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Distributed Population Dynamics for Searching Generalized Nash Equilibria of Population Games With Graphical Strategy Interactions.

Authors :
Tan, Shaolin
Wang, Yaonan
Vasilakos, Athanasios V.
Source :
IEEE Transactions on Systems, Man & Cybernetics. Systems; May2022, Vol. 52 Issue 5, p3263-3272, 10p
Publication Year :
2022

Abstract

Evolutionary games and population dynamics are finding increasing applications in design learning and control protocols for a variety of resource allocation problems. The implicit requirement for full communication has been the main limitation of the evolutionary game dynamic approach in engineering tasks with various information constraints. This article intends to build population games and dynamics with both static and dynamical graphical communication structures. To this end, we formulate a population game model with graphical strategy interactions and derive its corresponding population dynamics. In particular, we first introduce the concept of generalized Nash equilibria for population games with graphical strategy interactions, and establish the equivalence between the set of generalized Nash equilibria and the set of rest points of its distributed population dynamics. Furthermore, the conditions for convergence to generalized Nash equilibrium and particularly to Nash equilibrium are obtained for the distributed population dynamics with both static and dynamical graphical structures. These results provide a new approach to design distributed Nash equilibrium seeking algorithms for population games with both static and dynamical communication networks, and hence, expand the applicability of the population game dynamics in the design of learning and control protocols under distributed circumstances. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21682216
Volume :
52
Issue :
5
Database :
Complementary Index
Journal :
IEEE Transactions on Systems, Man & Cybernetics. Systems
Publication Type :
Academic Journal
Accession number :
156342710
Full Text :
https://doi.org/10.1109/TSMC.2021.3062827