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Strategic use of payoff information in k-hop evolutionary Best-shot networked public goods game

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  • Jin, Xing
  • Tao, Yuchen
  • Wang, Jingrui
  • Wang, Chao
  • Wang, Yongheng
  • Zhang, Zhouyang
  • Wang, Zhen

Abstract

Globalization has led to increasingly interconnected interactions among individuals. Their payoffs are affected by the investment decision of themselves and their neighbors, which will cause conflicting interests between individual and social investment. Such problems can be modeled as a networked public goods game (NPGG). In this paper, we study the Best-shot NPGG model by introducing three mechanisms: k-hop, payoff information use strategy, and access cost. We use evolutionary game theory and present the k-hop evolutionary Best-shot networked public goods game (k-EBNPG) to explore the impact of these three mechanisms on social welfare. The results show that social welfare will increase with a diminishing margin as k increases while introducing the payoff information use strategy can significantly improve social welfare when k>1. Finally, we study the impact of access cost on social welfare and surprisingly find that social welfare will achieve the highest when the access cost is half the investment cost.

Suggested Citation

  • Jin, Xing & Tao, Yuchen & Wang, Jingrui & Wang, Chao & Wang, Yongheng & Zhang, Zhouyang & Wang, Zhen, 2023. "Strategic use of payoff information in k-hop evolutionary Best-shot networked public goods game," Applied Mathematics and Computation, Elsevier, vol. 459(C).
    • Handle: RePEc:eee:apmaco:v:459:y:2023:i:c:s009630032300440x
    DOI: 10.1016/j.amc.2023.128271
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    1. Zhang, Huizhen & An, Tianbo & Yan, Pingping & Hu, Kaipeng & An, Jinjin & Shi, Lijuan & Zhao, Jian & Wang, Jingrui, 2024. "Exploring cooperative evolution with tunable payoff’s loners using reinforcement learning," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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