{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001507377,0.000103245,0.0002008569,0.0001645556,0.0001579931,0.0002540526,0.00150729,0.00003001515,0.003039984],"category_scores_gemma":[0.01638079,0.0000688648,0.00004073024,0.001111495,0.00007073556,0.0005224405,0.0011471,0.0002208435,0.003455433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004070679,"about_ca_system_score_gemma":0.0001578144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000152297,"about_ca_topic_score_gemma":0.00009972261,"domain_scores_codex":[0.9962729,0.0001770308,0.0003465084,0.001046956,0.001832267,0.0003243068],"domain_scores_gemma":[0.9951143,0.001547165,0.00006361971,0.002658705,0.0004927252,0.0001235125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004915342,0.000566753,0.002238243,0.000008091939,0.00005020397,0.0006736337,0.0003636621,0.02750941,0.006232892,0.002831742,0.1411715,0.8183047],"study_design_scores_gemma":[0.001774699,0.0001310694,0.01584247,0.00003433407,0.000009933296,0.00007650849,0.001794962,0.7035817,0.05042515,0.02206399,0.2036243,0.0006408802],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0175248,0.0002591696,0.9626105,0.003677236,0.0008456173,0.0002676228,0.00006556793,0.0001327611,0.01461675],"genre_scores_gemma":[0.5959871,0.00005298652,0.2656202,0.0009625776,0.0004771183,0.0000106284,0.00008362834,0.0000392679,0.1367665],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8176638,"threshold_uncertainty_score":0.9978714,"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."}}