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In the literature on nuclear proliferation, some argue that further proliferation decreases interstate conflict, some say that it increases interstate conflict, and others indicate a non-linear relationship between these two factors. However, there has been no systematic empirical investigation on the relationship between nuclear proliferation and a propensity for conflict at the interstate–systemic level. To fill this gap, the current paper uses the machine learning method Random Forests, which can investigate complex non-linear relationships between dependent and independent variables, and which can identify important regressors from a group of all potential regressors in explaining the relationship between nuclear proliferation and the propensity for conflict. The results indicate that, on average, a larger number of nuclear states decrease the systemic propensity for interstate conflict, while the emergence of new nuclear states does not have an important effect. This paper also notes, however, that scholars should investigate other risks of proliferation to assess whether nuclear proliferation is better or worse for international peace and security in general

Is more better or worse? New empirics on nuclear proliferation and interstate conflict by Random Forests