Revision as of 13:52, 14 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 how to correctly implement RDM in project management, and to inform future research in this area. 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

History

RDM was first developed in the 1950s and 1960s by RAND Corporation to evaluate the effectiveness of nuclear weapon systems ^{[2] [3]}. The approach evolved to include simulation techniques, sensitivity analysis, and real options analysis. In the 1990s and 2000s, RDM gained wider acceptance in project management and has been applied to complex infrastructure, software development, and environmental management. Today, RDM is an established approach in project management, helping project managers make effective decisions in the face of uncertainty and ambiguity ^{[4] [5] [6] [7]}.

Literature review and State of the Art

Key principles and practices of RDM

Application

Benefits & Limitations

Further research

References

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. ↑ 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.