DHB--Niches and Networks: Studying the Coevolution of Voluntary Groups and Social Networks

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Civic associations and the local networks that they create have been recognized as a key feature of American society since de Tocqueville argued that they were the reason for the great success of democracy in America. This project gathers data to answer fundamental questions raised by McPherson's evolutionary theory of association: How do the network connections between individuals and the affiliations of individuals with voluntary groups coevolve in a community structure? What types of significant social relationships are formed as a result of initial contact in social groups? What types of memberships in such groups occur by virtue of prior network connection with an other person? To what extent do voluntary groups and their internal structures serve to integrate or segregate society? In essence, the project seeks to understand the formation, organization and dynamics of social groups within a field of social networks. This project will be the first to test dynamic hypotheses about networks and groups with national data. It will also significantly expand current understanding by including features of bottom-up self-organization of social systems and of the relationship of internal complexity to environmental features. To answer these new questions, a multidisciplinary team will use insights from new models of complex self-assembly. Specifically, new measures of complexity developed in biological studies of long-range evolution and new network models in the physical sciences (e.g., domain boundaries in superlattices, the relationship between spatial distribution and evolutionary stability) will be applied to the study of the interrelationship of networks and groups within a self-organizing social system. The project will generate two types of data. The life history calendar approach will generate data from a nationally representative sample of 1000 U.S. adults to produce information on the timing of significant social network connections and memberships in social groups. This data set will allow dynamic analyses of joining and leaving voluntary groups, as well as the formation of significant network ties. In addition, the respondents will provide a sampling frame for generating information on voluntary groups including information on their composition, internal structure and external ties. This will create the first nationally representative sample of voluntary associations that includes informal, less organized groups. The broader impacts of the study will be important both for science and for public policy. The two data sets will constitute a major resource for scholars who study voluntary associations, civic engagement, social capital and social networks. They will be publicly released with the extensive 2004 General Social Survey interviews that are used here as a sampling frame. The multidisciplinary approach will generate new theoretical infusion from evolutionary biology, nanoscience, and computational network models to social scientific theory. At Duke, a current NSF training program at the Center for Philosophy of Biology, a developing Interdisciplinary Graduate Training program in bottom-up complex self-assembly, and the graduate certificate program in the Duke Center for Nonlinear and Complex Systems will all benefit from more extensive consideration of social systems, their organization and development.