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How behaviors spread in dynamic social networks

  • SI: Agent-Directed Simulation
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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.

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Notes

  1. For details, please check the original article at http://www.jesse-anderson.com/2011/08/a-few-more-million-amazonian-monkeys/.

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Acknowledgements

This work was supported in part by the U.S. National Science Foundation under Grants IIS 0755405 and CNS 0821585. We would also like to thank Sophia Goreczky and KangChon Kim for their contributions to the experiment.

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Authors and Affiliations

  1. Department of Computer Science, Trinity University, San Antonio, TX, 78216, USA

    Yu Zhang

  2. Department of Computer Science, Stanford University, Stanford, CA, 94305, USA

    Yu Wu

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  1. Yu Zhang
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  2. Yu Wu
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Correspondence to Yu Zhang.

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Zhang, Y., Wu, Y. How behaviors spread in dynamic social networks. Comput Math Organ Theory 18, 419–444 (2012). https://doi.org/10.1007/s10588-011-9105-7

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