{"id":"W4413237434","doi":"10.1007/s10589-025-00704-w","title":"Risk-averse constrained blackbox optimization under mixed aleatory/epistemic uncertainties","year":2025,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Uncertainty quantification; Mathematics; Mathematical optimization; Robust optimization; Mathematical economics; Epistemology; Applied mathematics; Statistics; Philosophy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008555712,0.0002669887,0.0003322395,0.000682969,0.0008522761,0.0004643426,0.0003733029,0.0001723386,0.00034823],"category_scores_gemma":[0.0004287807,0.0002526065,0.0001126666,0.00194645,0.0003874847,0.000446734,0.0001044241,0.0001727554,0.00006842682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008810704,"about_ca_system_score_gemma":0.0003076291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001698207,"about_ca_topic_score_gemma":0.000007836011,"domain_scores_codex":[0.997117,0.0002678462,0.0009899585,0.0007228367,0.0006621619,0.000240228],"domain_scores_gemma":[0.9964994,0.001147218,0.0005708268,0.0004498471,0.00117758,0.000155115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001651707,0.00006276186,0.001909998,0.000004334039,0.00003518331,2.483626e-7,0.00007856993,0.8625524,0.000002101228,0.1268668,0.002509028,0.005962083],"study_design_scores_gemma":[0.0007663913,0.00001745096,0.002008145,0.00001462233,0.00006267434,0.000005619348,0.000923818,0.9353393,0.000009761966,0.0531181,0.007500061,0.0002340886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00102381,0.0002159226,0.9862269,0.002315415,0.0002038146,0.0007750368,0.000099761,0.0001725731,0.008966748],"genre_scores_gemma":[0.6822724,0.001391885,0.3088034,0.001526851,0.0001482545,0.0003442786,0.001139451,0.00004105518,0.004332391],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6812486,"threshold_uncertainty_score":0.9999926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02741239218951086,"score_gpt":0.3305064136275489,"score_spread":0.303094021438038,"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."}}