On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Emergence of social conventions in complex networks
Artificial Intelligence
Emergence of Social Norms in Complex Networks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
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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.