{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002800213,0.001068848,0.0017135,0.0009136969,0.0004886609,0.002878733,0.001048559,0.002239497,0.004271115],"category_scores_gemma":[0.01044407,0.0006277906,0.0006894062,0.0007346254,0.001291943,0.003184516,0.001525698,0.001635383,0.0004523999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002394548,"about_ca_system_score_gemma":0.001611277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001047379,"about_ca_topic_score_gemma":0.0004437332,"domain_scores_codex":[0.9988952,0.0005333116,0.00004478759,0.0001610026,0.0001781202,0.0001875354],"domain_scores_gemma":[0.9975485,0.001779687,0.000184025,0.0001553828,0.0001562409,0.0001760881],"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":[0.0004167257,0.0001215028,0.0009942664,0.0002947144,0.0001013235,0.0001333303,0.0001408506,0.1332904,0.001934537,0.8253819,0.005933546,0.03125689],"study_design_scores_gemma":[0.00004915823,0.00007003097,0.0005371051,0.00005550159,0.00002559828,0.00009645242,0.000029947,0.3011484,0.0006643016,0.695856,0.001453194,0.00001424161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2712762,0.00423332,0.6467508,0.00991857,0.0004296998,0.0001250661,0.001119043,0.0007225116,0.0654249],"genre_scores_gemma":[0.9643507,0.001164493,0.02234053,0.0003440328,0.0003238629,0.00009809578,0.0001813819,0.0001500042,0.01104687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004271115,"threshold_uncertainty_score":0.01737368,"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."}}