Book Review: John Kay & Mervyn King: “Radical Uncertainty”

Looking back at over fifty years of professional work in economics in scholarly as well as professional positions – King was the Gouveneur of the Bank of England – the authors dive into different schools of economics and often encounter the same pathology. This pathology has afflicted large segments of the financial, business and political world.
In a nutshell, it is about fooling ourselves and others with made-up numbers.

The nature of uncertainty

In the first part of five of the book, the authors muse about the nature of uncertainty and introduce the concept of radical uncertainty. They recognize a general confusion about terms and their usage in different disciplines and common day usage.
Risk and uncertainty are not used for the same concepts. To make the difference more apparent, they create a new term: radical uncertainty.
They define resolvable uncertainty as “uncertainty which can be removed by looking something up (I am uncertain which city is the capital of Pennsylvania) or which can be represented by a known probability distribution of outcomes (the spin of a roulette wheel).” [KING]
For radical uncertainty, however, there “is no similar means of resolving the uncertainty – we simply do not know. Radical uncertainty has many dimensions: obscurity; ignorance; vagueness; ambiguity; ill-defined problems; and a lack of information that in some cases but not all we might hope to rectify at a future date.” [KING]
Radical uncertainty is even more general than the ideas of Taleb’s The Black Swan events. They are “not talking about ‘long tails’ – imaginable and well-defined events whose low probability can be estimated, such as a long losing streak at roulette. And we are not only talking [about] surprising events which no one could have anticipated until they happen, although these ‘black swans’ are examples of radical uncertainty.” [KING]
The concept is way more general and broader. It is “the vast range of possibilities that lie in between the world of unlikely events which can nevertheless be described with the aid of probability distributions, and the world of the unimaginable. This is a world of uncertain futures and unpredictable consequences, about which there is necessary speculation and inevitable disagreement – disagreement which often will never be resolved. ” [KING]
Interestingly enough, this concept is encountered in “the real world” far more often, than one would assume. They are the stuff of everyday experience. Radical uncertainty occurs, whenever we talk about “one of a kind” events, whenever there is no well defined probability distribution or these distributions are non-stationary, because the process is changing or reflexive.
A reflexive process occurs, when other agents are reacting to the actions of other agents. Like in almost any human endeavor.
A changing process could for example be technological innovation.
This view on uncertainty is not new. The famous economists Frank Knight and John Maynard Keynes were early proponents of these ideas. In fact, the economist “Knight believed that it was radical uncertainty that created profit opportunities for entrepreneurs and that it was their skill and luck in navigating radical uncertainty which drove technical and economic progress.” [KING]

