IDEAS home Printed from https://ideas.repec.org/p/mtl/montde/2019-02.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/1866/21631
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    2. Stelios Michalopoulos & Elias Papaioannou, 2016. "The Long-Run Effects of the Scramble for Africa," American Economic Review, American Economic Association, vol. 106(7), pages 1802-1848, July.
    3. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(02), pages 358-375, May.
    4. repec:aea:jecper:v:31:y:2017:i:2:p:87-106 is not listed on IDEAS
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    6. Olivier J. Blanchard & Daniel Leigh, 2013. "Growth Forecast Errors and Fiscal Multipliers," American Economic Review, American Economic Association, vol. 103(3), pages 117-120, May.
    7. Arnaud Costinot & Dave Donaldson & Cory Smith, 2016. "Evolving Comparative Advantage and the Impact of Climate Change in Agricultural Markets: Evidence from 1.7 Million Fields around the World," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 205-248.
    8. Ralph Sundberg & Erik Melander, 2013. "Introducing the UCDP Georeferenced Event Dataset," Journal of Peace Research, Peace Research Institute Oslo, vol. 50(4), pages 523-532, July.
    9. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    10. Oeindrila Dube & Juan F. Vargas, 2013. "Commodity Price Shocks and Civil Conflict: Evidence from Colombia," Review of Economic Studies, Oxford University Press, vol. 80(4), pages 1384-1421.
    11. Barbara Rossi & Tatevik Sekhposyan, 2015. "Macroeconomic Uncertainty Indices Based on Nowcast and Forecast Error Distributions," American Economic Review, American Economic Association, vol. 105(5), pages 650-655, May.
    12. Samuel Bazzi & Christopher Blattman, 2014. "Economic Shocks and Conflict: Evidence from Commodity Prices," American Economic Journal: Macroeconomics, American Economic Association, vol. 6(4), pages 1-38, October.
    13. Joan Esteban & Laura Mayoral & Debraj Ray, 2012. "Ethnicity and Conflict: An Empirical Study," American Economic Review, American Economic Association, vol. 102(4), pages 1310-1342, June.
    14. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2, May.
    15. Michael D Ward & Brian D Greenhill & Kristin M Bakke, 2010. "The perils of policy by p-value: Predicting civil conflicts," Journal of Peace Research, Peace Research Institute Oslo, vol. 47(4), pages 363-375, July.
    16. repec:eee:econom:v:210:y:2019:i:1:p:203-218 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mtl:montde:2019-02. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sharon BREWER). General contact details of provider: http://edirc.repec.org/data/demtlca.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.