{"id":"W4281568866","doi":"10.54932/nqvt3458","title":"Advantageous selection without moral hazard","year":2022,"lang":"en","type":"report","venue":"","topic":"Financial Literacy, Pension, Retirement Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fondation du Risque; Agence Nationale de la Recherche","keywords":"Moral hazard; Actuarial science; Selection (genetic algorithm); Business; Profit (economics); Morale hazard; Risk aversion (psychology); Profit maximization; Adverse selection; Microeconomics; Economics; Product (mathematics); Insurance policy; Incentive; Auto insurance risk selection; Key person insurance; Computer science; Expected utility hypothesis; Financial economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002833776,0.0004094989,0.0008330838,0.0006202517,0.001000356,0.001356494,0.0007180271,0.001181811,0.01416275],"category_scores_gemma":[0.01302439,0.0003065501,0.0007047387,0.0004988118,0.001692211,0.001732381,0.001605883,0.001339788,0.00112785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006544249,"about_ca_system_score_gemma":0.0009018561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001088315,"about_ca_topic_score_gemma":0.001588612,"domain_scores_codex":[0.997443,0.0007282222,0.000107629,0.0006435708,0.0004795219,0.0005980845],"domain_scores_gemma":[0.9923493,0.002313113,0.001633012,0.002386728,0.000538371,0.0007796064],"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":[0.0002960666,0.0003076425,0.0668338,0.0002164858,0.0001747571,0.002237926,0.0008066604,0.01028775,0.01101887,0.8129994,0.007806936,0.08701386],"study_design_scores_gemma":[0.000196205,0.0004558516,0.04932864,0.00006081145,0.0001230755,0.006171449,0.0005917786,0.05327021,0.003130837,0.8655227,0.02106653,0.00008190641],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6886311,0.0005068858,0.1895025,0.001670792,0.0001289245,0.0003099824,0.001029853,0.0002142598,0.1180057],"genre_scores_gemma":[0.976045,0.0001714502,0.01290966,0.0003415961,0.00005646522,0.00008190576,0.0001911364,0.00002793302,0.01017486],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01416275,"threshold_uncertainty_score":0.04737914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03277296537181795,"score_gpt":0.274631728086936,"score_spread":0.241858762715118,"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."}}