{"id":"W4226438160","doi":"10.1287/msom.2021.1065","title":"Pricing for Heterogeneous Products: Analytics for Ticket Reselling","year":2022,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Ticket; Computer science; Endogeneity; Machine learning; Causal inference; Econometrics; Analytics; Instrumental variable; Artificial intelligence; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004298345,0.001250493,0.001229935,0.002685972,0.0004471524,0.002548016,0.002523727,0.001667168,0.004402001],"category_scores_gemma":[0.02849611,0.0004068103,0.001117172,0.003260608,0.0009428455,0.003633878,0.001989736,0.002374958,0.0009228846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092013,"about_ca_system_score_gemma":0.0008831574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004135715,"about_ca_topic_score_gemma":0.002726517,"domain_scores_codex":[0.9977111,0.0009542571,0.0001596557,0.0005194003,0.000526358,0.0001292689],"domain_scores_gemma":[0.9849768,0.01011734,0.002106346,0.001478801,0.0008920975,0.0004285779],"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":[0.0006801894,0.001267842,0.1016168,0.0007053106,0.0002483677,0.0009659893,0.0005979694,0.4858007,0.00246504,0.08533552,0.0170029,0.3033134],"study_design_scores_gemma":[0.00001735978,0.00005320746,0.005404726,0.00002663367,0.0000116006,0.000067024,0.0001254009,0.9393347,0.0004539941,0.05271377,0.001775478,0.00001615548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2508424,0.002753747,0.7217197,0.006902921,0.0001416199,0.0005180157,0.006886756,0.002522947,0.007711863],"genre_scores_gemma":[0.8340747,0.001011453,0.1541857,0.0003928637,0.0003199811,0.000312927,0.006838345,0.0001598095,0.002704226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004402001,"threshold_uncertainty_score":0.02273208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03532865363291429,"score_gpt":0.2519949755523821,"score_spread":0.2166663219194678,"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."}}