RDM

Revision as of 18:30, 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 therefore 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^[3].

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 "^[4]. 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. RDM was original developed make policymakers able to make better decisions about issues that could have consequences on the long-term. 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""^[5]. 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. The RDM method gives the decision-maker a tool to identify trade-offs when testing strategies against different scenarios. In that way, decision-makers can now determine which 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.

RDM consist of the following key elements:

• Multiple plausible futures. The combination of scenarios for the future should be very diverse. The reason for this is that the main idea of RDM is that you are interested in analysing how different strategies perform in as many different futures as possible. These multiple scenarios can also correspond to different person view of the world.

• The goal is not to find the best strategies but to find the most robust ones. The main focus in RDM is to find strategies that perform well over most if not all possible scenarios.

• Apply adaptive strategies. One way to secure that strategies are robust is to choose some who adaptive. Adaptive means that when the future comes, the strategies can adapt to the new circumstances, and still perform well under the new conditions.

• The computer is used to ease the human job, not to give the final recommendation of which specific strategy that should be used.

Theory

Application

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

Steps in RDM

Steps in an RDM analysis^[1]

Step 1. Decision Framing The stakeholders start out with a decision framing exercise, in which they have to define the key factors relevant in the further analysis:

• Objectives and criteria
• Strategies/Alternatives that successfully can handle the objectives
• Uncertainties and their consequences
• Relationship between strategies, objectives and uncertainties

Step 2. Evaluate Strategy Across Futures Next step is to use an "agree-on-decision" approach". To do so, RDM uses simulations of the strategies found in Step 1, against a variety of plausible scenarios of the future. These simulations generate a large number of results, which is merged into a large database^[1].

Step 3 Vulnerability Analysis Step 3 tries to identify, explore and characterize vulnerabilities. The decision-making team can use visualisations and data analytics based on the database made in step 2. In order to identify the key factors that contribute to the success or failure of the strategies, it is common practice to use Scenario Discovery (SD) algorithms. The use of SD algorithms will result in clusters of futures that will enlighten which factors the strategies are most vulnerable to^[1].

Step 4 Tradeoff Analysis A tradeoff analysis is performed on the scenarios from step 3. Different strategies The scenarios found in step 3 can

Step 4 Analysts and decisionmakers may use these scenarios to display and eval- uate the tradeoffs among strategies.

For instance, one can plot the performance of one or more strategies as a function of the likelihood of the policy-relevant scenarios(e.g., see Fig. 2.2) to suggest the judgments about the future implied by choosing one strategy over another. Other analyses plot multi-objective tradeoff curves—for instance comparing reliability and cost (Groves et al. 2012)—for each of the policy- relevant scenarios to help decisionmakers decide how to best balance among their competing objectives.

Step 5 New Futures and Strategies

^[6]

Limitations

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

RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is " that it is better to be roughly right than precisely wrong", the models used to perform the RDM is relatively simple and fast models^[7].

One of the limitations of

RDM approaches adopt the philosophy that it is better to be roughly right than precisely wrong, by working with relatively fast and simple models or fit for purpose models (Haasnoot et al., 2014) and avoiding complex and detailed modelling processes (Walker et al., 2013). An example of such an approach is expert judgment which does not rely on intensive quantitative data analysis. However, for analysing potential consequences of a large number of scenarios, correspondingly large data requirements, computational capability, model simulation and visualization, becomes necessary

As RDM uses probabilistic modelling there has to be used computers and it requires some kind of quantitative information. Moreover, it is quite complicated to perform the modelling which means that you have to be an expert otherwise you will have to get help from an external expert to perform it. https://econadapt-toolbox.eu/robust-decision-making

Annotated bibliography

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

Bibliography

1. ↑ ^{1.0 1.1 1.2 1.3} 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., 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.
4. ↑ 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.
5. ↑ 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
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.
7. ↑ Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016) Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963,

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