{"id":"W2103315867","doi":"10.7939/r3q23r282","title":"Regret Minimization in Games with Incomplete Information","year":2007,"lang":"en","type":"article","venue":"","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":510,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Regret; Counterfactual thinking; Nash equilibrium; Complete information; Computer science; Limit (mathematics); Domain (mathematical analysis); Mathematical optimization; Mathematical economics; Exploit; Repeated game; Correlated equilibrium; Best response; Minification; Game theory; Equilibrium selection; Mathematics; Machine learning","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":[0.005965323,0.001205167,0.001527218,0.0007687496,0.0007500509,0.00231732,0.001720962,0.0013319,0.001418771],"category_scores_gemma":[0.01770632,0.0006933183,0.001360184,0.0008996877,0.003085959,0.004436418,0.002032584,0.002506052,0.0001935718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002097482,"about_ca_system_score_gemma":0.001301131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002230255,"about_ca_topic_score_gemma":0.002018246,"domain_scores_codex":[0.9943334,0.003753784,0.0001877876,0.0005422815,0.0008232694,0.000359551],"domain_scores_gemma":[0.9894577,0.008581103,0.0009132769,0.0005250818,0.0003117895,0.0002111191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009345283,0.00005028926,0.0005607415,0.0001279142,0.0001159281,0.0001254123,0.0001771187,0.6540182,0.0005828844,0.3315705,0.0007727061,0.01180485],"study_design_scores_gemma":[0.00002132729,0.00003033614,0.0001612017,0.00001658869,0.0000137284,0.00002409271,0.00002870238,0.6918927,0.0003491905,0.3067289,0.0007224269,0.00001087671],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04480651,0.0006930044,0.9470131,0.0009921029,0.00003510788,0.00007972146,0.0001137335,0.0001192735,0.006147524],"genre_scores_gemma":[0.800598,0.0009719415,0.1933136,0.000271077,0.0001580007,0.0004014481,0.0001874328,0.0000662748,0.004032308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005965323,"threshold_uncertainty_score":0.03154802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06042400712928032,"score_gpt":0.3601756060542456,"score_spread":0.2997515989249653,"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."}}