The early warning/early action (EWEA) community has been working for decades on analytics to help prevent conflict. The field has evolved significantly since its inception in the 1970s and 80s. The systems have served with variable success to predict conflict trends, alert communities to risk, inform decision makers, provide inputs to action strategies, and initiate a response to violent conflict. Present systems must now address the increasingly complex and protracted nature of conflicts in which factors previously considered peripheral have become core elements in conflict dynamics.
As our global and local environments become more interconnected, with junctions of multiple and cascading risks, being able to track these risks and anticipate their consequences is exceeding human capabilities. At the same time, advances made in quantitative and qualitative analytical tools, machine learning (ML), and artificial intelligence (AI) are providing us with new tools to tackle this analytical work. These same tools could support a revival of the EWEA field along with its effectiveness for prevention and peace-building work.
This report starts by surveying the data-driven techniques with the greatest potential to revolutionize the field, along with emerging trends in data and modeling. Then, it reviews contextual thematic issues most likely to shape EWEA (such as the COVID-19 pandemic and climate change), and concludes with recommendations for engaging emerging technologies in EWEA’s future development.