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Strategy Set and Payoff Optimization of a Type of Networked Evolutionary Games

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Abstract

In this paper, we consider the strategy set optimization and payoff optimization for a class of networked evolutionary games (NEGs) with “myopic best response adjustment” by using the semi-tensor product (STP) method, and present a number of new results. Firstly, the dominated strategies are defined, based on which an algorithm for removing dominated strategies is formulated for a given NEG. Secondly, using STP method, the dynamics of the NEG after removing the dominated strategies is converted into an algebraic form. Finally, the payoff optimization problem is considered by adding control players to the game and state feedback controls are designed to steer the game from an initial profile to the optimal profile which can maximize the overall payoff of the whole game. An illustrative example is studied to support our new results.

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Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

This work is supported by the National Natural Science Foundation (NNSF) of China under Grants 62103176 and the Natural Science Foundation of Shandong Province ZR2019BF023.

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Authors and Affiliations

  1. School of Mathematical Sciences, Liaocheng University, Liaocheng, 252026, Shandong, People’s Republic of China

    Wen Liu, Shihua Fu & Jianli Zhao

  2. Research Center of Semi-Tensor Product of Matrices: Theory and Applications, Liaocheng, 252026, Shandong, People’s Republic of China

    Wen Liu, Shihua Fu & Jianli Zhao

  3. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, Shandong, People’s Republic of China

    Yanan Pan

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  1. Wen Liu
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Correspondence to Yanan Pan.

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Liu, W., Pan, Y., Fu, S. et al. Strategy Set and Payoff Optimization of a Type of Networked Evolutionary Games. Circuits Syst Signal Process 41, 4413–4437 (2022). https://doi.org/10.1007/s00034-022-02000-y

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