Citation:
P. Kim, M. Yim, J.V. Hastings, P. Baxter, “Examination of Deep Learning Algorithm for Nuclear Proliferation Risk Modeling”, Nuclear Technology, 210,1 (2024).
Abstract:
Previous studies have explored the determinants of the nuclear proliferation levels (Explore, Pursue, and Acquire). However, these studies have weaknesses, including endogeneity and multicollinearity among the independent variables. This resulted in tentative predictions of a country™s nuclear program capabilities. The objective of this study is to develop a tool to predict future nuclear proliferation in a country, and thus facilitate its prevention. Specifically, we examine how applying deep learning algorithms can enhance nuclear proliferation risk prediction. We collected important determinants from the literature that were found to be significant in explaining nuclear proliferation. These determinants include economics, domestic and international security and threats, nuclear fuel cycle capacity, and tacit knowledge development in a country. We used multilayer perceptrons in the classification model. The results suggest that detecting a country™s proliferation behavior using deep learning algorithms may be less tentative and more viable than other existing methods. This study provides a policy tool to identify a country™s nuclear proliferation risk pattern. This information is important for developing efforts/strategies to hamper a potential proliferating country™s attempt toward developing a nuclear weapons program.