A Multi-Pathfinder for Developing Adaptive Robust Policies in System Dynamics

Adaptivity is essential for dynamically complex and uncertain systems. Adaptive policymaking is an approach to design policies that can be adapted over time to how the future unfolds. It is crucial for adaptive policymaking to specify under what conditions and how to adapt the policy. The performance of adaptive policy is critically depended on the proper timing of the actions. This paper illustrates that robust optimization can be used as decision support aid for appropriate specification of conditions to ensure adaptivity of policy under uncertainty. Furthermore, multiplicity of divergent objectives of different stakeholders is also important for policy support in dynamic systems. To address this issue, multi-objective optimization algorithms are good candidates for a proper solution. In this paper, we outline how to use multi-objective robust optimization in System Dynamics to support adaptive policy design. The outlined approach results, rather than a single set of conditions, in multiple alternative conditions under which to adapt policy. Thus, better informed policy debate on trade-offs is possible. The approach is illustrated through a SD model about the transition toward renewable energy systems in the EU. The study aims to propose a model-based simulation approach with multi-objective robust optimization for supporting informed adaptive policymaking.