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Evolutionary Games in Self-Organizing Populations

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Adaptive Networks

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

Social networks are dynamic: We make new friends and lose touch with old ones, depending on the interactions with them. Most analytic studies of social networks assume that links remain unchanged at all times. In this case, individuals have no control over the number, frequency or duration of their interactions with others. Here, we discuss analytical and numerical models in which individuals can break links and create new ones. Interactions are modeled as general symmetric two-player games. Once a link between two individuals has formed, the productivity of that link is evaluated. Links can be broken off at different rates. In the limiting cases where linking dynamics is much faster than evolutionary dynamics or vice-versa, the system can be tackled analytically for non-local linking dynamics. We show how the individual capacity of forming new links or severing inconvenient ones can change the nature of the game. If the linking rules are local, numerical simulations show that the resulting networks capture some of the features characteristic of real-world social networks.

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Author information

Authors and Affiliations

  1. Evolutionary Dynamics Group, Max-Planck-Institute for Evolutionary Biology, 24306, Plön, Germany

    Arne Traulsen

  2. Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle (IRIDIA) of the Université Libre de Bruxelles (ULB), 1050, Brussels, Belgium

    Francisco C. Santos

  3. ATP-Group and CFTC, Departamento de Física da Faculdade de Ciências, 1649-003, Lisboa, Codex, Portugal

    Jorge M. Pacheco

Authors
  1. Arne Traulsen
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  2. Francisco C. Santos
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  3. Jorge M. Pacheco
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Corresponding author

Correspondence to Arne Traulsen .

Editor information

Editors and Affiliations

  1. MPI für Physik Komplexer Systeme, Nöthnitzer Str. 38, Dresden, 01187, Germany

    Thilo Gross

  2. Binghamton University, State University of New York, Binghamton, 13902-6000, U.S.A.

    Hiroki Sayama

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© 2009 Springer-Verlag Berlin Heidelberg

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Traulsen, A., Santos, F.C., Pacheco, J.M. (2009). Evolutionary Games in Self-Organizing Populations. In: Gross, T., Sayama, H. (eds) Adaptive Networks. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01284-6_12

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  • DOI: https://doi.org/10.1007/978-3-642-01284-6_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01283-9

  • Online ISBN: 978-3-642-01284-6

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