IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v182y2024ics0960077924003060.html
   My bibliography  Save this article

The universal probabilistic reward based on the difference of payoff realizes the evolution of cooperation

Author

Listed:
  • Ohdaira, Tetsushi

Abstract

Many previous studies regarding the theoretical models of social dilemmas have described that rewarding opponents can help players to cooperate with each other. Those studies deal with reward not for defectors, but for cooperators. However, many prior researches concerning punishment that is also considered to be necessary for the evolution of cooperation discuss punishment on cooperators as well as defectors. Considering the group level, defectors who have too much payoff due to many cooperators around them will lose their superiority over cooperators by rewarding not only cooperators but also defectors surrounding them. As a result, cooperation among players can be realized.

Suggested Citation

  • Ohdaira, Tetsushi, 2024. "The universal probabilistic reward based on the difference of payoff realizes the evolution of cooperation," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
  • Handle: RePEc:eee:chsofr:v:182:y:2024:i:c:s0960077924003060
    DOI: 10.1016/j.chaos.2024.114754
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924003060
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2024.114754?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. Theodor Cimpeanu & The Anh Han & Francisco C. Santos, 2019. "Exogenous Rewards for Promoting Cooperation in Scale-Free Networks," Papers 1905.04964, arXiv.org, revised May 2019.
    2. E. Ahmed & A. S. Hegazi & A. S. Elgazzar, 2002. "On Spatial Asymmetric Games," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 433-443.
    3. Erez Lieberman & Christoph Hauert & Martin A. Nowak, 2005. "Evolutionary dynamics on graphs," Nature, Nature, vol. 433(7023), pages 312-316, January.
    4. Christoph Hauert & Michael Doebeli, 2004. "Spatial structure often inhibits the evolution of cooperation in the snowdrift game," Nature, Nature, vol. 428(6983), pages 643-646, April.
    5. Rand, David Gertler & Dreber, Anna & Fudenberg, Drew & Ellingson, Tore & Nowak, Martin A., 2009. "Positive Interactions Promote Public Cooperation," Scholarly Articles 3804483, Harvard University Department of Economics.
    6. Wang, Qiang & He, Nanrong & Chen, Xiaojie, 2018. "Replicator dynamics for public goods game with resource allocation in large populations," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 162-170.
    7. Tetsushi Ohdaira, 2021. "Cooperation evolves by the payoff-difference-based probabilistic reward," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(11), pages 1-8, November.
    8. Ohdaira, Tetsushi, 2017. "Characteristics of the evolution of cooperation by the probabilistic peer-punishment based on the difference of payoff," Chaos, Solitons & Fractals, Elsevier, vol. 95(C), pages 77-83.
    9. Martin A. Nowak & Akira Sasaki & Christine Taylor & Drew Fudenberg, 2004. "Emergence of cooperation and evolutionary stability in finite populations," Nature, Nature, vol. 428(6983), pages 646-650, April.
    10. Fort, H. & Sicardi, E., 2007. "Evolutionary Markovian strategies in 2×2 spatial games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(1), pages 323-335.
    11. Xiaojie Chen & Attila Szolnoki, 2018. "Punishment and inspection for governing the commons in a feedback-evolving game," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-15, July.
    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. Tetsushi Ohdaira, 2021. "Cooperation evolves by the payoff-difference-based probabilistic reward," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(11), pages 1-8, November.
    2. Xiang Wei & Peng Xu & Shuiting Du & Guanghui Yan & Huayan Pei, 2021. "Reputational preference-based payoff punishment promotes cooperation in spatial social dilemmas," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(10), pages 1-7, October.
    3. Chen, Qiao & Chen, Tong & Wang, Yongjie, 2019. "Cleverly handling the donation information can promote cooperation in public goods game," Applied Mathematics and Computation, Elsevier, vol. 346(C), pages 363-373.
    4. Li, Bin-Quan & Wu, Zhi-Xi & Guan, Jian-Yue, 2022. "Critical thresholds of benefit distribution in an extended snowdrift game model," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    5. Deng, Kuiying & Li, Zhuozheng & Kurokawa, Shun & Chu, Tianguang, 2012. "Rare but severe concerted punishment that favors cooperation," Theoretical Population Biology, Elsevier, vol. 81(4), pages 284-291.
    6. Du, Faqi & Fu, Feng, 2013. "Quantifying the impact of noise on macroscopic organization of cooperation in spatial games," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 35-44.
    7. Huang, Shaoxu & Liu, Xuesong & Hu, Yuhan & Fu, Xiao, 2023. "The influence of aggressive behavior on cooperation evolution in social dilemma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    8. 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).
    9. Hong, Lijun & Geng, Yini & Du, Chunpeng & Shen, Chen & Shi, Lei, 2021. "Average payoff-driven or imitation? A new evidence from evolutionary game theory in finite populations," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    10. Li, Kun & Mao, Yizhou & Wei, Zhenlin & Cong, Rui, 2021. "Pool-rewarding in N-person snowdrift game," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    11. Sarkar, Bijan, 2021. "The cooperation–defection evolution on social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    12. Flávio L Pinheiro & Jorge M Pacheco & Francisco C Santos, 2012. "From Local to Global Dilemmas in Social Networks," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-6, February.
    13. Cheng, Jiangjiang & Mei, Wenjun & Su, Wei & Chen, Ge, 2023. "Evolutionary games on networks: Phase transition, quasi-equilibrium, and mathematical principles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    14. Dhaker Kroumi, 2021. "Aspiration Can Promote Cooperation in Well-Mixed Populations As in Regular Graphs," Dynamic Games and Applications, Springer, vol. 11(2), pages 390-417, June.
    15. Shu, Feng, 2020. "A win-switch-lose-stay strategy promotes cooperation in the evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    16. Wes Maciejewski & Feng Fu & Christoph Hauert, 2014. "Evolutionary Game Dynamics in Populations with Heterogenous Structures," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-16, April.
    17. Ma, Xiaojian & Quan, Ji & Wang, Xianjia, 2023. "Evolution of cooperation with nonlinear environment feedback in repeated public goods game," Applied Mathematics and Computation, Elsevier, vol. 452(C).
    18. Guang Zhang & Nan He & Yanxia Dong, 2021. "A Proportional-Egalitarian Allocation Policy for Public Goods Problems with Complex Network," Mathematics, MDPI, vol. 9(17), pages 1-12, August.
    19. Hu, Liwen & He, Nanrong & Weng, Qifeng & Chen, Xiaojie & Perc, Matjaž, 2020. "Rewarding endowments lead to a win-win in the evolution of public cooperation and the accumulation of common resources," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    20. Benjamin Allen & Christine Sample & Robert Jencks & James Withers & Patricia Steinhagen & Lori Brizuela & Joshua Kolodny & Darren Parke & Gabor Lippner & Yulia A Dementieva, 2020. "Transient amplifiers of selection and reducers of fixation for death-Birth updating on graphs," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-20, January.

    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:chsofr:v:182:y:2024:i:c:s0960077924003060. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.