{"id":"W4210625983","doi":"10.1109/cdc45484.2021.9683060","title":"Distributed Nash equilibrium seeking resilient to adversaries","year":2021,"lang":"en","type":"article","venue":"2021 60th IEEE Conference on Decision and Control (CDC)","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nash equilibrium; Computer science; Best response; Correlated equilibrium; Computer security; Epsilon-equilibrium; Core (optical fiber); Adversarial system; Game theory; Mathematical optimization; Mathematical economics; Equilibrium selection; Repeated game; Artificial intelligence; Mathematics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001720109,0.0003129678,0.0006255507,0.0002288502,0.0003748973,0.001000707,0.0008014315,0.0001535472,0.002688635],"category_scores_gemma":[0.004372213,0.000247669,0.0001775798,0.001132009,0.0001813023,0.0002604568,0.0002552214,0.0002885243,0.001708401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000469043,"about_ca_system_score_gemma":0.0002926439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009036356,"about_ca_topic_score_gemma":0.00007889955,"domain_scores_codex":[0.9956876,0.0004086696,0.0008696432,0.001164472,0.001372217,0.0004974118],"domain_scores_gemma":[0.993904,0.002997724,0.0002187702,0.001317188,0.001043191,0.0005191209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001975873,0.0005292484,0.001893316,0.00001064747,0.0001106033,0.0002283434,0.001071393,0.002186604,0.1366852,0.188063,0.04579414,0.6214517],"study_design_scores_gemma":[0.008576365,0.0008133292,0.039803,0.000516018,0.0001645673,0.00009842335,0.007527167,0.04283422,0.02203077,0.4653067,0.4104782,0.001851279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6199558,0.0002500571,0.3528164,0.0139558,0.001020861,0.000595762,0.0004658436,0.00008720447,0.01085222],"genre_scores_gemma":[0.992259,0.00004134624,0.0005958145,0.002148026,0.0001311878,0.00005866024,0.00001912701,0.00001614307,0.004730674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6196004,"threshold_uncertainty_score":0.9999976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07272509218434094,"score_gpt":0.3584789924408153,"score_spread":0.2857539002564744,"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."}}