{"id":"W7108210594","doi":"10.1109/tac.2025.3639124","title":"Online Best-Response Algorithm in Open Noncooperative Games","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Regret; Interval (graph theory); Stability (learning theory); Upper and lower bounds; Trajectory; Nash equilibrium; Online algorithm; Online learning; Cournot competition","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":[0.00481175,0.001330771,0.00237965,0.0005850786,0.0009377407,0.001986274,0.002954423,0.00251743,0.002551081],"category_scores_gemma":[0.01731451,0.0006027156,0.0007305577,0.0006537882,0.002699317,0.002788813,0.002688512,0.002994507,0.000774878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001219527,"about_ca_system_score_gemma":0.00155073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001616156,"about_ca_topic_score_gemma":0.0009427557,"domain_scores_codex":[0.9953297,0.00204983,0.0001894746,0.001071064,0.0006850825,0.0006747521],"domain_scores_gemma":[0.9825081,0.01377917,0.001279438,0.0007692112,0.0009289791,0.0007350091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004690739,0.0003623134,0.0009035029,0.0001675214,0.00009559028,0.0003163777,0.000343323,0.8750687,0.002155982,0.08269969,0.001801608,0.03561635],"study_design_scores_gemma":[0.0000347066,0.00005953132,0.00004621877,0.000005753014,0.000006050153,0.000031779,0.00002290324,0.9662576,0.0003784288,0.03289308,0.0002551846,0.000008793365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02945899,0.0001903856,0.9670778,0.0003439173,0.00004942487,0.00009523286,0.00004046251,0.0003310049,0.002412719],"genre_scores_gemma":[0.8900752,0.0001925746,0.103021,0.000300427,0.00008443504,0.0003471013,0.0001246392,0.0001123113,0.005742346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00481175,"threshold_uncertainty_score":0.02544725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04965455923632081,"score_gpt":0.4201828295119084,"score_spread":0.3705282702755877,"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."}}