{"id":"W4255203801","doi":"10.1017/s0021900200003946","title":"Law of Large Numbers for Dynamic Bargaining Markets","year":2008,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":4,"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; Statistical physics; Mathematical economics; 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":[0.005191651,0.0009249767,0.001124746,0.001743374,0.001278395,0.002835622,0.002065795,0.00241782,0.005651119],"category_scores_gemma":[0.02192477,0.0004535769,0.001369397,0.001057092,0.004800003,0.006729776,0.002374949,0.004128442,0.0006492488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001882383,"about_ca_system_score_gemma":0.0009989381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001043902,"about_ca_topic_score_gemma":0.0007165277,"domain_scores_codex":[0.9983071,0.0007741561,0.00006900985,0.0002103783,0.00047672,0.0001625247],"domain_scores_gemma":[0.9894142,0.007816608,0.0009096773,0.0004567586,0.0006025438,0.0008001237],"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":[0.000005379798,0.00001129135,0.0001539807,0.00001669235,0.000006145687,0.00006496475,0.00007088151,0.007564968,0.0003219709,0.9904923,0.0003570187,0.0009344633],"study_design_scores_gemma":[0.00001312315,0.00001613563,0.0001293248,0.00001726301,0.000005088357,0.00007180672,0.00002788313,0.1745587,0.0001095457,0.824127,0.0009107063,0.00001350994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05612155,0.001533674,0.9120051,0.003554836,0.0002082067,0.00007383929,0.00009928575,0.0001766328,0.02622691],"genre_scores_gemma":[0.8911027,0.00177339,0.08673157,0.001277781,0.0007735342,0.0004586646,0.0002042621,0.0001811921,0.01749697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005651119,"threshold_uncertainty_score":0.0274564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02565010283066955,"score_gpt":0.2389039074660336,"score_spread":0.2132538046353641,"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."}}