Work

Nash Equilibrium Learning in Large Populations With First-Order Payoff Modifications

Year: 2025

Type: article

Abstract: We establish Nash equilibrium learning in large populations of noncooperative, strategic agents. Our analysis considers the broadest class to date of payoff mechanisms with first-order modifications, capable of modeling bounded rationality and anticipatory effects, averaging, or Padé delay approximations. We propose a framework that, for the first time, combines two nonstandard system-theoretic passivity notions. Our results hold for discontinuous best response dynamics alongside continuous learning rules, significantly extending prior work.

Source: IEEE Control Systems Letters

DOI: 10.1109/lcsys.2025.3573119

Authors: Matthew S. Hankins, Jair Certório, Tzuyu Jeng, Nuno C. Martins

Institution: University of Maryland, College Park

Language: English

Topic: Game Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: Air Force Office of Scientific Research

Award: G778008054