Robust Decision Making under Deep Uncertainty

Revision as of 00:40, 21 February 2022

Abstract

The uncertainty is highest in the beginning of a project, but it is also here decisions of crucial importance for the project's outcome are made. Furthermore, the world is constantly changing, making it impossible to make reliable decisions based on prediction of what the future holds. There is a need to move away from prediction to calculating the most robust decisions.

This article describes how to approach decisions under high uncertainty as a project manager or project stakeholder using robust decision making (RDM). RDM is a set of methods and tools developed over the last decade, primarily by researchers associated with the RAND Corporation. The article explains the XLRM Matrix for RDM and the Iterative Steps of RDM as well as how to apply these through exemplification. Limitations and challenges of the method are touched on, including the challenge of shifting to a new way of dealing with uncertainty.

It is inevitable to encounter uncertainties in projects, but you can do your best to investigate which strategies will perform best under these uncertainties. RDM is an iterative framework that offers a data-based assessment on future scenarios on which stakeholders and decision makers can base their decisions.

Deep Uncertainty and Project Management

A paradox of project planning is that when uncertainty is at its highest – in the beginning of a project – is also when the most crucial and determining decisions need to be taken. When time goes by and the project performer knows more about the project, there are less degrees of freedom to influence it. All projects face varying degrees of uncertainty which is often mentioned as a reason for their failures (source).

Uncertainty characterizes situations where the outcome of a project is likely to deviate from the estimated outcome. There are several levels of uncertainty and many operate with four levels. Level 1, 2 and 3 ranges from almost certainty of the outcome to a limited set of plausible outcomes with unknown probabilities. Level four represents the highest level of uncertainty, also called deep uncertainty. Level four can be distinguished into two types: a) where the future is bound around many plausible futures, and b) where the only thing that is known about the future is that it is not known. Type a is often due to lack of information, whereas type b is due to unpredictable events (Decision Making under Deep Uncertainty, V. A. W. J. Marchau et al.). We see the tendency that important problems faced by decision makers are characterized by a higher level of uncertainty and cannot be reduced simply by collecting information, because the uncertainties are unknowable at the time. Such situations are defined as decision making under deep uncertainty (source).

Different approaches are needed for handling decisions in deep uncertainty (level 4) than in level 1, 2 and 3. This is because these types of uncertainties involve factors of which probability distributions and possible outcomes are not known. There is a need for a new paradigm for decision making under deep uncertainty that is not based on predictions of the future (known as the “predict-then-act” paradigm). The “monitor and adapt” paradigm is more suited for deep uncertainty, which recognizes the need for taking the uncertainty into account and prepares and adapts for uncertain events.

Foundation of RMD

Robust Decision Making (RDM) is a set of tools, methods, and processes that are developed for Decision Making under Deep Uncertainty when predictions of the future are not possible or risky. RDM uses computation and quantitative models in a new way to provide better decisions under uncertain conditions. It combines decision analysis, Assumption-Based Planning, scenarios, and Exploratory Modelling to stress test strategies over a great number of possible paths into the future. By that, policy-relevant scenarios and robust adaptive strategies can be determined. RDM are often embedded in a decision support process called “deliberation with analysis” that promotes learning and consensus-building among stakeholders (Lempert et al. 2003).

Decision Analysis Empirical research shows that people make better decisions when using well-structured decision aids. RMD represents a type of decision analysis that relies on decision structuring frameworks, evaluation of consequences of alternative actions, identification of trade-offs among alternative options, and tools for comparing decision outcomes. (R. J. Lempert, 2019, s. 26) In RDM, the decision analysis is based on considering several strategies over a broad range of possible futures. The results are used to characterize weaknesses of the strategies and to identify and evaluate potential response to those weaknesses.

Assumption based planning RDM draws on assumption based planning. This implies the development of an organization’s plan and identification of load-bearing assumptions in this plan. This means identifying the potential break points that could cause the plan to fail. After identifying the assumptions, ABP considers shaping actions (actions designed to make the assumptions less likely to fail), hedging actions (those that can be taken if assumptions begin to fail), and signposts (trends and events to monitor in order to detect whether any assumptions are failing) (s. 28).

Scenarios The concept in RDM of multiplicity of plausible futures is drawn from the scenario analysis (Lempert et al. 2003). Scenarios are projected futures that require less confidence than probabilistic forecasts and that seek to represent different ways of looking at the world without a ranking of relative likelihood (Wack 1985). RDM draws from scenario analysis by analysing information about the future into small numbers of different cases that can help people to explore and communicate the deep uncertainty (s.28.)

Exploratory Modelling The concepts of decision analysis, assumption based planning and scenarios are combined in the exploratory modelling. Exploratory modelling provides RDM with a quantitative framework for stress testing and scenario analysis (s.29.)

Application of RDM

1. Decision Framing

XLRM Matrix

2. Evaluate Strategies across Futures

3. Vulnerability Analysis

4. Tradeoff Analysis

5. New Futures & Strategies

Example of application

Limitations and Challenges