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ViEWS: A political violence early-warning system
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This article presents ViEWS – a political violence early-warning system that seeks to be maximally transparent, publicly available, and have uniform coverage, and sketches the methodological innovations required to achieve these objectives. ViEWS produces monthly forecasts at the country and subnational level for 36 months into the future and all three UCDP types of organized violence: state-based conflict, non-state conflict, and one-sided violence in Africa. The article presents the methodology and data behind these forecasts, evaluates their predictive performance, provides selected forecasts for October 2018 through October 2021, and indicates future extensions. ViEWS is built as an ensemble of constituent models designed to optimize its predictions. Each of these represents a theme that the conflict research literature suggests is relevant, or implements a specific statistical/machine-learning approach. Current forecasts indicate a persistence of conflict in regions in Africa with a recent history of political violence but also alert to new conflicts such as in Southern Cameroon and Northern Mozambique. The subsequent evaluation additionally shows that ViEWS is able to accurately capture the long-term behavior of established political violence, as well as diffusion processes such as the spread of violence in Cameroon. The performance demonstrated here indicates that ViEWS can be a useful complement to non-public conflict-warning systems, and also serves as a reference against which future improvements can be evaluated.
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“We emphasize, however, that conflict onset is notoriously difficult to predict in general as it is a very rare event (174), providing an additional explanation for why onset studies yield weaker results in this field of inquiry.”
From Vicious Circles: Violence, Vulnerability, and Climate Change (2021) · cites this paper as [174]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Ethnicity, Insurgency, and Civil War
FEARON, J. D., & LAITIN, D. D. (2003) · American Political Science Review · doi:10.1017/s0003055403000534 · Cited by 7,366
Greed and grievance in civil war
Collier, P. D. (2004) · Oxford Economic Papers · doi:10.1093/oep/gpf064 · Cited by 6,295
From Mobilization to Revolution
Tilly, C. (2017) · Routledge eBooks · doi:10.4324/9781315205021-5 · Cited by 4,010
Armed Conflict 1946-2001: A New Dataset
GLEDITSCH, N. P., WALLENSTEEN, P., ERIKSSON, M., SOLLENBERG, M., & STRAND, H. (2002) · Journal of Peace Research · doi:10.1177/0022343302039005007 · Cited by 3,846
Amelia II: A Program for Missing Data
Honaker, J., King, G., & Blackwell, M. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i07 · Cited by 2,552
Introducing ACLED: An armed conflict location and event dataset
Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010) · Journal of Peace Research · doi:10.1177/0022343310378914 · Cited by 1,747
Self-citationToward a Democratic Civil Peace? Democracy, Political Change, and Civil War, 1816–1992
Hegre, H. (2001) · American Political Science Review · doi:10.1017/s0003055401000119 · Cited by 1,604
Self-citationThe political ecology of war: natural resources and armed conflicts
Le Billon, P. (2001) · Political Geography · doi:10.1016/s0962-6298(01)00015-4 · Cited by 1,508
Géron, A. (2017) · Cited by 1,443
Research works printed but not linked: 4
- Bueno de Mesquita, 2010, The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future
- Gurr, 1999, State failure task force report: Phase II findings
- Singer, 1973, The peace researcher and foreign policy prediction, Papers of the Peace Science Society (International), 21, 1
- World Bank, 2015, World development indicators, 10.1596/978-1-4648-0484-7_world_development_indicators · DOI
Cited by
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“We emphasize, however, that conflict onset is notoriously difficult to predict in general as it is a very rare event (174), providing an additional explanation for why onset studies yield weaker results in this field of inquiry.”
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Guo, W. (2019) · Royal Society Open Science · doi:10.1098/rsos.190645 · Cited by 19
Mueller, H., & Techasunthornwat, C. (2020) · World Bank, Washington, DC eBooks · doi:10.1596/1813-9450-9455 · Cited by 19
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Metadata
- Authors
- Hegre, H., Allansson, M., Basedau, M., Colaresi, M., Croicu, M., Fjelde, H., Hoyles, F., Hultman, L., Högbladh, S., Jansen, R., Mouhleb, N., Muhammad, S. A., Nilsson, D., Nygård, H. M., Olafsdottir, G., Petrova, K., Randahl, D., Rød, E. G., Schneider, G., von Uexkull, N., & Vestby, J.
- Affiliations
- Uppsala University (SE)
- Peace Research Institute Oslo (NO)
- Corresponding author
- Hegre, Håvard
- ORCID
- Hegre, Håvard
- Allansson, Marie
- Basedau, Matthias
- Colaresi, Michael
- Croicu, Mihai
- Fjelde, Hanne
- Hultman, Lisa
- Högbladh, Stina
- Jansen, Remco
- Muhammad, Sayyed Auwn
- Nilsson, Desirée
- Nygård, Håvard Mokleiv
- Olafsdottir, Gudlaug
- Petrova, Kristina
- Randahl, David
- Rød, Espen Geelmuyden
- Schneider, Gerald
- von Uexkull, Nina
- Vestby, Jonas
- Venue
- Journal of Peace Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-02-15
- Volume
- 56
- Issue
- 2
- Pages
- 155-174
- ISSN
- 0022-3433 (ISSN-L)
- 1460-3578
- OpenAlex
- W2914509225
- Cited by
- 173
- References
- 60
- Citations by year
- 2026: 31 · 2025: 22 · 2024: 22 · 2023: 25 · 2022: 28 · 2021: 24 · 2020: 9 · 2019: 9
- FWCI
- 35.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Peace Research (publishedVersion, cc-by-nc)
- KOPS (University of Konstanz) (submittedVersion, cc-by-nc)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) (submittedVersion, cc-by-nc)
- Funders
- European Research Council (694640)
- Topics
- Social Sciences › Sociology and Political Science › Political Conflict and Governance
- Social Sciences › Sociology and Political Science › Terrorism, Counterterrorism, and Political Violence
- Social Sciences › Sociology and Political Science › Peacebuilding and International Security
- Keywords
- political violence, early warning systems, conflict prediction, one-sided violence, ensemble forecasting, conflict persistence, Africa, southern Cameroon, northern Mozambique
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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