{"id":"W4387389765","doi":"10.48550/arxiv.2310.02384","title":"Constrained Optimization with Decision-Dependent Distributions","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Air Force Office of Scientific Research; National Science Foundation; Vetenskapsrådet; European Commission; Natural Sciences and Engineering Research Council of Canada; Knut och Alice Wallenbergs Stiftelse","keywords":"Mathematical optimization; Optimization problem; Stochastic optimization; Computer science; Constrained optimization; Convergence (economics); Dual (grammatical number); Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004916418,0.001754925,0.002067899,0.0008005984,0.0006203105,0.00224257,0.001876908,0.002158514,0.002351418],"category_scores_gemma":[0.02100223,0.001200151,0.001303771,0.00153416,0.002276364,0.002751366,0.002022492,0.003343819,0.0003585762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002112811,"about_ca_system_score_gemma":0.001874047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006315367,"about_ca_topic_score_gemma":0.003679743,"domain_scores_codex":[0.9961793,0.001972177,0.0001551898,0.0008174314,0.000503615,0.0003722363],"domain_scores_gemma":[0.9854935,0.01167395,0.001067803,0.0007184356,0.0007868455,0.0002595528],"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":[0.00005064957,0.00004812366,0.0004398535,0.00006149993,0.00005822707,0.00008575501,0.00002884699,0.9667293,0.0003930165,0.02536383,0.0004162538,0.006324683],"study_design_scores_gemma":[0.0000141896,0.00001842165,0.0001153878,0.000007586368,0.000006159884,0.00001310877,0.000007839399,0.9829741,0.0001838536,0.0163462,0.0003045449,0.000008660235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02504432,0.0004797409,0.9711291,0.0007511888,0.00006971078,0.00007221491,0.0001169235,0.0001031673,0.002233624],"genre_scores_gemma":[0.8075015,0.0007925172,0.1831687,0.0006262323,0.0001768152,0.0003718823,0.0004027028,0.0001667716,0.006792778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006315367,"threshold_uncertainty_score":0.0260008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0694946494386965,"score_gpt":0.2235197865783004,"score_spread":0.1540251371396039,"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."}}