{"id":"W3045016350","doi":"10.3982/ecta17105","title":"Statistical Inference in Games","year":2020,"lang":"en","type":"article","venue":"Econometrica","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Statistical inference; Inference; Sampling distribution; Sample (material); Econometrics; Nash equilibrium; Matching (statistics); Fiducial inference; Computer science; Indirect Inference; Frequentist inference; Mathematical economics; Economics; Mathematics; Statistics; Artificial intelligence; Bayesian inference; Estimator","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":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005759306,0.00005277999,0.0001560141,0.0003038923,0.00002853423,0.00009484982,0.0004467109,0.00002399712,0.00525801],"category_scores_gemma":[0.01134235,0.00004350095,0.00002494984,0.002397664,0.00006057231,0.0001288503,0.00008162711,0.00009248243,0.005745945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001256503,"about_ca_system_score_gemma":0.00002860406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004342926,"about_ca_topic_score_gemma":0.00000294062,"domain_scores_codex":[0.9990189,0.0000625459,0.0003408942,0.0002868492,0.0001636885,0.0001270973],"domain_scores_gemma":[0.9955977,0.003952721,0.0000649604,0.0002153139,0.00002937686,0.000139912],"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":[0.00002315181,0.0001005597,0.1338632,0.000002744532,0.000005312455,0.000007768856,0.0009692266,0.0005564785,0.00007778613,0.6176538,0.009383752,0.2373563],"study_design_scores_gemma":[0.0003586496,0.0001032624,0.3868294,0.000002059661,0.000002868941,7.670025e-7,0.0005998611,0.007792118,0.0002628338,0.3218,0.2820338,0.0002144726],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7009511,0.0001300358,0.1315865,0.0176168,0.0001079866,0.0002675276,0.0001220016,0.00007685921,0.1491412],"genre_scores_gemma":[0.9973223,0.000005643391,0.001587772,0.0007831128,0.00003167912,0.00001247258,0.000001925457,0.000002830847,0.0002522844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2963712,"threshold_uncertainty_score":0.9969856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2635588611489281,"score_gpt":0.432110466382174,"score_spread":0.168551605233246,"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."}}