Creating a data-driven and complex systems model of public opinion
Contemporary understanding of public opinion relies on the aggregation of individual opinions (e.g., surveys from polling organizations). However, this neglects the influence of interest groups, political and media elites and social mores, as well as individual variation on dimensions of political knowledge, party identification, ideology, self-interest, group identity, emotion and salient frames of reference, among others.
Using a complex systems framework, Osama Eshera and Chelsea Goforth developed a more multifaceted conceptualization of public opinion, which yielded more fruitful results for questions about public opinion posed by political, non-profit and community organizations.
A complex systems approach also offers fresh insight into the efficacy of computational tools for describing and understanding human behavior. Current methods in opinion dynamics modeling make fatal assumptions about how people formulate opinions and, as such, do not offer much predictive or descriptive utility.
Eshera and Goforth’s model was built on first principles elucidated from social science research and remaining true to mathematical foundations of complex systems thinking. In so doing, they modeled a fruitful approach for future collaborations between technical and social scientists.
Osama Eshera is a graduate student in the Department of Systems and Information Engineering. He studies complex systems theory and applies it to social networks via agent-based modeling.
Chelsea Goforth is a PhD student in the Woodrow Wilson Department of Politics. Her research interests are political psychology, public opinion and political behavior in American politics.