{"id":"W2910035100","doi":"10.1214/19-aap1540","title":"Conditional Optimal Stopping: A Time-Inconsistent Optimization","year":2019,"lang":"en","type":"preprint","venue":"The Annals of Applied Probability","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Optimal stopping; Uniqueness; Stochastic game; Bounded function; Stopping time; Conditional probability; Optional stopping theorem; Mathematical economics; Event (particle physics); Time horizon; Discrete time and continuous time; Optimal control; Mathematics; Mathematical optimization; Conditional expectation; Computer science; Physics; Econometrics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002161255,0.0003179961,0.0009310996,0.0001053696,0.0001025609,0.00007487486,0.0006959359,0.0003121028,0.001107058],"category_scores_gemma":[0.00007144172,0.0003057333,0.0003753355,0.00007957644,0.0004947874,0.00008265424,0.0007291436,0.000427818,0.0003714662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008558544,"about_ca_system_score_gemma":0.0001274831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004435781,"about_ca_topic_score_gemma":0.000001190613,"domain_scores_codex":[0.9975763,0.00003365202,0.001218799,0.0007763195,0.00006238384,0.0003325671],"domain_scores_gemma":[0.9970279,0.0001488473,0.001298424,0.001320901,0.0001298913,0.00007398033],"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":[0.0001083672,0.000082008,0.00004912305,0.0001326376,0.0001227227,7.223684e-8,0.0002443368,0.5477957,0.000002836379,0.4506617,0.0007240074,0.00007652224],"study_design_scores_gemma":[0.0003634877,0.00004760927,0.0002724568,0.00002842007,0.00001858179,9.250321e-7,0.00003655798,0.09691368,0.0002197577,0.8984762,0.00326119,0.0003610901],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5302758,0.002092706,0.1545139,0.01265078,0.001416774,0.006720815,0.006721274,0.0002314888,0.2853765],"genre_scores_gemma":[0.9922233,0.0001466922,0.005696167,0.0005964966,0.0001528479,0.0001828641,0.0004014027,0.00004018228,0.0005600868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4619475,"threshold_uncertainty_score":0.9999395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1078717276024866,"score_gpt":0.2644861972872913,"score_spread":0.1566144696848047,"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."}}