Accounting for variability in conflict dynamics: A pattern-based predictive model.
Existing models for predicting conflict fatalities frequently produce conservative forecasts that gravitate towards the mean. While these approaches have a low average prediction error, they offer limited insights into temporal variations in conflict-related fatalities. Yet, accounting for variabili...
| Publicado en: | Journal of Peace Research Vol. 62; no. 6; pp. 2052 - 2070 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Nov2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=191331036&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 191331036 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00223433 JPR jtl: Journal of Peace Research issn: 00223433 maglogo: Y pubinfo: dt: Nov2025 vid: 62 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 191331036 10.1177/00223433251330790 ppf: 2052 ppct: 18 formats: tig: atl: Accounting for variability in conflict dynamics: A pattern-based predictive model. aug: au: Schincariol, Thomas Frank, Hannah Chadefaux, Thomas affil: Department of Political Science, Trinity College Dublin, Ireland su: Death rate War Heterogeneity Prediction models Pattern perception sug: subj: Death rate War Heterogeneity Prediction models Pattern perception keyword: Armed conflict dynamic time warping pattern recognition prediction temporal patterns time series Armed conflict dynamic time warping pattern recognition prediction temporal patterns time series ab: Existing models for predicting conflict fatalities frequently produce conservative forecasts that gravitate towards the mean. While these approaches have a low average prediction error, they offer limited insights into temporal variations in conflict-related fatalities. Yet, accounting for variability is particularly relevant for policymakers, providing an indication on when to intervene. In this article, we introduce a novel risk-taking methodology, the 'Shape finder', designed to capture variability in fatality data, or rather the sudden surges and declines in the number of deaths over time. The method involves isolating historically analogous sequences of fatalities to create a reference repository. Comparing the shape of the input sequence to the historical references, the most similar historical cases are selected. Predictions are then generated using the average future outcomes of the selected matches. The Shape finder is derived from the theoretical understanding that strategic and adaptive interactions between the government and a non-state armed group produce recurring temporal patterns in fatality data, which are indicative of broader developments. In this article, we demonstrate that our approach maintains high accuracy while significantly enhancing the ability to predict shifts, surges, and declines in conflict fatalities over time. We show that combining the Shape finder with existing approaches, the Violence Early-Warning System ensemble, achieves a lower mean squared error and better accounts for variability in fatality data. The Shape finder methodology performs particularly well for high intensity cases, or rather country-months with substantial armed violence. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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