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Self-exciting point process models for political conflict forecasting

Published online by Cambridge University Press:  04 December 2017

N. JOHNSON ,
A. HITCHMAN ,
D. PHAN  and
L. SMITH
N. JOHNSON
Affiliation:
Department of Mathematics, California State University, Fullerton, CA, USA emails: [email protected], [email protected], [email protected], [email protected]
A. HITCHMAN
Affiliation:
Department of Mathematics, California State University, Fullerton, CA, USA emails: [email protected], [email protected], [email protected], [email protected]
D. PHAN
Affiliation:
Department of Mathematics, California State University, Fullerton, CA, USA emails: [email protected], [email protected], [email protected], [email protected]
L. SMITH
Affiliation:
Department of Mathematics, California State University, Fullerton, CA, USA emails: [email protected], [email protected], [email protected], [email protected]
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Abstract

In 2008, the Defense Advanced Research Project Agency commissioned a database known as the Integrated Crisis Early Warning System to serve as the foundation for models capable of detecting and predicting increases in political conflict worldwide. Such models, by signalling expected increases in political conflict, would help inform and prepare policymakers to react accordingly to conflict proliferation both domestically and internationally. Using data from the Integrated Crisis Early Warning System, we construct and test a self-exciting point process, or Hawkes process, model to describe and predict amounts of domestic, political conflict; we focus on Colombia and Venezuela as examples for this model. By comparing the accuracy of fitted models to the observed data, we find that we are able to closely describe occurrences of conflict in each country. Thus, using this model can allow policymakers to anticipate relative increases in the amount of domestic political conflict following major events.

Keywords

Applications to social sciencespoint processesprediction theory
Type
Papers
Information
European Journal of Applied Mathematics , Volume 29 , Issue 4 , August 2018 , pp. 685 - 707
Copyright
Copyright © Cambridge University Press 2017 

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