYRtp8nfH4OPSxG65mHgIH8qhBDCHupDdXJbHHKz8Nz1BwB6D29yu/ar9RgOxrqRne9Ah2b5AKze0YDYJBfWvn2Y3AIHRk5vyU0tc2kWXmR1P6XghU8b88XLx2nRsJCkNCfueKE1t3bJws3FyJPzm/D6Q/Hc0iaX+GRnJs1pztq3DuPipCqN552fmuDsrDh5Ek6dgm7dICoK/Pyst2vbFqKj/7k9dap1iZXvvgMfH9PvP/0EEybAgQOm288+CzNmmBJvUbukn6AQQgj0eog9kMfR5QfR79mPf7AOXaNwSbiFEMIOlFLEZMWxIWkHBfpCPBzd6B/eg6bekdd8wg3gvfUXtPpSALJ7DUE5Odd6DN9tCODO7hmW2+v+bMA9fdNx0IKPh4Hbu2WydmcDm/fVaCC3wNTbIK/QAR8PPY6Oiqw8BzJyddzSxtSyHRlSjJeb/kLreeWWbwvlkQmmonKNG0PfvqakuTJFRfD11zB58j/LzAk3QHa2dUJ+xx2wbp1puahd8m1KCCGuc1mZirhtZ8nfdwwfh1wcmwWjHGUqMCGEsKes4hwUigj3ELoEtsNRe+1VJ7dJKXw2lpmbO2q4XcLYfcyT8YNSLLeTzzsR6l9iuR0WUMKBk+7l7qfRwNzH43jyvSa4OhvIydfx3lOxOOkUTp4GAnz+n737jq+qvh8//jp3Z9/sDQRIkBUQVEQFBXfdUkertdbWtrbWgXbYVlttf1q1dbbODlut/bpArKsOcDJUUBEyIJOQve5NcnP3Ob8/DiSETbi5J+P99MHD5Nz1Bi43530+78/7HeSNdcmcOa+Tr6piqW50UN924J+p21pjGZ/f0/f9hAmwbdv+H7NsGUycCLNnDzx+xRWwapX+9euv9x+3WvWV8g8/hLPPPmBIIoIk6RZCiDEqHIZtW3w0vL8Vc101aRl2SMlDGwWrKEIIMRJpmoaiKCiKwtz0GWTGpjE+PndUrG7vFFvyGfYmPZv0TDuaQPZ4Q+Jo6rCSmhQ65MeFwvD4imweur6So47o4auqWH5832RW3LWZ5IQwf76hkvuey+XJ/2YxOdfLnKIezKb9l5YP1t/+NnCVe6ed5eP//Cf8/OcDE++sLNi+fUjCEfsh5eVCCDEGdXXBxndb2bbsUxKat+KcmAqpaTJ7WwghDKCXk1eyunk9mqYnaBaThQkJeaMq4QZI3mWV28gxYTE2FX+w/882OzVAwy4r0vWtNrJTA3s8rqw2lhaXlaOO0FelZ07sJSslQGlNLABHjPfyxM8qWPb7Uu65poZWl5XJu+0L35tx6b3U1vWnZjU1MG7cvu9fXQ1r18I3v7nv+3z72/qKd3t7/zGfD2JiDhiOiDBJuoUQYgwJh2FbZZCNL23B9+EnZDq6sBbko9ll9rYQQhjBHw7wcdNnfNleSr2nqa9L+WhkdreT8Jle9xxKTKF77kmGxVKU76W6sf9n3+nHdPLCe2mEVXD1mPUS8WM79nhcVmqAVpeVynr9sbXNdra12JmQrSfWra7+QuIXVqURY1c5dpq+x/vfb6dz33M5e43nouMbeewpfW97dTW89x6cf/6+4//73+GCCwbu4Xa5oKGh//uXX9YbsaXssjW9tBRmzdr384qhIeXlQggxRvT0QNUGF51ry3D2NuLIT0GNlVFgQghhlAHdyRUTs1OnkxObaXRYQ8b5wX9RwnpJt2vhuWAxbp/6acd08vFXiX1Nz849oZ1NVbGcefMMFAWuPLOFonw9kV65IYlVG5z87nu1pCWFuP2qWpb+eSImRUPVFH59xTZy0vTGcM+vTOfVNSloGkzK8fHQ9ZV9RWSV9Q7y0vdcPQf46QUVXPWfAiZNArMZ/vxnSEvTb3vsMT2ZvuMO/XtVhaee2rMLudsNF10EXq8+Miw9HV59tb+IraZGv/guSXf0KdrOGpYxqquri6SkJDo7O3HueqlIiBFKVVVaWlrIyMjAZJJiFqH/cG6oC1P78XYoLyM1zoeWmTViOpNraHjMPuLCDhRGV5mlGJvkPS00TWOLu4qN7WVoaMRbY5mfOZdk+4G7XA83B/1+VlUm3XwBttZ6ACr+9DLBjLwoRbknj8/EZXdM4dnbyol1qFF5zct/N4XHb96K3aricsORsyE+Hj0TbmrSB2knJw/Z6//iFzB5Mnzve0P2EqOCy+UiOTkZt9tNYmJiRJ5zZJxxCSGEGBSPRx8F1r52K053DbGZiaiJxp3kCCGEgA1tm6jsqgUgPz6Ho9Jnjp7u5PsQt2ldX8LdM3O+oQk3QJxD5eeXbae+1UZh/oH3XEfCM7eWA/qYTiPk5MBVVxnz2mOdJN1CCDEKaRo0NmhUr25ELSkjy+ZGmZCFKqPAhBDCcBMS8qjtrmdW6lQmJo4bdc3S9mbAmDADG6jtav70bqNDiKrrrjM6grFLkm4hhBhlvF6o3OyjZU0FzvYq4lPthJ35MgpMCCEMomka7kA3TrteqprqSObs8YuxmcfGhVBLZysJGz4AIOhMo3v2AoMjEiK6JOkWQohRQtOguRmq1rUS2FRONs0oeZmEHTIbRAghjOIPB/ik5QuavW2ckntCX+I9VhJuAOf7K1DUMACuk84Hi6QgYmyRd7wQQowCfj9UlAZpWldDYtMW0hIhnDoOTZrpCSGEYdp8naxtXk9vyIdJMdG1y2r3mKGGcb63HABNMeE68Xxj4xHCAJJ0CyHECNfaChWfufB+WU52uB5zdiphGQUmhBCG0TSNclcVX3Xs7E4ex/zMOSOyO/nhit+4Bmu7Pnu8Z9ZxhNKyDI5IiOiTpFsIIUaoQACqK1Xq19YRu72c/Bgvoexc1BEyCkwIIUajneXkjb0twNjpTr4vzndf6vvatXiJgZEIYRw5MxNCiBGovR0qvvTQvWELWb5abGkJhBJkFJgQQhitpns7jb0tmBQTR6ZNZ2LC2OhOvjeW9ibiv/wYgGBqJj2zjjM4IuOFVfi0LJ7aBis9WpDTjuvBbHRQYshJ0i2EECNIMAi1NRrb1jXiqC1jnNWNmptFWEaBCSHEsFCYVEB3sIdJiePHZDn5rpzvrUDRVABcJ10AprGdXr79qZM7n8mnuWPHz+ynIS8jwINLa7lwapOxwYkhJR12hBBihOjshA2rfVS9VkLatvWkJwUJZ+ejScIthBCG8YcDfN62mdCO7twmReGo9OIxn3ATDuF8/2UANJMZ14nnGRuPwd7+1MkND02kuWPgNoP6Fitf/8Vklq3JNigyEQ2y0i2EEMNcKATbtkHNp61Yq8oZr7SgZWQQklFgQghhqF27k6uaytz0mUaHNGzEf/4R1s5WAHqOXEAoOd3giIwTVuHOZ/LRABi41UBDQUHjhr9O57yb/FJqPkpJ0i2EEMOY2w1bS4J0bKgho2srcTEqoZR8kFFgQghhmL11J5+UON7osIaV5FXL+r7uHOMN1NaXx/eXlO+FhkJdWywfrglz0jlRDExEjSTdQggxTLW1waaP3SjlZUzQ6tFSUgjFJRgdlhBCjGm7dycfF5/D3PRirCY5rd7J2lJP3FdrAAik5+KZMc/giIy1te7gKtMam8dmw72xQD4dhBBiGHJ3qmx5uw5HRTmpsV5CqblgkY9sIYQwUqffzUeNn+IN+6Q7+X4433sZRdOLqV0nnT9mq7M6u8088d9s/v3WwZXWZ2dqQxyRMIqcwQkhxDDjafFQ9eoWzFtrcebFE0qSUWBCCDEc2M02wlqYeGscx2XOxWlPNDqk4ScUxPn+CgA0sxnXiecaHFD0ebwmnnojk6feyMTj23WXtsbue7oBFDTy0rwsmB+KWowiuiTpFkKI4ULT8Nc2UftaGYE6FwmTM1EddqOjEkKIMS2khrHsGHUVa4lhYfY8EmzxUk6+Dwnr38fS1QFA99xFhJNSDY4oevwBhedWpvP4K1l0dvd3KbdbVU6Y6ebdDU4UNLRdEm9lR3u1B763GbN5ctRjFtEhnxZCCDEc+P2EyivYvrKSjjYbSUfko1ikXFEIIYzU5u1gbcsGjkybQW5cFgApDqexQQ1zAxuoXWhgJNETCsOKj1L5y/Icmtr7G6aZTRpLTmrjmvMayUwJ7jmnG8jLDPLAjbVcOLURkKR7tJKkWwghjNbWhlpSRv2GFuo8GaROjpHt20IIYSC9O3klX3WUo6FR1llBTmym7N0+AGvTNuI2fwKAP2scvVOPMjiioaVp8PZnTh56MYeqhoHN0s6a38G1SxoYn+nvO3bq0S4Wz3XxSUk8tQ1Wjp0T5LTjejAThqZoRy+iSU7rhBDCKKEQ1NSglW+hoU6lKpBPcpZJEm4hhDDQvrqTS8J9YMmrlvd97TrpglHdQG31pgQeeD6XTdVxA44vnOXm+ovqmTreu9fHmU1w9BE9FGbDkbPBbAbCQx+vMJac2gkhhBHcbigrg/p6mgMpVPYmkOgEu2zhFkIIw7R5O1jTvEG6kw+CEgyQ9MErAKgWK+6Fo3Pg9MbKWO5/Ppd1JQOb6M0p6uGGi+s5akqPQZGJ4UySbiGEiCZVhe3b9YTb66XVlsvWOgsxMRAba3RwQggxdnUHeljVsAYNjQRrHPOlO/khSfh0JZYeNwDdR59MOMFpbEARVlHv4KEXc3jns+QBx6fk93LDxfUsnNWFXJsR+yJJtxBCRIvHA1u2QG0txMfTGZ9HRRlYzJCQYHRwQggxtiXY4pmYOI6gGmRuerF0Jz9EyStHZwO1+jYbf1mWzSsfpaJq/Vl1foafnyyp52vHdo7mKnoRIfJpIoQQQ03ToKkJysuhowOysugO2qko07d1p42daSpCCDGstHk7iLPGEmNxAHBk2nQUFCknP0SO+hriyj8HwJ9TgHfKkQZHdPja3RYefyWL51amEwz1Z9XpzgDXnN/IkhPbsEomJQ6SvFWEEGIo+f1QUQGVlWCzwbhxeH0KFRXg6YGMDKMDFEKIsUfTNMpclWzqKCc9JoWF2cdiUhRMiixZDkb6u6/0fd25+EJGcp11d6+Jp97I5Kk3MvH6zX3HE2NDfO+cJi47tYUYu2ZghGIkkqRbCCGGSlubvne7pQXS0yE2lkAAKirB5YKM9BF9XiKEECOSPxxgXcvnNPW2AuAw21E1FZNiPsAjxd4oAR9pH7wBgGq14z7hLIMjGhxfQOHZtzN48tUs3D39KVKMLczlp7dw1VnNJMVJm3ExOJJ0CyFEpO0YBcbWrRAOQ14emM2EQlBVBa0tkJ4xqiepCCHEsNTq7WDtju7kZsXEkWkzKEjIl3Lyw5D4ybtYevWO3V3zTkWNG1nN50JhWP5BGo8sz6a509Z33GLWuHhRKz84r5F0Z8jACMVoIEm3EEJEktutN0vbvh2Sk/s6pKmqnoc3NOiL3hZZUBFCiKjZtZxcupNHVvK7L/V97RpBDdRUFf73aTIPvZhDbZOj77iiaJx9XAfXXthAfkbAwAjFaCJJtxBCRMLOUWDl5dDbCzk5YNE/YjUN6ur0XykpfYeFEEJESVgLU9Ndh4bGuPgc6U4eIfZtW4mt+AoAX/5kvJNnGhzRgWkafPRVIg88n0tp7cBZnYuOdHH9RfUU5fsMik6MVvJpI4QQh6u3t38UWFycXk6+i6YmfZU7MRHsdmNCFEKIscxisjA/cy4dfpeUk0eQc/cxYcP8z/WLrXHc/3wun5YNnNN51JRubry4niOLPAZFJkY7SbqFEGKwdh0F1tkJmZl7ZNWtrXrjtJgYiI3dx/MIIYSIqJ3l5GbFRJFzIgBOe6KUk0eQ4vOStPp1AMJ2B+7jzjQ4on3bUufgwRdyWfW5c8DxqeN7ueHiek6Y2TXcrxeIEU6SbiGEGIxwWE+2Kyv1evH8/D2u8He69ITbYu7b2i2EEGKI+cJ+Pmn+giZvKwoK2bEZJNjijQ5r1Elc+z/MXn1luGP+yaix8Qy3vLWuxcafl+Xw6uoUNK0/uvFZPq77egOnH90pTU1FVEjSLYQQg1FVpSfdaWl7XcLu7tHHc4dCkJZqQHxCCDEGtXrbWdv8+YDu5PHWOKPDGpWSdyktbznl/GGVcLe6LDy+IpvnV6URCvdn1ZnJAX50QSPnL2jDKlmQiCJ5uwkhxKFqatL3cKek7DXh9nr1hNvTAxkZBsQnhBBjjHQnjy5HTRkx1SUAeCccQe/EIxgOI6y7PGb+9lomz/wvA2+gf0xIUnyIq89u4puntuCwaQZGKMYqSbqFEOJQdHfD5s1gNkP8nuWKgYBeUu5yQUb6sO8pI4QQI56maXzc9BkNvc0AjI/PZU76TOlOPoScK/vHhHUuusDASHRev8K/387gr69m0eXp/3uPsYe58sxmrjyzmYRY1cAIxVgnn0ZCCHGwgkEoKdET7906lINeSl5VBa0tkJ6B7BMTQogoUBSF9JhUmr2tHJk2Q7qTDzGTt4ek1W8CEHbE0TX/dMNiCYbgpffTePTlbFpdtr7jVovKJYtb+cG5TaQmhQyLT4idJOkWQoiDoWmwdSvU10Nu7h5L2KqqjwVraID0dL15mhBCiKGhaRq+sJ8YiwOAoqQCcuMyZf92FCSufhOT3wtA13FnoMbEAdGda62q8PraZB5+KYe6FkffcZOice7x7fz4wkZy0wNRjUmI/ZGkWwghDsb27XrSnZGhdyvfhabpN9fV6du8LfLJKoQQQ2Znd3JPqJdT8hZgNVlQFEUS7mjQtAEN1DoXL4n2y/PBl4k88EIu5dsG9lQ55ahOrvt6A5Nzo3sBQIiDIaeGQghxIJ2dUFqqN02Lidnj5qYmqK6GxMQ9xnQLIYSIIL07+Qa8YT9mxUSn30VGTJrRYY0ZjsrNOLZtAcA7aQb+8UVAdBqTfVYezwPP57Jhy8B+KvOmdXHDRfXMmtwblTiEGAxJuoUQYn98Pr1xmt8POTl73NzaqjdOi4nZayNzIYQQEdDfnbwMDUiwxjM/c450J4+y5FW7NFBbfGFUXrO0NoYHX8jlgy+TBhyfUeDhhovrOW5Gd1TiEOJwSNIthBD7oqpQVgYtLZCfv8fNnS494baYISEh+uEJIcRYsLOcvMnbCkh3cqOYPN0krn0LgHBsPF3zThvS16tttvPnl3J4bU3KgOMTc7xc9/UGTj3KJRNCxIghn1ZCCLEvNTV63XhW1h6tyLt79FncoRCkpRoTnhBCjAVftG2myduKWTExJ20mExLypDu5AZI+fg1TwA+A+/iz0OyOAzxicFo6rTzycjbL3k8jFO7/e85KDfDjCxo474R2aVYqRhxJuoUQYm9aWvRVbqdzj43aXq+ecHt69L5qQgghhs6s1Gn4Qn6OTJtOkpSTG0PTcA5ooBb50nJXj5m/vZrFM29l4A/2X+hOTgjyg3ObuGRxK3ZbdPaPCxFpknQLIcTuPB59Hrem6d3RdhEI6CXlLhdkpO8xOUwIIcRh8oX9bO9pZHLSBABiLA5Oyp1vbFBjXMyWL3HUVwHQWzSbQN6kiD13r8/E029l8PfXMunu7U9N4hxhrjyzmSvPbCYuRo3Y6wlhBEm6hRBiV6GQnnC7XJCXt8dNVVXQ2gLpGXtUnAshhDhMu3Ynt5ltjIvfs4GliL6haKAWCCm8sCqNx1Zk0+629h23WVW+cXIrV5/TREpiKCKvJYTRJOkWQoidNE2vG6+rg9zcAcvYqqpv8W5ogPR0ZD+ZEEJEkKZplLoq2NxRjgYkWuNJskmHyuHA3O0i4ZN3AQjFJ9F99MmH9XxhFV5bncLDL+VQ39a/fcukaFywsJ0fXdBAdmrwsF5DiOFGkm4hhNipoQG2bIG0NLD0fzxqGmzfrufiKSkDbhJCCHGYfGE/65q/oLmvO3kec9NnYJHu5MNC0kevYgoGAHAvOBvNZj/AI/ZO02DlhiQefCGXivqYAbeddnQn1329nok5/sOOV4jhSD7NhBACwO2G0lK9aVpc3ICbmpr0JuaJiXv0VBNCCHEYdi0n39mdvCBxzxGNwiCahnPV8r5vXYsuGNTTfFIaz/3P5/JlRfyA48fPdHPDRQ1ML+g9rDCFGO4k6RZCCL9f38ft8eyxj7u1VW+cFhMDsbEGxSeEEKNUUA3hDftJtMYzP2uulJQPM7Fl67E31gLgmTqXQPaEQ3r85upYHnghh4+/ShpwvHhSDzdeXM+8aT2RClWIYU2SbiHE2Kaqekl5Y+MeCXenS0+4LWZIkPNAIYSICE3T+uZs58RlcmzmHHJiM6ScfBhyvrtrA7UlB/246kY7D72Yw/8+SRlwfFKul+u/Xs/Jc90y/UOMKfLpJoQY2+rq9JbkmZlg7u+O1t2j91QLhSAt1cD4hBBiFGnxtvN52yYWZB9DrEXf1ysdyocns7uDxM9WARBKSKb7qEUHfExTh5VHlmez/IM0wmp/Vp2b5ufaJQ2cfVwHZpn8IcYgSbqFEGNXW5u+jzs+HhyOvsNer55we3ogI8PA+IQQYpTYvTv5po5yjsmYbXRYYj+SPvwvSlgf2eU68VywWPd5X1eXhUdW5PHsOxkEgv1ZdWpikB+c18jFi9qwWbUhj1mI4UqSbiHE2NTbC5s360vZ6el9hwMBvaTc5YKMdKT8TQghDpMv5Gddy8Du5EemzTA4KrFfqkryqmV937pO2nsDNY/XxFNvZvDUG5l4vP1pRXxMmKu+1sS3zmghzqEOebhCDHeSdAshxp5wGMrLoaNjwD7uUEivNG9tgfQMMEkJnBBCHJaWHd3JfdKdfESJ2/wJtpZ6AHpmzCOYObDnSSCo8NzKdB5fkUVHd/8KuN2qctmpLXzv7CacCeGoxizEcCZJtxBi7Kmq0meA5eT0ZdaqCjU1+qju9HS9eZoQQojBa/Q081HTp2gg3clHGOfK/gZqrl0aqIXC8MrHqfxlWTaN7f0zNM0mjQtPbONH5zeSmRKMaqxCjAQHnXR/8MEHexxbuHBhRIMRQogh19Skr3KnpoJVvzqvabB9u95TLSUFLHI5UgghDlt6TBqJtgSS7UnMSZsh3clHCIurjYQN+nl/KCmV7iMXomnwzmdOHnwxh6qGmAH3P/PYdr57UTVTM0BB9mQJsTcH/el30kknoSgKmqY3QVAUhXBYykaEECNIV5e+j9ti0Zun7dDUpC98JyaC3b6fxwshhNivTr+bJFsiJkXBYjKzOPc4rKZ9N+ASw0/S+ytQVP0c33XieawpT+aB53P5qipuwP0WFLu5/qJ6pk7oxWP2Qdixt6cTQnAISXd1dfVQxiGEEEMrENA7lff0QG5u3+HWVr1xWkwMxMYaGJ8QQoxgqqZRtqM7+fSUKUxLLgSQhHukUcMkr1oOgKYoXF16Iy+/UjTgLkcW9nDjxfUcdUSPfr+oBynEyHPQSff48eOHMg4hhBg6mgZbt0J9vZ5w72hJ3unSE26LGRJkm6EQQgyK3p38c5q9bQB4gr1omoYi4x9GnLiNa7C2NwHwunYmL2/t7zJflN/LDRc1cOJst0z2EOIQRWxzjaZprFq1Cr/fzwknnECCnMEKIYaL7dv1wdsZGX0btrt79EOhEKSlGhyfEEKMUC3eNtY2fy7dyUeB+jYbif94k3E7vn+cHwCQn+Hn2gsbOGt+h0z1EGKQBpV0/+pXv2L16tWsWrUK0BPu0047jZUrV6JpGuPGjePdd99l0qRJEQ1WCCEOWUcHlJRAXJxeQw54vXrC7enR83AhhBCHZtdyculOPrK1uy088d8sPnrHx9bwWwDUkccniady6wXbWHJSGzaLFJELcTgGdb3qpZde4phjjun7/sUXX+Tdd9/l97//Pa+++irhcJjf/va3kYpRCCEGx+vVE+5AAJKTAf3LikpwufTRYFIiJ4QQh64n6KGkcysaMCEhn1PyTpCEe4Tp8Zp4+KVsTr95Bk//L5Mrwv/AjArA5ukX89qfyvjGKa2ScAsRAYNa6a6vr2fy5Ml93y9btoxp06Zxyy23AHDNNdfw6KOPRiZCIYQYjHBYHw3W2gp5eYBeSl5VBa0tkJ6BlMkJIcQgJdrimZM2AxMKE6ScfETxBRT+8046T/43G1ePngqYCXE1fwVAU0xM+v7JhByqkWEKMaoMKum2WCz4/X5ALy1/9913ueKKK/puz8zMpK2tLTIRCiHEYNTU6L8yM8FkQlWhthYaGvQVbovZ6ACFEGLk2FlOnhWTTorDCcDExHH7f5AYVkJhePnDVP6yLIfmTlvfcYtZ4w/T/03uxnoAeo5cQCgl06gwhRiVBpV0z5gxg2eeeYbLLruM5cuX097ezllnndV3e21tLWlpaRELUgghDklLC5SVQVIS2O1omt5Lbds2SEnp66UmhBDiIOzanbzaso3T80/EYpIP0pFCVeGtT5089GIuNU39s7QVReOs+R1ce2EDx//rib7jnYsvNCJMIUa1QX1i3nbbbZxzzjl9ifXxxx/PokWL+m5/7bXXOProoyMToRBCHIqeHn0ft6JAYiIATU1QXa1/a7cbHJ8QQowgA7uTm5meMkUS7hFC0+DjrxJ54IUcSmriBtx20mwX11/UwJRxXqytDcR9tQaAQFoOnpnHGhGuEKPaoD41Tz31VDZs2MDbb7+N0+nkkksu6buts7OThQsXct5550UsSCGEOCjBIJSW6l3Sduzjbm3VG6fFxEBsrLHhCSHESKFqGqWdWynp3CLdyUegLyviuP/5XD4pHfj3NXdKNzdeXM+cIk/fMed7y1E0vVmaa9H5YJL9V0JE2qAvVU6bNo1p06btcTw5OZn777//sIISQohDpmlQWQl1dZCbC4pCp0tPuC1mSJDzRCGEOChBNcTqps9o9ur9eSYk5DMnbbqscI8AW+scPPBiLqs2OAccP2J8LzdcVM+C4q6BUztCIZzvvwKAZjbjWnhu9IIVYgw5rE/PtWvXsmrVKlpaWvjRj35EYWEhvb29lJWVUVRURHx8fKTiFEKI/WtogK1bd3RJs9Ddo8/iDoUgLdXo4IQQYuSwKGZMigmzYmZu+kwmJOQZHZI4gO0tNv68LIf/rk5B0/qz6vFZPq5b0sDpx3TudWJHwob3sLjbAeiecxJhp/RkEmIoDCrpDgQCXHrppaxYsQJN01AUhXPOOYfCwkJMJhOnnXYaN954I7/61a8iHa8QQuzJ5dL3cdvtEBuL16sn3J4eyMgwOjghhBj+VE1D01TMJjOKonBMxmx8Yb+Ukw9zrS4Lj6/I5vlVaYTC/Vl1RnKAH53fyAUL27Du52zfuXJZ39fSQE2IoTOoKbW33norr776Ko8++ijl5eVoO/aBADgcDi666CJWrFgRsSCFEGKf/H59H3dvL6SmEgjoJeWuTn3Re0AZnRBCiD34Qn4+aFzHZ60b+87p7GabJNzDWJfHzAMv5HDGTTN49p2MvoQ7MS7ETZdu580/buLixftPuK3NdcRv/gSAQEYevdOkCbIQQ2VQK93/+c9/uOaaa/j+979Pe3v7HrdPnTqVF1544bCDE0KI/VJVKC+HxkbIyyMUgqoqaG2B9Az2WkonhBCiX3NvG+tadulOHuol3hp34AcKQ3j9Cs++ncGTr2bR5ek/jY+xh/n2GS1852tNJMSqB/VcybuvcssPTSGGzKCS7paWFmbOnLnP281mM729vYMOSgghDsq2bXqWnZmJqpiprdG3dqel6c3ThBBC7N3O7uSbO7cAkGhN4LisOZJwD1PBECz7II1HlmfT6rL1HbeYVS5Z3MYPzmskLSl00M+nBAMkffhfAFSLFfeCcyIesxCi36CS7vz8fMrKyvZ5+8cff8zkyZMHHZQQQhxQW5teVp6QgGZ3sL1Oz8FTUsBqNTo4IYQYvrwhH+taPqfFq1crFiTkc2TaDCwyKmrYUVV4Y10yD7+Uw7ZmR99xRdE49/gOrr2wgdz0wCE/b8Jnq7B0uwDoPmoR4cTkSIUshNiLQSXd3/zmN7nvvvtYsmQJRUVFACg7Nk4++eSTPP/88/zhD3+IXJRCCLGr3l7YvFk/G3E6aWqE6mpITNR7qQkhhNg7TdP4sPETXIEu6U4+jGkafPBlIg+8kEv5ttgBt508t5Prvt5AYZ5v0M/vXPlS39euxUsG/TxCiINz0En3V1991VdS/qtf/Yq1a9eycOFCpk6diqIo3HjjjXR0dLB9+3a+9rWvceONNw5Z0EKIMSwU0le429th3Dha2/TGaTExEBt74IcLIcRYpigKs1Kn8kV7CfMz55AozdKGnfXlcdz/fC4btgz8u5k3tYsbLm5g1mTPYT2/raGGuLINAPhzJtB7xJzDej4hxIEddNI9d+5cbrrpJn7zm9/gcDh48803+fe//82LL75IOBzG7/dTXFzM73//e771rW/1rXwLIUREVVXpdeQ5OXS6FSoq9P3bCXLeKIQQe+UN+egO9pARo89gzoxN59SYhZjkXG1YKauN4cEXc3j/C+eA49MLPNx4UT3zZ3RHZCKHc1V/AzXXSRfImA8houCgk+7vfve73Hvvvbzwwgs8+uijnHrqqVx++eVcfvnlQxmfEEL0a2yELVsgJYVuv5WKCn3hOy3V6MCEEGJ42tmdPKSGOTV/AQk7GqVJwj181Dbb+fNLOby+NhlN6/97Kcj2cd3X6zntaFfE8mIl4MP54asAqFYbrgVnR+aJhRD7ddCzAR599FFWr15NQkICZ5xxBpdffjmtra1DGZsQQvTr6oKSErBa8ZrjqagATw+kphgdmBBCDD+qprG5YwvvN67FF/YTZ43RNwqLYaOl08rt/xjHOT+fzmtrUvoS7qyUAL/7bg0r7trM6cdELuEGSPjkXcyeLgC6jjkFNT4pck8uhNinQ2qkdswxx7B+/XoefPBBfvOb3/DGG2/whz/8gblz5+71/nPmyB4RIUQEBAJ6wt3TQyAjj4ot4OqEjAypihNCiN1Jd/Lhze0x87dXs3jmrQx8gf71L2d8iB+c28ilJ7ditw3NBZLkXUvLpYGaEFFzyN3LTSYTN954I+eeey7z5s3jhz/84R730TQNRVEIh8MRCVIIMYZpGmzdCg0NhDJzqaqC1hZIzwDTQdfqCCHE2LCznNwX9mPZ0Z18vHQnHxZ6fSaefiuDv7+WSXdv/yl4rCPMlWc2c+WZzcTHqEP2+va6CmK3fAmAL28S3sLiIXstIcRAgxoZ9u6773LNNdfgcrm45pprOProoyMdlxBC6OrqoKICNS2D2noLDQ2QlqY3TxNCCDFQQ28zvrCfJFsC8zPnkmiLNzqkMS8QUnhxVRqPrsim3W3tO261qHzj5Fa+f24TKYmhIY9jQAO1xRdKqZgQUXRISXdrays33ngj//nPfyguLmbNmjWScAshhk5HB5SWosXGsb09hm3bICUFrNYDP1QIIcai4tSp2M02ipImSjm5wcIqvLYmhT+/lMP2VnvfcZOicf6Cdn50QQM5acGoxKL4vCR99BoAqs2B+/izovK6QgjdQSfdTz75JL/4xS/w+/3cfffd3HjjjZjN8mEuhBgiXq++jzsYpIk0qqshMRHs9gM/VAghxorm3jYqu2o4NnMOJsWEWTExLbnQ6LDGNE2D9z5P4oEXctm6PWbAbacd3clPljQwKdcX1ZgS172F2avP9+469jTUWKmAECKaDjrp/sEPfsAZZ5zBo48+yvjx44cyJiHEWBcOQ1kZtLTQGpNPxRaIiYHYWKMDE0KI4UHVNEo6t1DSuRWAre4apjgnGhyV+LQ0nvufz+WLioFJ7fzpXdxwcT0zJ/YaElfyyv7S8s7FFxoSgxBj2UEn3f/5z3+45JJLhjIWIYTQ1dRATQ2dMdlUVJmwmCEhweighBBiePCGfKxr/pwWX3938kmJsiBipJKaGB54PpePvho4gmvmRA83XlzPsdO7DYoM7DVlxFRtBsA3fgq+idMNi0WIseqgk25JuIUQUdHcDGVldFuTqdhmIxSCtFSjgxJCiOFBupMPLzWNdh56KYc316UMOD4p18v1X2/g5LmRnbM9GLuOCeuUBmpCGOKgk+7FixfvcWzlypURDUYIMcb19EBJCd6AiYqOBDw9+ixuIYQQUNlVy/rWrwCkO7nBmjqsPLI8m+UfpBFW+5PYnDQ/117YwDnHd2AeBmMtTV4PiavfBCDsiKVr/hkGRyTE2HTQSbfs4xZCDKlgEEpLCbS5qfDk4erUE265IC+EELp0RyoWxUx+fA5Hps2Q7uQGcHWbeeK/WTz7TgaBYH9WnZIQ5AfnNXLJ4jZsVs3ACAdKXPMmZp++j7zruDNQY+IMjkiIsemgk+5//OMfQxmHEGIs0zSoqCBUU0dVby6trQrpGWAaBqsEQghhpN6Ql1iL3gE70RbP6fknEmeVrpLR5vGZ+NebGfzj9Sx6vP0XO+Jjwnzna01ccXoLcTGqgRHuhaYNbKC2SBqoCWGUQ5rTLYQQQ6K+HnXLVrZ502losZCWBhZZwBFCjGE7u5OXdVZwYs6xpMfozS0k4Y6uQFDhuZVpPL4im45ua99xu1Xlm6e2cPXZTTgTwgZGuG+O6hIcteUAeCdOwz/hCIMjEmLskqRbCGEslwutpJSGDge17bGkpIDVeuCHCSHEaLV7d/Km3ta+pFtER1iFVz5K5S/Ls2los/cdN5s0LlzYxjUXNJKVEjQwwgNLfvelvq87Fy8xMBIhhCTdQgjj+HxQUkJrnZdKdy6JiWC3H/hhQggxWjX3trK25XP84YB0JzeApsG765088EIOVQ0xA247Y14H1y1pYEK236DoDp6pt4fEtW8BEI6Jo2veaQZHJMTYJkm3EMIYqgpbttBR2kR5Vx4xMRArVZNCiDFqZzl5SedWQLqTG2Ht5gTufz6Xr6oGNhs7odjNDRfVM22C16DIDl3Sx69jCvgAcJ9wFpoj5gCPEEIMJUm6hRDGqKnB/UUVW7sysVjNJCQYHZAQQhinwdPUl3BPTBjH7LTp0p08Sr6qiuWB53NZszlxwPHZk3u48eJ6jp7aY1Bkg6RpOFf2l5a7pIGaEIYbdr2B//KXvzBhwgQcDgfz5s3jk08+2e/9XS4XP/7xj8nOzsZut1NUVMTrr78epWiFEIPS2krPhnIq2xIJmBw4nUYHJIQQxsqNy6IgIZ95GbM5KqNYEu4oqKx3cP2DE7nkN1MHJNyFeV7+cmMF/76tfOQl3EDM1o04tlcC0FtYjD9/ssERCSGG1Ur3c889x9KlS3nssceYN28eDzzwAKeffjrl5eVkZGTscf9AIMCpp55KRkYGL774Irm5udTW1uKUM3ghhi+PB+/6EmqqVLrMSWSkGB2QEEJEn6ZplLsqmZgwHpvZiqIoHJ0xy+iwxoSGNit/WZ7Dig9TUTWl73heup9rlzRw1vwOzMNuWerg7brKLQ3UhBgeDirpNplMKIpy4DvuJhw+tBEK9913H1dffTXf+c53AHjsscd47bXX+Pvf/84vfvGLPe7/97//nY6ODlavXo11R7vjCRMmHHKcQogoCYUIbCyj7ssOWk35ZKTDID5ahBBiRPOGfKxr2UCH10WHz8X8zLmDOs8Sh6ajy8ITr2Txn3fTCYb6s+rUpCDXnNfI1xe1YbNoBkZ4+Ew9bhI/eQeAcFwi3cecbHBEQgg4yKT7tttu2+OHwfLly9m8eTOnn346U6ZMAaCsrIy33nqLGTNmcP755x9SIIFAgPXr13PLLbf0HTOZTJxyyimsWbNmr4955ZVXmD9/Pj/+8Y9ZsWIF6enpfPOb3+TnP/85ZvPey7L8fj9+f3/Xya6uLgBUVUVV1UOKWYjhSFVVNE0bfu9nTSNUVkHd6lq2q9mkZYFi0hjZpzciGrRd/hNipGvubWVdyxd93clz47JAQd7fQ6jHa+Kp17P455uZ9Pr6zw8TYkN896wmLjuthViH/jNzpP8tJH30KqZgAADXCWeh2uwM9e9KPqMPnbbjl6rpv9C0/l/D7fxtDBqKc+iDSrp/+9vfDvj+iSeeoKWlhU2bNvUl3DuVlpayePFicnJyDimQtrY2wuEwmZmZA45nZmZSVla218dUVVWxcuVKLrvsMl5//XUqKir40Y9+RDAY5De/+c1eH3PXXXdx++2373G8tbWVQCBwSDELMRypqorb7UbTNEym4VMfp7Z10LaughZzIvH5YfzmMMN/6IoYDjQ0/GZ9Hq6CrAaKkUnTNLa2V1HRUQNAvD2OI7NnkGCLx4PP2OBGKX9A4cW3s3lqRT7uHmvfcbstzCWnN/Ctc+pJig+hAR7jwowcTaNgl9LyhlPOwmce+veWfEYfurACwRhoD0GvDwgE9NK/jg79a2Eot9sd8ecc1J7ue++9l2uvvXaPhBtg6tSpXHvttdxzzz1cffXVhx3g/qiqSkZGBk888QRms5m5c+dSX1/Pvffeu8+k+5ZbbmHp0qV933d1dZGfn096errsBRejgqqqKIpCenr6sEm6NXcX9esb6Kq1kTreiR3g0HafiDFs5+pJXNghJ3RiRPKFfKxt/oJWXwcABYn5FGZMJFGLQwnLezrSQmFY8WEajyzPoanD1nfcYlZZcmIbPzy/kYzkIGCB8LBqb3RYYkvXE9O4DQDPEXMwZ00hLgo/a+Uz+tCFQhDwQqoF4s1BcLmgoADGj4d9VOuK6LHZbAe+0yEa1CfN9u3b+/ZQ743VamX79u2H9JxpaWmYzWaam5sHHG9ubiYrK2uvj8nOzsZqtQ4oJZ86dSpNTU0EAoG9/oHZ7Xbsdvsex00m07BJUIQ4XIqiDJ/3dCBA4/tlNJZ7iJmQh2PPf35CHJCyy39CjDQmxUxPsBeLYmZuejHjEnLwmHwoYXlPR5KmwVufOnnoxVyqGx19xxVF42vHdnDtkkbGZ+6ssRp9f+7Jq5b3fe1afGFU31vyGX1olB2/TOEQpsZGPeGeMQP2k1+J6BmK8+dBPeOMGTN45JFHqK+v3+O27du388gjjzBz5sxDek6bzcbcuXN59913+46pqsq7777L/Pnz9/qY448/noqKigF191u2bCE7O3tIrlAIIQ6RptH68RbqP6lHyckiNtbogIQQIjo0rX9/q91s47isuZySt4DxCbkGRjU6aRp8/FUCF//mCG58eNKAhPvE2S5e+l0p9/6oZpeEe/Qxd3WS+Il+Dh1KcNJ91GKDIxIHFA6hNNZDfj5MmyYJ9yg3qJXu+++/n9NPP52ioiIuuOACJk/W5/9t3bqVl19+GU3TeOaZZw75eZcuXcq3v/1tjjrqKI455hgeeOABPB5PXzfzK664gtzcXO666y4ArrnmGv785z9z/fXX85Of/IStW7dy5513ct111w3mtyWEiLCOL+uoe7+ScFoWic7RU8InhBD74w35WNu8gYKEfCYk5gOQ6kg2OKrR6cuKOO5/PpdPShMGHJ9T1M2NF9czd8qo2K19QEkf/hclHALAveAcNKssPg1rapiY9nq02bkwcybspQpXjC6DOgs+4YQTWLduHbfeeivLly/H6/UCEBMTw+mnn87tt99+yCvdAJdccgmtra3cdtttNDU1MXv2bN58882+5mrbtm0bsNyfn5/P//73P2688UaKi4vJzc3l+uuv5+c///lgfltCiAjqqm5n2/9K8VvjcWY4DvwAIYQYBZp6W1nX/Dl+NUB30ENefA4Wk+zRjLSt2x08+EIuKzc4BxyfMq6XGy6qZ+GsrrEzklJVB5SWdy6+0MBgxAGpKrbWBrqSs1GnF4NDzpHGAkXbtf5pEFRVpbW1FWBYNW46WF1dXSQlJdHZ2SmN1MSooKoqLS0tZGRkGPbv0dPmpeK5z/A2uEg8ImfsnPiIIaGh4TH7pEmPGNZUTWVzxxZKXRUAOG2JzM+cQ4Itfo/7ynt68Opbbfx5WQ6vfJyCpvX/2eVn+Lju6w2cOa+TEXYqethiN61j/N0/BqBn+jHU/eKRqL6+vJ8PgaZhbanHF5fK9vQjOe7UOBITjQ5K7M7lcpGcnIzb7SYxQn9Bh13vaTKZcDgcxMfHj7iEWwgRef7eMFWvl+GtbSVhWr4k3EKIUW9nOfnO7uSTEsczO3UaZlnhjpg2t4XHVmTz/Mo0QuH+882M5ADXnN/IhQvbsI7RXUzJK5f1fe2SVe7hS9OwtNYTSkqmt2AWYV+c0RGJKBp0lvzZZ59xxhlnEBsbS2pqKu+//z6gz9s+77zzeO+99yIVoxBihAiFoOLtano21RBflI3JIhfihBCjWyAc5O3tH9Lq68CimDk240jmps+UhDtCuntNPPhCDqffNINn387oS7gT40LcdMl23rh3E5csHrsJt9nVRsKG9wAIJaXSPeckQ+MR+2Zpa0SNS8JbOAs1LuHADxCjyqA+olavXs3ixYvJzc3l8ssv569//WvfbWlpabjdbh5//HFOOumkSMUphBjmVBUqPm7Gta6cpPHJmB3SxEUIMfrZzFYKEvJp7G3ZZzm5OHS+gMK/387gyf9m0eXpP12NsYW54owWvvO1ZhKjMYR6mHN+8ApKWP9zcJ14LljG6NWHYc7S1oTqiMVbNAs1PgmCRkckom1Q/zJ/+ctfMnXqVNauXUt3d/eApBtg0aJF/POf/4xIgEKI4U/ToOrLblrf20xKiglTklzBFUKMXt6QD1VTibPqcxCnpxQxNblQGqZFQDAEyz5I49GXs2np7L94azGrXLy4jR+c20i6M2RghMOIGsa56mUANEXBddIFxsYj9srS0Yxms+Mtmk04UaYYjFWDSro//fRT7rrrLux2Oz09PXvcnpubS1NT02EHJ4QYGbZVBql/t5RUcxemzDyjwxFCiCGzszt5rDWGxbnHYVbMmBQTJulfcVhUFd5cl8xDL+Wwrbm/m7OiaJxzXAfXXthAXkbAwAiHn7iv1mJrawDAM3M+wfQcgyMSu7O42tBMZrxFswg7U40ORxhoUEm31WpFVdV93l5fX098vJRXCTEWNDZoVL+1lZSe7Zgn5CKd04QQo9Hu3cljNAeBcJAYi6xuHw5Ngw83JvLAC7mU1cYOuG3xHBfXf72ewnyfQdENb7s2UJMxYcOP2d0Omoa3aDah5HSjwxEGG1TSfeyxx/Liiy9yww037HGbx+PhH//4ByeeeOLhxiaEGOba2mDLqnoSWyqw5aWjyV4yIcQoJN3Jh8aGLXHc/3wu68sHbkk6Zmo3N1xUz+xCj0GRDX+Wjmbiv/gIgGByBj2zTzA4IrErc1cnSiioJ9xpWUaHI4aBQZ0h33777Zx44omcddZZfOMb3wDgyy+/pKqqij/+8Y+0trZy6623RjRQIcTw4nZD6epO7FUlxKfHEHbEHvhBQggxwuwsJ/erASyKhaMyihkXL2W8h6N8WwwPvJDD+184BxyfNsHDjRfXc9yMbimaOgDneytQ1B0N1E46D8xy0Xu4MPW4UQI+vIWzCGbkGh2OGCYG9S903rx5vP7661xzzTVcccUVANx0000ATJo0iddff53i4uLIRSmEGFY8Htj0mQ9tcwmpcT5CSfJDRQgx+miaxqaOMvxqAKctUbqTH6ZtzTYefimH19emoGn9WfWELB/Xfb2e0452YZJJkwcWDuF8fwUAmmLCddL5xsYj+pg83Zi8HnyFxQSz8o0ORwwjg74stnjxYsrLy/niiy/YunUrqqoyadIk5s6diyKXJ4UYtfx+2LRRxfdlGePMzYTSpHGaEGJ0UhSFYzPnUOGuYWbKEYMqJ7/hoYlceWYzsws9qCrc+Uw+H36ZiKZoXHF6C5ef2rbXxwWCCvc8m8dHXyVit2pMGdfLPdfUAPC9uwtpc1tQFIhzhPnlt+qYNsF7wFhqmuz88vEJdPZYSIgJ8/++X0Nh3p77pT8pjecH9xYyIbv/tv/8pgyHTdvvbeXbYvjTc7k88dOKAc/X6rLw6MvZvPheOqFw/zliVkqAH13QwPkL2pGt8Qcv/svVWDuaAeiZfTyhlEyDIxIAJm8PJo8b38QZBLLGGR2OGGYGlXS73W6SkpIAmD17NrNnz45kTEKIYSoUgpIS6Py8homhGsJpWSB7GoUQo0hTbwsufxdHJE8GIN4ax+y06YN6ro2Vsbg95r69yf9dnUJlvYPX7t1Esz/EFbccybypPXtNfO97LhcUeOPezSiKnrj23XZtVd+M6nc+c/KrJyaw/M7SA8Zz+9/HcdGiNi5Y2M7/PtEf9/wdZXu974RsH8v/396fc1+3TRnnxWbRWLs5gWOnd+P2mPnbq1k881YGvkD/ErYzPsT3z23kGye3YrdpB4xbDJS88qW+rzsXLzEwErGT4uvF3OXCN3EagbyJ0lRW7GFQRTwZGRmcd955PPvss3sdGSaEGH1UFcrLof6LVsZ7y9ASE9FsdqPDEkKIiFA1la/ay/ig8RM2dpTR4m0/7Od8fmU6Z8/v6Pv+jbUpXHRSG2YTJMWHOGNeJ6+vSdnjcb0+Ey+9n8YNF9X3nbvvOpt6Z8IN0N1rhoM4v293W9hUHcc5x+u/r9OOdtHYYaO2ObKf42fN7+A/76TzxCtZnL50Bn99Nasv4Y51hLnm/Abeuu8rrjyzRRLuQbC0NRK3cTUAwdQsPMXzDY5IKD4vFlc7vvFT8OdNkoRb7NWgVrqXLl3KCy+8wOWXX47D4eDMM8/k0ksv5eyzzyYmJibSMQohDKZpUFkJlRs9TPCUYLFohOKTjA5LCCEiojfkZW3z57Tt0p081e487Of9tCyBb5/R3Pd9Y7uNnLT+WdO56X42Vuy5R7yuxU5SfIgnXslmzeYE7FaVH1/YyPzp3X33+cVjE/ikVO/6/djNWw8YS1OHjXRnsK+MW1EgJzVAY5uN8Zn+vcaw5NdTMZs0LljYzjdOaT3gbYGQQm2TnXfWO3n7s+S++1stKpee3Mr3z2kiNSm0x2uJg5f83ssomn6xovOk86XazGBKwIfF1YpvXBH+8UVIUwKxL4NKuu+66y7uuusuPv30U5577jlefPFFli9fTlxcHGeffTaXXHIJX/va17DZbJGOVwhhgG3boGxTiJzuMmJ9HQQzpTmIEGJ0aOptYV3zF33dyY/OKCY/Qt3Jmzqsg0oywyo0tNmZlOtl6SX1lNTE8L27i3jlD5tJ2/F8f/hhDQAvf5jCn/4vj8d320d9OKZN6GXVgxtJiFVp6rDywz9OxpkQ4sx5nXu9LTEuRFhV+MuyHOpa+lfOTYrGeQva+dEFjeTucrFBDFIohPO9lwHQTGbc0kDNUEowgKW9GX9+If4JR0jCLfbrsOYLHH300Rx99NH88Y9/ZM2aNX0J+PPPP09iYiKdnZ2RilMIYZDGRtj0lUaquxKnu5Zgeo6UTgkhRoWSzq1s6igH0LuTZ80lwRoXseePsan4g/2fl9mpARrabMwq1L+vb7WTnbpnMpqdGsCkaJx9nL7yPm2Cl7x0P1vqYkhL6h5w3/MXdHD7P8bj6jbjTAjv8Vw7ZaUEaHVZCYXBYtYrmBrabWTvJRmOj1F3eVyQrx3byfryeM6c1zngtszkIEeM8/L//pWPq8c64DlOmdvJ9Rc1MCl3z/3qYnASPv8Ai1vfHtA9ZyEhZ5rBEY1hoSCWtkb8eRPxFUwFs1QciP2L2CWZ+fPn8+Mf/5irr76a+Ph4urq6IvXUQgiDtLXBV19BXFcj6Z1bCDvTwGI98AOFEGIEiDU7AL2c/OTc4yOacAMU5XupbnT0fX/6MZ288F4aYRXcPRbeXJfMmcd27PG45IQwx07v5qONiQBsb7GxvdXOpBwfXR4zLZ39n8PvfJaEMz5EUryecP/isQm885lzj+dMTQoxbUIv//04FYC3PnWSlRLYa2l5q8uCuiO39nhNvP9FElPH9w647bOyeL5x+xRe+Th1QMI9vcDD+EwfD91QJQl3hDl3aaDmkgZqxgmFsLY1EMgpwDdxOlhkRro4sMN+l1RXV/Pcc8/x/PPP8+WXX2IymVi0aBGXXHJJJOITQhjE7dYTbs3lJsdVgma1ocZE9oRUCCGiLaiGsJr0058Jifkk2OJJdSQf4FGDc9oxnXz8VSLHzdBXp889oZ1NVbF87eaZaIrKt89spihfT0xXbkhi1QYnv/teLQC/+U4tt/51Avc9l4dJ0fjtVbVkpgSpb7Ox9OGJ+AImTIpGcmKIR26q6CtA2lQdy+Wntew1nt9eVcsvn5jAE//NIj4mzP+7uqbvtlv/Op5Fc1wsnuPmrU+T+b9307GYNEKqwunHdHLhQn2F9V9vZvCfdzLo9Q9c2Zs50cMNF9ezoTwes0kapEWatXk78ZvWARDIyMUz/RiDIxqjwiGsrfUEM/PxTZwmCxHioCmaph3yJ2NdXR3PP/88zz33HOvXr0dRFBYsWMAll1zCkiVLSE9PH4pYh0RXVxdJSUl0dnbidDqNDkeIw6aqKi0tLWRkZGAa5P4ijwc+/xw6m/xM7t6ApaOFUKbM4xbG0NDwmH3EhR0oB9OmWYi9UDWVTR1b2NaznVPzFmI3D33fGY/PxGV3TOHZ28qJdfSXZQ/Ve7qjy8JPHyngb784cGO1Q1XTZOfhF3N4Y93AbusTc7xc//UGTjnKRTCscPFtR/CPW7aQvJ9Sd3Ho0p97mLRX/wlAyyXX0n72lcYGtIsx8xmthrE2byeYnou3aBaa3XHgx+xDMAjt7bBgASQmRjBGEREul4vk5GTcbjeJEfoLGtRK9/jx41EUhWOPPZb777+fiy66iOzs7IgEJIQwlt8PmzZBa7PK5OAWrO2NBDMk4RZCjFy7dyff3tPIpKTxQ/66cQ6Vn1+2nfpWG4X5Q19qnZIYinjC3dRh5dHl2Sz7II2wuuv+dD/XXtjIuSe0Y95xfbe+1caNF9dLwh1poSDO918BQDNbcC041+CAxiBVxdrSQCgtG29h8WEl3GJsGlTSfe+993LxxReTny8djIUYTUIhKCmB+nooMNcRU19JKDlTRpIIIUasxt4WPhmi7uQHY9cxXyOJq9vMk69m8ezbGfiD/VVTKQlBfnBeE5csbsVmHVgsWZDtpyB7zz3i4vAkfLYKS7fenLjrqEWEk/ac7S6GkKZhbW0g5EzTE26HjEcWh25QSfdNN90U6TiEEAZTVSgvh+pqyI9tJ66iDDUuQa7mCiFGJL2cvJwyVyUwNN3JRyOPz8TTb2bw99ez6PH2X3CNc4T5ztea+fYZzcTt0sFcDL3klcv6vpYGalGmaVha6wklJeMtmiW9bcSgHVTS/a9//WtQT37FFVcM6nFCiOjSNKishC1bIDupl4TqEpRQUMaRCCFGrJLOrX0J96TE8cxOnYZZqnb2KRBUeG5lGk+8kk17V39zKJtV5ZuntHD1OU1SNm4AW2MNcaWfAeDPGkfv1LkGRzS2WNoaUeOS8BbOQo1LMDocMYIdVNJ95ZVXHvITK4oiSbcQI8S2bVBaCilJYZIay7G42mQftxBiRCtKmkiDp5mpyZOjWk4+0oRV+O/HKfx5WQ4Nbfa+42aTxgUL27jm/EayU4MGRji2OVct7/vatfhC+trUiyFnaWtCdcTqK9zxSUaHI0a4g0q6q6urhzoOIYRBGhv1xmlxcZDqrsLWWEMoNQsG2flcCCGMoGoqdT0NjIvPRVEUbGYrp+YtQJEkZa80Dd5dn8SDL+ZSWT9wj+rpx3Rw3dcbZH+2wZSAn6QPXwVAtdpwn3C2wRGNHZaOZjSbHW/RbMKJQzNSUIwtB5V0jx8/9B0+hRDR19amz+K2WCAt1IR92xbCiSlo1qEfpSOEEJGya3fyoBpictIEAEm492Ht5gQeeCGHjZXxA46fMNPN9Rc1ML2g16DIxK4SPn0XS48bgO5jTiac4DQ2oDHC4mpDM5nxFs0i7Ew1OhwxSgyqkdquSkpKqK2tBfTkfNq0aYcdlBBi6LndesIdDEJOQjeOzZvBZEaNjT/wg4UQYpho9LSwruVzAmoQi2KJyvzt4SyswvryeFpdVtKdQeZO6ekb6bWpKpYHXshl9aaBc2dnTe7hxovrOWZqjwERi33ZtYFa5yJpoBYNZnc7aBreotmEktONDkeMIoNOulesWMHSpUupqakZcLygoID77ruPc8+VGYJCDFcej55wd3VBXmYQR1kJZk83wUzZxy2EGBl2706ebEvi2Kw5Y7o7+dufOrnzmXyaO/ovPGSmBPjuWU18VpbAW58OLJMtzPNy/UX1LDrSLVuFhxnb9kpit3wBgD93It6iWcYGNAaYuzpRQkE94U7LMjocMcoMKul+/fXXWbJkCePHj+fOO+9k6tSpAJSWlvLEE09w4YUX8uqrr3LGGWdENFghxOHz+/U93K2tkJerYa/dirW1nmBGrjRoEUKMCLuWkwNMTpzArLSpmJWx25387U+d3PDQRLTdjjd3WLnz6Xyg//M9N83PT5Y0cNZxHX2r4GJ4GbDKLQ3Uhpypx40S8OEtnKWfDwkRYYNKun/3u99RXFzMhx9+SFxc/xXlc889l2uvvZYTTjiB22+/XZJuIYaZUAhKSqC+HnJzwd66HXvdVkLJGWA+7N0mQggRFZ5gL+2+DqwmC0elF4/57uRhFe58Jn9Hwr17ctb/fUpikGvOb+SiRW3YLLun52K4UPw+kj5+DQDVZsd9/FkGRzS6mTzdmLwefIXFBLPyjQ5HjFKDur65ceNGvv3tbw9IuHeKi4vjyiuvZOPGjYcdnBAiclQVysuhuhqys8He24mjphTNHovmiDnwEwghxDCRHpPK0emzODVvwZhPuEHfw62XlO9/NfTO79dw2amtknAPc4nr3sLcq++v7zr2NJkPPYRM3h5MHje+gmkEssYZHY4YxQaVdDscDjo6OvZ5e0dHBw6HY9BBCSEiS9OgshK2bIHMTLBrPhyVm1ECfsJJKUaHJ4QQ+9Ub8vJ+wzq6At19xyYk5hM/hvdv76rVZT2o+3X3jt3y+5FEGqhFh+Lrxdzlwl8wjUDexKiW8AcC+mRW2TUwdgwq6V68eDEPPvgga9as2eO2devW8dBDD3HKKaccdnBCiMjYtg1KSyElBRw2FXttGdbOFn0etxBCDGONnmbeqvuAZm8rn7VuRNNklXZ36c5gRO8njGOvLSemchMAvnFF+CZNNzii0Unx+7C42vGNn4I/b1JUs1+fD9rbYfx42EvRsBilBrWJ85577mH+/PmccMIJHHPMMUyZMgWA8vJyPvnkEzIyMrj77rsjGqgQYnAaG/XGaXFxEB8Ptu012LdXE0zN0i+zCiHEMLRHd3J7EsdkzJbZ27vRNPh8y/7P3BU0MlP08WFieJMGakNPCfiwdLbgG1eEf3xRVM+F/H5oaYHJk2HKFDkNG0sG9VddUFDAxo0bue666+js7OS5557jueeeo7Ozk+uvv54vv/ySCRMmRDhUIcShamvTR4NZLOB0gqWjBUdNGeFEJ5rNbnR4QgixV70hL6sa1vQl3JMTJ7A49zgpJ9+NqsLd/87jwRd3Hfc4sBJA2fH9LZfXSafyYU7x9ZK4+k0AVHsMXcdJQ+JIU4IBLO3N+PMm459wRFSz3mAQmpqgoACmTgWz7PYYUwbdrjgjI4P777+f+++/P5LxCCEixO3WE+5gELKywOT14KgqATTUuESjwxNCiL1yB7pZVb+agBqU7uT7EQgp/OqJ8by2JrXv2NnHtfFpWeJuc7qD3HJ5Hace7TIgSnEoktb8D7PPA4B7/hmoMfEGRzTKhIJY2hrx503EVxDdrDcUgoYGPeGePl1fDBFjS0T/yquqqvD7/X1zu4UQxvD5YOtW6OrSR4MRCuGoKsHc7SKYmXfAxwshhFESrHEk2uIJayrzM+fI6vZeeHwmbnhoIh9/lQSASdG4/bu1LDmxnbCqdzNvdVlJd+ol5bLCPTI4V77U97Vr8YUGRjIKhUJY2xoI5BTgmxjdrDcU0ke15ufDtGlgPbi+h2KUGdQ77qGHHmL16tX83//9X9+xK6+8kqeffhqAI488ktdff52MjIzIRCmEOGh+P9TWQkcH5OXppYX2ugqszXUE03Nlf5gQYtjpDXmxm+2YFRMmxcRxWUdhNVkwK1J/ubuOLgvX/GkyX1XpFyPsVpU/XVvF4jluAMwmOGaq7N0eaRxVJcTUlAHgLZimr8SKyAiHsLbWE8zMxzdxGliil/WGw/oKd24uzJwJdtnZN2YN6trnX//6VzIzM/u+/9///se//vUvvv/97/Pwww9TVVXF7bffHrEghRAHJxTSu5S3t+uzuE0msLY2YN+2hbAzTeqZhBDDTsOO7uRftZf2HXOY7ZJw70V9m43LfzelL+FOjA3x159v6Uu4xcjlXNXfQE1WuSNIDWNtqSeYnot38syo9rNRVT3hzsrSE26Zpjy2DeoMvLa2dkAJ+fPPP09BQQGPPvooAE1NTX2r3kKI6FBVKC+Hmhp9NJjFAqYeN47qUjSbHTVGSjSFEMOHqql81VFGuasKgFZfB2E1jNkkyfbebKlz8P17C2np1PdrZyQHePKnWynM9xkcmThcpt4eknY0UAvHxOE+9jSDIxolVBVrSwOhtGy8hTPR7NHLejVNT7jT0vSEOzY2ai8thqlBJd27z8h86623OO+88/q+nzBhAk1NTYcXmRDioGkaVFbCli2wc1eHEvATU1WC4vMQypB93EKI4aM35GVN0wba/Z0ATE6awKzUqbK6vQ+flcfz4/sm0d2rn7YVZPt44mdbyU0LGByZiISk1W9gCugXT9zHfQ3NIRnaYdM0rK0NhJxpeAuLo/pnujPhdjqhuFgf1yrEoMrLi4qKWL58OaCXljc0NHDmmWf23b59+3acTmdEAhRCHNi2bXpZeUrKjvIlTcW+bSuW9kZCadlGhyeEEH12lpO3+zuxmiwclzmXOWkzJOHeh5Ubkrj67sK+hHvmRA/P3FomCfdooWnSQC3SNA1Laz2hpGS8RbOiXunX2AgJCTBrFiTKsBixw6BWum+++Wa++c1vkpycjMfjYerUqZx++ul9t69cuZLZs2dHKkYhxH40NsKmTRAXp19N1TSwuNqw11cTSskEKdUUQgwTgXCAdS2fE1RDJNuTpDv5Abz0Xiq/+ft4VE1vgHnCTDf3X1dFnEM1ODIRKTEVX+GoqwCgd3Ix/nGFBkc08lnaGlHjkvAWzkKNS4jqazc16aXks2bpK91C7DSopPvSSy8lNTWV119/HafTyY9+9CMsOxo0dXR0kJKSwre+9a2IBiqE2FNbmz6L22Lp/3A3u9oxNdehxsah2aRrhxBi+LCZbRyVXkyrr0PKyfdD0+DJ/2bxwAu5fcfOPq6d319di82i7eeRYqRxrpQGapFkaWtCdcTqK9zxSVF97dZWfRxYcbFeeSjErhRt9w3aY0xXVxdJSUl0dnZKSbwYUdxu2LABenv1zpgAiq+XmJJP8KrdOOKyUZDxYGLk09DwmH3EhR3ynh6BGjzNmBUzmbFpRocybOzvPa2q8Id/5/HMW/1TYr59RjM//cZ2TDJve1Qxeboo/MmZmIJ+wrEJbH34jRF5sXy4fEZbOprRLDZ6pxxJ2Jka1ddua9P/P3s27DLgSYxQLpeL5ORk3G43iRHaI3BY84Pq6+v54IMPaGlpYcmSJeTl5REOh3G73SQlJWE2yxVsIYaCx6OvcHd16bMfAQiHcdSUY3F3Es5JA6k+FEIYSNVUvmovo9xdhd1s47S8hcRYRl5CEU2BkMIvH5/A62v7l8luumQ7V53VjCLXm0adpI9ewxT0A+BecPaITLiHC4urDc1kxls0K+oJd2enPo9bEm6xP4O6ZqppGkuXLqWgoIDLLruMpUuXsmXLFgB6enqYMGECDz/8cEQDFULo/H59D3drK+Tk0HciZq+vwt5QTTAtCzk7E0IYyRP0sqp+DeVufRzYuPhcbGarwVENbx6viR/9aXJfwm02afz+6hq+e7Yk3KOSppG8S2l55yIpLR8ss7sdNA1v4SxCyelRfW23G3w+fSxYTk5UX1qMMINKuu+9914efPBBbr75Zt5+++0BI8SSkpK48MILeemll/bzDEKIwQiFoKQE6uv1D/edpYaWtibsteWEklLBIie2QgjjNHiaeXv7wO7kR6ZNl/3b+9HRZeE7dxWxepNexuiwqTx8QyUXLmw3ODIxVGLKP8feUA2AZ8ocArkFBkc0Mpm7XSihIN7JMwmlZUX1tbu79crDGTMgPz+qLy1GoEGVlz/55JNcccUV3HnnnbS37/kDobi4mDfeeOOwgxNC9FNVKC+H6mrIztabpwGYerpwVG0GswU1Nh4Y020ahBAG0TSNje2lfavbKfYkjs2cS7xVZg7vT32rje/dU0htk15anBgX4tGlFRxZ5DE4MjGUkqWB2mEz9bhR/F68hbMIZuQe+AER1NOjr3LPmAHjx0f1pcUINaiku66ujuOOO26ft8fFxdHV1TXooIQQA2kaVFbCli36fiGbTT+uBAM4qksx9/ZE/QeOEELszhfW96cWJhVQnDoVsyKdv/anfFsMP7i3kFaX/qGemRzgiZ9tpTDPZ3BkYiiZu10kfPouAKH4JLqPXmxwRCOPydONyevBV1hMMCu6y8y9vfo+7unTYeJE2dEnDs6gku6MjAzq6ur2efv69esZN27coIMSQgy0bRuUluojKBw7+6xoGrZtW7G21usJt3zqCyEMoGoaJkVBURTmpM9kXHwO2XHSTehAPi9L5OY/TqG7Vz8VK8j28eTPtpCTFjQ4MjHUkj74L6aQ/vfsXngumtVmcEQji8nbg8njxjdxBoGs6OYbPp/eqXzqVJg0SU69xMEb1CXoCy+8kMcee4yqqqq+Y8qOd91bb73FU089xUUXXRSZCIUY4xob9cZpcXEQH99/3Nq8Hfv2CkLJGWA+rEEEQghxyFRN5cv2ElY3fdbX28VqskjCfRDe/czJT+6a0ZdwF0/q4ZlbyyThHgs0Ded7y/u+7Vx0gYHBjDyKrxdzlwt/wTQCedFdZvb7obkZioqgsBAZ4ScOyaDeLrfffjvZ2dnMnj2bK664AkVRuPvuuznhhBM488wzKS4u5pe//GWkYxVizGlr00eDWSyw6xh5s7sDR3UJmiMOzRFjWHxCiLGprzu5q4qG3mZavNLw62C9+F4qNzw0iUBQPwVbUOzm77/YSnJC2ODIRDTElnyGvWkbAJ5pRxOM8krtSKb4fVhc7fjGT8GfF91l5kAAmppg8mSYMgVkKrI4VINKupOSkli7di0/+9nPqK+vx+Fw8P777+NyufjNb37Dhx9+SGysNE4R4nC43XrCHQxCWlr/ccXnxVFVghIMEE5MNi5AIcSYtLfu5JmxaQd+4BinafDYiixu+9sEVE1PFs45vp0/31hBrEM1ODoRLckr+6f7dC5eYmAkI4sS8GHpbME3rhD/+KKoLjOHQnrVYUGBXlZukeJCMQiDftvExMTw61//ml//+td7vb26upqCAhl/IMRgeDx6wt3VBbm79kcLh7FvK8fiaiWYkWdYfEKIsUfVVL5qL5Pu5IOgqnDnM/k8+3ZG37HLztrOLy5uwmySTaFjhdndTsL6VQCEElPonnuiwRGNDEowgKW9GX9+If4JR0Q94a6v1zuUT58OVpnKKgYp4u/ajRs38s1vfpMpU6ZE+qmFGBP8fn0Pd2urPot71+opW0MN9voaQimZsplICBFVn7R80ZdwFyYVsCj3eEm4D0IgqPDTRwoGJNw3XVrH9ZfVyMf4GON8/xWUsL6NwLXwXLBIBndAoSCWtkb8eRPxFUyNal13OAwNDZCXp48Gs0m/O3EYDmmle/PmzTz66KNUVlaSnJzMRRddxAUX6A0gNmzYwK9//Wv+97//YbVaufzyy4ckYCFGs1AISkr0q6q5uQPzaktHC47aMsIJSWg2u3FBCiHGpKKkiTR725ibNpO8+GyjwxkRPF4T1z04iTWbEwEwmzR+970azlvQjkzhHmNUFed7LwOgKQquRecbGs6IEAphbWsgkFOAb+L0qNZ1q6qecGdlwcyZYJfTLnGYDvrdu3btWhYvXozP1z878rnnnuO+++4jFArx85//nISEBH76059y/fXXk50tP5CFOBSqCuXlUF0N2dkDf7aYentwVJWAoqDGJRoXpBBizFA1lQ6/izRHCgApDidnjTsZi0k6CB2MdreFH/5pMpur4wBw2FTu/0klJ87uQjM4NhF9cZvWYWutB8Az41jZInYg4ZA+EjUzH9/EaVGtCtiZcKenQ3ExxEi/WhEBB51033HHHTgcDpYvX86CBQuorq7mO9/5Drfddhter5elS5fyq1/9iqSkpKGMV4hRSdOgshK2bIHMzN1KmEJBHNWlmLtdBDPlh7QQYuh5gr2sbd5AZ6CLk3OPJ9mu/2yXhPvgbG+x8b17CtnW7AAgMS7EYzdVMLtQ1rfHKmmgdgjUMNaWeoLpuXgnz4xqdZ+m6Ql3SoqecMfFRe2lxSh30En3unXr+PGPf8zpp58OwPTp07nvvvtYuHAhS5cu5Z577hmyIIUY7bZtg9JS/UPe4djlBk3DXleJtbmOYHpuVMdjCCHGpgZPM5+0fEFADWI1WfCF/UaHNKKU1cbw/XsLaXPrK3NZKQGe+NlWJuf6DvBIMVpZOluJ//xDAILJ6fQceYLBEQ1jqoq1uZ5QWjbewplodseBHxNBjY2QlKQn3AkJUX1pMcoddNLtcrkoKioacGzn94sXL45sVEKMIY2NeuO0uDiIjx94m7W1AXvdVkLJ6TKjQggxpMI7upNvke7kg/ZpaTw/vn8yPV69ImBijpcnf7aV7NSgwZEJIznfX4Gi7migduJ5YJaf53ulaVhbGwglp+MtLEZzRPezp6kJYmNh1iw98RYikg76X72maZh36xi483uHI7pXoYQYLdra9NFgFgs4nQNvM3e7cFSVoNnsUf/BI4QYWzzBXtY0b6DD7wL07uTFqVMxK9Je+2C985mTmx8pIBDU/8xmTe7h0aUVOBPCBkcmDKWGcb63HABNMeE68Xxj4xmuNA1Laz2hpGS8RbNQY6Jb193crDdLmzULkpOj+tJijDikS22vv/46TU1Nfd/39vaiKAovvPACX3zxxYD7KorCjTfeGJEghRiN3G494Q4G9e6Yu1ICfhzVpSj+XkLSbEUIMcS29TTQ4XdhNVk5JmMWuXFZB36Q6PPCqjRu/8c4VE3fArSg2M39P6ki1qEaHJkwWvyXq7G2NwPQM+t4Qmnyb2tvLG2NqHFJeAtnocZFt667rU2fRFZcDGlpUX1pMYYomqYdVBNN0yEOk1QUhXB4+F/d7erqIikpic7OTpy7LzUKMUQ8Hvj8c2hv10eDDdiqrao4Kjdh316hdzc9xMZFGhoes4+4sAMF2QMuRj55Tw89VdPY2F7K5KQJUk5+CDQNHl+RxUMv5fYdO/f4dn73vRqs+1nWkPf02JH3pxtJ+ELfz1239H56jlxgcESRd7jvZ0tbE6rdgfeIOYQTo7vM3NGhj2udPVufHCME6Nuqk5OTcbvdJCZGZmrQQa90V1dXR+QFhRjr/H59D3drK+Tl7dkbzda0DXt9NaGUzENOuIUQ4mB4gr1s7tzKnLQZWExmTIrC7LRpRoc1ooRVuOvpfJ59J6Pv2He+1sRNl9RziOsUYpSytDUR/+XHAARTM+mZdZzBEQ0/lo5mNJsdb9HsqCfcLhcEAnpJuSTcYqgddNI9fvz4oYxDiDEhFIKSEqiv11e4dz8xM7vacNSUEY5LQLNJrwQhROTVe5r4pOVLgju6kx+ZNt3okEacQFDhF49P4M11KX3Hbr50O1ed1WxgVGK4cb7/MoqmbzFwnXSBXEjfjcXVhmYy4y2aRdiZGtXX7uqC3l494c6TXXwiCqR9ohBRoqpQXg7V1foV1d2bkSu+XhxVmyEcRk2WtplCiMjaszu5k6KkAoOjGnl6vCZ+8sAk1pXoJYdmk8bvr67hvBM6DI5MDCuhEM73XgZAM5n1ruWij9ndDpqGt2i2PqElinp69KR75kwYNy6qLy3GMEm6hYgCTYPKStiyBTIzwWbb7Q6hEI7qUizuDoKZ+YbEKIQYvXbvTl6UVMBM6U5+yNrcFn74x8mU1OidlR02lft/UsmJs7sMjkwMNwlffIjV1QZA95yFUU8shzNztwslFNQT7ig3lvN49LLy6dOhQK45iiiSpFuIKNi2DUpLISUF9jZhz15fha1pG8G07D03eQshxGFo8bbxcdP6HeXk0p18sOpabFx9TyHbmvUP8aT4EI/dVMGsyR6DIxPDkXPlsr6vXYsuNDCS4cXU40bxe/EWziKYkXvgB0SQ16s3sJ02DSZOlNMtEV2SdAsxxBob9cZpcXEQH7/n7Za2RuzbthBOSgGLNfoBCiFGtThLHAp6Ofn8zDnESXfyQ1ZaG8MP7i2kza1/RmelBnjyp1uZlOszODIxHFlbthP/1RoAAum5eGbMMzii4cHk6cbk9eCbPJNgVnSr+nw+aGmBI46AwsI9e+oIMdQk6RZiCLW16bO4LRbY20Q6U08XjqoSsFhRY/aSkQshxCAEwkFsZj1BjLPGcFLOfBJs8VJOPgiflMZz7f2T6fHqTbAm5Xp54qdbyU4NGhyZGK6cq17u+9q16ALJ8ACTtweTx41v4gwC2dFtzhwI6Al3YSFMmSJ/HcIYg3rbXXXVVaxbt26ft3/yySdcddVVgw5KiNHA7dYT7mAQ0tL2vF0JBnBUl2Du7SHk3MsdhBBiEOo9Tby+bSX1nqa+Y057oiTcg/D2p06uvqewL+GePbmHp39dLgm32LdQEOcHrwCgmc24Fp5jcEDGU3y9mLtc+AumEciLbl13MKhXHBYUwNSpYJYG8sIgg/oJ/NRTT1FZWbnP26urq/nnP/856KCEGOk8Hj3h7urSG6ftQdOwbduKtbWBYJSbiAghRqewpvJF22Y+bvqMgBqksqvW6JBGtOdWpnHjwxMJhvRTpRNnu/jbL7bgjA8bHJkYzhLWv4elS+9k333UYsJJ0R2FNdwofh8WVzu+8VPw502KasIdCkFDg55wT5++59QYIaJpSN5+DQ0NxMTEDMVTCzHs+f36Hu7WVn32495+vlib63DUVRBKzgCz/BQQQhyePbuTT2Rm6hHGBjVCaRo8+nI2f16W03fs/BPauP27tVjl41ocQPIuDdQ6x3gDNSXgw9LZgm9cEf7xRVGt6w6FoL4e8vP1xmlWaZkjDHbQPz5WrFjBihUr+r5/4okneOedd/a4n8vl4p133uHoo4+OTIRCjCChEJSU6B/0ubl7//lidnfgqC5FjYlDc8jFKSHE4an3NPFJy5cE1SA2k5WjpTv5oIVVuPNf+fzn3Yy+Y989q4mll9RLp2NxQLbGWuJKPgXAnzWO3mlHGRyRcZRgAEt7M/78QvwTjohqwh0O6yvcOTn6LG67PWovLcQ+HXTSXVJSwgsvvACAoiisW7eO9evXD7iPoijExcWxcOFC7rvvvshGKsQwp6pQXg7V1ZCdvfcyJsXnxVFVghIKEtrbRm8hhDgEnX43Hzd9Bkh38sMVCCr87NEC3vo0ue/Yz75Zx5VnthgYlRhJnKuW933tWnTh2J1JFQpiaWvEnzcRX0F0N1Krqp5wZ2VBcfHex7QKYYSDTrpvueUWbrnlFgBMJhN/+9vf+OY3vzlkgQkxkmgaVFbCli36Hm6bbS93Coex15Zh6WwhmBndURlCiNEp2Z7EpMTxmBUzM1OPkGZpg9TjNfGTByaxriQRAItZ4/dX13Du8R0GRyZGCiXgJ+nD/wKgWqy4F5xtcEQGCYWwtjUQyCnANzG6G6k1TU+409L0Fe5Yuf4ohpFB/UtQVTXScQgxom3bBqWlkJKy76uqtoZq7A01hNKyZV6FEGLQ6j1NpNidxFj0D5s5aTNQxuqKWgS0uS384N5CSmv1M/QYW5j7r6ti4awugyMTI0nCZ6uw9LgB6D76ZMIJTmMDMkI4pDeIzczHN3EaWKK3kXpnwu106ivc8TKFVQwz0hJEiMPU2Kg3TouL2/eHvKW9GUdtOeGEZDTr3pbBhRBi/8Kaysb2Ura6q8mISWNh9jxMiiIJ92HY1mzj6nsKqWvRL2A440M8etNWZk3uNTgyMdIkr3yp7+vOk5cYGIlBNBVrSwPB9Fy8k2ei2aK7kbqpCRISYNYsSEyM6ksLcVAGvdz2xhtvcOqpp5KamorFYsFsNu/xS4jRrq1NHw1msehXV/fG1NuDo7oUFBNqXEJU4xNCjA49wV5W1X/MVnc1AE5bIqAZG9QIV1ITw2W/O6Iv4c5KDfDMrWWScItDZquvIrb8cwD8OQV4i2YbG1C0qSoWVzuhtCy8hTPR7NHdSN3UBDExesK9r3MxIYw2qKT7pZde4uyzz6a5uZlLL70UVVX5xje+waWXXkpMTAzFxcXcdtttkY5ViGHF7dYT7mBQ3z+0V6EgjupSzN0uQsnpUY1PCDE6bO9p5O3tH9Dhd2MzWTkh62hmp03DJPu3B21dSTzf/n9TaHfr5a+Tcr08e1sZE3P8BkcmRqLkXRqodS4eYw3UNA1rayPh+CR9hdsR3Y3Ura36OLDiYn2LnxDD1aDKy++66y6OOeYYPvroIzo7O3n00Ue56qqrWLx4MTU1NRx77LEUFBREOlYhhg2PR0+4u7r00WB7pWnY6yqwNtcRTM8dWz+EhRCHbddycoBUu5NjpTv5YfvfJ05+9mgBwZB+0eLIwh7+srQCZ3zY4MjESKQEfCR9+CoAqtWO+4SzDI4oijQNS2s9oSQn/pxx2GPiiOaZTnu7/v/iYkiXdQ0xzA3qMnlJSQmXXnopZrMZy46uhMFgEIAJEybwox/9iLvvvjtyUQoxjPj9+h7u1lZ9BuS+cmlrSz32bVv1Fe4odu8UQowOqqbS1KuPq5qSNJFFucdJwn2Y/u/dNJb+eWJfwn3SbBd//fkWSbjFoCWuewdzbzcAXfNORY0bOxuKLW2NqHFJeCcXozliovranZ0QCukJd1ZWVF9aiEEZVCYQGxuLbcdMJKfTid1up7Gxse/2zMxMqqurIxOhEMNIKAQlJVBfr69w76sJubnbhaO6FM3uiHqplRBidLCaLMzPnEtvyEtOXKbR4YxomgZ/WZ7NI8tz+o5dsLCN26+qxSItaMRhcO7SQM21+EIDI4kuS1sTqiMWb9Es1Pgk8EVvnr3bDT6fvoc7J+fA9xdiOBjUSveUKVMoKSnp+3727Nk8/fTThEIhfD4fzz77LOPGjYtYkEIMB6oK5eVQXQ3Z2ftevFb8PhxVJSgBL+Gk1OgGKYQYscKayudtmyl3VfUdc9oTJeE+TGEV7nhq3ICE+3tnN/H770nCLQ6PfdtWYiu+AsCXX4h38kyDI4oOS0czms2Ot2g24cTkqL52d7e+xW/GDMjPj+pLC3FYBpV0X3DBBaxYsQK/X2848qtf/Yr33nsPp9NJeno6H374Ib/4xS8iGqgQRtI0qKyELVsgMxNs+5r6parYt23B2t5EKFXqnYQQB2fX7uRftZfSG/IaHdKo4A8o3PTniTy3sn/D58+/WcfSS+qlzYY4bM6Vy/q+do2RBmoWVxuayYy3sJiwM7oLCz09+ir3tGkwfnxUX1qIw6ZomhaRmSMffvghy5Ytw2w2c9ZZZ7Fo0aJIPO2Q6+rqIikpic7OTpwyZ0DsQ20tfPklJCXtexY3gG17FTFbNxJKSUezRXdkxk4aGh6zj7iwAyWqLU2EGBqj/T29vaeRT1u/JKiGsJmsHJMxW1a3I6C718RPHpjMJ6X6qEaLWeP/XV3DOcd3GBzZ6H9PjwWKr5fCn5yJ2edBtcew9eE3UGP2c4IwCpjd7SiqSm/RbEJp/QsLmqbi87XgcGSgDNFUhd5evXHatGlQWDgmrm8IA7lcLpKTk3G73SRGaPB7xLo7LViwgAULFkTq6YQYNhob9cZpcXH7T7gtna04assJxycalnALIUaOPbuTJ+/oTh7dhkSjUavLwg/+WEhZrd5TI8Ye5sHrqjihuMvgyMRokbT2Lcw+DwDu+aeP/oS724USCuLdLeGOBp8P2tpg6lSYPFkSbjEySUtlIfajrU0fDWaxwP4KIUxeD46qElBVvaGIEELsh6ppvN+whjZfJwBTnBOZmXKEzN6OgG3NNq6+p4i6FjsAzvgQj928leJJvQZHJkaTAaXli0Z3AzVTjxvF78VbOItgxr7mpA4Nvx+am2HKFH2Fe18NbIUY7gb11tU0jccff5xjjjmGtLQ0zGbzHr8sMiJJjHBut55wB4OQlrafO4ZC2GvKMHd1EEqVklAhxIGZFIXcuGxsJisnZB3NrNRpknBHQElNDJfdcURfwp2d6ueZW8sk4RYR5aguJaZabyjsLZiKb+I0gyMaOiZPNyavB9+kGQSzotu5LBCApiZ9dXvKFDBL40Mxgg0qM/7Zz37Gfffdx+zZs7n88stJTo5u50IhhprHoyfcXV36aLB90jTs2yuxNdYSTN/P0G4hxJgX1lR8IV/frO2ipALGxecQY5HtKJGwdnMCP3lgEh6ffmZemOfliZ9uJTMlaHBkYrQZK6vcJm8P5h433kkzCGRHt3NZKKRv7yso0MvKZS1PjHSDegv/85//ZMmSJTz//PORjkcIw/n9+h7u1lbIy9t/Hm1ta8S+bYvewdNijV6QQogRpSfoYU3zBkJqiFPyFmA1WVAURRLuCHlznZOfP1ZAMKRXC8wp6uEvSytIigsbHJkYbUzeHpLWvAlA2BGHe/7pBkc0NBRfL2Z3J75J0wnkTYzqokIoBPX1eofy6dPBKqdXYhQYVNLt9Xo55ZRTIh2LEIYLhaCkRP+wz83d/94hU48be3UpWG2jvoGKEGLwdu9O3h3oIcXhNDqsUeM/76Tz+3/lo2l6UrDoSBd/urYKhy0iw1mEGCDx4zcx+fWRfl3Hn4nmiDU4oshT/D4srnZ8E47Anzcpqgl3OAwNDfo52PTp+xnRKsQIM6gNZCeffDKffvpppGMRwnA1NVBdDdnZ+y9lUoIBHNWlmHp7CDn3t+FbCDFWhbUwn7dtYnXzeoJqiFR7MqflL5SEO0I0DR5+KZvf/XNcX8J94cI2Hry+UhJuMTQ0jeRV/aXlnaOwtFwJ+LB0tuAbV4h/fFFUO5epqp5wZ2VBcTE4pBBIjCKD+pf0yCOPsHbtWu68807a29sjHZMQhujshIoKSE4+wJVVTcNeuwVra33Ux2YIIUaGnqCHlfWr2equAfTu5Ity5xNrkXFgkRBW4fanxvHoyzl9x64+p5Hffa8WizRbEkPEUbkZx7YtAHgnzdCT0lFECQawtDfjz5uMf8IRUU24NU1PuNPT9YQ7Rj4qxShzUOXlCQkJKLuVloRCIW699VZuvfVWHA4H5t1aCiqKgtvtjlykQgyhUAi2btX3c6en7/++1uY67NsrCaVkgVk6ewgh9vRleymdfjc2k5VjMmaTEyeTDSLFH1D42aMFvP1ZfxPXWy6v41untxgYlRgLkle+1Pd15+IlBkYyBEJBLG2N+PMm4iuYGtVW4Zqmb+tLTtYT7ri4qL20EFFzUBnDkiVL9ki6hRhNtm/XP/BzcvZ/P7OrHUd1KWpsPJpd6p6EEHs3N20mAEemTZfV7Qjq7jVx7f2T+bQsAQCLWePO71dz9nGdBkcmRjuTp4vEdW8BEI6Np2veqQZHFEGhENa2BgI5BfgmTo96q/DGRkhKglmzICEhqi8tRNQc1L+qp556aojDEMI4XV2wZQskJh5gH7fPi6O6BFMwQFD2cQshdtET9LDd08QRzkkAOCx2js86yuCoRpdWl4Xv31tI+Ta9cVWMPcxD11dy/MxugyMTY0HSR69jCvgBcB9/1ui58B4OYW2tJ5iZr88bj/IklqYmiI3VE+6kpKi+tBBRNajNGnfccQebNm3a5+2bN2/mjjvuGHRQQkSLqupl5b294HTu547hMI6aMiydrQRTZR+3EKLf9p5G3t7+IRvbS6nraTA6nFGpttnOZXcc0ZdwJycE+cctWyThFtGxewO1xaOkgZoaxtpSTzA9F+/kmWg2e1RfvqVF76Eza5ZeWi7EaDaopPu3v/0tGzdu3OftmzZt4vbbbx90UEJES3091NVB5gG2W9oaqrE11hBKy45qYxEhxPAV1sJs2LU7uSOZVIecOUba5upYLrt9Cttb9YQgJ83PM7eWUzyp1+DIxFgRs+VL7PVVAPQWzSaQN8ngiCJAVbE21xNKy8ZbODPqK/dtbfrp1KxZkCbFg2IMGJJNGx0dHdhksJ4Y5jwevaw8Nnb/3cot7c04assJJySjWeV9LYTQy8nXNG+g0683DJ3inMTMlCmYFLkoF0mrNyVw3YOT6PXpTZ2K8nt54qcVZCQHDY5MjCWjroGapmFtbSCUnI63sDjqs8Y7OvRKw9mzISMjqi8thGEOOun+4IMPeO+99/q+X7ZsGRUVFXvcz+Vy8dxzzzFz5syIBCjEUNA0vay8qwvy8/d9P5OnG0fVZlBMqHHS3UMIAfWeJj5p+YKgGsJmsjIvYzbZ0p084t5Yl8zPH51AKKxfyJhT1M0jSytJjAsbHJkYS8zdLhI+fReAUHwS3UcvNjiiw6RpWFrrCSUl4y2ahRoT3VbhLhcEAvoKd3Z2VF9aCEMddNK9atWqvpJxRVFYtmwZy5Yt2+t9p02bxsMPPxyZCIUYAo2NUFurX2HdZ2P+UBBHdSnmni6CmXlRjU8IMXwpKH3l5PMz50h38iHw7Nvp/L+n89E0/QN60RwXf/pxFQ6bZnBkYqxJ+uhVTMEAAO4FZ0d933OkWdoaUeOS8BbOivpiQleX3kOnuBjy5LRKjDEHnXT/7Gc/49prr0XTNDIyMnjsscdYsmRgiY2iKMTGxuJwjJKOjmJU8nr1snK7Hfb5VtU07Nu2Ym3ZTjA9dz+ZuRBiLFA1ta90PCcukxOyjiYrNl3KySNM0+Dhl3J4bEX/EtiSE9v4zXdqsURvbLAQOk3DubJ/gcm16AIDgzl8lrYmVEesvsIdH91W4T09etI9cyaMGxfVlxZiWDjopDsmJoaYGP1qfnV1Nenp6cTGRncPiBCHS9OgqkrfT7S/D31rSz32ugpCyelRn1cphBhe6noa2NhexqLc+X2r2jlSTh5xoTD87qlxvPBeet+xH5zbyHVfb5DrnsIQsaXrsTdtA8Az9SgC2ROMDegwWDqa0Wx2vEWzCSdGt+Gjx6OXlU+fDgUFso4hxqZBZRPjx4+PdBxCREVLi55076+s3NzViaO6BM0eE/XmIkKI4SOshfmyrYSKrloAylyVzEmbYXBUo5M/oHDzIwW8u74/Gfjlt7Zx+WmtBkYlxjrngAZqI3dMmMXVhmYy4y0sJuxMjepre73Q3g5Tp8LEiZJwi7FLlvDEmOH362XlJhPE7GMLpuL34agqQQn4CKXnRjdAIcSw0RP0sKZpA50BvTv5Ec5JzEiZYnBUo1OXx8y190/is3J9f6nFrPKHH9TwtfmdBkcmxjKzu4PEz1YBEEpIpvuoRQZHNDhmdztoGt6i2YRSotsq3OfTFzumTIGiIpm4KsY2SbrFmFFdrX/477OsXFWx15Zh7WgmII3ThBiz6noa+Kx1o3Qnj4JWl4Xv31NIeZ1eVRRjD/Pw9ZUcN7Pb4MjEWOf88BWUcAgA14nngsVqcESHztztQgkF9YQ7LSuqrx0IQHMzFBbCEUdIwi2EJN1iTGhrg8pKSEvb9we/raEGe30NwdQsMEnHnpEi96Gf03HmZXgLi0FVyXzmj8R/+TGg0HHGN+g89ZK9Pi7/7h9jcbfr4+AcsTR962b8E44AwNq0jZzHf4u5x4UaE0/D939DIG/SAWM55MdpGuPuugZHbRlbHn+v73D85x+S8Z8HUFQVX/5kGr//G9SYeMzudvLvu5Ga2/4OZvn4HgrbuutZ2/I5AGmOZI6V7uRDpqbJztV3F1LfpneDTk4I8vjNFcyY2GtwZGLMU1Wcq5b3fes6aeQ1UDP1uFH8XryFswhmRLdyLxjUp8RMnKiXlZvllEoI5LqTGPWCQX0mt6pCfPze72PpbMVRW044PnHEjwMZSxyVmzB7uvSEG0ha/Tr2+moq711G9e3/JPW1p7Ftr9zrY+uv/QPVd/4f1f/vWTrOvIycJ27vuy3773fiWnQBVfcuo/3sKwbctj+H+riUN/+9R1WF4usl+6+/Y/sNf6Lyj8sJOdNIe/lvAISTUvEWziLpo9cOKh5x6HLisnDaEjnCOYmTcuZLwj1ENlXFcvkdU/oS7pw0P/++tVwSbjEsxG3+BFtLPQA9M48dcWNDTZ5uTF4PvkkzCGblR/W1QyFoaIAJE2DaNOlFK8ROknSLUa+2Vr/imrmP6lCT14OjqgQ0NeojNMThSV65DPf80/u+T1z7Nq6TzgeTGTU+ia55p5K05n97feyu80lNvT193V3M7g4c1aW4jz8TgO6jT8ba0Yy1uW6/sRzq42zbK0lY/z7tZ1854Hj8l6vxjZ9CIGcCAJ2nXETiLr8H97Gnk7zLCBtx+Fq8baiaPv/ZYjJzcu7xFKdOlXFgQ2T1pgSuvKuIjm69XLcov5dnbytnQrbf4MiE0O3aQM21aGQ1UDN5ezD3uPEVTCOQHd3Gx6EQ1NdDfr7eqdxmi+rLCzGsDfr6U2lpKf/4xz+oqqqis7MTbccJy06KovDuu+8edoBCHI7OTqiogOTkfZQ3hULYa8owd3UQzIzu1WBx+GLL1tNxxjf7vre0NxFM65/vG0zPwVHx1T4fn/3YbcSVrgeg7uYHAbB2NBNypvaXbysKwdRMrG1N+32PHNLjQiGy//b/aPzerXvsd7C2NxHcZe9dMC0Hi6sNwiEwW/AVHIG9rgKTtwc1Zh+lG+KghNUwX7br3clnpExhWnIhAGbZXjJk3libzM8fm0AorL/v507p5i83VpIYFzY4MiF0ls5WEjZ8AEAoKZXuIxcaHNHBU3y9mN2d+CZNJ5AX3VbhqqovcOTkwIwZYJeiQSEGGFTS/fTTT/Od73wHq9XKlClTSE7ec97f7km4ENEWCundyv1+SE/fyx00Dfv2SmyNtQTTc2SOxQhk6WghlDT48SeNP7wDgKQPXyXj/x6i7qcPRSq0/Upf/gTdRy0ikFuAtbXh0B5sthCOS8DS2UZAku5B6w56WNO0HlegC9ATcDG0nnkrnbueyUfT9M/ak+d2cu+PqnHY5HxBDB9J769A2fF54Drp/BFTH634fVhc7fgmHIE/b1LUE+6ODn0ca3HxvifECDGWDeqT5Le//S1HHnkkb7zxBmlpaZGOSYiIqKvT9xXl5Oz9dmtbI/ZtWwg700ZkV1IBms2BEuwvSQ2lZmFta+zb421tbSCUeuCOre4FZ5P1j7swd7sIpmRicbX3rSyjaVjbmwesPu/NoTwutmwD1vYmkt95HiUcxuT1MOnGc6i5/V8EU7OI27Su777WtgZCzrQBjdNMwQCq9B4YtLqeBj5t2UhI29GdPPNIsmOjO0pnLNE0eOjFHB5/pb8K5aKTWrn1ym1YpKhADCdqmOT3XgZAUxQ6Tzrf0HAOlhLwYelswTeuCP/46M7m0jR9hTshAWbOhNjYqL20ECPKoP5VNjQ0cNVVV0nCLYatri69eVpi4t4vUpt63DiqStCsNtSYuOgHKCLCl1+IrbG27/uuY07B+d7LoIYx9bhJXPc2XceetsfjTJ5uLJ2tfd/Hf/Ye4fgk/VdSCr4JU0j6+A0AEj59l2BKRl+JePZjt5GwY3brrg70uF3V3vpXKh54lcr7/0vtrX9FjYmj8v7/Ek5MxlM8H0dNGbaGGgCS33lhwO/B7G5HUxRCKTLC6lCF1TDrW79iTfMGQlqINEcKp+UvlIR7CIXC8Ju/jxuQcP/wvEZ+e5Uk3GL4id+4Bmt7EwA9s44ntMt2peFKCQawtDfjz5usT+CIcsLd0ABJSVBQsO9mtUKIQa50FxcX09BwiCWRQkSJquoJt9cLeXtpOKoE/DiqS1C8HkIjrCOpGKj7mMXEf7WW3hnzAHCf8DUcVSVMuvlCUKD9zMvw508GIH7D+yRs+IDG792KydtD3sM/Rwn4QTERTkym7qb7+8rxmq76JdlP3E7qf/+BGhNH49W/6XvNmOpSOk+7dK/x7O9x2X/9Hd1zFtIz58T9/p7UmDgav/dr8h64CSUcxp83iYYf9HdBj9+4hp65J8nQ00HoCXqo7tYb2x3hnMyMlCJpljaEfAGFnz5SwLvr9S1oiqJxy+V1XH5a6wEeKYQxRlwDtVAQS1sj/ryJ+AqiP5urqUlf4Z41S9/KJ4TYN0UbxObrjz/+mIsuuogXX3yR4447bijiipquri6SkpLo7OzE6XQaHY6IgLo6WL9e71a+R+dMVcVRuRn79q0EM/JG5TxuDQ2P2Udc2IHC6N6nrvh6mXDHVdTc9g80x9BvIjN3dZLzyK+o+8UjQ/5a+zL+d9+j8apfEcgtMCyGaIvke7qmezt2s01Wt4dYl8fMj++fxPpyfUqAxaxy9w9rOPPYToMjGx7G0uf0SGFpb2LyjeeiaCrBlEwq7lsxYFvPsBMKYW2rJ5BdgHfyjKhvk2tqAocDZs+G5GSVlpYWMjIyMMkFYTEKuFwukpOTcbvdJCYmRuQ5B/Vpcvfdd5OUlMSCBQuYNm0a48aNw7zb1TVFUVixYkVEghTiYPX06M3T4uL2PqrC1lyHvb6KUHLmqEy4xxrNEUvzZUuxtdb3rWgPpXBisqEJt9ndTufJXx9TCffh0LuTlzIhIY8UhxOACQlS3TLUWjqtfP/eyWyp0zd3xjrCPHR9JcfN6DY4MiH2zfneChRNBXY0UBvOCXc4hLW1nmBmPr6J06KecLe2gtWqN01LTdUrDIUQ+zeoT5SNGzeiKArjxo2jp6eHkpKSPe6jSCdoEWWqqo8H6+rSZ0Tuzuxqx15Thhobj2Z3RD9AMSR6px9jdAhRE05Kpeu4M4wOY0TYtTt5Y28LZ4w7CbOUkg+5mkY737unkIY2vdFfSkKQx39aweTLAOoAANBKSURBVPSCXoMjE2I/wiGc778MgGYy4zrxPGPj2R81jLWlnmB6Lt7JM9Gi3FSzvV3/f3HxPibDCCH2alBJd01NTYTDEOLwNTVBba0+smL3az6Kr1ffxx0K6p2ghRCj1q7dye0mG3PSZ0jCHQVfVcXywz9OprNbX3XLTfPz5M+3MiFLNnuK4S3+i4+w7miu2TP7BEIpw3T7iapiba4nlJaNt3Bm1BcQXC4IBvWS8qwDDwYRQuxiGNfOCHHwvF69rNzh0H8NEA7jqCnH4mrT93ELIUalsBrmi/YSKrv0jvZpjhSOzTySWIsMjR1qq79K4CcPTsLr17ftTMnv5YmfbSXdGTI4MiEOLHnlsr6vO09eYmAk+6Fp+hjM5HS8hcVojujO5nK79XOtWbMgNzeqLy3EqHBYSff777/Pa6+9Rm2tfoIzfvx4zjrrLE48cf/deYWIJE3Ty8o7O/deVm6vr8LWWKPPa5YGH0KMSv5wgPcb1uIKdAEw1TmZ6dKdPCpeW5PMLY9PIBTW/6yPPqKbP99YQUKsbPQUw5+1pZ64r9YAEEjLwTPjWIMj2gtNw9JaTygpGW/RrKiPOu3u1nvmFBfv/TxLCHFgg0q6A4EA3/jGN3j55ZfRNK2v67fL5eJPf/oTF1xwAf/5z3+wWqPb2EGMTS0tUFOj7y3avazc0taEfdsWwokpaNa9dFYTQowKNpOVGIsDb8jHvMzZZEl38qh4+n8Z3PVM/1n4KUd1cu811dhthzwYRQhDON97GWXHIB/XovOH5cV5S1sjalwS3sJZqHEJUX3tnh59lXvGDBg/PqovLcSoMqhPlttvv53ly5dz00030djYSEdHBx0dHTQ1NXHzzTezbNky7rjjjkjHKsQe/H69rNxshpjdKkhNnm4cVZvBZEaNjTcmQCHEkAmrYUKqXr6sKArHZMzm1PwFknBHgabB/c/nDEi4L1rUyv0/qZKEW4wcoRDOD14BQDObcS081+CA9mRpa0J1xOor3PFJUX3t3l69ivCII2DixD0XNoQQB29QSfezzz7Lt7/9be655x4yMzP7jmdkZHD33XdzxRVX8PTTT0csSCH2pbpaH12xRwfNUBBHVQlmT7c0ThNiFOoO9PBu/cesb/0Kbccqld1sk/3bURAKw21/G8+T/83uO3bN+Q389jvbMA+/RUIh9ilhw3tY3Ho77u45JxEeZucLlo5mNJsdb9FswonJUX1tnw/a2mDKFCgslIRbiMM1qB+PjY2NzJs3b5+3z5s3j6ampkEH9Ze//IUJEybgcDiYN28en3zyyUE97v/+7/9QFIXzzz9/0K8tRo62NqishLS03arBNA177RasbQ0E07PlJ4WIPDVMbOlnJK55k9jSz0ANGx3RmLKtp4G3t3+EK9BFU28r3rDP6JDGDF9A4fqHJvHS+3pyoigav75iGz9Z0igftWLEGdBAbfGFBkayJ4urDc1kxltYTNiZGtXX9vuhuVlPtouKhmXFvRAjzqD2dOfl5fHee+/xwx/+cK+3v//+++TlDa5L9HPPPcfSpUt57LHHmDdvHg888ACnn3465eXlZGTsu2SwpqaGm2++mQULFgzqdcXIEgzqZeWaBnG79ROxNm/Hvr2CkDMdzNKgX0RWwqcryXzmj1g7WvqOBVMyaL78ZrqPXmxgZKNfWA3zZXupdCc3iNtj5sf3TWLDFn1PqcWscvc1NZw5r9PgyIQ4dNbmOuI264s6gcx8eqcdbXBE/czudtA0vEWzoz6+LBDQR7BOmqSXlZvNUX15IUatQV27+va3v83zzz/PD3/4Q8rLywmHw6iqSnl5Oddccw0vvPACV1555aACuu+++7j66qv5zne+w7Rp03jssceIjY3l73//+z4fEw6Hueyyy7j99tuZOHHioF5XjCy1tfoPhd2vw5i7OnHUlKLZY9EcciIuIivh05XkPvQzLLsk3ACWjhZyH/oZCZ+uNCiy0c8T6GVl/eq+hHuqczIn5RwrCXeUNHdYueL3U/oS7lhHmMd/WiEJtxixBqxyL7pg2CznmrtdKKEg3skzCaVFdxh2KASNjVBQANOmgUXWLYSImEH9c/rlL39JZWUlTzzxBE8++SSmHR9UqqqiaRrf/va3+eUvf3nIzxsIBFi/fj233HJL3zGTycQpp5zCmjVr9vm4O+64g4yMDL773e/y4YcfHvpvSIwonZ36iLCUlIFXYBW/D0dVCUrATyg9x7gAxeikhsl85o8A7F5FqwAakPnMn+ieeyKYZGkgkjRN49P6L+gNerGbbNKdPMqqG+1cfU8hDW12AFITgzz+061Mm+A1ODIhBkcJBkj68L8AqBYr7gXnGByRztTjRvF78RbOIpgR3WHYoRDU1+sdyqdPBxlAJERkDSrpNpvNPPXUUyxdupTXX399wJzur33taxQXFw8qmLa2NsLh8IDmbACZmZmUlZXt9TEfffQRf/vb3/jiiy8O6jX8fj9+v7/v+64ufaarqqqoqswUHe5CISgv1/cbpaXp5eUAqCr22lIsHc0EMvPQU6CxSdvlPxE5seWfDygp350CWDuaiSn/nN6pc6MX2FigwPSMKVS3b+PYzCOJsTjk/R0lGyvjuOaPk3H16Gfg+Rk+Hv/ZVsZn+uVv4DDI57SxEj5biaXbBUD30YsJJTox+rzB5OlG8fbgnTSTQGYuaNE7Jw2HoaEBcnJg6lR9hftQTol3LrrJebQYLYbivXxYhSPFxcWDTrAjobu7m29961s8+eSTpKUdXMfJu+66i9tvv32P462trQQCgUiHKCKsuVn/wZCaqnfW3MnS3kSwswZfVjKaZWz/PWpo+M1BAJQ91mTFYNm7Gg/qfuGuRjxmaex1uDyBXnqDXtLjUtHQSEhM4KjYWf+fvfuOj6JOHzj+mW3Z9E3vFRJ6QETFgr2cd5717AU7eufP7ikqiIDtTu+sp2LvvZ13Viwn9oKKhZ4AIX032U3bPvP7Y2ABaSFstiTP+16cu7M7Mw8QdueZ7/N9vqgK9CB/vpHwxY82rrmzGrdXr9yoKuvmrqt/IdvmpyfKscU7+ZyOruKPXg49bjr4D1H/zFZ8HoxqD96KUgIZVvBs/QZvuGkaOByQkQEFBdDZqf/aEaqq4nK50DQtVP0qRDxzuVxhP2ZMzdbIzs7GaDTS0tKyyfaWlhby8zef17Jy5UpWrVrFH/+4oSxo/Z0Jk8nE0qVLGTZs2Cb7TJ8+ncsvvzz0vLOzk5KSEnJycrDZbGH83Yhw6+zU53GnpW3aPM3U3kbSqgZUazqqkgpDvJH0+pGT5KBVLubCyJhWsP03AUmuHrxB6wBHM7jVdzfybesiQOGQkn1INicBkKzKz3Sk/OezTK57qJxAUL+A3m1UJ/dcupLUJCMEZfrEzpLP6eixNNSRtvgHALyF5ahVk0kORu/vQPH0YnJ24xk2mmDRMEwRXAZA0zYMZEyYsHlj2r5SVRVFUcjJyZGkWwwKFosl7MfsU9JtMBgwGAz09vZisVgwGAwo2/lQUBSFQCCwQ8FYLBZ23XVXPvjgg9CyX6qq8sEHH3DRRRdt