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Evolution of cooperation in networked heterogeneous fluctuating environments

Author

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  • Stojkoski, Viktor
  • Karbevski, Marko
  • Utkovski, Zoran
  • Basnarkov, Lasko
  • Kocarev, Ljupco

Abstract

Fluctuating environments are situations where the spatio-temporal stochasticity plays a significant role in the evolutionary dynamics. The study of the evolution of cooperation in these environments typically assumes a homogeneous, well mixed population, whose constituents are endowed with identical capabilities. In this paper, we generalize these results by developing a systematic study for the cooperation dynamics in fluctuating environments under the consideration of structured, heterogeneous populations with individual entities subjected to general behavioral rules. Considering complex network topologies, and a behavioral rule based on generalized reciprocity, we perform a detailed analysis of the effect of the underlying interaction structure on the evolutionary stability of cooperation. We find that, in the presence of environmental fluctuations, the cooperation dynamics can lead to the creation of multiple network components, each with distinct evolutionary properties. This is paralleled to the freezing state in the Random Energy Model. We utilize this result to examine the applicability of our generalized reciprocity behavioral rule in a variety of settings. We thereby show that the introduced rule leads to steady state cooperative behavior that is always greater than or equal to the one predicted by the evolutionary stability analysis of unconditional cooperation. As a consequence, the implementation of our results may go beyond explaining the evolution of cooperation. In particular, they can be directly applied in domains that deal with the development of artificial systems able to adequately mimic reality, such as reinforcement learning.

Suggested Citation

  • Stojkoski, Viktor & Karbevski, Marko & Utkovski, Zoran & Basnarkov, Lasko & Kocarev, Ljupco, 2021. "Evolution of cooperation in networked heterogeneous fluctuating environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    • Handle: RePEc:eee:phsmap:v:572:y:2021:i:c:s037843712100176x
    DOI: 10.1016/j.physa.2021.125904
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    Cited by:

    1. Viktor Stojkoski & Petar Jolakoski & Arnab Pal & Trifce Sandev & Ljupco Kocarev & Ralf Metzler, 2021. "Income inequality and mobility in geometric Brownian motion with stochastic resetting: theoretical results and empirical evidence of non-ergodicity," Papers 2109.01822, arXiv.org.
    2. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Li, Xiaopeng & Han, Weiwei & Yang, Wenjun & Wang, Juan & Xia, Chengyi & Li, Hui-jia & Shi, Yong, 2022. "Impact of resource-based conditional interaction on cooperation in spatial social dilemmas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    4. 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).

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