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Evolution of prosocial behaviours in multilayer populations

Author

Listed:
  • Qi Su

    (University of Pennsylvania
    University of Pennsylvania
    University of Pennsylvania)

  • Alex McAvoy

    (University of Pennsylvania
    University of Pennsylvania)

  • Yoichiro Mori

    (University of Pennsylvania
    University of Pennsylvania
    University of Pennsylvania)

  • Joshua B. Plotkin

    (University of Pennsylvania
    University of Pennsylvania
    University of Pennsylvania)

Abstract

Human societies include diverse social relationships. Friends, family, business colleagues and online contacts can all contribute to one’s social life. Individuals may behave differently in different domains, but success in one domain may engender success in another. Here, we study this problem using multilayer networks to model multiple domains of social interactions, in which individuals experience different environments and may express different behaviours. We provide a mathematical analysis and find that coupling between layers tends to promote prosocial behaviour. Even if prosociality is disfavoured in each layer alone, multilayer coupling can promote its proliferation in all layers simultaneously. We apply this analysis to six real-world multilayer networks, ranging from the socio-emotional and professional relationships in a Zambian community, to the online and offline relationships within an academic university. We discuss the implications of our results, which suggest that small modifications to interactions in one domain may catalyse prosociality in a different domain.

Suggested Citation

  • Qi Su & Alex McAvoy & Yoichiro Mori & Joshua B. Plotkin, 2022. "Evolution of prosocial behaviours in multilayer populations," Nature Human Behaviour, Nature, vol. 6(3), pages 338-348, March.
    • Handle: RePEc:nat:nathum:v:6:y:2022:i:3:d:10.1038_s41562-021-01241-2
    DOI: 10.1038/s41562-021-01241-2
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    Cited by:

    1. Beranek, L. & Remes, R., 2023. "The emergence of a core–periphery structure in evolving multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    2. Wang, Chaoqian & Sun, Chengbin, 2023. "Public goods game across multilayer populations with different densities," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Zhang, Yanling & Yang, Shuo & Chen, Xiaojie & Bai, Yanbing & Xie, Guangming, 2023. "Reputation update of responders efficiently promotes the evolution of fairness in the ultimatum game," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    4. Li, Wen-Jing & Chen, Zhi & Jin, Ke-Zhong & Wang, Jun & Yuan, Lin & Gu, Changgui & Jiang, Luo-Luo & Perc, Matjaž, 2022. "Options for mobility and network reciprocity to jointly yield robust cooperation in social dilemmas," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    5. Meliksah Turker & Haluk O. Bingol, 2023. "Multi-layer network approach in modeling epidemics in an urban town," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-13, February.
    6. Weida Zhang & Guoliang Yu & Wangqian Fu & Runqing Li, 2022. "Parental Psychological Control and Children’s Prosocial Behavior: The Mediating Role of Social Anxiety and the Moderating Role of Socioeconomic Status," IJERPH, MDPI, vol. 19(18), pages 1-12, September.

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