Embracing Uncertainty: An Economist’s Guide to the Future
Written by Lord Mervyn King
“We live in uncertain times” must be the most frequent line used by politicians and economists alike. “Uncertainty is the new normal” said IMF managing director Kristalina Georgieva recently. In his foreword to the UK’s defence strategy published in June, even Keir Starmer said that we live in an “era of radical uncertainty”, although I am not sure that when he wrote those words, he realised that it applied to his own position.
We are afraid of uncertainty in the same way that children are afraid of the dark. We fear the unknown. My aim today is to try to persuade you to embrace uncertainty, and to enjoy the serendipity which enriches our lives.
What is uncertainty? How do we measure it? Uncertainty reflects our incomplete information about the future, the present and sometimes even the past. In my 2016 book, The End of Alchemy, I introduced the concept of radical uncertainty, and in the 2020 book Radical Uncertainty co-authored with John Kay we distinguished two types of uncertainty: resolvable and radical uncertainty. Resolvable uncertainty is when the uncertainty can be removed or tamed, either by looking something up and gaining more information, or by representing the uncertainty as a probability distribution, as with the rolling of dice. We all know when King’s College was founded, but when was Trinity College founded? It doesn’t matter if we don’t know because we can easily look it up; if you are interested the answer is 1546. Gambling in a casino is a game against known odds. We can calculate the likelihood of various outcomes and decide whether or not to play.
Radical uncertainty is very different. It is a situation in which we do not know the odds. Covid-19 is an example of radical uncertainty. We knew pandemics were possible, even likely. But there was no way in which one could attach a probability to an event such as “the emergence of a contagious virus from Wuhan in China in December 2019”. And the attempt to quantify the risk of a pandemic would have inhibited a useful discussion about how to prepare for such an event and improve the resilience of our public health services.
Russia’s invasion of Ukraine is another example of radical uncertainty. It was a possible event, but the timing, scale and consequences of the invasion were something to which it would have been fruitless to waste time attaching probabilities. We knew something but not enough to pretend to quantify the uncertainty involved.
The inability to quantify uncertainty undermines much of traditional economics, and the economics profession is reluctant to accept it. We need to return to a debate of a century ago. Much of the intellectual foundations of how to think about uncertainty were laid here in King’s by two intellectual giants of the twentieth century, Frank Ramsey and John Maynard Keynes. In his Fellowship dissertation, published as A Treatise on Probability in 1921, Keynes argued persuasively that ignorance cannot be represented in terms of probabilities. But his colleague Frank Ramsey argued that people have subjective probabilities defined over all possible outcomes, and that we can infer those probabilities by observing or asking people about the odds on which they are prepared to bet. Those subjective probabilities must be coherent, Ramsey argued, because otherwise betting at those odds would be certain to lose money, and people do not bet when they are certain to lose. Once we make the leap of faith that to each possible future event people attach probabilities, economists then explain actions in terms of maximising the expected value of profits for a business, social welfare for a government, and happiness or utility for a household. Individuals are simply calculating machines.
Frank Ramsey had the good sense to die at the age of 26, thus cementing his reputation as a lost genius and ending the debate about the existence of subjective probabilities. But in this respect Ramsey was wrong, and Keynes had the better intuition. Ramsey overlooked the fact that most people do not bet on most things. The proposition that we can infer from the odds at which people are prepared to bet their subjective probabilities of all possible states of the world was refuted convincingly in 1950 in the wonderful musical Guys and Dolls when Sky Masterson explains the folly of betting to Nathan Detroit by describing his father’s advice:
“On the day I left home to make my way in the world, my daddy took me to one side. "Son," my daddy says to me, "I am sorry I am not able to bankroll you to a large start, but not having the necessary lettuce to get you rolling, instead, I'm going to stake you to some very valuable advice." "One of these days, a guy is going to show you a brand-new deck of cards on which the seal is not yet broken." "Then this guy is going to offer to bet you that he can make the jack of spades jump out of this brand-new deck of cards and squirt cider in your ear." "But, son, you do not accept this bet because, as sure as you stand there, you're going to wind up with an ear full of cider."
Most people are far too sensible to bet with people who propose a bet. We do not know enough about the context of such bets to be sure we understand how events could play out. We live in a world of radical uncertainty.
In such a world we cannot tame uncertainty by assuming the existence of subjective probabilities that allow us to reduce making decisions to a mathematical calculation. But instead of learning to cope with radical uncertainty, we turn away from reality and search desperately for certainty, and we are prepared to delude ourselves to satisfy our craving. Economists are often pressed for a forecast which is then presented as a single number. Spurious precision in both modelling and forecasts is an attempt to escape from the reality of radical uncertainty. Let me give you two examples.
