{"id":"W2241234830","doi":"","title":"Price Discrimination and Efficent Matching","year":2007,"lang":"en","type":"article","venue":"Economic Theory","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Matching (statistics); Mathematical economics; Revenue; Value (mathematics); Economics; Monopoly; Pairwise comparison; Price discrimination; Function (biology); Phone; Schedule; Microeconomics; Operations research; Computer science; Management; Mathematics; Finance; Artificial intelligence; Statistics","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":[0.003302878,0.0004364567,0.001104641,0.001173349,0.001246411,0.003565513,0.001775535,0.002591698,0.01819397],"category_scores_gemma":[0.02622845,0.0004844599,0.0005569623,0.001312041,0.002587525,0.006902596,0.002447564,0.001929158,0.001289632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001260129,"about_ca_system_score_gemma":0.0009030251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008777651,"about_ca_topic_score_gemma":0.0004676857,"domain_scores_codex":[0.9979483,0.0005381935,0.00009582101,0.0003783966,0.0005544661,0.0004848257],"domain_scores_gemma":[0.9906709,0.005391955,0.0009524607,0.00180214,0.0007462498,0.0004362257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006182003,0.0001060501,0.00136392,0.00002117239,0.000009014007,0.0000602198,0.0000812502,0.003515095,0.0006019997,0.9764783,0.0007859324,0.01691523],"study_design_scores_gemma":[0.0000197092,0.00002001476,0.0008888819,0.000007010127,0.000008276192,0.0001025716,0.00004700046,0.03171703,0.000462992,0.9658007,0.0009188247,0.000006904987],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4643066,0.0008780339,0.3101798,0.005102869,0.0002782646,0.0001193612,0.0001499921,0.0002465424,0.2187386],"genre_scores_gemma":[0.9812166,0.0001802896,0.006179809,0.0002341149,0.0001117981,0.00001851186,0.00004573153,0.00002572133,0.01198744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01819397,"threshold_uncertainty_score":0.06086487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01026720332497083,"score_gpt":0.2244869338298582,"score_spread":0.2142197305048873,"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."}}