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Background to the Research

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. Large-scale political violence still kills hundreds every day across the world. 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 in the build up to the onset of wars? Would better understanding of these patterns help us to avoid such conflicts?


Recent advances overcoming methodological and data barriers present an opportunity to identify these recurrences empirically and to examine whether these patterns can be classified to improve forecasts and inform theories of conflict.​Just as DNA sequencing has been critical to medical diagnoses, PaCE aims to diagnose international politics by uncovering the relevant patterns around conflict. The project aims to uncover, cluster, and classify such patterns in meaningful ways to help us improve future forecasts.

Overview of current research areas


Statistical Methods

Accounting for different speeds in comparative case studies: Dynamic Synthetic Controls

(Jian Cao & Thomas Chadefaux)

Synthetic controls are widely used to estimate the causal effect of a treatment. However, they do not account for the different speeds at which units respond to changes. Reactions may be inelastic or `sticky’ and thus slower due to varying regulatory, institutional, or political environments. We show that these discrepancies in reaction speeds can lead to biased estimates of causal effects. To address this issue, we introduce a dynamic synthetic control approach that accommodates varying speeds in time series, resulting in improved synthetic control estimates.

We find that our approach reduces errors in the estimates of true treatment effects by up to 70% compared to traditional synthetic controls, improving our ability to make robust inferences.

Leveraging Temporal Patterns in Forecasting

(Thomas Schincariol, Hannah Frank & Thomas Chadefaux)

Recurring temporal patterns naturally emerge from underlying processes and interactions in a variety of disciplines, ranging from epidemiology and ecology to social sciences and physics. These patterns and motifs hold considerable promise for enhancing the precision of time-series forecasting. This study introduces an innovative method that not only identifies these repeating patterns but also incorporates them as dynamic covariates in traditional time-series forecasting models.

By leveraging time series clustering techniques our approach actively seeks out recurring patterns in time series data, transforming them into dynamic covariates that augment prediction capabilities.

Dots on Grid

Temporal Patterns

Temporal Patterns in Migration Flows

(Thomas Schincariol & Thomas Chadefaux)

What explains the variation in migration flows over time and space?
Existing work has contributed to a rich understanding of the factors that affect why 
and when people leave, at the micro, the meso-, and the macro-level. What is less understood are the dynamics of migration flows over time.

Existing work typically focuses on static variables at the country-year level, and the temporal dynamics are
rarely investigated. Are there recurring temporal patterns in migration flows? And can we use these patterns to improve our forecasts of the number of migrants? Here, we develop new methods to uncover motifs in the number of migrants over time, and use these motifs for forecasting.

By clustering the time series into common shapes that allow for different time scales, we show that the inclusion of temporal clusters does improve our ability to forecast migrant flows. We apply the new method to the case of South Sudan.

Temporal Patterns in Conflict Fatalities
(Thomas Schincariol, Paola Vesco, Thomas Chadefaux & Bear Braumoeller)

Armed conflicts are complex processes characterised by non-linearities, feedback loops, and recurrent patterns of occurrence and diffusion. Recent progress in statistical and computational methods coupled with an increasing availability of highly granular data have increased our ability to forecast armed conflict occurrence. However, conflict dynamics and related variations in conflict related fatalities remain poorly understood.

Improving our understanding of the spatiotemporal patterns and recurrent motifs that characterise conflicts is paramount to better understand and forecast conflict dynamics. This study attempts to identify recurrent motifs and patterns underlying casualties in interstate wars. We apply matrix profiles on monthly data on battle death estimates to uncover, cluster, and classify patterns and motifs in interstate wars. Next, we empirically assess the contribution of the derived clusters in forecasting the future course of casualties, and discuss their implications for theoretical developments. The identification of underlying patterns characterising war fatalities enable us to increase forecasting performance and improve our understanding of conflict processes.

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