First, the global pandemic of Covid. Last week, the Covid-19 Inquiry published a report stating that “Had a mandatory lockdown been imposed on or immediately after 16 March 2020, modelling has established that the number of deaths in England in the first wave up until 1 July 2020 would have been reduced by 48% – equating to approximately 23,000 fewer deaths”. No such statement should have been made. Too often modelling becomes an exercise in bogus quantification. Epidemiological models are extraordinarily useful in describing the general shape of an epidemic. But they cannot provide a precise description without accurate information about key parameters, such as the fatality rate of the disease. And some of the most important parameters reflect human behaviour rather than unchanging scientific laws. Science, and especially models, cannot tell us what to do. The cost of a policy of “following the science” was the failure to make and explain decisions which reflected trade-offs between measures to slow the inevitable spread of the virus and their impact on the health and wealth of the population.
My second example is the announcement in this week’s budget that public sector net borrowing would be £138.3 billion in 2025-26, £112.1b. in 2026-27, £98.5 in ‘27-28, £86.9 in ‘28-29, £67.9 in ‘29-30, and £67.2 billion in 2030-31. You couldn’t get a better example of spurious precision. The chance of those numbers equalling the eventual outturn is close to zero. Although the Office for Budget Responsibility discusses risks to the outlook, in their speeches Chancellors reel off a series of precise numbers. This focus on the spurious precision of a point forecast, and the arbitrary fiscal rules relating to a four-year rolling forecast horizon that is never reached, rather than a qualitative assessment of risks is damaging to sensible decisions on public spending and taxation.
The focus on probabilities for every outcome and the spurious precision of forecasts give uncertainty a bad name. So I want to ask if uncertainty can ever be beneficial. Without uncertainty there is no possibility of new ideas, new products and innovation in general. But economic progress and increases in productivity depend on innovation. Innovation requires creativity. And creativity is the use of imagination to find a way to understand better the world of radical uncertainty. In business, as in the arts and sciences, and in life itself, uncertainty and creativity are inseparable. And many of our best ideas come as a by-product of looking in a different direction.
New ideas are the gradual conversion of uncertainty into knowledge. But this progress is not the result solely of chance but of the directed efforts of people to improve our understanding of how the world works. I describe this process as purposive serendipity. What do I mean by this? Purposive serendipity is the interface between radical uncertainty and the observation that necessity is the mother of invention. The implication of such a view of how progress is achieved is that uncertainty, far from being something we should fear, is a force for good. Serendipity is the source of much that is best in our world. Like all good explorers embarking on a journey, we should embrace uncertainty.
Many people think of innovation as the invention of something that had never been imagined until it had been created – the concept of “black swans” in the popular description of Nassim Taleb. They think that innovation is by definition a black swan. But innovation – indeed, the concept of radical uncertainty itself – goes way beyond inventions that are impossible to conceive until they have happened. Much innovation is the successful discovery of a way to achieve something that has already been imagined in principle – travel to outer space, for example. The path to innovation is one along which we know something but not enough. Eventually, the innovation becomes a reality and emerges as a commodity or service that can be sold on the market. It is a path of purposive serendipity.
The Concise Oxford Dictionary defines serendipity as “the faculty of making happy and unexpected discoveries by accident”. But there is more to be said. Serendipity is a fascinating word. It was the subject of a remarkable book by the famous sociologist Robert K Merton.[i] Written in the 1950s and published only in 2004, The Travels and Adventures of Serendipity explored the origins of the word and its various meanings. The word “serendipity” was coined by the English man of letters Horace Walpole in 1754. Writing to his friend Horace Mann, the British minister to the Court of Florence, Walpole described the making of discoveries by accident as “serendipity” after a “silly fairy tale” called The Travels and Adventures of Three Princes of Serendip that had been translated from Persian into French and then into English and published in London in 1722. We have come to use the word serendipity to describe knowledge acquired through happy accidents while seeking something altogether different.
My own experience of serendipity came one evening in the London Library. While searching for a particular history of the Bank of England, my eye was caught by a title on the shelf above: The Old Lady Unveiled by a Mr J.R. Jarvie. How could a book with such a risqué title have found its way into the section on money and central banking? Published in 1933, The Old Lady Unveiled turned out to be a brilliant attack on the Bank of England (whose moniker is The Old Lady of Threadneedle Street). The book was not mentioned in any history of the Bank, nor in any relevant biography or bibliography, and nor was it known to anyone of my acquaintance. Jarvie was not overly impressed by economists: ‘If you want to find violence of language, go to the economists. No zealot, religious or political, can work himself up to such a white heat as a professor of the dismal science in defence of a theory’.[ii] I shall try to avoid raising the temperature to that level.
