Special Collection: New Technologies for Peace and Development

Machine Learning and Conflict Prediction: A Use Case

Chris Perry


For at least the last two decades, the international community in general and the United Nations specifically have attempted to develop robust, accurate and effective conflict early warning system for conflict prevention. One potential and promising component of integrated early warning systems lies in the field of machine learning. This paper aims at giving conflict analysis a basic understanding of machine learning methodology as well as to test the feasibility and added value of such an approach. The paper finds that the selection of appropriate machine learning methodologies can offer substantial improvements in accuracy and performance. It also finds that even at this early stage in testing machine learning on conflict prediction, full models offer more predictive power than simply using a prior outbreak of violence as the leading indicator of current violence. This suggests that a refined data selection methodology combined with strategic use of machine learning algorithms could indeed offer a significant addition to the early warning toolkit. Finally, the paper suggests a number of steps moving forward to improve upon this initial test methodology.

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How to cite: Perry, C 2013. Machine Learning and Conflict Prediction: A Use Case. Stability: International Journal of Security and Development 2(3):56, DOI: http://dx.doi.org/10.5334/sta.cr

This is an Open Access article distributed under the terms of the Creative Commons Attribution License
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This article has been peer reviewed (journal peer review policy).

Published on 31 October 2013.

ISSN: 2165-2627 | Published by Ubiquity Press | Creative Commons License This work is licensed under a Creative Commons License.