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Do I Know You? How Individual Recognition Affects Group Formation and Structure

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  • Vitor Passos Rios
  • Roberto André Kraenkel

Abstract

Groups in nature can be formed by interactions between individuals, or by external pressures like predation. It is reasonable to assume that groups formed by internal and external conditions have different dynamics and structures. We propose a computational model to investigate the effects of individual recognition on the formation and structure of animal groups. Our model is composed of agents that can recognize each other and remember previous interactions, without any external pressures, in order to isolate the effects of individual recognition. We show that individual recognition affects the number and size of groups, and the modularity of the social networks. This model can be used as a null model to investigate the effects of external factors on group formation and persistence.

Suggested Citation

  • Vitor Passos Rios & Roberto André Kraenkel, 2017. "Do I Know You? How Individual Recognition Affects Group Formation and Structure," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0170737
    DOI: 10.1371/journal.pone.0170737
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    1. Larry J. Young & Roger Nilsen & Katrina G. Waymire & Grant R. MacGregor & Thomas R. Insel, 1999. "Increased affiliative response to vasopressin in mice expressing the V1a receptor from a monogamous vole," Nature, Nature, vol. 400(6746), pages 766-768, August.
    2. H. Hildenbrandt & C. Carere & C.K. Hemelrijk, 2010. "Self-organized aerial displays of thousands of starlings: a model," Behavioral Ecology, International Society for Behavioral Ecology, vol. 21(6), pages 1349-1359.
    3. Michael J. Sheehan & Thore J. Bergman, 2016. "Is there an evolutionary trade-off between quality signaling and social recognition?," Behavioral Ecology, International Society for Behavioral Ecology, vol. 27(1), pages 2-13.
    4. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
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