{"id":"W4403828469","doi":"10.2139/ssrn.4961447","title":"Market Simulation under Adverse Selection","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Adverse selection; Selection (genetic algorithm); Economics; Business; Computer science; Microeconomics; Artificial intelligence","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":[0.001834596,0.0004443537,0.001199596,0.001033494,0.000636725,0.001716195,0.0009808438,0.002709327,0.01431661],"category_scores_gemma":[0.01457793,0.0005249897,0.0008450075,0.0007100925,0.001384966,0.001800497,0.001415528,0.001576214,0.0009059134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000747802,"about_ca_system_score_gemma":0.00096055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005836357,"about_ca_topic_score_gemma":0.002211368,"domain_scores_codex":[0.9992687,0.0003580221,0.00002932377,0.00009812445,0.00009759613,0.0001482996],"domain_scores_gemma":[0.9886657,0.00867987,0.0007676899,0.0005217064,0.0006977205,0.0006672468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001764716,0.00008743736,0.001857836,0.00002890638,0.00003064295,0.0002649147,0.00008214379,0.8973936,0.0004772076,0.09562015,0.002201198,0.001779351],"study_design_scores_gemma":[0.00002715062,0.00001104099,0.000136911,0.000002191144,0.000002768954,0.00001703849,0.000008930539,0.9862331,0.00008003929,0.01333207,0.0001438966,0.000004891871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7453384,0.00037625,0.1995394,0.00531111,0.000411909,0.0002009188,0.001738117,0.0006990032,0.04638501],"genre_scores_gemma":[0.9847395,0.00009158362,0.006138193,0.000191564,0.00007219108,0.00007578783,0.0003253669,0.00006267806,0.008303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01431661,"threshold_uncertainty_score":0.04789388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01894117563792455,"score_gpt":0.241545603855814,"score_spread":0.2226044282178894,"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."}}