{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004969052,0.0008902221,0.001094402,0.001754421,0.00131272,0.00274289,0.00209401,0.002394487,0.005857703],"category_scores_gemma":[0.02170146,0.0004521818,0.001365012,0.001042174,0.004839144,0.006855442,0.002360237,0.00409923,0.0006645806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001850179,"about_ca_system_score_gemma":0.0009862623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001061852,"about_ca_topic_score_gemma":0.0007145897,"domain_scores_codex":[0.9983467,0.0007550925,0.00006571926,0.0002029064,0.0004655967,0.0001641065],"domain_scores_gemma":[0.9895557,0.007743852,0.0008746299,0.0004546415,0.0005819757,0.0007892642],"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":[0.000005192804,0.00001089784,0.0001448676,0.00001616759,0.000005569853,0.00006024944,0.0000663697,0.007013991,0.0002997647,0.9911294,0.0003545806,0.0008929275],"study_design_scores_gemma":[0.0000128316,0.00001555766,0.0001256522,0.00001665306,0.000004765434,0.0000688711,0.00002631714,0.1659425,0.000105974,0.8328058,0.0008620398,0.00001298566],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05854119,0.001528449,0.9084958,0.003566905,0.0002098099,0.00007439823,0.0001045044,0.000188439,0.02729048],"genre_scores_gemma":[0.8944359,0.001710775,0.08370838,0.001257206,0.0007744377,0.0004403541,0.0002023516,0.0001848144,0.01728569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005857703,"threshold_uncertainty_score":0.02627915,"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."}}