There have been more than 200 wars since the start of the 20th century, leading to 35 million battle deaths and countless more civilian casualties. International conflicts and civil wars also lead to forced migration, disastrous economic consequences, weakened political systems, and poverty.
The recurrence of wars despite their tremendous economic, social, and institutional costs, may suggest that we are doomed to repeat the errors of the past. Does history indeed repeat itself? Are there particular temporal patterns which occur in the build up to the onset of war? Would better understanding of these patterns help us to avoid such conflicts? This project aims to uncover, cluster, and classify such patterns in meaningful ways to help us improve future forecasts. In particular, it addresses three related central research questions:
1. What are the recurring pre-conflict patterns? Certain indicators may follow a typical path—a motif— prior to conflict events (inter- or intra-state). Or are the variables associated with conflict chaotic and therefore inherently unpredictable? Using novel methods, we search for patterns in the observable actions that international leaders and actors take prior to conflict events, as well as in their perceptions.
2. Can we exploit these patterns for prediction purposes? Can we cluster and classify sequences to understand where tensions are headed—escalation, diffusion, or decline? Answers to these questions could improve our ability to forecast wars by building an automated pattern recognition system for conflict and geopolitical crises. It will also help political scientists to answer theoretical questions that have so far remained out of reach because of the limits of existing methodological approaches and data.
3. Finally, can we apply the patterns to generate new theories about conflict processes, by identifying the key sequences and combinations that are particularly dangerous. To our knowledge, no current system directly addresses these questions. Real-time crisis monitoring systems have emerged both in academia and in policy arena, but they rely on methods which (i) do not attempt to measure how fundamentally chaotic (and therefore predictable) their data is, and (ii) do not treat entire sequences as units of analysis and hence may fail to identify important geometric shapes and redundancies in the data.
(Grant agreement no:101002240)
