IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v339y2018icp805-811.html
   My bibliography  Save this article

Promote of cooperation in networked multiagent system based on fitness control

Author

Listed:
  • Deng, Wenfeng
  • Huang, Keke
  • Yang, Chunhua
  • Zhu, Hongqiu
  • Yu, Zhaofei

Abstract

How did cooperative strategy evolve remains an open question across disciplines. In most previous studies, they mainly consider the analyzing of game dynamics on the networked multiagent system under different mechanisms. However, there often exists a “government” who regulates the strategies of agents centralized or decentralized in reality. Motivated by this fact, we introduce a fitness control method in this paper, and investigate the strength of external fitness control on the game dynamics in networked multiagent system. According to the classic Monte Carlo simulation, we found that the fitness control rule can significantly enhance the cooperation level in networked multiagent system. In particular, we found that the stronger the local fitness control is, the more widespread cooperative strategy becomes. More interestingly, we found that although the local fitness control is less information needed, it is more powerful in cooperation promotion than that of global fitness control rule. Thus, it is practically significant and will provide a new insight into the control of game dynamics in networked multiagent system for the further research.

Suggested Citation

  • Deng, Wenfeng & Huang, Keke & Yang, Chunhua & Zhu, Hongqiu & Yu, Zhaofei, 2018. "Promote of cooperation in networked multiagent system based on fitness control," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 805-811.
    • Handle: RePEc:eee:apmaco:v:339:y:2018:i:c:p:805-811
    DOI: 10.1016/j.amc.2018.08.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300318306416
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2018.08.002?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. Lucas Molleman & Pieter van den Berg & Franz J. Weissing, 2014. "Consistent individual differences in human social learning strategies," Nature Communications, Nature, vol. 5(1), pages 1-9, May.
    2. 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.
    3. Su, Qi & Li, Aming & Wang, Long, 2017. "Spatial structure favors cooperative behavior in the snowdrift game with multiple interactive dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 299-306.
    4. Swami Iyer & Timothy Killingback, 2016. "Evolution of Cooperation in Social Dilemmas on Complex Networks," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-25, February.
    5. Huang, Keke & Zheng, Xiaoping & Su, Yunpeng, 2015. "Effect of heterogeneous sub-populations on the evolution of cooperation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 681-687.
    6. Manfred Milinski & Christian Hilbe & Dirk Semmann & Ralf Sommerfeld & Jochem Marotzke, 2016. "Humans choose representatives who enforce cooperation in social dilemmas through extortion," Nature Communications, Nature, vol. 7(1), pages 1-9, April.
    7. Zhenhua Pei & Baokui Wang & Jinming Du, 2016. "Effects of income redistribution on the evolution of cooperation in spatial public goods games," Papers 1611.01531, arXiv.org.
    8. Huang, Keke & Chen, Xiaofang & Yu, Zhaofei & Yang, Chunhua & Gui, Weihua, 2018. "Heterogeneous cooperative belief for social dilemma in multi-agent system," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 572-579.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Li, Xiaopeng & Sun, Shiwen & Xia, Chengyi, 2019. "Reputation-based adaptive adjustment of link weight among individuals promotes the cooperation in spatial social dilemmas," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 810-820.
    2. Liao, Hui-Min & Hao, Qing-Yi & Qian, Jia-Li & Wu, Chao-Yun & Guo, Ning & Ling, Xiang, 2023. "Cooperative evolution under the joint influence of local popularity and global popularity," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    3. Zhang, Shuai & Clark, Ruaridh & Huang, Yunke, 2020. "Frequency-dependent strategy selection in a hunting game with a finite population," Applied Mathematics and Computation, Elsevier, vol. 382(C).
    4. Wu, Yu’e & Zhang, Zhipeng & Chang, Shuhua, 2019. "Reciprocal reward promotes the evolution of cooperation in structured populations," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 230-236.

    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. Huang, Keke & Liu, Yishun & Zhang, Yichi & Yang, Chunhua & Wang, Zhen, 2018. "Understanding cooperative behavior of agents with heterogeneous perceptions in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 234-240.
    2. Huang, Keke & Chen, Xiaofang & Yu, Zhaofei & Yang, Chunhua & Gui, Weihua, 2018. "Heterogeneous cooperative belief for social dilemma in multi-agent system," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 572-579.
    3. Wu, Yu’e & Zhang, Zhipeng & Wang, Xinyu & Chang, Shuhua, 2019. "Impact of probabilistic incentives on the evolution of cooperation in complex topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 307-314.
    4. Liu, Jinzhuo & Meng, Haoran & Wang, Wei & Xie, Zhongwen & Yu, Qian, 2019. "Evolution of cooperation on independent networks: The influence of asymmetric information sharing updating mechanism," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 234-241.
    5. Song, Qun & Cao, Zhaoheng & Tao, Rui & Jiang, Wei & Liu, Chen & Liu, Jinzhuo, 2020. "Conditional neutral punishment promotes cooperation in the spatial prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 368(C).
    6. Chen, Qiao & Chen, Tong & Wang, Yongjie, 2017. "Publishing the donation list incompletely promotes the emergence of cooperation in public goods game," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 48-56.
    7. Liu, Xuesong & Pan, Qiuhui & He, Mingfeng & Liu, Aizhi, 2019. "Promotion of cooperation in evolutionary game dynamics under asymmetric information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 258-266.
    8. 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.
    9. Sarkar, Bijan, 2018. "Moran-evolution of cooperation: From well-mixed to heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 319-334.
    10. Shu, Feng & Liu, Yaojun & Liu, Xingwen & Zhou, Xiaobing, 2019. "Memory-based conformity enhances cooperation in social dilemmas," Applied Mathematics and Computation, Elsevier, vol. 346(C), pages 480-490.
    11. 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.
    12. Zhang, Yifan & Shu, Gang & Li, Ya, 2017. "Strategy-updating depending on local environment enhances cooperation in prisoner’s dilemma game," Applied Mathematics and Computation, Elsevier, vol. 301(C), pages 224-232.
    13. Xiaochen Wang & Lei Zhou & Alex McAvoy & Aming Li, 2023. "Imitation dynamics on networks with incomplete information," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    14. Swami Iyer & Timothy Killingback, 2020. "Evolution of Cooperation in Social Dilemmas with Assortative Interactions," Games, MDPI, vol. 11(4), pages 1-31, September.
    15. Chen, Yunong & Belmonte, Andrew & Griffin, Christopher, 2021. "Imitation of success leads to cost of living mediated fairness in the Ultimatum Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    16. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    17. Christian Hilbe & Moshe Hoffman & Martin A. Nowak, 2015. "Cooperate without Looking in a Non-Repeated Game," Games, MDPI, vol. 6(4), pages 1-15, September.
    18. Takuya Sekiguchi, 2023. "Fixation Probabilities of Strategies for Trimatrix Games and Their Applications to Triadic Conflict," Dynamic Games and Applications, Springer, vol. 13(3), pages 1005-1033, September.
    19. Te Wu & Feng Fu & Long Wang, 2011. "Moving Away from Nasty Encounters Enhances Cooperation in Ecological Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-7, November.
    20. Wu, Yu’e & Zhang, Zhipeng & Yang, Guoli & Liu, Haixin & Zhang, Qingfeng, 2022. "Evolution of cooperation driven by diversity on a double-layer square lattice," Chaos, Solitons & Fractals, Elsevier, vol. 162(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:apmaco:v:339:y:2018:i:c:p:805-811. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.