Early-warning systems can greatly benefit all levels of society through the potential to assist policy-makers and practitioners in eliminating blind spots, justify targeted action, provide an evidence-based means to allocate scarce resources, and serve as scenario analysis tools for policy-making.

ViEWS surpasses its competitors, as it:

• Is the only system to produce forecasts for three types of violence at two levels of analysis;
• Uses the most comprehensive forecasting horizon (1-36 months) and set of input variables;
• Is highly accurate, typically predicting correctly 95% of all country-months in conflict with only 35% false positives;
• Has developed a procedure to evaluate forecasts for all steps (months) ahead against all months in the evaluation data partition, offering much more precise evaluations of performance than its competitors;
• Is highly cost-effective, being built on an infrastructure and automatization routine that allows for monthly updates with limited effort.

The ViEWS pilot (2017-22) achieved scientific breakthrough, which generated great interest from the international community and allowed us to make invaluable connections and secure funding for further developments in direct collaboration with our key user group.

We now generate global predictions at the country level, and cover both Africa and the Middle East sub-nationally. We also offer not only probabilistic assessments of the likelihood that a given conflict threshold will be surpassed in a certain time and place (the pilot), but also estimates of the number of fatalities expected in said conflict. The latter allowed us to embark on two new research projects through which we move from predicting conflict to also predicting – and understanding – the impacts of armed conflict on human development. Simultaneously, we are working closely with our key users to tailor the output from ViEWS to better meet the needs of each organisation and realize the full potential of our innovation.

Beyond the ERC and our host institutions, our key partners have been:

• UN ESCWA, with whom we expanded the geographic scope of the model, incorporated factors of particular importance to their operations, and developed an API through which users can explore our complete datasets, and/or easily feed them into internal risk dashboards for further analysis.
• UK FCDO, with whom we moved to predicting the number of fatalities in impending conflict, which opened up for research on conflict impacts.

The users of the ViEWS system are the key beneficiaries thereof. They range from students and researchers who use our open-source code and data for academic advancements, to policy-makers and practitioners who use the conflict predictions as a source of evidence-based and data-driven intelligence to assist and support dire policy decisions and strategic planning for targeted action or humanitarian interventions.

Using procedures subjected to careful academic peer review, we have carried out industry-standard out-of-sample evaluations of predictive performance with excellent results (typically predicting correctly 95% of all country-months in conflict with only 35% false positives), which have been presented in prestigious academic journals. This scientific breakthrough has generated significant interest in international fora, leading to a number of collaborations with governments, IGO agencies, and other research institutes, allowing us to test the applicability our research in different contexts and develop tailor-made solutions to maximise the usefulness of our research to a larger audience.

Our forecasts are now used by fellow researchers, governments, IGOs and NGOs alike, and we continue to closely engage with our key user groups to iteratively improve our forecasting system and provide a high-quality public good as we transition to also predicting impacts of armed conflict.

One of the greatest challenges the project has faced concerns the development of a robust, flexible, and cost-efficient data infrastructure. Over the course of the project, we have been forced to conduct no less than two comprehensive re-writes of this system. We have only just now been able to launch an infrastructure that is sufficiently robust and flexible to support the maintenance of our complex forecasting model.

Relatedly, implementing these re-writes while simultaneously conducting research on how best to develop the forecasting model itself has been challenging at best, not to mention obtaining the hardware to run ViEWS in a university setting.

Last, securing funding for regular maintenance of ViEWS has been a standing challenge. Most funders seek to support new research or novel developments rather than the daily operations that are key to any successful project, making it difficult to remain an attractive employer and keep key staff for longer periods of time.

One of the keys to the success of the ViEWS project lies in its embedment in two of the leading conflict research institutions – the Department of Peace and Conflict Research at Uppsala University, and Peace Research Institute Oslo. The former has also included a close collaboration with the Uppsala Conflict Data Program (UCDP), a global leader in conflict data collection. Together, this has allowed us to develop a system of high quality with strong links to state-of-the-art research.

Moreover, the value of human and financial resources cannot be understated. In addition to the need for strong leadership, a dedicated team is crucial to remain motivated and productive in difficult times and unexpected set-backs. Investing in your staff is certainly a key to success.

Last, having resources that are ear-marked for outreach activities have been key to ViEWS' success, allowing us to remain tuned to developments in international fora and to generate interest amongst key user groups.

The ViEWS system holds great potential for replication to other regions – by the project team or external users. This has already been cemented by our expansion from Africa to the Middle East at the sub-national level, and globally on the country level. The only constraint to geographic scalability is data availability, most importantly high-quality conflict data. Monthly updates are currently available for all countries in world, facilitating our global scope at this level of analysis, but is limited to Africa and the Middle East at the geographically disaggregated level.

The prediction system is also highly suitable for replication to other forecasting tasks, such as moving from conflict prediction to also predicting impacts of armed conflict on human development. The project team is currently embarking on this task by means of two separate but interlinked research projects: 'Societies at Risk' at Uppsala University, and 'ANTICIPATE' at Peace Research Institute Oslo.

Most importantly, don't underestimate the resources required for software development, nor the importance of thorough documentation thereof. The latter applies to all key tasks and routines, which otherwise risk being stalled for long periods of time in case of high workloads, sudden illness, or leaves of absence.

Moreover, consider carefully how to best evaluate the performance of your systems, which in turn are instrumental to optimizing it. Such evaluation criteria must be developed in dialogue with users that have the best qualitative understanding of what systems should seek to achieve.

As of 2022, ViEWS generates monthly predictions for the number of fatalities in impending conflict, as well as probabilistic assessments of the risk that a given conflict threshold will be surpassed in certain month and location. Country-level predictions are offered on a global scale, while sub-national forecasts cover Africa and the Middle East.

We also release monthly results from a set of interpretation models that highlight the relative importance of selected input variables (such as conflict history, democracy indices, infant mortality rate, or population size) to facilitate a greater understanding of the key drivers behind our forecasts.

Users can explore and download the complete datasets via the ViEWS API, or feed them into risk dashboards for further analysis. Users can also explore the source code via open-source repositories on GitHub.

To learn more about ViEWS, please contact us at [email protected].