IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v525y2019icp1323-1329.html
   My bibliography  Save this article

History loyalty-based reward promotes cooperation in the spatial public goods game

Author

Listed:
  • Fu, Mingjian
  • Guo, Wenzhong
  • Cheng, Linlin
  • Huang, Shouying
  • Chen, Dewang

Abstract

Reward has been proved to be an effective mechanism to sustain cooperation among selfish individuals. In this paper, we propose a history loyalty-based reward in which a cooperator can gain additional reward if the time he sticks to the cooperation strategy is over a loyalty threshold. Accordingly, defectors have to bear the cost of reward subsequently. The results on the spatial public goods game show that the cooperation could be immensely enhanced when the loyalty threshold and the reward factor are suitable. Besides, the time evolution of cooperator density and the spatial distribution of cooperators and defectors are investigated. Our work extends the form of reward in the evolution of spatial public goods game.

Suggested Citation

  • Fu, Mingjian & Guo, Wenzhong & Cheng, Linlin & Huang, Shouying & Chen, Dewang, 2019. "History loyalty-based reward promotes cooperation in the spatial public goods game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1323-1329.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:1323-1329
    DOI: 10.1016/j.physa.2019.03.108
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119303437
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.03.108?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lee, Hsuan-Wei & Cleveland, Colin & Szolnoki, Attila, 2024. "Supporting punishment via taxation in a structured population," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    2. Lee, Hsuan-Wei & Cleveland, Colin & Szolnoki, Attila, 2023. "Group-size dependent synergy in heterogeneous populations," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    3. Zhang, Yao & Hao, Qing-Yi & Qian, Jia-Li & Wu, Chao-Yun & Guo, Ning & Ling, Xiang, 2024. "The cooperative evolution in the spatial prisoner's dilemma game with the local loyalty of two-strategy," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    4. Quan, Ji & Li, Haoze & Zhang, Man & Wang, Xianjia, 2024. "Cooperation dynamics in nonlinear spatial public goods games with endogenous synergy and discounting feedback," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    5. Szolnoki, Attila & Chen, Xiaojie, 2020. "Blocking defector invasion by focusing on the most successful partner," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    6. Lee, Hsuan-Wei & Cleveland, Colin & Szolnoki, Attila, 2023. "Restoring spatial cooperation with myopic agents in a three-strategy social dilemma," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    7. Szolnoki, Attila & Chen, Xiaojie, 2020. "Gradual learning supports cooperation in spatial prisoner’s dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    8. Zhenghong Wu & Huan Huang & Qinghu Liao, 2021. "The study on the role of dedicators on promoting cooperation in public goods game," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-17, September.
    9. Szolnoki, Attila & Chen, Xiaojie, 2020. "Strategy dependent learning activity in cyclic dominant systems," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    10. Wang, Jianwei & Xu, Wenshu & Chen, Wei & Yu, Fengyuan & He, Jialu, 2021. "Inter-group selection of strategy promotes cooperation in public goods game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    11. Szolnoki, Attila & Chen, Xiaojie, 2022. "Tactical cooperation of defectors in a multi-stage public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    12. Zu, Jinjing & Xu, Fanxin & Jin, Tao & Xiang, Wei, 2022. "Reward and Punishment Mechanism with weighting enhances cooperation in evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    13. de Oliveira, Breno F. & Szolnoki, Attila, 2022. "Competition among alliances of different sizes," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:1323-1329. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.