{"id":"W4398182762","doi":"10.1287/mnsc.2022.01108","title":"Self-Adapting Network Relaxations for Weakly Coupled Markov Decision Processes","year":2024,"lang":"en","type":"article","venue":"Management Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov decision process; Computer science; Markov chain; Statistical physics; Markov process; Mathematical optimization; Econometrics; Mathematics; Machine learning; Physics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002705812,0.001192974,0.000879137,0.0006304829,0.0004973815,0.001188353,0.00125509,0.001066281,0.006669601],"category_scores_gemma":[0.01160239,0.0006818108,0.001176743,0.0005031585,0.001125957,0.001822926,0.001742613,0.003137026,0.0004012486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001738993,"about_ca_system_score_gemma":0.001769773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004100784,"about_ca_topic_score_gemma":0.004296352,"domain_scores_codex":[0.9991022,0.000411358,0.00003733742,0.0001691774,0.0001483329,0.0001316498],"domain_scores_gemma":[0.9930398,0.005503899,0.0005720339,0.0002857763,0.0003007031,0.000297749],"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":[0.00005903943,0.00004111974,0.0004218082,0.00005438505,0.00002066753,0.00004821641,0.00005347479,0.9529414,0.0005263801,0.03821289,0.0008441276,0.006776494],"study_design_scores_gemma":[0.000006549008,0.00001027422,0.00003987304,0.000005117249,0.000002984694,0.000004748152,0.000006825326,0.9882538,0.0001036075,0.01134121,0.0002221805,0.000002738708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03635963,0.0002205062,0.955852,0.0004882426,0.00004531996,0.0001042795,0.0002959624,0.0002256495,0.006408433],"genre_scores_gemma":[0.6990309,0.0004758255,0.2927894,0.000302103,0.00007151234,0.0005846648,0.0006714641,0.0002247129,0.005849423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006669601,"threshold_uncertainty_score":0.02231205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00595439663458187,"score_gpt":0.224123714524245,"score_spread":0.2181693178896631,"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."}}