{"id":"W2005751881","doi":"10.1239/jap/1208358950","title":"Law of Large Numbers for Dynamic Bargaining Markets","year":2008,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Law of large numbers; Sequence (biology); Jump; Markov chain; Markov process; Mathematical economics; Statistical physics; Random variable; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018932,0.000116209,0.0005880577,0.00006902449,0.00009760934,0.00001036499,0.0002246188,0.00009096573,0.000141803],"category_scores_gemma":[0.00006894382,0.0001230848,0.0002477628,0.00006505798,0.0001259314,0.0001547622,0.00004160507,0.0001451945,0.000009231524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001302933,"about_ca_system_score_gemma":0.00004736838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001023905,"about_ca_topic_score_gemma":0.00001338444,"domain_scores_codex":[0.9983802,0.000008163627,0.001143527,0.0002012816,0.00003028623,0.0002365748],"domain_scores_gemma":[0.9984186,0.0001310758,0.001047601,0.0002480925,0.00007475955,0.00007988888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003588646,0.0001397951,0.002696565,0.00006201774,0.00007324905,7.937056e-7,0.0006268201,0.0003463611,0.00003978731,0.9953126,0.0001632472,0.0001799054],"study_design_scores_gemma":[0.001608112,0.0001103313,0.002666755,0.0000115671,0.00001032081,0.00001714295,0.0001190779,0.001202194,0.0003100619,0.9797907,0.01398727,0.0001664449],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8859193,0.0002888585,0.01170587,0.0001149986,0.0002673442,0.0003432752,0.0001683154,0.000008731446,0.1011833],"genre_scores_gemma":[0.9874935,0.00006208345,0.01216335,0.0001197072,0.00005899476,0.00001058132,0.000003058158,0.0000169176,0.00007184711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1015742,"threshold_uncertainty_score":0.5019253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02754820784365621,"score_gpt":0.2285166788662751,"score_spread":0.2009684710226189,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}