A Computational Analysis of Civil Resistance Dynamics and Outcomes
The dilemma that civil resistance movements encounter is the inherent tension between structure and agency. Movements frequently face challenges that they have no control over, such as the type of government they oppose and the risks of mobilization. Nevertheless, the fate of civil resistance movements does not rely entirely on the structure of the system. Often, success is attributed to a combination of structural opportunities and strategic actions. The question becomes: which has more influence over the other? And can we identify the factors that are more likely to lead to the success of civil resistance? To address these questions, I take a computational social science approach to examine properties of civil resistance that are not easily captured with standard econometrics or country case studies. I implement a set of computational methods, some of them dynamic with emergent properties, and some empirical, to examine the connection between the characteristics of civil resistance movements, repression, and political outcomes. In my dissertation, I test a range of propositions using data analytics and machine learning algorithms and apply agent-based modeling to explore the emergent behavior in civil resistance processes in addition to investigating how different mobilization scenarios can lead to different outcomes. I also perform spatial analysis of civil resistance using Geographic Information Systems to examine the effect of proximity between same-year campaigns on civil resistance outcomes. With the complexities of the factors involved and their dynamic interactions, computational social science methods offer innovative tools that can help uncover the underlying relations between the different components of civil resistance and measure their effect on campaign outcomes.
Civil Resistance, Computational Methods, Machine Learning, Nonviolence, Peace Studies, Political Science
Networked Digital Library of Theses and Dissertations