{"id":"W4252202848","doi":"10.3982/qe666","title":"Identification of games of incomplete information with multiple equilibria and unobserved heterogeneity","year":2019,"lang":"en","type":"article","venue":"Quantitative Economics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Ministerio de Ciencia e Innovación; University of Pennsylvania","keywords":"Nonparametric statistics; Identification (biology); Stochastic game; Function (biology); Matching (statistics); Independence (probability theory); Complete information; Parameter identification problem; Mathematics; Econometrics; Computer science; Mathematical optimization; Mathematical economics; Applied mathematics; 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":[0.0002587924,0.00007860961,0.0001792603,0.0001363082,0.00002371845,0.0000574735,0.00007823805,0.00002327785,0.00002887736],"category_scores_gemma":[0.00004438601,0.00007544464,0.00002848704,0.00009508961,0.0000542675,0.001281844,0.00006346756,0.00003144032,0.00002392452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007974969,"about_ca_system_score_gemma":0.00001129204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003796208,"about_ca_topic_score_gemma":0.0002918273,"domain_scores_codex":[0.9994287,0.000008431414,0.0003482897,0.0001001127,0.00004030815,0.00007413032],"domain_scores_gemma":[0.9991227,0.00009293817,0.0005182854,0.0001364522,0.0001246976,0.000004933387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001626813,0.00001436923,0.9712692,0.0002775429,0.00003255081,4.25435e-8,0.0001520636,0.0001765245,0.01132047,0.013437,0.000006567032,0.003150939],"study_design_scores_gemma":[0.0007546919,0.00003088262,0.9277552,0.00003652721,0.00004234748,3.533124e-7,0.0005608406,0.06677992,0.002875995,0.0004366495,0.0005912996,0.0001353319],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986284,0.00002527864,0.0005440997,0.0000347271,0.0000821,0.0002140805,0.00002019869,0.00001112074,0.0004400403],"genre_scores_gemma":[0.9995275,0.000009757731,0.0003634068,0.00003147255,0.000008953661,0.000003965263,0.0000415275,0.000006575015,0.000006905909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06660339,"threshold_uncertainty_score":0.3076543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02894938234114203,"score_gpt":0.2397373934248076,"score_spread":0.2107880110836655,"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."}}