{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001777465,0.00005057399,0.00007811884,0.0002665521,0.00004737369,0.00008377623,0.0002123624,0.00002895796,0.0005190516],"category_scores_gemma":[0.0003286751,0.00003152631,0.00001704142,0.0009422346,0.00004880182,0.0007047402,0.00002490273,0.00004737811,0.0005000078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001474945,"about_ca_system_score_gemma":0.00001849331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002081822,"about_ca_topic_score_gemma":0.0002800483,"domain_scores_codex":[0.9990011,0.00003271925,0.0003695879,0.0001039537,0.0003799838,0.0001126212],"domain_scores_gemma":[0.9990416,0.0004286099,0.0001101211,0.0002513636,0.0001285701,0.00003975762],"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":[0.0002455771,0.00007858936,0.08295858,0.000003687305,0.000006205195,0.000002478434,0.004091748,0.002305248,0.0002160856,0.5785154,0.007369929,0.3242065],"study_design_scores_gemma":[0.001080328,0.0001118374,0.5563843,0.00001927265,0.000005097484,0.00001816277,0.008328038,0.005538891,0.002690201,0.1673598,0.2581792,0.0002849],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5249602,0.000003770095,0.3388018,0.0008890741,0.0000277668,0.0001757933,0.000003263674,0.00003861333,0.1350997],"genre_scores_gemma":[0.9920692,7.721577e-7,0.006439287,0.0004292942,0.00001168621,0.00000591582,0.0000098123,0.000001782935,0.00103222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4734257,"threshold_uncertainty_score":0.6426755,"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."}}