Weighted Networked Evolutionary Games with Switched Topologies and Time-delay Threshold

Wu, Yue; Ma, Fengyuan; Dang, Wenyan; Li, Lulu; Lu, Jianquan

doi:10.23919/ccc58697.2023.10239809

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Utilizing the STP method, Zhao et al presented a model focusing on NEGs with switched topologies [17], examining algebraic formulation and optimization control. Additionally, the authors delved into the dynamics and consensus of NEGs featuring switching network topologies among players [18,19]. Wang et al investigate the stability degree of profile for a class of NEGs with switching topologies [20].…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of networked evolutionary game based on incomplete information with switched topologies

Gui,

Gao,

2024

IET Control Theory & Appl

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In the realm of evolutionary game theory, the majority of scenarios involve players with incomplete knowledge, specially regarding their opponents' actions and payoffs compounded by the ever‐shifting landscape of players' interactions. These dynamics present formidable challenges in both the analysis and optimization of game evolution. To address this, a novel model named the networked evolutionary game (NEG) is proposed based on incomplete information with switched topologies. This model captures situations where players possess limited insight into their opponents' benefits, yet make decisions based on their own payoffs while adapting to different networks and new players. To bridge the gap between incomplete and complete information games, R. Selten's transformation method is leveraged, a renowned approach that converts an incomplete information game into an interim agent game, thereby establishing the equivalence of pure Nash equilibria (NE) in both scenarios. Employing the semi‐tensor product (STP) of matrices, a powerful tool in logistic system, the evolution of the model is articulated through algebraic relationships. This enables to unravel the patterns of game evolution and identify the corresponding pure Nash equilibria. By introducing control players, strategically positioned within the game, optimized control is facilitated over the evolutionary trajectory, ultimately leading to convergence towards an optimal outcome. Finally, these concepts are illustrated with a practical example within the paper.

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