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The hard problem of prediction for conflict prevention

Author

Listed:
  • Hannes Mueller

    (Institut d'Analisi Economica (CSIC))

  • Christopher Rauh

    (Université de Montréal)

Abstract

There is a rising interest in conflict prevention and this interest provides a strong motivation for better conflict forecasting. A key problem of conflict forecasting for prevention is that predicting the start of conflict in previously peaceful countries is extremely hard. To make progress in this hard problem this project exploits both supervised and unsupervised machine learning. Specifically, the latent Dirichlet allocation (LDA) model is used for feature extraction from 3.8 million newspaper articles and these features are then used in a random forest model to predict conflict. We find that several features are negatively associated with the outbreak of conflict and these gain importance when predicting hard onsets. This is because the decision tree uses the text features in lower nodes where they are evaluated conditionally on conflict history, which allows the random forest to adapt to the hard problem and provides useful forecasts for prevention.

Suggested Citation

  • Hannes Mueller & Christopher Rauh, 2019. "The hard problem of prediction for conflict prevention," Cahiers de recherche 2019-02, Universite de Montreal, Departement de sciences economiques.
    • Handle: RePEc:mtl:montde:2019-02
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    File URL: http://hdl.handle.net/1866/21631
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    Cited by:

    1. Hannes Mueller & Christopher Rauh, 2022. "Using past violence and current news to predict changes in violence," International Interactions, Taylor & Francis Journals, vol. 48(4), pages 579-596, July.
    2. Hannes Mueller & Christopher Rauh, 2024. "Building bridges to peace: a quantitative evaluation of power-sharing agreements," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 39(118), pages 411-467.
    3. Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    4. Marfè, Roberto & Pénasse, Julien, 2024. "Measuring macroeconomic tail risk," Journal of Financial Economics, Elsevier, vol. 156(C).
    5. Alonso-Alvarez, Irma & Molina, Luis, 2023. "How to foresee crises? A new synthetic index of vulnerabilities for emerging economies," Economic Modelling, Elsevier, vol. 125(C).
    6. Sidney Michelini & Barbora Šedová & Jacob Schewe & Katja Frieler, 2023. "Extreme weather impacts do not improve conflict predictions in Africa," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    7. Konstantin Boss & Finja Krueger & Conghan Zheng & Tobias Heidland & Andre Groeger, 2023. "Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques," Working Papers 1387, Barcelona School of Economics.
    8. Emanuele Colonnelli & Jorge Gallego & Mounu Prem, 2022. "What predicts corruption?," Chapters, in: Paolo Buonanno & Paolo Vanin & Juan Vargas (ed.), A Modern Guide to the Economics of Crime, chapter 16, pages 345-373, Edward Elgar Publishing.
    9. Mueller,Hannes Felix & Techasunthornwat,Chanon, 2020. "Conflict and Poverty," Policy Research Working Paper Series 9455, The World Bank.
    10. Diakonova, Marina & Molina, Luis & Mueller, Hannes & Pérez, Javier J. & Rauh, Christopher, 2024. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(4).
    11. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.

    More about this item

    JEL classification:

    • F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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