RDM

Revision as of 00:11, 18 February 2021

Abstract

Robust decision-making (RDM), is a relatively 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 in-depth with the concept of RDM, the theory behind RDM and it's purpose. Further in-depth 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.

RDM in general

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

History of RDM

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 more common approaches to handle decision making under deep uncertainty. The first person to describe the RDM methodology was Robert J. Lempert from the RAND cooperation in 1997 in "When we don't know the costs or the benefits: Adaptive strategies for abating climate change "^[4]. But R.J. Lempert was not the first one who tried to develop a new method to handle the increasing number of decisions under deep uncertainty. The company Envolving Logic had in the 1990s, parallel with the RAND corporation and R.J. Lempert's development of RDM, developed the first software tool for a DMDU methodology. Their software was called the Computer-Assisted Reasoning system (CARsTM) and they described it as a tool to be used in the Robust Adaptive Planning (RAP) methodology while the RAND corporation and R.J. Lempert instead adopted the Robust Decision Making (RDM) methodology ^[5].

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 to make policymakers able to make better decisions about issues that could have consequences in the long-term. RDM were more specifically focused on decision processes about climate changes and how this complex and very unpredictable problem could be handled, the reason for this is that climate change is commonly known as an issue embraced with deep uncertainty^[6]. RDM were therefore a particularly useful alternative for finding suitable strategies regarding the area of climate changes. With the arrival of the RDM, it was no longer necessary to determine the probabilities of the future consequences of climate changes, in order to make a strategy, which many also believe is something near impossible. 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""^[7]. 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:

• Testing of multiple plausible futures. The combination of scenarios for the future should be significantly 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 persons in the decisionmaking groups view of the world. Instead of spending time debating which prediction of the future too chose, as one would do in the traditional “predict-then-act” framework, the decisionmaking group can use their time more effective while testing everybody's idea of the future at the same time.

• 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 are 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.

The theoritical background and foundation of RDM

The primary theoretical focus lays around uncertainty theories more specifically in the area of Decision Making Under Deep Uncertainty (DMDU), the reason for this is that the RDM methodology is used for developing strategies under deep uncertainty. Uncertainties are in general just the result of lack or limited knowledge about a certain state in time and it is always essential for decisionmakers to consider in every decision they make^[8]. Many books have been written about decisionmaking under uncertainty, but only a very limited amount includes DMDU. Even though the literature about DMDU is limited, however increasing, a number of tools and different analytical approaches exist. They share the underlying paradigm that they all seeks to reduce strategies vulnerability against the uncertainties associated with the future, which in some literature is defined as Assumption Based Planning (ABP)^[9].

Decision Analysis

A part of RDM's foundation is Decision Analysis (DA). The reasoning behind DA is that we by including decision tools make better decisions. It applies whether we are a group or an individual. DA provides the decision-makers with tools to enlighten what affect certain decisions will have on different situations under uncertainty.^[10]. DA can take form as e.g. Decison Trees or Expected Value (EV) calculations, but for more complex decisions under deep uncertainty, it is necessary to include more advanced methods and computer modelling. It could e.g. be RDM which is categorised as a quantitative DA method^[1]. DA can be divided into two different approaches, the first approach is called "agree-on-assumption" or "predict-then-act" and the second one, which RDM follows, is an "agree-on-decisions" approach. The idea of stress testing is one concept RDM has derived directly from "agree-on-decision" approach. The first approach seeks to rank different strategies based on agreed probabilities of how likely it is that the world is in a certain state in the future, known from the traditional DA framework, while the second approach inverts this framework ^{[11] [12]}. According to C. Helgeson (2018), the "agree-on-decision" approach can be summarized as an approach to support decisionmakers in the search for categorized as the support the decisionmaking group in the process of framing decisions under deep uncertainty ^[13].

Assumption-Based Planning

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Scenarios

Scenario analysis is another concept RDM uses. It is a way to represent different descriptions of plausible future states. Scenario analysis contributes to dividing different future states into multiple scenarios which makes it easier for the decision-makers to analyse and discuss. In contrast to ranking future states based on probabilities, scenario analysis analyses different views of how a situation will develop. By focusing on possibilities instead of probabilities, gives the decision-makers the possibility to analyse more and diverse events, compared to if they had to focus on only the events that most likely would happen. At the same time, the decision-makers are faced with more extreme and unexpected future events ^[14]. Gong, M., Lempert, R. J., Parker, A. M., Mayer, L. A., Fischbach, J., Sisco, M., et al. (2017), Testing the scenario hypothesis: An experimental comparison of scenarios and forecasts for decision support in a complex decision environment. Environmental Modeling and Software, Volume 91, Page 135–155 </ref>. There exist different types of algorithms to do scenario analysis and the ones RDM uses is a quantitative approach of Scenario Discovery algorithms. In RDM, the decisionmakers use the final scenarios from the scenario analysis to capture the most important findings from the ABP street test, which at the end of the RDM makes it possible for the decision-makers to make adaptive strategies^[15].

