Theory and evidence in international conflict : a response to the Marchi, Gelpi, and Grynaviski
By: BECK, Nathaniel; KING, Gary; ZENG, Langche
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ArticlePublisher: New York : Cambridge University Press, May 2004American Political Science Review 98, 2, p. 379-389Abstract: In this article, we show that the Marchi, Gelpi, and Grynaviski´s substantive analyses are fully consistent with our prior theoretical conjecture about international conflict. We note that they also agree with our main methodological point that out-of-sample forecasting performance should be a primary standard use to evaluate international conflict studies. However, we demonstrate that all other methodological conclusions drawn by the Marchi, Gelpi, and Grynaviski are false. For example, by using the same evaluative criterion for both models, it is easy to see that their claim that properly specified logit models outperform neural network models is incorrect. Finally, we show that flexible neural network models are able to identify important empirical relationships between democracy and conflict that the logit model excludes a priori; this should not be surprising since the logit model is merely a limiting special case of the neural network model.
In this article, we show that the Marchi, Gelpi, and Grynaviski´s substantive analyses are fully consistent with our prior theoretical conjecture about international conflict. We note that they also agree with our main methodological point that out-of-sample forecasting performance should be a primary standard use to evaluate international conflict studies. However, we demonstrate that all other methodological conclusions drawn by the Marchi, Gelpi, and Grynaviski are false. For example, by using the same evaluative criterion for both models, it is easy to see that their claim that properly specified logit models outperform neural network models is incorrect. Finally, we show that flexible neural network models are able to identify important empirical relationships between democracy and conflict that the logit model excludes a priori; this should not be surprising since the logit model is merely a limiting special case of the neural network model.
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