In 2022, the world experienced the deadliest year of armed conflict since the 1994 Rwandan genocide. Much of the intensity and frequency of recent conflicts has drawn more attention to failures in forecasting—that is, a failure to anticipate conflicts. Such capabilities have the potential to greatly reduce the time, motivation, and opportunities peacemakers have to intervene through mediation or peacekeeping operations. In recent years, the growth in the volume of open-source data coupled with the wide-scale advancements in machine learning suggests that it may be possible for computational methods to help the international community forecast intrastate conflict more accurately, and in doing so reduce the rise of conflict. In this commentary, we argue for the promise of conflict forecasting under several technical and policy conditions. From a technical perspective, the success of this work depends on improvements in the quality of conflict-related data and an increased focus on model interpretability. In terms of policy implementation, we suggest that this technology should be used primarily to aid policy analysis heuristically and help identify unexpected conflicts.

Four theories are used to explain the causes and dynamics of violent conflict along the Oromia–Somali Border in eastern Ethiopia. Of these, political economy (greed versus grievance) and political ecology theories are instrumental in understanding the main drivers of violence. Politicization of ethnicity and the self-centered behavior of political elites have increased the complexity of the conflict. Fekadu Kenee demonstrates that peacebuilding efforts require respect for the outcomes of referendums on the disputed territorial units and revitalization of customary systems, among others. While customary institutions can resolve conflicts at least temporarily, sustained community dialogue needs to be part of the political reform to build lasting inter-regional peace.

Nigeria has experienced bouts of violent conflict in different regions since its independence leading to significant loss of life. In this article, we explore the average effect of exposure to violent conflict generally on labor supply in agriculture. Using a nationally representative panel dataset for Nigeria from 2010 to 2015, in combination with armed conflict data, we estimate the average effect of exposure to violent conflict on a household's farm labor supply. Our findings suggest that on average, exposure to violent conflict significantly reduces total family labor supply hours in agriculture. We also find that the decline in family labor supply is driven by a significant decline in the household head's total number of hours on the farm.