Dissertation Research: Simulating Social Structure

本博士论文研究项目将分两部分进行。 首先，将运行一系列基于代理的计算机模拟。 这些模拟将建立在以前的努力，复制偏见的网络，看起来像实际的社交网络，并将纳入经验信息的程度，参与者的个人网络在现有的研究是系统性的偏见的基础上的个人特征或结构属性的关系（聚类）。 其次，基于这些有偏见的网络内的选民投票率模拟的预测将与美国南部一个大城市县的记录投票行为进行测试。以及有关竞选活动和选民投票率的信息将使用地理软件绘制地图，以测试预测。该项目在实质性投票率的研究和社会模拟的方法研究。 实质上，该项目提供了一个正式的选民投票率模型，产生现实的投票率水平，是符合理性的自我利益和超理性的动机，并可以复制许多实证研究结果。 该模型提供了关于在地方和全国选举中保持和进一步提高选民投票率水平的最佳方法以及如何提高选民代表性的新颖预测。 在方法论上，这个项目有助于建模社会背景的具体问题。 上面详细描述的密集建模工作将允许改进和验证计算密集度较低，更用户友好的方法来建模社会背景，以提供给其他研究人员作为流行的模拟工具包中的网络构建类。 作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立的研究生涯。

This doctoral dissertation research project will be conducted in two parts. First, a series of agent-based computer simulations will be run. These simulations will build on previous efforts to replicate biased networks that look like actual social networks and will incorporate empirical information about the extent to which the personal networks of participants in existing studies are systematically biased on the basis of personal characteristics or structural properties of relationships (clustering). Second, predictions based on simulations of voter turnout within these biased networks will be tested against recorded voting behavior in a large, urban county in the Southern U.S. A series of interviews with candidates and political parties will be conducted, and information about campaign activity and voter turnout will be mapped using geographic software in order to test the predictions.This project advances understanding in both the substantive study of voter turnout and in the methodological study of social simulation. Substantively, the project provides a formal model of voter turnout that generates realistic turnout levels, is compatible with both rational self-interest and extra-rational motivation, and can replicate numerous empirical findings. This model provides novel predictions about the best ways to maintain and further increase voter turnout levels in both local and national elections, as well as how to increase the representativeness of the electorate. Methodologically, this project contributes to the specific problem of modeling social context. The intensive modeling efforts detailed above will allow improvements to and verification of a less computationally intensive, more user-friendly approach to modeling social context to be made available to other researchers as a network construction class in a popular simulation toolkit. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.