{"id":"W2775007035","doi":"10.1509/jmr.14.0102","title":"Modeling Simultaneous Multiple Goal Pursuit and Adaptation in Consumer Choice","year":2017,"lang":"en","type":"article","venue":"Journal of Marketing Research","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Identification (biology); A priori and a posteriori; Adaptation (eye); Context (archaeology); Discrete choice; Consumer choice; Valuation (finance); Decision maker; Key (lock); Machine learning; Management science; Economics; Microeconomics; Psychology","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":[0.00481768,0.0009259251,0.0009985565,0.0008514199,0.0005443352,0.001996447,0.001412372,0.002004536,0.003469287],"category_scores_gemma":[0.01267906,0.0007223158,0.001661184,0.001181098,0.001722679,0.00208411,0.002120222,0.002573072,0.0003522248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001969016,"about_ca_system_score_gemma":0.001184837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063001,"about_ca_topic_score_gemma":0.009031241,"domain_scores_codex":[0.997314,0.001716577,0.00007337171,0.0004145039,0.0001979793,0.0002835972],"domain_scores_gemma":[0.9898584,0.008281088,0.0009201156,0.0003353156,0.0002719053,0.0003330913],"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":[0.0004330661,0.0006565109,0.02014865,0.0001242809,0.0003034456,0.0004264364,0.001212028,0.799888,0.001238766,0.1516597,0.0008664928,0.02304254],"study_design_scores_gemma":[0.00003396315,0.00007304631,0.002470979,0.000008368585,0.00002465093,0.00003306026,0.00008113916,0.9558061,0.00006436172,0.0410737,0.0003086898,0.00002209103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5508441,0.0004946083,0.4336304,0.001340623,0.00006480731,0.0002627965,0.0002419573,0.0002005992,0.01292006],"genre_scores_gemma":[0.9690158,0.0001994629,0.02657158,0.00007971109,0.00001923637,0.0002577303,0.00009613976,0.00001993619,0.003740341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01063001,"threshold_uncertainty_score":0.0254786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2758548728248057,"score_gpt":0.3327602167891594,"score_spread":0.05690534396435371,"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."}}