{"id":"W4392697211","doi":"10.1016/j.ejor.2024.03.016","title":"Worst-case risk measures of stop-loss and limited loss random variables under distribution uncertainty with applications to robust reinsurance","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinsurance; Random variable; Variable (mathematics); Mathematics; Distribution (mathematics); Statistics; Econometrics; Actuarial science; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.01740459,0.0001214595,0.0002432987,0.0004911113,0.0004645071,0.0006791964,0.0003892161,0.00003036809,0.00006583527],"category_scores_gemma":[0.002801631,0.00007654612,0.00006624904,0.001841512,0.000276995,0.0004975779,0.00008486916,0.0004598464,0.00003682991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007182168,"about_ca_system_score_gemma":0.0004283658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003326688,"about_ca_topic_score_gemma":0.00002469007,"domain_scores_codex":[0.9946371,0.00178707,0.0008530567,0.0003282813,0.00218102,0.0002134368],"domain_scores_gemma":[0.9933681,0.002123536,0.0002108905,0.0002823261,0.003774347,0.0002408327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001190033,0.00009966507,0.00309194,0.00001011729,0.0001000704,0.0006581504,0.0006905695,0.9315757,0.0003005038,0.01755088,0.005115196,0.03961714],"study_design_scores_gemma":[0.01405908,0.005147465,0.1347896,0.001746856,0.0003743987,0.01636918,0.007395638,0.2832824,0.002003675,0.03168871,0.5014935,0.001649596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2126536,0.0009637905,0.7828013,0.002095614,0.00007825408,0.0003435029,0.0001801962,0.000009238614,0.0008744844],"genre_scores_gemma":[0.9918282,0.001274629,0.006094096,0.00003007109,0.0002408705,0.000008874508,0.00002446069,0.00001768098,0.0004811402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7791746,"threshold_uncertainty_score":0.6549506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1696763661460953,"score_gpt":0.4025862293248418,"score_spread":0.2329098631787465,"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."}}