9v6RI0fy008/bbLt+uuvp6uri7vuuouSkpLN9klISCAhIWGz7et/jyI2BYP68mAeD2zcGN/g7iGxbjGKoqAlp8ulyzrKRv8T4eEesQuqxYrBt+0RkfynbsfiaKHtuAvQLJt/1oitC6pBfnD8ukl3cqNi3OTnWX6mB96T7+Ry6zMbvj8PmdTB3y6sI8GisXlHA9Ff8jMdHRkfvRZ63HHAsShK9K79FK8Hk7MdT/lIfMXDUSJ4Hbo+4bbZ9IQ7NXXnjqcoilxLi0FjIH6O+5R0z5w5E0VRMK1rY7j++UC4/PLLmTp1KpMmTWL33XfnzjvvpKenh7POOguAM844g6KiIm655RasVitjx47dZP/1o9W/3S7iW2Mj1NfDJgUPgQDW2l8xdjnx5/VviToh+ir1u/+FEu7fph7rZwIq635lvfUUKT8soPH8WXiGyWdRX3T5uvmiZSFOn17XOMo2nDGZ1RgUg8x7jRBNg3++WMjD/9lQ1XHCgW3MmLoGo1xHi0FA8XmwffpfAFSzBdc+f4hqLKaOVjyl1XjLIr8YdnMzpKeHJ+EWQmxfn5LuWbNmbfN5OJ144om0tbUxc+ZMmpubmTBhAu+8806oudqaNWvkLtoQ092tr8mdnLxRN01NI6F+BeaWevw5RRDBciwx9JjaW8l/9KbQczU5DWPPhklvgcw8Wk69DHNbEzmv3I/B7yOhcRXlN56N449TsR99Hpo5/KVKg8Warga+bVtEQAuu606+C/lJOdEOa0gJBOGGR8t47ZMN/VH+fEwjfzmmST5exaCR9vX80Gd35x6HoKakRyUOxe/D5GjBW1KFt3xkVBLuxESoqdETbyHEwFM0TdvhIYTZs2dz7LHHbnU0+ZdffuGVV15h5syZOx3gQOvs7CQ9PZ2Ojg6Z0x2DVBUWLYK6Oigp2ZBbm1sbSFz8HWqqDTWxn5OQBikNjR6jR+YKhouqUvq3i0j+5WsAOnc7kIa/3EzSsh8wOe0EbNn0jtgltEyYpaGWwnmzSKz9NXQIT8lwGs+fpV9cic38YP+FZa46cqyZTM6bSKJp0znx8jM9sNxehSvureTjH2wAKIrGjDPWcNLB9ugGNojJz3R0lM0+m6TliwBYNeMR3NXjIx9EwI+5rRFvcSWeyrERXwy7tVU/5S676KvAhIOqqrS2tpKbmysDY2JQcDqdZGRk4HK5SEtLC8sx+/UvY9asWSxatGirr//8889b7BAuxI5qbobVqyEvb0PCbeh2Ya1bjGZJkIRbDLjMd58NJdz+jFyazr4OjCZ6R02ic8/f0Ttq0ibrcvuKKlk181Fa/3QhmlG/mLLWr6Bi1lSyX52nr3sn2Ph+77isUUzMHst+hZM3S7jFwHL1GDnvb9WhhNtsUvnHRbWScItBJ6F+RSjh9hQPw10VhdV3AgHM9kZ8hRV4KsdEPOG22/VB9Zqa8CXcQoi+GZDbUe3t7QPS9U0MLb29elm51Qrre98pPi+Jtb+ieHoI2uQbQwyshNXLyHnxvtDzxmmz+laOaDThOOoc6mY/haesGgAlGCTntXmU33gmCfUrBirkuLCmq4EFzV+jrluH1qgYGJ5ejiGKDY2GopZ2M6fPGcHCZSkAJFuDPHjlCg7b3RndwIQYALYPXwk9dh54XOSnpQUDmNsa8OeW4KkcDSbz9vcJo/Z2vXqwpkYfyBBCRFafb7F98sknfPzxx6Hnr776KitWbH7h6HQ6eeGFFxg3blxYAhRDk6bp3crb26G0dN1GVSVh9TJMjib8udI4TQwsxeeh6P7rMQT0tXQdh59G75jdd+gY3tIq6mY9QfYbj5L970dR1CCJq5ZQPvN07Mecj+MPp4MxplZuHFABNcgPjl+o7VwDQG1nPcPTy6Ic1dBU25jAeX+rosmh39HMSvfz4JXLGV3ujnJkQoSf4nGT/tlbAKgWK669fx/ZANQg5tYG/DlFuIePi/jKFk4n+Hwwfry+FrcQIvL6fLX30UcfhUrGFUXh1Vdf5dVXX93ie0ePHs0999wTngjFkNTSAqtWQW7uhpvRlpZ6EhpqCWTmbVLOK8RAyH3+HhIaagHwlFbTdvyf+3cgkxn7cdPonrgvBQ/egLWhFkPAT+5L95H63cc0TrsRX2F5+AKPUZt1J8+oojJt82UdxcD7cUUSF95RhbNbvwQoyfXy0F+XUZrni3JkQgyMtC/fxejuAaBz8qGoSSmRO7mqYm5pIJBdgLtqHFpCZKfQdHbqlYM1NZsuuSqEiKw+1/L99a9/pa2tjdbWVjRN44EHHqCtrW2TX3a7nd7eXn7++Wf22GOPgYxbDGJeLyxfDkaj3l0TwOi0Y61bjJqUgmaROZ9iYCX/+BmZ778AgGpOoOHPc3e6+7inYhSr5jyN/Ygz0daVUSfW/kLF9aeS+fbToAZ3Ou5YtaargffXLsDp6yTBYGHfgj0YlzlCysmjYMGiNM6+pTqUcI8s6+XpGUsk4RaDWsZHGwaJOg46LnIn1jTMbY0EMnJwV9WgWZMid2701V86O2HMmI2qBoUQUdHnke7ExEQS12VAdXV15Obmhp4LEU51ddDWpncrB1A8vVhrf4FggGCGLCMkBpbR1U7hQ7NDz1tPvgRfUWVYjq2ZLbSdeBFdu+5H4bxZJDStxuD3kvfsnaR++zGN59+AP29wjf4u7ljBT+1LALbanVxExpufZXLdQ+UEgnr50B6jOrnnspWkJKpRjkyIgWNdtSS0moSnbASeitGRObGmYWprIJBmw109PuKNX3t69LLy0aOhokJWVhUi2vo1zKCqKvPnz9/q62+++SarVq3qb0xiCGtr0+dyZ2evW7YyGMRatwSTq10vKxdiIGkaBQ/PweRyANA9fm86Dj4+7KfxDB9H3dxncBx+Ktq6K6GkZT9Qee3JZLz/gt7tZpAoSs7HpJgYnVEl3cmj6Im3c7n6gYpQwn3obh08cOUKSbjFoLdxA7WOCDZQM9mbUJPTcVdPQE1Ojcg513O79Z44I0bAsGGScAsRC/qVdF955ZXcfffdW339vvvu45prrul3UGJo8vn0snJNg+R1N4QTGmpJaFqFP7tgXRYuxMCxffgKqT8sACCQmkHjeTMH7GpFs1hpPeUyVl83D9+6xoAGn4f8J/9O6a1/xtzWOCDnjYROX1focZolhd+XHcBYKSePCk2DO54v4rZnN1RQnHRQK3dcVEuCRdvGnkLEP4O7h7Qv3gUgaE2ic8/DInJek70Z1Zqkj3D3ZcWLMPJ49AGMqiqorpZLJyFiRb/+KX7xxRcccsghW339oIMOYsGCBf0OSgxNq1fr63Ln5urPTfZmElYvJZCeFfGlNcTQY2lcRd6z/ww9bzpvJsH0rAE/r3vELtTe9BztG42oJy/+loprT8L24at61hQnAmqQb1sX8W79J7S5HaHtVmNkO/UKnT8A180r45H/5oe2/eWYRmZMrccoF+JiCEj74h2Mnl4AOvf6XURKvE3tLWiWBNzVEwimZQz4+Tbm8+mNaIcNg5EjJeEWIpb0659jR0cHqalbL5VJSUnB4XBs9XUhfqu9HVasgMxMvYGaobtTn8dtNEW2y6gYmgJ+Cu+/HoPPC0D7QcfTvcuUiJ1esybSMvVqVl9zP75sfT0Xo6eXgsdupuTvF2Nqb4lYLP3V6evmg4ZPqe1ag4ZGu9cV7ZCGNLdX4eK7hvH6p9kAKIrGzDNX85djm6TUVAwNmkbGxqXlBxw74Kc0Oe1oBiPuqhqCtoG/absxvx+amqCyUp/HbZRFXoSIKf1KuktLS/nss8+2+vqCBQsolnUJRB8FAnpZud8Pqamg+H1Y6xZj7O0mYMuOdnhiCMh55QESV+nNvryF5bSefElU4ugdsxt1Nz9Hx/7HhLal/PQFldNPJP2TN2N21Ht1VwPz1y7A5esiwWhhv4I9GGELT/M5seOc3UbOva2a//1gA8BsUvnn/9Vy0kH26AYmRARZa3/BunoZAO7KMXjLRw7o+YwuB2ga7qrxBDJzB/RcvxUIQGMjlJXpCbepz22ShRCR0q+k++STT+a5557j7rvvRt2o4U8wGOSuu+7ihRde4JRTTglbkGJwW7MGGhogLw/QNCxrlmNua8CfnS/dP8SAS1r8LVn/fRIAzWii4cK5EV9HdWNqYgrN51zHmqvuwZ+hX7gZe7spfOhGiv9xOSZn7CRO68vJv2r9noAWJMeaxaHF+5KXJKsMREtzu5nT547g++V6hVBKYpCH/rqcQ3dzRjcwISIs48ONlgk7cGBHuY1dTpSAH/fwcQSy87e/QxgFg/o1VEkJjB0Llp1b3VIIMUAUTdvxoROv18sf/vAHPvzwQ3JychgxYgQAS5cupa2tjf3335+3336bhITYn8fX2dlJeno6HR0d2Gy2aIcz5Lhc8OWX+l1Zmw3MzfUkLl1IMC0LzSpL0vWHhkaP0UNy0IqC3LTYFkNPJ5XXnox5Xfl2y0kX0/6HM6Ic1QaGni7ynr4D26f/CW0LJqfRPPWvdE4+LOo3pVZ3reWr1h8AGJ1RxeiMagwDEJP8TPfNygYr5/29imaHftWdle5n3lXLGVXmjnJk4rfkZ3pgGXq6qLr4dxh8XoJJKSy/6+0Bu6YwdLsweHpxV43Hnx/ZJR/XJ9wFBTB+PERrJV9VVWltbSU3NxeDTCQXg4DT6SQjIwOXy0VaWlpYjtmvfxkJCQm89957PPLII+y+++7Y7Xbsdju77747jz76KPPnz4+LhFtEVzCoz+P2ePSE2+hqx1r3K5o1WRJuMfA0jYLHbgkl3D2jJtF++GlRDmpTanIqTdNmUX/ZHXpDQcDY00nRv66n6J6rMbraoxpfaUoRw9LK2Ldgj3XdySV5iJYfVyRz2twRoYS7JNfDMzOWSMIthqT0z98K9ehw7f37gUu4e7owuHvwDBsb8YRbVfWS8rw8qKmJXsIthOibfs/6MBgMnHXWWZx11lnhjEcMIQ0NUF8P+fmgeNxYa39F8fsI5BRGOzQxBKR/9l/Svnof0EePG6fNitlWr90T96O2ajx5T/2d9HXL36R98yFJSxbSfNZ0unY7KCJxBNQgizuWM8I2DIvRjKIo7JozLiLnFlv3yY9pXHZ3JW6f3jlpVFkvD161nOz0QJQjEyIKNA3bBxsaqDkHqIGawd2NsduFe9hYfAVlA3KOrdE0PeHOytIT7qSkiJ5eCNEPsXmFKQa97m5Ytkxfj9tsCJKwZikmZxuBrMjOhRJDk7l1LXlP/C30vOmsa2P+Zy+YaqPxzzex9uLbCKTaADB1OSm++2oK/3Udxi7ngJ5/fXfyxc4VfNe2aEDPJfru359mctE/h4cS7j1Gd/LEdUsl4RZDVuLyH7E21ALQWz0eb8nwsJ9D8fRidHXgqRyNr7gyolN9NE3vUm6z6SXlKbLAixBxod8j3c3NzTzyyCMsXLgQl8u1SUM1AEVR+OCDD3Y6QDH4qKpeVt7dDcXFYFm7ioSGVQQy82J2pFEMIsEAhffPDK3d6pzyR7r2ODjKQfVd124H0Vu9C/mP30Latx8BkP7FuyT/+i1NZ19H98R9w37O1V1r+a7tJwJakASjhcq00rCfQ+y4x97K5e/PbShpPWz3dm67YBUWc2x2uRciEmwbN1AbgFFuxevB5HTgKR+Jt3hYxHtrNDfrAxY1NRCmqaZCiAjoV9K9aNEi9t9/f9xuNyNGjOCnn35i9OjROJ1OGhoaGDZsGCUlkZ3bIuJHUxOsXg25uWDuaMW6egnB1HQ0i/QBEAMv+9+PkbRCH6n15RbRcvqVUY5oxwXTM2m4+G90ffku+U/8DWNPJyaXg5J/Xo5znyNoOe0K1OTUnT5PQA3yvf1n6rrqAci1ZrFH3i4kmqLX3V3oI113PF/Eo29tqM44+aBWrj2jHqPctxRDmLHLSdrX8wF92lDX7uGdeqP4PJg6WvGUVuMtq474QEFLC1it+gh3RkZETy2E2En9+rS45pprSElJYenSpcyfPx9N07jrrruor6/nhRdeoKOjg1tvvTXcsYpBoLdXLyu3WiEx2I219ldQFNRkuV0rBp51xU9kv/4wAJrBSOMFc1ATk6McVT8pCp17/o7aW1+ka8KU0Gbbp/+hcvqJJC/6fKcO3+3v4YOGT0MJ9+iMKvYtnCwJd5T5A3DdvLJNEu7/O66B66dKwi1E+qf/xeD3AeCccgSaJXyfV4rfh8nRgrd4uL7md4QT7rY2faWX8eP1udxCiPjSr0+Mzz77jGnTplFaWhpaGmB9efnxxx/PqaeeylVXXRW+KMWgoGmwciV0dEB2uh9r3WKMXU4C69YiFmIgGdw9FN1/PYoaBMB+1Dm4q2qiHNXOC9iyWXv5P2g8fxbBJH1yn7mjldK/X0z+IzdhcPf067hmgxlf0I/VmMB+BZOlO3kM6PUY+L87h/P6p9kAGBSNWWet5sKjm6O9epwQ0adp2D7aUFoe1gZqAT8mexPe4ko8FaPAaAzfsfvA4dD/W1MDOTkRPbUQIkz6lXSrqkpeXh4ANpsNo9FIe/uGpWvGjRvHd999F54IxaDR0gJ1dZCXq5GwdiXm1rX4swuivtawGBrynrodS2sDAL3Da7AfdXaUIwojRcE15Qhqb3mB7nGTQ5szPn6NimtPIumXb/p0mKC2oTdHgtHCPgW7cUjxFPKSssMestgxzi4j59xWxSc/pgNgMav88/9qOeFAe5QjEyI2JC35joSm1QD0jNoVX2F5eA4cCGC2N+IrrMBTOUYfbo4gpxP8fhg3Tl/tRQgRn/qVdFdUVFBXV6cfwGCgoqKC+fPnh17//PPPsdlsYQlQDA4eDyxdqn9XpXY1klC/nIAtO+JfXmJoSv1qPrYFbwIQtCbReOFsMA6+n71AZh71V91D01nXErTqa8hY7E2U3XoheU/chuLZ+prNnb5u5q9dwKrO+tC2jIR0KSePAU0OM6fNHcGPK/RKhpTEIA9dtZxDdnNGNzAhYsjGDdTCNsodDGBua8CfW4KncjSYzOE5bh+5XOB26wl3UVFETy2ECLN+Jd2HHnooL730Uuj5hRdeyMMPP8zBBx/MQQcdxBNPPMEpp5wStiBF/Kut1cuj8q1OrLW/olkS0KyysKQYeKb2Fgoeuzn0vOWMv+LPLY5iRANMUXAeeCx1Nz9Pz6hJoc2Z81+i8rqTSVz6w2a7rO5ay/y1C3D5uvilYzmqpm72HhEdKxqsnDp7JLWNiYA+NefJ65ay26juKEcmROwwutpJ++ZDAAKpNromHbDzB1WDmFsb8OcU4R4+LuLNXru69FVexo6FUlkwQoi416+k+7rrruO5557D7/cDcOmllzJ79mwcDgcul4sZM2Ywd+7csAYq4ldbm15WnpPmJWn1YhRvL8F06QIiIkBVKXzgBow9nQB07nEIrn3+EOWgIsOfU8iaa/5F8xlXoa5rJmRpXUvZTeeR+8w/UHweAmqQb1p/5KvWHwhoQXITsziwaC8MinTkigU/LE/m9LkjaG63AFCa5+GZmUsYWbb1igUhhqL0BW+iBPW16Z37HolmtuzcAVUVc0sDgewC3FXj0BIiW/HT06OPco8eDWVlET21EGKAKJqm7dCCnpqm0dXVhcViwWqN/7LDzs5O0tPT6ejokJL4AeDzwbffQrtdpaL3ZxLWrtBHGQ2RbUIylGho9Bg9JAetKAzt+fKZ/32SvOfvBsCfmUftzc8NyU755pZ6CufNImnZj6Ft7vwS3jjmGJbmZQIwJqOaURlVMdksbSj+TP/vhzQuu2cYHp9+A2RMRQ8PXLGCrPRAlCMT4TAUf6YHjKoy7KpjsbSuBWDF7a/hz9uJZWs1DXNrA4H0LNwjd4n4ChduN9jtMGoUVFfHR9sbVVVpbW0lNzc31GBZiHjmdDrJyMjA5XKRlhae68Yd/pfh8/nIzMzk7rvvDksAYnBbtQqam6FYXUNCQx2BzDxJuEVEJKxaQu5L/wJAUxQap904JBNuAH9eCauvm0fLKZeirhsBSmyu54QH7uHQDz5i/+yJjMmsjsmEeyh649NMLvrn8FDCPXlMJ49PXyYJtxBbkPTrN6GEu3vM7judcJvaGgik2XBXj494wu3x6NWB1dVQVRUfCbcQom92OOlOSEggPz+fhITIzm0R8ae9XV8iLM9oJ7l+CcHk1LCumSnE1iheD0X3zwiVGzp+fzq9oydtZ69BzmCk/fDTqJv7LO7KMfomTWPPBf9jj1suxVq3OMoBCoBH/pvH9AcrCKr61fbv9mjngStWkJwo8+yF2JKMD18JPXYedNxOHctkb0JNTsddPQE1OXVnQ9shXq++yktVFYwYEfFlwIUQA6xf/6TPPPNMnnzySXw+X7jjEYNEIADLloHa3UtW6y8QDKKmpEc7LDFE5D53FwmN+goL7vKRtP3pwihHFH2dvm66/b34CstZNfMRWo7/C+q6TrzWhlrKZ51J9isPQsAf5UiHJlWFvz1bxB3Pb2jyd8ohrdz+5zos5h2aBSbEkGFy2kld+D8AAulZdO2yX/+PZW9GtSbpI9wRvl7x+6GpCYYNg5EjI74MuBAiAvq1Zs64ceN4/fXXGTNmDGeeeSbl5eUkJiZu9r5jjw3Tkg0i7qxZA81rAwzrWYzJ1b5z5V5C7ICU7xeQ+YG+uoJqSaDxwrkRX+Yl1qzuWst3bT+RaknhwKK9MBpNtB95Fj27TKHwwRuwrl6KogbJef0hUr//H43n34i3tCraYQ8Z/gDMeLicf3+2ocHkxX9qYNqRzVJeKsQ2pP/vDZRgEADnfkf2exlSU3sLmiUBd/UEgmkZ4QxxuwIBaGyEykq9cZqspCrE4NSvf9onn3xy6PGMGTO2+B5FUQiu+yAUQ4vLBcuXQ15PLYn2NfizC2RikogIo8tBwUOzQ89bTr4MX2F59AKKsoAa5Hv7z9R16Wtvmw0mAmoQ47phFG/JcOpmPUH2vx8l+9+PoASDWFcvo2Lm6bQdcx6OI6YOyvXMY0mvx8Bl91SyYJE+smZQNG44aw3HH2CPcmRCxDg1iO3j1wG9b4dz/2P6dRiT045mMOKuqiFoi+zKKoEANDToS4KNGQPmoX1/WIhBrV9XUx999FG44xCDRDCol5VrjU1ku5YRTM8c8qOMIkI0jcKHZmPq6gCga8KUnZ7fF886fd180fIdLl8XsI3u5CYT9mPPp3vivhTMm4W1fgVKMEDuy/eTulAf9fYVVUThdzD4ObuMXHDHcBatTAHAYla5/c91HDzJGd3AhIgDyT99icXeBEDPuD3x5xTu8DGMLgdoGu7qCQQyc8Md4jYFg3rCXVSkr8Vt2clVzoQQsa3PSfe1117LSSedRE1NDfvt1/85M2Jwa2iA5mWdDOv+FUxm1MSUaIckhoiM+S+R8uNnAATSMmk6d8aQrbBY1bWWhW0/EdCCWI0J7JG7C3lJ2dvcx1M+klU3Pkn26w+R9eYTKJpKYu2vVMw4lbbjLqD98FNl5YEwarSbOf/vVdQ26lOzUpMC3HfZSiaN7I5yZELEh40bqHUcuOPTGY1dTpSAX0+4s/PDGdp2qap+vVRQAOPGwSBYgVcIsR19bqR266238vPPP4eeOxwOjEYjH3744YAEJuJPdzcs/8VHXvuvmH3dBGzbvsgXIlwsDbXkPndX6Hnj+TfoVRZDkKqpLHfVEdCC5CZmcUjxlO0m3OtpZgttx/+FVTc8inddWb7B7yPv+bspm3se5uY1Axj50LF8rZXT5owMJdzZ6X6evG6ZJNxC9JGpvYWU7z8FwJ+RS/eEfXZof0O3C8Xrxj28Bn9u0UCEuFWaps/hzsmBmhpISoro6YUQUbJTCxJomnRUFTpVheXLNLTly8n0NEb8rrEYuhS/j6J/XY/B7wWg/ZAT6Bm/d5Sjih6DYmDPvImMzRzBvgWTSTTt+BCKZ9hY6uY8jeP3p6OtqxZIWr6IyutOJuPd5/V/8KJfvl+WzOlzR9DcrteSluV7eHbmEkaUuqMcmRDxw/bxGyia/jnk3P+oHeo9YejpwuDuwTNsLP78yDZ51TR9hNtmg/HjITmyy4ALIaJIVgEUYdHUBG0L6ynqXUEgI1eaL4mIyXn5fqxrlgHgLaqk9aSLoxxR5K3qWsuvHctDz1PMyYze0vztHaBZrLSefAmrr38I37rVBww+L/lP307pLRdgbl2703EPNR9/n845t1XT2aN/Po6p6OHp65dSnCvLbwrRZ8HARg3UDDj3P7rPuxrc3Ri7XXgqRuMrKBuY+LahuRnS02HCBEiN7DLgQogok6Rb7LTeXqj9tp2MlsUY05LRrJsvHyfEQEj65Wuy3noKANVkpuHCuWiWoTM5LqAG+ab1R75u/YGf25di93SE/Rzu6gnUzn2W9kNPCm1LXrKQymtPxvbBy/rQjdiu1z7J4v/uHIbHp3/t7jmmk8enLyMrPRDlyISILyk/foa5oxWA7l32IZCZ16f9FE8vRlcHnsrR+IorI97zo7kZEhP1kvL0yC4DLoSIATs0HLlq1SoWLlwIgMvlAmD58uXYbLYtvn/ixIk7F52IeZoGtb+4Cf78K7YkP4E0mcctIsPQ7aLwwVmh523H/wVvWXX0Aoowl6+LL5oX0unf0J08M8E2IOfSrIm0nH4lXZP2p2DebCz2RgxeNwWP30rqtx/RdO4MAlkypWRLNA0e/W8ed7xQHNr2+8nt3DxtFRaT3LAQYkdlfPhq6HHHAX1roKZ4PZicDjzlI/EWD4t4wt3aqncnHz8eModmuxEhhjxF6+PEbIPBgPKbDylN0zbbtvH2eFinu7Ozk/T0dDo6OrZ680BsXXNDkKUvLSK3pw5KSsAgxRPRpqHRY/SQHLSiMEi7d2saRfdcTdo3eiPHnjG7s+av9w6Zn79VXWv5ru0nguu6k0/O24XcxMjc8DK4e8h9/q5NLnyDicm0nHo5rn2PHJCL2Xj9mVZV+PvzxTzx9oaRuFMPaWX6afVD5UdVbEW8/kxHm7mtkWFXHIWiafiyC1h5x+vbXVVB8XkwtbfiKa3GWzEq4t8Tdrv+3wkTIK9vg/JxR1VVWltbyc3NxSAfbmIQcDqdZGRk4HK5SEtLC8sx+zzS/dhjj4XlhGLw8Hhg9cd1pLSvRqkoQJMPWhEh6QveDCXcgZR0Gs+fNWQS7oVtP7OicxUAuYnZTM7dBaspIWLnVxOTaT7rWromHUjBw3Mwt7dgdPdQ+PAc0r75kKZzrieQkROxeGKVL6Bw/UNl/OfzrNC2S45v4Pw/Ng/VleyE2Gm2j19HWTdW5Nz/6O0n3H4fJkcL3pIqvOUjI/490dGhr8c9mBNuIUTf9Dnpnjp16kDGIeLQqkWd+JesIK3Qhma2RDscMUSYW+rJe+r20PPms68lkJkbxYgiK9NqQ+mE0RnVjNrJZmk7o2fcZGpveYG8Z+7A9smbgD7XsvKaE2g+4yo69zp8yK6T3usxcOndlXz6kz5x06BozDp7NX/a3xHlyISIY4EAtv+9AYBmNOLa76jtvN+Pyd6Et7gST8UoMG47QQ83pxO8Xr2kvLAwoqcWQsSgoTE0JMKurQ0aF7WRkeCWFpwicgIBiu6fgdHTC4Bz3yPp2u2gKAc18DxBb+hxeWoxh5Xsx5jM6qgl3OupSSk0nXcD9Zf/k0C6PqJr7O2i6IGZFN91FUbX0EsyO7qMnHVLdSjhTjCr3HXJSkm4hdhJqd//D9O6z5SuifsRsG1jSk0ggNneiK+wAk/lGDBFdkWVzk7o6YGxY6G4ePvvF0IMfpJ0ix3m88GyXwNY7WsxZ6REOxwxhGS/8QiJK38GwJdXQvPpV0Y5ooEVUAN83foD79cvwBvcsKxUmiW2bnR17zKFlbe+iGuvw0PbUr/7mMprTiD1q/lRjCyyGuwWTpszkp9q9cV3U5MCPHT1cg7a1RXlyISIf7aN+kg4t9VALRjA3NaAP7cET+VoMJkjEN0G3d160j12LJSWRvTUQogYJkm32GGrVkHHCgfZRifBlPA0FxBiexKX/UD2G48AoBmMNFwwB82aFOWoBo7L18X8tZ+yqmstnqCHFrc92iFtk5qSTuOFc1h7yd8JpGYAYOp2UXzvNRTeOx1jlzO6AQ6w5fVWTps9gromfcm6HJuPp65fyqQR3VGOTIj4Z26pJ+XnrwDw5RbRM2b3Lb9RDWJubcCfU4R7+Dg0S+T6XYC+hKrTCaNHQ0XFkJ1hI4TYAkm6xQ5pb4eVKyGfJhSTAsbIlmyJocng7qbw/pkomgpA2zHn4Rk+NspRDZxVXWuZv/ZTOv3dWI0J7Fc4mdKU+JgU2DXpAGpvfZHOjcr+0796n8prTiDlu4+jF9gAWrgsmdPnjqClQ+9tUZ7v4dmZS6ku8UQ5MiEGB9tHr4Uedxxw7JYboqkq5pYGAtkFuKvGoSVYIxih3lzW4YARI2BY5FclE0LEOEm6RZ8FArBsGWjdPaS5W1BT0qMdkhgi8p78OxZ7IwC91eNxHHlWlCMaGOvLyb9u/YGgFiQvMZtDi/eN2HJg4RJMy6Dh4ttY+5ebCaz7nDB1tlNy55UUPDATQ09nlCMMn48WpnPOrdV09uo3IMdW9PD0jKUU5fi2s6cQoi8Uvy/UrFEzmnBN+ePmb9I0zG2NBDJycFfVRLwKyuPR1+KuqoLq6iGzmIYQYgfIx4LoszVroKkJCsx2DO4e1ESZzy0GXuqX72H79L8ABK3JNE6bvd1lYuLVz+3LWNW1FgUYk1HNlII9IrocWLh1TT6U2lteoGvivqFtts/eonL6iST/+FkUIwuPVz/J4uK7huH161+le49z8di1y8hMC0Q5MiEGj9RvP8LU1QFA56QDCKZnbvoGTcPU1kAgzYa7ejxqYnJE4/P5oKVFH90eGflVyYQQcUI+GkSfOJ2wfDmkp6okOtYO6rm0InaY7M0UPHZL6HnzmVfjzy2KYkQDa3RGFdnWDPYrnBwT3cnDIWjLZu2ld9Aw7UaCSfqNOnNHG6W3X0LBw3MwuONvzrOmwUNv5nH9Q+UEVf3v6A97tnPf5StJtqpRjk6IwcX20UYN1A46brPXTfYm1OR03NUTUJMj22TS79cHIyorYVTkVyUTQsQRSbrFdgWDesLt8UCm0oGxs51Aqi3aYYnBTg1S+OBMjL1dALgmH6qv/TyIBNQAK1yr0TQNAIvRzAGFe8VdOfl2KQqd+/yB2ltepLtmr9Bm2//eoHL6iSSta5AUD1QVbnummH++uGEdoNMObeG2C+qwmLQoRibE4GNpXEXy4u8A8BaU0Tty101eN9mbUa1J+gh3hKe8BQLQ2AhlZXrjNHNkm6QLIeKMJN1iu9au1X/l5YGpvQVFVSO+BIcYerLeeorkJQsB8Gfl03zm9EHVmWZ9d/KF9p9Y2bk6tF0ZRL/H3wpk5lJ/5V00nXM9QateAmp2tFB221/If/xWlHXrr8cqX0Dh6gfKefLdvNC2y05Yy/TT1kpJqRADYJNR7gOO3eQ7wNTegmZJwF09gWBaRkTjCgahoQFKSvSlwSyWiJ5eCBGH5DJBbFNXlz7KnZoKFtWDpa1RlgkTA85at5icl+8HQFMUGqfdGPGywYFU11m/SXfyWFt3e0ApCs79j6b2lufpGb1baHPGBy9Ted3JJK670RJrejwGLvrHMP77RRYABkVjzrmrOO+PLYPpXpAQMUPxeUlf189DNVtwTjki9JrJaUczGHFX1RC0ZUU0LlXVE+7CQj3hTojfthtCiAiSpFtslarCihXQ3Q0ZGWByOTC4u1GThlCCICJO8bgpvP96lGAQAMcRZ9I7atft7BUf1ncn/6btx990J4/sRWMsCGQXsObq+2ieejWqRV/ax9LaQNnN08h7+g4Ub+wst9XeaeLsW6r59Ce9fDXBrHL3pSs5bj9HlCMTYvBK/eYDTN0uALp2PyhUPm50OUDTcFeNJ5CZG9GY1ifceXkwbhwkJkb09EKIOCZJt9iqxka9Y3luLvpyHK0NaCbzoCrxFbEn77l/ktCkl1u7K0bRduz5UY4oPNaXk6/vTj42cwT7xnl38p1mMNBx8PHU3vw8vSN2AUDRNDLffY6K608hcfmiKAcIDXYLp80ZwU+1ejl8WlKAh69exoETXVGOTIjBLePDV0KPOw7UG6gZu5woAT/u4eMIZOdHNB5N06+LsrKgpgaSI9skXQgR5yTpFlvU26uvyW216qVTxm4XJmcbQWmgJgZQysL/kfGhPodPtVhpvHDOoOkf4Av66FpXTr5f4Z6Mzqga1PO3d4Q/r5jV1z5I86mXo5r1mxAJzWsom3MuOc/fjeLzRiWuZfVWTp09glXN+kh8boaPJ69fyq4jeqISjxBDRUL9CpKW/QiAp6gSd9V4DN0uFK8b9/CaiK9ioWl6l3KbDcaPhxRZMVUIsYMk6Rab0TS9rNzl0u/oApg62lD8PrR1ZaBChJvRaafg4Tmh5y2nXo6voDx6AYXB+q7kADmJWeyRN3HIlpNvl8FAx+9Ooe6mZ+gdPg4ARVPJ/u+TVMw8naTaJREN57ulyZwxdwStHXqHpIoCD8/MXEp1SeyUvQsxWG3SQO3AYzH0dmNw9+AZNhZ/fknE42lu1ke2a2ogTdraCCH6QZJusZnmZli1Si8rVxQg4Mfc2iBzucXA0TQKH5qNqcsJQNfE/XAecEx0Y9pJLl8XHzR8SqevK7StNKVwaJeT94GvoJzVMx6m5cT/Q11X5WBtqGP0zAv05noB/4DH8OHCdM69rZrOXhMA4yp7eHrGEoqyfQN+biGGOsXrIf2ztwBQLQl07bofxm4XnorR+ArKIh5PS4te9Td+vN7fRggh+kOSbrEJj0cvKzeb9S8ZAJPTgbHbJV3LxYDJeP8FUhZ9DkAgPYumc66P694BenfyBbR7XXxv/zXa4cQfg5H2I6ZSN+dp3BWjAFDUIDlvPErFzDNIWL10wE79ysdZXHznMLx+/etxn3EuHp2+jIzU4ICdUwixQdpX72Hs7Qagc7eDUPwBPJWj8RVXRvx7wW4Ho1Ef4c6SAiUhxE6QpFtsorYWHA7Izt6wzWxv0r/oDMboBSYGrYT6FeQ+f3foeeP5syK+5mq4bNqdXCUvMYc9cidEO6y45SsexqqZj9F63DRUo/75Y61fTsUNZ5D9+sMQCITtXJoG8/6dz4xHylE1/cL+D3s6uPfylSRb1bCdRwixbRkfbGig1jVxPzzlI/EWD4t4wu1w6N3Kx49f11BWCCF2giTdIqS1VU+6c3LAsO4nw9DbjamjVRqoiQGh+LwU3j8Dg18v220/7GR6avaMclT9s+Xu5LtLOfnOMpmwH30uv855CE9JFQBKMEjOKw9QPvssLGtX7vQpVBVufaaYO1/a0JzpjMNauO2CVVhM2jb2FEKEU8LqpSTW/gKAt7AC196/x1tWveGiJEKcTvD79RHu/Mg2SRdCDFKSdAsAfD69rFxRIClpw3aT047B04OaKGtjiPDLeek+rPXLAfAUD6P1hIuiHFH/tHuczF+7gE5/N4nGBPaX7uRh5y6vonb2E9iPOgdtXdVNYt1iKmacRtZ/Hge1f+XfvoDC1Q9U8NS7eaFtl5+4lqtPXRvp63whhrz1q1cAOH53Kt6KURFPuF0ufQWXceOgKLJN0oUQg5hcUggA6ur0ke6cnI02BoOYW+pRrZJwi/BL/ulLst55FgDVbKHxzzehWeJzVNiWkEZGQjp5iTkcUrwvOdKdfGCYzLT96UJW3fAo3qJKAAwBP7kv3EvZnHOxNK3aocP1uA38+Y7h/PeLTACMBo25563i3CNa4rmlgBBxyeDuIe3ztwEIJiTScupl+oTqCOru1n+NHQulpRE9tRBikJOkW+BwwMqVepOQjb/fTJ3tGDs7CKbYohabGJyMXU4K5s0KPW894SK8JcOjF1A/dPq6CWr6XF+DYmCf/N2lnDxCPJVjqJv9FPY/nIGm6F9jSSt+ouK6U8l8+xm9Xnw72jtNnHVLNZ//rDeITDCr3H3JSo7d1zGgsQshtizts7cxenoBaD/8VNT0zIiev6dHLysfPRrKyyN6aiHEECBJ9xAXDMLy5fp/U1I2fc3oaNG7C5lM0QlODE6aRv6jN2F22gHoHjeZjkNPinJQfadpGnWd9by/9hMWORaHtluMZiknjyDNkkDbSRezesbDePP1ISmD30ves/+k7OZpmFvWbnXfhjYLp84Zwc91ehVPWlKAR65ZxgETXRGJXQjxGwE/Ge+/EHra9qcLI3p6txva22HkSBgW+Z5tQoghQJLuIc7r1e/sZv7mhrLicWOxN6GmpEclLjF4pf/vDdK+/QiAQEo6TefPivicvf7Su5P/GOpO3uXrRtWks3U0uatqqJv7LI7DTkZbd6WctPR7Kq89iYz5L2026r10TSKnzB7B6mZ9TcS8DB9PzVjKxOqeiMcuhADUICkLP8HaWAdAz+jdcI+cGLHTezzQ1gbV1VBVJQm3EGJgxMeVrog4k9OOobcLNSll+28Woo/MzWvIf+r20POmc2YQsGVvY4/Y4fJ2Mn/tp6zu3tCdfErB7hgU+RiNNi3BSutpV7D62gfx5eidjww+D/lP3Ebp3y7CZG8C4NulKZxxUzVtTgsAFQUenpm5hKpiT9RiF2JIU1XMLQ2k/vBpaFPbsdMidnqvF1paYPhwGDEibu7/CiHikHy8iM1pGua2BrQEq9zyFeETCFB0//UYfHqC07H/MXRP2j+6MfXB+nLy+Q2fSnfyGOceOZHam5+j46A/hbYl//I1ldNPovHxdzn31uF09erTZcZV9vD0jCUUZvujFa4QQ5umYW5rRE1IJPWbDwAIJqfRcVhkphv5/dDcDJWVMGpUxHu2CSGGGEm6xWaMXU5MLoc0UBNhlfPaPBJrfwXAm19Ky6mXRzmivvEGffzg+IWgpkp38jigWZNoPvMaVl99H/4sfYFdo6eHgz64jtcDR1DEWvapcfHY9GVkpPZvmTEhxE7SNExtDQTSbCTW/RJqoOb4/ekRWaI0EIDGRqiogDFjpHWNEGLgSdItNmNqb0Xx++N2+SYRexKXfk/Wm48DoBmNNF44B82aGN2g+shqSmBSTg1jM0dId/I40jt2D1be/DxfVGwYNTucd1hiHMMzu99JUoIk3EJEi8nehJqcjrtqPFn/eSK0ve24gS8tX59wl5bqncrN5gE/pRBCSNItNqX4fZjbGlCTU6MdihgkDL3dFD4wE2Vdw7G2Y6fhqRwT5ai2TtM0ajvX0NLbFtpWklIo5eRxRlVh7suj2KvuOQ7nLRooBCAl2Enpw7MovvMKjOs66AshIsdkb0a1JuGuHk9i7a8krfgJgO6aPfEMHzeg5w4GoaEBCgv1tbgT5B6qECJCJOkWmzC6HBi7Owkmp0U7FDFI5D9xG5Z1jax6R+yC44ipUY5o6/xqgK9bf+DbtkV82fo9noA32iGJfvD5Fa76VwXPvJ8LwDsczgPHvotz79+H3pO68BMqp59I2hfv6ksjCiEGnKm9Bc2SgLt6AsG0DHJefTD0WtuxFwzouVVVT7gLCmDcOLBaB/R0QgixCUm6xSbMbY1oJpO08BRhkfb5O6R//jYAwaQUGi6YDYbY7FazoTt5AwoK1emVJBgt0Q5L7KAet4EL7xjO21/p6yAaDRo3nbeKU47x0nTBbOovvZ1Amv6aqdtF0b+uo+ieazB2dkQzbCEGPZPTjmYw4q6qIWjLwuhqJ2P+iwAE0jLoOPj4ATu3pukl5Tk5esKdlDRgpxJCiC2SzEqEGLo7Mbe3SgM1ERYmexP5j98Set585jUEsguiGNGWrS8nn9/wKV3+bhKNVvYvnMyojOFSTh5nHC4TZ91SzRe/6JU6VovKPZeu5Jh9HaH3dO+6P7W3vohrj0NC29K++YDKa04gdd368UKI8DK6HKBpuKvGE8jUK1Cy/vskBq++moXjD2cMWJ+P9Qm3zQY1NZAiK6EKIaJAkm4RYnLaMXjdcdPgSsQwNUjRAzMxunsAcO11OJ17/i7KQW1O1dRQOXlQU8lPzOGQkinSnTwOrW21cNrcEfxcp3c+TksO8MjVy9h/F9dm7w2m2mi86BbWXnQrgZR0AExdHRTfdRWF98/A0L35PkKI/jF2OVECftzDxxHI1lcUQNPI3qi03D6Aa3M3NUFaGowfr/9XCCGiQZJuoQsEMLfWE0ySW8Bi52X95wmSln4PgC+7gOapV0c5oi1TUNb9v8K4zJFMKdgdq1E668SbJasTOWX2SFY365M08zJ8PHX9Unap7tnmfl17HEztrS/Stev+oW3pn79N5fQTSfnh04EMWYghwdDtQvG6cQ+vwZ9bFNqe8v0CElctAaBr4r54KkYNyPmbm/VS8poafaRbCCGiRZJuAYC5qx1jl5PgulEfIfrLWvtLqDmOphhovGA2agzdzNE0jaCqLxelKAq75ozlgKK9pJw8Tn2zOIUzbhqB3aWv+1NZ6OaZmUuoKvb0af9gehZrL/k7DRfMIZikr9pgdtopueNSCh66EUNv94DFLsRgZujpwuDuwTNsLP78kk1ey3nlgdDjtgEa5W5tBYtFH+HOzByQUwghRJ9J0i0AMDma9QdGU3QDEXFN8fRSeP8MlKCe1Dr+eCbuEbtEOaoN1ncn/7zlO7R1HatNBhPZ1owoRyb6Y/63Ns77exXdbr05X82wbp6+fimF2f4dO5Ci0Ln34dTe+iLd4/cObbZ98iaV008k+acvwxm2EIOewd2NsduFp2I0voKyTV4zOu3YPnwFAL8tG+eBx4X9/Ha73g+2pgays8N+eCGE2GGSdAsMnl4s7c2o0kBN7KS8Z/5JQvMaANyVo2k75vwoR7SB09vJ/LULWN3dQHNvG+1eZ7RDEjvhpY+yufTuSnx+/WtsSo2LR69Zji012O9jBjJyqL/iThrPm0kwUZ8bbm5vofRvF5H/2M0Y3NsuVxdC6Ddfja4OPJWj8RVXwm8qiLLffByD3wfoN2Y1S3in9HR06OtxjxsHeXlhPbQQQvSbJN0CS6cdY2836rqLTCH6I/Xbj8j4+DUA1IREGi6cC6boV06s707+QcOndPl71nUn35MsGd2OS5oGD7yezw2PlqFq+sX8kXs7uPeyFSRZ1Z0/gaLg2vdIam9+ge6xe4Q2Z3z4KhXXnkzSr9/u/DmEGKQUrweT04GnfCTe4mGbJdyo6qYN1MJ8Y9bpBI9HT7gLC8N6aCGE2CmSdA91qkqCowE1IXHzL0ch+sjU0Ub+I3NDz5tPuwJ/fmkUI9KtLyffuDv5oSX7kpMoE/ziUVCFm54s4e5XNjRkOvPwZm4+fxXmMN/fCWTnU//Xe2k68xr98xGw2Bspu+UC8p78O4rHHd4TChHnFJ8HU0crntIqvGXVen33b6R++xHW+hUAdO5+EN7SqrCdv7MTenr0hLukZPvvF0KISJKke6hzOjF3OghIabnoL1Wl4KEbMa1bZqlz0gG49jsqykHpvmj+jtXdDZt0J08wWqIdlugHn1/hr/+q5Nn5uaFtV560lr+e0rCla/vwUBScB/2J2pufo2fkxNDmzPdfoPL6U0hc9sMAnViI+KL4fZgcLXiLh+MtH7nFhBvYZJQ7nA3Uurv1pHvMGCiN/v1eIYTYjCTdQ5zS1ooSDKCZJRER/ZPx3vOkrGs05c/Iofns62KmamJMZjXJpiT2L9xTupPHsR63gcv+NoZ3vtIrFIwGjVum1XH2H1oicn5/bjFrpj9A82lXoq6bf2ppqads7nnkPnsniq9vndKFGJQCfkz2JrzFlfrSX0bjFt9msjeT8ZE+BcmflRe2m7O9vfo87tGjoXLzKeRCCBETJOkeyrxeaGwgmJQW7UhEnEpYs5zcF+4JPW867waCqbaoxeNXA7T02kPPs6wZHF66v5STxzGHy8SZN4/gm19sAFgtKvdetoKj9mmPbCAGAx2HnUTd3GfpHV4DgKJpZL39NBUzTsO68ufIxiNELAgEMNsb8RVW4Kkcs80+HtlvPoYSDABgP/LssNzs93jA4YCRI2HYFqaQCyFErJCkeyiz21G6uwgkpkY7EhGHFJ+XwvuvxxDQl2dy/O4UesZNjlo867uTL2j+mg6vK7TdoMjHXLyqb7Vw6pwR/LpKb/KYlhzg0WuWsd+EzqjF5CsoY/WMh2g56RLUdUlDQuMqym88m5yX7kNZ15VZiEEvGMDc1oA/twRP5Wgwmbf+XlUl+7V5AGiKgv3o83b69B6PvhZ3VZX+a8CmmQghRBjIR9RQpWnQ2Kh/Sco3leiH3Bfvxbp2JQCekirajv9LVOLQNI2VnatD3ckTDBZULQxdrEVULVmdyKmzR7KmxQpAbqaXp2YsYUJVDCzbZTDS/ofTqZvzNO7K0QAomkr2vx+j/IYzSFi1JMoBCjHA1CDm1gb8OUW4h4/b7rJfaV++R0LjKgA69zwMX1HFTp3e54OWFn10e8SIrVa0CyFEzJBsa6jq7IS2NjSbLdqRiDiUvOgLMt99DgDVbKHhz3PDvtZqX/jVAF+1fs93bT8R1FQKknI5tGRfWQ4szn2zOIUzbhqB3aWPnFUWunnkxh8ZXhRbc6d9RZWsmvkorcf/Gc2ol9Va61dQMWsq2a/Og0AgyhEKMQBUFXNLA4HsAtxV49ASrNvdJZwN1Px+fcygogJGjYqJlSmFEGK7JOkeqtra9Nos6/a/LIXYmLGzg8J5s0LPW0+6GF/xsIjHsb6cfE13IwoKNZkj2Sd/N+lOHufe/8bGeX+votutD11NGN7NUzOWkJcVo2XbRhOOI8+mbvZTeMqqAVCCQXJem0fFrKkkrFseSYhBQdMwtzUSyMjBXVWDZk3a7i7m1gZsC94EwJdTiGufI/p9+kBAT7jLy/VO5eZtVLQLIUQskaR7KAoEoKEBUmUut9hBmkbBI3MwuRwAdNfsRcchJ0YllMbeFrr8PSQarRxQuCcjpTt53Hvhw2wuu6cSn1//atp3vItHrlmGLSUY5ci2z1taRd2sJ2g7+jw0g37DwLp6KRUzTiPr349BUEa9RZzTNExtDQTSbLirx6MmJvdpt+w3HkEJ6v+G7Uef2++h6WBQv3QpKYGxY8Ei91eFEHFEku6hyOEApxPSpGu52DG2j14jdeEnAARSbTSeNzNq7WJH2oYzOqOKQ0v2JVu6k8c1TYN/vVbAjY+VoWr6z9NR+zi459IVJCZoUY5uB5jM2I+bxqpZj+MpqgRACQbIfek+ymefg6WhLsoBCtF/JnsTanI67uoJqMl9vGkfCJD9+kMAaAYD9qPO7de5VVVPuAsL9YQ7IfKzmYQQYqdI0j0UNTXpzdNkIpTYAZamVeQ984/Q86ZzZxK0ZUfs/E5vJ583f0dA1UdMDIrC2MwRUk4e54IqzH2ihHtfLQxtO/v3zdx8/irMcfoR5akYxao5T2M/4ky0dd3zE2t/oWLGqWS+9TSosT9yL8TGTPZmVGuSPsKdkt7n/dI/fxtLy1oAXHv/Hn9+yQ6fe33CnZsL48ZBYuIOH0IIIaJOku6hpqdHb/kpo9xiRwT8FN4/A4NPb2TVceBxdE/cNyKn3rg7+dqeJn7pWBaR84qB5/MrXHlfBc99kBvadtXJ9Vx5ckPcr7ermS20nXgRq2Y+gregDACD30fec3dSdtP5mFvqoxyhEH1jam9BsyTgrp5AMG3HmlRu0kDtuAt2+NzrF1rJyoKaGkjuW0W7EELEHEm6hxq7XU+8U1KiHYmIIzmvziOxbjEA3oIyWk65LCLn3VJ38pG2yDdtE+HX7TYw7fbhvPu1PjXAZNS4dVodZ/2+NcqRhZdn+Djq5j6D4/BT0dbdSUha9iOV155ExnvP68N4QsQok9OOZjDirqohaMvaoX0tTatJ/+wtALz5pXTu+bsd2l/T9MI8m01PuKUNjRAinknSPZSoKtTXy61isUOSFn9H1n8eB0AzGmm8cG6flojZWU5vJ+9v0p18lHQnHyTsLhNTbxrBV7/qFTeJliD3XraCI/dpj3JkA0OzWGk95TJWXzcPX24xAAafl/ynbqf01j9jbmuMcoRCbM7ocoCm4a4aTyAzd/s7/Eb26w+jaHpPBvvR5+3wYtrNzZCUpCfc6X2vaBdCiJgkSfdQ0t6u/5JvL9FHhp4uCh+cGbpwavvThXgqRg34eRt6mpnf8Cnd67uTF+3JyIxh0p18EKhvtXDq7BEsXq0vNZSeEuDR6cvZd3xnlCMbeO4Ru1B703O0H3JCaFvy4m+puPYkbB++qg/tCREDjF1OlIAf9/BxBLLzd/wAAT9ZbzwC6DdrHUedvUO7t7ToK5pOmAAZO1bRLoQQMUmS7qGktVW/qJOFLUVfaBr5j9+C2dECQM/IiTh+f3pETm2zpGNSjBQk5erdya3SnXwwWLw6kVNmj6S+Va+UyM/y8fT1Sxk/vCfKkUWOZk2k5Yy/svqa+/FlFwBg9PRS8NjNlPz9/zA5mqMcoRjqDN0uFK8b9/Aa/LlF/TqG7ZM3sdibAHDueyT+nMLt7LGB3a4PitfU6HO5hRBiMJCke6jwePRuJNJATfRR2udvk/7lewAEk1JpvGA2GHasPHBHuAOe0ONkcyIHFe8j5eSDyFe/pnDG3BE4XPpNv2FFbp6duYRhRZ7t7Dk49Y7Zjbqbn6Nj/2NC21J++pLK6SeS/smbMuotosLQ04WhtxvPsLH96jS+Xn8bqDkc+ky48eP1buVCCDFYSNI9VNjt0NUlnUhEn5hbG8h//LbQ86azphPI6keJYR+s707+1poPaejZMMqXak6WcvJB4t2vbZz/9yp6PPpNm12qunnq+qXkZ/qjHFl0qYkpNJ9zHWuuugd/hp5hGN09FD50I8X/uByT0x7lCMVQYnB3Y+x24akcg29dx/3+sKytDd2w9RZV0rX7wX3az+kEv18f4c4fmK8bIYSIGkm6hwJN0xe5tFiI+3V4xMALBih8cCZGj17y69znD3RNPnRATvXb7uRru5sG5Dwiep7/IJvL763EH9C/bvab4OThq5dhS5G1qtfrqdmT2ltewLnPEaFtqT8soPKaE0j7/B0Z9RYDTvH0YnR14Kkcja+4cqeuFXJemxd63HbMeWDY/qVmZyf09urrcBf1r6JdCCFimiTdQ4HLpY9022zRjkTEgaw3Hydp2Y8A+HKKaDnjqgE5z2bdybNGsXvuhAE5l4g8TYP7Xi1g9uNlaJp+AX/0FDt3X7KSxARJIn9LTU6lados6i+7g0C6PpHV2NNJ0f3XU3T3XzG6BmdndxF9iteDyenAUz4Sb/GwnUq4Fb+PrDcfA0A1mXH88azt7tPdrRfijR0LpaX9PrUQQsQ0U7QDEBHQ1qbXbFkHfpknEd+sK34m57WHANAUA40XzEZNDO+a7pqmUdu1hu/tv6BqKkkmK5PzJkqztEEkqMJNT5bw/AcbJmWee0Qzl53QIMU229E9cT9qq8aT99TfSf/iXQDSvv2IpKXf03zmNX0u1RWiLxSfB1NHK57Sarxl1X0ald4W28evY25vBcB5wDEEsvK2+f6eHr2sfMwYKC/fqVMLIURMk5Huwc7vh7VrISW8iZMYfBRPL0X3X4+i6mW/9qPOwV09PuzncXg7+K7tJ1RNpSApl0OKpTv5YOL1KVxxb+UmCffVp9Rz+YmScPdVMNVG459vYu3FtxFItQFg6nJSfM81FN53LcYuZ1TjE4OD4vdhcrTgLR6Ot3zkTifcANmvPBB6bD922jbf63brjdNGjoRhOzfALoQQMU+S7sHO4dAnS0nXcrEd+U/djqV1LQDuYWOxH33OgJwn25rJ8PRyarJGSXfyQaar18C026t47xt9YV2TUePWC+qYenhrlCOLT127HUTtLS/SOemA0Lb0L9+jcvqJpCz8JIqRibgX8GOyN+EtrsRTMUpfo2snJaxeRtq3HwHgKa2ma6Of29/yePQivBEjoKpKEm4hxOAnSfdg19Sk370OwxeqGLxSv/kA2yf/BiBoTaLhwrlgDM/sE03TqO1cs8mSYBOzxzLSNky6kw8ibU4TU28ewdeL9RUSEi1B7rt8BUfuLXORd0YwPZOGi/9Gw5/nEkzWb56aXA5K/nk5BQ/OwtDTFeUIRdwJBDDbG/EVVuCpHAOm8HzWZ2/cQO3Y87eaSXu90NICw4frSXcYBtiFECLmyUfdYNbVpX+zSQM1sQ2m9lYKHrkp9Lzl9Cvx5xWH5dh+1c+Xrd/zbdsivmr9HlW6MA9Ka1osnDZnJEtWJwFgSwnw6PRlTKnpjHJkg4Si0Lnn76i99UW6JkwJbbZ9+h8qp59I8qLPoxiciCvBAOa2Bvy5JXgqR4PJHJbDKl4P2esbqJktOI6YusX3+f3Q3AyVlTAqPAPsQggRFyTpHszsdn0NjqSkaEciYpWqUjhvFsYePTnq3O0gXFP+GJZDd3hdvL92AfXrupPnJ+Ui49qDz6+rEjl19kjqWxMAKMjy8vSMJYwf3hvlyAafgC2btZf/g8bzZxFM0vt0mDtaKf37xeQ/chMGd3eUIxQxTQ1ibm3An1OEe/g4NEtC2A6d8eErmNZ12O84+HiCtuzN3hMIQGMjVFTojdPCNMAuhBBxQZLuwSoY1BuoJSdHOxIRwzLfeZbkX74GwJ+RS9PZ1+705DpN01jpWs0HDZ/R7e8lyWTlgKK9pJx8EPryl1Sm3jQCR6c+Wja8yM0zM5dSWeiNcmSDmKLgmnIEtbe8QPe4yaHNGR+/RuX0k0j65ZsoBidilqpibmkgkF2Au2ocWkJ4VzPJfvXB0OMtNVALBKChAUpKYPRoMIdngF0IIeKGJN2DVXs7dHRIabnYqoTVy8h56T4ANEWhcdqNqCnpO3VMvxrgy9bv+c7+2+7kGeEIWcSQd7+2Me324fR49PrQidXdPDVjKfmZ/ihHNjQEMvOov+oems66lqBVr2YyO5opu/VC8p64DcXjjnKEImZoGua2RgIZObiratCs4a1+s678hdTvFwDgrhxN94R9Nnk9GNRHuIuKYNw4SAjfALsQQsQNSboHq+Zm0DSp3xJbpPg8FN1/PYaAniC1H34avWN2C8uxnV4XCop0Jx/Enp+fzeX3VuIP6F8hB+zi5KG/LiM9ORjlyIYYRcF54LHU3fw8PaMmhTZnzn+JyutOInHp91EMTsQETcPU1kAgzYa7ejxqYvir3zZtoDZtk2opVdVHuPPz9YTbGt4BdiGEiBuSdA9GbreedKfv3KilGLxyn7+bhIZaADxl1bT96cJ+H0vTNLR1DdLMBhN75u0q5eSDlKbBPa8UMPuJMjRN/7s9Zl87d12yksQEaZIXLf6cQtZc8y+az7gK1aJnNZbWBspuOp/cZ/6B4vNs5whisDLZm1CT03FXT0BNTg378RVPL1n/eQIANcFK++9PD72mafoId06OnnBLexkhxFAmSfdgZLdDdzekpEQ7EhGDkn/4lMz3XwRANSfQcOFNaOb+jUav706+zFUX2mZLSJNy8kEoqMKNj5dy/+uFoW3nHtHE3HNXY5IOxNFnMNBxyInU3vwcvdXjAVA0jax3nqXiulOwrvgpygGKSDPZm1GtSfoI905OHdqazPdfxNTtAqD90JMIpumf/esTbpsNamrkckQIISTpHmw0Ta/lslp3uiGWGHyMrnYKH5odet568iX4iir6dayNu5P/3L4ET1CaZw1WXp/C5fdU8uKHOaFt15xaz+UnNsrHTIzx55Ww+rp5tJxyKeq6m2kJzWson30OOS/cg+L3RTlCEQmm9hY0SwLu6gmhRHggbK2BWlMTpKbC+PGQljZgpxdCiLghSfdg43SCwyGl5WJzmkbBw3MwderLunSP35uOg4/vx2E0VrhWbdKdfL/CPbEapTvOYNTVa2Da7VW8/61+4W4yavztwlrO+F1rlCMTW2Uw0n74adTNfRZ35RgAFE0l+z9PUD7jNKx1i6McoBhIJqcdzWDEXVVD0JY1YOdJXPYjKT99CUBvVQ09Y/cA9NltSUl6wi29XIUQQidJ92DT2gp+v7QHFZuxffAKqT/oHWYDqRk0njdzh6sh/KqfL1sWstD+M6qmUpiUJ93JB7E2p4kzbhrB14v1uaCJCUHuv2I5R+zVEeXIRF/4CstZNfMRWk+4CNWkr9FkbailfNaZZL/yIASk0/xgY3Q5QNNwV40nkJk7oOfaeJS77bgLQFFoa9OXA6upgczMAT29EELEFUm6BxOfT1+bOzX8zVJEfLM0riLvuX+GnjedfwPB9B0bAVE1lQ/WfkZ9TxMKCuOzRrN3/iTpTj5IrW5J4NTZI1m6Ru9+ZEsJ8Nj0Zew9rivKkYkdYjTh+OOZrJr9FJ6yEQAoapCc1x+iYtZUEtYsj3KAIlyMXU6UgB/38HEEsvMH9FyG3m6y3n4agGBiMu2/OxW7Xb+PO3683jxNCCHEBpJ0DyZ2O3R2ygQqsamAn6J/XYfBp8+5bj/o+M3WUe0Lg2KgIq2UJFMiBxbtxQhbpXQnH6R+qUvi1BtHsLZNr5gpyPLyzMwl1AzrjXJkor+8JcOpm/UEbcecj2bUO99ZVy+jYubpZL3xCAQDUY5Q7AxDtwvF68Y9vAZ/btGAny/z3ecw9ug34NoPOxmHP41gUO9Snpc34KcXQoi4I0n3YKFpeucSsxkM8tcqNsh5+X6sq5cC4C2soPXkS/q8r1/10+XvCT2vTq/g0OJ9yZJy8kHri19SmXpzNe1dejlyVbGbZ2cupaJAGuXFPZMJ+7Hns2rWE3hKhgOgBAPkvnw/5TeejWXdMoIivhh6ujD0duMZNhZ/fklEzrlxaXndoRfg8egJd2HhNnYSQoghTLKzwaKrS5/PLV1LxEaSfv2WrLeeAkAzmmj481y0BGuf9u3wuni/fgGfNn2NX9VHwRRFwWI0D1i8Irre/iqDC24fTq9HHwmdWN3Fk9cvJS9T5v4OJp7ykay68UnsR56FpuiXAYl1v1Ix4zQy//skqMEoRyj6JBjA1N6CobcLT+UYfAVlETlt0q/fkrz4OwC6qneluWhXxo2Dksjk+0IIEZck6R4s2trA7YbExGhHImKEoaeTwgdvQNE0AFqP/zPedXM6tyXUnXztZ3QHeglqKu6Ae6DDFVH27Ps5XHlfBf6A/rVwwEQnD1+9nPRkScAGI81soe34v7DqhkfxFpYDYPD7yHv+bsrmnIelaXV0AxRbpwYxtbdibmsimGKjd/QkfMWVEVsmdONR7pUHT2PMGCgtjciphRAibknSPRgEAnoDtZSUaEciYoWmUfDoLZjbWwDoGb0b7Yeftt3dNulOzvru5FNIs0hzvsFK0+DulwuZ+2QpmqZftB+7r527Ll6J1aJFOTox0DzDxlI35xkcvz8dbV3SlrRiERXXn0LGu8+BqkY5QhGiqpicdsytDQSTUugdsxs9Y3YnkF0QsYTb0N1J5rvPAeBPTCXlvJOpjFy+L4QQccsU7QBEGLS36+tz5w9st1IRP7IWvEP61/MBCCan0Tht1nbn+nd4XXzR/B3dgV4UFGqyRlGdXiHN0gaxoAqzHy/lpY82tBo+/8gmLvlTo1xEDyGaJYHWky+ha9f9KJx3I5aWegw+L/lP30Hqtx/RdN5M/LnF0Q5z6FJVjF0dGHq7Cdiy8FWMxp+dD6bIT/XJfOcZjG69z0f3UadRWZMinxVCCNEHMtI9GDQ16beZTXIPRYC5ZS1lj2+0PNhZ1xLI3H472Z/bl9Id6JXu5EOE16dw2T2VmyTc009bw6XHS8I9VLmrJ1A791naDz0ptC15yUIqrz0Z2wcv62URInI0DWNnB+aWejSTBfeoXekdt6feLC0KCTeaRvbLD4Sepl01Tfq2CiFEH8nHZbzr6YGWFkhPj3YkIhYEAxQ9MBOjR5+D7ZzyR7r2OLhPu07KqaEitYRDiqdId/JBrrPHyHl/q2L+t/rfs8mo8vc/13L6YW1RjkxEm2ZNpOX0K1l97QP4svVW1Aavm4LHb6Xktr9gsjdHOcIhQNMwdjkxt6xBUxTc1RPoqdkTX0EZmtkStbAs339F8opFeoh7TMY4cXzUYhFCiHgjQ6PxzuHQE+/MzGhHImJA9huPkrTiJwB8uUW0nH7lVt/b4XXR1NvK6IwqABJNVnbLlYuoLbn07krOPLyFCVU9qCrc/HQJC35MBzTO+F0rpx6y5WT13NuqsLtMKAokW4Nce3o9o8vd231tW1Y1J3Dtg+V0dJtITQxy0/mrqCr2bPa+hjYL184rZ/HqJIpyvLx202IA2pwmzrutimVrkwBQFI1hhR7GVuprcNtdJv7yj+E8M3MJJmN//rTEYNA7ahJ1Nz9H7vN3kfHhqwCk/PI1ldeeSMupl+Pa90iZyDsADD2dmDo7CCan6Wtu5xShWaPfINXvh6xnN4xyKxdMi2I0QggRfyTpjmeqqjdQS0yUix9B4vJFZL/xCACawUjDhXNQE5M3e5+maazsXM0P9l9RUUkzp1CcUhDpcOPGopVJuHqMTKjS5zG++XkmKxusvPX3n+nqNXLc9aPYfVTXFhPff1xUS9q67t/zv7Vx3bxyXrt58XZf25YbHy3l+APsHLOvg3e/1vd7cfaSzd6XnBjk4j810O02cudLRYCesJ93WxUN9gQAbCl+HrxqBZ8tSuPOFwv55//VkZ0eYEJVN298msVx+zn68ScmBgs1MZnms66la9KBFDw8B3N7C0Z3D4UPzyHtmw9pOud6Ahk52z+Q2C5DbzfGznbUxGTclWPw5xVv8fM7GgIBaFvWwe5fvKBvsNnghBOiGpMQQsQbKS+PZx0d+ki3lJYPeQZ3D4UPzEBZt75u47FTcQ8ft9n7fEE/X/ymO3luYlakw40rL36YwxF7toeev/1lJsfvb8doAFtKkMP36OCtL7ZcaZK20XJbXb1GUPr22tY4XCZ+rkvmj3vryfChuzlparewuiVhs/faUoLsOqKHxAS9+/QvdUmcNntEKOE2GVUe/utyxlb00u02kr/RWty/n9zOix9KMiV0PeMmU3vLCzj3/WNoW8qPn1F5zQmkffaWzPXeCQZ3D+bmehSfB0/ZCHpq9sJbPiJmEu5gEBoboebHpzB4191YPOMMSEqKbmBCCBFnZKQ7nrW26t+IlujN8RKxIe+p27G0NgDQW1VD41Gn89tLtnavky+aF9Kzrjv5+KxRVEl38u36ZkkqU3/XEnre5LBQmO0LPS/K8fHjiq1fIF/zQDlfL9aXXHvgyuV9fm1Lmtst5Nj8obJvRYHCLB9Ndgtled6t7tfrMTD15mp6PfqOVcW91Azr4fS5I0hKVMnL8PHEdctC7x9T0cuy+kS63QZSEmXJKAFqUgpN591A16QDyX/0JsxOO8beLooemKmPep81nWC63MDrK8XjxuR3YPBb8BYPw1dQhpqSFu2wNqGq0NAA+XkaJW9tKC1nmpSWCyHEjpKR7njl9eq3n9Ni60taRF7qV/OxLXgTgKA1mYYLbgTjpvfTajvX8OHaz+nZqDt5tXQn75PmdjNZ6YF+73/rBav48K6fuPhPDdzxfHGfXwuXr35Npb41IZRw7zqii2tOXUuDPYGP7v6J/929iMmju7jxsdLQPiYjpCUHaO2IQodkEdO6d5lC7S0v4Nrr8NC21O8+pvKaE0j96v0oRhYfFJ8HU8tajD0u/Fl59IzbE0/VuJhNuHNzYUL3pxiWrJv6MmUKjB4d3eCEECIOxWTSfd9991FeXo7VamWPPfbg66+/3up7H3roIaZMmUJGRgYZGRkcfPDB23z/oGG3Q2cnpKZGOxIRRSZHMwWP3hR63jL1qi2up2s1JoTKyQ+V7uQ7JNGi4vVvuDlRkOWj0b6huqShzUJBlm9Lu27i6CntfL04FWfX5t3JtvXaxvIzfbQ5zQTWVaZrGjQ6LBRkb/n8T7+Xw/2vF7C+dv3AiU4e+utyPvjOxh6ju0hLDmIwwFFTHKER9/W8fgMJFikbFptTU9JpvHAOay/5O4HUdR3wu10U3zudonunY+xyRjfAGKT4vJjaGjB2duDPL6Vn7J74CsoJpsbe9DBN0+/pZ2VBTQ0kPvnghhdllFsIIfol5pLuF154gcsvv5wbbriBhQsXMn78eA477DBaW1u3+P6PP/6Yk08+mY8++ogvvviCkpISDj30UBoaGiIceQSt/0a0WJBFMocwVaXwwVkYe7sA6NzjEFx7/yH0clDdMGe4MDmPAwr3ZO/8SViMMh1hR1SXuKlrsoaeH7Z7By99nE1QBWe3kbe/yuDwye2b7dfZY9xkpHj+t+nYUgKkpwS3+RroZefzv7Vtdsys9ACjy3t58zO9jPe9b2zkZ/o2Ky3XNLjrpUJufqqU9Qn3n/Zv486LV2K1aBTnevnq11R8Af21//2QTlXxhs7p67uqF2Ru/2aCGLq6Jh1A7a0v0rnbQaFtaV+9T+U1J5Dy7cfRCyyWBPyY2xoxOu34c4roGTcZ94gJBNMzYrYBalOT3iqmpgZSvXZ46SX9hawsOO646AYnhBBxStG02OqAsscee7Dbbrtx7733AqCqKiUlJfzf//0f11xzzXb3DwaDZGRkcO+993LGGWds9/2dnZ2kp6fT0dGBzWbb2fAjw+WCzz7TvxWt1u2/fxvcHvh+od4TJWHzXkwihmX+90nynr8bAH9mHrU3P4eanIaqqfzavYJax2oOLt6HJFP0l5uJZ0+/l0OTw8JVJ+s38oIq3PxkCQsWpaMocNqhrZx+mH5T8MOF6Xy00Macc1fTYLdw+T2VeHwGDIpGRlqAq05ey6gy9zZfAzji6tHcOm1VaBmvjdU1JXDtvHKc3SZSEoPcdN4qqkv0BkczHi5j3wlOFvyYzssfb2iEZlA0cmx+/ri3g8tPbMTnV5j7ZAkLl6VgMmpkpwe44azVlOTqSfbrCzL5bmkqc85dPaB/tn2lodFj9JActKL0peOciLjUL98j/4nbMHW7Qttcex9O8+lXoSbHVul0RAQCmJx2UAMEMvPxFVUQsGWHbpRrmorH04rVmouixM7N8+Zm/bJi4kTIyADuuAOuXLf05BVXwO23RzU+EZtUVaW1tZXc3FwMMhgkBgGn00lGRgYul4u0ME3ljamk2+fzkZSUxMsvv8zRRx8d2j516lScTidvvPHGdo/R1dVFbm4uL730EkccccRmr3u9XrzeDaNCnZ2dlJSU4HA44ifpXrkSfv4ZSkp2+lBuD/zwvSTd8ca6agkVs85CCQbQFIXV0++nd9Su+IN+vmlbRENPMwCjMoYzNnNElKONbz0eA6fNHskzM5eQZB34pmLtnSb++q9KHr5m2fbf/Bsen8Jf/1XJB9/pJb+KojH9tHpOPXTLlUJbc/qcEcw6ezXDijZfBi0aJOmOD0anncLHbiF14Sehbf6MHJrOvo7uCXtHMbIICgYwuRwo/gCBzBy8BRUEMnM2q0rTk+42rNacmEm6W1r0AroJE/RBbTQNZdQolOV6k0d18WKoro5qjCI2qapKW1sbOTk5knSLQcHpdJKVlRXWpDumupfb7XaCwSB5eXmbbM/Ly2PJks3Xod2Sq6++msLCQg4++OAtvn7LLbdw4403bra9ra0Nny8OSikDAVi1Sr8V7dn5C2KvD3yJoFggsO3ppCJGGLweKu6/HiWoN/dqPuIU2saOweVp5fumn+n1u1FQGJkznHJbCT1KbCROcSsZLj59JSvaYVjJwP9ZJmTAXdctomcH9+vqMXLFHaP5YYk+R9RkVJl14TIO3cu+Q8dyuMwcfUgj+aXOHY5hoGhoeI36kmaSdMewrBQ6r5hL1qfvUvrEXZh6uzF3tFF6x6W07f8H6k+7iGBSSrSjHBiairG3C8XvpzcjjUBmPoFUGxgU8Nk3f7um4ve7AC0mku7OTr3avaREXxSltRUsn35K5rqE27v33nTYbPoLQvyGqqq4XC40TZOkWwwKLpdr+2/aQTGVdO+sW2+9leeff56PP/4Y61bKrqdPn87ll18eer5+pDsnJyc+RrpbW6G3F/LzwbjzWbIbWOuGRAUS5Fo2LuQ/fReJjXrZr7t8JB3H/Jmm9kZ+tC9GRSXJlMj4gjEUWXJRVPlLDYcDRq2rjgnu3HSOgdLaYebCv1exrF5fOzfJGuTuS1aw59juHY45OQVKJ3fF1O9VQy/IkpHu+ODZ62hqR+5F4SNzSVn0BQA5H/8X20/f0njuDHrG7hHlCMNIVTF2tmN09+LPyMZXWkEwKw+TybTNCyxNUwElJka629e1pJgwQb+0WE958cXQY/NFF5GbmxvZwETcUFUVRVFkpFsMGpYBWI45ppLu7OxsjEYjLS0tm2xvaWkhf+Nvgi24/fbbufXWW5k/fz41NTVbfV9CQgIJW6ijNhgM8fFB0dys3442heevzqDobZbW/xKxLeX7BWR+8DIAqiWBxgvnUutu5nv7LwAUJecxKWc8fksQJahIgjIErGpK4Ly/VdFg1z/XMlP9PHDlinXzwQfP37+y0f9E7Atm5lF/5d2k/+8N8p75J0ZPD2ZHC2W3XUTHQX+i5aSL0axJ0Q6z/zQNY2cHBnc3wbRMeitG488uAJO5zz+hiqKgKIaoJt1OJ/j9esJdWLjRCy0t8Prr+uPcXAzHHiuNW8U2KYoSP9fSQmzHQPwcx9S/DIvFwq677soHH3wQ2qaqKh988AF77rnnVvf729/+xpw5c3jnnXeYNGlSJEKNju5u/YswHkbkRdgZXQ4KHpodet5yymX4CsspSykmIyGdCVmj2StvEhajrK08VPxUm8Spc0aEEu6ibC9Pz1y6xQZsQkScouDa/2hqb3menjG7hzZnfPAyldeeTNLi76IYXD9pGsYuJ+bmNWhGI+4Ru9BTsyf+/FIwxddnb2enXjg3bhwUFf3mxcce07NxgLPP1id7CyGE6LeYSroBLr/8ch566CGeeOIJFi9ezIUXXkhPTw9nnXUWAGeccQbTp08Pvf+2225jxowZPProo5SXl9Pc3ExzczPd3d3R+i0MHLtd/4ZMTo52JCLSNI3Ch2Zj6uoAoG3c7rQfcCwAJoORg4r2ptpWiRKjS9CI8Pv8p1TOvLmaji79Qn9ESS/PzFxKeb53O3sKEVmB7ALW/PVemqdejWrRpy1Y2hoou3kaeU/fgeKNj74Thm4X5pY1aIC7ajw9NXvhKyxHM8dfQtrdrSfdY8ZAaelvXlRVmDdvw/PzzotobEIIMRjFVHk5wIknnkhbWxszZ86kubmZCRMm8M4774Saq61Zs2aTIf/7778fn8/Hn/70p02Oc8MNNzBr1qxIhj6wgkFYu1ZvMy6GnIz5L5Hy42cA9Kak8fhh+1LpWsnojCoADDHQiEdEzn+/yGD6g+UEgvrf+6QRXdx72UrSkoPb2VOIKDEY6Dj4eLrH7UnhQzeStPR7ADLffY7kHz+j6bwbcFePj3KQW2bo6cLY1YGamIJn2Dh8uUVxXRrf0wMdHTB2LFRUbOEN778PdXX640MPhcrKiMYnhBCDUUwtGRYNcbNOt90On38OublgDl8Jm6zTHfssDbVUzDgdg18fwXzm1FOpraqmJms01bbNr5hkeaXB7al3c7jl6Q1DUwdP6uDvF9aRYBm8H+XyMz3IqCoZ7z1P7ov3hT7XNMVA++Gn0nbcBWiW2PgyMri7MTodqInJ+PLL8OeXoCaGp9IsWut0u93Q1gajR+urf22xOOrYY+G11/THr74KxxwTsfhEfJJ1usVgMxDrdMfcSLfYipYW0LSwJtwi9il+H0X/ui50YfrV7rvTNKqGA/ImkmXNiHJ0IpI0De56uZB5/y4IbTt+/zZmnrUGo1zjiHhiMNDxu1PoGb8XBfNuJGnFTyiaStZbT5Hyw6c0TpuFp3JM1MJTPL2YnA60BCueshH480tRk1OjFk+4eDz6AigjR0JV1VYS7sZG+Pe/9ccFBXDEERGNUQghBitJuuOBxwNNTRCmOy0ifmS+eA/WNfo6qa05Ofx69KkcUrgbFmP8zSEUOyaowndLU2hzmslM9fPfL7J49ZPs0OsXHt3IRcc2bfnCWYg44CsoZ/WMh8l862lyXnkAQ8BPQmMd5TeejeOIqbQdc15Em5MpXg8mpx3NZMZbPAxfQSlqSnrEzj+QvF793n1VFYwYsY1G5I88ok9nAzjnHLnRL4QQYSJJdzyw26GrC0pKoh2JiKCkX74m953nAAgYjfxy9lXsUbyXNEsbAt7/xsbNT5fQ0r75zRVF0bj29HpOPaQtCpEJEWYGI+1HTKV7wj4UzptFYt1iFDVI9r8fJeX7BTROm4W3bMSAhqD4PBiddjCa8BaW4c8vI5g2eCqJ/H59tdHKShg1CozGrbwxGISHHtIfGwzSQE0IIcJIihJjnabpDdQSErZSCyYGI2OXk8IHZ4Werzn2XPLHHigJ9xDw/jc2Lr27kpb2LY0waZzxuxZJuMWg4ysexqqZj9F63AVoRn08wFq/nIobziD79YchEAj7ORW/D1NbI0ZXO/68UnrGTsZTNX5QJdyBgF4xXlGhdyo3bWuo5Z13oL5ef3z44Vtoay6EEKK/JOmOdS4XOByQPjhK3MS2+YJ+vmj+jsyHb8Tc0QpA95jd8R5xTpQjE5EQVOHmp0vQW6Jt+QbLu19lElQjGZUQEWIy4Tj6XOpufBJPib4ygxIMkvPKA5TPPgvL2pXhOU/Aj8nehLGjjUBWPr3jJuMeMYGgLWtQ3dwOBKChQS+SGz26D5XiDzyw4fG0aQMamxBCDDWSdMe61lbw+cBqjXYkYoC1e5y8v3YBWZ++RfbCBQAEUtJpmnbjNibgicFCVeGZ93LWlZRv7cJfobndwndLUyIZmhAR5S2rpm72k9iPOgfNoNdCJ9YtpmLGaWT953FQ+7k0XiCAydGM2dFCwJZN79g96B21K4GMnEGVbIPepbyhAQoLYdy4PqxOUl8Pb72lPy4pgd//fsBjFEKIoUTmdMcyv18vLZcGaoOapmms6FzFj/ZfSXfYOfztt0OvNZ99nX5BKAatta0W3vg0izc+zWJtW9+WSmpzSnMjMciZzLT96UK6Ju5L4bwbSWioxRDwk/vCvaR89z+azr8BX0F5344VDGBytaP4vASy8vAWlhPIyN3G5Ob41d0N7e16kl1RoTdN69M9+4cf1u/8AZx77qD8sxFCiGiSpDuW2e3Q2anfqhaDki/o55u2H2noacYQDHLSG//B4vMB4NzvKLp2OzDKEYqB0OMx8P43Nl5fkM3Xi3d8KaIcm38AohIi9ngqx1A3+ymyX32QrLeeRtFUklb8RMV1p9J2/J9pP+zkrVcCqUFMnR0onl4Cthx8VTX4s/IHXUKpqvqlgssFycl6ol1YqM9K69MAfiCgJ92g/9mcI9OZhBAi3CTpjmVNTfoX4CC7QBA6d8DDhw2f0xPoxYDCCd/8Su6aOgB8eSU0n3ZFlCMU4aSuWwLstQVZvPt1Bm7vpv+uFUVjrzGd/FyXTGePEW0LJeYKGnmZfnYd0R2psIWIOs2SQNtJF9O96/4UzJtFQvMaDH4vec/+k9TvPqbxvBvw5xVv2EFVMXZ1YHD3EEjPxFcxGn92wXa6iMWfQACcTujp0RPsmhrIz4eUHZ198p//6N3WAP74RygqCneoQggx5A2ub6DBpKtLn89ts0U7EjFArMYE0iz61dGhXRaq330NAM1gpOHCOWjWpGiGJ8Jke+XjFQUejp5i5497t5Of6Q91L1fQNkm8lXXt1aafVo9RpviLIchdVUPd3GfJeek+Mt97HkXTSFr6PZXXnkTrSRfTceBxGHs6MfR0EkzLxD1yBP7sAjTz5kvvxTOfTy8h9/shK0tfBiwvrw/ztrdGGqgJIcSAk6Q7VtnteieUHJnPO5j4gn4URcFsMKEoCrvnTsDY28OIO6eiaPp8urZjzsMzbGyUIxU7Y3vl46lJAQ6f3MExUxzUDOvZpAT0kN2c3Hlx7WbrdOdl+pl+Wj2H7OaMwO9AiNikJVhpPe0KuiYdQOG8G7G0NWDwech/8m+kff42TVOvoXfcZPy5RWiW/mahscnt1pNtgNxcKCvTLxF2agC/rg7ee09/XF4Ohx66s2EKIYTYAkm6Y1EwqDdQS5KRzsGk3ePki5bvyLZmsnvuBBRFIcFooeCZuVjsemlfb/UEHEeeFeVIRX/0pXx877GdHD3FwYG7OrFatK0e65DdnBy4q5PvlqbQ5jSTY9NLymWEWwide+REam9+jryn7yDjf28AkLTiJ8pvuYD6y/+B46jBMy+5q0svI7dY9MbixcX6CHdYFrV46CHQ1n0WnX++rJQhhBADRJLuWNTeDh0der2YiHuaprHcVccix2JUNPB04FP9JBgtpH3xLrbP9GVagonJNF4wGwwyhz+e7Gj5eF8ZDbD7KJm7LcSWGHq7MboctB3/Z5z7HU3hgzNJaKnH2NtF+dzzyPjwFVZf99Cmc73jyG+bo1VX72BztL7w+eCRR/THJhOcJTd8hRBioEjSHYuam/U7z4Os6ctQ5Av61nUnbwGgKDmf3XLGYzGaMdmbyX/8ltB7m6degz9HOtXHg50pHxdC9J/B3YPR5UC1JuGpGI0/rxg1KQXngcdS8s/LyX5DTyLTP3+H0SeOpf7Ku2n/w+lxsw73lpqjFRToiXfYvfGG3jsG4Jhj9C5sQgghBoRkdbGmt1fvWp6eHu1IxE5yeDr4smUhPQE3BhTGZ49meFo5iqKAGqTwwZkYe/WRTNeeh9G59+FRjlhsSzjLx4UQO0bxuDE57WiWBDyl1fjzSlBT0kKvqylprJ7xMB0HHkfZ3HOxtDVi6nZRMWsqGR+8zOrr5hHIjt2kMuzN0friwQc3PJYGakIIMaAk6Y41Dod+izszM9qRiJ0Q1FS+aPmO3oCHZFMSe+ZNJNNqC72e9d+nSF6yEAB/Vj7NU6+JUqRiewaqfFwIsX2K14PRaQeTCW9xJf78UoKptq2+v3Pvw/n1hZ8puf0Sst56CgDbgjdJOXEMa/56Hx2HnhhTo95utz6bDMLYHK0vli+HDz7QHw8fDgccMMAnFEKIoU2S7liiqnoDNas1pi4KxI4zKgZ2yxnPys41TMqpwWI0h16z1i0m55X7AdAUhYYLZqMmb16iLKJHyseFiC7F58XosoNiwJ9fiq+gjGB6325GB9MyWDX7SX3U++bzMbe3YnK1U3ndyXR8+AprrvkXgYzorgzS1aXP1zabB6A5Wl/Mm7fh8bRp0kBNCCEGmCTdscTp1Ee6ZZQ7Ljk8HXiDPgqT9QZ4eUk55CVtemGneNwU/us6lGBQ3+eIM3GPnBjxWMXmpHxciOhT/D69jFzT8OcWrUu2s/p1I9q1/1H8MmFvSm+7iMz3XwAg44OXSVn4P9ZMfwDngceGO/xt+m1ztMJCqKqCjIyIhgFeLzz2mP7YYoEzz4xwAEIIMfRI0h1LWlv1LioDOolLhNvG3ckNipFDS6aQYt5y15u85/5JQvMaANwVo2k79vxIhiq2QMrHhYgBgQAmpx3UAP6sAnyF5fpo9E6WkQRt2dTd8jwdBx1H2S0XYnI5MHe0Meyvx+H43SnUX3VPn0fQ++u3zdHGj9dLydc/j7hXXtFv8AMcdxxkZ0chCCGEGFok6Y4VXi80NECqlBnHk992Jy9MysZisGzxvSnffUzGh68CoFqsNF44B0zmLb5XDCwpHxciRgQDmFwOlIAff2YevoJyApm5YS93dh58PN277EvpLReQ8fHrAGS98yxp33zI6usfwjXliLCeDzY0RwsE9AK2jZujqaqedEfFxg3ULrggSkEIIcTQIkl3rHA49EleRUXRjkT0kcPTwRctC+kNuDFgWNedvEzvTv4bRqedgkfmhp63nHYFvoKySIY75En5uBAxRA1icrWjeD0EMnPxFZbjz8wDo3H7+/ZTICuP2r+/SuY7z1Lyt4swdTkxO5oZftkfsf/xTOqvuBM1ZeeHnt1u/StdUfQku7Q0Qs3R+mLxYvjkE/3xqFEwZUp04xFCiCEiFr4ChKZBY6P+jSzNTOLCMmctixyLUdH07uT5E8lMsG35zapK4bwbMXU5AeiauB/O/Y+OVKhDnpSPCxFDVBVjZzsGTy8BWza+4eP0ZDtSGami0H74qXRNOoCyueeR/tlbAGS/+ThpX89n1YxH6Jp8aL8O3dWll5FbLHoX8uJifYQ7pr7WNx7lPv98adoqhBARIkl3LOjqgrY2sNmiHYnoo56AGxWN4uR8JuWM36Q7+W9lvP8iKT99AUAgPYumc2fIhc4Ak/JxIWKMqmLscmLo7SKYnkVvxWj82flRm2LjzylkxZ3/IevNxym541KMPZ1YWtZSfdFhtB1zPmsvvb1Pq0qoqt4YrbMTUlKgulpvkBaTX+duNzzxhP7YaoUzzohuPEIIMYRI0h0L2trA49E7q4iYpWlaqHS8JmsUmQk2SlMKt1hOvl5C/QpyX7g79Lxx2o3bXGNW9J+UjwsRgzQNY7cLQ08nwRQb7lG74s8uQDNvufdFRCkKjiPPonP3gyifcy5pX70PQM5r80j76j1WzXyU7klbXr96S83R8vP1ruQx68UX9aABTjhBVkoRQogIkqQ72gIBfW3ulJRoRyK2Yn138sbeFvYt2AODYsCoGChL3fb8e8XnpfD+6zH4fQC0H3YyPeMmRyLkIUXKx4WIQZqGoacTY7eTYHI67qrx+HMK0RKs0Y5sM/78Upbf+y7ZrzxI8V1XYnT3kNC4ihEXHEjrif9Hw0W3oCbq2fS2mqPFPGmgJoQQUSNJd7S1t+t3nvPzox2J2AJf0MfXrT/S2Kt3J6/vbqQstbhP++a8dB/W+hUAeIqH0XrCRQMW51Aj5eNCxC5DTxemznaCSam4h9fgzylCsyZGO6xtUxTsf7qAzsmHUj77bFIX/g+A3BfuIe3zt1l6zeOsKto7Npuj9cWiRfCFPs2JceNgstwAFkKISIqXr4vBq6lJn98bN9/cQ8dvu5NPyB5NaUrfussn//QlWe88C4BqttD455vQLPEwFBK7pHxciNhm6O3G2NmOak3CXTkGf15xaIQ4XviKK1n2wIfkvHgvRfdcg9Hrxlq/gpqLplBw2uUYbppDZlFibDVH64uNR7mnTZO+IkIIEWGS6UVTTw80N+sTwkTM0DSNZa46FjkWo6GRYkpiz/xdyUjo29+TsctJwbxZoeetJ/4f3pLhAxTt4Le98vHyfA/H7Cvl40JEi8Hdg9HVjpZgxVM2An9eSZ+akMUqFQPLDruYJWW/Y7f7zyLj189RNI3cp+6Ab/4Ljz8Oe+wR7TD7rqcHnn5af5yUBKedFt14hBBiCJKkO5rsdujthaysaEciNrLIsZilrloAipMLmJRTs83u5JvQNPIfvQmz0w5A97jJdBxy4kCFOmhJ+bgQsU/xuDG5HGgmM97iYfgKSsOyznW0bNwczWaDisOrsZzxCcz7J1x/PXi9sGQJ7LUXXH013HBDfEzmfv55vb06wMkny41+IYSIAkm6o0VVoaEBEhOlzCvGVKSVUtdVz9jMEQxLK9tmd/Lfsv3vDdK+/QiAQEo6TefPirFFWmOXlI8LER8Unwej0w5GE97CMvz5ZQTTMqIdVr/9tjna6NH6YiJ6Pm2EK6+E3/8ezjwTvvlG/7C65RZ48019Ca6JE6P8O9iOBx7Y8HjatOjFIYQQQ5gk3dHS0QEOB2RnRzuSIU/TNNq9TrKs+kVjmiWFP5QdhNmwY/88LE2ryXvq9tDzpnNnELDJ3+/2SPm4EPFB8XkxuhygKPjzSvEVrEu24/TGcW+vnmwbDHqSvc3maKNHw+efw9/+BrNmgd8PP/+sl5lfd53+yxydNce3aeFC+PZb/fHEiTBpUnTjEUKIIUqS7mhpaYFgECwxsFbpELa+O3lTbwv7F+5JTqJe6r+jCTeBAIUPzMDg8wDQccAxdO+6f5ijHTykfFyIOBLwY3LaQdPwZxfgKywnmJ4Vl8m2pkF3t15GbrFAWRkUF+sj3NstSjKZ4Npr4YgjYOpU+OEHfXj8xhvh3//WR73HjYvA72IHSAM1IYSICZJ0R4PHo3ctT0uLdiRD2m+7k/cE3OT081g5r80jsfZXALz5pbSccnn4Ah0kpHxciDgTCGBy2SEYIJCZj6+oQq/eicMpM6oKLpc+tTklBaqroaion9Oba2rgq6/gppv0X8EgfP897LILzJkDV1yh/3rrLT3JvfRSuGgrS0Z6vXDVVfDuu2C1wvjxetMzjwdOOgl+/VWfhpabC/ffD8P70JSztRXOOAOWL4e6On1bSoo+n3tj3d1w3HHw3XcbJrT35bWWFvjjH/WRf1l5RQgh+kQ+LaPB4dC/+Yv7tt6zCK+d7U7+W4lLvyfrzcf0YxuNNF44N/bXpI0gKR8XIs4EA5hcDhS/n0BmLt7CCgKZuXGZbAcC+mwut1tPsMePh/x8SN7ZlcwsFn2E+8gj4fjj9eQ2GNRHwh9+WK9TX7ZMz/R32QUOOADGjNnsMMr06XpivmyZ/t/m5g0vnn8+HH64vv3ee+Hcc+Hjj7cf2zXX6OtwH300XHihvu3kkyH1N1VFZrPeEC4zE/bfv++v5eXpzeSefBLOPnv78QghhJCkO+I0TW+gZrHE5QVMvFtfTt7Y2wJASXIBk3JrMBv6NxfP0NNF0f0zUDR9VLbt2AvwVI4OW7zxSsrHhYhDahCTqx3F6yFgy8ZXXIk/Mw+Mxu3vG2O8Xj3ZXt8cbcyYjZujhdGuu8K++8LIkfpotapCbS2sWQN33qmPcp94Ijz3HMydu8muSm8vPPoorF27oew7P1//r9WqN29bb/JkuP12+uTFF/VR7sMP37BtS83eEhLgwANh1aodew30JP7iiyXpFkKIPpKkO9JcLmhr09cjERHX0NNCY28LBsXAhKwxDEsr3aHu5L+V/8RtmB36yETPiIk4jjgjXKHGnb6Uj++1rnz8ICkfFyJ2qCrGznYMnl4C6Zn4ho3Fn5Ufl6XDGzdHy8uDkpJtNEcLlwUL4I03YOZMvcP50qV6tn/llfDaa3DwwXoi/hvGVav0OwI33wzz5+tl5LNmwUEHbX6Ou+6Co47afiwOh97kbc0a+PFHfVtmZvj7x+y6KyxapFftyVQ5IYTYrvj7Ro13dru+PonVGu1IhqTy1GI6fV2Uphb1u5x8vbTP3yH9i3cACCal0HjBjWCIvxGhndWX8vGjpzg4ch+HlI8LEUs0DWNnB4beLoJpmfRWjMKfXQCmGOzCvQ3rm6N1dOgDtDvUHC0c1q7VM/yxY/W53SUlevIL8Nln8PXXMGGCfmdy44ACAZTVq/XO6Lfequ97yCHwyy/68da7+WZYsQI++KDvMW3cQK2ycqd+e1tkMkFGBjQ2StIthBB9IEl3JPn9+pfzb+dViQHjDfr4uX0p4zJHYjGaURSF8dk7X/5tbmsk//FbQs+bz7yGQHbBTh83Xkj5uBBxTNMwdrswdLsIpqTjHjkRf3YBmiXctdcD67fN0UaM2InmaDsjKUlvfAb6aPUee8Buu+kN0Vau1L/7v/lGH8F+9FGoqAAgWFSEZjCgnHqqvu8uu+iv/fTThqT79tvh1Vf1kfCkpO3HkpWlJ8TPPac/T0/X70qUlob5N43+e06U/iVCCNEXknRHksOhXyEUDJ3kLJo27k7uV/1MztvCnLb+UIMUPngDRncPAK69D6dzz9+F59gxTMrHhYh/hm4Xxm4nweR03FXj8ecWoSXEV+XVgDVH66+aGr2kvKREf3788fDUU/oa2VdcoTdWA70JWk2Nnkifey5aVpY+b/rdd/X523V1+q9Ro/T3/+MfevI8f/7mU9KmT9fvMGypK/rYsfroOsBhh8Gnn8J++4X399zSos9DX/97FkIIsU2SdEdSc7P+JRWHTWniid6dvJZFjiV6d3JzEiNsw8J2/Kz/PEHS0u8B8GUX0HzG1WE7diyS8nEh4p+hpwtjZztqUiqeYePw5RahWfswchpDNm6OlpU1gM3RdtSf/qQnzgcfrD8//XR9ZHvCBP07/89/hv/+F1av1uvgL7gA5ZVXMNxyC9r996Ocd57eKdxg0MvCi4r0qrgrrtBLww84QD9uQoK+VBno87V33XXzWDRNv7m/3nff6SPu5nVTBmbOhMJCuOAC/XlNjd5nZv2KKgccoN8w2N5r77wDxxwjDWGFEKKPFE3ThvRwVGdnJ+np6XR0dGAbyOZm3d363K7ExCjejt+c2wPfL9Sr1qJ+4RIG3qCPb8LYnfy3rLW/UD77bJRgEE0xsPq6ebhHTAjLscNFQ6PH6CE5aEWhf3XdUj4uYkk4fqaHKoO7G6OrHdWahC+/DH9eMWpSSrTD2iG/bY5WWgrZ2THU5627W19C64svtv793tmpN1Z76KHQJjU1Fe68E8NZZ7FDH6LBoN7N/KuvNk96P/0UpkzRH++9t/58IEyZAvPmbRiVF0Oaqqq0traSm5uLQW7EiEHA6XSSkZGBy+UiLUx9K2LlK2vws9uhp0e/UhADwunt5NPmb+gNuDFgYEL2aIalle1Ud/KNKZ5eCu+fgRIMAuA48qyYS7h3hpSPCzF4KJ5eTE4HWoIVT2k1/vxS1OT46SeiadDVBU6n3nc04s3RdkRKCvzzn3pp+NixW35PWpqepB53HJxzDjQ0YOjq0h+/9pr+Wl+nnhmN+kj6lmzcQG39aHa4tbTo639Lwi2EEH0mSXckBINQXx9TI9yDUaLJiqbp5eR75u26093JfyvvmX+Q0LwGAHflGNqOPi+sx48WKR8XYvBQvB5MTjuayYy3uBJfQRlqSqQ7i/Xfb5ujjRypV0NHvDnajtrSMl9bcthh8PPPaJdcgvLkk/q2//xHr5W/9159/ev+3ih2OOCll/THmZl62ftAyMuDU04ZmGMLIcQgJUl3JHR06L82XgJEhIVfDWA26D/GCUYL+xbsTpI5MWzl5OulfvsRGR+/DoCakEjDhXNiqLZxx22vfDwlMcjhk9s5ZoqD8cOlfFyIWKf4PBiddjCa8BaW4c8vI5iWEe2w+izmmqMNJJsN7bHHcB54ILZrrkFpbtZ/86eeCi+/DA88oE9W31FPPKFPfAeYOlWWJhVCiBgSv1lDPGlp0Wvl4jhJi0V2TwdftixkTEY1FWl6B9X0hPCvF2rqaCP/kbmh582nXYE/fwCWXxlgUj4uxOCj+H16sg34c4vxFZQTTM/s/2hphHm9+nztQECffRUzzdEiwHvYYWi//z3KJZdsWOLrtddgwQK4//4dG6nWNL1Efb1p08IbrBBCiJ0iWeBAc7uhsTEOauPix2+7ky931VGWWoxhIC4yVZWCebMwdevdYDsnHYBrv6PCf54BJOXjQgxCAT8mpx1UlUB2Ab7CcgK27LhJtmO+OVqkZGXBs8/qc70vuEDv/2K368uOnXSSXnKelbX94/zvf/qyZQD7768vWi6EECJmDLWvt8hzOPTOprKW5Q659O5Kzjy8hQlVPagq3Px0CQt+TAc09t7zG0bushiAkpRCJuWMCyXcNz1Zwkffp9NoT+CVub8yqswdOubBl43FYtJIsKgAnP/HZg6f3LHNODLee56mnx1M5TPaDHlYW3O4qaGeqmLPFt//ysdZPPSffDRNYY/RncyYugazSR9lvv35Ij5dlE5QVdilqpuZZ63BYtKwu0z85R/DeWbmEkxhWk2u12Pg/S+zeEPKx4UYXIIBTE47SiCAPysPX2EFgYycGOwutrmtNUfLyoqbewUD57jj9I7gF14Ir76qb3v+efjoI30E+8gjt73/Aw9seCyj3EIIEXMk6R5ImqavtZmQIFcUO2DRyiRcPUYmVPUA8ObnmaxssPLknAX8b/Uv/PO+qWQX/8Jho3KpTCvdpDv5Ybt3cM4RzZw2Z8t3+e+4qHaTRHxbEtYsJ/eFeziFdzifeRxw1e681nsY180r58XZSzZ7/9pWC3e/UsjLcxaTnR7gon8O46WPcjjlkDZe+V82i1cl8fLcxZiNGjc8WspT7+Zyzh9ayE4PMKGqmzc+zeK4/Rz9+BPTSfm4EIOYGsTkdKD4vAQyc/EWVRDIyNU7Wce43zZHGzVKb9QtBWC/kZurz+l+/nn4y1/0ed4tLXDUUXDGGXDXXbClpU1bWzck6jk5+vrZQgghYkrs3xqPZ06nPtI9kOt/D0IvfpjDEXu2h56//WUmR+7byIKmL8DiZJeaZXSt+h3D0jdfDmzSyO6wlEgrPg+F91+PPWDjWyZx+GEqvWP34NDdnDS1W1jdsnmZ9rvfZHDARBc5tgCKAicc2MZbX2YCsHRNIpPHdmExaSgKTKnp5M3PMkP7/n5yOy9+mNOvWNe2Wrjv1QJ+d+VYpt48gtcXZG+ScJfne7j0+AY+uPMnHvrrCv6wZ4ck3ELECzWIyWnH3NpAMCmV3jG70TNmdwLZBTGfcAcC0Nam33s2GPTmaPvso3ckl4R7KxRF72D+yy9wxBEbtj/5pL4c2TvvbNgWDMLHH8Oll4J/3ffeWWcNjQnxQggRZ2SkeyC1toLPJ1+AO+ibJalM/V1L6HmTw0JFLhhtlXQHetmrIoefV6YCOz4qPP3BcjRNYVxlD5ef2EBmWmCL78t94V6sa1fyCxPJN9npOPFCQL8eKszy0WS3UJbn3WSfJoeFwixf6HlRjo9GhwWA0RW9vPhhNqce0kqCWeWdrzJo2Gh+9ZiKXpbVJ9LtNpCSqG7397G97uPJiQF+P7lDyseFiFeqirGrA0NvNwFbFr6K0fiz88EU3pUZBsL65mjBoF46PpSao4VNQQH8+996sn3JJXqpQEMDHH44nHsu7LcfTJ+u39HYWGn8NfkUQoihQJLugeLz6V+QaeHvpj3YNbebyUoPYPd0YDVaQtvHZuol46uU/o3uPHndUgqz/fgDcPfLRUx/sJwHr1qx2fuSF31O5nvPA6CaLARs2Wjmbc/93p5jpjhotFs446YRWM0qe47t5LOfN/xsmIyQlhygtcNMSqJ3i8foa/n4UVMcTN69mSyjBQXJtoWIK5qGscuJoaeTYFom7lG74s8uQDNbtr9vlElztDBTFH3pr4MOgnPOgffe07c//LD+a0v+7//0hP3YYyMXpxBCiO2Sr8KB4nDoE9gKC6MdSdxJtKj8al9Dk/Y9toQ08rNqaLRbmFClJ5ANbRYKNhpR7qvCbL38zmyCM37X8v/t3Xd4FVX+x/H3Lem9dwKE3iEqooKiCKtYsaAuIDZUQFFWV6zAWlBUxGVVbAsqKKwKLqtYEMUC/FSaFZGSCFLSe7ttfn+MREJCCSa5Sfi8nifP4505M/Od5Hi533vO+Q7n3NmjVhtbcQGJL0z/I5aLhpH9bhAut5kYGwbsyfMlIbr29ROiHOzK/mMoZ3fOHyPfFgtMHLGXiSP2ArB8bQQdkmquLa9yWvGrY9p3fauPGxiU2TzgPprfiog0C4aBrbQIa1kR7qAwKjr1wRmbhOHbvIeHVRytCSQnm9PKX3oJbr8dysoO3/6228x14M18+YGIyPFESXdj2bPH/AdP/+jVS5XbQVxcNl9sKyAtzSDYJ4izT8zjzVUxDOtfQEm5jfe/iuC5v9UeoT6c8korLreF0CAzE31vbSRdU8ur90+Z25Yh6QWM/XIy9iJz2nppr1OwXXAe3X4o53+ro7h4UB4ffRNOfKSj1tRygKEnFjDqwc5MuNhOdJiL/3wSwzknm2vTqxwWKp1WwoLcFJTYePHdeG69ZE/1sblFdiwWSIg0k/QjTR9X9XGR1sNaVoy9uAB3UCgVHXrhjEnC8A/wdliH5fGYiXZJCYSEqDhao7NY4IYbzF/2lVceup1hwK5d5rO+zzijycITEZHDU9LdGEpKzOoxKqBWL7mVBfxf1no6dOnFju0duPzkYNqHtMETU8jmjBDOuaMHFguMPSebTinmI7s+2RDGpxvCefD6XwGY+u82fL4pjNwiH8bN7Eigv5sPn/yRvGI7k/6ZhscDhmEhObaKR2/MrL72DxmB3By5iJANnwPgCglnzw0PgMXCtGt/5Z4X2vLC/+IJDnDz8A1/HHf/S6kM7lfImf2KSIl1MHHEXkY92AWAE7uUcPngHABKKmyMfaQTFov5mWjU0GwG9yuqPs+X34VyVr9CVR8XOY5Yy0uxFefjCQiion13HPEpGP6B3g7rsFwus6h2RYX5T1yfPhAfD4HNO+zWwzjK9/29exs3DhERqReLYRztO3jrVFxcTFhYGAUFBYQ3VJK8Ywds2mTOs2vmKiph4wbzA5O3itwYhsGWwh18n/8zBgY+nnD+/fJ1LJ62lUD/IxcV+7Pyi+3cNTuez39NxeowR7B33T6L0n6DGv3aYE4fv/qRTng8FrIKaq/bPHj6+JGY08srCXL7a023tAqtrU9bK8qwFeXh8Q/EEZeCM74NnsBgb4d1WAcXR0tNNddt+zb/pebNksfjITs7m9jYWKz1ecb6qlUwePCR2336qUa6pckcc38WaaYKCwuJiIigqKiI0Aaqz6WR7obmdptTu4