Exploratory Modeling

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Application of RDM

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

^[16]

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. In step 4 different strategies are evaluated in order to analyse the tradeoffs among different strategies. This is often done by making multi-objective tradeoff plots in which tradeoff curves can be used to e.g. compare cost against reliability for different scenarios. The tradeoff curves can be used by the decision-makers to determine what balance they evaluate to be most suitable among the objectives defined in step 1^[1].

Step 5 New Futures and Strategies Finally, the decision-making team can then use the findings from step 4 to evaluate if they have found any robust strategies, with tradeoffs that are better than for the existing strategies. The final strategies from the RDM will often be what you call, Adaptive Decision Strategies, which are strategies that adapt depending on how the future develops. The adaptive strategies consist of a short-term strategy with actions to be taken in the near future and so-called signpost which are predesignated signals that when observed, certain actions will be taken. These adaptive strategies can either be established from a jury of experts^[17], or the most suitable combination of near-term actions, signposts and actions to be taken if the signals are observed, can be determined with the help of optimization algorithms^[16].

Bennefits of the RDM methodogy

^[18]

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^[19].

One of the limitations of

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 1.4 1.5} Lempert R.J, Marchau V., Walker W., Bloemen P., Popper S. (2019) Robust Decision Making in Decision Making under Deep Uncertainty. Springer, Cham.
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. ↑ Bankes S.C., Lempert R.J., Popper S.W. (2001), Computer-assisted reasoning. Computing in Science & Engineering, Volume 3, Issue 2, Page 71–77
6. ↑ Marchau V.A.W.J., Walker W.E., Bloemen P.J.T.M, Popper S.W (2019), Decision Making under Deep Uncertainty, From Theory to Practice, Springer Cham, DOI: https://doi.org/10.1007/978-3-030-05252-2
7. ↑ 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
8. ↑ Walker W.E., Lempert R.J., Kwakkel J.H. (2013), Deep uncertainty, entry. In S. I. Gass & M. C. Fu (eds.), Encyclopedia of operations research and management science, Page 395–402, 3rd ed. New York: Springer
9. ↑ Dewar J.A., Builder C.H. W., Hix M., Levin M.H. (1993) Assumption-Based Planning: A planning tool for very uncertain times. MR-114-A, RAND, Santa Monica, California.
10. ↑ National Research Council (2001, Theoretical Foundations for Decision Making in Engineering Design, Washington, DC: The National Academies Press. https://doi.org/10.17226/10566
11. ↑ Kalra N., Hallegatte S., Lempert R.J., Brown C., Fozzard A., Gill S., et al. (2014), Agreeing on robust decisions: A new process of decision making under deep uncertainty, Policy Research Working Paper. World Bank
12. ↑ Lempert R.J., Nakicenovic N., Sarewitz D., Schlesinger M. (2004), Characterizing climate change uncertainties for decision-makers — An editorial essay. Climatic Change, Volume 65, Issue 1–2, Page 1–9
13. ↑ Helgeson C. (2018), Structuring decisions under deep uncertainty. Topoi, Page 1–13
14. ↑ Schoemaker P.J.H. (1993), Multiple scenario development: Its conceptual and behavioural foundation. Strategic Management Journal, Volume 14, Issue 3, Page 193–213
15. ↑ Groves D. G., Lempert R.J. (2007), A new analytic method for finding policy-relevant scenarios, Global Environmental Change, Volume 17, Page 73–85
16. ↑ ^{16.0 16.1} Lempert R.J., Collins M. (2007), Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches. Risk Analysis, Volume 27, Issue 4, Page 1009–1026
17. ↑ Lempert R.J., Popper S.W., Bankes S.C. (2003), Shaping the next one hundred years: New methods for quantitative, long-term policy analysis, Santa Monica, CA, The RAND Corporation, Technological Forecasting and Social Change — 2004, Volume 71, Issue 3, Page 305-307
18. ↑ 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.
19. ↑ 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,

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

https://www.tandfonline.com/doi/abs/10.1080/09544820010031580 - Robust decision-making for engineering design