Machine Learning Technique Helps Predict State Violence in Africa

Researchers at The University of Texas at Dallas have used automated machine learning in a new way to forecast state violence in Africa, and they expect the technology to have even wider predictive applications. Dr. Vito D'Orazio, associate professor of political science in the School of Economic, Political and Policy Sciences, and his team created the dynamic forecasting model as part of a competition sponsored by the Violence Early-Warning System (ViEWS) project at Uppsala University's Department of Peace and Conflict Research. The research was subsequently published online Jan. 15 in the journal International Interactions. The ViEWS contest challenged competitors to forecast -- up to six months out -- the change in the number of fatalities in a country or region stemming from state-based violence, which is armed conflict in which at least one party is a government. Forecasts from the UT Dallas team were so accurate on the subnational level -- consisting of randomly gridded map areas that don't take countries' borders into account -- that they won that part of the competition for predictive accuracy and split the win for originality.