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Evaluating Adaptive Policymaking using expert opinions

Author

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  • van der Pas, J.W.G.M.
  • Kwakkel, J.H.
  • Van Wee, B.

Abstract

Adaptive Policymaking (APM) is a policymaking approach for dealing involving policy problems with deep uncertainty. Two barriers to getting APM used are that (1) there are few actual examples of adaptive policies developed by policymakers or domain experts, and (2) APM has rarely been used in a real-world policy setting. More specifically, there are questions that remain about the costs and benefits of APM, and the efficacy and performance of adaptive policies compared to traditional static policies. This paper addresses these questions and barriers by reporting on a workshop with experts that was aimed at developing an adaptive policy for the implementation of ISA in the Netherlands. We report on their evaluation of both the process of developing the policy and their assessment of the efficacy of the developed policy compared to that of the current policy under consideration by the Dutch national government. The experts indicated that APM increases the chances of reaching policy goals and makes better use of decision support information that is gathered during the policy analysis process. However, they also indicated that, despite these potential benefits of APM, it may be hard to implement adaptive policies due to institutional issues.

Suggested Citation

  • van der Pas, J.W.G.M. & Kwakkel, J.H. & Van Wee, B., 2012. "Evaluating Adaptive Policymaking using expert opinions," Technological Forecasting and Social Change, Elsevier, vol. 79(2), pages 311-325.
    • Handle: RePEc:eee:tefoso:v:79:y:2012:i:2:p:311-325
    DOI: 10.1016/j.techfore.2011.07.009
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    Cited by:

    1. Zhang, Junyi & Hayashi, Yoshitsugu & Frank, Lawrence D., 2021. "COVID-19 and transport: Findings from a world-wide expert survey," Transport Policy, Elsevier, vol. 103(C), pages 68-85.
    2. Lyons, Glenn & Davidson, Cody, 2016. "Guidance for transport planning and policymaking in the face of an uncertain future," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 104-116.
    3. Hamarat, Caner & Kwakkel, Jan H. & Pruyt, Erik, 2013. "Adaptive Robust Design under deep uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 408-418.
    4. Mauksch, Stefanie & von der Gracht, Heiko A. & Gordon, Theodore J., 2020. "Who is an expert for foresight? A review of identification methods," Technological Forecasting and Social Change, Elsevier, vol. 154(C).

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