Date of Award


Document Type


Degree Name

Master of Science in Operations Research


Department of Operational Sciences

First Advisor

Darryl K. Ahner, PhD.


The field of statistical conflict prediction addresses region-wide analysis in eras of stable conflict and peace. This study improves upon those prediction rates in times of volatile conflict and peace seen during the Arab Spring of 2011 to 2015. During this time, higher rates of conflict transition in certain Middle Eastern and North African countries occurred than normally observed in previous studies. Due to the fact that previous prediction models decrease in accuracy during times of volatile conflict transition and since the proper strategy for handling the Arab Spring has been highly debated, this study considers alterations to previous studies to understand the effects of the Arab Spring on conflict prediction over a five-year period. This study identifies which countries were affected by the Arab Spring, and then applies logistic regression to predict a country’s tendency to suffer from high-intensity, violent conflict. A large number of open-source variables are incorporated by implementing an imputation methodology useful to conflict prediction studies in the future. The imputed variables are implemented in four model building techniques: Purposeful Selection of Covariates, Logical Selection of Covariates, Principal Component Regression, and Representative Principal Component Regression resulting in accuracies exceeding 90%. Analysis of the models produced by the four techniques supports hypotheses which propose political opportunity and quality of life factors as causations for increased instability following the Arab Spring.

AFIT Designator


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