Kb9weo5sSDwa6yPRgYtAlOJD2mF/Gj97A7x5eOv49oN6bIwApWuAZXJ9wFZ17S6Am3po+LHH8slRXYC3MxfP2oSu6AIyEVT3DzLrZZV3G0mBitnPKagQPNNd67d9c96m2xmPsHDmz62ERE5JCUdDe0vDxzoVt8vLcjaTFsFisD4vqRXZFHu5AULBYLA7qXNNn1Y95+noCMzQBUJaSSddXtjXKdo60+runjIq2LxVGJrSAX7HaqktvhjE/FHRLu7bAOqa7iaCkpEBmp4mheZ7PB00/DpZdSvV5pv/1/nNmz9a2IiEgzo6S7oWX9/nxpPSPlkMzp5NtxGe7qx4AF+wQR7BPU5LEEbl5P1HuvmHHZ7OwZ/zCGn3+DXqO+1cdFpHWwOKqwFeWCxYozvg2OhFTcoRHNNnNVcbQWYsQIeOst8/ndBz6nOznZTLj1uDARkWZHmWFDKi83i5eogNohVbkdfJ29ib3l2QAkBcUT4eedT3TWsmISn38Ay+8jBTmX3kRl2y4Ncm5NHxc5jrmc+BTkYBgGztgkM9kOa77P0NpfHK28HCIiVBytRRgxwnws2BdfmJ87EhLMKeUa4RYRaZaUdDek3FwoLTXn4EktuRX5rM3aQIW7EqvFSt/o7oT7emk9o2EQP28GPnnmzISyrunknTv6T51S08dFjnMuF/bCXPC4cEYl4Ehsiysiptkm2wcXR+veXcXRWhSbTcXSRERaCCXdDcXjMQubBAQ02w9Y3rJ/Ovn3+VswMAjxCWJAXDrhft4rIBS6ejlhX60AwB0Ywp4bp4P12EYINH1c5DjndmEvysPidOKMisWR0A5XZKxZfawZ2l8czWYzk+yUFBVHExERaUxKuhtKaak5P0+L32pZm7WB38rMZ4bur07uY/Ve1/PJ3k38KzOrX++99h5cUfUrfKfp4yKCx429KB9LZQWuyFgcSe1wRsY1y+z14OJobduaS4BVHE1ERKTxKeluKIZhjnargFotiYGx7CnPom90d9qHtMHizU94bheJc+/HVlkGQOFpwynpf7a56/fp4TmFPsSEO0nvXIrtgIEqTR8XEQA8HmzF+dgqynBGxOBI64EzKr5Zvv+rOJqIiIj3Nb9PCNLiGYZBhbuSQHsAAG1DU4gJiCbIJ8DLkUH0/+YTuPU7ABwxSWSNuROAFd+E88iCFLLy/1jMGBfp4J5Ru+iaWq7p4yJiJtslhVjLS3CHRVHWrivO6ASw+3g7slpcLnMKeUWFOZqt4mgiIiLeo6RbGtT+6uSFVcUMTRmEn81MYptDwu2/7Qeil74IgGGxsuemf+AJCGbFN+Hc9s/2HDwunZXvw6R/tgdqj8xr+rjIccQwsJUWYS0twh0STkWXfjhjEjF8ml/FsYOLo/XsCbGxKo4mIiLiTUq6pcEcWJ3cZrGSX1VIQmCst8MCwFpRRtJz92HxuAHIveg6Kjr1xu2BRxak/J5wH5w513yt6eMixxnDwFpWjK20EHdQGBUde+OMTcLw8/d2ZLWUlZllRVQcTUREpPlR0i1/WnOsTn6wuAVP4pv9GwDlHXqSe+F1gLlG+8Ap5Ydyyek5TBixV9PHRY4T1rISbCUFeAKCqUzriSM2GcPf+zN2DqTiaCIiIi2Dkm75U6rcDr7K3si+8hygeVQnP1jINysJ/3wZAG7/QPbc9CDYzPhyCo9uLebJ3UuUcIscB6wVpdiK8vH4B1LZrhvOuGQ8AUHeDquGuoqjJSZCaPP5nlNEREQO0HwyI2mRfsjfwr7yHGwWK32je9AuJMW71ckPYs/PIuHlh6tfZ42+E2dccvXr4AD3UZ0nJlwJt0hrZqksx16Yh+HnT2VqZ5xxKXiCaj8O0JtUHE1ERKRlUtItf0rPyC6UucrpFdm1WU0nB8DjIfGF6djKigEoPvEsigaeV707p9DO7DeTDnsKCwZxkebjw0Sk9bFUVmAvysOw+1CVnIYjoQ2e4Ob1PC0VRxMREWnZlHRLvVS5HWQU76JzeHssFgu+Nh8GJfT3dlh1ivzgdYJ+/BoAZ0Qse6+9p3qh484sX26Y2Yld2fsfAba/KNofo/SW37fdPWpXjed1i0jLZ3FUYivMBZudqsRUnPGpuEMjvB1WDSqOJiIi0joo6ZajllORz//9Xp3cbrXRIaytt0M6JL9ftxDz5jMAGBYLe26cXj169VNmADc+0ZG8InM9d3yUg2vP3cfL78Uf9JxuJ3eP2sXZJxY2efwi0jgsToeZbAPOuDY44tvgDms+lccOLI4WEKDiaCIiIq2Bkm45IsMw+LlwOz8cUJ082j/S22EdksVRSdKz92F1meuw888ZRXn3EwH46qdgJj7VgbJKc6ioQ1IFL/x9K/GRTq4cksP6LcHkFPoQE25OKdcIt0gr4XJiL8wFjwdXdAKOxLa4wqObTSZ7cHG0bt0gIUHF0URERFoDJd1yWLWrkyeRHtOzWVUnP1json/itycDgMrUTuRcejMAH34dzt+fa4fTZWbSfTuW8szkbYQHm8XUbFY4qavWbou0Ki4X9qJcLC4Xzqh4M9mOiAFr8/hGbX9xtMpKiIhQcTQREZHWqPlmTuJ1uZX5rN1nTidvrtXJDxa06UsiV/wHAI+PH7tvfhjDx5dFH0fz4KttMAwz9jP6FPLkxB0E+BmHO52ItFRuF/aifCyOKlxRcVQltsUVGddsku0Di6NFR6s4moiISGumpFsOyTAMKt2VhPgEMSAuvflVJz+IrSifxBf/Uf06+6rbqEpsxzNLEnh2aWL19osH5TL92l+xqxiRSOvjcWMvLsBSWYErPBpHx144o+KbTfWxg4ujtWljJt3NJDwRERFpBEq6pQaPYWD9fSQ7JiCKU+NPJCYgqllPJwfAMEh46R/Yi/MBKOlzGrmDL+Wh+W1Y/ElMdbPrz9vH7Zfvbi7LOEWkoXg82EoKsFaU4QqLxNGuG87oBLB7/73LMKC4GIqKVBxNRETkeOT9TyPSbORU5LEu5ztOjT+BUN8QABKD4rwc1dEJX/k2IZu+BMAVGsmvYx5g8r/SWLHuj0cA3XXVLq4+J9tbIYpIYzAMbCWFWMuKcYdGUtGlM87oBAwf78/TdrvNRFvF0URERI5vSroFwzD4pXg7Pxeb1cm/z9/CqfEneDuso+a7O4O415+qfr19zDSue/Fkvt5sfnFgtxk8fEMm55+a760QRaShGQa20iKspUW4g8Oo6NwXZ0wihq+ftyNTcTQRERGpQUn3ca7cUcXG0k3kOc3q5KnBSfSL6enlqOrB5STpufuwOqsA2D1wJBcuu44tO81PtwF+bp6+dQen9Sr2ZpQi0oCsZcXYiwtwB4VS0bE3ztgkDD9/b4dFZaWZbHs8EBOj4mgiIiJiUtJ9HPs1L4+3Nmyg1FmFFSv9Ypp/dfKDxbz1HP6/bgGgNLY9Z/w8l205ZsIdHuxi7h1b6ZVW7s0QRaSBWMtLsRfl4wkMpiKtB464ZAx/7w8fH1gcLT5exdFERESkJiXdx6nM3Fxe/eorDMMgyBrMSTH9iAluWQsNA39aR9Ty1wDw2OycV7aIbWXhACREVfHSXVtpl1DlxQhFpCFYK8qwu/KxuAOobNcNZ1wynsBgr8Z0cHG0du0gKUnF0URERKQ2Jd3HqTaRkSSHhxPiF0hcVU9CfVpWV7CWFpE49wEshvmc7fssD/NZ2YkAdEyu4IU7txIX6fRmiCLyJ1kqy7EX5uHx9cWRmIQR3QkjJNyrMR1YHC00VMXRRERE5MhaVqYlf8ruggLiw8KwWa1YrVZG9e+Py2lj08YWNixjGCTMm4FPgVmJ/BPLmTzqugOAfp1KeWbyNsKC3N6MUET+BEtVJbbCXLDbqUpujyMuBadPFTb/ULz1bnVwcbS+fc2p5AEBXgpIREREWgwl3ccBwzD4cvt2Pv35Z/q3a8ew7t0B8LXbcbu8HNwxCPvyPUK//hiAfCIYY7yCgZXB/Qp5csIO/H0NL0coIsfC4qjCVpQLVhvOhFQc8W1wh0ViGB6o9M7j/lQcTURERP4sJd2tXFlVFUs3bWJ7jlmdvNzhwDCMFlUs7UA+Wb8R9+rM6tfjeIHdJDNiUC7Trv0VuwoXibQ4FqcDe2EuBuCMScKRkIo7LMqri6NVHE1EREQaipLuVuzXvDze3rCBkqoq7FYr5/boQZ+UllWdvAa3i8Tn7sdWaVYj/zfX8DaXcsP5e7ntsj0qXiTS0rhc2AtzwePGGZ2AI7EtrvBoryXbdRVHS042p5Pr/UVERESOlZLuVsgwDL7cto1Pt2zBAKKDg7msXz9iW3iln4gl/yZw+/cAbCONSTzN3aN2MXqYd6adisgxcruwF+VhcTpxRsXhSGyHKyIGrFbvhOOGwkIoLVVxNBEREWl4SrpboeLKSlZv344B9EpKYnjPnvjaW/af2vj+e2KWvQSACxtjra8y9aYchg8o8HJkInLUPG7sRflYqipxRcZSldQOV0Ss1+ZsH1wcrV8/iItTcTQRERFpWC07E5M6hQUEcEHv3lS5XPRJTm6508l/l7+3kjZPTseGB4AZtvsY+7doTu2phFukRfB4sBXnY60sxxUejaNDT5yRceClLwNVHE1ERESakpLuVsAwDL7Yto3k8HDax8QA0C0hwctRNYzMfX6U3j+bU92ZAHxlHUDney6hZ6cS7wYmIkfm8WArKcBaXoo7LIrydt1wRseD3ccr4RxYHC0hAVJSVBxNREREGp+S7hbuwOrkQb6+TDjjDAJayXDNjxmBLH9kI/+uWgBAiSWE8r8/QM9ODi9HJiKHZRjYSgqxlhXjDomgoms6zugEDJ+mf29ScTQRERHxNiXdLVjm79XJS3+vTj6ka9dWk3Cv+SGEx2b78lXVhOptv/31LuK7x3gxKhE5LMPAVlqEtawId1AYFZ364IxJxPDzb/JQVBxNREREmgsl3S3Q/unkqw6sTp6eTmxIiLdDaxDvfxXB3c+m8IFnKBEUApB3wlAYOsy7gYnIIVnLirEXF+AOCqWiQy+cMUkY/k1fkezA4miRkdCpk4qjiYiIiHcp6W5hnG43i9etY3tODgC9k5M5t0ePFl+dfL+FK2J45LUU7jAeZzCrAHBExpF7/RTNBRVphqzlpdiK8/EEBFHRvjvOuGQ8AUFNHoeKo4mIiEhz1ToyteOI3WolyNcXu9XK8J496ZOS4u2QGoRhwJy3E5n73wT6soGHuM/cbrGw96bpeII0J1SkObFWlGErysfjH0Blamec8W3wBAY3eRylpeY0crtdxdFERESkeVLS3QIYhoHT7cbXbsdisTC8Z09O69CBmFYyndzlhgfnt+HNVTEEUM7rXIUvTgDyho+hvOsJXo5QRPazVFZgL8rD8PGlKjkNR0IqnuCm/VLs4OJo7dtDUpKKo4mIiEjzpKS7mSurqmLJxo3YrVauOPFELBYLvnZ7q0m4qxwW7ni2HSvXRwDwJH+jC1sAqGjbhZxLbvJmeCLyO4ujElthLtjsVCWm4kxoizskvEljcLvNRPvA4miJidBK3g5FRESklVLS3YwdXJ08p6SE2FZUere4zMbEp9JYt8X8xHyBdRk3e+YC4PH1Y8/ND3nteb4iYrI4qrAV5YLFijOuDY6EVNyhTTuk7HKZU8idThVHExERkZZHSXcz5DEMvjygOnlMcDCXtqLq5ADZBT6Me7wDv+wKBCDVdw+L7NdAubk/66+TcSS29V6AIsc7lxOfghxzeUtskplsh0U1abJ9YHG00FDo2NFMtn30XZyIiIi0IEq6m5n908l35OYC0Cc5mXNaUXVygMy9ftwwsyO7c/0AiAh2sDbhCgK25gNQ0m8QhYNHeDNEkeOXy4W9MBc8LpyR8TiS2uEKjwartclCOLA4WmKiuV7b44H4+CYNQ0RERKRBtJ5MrhUwDIPF69axq6Cg1VUn3++HHYHc+EQHCkrMoaqk6Co+PHU6Cf/9AgBXWBR7r7tf1ZBEmprbhb0oD4vTiSsylqrEdrgiY5ssy91fHK2wEAIDaxZHMwzIzm6SMEREREQanJLuZsRisTCsWzfe/f57Lu7bt1VNJwdY830It/4zjfJK81k+nVLKWTDqA7o+Mau6zZ5xU831oiLSNDxu7EX5WKoqcUXE4EhqhzMyrsmeueV2m4n2/uJo3bvXLo5mGE0SioiIiEijUNLtZWVVVewuLKRTXBwASRERjBs4EEsrG+ldvjaCKc+3xeU2R83SO5fw7MTN9Jw5BauzCoD8s0dS1usUb4YpcvzweLAV52OtLMcVFoUjrQfOqHhzTncTcDrN9doOhzmareJoIiIi0lop6faizNxc3t64kQqnk+tPPZX4sDCAVpdwv/ZhDDMWtKl+fVZ6AY+Pz6DNW3Pw37UVgMqk9mRfcYu3QhQ5fhgGtuICrBWluEMjKW/XFWd0QpM9KeDA4mgxMZCaCrGxKo4mIiIirZeSbi+oqzq5rRVWBzIMePqtRF5YllC97bIzcrh/7E5CN39F1PsLAPDYfdgz/mEMX39vhSrS+hkGttIirKVFuEPCqejcF2d0AoavX5Nc/uDiaMnJEB3dZLPYRURERLxGSXcTK62qYmkrr04O4HLD9HmpvP1ZdPW2my/aw8QRe7GXFpL4wrTq7TmXT6SqTUcvRClyfLCWFmErLcQdFEZFx944Y5Mw/Br/Sy7DgKIi8ycoqGZxtFY2oUdERETkkFpXpgdw2WUweTIMGGDOX5w0CZYvNz/h3XYbTJxY52HW7dvhllsgNxfCwmD+fLOiT2UlXHEF/PSTudgwNhaeew46dDhyLPn5MHUq/PYb+Piw9+abed3lorSqCh+bjXN79KiuTj7juq3clnUPAT4ujLQO/D3iRZZ8nYzFYjD7pDc4b/108346dDDPGRwMeXlUTvgba7PTeC7gNjKiTmT+/TvovvQh3F+sYXeuLwvDJ7A44ibKK63s2O1H9kffEhnmNuPbuRPfB6bRN6sQIyiYvTdOxZGcRl6RnaVPbOPq3x7BFwdRsTZKxt9FVWqnWre4amMYj7+RjNsDnVIqmHnFBtLm30/x1iymVQWQzbN8wUA6t6ngmnOzsGBgfeE5Ti14l7UMwOLrS8TKtwj4ZRN7x03FExAMQPDGL4h9YzYWj4fKlA7V+2xFeaTMup3MB/4NttbXfUUakrWsBFtxPp7AECrTeuKITcLwD2z06+4vjlZWZhZE69EDEhJqFkcTEREROV60rjnNX39tJroDBpivFywwk+VffjH3Pf44/PhjnYcG3H47jBtntr3rLhg79o+d48bBli3w7bdw4YVw/fVHF8+cOeanzaVLYepUImfMoLy8nJjgYG447bTqhHvdOoObfryVgH89DkuXsqUsmRPWPs0vS37g67kb6P/OFLZOmmOeJzoaXn7ZPH9UFGvyOtHX9wf+814Id43Zx3/+9n+QkYHtnbdp88GL3G17nE0Pvsu4i3M555SiPxJugEcewX3BxWy8fwlZfxlD4gvTAXh+YRDTd19P0IN38d39/+H64lkkPndfrdsrq7Ry/0upzLltGx888SOx4U6KZr3EG3tOJ6VqO9cwj9e5iqdu+oX0TqU8904CYZ8vo8OmtzmDT6nCj513/pPtTyzFFR5N9DvmfVkqy0l46UF+u+3JWvvcYVFUdOxN2JfvHd3fQOQ4ZK0oxWffTizOKirbdaOs9ylUtenY6Am30wlZWbBnD/j5Qd++cOqpZpE0JdwiIiJyvGpdSffzz8NVV/3xevFiuOEGc9FgZCSMHAlvvFHrsBjAvmkTjBplbrjkEti1C7ZtA39/OPfcP+ZCnnwyZGYeXTwff2yeC6B7d3zj47nEMLj+tNOIOeAT6DcvfUtZm67Qti0As10TOL/8TTPsH74gP74br/xwgtn4ssvgww8ByM63U1nsIMynwgz7rEIG5ixl38DLzHsOC4Ozz4YPP+Tl/0Zx3YW5f8SWnw+bN+Meeg4AReln4ZOfhU/WLjLW5WEND8WRnEbP9uVkxPbHmpOFf+bPNW7vi29D6ZpaTvtEs/r40BMLOGHP/5iaPwmADdYT8EuI5OKwlVwxJIflq8OIf+0JAG7gJTb69qeiSz8ACoZcRuha876Cv11DZWpnHIlta+0DKDp5GBGfLDm6v4HIccRSWY7P3p1YKyuobNOJsl6nUNWuC56AoEa9bmUl7N5tJtwREXDiiXDKKeZbmqqRi4iIyPGudSXdq1ZB//5/vN650yyNu1/btua2g6QAnri4Px6VY7FAmzZ1tuXpp83R7iMpLMRwuViwfTtOtzm6bElMpJvFUmv9dsEvuQS2i6t+vb6wAwEl2eBywb59uGIS2bnP19yZmGhOgXe52JXlSw9+xJKbA6WlWCzQ1vorv9n+qBROYiLZm3MpKLFz3mlFf2zPyoKoqBr37IyKo2pXNj97OuFbUUTAL98CMML2Dr6OMnxy9tSIe2+eLwnRDgAy9/rx2NwQfHCSRTwBfm7O7FeIX0ocPnn7SAovJ6fIF0+V2T6hfQCbXR0prTC7oDM6EXthLrhd+OTtwxkdX32dA/cBVLbrgt+ubVgrSo/8dxA5DliqKvHJ+g1bWQlVKWmU9hpAVVp3PEGNO7xcWmquniksNNdqDxgAJ51k/reqkYuIiIiYWtei2N9+Mx/02lgeecQc/V658rDNPIbB2u3b6e/xsD0nh9XbtnFG586HbF9UaiPA33NMIcW7d0NYiJmIBwfX2WbbLn/GnJt31I/fLbaEsfuWx4j5zzNYq8rpV34qOeEdMQ5RZvj7HYHc9EQHLCXFACRGVXHugHyKyuxQZraJenc+YI5qO+JSKE8fiO9vVWQX+BAcUFWfWwabHXdQCPaCXBwBdd+zyPHA4qjEVpgLNjtViak441Nxh0Y06jVVHE1ERESkflpX0h0YaM5z3K9NG/j11z/WeGdmmtsOsguwZmWZI8t2u/mpcufOmm2feAKWLDGnjAceel1kaVUVS77/nozcXE60WukfGsopaWnmzj17ID6+1jFZvsmwd2/16/TwbVSExBJot0N8PPacTbRJd/xxjuhosNtJiXNQRiA+DgcWPz8MAzI9qfRw7wTMQm+OnXv5cl8a1x44tRzMLyfy8sx7xrxnn7ws/FJisVsNfk08mfL7zCnt0x9ox5qidhQlta9xioQoB8v/L5Kxj3SiosoGROHCzluTv+T/9rZlyWfR+JTtwVpeRum7n5HAXmxW2HXzg/jk7iPR/Qn4GgD45O7BFR4NNjvOqHiCfviq+joH7tvP6nTgaaJHHYk0Nxanw0y2AWdsMo6EtrjDIhs1691fHK20FEJDVRxNRERE5Gi1runlvXqZBc/2u+wyePFF89Nifr65xnvkyFqH5QDuXr3MwmsAb79tPkR2f4XyWbPMteArVkB4eM2D774b/vUvADJKSnh+zRoycnPxsdkoPe00/rJ5szmd/McfIScH0tNrXX9f50HYfvm5eq34JPszvBtwqRl299OI2vcjY7qvMxu/+SYMHQpAbKSLPUEdcToMiIvj7ZXhfBl9IfFfvGnec1ERjvc+5of2F9Klbc3R5Ltf78HuyJ7YPnofgLD1K3FGxuKMS2HYSQW8v9wcef9+RyCj9j5OZY90nHFm4beYxf8iYsViSius/JgR+HvCDbHhDtYnnEeHdYsZ2KsYv+0/YcnJIfLDN3iOm7iCReSMGEdlWg92th1Ib89GUit/ASDi4zcpPtm8r7JeA/DP/BnfPZm19gHYivIwLBZckY04q0GkOXI5sefuxVaQgysqnvKeJ1PRpR/u8KhGS7gPLo7Wr5+Ko4mIiIjUR+sa6b70UrPI2JAh5uvRo+Gbb6BjR/MD6eTJ0LOnuW/ZMvNn1iwAyp96itBbbzWnkIeGwrx5ZrvffoO//c2cQzl4sLnNzw+++n0k9ttvIT2db3/6if/+8gsGEBMczGXp6USedBI88ABcfLG5wPHBB/9YQz13rjlifemlDD/bxcKIWVzzt7+B202X9mm8NOAlpoxIwQI8df4MLvjnLfCUm71hXXgk9XnmUABAcr9Ydn5dxrmX9iQ0yM28JwfA0pUwYgQALwVPYvAVcUAefPYZfP453H8/3/4SyPeX/4Oh//0bfbPnYQQGsffGqQBMvmI3BffPxuej/6M/LlxderDvhgcAmPN2ArdtzGRd+/488EW76l99gJ+bbm3LCf7rDQS8ch+91lzIf/z9uKF0DmtL0unBD7wWcSulQWahu89+SeDzbrO4/Z9/w+J2U5Wcxp4bzerpnoAg9l5/H8mza+8DCP5uLaXpZ4C1dX1nJHJILhf2olwsLhfOqDgcie1wRcQ06v8DlZXmZBiAmBhz4k9srNZqi4iIiNSXxTAMw9tBNJjSUrNk7tq15mLDo1BcXExYWBgFBQWEHzyKfSRut1nN/KuvKNqzh+fnz6dzfDzn9OxZq1jaYcMut3LKtZ1ZO28LQQH1XNt9zTXm4sqFC4+6TLDbDSdf04Wv5v9MlQM2bjBnzPsdxWxtw+0mcNI42hdtwPh9osRlg3N4YOxObAd9/g9d+yFJz95rXjMgiIyH38AZkwjAqAc7Mf3anaQlVVJfqQ9ez95r78WR1O7IjY9DBgZltkqC3P5Y0CLbFs3jxl6Yh8VRhSsqjqrEtrgiYs2nEzSS0lJzGvnvq1tISTFrLjbiJY/I4/GQnZ1NbGwsVn3ZJq2A+rS0JurP0toUFhYSERFBUVERoaGhDXLO1jXSHRwMTz0FGRnmgsNGVlBcTMQ33wAQFhLCzd26EZKUxFFXLPtdcKCHpyb/RsZuX3p0qEcSmpdnTpePjDSf17N/OvwR2Gzwzas/H7nhQVxumPbv9iwp2lS97eaL9jBxxN5aM1vtufuInz+j+vW+q6dUJ9y5RXauOCvnmBJuW1EeBWddqoRbWjePG3txAZbKclzhMTg69sIZGVfv95ajpeJoIiIiIo2ndSXdAGed1eiX8Hg8fPHFF3z22WeMHDmSzr9XJg/x9T3mc551Ukn9D4qKgr/85ZivWR8VVRbueKY9n24MB8BiMbh39C6uOjundmOPm8S5D2ArNx/pVTTgLxSfek717ugwF+edUnBMcbjDoig+pWnuWaTJeTzYSgqwlpfiCo/C0a6b+fg8e+PM6T6wOFpYmIqjiYiIiDSG1pd0N7LS0lKWLFlCRkYGAJmZmdVJd2tVVGZjwqwObPjFfDyXj93DYzdl8Jf+hXW2j3rvNYK2bADAGRXPvqvvaqpQRVomw8BWUoi1rBh3aCQVXdNxRidg+Bz7F3mH43SatSUdDnM0u3Nnc732Ua5QEREREZF6UNJdDxkZGSxZsoTS0lJ8fHwYPnw4vXv39nZYjSor34dxj3dk62/mp/EgfzdzbtvOyd3rHpn33/ETMW8/B4BhsbL75gfxBGnYTKROhoGttAhraRHu4DAqOvXBGZuE0UiPw1NxNBEREZGmp6T7KHg8Hj7//HM+++wzAGJjY7n00kuJiYnxcmSNa8ceP26Y2ZG9eWYCEBXq5Pk7t9KtbUWd7S2VFSQ+dx8WtxuAvPOvpqJz3yaLV6QlsZYVYy8uwB0USkXHXjhjkjD8G2eoubQUCgrM5DopySyOFh2tBwCIiIiINAUl3UchMzOzOuHu27cv55xzDj6tfGjou+2B3PRERwpLzS6SElvFC3/fSmpc1SGPiXt9Fn77dgJQ0a4bORff2CSxirQk1vJSbMX5eAKCqGjfHUd8CoZ/YINf5+DiaGlpkJwM4eEqjiYiIiLSlJR0H4X27dszYMAA4uLiWv10coAvvwtl0tPtqXCYzwjq3KacF+7cSky465DHBK9fRcSnSwHw+Pqz5+YHG63SskhLZK0ow1aUh8c/kMrUzjjj2+AJDG7w67jd5qh2WZmKo4mIiIg0B8qK6uDxeFizZg19+vQhONj8UDx06FAvR9U0/rc6kntfbIvLbQ6FndS1hDm3bSMk8NDPD7cX5pLw8kPVr7NG/Q1HQmqjxyrSElgqK7AX5mL4+lGV0hFHfBs8wQ3zzMcD7S+OVlVlPkWwSxeIiwN//wa/lIiIiIjUQ7Nc0ffMM8/Qtm1b/P396d+/P19//fVh27/55pt06dIFf39/evbsyfLly4/52qWlpSxYsICVK1eyZMkSDMM48kFuN3zxBXz2Gaxfb75uAdxu+HxDMCvWR7Du52DmL4/lrrntqhPus08o4Pk7ttadcHvcBG5eR+jq90meNRl7SSEAJelnUHjGRU13EyLNlKWqEnvWb9jKiqhKbkdZrwFUdujR4Al3ZSXs2QPZ2WYl8pNOglNOgdRUJdwiIiIizUGzG+levHgxkydPZu7cufTv35/Zs2czbNgwtmzZQmxsbK32a9as4corr2TGjBmcd955vP7661x00UVs2LCBHj161OvaO3bsYMmSJZSVleHj40OfPn2wHGnx45IlMGkS/PbbH9tiY+GOO+DMM+t1/aa05JNwJj2Rwm/ZdT+S6PIzc7j/6p3Y6vhaJuSbT4hb8AQ++dk1trsCQ9h73X1aMCrHNYujCltRLlisOOPb4EhIxR0W2eDXObA4WmKiiqOJiIiINFcW46iGcptO//79OfHEE/nXv/4FmFO9U1JSuOWWW5gyZUqt9iNHjqSsrIx33323etvJJ59Mnz59mDt37hGvV1xcTFhYGP/73/9Yv349YFYnv+yyy4iOjj78wUuWwKWXmhWL6jJzZrNMvJd8Es6lf2+PGXXtBHnYSfnMmphRZ+4c8s0nJP3z73UeaQC7b51JyYnN756PJwYGZbZKgtz+WOr4+0rjsDgd5jRyw8AZm/R7sh3VoF9C7S+OVlwMgYFmsn08FEfzeDxkZ2cTGxuLVd8qSCugPi2tifqztDaFhYVERERQVFREaGjDzFBsViPdDoeD9evXc/fdd1dvs1qtDBkyhLVr19Z5zNq1a5k8eXKNbcOGDeOdd96p17VXr16Nv7//0Vcnd7vNEe7DfWdx//3wwQfN6tOwYYDv6jAWH2Zlgf9GD0lzimqna4ZB8LergbpSdVPcgicpST8drLYGiVek2XO5sBfmgseFMyoBR2JbXBExDfr/fV3F0eLjVRxNREREpCVoVkl3bm4ubrebuLi4Gtvj4uL4+eef6zxm3759dbbft29fne2rqqqoqvrjsVdFRUWA+S3dWWedRY8ePSgrKztysF98gfXAKeV1Xww++eTI52pig47UwAl8U/eu0iMdm59F2ccfUNK2e73jkoZhAFXBHkpKrRrnbmQWlxuLx40jLJrK2I44gqKh2ArFRQ12DbfbLJIWHg4dO0JMjLlW2+2GwsIGu0yz5vF4KC4uxtfXV6Mo0iqoT0trov4srU3h7x+wGnJCeLNKupvCjBkzmD59eq3tM2fOZObMmV6IqBV6baq3IxARERERETlmeXl5hIWFNci5mlXSHR0djc1mIysrq8b2rKws4uPj6zwmPj6+Xu3vvvvuGtPRCwsLSU1NZefOnQ32SxXxpuLiYlJSUti1a1eDrUMR8Sb1aWlt1KelNVF/ltamqKiINm3aEBnZcIVwm1XS7evrS3p6OitXruSiiy4CzCkrK1euZOLEiXUeM2DAAFauXMltt91WvW3FihUMGDCgzvZ+fn74+fnV2h4WFqY3CmlVQkND1aelVVGfltZGfVpaE/VnaW0acrlEs0q6ASZPnszVV1/NCSecwEknncTs2bMpKyvjmmuuAWDMmDEkJSUxY8YMACZNmsTpp5/Ok08+yfDhw1m0aBHr1q3jhRde8OZtiIiIiIiIiDS/pHvkyJHk5OTwwAMPsG/fPvr06cMHH3xQXSxt586dNb51OOWUU3j99de57777uOeee+jYsSPvvPNOvZ/RLSIiIiIiItLQml3SDTBx4sRDTidftWpVrW2XXXYZl1122TFdy8/Pj6lTp9Y55VykJVKfltZGfVpaG/VpaU3Un6W1aYw+bTEasha6iIiIiIiIiFTTw/REREREREREGomSbhEREREREZFGoqRbREREREREpJEcF0n3M888Q9u2bfH396d///58/fXXh23/5ptv0qVLF/z9/enZsyfLly9vokhFjk59+vSLL77IwIEDiYiIICIigiFDhhzx/wGRplbf9+n9Fi1ahMVi4aKLLmrcAEXqob79ubCwkAkTJpCQkICfnx+dOnXSZw9pVurbp2fPnk3nzp0JCAggJSWF22+/ncrKyiaKVuTwPv/8c84//3wSExOxWCy88847Rzxm1apV9OvXDz8/Pzp06MD8+fPrdc1Wn3QvXryYyZMnM3XqVDZs2EDv3r0ZNmwY2dnZdbZfs2YNV155Jddddx0bN27koosu4qKLLuKHH35o4shF6lbfPr1q1SquvPJKPv30U9auXUtKSgpDhw5l9+7dTRy5SN3q26f3y8zM5I477mDgwIFNFKnIkdW3PzscDs4++2wyMzN566232LJlCy+++CJJSUlNHLlI3erbp19//XWmTJnC1KlT2bx5My+//DKLFy/mnnvuaeLIRepWVlZG7969eeaZZ46qfUZGBsOHD2fw4MFs2rSJ2267jeuvv54PP/zw6C9qtHInnXSSMWHChOrXbrfbSExMNGbMmFFn+8svv9wYPnx4jW39+/c3brzxxkaNU+Ro1bdPH8zlchkhISHGK6+80lghitTLsfRpl8tlnHLKKcZLL71kXH311caFF17YBJGKHFl9+/Nzzz1ntG/f3nA4HE0Voki91LdPT5gwwTjzzDNrbJs8ebJx6qmnNmqcIscCMJYuXXrYNn//+9+N7t2719g2cuRIY9iwYUd9nVY90u1wOFi/fj1Dhgyp3ma1WhkyZAhr166t85i1a9fWaA8wbNiwQ7YXaUrH0qcPVl5ejtPpJDIysrHCFDlqx9qn//GPfxAbG8t1113XFGGKHJVj6c/Lli1jwIABTJgwgbi4OHr06MEjjzyC2+1uqrBFDulY+vQpp5zC+vXrq6eg79ixg+XLl3Puuec2ScwiDa0h8kN7QwfVnOTm5uJ2u4mLi6uxPS4ujp9//rnOY/bt21dn+3379jVanCJH61j69MHuuusuEhMTa715iHjDsfTpL7/8kpdffplNmzY1QYQiR+9Y+vOOHTv45JNP+Otf/8ry5cvZtm0b48ePx+l0MnXq1KYIW+SQjqVPX3XVVeTm5nLaaadhGAYul4ubbrpJ08ulxTpUflhcXExFRQUBAQFHPEerHukWkZoeffRRFi1axNKlS/H39/d2OCL1VlJSwujRo3nxxReJjo72djgif5rH4yE2NpYXXniB9PR0Ro4cyb333svcuXO9HZrIMVm1ahWPPPIIzz77LBs2bGDJkiW89957PPjgg94OTcRrWvVId3R0NDabjaysrBrbs7KyiI+Pr/OY+Pj4erUXaUrH0qf3e+KJJ3j00Uf5+OOP6dWrV2OGKXLU6tunt2/fTmZmJueff371No/HA4DdbmfLli2kpaU1btAih3As79EJCQn4+Phgs9mqt3Xt2pV9+/bhcDjw9fVt1JhFDudY+vT999/P6NGjuf766wHo2bMnZWVljBs3jnvvvRerVWN+0rIcKj8MDQ09qlFuaOUj3b6+vqSnp7Ny5crqbR6Ph5UrVzJgwIA6jxkwYECN9gArVqw4ZHuRpnQsfRpg5syZPPjgg3zwwQeccMIJTRGqyFGpb5/u0qUL33//PZs2bar+ueCCC6oriqakpDRl+CI1HMt79Kmnnsq2bduqvzwC+OWXX0hISFDCLV53LH26vLy8VmK9/0sls26VSMvSIPlh/Wu8tSyLFi0y/Pz8jPnz5xs//fSTMW7cOCM8PNzYt2+fYRiGMXr0aGPKlCnV7VevXm3Y7XbjiSeeMDZv3mxMnTrV8PHxMb7//ntv3YJIDfXt048++qjh6+trvPXWW8bevXurf0pKSrx1CyI11LdPH0zVy6U5qW9/3rlzpxESEmJMnDjR2LJli/Huu+8asbGxxkMPPeStWxCpob59eurUqUZISIjxxhtvGDt27DA++ugjIy0tzbj88su9dQsiNZSUlBgbN240Nm7caADGrFmzjI0bNxq//vqrYRiGMWXKFGP06NHV7Xfs2GEEBgYad955p7F582bjmWeeMWw2m/HBBx8c9TVbfdJtGIYxZ84co02bNoavr69x0kknGf/3f/9Xve/00083rr766hrt//Of/xidOnUyfH19je7duxvvvfdeE0cscnj16dOpqakGUOtn6tSpTR+4yCHU9336QEq6pbmpb39es2aN0b9/f8PPz89o37698fDDDxsul6uJoxY5tPr0aafTaUybNs1IS0sz/P39jZSUFGP8+PFGQUFB0wcuUodPP/20zs/G+/vx1VdfbZx++um1junTp4/h6+trtG/f3pg3b169rmkxDM3zEBEREREREWkMrXpNt4iIiIiIiIg3KekWERERERERaSRKukVEREREREQaiZJuERERERERkUaipFtERERERESkkSjpFhEREREREWkkSrpFREREREREGomSbhEREREREZFGoqRbRESknqZNm4bFYvF2GEd0xhlncMYZZ3g7jGr7f2+5ubkNds62bdty3nnnHbHdqlWrsFgsrFq1qnrb2LFjadu2bY12FouFadOmNVh8IiIiSrpFRKTVePbZZ7FYLPTv39/bobQobdu2xWKxVP/ExsYycOBAli5d6u3QvG7NmjVMmzaNwsJCb4ciIiItlJJuERFpNRYuXEjbtm35+uuv2bZtW6Nd57777qOioqLRzu8Nffr04bXXXuO1117jjjvuYM+ePYwYMYK5c+d6O7QGMWjQICoqKhg0aNBh21VUVHDfffdVv16zZg3Tp09X0i0iIsdMSbeIiLQKGRkZrFmzhlmzZhETE8PChQsb7Vp2ux1/f/9GO783JCUlMWrUKEaNGsXf//53Vq9eTVBQEE899dQhj3G5XDgcjiaM8thZrVb8/f2xWg//0cff3x+73d5EUYmIyPFASbeIiLQKCxcuJCIiguHDh3PppZceMuletGgR6enphISEEBoaSs+ePXn66aer9zudTqZPn07Hjh3x9/cnKiqK0047jRUrVlS3qWtNd0VFBbfeeivR0dGEhIRwwQUXsHv37lprhPcfu23bNsaOHUt4eDhhYWFcc801lJeX14p3wYIFpKenExAQQGRkJFdccQW7du2q1e6FF14gLS2NgIAATjrpJL744ov6/gpriI+Pp2vXrmRkZACQmZmJxWLhiSeeYPbs2aSlpeHn58dPP/0EwCeffMLAgQMJCgoiPDycCy+8kM2bN9d57tzcXC6//HJCQ0OJiopi0qRJVFZW1mgzb948zjzzTGJjY/Hz86Nbt24899xzh4z3o48+ok+fPvj7+9OtWzeWLFlSY39da7rrcuDfa9q0adx5550AtGvXrnr6fWZmJqeffjq9e/eu8xydO3dm2LBhh72OiIgcP5R0i4hIq7Bw4UJGjBiBr68vV155JVu3buWbb76p0WbFihVceeWVRERE8Nhjj/Hoo49yxhlnsHr16uo206ZNY/r06QwePJh//etf3HvvvbRp04YNGzYc9vpjx45lzpw5nHvuuTz22GMEBAQwfPjwQ7a//PLLKSkpYcaMGVx++eXMnz+f6dOn12jz8MMPM2bMGDp27MisWbO47bbbWLlyJYMGDaox3fnll1/mxhtvJD4+npkzZ3LqqadywQUX1JmcHy2n08muXbuIioqqsX3evHnMmTOHcePG8eSTTxIZGcnHH3/MsGHDyM7OZtq0aUyePJk1a9Zw6qmnkpmZWee9V1ZWMmPGDM4991z++c9/Mm7cuBptnnvuOVJTU7nnnnt48sknSUlJYfz48TzzzDO1zrd161ZGjhzJOeecw4wZM7Db7Vx22WU1vig5FiNGjODKK68E4Kmnnqqefh8TE8Po0aP57rvv+OGHH2oc88033/DLL78watSoP3VtERFpRQwREZEWbt26dQZgrFixwjAMw/B4PEZycrIxadKkGu0mTZpkhIaGGi6X65Dn6t27tzF8+PDDXm/q1KnGgf+Erl+/3gCM2267rUa7sWPHGoAxderUWsdee+21NdpefPHFRlRUVPXrzMxMw2azGQ8//HCNdt9//71ht9urtzscDiM2Ntbo06ePUVVVVd3uhRdeMADj9NNPP+y9GIZhpKamGkOHDjVycnKMnJwc49tvvzWuuOIKAzBuueUWwzAMIyMjwwCM0NBQIzs7u8bxffr0MWJjY428vLzqbd9++61htVqNMWPG1Lr3Cy64oMbx48ePNwDj22+/rd5WXl5eK85hw4YZ7du3rxU7YLz99tvV24qKioyEhASjb9++1ds+/fRTAzA+/fTT6m1XX321kZqaWuN8B/+9Hn/8cQMwMjIyarQrLCw0/P39jbvuuqvG9ltvvdUICgoySktLa8UvIiLHJ410i4hIi7dw4ULi4uIYPHgwYE4RHjlyJIsWLcLtdle3Cw8Pp6ys7LAjoOHh4fz4449s3br1qK//wQcfADB+/Pga22+55ZZDHnPTTTfVeD1w4EDy8vIoLi4GYMmSJXg8Hi6//HJyc3Orf+Lj4+nYsSOffvopAOvWrSM7O5ubbroJX1/f6vONHTuWsLCwo76Hjz76iJiYGGJiYujduzdvvvkmo0eP5rHHHqvR7pJLLiEmJqb69d69e9m0aRNjx44lMjKyenuvXr04++yzWb58ea1rTZgwocbr/b+nA9sGBARU/3dRURG5ubmcfvrp7Nixg6KiohrHJyYmcvHFF1e/Dg0NZcyYMWzcuJF9+/Yd9e+gPsLCwrjwwgt54403MAwDALfbzeLFi7nooosICgpqlOuKiEjLo6RbRERaNLfbzaJFixg8eDAZGRls27aNbdu20b9/f7Kysli5cmV12/Hjx9OpUyfOOecckpOTufbaa6sT5v3+8Y9/UFhYSKdOnejZsyd33nkn33333WFj+PXXX7FarbRr167G9g4dOhzymDZt2tR4HRERAUBBQQFgTpk2DIOOHTtWJ8P7fzZv3kx2dnb1tQE6duxY43w+Pj60b9/+sHEfqH///qxYsYKPP/6YNWvWkJuby6uvvloj+QVq3eP+63fu3LnWObt27Upubi5lZWU1th8ca1paGlartcZU9NWrVzNkyJDqNeIxMTHcc889ALWS7g4dOtRaY9+pUyeAOqe3N5QxY8awc+fO6vXzH3/8MVlZWYwePbrRrikiIi2PynOKiEiL9sknn7B3714WLVrEokWLau1fuHAhQ4cOBSA2NpZNmzbx4Ycf8v777/P+++8zb948xowZwyuvvAKYj5bavn07//3vf/noo4946aWXeOqpp5g7dy7XX399g8Vts9nq3L5/1NTj8WCxWHj//ffrbBscHNxgsQBER0czZMiQI7Y7OAlvCAcnzNu3b+ess86iS5cuzJo1i5SUFHx9fVm+fDlPPfUUHo+nwWM4FsOGDSMuLo4FCxYwaNAgFixYQHx8/FH9HkVE5PihpFtERFq0hQsXEhsbW2eBrSVLlrB06VLmzp1bnSz6+vpy/vnnc/755+PxeBg/fjzPP/88999/f/XIdGRkJNdccw3XXHMNpaWlDBo0iGnTph0y6U5NTcXj8ZCRkVFjFPfPPCs8LS0NwzBo165d9ajtoa4N5sj4mWeeWb3d6XSSkZFxyArbDWX/9bds2VJr388//0x0dHStqdZbt26tMWK+bds2PB4Pbdu2BeB///sfVVVVLFu2rMaMgP1T6g+2bds2DMOokbz/8ssvANXnPFYHfyFwIJvNxlVXXcX8+fN57LHHeOedd7jhhhsO+YWKiIgcnzS9XEREWqyKigqWLFnCeeedx6WXXlrrZ+LEiZSUlLBs2TIA8vLyahxvtVrp1asXAFVVVXW2CQ4OpkOHDtX767L/8VDPPvtsje1z5sw55nsbMWIENpuN6dOnV49+72cYRnWcJ5xwAjExMcydO7fGM7Pnz59fo8J5Y0lISKBPnz688sorNa73ww8/8NFHH3HuuefWOubgL0j2/57OOecc4I9ZAAfed1FREfPmzaszhj179rB06dLq18XFxbz66qv06dOH+Pj4Y7ux3+3/wuBQv8vRo0dTUFDAjTfeSGlpqaqWi4hILRrpFhGRFmvZsmWUlJRwwQUX1Ln/5JNPJiYmhoULFzJy5Eiuv/568vPzOfPMM0lOTubXX39lzpw59OnTh65duwLQrVs3zjjjDNLT04mMjGTdunW89dZbTJw48ZBxpKenc8kllzB79mzy8vI4+eST+eyzz6pHWw83WnooaWlpPPTQQ9x9991kZmZy0UUXERISQkZGBkuXLmXcuHHccccd+Pj48NBDD3HjjTdy5plnMnLkSDIyMpg3b1691nT/GY8//jjnnHMOAwYM4LrrrqOiooI5c+YQFhZW4xnl+2VkZHDBBRfwl7/8hbVr17JgwQKuuuqq6lH5oUOHVs9I2J/Mvvjii8TGxrJ3795a5+vUqRPXXXcd33zzDXFxcfz73/8mKyvrkEl6faSnpwNw7733csUVV+Dj48P5559fnYz37duXHj168Oabb9K1a1f69ev3p68pIiKti0a6RUSkxVq4cCH+/v6cffbZde63Wq0MHz6cDz74gLy8PEaNGoW/vz/PPvss48eP55VXXmHkyJG8//77WK3mP4m33normZmZzJgxg1tvvZXPPvuMhx56iCeffPKwsbz66qtMmDCB9957j7vuuguHw8HixYsB8Pf3P6b7mzJlCm+//TZWq5Xp06dzxx13sGzZMoYOHVrji4Zx48bx7LPPsmfPHu68806++OILli1bRkpKyjFdt76GDBnCBx98QFRUFA888ABPPPEEJ598MqtXr65VeA1g8eLF+Pn5MWXKFN577z0mTpzIyy+/XL2/c+fOvPXWW1gsFu644w7mzp3LuHHjmDRpUp3X79ixI4sXL2b58uVMmTIFp9PJ4sWLq2cg/BknnngiDz74IN9++y1jx47lyiuvJCcnp0abMWPGAKiAmoiI1MliHDxnTURERBrEpk2b6Nu3LwsWLOCvf/2rt8ORRvL0009z++23k5mZWasqvYiIiEa6RUREGkBFRUWtbbNnz8ZqtTJo0CAvRCRNwTAMXn75ZU4//XQl3CIiUiet6RYREWkAM2fOZP369QwePBi73V79SLJx48Y12TRvaTplZWUsW7aMTz/9lO+//57//ve/3g5JRESaKU0vFxERaQArVqxg+vTp/PTTT5SWltKmTRtGjx7Nvffei92u77hbm8zMTNq1a0d4eDjjx4/n4Ycf9nZIIiLSTCnpFhEREREREWkkWtMtIiIiIiIi0kiUdIuIiIiIiIg0EiXdIiIiIiIiIo1ESbeIiIiIiIhII1HSLSIiIiIiItJIlHSLiIiIiIiINBIl3SIiIiIiIiKNREm3iIiIiIiISCNR0i0iIiIiIiLSSP4fFeeR3Q1SZGMAAAAASUVORK5CYII=", + "image/png": "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", "text/plain": [ "
" ] @@ -13475,17 +13466,20 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "# Map resolution to 0 and 1\n", - "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})" + "df_top_bot_pro_forecasts_all_binary['resolution'] = df_top_bot_pro_forecasts_all_binary['resolution'].map({'yes': 1, 'no': 0})\n", + "df_top_bot_pro_forecasts_all_binary = df_top_bot_pro_forecasts_all_binary[\n", + " df_top_bot_pro_forecasts_all_binary['resolution'].notna()\n", + "]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -13542,7 +13536,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.063\n", + " 0.1\n", " 0.013\n", " \n", " \n", @@ -13560,7 +13554,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.62\n", + " 0.5\n", " 0.45\n", " \n", " \n", @@ -13578,7 +13572,7 @@ " False\n", " 31294\n", " 1.0\n", - " 0.81\n", + " 0.835\n", " 0.95\n", " \n", " \n", @@ -13644,14 +13638,14 @@ "13 NaN NaN False False 31338 \n", "\n", " question_weight bot_team_median pro_median \n", - "2 1.0 0.063 0.013 \n", - "5 1.0 0.62 0.45 \n", - "8 1.0 0.81 0.95 \n", + "2 1.0 0.1 0.013 \n", + "5 1.0 0.5 0.45 \n", + "8 1.0 0.835 0.95 \n", "10 1.0 NaN NaN \n", "13 1.0 0.85 0.9 " ] }, - "execution_count": 76, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -13662,7 +13656,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -13679,8 +13673,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of pro forecasts: 50\n", - "Number of bot forecasts: 241\n" + "Number of pro forecasts: 48\n", + "Number of bot forecasts: 236\n" ] } ], diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 930eefb..7214749 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 -X_bot,-0.0,-0.0,-0.0,0.0,0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 -CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 +CumulativeBot,-0.0,-0.0,-0.0,0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 SynapseSeer,-0.1,-0.0,-0.0,0.0,0.0 -pianobot,-0.1,-0.1,-0.0,-0.0,0.0 Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 +pianobot,-0.1,-0.1,-0.0,-0.0,0.0 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 -4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 annabot,-0.1,-0.1,-0.1,-0.0,-0.0 +4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 MWG,-0.2,-0.2,-0.1,-0.0,-0.0 ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 -GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 -Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 +GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 -metac-o1,-0.3,-0.2,-0.1,-0.0,0.0 +Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-perplexity,-0.3,-0.3,-0.1,-0.0,0.1 +bot_median,-0.3,-0.2,-0.1,-0.0,0.1 +metac-o1,-0.3,-0.3,-0.1,-0.0,0.1 metac-deepseek-r1+asknews,-0.3,-0.2,-0.1,-0.1,-0.0 -wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.1 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-Gemini-Exp-1206,-0.3,-0.2,-0.2,-0.0,0.0 +wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.3,-0.1,-0.0,0.1 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -bot_median,-0.3,-0.2,-0.2,-0.1,0.0 -metac-claude-3-5-sonnet-20240620,-0.3,-0.3,-0.2,-0.1,0.0 -metac-perplexity,-0.4,-0.3,-0.2,-0.0,0.0 +metac-claude-3-5-sonnet-20240620,-0.3,-0.3,-0.2,-0.0,0.0 NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 +metac-claude-3-5-sonnet-latest,-0.3,-0.3,-0.2,-0.1,-0.1 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 +metac-o1-preview,-0.4,-0.3,-0.2,-0.1,-0.1 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.1 -pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 -metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.4,-0.4,-0.2,-0.1,-0.0 +pgodzinai,-0.4,-0.4,-0.3,-0.1,-0.1 +metac-grok-2-1212,-0.5,-0.4,-0.3,-0.1,-0.0 VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 -metac-Llama-3.1,-0.4,-0.4,-0.3,-0.2,-0.1 -metac-o1-preview,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-grok-2-1212,-0.5,-0.4,-0.3,-0.2,-0.1 metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index 477882c..cd9448c 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +RPM_bot,-0.6,7.0,-0.1,0.8206747298542999,0.31018589178137035,-0.2697293560809546,2.4469118511449692,0.7,-0.8,0.3982026167089623,0.796405 jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 +bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 -RPM_bot,-1.4,7.0,-0.2,0.8195427278689026,0.3097580352475143,-0.650312775083108,2.4469118511449692,0.6,-1.0,0.26978865902437565,0.539577 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 -annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 +annabot,-5.9,29.3,-0.2,0.5175750572467731,0.09561797207152893,-2.1122028342259047,2.0441825433909937,-0.0,-0.4,0.021810527148697016,0.043621 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.6,27.4,-0.2,0.7452828646172052,0.14237897258891655,-1.694618782556622,2.0495406495390753,0.1,-0.5,0.05095705221638959,0.101914 +cookics_bot_TEST,-6.6,27.4,-0.2,0.7470933569588007,0.14272484937169871,-1.6836598504701996,2.0495406495390753,0.1,-0.5,0.05201867599309354,0.104037 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 -GreeneiBot2,-10.7,58.4,-0.2,0.8492744520587402,0.11118024573783404,-1.6427768404571312,2.000831925930035,0.0,-0.4,0.05295076167168595,0.105902 +GreeneiBot2,-10.7,58.4,-0.2,0.8487135517179298,0.11110681713348293,-1.6470273617836275,2.000831925930035,0.0,-0.4,0.052510863710317504,0.105022 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 -metac-o1,-11.3,91.1,-0.1,0.885301596604543,0.09275387429075187,-1.342986841449772,1.9858289388460384,0.1,-0.3,0.09132478421461744,0.182650 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 +metac-perplexity,-12.0,89.1,-0.1,1.0008449184534645,0.10602979859799266,-1.2696037636515303,1.9864049297707018,0.1,-0.3,0.10378462460698391,0.207569 +bot_median,-12.2,92.1,-0.1,0.8759085051927877,0.0912701844746672,-1.448706262693777,1.9855502432148115,0.0,-0.3,0.07542649485602951,0.150853 +metac-o1,-12.4,91.1,-0.1,0.9413031092818035,0.09862120502513756,-1.3750355923383297,1.9858289388460384,0.1,-0.3,0.08626502997859752,0.172530 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 -metac-deepseek-r1+asknews,-13.3,52.1,-0.3,0.7808915178330472,0.10818619432038376,-2.3663082727832094,2.0053789762011176,-0.0,-0.5,0.010897575637344883,0.021795 +metac-deepseek-r1+asknews,-13.4,52.1,-0.3,0.6866418388462276,0.09512866474982715,-2.7023938246614656,2.0053789762011176,-0.1,-0.4,0.0046603987010819335,0.009321 +metac-Gemini-Exp-1206,-13.5,76.5,-0.2,1.0066063915806054,0.11508771463432003,-1.5277274660739493,1.9908217254774627,0.1,-0.4,0.06537953017362978,0.130759 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 -metac-Gemini-Exp-1206,-13.7,76.5,-0.2,0.9567011955687134,0.10938193429612067,-1.6400021546672607,1.9908217254774627,0.0,-0.4,0.05258248904380755,0.105165 -bot_median,-14.2,92.1,-0.2,0.8060563380929024,0.08399154733464013,-1.8298886724683292,1.9855502432148115,0.0,-0.3,0.03526855952035323,0.070537 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-claude-3-5-sonnet-20240620,-15.7,90.5,-0.2,0.9577206882239262,0.10067336366115942,-1.726279013247091,1.9860719790130024,0.0,-0.4,0.043873862980955504,0.087748 -metac-perplexity,-16.1,89.1,-0.2,1.04022365857026,0.11020159365499146,-1.6385490214880174,1.9864049297707018,0.0,-0.4,0.052436941119456015,0.104874 +metac-claude-3-5-sonnet-20240620,-14.7,90.5,-0.2,0.9429804683378815,0.09912390614679249,-1.6425851577449733,1.9860719790130024,0.0,-0.4,0.051988931836857315,0.103978 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 -metac-claude-3-5-sonnet-latest,-21.9,91.1,-0.2,0.8267775869528969,0.08662225919479004,-2.7788128175615063,1.9858289388460384,-0.1,-0.4,0.0033198064428072906,0.006640 +metac-claude-3-5-sonnet-latest,-18.9,91.1,-0.2,0.7317083930215759,0.07666177104402958,-2.699995118056715,1.9858289388460384,-0.1,-0.4,0.004140859358698023,0.008282 +minefrac1,-19.2,51.1,-0.4,0.8809897145082934,0.1232424683669797,-3.0436411347421197,2.0065449272360034,-0.1,-0.6,0.0018587451878251278,0.003717 +metac-o1-preview,-20.9,91.1,-0.2,0.802181404225052,0.08404529418137442,-2.7288070523371224,1.9858289388460384,-0.1,-0.4,0.003821400227265772,0.007643 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -pgodzinai,-23.5,76.4,-0.3,1.0010628527586396,0.11452878848708839,-2.684829528603297,1.9908489732268309,-0.1,-0.5,0.004459201995123589,0.008918 -metac-exa,-24.1,89.1,-0.3,0.8238773759897631,0.08728180623689599,-3.103267575628089,1.9864049297707018,-0.1,-0.4,0.0012863793448356026,0.002573 +metac-Llama-3.1,-23.2,89.1,-0.3,1.0312779661924496,0.1092538844308646,-2.379606259857792,1.9864049297707018,-0.0,-0.5,0.009744516632283914,0.019489 +metac-grok-2-1212,-23.5,91.1,-0.3,1.0680060472571526,0.11189599005467826,-2.303421178504194,1.9858289388460384,-0.0,-0.5,0.011778139872058951,0.023556 +pgodzinai,-24.0,76.4,-0.3,0.9765897737398795,0.11172889227393508,-2.8110851156332464,1.9908489732268309,-0.1,-0.5,0.0031442974859602537,0.006289 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-Llama-3.1,-26.6,89.1,-0.3,0.8904683193506574,0.09433646993436098,-3.1697302934806575,1.9864049297707018,-0.1,-0.5,0.001049393935170647,0.002099 +metac-exa,-26.2,89.1,-0.3,0.8302752742001319,0.0879596014139391,-3.3415454501401167,1.9864049297707018,-0.1,-0.5,0.0006119018080970774,0.001224 +metac-gpt-4o,-26.6,91.1,-0.3,0.8790866786848435,0.09210273154158923,-3.165570176683145,1.9858289388460384,-0.1,-0.5,0.0010559673026657784,0.002112 InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 -metac-o1-preview,-27.3,91.1,-0.3,0.8396846352431687,0.0879745426868476,-3.4074998848675455,1.9858289388460384,-0.1,-0.5,0.0004908622706364246,0.000982 -metac-grok-2-1212,-28.3,91.1,-0.3,1.0374739049385253,0.10869710901649764,-2.862896131089403,1.9858289388460384,-0.1,-0.5,0.00261020744989918,0.005220 -metac-gpt-4o,-28.7,91.1,-0.3,0.8937174262561063,0.09363560861558237,-3.3666300493101518,1.9858289388460384,-0.1,-0.5,0.0005601224288125974,0.001120 From 938f4a3d3c577158b1fdbbec11c25de42d3a42a6 Mon Sep 17 00:00:00 2001 From: Molly Hickman Date: Thu, 22 May 2025 13:01:38 -0400 Subject: [PATCH 26/26] calibration bug fix :bug: --- AI_BENCHMARKING_ANALYSIS.ipynb | 3093 +++++++++-------- functions.py | 6 +- .../bootstrapped_h2h_bot_vs_pros.csv | 40 +- .../weighted_t_test_h2h_bot_vs_pros.csv | 40 +- 4 files changed, 1653 insertions(+), 1526 deletions(-) diff --git a/AI_BENCHMARKING_ANALYSIS.ipynb b/AI_BENCHMARKING_ANALYSIS.ipynb index bb7044b..bf4055e 100644 --- a/AI_BENCHMARKING_ANALYSIS.ipynb +++ b/AI_BENCHMARKING_ANALYSIS.ipynb @@ -38,7 +38,8 @@ "%autoreload 2\n", "from functions import *\n", "from IPython.display import display, clear_output\n", - "import pandas as pd\n" + "import pandas as pd\n", + "from copy import deepcopy\n" ] }, { @@ -61,7 +62,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1441081/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_17143/1846409041.py:25: DtypeWarning: Columns (18,19) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df_bot_forecasts = pd.read_csv('https://data.heroku.com/dataclips/tfwiopapwgyjkawcpjmpibjlsars.csv')\n" ] }, @@ -832,12 +833,12 @@ " False\n", " \n", " \n", - " 5\n", + " 3\n", " 31268\n", - " darkives\n", + " SpottedBear\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 103907\n", + " 131523\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -849,16 +850,16 @@ " False\n", " False\n", " 31736\n", - " [0.001,0.49,0.365,0.1,0.044]\n", + " [0.001,0.59,0.35,0.044,0.015]\n", " False\n", " \n", " \n", - " 6\n", + " 4\n", " 31268\n", - " datscilly\n", + " Zaldath\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 103777\n", + " 139161\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -870,7 +871,7 @@ " False\n", " False\n", " 31736\n", - " [0.001,0.56,0.36,0.059,0.02]\n", + " [0.001,0.623,0.336,0.03,0.01]\n", " False\n", " \n", " \n", @@ -878,47 +879,54 @@ "" ], "text/plain": [ - " question_id forecaster question_title \\\n", - "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", - "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", - "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", - "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", - "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster \\\n", + "0 31268 Jgalt \n", + "1 31268 MaciekK \n", + "2 31268 OpenSystem \n", + "3 31268 SpottedBear \n", + "4 31268 Zaldath \n", + "\n", + " question_title \\\n", + "0 For Q1 2025, how many banks will be listed on ... \n", + "1 For Q1 2025, how many banks will be listed on ... \n", + "2 For Q1 2025, how many banks will be listed on ... \n", + "3 For Q1 2025, how many banks will be listed on ... \n", + "4 For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", "1 2025-01-17 19:06:22.013528+00 117580 1 \n", "2 2025-01-17 19:06:22.013528+00 120160 1 \n", - "5 2025-01-17 19:06:22.013528+00 103907 1 \n", - "6 2025-01-17 19:06:22.013528+00 103777 1 \n", + "3 2025-01-17 19:06:22.013528+00 131523 1 \n", + "4 2025-01-17 19:06:22.013528+00 139161 1 \n", "\n", " scheduled_close_time actual_close_time question_weight \\\n", "0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "1 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "2 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "3 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "4 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", " type options range_min range_max \\\n", "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "3 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "4 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "\n", " open_lower_bound open_upper_bound post_id forecast \\\n", "0 False False 31736 [0.001,0.568,0.366,0.041,0.024] \n", "1 False False 31736 [0.001,0.62,0.35,0.019,0.01] \n", "2 False False 31736 [0.005,0.7,0.25,0.04,0.005] \n", - "5 False False 31736 [0.001,0.49,0.365,0.1,0.044] \n", - "6 False False 31736 [0.001,0.56,0.36,0.059,0.02] \n", + "3 False False 31736 [0.001,0.59,0.35,0.044,0.015] \n", + "4 False False 31736 [0.001,0.623,0.336,0.03,0.01] \n", "\n", " is_median \n", "0 False \n", "1 True \n", "2 False \n", - "5 False \n", - "6 False " + "3 False \n", + "4 False " ] }, "execution_count": 15, @@ -961,13 +969,14 @@ { "data": { "text/plain": [ - "array(['metac-Llama-3.1', 'metac-Gemini-Exp-1206', 'acm_bot',\n", - " 'NextWorldLab', 'metac-o1-preview', 'metac-perplexity', 'mmBot',\n", - " 'metac-claude-3-5-sonnet-latest', 'Grizeu_Bot', 'GreeneiBot2',\n", - " 'InstitutPelFutur', 'metac-claude-3-5-sonnet-20240620', 'metac-o1',\n", - " 'metac-grok-2-1212', 'metac-gpt-4o', 'bot_median', 'pgodzinai',\n", - " 'metac-exa', 'jkraybill_bot', 'VeritasAI', 'MWG', 'twsummerbot',\n", - " 'CatrachoCaster', 'X_bot', 'manticAI', 'annabot', 'minefrac1',\n", + "array(['GreeneiBot2', 'Grizeu_Bot', 'InstitutPelFutur', 'NextWorldLab',\n", + " 'acm_bot', 'metac-Gemini-Exp-1206', 'metac-Llama-3.1', 'mmBot',\n", + " 'metac-claude-3-5-sonnet-latest', 'metac-gpt-4o',\n", + " 'metac-grok-2-1212', 'metac-o1', 'metac-o1-preview',\n", + " 'metac-perplexity', 'bot_median',\n", + " 'metac-claude-3-5-sonnet-20240620', 'pgodzinai', 'jkraybill_bot',\n", + " 'metac-exa', 'manticAI', 'MWG', 'CatrachoCaster', 'twsummerbot',\n", + " 'VeritasAI', 'X_bot', 'annabot', 'minefrac1',\n", " 'metac-deepseek-r1+asknews', 'Bot_Pepa', 'laylaps', 'ajf-bot',\n", " 'SynapseSeer', 'RPM_bot', 'cookics_bot_TEST', 'ProfessorSP',\n", " 'wunderplumb', 'CumulativeBot', 'pianobot', 'krm-bot',\n", @@ -1021,7 +1030,7 @@ " \n", " \n", " \n", - " 12\n", + " 11\n", " metac-o1\n", " 9.674740\n", " 3631.123492\n", @@ -1030,16 +1039,7 @@ " 1.738353\n", " \n", " \n", - " 15\n", - " bot_median\n", - " 8.546230\n", - " 3230.645695\n", - " 409\n", - " 5.546573\n", - " 1.525925\n", - " \n", - " \n", - " 4\n", + " 12\n", " metac-o1-preview\n", " 8.465638\n", " 3121.449998\n", @@ -1048,7 +1048,16 @@ " 2.298000\n", " \n", " \n", - " 24\n", + " 14\n", + " bot_median\n", + " 8.143307\n", + " 3078.332902\n", + " 409\n", + " 5.471228\n", + " 1.359286\n", + " \n", + " \n", + " 19\n", " manticAI\n", " 6.510835\n", " 2055.210309\n", @@ -1057,7 +1066,7 @@ " 3.029040\n", " \n", " \n", - " 1\n", + " 5\n", " metac-Gemini-Exp-1206\n", " 5.417367\n", " 1880.476418\n", @@ -1071,18 +1080,18 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "12 metac-o1 9.674740 3631.123492 406 6.257418 \n", - "15 bot_median 8.546230 3230.645695 409 5.546573 \n", - "4 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", - "24 manticAI 6.510835 2055.210309 337 0.552564 \n", - "1 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", + "11 metac-o1 9.674740 3631.123492 406 6.257418 \n", + "12 metac-o1-preview 8.465638 3121.449998 399 3.947903 \n", + "14 bot_median 8.143307 3078.332902 409 5.471228 \n", + "19 manticAI 6.510835 2055.210309 337 0.552564 \n", + "5 metac-Gemini-Exp-1206 5.417367 1880.476418 377 0.876988 \n", "\n", " weighted_se \n", - "12 1.738353 \n", - "15 1.525925 \n", - "4 2.298000 \n", - "24 3.029040 \n", - "1 2.309106 " + "11 1.738353 \n", + "12 2.298000 \n", + "14 1.359286 \n", + "19 3.029040 \n", + "5 2.309106 " ] }, "metadata": {}, @@ -1119,7 +1128,7 @@ " \n", " \n", " \n", - " 19\n", + " 23\n", " VeritasAI\n", " -4.854808\n", " -1602.183635\n", @@ -1137,7 +1146,7 @@ " 3.096816\n", " \n", " \n", - " 8\n", + " 1\n", " Grizeu_Bot\n", " -9.743831\n", " -1882.605577\n", @@ -1146,7 +1155,7 @@ " 3.931500\n", " \n", " \n", - " 14\n", + " 9\n", " metac-gpt-4o\n", " -5.987786\n", " -2235.360274\n", @@ -1169,17 +1178,17 @@ ], "text/plain": [ " forecaster weighted_mean weighted_sum n_questions ci_lower \\\n", - "19 VeritasAI -4.854808 -1602.183635 361 -8.860367 \n", + "23 VeritasAI -4.854808 -1602.183635 361 -8.860367 \n", "26 minefrac1 -9.333648 -1757.059251 202 -15.440064 \n", - "8 Grizeu_Bot -9.743831 -1882.605577 207 -17.494967 \n", - "14 metac-gpt-4o -5.987786 -2235.360274 404 -10.422687 \n", + "1 Grizeu_Bot -9.743831 -1882.605577 207 -17.494967 \n", + "9 metac-gpt-4o -5.987786 -2235.360274 404 -10.422687 \n", "30 ajf-bot -14.000701 -3208.260547 244 -24.482548 \n", "\n", " weighted_se \n", - "19 2.036820 \n", + "23 2.036820 \n", "26 3.096816 \n", - "8 3.931500 \n", - "14 2.255950 \n", + "1 3.931500 \n", + "9 2.255950 \n", "30 5.321344 " ] }, @@ -1740,7 +1749,7 @@ " \n", " 3\n", " bot_median\n", - " 8674.761163\n", + " 8721.511046\n", " \n", " \n", " 4\n", @@ -1761,7 +1770,7 @@ "Rank \n", "1 metac-o1 8861.959039\n", "2 metac-o1-preview 8849.559824\n", - "3 bot_median 8674.761163\n", + "3 bot_median 8721.511046\n", "4 acm_bot 7605.922314\n", "5 manticAI 7061.660958" ] @@ -1931,7 +1940,7 @@ " \n", " 2\n", " bot_median\n", - " 3544.710382\n", + " 3472.028144\n", " \n", " \n", " 3\n", @@ -2166,7 +2175,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3544.710382\n", + "2 bot_median 3472.028144\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -2414,12 +2423,12 @@ " False\n", " \n", " \n", - " 5\n", + " 3\n", " 31268\n", - " darkives\n", + " SpottedBear\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 103907\n", + " 131523\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -2431,16 +2440,16 @@ " False\n", " False\n", " 31736\n", - " [0.001,0.49,0.365,0.1,0.044]\n", + " [0.001,0.59,0.35,0.044,0.015]\n", " False\n", " \n", " \n", - " 6\n", + " 4\n", " 31268\n", - " datscilly\n", + " Zaldath\n", " For Q1 2025, how many banks will be listed on ...\n", " 2025-01-17 19:06:22.013528+00\n", - " 103777\n", + " 139161\n", " 1\n", " 2025-01-20 03:27:00+00\n", " 2025-01-20 03:27:00+00\n", @@ -2452,7 +2461,7 @@ " False\n", " False\n", " 31736\n", - " [0.001,0.56,0.36,0.059,0.02]\n", + " [0.001,0.623,0.336,0.03,0.01]\n", " False\n", " \n", " \n", @@ -2460,47 +2469,54 @@ "" ], "text/plain": [ - " question_id forecaster question_title \\\n", - "0 31268 Jgalt For Q1 2025, how many banks will be listed on ... \n", - "1 31268 MaciekK For Q1 2025, how many banks will be listed on ... \n", - "2 31268 OpenSystem For Q1 2025, how many banks will be listed on ... \n", - "5 31268 darkives For Q1 2025, how many banks will be listed on ... \n", - "6 31268 datscilly For Q1 2025, how many banks will be listed on ... \n", + " question_id forecaster \\\n", + "0 31268 Jgalt \n", + "1 31268 MaciekK \n", + "2 31268 OpenSystem \n", + "3 31268 SpottedBear \n", + "4 31268 Zaldath \n", + "\n", + " question_title \\\n", + "0 For Q1 2025, how many banks will be listed on ... \n", + "1 For Q1 2025, how many banks will be listed on ... \n", + "2 For Q1 2025, how many banks will be listed on ... \n", + "3 For Q1 2025, how many banks will be listed on ... \n", + "4 For Q1 2025, how many banks will be listed on ... \n", "\n", " created_at author_id resolution \\\n", "0 2025-01-17 19:06:22.013528+00 101465 1 \n", "1 2025-01-17 19:06:22.013528+00 117580 1 \n", "2 2025-01-17 19:06:22.013528+00 120160 1 \n", - "5 2025-01-17 19:06:22.013528+00 103907 1 \n", - "6 2025-01-17 19:06:22.013528+00 103777 1 \n", + "3 2025-01-17 19:06:22.013528+00 131523 1 \n", + "4 2025-01-17 19:06:22.013528+00 139161 1 \n", "\n", " scheduled_close_time actual_close_time question_weight \\\n", "0 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "1 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "2 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "5 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", - "6 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "3 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", + "4 2025-01-20 03:27:00+00 2025-01-20 03:27:00+00 1.0 \n", "\n", " type options range_min range_max \\\n", "0 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "1 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "2 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "5 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", - "6 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "3 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", + "4 multiple_choice [\"0\",\"1\",\"2-3\",\"4-6\",\">6\"] NaN NaN \n", "\n", " open_lower_bound open_upper_bound post_id forecast \\\n", "0 False False 31736 [0.001,0.568,0.366,0.041,0.024] \n", "1 False False 31736 [0.001,0.62,0.35,0.019,0.01] \n", "2 False False 31736 [0.005,0.7,0.25,0.04,0.005] \n", - "5 False False 31736 [0.001,0.49,0.365,0.1,0.044] \n", - "6 False False 31736 [0.001,0.56,0.36,0.059,0.02] \n", + "3 False False 31736 [0.001,0.59,0.35,0.044,0.015] \n", + "4 False False 31736 [0.001,0.623,0.336,0.03,0.01] \n", "\n", " is_median \n", "0 False \n", "1 True \n", "2 False \n", - "5 False \n", - "6 False " + "3 False \n", + "4 False " ] }, "execution_count": 27, @@ -2578,9 +2594,9 @@ " False\n", " False\n", " ...\n", - " [0.4,0.31,0.2,0.05600000000000001,0.034]\n", - " [0.01,0.7,0.25,0.03,0.01]\n", - " [0.30000000000000004,0.31,0.25,0.1060000000000...\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44...\n", " [0.014925742574257425,0.5137871287128712,0.334...\n", @@ -2603,7 +2619,7 @@ " True\n", " ...\n", " [0.05,0.0505882353,0.0511764706,0.0517647059,0...\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222...\n", @@ -2627,7 +2643,7 @@ " False\n", " ...\n", " 0.1\n", - " 0.1\n", + " 0.05\n", " 0.1\n", " NaN\n", " 0.2\n", @@ -2651,8 +2667,8 @@ " None\n", " ...\n", " [0.45,0.45,0.1]\n", - " [0.2,0.6,0.2]\n", - " [0.1,0.6,0.3]\n", + " [0.15,0.65,0.2]\n", + " [0.15000000000000002,0.54,0.31000000000000005]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -2713,24 +2729,24 @@ "4 False False ... \n", "\n", " metac-o1 \\\n", - "0 [0.4,0.31,0.2,0.05600000000000001,0.034] \n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0... \n", "2 0.1 \n", "3 [0.45,0.45,0.1] \n", "4 [0.0,0.0033333333,0.0066666667,0.01,0.01333333... \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.25,0.03,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", "\n", " metac-perplexity minefrac1 \\\n", - "0 [0.30000000000000004,0.31,0.25,0.1060000000000... NaN \n", + "0 [0.3,0.4,0.2,0.07,0.03] NaN \n", "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... NaN \n", "2 0.1 NaN \n", - "3 [0.1,0.6,0.3] NaN \n", + "3 [0.15000000000000002,0.54,0.31000000000000005] NaN \n", "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0... NaN \n", "\n", " mmBot \\\n", @@ -2842,7 +2858,7 @@ " False\n", " False\n", " ...\n", - " 0.3\n", + " 0.4\n", " 0.9\n", " NaN\n", " NaN\n", @@ -2866,7 +2882,7 @@ " False\n", " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.95\n", " NaN\n", " NaN\n", @@ -2914,8 +2930,8 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", + " 0.05\n", + " 0.05\n", " 0.03\n", " NaN\n", " 0.15\n", @@ -2947,10 +2963,10 @@ "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.3 0.9 NaN NaN 0.15 NaN \n", - "96 0.85 0.95 NaN NaN 0.9 NaN \n", + "95 0.4 0.9 NaN NaN 0.15 NaN \n", + "96 0.8 0.95 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.1 0.03 NaN 0.15 0.05 \n", + "98 0.05 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3100,7 +3116,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1441081/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + "/tmp/ipykernel_17143/199340000.