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An Informed Forensics Approach to Detecting Vote Irregularities

Published online by Cambridge University Press:  04 January 2017

Jacob M. Montgomery ,
Santiago Olivella ,
Joshua D. Potter  and
Brian F. Crisp
Jacob M. Montgomery*
Affiliation:
Political Science, Washington University in St. Louis Campus, Box 1063, St. Louis, MO 63130, USA
Santiago Olivella
Affiliation:
Political Science, University of Miami, 1300 Campo Sano Avenue, Coral Gables, FL 33146, USA, e-mail: olivella@miami.edu
Joshua D. Potter
Affiliation:
Political Science, Louisiana State University, 240 Stubbs Hall, Baton Rouge, LA 70803, USA, e-mail: jpotter@lsu.edu
Brian F. Crisp
Affiliation:
Political Science, Washington University in St. Louis Campus Box 1063, St. Louis, MO 63130, USA, e-mail: crisp@wustl.edu
*
e-mail: jacob.montgomery@wustl.edu (corresponding author)
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Abstract

Electoral forensics involves examining election results for anomalies to efficiently identify patterns indicative of electoral irregularities. However, there is disagreement about which, if any, forensics tool is most effective at identifying fraud, and there is no method for integrating multiple tools. Moreover, forensic efforts have failed to systematically take advantage of country-specific details that might aid in diagnosing fraud. We deploy a Bayesian additive regression trees (BART) model–a machine-learning technique–on a large cross-national data set to explore the dense network of potential relationships between various forensic indicators of anomalies and electoral fraud risk factors, on the one hand, and the likelihood of fraud, on the other. This approach allows us to arbitrate between the relative importance of different forensic and contextual features for identifying electoral fraud and results in a diagnostic tool that can be relatively easily implemented in cross-national research.

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Type
Articles
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Political Analysis , Volume 23 , Issue 4 , Autumn 2015 , pp. 488 - 505
Copyright
Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: Replication data and code are available at Montgomery et al. (2015). We are grateful for helpful comments we received from Chris Zorn and two anonymous reviewers. Supplementary Materials for this article are available on the Political Analysis Web site.

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