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

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== Literature review ==
== Literature review ==
TO SHORTEN BY A LOT AND TO NARROW DOWN TO RDM
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 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"/>. Knight further elaborates on this concept and proposes a distinction between risk and uncertainty. The former indicates situations in which the unknown can be measured (through probabilities) and, therefore, controlled. The latter indicates situations in which the unknown can't be quantified and can't, therefore, be measured <ref name="Knight"/>. Based on Knight’s distinction, academics categorised the various levels of uncertainty in decision-making, ranging from complete certainty to total ignorance <ref name="Courtney"/> <ref name="Walker"/> <ref name="Lempert RDM"/>. These levels are categorized based on the knowledge possessed about various aspects of a problem, including the future state of the world, the model of the relevant system, the outcomes from the system, and the weights that the various stakeholders will put on the outcomes. The four intermediate levels of uncertainty are defined as Level 1, where historical data can be used as predictors of the future <ref name="Hillier"/>; Level 2, where probability and statistics can be used to solve problems; Level 3, where plausible future worlds are specified through scenario analysis; and Level 4, where the decision maker only knows that nothing can be known due to unpredictable events,lack of knowledge, or unavailability of data <ref name="Taleb"/> <ref name="Schwartz"/>. When dealing with issues distinguished by  greater level of uncertainty (Level 4), a more sophisticated and in-depth data gathering is often unhelpful. The decision-making process in such situations is defined as ''decision making under deep uncertainty'' (DMDU) <ref name="Lempert RDM"/>. Instead of a "predict and act" paradigm, which attempts to anticipate potential future problems and act on that prediction, DMDU approaches are based on a "monitor and adapt" paradigm, which places more emphasis on efforts aimed at  preparing for unknown occurrences and adjust accordingly <ref name="Walker"/>. In order to make decisions for unpredictable occurrences and long-term changes, this "monitor and adapt" paradigm "explicitly identifies the deep uncertainty surrounding decision making and underlines the necessity to take this deep uncertainty into consideration” (<ref name="DMUDU"/>, p. 11). This article explores RDM under uncertainty, an approach dwelling under the realm of DMDU methodologies.
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"/>.
 
According to the "monitor and adapt" paradigm, RDM refers to a collection of ideas, procedures, and supportive technologies intended to rethink the function of quantitative models and data in guiding choices in situations affected by uncertainty. Models and data become tools for systematically exploring the consequences of assumptions, expanding the range of futures considered, creating innovative new responses to threats and opportunities, and sorting through a variety of scenarios, options, objectives, and problem framings to identify the most crucial trade-offs confronting decision makers. This contrasts with the traditional view of models as tools for prediction and the subsequent prescriptive ranking of decision options. This means that, rather than improving forecasts, models and data are used to facilitate decision makers in taking robust decisions <ref name="Popper"/>. As argued by Marchau et. al., robustness of decisions is, therefore, guaranteed by iterating several times the solution to a problem while straining the suggested decisions against a wide variety of potential scenarios. In doing so, RDM endure the decision-making process under deep uncertainty <ref name="Lempert RDM"/>.
 
Although scholars widely explored the practicalapplications of RDM in project management, the theoretical support of the application of this framework in project management practices remains largely unexplored. The remainder of the article will, therefore, concentrate on the fundamental principles of RDM, guide the reader through the methodology, give an illustration of how RDM has been successfully used in a large-scale project, and discuss benefits and limitation of the approach.
 


== Foundations of Robust Decision Making ==
== Foundations of Robust Decision Making ==
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= References =
= References =
<references>
<references>
<ref name="Rosenhead 1972"> Rosenhead, M. J., M. Elton, S. K. Gupta. 1972. Robustness and opti-mality as criteria for strategic decisions.Oper. Res. Quart.23(4)413–430. </ref>
<ref name="Metz 2001"> 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. </ref>
<ref name="Morgan 1990"> 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. </ref>
<ref name="Saltelli 2000"> Saltelli, A., K. Chan, E. M. Scott. 2000. Sensitivity Analysis. John Wiley & Sons, New York. </ref>
<ref name="Morgan et al. 1999"> 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. </ref>
<ref name="van Asselt 2000"> van Asselt, M. B. A. 2000. Perspectives on Uncertainty and Risk. Kluwer Academic Publishers, Dordrecht, The Netherlands. </ref>
<ref name=" Nakicenovic 2000"> 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. </ref>
<ref name=" Lempert et al. 2006"> Lempert et al.: A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios Management Science 52(4), pp. 514–528, 2006 INFORMS </ref>


<ref name="RAND corp"> https://www.rand.org/pardee/methods-and-tools/robust-decision-making.html </ref>
<ref name="RAND corp"> https://www.rand.org/pardee/methods-and-tools/robust-decision-making.html </ref>


<ref name="Lempert RDM"> Lempert, R., J. (2019). Robust Decision Making (RDM), in Decision Making Under Deep Uncertainty — 2019, pp. 23-51 </ref>
<ref name="Lempert"> Lempert, R. J., & Collins, M. T. (2007). Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches. Risk Analysis, 27(4), 1009-1026. </ref>


<ref name="DMUDU"> 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 </ref>
<ref name="Lempert RDM"> Lempert, R., J. (2019). Robust Decision Making (RDM), in Decision Making Under Deep Uncertainty — 2019, pp. 23-51 </ref>
 
<ref name="DMUDU"> 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 </ref>
 
<ref name="Ramanathan"> Ramanathan, R., & Ganesh, L. S. (1994). Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members’ weightages. European Journal of Operational Research, 79(2), 249-265. </ref>
 
