IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v490y2018icp1251-1258.html
   My bibliography  Save this article

Group learning versus local learning: Which is prefer for public cooperation?

Author

Listed:
  • Yang, Shi-Han
  • Song, Qi-Qing

Abstract

We study the evolution of cooperation in public goods games on various graphs, focusing on the effects that are brought by different kinds of strategy donors. This highlights a basic feature of a public good game, for which there exists a remarkable difference between the interactive players and the players who are imitated. A player can learn from all the groups where the player is a member or from the typically local nearest neighbors, and the results show that the group learning rules have better performance in promoting cooperation on many networks than the local learning rules. The heterogeneity of networks’ degree may be an effective mechanism for harvesting the cooperation expectation in many cases, however, we find that heterogeneity does not definitely mean the high frequency of cooperators in a population under group learning rules. It was shown that cooperators always hardly evolve whenever the interaction and the replacement do not coincide for evolutionary pairwise dilemmas on graphs, while for PG games we find that breaking the symmetry is conducive to the survival of cooperators.

Suggested Citation

  • Yang, Shi-Han & Song, Qi-Qing, 2018. "Group learning versus local learning: Which is prefer for public cooperation?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1251-1258.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1251-1258
    DOI: 10.1016/j.physa.2017.08.100
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117308245
    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.2017.08.100?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.

    References listed on IDEAS

    as
    1. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    2. Zhu, Cheng-jie & Sun, Shi-wen & Wang, Li & Ding, Shuai & Wang, Juan & Xia, Cheng-yi, 2014. "Promotion of cooperation due to diversity of players in the spatial public goods game with increasing neighborhood size," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 145-154.
    3. Song, Qi-Qing & Li, Zhen-Peng & Fu, Chang-He & Wang, Lai-Sheng, 2011. "Optional contributions have positive effects for volunteering public goods games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4236-4243.
    4. A. Szolnoki & M. Perc, 2009. "Promoting cooperation in social dilemmas via simple coevolutionary rules," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 337-344, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yunsheng Deng & Jihui Zhang, 2022. "The choice-decision based on memory and payoff favors cooperation in stag hunt game on interdependent networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(2), pages 1-13, February.
    2. Lv, Shaojie & Song, Feifei, 2022. "Particle swarm intelligence and the evolution of cooperation in the spatial public goods game with punishment," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    3. Zhang, Zhipeng & Wu, Yu’e & Zhang, Shuhua, 2022. "Reputation-based asymmetric comparison of fitness promotes cooperation on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    4. Yu, Fengyuan & Wang, Jianwei & He, Jialu, 2022. "Inequal dependence on members stabilizes cooperation in spatial public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    5. Cheng, Fei & Chen, Tong & Chen, Qiao, 2020. "Rewards based on public loyalty program promote cooperation in public goods game," Applied Mathematics and Computation, Elsevier, vol. 378(C).
    6. Lv, Ran & Qian, Jia-Li & Hao, Qing-Yi & Wu, Chao-Yun & Guo, Ning & Ling, Xiang, 2023. "The impact of current and historical reputation with non-uniform change on cooperation in spatial public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    7. Deng, Zheng-Hong & Huang, Yi-Jie & Gu, Zhi-Yang & Li-Gao,, 2018. "Multigames with social punishment and the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 164-170.
    8. Zhang, Hong, 2023. "Evolution of cooperation with tag-based expulsion in spatial public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    9. Geng, Yini & Shen, Chen & Hu, Kaipeng & Shi, Lei, 2018. "Impact of punishment on the evolution of cooperation in spatial prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 540-545.
    10. Haodong Niu & Keyu Li & Juan Wang, 2023. "Paid Access to Information Promotes the Emergence of Cooperation in the Spatial Prisoner’s Dilemma," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    11. Zhang, Xiaoyang & Chen, Tong & Chen, Qiao & Li, Xueya, 2020. "Increasing pool funds in public goods: The effects of deposit-based delayed rewards," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Liu, Chengwei & Wang, Juan & Li, Xiaopeng & Xia, Chengyi, 2020. "The link weight adjustment considering historical strategy promotes the cooperation in the spatial prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    13. Shang, Lihui & Sun, Sihao & Ai, Jun & Su, Zhan, 2022. "Cooperation enhanced by the interaction diversity for the spatial public goods game on regular lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    14. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    15. Shuhua Chang & Xinyu Wang & Zheng Wang, 2015. "Modeling and Computation of Transboundary Industrial Pollution with Emission Permits Trading by Stochastic Differential Game," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-29, September.
    16. Ping Zhu & Guiyi Wei, 2014. "Stochastic Heterogeneous Interaction Promotes Cooperation in Spatial Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-10, April.
    17. Yuan Cheng & Yanbo Xue & Meng Chang, 2019. "Career Choice as an Extended Spatial Evolutionary Public Goods Game," Papers 1907.13296, arXiv.org.
    18. Yang, Han-Xin & Chen, Xiaojie, 2018. "Promoting cooperation by punishing minority," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 460-466.
    19. Luo, Chao & Jiang, Zhipeng, 2017. "Coevolving allocation of resources and cooperation in spatial evolutionary games," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 47-57.
    20. Shen, Chen & Li, Xiaoping & Shi, Lei & Deng, Zhenghong, 2017. "Asymmetric evaluation promotes cooperation in network population," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 391-397.

    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:490:y:2018:i:c:p:1251-1258. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.