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Dynamic Early Warning and Action Model

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Abstract

This document presents the outcome of two modules developed for the UK Foreign, Commonwealth Development Office (FCDO): 1) a forecast model which uses machine learning and text downloads to predict outbreaks and intensity of internal armed conflict. 2) A decision making module that embeds these forecasts into a model of preventing armed conflict damages. The outcome is a quantitative benchmark which should provide a testing ground for internal FCDO debates on both strategic levels (i.e. the process of deciding on country priorities) and operational levels (i.e. identifying critical periods by the country experts). Our method allows the FCDO to simulate policy interventions and changes in its strategic focus. We show, for example, that the FCDO should remain engaged in recently stabilized armed conflicts and re-think its development focus in countries with the highest risks. The total expected economic benefit of reinforced preventive efforts, as defined in this report, would bring monthly savings in expected costs of 26 billion USD with a monthly gain to the UK of 630 million USD.

Suggested Citation

  • Mueller, H. & Rauh, C. & Ruggieri, A., 2022. "Dynamic Early Warning and Action Model," Cambridge Working Papers in Economics 2236, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2236
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    1. Gianmarco León & Edward Miguel, 2017. "Risky Transportation Choices and the Value of a Statistical Life," American Economic Journal: Applied Economics, American Economic Association, vol. 9(1), pages 202-228, January.
    2. United Nations & World Bank, 2018. "Pathways for Peace," World Bank Publications - Books, The World Bank Group, number 28337.
    3. Dominic Rohner & Mathias Thoenig, 2021. "The Elusive Peace Dividend of Development Policy: From War Traps to Macro Complementarities," Annual Review of Economics, Annual Reviews, vol. 13(1), pages 111-131, August.
    4. 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(2), pages 358-375, May.
    5. Malcolm Chalmers, 2007. "Spending To Save? The Cost-Effectiveness Of Conflict Prevention," Defence and Peace Economics, Taylor & Francis Journals, vol. 18(1), pages 1-23.
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    1. Mueller, H. & Rauh, C. & Seimon, B., 2024. "Introducing a Global Dataset on Conflict Forecasts and News Topics," Janeway Institute Working Papers 2402, Faculty of Economics, University of Cambridge.

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    More about this item

    Keywords

    dynamic optimisation; forecasting; internal armed conflict; prevention;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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