<ref name="Whang"> Whang, J., & Han, S. (2009). Optimal R&D investment strategies under uncertainty for the development of new technologies. Journal of Business Research, 62(4), 441-447. </ref>
 
<ref name="Xu"> Xu, Q., Zhang, L., & Zhang, X. (2013). The application of robust decision-making in the emergency evacuation of large-scale events. Safety Science, 57, 141-146. </ref>


<ref name="Rumsfeld"> Donald Rumsfeld, Department of Defense News Briefing, February 12, 2002. </ref>
<ref name="Rumsfeld"> Donald Rumsfeld, Department of Defense News Briefing, February 12, 2002. </ref>
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<ref name="Reclamation 2012"> 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.
<ref name="Reclamation 2012"> 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.
</ref>
</ref>
<ref name="Groves 2013"> Groves, D. G., Fischbach, J. R., Bloom, E., Knopman, D., & Keefe, R. (2013). Adapting to a changing Colorado River. RAND Corporation, Santa Monica, CA. Retrieved July 02, 2018. http://www.rand.org/pubs/research_reports/RR242.html.
</ref>
<ref name="NRC 2009"> National Research Council (NRC) (2009). Informing decisions in a changing climate. National Academies Press. </ref>

Revision as of 09:31, 29 April 2023

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 [1]. 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.


Conceptualising Robust Decision Making at times of Uncertainty

Origins

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 [2] [3]. 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.

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 [4] Cite error: Closing </ref> missing for <ref> tag

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  1. ↑ 1.0 1.1 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
  2. ↑ 2.0 2.1 https://www.rand.org/pardee/methods-and-tools/robust-decision-making.html
  3. ↑ 3.0 3.1 Lempert, R., J. (2019). Robust Decision Making (RDM), in Decision Making Under Deep Uncertainty — 2019, pp. 23-51
  4. ↑ 4.0 4.1 Donald Rumsfeld, Department of Defense News Briefing, February 12, 2002.
  5. ↑ 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.
  6. ↑ 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.
  7. ↑ Saltelli, A., K. Chan, E. M. Scott. 2000. Sensitivity Analysis. John Wiley & Sons, New York.
  8. ↑ 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.
  9. ↑ van Asselt, M. B. A. 2000. Perspectives on Uncertainty and Risk. Kluwer Academic Publishers, Dordrecht, The Netherlands.
  10. ↑ 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.
  11. ↑ Lempert et al.: A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios Management Science 52(4), pp. 514–528, 2006 INFORMS
  12. ↑ Lempert, R. J., & Collins, M. T. (2007). Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches. Risk Analysis, 27(4), 1009-1026.
  13. ↑ Ramanathan, R., & Ganesh, L. S. (1994). Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members’ weightages. European Journal of Operational Research, 79(2), 249-265.
  14. ↑ Whang, J., & Han, S. (2009). Optimal R&D investment strategies under uncertainty for the development of new technologies. Journal of Business Research, 62(4), 441-447.
  15. ↑ Xu, Q., Zhang, L., & Zhang, X. (2013). The application of robust decision-making in the emergency evacuation of large-scale events. Safety Science, 57, 141-146.
  16. ↑ "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.
  17. ↑ Knight, F. H. (1921). Risk, uncertainty and profit. New York: Houghton Mifflin Company (repub- lished in 2006 by Dover Publications, Inc., Mineola, N.Y.).
  18. ↑ Courtney, H. (2001). 20/20 foresight: Crafting strategy in an uncertain world. Boston: Harvard Business School Press.
  19. ↑ 19.0 19.1 Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., et al. (2003). Defining uncertainty: A conceptual basis for uncertainty management in model- based decision support. Integrated Assessment, 4(1), 5–17. Cite error: Invalid <ref> tag; name "Walker" defined multiple times with different content
  20. ↑ Hillier, F. S., & Lieberman, G. J. (2001). Introduction to operations research (7th ed.). New York: McGraw Hill.
  21. ↑ Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
  22. ↑ Schwartz, P. (1996). The art of the long view: Paths to strategic insight for yourself and your company. New York: Currency Doubleday.
  23. ↑ Popper, S. W., Lempert, R. J., & Bankes, S. C. (2005). Shaping the future. Scientific American, 292(4), 66–71.
  24. ↑ Morgan, M. G., & Henrion, M. (1990). Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge, UK: Cambridge University Press.
  25. ↑ Walley, P. (1991). Statistical reasoning with imprecise probabilities. London: Chapman and Hall.
  26. ↑ 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.
  27. ↑ 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.
  28. ↑ 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.
  29. ↑ Bankes, S. C. (1993). Exploratory modeling for policy analysis. Operations Research, 41(3), 435–449.
  30. ↑ National Research Council (NRC) (2009). Informing decisions in a changing climate. National Academies Press.
  31. ↑ 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)
  32. ↑ 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.
  33. ↑ 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.
  34. ↑ Lempert, R. J. (2013). Scenarios that illuminate vulnerabilities and robust responses. Climatic Change, 117, 627–646.
  35. ↑ 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.
  36. ↑ 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.
  37. ↑ Groves, D. G., Fischbach, J. R., Bloom, E., Knopman, D., & Keefe, R. (2013). Adapting to a changing Colorado River. RAND Corporation, Santa Monica, CA. Retrieved July 02, 2018. http://www.rand.org/pubs/research_reports/RR242.html.
  38. ↑ National Research Council (NRC) (2009). Informing decisions in a changing climate. National Academies Press.
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