My discovery of Jarvie’s book was serendipitous. I would not have found it in any other way. But it was not a random discovery – I was after all looking for histories of the Bank of England.[iii] Equally, the most cited example of serendipity – the discovery of penicillin in 1928 by the British Nobel Laureate Alexander Fleming when he found that mould which had developed accidentally on a staphylococcus culture plate had created a bacteria-free circle around itself – came through a programme of research into bacteriology. And Fleming had much to do before his serendipitous discovery resulted in penicillin. Apart from trivial examples such as my discovery of an unknown book, serendipity leads to important and useful results only when the recipient has a receptive mind. It represents a set of clues that require the right person to analyse and solve. Fleming was not awarded the Nobel Prize because he won a lottery but because he was alert enough to observe a clue that after further work led to the discovery of penicillin.[iv] Serendipity is, in the words of James Shulman, “a process hovering ambiguously between the clever and the clairvoyant, the incisive mind and the wheel of fortune”.[v]
It is clear then that serendipity and radical uncertainty are intimately related. And it shows us that uncertainty is not all downside. Serendipity is the potential benefit of the existence of radical uncertainty. We start by knowing something, but not enough. And we receive unexpected clues that direct us to our intended destination.
This takes us directly to the link between economic progress, serendipity and radical uncertainty. Innovation results from a search to improve the processes and products we currently employ. It is a process of looking, to use the apt biblical metaphor, “through a glass, darkly”. The rise and fall of technologies and businesses cannot be described in terms of stationary processes which remain unchanged over long periods of time and capable of being described by known probability distributions. We can imagine new technologies in general terms without being able to predict the specific path along which they will evolve – artificial intelligence being a good example. They are things we see through a glass, darkly. Progress can be leaps forward, as in the use of electric power, or the cumulation of large numbers of small improvements. To take one example emphasised by Paul Romer, combining different chemical elements generates new compounds with sometimes surprising properties, as when tin and copper combined yield bronze and carbon and iron combined yield steel.[vi] Further discoveries produced superconductors and the application of rare earths to the production of green energy. There are billions of such combinations that may yet yield useful new compounds.
In this sense economic progress is based on serendipity and radical uncertainty. Let me explain briefly why the idea of purposive serendipity differs from traditional theories of economic growth in two ways. The first concerns the nature of technological progress. With finite resources unlimited economic progress is impossible. But the world has experienced economic progress for several centuries and certainly since the Industrial Revolution. How is this compatible with the constraint implied by finite resources? The answer is the discovery of new ideas about how to combine those finite resources. The key to understanding the process of economic growth is to understand how new ideas are generated and spread throughout the economy.
In the early models of economic growth improvements in the relationship between inputs of various factors of production – labour, capital, and land – and outputs of final goods were exogenous to economic activity. They descended like manna from heaven.[vii] At first sight, serendipity might appear rather like manna from heaven. But as the examples of my search for a history of the Bank of England and the more important discovery of penicillin show, serendipitous discoveries result from purposive actions. It is the search for ways to improve productivity that leads to technical progress. Serendipitous discoveries are not purely accidental; they are endogenous to economic activity.
The second way in which I suggest serendipitous economic progress differs from conventional theories is the importance of collective rather than individual innovation. We learn from others. Progress today is rarely the result of an isolated individual producing a new idea; it is more likely to be the result of a collective effort by many individuals working together. Even elite sports players now require a team around them, each with a distinct skill. Human intelligence is collective intelligence. Pigeons can fly whereas humans cannot. But humans can build an aeroplane to fly from London to Perth in Australia without stopping, although the England cricket team probably wishes that their flight had stopped much earlier. The ability to construct such a complex machine is the product of a collective intelligence accumulated over a long period. To see the discovery of new ideas purely from an individual perspective rather than that of the group is to miss the essence of how modern economies function. Agglomerations of people living in dense urban environments – or even King’s College, Cambridge – provide a fertile source of contacts, both market-driven and serendipitous. The people we meet are central to the way we come to think. New ideas spread and mutate rather as a virus spreads through the community.
