Social Networks and Multi-agent Organizational Performance
SVM HeaderParse 0.2
Matthew E. Gaston, et al.
In studying the dynamical behavior of processes in artificial or natural social systems, a key factor is the topology of the network structure. It has been shown that real-world social networks tend to have non-random network structure with properties such as short average path length, excess clustering, and skewed degree distributions (Albert & Barab asi 2002; Newman 2003). We show in this paper that the nature of the network structure in a social network of artificial or simulated agents has a significant effect on the performance of the overall system. We conclude that finding "good" network structures for a particular application domain is critical to modeling artificial social systems and implementing multiagent systems. We argue that techniques for adapting network structure will be critical in large-scale agent communities.