{"id":"W3010190843","doi":"10.1287/mnsc.2020.3641","title":"Promotion Optimization for Multiple Items in Supermarkets","year":2020,"lang":"en","type":"article","venue":"Management Science","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Oracle; National Science Foundation","keywords":"Mathematical optimization; Rounding; Computer science; Integer programming; Pairwise comparison; Integer (computer science); Linear programming; Complementarity (molecular biology); Set (abstract data type); Parametric statistics; Robust optimization; Class (philosophy); Optimization problem; Mathematics; Artificial intelligence","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":[0.001472513,0.001149707,0.001998859,0.0005475004,0.0005663365,0.001709086,0.001317245,0.001321606,0.00690668],"category_scores_gemma":[0.002742693,0.0008944469,0.001271808,0.001468367,0.0008687116,0.0020968,0.001110996,0.001889858,0.0004460049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002319891,"about_ca_system_score_gemma":0.001559876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0104143,"about_ca_topic_score_gemma":0.01096804,"domain_scores_codex":[0.9992824,0.0002703035,0.00002055605,0.0001390574,0.0001226073,0.0001650389],"domain_scores_gemma":[0.9990765,0.000605769,0.0001040276,0.00005238468,0.00006630676,0.00009503116],"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":[0.0001182146,0.00009234612,0.001023312,0.0001056394,0.00002635164,0.000102677,0.000044769,0.9711612,0.0005003792,0.01227875,0.001345714,0.01320053],"study_design_scores_gemma":[0.00001228737,0.00003504822,0.0002891989,0.00001051941,0.00001002267,0.0000181678,0.00003757981,0.9906328,0.0001723821,0.007940676,0.0008352451,0.00000599263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3438549,0.002109941,0.6203789,0.001881149,0.0001625058,0.0002918707,0.000993445,0.0005088112,0.02981856],"genre_scores_gemma":[0.9052856,0.0007817177,0.08089154,0.000163462,0.00005636865,0.0001911172,0.000464942,0.0001214473,0.01204385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0104143,"threshold_uncertainty_score":0.02310514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04091497976598798,"score_gpt":0.2491322442065523,"score_spread":0.2082172644405643,"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."}}