{"id":"W3186527071","doi":"10.23919/acc50511.2021.9482649","title":"On Data-driven Multi-Product Pricing","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Estimator; Computer science; Boosting (machine learning); Mathematical optimization; Parametric statistics; Task (project management); Product (mathematics); Robust optimization; Machine learning; Artificial intelligence; Mathematics; Engineering","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":[0.009774202,0.001349488,0.002491919,0.001245426,0.000679152,0.00241457,0.002497683,0.002178199,0.002940679],"category_scores_gemma":[0.03649518,0.0009912674,0.001108497,0.001993795,0.002279747,0.004932007,0.002836667,0.003845931,0.0005021105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001955407,"about_ca_system_score_gemma":0.00185793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003925657,"about_ca_topic_score_gemma":0.002236498,"domain_scores_codex":[0.9961383,0.002239113,0.0001398105,0.0005272219,0.0006788936,0.0002767517],"domain_scores_gemma":[0.9805932,0.0152394,0.0009159193,0.001370344,0.001462791,0.0004183534],"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":[0.00007209564,0.0000749854,0.0007062514,0.0001006567,0.00005607241,0.0001005312,0.00004742689,0.8469212,0.0003338851,0.1273906,0.001550511,0.02264587],"study_design_scores_gemma":[0.000005318852,0.000009658092,0.00006434734,0.000006923991,0.000003902549,0.000009782606,0.000002667116,0.9673235,0.00007880521,0.03219607,0.0002941577,0.00000494557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008659198,0.0006921662,0.9878248,0.0007916861,0.00008718868,0.00003273647,0.00007562842,0.00009952978,0.001737101],"genre_scores_gemma":[0.7395601,0.002290433,0.2501001,0.0009129121,0.0007767489,0.0003105666,0.0004683015,0.000241305,0.005339515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009774202,"threshold_uncertainty_score":0.05169159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4151968970232139,"score_gpt":0.5222217993256624,"score_spread":0.1070249023024484,"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."}}