Armed conflict recurrence poses a persistent challenge to peace and stability with profound humanitarian and socio-economic consequences. Analysis of conflict data reveals that armed conflict of the past often recurs at a later point in time revolving around similar or even the same grievances, in several cases also involving new groups, alliances or incompatibilities. The tendency of conflict to perpetuate further violence in known and newly emerging conflicts underscores the importance of addressing underlying causes. Machine learning models offer valuable insights into general or escalatory conflict patterns, enabling stakeholders to anticipate potential hotspots. However, prediction models, thus far, cannot account for complex socio-political dynamics that drive conflict recurrence, especially when the feature group with the most predictive power is often past conflict itself. Thus, to some extent, new conflict patterns, emerging actors, and unexpected developments are disregarded. Foresight methods can complement quantitative analysis by exploring alternative futures and developing plausible scenarios for systemic risks and grievances. By combining data-driven analysis and foresight, we argue that stakeholders are better equipped to mitigate the risk of conflict recurrence and comprehensively anticipate critical developments.

The interactive sessions during the CCEW Symposium 2024 will involve an integrated approach combining the foresight method of scenario development and quantitative prediction results. Focussing on the combination, its advantages and challenges, participants will analyse three recently inactive conflicts in Kosovo, India, and the Central African Republic as data stresses that the risk of renewed violence is latent in post-conflict settings. Therefore, they offer a fruitful environment for risk and resilience analysis for conflict prevention. The interactive sessions comprise plenary sessions and exchanges in the respective working groups (corresponding to the three use cases).