The lure of probabilities

But this radical uncertainty has an unappealing effect: it precludes the application of standard tools of mathematics and physics.
For a lot of questions, this rules out “objectively” true answers and even renders some of the standard tools and definitions of “rationality” useless. The canonical model of rationality is the von Neumann–Morgenstern utility theorem. It states that under the conditions of axiomatic rationality, a decision-maker faced with probabilistic outcomes for different choices of action will behave as if he or she is maximizing the expected value of some utility function defined over the potential outcomes at some specified point in the future. For some of these events the probabilities are unknown. But that is generally not regarded as a problem, because subjective probabilities can be assigned. These can be obtained from “expert consultation” or alternatively by “reasoning” about them. It does not matter where these probabilities come from. They don’t have to relate to anything measurable in the real world. It is only important that they are consistent, i.e. conform to the formal axioms of probability. Applying these subjective probabilities transforms a real, confusing problem into a puzzle. “A puzzle has well-defined rules and a single solution, and we know when we have reached that solution. Puzzles deliver the satisfaction of a clear-cut task and a correct answer. Even when you can’t find the right answer, you know it exists. Puzzles can be solved; they have answers.”[KING]
Contrast this with what we are often faced with: mysteries. “Mysteries offer no such clarity of definition, and no objectively correct solution: they are imbued with vagueness and indeterminacy. We […] recognise that even afterwards our understanding is likely to be only partial. They provide none of the comfort and pleasure of reaching the ‘right’ answer.” [KING]
This doesn’t mean there is anything wrong with the mathematical theory of expected utility. The problem stems from a misapplication of the concept to areas of the real world, where the necessary conditions for its applications are not met. “The distinction between a small world in which people can solve problems by maximising expected utility and the large world in which people actually live is crucial [.]” [KING]
Reasoning about subjective probabilities can often be a good way to check assumptions or realize gaps in one’s knowledge. But one should always be aware of the source of these probability estimates and what we try to attach them to. “The claim that we can and should attach subjective probabilities to every event, far from enhancing understanding of the future, impedes that understanding.” [KING]
Unfortunately, it is an all too common occurrence that modelling experts are required to insert accurate numbers in predefined, standardized models and templates, about which they have no idea whatsoever. They default to a regrettable behavior and just invent the numbers they don’t know. The decision that are made on the basis of these numbers are far reaching: for example for public investments in transport infrastructure or actuarial valuations of “risk” in the portfolios of banks. The 2008-09 crisis revealed that models about risk and actual risk are not synonymous. The experts and modelers are in a tough spot. More often than not it is already known which are the demanded results. So modelers invent the numbers to fit the predetermined result. “Enthusiasm for ‘evidence-based policy’ is seen as the hallmark of sophisticated decision-making. The problem is not so much that these models give rise to bad decisions but that they provide supposedly objective cover for bad decisions which have been made on quite different grounds.” [KING]

Making sense of uncertainty & living with it

Will AI help us with these problems? The short answer is: nope.
“The ‘training base’ – the historical data series from which the experience of risk managers and the algorithms of machines were deduced – was largely irrelevant, drawn from a past which was very different from the present and the future.” [KING] So what we can reasonably expect is to “end up with forecasting models which work well so long as nothing much changes, and give us no insight into when things might change, or why.” [KING]
The authors are advocating for a different mode of reasoing. Abductive reasoning.
“[This] seeks to provide the best explanation of a unique event. For example, an abductive approach might assert that Donald Trump won the 2016 presidential election because of concerns in particular swing states over economic conditions and identity, and because his opponent was widely disliked. Deductive, inductive and abductive reasoning each have a role to play in understanding the world, and as we move to larger worlds the role of the inductive and abductive increases relative to the deductive.” [KING] The most important question decision makers have to answer is “What is going on here?“. Data and theory are not seen as a reliable guide to decisions in a world of radical uncertainty.

The authors conclude that there is a different interpretation to the often observed failure of human beings to act rationally as defined by common economic theories. “If we do not act in accordance with axiomatic rationality and maximise our subjective expected utility, it is not because we are stupid but because we are smart. And it is because we are smart that humans have become the dominant species on Earth. Our intelligence is designed for large worlds, not small. Human intelligence is effective at understanding complex problems within an imperfectly defined context, and at finding courses of action which are good enough to get us through the remains of the day and the rest of our lives. The idea that our intelligence is defective because we are inferior to computers in solving certain kinds of routine mathematical puzzles fails to recognise that few real problems have the character of mathematical puzzles. The assertion that our cognition is defective by virtue of systematic ‘biases’ or ‘natural stupidity’ is implausible in the light of the evolutionary origins of that cognitive ability. If it were adaptive to be like computers we would have evolved to be more like computers than we are. ” [KING]

The book is a very thorough venture into the follies of people that are fooled by randomness and forced to provide a sense of certainty in a world of genuine uncertainty.
It exposes how made up numbers nevertheless instill a feeling of security and all too often provide a convenient excuse for decisions. It casts a lot of doubt on the mainstream schools of thought in economics.

An interesting book, but far too long-winded and lengthy for my personal taste.

Source:
[KING] King, Mervyn. Radical Uncertainty: Decision-making for an unknowable future . Little, Brown Book Group. Kindle-Version.