{"id":"W4318479566","doi":"10.1287/opre.2022.2429","title":"Technical Note—Risk-Averse Regret Minimization in Multistage Stochastic Programs","year":2023,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Regret; Minification; Mathematical optimization; Computer science; Stochastic programming; Mathematics; Machine learning","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00731458,0.0001171267,0.0001911412,0.001654086,0.0006239957,0.0006955197,0.0006054863,0.0001697195,0.0004756059],"category_scores_gemma":[0.01146889,0.00009463583,0.00007126898,0.008063276,0.0002220619,0.0004739073,0.0002189399,0.0005175072,0.003233535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001236821,"about_ca_system_score_gemma":0.0002959628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005544746,"about_ca_topic_score_gemma":0.004669443,"domain_scores_codex":[0.9952347,0.0008879166,0.0006626886,0.0005977832,0.002114948,0.0005019139],"domain_scores_gemma":[0.9971709,0.0009677087,0.00004814389,0.0007333282,0.0009308237,0.0001491333],"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":[0.00004721192,0.0001743022,0.006963098,0.000001915183,0.000002649859,0.0000349867,0.001336013,0.8355046,0.0003482874,0.002330526,0.01187333,0.1413831],"study_design_scores_gemma":[0.0004542362,0.00009851023,0.0107104,0.0000173881,0.000002830783,0.000004121966,0.000978209,0.9777057,0.00004423261,0.0009589501,0.00889371,0.0001317532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3172392,0.0001159785,0.6568306,0.005816616,0.0007116846,0.005029885,0.0001738421,0.0006382251,0.01344393],"genre_scores_gemma":[0.9692381,0.0002962278,0.01368008,0.00001688124,0.00009207371,0.0002976456,0.0002605537,0.00002425188,0.01609417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6519989,"threshold_uncertainty_score":0.9975426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.247145376137846,"score_gpt":0.510228524390415,"score_spread":0.263083148252569,"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."}}