{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001233109,0.0002208133,0.000198382,0.0003176467,0.0003591113,0.000151664,0.0009414719,0.00007496351,0.00001420314],"category_scores_gemma":[0.00005865538,0.0001799355,0.0001230993,0.001431994,0.00004380459,0.000683513,0.0002124592,0.001164236,0.000236024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334667,"about_ca_system_score_gemma":0.002761849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004000188,"about_ca_topic_score_gemma":0.001008487,"domain_scores_codex":[0.9960413,0.0001024357,0.0003236436,0.0003790986,0.0005872675,0.002566259],"domain_scores_gemma":[0.9990777,0.00007934438,0.0001753604,0.0003404245,0.0001686485,0.0001584822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002790294,0.0005734176,0.03773993,0.00006813324,0.001256415,0.0003531667,0.003723295,0.05852992,0.0007603465,0.1635881,0.002169345,0.7309589],"study_design_scores_gemma":[0.005279435,0.006095346,0.03690117,0.00008603239,0.00009033587,0.01800155,0.001283526,0.5284781,0.001169094,0.3855698,0.01518991,0.00185573],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5399931,0.0001071594,0.4571643,0.0008090916,0.0004963513,0.0002515389,6.914244e-7,0.0006072822,0.0005704115],"genre_scores_gemma":[0.9938652,0.0004172021,0.004270602,0.0001651427,0.0002662195,0.00001372896,0.000003242731,0.00002433669,0.0009743162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7291031,"threshold_uncertainty_score":0.7337555,"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."}}