{"id":"W2949873667","doi":"10.48550/arxiv.1308.3814","title":"A Mixed Value and Policy Iteration Method for Stochastic Control with Universally Measurable Policies","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Bounded function; Convergence (economics); Bellman equation; Function (biology); Mathematical optimization; Power iteration; Average cost; Value (mathematics); Context (archaeology); Optimal control; Markov decision process; Applied mathematics; Iterative method; Economics; Markov process","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":[0.00115662,0.0003470722,0.0005610115,0.0009217309,0.0003129397,0.0004656987,0.0006379628,0.0002929819,0.00002945667],"category_scores_gemma":[0.0006734317,0.0002953035,0.0001575812,0.0008648238,0.0001595545,0.000589152,0.0002303995,0.000248715,0.00002447606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001678835,"about_ca_system_score_gemma":0.000534869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001860605,"about_ca_topic_score_gemma":0.0003460799,"domain_scores_codex":[0.9975786,0.0004135721,0.0003532867,0.001008014,0.0002865106,0.0003600457],"domain_scores_gemma":[0.9963214,0.001097608,0.0005874212,0.000721065,0.001042005,0.0002304482],"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":[0.000241259,0.00002756481,0.0009600981,0.00001034596,0.0000906952,0.000006176752,0.0003892134,0.8712118,0.00002681977,0.1252017,0.0005111384,0.00132321],"study_design_scores_gemma":[0.001625393,0.0001501755,0.002026606,0.00004432947,0.0002069416,0.00000721691,0.0004954348,0.8900462,0.0000244641,0.1043206,0.0006951814,0.0003574677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1235485,0.00004207696,0.8736938,0.0004605452,0.0001619131,0.0009771775,0.0001009127,0.0000660967,0.0009489094],"genre_scores_gemma":[0.9843482,0.00009303456,0.01036937,0.0001516446,0.0001244176,0.000005902195,0.00003289115,0.00002868587,0.004845862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8633245,"threshold_uncertainty_score":0.9999499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1365423791899294,"score_gpt":0.272731797113564,"score_spread":0.1361894179236346,"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."}}