{"id":"W3014557966","doi":"10.1515/jem-2019-0029","title":"Identification of Non-Equilibrium Beliefs in Games of Incomplete Information Using Experimental Data","year":2020,"lang":"en","type":"article","venue":"Journal of Econometric Methods","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada; University of Toronto","funders":"","keywords":"Identification (biology); Matching (statistics); Stochastic game; Mathematical economics; Set (abstract data type); Complete information; Rationalizability; Economics; Econometrics; Computer science; Mathematics; Nash equilibrium; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.05750648,0.001139295,0.002605252,0.001810404,0.0009765117,0.004126086,0.002732827,0.003092403,0.002875509],"category_scores_gemma":[0.2731869,0.001365854,0.001557201,0.001118953,0.005155892,0.006313811,0.002610472,0.002744589,0.000318224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0030762,"about_ca_system_score_gemma":0.001605037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003458797,"about_ca_topic_score_gemma":0.002236726,"domain_scores_codex":[0.9665025,0.02531077,0.001614114,0.003180567,0.002150141,0.001241908],"domain_scores_gemma":[0.521519,0.4210294,0.03184074,0.01902401,0.005207764,0.001378932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003171347,0.002777274,0.08206896,0.001258237,0.001157117,0.0007367909,0.004153993,0.3840952,0.005661628,0.4703125,0.0008624385,0.04374459],"study_design_scores_gemma":[0.0004255868,0.000448903,0.01459998,0.0001092211,0.0001212594,0.00009237701,0.0006047325,0.7375968,0.003831465,0.24145,0.0006014708,0.0001182319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7218096,0.0001307576,0.2741299,0.0005560315,0.0000189538,0.0004358531,0.0003301706,0.0001142505,0.002474496],"genre_scores_gemma":[0.9691234,0.00006488639,0.02961814,0.00007734264,0.00001390264,0.0003641558,0.0002558047,0.00001224981,0.0004702419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05750648,"threshold_uncertainty_score":0.304127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2448318482841166,"score_gpt":0.4821901653592673,"score_spread":0.2373583170751506,"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."}}