RDM

Revision as of 13:38, 15 February 2021

Abstract

Robust decision-making (RDM), is a new and innovative methodology in the science of decision support. The RDM framework is a key tool in decision-making processes under deep uncertainty and provides decision-makers with a method to make a plan for the future without having to predict it. When decision-makers have to make long-term decisions they're often called upon to anticipate future needs, resources and circumstances. The problem is that decisions made on predictions are less reliable the farther the prediction reach forward in time, as time entails the prediction to become more vulnerable to uncertainty. This uncertainty could e.g. be unforeseen economic crashes terrorism attacks, political instability, climate change, chains of actions or reactions leading to more possible outcomes than a single prediction can handle. In order to handle a large amount of possible futures, RDM uses computer simulations and advanced modelling techniques to stress-test strategies not only against one predicted future but against thousands or millions of possible futures ^[1]. Thus the RDM framework is used for Decision Making under Deep Uncertainty (DMDU) ^[2]. The main purpose of RDM is not to make a better prediction but through its concepts and processes to contribute with knowledge for the decision-maker to be able to design more robust strategies, that perform no matter what the future holds.

This paper will go more in-depth with the history of RDM, the theory behind RDM and it's purpose. Further guidance on how RDM is implemented and used, as well as an analysis of when RDM is applicable, will be presented. Finally, a critical reflection on RDM will seek to find and enlighten the limitations of RDM.

Big idea

Describe the tool, concept or theory and explain its purpose. The section should reflect the current state of the art on the topic

RDM in general

Decision-making under uncertainty is not a new problem, but the way to approach and handle these often complex issues has changed over time. RDM has become one of the most common approaches to handle decision making under uncertainty. The first person to describe the RDM methodology was Robert J. Lempert in 1997 "When we don't know the costs or the benefits: Adaptive strategies for abating climate change "^[3]. According to R.J Lempert, the traditional “predict-then-act” framework has proven to be successful for decades, which both a large number of theories and mathematical techniques is proof on. The need for RDM is therefore targeted more towards complex decision processes under deep uncertainty, where relatively simple prediction analysis is not enough and often will be misleading. R.J. Lempert thought it would be better to seek a robust solution rather than trying to predict the future and then choose the solution that fits the predicted future best. He disagreed with the traditional framework for assessing alternatives in decision processes, in which he thinks predictions were imposed too great a significance. He argued that the reason for decision-makers to use predictions were to make the future less scary and that the "the quest for prediction probably fills some deep human need. Even though the accuracy of most predictions has proven to be poor""^[4]. A combination of new technologies and the need for a method to handle more complex issues resulted in the development of RDM. Instead of finding the alternative that seems to be the best based on specific predictions, decision-makers can now use modern technology to analyse and evaluate alternatives against thousands or even millions of possible scenarios. In that way, decision-makers can now determine which alternatives/strategies that perform best under a large range of possible scenarios. It results in not necessarily the best strategy, but the most robust strategy that can handle the future, regardless of how the future ends up being

Theory

Application

Provide guidance on how to use the tool, concept or theory and when it is applicable

Step-by-Step guide

• Structure problem
• Choose candidate strategy
• Evaluate strategy against large ensemble of scenarios
• Characterize vulnerabilities
• Identify and assess options for ameliorationg vulnerabilities

^[5]

^[6]

Limitations

Critically reflect on the tool/concept/theory. When possible, substantiate your claims with literature

Annotated bibliography

Provide key references (3-10), where a reader can find additional information on the subject.

Bibliography

1. ↑ Lempert R.J. (2019) Robust Decision Making (RDM). In: Marchau V., Walker W., Bloemen P., Popper S. (eds) Decision Making under Deep Uncertainty. Springer, Cham. https://doi.org/10.1007/978-3-030-05252-2_2.
2. ↑ Walker W.E., Lempert R.J., Kwakkel J.H. (2013) Deep Uncertainty. In: Gass S.I., Fu M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_1140
3. ↑ Lempert R.J., Schlesinger M.E., & Bankes S.C. (1996). When we don’t know the costs or the benefits: Adaptive strategies for abating climate change. Climatic Change, Volume 37, Issue 1, 207–208.
4. ↑ Lempert R.J., Schlesinger M.E. (2000) Robust strategies for abating climate change. Climatic Change, Springer Netherlands, Volume 45, Page 387–401, DOI: 10.1023/a:1005698407365
5. ↑ Lempert R.J., Groves D.G. (2010) Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west, Technological Forecasting and Social Change, Volume 77, Issue 6, Pages 960-974, ISSN 0040-1625, https://doi.org/10.1016/j.techfore.2010.04.007.
6. ↑ Lempert R.J., Groves D.G., Popper S.W., Bankes S.S. (2016) A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios, Volume 52, Issue 4, https://doi-org.proxy.findit.dtu.dk/10.1287/mnsc.1050.0472.

https://findit.dtu.dk/en/catalog/2281533929 - 7.2.1 Robust Decision Making

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