{"id":"W4226388878","doi":"10.1017/asb.2021.36","title":"MEAN–VARIANCE INSURANCE DESIGN WITH COUNTERPARTY RISK AND INCENTIVE COMPATIBILITY","year":2021,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Indemnity; Actuarial science; Incentive compatibility; Incentive; Moral hazard; Insurance policy; Auto insurance risk selection; Economics; Econometrics; Business; Liability insurance; Microeconomics","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":[0.000687873,0.0001925764,0.0004096468,0.00005052348,0.0002107657,0.00009056427,0.0001426157,0.00006464145,0.0003930609],"category_scores_gemma":[0.0001938407,0.0002031686,0.00004686087,0.0002422322,0.0001390753,0.00009296269,0.00009066975,0.0002134212,0.0005334419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006032332,"about_ca_system_score_gemma":0.00002560081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004263615,"about_ca_topic_score_gemma":0.0001080546,"domain_scores_codex":[0.9984743,0.00006878775,0.0004391935,0.0006369895,0.00006311482,0.0003176352],"domain_scores_gemma":[0.9990008,0.0001287849,0.0003237843,0.0003840873,0.00009700697,0.00006552425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000208204,0.0001849742,0.9342142,0.00006009794,0.00007240356,0.00006154743,0.0008100467,0.0005308171,0.000007365511,0.05373905,0.003642107,0.006469157],"study_design_scores_gemma":[0.00114544,0.0001229592,0.8053658,0.00006700311,0.00001069684,0.000005175932,0.0001170295,0.0007460194,0.0001606279,0.006587825,0.1852968,0.0003746953],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7299955,0.005141055,0.2424785,0.001161387,0.0003936688,0.0005619824,0.0004310827,0.00008152417,0.01975533],"genre_scores_gemma":[0.9873638,0.0009059942,0.01038559,0.0004417275,0.00006126588,0.00003831984,0.00001120525,0.00002136517,0.0007707091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2573684,"threshold_uncertainty_score":0.8284973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0190185002477444,"score_gpt":0.1931729647853969,"score_spread":0.1741544645376525,"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."}}