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Predicting Interstate Conflict Outcomes Using a Bootstrapped ID3 Algorithm

Published online by Cambridge University Press:  04 January 2017

Philip A. Schrodt
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Abstract

The ID3 algorithm is an inductive artificial intelligence technique that generates classification trees. These trees are similar to those used in simple expert systems; with ID3 they are generated by machine rather than using human experts. This article applies a bootstrapped ID3 to the Butterworth data set on interstate conflict management. By generating a number of classification trees from randomly selected subsets of the complete data set, the variables that most effectively predict the outcome of the conflict management effort are identified, and the degree of unpredictability in the data is estimated from the accuracy of the classification tree in predicting cases not in the training set. The original set of 38 independent variables can be reduced to 5 or less with almost no loss of accuracy; classification trees using these variables have 95–100 percent accuracy when fitted to the entire data set and an average accuracy of 50–60 percent in predicting new cases in split-sample tests. Unlike many existing statistical techniques, the classification tree is a plausible model of human inductive knowledge representation since it is compatible with the cognitive constraints of the human brain.

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Type
Research Article
Information
Political Analysis , Volume 2 , 1990 , pp. 31 - 56
Copyright
Copyright © by the University of Michigan 1991 

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