{"id":"W4414832767","doi":"10.1002/nav.70020","title":"Pricing and Assortment Optimization Under Logit‐Based Choice Models With Tree‐Structured Consideration Sets","year":2025,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute on Governance","funders":"National Natural Science Foundation of China","keywords":"Tree (set theory); Set (abstract data type); Multinomial logistic regression; Node (physics); Path (computing); Revenue; Product (mathematics); Nested logit","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001117276,0.0002047431,0.0002236681,0.0005688765,0.000554982,0.0007703,0.0001659758,0.0001277718,0.00007637963],"category_scores_gemma":[0.001184877,0.0001734964,0.00003098766,0.0007937676,0.0002174591,0.0004810484,0.0001952905,0.0004044548,0.000005233788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001351209,"about_ca_system_score_gemma":0.0002081187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001074701,"about_ca_topic_score_gemma":0.0009354505,"domain_scores_codex":[0.9980569,0.00008454922,0.0002795257,0.0004466683,0.0006746592,0.0004577233],"domain_scores_gemma":[0.998044,0.0008979992,0.0001063151,0.0002879552,0.0006324294,0.0000312984],"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":[0.0009741065,0.0003617052,0.1117487,0.0009990785,0.0002016649,0.0001081372,0.0000881274,0.695932,0.001551883,0.09627517,0.004313625,0.08744579],"study_design_scores_gemma":[0.001607651,0.00004379969,0.03099673,0.0001640368,0.0001466733,0.000001863771,0.0001508542,0.9495048,0.0001783766,0.01542395,0.001483602,0.0002976667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1057939,0.0003251481,0.8280228,0.003891504,0.0003655389,0.001718819,0.0000132458,0.0002740025,0.0595951],"genre_scores_gemma":[0.994172,0.00002826791,0.004675173,0.0006042513,0.000123411,0.00003974683,0.00008754789,0.00002363301,0.0002459408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8883781,"threshold_uncertainty_score":0.742802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1143954620973993,"score_gpt":0.3586934059996934,"score_spread":0.2442979439022941,"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."}}