{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0008319023,0.0002771681,0.0002386758,0.0004642982,0.001972691,0.0006511245,0.0005205575,0.00002938154,0.000278701],"category_scores_gemma":[0.0000317958,0.0003054939,0.0001186737,0.0004907012,0.00001250318,0.0005072706,0.000606606,0.0001473918,0.00002747788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001286942,"about_ca_system_score_gemma":0.00001919664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003242845,"about_ca_topic_score_gemma":0.0003719133,"domain_scores_codex":[0.9980614,0.00001873542,0.0004533744,0.0006215971,0.0003370483,0.0005078205],"domain_scores_gemma":[0.9990509,0.000087468,0.0001324543,0.0005482202,0.0001621092,0.00001887077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004238753,0.0006320786,0.0007618138,0.004930034,0.0005214995,0.00003405422,0.0006573971,0.8935294,0.0007065136,0.007813178,0.005701022,0.08428918],"study_design_scores_gemma":[0.001446149,0.00004116333,0.0008351742,0.00004534716,0.0007402608,0.000005563732,0.001449753,0.1823168,0.001801613,0.0008130037,0.8097836,0.0007215428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8880356,0.0002602903,0.07524999,0.01217901,0.002889972,0.01111724,0.00009665328,0.0008951533,0.009276069],"genre_scores_gemma":[0.9793699,0.00001862389,0.00938641,0.004994863,0.000762419,0.00241035,0.0005260363,0.0001115723,0.002419814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8040826,"threshold_uncertainty_score":0.9999397,"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."}}