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Improving Quantitative Studies of International Conflict: A Conjecture
Nathaniel Beck, Gary King and Langche Zeng
The American Political Science Review
Vol. 94, No. 1 (Mar., 2000), pp. 21-35
Published by: American Political Science Association
Stable URL: http://www.jstor.org/stable/2586378
Page Count: 15
You can always find the topics here!Topics: Statistical models, Dyadic relations, Artificial neural networks, Analytical forecasting, International hostility, Probabilities, Political science, Modeling, Forecasting models, Democracy
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We address a well-known but infrequently discussed problem in the quantitative study of international conflict: Despite immense data collections, prestigious journals, and sophisticated analyses, empirical findings in the literature on international conflict are often unsatisfying. Many statistical results change from article to article and specification to specification. Accurate forecasts are nonexistent. In this article we offer a conjecture about one source of this problem: The causes of conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large, stable, and replicable wherever the ex ante probability of conflict is large. This simple idea has an unexpectedly rich array of observable implications, all consistent with the literature. We directly test our conjecture by formulating a statistical model that includes its critical features. Our approach, a version of a "neural network" model, uncovers some interesting structural features of international conflict and, as one evaluative measure, forecasts substantially better than any previous effort. Moreover, this improvement comes at little cost, and it is easy to evaluate whether the model is a statistical improvement over the simpler models commonly used.
The American Political Science Review © 2000 American Political Science Association