{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003361057,0.001047754,0.001923807,0.001324414,0.001011469,0.00104733,0.001899484,0.001869406,0.00961647],"category_scores_gemma":[0.00789944,0.001031378,0.001792931,0.001206683,0.001293229,0.001243966,0.00316151,0.002735875,0.001404585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077577,"about_ca_system_score_gemma":0.003363814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01118066,"about_ca_topic_score_gemma":0.007917768,"domain_scores_codex":[0.99914,0.0004752173,0.00004121778,0.00007340629,0.0002148308,0.00005527855],"domain_scores_gemma":[0.9958481,0.003118586,0.0001411403,0.0001750957,0.0005206308,0.000196382],"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":[0.0001283379,0.0001132706,0.0006127387,0.0001408931,0.0001160269,0.0001230229,0.00008753926,0.8064019,0.002067096,0.1495866,0.00162666,0.03899587],"study_design_scores_gemma":[0.00002445795,0.00001498207,0.00003455148,0.00001201973,0.0000100001,0.00001030109,0.000003237151,0.9763784,0.0001486574,0.0223082,0.001048385,0.000006853761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001298404,0.00007660581,0.9969068,0.0001049304,0.00005489912,0.00003477186,0.00004146694,0.0001091692,0.001372875],"genre_scores_gemma":[0.2052133,0.0005982369,0.7716961,0.0003283897,0.0002397917,0.001156146,0.0004557561,0.0006700395,0.01964223],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01118066,"threshold_uncertainty_score":0.0321703,"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."}}