{"id":"W2232678697","doi":"10.1287/opre.2017.1712","title":"Informational Braess’ Paradox: The Effect of Information on Traffic Congestion","year":2018,"lang":"en","type":"preprint","venue":"Operations Research","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inefficiency; Computer science; Class (philosophy); Complement (music); Set (abstract data type); Theoretical computer science; Focus (optics); Embedding; Graph; Mathematical economics; Mathematical optimization; Mathematics; Artificial intelligence; Economics","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":[0.004354575,0.0008548407,0.001207595,0.000932043,0.001055918,0.002854436,0.002389984,0.001939057,0.00509315],"category_scores_gemma":[0.02812645,0.0005387587,0.0009532524,0.001130502,0.003143834,0.008011201,0.002544614,0.003498323,0.0003259202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00181048,"about_ca_system_score_gemma":0.001695719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001255873,"about_ca_topic_score_gemma":0.0010152,"domain_scores_codex":[0.9968908,0.001549979,0.0001234785,0.0004477244,0.0006058508,0.0003821246],"domain_scores_gemma":[0.9736841,0.02038297,0.001743352,0.002355629,0.001044009,0.0007898887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004746022,0.0002247286,0.002304214,0.0003227805,0.0001384463,0.0008077575,0.0005190569,0.2868836,0.00757159,0.6629638,0.004866387,0.03292308],"study_design_scores_gemma":[0.00007227123,0.0001191526,0.0004754521,0.00003551191,0.00004407693,0.0003078899,0.0001697093,0.5632066,0.00325964,0.4303223,0.001944227,0.00004316212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3165266,0.0005975598,0.6490372,0.004245086,0.0001883597,0.00019766,0.0004104561,0.0003143286,0.0284828],"genre_scores_gemma":[0.9637706,0.0003871809,0.03312852,0.0004511317,0.00008163678,0.0001043608,0.00009165768,0.00005643819,0.001928386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00509315,"threshold_uncertainty_score":0.02302951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05161637199998291,"score_gpt":0.4102067467891671,"score_spread":0.3585903747891842,"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."}}