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " multiple_choice_rows_with_empty_options = df_pro_bot_forecasts[df_pro_bot_forecasts['options'] == '[]'][df_pro_bot_forecasts['type'] == 'multiple_choice']\n" ] }, @@ -3162,9 +3178,9 @@ " False\n", " False\n", " ...\n", - " [0.4,0.31,0.2,0.05600000000000001,0.034]\n", - " [0.01,0.7,0.25,0.03,0.01]\n", - " [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991]\n", + " [0.25,0.3,0.3,0.1,0.05]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", + " [0.3,0.4,0.2,0.07,0.03]\n", " NaN\n", " [0.009900990099009901,0.39603960396039606,0.44554455445544555,0.1188118811881188,0.0297029702970297]\n", " [0.014925742574257425,0.5137871287128712,0.3349009900990099,0.10168316831683169,0.03470297029702965]\n", @@ -3186,8 +3202,8 @@ " True\n", " True\n", " ...\n", - " [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.44,0.48,0.52,0.56,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007142857,0.9014285714,0.9021428571,0.9028571429,0.9035714286,0.9042857143,0.905,0.9057142857,0.9064285714,0.9071428571,0.9078571429,0.9085714286,0.9092857143,0.91,0.9107142857,0.9114285714,0.9121428571,0.9128571429,0.9135714286,0.9142857143,0.915,0.9157142857,0.9164285714,0.9171428571,0.9178571429,0.9185714286,0.9192857143,0.92,0.9207142857,0.9214285714,0.9221428571,0.9228571429,0.9235714286,0.9242857143,0.925,0.9257142857,0.9264285714,0.9271428571,0.9278571429,0.9285714286,0.9292857143,0.93,0.9307142857,0.9314285714,0.9321428571,0.9328571429,0.9335714286,0.9342857143,0.935,0.9357142857,0.9364285714,0.9371428571,0.9378571429,0.9385714286,0.9392857143,0.94,0.9407142857,0.9414285714,0.9421428571,0.9428571429,0.9435714286,0.9442857143,0.945,0.9457142857,0.9464285714,0.9471428571,0.9478571429,0.9485714286,0.9492857143,0.95]\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95]\n", + " [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95]\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95]\n", " NaN\n", " [0.0215944348,0.0218024136,0.0220262706,0.0222657692,0.0225205234,0.0227900084,0.0230735761,0.0233704727,0.0236798595,0.0240008339,0.0243324518,0.0246737484,0.0250237592,0.0253815375,0.0257461704,0.0261167925,0.0264925953,0.0268728349,0.0272568365,0.0276439961,0.0280337803,0.0284257242,0.0288194274,0.0292145496,0.0296108048,0.0300079559,0.0304058088,0.0308042061,0.031203022,0.0316021576,0.0320015358,0.0324010988,0.0328008038,0.033200622,0.0336005361,0.0340005406,0.0344006419,0.0348008594,0.0352012288,0.0356018064,0.0360026751,0.0364039532,0.0368058059,0.0372084598,0.0376122217,0.0380175022,0.0384248443,0.0388349581,0.0392487619,0.0396674303,0.040092449,0.0405256766,0.040969412,0.0414264662,0.0419002382,0.0423947905,0.0429149226,0.0434662384,0.0440552034,0.0446891875,0.0453764888,0.0461263346,0.0469488546,0.047855024,0.0488565752,0.0499658763,0.0511957788,0.0525594355,0.0540700958,0.0557408822,0.0575845575,0.0596132911,0.061838434,0.0642703126,0.0669180506,0.0697894271,0.0728907793,0.0762269529,0.0798013046,0.0836157568,0.0876709009,0.091966147,0.096499911,0.1012698318,0.1062730078,0.1115062433,0.116966291,0.1226500836,0.1285549408,0.1346787459,0.1410200827,0.1475783286,0.1543537019,0.1613472593,0.1685608481,0.1759970129,0.1836588644,0.1915499147,0.1996738871,0.208034508,0.2166352903,0.225479315,0.2345690212,0.24390601,0.2534908708,0.2633230334,0.2734006526,0.283720526,0.2942780484,0.3050672012,0.316080577,0.3273094353,0.3387437886,0.3503725099,0.3621834602,0.3741636271,0.3862992712,0.3985760721,0.4109792702,0.4234937993,0.4361044066,0.4487957561,0.4615525185,0.4743594438,0.4872014199,0.5000635204,0.5129310433,0.5257895463,0.5386248816,0.5514232322,0.5641711536,0.5768556211,0.589464083,0.6019845173,0.6144054896,0.6267162064,0.6389065595,0.6509671563,0.6628893291,0.6746651196,0.6862872355,0.6977489765,0.7090441313,0.7201668477,0.7311114815,0.7418724312,0.7524439675,0.7628200682,0.7729942685,0.7829595382,0.7927081941,0.8022318565,0.8115214549,0.8205672863,0.8293591256,0.8378863854,0.8461383197,0.8541042651,0.8617739066,0.8691375599,0.8761864572,0.8829130238,0.8893111359,0.8953763492,0.9011060878,0.9064997881,0.9115589931,0.9162873921,0.9206908074,0.9247771276,0.9285561903,0.9320396198,0.9352406245,0.9381737618,0.9408546777,0.9432998299,0.945526202,0.9475510194,0.949391472,0.9510644542,0.9525863264,0.953972705,0.955238285,0.9563966974,0.9574604037,0.9584406278,0.9593473236,0.960189177,0.9609736386,0.9617069836,0.9623943945,0.9630400616,0.9636472966,0.9642186545,0.9647560591,0.9652609283,0.9657342945,0.9661769175,0.9665893865,0.9669722099,0.9673258911]\n", @@ -3211,7 +3227,7 @@ " False\n", " ...\n", " 0.1\n", - " 0.1\n", + " 0.05\n", " 0.1\n", " NaN\n", " 0.2\n", @@ -3235,8 +3251,8 @@ " None\n", " ...\n", " [0.45,0.45,0.1]\n", - " [0.2,0.6,0.2]\n", - " [0.1,0.6,0.3]\n", + " [0.15,0.65,0.2]\n", + " [0.15000000000000002,0.54,0.31000000000000005]\n", " NaN\n", " [0.25,0.5,0.25]\n", " [0.27499999999999997,0.5125,0.21249999999999997]\n", @@ -3260,7 +3276,7 @@ " ...\n", " [0.0,0.0033333333,0.0066666667,0.01,0.0133333333,0.0166666667,0.02,0.0233333333,0.0266666667,0.03,0.0333333333,0.0366666667,0.04,0.0433333333,0.0466666667,0.05,0.0533333333,0.0566666667,0.06,0.0633333333,0.0666666667,0.07,0.0733333333,0.0766666667,0.08,0.0833333333,0.0866666667,0.09,0.0933333333,0.0966666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.6057142857,0.6114285714,0.6171428571,0.6228571429,0.6285714286,0.6342857143,0.64,0.6457142857,0.6514285714,0.6571428571,0.6628571429,0.6685714286,0.6742857143,0.68,0.6857142857,0.6914285714,0.6971428571,0.7028571429,0.7085714286,0.7142857143,0.72,0.7257142857,0.7314285714,0.7371428571,0.7428571429,0.7485714286,0.7542857143,0.76,0.7657142857,0.7714285714,0.7771428571,0.7828571429,0.7885714286,0.7942857143,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", " [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9013333333,0.9026666667,0.904,0.9053333333,0.9066666667,0.908,0.9093333333,0.9106666667,0.912,0.9133333333,0.9146666667,0.916,0.9173333333,0.9186666667,0.92,0.9213333333,0.9226666667,0.924,0.9253333333,0.9266666667,0.928,0.9293333333,0.9306666667,0.932,0.9333333333,0.9346666667,0.936,0.9373333333,0.9386666667,0.94,0.9413333333,0.9426666667,0.944,0.9453333333,0.9466666667,0.948,0.9493333333,0.9506666667,0.952,0.9533333333,0.9546666667,0.956,0.9573333333,0.9586666667,0.96,0.9613333333,0.9626666667,0.964,0.9653333333,0.9666666667,0.968,0.9693333333,0.9706666667,0.972,0.9733333333,0.9746666667,0.976,0.9773333333,0.9786666667,0.98,0.9813333333,0.9826666667,0.984,0.9853333333,0.9866666667,0.988,0.9893333333,0.9906666667,0.992,0.9933333333,0.9946666667,0.996,0.9973333333,0.9986666667,1.0]\n", - " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0]\n", + " [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.902,0.904,0.906,0.908,0.91,0.912,0.914,0.916,0.918,0.92,0.922,0.924,0.926,0.928,0.93,0.932,0.934,0.936,0.938,0.94,0.942,0.944,0.946,0.948,0.95,0.952,0.954,0.956,0.958,0.96,0.962,0.964,0.966,0.968,0.97,0.972,0.974,0.976,0.978,0.98,0.982,0.984,0.986,0.988,0.99,0.992,0.994,0.996,0.998,1.0]\n", " NaN\n", " [0.0,0.0006552097,0.0013605064,0.0021151815,0.0029180701,0.0037675922,0.0046618077,0.0055984833,0.0065751692,0.0075892831,0.0086381998,0.0097193446,0.0108302867,0.0119688337,0.0131331257,0.014321727,0.0155337159,0.0167687729,0.0180272663,0.0193103356,0.020619972,0.0219590952,0.0233316264,0.024742554,0.0261979914,0.0277052245,0.0292727448,0.030910267,0.0326287265,0.034440256,0.0363581376,0.0383967303,0.0405713707,0.042898249,0.0453942605,0.0480768342,0.0509637431,0.0540728987,0.0574221344,0.0610289827,0.0649104508,0.069082799,0.0735613277,0.0783601755,0.0834921337,0.0889684789,0.0947988278,0.1009910149,0.1075509944,0.1144827695,0.1217883466,0.1294677162,0.1375188601,0.1459377845,0.1547185775,0.1638534906,0.173333043,0.183146147,0.1932802518,0.2037215056,0.2144549309,0.2254646117,0.2367338883,0.2482455564,0.2599820665,0.2719257181,0.2840588463,0.2963639938,0.308824066,0.3214224646,0.3341431959,0.3469709515,0.3598911602,0.3728900098,0.3859544391,0.3990721017,0.4122313044,0.4254209242,0.4386303077,0.4518491587,0.4650674199,0.4782751541,0.4914624335,0.5046192399,0.5177353826,0.5308004395,0.5438037232,0.5567342756,0.5695808913,0.5823321691,0.5949765903,0.6075026181,0.6198988152,0.6321539735,0.6442572471,0.6561982838,0.6679673464,0.679555418,0.6909542849,0.7021565932,0.7131558737,0.7239465364,0.7345238314,0.7448837818,0.7550230879,0.7649390101,0.7746292356,0.7840917363,0.793324625,0.8023260164,0.8110939019,0.8196260428,0.8279198893,0.8359725294,0.84378067,0.8513406529,0.8586485067,0.8657000313,0.8724909149,0.8790168773,0.8852738353,0.8912580844,0.8969664881,0.9023966684,0.9075471904,0.9124177307,0.9170092252,0.9213239875,0.9253657928,0.9291399243,0.9326531773,0.9359138212,0.9389315199,0.9417172132,0.9442829632,0.9466417713,0.9488073729,0.9507940179,0.9526162437,0.9542886507,0.9558256867,0.957241447,0.9585494976,0.9597627233,0.9608932066,0.9619521358,0.9629497455,0.9638952848,0.9647970143,0.9656622247,0.9664972774,0.9673076585,0.9680980464,0.9688723855,0.9696339648,0.9703854957,0.9711291891,0.9718668279,0.9725998336,0.9733293276,0.9740561839,0.9747810757,0.9755045151,0.9762268859,0.9769484703,0.9776694709,0.9783900269,0.9791102268,0.9798301173,0.9805497088,0.9812689786,0.981987871,0.9827062964,0.9834241265,0.9841411897,0.9848572642,0.98557207,0.9862852591,0.9869964062,0.9877049976,0.9884104215,0.9891119579,0.9898087704,0.990499899,0.9911842569,0.9918606294,0.9925276775,0.9931839465,0.9938278782,0.99445783,0.9950720981,0.9956689463,0.9962466383,0.9968034747,0.9973378313,0.9978481983,0.9983332192,0.9987917276,0.9992227789,0.9996256782,1.0]\n", " [0.0,0.0001141583,0.0002446967,0.0003862688,0.0005272579,0.0006650709,0.0008243437,0.0011074433,0.0016696544,0.0025699094,0.0037138357,0.0049708626,0.0062610152,0.0075426566,0.0089765864,0.0111726822,0.0147311078,0.0195212559,0.0249547717,0.0306181288,0.0363105138,0.0419407763,0.0476011969,0.053516341,0.0598014349,0.0663689162,0.0730761187,0.0798334547,0.0865904866,0.0933196582,0.1000172031,0.1066924089,0.1133554776,0.1200140176,0.1266729489,0.1333343989,0.1399984689,0.1466644317,0.1533314439,0.1599988203,0.1666661444,0.1733332523,0.1800001372,0.1866668598,0.1933334943,0.2000000995,0.2066667101,0.2133333393,0.2199999878,0.22666665,0.2333333196,0.2399999916,0.2466666631,0.2533333329,0.2600000011,0.2666666681,0.2733333345,0.2800000007,0.286666667,0.2933333334,0.2999999999,0.3066666665,0.3133333332,0.3199999999,0.3266666666,0.3333333333,0.34,0.3466666667,0.3533333333,0.36,0.3666666667,0.3733333333,0.38,0.3866666667,0.3934628939,0.400837331,0.40925763,0.4186848364,0.428718413,0.4390353607,0.4494419812,0.4597974687,0.4700329298,0.4801500685,0.4901790777,0.500153105,0.5101028922,0.5200515519,0.5300114112,0.5398722838,0.5492279015,0.5576212737,0.5650210292,0.571743695,0.5780856137,0.5842571713,0.5904328096,0.5967209586,0.603152213,0.6097133168,0.6163738459,0.6230958146,0.6298433017,0.6365902337,0.6433215069,0.6500308134,0.656718392,0.6633885674,0.6700472479,0.6767001542,0.6833518918,0.6900055659,0.6966627826,0.7033239321,0.7099885835,0.7166558627,0.723324761,0.7299943545,0.7366639271,0.7433330133,0.7500013847,0.7566690034,0.7633359628,0.770002427,0.7766685825,0.7833346018,0.7900006228,0.7966667394,0.8033330023,0.8099994258,0.8166659972,0.8233326871,0.8299994586,0.8366662749,0.8433331037,0.8499999207,0.8566667097,0.8633334627,0.8700001785,0.8766668606,0.8833335157,0.8899751517,0.8964699017,0.9025861327,0.9081211655,0.9130226546,0.9173491712,0.921198292,0.9246959323,0.9279877368,0.9312103051,0.934472912,0.9378540969,0.9414005467,0.9450901244,0.9487670554,0.9522009139,0.9552513327,0.9578998205,0.9601715711,0.96211589,0.9638162438,0.9653702301,0.9668664828,0.9683781475,0.9699605983,0.9716476808,0.9734519305,0.9753688047,0.9773815283,0.9794657325,0.9815941718,0.9837408125,0.9858836701,0.9879773814,0.9898993305,0.9914888717,0.9926681205,0.9934599632,0.9939261174,0.9941560479,0.9942611072,0.9943265488,0.9943865488,0.9944537386,0.9945561009,0.9947328687,0.9950042368,0.9953660612,0.9958058993,0.9963078442,0.9968511117,0.9974139813,0.9979781729,0.9985251814,0.999027536,0.9994498435,0.999736686,0.9998734993,0.99994,1.0]\n", @@ -3296,26 +3312,26 @@ "3 None None ... \n", "4 False False ... \n", "\n", - " metac-o1 \\\n", - "0 [0.4,0.31,0.2,0.05600000000000001,0.034] \n", - "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.44,0.48,0.52,0.56,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007142857,0.9014285714,0.9021428571,0.9028571429,0.9035714286,0.9042857143,0.905,0.9057142857,0.9064285714,0.9071428571,0.9078571429,0.9085714286,0.9092857143,0.91,0.9107142857,0.9114285714,0.9121428571,0.9128571429,0.9135714286,0.9142857143,0.915,0.9157142857,0.9164285714,0.9171428571,0.9178571429,0.9185714286,0.9192857143,0.92,0.9207142857,0.9214285714,0.9221428571,0.9228571429,0.9235714286,0.9242857143,0.925,0.9257142857,0.9264285714,0.9271428571,0.9278571429,0.9285714286,0.9292857143,0.93,0.9307142857,0.9314285714,0.9321428571,0.9328571429,0.9335714286,0.9342857143,0.935,0.9357142857,0.9364285714,0.9371428571,0.9378571429,0.9385714286,0.9392857143,0.94,0.9407142857,0.9414285714,0.9421428571,0.9428571429,0.9435714286,0.9442857143,0.945,0.9457142857,0.9464285714,0.9471428571,0.9478571429,0.9485714286,0.9492857143,0.95] \n", - "2 0.1 \n", - "3 [0.45,0.45,0.1] \n", - "4 [0.0,0.0033333333,0.0066666667,0.01,0.0133333333,0.0166666667,0.02,0.0233333333,0.0266666667,0.03,0.0333333333,0.0366666667,0.04,0.0433333333,0.0466666667,0.05,0.0533333333,0.0566666667,0.06,0.0633333333,0.0666666667,0.07,0.0733333333,0.0766666667,0.08,0.0833333333,0.0866666667,0.09,0.0933333333,0.0966666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.6057142857,0.6114285714,0.6171428571,0.6228571429,0.6285714286,0.6342857143,0.64,0.6457142857,0.6514285714,0.6571428571,0.6628571429,0.6685714286,0.6742857143,0.68,0.6857142857,0.6914285714,0.6971428571,0.7028571429,0.7085714286,0.7142857143,0.72,0.7257142857,0.7314285714,0.7371428571,0.7428571429,0.7485714286,0.7542857143,0.76,0.7657142857,0.7714285714,0.7771428571,0.7828571429,0.7885714286,0.7942857143,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", - "\n", - " metac-o1-preview \\\n", - "0 [0.01,0.7,0.25,0.03,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.0526666667,0.0533333333,0.054,0.0546666667,0.0553333333,0.056,0.0566666667,0.0573333333,0.058,0.0586666667,0.0593333333,0.06,0.0606666667,0.0613333333,0.062,0.0626666667,0.0633333333,0.064,0.0646666667,0.0653333333,0.066,0.0666666667,0.0673333333,0.068,0.0686666667,0.0693333333,0.07,0.0706666667,0.0713333333,0.072,0.0726666667,0.0733333333,0.074,0.0746666667,0.0753333333,0.076,0.0766666667,0.0773333333,0.078,0.0786666667,0.0793333333,0.08,0.0806666667,0.0813333333,0.082,0.0826666667,0.0833333333,0.084,0.0846666667,0.0853333333,0.086,0.0866666667,0.0873333333,0.088,0.0886666667,0.0893333333,0.09,0.0906666667,0.0913333333,0.092,0.0926666667,0.0933333333,0.094,0.0946666667,0.0953333333,0.096,0.0966666667,0.0973333333,0.098,0.0986666667,0.0993333333,0.1,0.1066666667,0.1133333333,0.12,0.1266666667,0.1333333333,0.14,0.1466666667,0.1533333333,0.16,0.1666666667,0.1733333333,0.18,0.1866666667,0.1933333333,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.408,0.416,0.424,0.432,0.44,0.448,0.456,0.464,0.472,0.48,0.488,0.496,0.504,0.512,0.52,0.528,0.536,0.544,0.552,0.56,0.568,0.576,0.584,0.592,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.90125,0.9025,0.90375,0.905,0.90625,0.9075,0.90875,0.91,0.91125,0.9125,0.91375,0.915,0.91625,0.9175,0.91875,0.92,0.92125,0.9225,0.92375,0.925,0.92625,0.9275,0.92875,0.93,0.93125,0.9325,0.93375,0.935,0.93625,0.9375,0.93875,0.94,0.94125,0.9425,0.94375,0.945,0.94625,0.9475,0.94875,0.95] \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", - "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9013333333,0.9026666667,0.904,0.9053333333,0.9066666667,0.908,0.9093333333,0.9106666667,0.912,0.9133333333,0.9146666667,0.916,0.9173333333,0.9186666667,0.92,0.9213333333,0.9226666667,0.924,0.9253333333,0.9266666667,0.928,0.9293333333,0.9306666667,0.932,0.9333333333,0.9346666667,0.936,0.9373333333,0.9386666667,0.94,0.9413333333,0.9426666667,0.944,0.9453333333,0.9466666667,0.948,0.9493333333,0.9506666667,0.952,0.9533333333,0.9546666667,0.956,0.9573333333,0.9586666667,0.96,0.9613333333,0.9626666667,0.964,0.9653333333,0.9666666667,0.968,0.9693333333,0.9706666667,0.972,0.9733333333,0.9746666667,0.976,0.9773333333,0.9786666667,0.98,0.9813333333,0.9826666667,0.984,0.9853333333,0.9866666667,0.988,0.9893333333,0.9906666667,0.992,0.9933333333,0.9946666667,0.996,0.9973333333,0.9986666667,1.0] \n", - "\n", - " metac-perplexity \\\n", - "0 [0.30000000000000004,0.31,0.25,0.10600000000000001,0.03399999999999991] \n", - "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", - "2 0.1 \n", - "3 [0.1,0.6,0.3] \n", - "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.2066666667,0.2133333333,0.22,0.2266666667,0.2333333333,0.24,0.2466666667,0.2533333333,0.26,0.2666666667,0.28,0.2933333333,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", + " metac-o1 \\\n", + "0 [0.25,0.3,0.3,0.1,0.05] \n", + "1 [0.05,0.0505882353,0.0511764706,0.0517647059,0.0523529412,0.0529411765,0.0535294118,0.0541176471,0.0547058824,0.0552941176,0.0558823529,0.0564705882,0.0570588235,0.0576470588,0.0582352941,0.0588235294,0.0594117647,0.06,0.0605882353,0.0611764706,0.0617647059,0.0623529412,0.0629411765,0.0635294118,0.0641176471,0.0647058824,0.0652941176,0.0658823529,0.0664705882,0.0670588235,0.0676470588,0.0682352941,0.0688235294,0.0694117647,0.07,0.0705882353,0.0711764706,0.0717647059,0.0723529412,0.0729411765,0.0735294118,0.0741176471,0.0747058824,0.0752941176,0.0758823529,0.0764705882,0.0770588235,0.0776470588,0.0782352941,0.0788235294,0.0794117647,0.08,0.0805882353,0.0811764706,0.0817647059,0.0823529412,0.0829411765,0.0835294118,0.0841176471,0.0847058824,0.0852941176,0.0858823529,0.0864705882,0.0870588235,0.0876470588,0.0882352941,0.0888235294,0.0894117647,0.09,0.0905882353,0.0911764706,0.0917647059,0.0923529412,0.0929411765,0.0935294118,0.0941176471,0.0947058824,0.0952941176,0.0958823529,0.0964705882,0.0970588235,0.0976470588,0.0982352941,0.0988235294,0.0994117647,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.22,0.24,0.26,0.28,0.3,0.32,0.34,0.36,0.38,0.4,0.42,0.44,0.46,0.48,0.5,0.52,0.54,0.56,0.58,0.6,0.62,0.64,0.66,0.68,0.7,0.72,0.74,0.76,0.78,0.8,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.89,0.9,0.9007692308,0.9015384615,0.9023076923,0.9030769231,0.9038461538,0.9046153846,0.9053846154,0.9061538462,0.9069230769,0.9076923077,0.9084615385,0.9092307692,0.91,0.9107692308,0.9115384615,0.9123076923,0.9130769231,0.9138461538,0.9146153846,0.9153846154,0.9161538462,0.9169230769,0.9176923077,0.9184615385,0.9192307692,0.92,0.9207692308,0.9215384615,0.9223076923,0.9230769231,0.9238461538,0.9246153846,0.9253846154,0.9261538462,0.9269230769,0.9276923077,0.9284615385,0.9292307692,0.93,0.9307692308,0.9315384615,0.9323076923,0.9330769231,0.9338461538,0.9346153846,0.9353846154,0.9361538462,0.9369230769,0.9376923077,0.9384615385,0.9392307692,0.94,0.9407692308,0.9415384615,0.9423076923,0.9430769231,0.9438461538,0.9446153846,0.9453846154,0.9461538462,0.9469230769,0.9476923077,0.9484615385,0.9492307692,0.95] \n", + "2 0.1 \n", + "3 [0.45,0.45,0.1] \n", + "4 [0.0,0.0033333333,0.0066666667,0.01,0.0133333333,0.0166666667,0.02,0.0233333333,0.0266666667,0.03,0.0333333333,0.0366666667,0.04,0.0433333333,0.0466666667,0.05,0.0533333333,0.0566666667,0.06,0.0633333333,0.0666666667,0.07,0.0733333333,0.0766666667,0.08,0.0833333333,0.0866666667,0.09,0.0933333333,0.0966666667,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.6057142857,0.6114285714,0.6171428571,0.6228571429,0.6285714286,0.6342857143,0.64,0.6457142857,0.6514285714,0.6571428571,0.6628571429,0.6685714286,0.6742857143,0.68,0.6857142857,0.6914285714,0.6971428571,0.7028571429,0.7085714286,0.7142857143,0.72,0.7257142857,0.7314285714,0.7371428571,0.7428571429,0.7485714286,0.7542857143,0.76,0.7657142857,0.7714285714,0.7771428571,0.7828571429,0.7885714286,0.7942857143,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.9025,0.905,0.9075,0.91,0.9125,0.915,0.9175,0.92,0.9225,0.925,0.9275,0.93,0.9325,0.935,0.9375,0.94,0.9425,0.945,0.9475,0.95,0.9525,0.955,0.9575,0.96,0.9625,0.965,0.9675,0.97,0.9725,0.975,0.9775,0.98,0.9825,0.985,0.9875,0.99,0.9925,0.995,0.9975,1.0] \n", + "\n", + " metac-o1-preview \\\n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.208,0.216,0.224,0.232,0.24,0.248,0.256,0.264,0.272,0.28,0.288,0.296,0.304,0.312,0.32,0.328,0.336,0.344,0.352,0.36,0.368,0.376,0.384,0.392,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.8066666667,0.8133333333,0.82,0.8266666667,0.8333333333,0.84,0.8466666667,0.8533333333,0.86,0.8666666667,0.8733333333,0.88,0.8866666667,0.8933333333,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", + "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,0.032,0.036,0.04,0.044,0.048,0.052,0.056,0.06,0.064,0.068,0.072,0.076,0.08,0.084,0.088,0.092,0.096,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.5,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.6,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.7,0.71,0.72,0.73,0.74,0.75,0.76,0.77,0.78,0.79,0.8,0.805,0.81,0.815,0.82,0.825,0.83,0.835,0.84,0.845,0.85,0.855,0.86,0.865,0.87,0.875,0.88,0.885,0.89,0.895,0.9,0.9013333333,0.9026666667,0.904,0.9053333333,0.9066666667,0.908,0.9093333333,0.9106666667,0.912,0.9133333333,0.9146666667,0.916,0.9173333333,0.9186666667,0.92,0.9213333333,0.9226666667,0.924,0.9253333333,0.9266666667,0.928,0.9293333333,0.9306666667,0.932,0.9333333333,0.9346666667,0.936,0.9373333333,0.9386666667,0.94,0.9413333333,0.9426666667,0.944,0.9453333333,0.9466666667,0.948,0.9493333333,0.9506666667,0.952,0.9533333333,0.9546666667,0.956,0.9573333333,0.9586666667,0.96,0.9613333333,0.9626666667,0.964,0.9653333333,0.9666666667,0.968,0.9693333333,0.9706666667,0.972,0.9733333333,0.9746666667,0.976,0.9773333333,0.9786666667,0.98,0.9813333333,0.9826666667,0.984,0.9853333333,0.9866666667,0.988,0.9893333333,0.9906666667,0.992,0.9933333333,0.9946666667,0.996,0.9973333333,0.9986666667,1.0] \n", + "\n", + " metac-perplexity \\\n", + "0 [0.3,0.4,0.2,0.07,0.03] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.057,0.058,0.059,0.06,0.061,0.062,0.063,0.064,0.065,0.066,0.067,0.068,0.069,0.07,0.071,0.072,0.073,0.074,0.075,0.076,0.077,0.078,0.079,0.08,0.081,0.082,0.083,0.084,0.085,0.086,0.087,0.088,0.089,0.09,0.091,0.092,0.093,0.094,0.095,0.096,0.097,0.098,0.099,0.1,0.104,0.108,0.112,0.116,0.12,0.124,0.128,0.132,0.136,0.14,0.144,0.148,0.152,0.156,0.16,0.164,0.168,0.172,0.176,0.18,0.184,0.188,0.192,0.196,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.6133333333,0.6266666667,0.64,0.6533333333,0.6666666667,0.68,0.6933333333,0.7066666667,0.72,0.7333333333,0.7466666667,0.76,0.7733333333,0.7866666667,0.8,0.804,0.808,0.812,0.816,0.82,0.824,0.828,0.832,0.836,0.84,0.844,0.848,0.852,0.856,0.86,0.864,0.868,0.872,0.876,0.88,0.884,0.888,0.892,0.896,0.9,0.901,0.902,0.903,0.904,0.905,0.906,0.907,0.908,0.909,0.91,0.911,0.912,0.913,0.914,0.915,0.916,0.917,0.918,0.919,0.92,0.921,0.922,0.923,0.924,0.925,0.926,0.927,0.928,0.929,0.93,0.931,0.932,0.933,0.934,0.935,0.936,0.937,0.938,0.939,0.94,0.941,0.942,0.943,0.944,0.945,0.946,0.947,0.948,0.949,0.95] \n", + "2 0.1 \n", + "3 [0.15000000000000002,0.54,0.31000000000000005] \n", + "4 [0.0,0.0025,0.005,0.0075,0.01,0.0125,0.015,0.0175,0.02,0.0225,0.025,0.0275,0.03,0.0325,0.035,0.0375,0.04,0.0425,0.045,0.0475,0.05,0.0525,0.055,0.0575,0.06,0.0625,0.065,0.0675,0.07,0.0725,0.075,0.0775,0.08,0.0825,0.085,0.0875,0.09,0.0925,0.095,0.0975,0.1,0.105,0.11,0.115,0.12,0.125,0.13,0.135,0.14,0.145,0.15,0.155,0.16,0.165,0.17,0.175,0.18,0.185,0.19,0.195,0.2,0.21,0.22,0.23,0.24,0.25,0.26,0.27,0.28,0.29,0.3,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.4133333333,0.4266666667,0.44,0.4533333333,0.4666666667,0.48,0.4933333333,0.5066666667,0.52,0.5333333333,0.5466666667,0.56,0.5733333333,0.5866666667,0.6,0.608,0.616,0.624,0.632,0.64,0.648,0.656,0.664,0.672,0.68,0.688,0.696,0.704,0.712,0.72,0.728,0.736,0.744,0.752,0.76,0.768,0.776,0.784,0.792,0.8,0.8033333333,0.8066666667,0.81,0.8133333333,0.8166666667,0.82,0.8233333333,0.8266666667,0.83,0.8333333333,0.8366666667,0.84,0.8433333333,0.8466666667,0.85,0.8533333333,0.8566666667,0.86,0.8633333333,0.8666666667,0.87,0.8733333333,0.8766666667,0.88,0.8833333333,0.8866666667,0.89,0.8933333333,0.8966666667,0.9,0.902,0.904,0.906,0.908,0.91,0.912,0.914,0.916,0.918,0.92,0.922,0.924,0.926,0.928,0.93,0.932,0.934,0.936,0.938,0.94,0.942,0.944,0.946,0.948,0.95,0.952,0.954,0.956,0.958,0.96,0.962,0.964,0.966,0.968,0.97,0.972,0.974,0.976,0.978,0.98,0.982,0.984,0.986,0.988,0.99,0.992,0.994,0.996,0.998,1.0] \n", "\n", " minefrac1 \\\n", "0 NaN \n", @@ -3447,7 +3463,7 @@ " False\n", " False\n", " ...\n", - " 0.3\n", + " 0.4\n", " 0.9\n", " NaN\n", " NaN\n", @@ -3471,7 +3487,7 @@ " False\n", " False\n", " ...\n", - " 0.85\n", + " 0.8\n", " 0.95\n", " NaN\n", " NaN\n", @@ -3519,8 +3535,8 @@ " False\n", " False\n", " ...\n", - " 0.1\n", - " 0.1\n", + " 0.05\n", + " 0.05\n", " 0.03\n", " NaN\n", " 0.15\n", @@ -3552,10 +3568,10 @@ "\n", " metac-o1 metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "94 0.9 0.9 NaN NaN 0.95 0.95 \n", - "95 0.3 0.9 NaN NaN 0.15 NaN \n", - "96 0.85 0.95 NaN NaN 0.9 NaN \n", + "95 0.4 0.9 NaN NaN 0.15 NaN \n", + "96 0.8 0.95 NaN NaN 0.9 NaN \n", "97 0.8 0.85 0.3 NaN 0.85 0.85 \n", - "98 0.1 0.1 0.03 NaN 0.15 0.05 \n", + "98 0.05 0.05 0.03 NaN 0.15 0.05 \n", "\n", " pianobot swingswish twsummerbot wunderplumb \n", "94 NaN 0.9 0.762 0.9 \n", @@ -3636,61 +3652,61 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n", - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n" ] } @@ -3771,7 +3787,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 5.521275\n", + " 4.605170\n", " \n", " \n", " 3\n", @@ -3786,8 +3802,8 @@ " None\n", " None\n", " ...\n", - " 0.310155\n", - " 0.310155\n", + " 0.390198\n", + " 0.204794\n", " NaN\n", " 0.127833\n", " 0.152526\n", @@ -3810,16 +3826,16 @@ " False\n", " False\n", " ...\n", - " 0.116534\n", - " -0.106610\n", + " 0.298855\n", + " 0.211844\n", " NaN\n", " -0.184571\n", - " 0.111521\n", + " 0.112526\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 0.111521\n", + " 0.112526\n", " \n", " \n", " 9\n", @@ -3835,7 +3851,7 @@ " None\n", " ...\n", " -0.518794\n", - " -0.806476\n", + " -1.211941\n", " NaN\n", " -0.806476\n", " -0.494101\n", @@ -3843,7 +3859,7 @@ " NaN\n", " -0.624154\n", " NaN\n", - " -0.518794\n", + " -0.681313\n", " \n", " \n", " 13\n", @@ -3858,7 +3874,7 @@ " None\n", " None\n", " ...\n", - " 0.441833\n", + " 0.330943\n", " 0.510826\n", " 0.021979\n", " 0.200671\n", @@ -3905,16 +3921,16 @@ "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", "0 2.302585 5.703782 NaN 2.292635 2.703087 \n", - "3 0.310155 0.310155 NaN 0.127833 0.152526 \n", - "6 0.116534 -0.106610 NaN -0.184571 0.111521 \n", - "9 -0.518794 -0.806476 NaN -0.806476 -0.494101 \n", - "13 0.441833 0.510826 0.021979 0.200671 0.253781 \n", + "3 0.390198 0.204794 NaN 0.127833 0.152526 \n", + "6 0.298855 0.211844 NaN -0.184571 0.112526 \n", + "9 -0.518794 -1.211941 NaN -0.806476 -0.494101 \n", + "13 0.330943 0.510826 0.021979 0.200671 0.253781 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", - "0 NaN NaN NaN NaN 5.521275 \n", + "0 NaN NaN NaN NaN 4.605170 \n", "3 NaN NaN -0.046520 NaN 0.310155 \n", - "6 NaN NaN NaN NaN 0.111521 \n", - "9 NaN NaN -0.624154 NaN -0.518794 \n", + "6 NaN NaN NaN NaN 0.112526 \n", + "9 NaN NaN -0.624154 NaN -0.681313 \n", "13 NaN NaN NaN NaN 0.158111 \n", "\n", "[5 rows x 58 columns]" @@ -3982,7 +3998,7 @@ " False\n", " ...\n", " -2.879198\n", - " -0.933288\n", + " -2.186051\n", " -3.007032\n", " -2.879198\n", " -3.795489\n", @@ -3990,7 +4006,7 @@ " NaN\n", " -2.348570\n", " -2.409195\n", - " -2.879198\n", + " -2.186051\n", " \n", " \n", " 82\n", @@ -4029,8 +4045,8 @@ " None\n", " None\n", " ...\n", - " -0.693147\n", - " -0.182322\n", + " -0.899761\n", + " -0.405465\n", " NaN\n", " -0.182322\n", " NaN\n", @@ -4053,8 +4069,8 @@ " False\n", " False\n", " ...\n", - " -0.069566\n", - " -0.080377\n", + " -0.054625\n", + " -0.102356\n", " NaN\n", " -0.124829\n", " -0.080377\n", @@ -4077,8 +4093,8 @@ " False\n", " False\n", " ...\n", - " -0.788457\n", - " -1.011601\n", + " -1.704748\n", + " -4.007333\n", " NaN\n", " -1.704748\n", " -0.318454\n", @@ -4118,19 +4134,19 @@ " range_max open_upper_bound open_lower_bound ... metac-o1-preview \\\n", "81 NaN False False ... -2.879198 \n", "82 NaN None None ... -0.076961 \n", - "83 NaN None None ... -0.693147 \n", - "91 NaN False False ... -0.069566 \n", - "92 NaN False False ... -0.788457 \n", + "83 NaN None None ... -0.899761 \n", + "91 NaN False False ... -0.054625 \n", + "92 NaN False False ... -1.704748 \n", "\n", " metac-perplexity minefrac1 mmBot pgodzinai pianobot swingswish \\\n", - "81 -0.933288 -3.007032 -2.879198 -3.795489 NaN NaN \n", + "81 -2.186051 -3.007032 -2.879198 -3.795489 NaN NaN \n", "82 -0.300105 -0.523248 0.105361 0.259511 NaN NaN \n", - "83 -0.182322 NaN -0.182322 NaN NaN NaN \n", - "91 -0.080377 NaN -0.124829 -0.080377 NaN -0.113529 \n", - "92 -1.011601 NaN -1.704748 -0.318454 NaN -0.480973 \n", + "83 -0.405465 NaN -0.182322 NaN NaN NaN \n", + "91 -0.102356 NaN -0.124829 -0.080377 NaN -0.113529 \n", + "92 -4.007333 NaN -1.704748 -0.318454 NaN -0.480973 \n", "\n", " twsummerbot wunderplumb bot_team_median \n", - "81 -2.348570 -2.409195 -2.879198 \n", + "81 -2.348570 -2.409195 -2.186051 \n", "82 0.276509 -0.644609 -0.587787 \n", "83 -0.178330 -0.567984 -0.693147 \n", "91 NaN -0.147818 -0.124829 \n", @@ -4200,7 +4216,7 @@ " False\n", " False\n", " ...\n", - " -0.092275\n", + " -0.038208\n", " -0.092275\n", " NaN\n", " -0.210058\n", @@ -4225,7 +4241,7 @@ " None\n", " ...\n", " -0.251314\n", - " 0.200671\n", + " 0.441833\n", " NaN\n", " 0.510826\n", " 0.320472\n", @@ -4233,7 +4249,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 0.287682\n", + " 0.367725\n", " \n", " \n", " 8\n", @@ -4248,8 +4264,8 @@ " False\n", " False\n", " ...\n", - " -0.111226\n", " -0.054067\n", + " 0.000000\n", " NaN\n", " -0.111226\n", " -0.147158\n", @@ -4328,15 +4344,15 @@ "16 None NaN NaN False False ... \n", "\n", " metac-o1-preview metac-perplexity minefrac1 mmBot pgodzinai \\\n", - "2 -0.092275 -0.092275 NaN -0.210058 -0.059485 \n", - "5 -0.251314 0.200671 NaN 0.510826 0.320472 \n", - "8 -0.111226 -0.054067 NaN -0.111226 -0.147158 \n", + "2 -0.038208 -0.092275 NaN -0.210058 -0.059485 \n", + "5 -0.251314 0.441833 NaN 0.510826 0.320472 \n", + "8 -0.054067 0.000000 NaN -0.111226 -0.147158 \n", "12 -0.057158 0.000000 NaN 0.054067 -0.057158 \n", "16 0.008457 0.008457 NaN -0.068083 NaN \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "2 NaN NaN NaN NaN -0.149434 \n", - "5 NaN NaN NaN NaN 0.287682 \n", + "5 NaN NaN NaN NaN 0.367725 \n", "8 NaN NaN -0.398124 NaN -0.147158 \n", "12 NaN NaN -0.499776 NaN -0.057158 \n", "16 NaN NaN -0.076070 NaN -0.096728 \n", @@ -4462,7 +4478,7 @@ " -0.132060\n", " -0.158283\n", " -0.132060\n", - " -0.132060\n", + " -0.158283\n", " \n", " \n", " 97\n", @@ -4501,7 +4517,7 @@ " False\n", " False\n", " ...\n", - " -0.063666\n", + " -0.017709\n", " 0.000000\n", " NaN\n", " -0.112251\n", @@ -4537,12 +4553,12 @@ "95 -2.251292 NaN NaN -0.111226 NaN \n", "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.063666 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", "95 NaN -0.054067 -0.083382 -2.944439 -0.111226 \n", - "96 NaN -0.132060 -0.158283 -0.132060 -0.132060 \n", + "96 NaN -0.132060 -0.158283 -0.132060 -0.158283 \n", "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", "\n", @@ -4603,7 +4619,7 @@ " \n", " 2\n", " bot_median\n", - " 3544.710382\n", + " 3472.028144\n", " \n", " \n", " 3\n", @@ -4838,7 +4854,7 @@ " bot Peer Score\n", "Rank \n", "1 metac-o1 3864.168122\n", - "2 bot_median 3544.710382\n", + "2 bot_median 3472.028144\n", "3 metac-o1-preview 3162.155445\n", "4 manticAI 2142.538438\n", "5 metac-Gemini-Exp-1206 2072.216227\n", @@ -4906,13 +4922,13 @@ "text": [ "mean pro median forecast on questions that resolved yes: 74.0%\n", "mean pro median forecast on questions that resolved no: 22.0%\n", - "mean metac-o1 forecast on questions that resolved yes: 73.0%\n", - "mean metac-o1 forecast on questions that resolved no: 27.0%\n" + "mean metac-o1 forecast on questions that resolved yes: 75.0%\n", + "mean metac-o1 forecast on questions that resolved no: 28.999999999999996%\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -4988,7 +5004,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1441081/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_17143/946735765.py:22: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " weighted_scores = df_long.groupby('forecaster').apply(lambda x: (x['score'] * x['question_weight']).sum(axis=0))\n" ] } @@ -5113,18 +5129,18 @@ " \n", " 3\n", " 4\n", - " acm_bot\n", - " 2239.058675\n", - " 85\n", - " 81.25\n", + " bot_median\n", + " 2374.216338\n", + " 97\n", + " 93.10\n", " \n", " \n", " 4\n", " 5\n", - " bot_median\n", - " 2138.701789\n", - " 97\n", - " 93.10\n", + " acm_bot\n", + " 2239.058675\n", + " 85\n", + " 81.25\n", " \n", " \n", " 5\n", @@ -5471,8 +5487,8 @@ "0 1 pro_median 4238.561607 97 \n", "1 2 metac-o1 3010.353788 96 \n", "2 3 metac-perplexity 2774.080331 94 \n", - "3 4 acm_bot 2239.058675 85 \n", - "4 5 bot_median 2138.701789 97 \n", + "3 4 bot_median 2374.216338 97 \n", + "4 5 acm_bot 2239.058675 85 \n", "5 6 metac-claude-3-5-sonnet-20240620 2018.110211 95 \n", "6 7 manticAI 1865.126260 74 \n", "7 8 metac-exa 1826.275681 94 \n", @@ -5520,8 +5536,8 @@ "0 93.10 \n", "1 92.10 \n", "2 90.10 \n", - "3 81.25 \n", - "4 93.10 \n", + "3 93.10 \n", + "4 81.25 \n", "5 91.50 \n", "6 70.45 \n", "7 90.10 \n", @@ -5716,6 +5732,20 @@ " 0.000036\n", " \n", " \n", + " bot_median\n", + " 2374.2\n", + " 93.1\n", + " 25.5\n", + " 56.712830\n", + " 5.877687\n", + " 4.338745\n", + " 1.985277\n", + " 37.2\n", + " 13.8\n", + " 0.999982\n", + " 0.000037\n", + " \n", + " \n", " acm_bot\n", " 2239.1\n", " 81.2\n", @@ -5730,20 +5760,6 @@ " 0.000025\n", " \n", " \n", - " bot_median\n", - " 2138.7\n", - " 93.1\n", - " 23.0\n", - " 64.275382\n", - " 6.661466\n", - " 3.448504\n", - " 1.985277\n", - " 36.2\n", - " 9.7\n", - " 0.999574\n", - " 0.000852\n", - " \n", - " \n", " metac-claude-3-5-sonnet-20240620\n", " 2018.1\n", " 91.5\n", @@ -6340,8 +6356,8 @@ "pro_median 4238.6 93.1 45.5 62.229168 \n", "metac-o1 3010.4 92.1 32.7 57.756859 \n", "metac-perplexity 2774.1 90.1 30.8 67.210383 \n", + "bot_median 2374.2 93.1 25.5 56.712830 \n", "acm_bot 2239.1 81.2 27.6 55.554054 \n", - "bot_median 2138.7 93.1 23.0 64.275382 \n", "metac-claude-3-5-sonnet-20240620 2018.1 91.5 22.1 64.219307 \n", "manticAI 1865.1 70.4 26.5 66.353059 \n", "metac-exa 1826.3 90.1 20.3 82.219585 \n", @@ -6389,8 +6405,8 @@ "pro_median 6.449398 7.059105 1.985277 58.3 \n", "metac-o1 6.018299 5.431054 1.985550 44.6 \n", "metac-perplexity 7.080664 4.348308 1.986114 44.9 \n", + "bot_median 5.877687 4.338745 1.985277 37.2 \n", "acm_bot 6.163169 4.471343 1.988985 39.8 \n", - "bot_median 6.661466 3.448504 1.985277 36.2 \n", "metac-claude-3-5-sonnet-20240620 6.713594 3.285252 1.985788 35.4 \n", "manticAI 7.905338 3.348936 1.993488 42.2 \n", "metac-exa 8.661894 2.340069 1.986114 37.5 \n", @@ -6438,8 +6454,8 @@ "pro_median 32.7 1.000000 0.000000 \n", "metac-o1 20.7 1.000000 0.000000 \n", "metac-perplexity 16.7 0.999982 0.000036 \n", + "bot_median 13.8 0.999982 0.000037 \n", "acm_bot 15.3 0.999987 0.000025 \n", - "bot_median 9.7 0.999574 0.000852 \n", "metac-claude-3-5-sonnet-20240620 8.7 0.999275 0.001450 \n", "manticAI 10.7 0.999343 0.001314 \n", "metac-exa 3.1 0.989243 0.021514 \n", @@ -6573,18 +6589,18 @@ " NA\n", " \n", " \n", - " RPM_bot\n", + " bean_bot\n", " -0.6\n", - " 7.0\n", + " 4.7\n", " -0.1\n", - " 0.820675\n", - " 0.310186\n", - " -0.269729\n", - " 2.446912\n", - " 0.7\n", - " -0.8\n", - " 0.398203\n", - " 0.796405\n", + " 0.069849\n", + " 0.032219\n", + " -4.265106\n", + " 2.784843\n", + " -0.0\n", + " -0.2\n", + " 0.007674\n", + " 0.015349\n", " \n", " \n", " jonahsingerbot\n", @@ -6601,20 +6617,6 @@ " 0.007677\n", " \n", " \n", - " bean_bot\n", - " -0.6\n", - " 4.7\n", - " -0.1\n", - " 0.069849\n", - " 0.032219\n", - " -4.265106\n", - " 2.784843\n", - " -0.0\n", - " -0.2\n", - " 0.007674\n", - " 0.015349\n", - " \n", - " \n", " X_bot\n", " -0.7\n", " 7.0\n", @@ -6657,6 +6659,20 @@ " 0.018953\n", " \n", " \n", + " RPM_bot\n", + " -1.3\n", + " 7.0\n", + " -0.2\n", + " 0.803163\n", + " 0.303567\n", + " -0.601802\n", + " 2.446912\n", + " 0.6\n", + " -0.9\n", + " 0.284666\n", + " 0.569332\n", + " \n", + " \n", " SynapseSeer\n", " -1.3\n", " 26.2\n", @@ -6742,17 +6758,17 @@ " \n", " \n", " annabot\n", - " -5.9\n", + " -6.2\n", " 29.3\n", " -0.2\n", - " 0.517575\n", - " 0.095618\n", - " -2.112203\n", + " 0.520869\n", + " 0.096226\n", + " -2.211795\n", " 2.044183\n", " -0.0\n", " -0.4\n", - " 0.021811\n", - " 0.043621\n", + " 0.017610\n", + " 0.035221\n", " \n", " \n", " 4Shadower\n", @@ -6770,17 +6786,17 @@ " \n", " \n", " cookics_bot_TEST\n", - " -6.6\n", + " -6.7\n", " 27.4\n", " -0.2\n", - " 0.747093\n", - " 0.142725\n", - " -1.683660\n", + " 0.748050\n", + " 0.142908\n", + " -1.722004\n", " 2.049541\n", - " 0.1\n", + " 0.0\n", " -0.5\n", - " 0.052019\n", - " 0.104037\n", + " 0.048384\n", + " 0.096767\n", " \n", " \n", " jkraybill_bot\n", @@ -6853,18 +6869,18 @@ " 0.201592\n", " \n", " \n", - " GreeneiBot2\n", - " -10.7\n", - " 58.4\n", - " -0.2\n", - " 0.848714\n", - " 0.111107\n", - " -1.647027\n", - " 2.000832\n", - " 0.0\n", - " -0.4\n", - " 0.052511\n", - " 0.105022\n", + " metac-o1\n", + " -10.8\n", + " 91.1\n", + " -0.1\n", + " 0.866824\n", + " 0.090818\n", + " -1.303018\n", + " 1.985829\n", + " 0.1\n", + " -0.3\n", + " 0.097944\n", + " 0.195889\n", " \n", " \n", " ajf-bot\n", @@ -6881,6 +6897,34 @@ " 0.094289\n", " \n", " \n", + " metac-deepseek-r1+asknews\n", + " -11.2\n", + " 52.1\n", + " -0.2\n", + " 0.634257\n", + " 0.087871\n", + " -2.445043\n", + " 2.005379\n", + " -0.0\n", + " -0.4\n", + " 0.008985\n", + " 0.017970\n", + " \n", + " \n", + " GreeneiBot2\n", + " -11.4\n", + " 58.4\n", + " -0.2\n", + " 0.846228\n", + " 0.110781\n", + " -1.766811\n", + " 2.000832\n", + " 0.0\n", + " -0.4\n", + " 0.041290\n", + " 0.082581\n", + " \n", + " \n", " Bot_Pepa\n", " -11.5\n", " 44.0\n", @@ -6895,46 +6939,18 @@ " 0.023810\n", " \n", " \n", - " metac-perplexity\n", - " -12.0\n", - " 89.1\n", - " -0.1\n", - " 1.000845\n", - " 0.106030\n", - " -1.269604\n", - " 1.986405\n", - " 0.1\n", - " -0.3\n", - " 0.103785\n", - " 0.207569\n", - " \n", - " \n", - " bot_median\n", - " -12.2\n", - " 92.1\n", - " -0.1\n", - " 0.875909\n", - " 0.091270\n", - " -1.448706\n", - " 1.985550\n", - " 0.0\n", - " -0.3\n", - " 0.075426\n", - " 0.150853\n", - " \n", - " \n", - " metac-o1\n", - " -12.4\n", - " 91.1\n", - " -0.1\n", - " 0.941303\n", - " 0.098621\n", - " -1.375036\n", - " 1.985829\n", + " metac-Gemini-Exp-1206\n", + " -11.5\n", + " 76.5\n", + " -0.2\n", + " 0.895210\n", + " 0.102351\n", + " -1.471849\n", + " 1.990822\n", " 0.1\n", - " -0.3\n", - " 0.086265\n", - " 0.172530\n", + " -0.4\n", + " 0.072609\n", + " 0.145218\n", " \n", " \n", " laylaps\n", @@ -6951,32 +6967,18 @@ " 0.017488\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -13.4\n", - " 52.1\n", - " -0.3\n", - " 0.686642\n", - " 0.095129\n", - " -2.702394\n", - " 2.005379\n", + " bot_median\n", + " -13.3\n", + " 92.1\n", " -0.1\n", - " -0.4\n", - " 0.004660\n", - " 0.009321\n", - " \n", - " \n", - " metac-Gemini-Exp-1206\n", - " -13.5\n", - " 76.5\n", - " -0.2\n", - " 1.006606\n", - " 0.115088\n", - " -1.527727\n", - " 1.990822\n", - " 0.1\n", - " -0.4\n", - " 0.065380\n", - " 0.130759\n", + " 0.757201\n", + " 0.078901\n", + " -1.830058\n", + " 1.985550\n", + " 0.0\n", + " -0.3\n", + " 0.035256\n", + " 0.070512\n", " \n", " \n", " wunderplumb\n", @@ -6993,6 +6995,20 @@ " 0.006348\n", " \n", " \n", + " metac-perplexity\n", + " -14.4\n", + " 89.1\n", + " -0.2\n", + " 1.102601\n", + " 0.116810\n", + " -1.384952\n", + " 1.986405\n", + " 0.1\n", + " -0.4\n", + " 0.084782\n", + " 0.169564\n", + " \n", + " \n", " manticAI\n", " -14.6\n", " 69.4\n", @@ -7007,20 +7023,6 @@ " 0.011014\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -14.7\n", - " 90.5\n", - " -0.2\n", - " 0.942980\n", - " 0.099124\n", - " -1.642585\n", - " 1.986072\n", - " 0.0\n", - " -0.4\n", - " 0.051989\n", - " 0.103978\n", - " \n", - " \n", " NextWorldLab\n", " -16.9\n", " 80.2\n", @@ -7035,46 +7037,32 @@ " 0.040909\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -18.9\n", - " 91.1\n", - " -0.2\n", - " 0.731708\n", - " 0.076662\n", - " -2.699995\n", - " 1.985829\n", - " -0.1\n", - " -0.4\n", - " 0.004141\n", - " 0.008282\n", - " \n", - " \n", " minefrac1\n", - " -19.2\n", + " -18.8\n", " 51.1\n", " -0.4\n", - " 0.880990\n", - " 0.123242\n", - " -3.043641\n", + " 0.874752\n", + " 0.122370\n", + " -3.013581\n", " 2.006545\n", " -0.1\n", " -0.6\n", - " 0.001859\n", - " 0.003717\n", + " 0.002021\n", + " 0.004043\n", " \n", " \n", - " metac-o1-preview\n", - " -20.9\n", + " metac-claude-3-5-sonnet-latest\n", + " -21.6\n", " 91.1\n", " -0.2\n", - " 0.802181\n", - " 0.084045\n", - " -2.728807\n", + " 0.784073\n", + " 0.082148\n", + " -2.885581\n", " 1.985829\n", " -0.1\n", " -0.4\n", - " 0.003821\n", - " 0.007643\n", + " 0.002444\n", + " 0.004888\n", " \n", " \n", " mmBot\n", @@ -7091,46 +7079,46 @@ " 0.002208\n", " \n", " \n", - " metac-Llama-3.1\n", - " -23.2\n", - " 89.1\n", - " -0.3\n", - " 1.031278\n", - " 0.109254\n", - " -2.379606\n", - " 1.986405\n", + " metac-claude-3-5-sonnet-20240620\n", + " -22.1\n", + " 90.5\n", + " -0.2\n", + " 0.992190\n", + " 0.104297\n", + " -2.344713\n", + " 1.986072\n", " -0.0\n", " -0.5\n", - " 0.009745\n", - " 0.019489\n", + " 0.010627\n", + " 0.021254\n", " \n", " \n", " metac-grok-2-1212\n", - " -23.5\n", + " -23.2\n", " 91.1\n", " -0.3\n", - " 1.068006\n", - " 0.111896\n", - " -2.303421\n", + " 0.969180\n", + " 0.101542\n", + " -2.504438\n", " 1.985829\n", - " -0.0\n", + " -0.1\n", " -0.5\n", - " 0.011778\n", - " 0.023556\n", + " 0.007032\n", + " 0.014063\n", " \n", " \n", " pgodzinai\n", - " -24.0\n", + " -23.2\n", " 76.4\n", " -0.3\n", - " 0.976590\n", - " 0.111729\n", - " -2.811085\n", + " 1.002923\n", + " 0.114742\n", + " -2.649317\n", " 1.990849\n", " -0.1\n", " -0.5\n", - " 0.003144\n", - " 0.006289\n", + " 0.004910\n", + " 0.009821\n", " \n", " \n", " VeritasAI\n", @@ -7147,32 +7135,46 @@ " 0.000076\n", " \n", " \n", - " metac-exa\n", - " -26.2\n", - " 89.1\n", + " metac-o1-preview\n", + " -24.4\n", + " 91.1\n", " -0.3\n", - " 0.830275\n", - " 0.087960\n", - " -3.341545\n", - " 1.986405\n", + " 0.852432\n", + " 0.089310\n", + " -2.999396\n", + " 1.985829\n", " -0.1\n", - " -0.5\n", - " 0.000612\n", - " 0.001224\n", + " -0.4\n", + " 0.001749\n", + " 0.003497\n", " \n", " \n", " metac-gpt-4o\n", - " -26.6\n", + " -25.1\n", " 91.1\n", " -0.3\n", - " 0.879087\n", - " 0.092103\n", - " -3.165570\n", + " 0.873597\n", + " 0.091528\n", + " -3.009707\n", " 1.985829\n", " -0.1\n", " -0.5\n", - " 0.001056\n", - " 0.002112\n", + " 0.001696\n", + " 0.003391\n", + " \n", + " \n", + " metac-exa\n", + " -26.1\n", + " 89.1\n", + " -0.3\n", + " 0.791935\n", + " 0.083898\n", + " -3.495695\n", + " 1.986405\n", + " -0.1\n", + " -0.5\n", + " 0.000371\n", + " 0.000743\n", " \n", " \n", " InstitutPelFutur\n", @@ -7188,6 +7190,20 @@ " 0.002292\n", " 0.004584\n", " \n", + " \n", + " metac-Llama-3.1\n", + " -28.0\n", + " 89.1\n", + " -0.3\n", + " 0.907200\n", + " 0.096109\n", + " -3.270200\n", + " 1.986405\n", + " -0.1\n", + " -0.5\n", + " 0.000767\n", + " 0.001534\n", + " \n", " \n", "\n", "" @@ -7196,146 +7212,146 @@ " W_score W_count W_ave W_stdev std_err \\\n", "cobyj-bot 0.0 0.0 NaN NaN NaN \n", "andrewsiah 0.0 0.0 NaN NaN NaN \n", - "RPM_bot -0.6 7.0 -0.1 0.820675 0.310186 \n", - "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "bean_bot -0.6 4.7 -0.1 0.069849 0.032219 \n", + "jonahsingerbot -0.6 4.7 -0.1 0.050272 0.023189 \n", "X_bot -0.7 7.0 -0.1 0.354068 0.133825 \n", "CumulativeBot -1.1 10.2 -0.1 0.257798 0.080522 \n", "swingswish -1.2 7.7 -0.2 0.140275 0.050552 \n", + "RPM_bot -1.3 7.0 -0.2 0.803163 0.303567 \n", "SynapseSeer -1.3 26.2 -0.1 0.452555 0.088498 \n", "KevinTestBot -1.5 8.4 -0.2 0.589466 0.203385 \n", "Grizeu_Bot -1.7 51.4 -0.0 1.173392 0.163747 \n", "pianobot -2.7 4.7 -0.6 0.916204 0.422613 \n", "CatrachoCaster -3.2 19.7 -0.2 0.520901 0.117361 \n", "krm-bot -5.1 9.5 -0.5 0.511546 0.165967 \n", - "annabot -5.9 29.3 -0.2 0.517575 0.095618 \n", + "annabot -6.2 29.3 -0.2 0.520869 0.096226 \n", "4Shadower -6.2 14.0 -0.4 0.767322 0.205075 \n", - "cookics_bot_TEST -6.6 27.4 -0.2 0.747093 0.142725 \n", + "cookics_bot_TEST -6.7 27.4 -0.2 0.748050 0.142908 \n", "jkraybill_bot -7.5 44.0 -0.2 0.512853 0.077272 \n", "twsummerbot -8.9 58.4 -0.2 0.659710 0.086327 \n", "MWG -9.6 28.6 -0.3 0.711160 0.132979 \n", "ProfessorSP -10.0 18.6 -0.5 0.936277 0.217094 \n", "acm_bot -10.5 80.2 -0.1 0.914265 0.102059 \n", - "GreeneiBot2 -10.7 58.4 -0.2 0.848714 0.111107 \n", + "metac-o1 -10.8 91.1 -0.1 0.866824 0.090818 \n", "ajf-bot -10.9 34.2 -0.3 1.085589 0.185496 \n", + "metac-deepseek-r1+asknews -11.2 52.1 -0.2 0.634257 0.087871 \n", + "GreeneiBot2 -11.4 58.4 -0.2 0.846228 0.110781 \n", "Bot_Pepa -11.5 44.0 -0.3 0.737537 0.111125 \n", - "metac-perplexity -12.0 89.1 -0.1 1.000845 0.106030 \n", - "bot_median -12.2 92.1 -0.1 0.875909 0.091270 \n", - "metac-o1 -12.4 91.1 -0.1 0.941303 0.098621 \n", + "metac-Gemini-Exp-1206 -11.5 76.5 -0.2 0.895210 0.102351 \n", "laylaps -12.9 64.1 -0.2 0.661905 0.082674 \n", - "metac-deepseek-r1+asknews -13.4 52.1 -0.3 0.686642 0.095129 \n", - "metac-Gemini-Exp-1206 -13.5 76.5 -0.2 1.006606 0.115088 \n", + "bot_median -13.3 92.1 -0.1 0.757201 0.078901 \n", "wunderplumb -13.6 25.6 -0.5 0.900051 0.178062 \n", + "metac-perplexity -14.4 89.1 -0.2 