{"id":"W3111857876","doi":"10.1609/aaai.v35i6.16702","title":"Hindsight and Sequential Rationality of Correlated Play","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Alberta","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Hindsight bias; Rationality; Mathematical economics; Computer science; Counterfactual thinking; Regret; Bounded rationality; Fictitious play; Game theory; Artificial intelligence; Mathematics; Epistemology; Machine learning; Psychology; Cognitive psychology; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01333729,0.0006256785,0.0009470725,0.0008949401,0.001056062,0.003988117,0.001599701,0.001563746,0.00312372],"category_scores_gemma":[0.04827412,0.00041092,0.001122853,0.0005952649,0.008502314,0.003838098,0.002866233,0.00271895,0.0004328813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002514956,"about_ca_system_score_gemma":0.003378438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003956025,"about_ca_topic_score_gemma":0.002951611,"domain_scores_codex":[0.9856711,0.007752326,0.0007552203,0.002176959,0.002409964,0.001234501],"domain_scores_gemma":[0.9668235,0.02054271,0.003838268,0.004986405,0.002812248,0.0009968807],"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":[0.0001165509,0.00005254183,0.003209678,0.00005128131,0.00005800917,0.00009885711,0.0006460682,0.03217167,0.0007164792,0.9482015,0.0006037446,0.01407362],"study_design_scores_gemma":[0.00004857948,0.00005227109,0.001009362,0.00002943986,0.00002069065,0.00006927449,0.0001358098,0.08211793,0.0007380546,0.9143433,0.001410316,0.0000248628],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2329812,0.0003619475,0.7028697,0.003325811,0.0000666169,0.0002389186,0.0001350467,0.0003118999,0.05970877],"genre_scores_gemma":[0.9595006,0.0001122679,0.03705859,0.0002438514,0.00002768484,0.0001414975,0.00004558142,0.000040198,0.00282963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01333729,"threshold_uncertainty_score":0.07053512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08312385965456567,"score_gpt":0.3069914782112599,"score_spread":0.2238676185566942,"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."}}