Collaborative Research: Dynamics for Social Networks Processes: Comparing Statistical Models with Intelligent Agents

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The goal of this project is to reconcile two methods for modeling change insocial networks over time: a class of statistical models and intelligent agentmodels. The research contrasts the properties of these two approaches,exploring what qualitative dynamic behaviors in social networks are capturedusefully and interpretably by each. The primary tools are latent variablerepresentations and dynamical systems analysis. The result is a framework thatcharacterizes the strengths and limitations of the two classes of models inmultiple settings, and provides feedback that stimulates refinement ofexisting theories for social networks and development of new theory.Social network theory has produced a wealth of paradigms to describe thenature and evolution of groups of people who interact, possibly in subgroups,with one another. Settings in which social network theory has been appliedrange from friend to corporations to political blocs to religious groups.Counterterrorism is one application of particular current importance, in whichthe notion that the network might have latent (unobservable) characteristicsthat are important to its evolution over time is especially germane.Currently, however, there is not good understanding of strengths andlimitations of different models for the dynamics of social networks.The researchers on this project, drawn from the social sciences,statistics and applied mathematics, are constructing a conceptual andoperational framework that allows the principled comparison of differentmodels in multiple contexts. In addition to advances in theory andmethodology, the framework enables social sciences researchers and otheranalysts to choose a model whose dynamic properties are most appropriate toeach application.