{"id":"W3093001417","doi":"10.1007/s00780-023-00497-y","title":"Optimal insurance under maxmin expected utility","year":2023,"lang":"en","type":"article","venue":"Finance and Stochastics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Indemnity; Expected utility hypothesis; Ambiguity; Mathematical economics; Unobservable; Mathematical finance; Ex-ante; Actuarial science; Prior probability; Knightian uncertainty; Econometrics; Ambiguity aversion; Economics; Mathematics; Computer science; Bayesian probability; Financial economics; Statistics","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":[0.00053493,0.0001110835,0.0001942649,0.0001432356,0.0001863727,0.000101968,0.0002117872,0.00007921584,0.00004762666],"category_scores_gemma":[0.0005525726,0.00008993479,0.00003917858,0.001112518,0.0001076222,0.0001789656,0.00008189416,0.00009646243,0.0002626732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000684745,"about_ca_system_score_gemma":0.00004433283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009105598,"about_ca_topic_score_gemma":0.000007716209,"domain_scores_codex":[0.9985555,0.00003760532,0.0003382126,0.0003529477,0.000480412,0.0002353035],"domain_scores_gemma":[0.9990038,0.0003192778,0.0001151861,0.0003347986,0.0001731635,0.00005374563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003104427,0.0001515683,0.0891309,0.00001136019,0.00002740281,0.00008527488,0.004330845,0.4699009,0.0001563302,0.03029423,0.09062454,0.3149762],"study_design_scores_gemma":[0.0004165414,0.00008118319,0.7329521,0.0000142015,0.000005390933,0.000008604537,0.0005828338,0.2247194,0.0000658258,0.01418397,0.02674028,0.0002296869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7371059,0.0001795325,0.2609153,0.000254557,0.0003220096,0.0001007779,0.0000555498,0.00007189827,0.000994434],"genre_scores_gemma":[0.9937441,0.0005264312,0.002510739,0.00007850834,0.0000669765,0.00001101768,0.00001510689,0.000008171488,0.003038944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6438212,"threshold_uncertainty_score":0.3667434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09140263138684938,"score_gpt":0.3585068105406871,"score_spread":0.2671041791538377,"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."}}