{"id":"W3093312274","doi":"10.2139/ssrn.3628684","title":"Algorithmic Personalized Pricing","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Digitalization, Law, and Regulation","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Business; Economics","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":[0.0008297118,0.00006934507,0.00009541492,0.00003611765,0.0005591502,0.000136793,0.0001678546,0.00005265015,0.00007893605],"category_scores_gemma":[0.0001288614,0.00006288956,0.0000763949,0.0002860606,0.0001053239,0.0003694582,0.00001011188,0.0001175311,0.000006170547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004519053,"about_ca_system_score_gemma":0.002002231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004566693,"about_ca_topic_score_gemma":0.001602286,"domain_scores_codex":[0.998235,0.0001043451,0.0001530071,0.0001152747,0.0003579119,0.001034422],"domain_scores_gemma":[0.9996251,0.00002188733,0.00009844884,0.00003627803,0.00009432064,0.0001239751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001688029,0.00001885556,0.001362754,0.000003066268,0.0000538629,0.000001585391,0.01105135,0.00007620618,0.00009461644,0.9558148,0.001155882,0.03035011],"study_design_scores_gemma":[0.001208244,0.0001933406,0.0001956714,0.00002483227,0.0000541975,0.00005019271,0.0384477,0.00167873,0.00004189512,0.3395261,0.6182241,0.0003549937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3432852,0.007853545,0.3732732,0.06365796,0.001441131,0.0008684479,0.000005056317,0.0005682734,0.2090472],"genre_scores_gemma":[0.9880965,0.002052169,0.000114007,0.000353718,0.001415927,0.000001418454,0.000004076443,0.0000143163,0.007947919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6448113,"threshold_uncertainty_score":0.4300588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402804595886032,"score_gpt":0.2677916340190308,"score_spread":0.2537635880601705,"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."}}