{"id":"W4320006265","doi":"10.3982/qe1861","title":"Random utility and limited consideration","year":2023,"lang":"en","type":"article","venue":"Quantitative Economics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Social Sciences and Humanities Research Council of Canada; California Institute of Technology","keywords":"Contrast (vision); Set (abstract data type); Choice set; Econometrics; Population; Preference; Mixed logit; Computer science; Revealed preference; Logit; Frame (networking); Mathematical economics; Logistic regression; Economics; Mathematics; Statistics; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007824294,0.000129934,0.0003115701,0.000173911,0.0001426135,0.0000738146,0.00006574779,0.00007600375,0.0004142254],"category_scores_gemma":[0.0001580512,0.0001689342,0.00006181184,0.00009369684,0.0001283775,0.0003823796,0.00005271378,0.00007745536,0.003214445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007357687,"about_ca_system_score_gemma":0.0000100341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005615546,"about_ca_topic_score_gemma":0.00003783304,"domain_scores_codex":[0.9988557,0.00002379826,0.0005121364,0.000400348,0.0000100579,0.0001979753],"domain_scores_gemma":[0.9992889,0.0002258258,0.0002333467,0.0001774246,0.000008473232,0.00006607108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004837294,0.00003177236,0.3108506,0.00001647763,0.00007707987,0.000001096857,0.0009982088,0.0009556974,0.00002954934,0.6841563,0.001808395,0.001026401],"study_design_scores_gemma":[0.001789389,0.00009018349,0.6708102,0.000005553741,0.000007616716,0.000002800708,0.0007133987,0.1515353,0.0001061754,0.1640987,0.01051027,0.0003303623],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846736,0.0003705758,0.002045686,0.0008686789,0.0002991579,0.00023229,0.0001461959,0.00007262569,0.01129125],"genre_scores_gemma":[0.9963886,0.001116692,0.001431765,0.0002671395,0.00003436816,0.00003016616,0.0001114489,0.0000209996,0.0005987938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5200576,"threshold_uncertainty_score":0.9975617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2335246844077759,"score_gpt":0.2658783762014983,"score_spread":0.03235369179372244,"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."}}