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Untangling Neural Nets

Published online by Cambridge University Press:  21 June 2004

SCOTT DE MARCHI ,
CHRISTOPHER GELPI  and
JEFFREY D. GRYNAVISKI
SCOTT DE MARCHI
Affiliation:
Duke University
CHRISTOPHER GELPI
Affiliation:
Duke University
JEFFREY D. GRYNAVISKI
Affiliation:
University of Chicago
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Abstract

Beck, King, and Zeng (2000) offer both a sweeping critique of the quantitative security studies field and a bold new direction for future research. Despite important strengths in their work, we take issue with three aspects of their research: (1) the substance of the logit model they compare to their neural network, (2) the standards they use for assessing forecasts, and (3) the theoretical and model-building implications of the nonparametric approach represented by neural networks. We replicate and extend their analysis by estimating a more complete logit model and comparing it both to a neural network and to a linear discriminant analysis. Our work reveals that neural networks do not perform substantially better than either the logit or the linear discriminant estimators. Given this result, we argue that more traditional approaches should be relied upon due to their enhanced ability to test hypotheses.

Type
FORUM
Information
American Political Science Review , Volume 98 , Issue 2 , May 2004 , pp. 371 - 378
Copyright
© 2004 by the American Political Science Association

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