Robust Decision Making: better decisions under uncertainty
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Robust Decision Making (RDM) is a set of concepts, processes, and enabling tools that use computation to make better decisions under conditions of deep uncertainty. It combines decision analysis, Assumption-Based Planning, scenarios, and Exploratory Modeling to stress test strategies over myriad plausible paths into the future and then to identify policy-relevant scenarios and robust adaptive strategies. The RDM analytic tools are often embedded in a decision support process called "deliberation with analysis" that promotes learning and consensus-building among stakeholders. This paper 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 paper 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 paper 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 set of concepts, processes, and enabling tools that use computation to make better decisions under conditions of deep uncertainty. It combines decision analysis, Assumption-Based Planning, scenarios, and Exploratory Modeling to stress test strategies over myriad plausible paths into the future and then to identify policy-relevant scenarios and robust adaptive strategies. The RDM analytic tools are often embedded in a decision support process called "deliberation with analysis" that promotes learning and consensus-building among stakeholders. This paper 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 paper 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 paper 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. | ||
Revision as of 11:55, 12 February 2023
Contents |
Abstract
Robust Decision Making (RDM) is a set of concepts, processes, and enabling tools that use computation to make better decisions under conditions of deep uncertainty. It combines decision analysis, Assumption-Based Planning, scenarios, and Exploratory Modeling to stress test strategies over myriad plausible paths into the future and then to identify policy-relevant scenarios and robust adaptive strategies. The RDM analytic tools are often embedded in a decision support process called "deliberation with analysis" that promotes learning and consensus-building among stakeholders. This paper 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 paper 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 paper 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
History
RDM was first developed in the 1950s and 1960s by RAND Corporation to evaluate the effectiveness of nuclear weapon systems [1] [2]. 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 [3] [4] [5] [6].
Literature review and State of the Art
Key principles and practices of RDM
Application
Benefits & Limitations
Further research
References
- ↑ https://www.rand.org/pardee/methods-and-tools/robust-decision-making.html
- ↑ Lempert, R., J. (2019). Robust Decision Making (RDM), in Decision Making Under Deep Uncertainty — 2019, pp. 23-51
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
