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How behaviors spread in dynamic social networks

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Listed:
  • Yu Zhang

    (Trinity University)

  • Yu Wu

    (Stanford University)

Abstract

In this paper, we explore how decentralized local interactions of autonomous agents in a network relate to collective behaviors. Earlier work in this area has modeled social networks with fixed agent relations. We instead focus on dynamic social networks in which agents can rationally adjust their neighborhoods based on their individual interests. We propose a new connection evaluation theory, the Highest Weighted Reward (HWR) rule: agents dynamically choose their neighbors in order to maximize their own utilities based on rewards from previous interactions. We prove that, in the two-action pure coordination game, our system would stabilize to a clustering state in which all relationships in the network are rewarded with an optimal payoff. Our experiments verify this theory and also reveal additional interesting patterns in the network.

Suggested Citation

  • Yu Zhang & Yu Wu, 2012. "How behaviors spread in dynamic social networks," Computational and Mathematical Organization Theory, Springer, vol. 18(4), pages 419-444, December.
  • Handle: RePEc:spr:comaot:v:18:y:2012:i:4:d:10.1007_s10588-011-9105-7
    DOI: 10.1007/s10588-011-9105-7
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    References listed on IDEAS

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    1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    2. David Banks, 2009. "Dynamic network models: introduction to new and interdisciplinary approaches," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 259-260, December.
    3. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    4. Paul Davidsson, 2002. "Agent Based Social Simulation: a Computer Science View," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(1), pages 1-7.
    5. Terrill L. Frantz & Marcelo Cataldo & Kathleen M. Carley, 2009. "Robustness of centrality measures under uncertainty: Examining the role of network topology," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 303-328, December.
    6. Eric A. Vance & Elizabeth A. Archie & Cynthia J. Moss, 2009. "Social networks in African elephants," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 273-293, December.
    7. López-Pintado, Dunia, 2008. "Diffusion in complex social networks," Games and Economic Behavior, Elsevier, vol. 62(2), pages 573-590, March.
    8. Mark S. Handcock & Martina Morris, 2009. "A curved exponential family model for complex networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 294-302, December.
    9. Yonghong Chen & Steven L. Bressler & Mingzhou Ding, 2009. "Dynamics on networks: assessing functional connectivity with Granger causality," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 329-350, December.
    10. Peter D. Hoff, 2009. "Multiplicative latent factor models for description and prediction of social networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 261-272, December.
    11. Emily M. Jin & Michelle Girvan & M. E. J. Newman, 2001. "The Structure of Growing Social Networks," Working Papers 01-06-032, Santa Fe Institute.
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    Cited by:

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    2. Yufei Wang & Mangirdas Morkūnas & Jinzhao Wei, 2024. "Strategic Synergies: Unveiling the Interplay of Game Theory and Cultural Dynamics in a Globalized World," Games, MDPI, vol. 15(4), pages 1-25, June.

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