Robust Decision Making: better decisions under uncertainty: Difference between revisions

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= Abstract =
= Abstract =
Robust Decision Making (RDM) involves a set of ideas, methods, and tools that employ computation to facilitate better decision-making when dealing with situations of significant uncertainty. It integrates Decision Analysis, Assumption-Based Planning, Scenario Analysis, and Exploratory Modelling to simulate multiple possible outcomes in the future, with the aim of identifying policy-relevant scenarios and robust adaptive strategies. These RDM analytic tools are frequently embedded in a decision support process referred to as "deliberation with analysis," which fosters learning and agreement among stakeholders <ref name="DMUDU"/>. This article provides a review of the current state of the art in RDM in project management, including the key principles and practices of RDM, such as the importance of data gathering and analysis, considering different options, and involving stakeholders. Furthermore, this article examines the benefits, challenges, and limitations of RDM in project management and provides insights into future directions for research in this area. Its aim is to provide project managers with a deeper understanding of the principles and practices of RDM, along with insights on and example of how to correctly implement RDM in project management. Ultimately, this article aims to contribute to the development of more effective and efficient approaches to project management and decision making by promoting the use of RDM in project management.
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=
= Conceptualising Robust Decision Making at times of Uncertainty=


== Origins ==
== 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 <ref name="RAND corp"/> <ref name="Lempert RDM"/>. 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 ad at times of uncertainty.
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 <ref name=”RAND corp”/> <ref name=”Lempert RDM”/>. 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 <ref name="Rumsfeld"/> <ref name="Defence">. The decision-making process in situations affected by a great level of uncertainty is defined as ''decision making under deep uncertainty'' (DMDU) <ref name="Lempert RDM"/>.
== Literature review ==
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 <ref name="Rumsfeld"/> <ref name="Defence"/>. The decision-making process in situations affected by a great level of uncertainty is defined as ''decision making under deep uncertainty'' (DMDU) <ref name="Lempert RDM"/>.
 
Robustness is a crucial aspect of effective DMDU <ref name="Rosenhead 1972"/> <ref name="Metz 2001"/>. Conventional decision-analytic techniques for risk and decision analysis are designed to identify optimal strategies based on a characterization of uncertainty that follows the axioms of probability theory <ref name="Morgan 1990"/>. However, in scenarios where there is uncertainty about the system model or the distributions of its inputs, traditional decision-analytic approaches often utilize sensitivity analyses to assess the dependence of the optimum strategy on the specification of model and distributions <ref name="Saltelli 2000"/>. While this approach may be suitable when the optimum strategy is relatively insensitive to these key assumptions, it can pose both conceptual and practical challenges when this is not the case. RDM is part of a new breed of computational, multi-scenario simulation approaches that aim to integrate ideas from scenario-based planning into a quantitative framework <ref name="Morgan et al. 1999"/>  <ref name="van Asselt 2000"/> <ref name="Metz 2001"/>  <ref name=" Nakicenovic 2000"/>. It inverts traditional sensitivity analysis by seeking optimization strategies whose good performance is insensitive to the most significant uncertainties. Beginning with one or more system models that link optimization strategies to outcomes and a collection of several plausible probability distributions over the uncertain input parameters to these models, RDM describes uncertainty with various, plausible perspectives of the future <ref name=" Lempert et al. 2006"/>. RDM suggests robust strategies, identifies vulnerabilities, and suggests new or modified strategies.


According to Lempert et al., RDM is a prescriptive, methodical, and quantitative methodology for creating and choosing from a pool of strategies that perform satisfactorily across a wide range of model and prior probability distribution assumptions. By encouraging analysts and project managers to consider a wide range of likely futures, it aims to reduce problems of overconfidence. It also aims to promote consensus by offering a theoretical framework within stakeholders can agree on near-term actions that are resilient across a wide range of expectations and values. Project managers can use RDM to develop solid plans whose components might not be trivial and deterministic <ref name=" Lempert et al. 2006"/>.
Robustness is a crucial aspect of effective DMDU <ref name="Rosenhead 1972"/> <ref name="Metz 2001"/>. Conventional decision-analytic techniques for risk and decision analysis are designed to identify optimal strategies based on a characterization of uncertainty that follows the axioms of probability theory <ref name="Morgan 1990"/>. However, in scenarios where there is uncertainty about the system model or the distributions of its inputs, traditional decision-analytic approaches often utilize sensitivity analyses to assess the dependence of the optimum strategy on the specification of model and distributions <ref name="Saltelli 2000"/>. While this approach may be suitable when the optimum strategy is relatively insensitive to these key assumptions, it can pose both conceptual and practical challenges when this is not the case. RDM is part of a new breed of computational, multi-scenario simulation approaches that integrates ideas from scenario-based planning into a quantitative framework <ref name="Morgan et al. 1999"/>  <ref name="van Asselt 2000"/> <ref name="Metz 2001"/>  <ref name=" Nakicenovic 2000"/>. It inverts traditional sensitivity analysis by seeking optimization strategies which good performance is insensitive to the most significant uncertainties. Beginning with one or more system models that link optimization strategies to outcomes and a collection of several plausible probability distributions over the uncertain input parameters to these models, RDM describes uncertainty with various, plausible perspectives of the future <ref name=" Lempert et al. 2006"/>. RDM suggests robust strategies, identifies vulnerabilities, and suggests new or modified strategies.


== Foundations of Robust Decision Making ==
== Foundations of Robust Decision Making ==
RDM finds its grounds in four key notions, from which it both takes some legacy, and offers a fresh expression. These are Decision Analysis, Assumption-Based Planning, Scenario Analysis, and Exploratory Modelling.
RDM combines four crucial concepts - Decision Analysis, Assumption-Based Planning, scenarios, and Exploratory Modelling - to provide evidence-based analysis that informs choices. This approach empowers decision-makers to navigate uncertainty, assess vulnerabilities, and identify robust strategies that can withstand potential futures.


'''Decision Analysis (DA)'''
'''Decision Analysis (DA)'''

Revision as of 08:52, 2 May 2023

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

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[3]

[14]

[15]

[16]

[17]

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  1. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named ”RAND
  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.
  6. ↑ Saltelli, A., K. Chan, E. M. Scott. 2000. Sensitivity Analysis. John Wiley & Sons, New York.
  7. ↑ Morgan, M. G., M. Kandlikar, J. Risebey, H. Dowlatabadi. 1999. Why conventional tools for policy analysis are often inad- equate for problems of global change. Climatic Change 41 271–281.
  8. ↑ van Asselt, M. B. A. 2000. Perspectives on Uncertainty and Risk. Kluwer Academic Publishers, Dordrecht, The Netherlands.
  9. ↑ Nakicenovic, N., J. Alcamo, G. Davis, B. de Vries, J. Fenhann, S. Gaffin, K. Gregory, A. Grübler. 2000. Special report on emissions scenarios. Working Group III, Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, UK.
  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.
  16. ↑ Walley, P. (1991). Statistical reasoning with imprecise probabilities. London: Chapman and Hall.
  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.
  20. ↑ Bankes, S. C. (1993). Exploratory modeling for policy analysis. Operations Research, 41(3), 435–449.
  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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