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Evolutionary dynamics of social dilemmas in structured heterogeneous populations

Santos, F. C., Pacheco, J. M., Lenaerts, Tom·2006·Proceedings of the National Academy of Sciences

Abstract

Real populations have been shown to be heterogeneous, in which some individuals have many more contacts than others. This fact contrasts with the traditional homogeneous setting used in studies of evolutionary game dynamics. We incorporate heterogeneity in the population by studying games on graphs, in which the variability in connectivity ranges from single-scale graphs, for which heterogeneity is small and associated degree distributions exhibit a Gaussian tale, to scale-free graphs, for which heterogeneity is large with degree distributions exhibiting a power-law behavior. We study the evolution of cooperation, modeled in terms of the most popular dilemmas of cooperation. We show that, for all dilemmas, increasing heterogeneity favors the emergence of cooperation, such that long-term cooperative behavior easily resists short-term noncooperative behavior. Moreover, we show how cooperation depends on the intricate ties between individuals in scale-free populations.

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Metadata

Authors
Santos, F. C., Pacheco, J. M., & Lenaerts, T.
Venue
Proceedings of the National Academy of Sciences
Published
2006-02-16
Volume
103
Issue
9
Pages
3490-3494
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2035945778
Cited by
1,011
References
4
FWCI
170.61
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Evolutionary dynamics of social dilemmas in structured heterogeneous populations. https://app.cochat.ai/papers/10.1073/pnas.0508201103
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Santos, F. C., Pacheco, J. M., & Lenaerts, T. (2006). Evolutionary dynamics of social dilemmas in structured heterogeneous populations. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0508201103

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.