Robust Decision Making: better decisions under uncertainty

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Abstract

Robust Decision Making (RDM) is a computational framework integrating Decision Analysis, Assumption-Based Planning, Scenario Analysis, and Exploratory Modelling. This article critically reviews RDM, its principles, and applications in project management. The article suggests that RDM enables project managers to effectively address uncertainty, offering a powerful analytical framework.

Conceptualising Robust Decision Making at times of Uncertainty

Origins and Functions

Robust Decision Making (RDM) emerged in the 1980s, when analysts of the RAND Corporation, a California-based think tank affiliated with the U.S. Government, developed a framework to evaluate the effectiveness of nuclear weapon systems [1] [2]. Designed to mitigate the uncertainty and ambiguity experienced by U.S. Government officials involved in the planning and implementation of nuclear deterrence strategies, RDM included simulation techniques, sensitivity analysis, and real options analysis. In the 1990s and 2000s, RDM received increasing interest from private companies interested in exploring new project management techniques applicable to a wide range of industries, including construction, software development, and environmental management. Today, RDM is an established approach in project management, recognized for its ability to help project managers making well-informed and timely decisions under pressure and at times of uncertainly. According to former United States Secretary of Defence Donald Rumsfeld, there are different types of knowledge: known knowns, known unknowns, and unknown unknowns. Known knowns refer to things that we know for sure. Known unknowns refer to things that we know we do not know. However, the most challenging category is the unknown unknowns, which refers to things that we do not know we do not know [3] Cite error: Closing </ref> missing for <ref> tag

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  2. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named ”Lempert
  3. ↑ 3.0 3.1 Donald Rumsfeld, Department of Defense News Briefing, February 12, 2002.
  4. ↑ Metz, B., O. Davidson, R. Swart, J. Pan, eds. 2001. Climate change 2001: Mitigation. Contribution of Working Group III to the Third Assessment [TAR] Report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, UK.
  5. ↑ Morgan, M. G., M. Henrion. 1990. Uncertainty: A Guide to Deal- ing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press, Cambridge, UK.
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  10. ↑ Lempert et al.: A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios Management Science 52(4), pp. 514–528, 2006 INFORMS
  11. ↑ https://www.rand.org/pardee/methods-and-tools/robust-decision-making.html
  12. ↑ Lempert, R., J. (2019). Robust Decision Making (RDM), in Decision Making Under Deep Uncertainty — 2019, pp. 23-51
  13. ↑ Vincent A. W. J. Marchau, Warren E. Walker, Pieter J. T. M. Bloemen, Steven W. Popper (2019). Decision Making under Deep Uncertainty. From Theory to Practice
  14. ↑ "Defense.gov News Transcript: DoD News Briefing – Secretary Rumsfeld and Gen. Myers, United States Department of Defense (defense.gov)". February 12, 2002. Archived from the original on March 20, 2018.
  15. ↑ Morgan, M. G., & Henrion, M. (1990). Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge, UK: Cambridge University Press.
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  17. ↑ Dewar, J. A., Builder, C. H., Hix, W. M., & Levin, M. H. (1993). Assumption-based planning—A planning tool for very uncertain times. Santa Monica, CA, RAND Corporation. https://www.rand.org/pubs/monograph_reports/MR114.html. Retrieved July 20, 2018.
  18. ↑ Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-term Policy Analysis. Santa Monica, CA, RAND Corporation, MR-1626-RPC.
  19. ↑ Wack, P. (1985). The gentle art of reperceiving—scenarios: Uncharted waters ahead (part 1 of a two-part article). Harvard Business Review (September–October): 73–89.
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  21. ↑ National Research Council (NRC) (2009). Informing decisions in a changing climate. National Academies Press.
  22. ↑ Jan H. Kwakkel, The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making, Environmental Modelling & Software, Volume 96, 2017, Pages 239-250, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2017.06.054. (https://www.sciencedirect.com/science/article/pii/S1364815217301251)
  23. ↑ Hall, J. M., Lempert, R. J., Keller, K., Hackbarth, A., Mijere, C., & McInerney, D. (2012). Robust Climate Policies under uncertainty: A comparison of Info-Gap and RDM methods. Risk Analysis, 32(10), 1657–1672.
  24. ↑ Popper, S. W., Berrebi, C., Griffin, J., Light, T., Min, E. Y., & Crane, K. (2009). Natural gas and Israel’s energy future: Near-term decisions from a strategic perspective. Santa Monica, CA, RAND Corporation, MG-927.
  25. ↑ Lempert, R. J. (2013). Scenarios that illuminate vulnerabilities and robust responses. Climatic Change, 117, 627–646.
  26. ↑ Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A general, analytic method for generating robust strategies and narrative scenarios. Management Science, 52(4), 514–528.
  27. ↑ Bureau of Reclamation. (2012). Colorado River Basin water supply and demand study: study report United States Bureau of Reclamation (Ed.). Retrieved July 11, 2018 from http://www.usbr.gov/ lc/region/programs/crbstudy/finalreport/studyrpt.html.
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