These two factors – the purposive nature of serendipitous discoveries and the role of collective intelligence – lie behind the nature of technological progress. There is a demonstration effect from observations of new ideas embodied in new investment projects to productivity elsewhere. We might describe this as “learning by looking”. Once an idea is embodied in an investment project, the spillover effect occurs up-front. Even though the project continues to operate there is no subsequent additional demonstration effect. This means that it is the new investment that creates the spillover. Of course, efforts to learn from others do not always lead to progress. Imagining how new ideas could be adapted to a different setting is by its nature serendipitous. But it suggests that, on average, the rate of productivity growth in the economy as a whole will be related to the proportion of economic activity that takes the form of new investment projects.
The nature of such a relationship is captured by analogy with the spread of a new virus through a population. Initially, the higher the rate of investment the greater the number of others that will come into contact with a new idea. But conditional upon contact with a new idea, the addition to the growth of productivity of existing firms declines with the number of new ideas with which contact occurs. In other words, there are decreasing returns to learning by looking. Consider the example of research in a university. Too few people doing research means little prospect of contact with the new ideas of others that may prove helpful to one’s own research. But as the number of active colleagues rises, the benefit provided by the marginal idea falls because of the opportunity cost of absorbing new ideas. These assumptions lead to a non-linear relationship in which, like the spread of an epidemic among an initially uninfected population, productivity growth rises very slowly as investment increases above zero, then starts to accelerate but then decelerates as more and more ideas are generated.
Without going into detail, let me mention an interesting property of such a theory. The nonlinear relationship between productivity growth and the share of investment in economic output leads to multiple equilibria in which an economy can find itself in one of several different equilibrium growth rates. A low rate of investment generates little learning by looking, a low rate of productivity growth, and hence a low demand for investment. But another equilibrium can exist in which a high rate of investment leads to higher productivity growth which in turn generates the higher demand for investment. And allowing for the uncertainty of serendipity creates dynamics in which an economy cycles around either a low or high growth rate for a period before suddenly jumping to a different growth rate around which it would then cycle. Two economies that appear identical in all respects can experience different paths of output and growth depending on their past history. Moreover, it is possible that disruptions to a country’s capital stock can lead to an acceleration of investment that results in the economy jumping from a low to high growth rate path – a formalisation of the idea of taking one step back to take two steps forward. That could explain the high growth rates experienced by continental Europe in the decades immediately after the Second World War. History matters.
In, conclusion, uncertainty does create risks. But it also leads to opportunities. At graduation ceremonies I have heard new graduates worrying about the uncertainty of their new lives. My riposte is to say that if I could tell them exactly how their future will unfold – which jobs they will do and when, who their life partner will be, where they will live – all excitement and hope would disappear.
Most of the important things in life – the people we meet, the ideas we form and the actions we take – reflect a high degree of serendipity. Without it, life would not be worth living. And to those students and graduates still worried about the future, I would commend the words of Vaclav Havel in 1991 about the importance of hope[viii]:
“The kind of hope I often think about (especially in situations that are particularly hopeless . . .), [is] a state of mind, not a state of the world. Hope, in this deep and powerful sense, is not the same as joy that things are going well . . . but rather, an ability to work for something because it is good, not just because it stands a chance to succeed”.
Serendipity truly is the spice of life, so please embrace uncertainty.
[i] The work was co-authored with Elinor Barber who sadly did not live to see the book’s publication first in Italian by Il Mulino in 2002 and subsequently in English by Princeton University Press in 2004.
[ii] Jarvie (1933), p. 75.
[iii] There is a certain parallel between my serendipitous discovery of Jarvie’s book and Merton’s own discovery of the word serendipity. While searching for the history of a different word beginning with “se” he was “riffling through the pages of volume 9 of the incomparable Oxford English Dictionary … when my eye happened upon the strange-looking and melodious-sounding word serendipity” (Merton and Barber, 2004), p.233-34.
[iv] As Merton explained, Walpole used the word serendipity to describe something for which he had in fact been looking (certain information about Venetian heraldry), and the three Princes of the “silly fairy tale” discovered nothing at all.
[v] James Shulman in Introduction to Merton and Barber (2004), p. xiv.
[vi] Romer (2019).
[vii] As in the basic growth model of Solow (1956).
[viii] I am gratel to Robert Hetzel for drawing this quotation to my attention.
Lord Mervyn King is the former Governor of the Bank of England. He studied economics at King’s College Cambridge, graduating with a first-class master's degree in 1969, and later became an Honorary Fellow. Lord King has built a distinguished career as an economist, central banker, and author, and is well-known for guiding the UK through the 2008 financial crisis and writing on monetary systems. He was a prominent figure at the London School of Economics (LSE) before his tenure at the Bank of England, holding chairs at various universities and publishing influential books like The End of Alchemy.