{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03713919,0.002646317,0.004344597,0.004448276,0.00189612,0.007051906,0.004263222,0.004543076,0.005763577],"category_scores_gemma":[0.135235,0.001485857,0.003242075,0.004132471,0.01281259,0.009767536,0.004995236,0.007824532,0.001007297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00553167,"about_ca_system_score_gemma":0.004795121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006056933,"about_ca_topic_score_gemma":0.002767144,"domain_scores_codex":[0.950693,0.03588699,0.002231388,0.004952718,0.00520602,0.001029942],"domain_scores_gemma":[0.7869439,0.1924383,0.006773639,0.008045782,0.004545191,0.001253273],"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":[0.00001825574,0.00004065383,0.0008309219,0.0002306524,0.0001611584,0.0000906534,0.0002192939,0.03054393,0.000100831,0.9540656,0.001908694,0.01178944],"study_design_scores_gemma":[0.00001814626,0.00002058627,0.0001046927,0.00005262862,0.00001659555,0.00002804232,0.00003139893,0.0661255,0.00005996567,0.9308602,0.002669282,0.00001276138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002984262,0.002138974,0.9825481,0.004006273,0.000287389,0.0001450061,0.0002241785,0.0001407529,0.007525187],"genre_scores_gemma":[0.3618754,0.007824227,0.6121134,0.005304973,0.003455788,0.002079405,0.0008815452,0.0003140437,0.006151203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03713919,"threshold_uncertainty_score":0.1964132,"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."}}