{"id":"W4400732905","doi":"10.1287/moor.2023.0211","title":"Risk-Averse Markov Decision Processes Through a Distributional Lens","year":2024,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov decision process; Mathematics; Partially observable Markov decision process; Mathematical optimization; Mathematical economics; Decision theory; Through-the-lens metering; Markov chain; Lens (geology); Markov process; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.006035978,0.0001228166,0.0002363263,0.0005217476,0.0005400178,0.0008832877,0.0006390593,0.00009764711,0.001320269],"category_scores_gemma":[0.01885914,0.00008427719,0.00009836054,0.00324061,0.0002660858,0.001016413,0.0002028085,0.0003181223,0.0009634946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006443247,"about_ca_system_score_gemma":0.0007075529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000731878,"about_ca_topic_score_gemma":0.0001318597,"domain_scores_codex":[0.9953341,0.0002342373,0.0008512337,0.0004098183,0.002886926,0.0002836201],"domain_scores_gemma":[0.9922287,0.004367286,0.00005868383,0.0006355786,0.002639645,0.00007003669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001108853,0.001617856,0.002630486,0.0004325899,0.0001847286,0.00006014962,0.01905817,0.1213029,0.001167233,0.5539454,0.2359313,0.06355833],"study_design_scores_gemma":[0.0003316934,0.0001889239,0.0005471361,0.0003425631,0.00003461723,0.00003942043,0.003306743,0.6122842,0.003402289,0.3015893,0.077655,0.0002781013],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2309448,0.001087753,0.7456158,0.001179173,0.0002864733,0.0007402826,0.0006202887,0.00008138386,0.01944402],"genre_scores_gemma":[0.8735773,0.002679261,0.1188997,0.00000938516,0.0001064717,0.00006958624,0.00006916944,0.00002143972,0.004567722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6426325,"threshold_uncertainty_score":0.9998144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2286279225640828,"score_gpt":0.5019646536223583,"score_spread":0.2733367310582755,"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."}}