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How Robust is a Robust Policy? Comparing Alternative Robustness Metrics for Robust Decision-Making

In: Robustness Analysis in Decision Aiding, Optimization, and Analytics

Author

Listed:
  • Jan H. Kwakkel

    (Delft University of Technology)

  • Sibel Eker

    (Delft University of Technology)

  • Erik Pruyt

    (Delft University of Technology)

Abstract

Nowadays, decision-makers face deep uncertainties from a myriad of external factors such as climate change, population growth, new technologies, and economic developments. The challenge is to develop robust policies, which perform well across all possible resolutions of the uncertainties. One approach for achieving this is to design a policy to be adapted over time in response to how the future actually unfolds. A key determinant for the efficacy of such an adaptive policy is the specification of when and how to adapt it. This specification depends on how robustness is being operationalized. To date, there is little guidance for selecting an appropriate robustness metric. In this chapter we address this problem, using a case study of designing a policy for stimulating the transition of the European energy system towards more sustainable functioning using five different robustness metrics. We compare the policies as identified by each metric and discuss their relative merits. We highlight that the different robustness metrics emphasize different aspects of what makes a policy robust. More specifically, measures that separate dispersion and the mean, effectively doubling the number of objectives, provide very valuable information on the trade-offs between the mean performance of the policy and dispersion around this mean. We also discuss, based on our case, why analysts should use multiple robustness metrics.

Suggested Citation

  • Jan H. Kwakkel & Sibel Eker & Erik Pruyt, 2016. "How Robust is a Robust Policy? Comparing Alternative Robustness Metrics for Robust Decision-Making," International Series in Operations Research & Management Science, in: Michael Doumpos & Constantin Zopounidis & Evangelos Grigoroudis (ed.), Robustness Analysis in Decision Aiding, Optimization, and Analytics, chapter 0, pages 221-237, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-33121-8_10
    DOI: 10.1007/978-3-319-33121-8_10
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    Cited by:

    1. Hurford, A.P. & McCartney, M.P. & Harou, J.J. & Dalton, J. & Smith, D.M. & Odada, E., 2020. "Balancing services from built and natural assets via river basin trade-off analysis," Ecosystem Services, Elsevier, vol. 45(C).
    2. Julia Reis & Julie Shortridge, 2022. "Robust decision outcomes with induced correlations in climatic and economic parameters," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(1), pages 1-23, January.
    3. Hurford, A.P. & Harou, J.J. & Bonzanigo, L. & Ray, P.A. & Karki, P. & Bharati, L. & Chinnasamy, P., 2020. "Efficient and robust hydropower system design under uncertainty - A demonstration in Nepal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    4. Daniel Puig & Fatemeh Bakhtiari, 2019. "Incorporating uncertainty in national-level climate change-mitigation policy: possible elements for a research agenda," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 9(1), pages 86-89, March.

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