{"id":"W3124874316","doi":"10.2139/ssrn.3319160","title":"A Backward Simulation Method for Stochastic Optimal Control Problems","year":2019,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Stochastic control; Computer science; Bellman equation; Monte Carlo method; Selection (genetic algorithm); Optimal control; Mathematics; Artificial intelligence","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01288041,0.000445236,0.0007260023,0.0003987645,0.0008029093,0.0004148534,0.0009972474,0.0004850633,0.00004359063],"category_scores_gemma":[0.000371979,0.0004457547,0.0007696101,0.0002552985,0.0001458127,0.000254875,0.0001561885,0.003431851,0.00003764118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002198837,"about_ca_system_score_gemma":0.005231886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009147386,"about_ca_topic_score_gemma":0.003635677,"domain_scores_codex":[0.9929926,0.0008452922,0.0007820252,0.0006974787,0.001066267,0.003616356],"domain_scores_gemma":[0.9972715,0.0005301974,0.0009895898,0.0004422503,0.000598749,0.0001677634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001521255,0.00009396562,0.0005517245,0.0000903185,0.0008766801,8.954849e-7,0.001453772,0.8910861,0.000004372308,0.09370334,0.00008842539,0.01189831],"study_design_scores_gemma":[0.003582521,0.0005620376,0.0007339899,0.0002290283,0.0009911766,0.00001145352,0.004162546,0.3205268,0.000001342158,0.6543819,0.01371307,0.001104172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007739843,0.002059012,0.9817209,0.001150982,0.001980559,0.003874797,0.00004762357,0.0001130608,0.001313286],"genre_scores_gemma":[0.9905398,0.001248726,0.004223795,0.0001494035,0.001687636,0.0002309859,0.00002778114,0.00007919593,0.001812634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9828,"threshold_uncertainty_score":0.9997994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120854266233264,"score_gpt":0.3457721743688126,"score_spread":0.32456363170648,"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."}}