{"id":"W4387478948","doi":"10.2139/ssrn.4598615","title":"Stochastic Control for Backward Stochastic Differential Equations with Semi-Markov Chain Noises","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Markov chain; Stochastic differential equation; Control (management); Stochastic control; Computer science; Chain (unit); Continuous-time Markov chain; Control theory (sociology); Mathematics; Mathematical optimization; Applied mathematics; Markov property; Markov model; Optimal control; Artificial intelligence; Physics; Machine learning","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":[0.005311601,0.002600397,0.002986655,0.002141639,0.001083955,0.00442884,0.002879804,0.003575018,0.006948443],"category_scores_gemma":[0.01710076,0.001382129,0.00219405,0.001774954,0.004596431,0.003935106,0.004128304,0.003546112,0.000587504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004547128,"about_ca_system_score_gemma":0.005178482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02028065,"about_ca_topic_score_gemma":0.008627796,"domain_scores_codex":[0.9975179,0.001056045,0.0001262008,0.0004746076,0.0005351636,0.0002901626],"domain_scores_gemma":[0.9901952,0.006883185,0.001006618,0.0002980612,0.001049211,0.0005675937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001354618,0.0001149295,0.0006595997,0.000175065,0.0001496,0.0001815557,0.0001401328,0.2336902,0.001455135,0.7548246,0.00182621,0.006647591],"study_design_scores_gemma":[0.00004717147,0.00003389209,0.0002217357,0.00002450397,0.00002724971,0.00002676867,0.00001973057,0.8175334,0.0001863351,0.1811503,0.0006975462,0.00003137608],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03930964,0.002246781,0.9405885,0.002473846,0.000643935,0.000109525,0.000411196,0.0002318345,0.01398479],"genre_scores_gemma":[0.8965859,0.004091444,0.04280088,0.0007375742,0.0009527176,0.0006057959,0.0009988577,0.0003657164,0.05286106],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02028065,"threshold_uncertainty_score":0.04032522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02288008234334556,"score_gpt":0.236550653836241,"score_spread":0.2136705714928954,"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."}}