The most significant achievement is the establishment of ViEWS, a live early-warning system for armed conflict that is more comprehensive and performant than any other publicly available system. ViEWS is used regularly by various IGOs, embodies a wide range of methodological optimizations and is published through a series of monthly reports, an API, and a website (
https://viewsforecasting.org(öffnet in neuem Fenster)).
Another significant achievement is a first-of-its-kind, efficient, robust, versatile, dynamic, and open-source computational infrastructure for producing regularly updated armed conflict forecasts, based on state-of-the-art machine-learning principles. The compilation and coordination of a broad set of best practices constitutes what can be called an entire new methodology for forecasting armed conflict. Most steps are documented in research papers and in the various GitHub repositories reported in
https://viewsforecasting.org/resources/#source-code(öffnet in neuem Fenster).
Another major achievement was the successful execution of a prediction competition, leveraging the infrastructure developed by the project. The competition sought contributions to the problem of forecasting changes in fatalities in armed conflict, what research by ourselves and others identified as one of the most difficult problems an armed conflict early-warning system must solve. The challenge was presented with a uniform dataset, a clearly defined problem, and evaluation metrics specified in advance. The competition attracted 15 teams, including several leading scholars within the field, and increased our understanding of useful predictors, optimal algorithms, and ensemble techniques. The contributors were from the US, Scandinavia, and a number of European countries, spanning the disciplines of statistics, computer science, political science, economics, and conflict research, and included some of the leading forecasting environments in our field. A major reason for the success was the ability to make accessible to all teams the computational infrastructure, associated standardized datasets, and a joint evaluation process conducted by team members.
A final achievement is the collaboration networks we have established with stakeholders. The PI has presented ViEWS to a long series of UN organizations and at multiple international early-warning workshops, arranged by UN agencies, the New York University Center for International Cooperation, as well as by the German and Dutch MFAs. Collaboration has been particularly deep with the UN Economic and Social Commission for West Asia (ESCWA). The UN ESCWA funded an extension of ViEWS to cover the Middle East and incorporate some new predictors, and publishes the product through a `dashboard' (
https://risks.unescwa.org(öffnet in neuem Fenster)). ViEWS also obtained funding from the UNHCR to collaborate on a report on `predictive analytics' in the Sahel. Moreover, ViEWS has written reports for internal use by the UN ESCWA and other UN organizations, and the UK Foreign and Commonwealth Development Office (FCDO) (
https://github.com/prio-data/FCDO_predicting_fatalities(öffnet in neuem Fenster)).
