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Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi, and Grynaviski
Nathaniel Beck, Gary King and Langche Zeng
The American Political Science Review
Vol. 98, No. 2 (May, 2004), pp. 379-389
Published by: American Political Science Association
Stable URL: http://www.jstor.org/stable/4145319
Page Count: 11
You can always find the topics here!Topics: Artificial neural networks, Modeling, Forecasting models, Statistical models, Dyadic relations, International hostility, Democracy, War, Analytical forecasting
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In this article, we show that de Marchi, Gelpi, and Grynavisk'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 used to evaluate international conflict studies. However, we demonstrate that all other methodological conclusions drawn by de Marchi, Gelpi, and Gryanaviski 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.
The American Political Science Review © 2004 American Political Science Association