1.102601 0.116810 \n", "manticAI -14.6 69.4 -0.2 0.670946 0.080510 \n", - "metac-claude-3-5-sonnet-20240620 -14.7 90.5 -0.2 0.942980 0.099124 \n", "NextWorldLab -16.9 80.2 -0.2 0.906964 0.101244 \n", - "metac-claude-3-5-sonnet-latest -18.9 91.1 -0.2 0.731708 0.076662 \n", - "minefrac1 -19.2 51.1 -0.4 0.880990 0.123242 \n", - "metac-o1-preview -20.9 91.1 -0.2 0.802181 0.084045 \n", + "minefrac1 -18.8 51.1 -0.4 0.874752 0.122370 \n", + "metac-claude-3-5-sonnet-latest -21.6 91.1 -0.2 0.784073 0.082148 \n", "mmBot -21.9 92.1 -0.2 0.725010 0.075546 \n", - "metac-Llama-3.1 -23.2 89.1 -0.3 1.031278 0.109254 \n", - "metac-grok-2-1212 -23.5 91.1 -0.3 1.068006 0.111896 \n", - "pgodzinai -24.0 76.4 -0.3 0.976590 0.111729 \n", + "metac-claude-3-5-sonnet-20240620 -22.1 90.5 -0.2 0.992190 0.104297 \n", + "metac-grok-2-1212 -23.2 91.1 -0.3 0.969180 0.101542 \n", + "pgodzinai -23.2 76.4 -0.3 1.002923 0.114742 \n", "VeritasAI -24.3 77.1 -0.3 0.660703 0.075245 \n", - "metac-exa -26.2 89.1 -0.3 0.830275 0.087960 \n", - "metac-gpt-4o -26.6 91.1 -0.3 0.879087 0.092103 \n", + "metac-o1-preview -24.4 91.1 -0.3 0.852432 0.089310 \n", + "metac-gpt-4o -25.1 91.1 -0.3 0.873597 0.091528 \n", + "metac-exa -26.1 89.1 -0.3 0.791935 0.083898 \n", "InstitutPelFutur -26.9 90.1 -0.3 0.973767 0.102587 \n", + "metac-Llama-3.1 -28.0 89.1 -0.3 0.907200 0.096109 \n", "\n", " t_stat t_crit upper_bound \\\n", "cobyj-bot NaN NaN NaN \n", "andrewsiah NaN NaN NaN \n", - "RPM_bot -0.269729 2.446912 0.7 \n", - "jonahsingerbot -5.273630 2.784843 -0.1 \n", "bean_bot -4.265106 2.784843 -0.0 \n", + "jonahsingerbot -5.273630 2.784843 -0.1 \n", "X_bot -0.747195 2.446912 0.2 \n", "CumulativeBot -1.315132 2.231848 0.1 \n", "swingswish -3.074947 2.367123 -0.0 \n", + "RPM_bot -0.601802 2.446912 0.6 \n", "SynapseSeer -0.568910 2.053076 0.1 \n", "KevinTestBot -0.897116 2.311496 0.3 \n", "Grizeu_Bot -0.206616 2.006447 0.3 \n", "pianobot -1.384327 2.798986 0.6 \n", "CatrachoCaster -1.365532 2.088777 0.1 \n", "krm-bot -3.229846 2.264709 -0.2 \n", - "annabot -2.112203 2.044183 -0.0 \n", + "annabot -2.211795 2.044183 -0.0 \n", "4Shadower -2.143194 2.147239 0.0 \n", - "cookics_bot_TEST -1.683660 2.049541 0.1 \n", + "cookics_bot_TEST -1.722004 2.049541 0.0 \n", "jkraybill_bot -2.197133 2.014642 -0.0 \n", "twsummerbot -1.758391 2.000855 0.0 \n", "MWG -2.535384 2.046561 -0.1 \n", "ProfessorSP -2.484480 2.095243 -0.1 \n", "acm_bot -1.287717 1.989344 0.1 \n", - "GreeneiBot2 -1.647027 2.000832 0.0 \n", + "metac-o1 -1.303018 1.985829 0.1 \n", "ajf-bot -1.722395 2.030778 0.1 \n", + "metac-deepseek-r1+asknews -2.445043 2.005379 -0.0 \n", + "GreeneiBot2 -1.766811 2.000832 0.0 \n", "Bot_Pepa -2.343166 2.014642 -0.0 \n", - "metac-perplexity -1.269604 1.986405 0.1 \n", - "bot_median -1.448706 1.985550 0.0 \n", - "metac-o1 -1.375036 1.985829 0.1 \n", + "metac-Gemini-Exp-1206 -1.471849 1.990822 0.1 \n", "laylaps -2.440461 1.996907 -0.0 \n", - "metac-deepseek-r1+asknews -2.702394 2.005379 -0.1 \n", - "metac-Gemini-Exp-1206 -1.527727 1.990822 0.1 \n", + "bot_median -1.830058 1.985550 0.0 \n", "wunderplumb -2.984094 2.056603 -0.2 \n", + "metac-perplexity -1.384952 1.986405 0.1 \n", "manticAI -2.613354 1.993968 -0.0 \n", - "metac-claude-3-5-sonnet-20240620 -1.642585 1.986072 0.0 \n", "NextWorldLab -2.078393 1.989344 -0.0 \n", - "metac-claude-3-5-sonnet-latest -2.699995 1.985829 -0.1 \n", - "minefrac1 -3.043641 2.006545 -0.1 \n", - "metac-o1-preview -2.728807 1.985829 -0.1 \n", + "minefrac1 -3.013581 2.006545 -0.1 \n", + "metac-claude-3-5-sonnet-latest -2.885581 1.985829 -0.1 \n", "mmBot -3.150104 1.985550 -0.1 \n", - "metac-Llama-3.1 -2.379606 1.986405 -0.0 \n", - "metac-grok-2-1212 -2.303421 1.985829 -0.0 \n", - "pgodzinai -2.811085 1.990849 -0.1 \n", + "metac-claude-3-5-sonnet-20240620 -2.344713 1.986072 -0.0 \n", + "metac-grok-2-1212 -2.504438 1.985829 -0.1 \n", + "pgodzinai -2.649317 1.990849 -0.1 \n", "VeritasAI -4.185910 1.990482 -0.2 \n", - "metac-exa -3.341545 1.986405 -0.1 \n", - "metac-gpt-4o -3.165570 1.985829 -0.1 \n", + "metac-o1-preview -2.999396 1.985829 -0.1 \n", + "metac-gpt-4o -3.009707 1.985829 -0.1 \n", + "metac-exa -3.495695 1.986405 -0.1 \n", "InstitutPelFutur -2.908524 1.986114 -0.1 \n", + "metac-Llama-3.1 -3.270200 1.986405 -0.1 \n", "\n", " lower_bound cdf p_value \n", "cobyj-bot NaN NaN NA \n", "andrewsiah NaN NaN NA \n", - "RPM_bot -0.8 0.398203 0.796405 \n", - "jonahsingerbot -0.2 0.003839 0.007677 \n", "bean_bot -0.2 0.007674 0.015349 \n", + "jonahsingerbot -0.2 0.003839 0.007677 \n", "X_bot -0.4 0.241594 0.483189 \n", "CumulativeBot -0.3 0.110066 0.220132 \n", "swingswish -0.3 0.009476 0.018953 \n", + "RPM_bot -0.9 0.284666 0.569332 \n", "SynapseSeer -0.2 0.287231 0.574463 \n", "KevinTestBot -0.7 0.198952 0.397903 \n", "Grizeu_Bot -0.4 0.418571 0.837143 \n", "pianobot -1.8 0.121941 0.243882 \n", "CatrachoCaster -0.4 0.094144 0.188288 \n", "krm-bot -0.9 0.005563 0.011127 \n", - "annabot -0.4 0.021811 0.043621 \n", + "annabot -0.4 0.017610 0.035221 \n", "4Shadower -0.9 0.025797 0.051593 \n", - "cookics_bot_TEST -0.5 0.052019 0.104037 \n", + "cookics_bot_TEST -0.5 0.048384 0.096767 \n", "jkraybill_bot -0.3 0.016721 0.033441 \n", "twsummerbot -0.3 0.042006 0.084012 \n", "MWG -0.6 0.008595 0.017191 \n", "ProfessorSP -1.0 0.011644 0.023289 \n", "acm_bot -0.3 0.100796 0.201592 \n", - "GreeneiBot2 -0.4 0.052511 0.105022 \n", + "metac-o1 -0.3 0.097944 0.195889 \n", "ajf-bot -0.7 0.047145 0.094289 \n", + "metac-deepseek-r1+asknews -0.4 0.008985 0.017970 \n", + "GreeneiBot2 -0.4 0.041290 0.082581 \n", "Bot_Pepa -0.5 0.011905 0.023810 \n", - "metac-perplexity -0.3 0.103785 0.207569 \n", - "bot_median -0.3 0.075426 0.150853 \n", - "metac-o1 -0.3 0.086265 0.172530 \n", + "metac-Gemini-Exp-1206 -0.4 0.072609 0.145218 \n", "laylaps -0.4 0.008744 0.017488 \n", - "metac-deepseek-r1+asknews -0.4 0.004660 0.009321 \n", - "metac-Gemini-Exp-1206 -0.4 0.065380 0.130759 \n", + "bot_median -0.3 0.035256 0.070512 \n", "wunderplumb -0.9 0.003174 0.006348 \n", + "metac-perplexity -0.4 0.084782 0.169564 \n", "manticAI -0.4 0.005507 0.011014 \n", - "metac-claude-3-5-sonnet-20240620 -0.4 0.051989 0.103978 \n", "NextWorldLab -0.4 0.020455 0.040909 \n", - "metac-claude-3-5-sonnet-latest -0.4 0.004141 0.008282 \n", - "minefrac1 -0.6 0.001859 0.003717 \n", - "metac-o1-preview -0.4 0.003821 0.007643 \n", + "minefrac1 -0.6 0.002021 0.004043 \n", + "metac-claude-3-5-sonnet-latest -0.4 0.002444 0.004888 \n", "mmBot -0.4 0.001104 0.002208 \n", - "metac-Llama-3.1 -0.5 0.009745 0.019489 \n", - "metac-grok-2-1212 -0.5 0.011778 0.023556 \n", - "pgodzinai -0.5 0.003144 0.006289 \n", + "metac-claude-3-5-sonnet-20240620 -0.5 0.010627 0.021254 \n", + "metac-grok-2-1212 -0.5 0.007032 0.014063 \n", + "pgodzinai -0.5 0.004910 0.009821 \n", "VeritasAI -0.5 0.000038 0.000076 \n", - "metac-exa -0.5 0.000612 0.001224 \n", - "metac-gpt-4o -0.5 0.001056 0.002112 \n", - "InstitutPelFutur -0.5 0.002292 0.004584 " + "metac-o1-preview -0.4 0.001749 0.003497 \n", + "metac-gpt-4o -0.5 0.001696 0.003391 \n", + "metac-exa -0.5 0.000371 0.000743 \n", + "InstitutPelFutur -0.5 0.002292 0.004584 \n", + "metac-Llama-3.1 -0.5 0.000767 0.001534 " ] }, "execution_count": 42, @@ -8563,9 +8579,23 @@ "outputId": "e83d6794-13a2-454d-cb70-0a38b065d9e7" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n", + "<>:29: SyntaxWarning: invalid escape sequence '\\s'\n", + "/tmp/ipykernel_17143/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\m'\n", + " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n", + "/tmp/ipykernel_17143/2856056443.py:29: SyntaxWarning: invalid escape sequence '\\s'\n", + " textstr = f'$\\mu={mu:.2f}$\\n$\\sigma={std:.2f}$'\n" + ] + }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -9087,363 +9117,363 @@ " \n", " \n", " metac-o1\n", - " 6.0\n", - " 7.6\n", - " 9.7\n", - " 12.0\n", - " 13.2\n", + " 5.9\n", + " 7.2\n", + " 9.5\n", + " 11.8\n", + " 12.9\n", " \n", " \n", " metac-o1-preview\n", - " 3.4\n", - " 5.2\n", + " 3.5\n", + " 5.3\n", " 8.3\n", " 11.2\n", - " 12.5\n", + " 12.7\n", " \n", " \n", " manticAI\n", - " 0.2\n", + " 0.3\n", " 2.2\n", - " 5.3\n", - " 8.6\n", - " 10.3\n", + " 5.4\n", + " 8.7\n", + " 10.4\n", " \n", " \n", " metac-Gemini-Exp-1206\n", " 0.4\n", - " 2.1\n", - " 4.9\n", + " 2.2\n", + " 5.0\n", " 7.7\n", - " 9.1\n", + " 9.5\n", " \n", " \n", " acm_bot\n", - " -0.0\n", - " 1.3\n", - " 4.7\n", + " 0.4\n", + " 1.9\n", + " 4.6\n", " 7.4\n", " 8.8\n", " \n", " \n", " metac-perplexity\n", - " -2.0\n", - " 0.6\n", - " 4.3\n", - " 8.2\n", - " 9.8\n", + " -1.8\n", + " 0.1\n", + " 4.2\n", + " 7.8\n", + " 9.5\n", " \n", " \n", " GreeneiBot2\n", - " -1.5\n", - " 0.7\n", + " -0.6\n", + " 0.8\n", " 4.0\n", - " 7.0\n", - " 8.8\n", + " 7.2\n", + " 8.7\n", " \n", " \n", " twsummerbot\n", " 0.2\n", - " 1.6\n", - " 3.7\n", - " 6.2\n", - " 7.3\n", + " 1.4\n", + " 3.8\n", + " 6.3\n", + " 7.4\n", " \n", " \n", " cookics_bot_TEST\n", - " 0.0\n", - " 1.1\n", - " 3.1\n", + " -0.2\n", + " 0.8\n", + " 3.0\n", " 5.1\n", " 6.2\n", " \n", " \n", " pgodzinai\n", - " -3.6\n", + " -3.0\n", " -1.1\n", - " 3.1\n", - " 6.5\n", + " 3.0\n", + " 6.8\n", " 9.0\n", " \n", " \n", - " CumulativeBot\n", - " -0.1\n", - " 0.9\n", + " metac-claude-3-5-sonnet-latest\n", + " -1.2\n", + " 0.2\n", " 2.6\n", - " 4.5\n", - " 5.4\n", + " 5.2\n", + " 6.6\n", " \n", " \n", " SynapseSeer\n", - " 0.3\n", + " 0.4\n", " 1.1\n", " 2.6\n", - " 4.1\n", - " 4.9\n", + " 4.0\n", + " 4.8\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", - " -1.4\n", - " -0.2\n", + " CumulativeBot\n", + " -0.5\n", + " 0.6\n", " 2.6\n", - " 5.1\n", - " 6.3\n", + " 4.5\n", + " 5.4\n", " \n", " \n", " jkraybill_bot\n", - " -3.6\n", - " -1.7\n", - " 1.8\n", - " 5.1\n", + " -3.2\n", + " -1.3\n", + " 1.7\n", + " 4.9\n", " 6.5\n", " \n", " \n", " metac-exa\n", " -4.8\n", - " -2.7\n", - " 1.8\n", - " 5.6\n", - " 7.3\n", + " -2.6\n", + " 1.7\n", + " 5.7\n", + " 7.4\n", " \n", " \n", " metac-deepseek-r1+asknews\n", - " -2.1\n", - " -0.8\n", - " 1.3\n", - " 3.3\n", - " 4.5\n", + " -1.7\n", + " -0.7\n", + " 1.4\n", + " 3.6\n", + " 4.6\n", " \n", " \n", " MWG\n", - " -1.7\n", + " -1.5\n", " -0.8\n", " 0.7\n", " 2.0\n", - " 2.9\n", + " 2.8\n", " \n", " \n", " andrewsiah\n", " -0.9\n", " -0.6\n", - " 0.0\n", - " 0.6\n", - " 1.0\n", - " \n", - " \n", - " pianobot\n", - " -1.3\n", - " -0.8\n", " -0.0\n", - " 0.7\n", - " 1.1\n", + " 0.6\n", + " 0.9\n", " \n", " \n", " X_bot\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.0\n", " 0.1\n", " 0.2\n", " \n", " \n", + " pianobot\n", + " -1.3\n", + " -0.8\n", + " -0.0\n", + " 0.6\n", + " 1.0\n", + " \n", + " \n", " cobyj-bot\n", " -1.5\n", " -0.9\n", - " -0.1\n", - " 0.8\n", + " -0.0\n", + " 0.9\n", " 1.3\n", " \n", " \n", - " annabot\n", - " -3.2\n", - " -2.1\n", - " -0.3\n", - " 1.2\n", - " 2.0\n", - " \n", - " \n", " KevinTestBot\n", - " -4.0\n", - " -2.6\n", + " -4.1\n", + " -2.9\n", " -0.4\n", - " 1.6\n", - " 2.6\n", + " 1.5\n", + " 2.7\n", + " \n", + " \n", + " annabot\n", + " -3.7\n", + " -2.3\n", + " -0.5\n", + " 1.2\n", + " 2.1\n", " \n", " \n", " bean_bot\n", - " -3.3\n", + " -3.1\n", " -2.2\n", " -0.5\n", - " 1.0\n", + " 1.1\n", " 1.9\n", " \n", " \n", " CatrachoCaster\n", - " -2.3\n", - " -1.8\n", - " -0.8\n", + " -2.2\n", + " -1.7\n", + " -0.7\n", " 0.2\n", - " 0.8\n", + " 0.7\n", " \n", " \n", " jonahsingerbot\n", - " -3.0\n", + " -2.9\n", " -2.3\n", - " -0.9\n", + " -0.8\n", " 0.4\n", " 1.0\n", " \n", " \n", " krm-bot\n", - " -3.8\n", - " -2.7\n", - " -1.0\n", - " 0.7\n", - " 1.6\n", + " -3.5\n", + " -2.6\n", + " -0.9\n", + " 0.6\n", + " 1.5\n", " \n", " \n", " ProfessorSP\n", - " -4.6\n", - " -3.3\n", - " -1.1\n", - " 0.9\n", - " 1.9\n", + " -4.4\n", + " -3.2\n", + " -1.0\n", + " 1.0\n", + " 2.2\n", " \n", " \n", " metac-grok-2-1212\n", - " -6.7\n", + " -6.6\n", " -4.8\n", - " -1.3\n", - " 1.7\n", - " 3.4\n", + " -1.4\n", + " 1.8\n", + " 3.1\n", " \n", " \n", " mmBot\n", - " -7.2\n", - " -5.5\n", + " -7.5\n", + " -5.4\n", " -1.6\n", - " 2.4\n", - " 4.5\n", + " 2.5\n", + " 4.7\n", " \n", " \n", " 4Shadower\n", - " -4.8\n", - " -3.7\n", - " -1.6\n", - " 0.2\n", - " 1.1\n", - " \n", - " \n", - " swingswish\n", - " -5.3\n", - " -3.9\n", - " -2.0\n", - " -0.1\n", - " 0.8\n", + " -4.9\n", + " -3.8\n", + " -1.8\n", + " 0.1\n", + " 1.2\n", " \n", " \n", " metac-claude-3-5-sonnet-20240620\n", - " -6.6\n", - " -5.0\n", - " -2.1\n", - " 0.8\n", - " 2.4\n", + " -6.2\n", + " -4.8\n", + " -2.0\n", + " 0.7\n", + " 2.0\n", " \n", " \n", " RPM_bot\n", " -4.7\n", " -3.8\n", - " -2.1\n", + " -2.0\n", " -0.7\n", - " -0.1\n", + " -0.2\n", + " \n", + " \n", + " swingswish\n", + " -5.5\n", + " -4.3\n", + " -2.1\n", + " -0.3\n", + " 0.5\n", " \n", " \n", " InstitutPelFutur\n", - " -9.3\n", - " -6.4\n", - " -2.2\n", - " 1.8\n", - " 4.2\n", + " -8.5\n", + " -6.5\n", + " -2.1\n", + " 1.9\n", + " 4.1\n", " \n", " \n", " metac-Llama-3.1\n", - " -6.7\n", - " -5.4\n", + " -6.6\n", + " -5.3\n", " -2.6\n", " 0.1\n", " 1.4\n", " \n", " \n", " wunderplumb\n", - " -6.3\n", - " -4.9\n", - " -2.6\n", - " -0.4\n", - " 0.7\n", - " \n", - " \n", - " NextWorldLab\n", - " -8.7\n", - " -6.9\n", - " -3.6\n", + " -6.2\n", + " -5.0\n", + " -2.7\n", " -0.2\n", - " 1.1\n", + " 0.6\n", " \n", " \n", - " laylaps\n", - " -10.4\n", - " -7.7\n", - " -3.8\n", - " -0.1\n", - " 1.6\n", + " NextWorldLab\n", + " -9.0\n", + " -6.8\n", + " -3.4\n", + " -0.4\n", + " 1.0\n", " \n", " \n", " Bot_Pepa\n", - " -7.0\n", + " -7.1\n", " -5.8\n", " -3.9\n", " -2.0\n", - " -1.1\n", + " -1.0\n", + " \n", + " \n", + " laylaps\n", + " -9.9\n", + " -7.7\n", + " -4.0\n", + " -0.1\n", + " 1.6\n", " \n", " \n", " VeritasAI\n", - " -8.1\n", - " -6.8\n", + " -7.7\n", + " -6.4\n", " -4.3\n", " -1.7\n", - " -0.9\n", + " -0.5\n", " \n", " \n", " minefrac1\n", - " -7.8\n", + " -7.9\n", " -6.8\n", - " -4.6\n", + " -4.5\n", " -2.6\n", - " -1.5\n", + " -1.7\n", " \n", " \n", " Grizeu_Bot\n", " -9.4\n", - " -7.8\n", - " -4.9\n", - " -2.2\n", - " -0.9\n", + " -7.5\n", + " -5.0\n", + " -2.4\n", + " -1.0\n", " \n", " \n", " metac-gpt-4o\n", - " -10.3\n", + " -10.2\n", " -8.9\n", - " -5.9\n", - " -3.1\n", - " -1.6\n", + " -5.8\n", + " -2.9\n", + " -1.5\n", " \n", " \n", " ajf-bot\n", " -14.8\n", - " -12.9\n", - " -8.3\n", - " -4.4\n", - " -2.1\n", + " -12.6\n", + " -8.4\n", + " -4.6\n", + " -2.2\n", " \n", " \n", "\n", @@ -9451,51 +9481,51 @@ ], "text/plain": [ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", - "metac-o1 6.0 7.6 9.7 12.0 13.2\n", - "metac-o1-preview 3.4 5.2 8.3 11.2 12.5\n", - "manticAI 0.2 2.2 5.3 8.6 10.3\n", - "metac-Gemini-Exp-1206 0.4 2.1 4.9 7.7 9.1\n", - "acm_bot -0.0 1.3 4.7 7.4 8.8\n", - "metac-perplexity -2.0 0.6 4.3 8.2 9.8\n", - "GreeneiBot2 -1.5 0.7 4.0 7.0 8.8\n", - "twsummerbot 0.2 1.6 3.7 6.2 7.3\n", - "cookics_bot_TEST 0.0 1.1 3.1 5.1 6.2\n", - "pgodzinai -3.6 -1.1 3.1 6.5 9.0\n", - "CumulativeBot -0.1 0.9 2.6 4.5 5.4\n", - "SynapseSeer 0.3 1.1 2.6 4.1 4.9\n", - "metac-claude-3-5-sonnet-latest -1.4 -0.2 2.6 5.1 6.3\n", - "jkraybill_bot -3.6 -1.7 1.8 5.1 6.5\n", - "metac-exa -4.8 -2.7 1.8 5.6 7.3\n", - "metac-deepseek-r1+asknews -2.1 -0.8 1.3 3.3 4.5\n", - "MWG -1.7 -0.8 0.7 2.0 2.9\n", - "andrewsiah -0.9 -0.6 0.0 0.6 1.0\n", - "pianobot -1.3 -0.8 -0.0 0.7 1.1\n", - "X_bot -0.4 -0.3 -0.0 0.1 0.2\n", - "cobyj-bot -1.5 -0.9 -0.1 0.8 1.3\n", - "annabot -3.2 -2.1 -0.3 1.2 2.0\n", - "KevinTestBot -4.0 -2.6 -0.4 1.6 2.6\n", - "bean_bot -3.3 -2.2 -0.5 1.0 1.9\n", - "CatrachoCaster -2.3 -1.8 -0.8 0.2 0.8\n", - "jonahsingerbot -3.0 -2.3 -0.9 0.4 1.0\n", - "krm-bot -3.8 -2.7 -1.0 0.7 1.6\n", - "ProfessorSP -4.6 -3.3 -1.1 0.9 1.9\n", - "metac-grok-2-1212 -6.7 -4.8 -1.3 1.7 3.4\n", - "mmBot -7.2 -5.5 -1.6 2.4 4.5\n", - "4Shadower -4.8 -3.7 -1.6 0.2 1.1\n", - "swingswish -5.3 -3.9 -2.0 -0.1 0.8\n", - "metac-claude-3-5-sonnet-20240620 -6.6 -5.0 -2.1 0.8 2.4\n", - "RPM_bot -4.7 -3.8 -2.1 -0.7 -0.1\n", - "InstitutPelFutur -9.3 -6.4 -2.2 1.8 4.2\n", - "metac-Llama-3.1 -6.7 -5.4 -2.6 0.1 1.4\n", - "wunderplumb -6.3 -4.9 -2.6 -0.4 0.7\n", - "NextWorldLab -8.7 -6.9 -3.6 -0.2 1.1\n", - "laylaps -10.4 -7.7 -3.8 -0.1 1.6\n", - "Bot_Pepa -7.0 -5.8 -3.9 -2.0 -1.1\n", - "VeritasAI -8.1 -6.8 -4.3 -1.7 -0.9\n", - "minefrac1 -7.8 -6.8 -4.6 -2.6 -1.5\n", - "Grizeu_Bot -9.4 -7.8 -4.9 -2.2 -0.9\n", - "metac-gpt-4o -10.3 -8.9 -5.9 -3.1 -1.6\n", - "ajf-bot -14.8 -12.9 -8.3 -4.4 -2.1" + "metac-o1 5.9 7.2 9.5 11.8 12.9\n", + "metac-o1-preview 3.5 5.3 8.3 11.2 12.7\n", + "manticAI 0.3 2.2 5.4 8.7 10.4\n", + "metac-Gemini-Exp-1206 0.4 2.2 5.0 7.7 9.5\n", + "acm_bot 0.4 1.9 4.6 7.4 8.8\n", + "metac-perplexity -1.8 0.1 4.2 7.8 9.5\n", + "GreeneiBot2 -0.6 0.8 4.0 7.2 8.7\n", + "twsummerbot 0.2 1.4 3.8 6.3 7.4\n", + "cookics_bot_TEST -0.2 0.8 3.0 5.1 6.2\n", + "pgodzinai -3.0 -1.1 3.0 6.8 9.0\n", + "metac-claude-3-5-sonnet-latest -1.2 0.2 2.6 5.2 6.6\n", + "SynapseSeer 0.4 1.1 2.6 4.0 4.8\n", + "CumulativeBot -0.5 0.6 2.6 4.5 5.4\n", + "jkraybill_bot -3.2 -1.3 1.7 4.9 6.5\n", + "metac-exa -4.8 -2.6 1.7 5.7 7.4\n", + "metac-deepseek-r1+asknews -1.7 -0.7 1.4 3.6 4.6\n", + "MWG -1.5 -0.8 0.7 2.0 2.8\n", + "andrewsiah -0.9 -0.6 -0.0 0.6 0.9\n", + "X_bot -0.4 -0.2 -0.0 0.1 0.2\n", + "pianobot -1.3 -0.8 -0.0 0.6 1.0\n", + "cobyj-bot -1.5 -0.9 -0.0 0.9 1.3\n", + "KevinTestBot -4.1 -2.9 -0.4 1.5 2.7\n", + "annabot -3.7 -2.3 -0.5 1.2 2.1\n", + "bean_bot -3.1 -2.2 -0.5 1.1 1.9\n", + "CatrachoCaster -2.2 -1.7 -0.7 0.2 0.7\n", + "jonahsingerbot -2.9 -2.3 -0.8 0.4 1.0\n", + "krm-bot -3.5 -2.6 -0.9 0.6 1.5\n", + "ProfessorSP -4.4 -3.2 -1.0 1.0 2.2\n", + "metac-grok-2-1212 -6.6 -4.8 -1.4 1.8 3.1\n", + "mmBot -7.5 -5.4 -1.6 2.5 4.7\n", + "4Shadower -4.9 -3.8 -1.8 0.1 1.2\n", + "metac-claude-3-5-sonnet-20240620 -6.2 -4.8 -2.0 0.7 2.0\n", + "RPM_bot -4.7 -3.8 -2.0 -0.7 -0.2\n", + "swingswish -5.5 -4.3 -2.1 -0.3 0.5\n", + "InstitutPelFutur -8.5 -6.5 -2.1 1.9 4.1\n", + "metac-Llama-3.1 -6.6 -5.3 -2.6 0.1 1.4\n", + "wunderplumb -6.2 -5.0 -2.7 -0.2 0.6\n", + "NextWorldLab -9.0 -6.8 -3.4 -0.4 1.0\n", + "Bot_Pepa -7.1 -5.8 -3.9 -2.0 -1.0\n", + "laylaps -9.9 -7.7 -4.0 -0.1 1.6\n", + "VeritasAI -7.7 -6.4 -4.3 -1.7 -0.5\n", + "minefrac1 -7.9 -6.8 -4.5 -2.6 -1.7\n", + "Grizeu_Bot -9.4 -7.5 -5.0 -2.4 -1.0\n", + "metac-gpt-4o -10.2 -8.9 -5.8 -2.9 -1.5\n", + "ajf-bot -14.8 -12.6 -8.4 -4.6 -2.2" ] }, "execution_count": 49, @@ -9599,7 +9629,7 @@ " NaN\n", " NaN\n", " NaN\n", - " 5.521275\n", + " 4.605170\n", " \n", " \n", " 1\n", @@ -9614,7 +9644,7 @@ " True\n", " True\n", " ...\n", - " -0.270414\n", + " -0.158842\n", " -0.616988\n", " NaN\n", " -0.050442\n", @@ -9638,7 +9668,7 @@ " False\n", " False\n", " ...\n", - " -0.092275\n", + " -0.038208\n", " -0.092275\n", " NaN\n", " -0.210058\n", @@ -9662,8 +9692,8 @@ " None\n", " None\n", " ...\n", - " 0.310155\n", - " 0.310155\n", + " 0.390198\n", + " 0.204794\n", " NaN\n", " 0.127833\n", " 0.152526\n", @@ -9719,9 +9749,9 @@ "\n", " open_upper_bound open_lower_bound ... metac-o1-preview metac-perplexity \\\n", "0 False False ... 2.302585 5.703782 \n", - "1 True True ... -0.270414 -0.616988 \n", - "2 False False ... -0.092275 -0.092275 \n", - "3 None None ... 0.310155 0.310155 \n", + "1 True True ... -0.158842 -0.616988 \n", + "2 False False ... -0.038208 -0.092275 \n", + "3 None None ... 0.390198 0.204794 \n", "4 False False ... 0.243782 -0.102791 \n", "\n", " minefrac1 mmBot pgodzinai pianobot swingswish twsummerbot \\\n", @@ -9732,7 +9762,7 @@ "4 NaN 0.265372 0.041050 NaN NaN -0.771754 \n", "\n", " wunderplumb bot_team_median \n", - "0 NaN 5.521275 \n", + "0 NaN 4.605170 \n", "1 NaN -1.512868 \n", "2 NaN -0.149434 \n", "3 NaN 0.310155 \n", @@ -9859,7 +9889,7 @@ " -0.132060\n", " -0.158283\n", " -0.132060\n", - " -0.132060\n", + " -0.158283\n", " \n", " \n", " 97\n", @@ -9898,7 +9928,7 @@ " False\n", " False\n", " ...\n", - " -0.063666\n", + " -0.017709\n", " 0.000000\n", " NaN\n", " -0.112251\n", @@ -9934,12 +9964,12 @@ "95 -2.251292 NaN NaN -0.111226 NaN \n", "96 -0.020834 NaN NaN -0.074901 NaN \n", "97 -0.680430 0.628948 NaN -0.680430 -0.680430 \n", - "98 -0.063666 0.000000 NaN -0.112251 -0.017709 \n", + "98 -0.017709 0.000000 NaN -0.112251 -0.017709 \n", "\n", " pianobot swingswish twsummerbot wunderplumb bot_team_median \n", "94 NaN -0.054067 -0.220515 -0.054067 -0.054067 \n", "95 NaN -0.054067 -0.083382 -2.944439 -0.111226 \n", - "96 NaN -0.132060 -0.158283 -0.132060 -0.132060 \n", + "96 NaN -0.132060 -0.158283 -0.132060 -0.158283 \n", "97 NaN -0.091255 0.811793 0.628948 -0.091255 \n", "98 NaN -0.163782 -0.241614 -0.163782 -0.112251 \n", "\n", @@ -10007,8 +10037,8 @@ " 0.0\n", " \n", " \n", - " RPM_bot\n", - " -0.1\n", + " X_bot\n", + " -0.0\n", " -0.0\n", " -0.0\n", " 0.0\n", @@ -10031,8 +10061,8 @@ " -0.0\n", " \n", " \n", - " X_bot\n", - " -0.0\n", + " RPM_bot\n", + " -0.1\n", " -0.0\n", " -0.0\n", " 0.0\n", @@ -10043,7 +10073,7 @@ " -0.0\n", " -0.0\n", " -0.0\n", - " 0.0\n", + " -0.0\n", " 0.0\n", " \n", " \n", @@ -10103,7 +10133,7 @@ " -0.0\n", " \n", " \n", - " annabot\n", + " 4Shadower\n", " -0.1\n", " -0.1\n", " -0.1\n", @@ -10111,7 +10141,7 @@ " -0.0\n", " \n", " \n", - " 4Shadower\n", + " annabot\n", " -0.1\n", " -0.1\n", " -0.1\n", @@ -10160,30 +10190,30 @@ " \n", " \n", " ajf-bot\n", - " -0.3\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.0\n", " 0.0\n", " \n", " \n", - " GreeneiBot2\n", + " acm_bot\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.0\n", " 0.0\n", + " 0.1\n", " \n", " \n", - " acm_bot\n", + " GreeneiBot2\n", " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", - " 0.1\n", + " 0.0\n", " \n", " \n", - " Bot_Pepa\n", + " metac-deepseek-r1+asknews\n", " -0.2\n", " -0.2\n", " -0.1\n", @@ -10191,15 +10221,7 @@ " -0.0\n", " \n", " \n", - " metac-perplexity\n", - " -0.3\n", - " -0.3\n", - " -0.1\n", - " -0.0\n", - " 0.1\n", - " \n", - " \n", - " bot_median\n", + " metac-Gemini-Exp-1206\n", " -0.3\n", " -0.2\n", " -0.1\n", @@ -10209,14 +10231,14 @@ " \n", " metac-o1\n", " -0.3\n", - " -0.3\n", + " -0.2\n", " -0.1\n", - " -0.0\n", + " 0.0\n", " 0.1\n", " \n", " \n", - " metac-deepseek-r1+asknews\n", - " -0.3\n", + " Bot_Pepa\n", + " -0.2\n", " -0.2\n", " -0.1\n", " -0.1\n", @@ -10239,44 +10261,36 @@ " -0.0\n", " \n", " \n", - " metac-Gemini-Exp-1206\n", - " -0.3\n", + " bot_median\n", " -0.3\n", + " -0.2\n", " -0.1\n", " -0.0\n", - " 0.1\n", + " 0.0\n", " \n", " \n", - " manticAI\n", + " metac-perplexity\n", + " -0.4\n", " -0.3\n", - " -0.2\n", - " -0.2\n", " -0.1\n", " -0.0\n", + " 0.1\n", " \n", " \n", - " metac-claude-3-5-sonnet-20240620\n", - " -0.3\n", + " manticAI\n", " -0.3\n", " -0.2\n", - " -0.0\n", - " 0.0\n", - " \n", - " \n", - " NextWorldLab\n", - " -0.3\n", - " -0.3\n", " -0.2\n", " -0.1\n", " -0.0\n", " \n", " \n", - " metac-claude-3-5-sonnet-latest\n", + " NextWorldLab\n", " -0.3\n", " -0.3\n", " -0.2\n", " -0.1\n", - " -0.1\n", + " 0.0\n", " \n", " \n", " minefrac1\n", @@ -10287,7 +10301,7 @@ " -0.1\n", " \n", " \n", - " metac-o1-preview\n", + " metac-claude-3-5-sonnet-latest\n", " -0.4\n", " -0.3\n", " -0.2\n", @@ -10303,7 +10317,7 @@ " -0.1\n", " \n", " \n", - " metac-Llama-3.1\n", + " metac-claude-3-5-sonnet-20240620\n", " -0.4\n", " -0.4\n", " -0.2\n", @@ -10314,24 +10328,40 @@ " pgodzinai\n", " -0.4\n", " -0.4\n", - " -0.3\n", + " -0.2\n", " -0.1\n", " -0.1\n", " \n", " \n", " metac-grok-2-1212\n", - " -0.5\n", " -0.4\n", - " -0.3\n", + " -0.4\n", + " -0.2\n", + " -0.1\n", " -0.1\n", - " -0.0\n", " \n", " \n", " VeritasAI\n", " -0.4\n", " -0.3\n", - " -0.3\n", " -0.2\n", + " -0.2\n", + " -0.1\n", + " \n", + " \n", + " metac-o1-preview\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", + " -0.1\n", + " -0.1\n", + " \n", + " \n", + " metac-gpt-4o\n", + " -0.4\n", + " -0.4\n", + " -0.3\n", + " -0.1\n", " -0.1\n", " \n", " \n", @@ -10351,7 +10381,7 @@ " -0.1\n", " \n", " \n", - " metac-gpt-4o\n", + " metac-Llama-3.1\n", " -0.5\n", " -0.4\n", " -0.3\n", @@ -10366,11 +10396,11 @@ " 2.5% CI 10% CI Median 90% CI 97.5% CI\n", "cobyj-bot 0.0 0.0 0.0 0.0 0.0\n", "andrewsiah 0.0 0.0 0.0 0.0 0.0\n", - "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", + "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", "jonahsingerbot -0.0 -0.0 -0.0 -0.0 -0.0\n", "bean_bot -0.0 -0.0 -0.0 -0.0 -0.0\n", - "X_bot -0.0 -0.0 -0.0 0.0 0.0\n", - "CumulativeBot -0.0 -0.0 -0.0 0.0 0.0\n", + "RPM_bot -0.1 -0.0 -0.0 0.0 0.0\n", + "CumulativeBot -0.0 -0.0 -0.0 -0.0 0.0\n", "swingswish -0.0 -0.0 -0.0 -0.0 -0.0\n", "KevinTestBot -0.1 -0.0 -0.0 0.0 0.0\n", "SynapseSeer -0.1 -0.0 -0.0 0.0 0.0\n", @@ -10378,38 +10408,38 @@ "pianobot -0.1 -0.1 -0.0 -0.0 0.0\n", "CatrachoCaster -0.1 -0.1 -0.0 -0.0 0.0\n", "krm-bot -0.1 -0.1 -0.1 -0.0 -0.0\n", - "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", "4Shadower -0.1 -0.1 -0.1 -0.0 -0.0\n", + "annabot -0.1 -0.1 -0.1 -0.0 -0.0\n", "cookics_bot_TEST -0.2 -0.1 -0.1 -0.0 0.0\n", "jkraybill_bot -0.2 -0.1 -0.1 -0.0 -0.0\n", "twsummerbot -0.2 -0.2 -0.1 -0.0 0.0\n", "MWG -0.2 -0.2 -0.1 -0.0 -0.0\n", "ProfessorSP -0.2 -0.2 -0.1 -0.0 -0.0\n", - "ajf-bot -0.3 -0.2 -0.1 -0.0 0.0\n", + "ajf-bot -0.2 -0.2 -0.1 -0.0 0.0\n", + "acm_bot -0.3 -0.2 -0.1 0.0 0.1\n", "GreeneiBot2 -0.3 -0.2 -0.1 -0.0 0.0\n", - "acm_bot -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-deepseek-r1+asknews -0.2 -0.2 -0.1 -0.1 -0.0\n", + "metac-Gemini-Exp-1206 -0.3 -0.2 -0.1 -0.0 0.1\n", + "metac-o1 -0.3 -0.2 -0.1 0.0 0.1\n", "Bot_Pepa -0.2 -0.2 -0.1 -0.1 -0.0\n", - "metac-perplexity -0.3 -0.3 -0.1 -0.0 0.1\n", - "bot_median -0.3 -0.2 -0.1 -0.0 0.1\n", - "metac-o1 -0.3 -0.3 -0.1 -0.0 0.1\n", - "metac-deepseek-r1+asknews -0.3 -0.2 -0.1 -0.1 -0.0\n", "laylaps -0.2 -0.2 -0.1 -0.1 -0.0\n", "wunderplumb -0.3 -0.2 -0.1 -0.1 -0.0\n", - "metac-Gemini-Exp-1206 -0.3 -0.3 -0.1 -0.0 0.1\n", + "bot_median -0.3 -0.2 -0.1 -0.0 0.0\n", + "metac-perplexity -0.4 -0.3 -0.1 -0.0 0.1\n", "manticAI -0.3 -0.2 -0.2 -0.1 -0.0\n", - "metac-claude-3-5-sonnet-20240620 -0.3 -0.3 -0.2 -0.0 0.0\n", - "NextWorldLab -0.3 -0.3 -0.2 -0.1 -0.0\n", - "metac-claude-3-5-sonnet-latest -0.3 -0.3 -0.2 -0.1 -0.1\n", + "NextWorldLab -0.3 -0.3 -0.2 -0.1 0.0\n", "minefrac1 -0.3 -0.3 -0.2 -0.1 -0.1\n", - "metac-o1-preview -0.4 -0.3 -0.2 -0.1 -0.1\n", + "metac-claude-3-5-sonnet-latest -0.4 -0.3 -0.2 -0.1 -0.1\n", "mmBot -0.4 -0.3 -0.2 -0.1 -0.1\n", - "metac-Llama-3.1 -0.4 -0.4 -0.2 -0.1 -0.0\n", - "pgodzinai -0.4 -0.4 -0.3 -0.1 -0.1\n", - "metac-grok-2-1212 -0.5 -0.4 -0.3 -0.1 -0.0\n", - "VeritasAI -0.4 -0.3 -0.3 -0.2 -0.1\n", + "metac-claude-3-5-sonnet-20240620 -0.4 -0.4 -0.2 -0.1 -0.0\n", + "pgodzinai -0.4 -0.4 -0.2 -0.1 -0.1\n", + "metac-grok-2-1212 -0.4 -0.4 -0.2 -0.1 -0.1\n", + "VeritasAI -0.4 -0.3 -0.2 -0.2 -0.1\n", + "metac-o1-preview -0.4 -0.4 -0.3 -0.1 -0.1\n", + "metac-gpt-4o -0.4 -0.4 -0.3 -0.1 -0.1\n", "metac-exa -0.4 -0.4 -0.3 -0.2 -0.1\n", "InstitutPelFutur -0.5 -0.4 -0.3 -0.2 -0.1\n", - "metac-gpt-4o -0.5 -0.4 -0.3 -0.2 -0.1" + "metac-Llama-3.1 -0.5 -0.4 -0.3 -0.2 -0.1" ] }, "execution_count": 50, @@ -10458,7 +10488,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0kAAAIjCAYAAADWYVDIAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAASUpJREFUeJzt3Xl4FFXe9vG7s3RnIwQSkoCEfQdZhAGCLILBgMgiUXFBlsHtEUUNqA/jsIkKgoIOAi4jAcdRRgYFF1YjMoqAgkQUGAQEIwYCAUMgmLXP+4dP+q0mCSQhpEP4fq6rL61Tp6t/dVJp+k5VnbYZY4wAAAAAAJIkL08XAAAAAACVCSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkA/k+DBg00atQoT5dR5c2ePVuNGjWSt7e32rdv7+lyKpXU1FTdcsstCg0Nlc1m00svveTpkjyuQYMGuummmzxdBoArDCEJQJW0ePFi2Ww2bdu2rcj11113ndq0aXPRr7Nq1SpNnTr1ordzpVi3bp2eeOIJXXvttUpISNBzzz133v4fffSRevXqpfDwcAUEBKhRo0a67bbbtGbNmgqquGI99thjWrt2rSZOnKh//OMf6tevn6dLqnJSUlI0depUJSUleboUAJWYj6cLAIDKYu/evfLyKt3fjlatWqX58+cTlEros88+k5eXl958803Z7fbz9n3hhRf0+OOPq1evXpo4caICAgK0f/9+ffrpp1q6dGmVDBCfffaZBg8erAkTJni6lCorJSVF06ZNU4MGDTiTCaBYhCQA+D8Oh8PTJZRaZmamAgMDPV1GiR07dkz+/v4XDEh5eXmaPn26+vbtq3Xr1hW5nYridDqVk5MjPz+/S/5ax44dU0hISLltLysrS3a7vdThHwCudLxrAsD/OfeepNzcXE2bNk1NmzaVn5+fQkND1b17d61fv16SNGrUKM2fP1+SZLPZXI8CmZmZGj9+vKKiouRwONS8eXO98MILMsa4ve7vv/+ucePGKSwsTNWqVdOgQYP066+/ymazuZ2hmjp1qmw2m3bv3q0777xTNWrUUPfu3SVJO3fu1KhRo9SoUSP5+fkpMjJSf/7zn3XixAm31yrYxo8//qjhw4erevXqqlWrliZNmiRjjH755RcNHjxYwcHBioyM1IsvvliisSsINY0bN5bD4VCDBg30l7/8RdnZ2a4+NptNCQkJyszMdI3V4sWLi9xeWlqaMjIydO211xa5Pjw83G05KytLU6dOVbNmzeTn56fatWtr6NChOnDggKtPSX8eNptNDz30kP75z3+qdevWcjgcrsv7fv31V/35z39WRESEHA6HWrdurUWLFhWqb968eWrdurUCAgJUo0YNderUSe+8806x41dweagxRvPnzy90LP3000+69dZbVbNmTQUEBKhr16765JNP3Lbx+eefy2azaenSpfrrX/+qq666SgEBAcrIyCj2dV944QV169ZNoaGh8vf3V8eOHfXvf/+7UL+CMVmxYoXatGnj2vdzL3ssOL7279+vUaNGKSQkRNWrV9fo0aN19uxZt74JCQnq06ePwsPD5XA41KpVKy1cuLDYWtetW6f27dvLz89PrVq10vvvv1+oz4XG6fPPP9ef/vQnSdLo0aMveBwCuHJxJglAlXbq1CmlpaUVas/Nzb3gc6dOnaoZM2bonnvuUefOnZWRkaFt27bp22+/Vd++fXX//fcrJSVF69ev1z/+8Q+35xpjNGjQIG3YsEFjxoxR+/bttXbtWj3++OP69ddfNXfuXFffUaNG6b333tPdd9+trl27auPGjRowYECxdd16661q2rSpnnvuOdcH/PXr1+unn37S6NGjFRkZqV27dun111/Xrl27tGXLFrcP3JI0bNgwtWzZUjNnztQnn3yiZ555RjVr1tRrr72mPn366Pnnn9c///lPTZgwQX/605/Us2fP847VPffcoyVLluiWW27R+PHjtXXrVs2YMUN79uzRBx98IEn6xz/+oddff11ff/21/v73v0uSunXrVuT2wsPD5e/vr48++kgPP/ywatasWexr5+fn66abblJiYqJuv/12PfLIIzp9+rTWr1+vH374QY0bNy7Vz0P647K39957Tw899JDCwsLUoEEDpaamqmvXrq7AUKtWLa1evVpjxoxRRkaGHn30UUnSG2+8oXHjxumWW27RI488oqysLO3cuVNbt27VnXfeWeQ+9OzZU//4xz909913q2/fvhoxYoRrXWpqqrp166azZ89q3LhxCg0N1ZIlSzRo0CD9+9//1s033+y2renTp8tut2vChAnKzs4+71m7l19+WYMGDdJdd92lnJwcLV26VLfeeqs+/vjjQsfgl19+qffff18PPvigqlWrpr/97W+Ki4tTcnKyQkND3fredtttatiwoWbMmKFvv/1Wf//73xUeHq7nn3/e1WfhwoVq3bq1Bg0aJB8fH3300Ud68MEH5XQ6NXbsWLft7du3T8OGDdMDDzygkSNHKiEhQbfeeqvWrFmjvn37lnicWrZsqaefflqTJ0/Wfffdpx49ekgq/jgEcAUzAFAFJSQkGEnnfbRu3drtOfXr1zcjR450Lbdr184MGDDgvK8zduxYU9Rb6YoVK4wk88wzz7i133LLLcZms5n9+/cbY4zZvn27kWQeffRRt36jRo0yksyUKVNcbVOmTDGSzB133FHo9c6ePVuo7d133zWSzH/+859C27jvvvtcbXl5eaZu3brGZrOZmTNnutp/++034+/v7zYmRUlKSjKSzD333OPWPmHCBCPJfPbZZ662kSNHmsDAwPNur8DkyZONJBMYGGj69+9vnn32WbN9+/ZC/RYtWmQkmTlz5hRa53Q6jTEl/3kYY4wk4+XlZXbt2uXWd8yYMaZ27domLS3Nrf3222831atXd/0MBg8eXOjYKilJZuzYsW5tjz76qJFkvvjiC1fb6dOnTcOGDU2DBg1Mfn6+McaYDRs2GEmmUaNGRR4PRTm3X05OjmnTpo3p06dPobrsdrvbOH333XdGkpk3b56rreD4+vOf/+z2/JtvvtmEhoae97WNMSY2NtY0atTIra1+/fpGklm+fLmr7dSpU6Z27dqmQ4cOrraSjtM333xjJJmEhIQixwQAjDGGy+0AVGnz58/X+vXrCz3atm17weeGhIRo165d2rdvX6lfd9WqVfL29ta4cePc2sePHy9jjFavXi1JrsuVHnzwQbd+Dz/8cLHbfuCBBwq1+fv7u/4/KytLaWlp6tq1qyTp22+/LdT/nnvucf2/t7e3OnXqJGOMxowZ42oPCQlR8+bN9dNPPxVbi/THvkpSfHy8W/v48eMlqdBlYSU1bdo0vfPOO+rQoYPWrl2rp556Sh07dtQ111yjPXv2uPotX75cYWFhRY5ZwRm0kv48CvTq1UutWrVyLRtjtHz5cg0cOFDGGKWlpbkesbGxOnXqlGucQ0JCdPjwYX3zzTdl2u9zrVq1Sp07d3ZdWilJQUFBuu+++3To0CHt3r3brf/IkSPdjofzsfb77bffdOrUKfXo0aPIYyYmJkaNGzd2Lbdt21bBwcFFHh/nHqM9evTQiRMn3C79s752wRnfXr166aefftKpU6fcnl+nTh23M2bBwcEaMWKEduzYoaNHj0oq/TgBwPkQkgBUaZ07d1ZMTEyhR40aNS743Kefflrp6elq1qyZrr76aj3++OPauXNniV73559/Vp06dVStWjW39pYtW7rWF/zXy8tLDRs2dOvXpEmTYrd9bl9JOnnypB555BFFRETI399ftWrVcvU79wOnJNWrV89tuXr16vLz81NYWFih9t9++63YWqz7cG7NkZGRCgkJce1rWdxxxx364osv9Ntvv2ndunW68847tWPHDg0cOFBZWVmSpAMHDqh58+by8Sn+CvKS/jwKnDvGx48fV3p6ul5//XXVqlXL7TF69GhJ/38yiSeffFJBQUHq3LmzmjZtqrFjx2rTpk1lHoOff/5ZzZs3L9Re0trP5+OPP1bXrl3l5+enmjVrqlatWlq4cGGJjhlJqlGjRpHHx7l9C37frH03bdqkmJgYBQYGKiQkRLVq1dJf/vIXSYWP2SZNmhS6ZLRZs2aSpEOHDkkq/TgBwPlwTxIAFKNnz546cOCAVq5cqXXr1unvf/+75s6dq1dffdXtTExFK+oswW233aavvvpKjz/+uNq3b6+goCA5nU7169dPTqezUH9vb+8StUkqNLFBcc79EFuegoOD1bdvX/Xt21e+vr5asmSJtm7dql69el2S1zt3jAvGcPjw4Ro5cmSRzyk4O9myZUvt3btXH3/8sdasWaPly5drwYIFmjx5sqZNm3ZJ6rUq6VmkL774QoMGDVLPnj21YMEC1a5dW76+vkpISChykonSHB8X6nvgwAFdf/31atGihebMmaOoqCjZ7XatWrVKc+fOLfKYBYCKREgCgPOoWbOmRo8erdGjR+vMmTPq2bOnpk6d6gpJxQWD+vXr69NPP9Xp06fdzl7897//da0v+K/T6dTBgwfVtGlTV7/9+/eXuMbffvtNiYmJmjZtmiZPnuxqL8tlgmVRsA/79u1z/dVe+uNG+vT0dNe+lpdOnTppyZIlOnLkiCSpcePG2rp1q3Jzc+Xr61tsjSX5eRSnVq1aqlatmvLz8xUTE3PBGgMDAzVs2DANGzZMOTk5Gjp0qJ599llNnDix1FOJ169fX3v37i3UXtLai7N8+XL5+flp7dq1btPfJyQklGl7pfHRRx8pOztbH374odtZpw0bNhTZf//+/TLGuP2+/fjjj5L+mJVSKvk4XcowD6Dq4HI7ACjGudNnBwUFqUmTJm7TWhd8R1F6erpb3xtvvFH5+fl65ZVX3Nrnzp0rm82m/v37S5JiY2MlSQsWLHDrN2/evBLXWfBX+3P/ov/SSy+VeBsX48Ybbyzy9ebMmSNJ552przhnz57V5s2bi1xXcP9QwaVVcXFxSktLKzTW0v8fk5L+PIrj7e2tuLg4LV++XD/88EOh9cePH3f9/7nHjd1uV6tWrWSMKdGsiue68cYb9fXXX7uNR2Zmpl5//XU1aNDA7d6p0vD29pbNZlN+fr6r7dChQ1qxYkWZtlfa15bcj9lTp04VG9BSUlJcsyRKUkZGht566y21b99ekZGRkko+TsX9zgKAFWeSAKAYrVq10nXXXaeOHTuqZs2a2rZtm/7973/roYcecvXp2LGjJGncuHGKjY2Vt7e3br/9dg0cOFC9e/fWU089pUOHDqldu3Zat26dVq5cqUcffdR1A3zHjh0VFxenl156SSdOnHBNAV7wV/KS/NU7ODhYPXv21KxZs5Sbm6urrrpK69at08GDBy/BqBTWrl07jRw5Uq+//rrS09PVq1cvff3111qyZImGDBmi3r17l3qbZ8+eVbdu3dS1a1f169dPUVFRSk9P14oVK/TFF19oyJAh6tChgyRpxIgReuuttxQfH6+vv/5aPXr0UGZmpj799FM9+OCDGjx4cIl/Huczc+ZMbdiwQV26dNG9996rVq1a6eTJk/r222/16aef6uTJk5KkG264QZGRkbr22msVERGhPXv26JVXXtGAAQMK3RNVEv/7v/+rd999V/3799e4ceNUs2ZNLVmyRAcPHtTy5cvL/EWxAwYM0Jw5c9SvXz/deeedOnbsmObPn68mTZqU+N67srrhhhtkt9s1cOBA3X///Tpz5ozeeOMNhYeHu84QWjVr1kxjxozRN998o4iICC1atEipqaluoaqk49S4cWOFhITo1VdfVbVq1RQYGKguXbqU6l4uAFcAj8ypBwCXWMEU4N98802R63v16nXBKcCfeeYZ07lzZxMSEmL8/f1NixYtzLPPPmtycnJcffLy8szDDz9satWqZWw2m9t04KdPnzaPPfaYqVOnjvH19TVNmzY1s2fPdk1LXSAzM9OMHTvW1KxZ0wQFBZkhQ4aYvXv3GkluU3IXTK98/PjxQvtz+PBhc/PNN5uQkBBTvXp1c+utt5qUlJRipxE/dxvFTc1d1DgVJTc310ybNs00bNjQ+Pr6mqioKDNx4kSTlZVVotcpantvvPGGGTJkiKlfv75xOBwmICDAdOjQwcyePdtkZ2e79T979qx56qmnXK8fGRlpbrnlFnPgwAFXn5L+PFTENNwFUlNTzdixY01UVJTrda6//nrz+uuvu/q89tprpmfPniY0NNQ4HA7TuHFj8/jjj5tTp05dcL+Le+0DBw6YW265xYSEhBg/Pz/TuXNn8/HHH7v1KZgCfNmyZRd8nQJvvvmmadq0qXE4HKZFixYmISHBdYyUpK5zf2eKO74Kfh8PHjzoavvwww9N27ZtjZ+fn2nQoIF5/vnnXdO5W/vVr1/fDBgwwKxdu9a0bdvWVWtR+1mScTLGmJUrV5pWrVoZHx8fpgMHUCSbMSW8IxcAUGGSkpLUoUMHvf3227rrrrs8XQ4AAFcU7kkCAA/7/fffC7W99NJL8vLyUs+ePT1QEQAAVzbuSQIAD5s1a5a2b9+u3r17y8fHR6tXr9bq1at13333KSoqytPlAQBwxeFyOwDwsPXr12vatGnavXu3zpw5o3r16unuu+/WU089dd4vSAUAAJcGIQkAAAAALLgnCQAAAAAsCEkAAAAAYFHlL3Z3Op1KSUlRtWrVSvSljAAAAACqJmOMTp8+rTp16pz3y7irfEhKSUlhdigAAAAALr/88ovq1q1b7PoqH5KqVasm6Y+BCA4O9nA1AAAAADwlIyNDUVFRroxQnCofkgousQsODiYkAQAAALjgbThM3AAAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACw8PF0AQAAAJVRcnKy0tLSPF1GpRQWFqZ69ep5ugzgkiEkAQAAnCM5OVnNW7RU1u9nPV1KpeTnH6C9/91DUEKVRUgCAAA4R1pamrJ+P6vQm8bLNzTK0+VUKrknftGJj19UWloaIQlVFiEJAACgGL6hUXJENvF0GQAqGBM3AAAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsPBqSpk6dKpvN5vZo0aKFa31WVpbGjh2r0NBQBQUFKS4uTqmpqR6sGAAAAEBV5/EzSa1bt9aRI0dcjy+//NK17rHHHtNHH32kZcuWaePGjUpJSdHQoUM9WC0AAACAqs7H4wX4+CgyMrJQ+6lTp/Tmm2/qnXfeUZ8+fSRJCQkJatmypbZs2aKuXbtWdKkAAAAArgAeD0n79u1TnTp15Ofnp+joaM2YMUP16tXT9u3blZubq5iYGFffFi1aqF69etq8eXOxISk7O1vZ2dmu5YyMDElSXl6e8vLyLu3OAACAKsHpdMput8vX2yZfL+PpcioVp7dNdrtdTqeTz1a47JT0mPVoSOrSpYsWL16s5s2b68iRI5o2bZp69OihH374QUePHpXdbldISIjbcyIiInT06NFitzljxgxNmzatUPu2bdsUGBhY3rsAAACqoNOnT2vSpEmyR4bLy+70dDmVirNRuHIaTlJaWpq2bt3q6XKAUsnMzCxRP5sxptL8eSQ9PV3169fXnDlz5O/vr9GjR7udFZKkzp07q3fv3nr++eeL3EZRZ5KioqJ04sQJBQcHX9L6AQBA1ZCUlKRrr71WEcNnyxHRyNPlVCrZqT8p9e3HtWnTJrVv397T5QClkpGRodDQUJ06deq82cDjl9tZhYSEqFmzZtq/f7/69u2rnJwcpaenu51NSk1NLfIepgIOh0MOh6NQu4+Pj3x8KtXuAgCASsrLy0s5OTnKzTfycto8XU6lkptvlJOTIy8vLz5b4bJT0mPW47PbWZ05c0YHDhxQ7dq11bFjR/n6+ioxMdG1fu/evUpOTlZ0dLQHqwQAAABQlXk0/k+YMEEDBw5U/fr1lZKSoilTpsjb21t33HGHqlevrjFjxig+Pl41a9ZUcHCwHn74YUVHRzOzHQAAAIBLxqMh6fDhw7rjjjt04sQJ1apVS927d9eWLVtUq1YtSdLcuXPl5eWluLg4ZWdnKzY2VgsWLPBkyQAAAACqOI+GpKVLl553vZ+fn+bPn6/58+dXUEUAAAAArnSV6p4kAAAAAPA0QhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAACLShOSZs6cKZvNpkcffdTVlpWVpbFjxyo0NFRBQUGKi4tTamqq54oEAAAAUOVVipD0zTff6LXXXlPbtm3d2h977DF99NFHWrZsmTZu3KiUlBQNHTrUQ1UCAAAAuBJ4PCSdOXNGd911l9544w3VqFHD1X7q1Cm9+eabmjNnjvr06aOOHTsqISFBX331lbZs2eLBigEAAABUZT6eLmDs2LEaMGCAYmJi9Mwzz7jat2/frtzcXMXExLjaWrRooXr16mnz5s3q2rVrkdvLzs5Wdna2azkjI0OSlJeXp7y8vEu0FwAAoCpxOp2y2+3y9bbJ18t4upxKxeltk91ul9Pp5LMVLjslPWY9GpKWLl2qb7/9Vt98802hdUePHpXdbldISIhbe0REhI4ePVrsNmfMmKFp06YVat+2bZsCAwMvumYAAFD1nT59WpMmTZI9Mlxedqeny6lUnI3CldNwktLS0rR161ZPlwOUSmZmZon6eSwk/fLLL3rkkUe0fv16+fn5ldt2J06cqPj4eNdyRkaGoqKi1KlTJwUHB5fb6wAAgKorKSlJ06dPV8Tw2XJENPJ0OZVKduoxpb49XZs2bVL79u09XQ5QKgVXmV2Ix0LS9u3bdezYMV1zzTWutvz8fP3nP//RK6+8orVr1yonJ0fp6eluZ5NSU1MVGRlZ7HYdDoccDkehdh8fH/n4ePzqQgAAcBnw8vJSTk6OcvONvJw2T5dTqeTmG+Xk5MjLy4vPVrjslPSY9diRff311+v77793axs9erRatGihJ598UlFRUfL19VViYqLi4uIkSXv37lVycrKio6M9UTIAAACAK4DHQlK1atXUpk0bt7bAwECFhoa62seMGaP4+HjVrFlTwcHBevjhhxUdHV3spA0AAAAAcLEq9TnSuXPnysvLS3FxccrOzlZsbKwWLFjg6bIAAAAAVGGVKiR9/vnnbst+fn6aP3++5s+f75mCAAAAAFxxPP5lsgAAAABQmRCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwKFNI+umnn8q7DgAAAACoFMoUkpo0aaLevXvr7bffVlZWVnnXBAAAAAAeU6aQ9O2336pt27aKj49XZGSk7r//fn399dflXRsAAAAAVLgyhaT27dvr5ZdfVkpKihYtWqQjR46oe/fuatOmjebMmaPjx4+Xd50AAAAAUCEuauIGHx8fDR06VMuWLdPzzz+v/fv3a8KECYqKitKIESN05MiR8qoTAAAAACrERYWkbdu26cEHH1Tt2rU1Z84cTZgwQQcOHND69euVkpKiwYMHl1edAAAAAFAhyhSS5syZo6uvvlrdunVTSkqK3nrrLf3888965pln1LBhQ/Xo0UOLFy/Wt99+e97tLFy4UG3btlVwcLCCg4MVHR2t1atXu9ZnZWVp7NixCg0NVVBQkOLi4pSamlqWkgEAAACgRMoUkhYuXKg777xTP//8s1asWKGbbrpJXl7umwoPD9ebb7553u3UrVtXM2fO1Pbt27Vt2zb16dNHgwcP1q5duyRJjz32mD766CMtW7ZMGzduVEpKioYOHVqWkgEAAACgRHzK8qR9+/ZdsI/dbtfIkSPP22fgwIFuy88++6wWLlyoLVu2qG7dunrzzTf1zjvvqE+fPpKkhIQEtWzZUlu2bFHXrl3LUjoAAAAAnFeZQlJCQoKCgoJ06623urUvW7ZMZ8+evWA4Kkp+fr6WLVumzMxMRUdHa/v27crNzVVMTIyrT4sWLVSvXj1t3ry52JCUnZ2t7Oxs13JGRoYkKS8vT3l5eaWuCwAAXHmcTqfsdrt8vW3y9TKeLqdScXrbZLfb5XQ6+WyFy05Jj9kyhaQZM2botddeK9QeHh6u++67r1Qh6fvvv1d0dLSysrIUFBSkDz74QK1atVJSUpLsdrtCQkLc+kdEROjo0aPnrW3atGmF2rdt26bAwMAS1wUAAK5cp0+f1qRJk2SPDJeX3enpcioVZ6Nw5TScpLS0NG3dutXT5QClkpmZWaJ+ZQpJycnJatiwYaH2+vXrKzk5uVTbat68uZKSknTq1Cn9+9//1siRI7Vx48aylCVJmjhxouLj413LGRkZioqKUqdOnRQcHFzm7QIAgCtHUlKSpk+frojhs+WIaOTpciqV7NRjSn17ujZt2qT27dt7uhygVAquMruQMoWk8PBw7dy5Uw0aNHBr/+677xQaGlqqbdntdjVp0kSS1LFjR33zzTd6+eWXNWzYMOXk5Cg9Pd3tbFJqaqoiIyOL3Z7D4ZDD4SjU7uPjIx+fMu0uAAC4wnh5eSknJ0e5+UZeTpuny6lUcvONcnJy5OXlxWcrXHZKesyWaXa7O+64Q+PGjdOGDRuUn5+v/Px8ffbZZ3rkkUd0++23l2WTLk6nU9nZ2erYsaN8fX2VmJjoWrd3714lJycrOjr6ol4DAAAAAIpTpvg/ffp0HTp0SNdff70rjTmdTo0YMULPPfdcibczceJE9e/fX/Xq1dPp06f1zjvv6PPPP9fatWtVvXp1jRkzRvHx8apZs6aCg4P18MMPKzo6mpntAAAAAFwyZQpJdrtd//rXvzR9+nR999138vf319VXX6369euXajvHjh3TiBEjdOTIEVWvXl1t27bV2rVr1bdvX0nS3Llz5eXlpbi4OGVnZys2NlYLFiwoS8kAAAAAUCIXdSFps2bN1KxZszI//0JfNuvn56f58+dr/vz5ZX4NAAAAACiNMoWk/Px8LV68WImJiTp27JicTvepMT/77LNyKQ4AAAAAKlqZQtIjjzyixYsXa8CAAWrTpo1sNmZ9AQAAAFA1lCkkLV26VO+9955uvPHG8q4HAAAAADyqTFOAW7/bCAAAAACqkjKFpPHjx+vll1+WMaa86wEAAAAAjyrT5XZffvmlNmzYoNWrV6t169by9fV1W//++++XS3EAAAAAUNHKFJJCQkJ08803l3ctAAAAAOBxZQpJCQkJ5V0HAAAAAFQKZbonSZLy8vL06aef6rXXXtPp06clSSkpKTpz5ky5FQcAAAAAFa1MZ5J+/vln9evXT8nJycrOzlbfvn1VrVo1Pf/888rOztarr75a3nUCAAAAQIUo05mkRx55RJ06ddJvv/0mf39/V/vNN9+sxMTEcisOAAAAACpamc4kffHFF/rqq69kt9vd2hs0aKBff/21XAoDAAAAAE8o05kkp9Op/Pz8Qu2HDx9WtWrVLrooAAAAAPCUMoWkG264QS+99JJr2Waz6cyZM5oyZYpuvPHG8qoNAAAAACpcmS63e/HFFxUbG6tWrVopKytLd955p/bt26ewsDC9++675V0jAAAAAFSYMoWkunXr6rvvvtPSpUu1c+dOnTlzRmPGjNFdd93lNpEDAAAAAFxuyhSSJMnHx0fDhw8vz1oAAAAAwOPKFJLeeuut864fMWJEmYoBAAAAAE8rU0h65JFH3JZzc3N19uxZ2e12BQQEEJIAAAAAXLbKNLvdb7/95vY4c+aM9u7dq+7duzNxAwAAAIDLWplCUlGaNm2qmTNnFjrLBAAAAACXk3ILSdIfkzmkpKSU5yYBAAAAoEKV6Z6kDz/80G3ZGKMjR47olVde0bXXXlsuhQEAAACAJ5QpJA0ZMsRt2WazqVatWurTp49efPHF8qgLAAAAADyiTCHJ6XSWdx0AAAAAUCmU6z1JAAAAAHC5K9OZpPj4+BL3nTNnTlleAgAAAAA8okwhaceOHdqxY4dyc3PVvHlzSdKPP/4ob29vXXPNNa5+NputfKoEAAAAgApSppA0cOBAVatWTUuWLFGNGjUk/fEFs6NHj1aPHj00fvz4ci0SAAAAACpKme5JevHFFzVjxgxXQJKkGjVq6JlnnmF2OwAAAACXtTKFpIyMDB0/frxQ+/Hjx3X69OmLLgoAAAAAPKVMIenmm2/W6NGj9f777+vw4cM6fPiwli9frjFjxmjo0KHlXSMAAAAAVJgy3ZP06quvasKECbrzzjuVm5v7x4Z8fDRmzBjNnj27XAsEAAAAgIpUppAUEBCgBQsWaPbs2Tpw4IAkqXHjxgoMDCzX4gAAAACgol3Ul8keOXJER44cUdOmTRUYGChjTHnVBQAAAAAeUaaQdOLECV1//fVq1qyZbrzxRh05ckSSNGbMGKb/BgAAAHBZK1NIeuyxx+Tr66vk5GQFBAS42ocNG6Y1a9aUW3EAAAAAUNHKdE/SunXrtHbtWtWtW9etvWnTpvr555/LpTAAAAAA8IQynUnKzMx0O4NU4OTJk3I4HBddFAAAAAB4SplCUo8ePfTWW2+5lm02m5xOp2bNmqXevXuXW3EAAAAAUNHKdLndrFmzdP3112vbtm3KycnRE088oV27dunkyZPatGlTedcIAAAAABWmTGeS2rRpox9//FHdu3fX4MGDlZmZqaFDh2rHjh1q3LhxedcIAAAAABWm1GeScnNz1a9fP7366qt66qmnLkVNAAAAAOAxpT6T5Ovrq507d16KWgAAAADA48p0ud3w4cP15ptvlnctAAAAAOBxZZq4IS8vT4sWLdKnn36qjh07KjAw0G39nDlzyqU4AAAAAKhopQpJP/30kxo0aKAffvhB11xzjSTpxx9/dOtjs9nKrzoAAAAAqGClCklNmzbVkSNHtGHDBknSsGHD9Le//U0RERGXpDgAAAAAqGiluifJGOO2vHr1amVmZpZrQQAAAADgSWWauKHAuaEJAAAAAC53pQpJNput0D1H3IMEAAAAoCop1T1JxhiNGjVKDodDkpSVlaUHHnig0Ox277//fvlVCAAAAAAVqFQhaeTIkW7Lw4cPL9diAAAAAMDTShWSEhISLlUdAAAAAFApXNTEDQAAAABQ1RCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALHw8XQAAAAAuP3v27PF0CZVOWFiY6tWr5+kyUA4ISQAAACix/DO/STabhg8f7ulSKh0//wDt/e8eglIVQEgCAABAiTmzz0jGKPSm8fINjfJ0OZVG7olfdOLjF5WWlkZIqgIISQAAACg139AoOSKbeLoM4JJg4gYAAAAAsCAkAQAAAICFR0PSjBkz9Kc//UnVqlVTeHi4hgwZor1797r1ycrK0tixYxUaGqqgoCDFxcUpNTXVQxUDAAAAqOo8GpI2btyosWPHasuWLVq/fr1yc3N1ww03KDMz09Xnscce00cffaRly5Zp48aNSklJ0dChQz1YNQAAAICqzKMTN6xZs8ZtefHixQoPD9f27dvVs2dPnTp1Sm+++abeeecd9enTR5KUkJCgli1basuWLeratWuhbWZnZys7O9u1nJGRIUnKy8tTXl7eJdwbAABQVTidTtntdvl62+TrZTxdTqVi9/ZibIrg9LbJbrfL6XTymbMSK+nPplLNbnfq1ClJUs2aNSVJ27dvV25urmJiYlx9WrRooXr16mnz5s1FhqQZM2Zo2rRphdq3bdumwMDAS1Q5AACoSk6fPq1JkybJHhkuL7vT0+VUKvlRrZXbibE5l7NRuHIaTlJaWpq2bt3q6XJQDOsVa+dTaUKS0+nUo48+qmuvvVZt2rSRJB09elR2u10hISFufSMiInT06NEitzNx4kTFx8e7ljMyMhQVFaVOnTopODj4ktUPAACqjqSkJE2fPl0Rw2fLEdHI0+VUKpm7d+nE6pcZm3Nkpx5T6tvTtWnTJrVv397T5aAYBVeZXUilCUljx47VDz/8oC+//PKituNwOORwOAq1+/j4yMen0uwuAACoxLy8vJSTk6PcfCMvp83T5VQqOflOxqYIuflGOTk58vLy4jNnJVbSn02lmAL8oYce0scff6wNGzaobt26rvbIyEjl5OQoPT3drX9qaqoiIyMruEoAAAAAVwKPhiRjjB566CF98MEH+uyzz9SwYUO39R07dpSvr68SExNdbXv37lVycrKio6MrulwAAAAAVwCPngscO3as3nnnHa1cuVLVqlVz3WdUvXp1+fv7q3r16hozZozi4+NVs2ZNBQcH6+GHH1Z0dHSRkzYAAAAAwMXyaEhauHChJOm6665za09ISNCoUaMkSXPnzpWXl5fi4uKUnZ2t2NhYLViwoIIrBQAAAHCl8GhIMubCc+v7+flp/vz5mj9/fgVUBAAAAOBKVykmbgAAAACAyoKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwMKjIek///mPBg4cqDp16shms2nFihVu640xmjx5smrXri1/f3/FxMRo3759nikWAAAAwBXBoyEpMzNT7dq10/z584tcP2vWLP3tb3/Tq6++qq1btyowMFCxsbHKysqq4EoBAAAAXCl8PPni/fv3V//+/YtcZ4zRSy+9pL/+9a8aPHiwJOmtt95SRESEVqxYodtvv70iSwUAAABwhfBoSDqfgwcP6ujRo4qJiXG1Va9eXV26dNHmzZuLDUnZ2dnKzs52LWdkZEiS8vLylJeXd2mLBgAAVYLT6ZTdbpevt02+XsbT5VQqdm8vxqYITm+b7Ha79uzZI6fT6elyKp2wsDDVrVvX02WUOA9U2pB09OhRSVJERIRbe0REhGtdUWbMmKFp06YVat+2bZsCAwPLt0gAAFAlnT59WpMmTZI9Mlxedj7wWuVHtVZuJ8bmXPn1ayq34SQdPHhQBw8e9HQ5lY6Xl5e6dOkiPz8/j9aRmZlZon6VNiSV1cSJExUfH+9azsjIUFRUlDp16qTg4GAPVgYAAC4XSUlJmj59uiKGz5YjopGny6lUMnfv0onVLzM258jcvVMnVr+s0P6PyCfU82dMKpO8E4d1YvXL2rRpk9q3b+/RWgquMruQShuSIiMjJUmpqamqXbu2qz01NfW8g+twOORwOAq1+/j4yMen0u4uAACoRLy8vJSTk6PcfCMvp83T5VQqOflOxqYIBeNiQq6SV63Gni6nUjH5Rjk5OfLy8vL45/GSvn6l/Z6khg0bKjIyUomJia62jIwMbd26VdHR0R6sDAAAAEBV5tEod+bMGe3fv9+1fPDgQSUlJalmzZqqV6+eHn30UT3zzDNq2rSpGjZsqEmTJqlOnToaMmSI54oGAAAAUKV5NCRt27ZNvXv3di0X3Es0cuRILV68WE888YQyMzN13333KT09Xd27d9eaNWs8fsMXAAAAgKrLoyHpuuuukzHFTx1ps9n09NNP6+mnn67AqgAAAABcySrtPUkAAAAA4AmEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFj6eLgAAAHhWcnKy0tLSPF1GpbJnzx5PlwDAgwhJAABcwZKTk9W8RUtl/X7W06UAQKVBSAIA4AqWlpamrN/PKvSm8fINjfJ0OZXG7z9t06kv3vZ0GQA8hJAEAADkGxolR2QTT5dRaeSe+MXTJQDwICZuAAAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsfDxdwJUmOTlZaWlpni6jUgoLC1O9evU8XQaAKor336Lt2bPH0yUAQKVDSKpAycnJat6ipbJ+P+vpUiolP/8A7f3vHoISgHLH+y8AoDQISRUoLS1NWb+fVehN4+UbGuXpciqV3BO/6MTHLyotLY2QBKDc8f5bvN9/2qZTX7zt6TIAoFIhJHmAb2iUHJFNPF0GAFxxeP8tLPfEL54uAQAqHSZuAAAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFkzcgEqF7+sojO+PQmnxfUCF8d4CACgNQhIqhfwzv0k2m4YPH+7pUiodvj8KpcH3AQEAcPEISagUnNlnJGP4DpNz8P1RKC2+D6hofBcQAKA0CEmoVPgOE6B88Lvkju8CAgCUBhM3AAAAAIAFIQkAAAAALC6LkDR//nw1aNBAfn5+6tKli77++mtPlwQAAACgiqr0Ielf//qX4uPjNWXKFH377bdq166dYmNjdezYMU+XBgAAAKAKqvQhac6cObr33ns1evRotWrVSq+++qoCAgK0aNEiT5cGAAAAoAqq1LPb5eTkaPv27Zo4caKrzcvLSzExMdq8eXORz8nOzlZ2drZr+dSpU5KkkydPKi8v79IWfAEZGRny9fWVOf6T8vKzL/yEK8mpFMamCObkr/L19dX27duVkZHh6XIqFS8vLzmdTk+XUens27eP36Wi8B5TPMamaIxL8RibojEuxSr4PJORkaGTJ096tJaCz1PGmPP2s5kL9fCglJQUXXXVVfrqq68UHR3tan/iiSe0ceNGbd26tdBzpk6dqmnTplVkmQAAAAAuI7/88ovq1q1b7PpKfSapLCZOnKj4+HjXstPp1MmTJxUaGiqbzXZR287IyFBUVJR++eUXBQcHX2ypOA/GuuIw1hWHsa5YjHfFYawrDmNdsRjvilNRY22M0enTp1WnTp3z9qvUISksLEze3t5KTU11a09NTVVkZGSRz3E4HHI4HG5tISEh5VpXcHAwvygVhLGuOIx1xWGsKxbjXXEY64rDWFcsxrviVMRYV69e/YJ9KvXEDXa7XR07dlRiYqKrzel0KjEx0e3yOwAAAAAoL5X6TJIkxcfHa+TIkerUqZM6d+6sl156SZmZmRo9erSnSwMAAABQBVX6kDRs2DAdP35ckydP1tGjR9W+fXutWbNGERERFV6Lw+HQlClTCl3Oh/LHWFccxrriMNYVi/GuOIx1xWGsKxbjXXEq21hX6tntAAAAAKCiVep7kgAAAACgohGSAAAAAMCCkAQAAAAAFoQkAAAAALAgJBVh0KBBqlevnvz8/FS7dm3dfffdSklJceuzc+dO9ejRQ35+foqKitKsWbMKbWfZsmVq0aKF/Pz8dPXVV2vVqlUVtQuXhUOHDmnMmDFq2LCh/P391bhxY02ZMkU5OTlufWw2W6HHli1b3LbFWF9YScZb4tguL88++6y6deumgICAYr/Quqhje+nSpW59Pv/8c11zzTVyOBxq0qSJFi9efOmLv8yUZKyTk5M1YMAABQQEKDw8XI8//rjy8vLc+jDWZdOgQYNCx/HMmTPd+pTkfQUlM3/+fDVo0EB+fn7q0qWLvv76a0+XdNmbOnVqoWO4RYsWrvVZWVkaO3asQkNDFRQUpLi4OKWmpnqw4svHf/7zHw0cOFB16tSRzWbTihUr3NYbYzR58mTVrl1b/v7+iomJ0b59+9z6nDx5UnfddZeCg4MVEhKiMWPG6MyZM5e+eINC5syZYzZv3mwOHTpkNm3aZKKjo010dLRr/alTp0xERIS56667zA8//GDeffdd4+/vb1577TVXn02bNhlvb28za9Yss3v3bvPXv/7V+Pr6mu+//94Tu1QprV692owaNcqsXbvWHDhwwKxcudKEh4eb8ePHu/ocPHjQSDKffvqpOXLkiOuRk5Pj6sNYl0xJxptju/xMnjzZzJkzx8THx5vq1asX2UeSSUhIcDu2f//9d9f6n376yQQEBJj4+Hize/duM2/ePOPt7W3WrFlTQXtxebjQWOfl5Zk2bdqYmJgYs2PHDrNq1SoTFhZmJk6c6OrDWJdd/fr1zdNPP+12HJ85c8a1viTvKyiZpUuXGrvdbhYtWmR27dpl7r33XhMSEmJSU1M9XdplbcqUKaZ169Zux/Dx48dd6x944AETFRVlEhMTzbZt20zXrl1Nt27dPFjx5WPVqlXmqaeeMu+//76RZD744AO39TNnzjTVq1c3K1asMN99950ZNGiQadiwodu/hf369TPt2rUzW7ZsMV988YVp0qSJueOOOy557YSkEli5cqWx2WyuD+YLFiwwNWrUMNnZ2a4+Tz75pGnevLlr+bbbbjMDBgxw206XLl3M/fffXzFFX6ZmzZplGjZs6FouCEk7duwo9jmMddmdO94c2+UvISHhvCHp3H8wrJ544gnTunVrt7Zhw4aZ2NjYcqyw6ihurFetWmW8vLzM0aNHXW0LFy40wcHBrmOdsS67+vXrm7lz5xa7viTvKyiZzp07m7Fjx7qW8/PzTZ06dcyMGTM8WNXlb8qUKaZdu3ZFrktPTze+vr5m2bJlrrY9e/YYSWbz5s0VVGHVcO6/eU6n00RGRprZs2e72tLT043D4TDvvvuuMcaY3bt3G0nmm2++cfVZvXq1sdls5tdff72k9XK53QWcPHlS//znP9WtWzf5+vpKkjZv3qyePXvKbre7+sXGxmrv3r367bffXH1iYmLcthUbG6vNmzdXXPGXoVOnTqlmzZqF2gcNGqTw8HB1795dH374ods6xrrszh1vju2KN3bsWIWFhalz585atGiRjOWr6xjr8rF582ZdffXVbl9CHhsbq4yMDO3atcvVh7Euu5kzZyo0NFQdOnTQ7Nmz3S5lLMn7Ci4sJydH27dvdztOvby8FBMTw3FaDvbt26c6deqoUaNGuuuuu5ScnCxJ2r59u3Jzc93GvUWLFqpXrx7jfpEOHjyoo0ePuo1t9erV1aVLF9fYbt68WSEhIerUqZOrT0xMjLy8vLR169ZLWh8hqRhPPvmkAgMDFRoaquTkZK1cudK17ujRo27/2EpyLR89evS8fQrWo7D9+/dr3rx5uv/++11tQUFBevHFF7Vs2TJ98skn6t69u4YMGeIWlBjrsilqvDm2K9bTTz+t9957T+vXr1dcXJwefPBBzZs3z7W+uLHOyMjQ77//XtHlXrYu5rhmrC9s3LhxWrp0qTZs2KD7779fzz33nJ544gnX+pKMPy4sLS1N+fn5vP9eAl26dNHixYu1Zs0aLVy4UAcPHlSPHj10+vRpHT16VHa7vdD9joz7xSsYv/Md00ePHlV4eLjbeh8fH9WsWfOSj/8VE5L+93//t8ibpK2P//73v67+jz/+uHbs2KF169bJ29tbI0aMcPsLL4pX2rGWpF9//VX9+vXTrbfeqnvvvdfVHhYWpvj4eHXp0kV/+tOfNHPmTA0fPlyzZ8+u6N2qtMpzvHF+ZRnr85k0aZKuvfZadejQQU8++aSeeOIJju3/U95jjdIpzfjHx8fruuuuU9u2bfXAAw/oxRdf1Lx585Sdne3hvQBKpn///rr11lvVtm1bxcbGatWqVUpPT9d7773n6dLgQT6eLqCijB8/XqNGjTpvn0aNGrn+PywsTGFhYWrWrJlatmypqKgobdmyRdHR0YqMjCw0q0nBcmRkpOu/RfUpWF+VlXasU1JS1Lt3b3Xr1k2vv/76BbffpUsXrV+/3rV8JY+1VL7jzbF9fqUd69Lq0qWLpk+fruzsbDkcjmLHOjg4WP7+/mV+nctBeY51ZGRkoRnASnpcXwljXZSLGf8uXbooLy9Phw4dUvPmzUv0voILCwsLk7e39xX7/luRQkJC1KxZM+3fv199+/ZVTk6O0tPT3c4mMe4Xr2D8UlNTVbt2bVd7amqq2rdv7+pz7Ngxt+fl5eXp5MmTl3z8r5iQVKtWLdWqVatMz3U6nZLk+qtYdHS0nnrqKeXm5rruU1q/fr2aN2+uGjVquPokJibq0UcfdW1n/fr1io6Ovoi9uDyUZqx//fVX9e7dWx07dlRCQoK8vC58cjMpKcntl+lKHmupfMebY/v8LuZ9pCSSkpJUo0YNORwOSX+M9bnTqzPWpRcdHa1nn31Wx44dc122sX79egUHB6tVq1auPlfqWBflYsY/KSlJXl5errEuyfsKLsxut6tjx45KTEzUkCFDJP3x+SQxMVEPPfSQZ4urYs6cOaMDBw7o7rvvVseOHeXr66vExETFxcVJkvbu3avk5OQr9v2hvDRs2FCRkZFKTEx0haKMjAxt3bpV//M//yPpj/eP9PR0bd++XR07dpQkffbZZ3I6nerSpculLfCSTgtxGdqyZYuZN2+e2bFjhzl06JBJTEw03bp1M40bNzZZWVnGmD9m3oiIiDB33323+eGHH8zSpUtNQEBAoWmSfXx8zAsvvGD27NljpkyZwjTJ5zh8+LBp0qSJuf76683hw4fdpt4ssHjxYvPOO++YPXv2mD179phnn33WeHl5mUWLFrn6MNYlU5Lx5tguPz///LPZsWOHmTZtmgkKCjI7duwwO3bsMKdPnzbGGPPhhx+aN954w3z//fdm3759ZsGCBSYgIMBMnjzZtY2Caakff/xxs2fPHjN//nympS7Chca6YArwG264wSQlJZk1a9aYWrVqFTkFOGNdOl999ZWZO3euSUpKMgcOHDBvv/22qVWrlhkxYoSrT0neV1AyS5cuNQ6HwyxevNjs3r3b3HfffSYkJMRt5kaU3vjx483nn39uDh48aDZt2mRiYmJMWFiYOXbsmDHmjynA69WrZz777DOzbdu2Ql8Ng+KdPn3a9Z4sycyZM8fs2LHD/Pzzz8aYP6YADwkJMStXrjQ7d+40gwcPLnIK8A4dOpitW7eaL7/80jRt2pQpwD1h586dpnfv3qZmzZrG4XCYBg0amAceeMAcPnzYrd93331nunfvbhwOh7nqqqvMzJkzC23rvffeM82aNTN2u920bt3afPLJJxW1G5eFhIQEI6nIR4HFixebli1bmoCAABMcHGw6d+7sNg1nAcb6wkoy3sZwbJeXkSNHFjnWGzZsMMb8MYVp+/btTVBQkAkMDDTt2rUzr776qsnPz3fbzoYNG0z79u2N3W43jRo1MgkJCRW/M5XchcbaGGMOHTpk+vfvb/z9/U1YWJgZP368yc3NddsOY11627dvN126dDHVq1c3fn5+pmXLlua5555z/VGxQEneV1Ay8+bNM/Xq1TN2u9107tzZbNmyxdMlXfaGDRtmateubex2u7nqqqvMsGHDzP79+13rf//9d/Pggw+aGjVqmICAAHPzzTe7/YERxduwYUOR788jR440xvwxDfikSZNMRESEcTgc5vrrrzd79+5128aJEyfMHXfcYYKCgkxwcLAZPXq0649gl5LNGGYjAAAAAIACV8zsdgAAAABQEoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAQKV3/Phx/c///I/q1asnh8OhyMhIxcbGatOmTZ4uDQBQBfl4ugAAAC4kLi5OOTk5WrJkiRo1aqTU1FQlJibqxIkTl+T1cnJyZLfbL8m2AQCVH2eSAACVWnp6ur744gs9//zz6t27t+rXr6/OnTtr4sSJGjRokKvP/fffr4iICPn5+alNmzb6+OOPXdtYvny5WrduLYfDoQYNGujFF190e40GDRpo+vTpGjFihIKDg3XfffdJkr788kv16NFD/v7+ioqK0rhx45SZmVlxOw8A8AhCEgCgUgsKClJQUJBWrFih7OzsQuudTqf69++vTZs26e2339bu3bs1c+ZMeXt7S5K2b9+u2267Tbfffru+//57TZ06VZMmTdLixYvdtvPCCy+oXbt22rFjhyZNmqQDBw6oX79+iouL086dO/Wvf/1LX375pR566KGK2G0AgAfZjDHG00UAAHA+y5cv17333qvff/9d11xzjXr16qXbb79dbdu21bp169S/f3/t2bNHzZo1K/Tcu+66S8ePH9e6detcbU888YQ++eQT7dq1S9IfZ5I6dOigDz74wNXnnnvukbe3t1577TVX25dffqlevXopMzNTfn5+l3CPAQCexJkkAEClFxcXp5SUFH344Yfq16+fPv/8c11zzTVavHixkpKSVLdu3SIDkiTt2bNH1157rVvbtddeq3379ik/P9/V1qlTJ7c+3333nRYvXuw6kxUUFKTY2Fg5nU4dPHiw/HcSAFBpMHEDAOCy4Ofnp759+6pv376aNGmS7rnnHk2ZMkUTJkwol+0HBga6LZ85c0b333+/xo0bV6hvvXr1yuU1AQCVEyEJAHBZatWqlVasWKG2bdvq8OHD+vHHH4s8m9SyZctCU4Vv2rRJzZo1c923VJRrrrlGu3fvVpMmTcq9dgBA5cbldgCASu3EiRPq06eP3n77be3cuVMHDx7UsmXLNGvWLA0ePFi9evVSz549FRcXp/Xr1+vgwYNavXq11qxZI0kaP368EhMTNX36dP34449asmSJXnnllQuegXryySf11Vdf6aGHHlJSUpL27dunlStXMnEDAFwBOJMEAKjUgoKC1KVLF82dO1cHDhxQbm6uoqKidO+99+ovf/mLpD8mdpgwYYLuuOMOZWZmqkmTJpo5c6akP84Ivffee5o8ebKmT5+u2rVr6+mnn9aoUaPO+7pt27bVxo0b9dRTT6lHjx4yxqhx48YaNmzYpd5lAICHMbsdAAAAAFhwuR0AAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAW/w+EThIoxR6R3QAAAABJRU5ErkJggg==", + "image/png": "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", "text/plain": [ "
