{"id":"W3201081331","doi":"10.1287/opre.2022.2347","title":"Differential Privacy in Personalized Pricing with Nonparametric Demand Models","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"News aggregator; Computer science; Differential privacy; Dynamic pricing; Big data; Information privacy; Revenue; Computer security; Privacy software; Business; Data mining; Marketing; Finance; World Wide Web","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":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001283444,0.00011518,0.0001613859,0.001268807,0.001003793,0.0004875118,0.01666954,0.00004568998,0.0001824981],"category_scores_gemma":[0.004162235,0.0001020962,0.00002479155,0.004689317,0.0001560942,0.0009337779,0.06561395,0.0009948313,0.00001626258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003919906,"about_ca_system_score_gemma":0.0003533674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004043292,"about_ca_topic_score_gemma":0.00009853635,"domain_scores_codex":[0.996793,0.0005392557,0.000220723,0.0006221286,0.001269449,0.0005554266],"domain_scores_gemma":[0.9951404,0.0002927536,0.00001841745,0.004313115,0.0001699057,0.00006544544],"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":[0.0003321508,0.003238424,0.008214178,0.0001362031,0.0001956976,0.0007024794,0.01024934,0.4473753,0.01619372,0.3748096,0.08751817,0.05103474],"study_design_scores_gemma":[0.0005977835,0.0001615074,0.0004025988,0.00001165853,0.000001287739,0.00002212337,0.0001839215,0.980444,0.0006598909,0.01692701,0.0004515557,0.0001367317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3586338,0.0001426342,0.6273565,0.01207922,0.00005876852,0.0006103902,0.00001327775,0.0001873211,0.0009180384],"genre_scores_gemma":[0.8533443,0.00003600326,0.1457516,0.00002990831,0.00001273707,0.0004361619,0.0000170725,0.00001206855,0.0003601873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5330686,"threshold_uncertainty_score":0.9886507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1014599156425495,"score_gpt":0.3601343908762298,"score_spread":0.2586744752336804,"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."}}