Adaptive Robust Design under deep uncertainty

Abstract

Developing strategies, or policies, that automatically adapt to changing conditions is called adaptive decision-making, respectively adaptive policy-making. In this paper, we propose an iterative computational model-based approach to support adaptive decision-making under deep uncertainty. This approach combines an adaptive policy-making framework with a computational approach to generate and explore thousands of plausible scenarios using simulation models, data mining techniques, and robust optimization. The proposed approach, which is very useful for Future-Oriented Technology Analysis (FTA) studies, is illustrated on a policy-making case related to energy transitions. This case demonstrates how the performance of a policy can be improved iteratively by exploring its performance across thousands of plausible scenarios, identifying problematic subsets that require improvement, identifying adaptive high leverage actions with which the adaptive policy needs to be extended until a satisfying dynamic adaptive policy is found for the entire ensemble of plausible scenarios. The approach is not only appropriate for energy transitions; it is also appropriate for any long-term structural and systematic transformation characterized by dynamic complexity and deep uncertainty.