SUBJECT: M.S. Thesis Presentation
BY: Jesse Coyle
TIME: Friday, December 16, 2011, 1:00 p.m.
PLACE: Boggs, 3-39
TITLE: Optimization of Nuclear, Radiological, Biological, and Chemical Terrorism Incidence Models Through the Use of Simulated Annealing Monte Carlo and Iterative Methods
COMMITTEE: Dr. Chaitanya Deo, Chair (NRE)
Dr. Shatakshee Dhongde (ECON)
Dr. Nolan Hertel (NRE)


A random search optimization method based off an analogous process for the slow cooling of metals is explored and used to find the optimum solution for a number of regression models that analyze nuclear, radiological, biological, and chemical terrorism targets. A non-parametric simulation based off of historical data is also explored. Simulated series of 30 years, 60 years, and a 30 year extrapolation of historical data are provided. The inclusion of independent variables used in the regression analysis is based off existing work in the reviewed literature. CBRN terrorism data is collected from both the Monterey Institute's Weapons of Mass Destruction Terrorism Database as well as from the START Global Terrorism Database. Building similar models to those found in the literature and running them against CBRN terrorism incidence data determines if conventional terrorism indicator variables are also significant predictors of CBRN terrorism targets. The negative binomial model was determined to be the best regression model available for the data analysis. Two general types of models are developed, including an economic development model and a governance model. From the economic development model we find that national GDP, GDP per capita, trade openness, and democracy to significant indicators of CBRN terrorism targets. Additionally from the governance model we find corrupt, stable, and democratic regimes more likely to experience a CBRN event. We do not find language/religious fractionalization to be a significant predictive variable. Similarly we do not find ethnic tensions, involvement in external conflict, or a military government to have significant predictive value.