{"id":"W1985136372","doi":"10.1016/s1755-5345(13)70025-3","title":"Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information","year":2008,"lang":"en","type":"article","venue":"Journal of Choice Modelling","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":241,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; University of Guelph","funders":"","keywords":"Discrete choice; Preference; Computer science; Aggregate (composite); Variance (accounting); Limit (mathematics); Econometrics; Choice set; Revealed preference; Mathematical optimization; Economics; Microeconomics; Mathematics; Machine learning","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":[0.05212422,0.002320614,0.003630075,0.001342534,0.0004628713,0.003445379,0.002722825,0.003626863,0.004051166],"category_scores_gemma":[0.09129903,0.002214893,0.003308101,0.001907031,0.001905431,0.004776482,0.002108073,0.003215929,0.0004233479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001285936,"about_ca_system_score_gemma":0.001919004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001067092,"about_ca_topic_score_gemma":0.001424431,"domain_scores_codex":[0.9535795,0.0406297,0.001102675,0.002311689,0.001465537,0.0009109763],"domain_scores_gemma":[0.7847285,0.1942742,0.008861112,0.009351005,0.001759518,0.00102564],"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":[0.005922458,0.002810451,0.01402813,0.0008293291,0.002641892,0.0002586849,0.000928359,0.7682878,0.00311548,0.1299773,0.0005960328,0.07060397],"study_design_scores_gemma":[0.0008691867,0.0009390095,0.001383272,0.00002847193,0.0003722551,0.00004700679,0.00004479203,0.8954757,0.0009405582,0.09942921,0.0003975561,0.0000730252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1528012,0.0001545456,0.8447719,0.0004035946,0.00006791117,0.0004698812,0.0002256462,0.000100796,0.001004548],"genre_scores_gemma":[0.632811,0.0003019778,0.3628619,0.0002270196,0.0001072142,0.002085008,0.0003030587,0.0000310487,0.00127178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05212422,"threshold_uncertainty_score":0.2756625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1841817718579511,"score_gpt":0.2408156124094176,"score_spread":0.05663384055146645,"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."}}