Complexity theory and models for social networks

Abstract

Much work in complexity theory employs agent-based models in simulations of systems of multiple agents. Agent interaction follows some standard types of network topologies. My aim is to assess how recent advances in the statistical modeling of social networks may contribute to agent-based modeling traditions, specifically, by providing structural characterizations of a variety of network topologies. I illustrate the points by reference to a computational model for the evolution of cooperation among agents embedded in neighborhoods and by reference to complex, real social networks defined by the ties of political support between US Senators as revealed through ties of cosponsorship of legislation. © 2003 Wiley Periodicals, Inc.