Cited by 29Evolutionary Game Theory and CooperationComplex Network Analysis TechniquesMathematical and Theoretical Epidemiology and Ecology Models

Evolution of cooperation on dynamical graphs

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Kun, Ádám, Scheuring, István·2009·Biosystems

Abstract

There are two key characteristic of animal and human societies: (1) degree heterogeneity, meaning that not all individual have the same number of associates; and (2) the interaction topology is not static, i.e. either individuals interact with different set of individuals at different times of their life, or at least they have different associations than their parents. Earlier works have shown that population structure is one of the mechanisms promoting cooperation. However, most studies had assumed that the interaction network can be described by a regular graph (homogeneous degree distribution). Recently there are an increasing number of studies employing degree heterogeneous graphs to model interaction topology. But mostly the interaction topology was assumed to be static. Here we investigate the fixation probability of the cooperator strategy in the prisoner's dilemma, when interaction network is a random regular graph, a random graph or a scale-free graph and the interaction network is allowed to change. We show that the fixation probability of the cooperator strategy is lower when the interaction topology is described by a dynamical graph compared to a static graph. Even a limited network dynamics significantly decreases the fixation probability of cooperation, an effect that is mitigated stronger by degree heterogeneous networks topology than by a degree homogeneous one. We have also found that from the considered graph topologies the decrease of fixation probabilities due to graph dynamics is the lowest on scale-free graphs.

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Crossref · 10.1016/j.biosystems.2008.11.009

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  • Switching between two random networks tends to impede cooperation relative to each separate network.

    “For example, switching between two random networks tends to impede cooperation relative to each separate network [12].”

    From Strategy evolution on dynamic networks (2023) · cites this paper as [12]

    The Results section reports that a cooperator’s fixation probability is lower on a changing graph than on the corresponding static graph, including random and random-regular graphs. The Methods section describes random rewiring after updates, not switching between two separate random networks; that specific comparison is unsupported. (LLM, conf=0.88)

    Experimental — accuracy not yet measured
  • The result that spatial burstiness inhibits cooperation aligns with previous simulation studies of random network rewiring.

    “This result aligns with previous simulation studies of random network rewiring [12].”

    From Strategy evolution on dynamic networks (2023) · cites this paper as [12]

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Research works printed but not linked: 1

  1. Barabási, A.-L., 2002. Linked. Perseus Publishing, Cambridge, MA.

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Authors
Kun, Á., & Scheuring, I.
Affiliations
Eötvös Loránd University (HU)
Corresponding author
Kun, Ádám
Venue
Biosystems
Publisher
Elsevier BV
Published
2009-04
Volume
96
Issue
1
Pages
65-68
ISSN
0303-2647 (ISSN-L)
OpenAlex
W2149500634
Cited by
29
References
46
Citations by year
2025: 1 · 2024: 2 · 2023: 2 · 2022: 2 · 2019: 1 · 2018: 1 · 2017: 1 · 2015: 1 · 2013: 1 · 2012: 4
FWCI
14.99
Type
journal article
Language
en
Open access
Locations
  • Biosystems (publishedVersion)
  • PubMed (publishedVersion)
  • IIASA PURE (International Institute of Applied Systems Analysis) (acceptedVersion)
  • Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
  • IIASA PURE (International Institute of Applied Systems Analysis) (submittedVersion)
Funders
Hungarian Scientific Research Fund (T049692)
Topics
  • Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
Keywords
evolution of cooperation, fixation probability, prisoner's dilemma, degree heterogeneity, scale-free networks, random regular graphs, random graphs, network dynamics, population structure
MeSH terms
Algorithms, Animals, Computer Simulation, Cooperative Behavior, Biological Evolution, Game Theory, Genetics, Population, Humans, Models, Genetic

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