I have covered some of the ideas in the book in previous posts (here and here) but have now had the chance the read the book in full and can recommend it. I have included more detailed notes on the book here but this post offers a short introduction to some of the key ideas.
Kay and King cover a lot of ground but, simply put, their book is about
“… how real people make choices in a radically uncertain world, in which probabilities cannot meaningfully be attached to alternative futures.”
One of the things that makes the book interesting is that they were once true believers in decision making models based on rational economic agents seeking to maximise or optimise expected value.
As students and academics we pursued the traditional approach of trying to understand economic behaviour through the assumption that households, businesses, and indeed governments take actions in order to optimise outcomes. We learnt to approach economic problems by asking what rational individuals were maximising. Businesses were maximising shareholder value, policy-makers were trying to maximise social welfare, and households were maximising their happiness or ‘utility’. And if businesses were not maximising shareholder value, we inferred that they must be maximising something else – their growth, or the remuneration of their senior executives.
The limits on their ability to optimise were represented by constraints: the relationship between inputs and outputs in the case of businesses, the feasibility of different policies in the case of governments, and budget constraints in the case of households. This ‘optimising’ description of behaviour was well suited to the growing use of mathematical techniques in the social sciences. If the problems facing businesses, governments and families could be expressed in terms of well-defined models, then behaviour could be predicted by evaluating the ‘optimal’ solution to those problems.
Kay and King are not saying that these models are useless. They continue to see some value in the utility maximisation model but have come to believe that it is not the complete answer that many economists, finance academics and politicians came to believe.
Although much can be learnt by thinking in this way, our own practical experience was that none of these economic actors were trying to maximise anything at all. This was not because they were stupid, although sometimes they were, nor because they were irrational, although sometimes they were. It was because an injunction to maximise shareholder value, or social welfare, or household utility, is not a coherent guide to action.
They argue that the approach works up to a point but fails to deal with decisions that are in the domain of radical uncertainty
But we show in this book that the axiomatic approach to the definition of rationality comprehensively fails when applied to decisions made by businesses, governments or households about an uncertain future. And this failure is not because these economic actors are irrational, but because they are rational, and – mostly – do not pretend to knowledge they do not and could not have. Frequently they do not know what is going to happen and cannot successfully describe the range of things that might happen, far less know the relative likelihood of a variety of different possible events.
There are many factors that explain the current state of affairs but a key inflexion point in Kay and King’s account can be found in what they label “A Forgotten Dispute” (Chapter 5) between Frank Knight and John Maynard Keynes on one side and Frank Ramsey and Bruno de Frinetti on the other, regarding the distinction between risk and uncertainty. Knight and Keynes argued that probability is an objective concept confined to problems with a defined and knowable frequency distribution. Ramsey argued that “subjective probability” is equally valid and used the mathematics developed for the analysis of frequency based probabilities to apply these subjective probabilities.
“Economists (used to) distinguish risk, by which they meant unknowns which could be described with probabilities, from uncertainty, which could not….. over the last century economists have attempted to elide that historic distinction between risk and uncertainty, and to apply probabilities to every instance of our imperfect knowledge of the future.”
Keynes and Knight lost the debate
Ramsey and de Finetti won, and Keynes and Knight lost, that historic battle of ideas over the nature of uncertainty. The result was that the concept of radical uncertainty virtually disappeared from the mainstream of economics for more than half a century. The use of subjective probabilities, and the associated mathematics, seemed to turn the mysteries of radical uncertainty into puzzles with calculable solutions.
Ramsey and de Finetti laid the foundations for economists to expand the application of probability based thinking and decision making. Milton Friedman picked up the baton and ran with it.
There is a lot more to the book than interesting historical anecdotes on the history of economic ideas. The subject matter is rich and it crosses over topics covered previously in this blog including:
- The way in which using probability can disguise uncertainty
- A “Bank Underground” post discussing possible pitfalls in what they describe as “a 1-in-X approach to financial stability” which approaches the same point from a different angle
- The New York Federal Reserve’s use of an “Applied Critical Thinking” unit to challenge conventional thinking
- Applying the concept of a “zone of validity” to the models used to measure bank capital requirements
- A book by Paul Wilmott and David Orrell titled “The Money Formula” where I picked up the zone of validity concept
- A great paper by Andrew Haldane and Benjamin Nelson titled “Tails of the Unexpected”
- Ed Catmul’s book titled “Creativity, Inc” which recounts his experience in how Pixar dealt with uncertainty
There are also overlaps with a book by Richard Bookstaber titled “The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction”. I am yet to review this book but have some detailed notes here.
One quibble with the book is that I think their critique of the Bayesian method is a bit harsh. I understand their concern to push back on the idea that Bayes solves the problem of using probability to understand uncertainty. At times however it reads like Bayes has no value at all. Read “The Theory that Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy” by Sharon Bertsch McGrayne for an alternative perspective.
Bayes may not help with mysteries but its application in puzzles should not be undervalued. I don’t entirely agree with their perspective on behavioural finance either.
I want to come back to the topics of risk and uncertainty in a future post but it will take time to process all of the overlapping pieces. In the interim, I hope you found the overview above useful.
Tony (From the Outside)
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