Improvised explosive devices, or IEDs, were extensively used during the US wars in Iraq and Afghanistan, causing half of all US and coalition casualties despite increasingly sophisticated countermeasures. Although both of these wars have come to a close, it is unlikely that the threat of IEDs will disappear. If anything, their success implies that US and European forces are more likely to face them in similar future conflicts. As a result there is value in understanding the process by which they are employed, and being able to predict where and when they will be used. This is a goal we have been working on for some time now as part of a project funded by the Office of Naval Research, using SIGACT event data on IEDs and other forms of violence in Afghanistan.
ICEWS is an early warning system designed to help US policy analysts predict a variety of international crises. This project was created at the Defense Advanced Research Projects Agency in 2007, but has since been funded (through 2013) by the Office of Naval Research. ICEWS has not been widely written about, in part because of its operational nature, and in part because articles about prediction in politics face special hurdles in the publication process. An academic article (gated) described the early phase of the project in 2010, including assessments of its accuracy, and a WIRED article in 2011 criticized ICEWS for missing the Arab Spring–at a time when the project was only focused on Asia.
In an article (here for now) forthcoming in the International Studies Review, as one of the original teams on the ICEWS project, we highlight the basic framework used in the more recent, worldwide version of ICEWS. Specifically, we discuss our model that is focused on forecasting, which is our main contribution to the larger, overall project. We call this CRISP. We argue that forecasting not only increases the dialogue between academia and the policy community, but that it also provides a gold standard for evaluating the empirical content of models. Thus, this gold standard improves not only the dialogue, but actually augments the science itself. In an earlier article in Foreign Policy, with Nils Metternich, we compared Billy Beane and Lewis Frye Richardson (sort of).
GDELT (gdelt.utdallas.edu) is a global database of events which have been coded from vast quantities of publicly available text that is produced by the world’s new media. It has created a great deal of excitement in the social science community, especially within the field of international relations. But it has had wider visibility as well: in August 2013, there were 150,000 views of a map of protest activity around the world, based on the GDELT database. Event data have been around for several decades, but the GDELT project has generated new interest.
ICEWS is an early warning system designed to help US policy analysts predict a variety of international crises to which the US might have to respond. These include international and domestic crises, ethnic and religious violence, as well as rebellion and insurgency. This project was created at the Defense Advanced Research Projects Agency, but has since been funded (through 2013) by the Office of Naval Research. ICEWS also produces a rich corpus of text which is analyzed with powerful techniques of automated event-data production. Since GDELT and ICEWS are based on similar, though not identical methods and sources, it is interesting to compare them.
One area in which they are most conceptually different is that ICEWS follows a more traditional approach to event data in seeking to encode a chronology of events that reflects in some sense the putative ground truth of what occurred. The figure on the right shows the corpus of stories in ICEWS (gray) and the resulting events (black): total events are fairly stable over time event though the number of media stories increases. GDELT is more concerned with getting a comprehensive catalogue of all media stories (and other text) on reported events, and the corpus of those media stories is increasing exponentially, as the figure below shows. As a result, the number of events in GDELT is also increasing over time, much more so than ICEWS.