{"id":"W4389494360","doi":"10.2139/ssrn.4649617","title":"Adverse Selection in Insurance","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Law, Economics, and Judicial Systems","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Adverse selection; Business; Actuarial science; Selection (genetic algorithm); Computer science; 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":[0.007349008,0.0003553112,0.001024102,0.001093212,0.001387408,0.003663059,0.0006293989,0.003873358,0.00918818],"category_scores_gemma":[0.02423692,0.0003070825,0.0005854607,0.0007210297,0.003880834,0.002693947,0.001475281,0.00339679,0.0007115266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00162038,"about_ca_system_score_gemma":0.001342747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002055484,"about_ca_topic_score_gemma":0.001571938,"domain_scores_codex":[0.9965869,0.001989686,0.0001145162,0.0003806613,0.000486446,0.0004416976],"domain_scores_gemma":[0.9816731,0.01342235,0.001841665,0.0009324732,0.001064015,0.001066471],"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":[0.00005162835,0.00005527642,0.004940988,0.00004264259,0.00005156104,0.0001942119,0.0002187451,0.003204357,0.0001446526,0.9734699,0.008445698,0.009180365],"study_design_scores_gemma":[0.00002266047,0.00001909649,0.001936969,0.00001719475,0.00001269177,0.00006776738,0.00005655555,0.006347937,0.00003334735,0.9894594,0.002018166,0.000008173882],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4238043,0.01967908,0.1179126,0.1429703,0.002415693,0.0001825894,0.000487634,0.0002410199,0.2923068],"genre_scores_gemma":[0.9774974,0.00189629,0.001036583,0.00203456,0.001695143,0.00004021619,0.00003989581,0.00001696406,0.015743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00918818,"threshold_uncertainty_score":0.03886575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01579736135908288,"score_gpt":0.2104875901126197,"score_spread":0.1946902287535368,"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."}}