Octavian Report: What do you mean by radical uncertainty, from an economic decisionmaking perspective?
Sir Mervyn King: What my co-author John Kay and I mean by radical uncertainty is, in essence, uncertainty that cannot sensibly be quantified.
We've all grown up with the idea that if you are intelligent, you think about uncertainty in terms of probabilities, and there are many people who will try to interpret any kind of future uncertainty in terms of some probability. I think this is a serious mistake and it detracts from good decisionmaking.
Two examples illustrate this very clearly. One is the financial crisis and the other is the COVID-19 crisis we're going through now. In both cases, we knew something. We knew that banking crises could occur. We've seen many in the past. We knew that pandemics could occur. But there was no way that we could have said: “What's the probability that there will be a virus emanating from Wuhan in China in December 2019?”. It didn't make sense to ask that question.
What it made sense to do was to say: “We can't anticipate precisely when or where, but we know that it's quite possible that there will be a virus coming from a country and that will spread to the rest of the world. So what is the right basis for being prepared or ready for this kind of event?” In the financial crisis, I think what you saw very clearly was that people relied on what we call bogus quantification. Recall the famous statement coming out of Goldman Sachs that what we were seeing was 25 standard deviations several days in a row — literally virtually impossible. What we saw was that the model that they had been using was wrong. It just didn't apply. We live in a world which is non-stationary, to use the technical phrase: things keep changing. It's not simply changing in the sense that you toss a coin and you sometimes get heads and sometimes get tails, and you can't tell in advance which you'll get, but you know that on average it'll balance out at 50/50. This was something where you just didn't know what would happen. To pretend that we can quantify it is potentially dangerous because it makes you feel that you can somehow manage these uncertainties in the future by pricing them if they're financial or by making accurate quantitative predictions — when in fact we can't.
OR: Once the virus is under control, do you think that there will be a strong snapback or do you think there's been permanent damage done to the economy in the Western world?
King: There are two parts to an answer to that question. The first one is getting the virus under control. I'm not sure that we ever will until we have a vaccine. Of course the risk of relaxing the restrictions too early is that you then get a second wave of infections (something there is already evidence of). This is the great challenge, which is to find a way essentially of flattening the peak of critical cases and deaths that place an enormous burden on our health services. By doing that, you are going to be pushing many of these cases out into the future that may have real benefits in terms of spreading the burden on health services and improving the quality of care delivered to those patients who need critical care. It doesn't in itself guarantee that you reduce significantly the total number of deaths or cases. To some extent, until we get a vaccine, we are always going to be in a position of trying to juggle the extent to which we use lockdowns to prevent the further spread of the virus against the enormous damage which the lockdown is doing, not just to incomes and output in the economy, but to people's wellbeing and health.
If you try to constrain people to stay indoors all the time, you are going to be doing great incidental damage, and by shutting schools and universities we have done damage to the younger generation. That's the first known dimension of all this. I think that we can't necessarily talk in terms of controlling the virus, but I do think that it would be sensible for governments to think about explaining carefully how they think about an exit strategy. They shouldn't give timescales where we don't know what the timescale is.
This is why this episode is an example of radical uncertainty. The models give us a good feel for the shape of the epidemic, and they actually are very helpful in telling us what data we should be trying to discover now. I think that the government is going to have to balance these considerations by making what essentially will be pragmatic judgments. The science will not tell us exactly when we should relax the restrictions. It will be gradual, I think.
I think there's a lot to be said for trying to allow those groups for which the virus is much less serious, like younger people, to be able to escape from the lockdown and go about their normal work and resume schools. That will be the first step in order to try to protect the really vulnerable groups, the elderly and people with existing health conditions. But the fact that we can't easily quantify this, the fact that we can't predict the date when the lockdown will end, is not a good reason for governments to say nothing. I think they would be better off saying: this is what we know and this is what we don't know. This is going to be our strategy towards the exit. I can't tell you today when that will be, but these are the criteria we will use. This is how we are thinking of implementing the exit strategy. The only way to get people to follow medical advice is to persuade them that governments are being honest about it and are being absolutely straightforward in saying what they know and what they don't know.