{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006897959,0.00009962506,0.00009213329,0.0003008855,0.0001931598,0.0003289804,0.0003361073,0.00001702231,0.0001331405],"category_scores_gemma":[0.0001523472,0.0001010098,0.00003018016,0.001395357,0.00006372949,0.001485385,0.0002283239,0.00004457221,0.0000293831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002650588,"about_ca_system_score_gemma":0.000007094394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004593018,"about_ca_topic_score_gemma":0.00002277514,"domain_scores_codex":[0.998892,0.00000496116,0.0001859596,0.0003774854,0.0002640035,0.0002755598],"domain_scores_gemma":[0.9997074,0.00002155707,0.00006450277,0.0001309862,0.00005943597,0.00001612064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000182464,0.0002771821,0.7678018,0.001064193,0.00001151343,0.00002255013,0.0003244522,0.0264179,0.002999754,0.00836386,0.002272635,0.1902617],"study_design_scores_gemma":[0.0007943675,0.000009539286,0.1213463,0.00003460985,0.00002382606,1.588281e-7,0.0001691827,0.8648191,0.0000598753,0.0001134287,0.01243275,0.000196813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8060434,0.00003988434,0.1225277,0.00873808,0.001002115,0.003571968,0.000003656679,0.0004157709,0.05765748],"genre_scores_gemma":[0.9962426,0.000004810544,0.002488178,0.0009650966,0.0001174037,0.00007884468,0.00001365047,0.00001041615,0.00007902863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8384012,"threshold_uncertainty_score":0.4119058,"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."}}