C.R. Freeman “Bayesian Network Analysis of Nuclear Acquisitions”, M.S. Thesis, Texas A & M University, December 2008.
Nuclear weapons proliferation produces a vehement global safety and security concern. Perhaps most threatening is the scenario of a rogue nation or a terrorist organization acquiring nuclear weapons where the conventional ideas of nuclear deterrence may not apply. To combat this threat, innovative tools are needed that will help to improve understanding of the pathways an organization will take in attempting to obtain nuclear weapons and in predicting those pathways based on existing evidence. In this work, a methodology was developed for predicting these pathways. This methodology uses a Bayesian network. An organization’s motivations and key resources are evaluated to produce the prior probability distributions for various pathways. These probability distributions are updated as evidence is added. The methodology is implemented through the use of the commercially available Bayesian network software package, Netica. A few simple scenarios are considered to show that the model’s predictions agree with intuition. These scenarios are also used to explore the model’s strengths and limitations. The model provides a means to measure the relative threat that an organization poses to nuclear proliferation and can identify potential pathways that an organization will likely pursue. Thus, the model can serve to facilitate preventative efforts in nuclear proliferation. The model shows that an organization’s motivations biased the various pathways more than their resources; however, resources had a greater impact on an organization’s overall chance of success. Limitations of this model are that (1) it can not account for deception, (2) it can not account for parallel weapon programs, and (3) the accuracy of the output can only be as good as the user input. This work developed the first, published, quantitative methodology for predicting nuclear proliferation with consideration for how an organization’s motivations impact their pathway probabilities.