The gaps inherent in modellings of known and unknown risks are clearly illustrated in the above image of the field of poppies: you can pick out the poppies but you cannot count them all in the short time available. Typical risk managers would break down the poppy problem into sub-problems and then aggregate the respective results: they would count the poppies at the front of the image, in this case four, and estimate those in the background that are more difficult to count in the few seconds available, in this case around 200. The sum total of the two calculations would be 204.

Two principles are being unconsciously applied here: meticulousness and bold assumptions. People have a tendency to model well-understood problems, e.g. the poppies in the foreground or, to use another example, physical climate risks, in exact detail. Other less well-understood problems for which little knowledge is available, e.g. the poppies in the background, or transitory risks – are estimated, if at all, on the basis of rough assumptions. Both principles are crucial and correct for navigating risks. But the way in which they are unconsciously applied and, in particular, the process of combining the individual results without rendering the underlying assumptions and uncertainties transparent leads ultimately to a misleading semblance of exactitude. The relevance of ESG risks does not depend on how well we are able to understand and model them. For a more realistic estimate and assessment, we need to improve the way in which we deal with the risks that are not understood, that are non-quantifiable. It is uneconomical to demand of supervised companies that they model all ESG risks exactly. Instead, there are better ways to address the topic of uncertainty.

The major challenges that ESG risks pose to risk managers are the high level of uncertainty with regard to the probability of occurrence and the direct and indirect consequences associated with this. Given the lack of data and high level of uncertainty, traditional methods and procedures are not suitable for managing these risks. ESG risks entail multiple and unclear repercussions and contagion effects for companies, people and society.

The time horizons are far longer than in traditional risk management, often encompassing several decades. Navigating ESG risks is comparable to managing the risks associated with future technologies such as genetic engineering and geoengineering. The challenges here are similar.

Viewing uncertainty from a different perspective

The best course of action is to approach uncertainty from a constructive perspective. There is a strong correlation between a successful application of the hard tools, i.e. the established instruments, of risk management and the level of uncertainty (see figure 1). Risk is a category close to certainty. While it is not possible to make exact predictions here, it is still possible to calculate the probability and consequences of events occurring on the basis of mathematical instruments. This is where conventional risk management is located – also in the financial industry.

The next category in the spectrum of uncertainty is epistemic uncertainty, the area in which it is no longer possible to quantify all things. Data are either unavailable or do not exist. But knowledge exists on principle, and there are possibilities of approaching the topic by means of scenarios or emerging risk management, for example. Paradigmatic uncertainty lies further down the spectrum of uncertainty towards ignorance. Here there is disagreement over the problem as such and the methods that can be used for modelling it. Paradigmatic uncertainty is characteristic of the behaviour and management of complex systems. This category of uncertainty encompasses, for example, the problem of systemic risks that have contagion effects and cross-border consequences, neither of which can be adequately mapped in scenarios. Even further down the spectrum towards ignorance is the category of ambiguity. Ambiguity encompasses value systems, scientifically unprovable beliefs and convictions that could compete with each other and that play a key role in navigating uncertainty.

Figure 1: Need for a constructive approach to uncertainty governance

© Dr. Sachs

The success prospects of traditional risk management – with its established instruments such as (natural) science, data and models – are all the worse, the further down the particular category is on the axis from certainty towards ignorance, i.e. absolute unknowledge. However, there are a number of approaches that are very useful for navigating uncertainty if no data or figures are available. In contrast to the hard tools of conventional risk management, these can be referred to as the soft tools. The ability to learn, cooperation, visions and trust all play an essential role in these approaches.

Rendering unknowledge transparent

A key success criterion – also in the context of managing ESG risks – lies in applying multiple strategies. By applying a wide range of methods, the prospects of generally successful governance increase. Given the considerable uncertainties, disastrous consequences could ensue if a risk manager were to rely entirely on a single method. Moreover, companies need to think proactively and learn how to deal better with surprises. There are limits to the extent to which strategies based primarily on planning and monitoring can be applied when navigating uncertainty. What is more, competences need to be fostered in companies in order to deal more effectively with complexity and thus be able to understand and accept the limitations of models, for example.

Practice shows that decision-makers in companies are often not interested in confidence intervals or error bars. What they seek is quantification in the form of a number as the output of a model. However, they are less interested in how exact this number is. A better approach would be to ensure that what we do not know, what we do not understand, what we can and cannot map by way of models, is rendered transparent.

Away from “what“ to “how“

The soft tools refer not only to what but also to how something is done. Soft tools are aimed at actively improving the risk culture in companies and in society – i.e. clarifying and allocating responsibility in decision-making processes, ensuring honest and comprehensive communication about opportunities and risks, and systematically learning from errors in organisations.

Particularly in the financial industry and in supervisory practice, stability and planning certainty are key values. However, given that the world is permanently changing, it is vital that both companies and supervisors are able to react to these changes. The idea of being able to develop a framework for ESG risks that will be applicable for the next ten to 20 years is an illusion.

A pertinent example for learning organisations is adaptive regulation. Its structures and processes make it possible to quickly revise and adapt to altered framework conditions. The practicality of such an approach could be seen in the EU regulation for biofuels, which was gradually edited in several phases between 2003 and 2018 in order to take experiences and market developments into account.

A change in perspective: from quantity to quality

As can be seen, there are suitable strategies for navigating uncertainty. However, what we need is a change in perspective: ESG risks should not be treated like conventional risks that seek quantification. Such a focus restricts the solution space. Instead of simply more of the same, we need to apply other approaches. An example is one that attaches greater value to qualitative risk management. This is a seemingly thankless task at first, with no figure to tick off in the end. What we need instead is a contents-focussed examination of the result.

It is also important to navigate uncertainty systematically and constructively. Uncertainty needs to be accepted. Essentially, this means not modelling away uncertainty or processing it in a way that lulls us into a (false) sense of having it under control. There are many suitable strategies for governing uncertainty constructively. A number of examples have been mentioned in this article.¹ As is so often the case, the challenge lies in putting them into practice. This presupposes that organisations deal actively with the topic, explore new methodical paths rather than adhere to previous strategies – such as measuring, describing and report publishing.

Footnote: