{"id":"W3122648087","doi":"10.2139/ssrn.3017523","title":"Adverse Selection with Heterogeneously Informed Agents","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Selection (genetic algorithm); Adverse selection; Medicine; Adverse effect; Intensive care medicine; Psychology; Computer science; Artificial intelligence; Business; Pharmacology; Actuarial science","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":[0.008762916,0.0008649255,0.002143538,0.001239202,0.001069077,0.004241182,0.001774643,0.004417608,0.01222901],"category_scores_gemma":[0.0339027,0.0008831481,0.001015094,0.0009980296,0.003643668,0.00409008,0.002585895,0.003393584,0.001428275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008196204,"about_ca_system_score_gemma":0.0006282362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340154,"about_ca_topic_score_gemma":0.000855293,"domain_scores_codex":[0.9960153,0.002347261,0.000157217,0.0004777335,0.0004360279,0.0005665673],"domain_scores_gemma":[0.9600229,0.02910835,0.005367888,0.002671254,0.001180717,0.001648951],"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":[0.0006112391,0.000240373,0.01004041,0.0001270144,0.0002588283,0.002278734,0.0005528599,0.07912216,0.0006206143,0.8789493,0.00788885,0.01930954],"study_design_scores_gemma":[0.0003870558,0.0001451344,0.002448245,0.00002593983,0.00008171602,0.0005897535,0.0002047444,0.2018749,0.0002504085,0.7922539,0.001680597,0.00005768883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.604472,0.001634969,0.3110807,0.0220162,0.0008105767,0.0003389385,0.0004895225,0.0003009933,0.05885616],"genre_scores_gemma":[0.9651594,0.000691121,0.00483471,0.0007255562,0.0006194034,0.00008776593,0.0000775818,0.00001946609,0.02778502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01222901,"threshold_uncertainty_score":0.04634327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0204969687335236,"score_gpt":0.2277822225831794,"score_spread":0.2072852538496558,"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."}}