Robust Decision Making: better decisions under uncertainty
Contents |
Abstract
Robust Decision Making (RDM) is a critical aspect of project management that involves making well-informed, confident decisions in the face of uncertainty and ambiguity. This paper will provide a comprehensive review of the current state of the art in RDM in project management, explore the key principles and practices of RDM, including the importance of data gathering and analysis, considering different options, involving stakeholders, anticipating uncertainty, and continuously monitoring and adapting. The paper will also examine the benefits, challenges and limitations of RDM in project management, and provide insights into future directions for research in this area. This paper aims at providing project managers with a deeper understanding of the principles and practices of RDM, give insights on how to correctly implement RDM in project management, and informing future research in this area. By doing so, this paper aims also at contributing to the development of more effective and efficient approaches to project management and decision making.
Big Idea
History
RDM was first developed in the 1950s and 1960s by RAND Corporation to evaluate the effectiveness of nuclear weapon systems [1]. 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 [2] [3] [4] [5].
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., & 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.
