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

自从托克维尔（de Tocqueville）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，公民协会和他们创建的地方网络就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联合进化理论提出的基本问题：在一个社区结构中，个人之间的网络连接和个人与自愿团体的隶属关系是如何共同进化的？什么样的重要社会关系是由于社会群体的初次接触而形成的？在这样的群体中，什么样的成员关系是由于先前与其他人的网络连接而产生的？志愿团体及其内部结构在多大程度上有助于社会的整合或隔离？从本质上讲，该项目旨在了解社会网络领域内社会群体的形成、组织和动态。这个项目将是第一个用国家数据测试关于网络和群体的动态假设的项目。它还将通过包括社会系统自下而上的自组织特征和内部复杂性与环境特征的关系，显著扩展当前的理解。为了回答这些新问题，一个多学科团队将利用复杂自组装新模型的见解。具体地说，在远距离进化的生物学研究和物理科学中的新网络模型（例如，超晶格中的域边界、空间分布与进化稳定性之间的关系）中发展起来的复杂性的新措施将被应用于研究自组织社会系统中网络和群体之间的相互关系。该项目将生成两种类型的数据。生活史日历方法将从1000名美国成年人的全国代表性样本中产生数据，以产生有关重要社会网络连接和社会群体成员资格的时间信息。这个数据集将允许动态分析加入和离开志愿团体，以及形成重要的网络关系。此外，答复国将提供一个抽样框架，以产生关于自愿团体的资料，包括关于其组成、内部结构和外部联系的资料。这将产生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。这项研究的广泛影响将对科学和公共政策都很重要。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年综合社会调查访谈一起公开发布，这些访谈在这里用作抽样框架。多学科的方法将产生新的理论注入从进化生物学，纳米科学，计算网络模型到社会科学理论。在杜克大学，目前在生物学哲学中心的一个NSF培训项目，一个正在发展的跨学科研究生培训项目，一个自下而上的复杂自组装项目，以及杜克大学非线性和复杂系统中心的研究生证书项目，都将受益于对社会系统、它们的组织和发展的更广泛的考虑。

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.