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

自从德·托克维尔认为公民协会和他们创建的地方网络是美国民主取得巨大成功的原因以来，它们一直被认为是美国社会的一个关键特征。这个项目收集数据来回答麦克弗森的进化论提出的基本问题：个人之间的网络联系和个人与志愿团体的从属关系如何在社区结构中共同进化？在社会团体中，由于初次接触，形成了哪些类型的重要社会关系？这类群组中哪些类型的成员身份是通过先前与他人的网络连接而发生的？志愿团体及其内部结构在多大程度上有助于社会的融合或隔离？本质上，该项目寻求了解社会网络领域中社会群体的形成、组织和动态。该项目将首次用国家数据检验有关网络和团体的动态假说。它还将通过包括社会系统自下而上的自组织特征以及内部复杂性与环境特征的关系的特征，极大地扩展当前的理解。为了回答这些新问题，一个多学科团队将使用复杂自组装的新模型的见解。具体地说，在长期进化的生物学研究中开发的新的复杂性衡量标准和物理科学中的新网络模型(例如，超晶格中的区域边界、空间分布和进化稳定性之间的关系)将被应用于研究自组织社会系统中网络和群体的相互关系。该项目将生成两种类型的数据。生活史日历方法将从具有全国代表性的1000名美国成年人样本中产生数据，以产生有关重要社交网络联系和社交团体成员资格的时间信息。这一数据集将使人们能够动态分析自愿团体的加入和离开，以及形成重要的网络联系。此外，受访者将提供一个抽样框架，以生成有关志愿团体的信息，包括其组成、内部结构和对外联系的信息。这将创建第一个具有全国代表性的自愿协会样本，其中包括非正式的、组织较少的团体。这项研究的更广泛影响对科学和公共政策都将是重要的。这两个数据集将构成研究志愿协会、公民参与、社会资本和社会网络的学者的主要资源。它们将与广泛的2004年一般社会调查访谈一起公开发布，这里使用这些访谈作为抽样框架。多学科方法将产生从进化生物学、纳米科学和计算网络模型到社会科学理论的新的理论灌输。在杜克大学，生物哲学中心目前的NSF培训计划，正在开发的自下而上复杂自组装的跨学科研究生培训计划，以及杜克大学非线性和复杂系统中心的研究生证书计划，都将受益于对社会系统及其组织和发展的更广泛考虑。

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.