Abstract
In this paper, we explore how decentralized local interactions of autonomous agents in a network relate to collective behaviors. Most existing work in this area models social network in which agent relations are fixed; instead, we focus on dynamic social networks where agents can rationally adjust their neighborhoods based on their individual interests. We propose a new connection evaluation rule called the Highest Weighted Reward (HWR) rule, with which agents dynamically choose their neighbors in order to maximize their own utilities based on the rewards from previous interactions. Our experiments show that in the 2-action pure coordination game, our system will stabilize to a clustering state where all relationships in the network are rewarded with the optimal payoff. Our experiments also reveal additional interesting patterns in the network.
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© 2011 Springer-Verlag Berlin Heidelberg
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Wu, Y., Zhang, Y. (2011). Pattern Analysis in Social Networks with Dynamic Connections. In: Salerno, J., Yang, S.J., Nau, D., Chai, SK. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2011. Lecture Notes in Computer Science, vol 6589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19656-0_25
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DOI: https://doi.org/10.1007/978-3-642-19656-0_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19655-3
Online ISBN: 978-3-642-19656-0
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