Journal Articles

Schincariol, T., Frank, H., & Chadefaux, T. (forthcoming). Accounting for variability in conflict dynamics: A pattern-based predictive model. Accepted at Journal of Peace Research.

Hegre, H., et al. (forthcoming) The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty. Accepted at Journal of Peace Research (preprint: link).

Working Papers

Schincariol, T., Frank, H., & Chadefaux, T. (2023). Temporal Patterns in Conflict Prediction: An Improved Shape-Based Approach (working paper: link).

Work in Progress and Under Review

Schincariol, T., Frank, H., & Chadefaux, T. (2024). Leveraging Temporal Patterns in Forecasting. Journal of Forecasting (revise & resubmit).

Recurring temporal patterns emerge naturally 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 a method that identifies these repeating patterns and incorporates them as dynamic covariates in traditional time series forecasting models. Using subsequence time series clustering, we obtain cluster solutions for various window length and k combinations, and only retain the ones with the lowest standard deviation at t+1, indicative of the level of signal entailed in the clusters. Our methodology is evaluated with three widely used forecasting models—Autoregressive Integrated Moving Average (ARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM). Each model is implemented in its standard form and subsequently augmented with dynamic covariates. The empirical evaluation draws on a data set comprising 1,000 time series spanning a broad array of domains. The results show that the introduction of dynamic covariates significantly improves the prediction accuracy. Through combining static and dynamic variants in a compound model, our algorithm effectively filters out conditions under which time series clusters pick up meaningful signal and discards noise. Including dynamic covariates is particularly salient for time series with disrupted seasonality. Our findings underline the potential of adding recurring motifs to prediction tasks for a variety of algorithms and problems.

Frank, H., & Chadefaux, T. (2025). The Dynamics of Dissent: Patterns in Protest Cycles (under review at International Interactions).

Protests often follow cycles of escalation and decline, influenced in part by the interaction between protest tactics and government responses. These dynamics can produce protest waves that vary in intensity and timing. Understanding these patterns is critical to anticipating shifts in political instability, assessing state responses, and analyzing protesters’ strategic behavior over time. Using protest data for India (2016–2023) at the local level, we apply Dynamic Time Warping and k-Means clustering to identify recurring patterns in protest time series. We find that protests evolve in sequences characterized by alternating phases of increasing and decreasing intensity. Incorporating these patterns greatly improves protest prediction accuracy. Protest patterns enhance our ability to distinguish between spontaneous, short-lived mobilization and sustained protest waves.

Frank, H., & Chadefaux, T. (2025). From Protests to Fatalities: Identifying Dangerous Temporal Patterns in Civil Conflict Transitions (under review at World Politics).

Why do some protests escalate into deadly civil conflict while others do not? Protest can turn violent when participants see non-violent action as ine!ective, or when rebel groups perceive protests as a strategic opportunity. Yet, armed violence is rarely spontaneous; escalation typically follows periods of calculation and adaptation. This suggests that recent protest activity (e.g., at t-1) is insuffcient to explain transitions to civil conflict. Instead, it is the evolving interaction between protesters and the state that gives rise to “dangerous” protest patterns. We identify three such patterns that consistently precede high fatalities in civil conflict: a gradual escalation of protest intensity, a sharp decline following peak activity, and a U-shaped trajectory. Using country-month data on protest and battle events, we identify these patterns through statistical inference and predictive validation. Our results show that protest-to-conflict transitions depend not just on protest volume, but on protest sequences.

Frank, H., & Chadefaux, T. (2025). Tactical Shifts in Armed Conflict: From Battle Losses to Civilian Targeting

Why do rebel groups target civilians in some cases, and not in others? Existing research suggests that civilian targeting might constitute a “cheap” alternative to conventional tactics if the group is otherwise close to defeat, as measured by the number of battle deaths. Here, we argue that the number of deaths is not sufficient to understand the rebel groups’ strategic decisions. Instead, the decision to shift towards unconventional tactics originates from the groups’ evaluation of the likely path the armed conflict might take in the future, based on analyzing preceding dynamics in armed conflict, most importantly battle losses. We argue that preceding dynamics in rebel casualties serve to predict civilian targeting. To validate our theoretical proposition, we apply dynamic time series techniques to uncover relevant sequences of events and patterns in time series data. Time series of rebel deaths are clustered using k-Means and the Euclidean distance of time warped time sequences as distance metric. The derived clusters are included as additional covariates in a model, predicting civilian deaths based on the specific past n observations of rebel casualties. We show that accounting for temporal patterns in rebel deaths adds important information when predicting civilian targeting.

Dworschak, C., Frank, H., Leis, M., Oswald, C., & Schumann, M. (2025). Protest in the Streets, Data in the Sheets: A Conceptual and Empirical Appraisal of Protest Event Data.

Research on violent and nonviolent conflict is becoming increasingly disaggregated. While past literature observes contentious processes at the level of countries and campaigns, nascent literature seeks to generate new insights and improve inference by zooming in on the level of individual events. Therefore, violent event datasets have come under increasing scrutiny in recent years, with methodological research critically comparing their scope, validity, reliability, and transparency. For nonviolent event datasets, however, we still lack a systematic understanding of how they compare, and of how sensitive research findings are to their choice of data. How do protest event datasets differ in their conceptualization and operationalization? We are the first to offer a conceptual and empirical appraisal of all major data projects that record information on protest events, systematically comparing definitions, measurement, and coverage. By outlining the strengths and unique features of different data projects, our study provides an important resource for future work on contentious politics, and helps researchers and stakeholders make informed decisions on their choice of data for scholarship and practice.

Buhaug, H., Frank, H., & Petrova, K. (2025). Projecting Income Inequality to the End of the 21st Century.

Income inequality is on the rise almost everywhere—within countries as well as among them. High and increasing inequality breaks with common norms of fairness and equity, and it has a range of well-documented adverse effects, including for social cohesion and wellbeing. Although conflict sometimes may be a consequence of unequal income distribution, it also is a central driver of increasing inequality. This study presents a novel scenario-based analysis of how inequality might evolve over the course of the 21st century. To this end, we first quantify the historical (1960-2020) association between armed civil conflict and indicators of income inequality within and between countries (Gini scores, 80/20 income ratio). In a subsequent step, we draw on long-term (2021-2100) projections of country-level characteristics from the Shared Socioeconomic Pathways (SSP) scenario framework, in combination with SSP-consistent conflict-adjusted GDP per capita projections from our earlier work, to estimate future changes in inequality along five distinct scenarios. Moreover, we conduct a counterfactual analysis to explore the potential for successful peacebuilding to contribute to common global goals on reducing inequalities.

Thesis

Frank, H. (2025). Grievances and Opportunity: Uncovering Causal Complexity in Civil Conflict Data.

Frank, H. (2025). To Demonstrate or Fight: Similarities and Differences in the Causes of Collective Action.

Frank, H. (2025). From Structure to Action: Anticipating Conflict Onsets Conditional on Preceding Collective Action.

Frank, H. (2021). Unpacking the Black Box—How Does Rebel-Government Interaction Affect the Transition to Active Civil Conflict after Rebel Formation has Occurred? (master thesis: link).