Brian Easton reviews the ideas raised in a John Kay/Mervyn King book about where historical data provides no useful guidance to future outcomes, challenging the foundations of modern economics

This is a re-post of an article originally published on pundit.co.nz. It is here with permission.

Two senior economists challenge some of the foundations of current economics.

It is easy to criticise economic science by misrepresenting it, by selective quotations, and by ignoring that it progresses, like all sciences, by improving and abandoning old theories. The critics may go on to attack physics by citing Newton.

So it is with considerable pleasure that one engages with John Kay and Mervyn King’s Radical Uncertainty. The authors are senior economists who have taught at major universities. Each has much practical experience. Kay left senior positions in the academy to work as a consultant and as a columnist and he has written a number of earlier books (some of which I should have reviewed); King was Governor of the Bank of England from 2003 to 2013 so that he oversaw the central bank during the Global Financial Crisis.

Because it is a serious critique of economics, the book has to go back into the foundations of the discipline. A useful starting point is the distinction between ‘risk’ and ‘uncertainty’ which faces anybody thinking about, or preparing for, the future. Briefly, risk is about where there are probabilities attached to future events; uncertainty is where there are not. The latter events include the ‘unknown unknowns’, or as Kay and King call them ‘radical uncertainties’.

The two situations involve quite different responses. In particular there is an elaborate economic analysis which shows how to approach a decision if the probabilities are known (using insurance, for example). However, often they are not. The example of uncertainty which Kay and King repeatedly use is former US president Barack Obama’s decision to launch the attack on Osama bin Laden. His advisers gave him probabilities of key considerations, but what is the meaning of their advice that there was an 80 percent chance bin Laden would be in the residence they were considering targeting? Obama had to make an intuitive decision based on his judgements (which, as it happened, successfully achieved its aim).

This is but one in a wealth of examples in the book. One suspects that the two could add many more from their own experience. The Governor of our Reserve Bank would nod, given the amount of judgement required in setting the Official Cash Rate.

However, the risk models are so powerful that we use them, often unconsciously, with guesstimates of the probabilities. Making probabilities up does not convert radical uncertaitiny into risk, even if it seems like it.

Poor quality estimates may not matter much in normal times, but when things go wrong they can be disastrous. An American bank developed an elaborate model for financial investment, but could not apply it because it required a key probability which was not known. To their surprise, competitor banks began using the model for their financial investment decisions. It turned out that the quants – quantitative analysts – running the models made up the key probability figures. (‘I’ve got my model; let not ignorance of reality get in the way of using it.’) Everyone learned this in the Global Financial Crisis when the American financial system imploded. The wrong assumptions underpinning the financial models almost brought down the capitalist system.

The meaning of probability is a philosophically deep issue. The book is rich in detail about the difference between risk and uncertainty. It presents a challenge to much of economics since we are all facing unknown unknowns in our everyday-life economic decisions, whether we are in households, businesses or government. Economists have tended to slip in the assumption that decisions makers are dealing with risk rather than uncertainty in a rational way. There is, however, a long tradition going back to Keynes and Frank Knight (who founded the Chicago School) contesting this approach.

It is not always appreciated that Keynes’ General Theory had radical uncertainty at its heart. (Joan Robinson, a colleague of Keynes, talked about ‘animal spirits’.) Subsequently, Keynes’ underlying model has been simplified and that critical role of radical uncertainty got lost.

Kay and King offer various guides about dealing with radical uncertainty. Perhaps the most useful is when facing a problem to first ask, ‘what is going on here?’ There is a human tendency, from which economists are not immune, to jump in with a solution without any analysis of the underlying situation.

Radical uncertainty also reinforces the research finding that the more certain one is, the more likely one is wrong. This applies to many critics of economics and many economists too. As in many professions, greater humility is an advantage. One difficulty though is that the media prefers certainty, even though those they go to regularly get it wrong.

Because probability theory is technical, many people may find much of the book opaque. My advice is to read the preface added to the 2021 ‘updated paperback edition’, which sets the concerns of the book into a critique of the current state of economics, plus Part I (the nature of uncertainty) and the final Part V (living with uncertainty), skipping the meatier intervening parts. (If you can cope with them, you should read them.) The issues they are covering are much wider than just economics; if you have the mildest introspection into personal decision making you will find the contents valuable.

The preface, in particular, challenges many of those who criticise economics. If they have not read the preface, and their criticism has not engaged with it, what they are saying is probably superficial.

A final point. I claim no expertise on artificial intelligence of the chatbot sort and the book was written before it became prominent. But the existence of ‘radical uncertainty’ suggests there are limits to what AI/chatbots can achieve. Imagine, if he could have at the time, Obama asking it whether to attack bin Laden. Perhaps part of the panic that educators are voicing, arises from they having been too concerned with teaching about known knowns and not enough with teaching about how to handle radical uncertainty.                                                                

Radical Uncertainty: Decision-Making Beyond the Numbers by John Kay and Mervyn King (paperback edition 2021)

*Brian Easton, an independent scholar, is an economist, social statistician, public policy analyst and historian. He was the Listener economic columnist from 1978 to 2014. This is a re-post of an article originally published on pundit.co.nz. It is here with permission.