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Updated: Jun 23, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
Distributed Nash equilibrium seeking in noncooperative game with partial decision information of neighbors
Xin Jin1, Kaihong Lu2, Zhengxin Wang3
1School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China.
Abstract:
In the real world, individuals may conceal some of their real decision information to their neighbors due to competition. It is a challenge to explore the distributed Nash equilibrium when individuals play the noncooperative game with partial decision information in complex networks. In this paper, we investigate the distributed Nash equilibrium seeking problem with partial decision information of neighbors. Specifically, we construct a two-layer network model, where players in the first layer engage in game interactions and players in the second layer exchange estimations of real actions with each other. We also consider the case where the actions of some players remain unchanged due to the cost of updating or personal reluctance. By means of the Lyapunov function method and LaSalle's invariance principle, we obtain the sufficient conditions in which the consensus of individual actions and estimations can be achieved and the population actions can converge to the Nash equilibrium point. Furthermore, we investigate the case with switched topologies and derive the sufficient conditions for the convergence of individual actions to Nash equilibrium by the average dwell time method. Finally, we give numerical examples for cases of fixed and switched topologies to verify our theoretical results.
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