The presentation will showcase the approach to conflict forecasting used for conflictforecast.org that leverages the power of machine learning and natural language processing. By analyzing patterns in newspaper text, algorithms can identify indicators of potential conflict and develop early warning systems for policymakers and other stakeholders. The presentation will highlight the key features and trade-offs of this approach, including its scalability and accuracy. We will also discuss some of the key challenges and limitations of conflict forecasting in general, and our approach in particular. Finally, we will illustrate how predictions can be used to support decision-making when considering when and where to prevent conflict or to intervene. The presentation will feature case studies and real-world examples to illustrate the potential of this approach.
Christopher Rauh is a Professor at the University of Cambridge, Research Professor at PRIO, Fellow of Trinity College Cambridge, and a Research Affiliate at CEPR and HCEO. His fields are Labor Economics and Political Economy. He is a co-founder of conflictforecast.org, a website providing monthly predictions about conflict risk. He has published in top Economics and Political Science journals, such as American Political Science Review, Journal of European Economic Association, and Journal of Public Economics, and has led to projects with the German Foreign Office and the Foreign, Commonwealth & Development Office. His work has been featured widely across the media including the Economist, The Guardian, Washington Post, the BBC, FAZ, and Der Spiegel, and Bloomberg.
Hannes Mueller is a tenured researcher at the Institute for Economic Analysis (IAE/CSIC) and an Associated Research Professor at the Barcelona School of Economics (BSE). He is affiliated to the CEPR Development Economics program since 2015 and a Research Fellow since 2022. He publishes in leading journals in science, economics and political science such as the American Economic Review (AER), the American Political Science Review (APSR), the Journal of the European Economic Association (JEEA) and the Proceedings of the National Academy of Sciences (PNAS). In the last five years Hannes has specialized in the use of supervised and unsupervised machine learning methods in applications in economic and political science. He directs the Masters in Data Science for Decision Making at the BSE and numerous projects that introduce heterogenous data like text or images into social science research. One of the projects is the development of the conflict forecast webpage conflictforecast.org. This work has become a key resource for governments and international organizations engaged in conflict prevention and has led to collaborations and research contracts with the Spanish central bank (BdE), the German foreign office, the UK Foreign, Commonwealth & Development Office, the IMF, several UN organizations, the World Bank and numerous NGOs.