Abstract for: Adaptive Policymaking under Deep Uncertainty: Optimal Preparedness for the next pandemic
The recent flu pandemic in 2009 caused a panic about the possible consequences due to deep uncertainty about an unknown virus. Overstock of vaccines or unnecessary social measures to be taken were all due to uncertainty. However, what should be the necessary actions to take in such deeply uncertain situation where there is no or very little information available? For uncertain and complex future, adaptivity and flexibility should be the main aim for designing robust policies. Here, we propose an iterative approach for designing adaptive and robust policies in the presence of deep uncertainty. A crucial part of this approach is the use of monitoring systems that provide the adaptivity and flexibility of the policy design. In the monitoring system, signposts to track specific information are defined. Specific values of these signposts are called triggers and they are triggered when pre-specified conditions occur in the system. The specification of trigger values is crucial for the policy performance but has not been studied in depth. Here, we use robust optimization to optimize the trigger values. This paper shows that our proposed approach with robust optimization improves policy design in deeply uncertain and complex situations where very little information is available.