Levels of Uncertainty

Source: adapted from Walker, W.E. (2011). Policy Analysis, 1962-2012: From Predict and Act to Monitor and Adapt, Farewell lecture at TU Delft, 19 October 2011.

D ecision making under uncertainty

Strategic decision making requires different approaches depending on the encountered level of uncertainty. I have been working on a scientific guiding framework for selecting the appropriate anticipatory and policy approaches for strategic decision making under uncertainty since 2009. This framework builds on a structure for classifying policy issues according to their location and level of uncertainty as proposed by prof. Walker . The level of uncertainty includes two extreme levels (complete certainty and total uncertainty) and four intermediate levels. Level 1 uncertainty is any uncertainty that can be described adequately by means of a point estimate and its sensitivity. Level 2 uncertainty represents the situation in which one is able to describe alternative futures and indicate their likelihoods. Level 3 uncertainty represents the situation in which one is able to enumerate multiple plausible alternatives without being able to judge how probable they are. Level 4 uncertainty involves the deepest level of recognized uncertainty. In this case, we know only that we do not know.

Futures Cone

Source: adapted from van Dorsser, C., Walker, W., Taneja, P., & Marchau, V. (2018). Improving the link between the futures field and policymaking. Futures, 104, 75-84; based on Voros, J. (2003). A generic foresight process framework. Foresight,5(3), 10-21.

T he type of future

When assessing future developments different types of future can be concerned depending on how likely they are. Trends & Transport applies the commonly applied taxonomy of dr. Voros who describes the types of futures as follows:

• Potential —everything beyond the present moment is a potential future. This comes from the assumption that the future is undetermined and ‘open’, not inevitable or ‘fixed’.

• Possible —those futures that we think ‘can’ happen, based on some future knowledge we do not yet possess, but which we might possess someday (e.g., warp drive).

• Plausible —those futures that we think ‘could’ happen based on our current understanding of how the world works (physical laws, social processes, etc.).

• Probable —those futures that we think are ‘likely to’ happen, based on current trends.

• Projected —the (singular) default, business as usual, ‘baseline’, extrapolated ‘continuation of the past through the present’ future, that could also be considered as being ‘the most probable’ (or most expected) of the Probable futures.

• Preferable —those futures we think ‘should’ or ‘ought to’ happen. These are based on normative values and can fall into any one of the above future types.

Futures Pyramid

Source: adapted from van Dorsser, C., Walker, W., Taneja, P., & Marchau, V. (2018). Improving the link between the futures field and policymaking. Futures, 104, 75-84.

The four disciplines of the Futures field

In a scientific paper that published in 2018, I have shown that the disciplines in the futures field can be linked to the four levels of uncertainty of prof. Walker and the typology of futures of dr. Voros. The disciplines are described as follows:

• Deterministic Forecasting aims to provide a single reliable forecast of the future state of the system on the basis of trend extrapolation techniques and expert judgement. Deterministic forecasts are intended to provide, with a high level of certainty, a single most probable estimate of detailed attributes of the system under consideration for a relatively short time period ahead and are often accompanied by a sensitivity analysis.

• Probabilistic Forecasting aims to provide a clear view of the possible outcomes of a system for which the variation in the main parameters is expected to be known with reasonable certainty. The art of Probabilistic Forecasting is fully explored in Bayesian Forecasting, which is founded on the premise that all uncertainties should be presented and measured by probabilities.

• Foresight aims to develop a coherent forward view of plausible futures without the need to be explicit on the probabilities with which these futures occur, since at this level of uncertainty it is no longer possible to assign probabilities. The word ‘coherent’ refers to the fact that the forward view is intended to cover the full expected range of plausible futures that lay within the realm of normal expectations, not including the more extreme ones that are addressed by the Futures discipline.

• Futures (studies or research) aims to provide a systematic view of possible futures (including ‘unknown unknowns’ and ‘wildcard’ surprises).

Guidelines

Source: Trends & Transport based on van Dorsser, C., Walker, W., Taneja, P., & Marchau, V. (2018). Improving the link between the futures field and policymaking. Futures, 104, 75-84 ; and Agusdinata, B. (2008). Exploratory Modelling and Analysis: A promising method to deal with deep uncertainty.

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Guidance on strategic decision making

Trends & Transport supports private and public entities in strategic decision making, by assessing the level of uncertainty for the challenges faced, choosing the appropriate projection methods, developing state-of-the-art projections, and applying appropriate methods for dealing with uncertainty. These steps seem obvious but can often be improved in practice. To give an example, in port planning it is common to apply forecasting techniques and develop a low, medium and high forecast for a certain port activity. However, apart from that the low and high forecast would be better represented by a sensitivity analysis, the use of Level 1 forecasting techniques is often not appropriate for dealing with the uncertainties confronting the business. Modern port authorities, such as those of the Port of Rotterdam, increasingly recognise this mismatch and shift to the use of scenarios. Scenarios are well suited to anticipate possible changes, but commercial plans designed to accommodate a broad set of possible future requirements (represented by a broad set of possible scenarios), require high investments and risk becoming non-competitive. In a commercial environment, the challenge is not just to envision future developments with a possible effect on the business, but more importantly, to assess which of them are plausible based on a thorough understanding of the underlaying system. To stay competitive in a commercial environment, one usually does not have the luxury to invest in plans that are robust under numerous possible developments. Instead one has to focus on the more plausible ones. For these cases, that typically concern Level 3 uncertainty, I have, together with dr. Poonam Taneja (my colleague at the TU Delft), developed an integrated foresight framework and method to support decision makers who are confronted with today’s complex and rapidly changing world. The method aims at reducing the degree of uncertainty by addressing the inertia (or duration) of unfolding trends and by placing individual trends in a broader context. Further discussion on the framework can be found on the page on trends .