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Aspiration-Based Learning in k -Hop Best-Shot Binary Networked Public Goods Games

Author

Listed:
  • Ziyi Chen

    (School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Kaiyan Dai

    (School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Xing Jin

    (School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
    Experimental Center of Data Science and Intelligent Decision-Making, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Liqin Hu

    (School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Yongheng Wang

    (Research Center for Big Data Intelligence, Zhejiang Lab, Hangzhou 310000, China)

Abstract

In public goods games, it is common for agents to learn strategies from those who possess the highest utility. However, in reality, because of the lack of information, strategies and utilities from others cannot be obtained or predicted during learning and updating. To address this issue, we introduce a learning update mechanism based on aspirations. To make this model more universal, we study goods that can be shared with k -hop neighbors. Additionally, when a free rider accesses an investor, it is required to pay an access cost to him. We investigate the influence of aspiration, shared scope k , and access cost on the social invest level and utility. It is shown that large shared scope k , moderate aspiration, and moderate access cost are conducive to the maximum utilization of social benefits. However, with low aspiration, the utilities of investors are very close and limited, while both the high aspiration and high access cost could disrupt the social stability.

Suggested Citation

  • Ziyi Chen & Kaiyan Dai & Xing Jin & Liqin Hu & Yongheng Wang, 2023. "Aspiration-Based Learning in k -Hop Best-Shot Binary Networked Public Goods Games," Mathematics, MDPI, vol. 11(14), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3037-:d:1189579
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    References listed on IDEAS

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