{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002549618,0.0001069929,0.0003283677,0.0004518034,0.0001465363,0.00004087113,0.0002094259,0.0001051429,0.00006486389],"category_scores_gemma":[0.00007384239,0.0001542826,0.0001226144,0.0005840442,0.00003667263,0.0003418119,0.0000229954,0.0008402827,0.001836563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300428,"about_ca_system_score_gemma":0.0002795802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008527209,"about_ca_topic_score_gemma":0.004508949,"domain_scores_codex":[0.9973162,0.00002511558,0.0006892826,0.0002730956,0.00003336621,0.001662932],"domain_scores_gemma":[0.9994627,0.00002460724,0.0003312382,0.0000992534,0.00001916311,0.00006309904],"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":[0.00001946663,0.00002621846,0.3353119,0.000005159128,0.00004512558,0.00000292168,0.0001843552,0.0004778969,0.00001578396,0.6623505,0.000221216,0.001339446],"study_design_scores_gemma":[0.0008833969,0.0001036955,0.1037021,0.00001400987,0.000001742308,0.00007026712,0.0004774264,0.001029554,0.000009676669,0.8855822,0.007896043,0.0002298725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762244,0.001485885,0.001043513,0.0004873658,0.0008736656,0.0001486027,0.00001431173,0.00007285357,0.01964936],"genre_scores_gemma":[0.9917961,0.005666336,0.000009690847,0.00007774983,0.00050314,0.00001148412,0.000003803825,0.00002802508,0.001903718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2316098,"threshold_uncertainty_score":0.9989406,"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."}}