An exploratory approach for adaptive policymaking by using multi-objective robust optimization Caner Hamarat a,⇑ , Jan H. Kwakkel a , Erik Pruyt a , Erwin T. Loonen b a Delft University of Technology, The Netherlands b GEN Nederland, The Netherlands article info Article history: Available online xxxx Keywords: Adaptive policymaking Deep uncertainty Multi-objective robust optimization Simulation optimization Complex systems Exploratory modeling and analysis abstract Developing robust policies for complex systems is a profound challenge because of their nonlinear and unpredictable nature. Dealing with these characteristics requires innovative approaches. A possible approach is to design policies that can be adapted over time in response to how the future unfolds. An essential part of adaptive policymaking is specify- ing under what conditions, and in which way, to adapt the policy. The performance of an adaptive policy is critically dependent on this: if the policy is adapted too late or too early, significant deterioration in policy performance can be incurred. An additional complicating factor is that in almost any policy problem, a multiplicity of divergent and potentially con- flicting objectives has to be considered. In this paper we tackle both problems simulta- neously through the use of multi-objective robust simulation optimization. Robust optimization helps in specifying appropriate conditions for adapting a policy, by identify- ing conditions that produce satisfactory results across a large ensemble of scenarios. Multi-objective optimization helps in identifying such conditions for a set of criteria, and providing insights into the tradeoffs between these criteria. Simulation is used for evaluat- ing policy performance. This approach results in the identification of multiple alternative conditions under which to adapt a policy, rather than a single set of conditions. This creates the possibility of an informed policy debate on trade-offs. The approach is illustrated through a case study on designing a robust policy for supporting the transition toward renewable energy systems in the European Union. The results indicate that the proposed approach can be efficiently used for developing policy suggestions and for improving deci- sion support for policymakers. By extension, it is possible to apply this methodology in dynamically complex and deeply uncertain systems such as public health, financial systems, transportation, and housing. Ó 2014 Elsevier B.V. All rights reserved. 1. Introduction Policymaking for complex adaptive systems requires dealing with dynamic complexity and deep uncertainty. Complex adaptive systems are composed of interacting heterogeneous agents that act independently, interact with each other, and adapt their behavior over time [1,2]. Out of these interactions emerge global regularities that show dynamic behavior over time due to the intrinsic adaptations taking place by the individual heterogeneous agents. The result of this is that when http://dx.doi.org/10.1016/j.simpat.2014.02.008 1569-190X/Ó 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author. Address: Jaffalaan 5, 2628 BX Delft, The Netherlands. Tel.: +31 15 278 8080; fax: +31 15 278 6233. E-mail address: c.hamarat@tudelft.nl (C. Hamarat). Simulation Modelling Practice and Theory xxx (2014) xxx–xxx Contents lists available at ScienceDirect Simulation Modelling Practice and Theory journal homepage: www.elsevier.com/locate/simpat Please cite this article in press as: C. Hamarat et al., An exploratory approach for adaptive policymaking by using multi-objective robust optimization, Simulat. Modell. Pract. Theory (2014), http://dx.doi.org/10.1016/j.simpat.2014.02.008