{"id":"W3203655594","doi":"10.2139/ssrn.3919807","title":"Differential Privacy in Personalized Pricing with Nonparametric Demand Models","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Differential privacy; Nonparametric statistics; Computer science; Differential (mechanical device); Business; Economics; Econometrics; Internet privacy; Data mining; 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":[],"consensus_categories":[],"category_scores_codex":[0.0007506429,0.0002186941,0.0003002094,0.0005106184,0.0002279101,0.000391405,0.0002409963,0.00006453759,0.0002370028],"category_scores_gemma":[0.00007489559,0.0001836998,0.0001105125,0.001206985,0.00003311374,0.0009906052,0.0001191393,0.001355265,0.00001547487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002976529,"about_ca_system_score_gemma":0.0007225449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002221264,"about_ca_topic_score_gemma":0.001289745,"domain_scores_codex":[0.9972966,0.00003924063,0.0003215548,0.0002987272,0.0003835032,0.001660427],"domain_scores_gemma":[0.9993879,0.00005835115,0.0001940046,0.0001737652,0.0001650295,0.00002090132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001283807,0.0009698745,0.5836965,0.000254196,0.0005910743,0.0005889172,0.0008008086,0.001564248,0.003969809,0.2100123,0.0001190087,0.1961494],"study_design_scores_gemma":[0.03975138,0.0003704672,0.2456279,0.001530174,0.00281729,0.005026494,0.0168972,0.1215379,0.0006801476,0.5484718,0.01204704,0.005242161],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618116,0.001722329,0.03281287,0.0003856414,0.0001460666,0.0001295455,2.774236e-7,0.00003891791,0.002952766],"genre_scores_gemma":[0.9980011,0.0006101365,0.0001153393,0.0001585328,0.0003603953,0.000006515173,0.000006997233,0.00003561215,0.0007053998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3384595,"threshold_uncertainty_score":0.7491061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0145168819713433,"score_gpt":0.2282963004597405,"score_spread":0.2137794184883972,"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."}}