Roots of Conflict: Classification and Regression Trees and the Complexity of Organized Violence

Abstract: Conflict researchers have validated many different theories on the causes of organized violence, but there are significant gaps in knowledge concerning how these theories interact with one another. In this thesis, I identify a body of the most prominent theories of organized violence and model them in an environment suitable for capturing these complex interactions. I formulate six causal categories to which these theories belong: Geography; Economy; Conflict History & Insecurity; Liberty & Inclusion; Natural Resources; and Structures of Governance. I then construct a cross-national, time-series sample of country-year observations and create a general model of organized violence using a machine learning technique called Classification and Regression Trees (CART). The results from this first model indicates a substantial negative effect owing to Peace Years, a count of the number of years since the country last experienced an internal conflict. Subsequently, I construct three more models, each investigating different subsets of country-year observations based on their Peace Years value. My models indicate that the country-years most likely to experience a high number of deaths from organized violence are those where conflict occurred in the previous year, the population size is high, and the net rate of male secondary school enrollment is low. The models also reveal several novel results under the presence of certain conditions, including: nonlinear relationships between deaths from organized violence and both oil exports and mass education; and a negative relationship between economic inequality and deaths from organized violence, wherein higher inequality results in fewer deaths. These findings highlight the importance of complexity-based modeling for both future conflict research and policymaking oriented towards violence reduction.