Can Machines Learn to Predict a Violent Conflict? - Chris Perry
▻http://theglobalobservatory.org/analysis/633-can-machines-learn-to-predict-a-violent-conflict.html
Automated early warning systems can help NGOs and IOs in a number of ways. They can help organizations develop an evidence base to create the political will to do preventative work to intervene or mitigate negative effects of large-scale conflict as tensions ramp up. In the case of predicting conflict, organizations can use early warning risk assessments for better planning and try to target non-conflict interventions that have conflict-mitigating knock-on effects in high-risk areas.
Yet there are relatively few examples of systematic attempts to create open source tools to forecast violent conflict. Instead, existing efforts to use statistical forecasting are 1) classified, 2) proprietary and very expensive, or 3) rudimentary, often relying heavily on data of violent occurrences as the primary source of information about trends in violence. (...)
As part of IPI’s new Data Lab project, we have been looking at ways to leverage data science methods into our policy research on peace, security, and conflict prevention. One area of research over the last year has been to research the application of machine learning specifically to the conflict prevention and early warning problems.
Data Map Shows Protests Around the World Increase, With Caveat - Jill Stoddard
▻http://theglobalobservatory.org/analysis/576-mapping-some-of-the-worlds-unrest-.html
But, with data comes problems. Commenters on the Foreign Policy post quickly pointed out what they saw were the map’s limitations. “Maybe I’m being nit-picky, but I’m just looking at Latin America and I can tell you this is severely underestimating the number of protests, both now and prior to the third wave of democratization,” wrote one user. “Clearly, wrong!! In México we have an permanente[sic] wave of protetest [sic] since 1950 and in the map only appears since 1994,” wrote another.
