{"id":"W3164239396","doi":"10.1093/restud/rdad100","title":"Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness","year":2021,"lang":"en","type":"preprint","venue":"The Review of Economic Studies","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mechanism design; Robustness (evolution); Outcome (game theory); Computer science; Mechanism (biology); Mathematical optimization; SIGNAL (programming language); Mathematical economics; Microeconomics; Economics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"grok","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"opus","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004170442,0.0001971266,0.0008084665,0.00007874001,0.0003585629,0.000073467,0.0004699251,0.0000626305,0.0004384794],"category_scores_gemma":[0.001813674,0.0001238206,0.0002321387,0.0001037288,0.0004175226,0.0001526431,0.001651944,0.0003507961,0.00005728809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003402752,"about_ca_system_score_gemma":0.0001244853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004034322,"about_ca_topic_score_gemma":0.000003705999,"domain_scores_codex":[0.9977869,0.000306134,0.001153871,0.0003678454,0.0002635304,0.0001217373],"domain_scores_gemma":[0.9959918,0.001963167,0.00117124,0.0004519376,0.0003814284,0.00004042691],"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":[0.00004643431,0.00009801712,0.001211702,0.005263889,0.001834742,0.000001747686,0.003460719,0.8022462,0.00001664888,0.01581244,0.01930994,0.1506975],"study_design_scores_gemma":[0.002621815,0.00018091,0.01430244,0.04980836,0.001977379,0.0004014554,0.06578208,0.4143836,0.0003899125,0.02741253,0.4193897,0.003349758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.1803768,0.7930305,0.01321156,0.009749016,0.0009485521,0.001235316,0.00009308038,0.00005571949,0.001299397],"genre_scores_gemma":[0.2709358,0.6756944,0.04361123,0.001673773,0.0006920397,0.0006774845,0.0001595329,0.00005148917,0.006504276],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.4000798,"threshold_uncertainty_score":0.5049256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1862425529168565,"score_gpt":0.4321111945742247,"score_spread":0.2458686416573682,"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."}}