" ] @@ -11023,7 +11053,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -11072,47 +11102,47 @@ "name": "stdout", "output_type": "stream", "text": [ - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.35]\n", - " >>> Collected 1 forecasts: [0.85]\n", + " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.05]\n", - " >>> Collected 1 forecasts: [0.6]\n", + " >>> Collected 1 forecasts: [0.8]\n", " >>> Collected 1 forecasts: [0.7]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.25]\n", " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.95]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.15]\n", + " >>> Collected 1 forecasts: [0.02]\n", " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.4]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.4]\n", - " >>> Collected 1 forecasts: [0.3]\n", - " >>> Collected 1 forecasts: [0.65]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.98]\n", " >>> Collected 1 forecasts: [0.7]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.25]\n", + " >>> Collected 1 forecasts: [0.85]\n", " >>> Collected 1 forecasts: [0.99]\n", - " >>> Collected 1 forecasts: [0.97]\n", - " >>> Collected 1 forecasts: [0.99]\n", + " >>> Collected 1 forecasts: [0.2]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.9]\n", + " >>> Collected 1 forecasts: [0.35]\n", " >>> Collected 1 forecasts: [0.9]\n", - " >>> Collected 1 forecasts: [0.6]\n", - " >>> Collected 1 forecasts: [0.8]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 1 forecasts: [0.25]\n", - " >>> Collected 1 forecasts: [0.65]\n", + " >>> Collected 1 forecasts: [0.05]\n", " >>> Collected 1 forecasts: [0.2]\n", - " >>> Collected 1 forecasts: [0.1]\n", + " >>> Collected 1 forecasts: [0.75]\n", + " >>> Collected 1 forecasts: [0.3]\n", + " >>> Collected 1 forecasts: [0.15]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.1]\n", " >>> Collected 1 forecasts: [0.1]\n", @@ -11121,457 +11151,457 @@ " >>> Collected 1 forecasts: [0.9]\n", " >>> Collected 1 forecasts: [0.95]\n", " >>> Collected 1 forecasts: [0.85]\n", - " >>> Collected 1 forecasts: [0.1]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.35, 0.6]\n", - " >>> Collected 2 forecasts: [0.85, 0.9]\n", + " >>> Collected 1 forecasts: [0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.35, 0.7]\n", + " >>> Collected 2 forecasts: [0.9, 0.9]\n", " >>> Collected 2 forecasts: [0.85, 0.85]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.6, 0.4]\n", - " >>> Collected 2 forecasts: [0.7, 0.4]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.2, 0.25]\n", - " >>> Collected 2 forecasts: [0.15, 0.15]\n", + " >>> Collected 2 forecasts: [0.8, 0.6]\n", + " >>> Collected 2 forecasts: [0.7, 0.6]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.2]\n", + " >>> Collected 2 forecasts: [0.2, 0.15]\n", " >>> Collected 2 forecasts: [0.6, 0.85]\n", - " >>> Collected 2 forecasts: [0.25, 0.65]\n", - " >>> Collected 2 forecasts: [0.25, 0.2]\n", + " >>> Collected 2 forecasts: [0.15, 0.5]\n", + " >>> Collected 2 forecasts: [0.25, 0.3]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.15, 0.2]\n", + " >>> Collected 2 forecasts: [0.15, 0.25]\n", " >>> Collected 2 forecasts: [0.95, 0.95]\n", - " >>> Collected 2 forecasts: [0.1, 0.25]\n", + " >>> Collected 2 forecasts: [0.15, 0.35]\n", + " >>> Collected 2 forecasts: [0.02, 0.05]\n", " >>> Collected 2 forecasts: [0.05, 0.05]\n", - " >>> Collected 2 forecasts: [0.05, 0.02]\n", + " >>> Collected 2 forecasts: [0.1, 0.4]\n", " >>> Collected 2 forecasts: [0.25, 0.35]\n", - " >>> Collected 2 forecasts: [0.4, 0.3]\n", - " >>> Collected 2 forecasts: [0.2, 0.15]\n", - " >>> Collected 2 forecasts: [0.97, 0.96]\n", - " >>> Collected 2 forecasts: [0.4, 0.3]\n", - " >>> Collected 2 forecasts: [0.3, 0.4]\n", - " >>> Collected 2 forecasts: [0.65, 0.7]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", - " >>> Collected 2 forecasts: [0.7, 0.75]\n", - " >>> Collected 2 forecasts: [0.99, 0.7]\n", - " >>> Collected 2 forecasts: [0.97, 0.99]\n", - " >>> Collected 2 forecasts: [0.99, 0.15]\n", - " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.65]\n", - " >>> Collected 2 forecasts: [0.6, 0.4]\n", - " >>> Collected 2 forecasts: [0.8, 0.9]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 2 forecasts: [0.25, 0.3]\n", - " >>> Collected 2 forecasts: [0.65, 0.75]\n", " >>> Collected 2 forecasts: [0.2, 0.2]\n", - " >>> Collected 2 forecasts: [0.1, 0.3]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", + " >>> Collected 2 forecasts: [0.98, 0.97]\n", + " >>> Collected 2 forecasts: [0.7, 0.4]\n", + " >>> Collected 2 forecasts: [0.25, 0.4]\n", + " >>> Collected 2 forecasts: [0.9, 0.7]\n", + " >>> Collected 2 forecasts: [0.25, 0.02]\n", + " >>> Collected 2 forecasts: [0.85, 0.75]\n", + " >>> Collected 2 forecasts: [0.99, 0.99]\n", + " >>> Collected 2 forecasts: [0.2, 0.99]\n", + " >>> Collected 2 forecasts: [0.3, 0.15]\n", + " >>> Collected 2 forecasts: [0.95, 0.9]\n", + " >>> Collected 2 forecasts: [0.9, 0.75]\n", + " >>> Collected 2 forecasts: [0.35, 0.6]\n", + " >>> Collected 2 forecasts: [0.9, 0.85]\n", + " >>> Collected 2 forecasts: [0.05, 0.1]\n", + " >>> Collected 2 forecasts: [0.2, 0.25]\n", + " >>> Collected 2 forecasts: [0.75, 0.7]\n", + " >>> Collected 2 forecasts: [0.3, 0.15]\n", + " >>> Collected 2 forecasts: [0.15, 0.3]\n", + " >>> Collected 2 forecasts: [0.1, 0.15]\n", " >>> Collected 2 forecasts: [0.1, 0.15]\n", - " >>> Collected 2 forecasts: [0.1, 0.05]\n", + " >>> Collected 2 forecasts: [0.1, 0.1]\n", " >>> Collected 2 forecasts: [0.8, 0.9]\n", " >>> Collected 2 forecasts: [0.9, 0.9]\n", - " >>> Collected 2 forecasts: [0.9, 0.3]\n", - " >>> Collected 2 forecasts: [0.95, 0.85]\n", + " >>> Collected 2 forecasts: [0.9, 0.4]\n", + " >>> Collected 2 forecasts: [0.95, 0.8]\n", " >>> Collected 2 forecasts: [0.85, 0.8]\n", - " >>> Collected 2 forecasts: [0.1, 0.1]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.07]\n", - " >>> Collected 3 forecasts: [0.35, 0.6, 0.62]\n", - " >>> Collected 3 forecasts: [0.85, 0.9, 0.82]\n", + " >>> Collected 2 forecasts: [0.05, 0.05]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.07]\n", + " >>> Collected 3 forecasts: [0.35, 0.7, 0.62]\n", + " >>> Collected 3 forecasts: [0.9, 0.9, 0.82]\n", " >>> Collected 3 forecasts: [0.85, 0.85, 0.85]\n", " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.6, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.7, 0.4, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, nan]\n", - " >>> Collected 3 forecasts: [0.2, 0.25, 0.25]\n", - " >>> Collected 3 forecasts: [0.15, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.8, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.7, 0.6, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, nan]\n", + " >>> Collected 3 forecasts: [0.1, 0.2, 0.25]\n", + " >>> Collected 3 forecasts: [0.2, 0.15, nan]\n", " >>> Collected 3 forecasts: [0.6, 0.85, nan]\n", - " >>> Collected 3 forecasts: [0.25, 0.65, 0.108]\n", - " >>> Collected 3 forecasts: [0.25, 0.2, 0.16]\n", + " >>> Collected 3 forecasts: [0.15, 0.5, 0.108]\n", + " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", " >>> Collected 3 forecasts: [0.05, 0.05, 0.95]\n", - " >>> Collected 3 forecasts: [0.15, 0.2, 0.15]\n", + " >>> Collected 3 forecasts: [0.15, 0.25, 0.15]\n", " >>> Collected 3 forecasts: [0.95, 0.95, 0.05]\n", - " >>> Collected 3 forecasts: [0.1, 0.25, 0.125]\n", - " >>> Collected 3 forecasts: [0.05, 0.05, 0.034]\n", - " >>> Collected 3 forecasts: [0.05, 0.02, 0.03]\n", + " >>> Collected 3 forecasts: [0.15, 0.35, 0.125]\n", + " >>> Collected 3 forecasts: [0.02, 0.05, 0.034]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.03]\n", + " >>> Collected 3 forecasts: [0.1, 0.4, 0.35]\n", " >>> Collected 3 forecasts: [0.25, 0.35, 0.35]\n", - " >>> Collected 3 forecasts: [0.4, 0.3, 0.35]\n", - " >>> Collected 3 forecasts: [0.2, 0.15, 0.115]\n", - " >>> Collected 3 forecasts: [0.97, 0.96, 0.97]\n", - " >>> Collected 3 forecasts: [0.4, 0.3, 0.285]\n", - " >>> Collected 3 forecasts: [0.3, 0.4, 0.3833333333333333]\n", - " >>> Collected 3 forecasts: [0.65, 0.7, 0.17]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, 0.12]\n", - " >>> Collected 3 forecasts: [0.7, 0.75, 0.875]\n", - " >>> Collected 3 forecasts: [0.99, 0.7, 0.99]\n", - " >>> Collected 3 forecasts: [0.97, 0.99, 0.9233333333333332]\n", - " >>> Collected 3 forecasts: [0.99, 0.15, 0.4166666666666666]\n", - " >>> Collected 3 forecasts: [0.9, 0.9, 0.8340000000000001]\n", - " >>> Collected 3 forecasts: [0.9, 0.65, 0.7666666666666667]\n", - " >>> Collected 3 forecasts: [0.6, 0.4, 0.875]\n", - " >>> Collected 3 forecasts: [0.8, 0.9, 0.84]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.026]\n", - " >>> Collected 3 forecasts: [0.25, 0.3, 0.16]\n", - " >>> Collected 3 forecasts: [0.65, 0.75, 0.67]\n", - " >>> Collected 3 forecasts: [0.2, 0.2, nan]\n", - " >>> Collected 3 forecasts: [0.1, 0.3, 0.3925]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.086]\n", + " >>> Collected 3 forecasts: [0.2, 0.2, 0.115]\n", + " >>> Collected 3 forecasts: [0.98, 0.97, 0.97]\n", + " >>> Collected 3 forecasts: [0.7, 0.4, 0.285]\n", + " >>> Collected 3 forecasts: [0.25, 0.4, 0.3833333333333333]\n", + " >>> Collected 3 forecasts: [0.9, 0.7, 0.17]\n", + " >>> Collected 3 forecasts: [0.25, 0.02, 0.12]\n", + " >>> Collected 3 forecasts: [0.85, 0.75, 0.875]\n", + " >>> Collected 3 forecasts: [0.99, 0.99, 0.99]\n", + " >>> Collected 3 forecasts: [0.2, 0.99, 0.9233333333333332]\n", + " >>> Collected 3 forecasts: [0.3, 0.15, 0.4166666666666666]\n", + " >>> Collected 3 forecasts: [0.95, 0.9, 0.8340000000000001]\n", + " >>> Collected 3 forecasts: [0.9, 0.75, 0.7666666666666667]\n", + " >>> Collected 3 forecasts: [0.35, 0.6, 0.875]\n", + " >>> Collected 3 forecasts: [0.9, 0.85, 0.84]\n", + " >>> Collected 3 forecasts: [0.05, 0.1, 0.026]\n", + " >>> Collected 3 forecasts: [0.2, 0.25, 0.16]\n", + " >>> Collected 3 forecasts: [0.75, 0.7, 0.67]\n", + " >>> Collected 3 forecasts: [0.3, 0.15, nan]\n", + " >>> Collected 3 forecasts: [0.15, 0.3, 0.3925]\n", + " >>> Collected 3 forecasts: [0.1, 0.15, 0.086]\n", " >>> Collected 3 forecasts: [0.1, 0.15, 0.285]\n", - " >>> Collected 3 forecasts: [0.1, 0.05, 0.02]\n", + " >>> Collected 3 forecasts: [0.1, 0.1, 0.02]\n", " >>> Collected 3 forecasts: [0.8, 0.9, nan]\n", " >>> Collected 3 forecasts: [0.9, 0.9, 0.95]\n", - " >>> Collected 3 forecasts: [0.9, 0.3, nan]\n", - " >>> Collected 3 forecasts: [0.95, 0.85, nan]\n", + " >>> Collected 3 forecasts: [0.9, 0.4, nan]\n", + " >>> Collected 3 forecasts: [0.95, 0.8, nan]\n", " >>> Collected 3 forecasts: [0.85, 0.8, 0.85]\n", - " >>> Collected 3 forecasts: [0.1, 0.1, 0.05]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.35, 0.6, 0.62, 0.7]\n", - " >>> Collected 4 forecasts: [0.85, 0.9, 0.82, 0.794]\n", + " >>> Collected 3 forecasts: [0.05, 0.05, 0.05]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.35, 0.7, 0.62, 0.7]\n", + " >>> Collected 4 forecasts: [0.9, 0.9, 0.82, 0.794]\n", " >>> Collected 4 forecasts: [0.85, 0.85, 0.85, 0.884]\n", " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.7, 0.4, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, nan, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.25, 0.25, nan]\n", - " >>> Collected 4 forecasts: [0.15, 0.15, nan, 0.242]\n", + " >>> Collected 4 forecasts: [0.8, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.7, 0.6, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, nan, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.2, 0.25, nan]\n", + " >>> Collected 4 forecasts: [0.2, 0.15, nan, 0.242]\n", " >>> Collected 4 forecasts: [0.6, 0.85, nan, 0.936]\n", - " >>> Collected 4 forecasts: [0.25, 0.65, 0.108, 0.264]\n", - " >>> Collected 4 forecasts: [0.25, 0.2, 0.16, 0.652]\n", + " >>> Collected 4 forecasts: [0.15, 0.5, 0.108, 0.264]\n", + " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, 0.652]\n", " >>> Collected 4 forecasts: [0.05, 0.05, 0.95, 0.052]\n", - " >>> Collected 4 forecasts: [0.15, 0.2, 0.15, 0.12]\n", + " >>> Collected 4 forecasts: [0.15, 0.25, 0.15, 0.144]\n", " >>> Collected 4 forecasts: [0.95, 0.95, 0.05, 0.866]\n", - " >>> Collected 4 forecasts: [0.1, 0.25, 0.125, 0.212]\n", - " >>> Collected 4 forecasts: [0.05, 0.05, 0.034, nan]\n", - " >>> Collected 4 forecasts: [0.05, 0.02, 0.03, 0.072]\n", - " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.226]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.35, 0.5]\n", - " >>> Collected 4 forecasts: [0.2, 0.15, 0.115, 0.102]\n", - " >>> Collected 4 forecasts: [0.97, 0.96, 0.97, 0.932]\n", - " >>> Collected 4 forecasts: [0.4, 0.3, 0.285, 0.34]\n", - " >>> Collected 4 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42]\n", - " >>> Collected 4 forecasts: [0.65, 0.7, 0.17, 0.236]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, 0.12, 0.29]\n", - " >>> Collected 4 forecasts: [0.7, 0.75, 0.875, 0.92]\n", - " >>> Collected 4 forecasts: [0.99, 0.7, 0.99, 0.99]\n", - " >>> Collected 4 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954]\n", - " >>> Collected 4 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2]\n", - " >>> Collected 4 forecasts: [0.9, 0.9, 0.8340000000000001, nan]\n", - " >>> Collected 4 forecasts: [0.9, 0.65, 0.7666666666666667, nan]\n", - " >>> Collected 4 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999]\n", - " >>> Collected 4 forecasts: [0.8, 0.9, 0.84, 0.86]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999]\n", - " >>> Collected 4 forecasts: [0.25, 0.3, 0.16, nan]\n", - " >>> Collected 4 forecasts: [0.65, 0.75, 0.67, nan]\n", - " >>> Collected 4 forecasts: [0.2, 0.2, nan, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.3, 0.3925, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.086, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.35, 0.125, 0.212]\n", + " >>> Collected 4 forecasts: [0.02, 0.05, 0.034, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.03, 0.072]\n", + " >>> Collected 4 forecasts: [0.1, 0.4, 0.35, 0.226]\n", + " >>> Collected 4 forecasts: [0.25, 0.35, 0.35, 0.5]\n", + " >>> Collected 4 forecasts: [0.2, 0.2, 0.115, 0.102]\n", + " >>> Collected 4 forecasts: [0.98, 0.97, 0.97, 0.932]\n", + " >>> Collected 4 forecasts: [0.7, 0.4, 0.285, 0.34]\n", + " >>> Collected 4 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42]\n", + " >>> Collected 4 forecasts: [0.9, 0.7, 0.17, 0.236]\n", + " >>> Collected 4 forecasts: [0.25, 0.02, 0.12, 0.29]\n", + " >>> Collected 4 forecasts: [0.85, 0.75, 0.875, 0.92]\n", + " >>> Collected 4 forecasts: [0.99, 0.99, 0.99, 0.99]\n", + " >>> Collected 4 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954]\n", + " >>> Collected 4 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2]\n", + " >>> Collected 4 forecasts: [0.95, 0.9, 0.8340000000000001, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.75, 0.7666666666666667, nan]\n", + " >>> Collected 4 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999]\n", + " >>> Collected 4 forecasts: [0.9, 0.85, 0.84, 0.86]\n", + " >>> Collected 4 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999]\n", + " >>> Collected 4 forecasts: [0.2, 0.25, 0.16, nan]\n", + " >>> Collected 4 forecasts: [0.75, 0.7, 0.67, nan]\n", + " >>> Collected 4 forecasts: [0.3, 0.15, nan, nan]\n", + " >>> Collected 4 forecasts: [0.15, 0.3, 0.3925, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.15, 0.086, nan]\n", " >>> Collected 4 forecasts: [0.1, 0.15, 0.285, nan]\n", - " >>> Collected 4 forecasts: [0.1, 0.05, 0.02, nan]\n", + " >>> Collected 4 forecasts: [0.1, 0.1, 0.02, nan]\n", " >>> Collected 4 forecasts: [0.8, 0.9, nan, nan]\n", " >>> Collected 4 forecasts: [0.9, 0.9, 0.95, 0.905]\n", - " >>> Collected 4 forecasts: [0.9, 0.3, nan, nan]\n", - " >>> Collected 4 forecasts: [0.95, 0.85, nan, nan]\n", + " >>> Collected 4 forecasts: [0.9, 0.4, nan, nan]\n", + " >>> Collected 4 forecasts: [0.95, 0.8, nan, nan]\n", " >>> Collected 4 forecasts: [0.85, 0.8, 0.85, 0.71]\n", - " >>> Collected 4 forecasts: [0.1, 0.1, 0.05, 0.02]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan]\n", - " >>> Collected 5 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676]\n", - " >>> Collected 5 forecasts: [0.85, 0.9, 0.82, 0.794, nan]\n", + " >>> Collected 4 forecasts: [0.05, 0.05, 0.05, 0.02]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676]\n", + " >>> Collected 5 forecasts: [0.9, 0.9, 0.82, 0.794, nan]\n", " >>> Collected 5 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76]\n", " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.4, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.7, 0.4, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, nan, nan, nan]\n", - " >>> Collected 5 forecasts: [0.2, 0.25, 0.25, nan, nan]\n", - " >>> Collected 5 forecasts: [0.15, 0.15, nan, 0.242, nan]\n", + " >>> Collected 5 forecasts: [0.8, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.7, 0.6, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, nan, nan, nan]\n", + " >>> Collected 5 forecasts: [0.1, 0.2, 0.25, nan, nan]\n", + " >>> Collected 5 forecasts: [0.2, 0.15, nan, 0.242, nan]\n", " >>> Collected 5 forecasts: [0.6, 0.85, nan, 0.936, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.65, 0.108, 0.264, nan]\n", - " >>> Collected 5 forecasts: [0.25, 0.2, 0.16, 0.652, nan]\n", + " >>> Collected 5 forecasts: [0.15, 0.5, 0.108, 0.264, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, 0.652, nan]\n", " >>> Collected 5 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999]\n", - " >>> Collected 5 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05]\n", + " >>> Collected 5 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05]\n", " >>> Collected 5 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925]\n", - " >>> Collected 5 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085]\n", - " >>> Collected 5 forecasts: [0.05, 0.05, 0.034, nan, 0.0925]\n", - " >>> Collected 5 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1]\n", - " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375]\n", - " >>> Collected 5 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425]\n", - " >>> Collected 5 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475]\n", - " >>> Collected 5 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2]\n", - " >>> Collected 5 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4]\n", - " >>> Collected 5 forecasts: [0.65, 0.7, 0.17, 0.236, nan]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06]\n", - " >>> Collected 5 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999]\n", - " >>> Collected 5 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95]\n", - " >>> Collected 5 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", - " >>> Collected 5 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336]\n", - " >>> Collected 5 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan]\n", - " >>> Collected 5 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan]\n", - " >>> Collected 5 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999]\n", - " >>> Collected 5 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05]\n", - " >>> Collected 5 forecasts: [0.25, 0.3, 0.16, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.65, 0.75, 0.67, nan, 0.76]\n", - " >>> Collected 5 forecasts: [0.2, 0.2, nan, nan, 0.2]\n", - " >>> Collected 5 forecasts: [0.1, 0.3, 0.3925, nan, 0.38]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.086, nan, 0.12]\n", + " >>> Collected 5 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085]\n", + " >>> Collected 5 forecasts: [0.02, 0.05, 0.034, nan, 0.0925]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1]\n", + " >>> Collected 5 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999]\n", + " >>> Collected 5 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375]\n", + " >>> Collected 5 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425]\n", + " >>> Collected 5 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475]\n", + " >>> Collected 5 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2]\n", + " >>> Collected 5 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4]\n", + " >>> Collected 5 forecasts: [0.9, 0.7, 0.17, 0.236, nan]\n", + " >>> Collected 5 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06]\n", + " >>> Collected 5 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999]\n", + " >>> Collected 5 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95]\n", + " >>> Collected 5 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002]\n", + " >>> Collected 5 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336]\n", + " >>> Collected 5 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan]\n", + " >>> Collected 5 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan]\n", + " >>> Collected 5 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999]\n", + " >>> Collected 5 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999]\n", + " >>> Collected 5 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05]\n", + " >>> Collected 5 forecasts: [0.2, 0.25, 0.16, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.75, 0.7, 0.67, nan, 0.76]\n", + " >>> Collected 5 forecasts: [0.3, 0.15, nan, nan, 0.2]\n", + " >>> Collected 5 forecasts: [0.15, 0.3, 0.3925, nan, 0.38]\n", + " >>> Collected 5 forecasts: [0.1, 0.15, 0.086, nan, 0.12]\n", " >>> Collected 5 forecasts: [0.1, 0.15, 0.285, nan, 0.096]\n", - " >>> Collected 5 forecasts: [0.1, 0.05, 0.02, nan, 0.098]\n", + " >>> Collected 5 forecasts: [0.1, 0.1, 0.02, nan, 0.098]\n", " >>> Collected 5 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999]\n", " >>> Collected 5 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78]\n", - " >>> Collected 5 forecasts: [0.9, 0.3, nan, nan, 0.05]\n", - " >>> Collected 5 forecasts: [0.95, 0.85, nan, nan, 0.744]\n", + " >>> Collected 5 forecasts: [0.9, 0.4, nan, nan, 0.05]\n", + " >>> Collected 5 forecasts: [0.95, 0.8, nan, nan, 0.744]\n", " >>> Collected 5 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55]\n", - " >>> Collected 5 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", - " >>> Collected 6 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5]\n", - " >>> Collected 6 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75]\n", + " >>> Collected 5 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175]\n", + " >>> Collected 6 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5]\n", + " >>> Collected 6 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75]\n", " >>> Collected 6 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85]\n", " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.6, 0.4, nan, nan, nan, 0.7]\n", - " >>> Collected 6 forecasts: [0.7, 0.4, nan, nan, nan, 0.65]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, nan, nan, nan, 0.15]\n", - " >>> Collected 6 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225]\n", - " >>> Collected 6 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.8, 0.6, nan, nan, nan, 0.7]\n", + " >>> Collected 6 forecasts: [0.7, 0.6, nan, nan, nan, 0.65]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, nan, nan, nan, 0.15]\n", + " >>> Collected 6 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225]\n", + " >>> Collected 6 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85]\n", - " >>> Collected 6 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2]\n", - " >>> Collected 6 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275]\n", + " >>> Collected 6 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2]\n", + " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275]\n", " >>> Collected 6 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125]\n", - " >>> Collected 6 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15]\n", + " >>> Collected 6 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15]\n", " >>> Collected 6 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85]\n", - " >>> Collected 6 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725]\n", - " >>> Collected 6 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125]\n", - " >>> Collected 6 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075]\n", - " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35]\n", - " >>> Collected 6 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275]\n", - " >>> Collected 6 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5]\n", - " >>> Collected 6 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35]\n", - " >>> Collected 6 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", - " >>> Collected 6 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05]\n", - " >>> Collected 6 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5]\n", - " >>> Collected 6 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", - " >>> Collected 6 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", - " >>> Collected 6 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan]\n", - " >>> Collected 6 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", - " >>> Collected 6 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225]\n", - " >>> Collected 6 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725]\n", - " >>> Collected 6 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2]\n", - " >>> Collected 6 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1]\n", + " >>> Collected 6 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725]\n", + " >>> Collected 6 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075]\n", + " >>> Collected 6 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275]\n", + " >>> Collected 6 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35]\n", + " >>> Collected 6 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275]\n", + " >>> Collected 6 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5]\n", + " >>> Collected 6 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35]\n", + " >>> Collected 6 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35]\n", + " >>> Collected 6 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3]\n", + " >>> Collected 6 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05]\n", + " >>> Collected 6 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5]\n", + " >>> Collected 6 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5]\n", + " >>> Collected 6 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325]\n", + " >>> Collected 6 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan]\n", + " >>> Collected 6 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75]\n", + " >>> Collected 6 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085]\n", + " >>> Collected 6 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225]\n", + " >>> Collected 6 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725]\n", + " >>> Collected 6 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2]\n", + " >>> Collected 6 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675]\n", + " >>> Collected 6 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1]\n", " >>> Collected 6 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15]\n", - " >>> Collected 6 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05]\n", + " >>> Collected 6 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05]\n", " >>> Collected 6 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935]\n", " >>> Collected 6 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935]\n", - " >>> Collected 6 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055]\n", - " >>> Collected 6 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8]\n", + " >>> Collected 6 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055]\n", + " >>> Collected 6 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8]\n", " >>> Collected 6 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475]\n", - " >>> Collected 6 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15]\n", - " >>> Collected 7 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35]\n", - " >>> Collected 7 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92]\n", - " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85]\n", + " >>> Collected 6 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.27]\n", + " >>> Collected 7 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.88]\n", + " >>> Collected 7 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75]\n", " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75]\n", - " >>> Collected 7 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1]\n", - " >>> Collected 7 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28]\n", - " >>> Collected 7 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25]\n", + " >>> Collected 7 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75]\n", + " >>> Collected 7 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1]\n", + " >>> Collected 7 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225, 0.18]\n", + " >>> Collected 7 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2]\n", " >>> Collected 7 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan]\n", - " >>> Collected 7 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35]\n", - " >>> Collected 7 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05]\n", - " >>> Collected 7 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15]\n", + " >>> Collected 7 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.35]\n", + " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02]\n", + " >>> Collected 7 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1]\n", " >>> Collected 7 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9]\n", - " >>> Collected 7 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15]\n", - " >>> Collected 7 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", - " >>> Collected 7 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05]\n", - " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan]\n", - " >>> Collected 7 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan]\n", - " >>> Collected 7 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan]\n", - " >>> Collected 7 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65]\n", - " >>> Collected 7 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan]\n", - " >>> Collected 7 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", - " >>> Collected 7 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99]\n", - " >>> Collected 7 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", - " >>> Collected 7 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9]\n", - " >>> Collected 7 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27]\n", - " >>> Collected 7 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15]\n", - " >>> Collected 7 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35]\n", - " >>> Collected 7 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78]\n", - " >>> Collected 7 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2]\n", - " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05]\n", - " >>> Collected 7 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05]\n", + " >>> Collected 7 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2]\n", + " >>> Collected 7 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15]\n", + " >>> Collected 7 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15]\n", + " >>> Collected 7 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.38]\n", + " >>> Collected 7 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan]\n", + " >>> Collected 7 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan]\n", + " >>> Collected 7 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan]\n", + " >>> Collected 7 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28]\n", + " >>> Collected 7 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3, 0.35]\n", + " >>> Collected 7 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan]\n", + " >>> Collected 7 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7]\n", + " >>> Collected 7 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99]\n", + " >>> Collected 7 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98]\n", + " >>> Collected 7 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2]\n", + " >>> Collected 7 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan, 0.38]\n", + " >>> Collected 7 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65]\n", + " >>> Collected 7 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27]\n", + " >>> Collected 7 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85]\n", + " >>> Collected 7 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05]\n", + " >>> Collected 7 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225, 0.9]\n", + " >>> Collected 7 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.78]\n", + " >>> Collected 7 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2]\n", + " >>> Collected 7 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.75]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.1]\n", + " >>> Collected 7 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07]\n", + " >>> Collected 7 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05, 0.1]\n", " >>> Collected 7 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85]\n", - " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92]\n", - " >>> Collected 7 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65]\n", - " >>> Collected 7 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75]\n", - " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85]\n", - " >>> Collected 7 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan]\n", - " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan]\n", + " >>> Collected 7 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95]\n", + " >>> Collected 7 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055, 0.65]\n", + " >>> Collected 7 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75]\n", + " >>> Collected 7 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1]\n", + " >>> Collected 7 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.27, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan]\n", + " >>> Collected 8 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan]\n", + " >>> Collected 8 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan]\n", " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan]\n", - " >>> Collected 8 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan]\n", + " >>> Collected 8 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225, 0.18, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan]\n", " >>> Collected 8 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", - " >>> Collected 8 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan]\n", - " >>> Collected 8 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan]\n", + " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.15, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan]\n", + " >>> Collected 8 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan]\n", " >>> Collected 8 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", - " >>> Collected 8 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124]\n", - " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55]\n", - " >>> Collected 8 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", - " >>> Collected 8 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", - " >>> Collected 8 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", - " >>> Collected 8 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513]\n", - " >>> Collected 8 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", - " >>> Collected 8 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", - " >>> Collected 8 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", - " >>> Collected 8 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", - " >>> Collected 8 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan]\n", - " >>> Collected 8 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847]\n", - " >>> Collected 8 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615]\n", - " >>> Collected 8 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55]\n", - " >>> Collected 8 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", - " >>> Collected 8 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223]\n", - " >>> Collected 8 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999]\n", - " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125]\n", - " >>> Collected 8 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073]\n", + " >>> Collected 8 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan]\n", + " >>> Collected 8 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124]\n", + " >>> Collected 8 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765]\n", + " >>> Collected 8 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55]\n", + " >>> Collected 8 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195]\n", + " >>> Collected 8 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95]\n", + " >>> Collected 8 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375]\n", + " >>> Collected 8 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513]\n", + " >>> Collected 8 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001]\n", + " >>> Collected 8 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345]\n", + " >>> Collected 8 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85]\n", + " >>> Collected 8 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan]\n", + " >>> Collected 8 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95]\n", + " >>> Collected 8 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34]\n", + " >>> Collected 8 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan, 0.38, nan]\n", + " >>> Collected 8 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan]\n", + " >>> Collected 8 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847]\n", + " >>> Collected 8 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001]\n", + " >>> Collected 8 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615]\n", + " >>> Collected 8 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225, 0.9, 0.55]\n", + " >>> Collected 8 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.78, 0.85]\n", + " >>> Collected 8 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223]\n", + " >>> Collected 8 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999]\n", + " >>> Collected 8 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125]\n", + " >>> Collected 8 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05, 0.1, 0.073]\n", " >>> Collected 8 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94]\n", - " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785]\n", - " >>> Collected 8 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", - " >>> Collected 8 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", - " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708]\n", - " >>> Collected 8 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7]\n", - " >>> Collected 9 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85]\n", + " >>> Collected 8 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785]\n", + " >>> Collected 8 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067]\n", + " >>> Collected 8 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001]\n", + " >>> Collected 8 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708]\n", + " >>> Collected 8 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.27, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.75]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8]\n", + " >>> Collected 9 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.9]\n", " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.65]\n", - " >>> Collected 9 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.75]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35]\n", + " >>> Collected 9 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2]\n", + " >>> Collected 9 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.25]\n", " >>> Collected 9 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95]\n", - " >>> Collected 9 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan, 0.25]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15]\n", " >>> Collected 9 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15]\n", - " >>> Collected 9 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", - " >>> Collected 9 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4]\n", - " >>> Collected 9 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15]\n", - " >>> Collected 9 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", - " >>> Collected 9 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", - " >>> Collected 9 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65]\n", - " >>> Collected 9 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01]\n", - " >>> Collected 9 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", - " >>> Collected 9 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", - " >>> Collected 9 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.4]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85]\n", - " >>> Collected 9 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35]\n", - " >>> Collected 9 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15]\n", - " >>> Collected 9 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.35]\n", - " >>> Collected 9 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", - " >>> Collected 9 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.65]\n", - " >>> Collected 9 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.2]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15]\n", - " >>> Collected 9 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15]\n", + " >>> Collected 9 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15]\n", + " >>> Collected 9 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25]\n", + " >>> Collected 9 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4]\n", + " >>> Collected 9 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25]\n", + " >>> Collected 9 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92]\n", + " >>> Collected 9 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35]\n", + " >>> Collected 9 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65]\n", + " >>> Collected 9 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35]\n", + " >>> Collected 9 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05]\n", + " >>> Collected 9 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.75]\n", + " >>> Collected 9 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99]\n", + " >>> Collected 9 