Prediction of Interstate Conflict Outcomes Using a Neural Network

This article uses a neural network to predict international conflict outcomes, comparing its accuracy to that of models constructed using discriminant analysis, logit analysis, and the rule-based ID3 algorithm. While neural networks originally attracted attention because they mirrored the structure of biological nervous systems, they are used increasingly to solve practical problems of prediction and classification. Neural networks may also be important for modeling international behavior because of structural similarities with some organizational processes used to determine foreign policy. In split-sample tests using the Butterworth international conflict data set, the neural network outperforms both discriminant analysis and ID3 in terms of accuracy; it is roughly comparable in accuracy to multinomial logit. The neural network is less successful than discriminant and logit at predicting nonmodal values of the dependent variable. The variables identified as important in the neural network appear to be similar to those significant in the discriminant analysis, but are quite different than those used by ID3. Keywords: neural networks, artificial intelligence, international conflict, ID3, logit analysis, discriminant analysis.