Pattern Recognition of International Crises using Hidden Markov Models

Event data are one of the most widely used indicators in quantitative international relations research. To date, most of the models using event data have constructed numerical indicators based on the characteristics of the events measured in isolation and then aggregated. An alternative approach is to use quantitative pattern recognition techniques to compare an existing sequence of behaviors to a set of similar historical cases. This has much in common with human reasoning by historical analogy while providing the advantages of systematic and replicable analysis possible using machine-coded event data and statistical models. This chapter uses "hidden Markov models"—a recently developed sequence-comparison technique widely used in computational speech recognition—to measure similarities among international crises. The models are first estimated using the Behavioral Correlates of War data set of historical crises, then applied to an event data set covering political behavior in the contemporary Middle East for the period April 1979 through February 1997. A split-sample test of the hidden Markov models perfectly differentiates crises involving war from those not involving war in the cases used to estimate the models. The models also provide a high level of discrimination in a set of test cases not used in the estimated, and most of the erroneously-classified cases have plausible distinguishing features. The difference between the war and nonwar models also correlates significantly with a scaled measure of conflict in the contemporary Middle East. This suggests that hidden Markov models could be used to develop conflict measures based on event similarities to historical conflicts rather than on aggregated event scores. Schrodt: Hidden Markov Models Page 1