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The Dynamic Consequences of Cooperation and Competition in Small-World Networks

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  • Iván Y Fernández-Rosales
  • Larry S Liebovitch
  • Lev Guzmán-Vargas

Abstract

We present a study of the social dynamics among cooperative and competitive actors interacting on a complex network that has a small-world topology. In this model, the state of each actor depends on its previous state in time, its inertia to change, and the influence of its neighboring actors. Using numerical simulations, we determine how the distribution of final states of the actors and measures of the distances between the values of the actors at local and global levels, depend on the number of cooperative to competitive actors and the connectivity of the actors in the network. We find that similar numbers of cooperative and competitive actors yield the lowest values for the local and global measures of the distances between the values of the actors. On the other hand, when the number of either cooperative or competitive actors dominate the system, then the divergence is largest between the values of the actors. Our findings make new testable predictions on how the dynamics of a conflict depends on the strategies chosen by groups of actors and also have implications for the evolution of behaviors.

Suggested Citation

  • Iván Y Fernández-Rosales & Larry S Liebovitch & Lev Guzmán-Vargas, 2015. "The Dynamic Consequences of Cooperation and Competition in Small-World Networks," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-13, April.
    • Handle: RePEc:plo:pone00:0126234
    DOI: 10.1371/journal.pone.0126234
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    References listed on IDEAS

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    1. Rojas-Pacheco, A. & Obregón-Quintana, B. & Liebovitch, L.S. & Guzmán-Vargas, L., 2013. "Time-delay effects on dynamics of a two-actor conflict model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 458-467.
    2. Liebovitch, Larry S. & Naudot, Vincent & Vallacher, Robin & Nowak, Andrzej & Bui-Wrzosinska, Lan & Coleman, Peter, 2008. "Dynamics of two-actor cooperation–competition conflict models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(25), pages 6360-6378.
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    Cited by:

    1. Yanzhu Hu & Huiyang Zhao & Xinbo Ai, 2016. "Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-25, November.
    2. Diep, H.T. & Kaufman, Miron & Kaufman, Sanda, 2017. "Dynamics of two-group conflicts: A statistical physics model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 183-199.

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