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98]\n", + " >>> Collected 9 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25]\n", + " >>> Collected 9 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85]\n", + " >>> Collected 9 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35]\n", + " >>> Collected 9 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15]\n", + " >>> Collected 9 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.35]\n", + " >>> Collected 9 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85]\n", + " >>> Collected 9 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65]\n", + " >>> Collected 9 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15]\n", + " >>> Collected 9 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15]\n", " >>> Collected 9 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85]\n", - " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785, 0.9]\n", - " >>> Collected 9 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15]\n", - " >>> Collected 9 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", - " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85]\n", - " >>> Collected 9 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.35, 0.6, 0.62, 0.7, 0.324676, 0.5, 0.35, nan, 0.7, nan]\n", - " >>> Collected 10 forecasts: [0.85, 0.9, 0.82, 0.794, nan, 0.75, 0.92, nan, 0.85, 0.638]\n", - " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.85, nan, 0.85, 0.546]\n", + " >>> Collected 9 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9]\n", + " >>> Collected 9 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.8]\n", + " >>> Collected 9 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9]\n", + " >>> Collected 9 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85]\n", + " >>> Collected 9 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.07, 0.0559999999999999, nan, 0.175, 0.27, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.7, 0.62, 0.7, 0.324676, 0.5, 0.3, nan, 0.75, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.9, 0.82, 0.794, nan, 0.75, 0.88, nan, 0.8, 0.638]\n", + " >>> Collected 10 forecasts: [0.85, 0.85, 0.85, 0.884, 0.76, 0.85, 0.75, nan, 0.9, 0.546]\n", " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, 0.127]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, nan, nan, nan, 0.7, 0.75, nan, 0.65, 0.319]\n", - " >>> Collected 10 forecasts: [0.7, 0.4, nan, nan, nan, 0.65, 0.65, nan, 0.75, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.25, 0.25, nan, nan, 0.225, 0.28, nan, 0.25, 0.1939999999999999]\n", - " >>> Collected 10 forecasts: [0.15, 0.15, nan, 0.242, nan, 0.275, 0.25, nan, 0.25, 0.281]\n", + " >>> Collected 10 forecasts: [0.8, 0.6, nan, nan, nan, 0.7, 0.75, nan, 0.35, 0.319]\n", + " >>> Collected 10 forecasts: [0.7, 0.6, nan, nan, nan, 0.65, 0.78, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.15, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, nan, nan, nan, 0.15, 0.1, nan, 0.05, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.2, 0.25, nan, nan, 0.225, 0.18, nan, 0.2, 0.1939999999999999]\n", + " >>> Collected 10 forecasts: [0.2, 0.15, nan, 0.242, nan, 0.275, 0.2, nan, 0.25, 0.281]\n", " >>> Collected 10 forecasts: [0.6, 0.85, nan, 0.936, nan, 0.85, nan, nan, 0.95, 0.946]\n", - " >>> Collected 10 forecasts: [0.25, 0.65, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.2, 0.16, 0.652, nan, 0.275, 0.1, nan, 0.25, nan]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.05, nan, 0.05, nan]\n", - " >>> Collected 10 forecasts: [0.15, 0.2, 0.15, 0.12, 0.05, 0.15, 0.15, nan, 0.15, 0.154]\n", + " >>> Collected 10 forecasts: [0.15, 0.5, 0.108, 0.264, nan, 0.2, 0.35, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, 0.652, nan, 0.275, 0.15, nan, 0.25, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.95, 0.052, 0.0699999999999999, 0.125, 0.02, nan, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.15, 0.25, 0.15, 0.144, 0.05, 0.15, 0.1, nan, 0.15, 0.154]\n", " >>> Collected 10 forecasts: [0.95, 0.95, 0.05, 0.866, 0.8925, 0.85, 0.9, nan, 0.85, 0.85]\n", - " >>> Collected 10 forecasts: [0.1, 0.25, 0.125, 0.212, 0.085, 0.725, 0.15, nan, 0.15, 0.408]\n", - " >>> Collected 10 forecasts: [0.05, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", - " >>> Collected 10 forecasts: [0.05, 0.02, 0.03, 0.072, 0.1, 0.075, 0.05, 0.124, 0.15, 0.063]\n", - " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.226, 0.1149999999999999, 0.275, 0.27, 0.6765, 0.25, 0.289]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.35, 0.5, 0.1375, 0.35, 0.65, 0.55, 0.4, 0.293]\n", - " >>> Collected 10 forecasts: [0.2, 0.15, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.15, 0.201]\n", - " >>> Collected 10 forecasts: [0.97, 0.96, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", - " >>> Collected 10 forecasts: [0.4, 0.3, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", - " >>> Collected 10 forecasts: [0.3, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.65, 0.513, 0.65, 0.425]\n", - " >>> Collected 10 forecasts: [0.65, 0.7, 0.17, 0.236, nan, 0.3, 0.65, 0.6485000000000001, 0.35, 0.155]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.01, 0.161]\n", - " >>> Collected 10 forecasts: [0.7, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.85, 0.6659999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.7, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", - " >>> Collected 10 forecasts: [0.97, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", - " >>> Collected 10 forecasts: [0.99, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.4, 0.408]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.8340000000000001, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.9, 0.65, 0.7666666666666667, nan, nan, nan, 0.9, nan, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.6, 0.4, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.8, 0.9, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.15, 0.1615, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.25, 0.3, 0.16, nan, 0.05, 0.225, 0.35, 0.55, 0.35, nan]\n", - " >>> Collected 10 forecasts: [0.65, 0.75, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", - " >>> Collected 10 forecasts: [0.2, 0.2, nan, nan, 0.2, 0.2, 0.15, 0.223, 0.65, 0.088]\n", - " >>> Collected 10 forecasts: [0.1, 0.3, 0.3925, nan, 0.38, 0.675, 0.15, 0.58, 0.2, 0.574]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.086, nan, 0.12, 0.1, 0.2, 0.1109999999999999, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.05, 0.125, 0.15, nan]\n", - " >>> Collected 10 forecasts: [0.1, 0.05, 0.02, nan, 0.098, 0.05, 0.05, 0.073, 0.15, 0.086]\n", + " >>> Collected 10 forecasts: [0.15, 0.35, 0.125, 0.212, 0.085, 0.725, 0.2, nan, 0.15, 0.408]\n", + " >>> Collected 10 forecasts: [0.02, 0.05, 0.034, nan, 0.0925, 0.125, nan, nan, 0.05, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.03, 0.072, 0.1, 0.075, 0.15, 0.124, 0.15, 0.063]\n", + " >>> Collected 10 forecasts: [0.1, 0.4, 0.35, 0.226, 0.1149999999999999, 0.275, 0.15, 0.6765, 0.25, 0.289]\n", + " >>> Collected 10 forecasts: [0.25, 0.35, 0.35, 0.5, 0.1375, 0.35, 0.38, 0.55, 0.4, 0.293]\n", + " >>> Collected 10 forecasts: [0.2, 0.2, 0.115, 0.102, 0.1425, 0.275, nan, 0.195, 0.25, 0.201]\n", + " >>> Collected 10 forecasts: [0.98, 0.97, 0.97, 0.932, 0.9475, 0.5, nan, 0.95, 0.92, 0.955]\n", + " >>> Collected 10 forecasts: [0.7, 0.4, 0.285, 0.34, 0.2, 0.35, nan, 0.4375, 0.35, 0.126]\n", + " >>> Collected 10 forecasts: [0.25, 0.4, 0.3833333333333333, 0.42, 0.4, 0.35, 0.28, 0.513, 0.65, 0.425]\n", + " >>> Collected 10 forecasts: [0.9, 0.7, 0.17, 0.236, nan, 0.3, 0.35, 0.6485000000000001, 0.35, 0.155]\n", + " >>> Collected 10 forecasts: [0.25, 0.02, 0.12, 0.29, 0.06, 0.05, nan, 0.345, 0.05, 0.161]\n", + " >>> Collected 10 forecasts: [0.85, 0.75, 0.875, 0.92, 0.6599999999999999, 0.75, 0.7, 0.85, 0.75, 0.6659999999999999]\n", + " >>> Collected 10 forecasts: [0.99, 0.99, 0.99, 0.99, 0.95, 0.5, 0.99, nan, 0.99, 0.959]\n", + " >>> Collected 10 forecasts: [0.2, 0.99, 0.9233333333333332, 0.954, 0.9280000000000002, 0.5, 0.98, 0.95, 0.98, 0.7759999999999999]\n", + " >>> Collected 10 forecasts: [0.3, 0.15, 0.4166666666666666, 0.2, 0.336, 0.325, 0.2, 0.34, 0.25, 0.408]\n", + " >>> Collected 10 forecasts: [0.95, 0.9, 0.8340000000000001, nan, nan, nan, 0.38, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.75, 0.7666666666666667, nan, nan, nan, 0.65, nan, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.35, 0.6, 0.875, 0.7759999999999999, 0.2299999999999999, 0.75, 0.27, 0.847, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.9, 0.85, 0.84, 0.86, 0.8019999999999999, 0.75, 0.85, 0.8620000000000001, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.05, 0.1, 0.026, 0.0559999999999999, 0.05, 0.085, 0.05, 0.1615, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.2, 0.25, 0.16, nan, 0.05, 0.225, 0.9, 0.55, 0.35, nan]\n", + " >>> Collected 10 forecasts: [0.75, 0.7, 0.67, nan, 0.76, 0.725, 0.78, 0.85, 0.85, nan]\n", + " >>> Collected 10 forecasts: [0.3, 0.15, nan, nan, 0.2, 0.2, 0.2, 0.223, 0.65, 0.088]\n", + " >>> Collected 10 forecasts: [0.15, 0.3, 0.3925, nan, 0.38, 0.675, 0.75, 0.58, 0.25, 0.574]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.086, nan, 0.12, 0.1, 0.1, 0.1109999999999999, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.15, 0.285, nan, 0.096, 0.15, 0.07, 0.125, 0.15, nan]\n", + " >>> Collected 10 forecasts: [0.1, 0.1, 0.02, nan, 0.098, 0.05, 0.1, 0.073, 0.15, 0.086]\n", " >>> Collected 10 forecasts: [0.8, 0.9, nan, nan, 0.5599999999999999, 0.935, 0.85, 0.94, 0.85, 0.8220000000000001]\n", - " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.92, 0.785, 0.9, 0.762]\n", - " >>> Collected 10 forecasts: [0.9, 0.3, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.15, 0.126]\n", - " >>> Collected 10 forecasts: [0.95, 0.85, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", - " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.85, 0.708, 0.85, 0.132]\n", - " >>> Collected 10 forecasts: [0.1, 0.1, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" + " >>> Collected 10 forecasts: [0.9, 0.9, 0.95, 0.905, 0.78, 0.935, 0.95, 0.785, 0.9, 0.762]\n", + " >>> Collected 10 forecasts: [0.9, 0.4, nan, nan, 0.05, 0.055, 0.65, 0.067, 0.8, 0.126]\n", + " >>> Collected 10 forecasts: [0.95, 0.8, nan, nan, 0.744, 0.8, 0.75, 0.7240000000000001, 0.9, 0.828]\n", + " >>> Collected 10 forecasts: [0.85, 0.8, 0.85, 0.71, 0.55, 0.475, 0.1, 0.708, 0.85, 0.132]\n", + " >>> Collected 10 forecasts: [0.05, 0.05, 0.05, 0.02, 0.052, 0.04, 0.02, 0.042, 0.05, 0.27]\n" ] } ], @@ -11652,16 +11682,16 @@ " multiple_choice\n", " [0, 1, 2-3, 4-6, >6]\n", " 0\n", - " [0.01,0.7,0.25,0.03,0.01]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.057462871287128715, 0.0001, 0.0001, 0.0001,...\n", " \n", " \n", " 1\n", " numeric\n", " NaN\n", " 86.82\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", " \n", @@ -11670,16 +11700,16 @@ " binary\n", " NaN\n", " no\n", - " 0.1\n", + " 0.05\n", + " 0.063\n", " 0.085\n", - " 0.1\n", " \n", " \n", " 3\n", " multiple_choice\n", " [0-4, 5-9, >9]\n", " 5-9\n", - " [0.2,0.6,0.2]\n", + " [0.15,0.65,0.2]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.45, 0.0001]\n", " \n", @@ -11716,8 +11746,8 @@ " NaN\n", " no\n", " 0.9\n", - " 0.3\n", - " 0.1835\n", + " 0.4\n", + " 0.2335\n", " \n", " \n", " 355\n", @@ -11725,7 +11755,7 @@ " NaN\n", " yes\n", " 0.95\n", - " 0.85\n", + " 0.8\n", " 0.775\n", " \n", " \n", @@ -11735,15 +11765,15 @@ " no\n", " 0.85\n", " 0.8\n", - " 0.755\n", + " 0.709\n", " \n", " \n", " 364\n", " binary\n", " NaN\n", " no\n", - " 0.1\n", - " 0.052\n", + " 0.05\n", + " 0.05\n", " 0.046\n", " \n", " \n", @@ -11766,42 +11796,42 @@ "364 binary NaN no \n", "\n", " metac-o1-preview \\\n", - "0 [0.01,0.7,0.25,0.03,0.01] \n", - "1 [0.05,0.0506666667,0.0513333333,0.052,0.052666... \n", - "2 0.1 \n", - "3 [0.2,0.6,0.2] \n", + "0 [0.01,0.7,0.2,0.07,0.02] \n", + "1 [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... \n", + "2 0.05 \n", + "3 [0.15,0.65,0.2] \n", "4 [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... \n", ".. ... \n", "342 0.9 \n", "351 0.9 \n", "355 0.95 \n", "361 0.85 \n", - "364 0.1 \n", + "364 0.05 \n", "\n", " median_forecast_5_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", ".. ... \n", "342 0.9 \n", - "351 0.3 \n", - "355 0.85 \n", + "351 0.4 \n", + "355 0.8 \n", "361 0.8 \n", - "364 0.052 \n", + "364 0.05 \n", "\n", " median_forecast_8_bots \n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.057462871287128715, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", + "2 0.085 \n", "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", ".. ... \n", "342 0.9025 \n", - "351 0.1835 \n", + "351 0.2335 \n", "355 0.775 \n", - "361 0.755 \n", + "361 0.709 \n", "364 0.046 \n", "\n", "[99 rows x 6 columns]" @@ -11892,52 +11922,52 @@ " \n", " 0\n", " 1\n", - " 1399.41\n", + " 1252.60\n", " \n", " \n", " 1\n", " 2\n", - " 2492.32\n", + " 2269.15\n", " \n", " \n", " 2\n", " 3\n", - " 2451.57\n", + " 2400.04\n", " \n", " \n", " 3\n", " 4\n", - " 2407.46\n", + " 2413.81\n", " \n", " \n", " 4\n", " 5\n", - " 2500.43\n", + " 2591.97\n", " \n", " \n", " 5\n", " 6\n", - " 2492.29\n", + " 2483.23\n", " \n", " \n", " 6\n", " 7\n", - " 2620.65\n", + " 2478.69\n", " \n", " \n", " 7\n", " 8\n", - " 2688.63\n", + " 2536.53\n", " \n", " \n", " 8\n", " 9\n", - " 2505.22\n", + " 2388.76\n", " \n", " \n", " 9\n", " 10\n", - " 2396.81\n", + " 2370.53\n", " \n", " \n", "\n", @@ -11945,16 +11975,16 @@ ], "text/plain": [ " Bot_Team_Size Weighted_Baseline_Score_for_Bot_Team_Median\n", - "0 1 1399.41\n", - "1 2 2492.32\n", - "2 3 2451.57\n", - "3 4 2407.46\n", - "4 5 2500.43\n", - "5 6 2492.29\n", - "6 7 2620.65\n", - "7 8 2688.63\n", - "8 9 2505.22\n", - "9 10 2396.81" + "0 1 1252.60\n", + "1 2 2269.15\n", + "2 3 2400.04\n", + "3 4 2413.81\n", + "4 5 2591.97\n", + "5 6 2483.23\n", + "6 7 2478.69\n", + "7 8 2536.53\n", + "8 9 2388.76\n", + "9 10 2370.53" ] }, "execution_count": 60, @@ -11994,14 +12024,7 @@ { "data": { "text/plain": [ - "['metac-o1-preview',\n", - " 'metac-o1',\n", - " 'pgodzinai',\n", - " 'GreeneiBot2',\n", - " 'manticAI',\n", - " 'acm_bot',\n", - " 'metac-Gemini-Exp-1206',\n", - " 'SynapseSeer']" + "['metac-o1-preview', 'metac-o1', 'pgodzinai', 'GreeneiBot2', 'manticAI']" ] }, "execution_count": 61, @@ -12018,7 +12041,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -12115,16 +12138,16 @@ " NaN\n", " False\n", " False\n", - " [0.01,0.7,0.25,0.03,0.01]\n", + " [0.01,0.7,0.2,0.07,0.02]\n", " ...\n", " [0.01, 0.0001, 0.0001, 0.0001, 0.0001]\n", - " [0.20500000000000002, 0.0001, 0.0001, 0.0001, ...\n", + " [0.13, 0.0001, 0.0001, 0.0001, 0.0001]\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.014925742574257425, 0.0001, 0.0001, 0.0001,...\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.057462871287128715, 0.0001, 0.0001, 0.0001,...\n", + " [0.057462871287128715, 0.0001, 0.0001, 0.0001,...\n", " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", " [0.01623640201331385, 0.0001, 0.0001, 0.0001, ...\n", " \n", @@ -12139,10 +12162,10 @@ " 100.0\n", " True\n", " True\n", - " [0.05,0.0506666667,0.0513333333,0.052,0.052666...\n", + " [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05...\n", " ...\n", - " [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05...\n", - " [0.05, 0.050627451000000004, 0.05125490195, 0....\n", + " [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05...\n", + " [0.05, 0.05079411765, 0.0515882353, 0.05238235...\n", " [0.05, 0.0505882353, 0.0511764706, 0.051764705...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", @@ -12163,18 +12186,18 @@ " NaN\n", " False\n", " False\n", - " 0.1\n", + " 0.05\n", " ...\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", + " 0.05\n", + " 0.075\n", + " 0.07\n", + " 0.063\n", + " 0.063\n", + " 0.07\n", " 0.085\n", " 0.085\n", " 0.1\n", " 0.1\n", - " 0.1\n", - " 0.1\n", - " 0.1\n", " \n", " \n", " 3\n", @@ -12187,18 +12210,18 @@ " NaN\n", " NaN\n", " NaN\n", - " [0.2,0.6,0.2]\n", + " [0.15,0.65,0.2]\n", " ...\n", - " [0.0001, 0.6, 0.0001]\n", - " [0.0001, 0.525, 0.0001]\n", + " [0.0001, 0.65, 0.0001]\n", + " [0.0001, 0.55, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", - " [0.0001, 0.5562499999999999, 0.0001]\n", + " [0.0001, 0.5662499999999999, 0.0001]\n", " [0.0001, 0.5125, 0.0001]\n", " [0.0001, 0.48124999999999996, 0.0001]\n", " [0.0001, 0.45, 0.0001]\n", " [0.0001, 0.45, 0.0001]\n", - " [0.0001, 0.442, 0.0001]\n", - " [0.0001, 0.434, 0.0001]\n", + " [0.0001, 0.48124999999999996, 0.0001]\n", + " [0.0001, 0.45, 0.0001]\n", " \n", " \n", " 4\n", @@ -12221,7 +12244,7 @@ " [0.0, 0.00183065955, 0.00366131905, 0.00549197...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", - " [0.0, 0.00217156865, 0.00434313725, 0.00651470...\n", + " [0.0, 0.002254902, 0.0045098039, 0.0067647059,...\n", " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", " \n", " \n", @@ -12245,65 +12268,65 @@ "4 NaN 0.0 400.0 False \n", "\n", " open_upper_bound metac-o1-preview ... \\\n", - "0 False [0.01,0.7,0.25,0.03,0.01] ... \n", - "1 True [0.05,0.0506666667,0.0513333333,0.052,0.052666... ... \n", - "2 False 0.1 ... \n", - "3 NaN [0.2,0.6,0.2] ... \n", + "0 False [0.01,0.7,0.2,0.07,0.02] ... \n", + "1 True [0.05,0.051,0.052,0.053,0.054,0.055,0.056,0.05... ... \n", + "2 False 0.05 ... \n", + "3 NaN [0.15,0.65,0.2] ... \n", "4 False [0.0,0.004,0.008,0.012,0.016,0.02,0.024,0.028,... ... \n", "\n", " median_forecast_1_bots \\\n", "0 [0.01, 0.0001, 0.0001, 0.0001, 0.0001] \n", - "1 [0.05, 0.0506666667, 0.0513333333, 0.052, 0.05... \n", - "2 0.1 \n", - "3 [0.0001, 0.6, 0.0001] \n", + "1 [0.05, 0.051, 0.052, 0.053, 0.054, 0.055, 0.05... \n", + "2 0.05 \n", + "3 [0.0001, 0.65, 0.0001] \n", "4 [0.0, 0.004, 0.008, 0.012, 0.016, 0.02, 0.024,... \n", "\n", " median_forecast_2_bots \\\n", - "0 [0.20500000000000002, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.050627451000000004, 0.05125490195, 0.... \n", - "2 0.1 \n", - "3 [0.0001, 0.525, 0.0001] \n", + "0 [0.13, 0.0001, 0.0001, 0.0001, 0.0001] \n", + "1 [0.05, 0.05079411765, 0.0515882353, 0.05238235... \n", + "2 0.075 \n", + "3 [0.0001, 0.55, 0.0001] \n", "4 [0.0, 0.00366666665, 0.00733333335, 0.011, 0.0... \n", "\n", " median_forecast_3_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505882353, 0.0511764706, 0.051764705... \n", - "2 0.1 \n", + "2 0.07 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0033333333, 0.0066666667, 0.01, 0.0133... \n", "\n", " median_forecast_4_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", - "3 [0.0001, 0.5562499999999999, 0.0001] \n", + "2 0.063 \n", + "3 [0.0001, 0.5662499999999999, 0.0001] \n", "4 [0.0, 0.00257575755, 0.00515151515, 0.00772727... \n", "\n", " median_forecast_5_bots \\\n", "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", - "2 0.085 \n", + "2 0.063 \n", "3 [0.0001, 0.5125, 0.0001] \n", "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " median_forecast_6_bots \\\n", "0 [0.014925742574257425, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", + "2 0.07 \n", "3 [0.0001, 0.48124999999999996, 0.0001] \n", "4 [0.0, 0.00183065955, 0.00366131905, 0.00549197... \n", "\n", " median_forecast_7_bots \\\n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.057462871287128715, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", + "2 0.085 \n", "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", " median_forecast_8_bots \\\n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", + "0 [0.057462871287128715, 0.0001, 0.0001, 0.0001,... \n", "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", + "2 0.085 \n", "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", @@ -12311,14 +12334,14 @@ "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", "2 0.1 \n", - "3 [0.0001, 0.442, 0.0001] \n", - "4 [0.0, 0.00217156865, 0.00434313725, 0.00651470... \n", + "3 [0.0001, 0.48124999999999996, 0.0001] \n", + "4 [0.0, 0.002254902, 0.0045098039, 0.0067647059,... \n", "\n", " median_forecast_10_bots \n", "0 [0.01623640201331385, 0.0001, 0.0001, 0.0001, ... \n", "1 [0.05, 0.0506374696, 0.051274939150000004, 0.0... \n", "2 0.1 \n", - "3 [0.0001, 0.434, 0.0001] \n", + "3 [0.0001, 0.45, 0.0001] \n", "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", "\n", "[5 rows x 29 columns]" @@ -12398,7 +12421,7 @@ " False\n", " 31268\n", " 1.0\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " \n", " \n", @@ -12416,7 +12439,7 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", " \n", " \n", @@ -12434,7 +12457,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.063\n", " 0.013\n", " \n", " \n", @@ -12452,7 +12475,7 @@ " NaN\n", " 31280\n", " 1.0\n", - " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", " \n", " \n", @@ -12470,7 +12493,7 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", " \n", " \n", @@ -12507,11 +12530,11 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.45, 0.0001] \n", - "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.063 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median \n", "0 [0.001,0.62,0.35,0.019,0.01] \n", @@ -12578,7 +12601,7 @@ " False\n", " 35380\n", " 1.00\n", - " 0.9025\n", + " 0.9\n", " 0.95\n", " \n", " \n", @@ -12596,7 +12619,7 @@ " False\n", " 35381\n", " 1.00\n", - " 0.1835\n", + " 0.4\n", " 0.05\n", " \n", " \n", @@ -12614,7 +12637,7 @@ " False\n", " 35385\n", " 1.00\n", - " 0.775\n", + " 0.8\n", " 0.97\n", " \n", " \n", @@ -12632,7 +12655,7 @@ " False\n", " 35386\n", " 0.85\n", - " 0.755\n", + " 0.8\n", " 0.666\n", " \n", " \n", @@ -12650,7 +12673,7 @@ " False\n", " 35387\n", " 0.85\n", - " 0.046\n", + " 0.05\n", " 0.03\n", " \n", " \n", @@ -12680,11 +12703,11 @@ "364 NaN NaN False False 35387 \n", "\n", " question_weight bot_team_median pro_median \n", - "342 1.00 0.9025 0.95 \n", - "351 1.00 0.1835 0.05 \n", - "355 1.00 0.775 0.97 \n", - "361 0.85 0.755 0.666 \n", - "364 0.85 0.046 0.03 " + "342 1.00 0.9 0.95 \n", + "351 1.00 0.4 0.05 \n", + "355 1.00 0.8 0.97 \n", + "361 0.85 0.8 0.666 \n", + "364 0.85 0.05 0.03 " ] }, "metadata": {}, @@ -12694,7 +12717,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/benwilson/Desktop/LogipediaStuff/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", + "/home/molly/metaculus/aib-analysis/refactored_notebook/scoring.py:38: RuntimeWarning: invalid value encountered in scalar divide\n", " peer_score = np.log(forecast_for_resolution / geometric_mean)\n" ] } @@ -12726,7 +12749,7 @@ " how='left'\n", ")\n", "\n", - "# Copy with union (not just overlapping questions)\n", + "# Copy with union (not just questions at the intersection)\n", "df_top_bot_pro_forecasts_all = df_top_bot_pro_forecasts.copy()\n", "\n", "# Filter to only those rows where pro_median is not NA\n", @@ -12747,7 +12770,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Weighted Total Score: -0.1115\n" + "Weighted Total Score: -0.1312\n" ] } ], @@ -12769,7 +12792,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAIjCAYAAAA0vUuxAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAbNRJREFUeJzt3Xd4FFXfxvF7k5BCKiWFEghNOgFpAtI0GlqkiCIoEBAVBRRRX0F9wPZQlKpSrGABpIig0kGKFKUJitJbkF4kISAJJPP+MU82rAlsAkkm5fu5rrnIOTM7+9sl4t47c86xGYZhCAAAAABwQy5WFwAAAAAAuR3BCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAkK0OHz4sm82madOmWV1KrsT7AwB5A8EJgKWmTZsmm83msAUFBally5ZavHjxLZ93+PDhmj9/vtPjWrRokeb509tef/31W64lp4SFhaldu3bp7lu9erVsNpvmzp2bw1VlzowZMzR+/PhsOXd+eH8OHz6sXr16qUKFCvL09FRISIiaNWumYcOGWV1anhQWFpbm356mTZvq22+/tbo0ALmQm9UFAIAkvfnmmypXrpwMw9CpU6c0bdo0tWnTRt9///0NP+zezPDhw9W5c2d16NDhpse9+uqr6tOnj729efNmvffee3rllVdUtWpVe3+tWrUyXQMyb8aMGdq5c6cGDhxodSm5zv79+1W/fn15eXmpd+/eCgsL04kTJ7Rt2zaNGjVKb7zxhtUl5km1a9fWCy+8IEk6fvy4PvzwQ3Xq1EmTJ09W3759La4OQG5CcAKQK7Ru3Vr16tWztx9//HEFBwdr5syZtxScMuq+++5zaHt6euq9997TfffdpxYtWmTb8wKZNW7cOMXHx2v79u0qW7asw77Tp0/naC2XLl2St7d3jj5ndilVqpQee+wxe7tHjx6qWLGixo0bd8PgdO3aNSUnJ8vd3T2nygSQC3CrHoBcKSAgQF5eXnJzc/x+59KlS3rhhRcUGhoqDw8PVa5cWaNHj5ZhGPZjbDabLl26pM8//9x+C050dPRt1bN48WI1bdpU3t7e8vX1Vdu2bfXHH384HPPbb78pOjpa5cuXt99G1bt3b507d87huNdff102m0179+7VY489Jn9/fwUGBuo///mPDMPQ0aNH1b59e/n5+SkkJERjxoy5rdpv5tixY+rdu7eCg4Pl4eGh6tWr67PPPnM4JjExUUOHDlXdunXl7+8vb29vNW3aVKtWrUpzvgsXLig6Olr+/v4KCAhQz549deHChQzV0qJFCy1cuFBHjhyx/72FhYXZ958+fdoeqD09PRUeHq7PP//8dl6+U7np/Tlw4IBKly6dJjRJUlBQUJq+xYsXq3nz5vL19ZWfn5/q16+vGTNmOBwzZ84c1a1bV15eXipevLgee+wxHTt2zOGY6Oho+fj46MCBA2rTpo18fX316KOPSpKSk5M1fvx4Va9eXZ6engoODtZTTz2lv//+2+EcW7ZsUWRkpIoXLy4vLy+VK1dOvXv3vunrbdeuncqXL5/uvkaNGjl80bJ8+XLdfffdCggIkI+PjypXrqxXXnnlpue/kZCQEFWtWlWHDh2SlDoGbfTo0Ro/frwqVKggDw8P/fnnn5KkH3/80f5vQ0BAgNq3b69du3Y5nPPixYsaOHCgwsLC5OHhoaCgIN13333atm3bLdUIwBpccQKQK8TGxurs2bMyDEOnT5/W+++/r/j4eIdvgg3D0AMPPKBVq1bp8ccfV+3atbV06VK99NJLOnbsmMaNGydJ+vLLL9WnTx81aNBATz75pCSpQoUKt1zbl19+qZ49eyoyMlKjRo3S5cuXNXnyZN1999369ddf7R/uly9froMHD6pXr14KCQnRH3/8oY8++kh//PGHfv75Z9lsNofzdunSRVWrVtXIkSO1cOFCvf322ypatKg+/PBD3XPPPRo1apSmT5+uF198UfXr11ezZs2c1nr16lWdPXs2TX9sbGyavlOnTumuu+6SzWZT//79FRgYqMWLF+vxxx9XXFyc/Xa5uLg4ffLJJ+rataueeOIJXbx4UZ9++qkiIyO1adMm1a5dW5L599O+fXutW7dOffv2VdWqVfXtt9+qZ8+eGXqfX331VcXGxuqvv/6y/136+PhIkv755x+1aNFC+/fvV//+/VWuXDnNmTNH0dHRunDhgp577rkMPUdefn/Kli2rFStW6Mcff9Q999xz02OnTZum3r17q3r16hoyZIgCAgL066+/asmSJerWrZv9mF69eql+/foaMWKETp06pQkTJmj9+vX69ddfFRAQYD/ftWvXFBkZqbvvvlujR49W4cKFJUlPPfWU/TzPPvusDh06pA8++EC//vqr1q9fr0KFCun06dO6//77FRgYqMGDBysgIECHDx/WvHnzbvoaunTpoh49emjz5s2qX7++vf/IkSP6+eef9e6770qS/vjjD7Vr1061atXSm2++KQ8PD+3fv1/r16/P0Pv6b1evXtXRo0dVrFgxh/6pU6fqypUrevLJJ+Xh4aGiRYtqxYoVat26tcqXL6/XX39d//zzj95//301adJE27Zts//b0LdvX82dO1f9+/dXtWrVdO7cOa1bt067du3SnXfeeUt1ArCAAQAWmjp1qiEpzebh4WFMmzbN4dj58+cbkoy3337bob9z586GzWYz9u/fb+/z9vY2evbsmel65syZY0gyVq1aZRiGYVy8eNEICAgwnnjiCYfjTp48afj7+zv0X758Oc35Zs6caUgy1q5da+8bNmyYIcl48skn7X3Xrl0zSpcubdhsNmPkyJH2/r///tvw8vLK0GspW7Zsuu/l9ducOXPsxz/++ONGiRIljLNnzzqc55FHHjH8/f3tr+fatWtGQkKCwzF///23ERwcbPTu3dvel/L388477zi8rqZNmxqSjKlTpzp9DW3btjXKli2bpn/8+PGGJOOrr76y9yUmJhqNGjUyfHx8jLi4OKfnzuvvz86dOw0vLy9DklG7dm3jueeeM+bPn29cunTJ4bgLFy4Yvr6+RsOGDY1//vnHYV9ycrJhGOZ7FxQUZNSoUcPhmB9++MGQZAwdOtTe17NnT0OSMXjwYIdz/fTTT4YkY/r06Q79S5Yscej/9ttvDUnG5s2bb/r6/i02Ntbw8PAwXnjhBYf+d955x7DZbMaRI0cMwzCMcePGGZKMM2fOZOr8hmH+Ttx///3GmTNnjDNnzhg7duwwHnnkEUOSMWDAAMMwDOPQoUOGJMPPz884ffq0w+Nr165tBAUFGefOnbP37dixw3BxcTF69Ohh7/P39zf69euX6foA5C7cqgcgV5g4caKWL1+u5cuX66uvvlLLli3Vp08fh2+lFy1aJFdXVz377LMOj33hhRdkGMZtzcJ3I8uXL9eFCxfUtWtXnT171r65urqqYcOGDrdjeXl52X++cuWKzp49q7vuukuS0r0l5/pJKVxdXVWvXj0ZhqHHH3/c3h8QEKDKlSvr4MGDGaq3YcOG9vfx+m306NEOxxmGoW+++UZRUVEyDMPhtUVGRio2NtZes6urq30sR3Jyss6fP69r166pXr16Dq9r0aJFcnNz09NPP+3wugYMGJCh2m9m0aJFCgkJUdeuXe19hQoV0rPPPqv4+HitWbMmQ+fJy+9P9erVtX37dj322GM6fPiwJkyYoA4dOig4OFgff/yx/bjly5fr4sWLGjx4sDw9PR3OkXLVc8uWLTp9+rSeeeYZh2Patm2rKlWqaOHChWme//q6JfM2P39/f913330O70/dunXl4+Nj/28j5crVDz/8oKtXr2botUqSn5+fWrdurdmzZzvcijtr1izdddddKlOmjMP5FyxYoOTk5AyfP8WyZcsUGBiowMBAhYeHa86cOerevbtGjRrlcNyDDz6owMBAe/vEiRPavn27oqOjVbRoUXt/rVq1dN9992nRokX2voCAAP3yyy86fvx4pusDkHtwqx6AXKFBgwYOYxa6du2qOnXqqH///mrXrp3c3d115MgRlSxZUr6+vg6PTZn97siRI1le1759+yTphrdG+fn52X8+f/683njjDX399ddpBuundytYyge/FP7+/vL09FTx4sXT9P97nNSNFC9eXBEREWn6/z1W7MyZM7pw4YI++ugjffTRR+me6/rX8Pnnn2vMmDHavXu3w4ffcuXK2X8+cuSISpQoYb+9LkXlypUd2v/880+a9yMkJOSmr+vIkSOqVKmSXFwcv+/79999bGys/vnnH/t+d3d3hw+1eeH9uZk77rhDX375pZKSkvTnn3/qhx9+0DvvvKMnn3xS5cqVU0REhA4cOCBJqlGjxg3Pk/J+pffcVapU0bp16xz63NzcVLp0aYe+ffv2KTY2Nt3xVVLq+9O8eXM9+OCDeuONNzRu3Di1aNFCHTp0ULdu3eTh4XHT19ulSxfNnz9fGzduVOPGjXXgwAFt3brVYcr6Ll266JNPPlGfPn00ePBg3XvvverUqZM6d+6c5vclPQ0bNtTbb78tm82mwoULq2rVqg63Kaa4/u9Suvl7WLVqVS1dutQ+icY777yjnj17KjQ0VHXr1lWbNm3Uo0ePG47hApA7EZwA5EouLi5q2bKlJkyYoH379ql69eqW1JHyDfaXX36Z7of76z9wP/zww9qwYYNeeukl1a5dWz4+PkpOTlarVq3S/Sbc1dU1Q32SHL5xzwop9Tz22GM3HGOTMgX7V199pejoaHXo0EEvvfSSgoKC5OrqqhEjRtg/pGfGrFmz1KtXL4e+rHp9zz33nMOEEc2bN9fq1aszfR4r35+McHV1Vc2aNVWzZk01atRILVu21PTp09MNhVnBw8MjTQhJTk5WUFCQpk+fnu5jUq7OpKyP9fPPP+v777/X0qVL1bt3b40ZM0Y///xzmiB5vaioKBUuXFizZ89W48aNNXv2bLm4uOihhx6yH+Pl5aW1a9dq1apVWrhwoZYsWaJZs2bpnnvu0bJly27431SKG4Xpf7v+inJmPfzww/b1oZYtW6Z3331Xo0aN0rx589S6detbPi+AnEVwApBrXbt2TZIUHx8vKXVw/MWLFx2uOu3evdu+P8W/J2K4VSmTSgQFBd30w9Xff/+tlStX6o033tDQoUPt/SlXrHKbwMBA+fr6KikpyemHxrlz56p8+fKaN2+ew/v670VXy5Ytq5UrVyo+Pt7hw/CePXscjouMjNTy5cvTfa4b/b2VLVtWv/32m5KTkx0+wP/77/7//u//HCYUKVKkyE1f241Y+f5kVsqV2hMnTkhK/Z3duXOnKlasmO5jUt6vPXv2pLmaumfPnnRn7vu3ChUqaMWKFWrSpEmGQsVdd92lu+66S//97381Y8YMPfroo/r6668dbln9N29vb7Vr105z5szR2LFjNWvWLDVt2lQlS5Z0OM7FxUX33nuv7r33Xo0dO1bDhw/Xq6++qlWrVmVbmLz+Pfy33bt3q3jx4g5TtpcoUULPPPOMnnnmGZ0+fVp33nmn/vvf/xKcgDyEMU4AcqWrV69q2bJlcnd3t9+O1aZNGyUlJemDDz5wOHbcuHGy2WwOH0C8vb0zPM3zzURGRsrPz0/Dhw9Pd3zGmTNnJKVeKfr3lZPrbynKTVxdXfXggw/qm2++0c6dO9PsT3ldKcdKjq/tl19+0caNGx0e06ZNG127dk2TJ0+29yUlJen99993OK5EiRKKiIhw2FJ4e3une1tjmzZtdPLkSc2aNcved+3aNb3//vvy8fFR8+bNJUnVqlVzOG/dunUz9H78m5Xvz4389NNP6f4OpoylSbll7P7775evr69GjBihK1euOBybUmO9evUUFBSkKVOmKCEhwb5/8eLF2rVrl9q2beu0nocfflhJSUl666230uy7du2a/b+/v//+O81/FykzDV7/3DfSpUsXHT9+XJ988ol27NihLl26OOw/f/58msdk5vy3qkSJEqpdu7Y+//xzh39rdu7cqWXLlqlNmzaSzL/jf/9OBwUFqWTJktlaH4CsxxUnALnC4sWL7VcPTp8+rRkzZmjfvn0aPHiwfRxRVFSUWrZsqVdffVWHDx9WeHi4li1bpgULFmjgwIEOU47XrVtXK1as0NixY1WyZEmVK1dODRs2zHRdfn5+mjx5srp3764777xTjzzyiAIDAxUTE6OFCxeqSZMm+uCDD+Tn56dmzZrpnXfe0dWrV1WqVCktW7bMvhZMbjRy5EitWrVKDRs21BNPPKFq1arp/Pnz2rZtm1asWGH/QNquXTvNmzdPHTt2VNu2bXXo0CFNmTJF1apVs18NlMy/nyZNmmjw4ME6fPiwqlWrpnnz5qUbhG6kbt26mjVrlgYNGqT69evLx8dHUVFRevLJJ/Xhhx8qOjpaW7duVVhYmObOnav169dr/Pjxaca95cf3Z9SoUdq6das6depkv01w27Zt+uKLL1S0aFH79Oh+fn4aN26c+vTpo/r166tbt24qUqSIduzYocuXL+vzzz9XoUKFNGrUKPXq1UvNmzdX165d7dORh4WF6fnnn3daT/PmzfXUU09pxIgR2r59u+6//34VKlRI+/bt05w5czRhwgR17txZn3/+uSZNmqSOHTuqQoUKunjxoj7++GP5+fnZw8XNpKwd9eKLL9oD7fXefPNNrV27Vm3btlXZsmV1+vRpTZo0SaVLl9bdd9+doff2Vr377rtq3bq1GjVqpMcff9w+Hbm/v79ef/11SeYaTqVLl1bnzp0VHh4uHx8frVixQps3b87WNdoAZAMrpvIDgBTpTUfu6elp1K5d25g8ebJ9+uQUFy9eNJ5//nmjZMmSRqFChYxKlSoZ7777bprjdu/ebTRr1sw+fXNGpyb/93TkKVatWmVERkYa/v7+hqenp1GhQgUjOjra2LJli/2Yv/76y+jYsaMREBBg+Pv7Gw899JBx/PhxQ5IxbNgw+3Ep05H/e/rknj17Gt7e3mlqat68uVG9enWntZctW9Zo27ZtuvtWrVqVZrptwzCMU6dOGf369TNCQ0ONQoUKGSEhIca9995rfPTRR/ZjkpOTjeHDhxtly5Y1PDw8jDp16hg//PCD0bNnzzRTh587d87o3r274efnZ/j7+xvdu3c3fv311wxPRx4fH29069bNCAgIMCQ5nP/UqVNGr169jOLFixvu7u5GzZo1M3TOFHn9/Vm/fr3Rr18/o0aNGoa/v79RqFAho0yZMkZ0dLRx4MCBNMd/9913RuPGjQ0vLy/Dz8/PaNCggTFz5kyHY2bNmmXUqVPH8PDwMIoWLWo8+uijxl9//eVwzI1+L1N89NFHRt26dQ0vLy/D19fXqFmzpvF///d/xvHjxw3DMIxt27YZXbt2NcqUKWN4eHgYQUFBRrt27Rz+23Hm0UcfNSQZERERafatXLnSaN++vVGyZEnD3d3dKFmypNG1a1dj7969Ts97s9+JFCnTkb/77rvp7l+xYoXRpEkT+/scFRVl/Pnnn/b9CQkJxksvvWSEh4cbvr6+hre3txEeHm5MmjTJaX0AchebYWTxiGMAAAAAyGcY4wQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcKHAL4CYnJ+v48ePy9fWVzWazuhwAAAAAFjEMQxcvXlTJkiXl4nLza0oFLjgdP35coaGhVpcBAAAAIJc4evSoSpcufdNjClxw8vX1lWS+OX5+fhZXAwAAAMAqcXFxCg0NtWeEmylwwSnl9jw/Pz+CEwAAAIAMDeFhcggAAAAAcILgBAAAAABOEJwAAAAAwIkCN8YJAAAA1jMMQ9euXVNSUpLVpSCfK1SokFxdXW/7PAQnAAAA5KjExESdOHFCly9ftroUFAA2m02lS5eWj4/PbZ2H4AQAAIAck5ycrEOHDsnV1VUlS5aUu7t7hmY0A26FYRg6c+aM/vrrL1WqVOm2rjwRnAAAAJBjEhMTlZycrNDQUBUuXNjqclAABAYG6vDhw7p69eptBScmhwAAAECOc3HhYyhyRlZd0eQ3FgAAAACcIDgBAAAAgBMEJwAAAOA2tWjRQgMHDsyx55s2bZoCAgJy7PmyU06/d7eK4AQAAABkQHR0tGw2W5pt//79mjdvnt566y37sWFhYRo/frzD460IO6tWrVK7du0UGBgoT09PVahQQV26dNHatWtztI6b+fd7l1sRnAAAAIAMatWqlU6cOOGwlStXTkWLFpWvr6/V5TmYNGmS7r33XhUrVkyzZs3Snj179O2336px48Z6/vnnrS7PLje+d+khOAEAAAAZ5OHhoZCQEIfN1dXV4XazFi1a6MiRI3r++eftV6VWr16tXr16KTY21t73+uuvS5ISEhL04osvqlSpUvL29lbDhg21evVqh+edNm2aypQpo8KFC6tjx446d+7cTeuMiYnRwIEDNXDgQH3++ee65557VLZsWdWqVUvPPfectmzZYj/23Llz6tq1q0qVKqXChQurZs2amjlzpsP50ruCVrt2bftrMAxDr7/+usqUKSMPDw+VLFlSzz77rP3YSZMmqVKlSvL09FRwcLA6d+5s3/fvW/W+/PJL1atXT76+vgoJCVG3bt10+vRp+/7Vq1fLZrNp5cqVqlevngoXLqzGjRtrz549N31PbpelwWnt2rWKiopSyZIlZbPZNH/+fKePSUhI0KuvvqqyZcvKw8NDYWFh+uyzz7K/WAAAAGSfevWk0qVzfqtXL8tfyrx581S6dGm9+eab9qtSjRs31vjx4+Xn52fve/HFFyVJ/fv318aNG/X111/rt99+00MPPaRWrVpp3759kqRffvlFjz/+uPr376/t27erZcuWevvtt29awzfffKOrV6/q//7v/9Ldf/0U3VeuXFHdunW1cOFC7dy5U08++aS6d++uTZs2Zfg1f/PNNxo3bpw+/PBD7du3T/Pnz1fNmjUlSVu2bNGzzz6rN998U3v27NGSJUvUrFmzG57r6tWreuutt7Rjxw7Nnz9fhw8fVnR0dJrjXn31VY0ZM0ZbtmyRm5ubevfuneF6b4WlC+BeunRJ4eHh6t27tzp16pShxzz88MM6deqUPv30U1WsWFEnTpxQcnJyNlcKAACAbHXypHTsmNVVOPXDDz/Ix8fH3m7durXmzJnjcEzRokXl6upqv2KSwt/fXzabzaEvJiZGU6dOVUxMjEqWLClJevHFF7VkyRJNnTpVw4cP14QJE9SqVSt7CLrjjju0YcMGLVmy5IZ17t27V35+fg7P9c0336hnz5729saNG1WzZk2VKlXKHuIkacCAAVq6dKlmz56tBg0aZOh9iYmJUUhIiCIiIlSoUCGVKVPG/tiYmBh5e3urXbt28vX1VdmyZVWnTp0bnuv6AFS+fHm99957ql+/vuLj4x3e+//+979q3ry5JGnw4MFq27atrly5Ik9PzwzVnFmWBqfWrVurdevWGT5+yZIlWrNmjQ4ePKiiRYtKMi8bAgAAII+77gN+bn7eli1bavLkyfa2t7f3bT3977//rqSkJN1xxx0O/QkJCSpWrJgkadeuXerYsaPD/kaNGt00OElpF36NjIzU9u3bdezYMbVo0UJJSUmSpKSkJA0fPlyzZ8/WsWPHlJiYqISEBBUuXDjDr+Ohhx7S+PHjVb58ebVq1Upt2rRRVFSU3NzcdN9996ls2bL2fa1atVLHjh1veP6tW7fq9ddf144dO/T333/bL5LExMSoWrVq9uNq1apl/7lEiRKSpNOnT6tMmTIZrjszLA1OmfXdd9+pXr16euedd/Tll1/K29tbDzzwgN566y15eXml+5iEhAQlJCTY23FxcTlVLgAAADLqujE3uZm3t7cqVqyYZeeLj4+Xq6urtm7dKldXV4d9119dyaxKlSopNjZWJ0+etF918vHxUcWKFeXm5hgB3n33XU2YMEHjx49XzZo15e3trYEDByoxMdF+jIuLiwzDcHjc1atX7T+HhoZqz549WrFihZYvX65nnnlG7777rtasWSNfX19t27ZNq1ev1rJlyzR06FC9/vrr2rx5c5pZBi9duqTIyEhFRkZq+vTpCgwMVExMjCIjIx3qkaRChQrZf04Jidl5J1qemhzi4MGDWrdunXbu3Klvv/1W48eP19y5c/XMM8/c8DEjRoyQv7+/fQsNDc3BigEAAFAQubu726/o3KyvTp06SkpK0unTp1WxYkWHLSXwVK1aVb/88ovD437++eebPn/nzp1VqFAhjRo1ymmt69evV/v27fXYY48pPDxc5cuX1969ex2OCQwM1IkTJ+ztuLg4HTp0yOEYLy8vRUVF6b333tPq1au1ceNG/f7775IkNzc3RURE6J133tFvv/2mw4cP68cff0xTy+7du3Xu3DmNHDlSTZs2VZUqVRwmhrBSnrrilJycLJvNpunTp8vf31+SNHbsWHXu3FmTJk1K96rTkCFDNGjQIHs7Li6O8AQg0+YciM3W8z9UwT9bzw8AyFlhYWFau3atHnnkEXl4eKh48eIKCwtTfHy8Vq5cqfDwcBUuXFh33HGHHn30UfXo0UNjxoxRnTp1dObMGa1cuVK1atVS27Zt9eyzz6pJkyYaPXq02rdvr6VLlzq9Ta9MmTIaM2aMnnvuOZ0/f17R0dEqV66czp8/r6+++kqS7Fe4KlWqpLlz52rDhg0qUqSIxo4dq1OnTjncFnfPPfdo2rRpioqKUkBAgIYOHepwhWzatGlKSkpSw4YNVbhwYX311Vfy8vJS2bJl9cMPP+jgwYNq1qyZihQpokWLFik5OVmVK1dOt253d3e9//776tu3r3bu3Jlr1njKU1ecSpQooVKlStlDk2QmcMMw9Ndff6X7GA8PD/n5+TlsAAAAQHZ68803dfjwYVWoUEGBgYGSpMaNG6tv377q0qWLAgMD9c4770iSpk6dqh49euiFF15Q5cqV1aFDB23evNk+Vueuu+7Sxx9/rAkTJig8PFzLli3Ta6+95rSGAQMGaNmyZTpz5ow6d+6sSpUqqU2bNjp06JCWLFlin/Xutdde05133qnIyEi1aNFCISEh6tChg8O5hgwZoubNm6tdu3Zq27atOnTooAoVKtj3BwQE6OOPP1aTJk1Uq1YtrVixQt9//72KFSumgIAAzZs3T/fcc4+qVq2qKVOmaObMmapevXqamgMDAzVt2jTNmTNH1apV08iRIzV69Ohb+jvIajbj3zcrWsRms+nbb79N85d0vY8++kgDBw7U6dOn7fd8LliwQJ06dVJ8fPwNxzldLy4uTv7+/oqNjSVEAcgwrjgBQNa4cuWKDh06pHLlymXb7GfA9W72O5eZbGDpFaf4+Hht375d27dvlyQdOnRI27dvV0xMjCQz2fbo0cN+fLdu3VSsWDH16tVLf/75p9auXauXXnpJvXv3zlBoAgAAAIBbYWlw2rJli+rUqWOfx33QoEGqU6eOhg4dKkk6ceKEPURJ5kwgy5cv14ULF1SvXj09+uij9gFoAAAAAJBdLJ0cokWLFmmmNbzetGnT0vRVqVJFy5cvz8aqAAAAAMBRnpocAgAAAACsQHACAABAjssl85OhAMiq3zWCEwAAAHJMoUKFJEmXL1+2uBIUFImJiZLksO7UrchTC+ACAAAgb3N1dVVAQIBOnz4tSSpcuLBsNpvFVSG/Sk5O1pkzZ1S4cGG5ud1e9CE4AQAAIEeFhIRIkj08AdnJxcVFZcqUue2ATnACAABAjrLZbCpRooSCgoJ09epVq8tBPufu7i4Xl9sfoURwAgAAgCVcXV1ve9wJkFOYHAIAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJS4PT2rVrFRUVpZIlS8pms2n+/PkZfuz69evl5uam2rVrZ1t9AAAAACBZHJwuXbqk8PBwTZw4MVOPu3Dhgnr06KF77703myoDAAAAgFRuVj5569at1bp160w/rm/fvurWrZtcXV0zdZUKAAAAAG5FnhvjNHXqVB08eFDDhg3L0PEJCQmKi4tz2AAAAAAgM/JUcNq3b58GDx6sr776Sm5uGbtYNmLECPn7+9u30NDQbK4SAAAAQH6TZ4JTUlKSunXrpjfeeEN33HFHhh83ZMgQxcbG2rejR49mY5UAAAAA8iNLxzhlxsWLF7Vlyxb9+uuv6t+/vyQpOTlZhmHIzc1Ny5Yt0z333JPmcR4eHvLw8MjpcgEAAADkI3kmOPn5+en333936Js0aZJ+/PFHzZ07V+XKlbOoMgAAAAD5naXBKT4+Xvv377e3Dx06pO3bt6to0aIqU6aMhgwZomPHjumLL76Qi4uLatSo4fD4oKAgeXp6pukHAAAAgKxkaXDasmWLWrZsaW8PGjRIktSzZ09NmzZNJ06cUExMjFXlAQAAAIAkyWYYhmF1ETkpLi5O/v7+io2NlZ+fn9XlAMgj5hyIzdbzP1TBP1vPDwAA0spMNsgzs+oBAAAAgFUITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATlgantWvXKioqSiVLlpTNZtP8+fNvevy8efN03333KTAwUH5+fmrUqJGWLl2aM8UCAAAAKLAsDU6XLl1SeHi4Jk6cmKHj165dq/vuu0+LFi3S1q1b1bJlS0VFRenXX3/N5koBAAAAFGRuVj5569at1bp16wwfP378eIf28OHDtWDBAn3//feqU6dOFlcHAAAAACZLg9PtSk5O1sWLF1W0aNEbHpOQkKCEhAR7Oy4uLidKAwAAAJCP5OnJIUaPHq34+Hg9/PDDNzxmxIgR8vf3t2+hoaE5WCEAAACA/CDPBqcZM2bojTfe0OzZsxUUFHTD44YMGaLY2Fj7dvTo0RysEgAAAEB+kCdv1fv666/Vp08fzZkzRxERETc91sPDQx4eHjlUGQAAAID8KM9dcZo5c6Z69eqlmTNnqm3btlaXAwAAAKAAsPSKU3x8vPbv329vHzp0SNu3b1fRokVVpkwZDRkyRMeOHdMXX3whybw9r2fPnpowYYIaNmyokydPSpK8vLzk7+9vyWsAAAAAkP9ZesVpy5YtqlOnjn0q8UGDBqlOnToaOnSoJOnEiROKiYmxH//RRx/p2rVr6tevn0qUKGHfnnvuOUvqBwAAAFAw2AzDMKwuIifFxcXJ399fsbGx8vPzs7ocAHnEnAOx2Xr+hypw1RwAgJyWmWyQ58Y4AQAAAEBOIzgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAATlganNauXauoqCiVLFlSNptN8+fPd/qY1atX684775SHh4cqVqyoadOmZXudAAAAAAo2S4PTpUuXFB4erokTJ2bo+EOHDqlt27Zq2bKltm/froEDB6pPnz5aunRpNlcKAAAAoCBzs/LJW7durdatW2f4+ClTpqhcuXIaM2aMJKlq1apat26dxo0bp8jIyOwqEwAAAEABl6fGOG3cuFEREREOfZGRkdq4ceMNH5OQkKC4uDiHDQAAAAAyI08Fp5MnTyo4ONihLzg4WHFxcfrnn3/SfcyIESPk7+9v30JDQ3OiVAAAAAD5SJ4KTrdiyJAhio2NtW9Hjx61uiQAAAAAeYylY5wyKyQkRKdOnXLoO3XqlPz8/OTl5ZXuYzw8POTh4ZET5QEAAADIp/LUFadGjRpp5cqVDn3Lly9Xo0aNLKoIAAAAQEFgaXCKj4/X9u3btX37dknmdOPbt29XTEyMJPM2ux49etiP79u3rw4ePKj/+7//0+7duzVp0iTNnj1bzz//vBXlAwAAACggLA1OW7ZsUZ06dVSnTh1J0qBBg1SnTh0NHTpUknTixAl7iJKkcuXKaeHChVq+fLnCw8M1ZswYffLJJ0xFDgAAACBb2QzDMKwuIifFxcXJ399fsbGx8vPzs7ocAHnEnAOx2Xr+hyr4Z+v5AQBAWpnJBnlqjBMAAAAAWIHgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcuKXgdPDgwayuAwAAAAByrVsKThUrVlTLli311Vdf6cqVK1ldEwAAAADkKrcUnLZt26ZatWpp0KBBCgkJ0VNPPaVNmzZldW0AAAAAkCvcUnCqXbu2JkyYoOPHj+uzzz7TiRMndPfdd6tGjRoaO3aszpw5k9V1AgAAAIBlbmtyCDc3N3Xq1Elz5szRqFGjtH//fr344osKDQ1Vjx49dOLEiayqEwAAAAAsc1vBacuWLXrmmWdUokQJjR07Vi+++KIOHDig5cuX6/jx42rfvn1W1QkAAAAAlnG7lQeNHTtWU6dO1Z49e9SmTRt98cUXatOmjVxczBxWrlw5TZs2TWFhYVlZKwAAAABY4paC0+TJk9W7d29FR0erRIkS6R4TFBSkTz/99LaKAwAAAIDc4JaC0/Lly1WmTBn7FaYUhmHo6NGjKlOmjNzd3dWzZ88sKRIAAAAArHRLY5wqVKigs2fPpuk/f/68ypUrd9tFAQAAAEBuckvByTCMdPvj4+Pl6el5WwUBAAAAQG6TqVv1Bg0aJEmy2WwaOnSoChcubN+XlJSkX375RbVr187SAgEAAADAapkKTr/++qsk84rT77//Lnd3d/s+d3d3hYeH68UXX8zaCgEAAADAYpkKTqtWrZIk9erVSxMmTJCfn1+2FAUAAAAAucktzao3derUrK4DAAAAAHKtDAenTp06adq0afLz81OnTp1ueuy8efNuuzAAAAAAyC0yHJz8/f1ls9nsPwMAAABAQZHh4HT97XncqgcAAACgILmldZz++ecfXb582d4+cuSIxo8fr2XLlmVZYQAAAACQW9xScGrfvr2++OILSdKFCxfUoEEDjRkzRu3bt9fkyZOztEAAAAAAsNotBadt27apadOmkqS5c+cqJCRER44c0RdffKH33nsvSwsEAAAAAKvdUnC6fPmyfH19JUnLli1Tp06d5OLiorvuuktHjhzJ0gIBAAAAwGq3FJwqVqyo+fPn6+jRo1q6dKnuv/9+SdLp06dZFBcAAABAvnNLwWno0KF68cUXFRYWpoYNG6pRo0aSzKtPderUydICAQAAAMBqGZ6O/HqdO3fW3XffrRMnTig8PNzef++996pjx45ZVhwAAAAA5Aa3FJwkKSQkRCEhIQ59DRo0uO2CAAAAACC3uaXgdOnSJY0cOVIrV67U6dOnlZyc7LD/4MGDWVIcAAAAAOQGtxSc+vTpozVr1qh79+4qUaKEbDZbVtcFAAAAALnGLQWnxYsXa+HChWrSpElW1wMAAAAAuc4tzapXpEgRFS1aNKtrAQAAAIBc6ZaC01tvvaWhQ4fq8uXLWVLExIkTFRYWJk9PTzVs2FCbNm266fHjx49X5cqV5eXlpdDQUD3//PO6cuVKltQCAAAAAP92S7fqjRkzRgcOHFBwcLDCwsJUqFAhh/3btm3L8LlmzZqlQYMGacqUKWrYsKHGjx+vyMhI7dmzR0FBQWmOnzFjhgYPHqzPPvtMjRs31t69exUdHS2bzaaxY8feyssBAAAAgJu6peDUoUOHLCtg7NixeuKJJ9SrVy9J0pQpU7Rw4UJ99tlnGjx4cJrjN2zYoCZNmqhbt26SpLCwMHXt2lW//PJLuudPSEhQQkKCvR0XF5dltQMAAAAoGG4pOA0bNixLnjwxMVFbt27VkCFD7H0uLi6KiIjQxo0b031M48aN9dVXX2nTpk1q0KCBDh48qEWLFql79+7pHj9ixAi98cYbWVIvAAAAgILplsY4SdKFCxf0ySefaMiQITp//rwk8xa9Y8eOZfgcZ8+eVVJSkoKDgx36g4ODdfLkyXQf061bN7355pu6++67VahQIVWoUEEtWrTQK6+8ku7xQ4YMUWxsrH07evRohusDAAAAAOkWg9Nvv/2mO+64Q6NGjdLo0aN14cIFSdK8efMcrh5lh9WrV2v48OGaNGmStm3bpnnz5mnhwoV666230j3ew8NDfn5+DhsAAAAAZMYtBadBgwYpOjpa+/btk6enp72/TZs2Wrt2bYbPU7x4cbm6uurUqVMO/adOnVJISEi6j/nPf/6j7t27q0+fPqpZs6Y6duyo4cOHa8SIEUpOTr6VlwMAAAAAN3VLwWnz5s166qmn0vSXKlXqhrfYpcfd3V1169bVypUr7X3JyclauXKlGjVqlO5jLl++LBcXx7JdXV0lSYZhZPi5AQAAACCjbmlyCA8Pj3Rnp9u7d68CAwMzda5BgwapZ8+eqlevnho0aKDx48fr0qVL9ln2evTooVKlSmnEiBGSpKioKI0dO1Z16tRRw4YNtX//fv3nP/9RVFSUPUABAAAAQFa6peD0wAMP6M0339Ts2bMlSTabTTExMXr55Zf14IMPZupcXbp00ZkzZzR06FCdPHlStWvX1pIlS+wTRsTExDhcYXrttddks9n02muv6dixYwoMDFRUVJT++9//3spLAQAAAACnbMYt3N8WGxurzp07a/PmzYqPj1fJkiV18uRJNWrUSIsWLZK3t3d21Jol4uLi5O/vr9jYWCaKAJBhcw7EZuv5H6rgn63nBwAAaWUmG9zSFSd/f38tX75c69ev144dOxQfH68777xTERERt1QwAAAAAORmmQ5OycnJmjZtmubNm6fDhw/LZrOpXLlyCgkJkWEYstls2VEnAAAAAFgmU7PqGYahBx54QH369NGxY8dUs2ZNVa9eXUeOHFF0dLQ6duyYXXUCAAAAgGUydcVp2rRpWrt2rVauXKmWLVs67Pvxxx/VoUMHffHFF+rRo0eWFgkAAAAAVsrUFaeZM2fqlVdeSROaJOmee+7R4MGDNX369CwrDgAAAAByg0wFp99++02tWrW64f7WrVtrx44dt10UAAAAAOQmmQpO58+ft6+vlJ7g4GD9/ffft10UAAAAAOQmmQpOSUlJcnO78bAoV1dXXbt27baLAgAAAIDcJFOTQxiGoejoaHl4eKS7PyEhIUuKAgAAAIDcJFPBqWfPnk6PYUY9AAAAAPlNpoLT1KlTs6sOAAAAAMi1MjXGCQAAAAAKIoITAAAAADhBcAIAAAAAJzI1xgkAcrM5B2KtLgEAAORTXHECAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACcIDgBAAAAgBMEJwAAAABwguAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHAiVwSniRMnKiwsTJ6enmrYsKE2bdp00+MvXLigfv36qUSJEvLw8NAdd9yhRYsW5VC1AAAAAAoaN6sLmDVrlgYNGqQpU6aoYcOGGj9+vCIjI7Vnzx4FBQWlOT4xMVH33XefgoKCNHfuXJUqVUpHjhxRQEBAzhcPAAAAoECwPDiNHTtWTzzxhHr16iVJmjJlihYuXKjPPvtMgwcPTnP8Z599pvPnz2vDhg0qVKiQJCksLCwnSwYAAABQwFh6q15iYqK2bt2qiIgIe5+Li4siIiK0cePGdB/z3XffqVGjRurXr5+Cg4NVo0YNDR8+XElJSeken5CQoLi4OIcNAAAAADLD0uB09uxZJSUlKTg42KE/ODhYJ0+eTPcxBw8e1Ny5c5WUlKRFixbpP//5j8aMGaO333473eNHjBghf39/+xYaGprlrwMAAABA/pYrJofIjOTkZAUFBemjjz5S3bp11aVLF7366quaMmVKuscPGTJEsbGx9u3o0aM5XDEAAACAvM7SMU7FixeXq6urTp065dB/6tQphYSEpPuYEiVKqFChQnJ1dbX3Va1aVSdPnlRiYqLc3d0djvfw8JCHh0fWFw8AAACgwLD0ipO7u7vq1q2rlStX2vuSk5O1cuVKNWrUKN3HNGnSRPv371dycrK9b+/evSpRokSa0AQAAAAAWcHyW/UGDRqkjz/+WJ9//rl27dqlp59+WpcuXbLPstejRw8NGTLEfvzTTz+t8+fP67nnntPevXu1cOFCDR8+XP369bPqJQAAAADI5yyfjrxLly46c+aMhg4dqpMnT6p27dpasmSJfcKImJgYubik5rvQ0FAtXbpUzz//vGrVqqVSpUrpueee08svv2zVSwAAAACQz9kMwzCsLiInxcXFyd/fX7GxsfLz87O6HABZaM6BWKtLuGUPVfC3ugQAAAqczGQDy2/VAwAAAIDcjuAEAAAAAE4QnAAAAADACYITAAAAADhBcAIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJwgOAEAAACAEwQnAAAAAHCC4AQAAAAAThCcAAAAAMAJghMAAAAAOEFwAgAAAAAnCE4AAAAA4ATBCQAAAACccLO6AADIrVwvX5LP4QPyPbRfhY//JcPNTUmeXkry9FSSx//+9PTSNa/Culihkq76BVhdMgAAyCYEJwAwDPnt/VNBv6yT78H98j20T74H96vwib8ydZq4CpV1rk59navTQOfq1FdcxcqSCxf2AQDIDwhOAAosv727VHrRtwpdPF9+B/be/vkO7JHfgT0qN/crSdJVHz+dC6+rEy3v15EOj+hqQJHbfg4AAGANghOAAsVv7y6VXjxfoYvmy+/Anhsel+gfoIvlKuli+Yq6WK6S4suEyWYYcv3nslyvXJFrwhW5XvlHrleuyD3ugor8/qsCdv0ul2vX7OcoFB+nkPWrFLJ+lWq987r+at1eB7r20rm6d0k2W068XAAAkEUITgDyP8NQyJrlqvb+KBXbsTXtbptNZ+s10l+tHtDf1cN1sVwlJRYtlulw4/rPZRXZuV1Ft29RsV83qdivm+V15pS5LzFBZRfMVtkFsxVbsYoOPtJTRzp21VX/gKx4hQAAIJvZDMMwrC4iJ8XFxcnf31+xsbHy8/OzuhwAWWjOgVjHDsNQ8LpVqj5+uIrt2OK4y2bT2Xp36a/WHfRX5AO6Elwi6wsyDPnt261ys79Q2Lcz5R57wWF3koenjrR/WH88N0RRjatk/fMDAICbykw2IDgByDeuD06BG9eoxvgRKr71Z4djLlSprkMPdTfDUkjJHKvNJeGKSi9eoPJfT1Pglo0O+64V9pbbkMHSCy9IXl45VhMAAAUdwekmCE5A/jXnQKyKbf1ZNca+raBf1jnsi72jmv54brCO3dfO8pnufPftVvlZ01Ru7gwVio9L3VGmjDRqlNSlC2OgAADIAQSnmyA4AfnUxYs60HegKsz4zKE7rkJl/fHsYP3Vur3lgenf3M+fU/X3Rqr8zM/kkpSUuqNRI2ncOKlhQ+uKAwCgACA43QTBCciHVq6UHn9cOnLE3nWxXEX98exgHW3TUXJ1tbA453z37VarCa9LS5Y47ujeXZowQSrCNOYAAGSHzGSD3PX1KwBkxsWLUt++UkSEPTRdK+ytbUPf0dLFP+toVOdcH5ok6WKlKtLixdKiRVLVqqk7vvxSCg+XfvrJuuIAAIAkghOAvGrFCqlGDenDD+1dpxverWU/rNeBHk/KcMuDqy20bi3t2CG9/74UEGD2HT0qtWghDR0qXbdGFAAAyFkEJwB5yz//mFeZ7rtPiokx+7y9pYkTtebL73SpTJil5d22QoWk/v2l33+Xmjc3+5KTpbfekpo1kw4dsrY+AAAKKIITgLwjJka6+26Hq0xq2dIMGc88k+smf7gtpUubY7f++9/U2w03bpRq15ZmzLC0NAAACqJ89CkDQL62Zo1Ur560bZvZLlxYmjTJvGWvXDlra8surq7SK69I69envsa4OOnRR6WePaXLl62tDwCAAoTgBCB3Mwzpgw+ke++Vzpwx+ypUkH75RXr66fx1lelGGjaUtm+XHnsste+LL8xb+U6csKwsAAAKkgLwiQNAnnXlijnN+IABUso6R5GR0ubN5sQQBYmfnznL3pdfSj4+Zt+WLVKDBuaEEgAAIFsRnADkTseOmVdUpk5N7Xv5ZWnhwoK9rtFjj5ljncqUMdt//WWO+1q40Nq6AADI5whOAHKfLVukunWlTZvMtpeXNHOmNHJknliXKdvVqGHeqtiggdmOj5ceeEB67z3z1kYAAJDlCE4AcpcffzRnyjt1ymyHhUkbNkiPPGJpWblOSIi0erX00ENmOzlZeu45cypz1nsCACDLEZwA5B7z5pmLwMbHm+2mTc3xTLVrW1pWruXlJX39tfTqq6l9kyZJUVHm7HsAACDL5IrgNHHiRIWFhcnT01MNGzbUppTbc5z4+uuvZbPZ1KFDh+wtEED2++QT8+pJYqLZjoqSli6Vihe3tq7czsVFevttado0c/FcSVqyxFwg+Px5S0sDACA/sTw4zZo1S4MGDdKwYcO0bds2hYeHKzIyUqdPn77p4w4fPqwXX3xRTZs2zaFKAWQLw5BGjZKeeMK83Uwy1yiaN8+8ooKM6dlTWr48deKMTZuke+6RnPxbCgAAMsby4DR27Fg98cQT6tWrl6pVq6YpU6aocOHC+uyzz274mKSkJD366KN64403VL58+RysFkCWMgzppZekwYNT+wYNkj77THJzs66uvKp5c3Oh4OBgs71jh9l3/Li1dQEAkA9YGpwSExO1detWRURE2PtcXFwUERGhjRs33vBxb775poKCgvT44487fY6EhATFxcU5bABygWvXpN69pTFjUvtGjJBGjy4Yi9pml5o1zfBUqpTZ3r1batZMOnLE2roAAMjjLP10cvbsWSUlJSk45dvR/wkODtbJkyfTfcy6dev06aef6uOPP87Qc4wYMUL+/v72LTQ09LbrBnCbrl2THn3UHJcjmUHpo4/MK082m6Wl5QuVK0s//SSVK2e2DxwwJ9rYt8/augAAyMPy1Ne6Fy9eVPfu3fXxxx+reAYHjA8ZMkSxsbH27ejRo9lcJYCbunZN6tFDmj3bbLu7mz8/8YS1deU35cpJa9dKd9xhto8eNa88/fGHtXUBAJBHWTqIoHjx4nJ1ddWplPVa/ufUqVMKCQlJc/yBAwd0+PBhRUVF2fuS/zeY3M3NTXv27FGFChUcHuPh4SEPD49sqB5ApiUlSb16mYvZSmZo+vZbqU0ba+vKr0qXNsPTffdJv/8unTxpjnlasYIp3gEAyCRLrzi5u7urbt26Wrlypb0vOTlZK1euVKNGjdIcX6VKFf3+++/avn27fXvggQfUsmVLbd++ndvwgNwsKUl6/HHpq6/MdqFC0jffEJqyW3CwuVBuvXpm+9w5M0hx5QkAgEyxfNqqQYMGqWfPnqpXr54aNGig8ePH69KlS+rVq5ckqUePHipVqpRGjBghT09P1ahRw+HxAQEBkpSmH0AukpwsPfmk9PnnZtvNTZozR2rXztq6CoqiRc2rTG3aSBs2SGfPShER5tWoSpWsrg4AgDzB8uDUpUsXnTlzRkOHDtXJkydVu3ZtLVmyxD5hRExMjFyYYQvIu5KTpb59zSnGJTM0zZ4ttW9vbV0Fjb+/tGiRGZi2bDFv27v3XjM8hYVZXR0AALmezTAMw+oiclJcXJz8/f0VGxsrPz8/q8sB8jfDkJ55RpoyxWy7ukpffy117pwtTzfnQGy2nDcnPFTBP2ee6Px5qWVL6bffzHb58mZ4Spm+HACAAiQz2YBLOQCyh2FIzz+fGppcXKTp07MtNCGDihaVli+XqlQx2wcPmlee/jVJDwAAcERwApA93npLmjDB/NnFxZwUoksXa2uCKSjIHPNUvrzZ3rPHnDDi/Hlr6wIAIBcjOAHIeh98IA0bltr+9FOpa1fr6kFapUpJK1dKKbOR/v67FBkpxebd2x0BAMhOBCcAWWv6dGnAgNT22LFSdLRl5eAmwsLM8JSybt6WLeZMh//8Y2lZAADkRgQnAFln4UKpZ8/U9quvmuOckHtVqmSGp+LFzfa6ddIjj0jXrllbFwAAuQzBCUDW+Oknc+KHpCSz3bevOc4JuV+1atKSJZKPj9n+7jvpqafMCT4AAIAkghOArPDrr+YtXleumO0uXcxxTjabtXUh4+rWlebPl9zdzfZnn0mvvGJpSQAA5CYEJwC3Z98+qVUrKS7ObLdqJX3xhblmE/KWe+81Zz9MCbwjR0rjx1taEgAAuYWb1QUAyMNOnpTuv186fdpsN24szZ2betUCGZadi/dmanHdhx6Szp41Fy6WzDFqgYHSo49mT3EAAOQRXHECcGvi4qQ2baTDh812zZrSDz9I3t6WloUs8PTTjtPJR0ebY6AAACjACE4AMi8xUXrwQXNskySVKWN+sC5SxNq6kHWGDTMn+JDMGfYefFD6+WdrawIAwEIEJwCZk5ws9e4trVhhtosUMUNTyZLW1oWsZbOZE3x07my2L182JwDZu9faugAAsAjBCUDmDBliLnIrSZ6e0vffS1WrWlsTsoerqzlZRMuWZvvcOXPyj1OnrK0LAAALEJwAZNx770nvvGP+7OIizZwpNWlibU3IXh4e0rffSrVqme1Dh6S2baX4eGvrAgAghxGcAGTMnDnSwIGp7YkTpQ4drKoGOcnfX1q0SAoNNdtbt0oPPyxdvWptXQAA5CCCEwDn1qyRHntMMgyz/dprqRMHoGAoVUpavFgKCDDbixebvwMpvxMAAORzBCcAN/fHH1L79uZMepLUq5f05pvW1gRrVK8uzZ+fuk7XZ59Jb7xhaUkAAOQUghOAGzt+XGrdWor93+KsrVtLH35ozriGgql5c+nLL1Pbb7whffKJdfUAAJBDCE4A0peywO3Ro2a7bl1p9mypUCFr64L1Hn5YGjs2td23rzkGCgCAfIzgBCCtq1fN9Xt27DDbYWHSDz9IPj6WloVc5PnnzU2SkpKkhx6StmyxtiYAALIRwQmAI8OQnnhCWr7cbBctai5wGxJibV3IfUaPNq8+SeYCuW3bSgcPWlsTAADZhOAEwNHrr0uff27+7OEhffedVLmypSUhl3JxMX9XmjUz26dPm+Pgzp2zti4AALIBwQlAqk8/TZ0xz2aTpk9ngVvcnKenuUBu1apme+9e6YEHpH/+sbYuAACyGMEJgGnxYumpp1LbY8dKDz5oXT3IO4oWNX9/Um7n3LDBXPcrKcnaugAAyEIEJwDS1q3m4P6UD7rPPy8NHGhpSchjypY1Z9ZLmUBk3jzphResrQkAgCxEcAIKukOHzEH9ly6Z7c6dzUH/QGbVqSPNnSu5uprtCROkceOsrQkAgCxCcAIKsrNnpVatpFOnzHbTpubipi7804BbFBkpffRRanvQIHP9LwAA8jg+HQEF1eXL5iD+vXvNdtWq0vz55mB/4Hb07i0NG5ba7t5dWrPGunoAAMgCBCegIEpKkrp1kzZuNNslSphrNRUtam1dyD+GDTMDlCQlJkrt20s7d1pbEwAAt8HN6gIA5DDDkAYMkBYsMNu+vuaMaGXKSJLmHIi1sDjkGzabNGWKdOKE+fsVG2veFrpxoxQaanV1AABkGlecgIJm5Ehp8mTzZzc3c/az8HBra0L+VKiQOb6pXj2zfeyYuUDu339bWxcAALeA4AQUJF9+Kb3ySmp76lQpIsK6epD/+fhICxdKFSua7T/+kDp0kK5csbQsAAAyi+AEFBRLl6aOOZGkESPMRUqB7BYUZI6hCww022vXskAuACDPITgBBcGmTdKDD0rXrpntZ56RXn7Z2ppQsFSoYC6Q6+1ttr/5xlxk2TAsLQsAgIwiOAH53e7dUps2qQvcPvig9N575uB9ICfVq2cukOv2v3mJPvhAGjXK2poAAMggghOQn/31l7kg6blzZrtFC+mrryRXV0vLQgHWqpX0ySep7SFDzLF2AADkcgQnIL86f978kBoTY7Zr1zanIGeBW1itZ09p+PDUdp8+5uLLAADkYgQnID+6fFmKijJnMJOk8uXNtXT8/KytC0gxeLA5xkmSkpOlRx6RVq2ytCQAAG6G4ATkN1evSl26SBs2mO3gYGnZMikkxNq6gOvZbNKYMVL37mY7IUFq317autXaugAAuAGCE5CfGIb05JPSDz+YbV9f80pThQrW1gWkx8VF+vRTqV07s33xonl76Z491tYFAEA6CE5AfmEY0vPPS9OmmW13d3NMU506lpYF3FShQtLs2VLTpmb77Fnp/vvNiU0AAMhFCE5AfjF0qDRhgvmzi4s0Y4bUsqW1NQEZ4eUlffedFB5utmNizPCUMhskAAC5AMEJyA/eeUd6++3U9iefmOs1AXlFQIC0ZEnqbaW7dpnrj128aGlZAACkIDgBed2kSdLLL6e233tP6tXLunqAWxUS4jiRyaZN5viny5etrQsAAOWS4DRx4kSFhYXJ09NTDRs21KZNm2547Mcff6ymTZuqSJEiKlKkiCIiIm56PJCvffGF1K9favu//5UGDLCuHuB2lS9vhqciRcz22rXmbHtXrlhbFwCgwLM8OM2aNUuDBg3SsGHDtG3bNoWHhysyMlKnT59O9/jVq1era9euWrVqlTZu3KjQ0FDdf//9OnbsWA5XDljsm28crywNHiy98op19QBZpWZNaenS1HXHVqwwbz1NTLS2LgBAgWYzDMOwsoCGDRuqfv36+uCDDyRJycnJCg0N1YABAzR48GCnj09KSlKRIkX0wQcfqEePHk6Pj4uLk7+/v2JjY+XHYqDIqxYvNr+Fv3rVbPfvb96iZ7Pd9qnnHIi97XMgd3mogr/VJdyaDRvMSSIuXTLbHTtKs2aZM/EBAJAFMpMNLL3ilJiYqK1btyoiIsLe5+LiooiICG3cuDFD57h8+bKuXr2qokWLprs/ISFBcXFxDhuQp/34o9SpU2poio42Z9PLgtAE5CqNG0sLF5qz7knSt9+aC+YmJVlbFwCgQLI0OJ09e1ZJSUkKDg526A8ODtbJkyczdI6XX35ZJUuWdAhf1xsxYoT8/f3tW2ho6G3XDVhm5UqpbdvU8R6dO0sff2xOPw7kR82bm+uReXiY7VmzpN69peRka+sCABQ4efrT1siRI/X111/r22+/laenZ7rHDBkyRLGxsfbt6NGjOVwlkEVWrDBnGEsJTVFR0vTpkpubtXUB2e2++6S5c1Nv0fviC6lvX3PRZwAAcoilwal48eJydXXVqVOnHPpPnTqlkJTpaG9g9OjRGjlypJYtW6ZatWrd8DgPDw/5+fk5bECes2yZGZRSQlP79uYHSXd3a+sCckq7dtLXX0uurmb744/N8MSVJwBADrE0OLm7u6tu3bpauXKlvS85OVkrV65Uo0aNbvi4d955R2+99ZaWLFmievXq5USpgHWWLpUeeCA1NHXsKM2eTWhCwdOpk/TVV6m3pn70kXnbHmOeAAA5wPJ7fAYNGqSePXuqXr16atCggcaPH69Lly6p1/+mWe7Ro4dKlSqlESNGSJJGjRqloUOHasaMGQoLC7OPhfLx8ZGPj49lrwPIFkuWSB06SAkJZrtTJ/Nbd2YVQ0H1yCPmLXopk0R8/rn538cXX/DfBQAgW1kenLp06aIzZ85o6NChOnnypGrXrq0lS5bYJ4yIiYmRy3UD3ydPnqzExER17tzZ4TzDhg3T66+/npOlA9lr0SLz6lLK2jUPPijNnMmHQ6BrV3OyiEceMWeX/PprMzzNnJk6iQQAAFnM8nWcchrrOCFPWLBAevjh1ND00EPmRBA5EJpYxyn/ybPrODmzcKH5hULKFdk2bcyFoW8wWRAAAP+WZ9ZxApCOadPMW/JSQlOXLtKMGVxpAv6tbVvp++9T13latMicRCVlwVwAALIQwQnITcaMkXr1Sp0p7LHHzMHwTDkOpO+++6TFiyVvb7O9YoXUurXEYucAgCxGcAJyA8OQhgyRXnwxte/ZZ82B74Qm4OaaNzen7E+5xeKnn6SWLaUMLqQOAEBGEJwAqyUlSU8+KY0cmdr35pvS+PGp0y4DuLnGjaWVK6WiRc32tm1m39691tYFAMg3+FQGWCkhwZwE4pNPzLbNJk2aJP3nP+bPADKuXj3zalNoqNk+dEhq0kT65Rdr6wIA5AsEJ8AqFy+ag9vnzTPbbm7mJBBPP21tXUBeVq2atHGjVLOm2T57VrrnHnMGPgAAbgPBCbBCTIx0993mrUWSVLiwOTvYI49YWxeQH5QqJa1dK7VoYbYvX5bat5c+/dTSsgAAeRvBCchpv/wiNWgg/fab2S5SxJwJrFUra+sC8pOAAGnJEvNWWMkcS9inj/TWW+ZkLAAAZBLBCchJs2eb34KfOmW2K1SQNmyQGjWytCwgX/LwkGbOlAYOTO0bOlR6/PHURXMBAMggghOQEwzD/Ka7SxfpyhWzr1kz8+pTlSrW1gbkZy4u0tix0rvvpvZNnWp+gXHihGVlAQDyHoITkN2uXJG6dze/6U7Rq5e0fLlUrJh1dQEFhc1mrpH29deSl5fZ9/PPUv360ubN1tYGAMgzCE5Adjp9Wrr3Xmn6dLNts0mjRpmD1N3dra0NKGi6dJHWrUudrvzYMalpU+nLL62tCwCQJxCcgOyybp1Up445hkkyZ8775hvp//6PNZoAq9x5p7RlizmrpWSOderRw7wide2atbUBAHI1ghOQ1QxDGj3aHENx/LjZV7KkuTBnx46WlgZAUlCQuRTAU0+l9o0ZY66r9vff1tUFAMjVCE5AVvr7bzMcvfSSOf2xZAaorVvNb7oB5A7u7tKUKdLkyebi05K0bJl5lXjjRmtrAwDkSgQnIKts3SrVrSstWJDa98or5iQQISHW1QXgxvr2Na8+FS9uto8cMcc9DR+e+uUHAACS3KwuAMjzDMP85nrgQCkx0ewrWtQccN6mTbY85ZwDsdlyXqBAatZM2rZN6tbNHJuYlCS9+qoZqL780rzVFgBQ4HHFCbgdZ8+aM3U980xqaGrQwPwQlk2hCUA2CA2VVq0ylw1w+d//Gn/8UQoPlxYtsrY2AECuQHACbtV330k1akhz5qT2DRhgTgJRtqx1dQG4NW5u0htvmIGpVCmz7+xZc9KIQYPMGfgAAAUWwQnIrAsXpOhoqX176dQps69IETNAvfce6zMBeV3z5tL27VJUVGrfuHHm1eQtWywrCwBgLYITkBnLlkk1a0qff57a166d9McfUufO1tUFIGsVL25O9HL9lyG//SY1bGjOmnn5srX1AQByHMEJyIj4eOnpp6XISOmvv8w+Pz/ps8/MW/ZKlLC2PgBZz2Yzb7/95Repdm2zLznZXKetVi1zTBQAoMAgOAE3YxjS3LlStWrmzHkpIiKk33+XevUyP1wByL9q15Y2bTKnKPfwMPsOHJDuuUd68knz9l0AQL5HcAJuZNcu6f77pYceko4eNfu8vaVJk8xb9sqUsbY+ADmnUCFpyBBpxw7p7rtT+z/+2Pxi5ZtvzC9aAAD5FsEJ+LeLF80xDLVqSStWpPa3amV+aHr6aa4yAQVV5crSmjXmFyg+PmbfiRPmGMeWLc2lCAAA+RLBCUhhGNKMGeYHo9GjpWvXzP6wMGn+fHMtlwoVrKwQQG7g4mJ+gfLnn+ZU5SnWrJHq1TNv4T1+3Lr6AADZguAESOYHnqZNpUcfNb89lsyxDMOGmR+O2rfnKhMAR6Gh0vffm1+sVKxo9hmGNG2aVKmS9OabzL4HAPmIzTAK1k3ZcXFx8vf3V2xsrPz8/KwuB1b7+WfpP/9xvCVPkh54wFy3pXx5a+pyYs6BWKtLAOwequBvdQnWS0yUJk40w9L1k0WUKiX997/mlzJubpaVBwBIX2ayAVecUDClLG7ZqJFjaKpSRVq40Fy/JZeGJgC5kLu79Pzz0v795hTmrq5m/7Fj5oLZVapIn35qBiwAQJ5EcELB8scf0sMPS3XqSD/8kNpfvrz0xRfSzp1SmzbW1QcgbytWzFw0d+dOx/FPBw5IffqYt/BNmiRduWJdjQCAW0JwQv5nGNLy5VLr1lKNGtKcOan7SpeWPvpI2r1b6t499VtiALgdVaqYX86sWWOu+5YiJkbq18+caGb8eMZAAUAeQnBC/nXlinlrTK1a5npMS5ak7gsOliZMkPbtk554wlyjBQCyWrNm5hc3GzY4Xs0+fty8ta9sWWnwYOnQIetqBABkCMEJ+c+pU+ZseGXKmLfG7NyZuq9sWWnMGPO2mWeflTw9rasTQMHRqJE5fnLLFqljx9T+s2elUaPMK1BRUdLixVJysnV1AgBuiOCE/CEx0ZwSuEMH8/a7N9+UzpxJ3d+4sXmL3v790qBBkre3VZUCKMjq1pXmzZN++82caS/lardhmLf2tWljjoMaPVo6d87aWgEADghOyLsMQ/r1V+m558wpfzt2NGfDS1m41tVVeuQR6ZdfpPXrpc6dmQ4YQO5Qs6b01VfS0aPmdOWhoan7Dh6UXnpJKlnS/DJo9mzGQgFALsA6Tsh7Dh0yv7H94gvzW9t/K1FC6tFDeuYZ83a9fIh1nJCbsI5TFrh2zbyVb+JEc0zUv/n4mCGqWzdzsgnGZQJAlshMNiA4IfczDHPdpfnzzS29sOThYX6oiI42P1Tk8ytLBCfkJgSnLLZ3r/Thh9KMGdLJk2n3FytmXkGPipJatpQKF875GgEgnyA43URuDE7Z+SE4z36gSUgwb69bsMAMSzEx6R93111mWHr4YalIkUw9BeEDyBp59t8Z5fJ/B5KSFPTLOoV+P1ellyyQ+8W4tMd4ekr33GOOjWrbVgoLy/EyCxr+n42CpCD8vmcmG+Tvr+WRd1y7Jm3dKv34o7mtW3fjBSIbNDCvLnXqJFWunKNlAkCOcXXV6cbNdbpxc/36+miFrF2hMt/PVcmVi+Wa8L9/H69ckRYtMrf+/aVq1cw161q0kJo0yfQXSgCAGyM4wRr//GPefrdxo7RqlblI5MWL6R/r5mZ+o9qhg/TAA+ZEEABQgCR7eOj4fW11/L62cou/qKANq9Vk2xozMB0/nnrgn3+a25gxks1mTkLRtGnqVrKkdS8CAPI4ghOyX1KStGuXtGlT6vb776mz36UnNFS6915z4drWraWAgBwrFwBys2s+vjp+f5T09GPmGNAdO8yJJRYtkn7+OXUdKMMwx4T+9ps56YQklS8v1a8v3XmnudWpY46ZAgA4RXBC1jEM85vPnTulP/4wt5SfL126+WODgsyrSilb+fLmt6UAgBuz2aTatc3t1VfNtZ/WrJHWrpV++sm8sn/9groHD5rbrFmpfWXKpIao6tWlKlWkihXNSXcAAHYEJ2SOYZgr3R88KB04YG4HD0r79pkhKTYDgwhtNvN/zg0amN98Nm1q3pdPUAKA21OsmDn+s1Mnsx0XZ94SnRKkNm9OO340Jsbc5s9P7XNxMb/AqlLF3CpXlipUMCefKF2a6dABFEgEJ6QyDHOc0V9/SceOpW4p7ZgYMyTdaCzSjZQrJ9WtawalBg3MbzZ9fbPnNQAAUvn5SZGR5iaZt0jv3i1t25a6/fqrFB/v+LjkZGn/fnP74QfHfS4u5ljTsLDUrVQpcw29lC04mHAFIN/JFcFp4sSJevfdd3Xy5EmFh4fr/fffV4MGDW54/Jw5c/Sf//xHhw8fVqVKlTRq1Ci1adMmByvOA5KSVOhinHTwnPT339KFC+b299/mrRxnzpjb6dOOP99oJruMCA01ryTVqGH+Wb26VLWquXAjAMB6bm7mv9E1apgLhUtmSDpwwLytb/dux+3y5bTnSE6Wjh41t59+uvFzFS9uhqigIPPnYsVSt5R2kSKSv3/q5uXF3QcAci3Lg9OsWbM0aNAgTZkyRQ0bNtT48eMVGRmpPXv2KCgoKM3xGzZsUNeuXTVixAi1a9dOM2bMUIcOHbRt2zbVqFHDgldwG/76S4qLU8D+s3JJTJRLYoJc//enS2KiXBOuyDUhQa5XLsv1yhW5XvnH7PvHbLv9c1lul+JVKP6i3C5fktulePufhS7FO3/+zHJzk8qWNW/XKF8+7Z9cRQKAvMfFRapUydyul5xs3m2we7e0Z490+LDjdu7czc979qy5ZYabm3mVzN/f/NPb29x8fBx/LlzYDFmenmn/9PSU3N3NMVru7mm3QoXMzc0t9WdXVwIbAKcsXwC3YcOGql+/vj744ANJUnJyskJDQzVgwAANHjw4zfFdunTRpUuX9MN1tw7cddddql27tqZMmeL0+XLVArgNG5ozzFnNxcX89i8w0PxmsHRp87aLlC2lHRxs/s8ln8jVC18CeUhuWcTwVuTlfwcsf9/j46UjR8wQdfy4dPKkdOKE43bypJSYaG2dGeXmZv4/LuXPf//8vy0+STJcXGS4usqwuchwdZFcXGTIZv7p4iLZbOa+//1stm2STZJSfr6+39yCCxey/ywp7c83+9NZ343aWbHvZgikedrR+KvZdu5Qn0Lm4t29emXbc2REnlkANzExUVu3btWQIUPsfS4uLoqIiNDGjRvTfczGjRs1aNAgh77IyEjNv35Q63USEhKUkJBgb8f+b/KCuLh0VmDPaW5Z9/Ynu7jomrePrhX20bXChXXN21eJfv4KKVHMnMo75TaIgABzCww0w1Lx4uatEi4uzp/E2cx4eczli7ngdwDIB+Li8u4Ho7z870CueN9DQ83tRlLGzp47J50/n7ql3DYeG2tucXHmdvGi+WdsrPnz1ez70JbGtWvmdt1nBmds/9uySt79bUR+lZ1fz8RJ5ufQBx/MxmfJQB3/ywQZuZZkaXA6e/askpKSFBwc7NAfHBys3bt3p/uYkydPpnv8yZMn0z1+xIgReuONN9L0h97sH/q8KDlZuhhnbgCQg6KtLqCAira6AAC4XVOmmFsucPHiRfn73zwqWj7GKbsNGTLE4QpVcnKyzp8/r2LFisnG5eM8JS4uTqGhoTp69Kj1t1kCTvD7iryC31XkJfy+IqsZhqGLFy+qZMmSTo+1NDgVL15crq6uOnXqlEP/qVOnFBISku5jQkJCMnW8h4eHPP61iF9AQMCtFw3L+fn58Y8l8gx+X5FX8LuKvITfV2QlZ1eaUmRgYEv2cXd3V926dbVy5Up7X3JyslauXKlGjRql+5hGjRo5HC9Jy5cvv+HxAAAAAHC7LL9Vb9CgQerZs6fq1aunBg0aaPz48bp06ZJ6/W+GjR49eqhUqVIaMWKEJOm5555T8+bNNWbMGLVt21Zff/21tmzZoo8++sjKlwEAAAAgH7M8OHXp0kVnzpzR0KFDdfLkSdWuXVtLliyxTwARExMjl+tmfGvcuLFmzJih1157Ta+88ooqVaqk+fPn5701nJBpHh4eGjZsWJpbL4HciN9X5BX8riIv4fcVVrJ8HScAAAAAyO0sHeMEAAAAAHkBwQkAAAAAnCA4AQAAAIATBCcAAAAAcILghDzn8OHDevzxx1WuXDl5eXmpQoUKGjZsmBITE60uDZAkTZw4UWFhYfL09FTDhg21adMmq0sC0hgxYoTq168vX19fBQUFqUOHDtqzZ4/VZQFOjRw5UjabTQMHDrS6FBQwBCfkObt371ZycrI+/PBD/fHHHxo3bpymTJmiV155xerSAM2aNUuDBg3SsGHDtG3bNoWHhysyMlKnT5+2ujTAwZo1a9SvXz/9/PPPWr58ua5evar7779fly5dsro04IY2b96sDz/8ULVq1bK6FBRATEeOfOHdd9/V5MmTdfDgQatLQQHXsGFD1a9fXx988IEkKTk5WaGhoRowYIAGDx5scXXAjZ05c0ZBQUFas2aNmjVrZnU5QBrx8fG68847NWnSJL399tuqXbu2xo8fb3VZKEC44oR8ITY2VkWLFrW6DBRwiYmJ2rp1qyIiIux9Li4uioiI0MaNGy2sDHAuNjZWkvi3FLlWv3791LZtW4d/Y4Gc5GZ1AcDt2r9/v95//32NHj3a6lJQwJ09e1ZJSUkKDg526A8ODtbu3bstqgpwLjk5WQMHDlSTJk1Uo0YNq8sB0vj666+1bds2bd682epSUIBxxQm5xuDBg2Wz2W66/fvD57Fjx9SqVSs99NBDeuKJJyyqHADytn79+mnnzp36+uuvrS4FSOPo0aN67rnnNH36dHl6elpdDgowxjgh1zhz5ozOnTt302PKly8vd3d3SdLx48fVokUL3XXXXZo2bZpcXPgeANZKTExU4cKFNXfuXHXo0MHe37NnT124cEELFiywrjjgBvr3768FCxZo7dq1KleunNXlAGnMnz9fHTt2lKurq70vKSlJNptNLi4uSkhIcNgHZBdu1UOuERgYqMDAwAwde+zYMbVs2VJ169bV1KlTCU3IFdzd3VW3bl2tXLnSHpySk5O1cuVK9e/f39rigH8xDEMDBgzQt99+q9WrVxOakGvde++9+v333x36evXqpSpVqujll18mNCHHEJyQ5xw7dkwtWrRQ2bJlNXr0aJ05c8a+LyQkxMLKAGnQoEHq2bOn6tWrpwYNGmj8+PG6dOmSevXqZXVpgIN+/fppxowZWrBggXx9fXXy5ElJkr+/v7y8vCyuDkjl6+ubZuydt7e3ihUrxpg85CiCE/Kc5cuXa//+/dq/f79Kly7tsI87T2G1Ll266MyZMxo6dKhOnjyp2rVra8mSJWkmjACsNnnyZElSixYtHPqnTp2q6OjonC8IAHI5xjgBAAAAgBMMDAEAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAOSI1atXy2az6cKFC1aXkmPCwsI0fvx4q8sAAGQBghMAFBDR0dHq0KFDmv7cFGhef/111a5dO0vOdbPXlVsCzeXLlzVkyBBVqFBBnp6eCgwMVPPmzbVgwQKrSwMA/Iub1QUAAFBQ9e3bV7/88ovef/99VatWTefOndOGDRt07ty5bHvOxMREubu7Z9v5ASC/4ooTACCNdevWqWnTpvLy8lJoaKieffZZXbp0yb7/yy+/VL169eTr66uQkBB169ZNp0+fdjjHokWLdMcdd8jLy0stW7bU4cOHb/qc06ZN0xtvvKEdO3bIZrPJZrNp2rRpkqSYmBi1b99ePj4+8vPz08MPP6xTp05l2eu9cOGC+vTpo8DAQPn5+emee+7Rjh077PsPHDig9u3bKzg4WD4+Pqpfv75WrFjhcI7Tp08rKipKXl5eKleunKZPn+70eb/77ju98soratOmjcLCwlS3bl0NGDBAvXv3th+TkJCgl19+WaGhofLw8FDFihX16aef2vevWbNGDRo0kIeHh0qUKKHBgwfr2rVr9v0tWrRQ//79NXDgQBUvXlyRkZGSpJ07d6p169by8fFRcHCwunfvrrNnz97yewgA+R3BCQDg4MCBA2rVqpUefPBB/fbbb5o1a5bWrVun/v3724+5evWq3nrrLe3YsUPz58/X4cOHFR0dbd9/9OhRderUSVFRUdq+fbv69OmjwYMH3/R5u3TpohdeeEHVq1fXiRMndOLECXXp0kXJyclq3769zp8/rzVr1mj58uU6ePCgunTpkmWv+aGHHtLp06e1ePFibd26VXfeeafuvfdenT9/XpIUHx+vNm3aaOXKlfr111/VqlUrRUVFKSYmxn6O6OhoHT16VKtWrdLcuXM1adKkNGHy30JCQrRo0SJdvHjxhsf06NFDM2fO1Hvvvaddu3bpww8/lI+PjyTp2LFjatOmjerXr68dO3Zo8uTJ+vTTT/X22287nOPzzz+Xu7u71q9frylTpujChQu65557VKdOHW3ZskVLlizRqVOn9PDDD9/qWwgA+Z8BACgQevbsabi6uhre3t4Om6enpyHJ+Pvvvw3DMIzHH3/cePLJJx0e+9NPPxkuLi7GP//8k+65N2/ebEgyLl68aBiGYQwZMsSoVq2awzEvv/yyw/OkZ9iwYUZ4eLhD37JlywxXV1cjJibG3vfHH38YkoxNmzbd8FyrVq0yJKV5vd7e3obNZjPGjRtnf21+fn7GlStXHB5foUIF48MPP7zh+atXr268//77hmEYxp49e9LUs2vXLkOS/XnSs2bNGqN06dJGoUKFjHr16hkDBw401q1bZ9+fct7ly5en+/hXXnnFqFy5spGcnGzvmzhxouHj42MkJSUZhmEYzZs3N+rUqePwuLfeesu4//77HfqOHj1qSDL27Nlzw3oBoCDjihMAFCAtW7bU9u3bHbZPPvnE4ZgdO3Zo2rRp8vHxsW+RkZFKTk7WoUOHJElbt25VVFSUypQpI19fXzVv3lyS7Fdgdu3apYYNGzqct1GjRg7t68/ft2/fG9a8a9cuhYaGKjQ01N5XrVo1BQQEaNeuXZKk6tWr28/VunVrh8f/9NNPaV5zyZIlHV5vfHy8ihUr5lDToUOHdODAAUnmFacXX3xRVatWVUBAgHx8fLRr1y6H1+vm5qa6devaz1ulShUFBATc8HVJUrNmzXTw4EGtXLlSnTt31h9//KGmTZvqrbfekiRt375drq6u9vc3vfemUaNGstls9r4mTZooPj5ef/31l73v+rpSXvOqVascXm+VKlUkyf6aAQCOmBwCAAoQb29vVaxY0aHv+g/YkhkSnnrqKT377LNpHl+mTBldunRJkZGRioyM1PTp0xUYGKiYmBhFRkYqMTExw7Vs377d/rOfn1/mXsi/LFq0SFevXpUkeXl5OewrV65cmgDj5pb6v7/4+HiVKFFCq1evTnPelMe9+OKLWr58uUaPHq2KFSvKy8tLnTt3ztTrvZFChQqpadOmatq0qV5++WW9/fbbevPNN/Xyyy+neS23ytvb26EdHx+vqKgojRo1Ks2xJUqUyJLnBID8huAEAHBw55136s8//0wTsFL8/vvvOnfunEaOHGm/CrRlyxaHY6pWrarvvvvOoe/nn392aKd3fnd3dyUlJaU519GjR3X06FH78/3555+6cOGCqlWrJkkqW7ZsJl6hozvvvFMnT56Um5ubwsLC0j1m/fr1io6OVseOHSWZweP6yS6qVKmia9euaevWrapfv74kac+ePbc0xXu1atV07do1XblyRTVr1lRycrLWrFmjiIiINMdWrVpV33zzjQzDsF91Wr9+vXx9fVW6dOmbvuZvvvlGYWFhDiESAHBj3KoHAHDw8ssva8OGDerfv7+2b9+uffv2acGCBfbJIcqUKSN3d3e9//77OnjwoL777jv7rWUp+vbtq3379umll17Snj17NGPGDPsMeTcTFhamQ4cOafv27Tp79qwSEhIUERGhmjVr6tFHH9W2bdu0adMm9ejRQ82bN1e9evVu+/VGRESoUaNG6tChg5YtW6bDhw9rw4YNevXVV+2BsFKlSpo3b562b9+uHTt2qFu3bkpOTrafo3LlymrVqpWeeuop/fLLL9q6dav69Onj9IpRixYt9OGHH2rr1q06fPiwFi1apFdeeUUtW7aUn5+fwsLC1LNnT/Xu3Vvz58/XoUOHtHr1as2ePVuS9Mwzz+jo0aMaMGCAdu/erQULFmjYsGEaNGiQXFxu/L/4fv366fz58+ratas2b96sAwcOaOnSperVq1ea4AoAMBGcAAAOatWqpTVr1mjv3r1q2rSp6tSpo6FDh9rHBQUGBmratGmaM2eOqlWrppEjR2r06NEO5yhTpoy++eYbzZ8/X+Hh4ZoyZYqGDx/u9LkffPBBtWrVSi1btlRgYKBmzpwpm82mBQsWqEiRImrWrJkiIiJUvnx5zZo1K0ter81m06JFi9SsWTP16tVLd9xxhx555BEdOXJEwcHBkqSxY8eqSJEiaty4saKiohQZGak777zT4TxTp05VyZIl1bx5c3Xq1ElPPvmkgoKCbvrckZGR+vzzz3X//feratWqGjBggCIjI+3BSJImT56szp0765lnnlGVKlX0xBNP2KeGL1WqlBYtWqRNmzYpPDxcffv21eOPP67XXnvtps9bsmRJrV+/XklJSbr//vtVs2ZNDRw4UAEBATcNXABQkNkMwzCsLgIAAAAAcjO+VgIAAAAAJwhOAAAAAOAEwQkAAAAAnCA4AQAAAIATBCcAAAAAcILgBAAAAABOEJwAAAAAwAmCEwAAAAA4QXACAAAAACcITgAAAADgBMEJAAAAAJz4f0/digH3Ewr8AAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ] @@ -12781,7 +12804,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average of 'head_to_head' is: -0.12\n" + "The average of 'head_to_head' is: -0.14\n" ] } ], @@ -12837,17 +12860,17 @@ " \n", " \n", " head_to_head\n", - " -10.6\n", + " -12.5\n", " 92.1\n", " -0.1\n", - " 0.846125\n", - " 0.088167\n", - " -1.304254\n", + " 0.669453\n", + " 0.069757\n", + " -1.939479\n", " 1.98555\n", - " 0.1\n", + " 0.0\n", " -0.3\n", - " 0.097716\n", - " 0.195433\n", + " 0.027769\n", + " 0.055537\n", " \n", " \n", "\n", @@ -12855,10 +12878,10 @@ ], "text/plain": [ " W_score W_count W_ave W_stdev std_err t_stat t_crit \\\n", - "head_to_head -10.6 92.1 -0.1 0.846125 0.088167 -1.304254 1.98555 \n", + "head_to_head -12.5 92.1 -0.1 0.669453 0.069757 -1.939479 1.98555 \n", "\n", " upper_bound lower_bound cdf p_value \n", - "head_to_head 0.1 -0.3 0.097716 0.195433 " + "head_to_head 0.0 -0.3 0.027769 0.055537 " ] }, "execution_count": 68, @@ -12930,34 +12953,34 @@ " \n", " 121\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.14]\n", + " [0.0001, 0.0001, 0.0001, 0.125]\n", " [0.005,0.017,0.157,0.821]\n", " 3 or more\n", - " -1.8\n", + " -1.9\n", " \n", " \n", - " 247\n", - " Will the 500th richest person on Bloomberg's B...\n", - " 0.833333\n", - " 0.333\n", - " no\n", - " -1.4\n", + " 47\n", + " What will be Donald Trump's net worth, accordi...\n", + " [0.16999999999999998, 0.0001, 0.0001, 0.0001, ...\n", + " [0.6,0.2,0.1,0.075,0.025]\n", + " 0-$6 billion, inclusive\n", + " -1.3\n", " \n", " \n", " 232\n", " How many movies will be new on Netflix's top 1...\n", - " [0.0001, 0.0001, 0.0001, 0.27130390143737165]\n", + " [0.0001, 0.0001, 0.0001, 0.2963039014373716]\n", " [0.002,0.008,0.09,0.9]\n", " 3 or more\n", - " -1.2\n", + " -1.1\n", " \n", " \n", - " 47\n", - " What will be Donald Trump's net worth, accordi...\n", - " [0.185, 0.0001, 0.0001, 0.0001, 0.0001]\n", - " [0.6,0.2,0.1,0.075,0.025]\n", - " 0-$6 billion, inclusive\n", - " -1.2\n", + " 247\n", + " Will the 500th richest person on Bloomberg's B...\n", + " 0.766667\n", + " 0.333\n", + " no\n", + " -1.1\n", " \n", " \n", "\n", @@ -12967,23 +12990,23 @@ " title \\\n", "279 What will Kalshi's rank in the iPhone Top Free... \n", "121 How many movies will be new on Netflix's top 1... \n", - "247 Will the 500th richest person on Bloomberg's B... \n", - "232 How many movies will be new on Netflix's top 1... \n", "47 What will be Donald Trump's net worth, accordi... \n", + "232 How many movies will be new on Netflix's top 1... \n", + "247 Will the 500th richest person on Bloomberg's B... \n", "\n", - " bot_team_median \\\n", - "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", - "121 [0.0001, 0.0001, 0.0001, 0.14] \n", - "247 0.833333 \n", - "232 [0.0001, 0.0001, 0.0001, 0.27130390143737165] \n", - "47 [0.185, 0.0001, 0.0001, 0.0001, 0.0001] \n", + " bot_team_median \\\n", + "279 [0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.05] \n", + "121 [0.0001, 0.0001, 0.0001, 0.125] \n", + "47 [0.16999999999999998, 0.0001, 0.0001, 0.0001, ... \n", + "232 [0.0001, 0.0001, 0.0001, 0.2963039014373716] \n", + "247 0.766667 \n", "\n", " pro_median resolution head_to_head \n", "279 [0.02,0.01,0.015,0.015,0.05,0.89] Not in top 50 -2.9 \n", - "121 [0.005,0.017,0.157,0.821] 3 or more -1.8 \n", - "247 0.333 no -1.4 \n", - "232 [0.002,0.008,0.09,0.9] 3 or more -1.2 \n", - "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -1.2 " + "121 [0.005,0.017,0.157,0.821] 3 or more -1.9 \n", + "47 [0.6,0.2,0.1,0.075,0.025] 0-$6 billion, inclusive -1.3 \n", + "232 [0.002,0.008,0.09,0.9] 3 or more -1.1 \n", + "247 0.333 no -1.1 " ] }, "execution_count": 69, @@ -13049,25 +13072,25 @@ " \n", " \n", " \n", - " 189\n", - " What will the highest rank of metac-GPT4o or m...\n", - " [0.0, 0.0106785714, 0.0213571429, 0.0320357143...\n", - " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", - " 34.0\n", - " 2.9\n", - " \n", - " \n", " 0\n", " For Q1 2025, how many banks will be listed on ...\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", " 0\n", - " 5.3\n", + " 2.5\n", + " \n", + " \n", + " 189\n", + " What will the highest rank of metac-GPT4o or m...\n", + " [0.0, 0.0369946063, 0.07475, 0.10485, 0.1198, ...\n", + " [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0...\n", + " 34.0\n", + " 2.8\n", " \n", " \n", " 151\n", " How many earthquakes of magnitude ≥ 4 will hap...\n", - " [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0...\n", + " [0.0, 0.0035714286, 0.0071428571, 0.0107142857...\n", " [0.0,0.0158237002,0.0235315723,0.0279864362,0....\n", " 0.0\n", " NaN\n", @@ -13083,7 +13106,7 @@ " \n", " 214\n", " Will the state of Rhode Island have any recrea...\n", - " 0.952\n", + " 0.928\n", " 0.95\n", " annulled\n", " NaN\n", @@ -13094,29 +13117,29 @@ ], "text/plain": [ " title \\\n", - "189 What will the highest rank of metac-GPT4o or m... \n", "0 For Q1 2025, how many banks will be listed on ... \n", + "189 What will the highest rank of metac-GPT4o or m... \n", "151 How many earthquakes of magnitude ≥ 4 will hap... \n", "211 Will Nikola Corporation file for bankruptcy be... \n", "214 Will the state of Rhode Island have any recrea... \n", "\n", " bot_team_median \\\n", - "189 [0.0, 0.0106785714, 0.0213571429, 0.0320357143... \n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", - "151 [0.0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "189 [0.0, 0.0369946063, 0.07475, 0.10485, 0.1198, ... \n", + "151 [0.0, 0.0035714286, 0.0071428571, 0.0107142857... \n", "211 0.99 \n", - "214 0.952 \n", + "214 0.928 \n", "\n", " pro_median resolution \\\n", - "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", "0 [0.001,0.62,0.35,0.019,0.01] 0 \n", + "189 [0.0,5.19918e-05,0.0001040776,0.0001562618,0.0... 34.0 \n", "151 [0.0,0.0158237002,0.0235315723,0.0279864362,0.... 0.0 \n", "211 0.999 annulled \n", "214 0.95 annulled \n", "\n", " head_to_head \n", - "189 2.9 \n", - "0 5.3 \n", + "0 2.5 \n", + "189 2.8 \n", "151 NaN \n", "211 NaN \n", "214 NaN " @@ -13234,10 +13257,10 @@ " False\n", " 31268\n", " 1.0\n", - " [0.20746287128712873, 0.0001, 0.0001, 0.0001, ...\n", + " [0.012462871287128714, 0.0001, 0.0001, 0.0001,...\n", " [0.001,0.62,0.35,0.019,0.01]\n", - " 5.334952\n", - " 5.334952\n", + " 2.522754\n", + " 2.522754\n", " \n", " \n", " 1\n", @@ -13254,10 +13277,10 @@ " True\n", " 31269\n", " 1.0\n", - " [0.05, 0.0506082725, 0.051216545, 0.0518248175...\n", + " [0.05, 0.0505982539, 0.0511965078, 0.051794761...\n", " [0.0013749738,0.0014499743,0.001526641,0.00160...\n", - " -0.250003\n", - " -0.250003\n", + " -0.158842\n", + " -0.158842\n", " \n", " \n", " 2\n", @@ -13274,10 +13297,10 @@ " False\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.063\n", " 0.013\n", - " -0.092275\n", - " -0.092275\n", + " -0.051987\n", + " -0.051987\n", " \n", " \n", " 3\n", @@ -13294,10 +13317,10 @@ " NaN\n", " 31280\n", " 1.0\n", - " [0.0001, 0.45, 0.0001]\n", + " [0.0001, 0.5125, 0.0001]\n", " [0.16,0.44,0.4]\n", - " 0.022473\n", - " 0.022473\n", + " 0.152526\n", + " 0.152526\n", " \n", " \n", " 4\n", @@ -13314,10 +13337,10 @@ " False\n", " 31281\n", " 1.0\n", - " [0.0, 0.0018431373, 0.0036862745, 0.0055294118...\n", + " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " -0.102791\n", - " -0.102791\n", + " 0.132210\n", + " 0.132210\n", " \n", " \n", "\n", @@ -13353,25 +13376,25 @@ "4 False 31281 1.0 \n", "\n", " bot_team_median \\\n", - "0 [0.20746287128712873, 0.0001, 0.0001, 0.0001, ... \n", - "1 [0.05, 0.0506082725, 0.051216545, 0.0518248175... \n", - "2 0.1 \n", - "3 [0.0001, 0.45, 0.0001] \n", - "4 [0.0, 0.0018431373, 0.0036862745, 0.0055294118... \n", + "0 [0.012462871287128714, 0.0001, 0.0001, 0.0001,... \n", + "1 [0.05, 0.0505982539, 0.0511965078, 0.051794761... \n", + "2 0.063 \n", + "3 [0.0001, 0.5125, 0.0001] \n", + "4 [0.0, 0.0018181818, 0.0036363636, 0.0054545455... \n", "\n", " pro_median head_to_head \\\n", - "0 [0.001,0.62,0.35,0.019,0.01] 5.334952 \n", - "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.250003 \n", - "2 0.013 -0.092275 \n", - "3 [0.16,0.44,0.4] 0.022473 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... -0.102791 \n", + "0 [0.001,0.62,0.35,0.019,0.01] 2.522754 \n", + "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", + "2 0.013 -0.051987 \n", + "3 [0.16,0.44,0.4] 0.152526 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.132210 \n", "\n", " weighted_score \n", - "0 5.334952 \n", - "1 -0.250003 \n", - "2 -0.092275 \n", - "3 0.022473 \n", - "4 -0.102791 " + "0 2.522754 \n", + "1 -0.158842 \n", + "2 -0.051987 \n", + "3 0.152526 \n", + "4 0.132210 " ] }, "execution_count": 72, @@ -13385,7 +13408,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -13410,7 +13433,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 74, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13422,7 +13445,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -13466,7 +13489,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -13479,7 +13502,35 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 0.0\n", + "5 1.0\n", + "8 1.0\n", + "10 1.0\n", + "13 1.0\n", + " ... \n", + "417 0.0\n", + "418 0.0\n", + "419 0.0\n", + "420 1.0\n", + "421 0.0\n", + "Name: resolution, Length: 236, dtype: float64\n" + ] + } + ], + "source": [ + "print(df_top_bot_pro_forecasts_all_binary['resolution'])" + ] + }, + { + "cell_type": "code", + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -13536,7 +13587,7 @@ " False\n", " 31270\n", " 1.0\n", - " 0.1\n", + " 0.063\n", " 0.013\n", " \n", " \n", @@ -13554,7 +13605,7 @@ " NaN\n", " 31282\n", " 1.0\n", - " 0.5\n", + " 0.62\n", " 0.45\n", " \n", " \n", @@ -13572,7 +13623,7 @@ " False\n", " 31294\n", " 1.0\n", - " 0.835\n", + " 0.86\n", " 0.95\n", " \n", " \n", @@ -13638,14 +13689,14 @@ "13 NaN NaN False False 31338 \n", "\n", " question_weight bot_team_median pro_median \n", - "2 1.0 0.1 0.013 \n", - "5 1.0 0.5 0.45 \n", - "8 1.0 0.835 0.95 \n", + "2 1.0 0.063 0.013 \n", + "5 1.0 0.62 0.45 \n", + "8 1.0 0.86 0.95 \n", "10 1.0 NaN NaN \n", "13 1.0 0.85 0.9 " ] }, - "execution_count": 95, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -13656,12 +13707,84 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bot_question_id Int64\n", + "title object\n", + "resolution float64\n", + "scheduled_close_time datetime64[ns]\n", + "actual_close_time datetime64[ns]\n", + "type object\n", + "options object\n", + "range_min float64\n", + "range_max float64\n", + "open_upper_bound object\n", + "open_lower_bound object\n", + "pro_question_id Int64\n", + "question_weight float64\n", + "bot_team_median object\n", + "pro_median object\n", + "dtype: object" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_top_bot_pro_forecasts_all_binary.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "bot_question_id Int64\n", + "title object\n", + "resolution float64\n", + "scheduled_close_time datetime64[ns]\n", + "actual_close_time datetime64[ns]\n", + "type object\n", + "options object\n", + "range_min float64\n", + "range_max float64\n", + "open_upper_bound object\n", + "open_lower_bound object\n", + "pro_question_id Int64\n", + "question_weight float64\n", + "bot_team_median object\n", + "pro_median object\n", + "head_to_head float64\n", + "weighted_score float64\n", + "dtype: object" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_top_bot_pro_forecasts_binary.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -13707,7 +13830,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -13719,7 +13842,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -13738,7 +13861,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13751,9 +13874,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bot average forecast difference (1 - 0): 0.4288\n", + "Bot average forecast difference (1 - 0): 0.4355\n", "Pro average forecast difference (1 - 0): 0.5238\n", - "Difference between pro and bot differences: 0.0950\n" + "Difference between pro and bot differences: 0.0882\n" ] } ], @@ -13780,7 +13903,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -13820,7 +13943,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -13961,8 +14084,8 @@ " 1.0\n", " [0.0, 0.0018181818, 0.0036363636, 0.0054545455...\n", " [0.0,0.0005044914,0.0010323506,0.0015847475,0....\n", - " 0.387623\n", - " 0.387623\n", + " 0.132210\n", + " 0.132210\n", " \n", " \n", "\n", @@ -14009,14 +14132,14 @@ "1 [0.0013749738,0.0014499743,0.001526641,0.00160... -0.158842 \n", "2 0.013 -0.051987 \n", "3 [0.16,0.44,0.4] 0.152526 \n", - "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.387623 \n", + "4 [0.0,0.0005044914,0.0010323506,0.0015847475,0.... 0.132210 \n", "\n", " weighted_score \n", "0 2.522754 \n", "1 -0.158842 \n", "2 -0.051987 \n", "3 0.152526 \n", - "4 0.387623 " + "4 0.132210 " ] }, "metadata": {}, @@ -14098,10 +14221,10 @@ " False\n", " 35381\n", " 1.00\n", - " 0.3\n", + " 0.4\n", " 0.05\n", - " -0.305382\n", - " -0.305382\n", + " -0.459532\n", + " -0.459532\n", " \n", " \n", " 355\n", @@ -14118,10 +14241,10 @@ " False\n", " 35385\n", " 1.00\n", - " 0.85\n", + " 0.8\n", " 0.97\n", - " -0.132060\n", - " -0.132060\n", + " -0.192684\n", + " -0.192684\n", " \n", " \n", " 361\n", @@ -14191,8 +14314,8 @@ "\n", " question_weight bot_team_median pro_median head_to_head weighted_score \n", "342 1.00 0.9 0.95 -0.054067 -0.054067 \n", - "351 1.00 0.3 0.05 -0.305382 -0.305382 \n", - "355 1.00 0.85 0.97 -0.132060 -0.132060 \n", + "351 1.00 0.4 0.05 -0.459532 -0.459532 \n", + "355 1.00 0.8 0.97 -0.192684 -0.192684 \n", "361 0.85 0.8 0.666 -0.435900 -0.370515 \n", "364 0.85 0.05 0.03 -0.017709 -0.015053 " ] @@ -14207,15 +14330,15 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[81], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/functions.py:750\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 740\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 741\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 748\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 749\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 750\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 752\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 753\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 754\u001b[0m bins\n\u001b[1;32m 755\u001b[0m )\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/Desktop/LogipediaStuff/aib-analysis/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:48\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 47\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[84], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Calculate confidence scores for bot_team_median and pro_median\u001b[39;00m\n\u001b[1;32m 2\u001b[0m display_head_and_tail(df_top_bot_pro_forecasts)\n\u001b[0;32m----> 3\u001b[0m bot_confidence \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_confidence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbot_team_median\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_top_bot_pro_forecasts\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresolution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m pro_confidence \u001b[38;5;241m=\u001b[39m calculate_confidence(df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpro_median\u001b[39m\u001b[38;5;124m'\u001b[39m], df_top_bot_pro_forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresolution\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBot team confidence score: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbot_confidence\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/metaculus/aib-analysis/functions.py:782\u001b[0m, in \u001b[0;36mcalculate_confidence\u001b[0;34m(predictions, outcomes)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 772\u001b[0m \u001b[38;5;124;03mCalculates over- or under-confidence for a set of predictions.\u001b[39;00m\n\u001b[1;32m 773\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 779\u001b[0m \u001b[38;5;124;03m float: Confidence score (positive for overconfidence, negative for underconfidence).\u001b[39;00m\n\u001b[1;32m 780\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 781\u001b[0m \u001b[38;5;66;03m# Bin predictions into 10 equally spaced bins\u001b[39;00m\n\u001b[0;32m--> 782\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 784\u001b[0m \u001b[38;5;66;03m# Calculate mean prediction and actual outcome for each bin\u001b[39;00m\n\u001b[1;32m 785\u001b[0m grouped \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: predictions, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutcome\u001b[39m\u001b[38;5;124m\"\u001b[39m: outcomes})\u001b[38;5;241m.\u001b[39mgroupby(\n\u001b[1;32m 786\u001b[0m bins\n\u001b[1;32m 787\u001b[0m )\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/reshape/tile.py:246\u001b[0m, in \u001b[0;36mcut\u001b[0;34m(x, bins, right, labels, retbins, precision, include_lowest, duplicates, ordered)\u001b[0m\n\u001b[1;32m 243\u001b[0m x_idx, _ \u001b[38;5;241m=\u001b[39m _coerce_to_type(x_idx)\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(bins):\n\u001b[0;32m--> 246\u001b[0m bins \u001b[38;5;241m=\u001b[39m \u001b[43m_nbins_to_bins\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(bins, IntervalIndex):\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bins\u001b[38;5;241m.\u001b[39mis_overlapping:\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/reshape/tile.py:363\u001b[0m, in \u001b[0;36m_nbins_to_bins\u001b[0;34m(x_idx, nbins, right)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x_idx\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cut empty array\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 363\u001b[0m rng \u001b[38;5;241m=\u001b[39m (\u001b[43mx_idx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, x_idx\u001b[38;5;241m.\u001b[39mmax())\n\u001b[1;32m 364\u001b[0m mn, mx \u001b[38;5;241m=\u001b[39m rng\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_numeric_dtype(x_idx\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m (np\u001b[38;5;241m.\u001b[39misinf(mn) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39misinf(mx)):\n\u001b[1;32m 367\u001b[0m \u001b[38;5;66;03m# GH#24314\u001b[39;00m\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/indexes/base.py:7467\u001b[0m, in \u001b[0;36mIndex.min\u001b[0;34m(self, axis, skipna, *args, **kwargs)\u001b[0m\n\u001b[1;32m 7464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_multi \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 7465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\u001b[38;5;241m.\u001b[39m_reduce(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, skipna\u001b[38;5;241m=\u001b[39mskipna)\n\u001b[0;32m-> 7467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 145\u001b[0m result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike \u001b[38;5;129;01mand\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n\u001b[1;32m 407\u001b[0m result \u001b[38;5;241m=\u001b[39m _wrap_results(result, orig_values\u001b[38;5;241m.\u001b[39mdtype, fill_value\u001b[38;5;241m=\u001b[39miNaT)\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/pandas/core/nanops.py:1098\u001b[0m, in \u001b[0;36m_nanminmax..reduction\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _na_for_min_count(values, axis)\n\u001b[1;32m 1095\u001b[0m values, mask \u001b[38;5;241m=\u001b[39m _get_values(\n\u001b[1;32m 1096\u001b[0m values, skipna, fill_value_typ\u001b[38;5;241m=\u001b[39mfill_value_typ, mask\u001b[38;5;241m=\u001b[39mmask\n\u001b[1;32m 1097\u001b[0m )\n\u001b[0;32m-> 1098\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1099\u001b[0m result \u001b[38;5;241m=\u001b[39m _maybe_null_out(result, axis, mask, values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/.local/lib/python3.12/site-packages/numpy/_core/_methods.py:49\u001b[0m, in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 48\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mumr_minimum\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (201,) (5,) " ] } @@ -15209,7 +15332,7 @@ "provenance": [] }, "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -15223,7 +15346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/functions.py b/functions.py index 11257c4..08b3fd0 100644 --- a/functions.py +++ b/functions.py @@ -647,7 +647,10 @@ def plot_calibration_curve(df: pd.DataFrame, column_name: str, label: str, color """ _assert_calibration_dataframe_matches_assumptions(df) # Filter to binary questions in case the DataFrame has other types (0 or 1 INT or 'yes'/'no' STR) - df = df[df["resolution"].isin(["yes", "no", 1, 0])] + df = df[df["resolution"].isin(["yes", "no", 1.0, 0.0])] + + # If any of df[column_name] are None, drop those rows + df = df[df[column_name].notnull()] y_true = df["resolution"] y_pred = df[column_name] @@ -655,6 +658,7 @@ def plot_calibration_curve(df: pd.DataFrame, column_name: str, label: str, color calibration_curve = _calculate_calibration_curve(y_pred, y_true, weights)[ "calibration_curve" ] + prob_true = [item["average_resolution"] for item in calibration_curve] bin_center = [ (item["bin_lower"] + item["bin_upper"]) / 2 for item in calibration_curve diff --git a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv index 7214749..6b92b92 100644 --- a/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv +++ b/notebook_outputs/bootstrapped_h2h_bot_vs_pros.csv @@ -1,11 +1,11 @@ ,2.5% CI,10% CI,Median,90% CI,97.5% CI cobyj-bot,0.0,0.0,0.0,0.0,0.0 andrewsiah,0.0,0.0,0.0,0.0,0.0 -RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 +X_bot,-0.0,-0.0,-0.0,0.0,0.0 jonahsingerbot,-0.0,-0.0,-0.0,-0.0,-0.0 bean_bot,-0.0,-0.0,-0.0,-0.0,-0.0 -X_bot,-0.0,-0.0,-0.0,0.0,0.0 -CumulativeBot,-0.0,-0.0,-0.0,0.0,0.0 +RPM_bot,-0.1,-0.0,-0.0,0.0,0.0 +CumulativeBot,-0.0,-0.0,-0.0,-0.0,0.0 swingswish,-0.0,-0.0,-0.0,-0.0,-0.0 KevinTestBot,-0.1,-0.0,-0.0,0.0,0.0 SynapseSeer,-0.1,-0.0,-0.0,0.0,0.0 @@ -13,35 +13,35 @@ Grizeu_Bot,-0.2,-0.1,-0.0,0.1,0.2 pianobot,-0.1,-0.1,-0.0,-0.0,0.0 CatrachoCaster,-0.1,-0.1,-0.0,-0.0,0.0 krm-bot,-0.1,-0.1,-0.1,-0.0,-0.0 -annabot,-0.1,-0.1,-0.1,-0.0,-0.0 4Shadower,-0.1,-0.1,-0.1,-0.0,-0.0 +annabot,-0.1,-0.1,-0.1,-0.0,-0.0 cookics_bot_TEST,-0.2,-0.1,-0.1,-0.0,0.0 jkraybill_bot,-0.2,-0.1,-0.1,-0.0,-0.0 twsummerbot,-0.2,-0.2,-0.1,-0.0,0.0 MWG,-0.2,-0.2,-0.1,-0.0,-0.0 ProfessorSP,-0.2,-0.2,-0.1,-0.0,-0.0 -ajf-bot,-0.3,-0.2,-0.1,-0.0,0.0 +ajf-bot,-0.2,-0.2,-0.1,-0.0,0.0 +acm_bot,-0.3,-0.2,-0.1,0.0,0.1 GreeneiBot2,-0.3,-0.2,-0.1,-0.0,0.0 -acm_bot,-0.3,-0.2,-0.1,-0.0,0.1 +metac-deepseek-r1+asknews,-0.2,-0.2,-0.1,-0.1,-0.0 +metac-Gemini-Exp-1206,-0.3,-0.2,-0.1,-0.0,0.1 +metac-o1,-0.3,-0.2,-0.1,0.0,0.1 Bot_Pepa,-0.2,-0.2,-0.1,-0.1,-0.0 -metac-perplexity,-0.3,-0.3,-0.1,-0.0,0.1 -bot_median,-0.3,-0.2,-0.1,-0.0,0.1 -metac-o1,-0.3,-0.3,-0.1,-0.0,0.1 -metac-deepseek-r1+asknews,-0.3,-0.2,-0.1,-0.1,-0.0 laylaps,-0.2,-0.2,-0.1,-0.1,-0.0 wunderplumb,-0.3,-0.2,-0.1,-0.1,-0.0 -metac-Gemini-Exp-1206,-0.3,-0.3,-0.1,-0.0,0.1 +bot_median,-0.3,-0.2,-0.1,-0.0,0.0 +metac-perplexity,-0.4,-0.3,-0.1,-0.0,0.1 manticAI,-0.3,-0.2,-0.2,-0.1,-0.0 -metac-claude-3-5-sonnet-20240620,-0.3,-0.3,-0.2,-0.0,0.0 -NextWorldLab,-0.3,-0.3,-0.2,-0.1,-0.0 -metac-claude-3-5-sonnet-latest,-0.3,-0.3,-0.2,-0.1,-0.1 +NextWorldLab,-0.3,-0.3,-0.2,-0.1,0.0 minefrac1,-0.3,-0.3,-0.2,-0.1,-0.1 -metac-o1-preview,-0.4,-0.3,-0.2,-0.1,-0.1 +metac-claude-3-5-sonnet-latest,-0.4,-0.3,-0.2,-0.1,-0.1 mmBot,-0.4,-0.3,-0.2,-0.1,-0.1 -metac-Llama-3.1,-0.4,-0.4,-0.2,-0.1,-0.0 -pgodzinai,-0.4,-0.4,-0.3,-0.1,-0.1 -metac-grok-2-1212,-0.5,-0.4,-0.3,-0.1,-0.0 -VeritasAI,-0.4,-0.3,-0.3,-0.2,-0.1 +metac-claude-3-5-sonnet-20240620,-0.4,-0.4,-0.2,-0.1,-0.0 +pgodzinai,-0.4,-0.4,-0.2,-0.1,-0.1 +metac-grok-2-1212,-0.4,-0.4,-0.2,-0.1,-0.1 +VeritasAI,-0.4,-0.3,-0.2,-0.2,-0.1 +metac-o1-preview,-0.4,-0.4,-0.3,-0.1,-0.1 +metac-gpt-4o,-0.4,-0.4,-0.3,-0.1,-0.1 metac-exa,-0.4,-0.4,-0.3,-0.2,-0.1 InstitutPelFutur,-0.5,-0.4,-0.3,-0.2,-0.1 -metac-gpt-4o,-0.5,-0.4,-0.3,-0.2,-0.1 +metac-Llama-3.1,-0.5,-0.4,-0.3,-0.2,-0.1 diff --git a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv index cd9448c..8eb9a70 100644 --- a/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv +++ b/notebook_outputs/weighted_t_test_h2h_bot_vs_pros.csv @@ -1,47 +1,47 @@ ,W_score,W_count,W_ave,W_stdev,std_err,t_stat,t_crit,upper_bound,lower_bound,cdf,p_value cobyj-bot,0.0,0.0,,,,,,,,,NA andrewsiah,0.0,0.0,,,,,,,,,NA -RPM_bot,-0.6,7.0,-0.1,0.8206747298542999,0.31018589178137035,-0.2697293560809546,2.4469118511449692,0.7,-0.8,0.3982026167089623,0.796405 -jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 bean_bot,-0.6,4.7,-0.1,0.0698490092484186,0.03221894544078219,-4.26510566168152,2.7848427377534137,-0.0,-0.2,0.007674496502235436,0.015349 +jonahsingerbot,-0.6,4.7,-0.1,0.0502720475429557,0.023188766374944235,-5.273629910349656,2.7848427377534137,-0.1,-0.2,0.003838655509487954,0.007677 X_bot,-0.7,7.0,-0.1,0.35406799582281046,0.13382512345060182,-0.7471946105725911,2.4469118511449692,0.2,-0.4,0.24159443667404312,0.483189 CumulativeBot,-1.1,10.2,-0.1,0.25779754004448213,0.08052242326875068,-1.3151322887765264,2.2318482470257073,0.1,-0.3,0.1100659836303239,0.220132 swingswish,-1.2,7.7,-0.2,0.14027522342155058,0.05055168154738577,-3.0749473143902657,2.367122926859399,-0.0,-0.3,0.009476427450502594,0.018953 +RPM_bot,-1.3,7.0,-0.2,0.803162845690475,0.3035670217119917,-0.6018020851526737,2.4469118511449692,0.6,-0.9,0.2846659989090443,0.569332 SynapseSeer,-1.3,26.2,-0.1,0.45255474982575933,0.08849837184875071,-0.568910320013585,2.0530763092739437,0.1,-0.2,0.2872314409451841,0.574463 KevinTestBot,-1.5,8.4,-0.2,0.5894659867910315,0.20338508794412294,-0.8971155260320279,2.3114957148363993,0.3,-0.7,0.19895153497848572,0.397903 Grizeu_Bot,-1.7,51.4,-0.0,1.1733916577534336,0.16374678141052051,-0.20661633211162028,2.0064473532408944,0.3,-0.4,0.4185713925307672,0.837143 pianobot,-2.7,4.7,-0.6,0.9162042335005162,0.42261349916620494,-1.3843270734534352,2.798986372998989,0.6,-1.8,0.12194093069402845,0.243882 -CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032241,2.0887774106971415,0.1,-0.4,0.09414402174256528,0.188288 +CatrachoCaster,-3.2,19.7,-0.2,0.5209013833112408,0.11736062067861285,-1.3655317032240997,2.0887774106971415,0.1,-0.4,0.0941440217425653,0.188288 krm-bot,-5.1,9.5,-0.5,0.5115460847961517,0.1659674656990186,-3.2298461551560385,2.2647088573190035,-0.2,-0.9,0.005563489501517069,0.011127 -annabot,-5.9,29.3,-0.2,0.5175750572467731,0.09561797207152893,-2.1122028342259047,2.0441825433909937,-0.0,-0.4,0.021810527148697016,0.043621 +annabot,-6.2,29.3,-0.2,0.5208688899467946,0.0962264820812545,-2.2117952878836604,2.0441825433909937,-0.0,-0.4,0.017610432479673904,0.035221 4Shadower,-6.2,14.0,-0.4,0.7673219105043008,0.20507540674799357,-2.1431944516704484,2.1472386339670253,0.0,-0.9,0.025796646516944247,0.051593 -cookics_bot_TEST,-6.6,27.4,-0.2,0.7470933569588007,0.14272484937169871,-1.6836598504701996,2.0495406495390753,0.1,-0.5,0.05201867599309354,0.104037 +cookics_bot_TEST,-6.7,27.4,-0.2,0.7480496337801963,0.14290753666776426,-1.7220041694550487,2.0495406495390753,0.0,-0.5,0.048383645251144566,0.096767 jkraybill_bot,-7.5,44.0,-0.2,0.5128530627973333,0.07727161640565941,-2.197133074819885,2.0146422768105463,-0.0,-0.3,0.01672059935283912,0.033441 twsummerbot,-8.9,58.4,-0.2,0.6597096411583532,0.08632695203642188,-1.758390985166895,2.0008548266793613,0.0,-0.3,0.042005771996978254,0.084012 MWG,-9.6,28.6,-0.3,0.7111599387639217,0.13297936883238545,-2.5353840992759586,2.0465614134207835,-0.1,-0.6,0.008595358294567833,0.017191 ProfessorSP,-10.0,18.6,-0.5,0.9362765859321275,0.2170939350431325,-2.484479782313461,2.0952434689972526,-0.1,-1.0,0.011644425230897355,0.023289 acm_bot,-10.5,80.2,-0.1,0.9142649133881292,0.10205858264251064,-1.2877165899437122,1.9893443508950648,0.1,-0.3,0.10079615172895406,0.201592 -GreeneiBot2,-10.7,58.4,-0.2,0.8487135517179298,0.11110681713348293,-1.6470273617836275,2.000831925930035,0.0,-0.4,0.052510863710317504,0.105022 +metac-o1,-10.8,91.1,-0.1,0.8668236222209089,0.09081791967404183,-1.3030182446846603,1.9858289388460384,0.1,-0.3,0.09794439270715757,0.195889 ajf-bot,-10.9,34.2,-0.3,1.0855889019420977,0.1854962383013122,-1.722394508253831,2.0307781947345034,0.1,-0.7,0.04714462059329925,0.094289 +metac-deepseek-r1+asknews,-11.2,52.1,-0.2,0.6342566612198152,0.08787112272667183,-2.4450432699738145,2.0053789762011176,-0.0,-0.4,0.008984924011519364,0.017970 +GreeneiBot2,-11.4,58.4,-0.2,0.8462281442135139,0.1107814473823621,-1.7668111287097124,2.000831925930035,0.0,-0.4,0.041290471840402215,0.082581 Bot_Pepa,-11.5,44.0,-0.3,0.7375369985271071,0.1111247649069599,-2.3431659801868907,2.0146422768105463,-0.0,-0.5,0.011904916896884948,0.023810 -metac-perplexity,-12.0,89.1,-0.1,1.0008449184534645,0.10602979859799266,-1.2696037636515303,1.9864049297707018,0.1,-0.3,0.10378462460698391,0.207569 -bot_median,-12.2,92.1,-0.1,0.8759085051927877,0.0912701844746672,-1.448706262693777,1.9855502432148115,0.0,-0.3,0.07542649485602951,0.150853 -metac-o1,-12.4,91.1,-0.1,0.9413031092818035,0.09862120502513756,-1.3750355923383297,1.9858289388460384,0.1,-0.3,0.08626502997859752,0.172530 +metac-Gemini-Exp-1206,-11.5,76.5,-0.2,0.8952097471246512,0.10235147002510721,-1.4718494129042066,1.9908217254774627,0.1,-0.4,0.07260889665750306,0.145218 laylaps,-12.9,64.1,-0.2,0.6619045107450789,0.08267350038122044,-2.44046054763956,1.9969065741038698,-0.0,-0.4,0.008744061158659102,0.017488 -metac-deepseek-r1+asknews,-13.4,52.1,-0.3,0.6866418388462276,0.09512866474982715,-2.7023938246614656,2.0053789762011176,-0.1,-0.4,0.0046603987010819335,0.009321 -metac-Gemini-Exp-1206,-13.5,76.5,-0.2,1.0066063915806054,0.11508771463432003,-1.5277274660739493,1.9908217254774627,0.1,-0.4,0.06537953017362978,0.130759 +bot_median,-13.3,92.1,-0.1,0.7572006546947513,0.07890075621895877,-1.8300583290868744,1.9855502432148115,0.0,-0.3,0.03525575647024838,0.070512 wunderplumb,-13.6,25.6,-0.5,0.9000512561955677,0.17806222265862548,-2.9840941451614404,2.05660303322038,-0.2,-0.9,0.0031741533534496535,0.006348 +metac-perplexity,-14.4,89.1,-0.2,1.1026009344968866,0.11680986021222348,-1.3849519746718768,1.9864049297707018,0.1,-0.4,0.08478215225308733,0.169564 manticAI,-14.6,69.4,-0.2,0.6709463826178552,0.08051034556472575,-2.613354492497458,1.9939680506212867,-0.0,-0.4,0.005507180276996954,0.011014 -metac-claude-3-5-sonnet-20240620,-14.7,90.5,-0.2,0.9429804683378815,0.09912390614679249,-1.6425851577449733,1.9860719790130024,0.0,-0.4,0.051988931836857315,0.103978 NextWorldLab,-16.9,80.2,-0.2,0.9069642286328539,0.10124361366849416,-2.078393214767385,1.9893443508950648,-0.0,-0.4,0.020454686442219806,0.040909 -metac-claude-3-5-sonnet-latest,-18.9,91.1,-0.2,0.7317083930215759,0.07666177104402958,-2.699995118056715,1.9858289388460384,-0.1,-0.4,0.004140859358698023,0.008282 -minefrac1,-19.2,51.1,-0.4,0.8809897145082934,0.1232424683669797,-3.0436411347421197,2.0065449272360034,-0.1,-0.6,0.0018587451878251278,0.003717 -metac-o1-preview,-20.9,91.1,-0.2,0.802181404225052,0.08404529418137442,-2.7288070523371224,1.9858289388460384,-0.1,-0.4,0.003821400227265772,0.007643 +minefrac1,-18.8,51.1,-0.4,0.8747517828376596,0.12236983831928097,-3.0135811013395264,2.0065449272360034,-0.1,-0.6,0.0020214088297449183,0.004043 +metac-claude-3-5-sonnet-latest,-21.6,91.1,-0.2,0.7840729022099676,0.08214804952944678,-2.8855809804350296,1.9858289388460384,-0.1,-0.4,0.002444218354964672,0.004888 mmBot,-21.9,92.1,-0.2,0.7250100357901175,0.0755464746834313,-3.1501040673463705,1.9855502432148115,-0.1,-0.4,0.0011040926153361213,0.002208 -metac-Llama-3.1,-23.2,89.1,-0.3,1.0312779661924496,0.1092538844308646,-2.379606259857792,1.9864049297707018,-0.0,-0.5,0.009744516632283914,0.019489 -metac-grok-2-1212,-23.5,91.1,-0.3,1.0680060472571526,0.11189599005467826,-2.303421178504194,1.9858289388460384,-0.0,-0.5,0.011778139872058951,0.023556 -pgodzinai,-24.0,76.4,-0.3,0.9765897737398795,0.11172889227393508,-2.8110851156332464,1.9908489732268309,-0.1,-0.5,0.0031442974859602537,0.006289 +metac-claude-3-5-sonnet-20240620,-22.1,90.5,-0.2,0.9921895725908227,0.10429665234389453,-2.3447130845077018,1.9860719790130024,-0.0,-0.5,0.010626881125878994,0.021254 +metac-grok-2-1212,-23.2,91.1,-0.3,0.9691804386011083,0.10154193882835436,-2.504438328301395,1.9858289388460384,-0.1,-0.5,0.007031732032192213,0.014063 +pgodzinai,-23.2,76.4,-0.3,1.00292283111273,0.11474158338495037,-2.6493172344887146,1.9908489732268309,-0.1,-0.5,0.004910376705596484,0.009821 VeritasAI,-24.3,77.1,-0.3,0.6607028010672139,0.0752452273943661,-4.185910498866988,1.9904817922115374,-0.2,-0.5,3.7752868903447694e-05,0.000076 -metac-exa,-26.2,89.1,-0.3,0.8302752742001319,0.0879596014139391,-3.3415454501401167,1.9864049297707018,-0.1,-0.5,0.0006119018080970774,0.001224 -metac-gpt-4o,-26.6,91.1,-0.3,0.8790866786848435,0.09210273154158923,-3.165570176683145,1.9858289388460384,-0.1,-0.5,0.0010559673026657784,0.002112 +metac-o1-preview,-24.4,91.1,-0.3,0.8524321835897993,0.08931011522099137,-2.9993955258512948,1.9858289388460384,-0.1,-0.4,0.0017486358986007922,0.003497 +metac-gpt-4o,-25.1,91.1,-0.3,0.8735971368751565,0.09152758712427154,-3.0097067040559993,1.9858289388460384,-0.1,-0.5,0.0016956535070904697,0.003391 +metac-exa,-26.1,89.1,-0.3,0.7919348200357222,0.08389780266944466,-3.4956946250034493,1.9864049297707018,-0.1,-0.5,0.0003713213076391189,0.000743 InstitutPelFutur,-26.9,90.1,-0.3,0.9737673821897402,0.10258711760941522,-2.90852403334722,1.9861137662360124,-0.1,-0.5,0.0022918503861915234,0.004584 +metac-Llama-3.1,-28.0,89.1,-0.3,0.9072003561919431,0.09610906673103263,-3.2702003829748127,1.9864049297707018,-0.1,-0.5,0.0007672454772695423,0.001534