{"id":"W4402386394","doi":"10.48550/arxiv.2408.05146","title":"Performative Prediction on Games and Mechanism Design","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Samsung; European Commission; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Performative utterance; Mechanism design; Mechanism (biology); Computer science; Human–computer interaction; Mathematical economics; Aesthetics; Art; Mathematics; Epistemology; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01298914,0.002086462,0.002026247,0.001566178,0.001226929,0.004918889,0.002302548,0.00425648,0.004930009],"category_scores_gemma":[0.04405808,0.0008465145,0.00142938,0.001679404,0.006181283,0.006674907,0.003059471,0.004182752,0.0005559797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004939768,"about_ca_system_score_gemma":0.002071533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002900248,"about_ca_topic_score_gemma":0.001863098,"domain_scores_codex":[0.9903293,0.006835663,0.00040293,0.001051149,0.0008848038,0.0004960848],"domain_scores_gemma":[0.9544901,0.03879586,0.002589414,0.002205544,0.001114017,0.0008050338],"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":[0.00004867885,0.00004189798,0.0007586196,0.0001002552,0.00005939555,0.00007172072,0.0001321058,0.1200156,0.0002149947,0.8677598,0.001183316,0.009613564],"study_design_scores_gemma":[0.0000306884,0.00002654743,0.0001346403,0.00002887556,0.000009550478,0.00001491476,0.0000210477,0.1560283,0.00007166043,0.8422416,0.001379663,0.00001252475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03796552,0.00279711,0.9264536,0.007814053,0.0002418323,0.000169442,0.0002462529,0.0002404855,0.02407163],"genre_scores_gemma":[0.875525,0.003257064,0.1104295,0.000911842,0.0006686825,0.0006140388,0.000215115,0.0001064376,0.008272352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01298914,"threshold_uncertainty_score":0.06869394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080047993888678,"score_gpt":0.1632515457243222,"score_spread":0.05524674633545432,"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."}}