{"id":"W7116295801","doi":"10.1016/j.insmatheco.2025.103202","title":"Optimal reinsurance design under convex premium principles and distortion risk measures","year":2025,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Shenzhen Science and Technology Innovation Program; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Reinsurance; Distortion (music); Regular polygon; Convex optimization; Noise (video); Risk premium","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":[0.005117683,0.001308298,0.001881755,0.0006808427,0.0003709318,0.001964099,0.001650099,0.001677048,0.002186945],"category_scores_gemma":[0.009194911,0.0008430515,0.0009786199,0.0004325793,0.001443092,0.002782134,0.001732775,0.001491939,0.0002549724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001745557,"about_ca_system_score_gemma":0.001290336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001019038,"about_ca_topic_score_gemma":0.0004273653,"domain_scores_codex":[0.9968791,0.001576646,0.0001469503,0.0005329307,0.0004268509,0.0004375041],"domain_scores_gemma":[0.9957968,0.00251625,0.0006573223,0.0003098676,0.0003597102,0.0003600861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003519228,0.0001560086,0.001246715,0.0001988521,0.0001318515,0.0002747639,0.0002359174,0.6792316,0.005143794,0.2800023,0.001150103,0.03187615],"study_design_scores_gemma":[0.0000866776,0.0002898534,0.0007299831,0.000038419,0.00005014998,0.0001289713,0.00007774799,0.8779951,0.002397157,0.116901,0.001266848,0.00003817378],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08583912,0.0007506438,0.9059815,0.0007827679,0.00003768899,0.0001464774,0.00008632297,0.00011207,0.006263367],"genre_scores_gemma":[0.9379405,0.0006565709,0.05733269,0.0001124428,0.00004785307,0.0001188119,0.00006316039,0.0000516837,0.003676236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005117683,"threshold_uncertainty_score":0.02706522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07792061275035057,"score_gpt":0.3047626779010117,"score_spread":0.2268420651506611,"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."}}