Revision as of 10:42, 17 February 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, scenarios, and Exploratory Modeling 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 comprehensive 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.

Big Idea: Robust Decision Making under uncertainty

Brief history

Robust Decision Making (RDM) emerged in the 1950s and 1960s, when the RAND Corporation developed it to evaluate the effectiveness of nuclear weapon systems ^{[2] [3]}. The approach was designed to address uncertainty and ambiguity inherent in strategic planning, and it evolved to include simulation techniques, sensitivity analysis, and real options analysis. In the 1990s and 2000s, as the complexity and uncertainty of projects increased, RDM gained wider acceptance in project management and was applied to fields such as infrastructure, software development, and environmental management. Today, RDM is an established approach in project management, recognized for its ability to help project managers make well-informed and confident decisions, anticipate and manage uncertainty, and continuously adapt and monitor. RDM has also been applied in various contexts beyond project management, such as climate change policy and disaster risk reduction. ^{[4] [5] [6] [7]}.

Literature review and state of the art

According to 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 difficult category is the unknown unknowns, which refers to things that we do not know we do not know ^{[8] [9]}. Knight further elaborates and proposes a distinction between risk and uncertainty. The first 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 ^[10]. Based on Knight distinction, academics differentiated the various levels of uncertainty in decision-making, ranging from complete certainty to total ignorance ^{[11] [12] [3]}. These levels are categorized based on the knowledge assumed 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 ^[13]; 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 or lack of knowledge or data ^{[14] [15]}. It is believed that with issues dealing with a greater level of uncertainty (Level 4), a more sophisticated and in-depth data gathering is not helpful. Such situations are defined as decision making under deep uncertainty (DMDU) ^[3]. Instead of a "predict and act" paradigm, which attempts to anticipate the future and take action on that prediction, DMDU approaches are based on a "monitor and adapt" paradigm, which aims to prepare for unknown occurrences and adjust accordingly ^[12]. 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." (^[1], p. 11) This article explores RDM under uncertainty, an approach dwelling under the realm of DMDU methodologies.

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A literature review on RDM in project management found that the approach can be applied in various stages of the project life cycle, including project initiation, planning, execution, monitoring, and control. RDM can also be used to address various project-related issues, such as risk management, resource allocation, cost estimation, and schedule planning.

Several studies have examined the use of RDM in project risk management, where it is used to identify and analyze potential risks, develop contingency plans, and assess the effectiveness of risk mitigation strategies. For example, RDM has been applied to assess the impact of climate change on infrastructure projects and to identify strategies to improve the resilience of these projects.

Other studies have focused on the use of RDM in resource allocation, where it is used to optimize resource allocation decisions under uncertain conditions. RDM has also been applied to project scheduling, where it is used to develop robust schedules that can accommodate potential delays and unexpected events.

Overall, the literature suggests that RDM can help project managers make more informed decisions and improve project outcomes by considering the potential impact of uncertainties and unexpected events. However, the effectiveness of RDM in project management depends on the quality of the available data, the accuracy of the models used, and the ability of project managers to make informed decisions based on the RDM results.

Key principles and practices of RDM

Application

Benefits & Limitations

Further research

References

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. ↑ https://www.rand.org/pardee/methods-and-tools/robust-decision-making.html
3. ↑ ^{3.0 3.1 3.2} Lempert, R., J. (2019). Robust Decision Making (RDM), in Decision Making Under Deep Uncertainty — 2019, pp. 23-51
4. ↑ 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.
5. ↑ 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.
6. ↑ 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.
7. ↑ 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.
8. ↑ Donald Rumsfeld, Department of Defense News Briefing, February 12, 2002.
9. ↑ "The Johari Window", http://wiki.doing-projects.org/index.php/The_Johari_Window, 27 February 2021
10. ↑ Knight, F. H. (1921). Risk, uncertainty and profit. New York: Houghton Mifflin Company (repub- lished in 2006 by Dover Publications, Inc., Mineola, N.Y.).
11. ↑ Courtney, H. (2001). 20/20 foresight: Crafting strategy in an uncertain world. Boston: Harvard Business School Press.
12. ↑ ^{12.0 12.1} Walker, W.E., Marchau, V.A.W.J., Kwakkel, J.H., 2013. Uncertainty in the framework of policy analysis, in: Walker, W.E., Thissen, W.A.H. (Eds.), Public Policy Analysis: New Developments.
13. ↑ Hillier, F. S., & Lieberman, G. J. (2001). Introduction to operations research (7th ed.). New York: McGraw Hill.
14. ↑ Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
15. ↑ Schwartz, P. (1996). The art of the long view: Paths to strategic insight for yourself and your company. New York: Currency Doubleday.