‘If You Only Have a Hammer’: Optimal Dynamic Prevention Policy

• Authors: Christopher Rauh, Ben Seimon, Alessandro Ruggieri and Hannes Mueller.
• BSE Working Paper: 1465 | November 24
• Keywords: dynamic optimization , machine learning , armed conflict , prevention , information gain
• JEL codes: O1, F5, F1, L8
• dynamic optimization
• machine learning
• armed conflict
• prevention
• information gain

We study the gains of improving forecasting when a policymaker is facing a recurring risk and has the choice between a preventive early action and a de-escalating late action. We first introduce a simple two-stage Markov model to illustrate how prevention and de-escalation interact. We then study the role of forecasting for optimal armed conflict prevention in a 12-stage model which is calibrated using a large cross-country panel. Prevention benefits are substantial but critically depend on the systematic use of forecasting. The information rent of using a forecast is larger than 60% of GDP. In line with the theory we find that de-escalation policies reduce the incentives for prevention, whereas prevention increases incentives for de-escalation.