The Black Swan Problem. Håkan Jankensgård
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So what is this famed and metaphorical creature more precisely? A Black Swan, according to Taleb, has three attributes. Before it occurs, it is considered extremely unlikely, to the extent we grant it for possible at all. When it occurs, its consequences are massive. After it has occurred, it makes perfect sense to us that something like that could happen. The last attribute is due to our brains being awesome ‘explanation machines’ that crave coherence. Dots will be ambitiously connected until coherence is established. The world inhabited by these Black Swans is characterized by uncertainty that is ‘wild’ rather than benign, meaning that there is always a potential for great dislocations and non‐linearities. Even when we manage to bring our focus to the tail end of the distribution, we frequently find that it is in flux, with events greatly surpassing anything we have seen or heard before.
The impact of the Black Swan idea has been substantial. While it may not have trickled down into popular culture just yet, the educated public seems largely aware of it, whether or not they have actually read the book. Admittedly, Taleb has drawn a fair amount of criticism for his work. Often, the target has been his abrasive and self‐aggrandizing style of writing and the use of fictional characters (the Tonys and the Valerias). Critics also claim to have found more than a few inconsistencies in his web of ideas. I do not wish to dwell on such trivial matters here. Instead, I would like to point to a habit among takers of the Black Swan concept to render it synonymous with low‐probability high‐impact events (usually disastrous ones). We certainly did not need a risk philosopher, somebody might go on to say, to point out the existence of that. In this view, Taleb just popularized what we already knew.
Reducing the Black Swan to be merely a shorthand for describing low‐probability high‐impact events is to do the construct significant injustice. It trivializes it and detracts from its core ideas. In this book, I will re‐emphasize the importance of expectations in the formation of Black Swans. This is a crucial point because it means that we can turn at least some Black Swans into ‘mere’ tail risk of a more calculated sort by controlling and improving our expectations in various ways. I will also stress how differences in expectations have strategic implications. No doubt, a more realistic appreciation of the nature of randomness is valuable for its own sake, but it gets even more interesting when we realize that others may systematically get themselves into perilous positions because they operate on unrealistic assumptions about randomness. What is a Black Swan to others need not be to us, and we may stand to benefit from this in various ways.
The main ambition of this book, however, is to place the Black Swan framework in a corporate context and explore its ramifications for business strategy. Taleb, motivated as he is by the grand sweep of history and civilization, and the philosophical underpinnings of randomness, does not take more than a fleeting interest in the firm as an institution. Yet, if we accept the premises of the Black Swan framework, we should ask what the implications are for how businesses are to be run. Businesses, after all, are stewards of a large part of humanity's resources, and innumerable people depend on them for their livelihood as well as the hope of making it big. Extreme uncertainty may deliver great risk but also holds out the promise of great opportunity.
As it turns out, firms are peculiar entities in several ways. To begin with, they operate under a mandate to maximize value, which introduces a focus on cost minimization that hugely shapes the corporate attention span for extreme events. There are also interesting features like separation between ownership and control, limited liability and multiple layers of stakeholders with different viewpoints. All these features profoundly influence how we should process the fact that uncertainty is wild rather than benign, and this book is about exploring them. How are massive consequences to be understood when we take the vantage point of a firm? When is it justifiable to spend corporate resources in preparation for highly improbable catastrophes? When should we have a strategy for Black Swan events? When and how can we make wild uncertainty work for us rather than against?
We start out, however, by recapping some of the core themes in the Black Swan framework (Chapter 1, ‘The Swans Revisited’). In this chapter, we take a fresh look at the question of what constitutes a Black Swan. We also revisit the issue of what kind of randomness we face as decision‐makers in the real world, as well as the crucial role of expectations. We establish the relative nature of Swans: players in the same industry can have very diverging expectations and are therefore likely to display different levels of preparation and vulnerability.
In Chapter 2 (‘Corporate Swans’), we begin our exploration of what the Black Swan framework means for corporate management. I argue that it is the expectations of the firm's Board of Directors that is the correct perspective for determining what counts as a Black Swan. This has the interesting effect of increasing the number of Swans because of the structural information disadvantage the directors find themselves in as compared to the executive team. Even more troubling, the rate also goes up because the executives are a potential source of Black Swans. We go on to explore several other Swans that are specific to the corporate domain, over and above the ones generated by the natural world and complex systems at the level of society.
In Chapter 3 (‘The Black Swan Problem’), we dive deeper into the question of how risk, and tail risk more specifically, relates to firm value. The risk of wipeout and strategy disruption provide economic arguments in favour of managing tail risk. Ultimately, though, we want to be able to demonstrate that managing tail risk produces benefits that are larger than the costs. This proves somewhat harder than expected, because when the probability of something goes towards zero – as they do with Black Swans – the cost of risk also tends towards zero. The conundrum is that when the cost of risk is near‐zero, there is little apparent justification for spending corporate resources on tail risk management. The second part of the Black Swan problem is the difficulty of mustering a proactive response to extreme events. A whole host of biases, both on the individual and organizational level, work against it.
In Chapter 4 (‘Greeting the Swan’), we discuss the need to alter our attitude to randomness as a first basic step in developing a strategy for dealing with the Black Swan phenomenon. We also need to recognize that the resources and patience available for managing the risk associated with extreme events are very small. Running through this book is a persistent tension between tail risk management and economic efficiency. Another part of adapting our mindset to a world of wild uncertainty is to own up to the delicate matter that Black Swans are sometimes made closer to home by people we want to trust. The Board of Directors need to extend their Swan map to cover known company‐wreckers like acquisitions, derivative portfolios and, yes, the executive team.
In Chapter 5 (‘Taming the Swan’), we arrive at the issue of how to make ourselves robust to wild uncertainty, to continue to survive and thrive even in much‐worse‐than‐expected scenarios. We dig deeply into the role of buffers and flexibility in achieving resilience, with particular emphasis on risk capital, a set of financial resources that absorbs shocks and ensures survival and strategy execution. Stress testing, I argue, is a particularly important, but currently under‐used, tool in the corporate battle against Black Swans. In them, we push resilience to the limits to learn about breaking points that can inform risk management strategies. The chapter contains extensive discussions on the role of models in the process of developing resilience. On the one hand, they help us see by reducing complexity and clarifying important mechanisms. On the other hand, they can make us blind to whatever is outside the model, creating a potential source of vulnerability.