The authors take a new look at the relationship between regime type and deadly militarized conflict among pairs of states (dyads) in the international system. With the goal of describing the general functional form, they evaluate three perspectives: democratic peace, regime similarity and regime rationality. They employ both standard logistic regression (logit) and a recently developed machine learning technique, a support vector machine (SVM). Logit is dependent on assumptions that limit flexibility and make it difficult to discern the appropriate functional form. SVM estimation, on the other hand, is highly flexible and appears capable of discovering a relationship that is contingent on other variables in the model. SVM results indicate that regime similarity and joint democracy are important in most dyadic interactions. However, for the special but important case of the most dangerous dyads, regime rationality plays a role and the democratic peace effect is dominant. The results suggest that models of international conflict excluding distinct indicators for political similarity, joint democracy and joint autocracy may be misspecified. SVMs are an especially useful complement to conventional statistical methods.
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