{"id":"W2129589037","doi":"10.1287/msom.1110.0344","title":"Pricing Multiple Products with the Multinomial Logit and Nested Logit Models: Concavity and Implications","year":2011,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":335,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multinomial logistic regression; Revenue management; Econometrics; Economics; Context (archaeology); Oligopoly; Profit (economics); Nested logit; Monopoly; Logit; Mixed logit; Revenue; Microeconomics; Logistic regression; Mathematics; Statistics","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":[0.005953538,0.00100328,0.001576337,0.001062528,0.000909464,0.003563357,0.002936583,0.002136249,0.006161895],"category_scores_gemma":[0.02483611,0.001614543,0.00195655,0.001660838,0.002912897,0.006828947,0.002837796,0.002642007,0.0005155401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003297949,"about_ca_system_score_gemma":0.002238584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01757846,"about_ca_topic_score_gemma":0.01662078,"domain_scores_codex":[0.9961487,0.002117317,0.0001233533,0.0004176193,0.0007591568,0.0004337843],"domain_scores_gemma":[0.9871552,0.009066396,0.001986011,0.0006792027,0.0006959291,0.0004171626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009095729,0.0002166692,0.002348883,0.00009616134,0.00008681539,0.0003392741,0.0002311975,0.5620233,0.0004610632,0.4194735,0.000811077,0.01382099],"study_design_scores_gemma":[0.00002276765,0.00002487726,0.0003941015,0.00001479646,0.0000170254,0.00008208616,0.00003421485,0.8322616,0.00008805107,0.1664589,0.000578891,0.00002266784],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1339783,0.001100261,0.8438345,0.002616017,0.00008678104,0.0001337266,0.0002025655,0.0001419888,0.01790578],"genre_scores_gemma":[0.9189168,0.0009433944,0.07002503,0.0002215612,0.0001239463,0.0001211295,0.00009762395,0.00005216252,0.009498283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01757846,"threshold_uncertainty_score":0.03495228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04643618843467594,"score_gpt":0.2106872157621677,"score_spread":0.1642510273274917,"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."}}