{"id":"W3021134829","doi":"10.2139/ssrn.2358415","title":"A Generalized Random Regret Minimization Model","year":2013,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Transport Canada","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Regret; Mathematics; Random variable; Minification; Maximization; Constant (computer programming); Function (biology); Value (mathematics); Mathematical optimization; Econometrics; Statistics; Computer science","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":[0.002998603,0.001996271,0.003117358,0.0009423502,0.0007023648,0.002799617,0.00475111,0.005382681,0.01068059],"category_scores_gemma":[0.007487924,0.001323372,0.00136005,0.001814827,0.002088649,0.002978411,0.002328387,0.003483464,0.001702538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002385636,"about_ca_system_score_gemma":0.00137104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005306559,"about_ca_topic_score_gemma":0.004108224,"domain_scores_codex":[0.99725,0.001582823,0.00006162374,0.0005359933,0.0002845363,0.0002850322],"domain_scores_gemma":[0.9964869,0.002320326,0.0004305845,0.0002585062,0.0002571914,0.0002464682],"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":[0.0001242726,0.00007586107,0.0002815846,0.0001262234,0.00009168551,0.0002263916,0.00005001789,0.8033385,0.0002617333,0.1803771,0.007539529,0.007507103],"study_design_scores_gemma":[0.00004889305,0.00003475912,0.00009479611,0.00001572145,0.00002283835,0.00003912048,0.00001259271,0.9121403,0.00004942944,0.08626422,0.001259626,0.00001765602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05349855,0.002291888,0.905921,0.008038323,0.0003899542,0.0001614766,0.001799959,0.0006486218,0.02725028],"genre_scores_gemma":[0.8237736,0.001985331,0.095945,0.001983924,0.0008615252,0.0005145855,0.001546966,0.0004334724,0.07295562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01068059,"threshold_uncertainty_score":0.03573012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860271378879293,"score_gpt":0.2835338828880677,"score_spread":0.2649311690992748,"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."}}