Dynamic Early Warning and Action Model

Hannes Mueller, Christopher Rauh and Alessandro Ruggieri

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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.

Keywords: dynamic optimisation; forecasting; internal armed conflict; prevention (search for similar items in EconPapers)
Date: 2022-06-14
New Economics Papers: this item is included in nep-big and nep-for
Note: cr542
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Citations: View citations in EconPapers (2)

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Working Paper: Dynamic Early Warning and Action Model (2022)
Working Paper: Dynamic Early Warning and Action Model (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2236

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