How behaviors spread in dynamic social networks

Yu Zhang () and Yu Wu ()
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Yu Zhang: Trinity University
Yu Wu: Stanford University

Computational and Mathematical Organization Theory, 2012, vol. 18, issue 4, No 4, 419-444

Abstract: 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.

Keywords: Social networks; Dynamic networks; Emergence of social norms; Local social interactions; Pure coordination game (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10588-011-9105-7

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