{"id":"W2098591116","doi":"10.1257/aer.20141141","title":"Perceiving Prospects Properly","year":2016,"lang":"en","type":"article","venue":"American Economic Review","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Curse; Economics; Action (physics); Perception; Mathematical economics; Noise (video); Microeconomics; Econometrics; Winner's curse; Feature (linguistics); Key (lock); Computer science; Artificial intelligence; Psychology; Computer security; Common value auction; Physics; Sociology","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":[0.001432089,0.0005089694,0.0002940155,0.0006967023,0.0004207402,0.005817551,0.0004682033,0.001601787,0.01385604],"category_scores_gemma":[0.0127203,0.0002755703,0.0004290771,0.0006193004,0.001757688,0.009535204,0.001706747,0.001810151,0.002462287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004196799,"about_ca_system_score_gemma":0.0004720927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008320013,"about_ca_topic_score_gemma":0.0005841016,"domain_scores_codex":[0.9991599,0.0002722621,0.0000494025,0.0002138888,0.0002250437,0.00007955216],"domain_scores_gemma":[0.998061,0.0006331727,0.0003801049,0.000320446,0.0003492176,0.0002560429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003412681,0.00006221097,0.0116529,0.0003138404,0.000109327,0.0004494509,0.004530507,0.006300883,0.009497084,0.8315181,0.01627309,0.1189514],"study_design_scores_gemma":[0.00004136307,0.0001524766,0.01288501,0.0001703353,0.00006674652,0.0004595192,0.003258996,0.01059391,0.001749073,0.9074317,0.06312572,0.00006511148],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2409249,0.003408283,0.3025954,0.0202125,0.001476857,0.0001727756,0.001644608,0.0005395061,0.4290251],"genre_scores_gemma":[0.9642934,0.001595802,0.02256946,0.00108531,0.0002733682,0.00006322837,0.0005559437,0.0001167024,0.009446857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01385604,"threshold_uncertainty_score":0.04635304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02283799274999525,"score_gpt":0.2324071389899997,"score_spread":0.2095691462400044,"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."}}