{"id":"W3126359288","doi":"10.2139/ssrn.3711743","title":"Optimal Insurance under Maxmin Expected Utility","year":2020,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Indemnity; Expected utility hypothesis; Ambiguity; Ex-ante; Mathematical economics; Actuarial science; Prior probability; Subjective expected utility; Economics; Unobservable; Econometrics; Mathematics; Computer science; Statistics; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006078785,0.0004686333,0.000772694,0.0003946717,0.000358289,0.0007588941,0.002078072,0.0004657378,0.0004072827],"category_scores_gemma":[0.001271415,0.000375836,0.0005259789,0.0008730947,0.0001286301,0.0003680354,0.000616559,0.006835348,0.0003055463],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007362261,"about_ca_system_score_gemma":0.007141075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000797417,"about_ca_topic_score_gemma":0.0002552779,"domain_scores_codex":[0.99227,0.00066871,0.001479957,0.001051574,0.002157422,0.002372346],"domain_scores_gemma":[0.996507,0.0002843653,0.001270014,0.0009212603,0.000714203,0.0003031467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00181558,0.0006910264,0.07851955,0.00002837666,0.001533116,0.0001474737,0.003786959,0.3530058,0.0002490892,0.1042291,0.02256917,0.4334248],"study_design_scores_gemma":[0.0007115197,0.000188483,0.0227954,0.00003213851,0.00005577859,0.0003234602,0.00249932,0.01379203,0.00006842774,0.9514387,0.007493159,0.0006016059],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4341052,0.006696532,0.5480095,0.004918712,0.001928143,0.0004099296,0.00005603332,0.000134422,0.003741591],"genre_scores_gemma":[0.9802367,0.01439845,0.00184421,0.0002137509,0.0008017914,0.00001225803,0.00003461066,0.00004651349,0.002411708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8472096,"threshold_uncertainty_score":0.9998693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07313037717453885,"score_gpt":0.3575137264571637,"score_spread":0.2843833492826248,"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."}}