https://doi.org/10.1140/epjds/s13688-025-00599-x
Research
Endogenous conflict and the limits of predictive optimization
Trinity College Dublin, College Green, Dublin, Ireland
Received:
30
July
2025
Accepted:
6
November
2025
Published online:
21
November
2025
Forecasting models in political violence research increasingly rely on high-dimensional covariates and machine learning. Yet in practice, the most reliable conflict forecasts often come from much simpler systems: autoregressive models that predict future events based solely on recent past outcomes. This paper argues that such models are not merely convenient baselines but theoretically appropriate tools for sparse, dynamic environments like armed conflict. We show that autoregressive models consistently outperform or match more complex alternatives across multiple countries and specifications, while structural covariates frequently add little or degrade performance. We explain this pattern both theoretically and empirically: conflict is driven by internal feedback, burstiness, and short-term adaptation—not by slow-changing structural conditions. By foregrounding the limits of causal modeling in high-entropy settings, we make a broader case for epistemic modesty in prediction. Autoregression, we argue, is not a shortcut, but a principled strategy in systems that resist control.
Key words: Conflict forecasting / Autoregression / Predictive modeling / Political violence / Temporal dependence
© The Author(s) 2025
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
