{"id":"W4321333045","doi":"10.2139/ssrn.4360725","title":"Adverse Selection with Heterogeneously Informed Agents","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Adverse selection; Selection (genetic algorithm); Psychology; Medicine; Business; Computer science; Artificial intelligence; 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":[0.01434202,0.001003049,0.002173302,0.001178199,0.001257838,0.004393999,0.002565096,0.004309367,0.01089779],"category_scores_gemma":[0.05777474,0.0008836338,0.0009804799,0.001137031,0.004241803,0.004811705,0.003132616,0.003486229,0.001269435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175496,"about_ca_system_score_gemma":0.0007313755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001154915,"about_ca_topic_score_gemma":0.000950097,"domain_scores_codex":[0.9919442,0.005511623,0.0002664685,0.0008262098,0.0007811491,0.0006703798],"domain_scores_gemma":[0.9219209,0.06511778,0.004974941,0.00449014,0.001768055,0.001728215],"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":[0.0007535692,0.0001603956,0.005782426,0.000136901,0.0002303012,0.001175532,0.0004333043,0.1184562,0.0004805344,0.84249,0.006015514,0.02388532],"study_design_scores_gemma":[0.0002345624,0.00009907928,0.0007300279,0.00001580764,0.00005330947,0.0002675411,0.0001182307,0.2089445,0.0002020213,0.7879826,0.001322039,0.00003011733],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3379134,0.001103987,0.5751314,0.01684755,0.0005962256,0.0003772035,0.0004319952,0.0002678232,0.06733043],"genre_scores_gemma":[0.9641805,0.0004017866,0.01340138,0.0006434471,0.0004180483,0.0001532549,0.00007137708,0.00002081844,0.02070939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01434202,"threshold_uncertainty_score":0.07584876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01324936740220667,"score_gpt":0.2499786841109469,"score_spread":0.2367293167087402,"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."}}