The only way to make sense of these events was to do so in terms of what we call radical uncertainty. The idea that risk could be priced by the use of probabilities had led people into a false position in which they felt they could genuinely price all risk. It turned out in the financial crisis that it's not just that the risk model is used by banks were useless because the models didn't apply anymore, but the calculation of risk weights used by regulators of banks also turned out to be a very poor guide to which banks would be in serious trouble and which not. That's because they had believed that they could use past data to capture and quantify the risk and price it — and they believed that somehow if you could price it, you could tame risk.
John and I felt this was quite wrong, and so, coming from different directions, we decided that we would join forces again and write another book on radical uncertainty. We learnt a great deal in writing that book. We give lots of examples in the book about the nature of uncertainty when you can't easily quantify it. The example we like best — and we use it at the beginning of the book — is the challenge that President Obama faced when he was in the Situation Room and was asked to decide whether to send the Navy SEAL team in to the compound in Abbottabad in Pakistan for the capture of Osama Bin Laden. Everyone knew there was a person living in the compound. The question was: was that person Bin Laden?
Now, there are only two answers to that question, yes or no. Interestingly, after the Iraq War, Congress basically mandated the CIA when giving advice to the President to do so in probabilistic form. They had to give probabilities for these things. The different members of the CIA team when briefing the President said, "Well, I think it's 90 percent certain that the man is Bin Laden." Someone else said, "I think it's only 60 percent likely." Someone else said 70 percent and someone else said 40 percent. At the end of this — and we know this because President Obama gave an interview afterwards — President Obama said, "Look guys, this is 50/50. I can't decide on the basis of your probabilities."
Now, he did not mean by that that his own judgment was that it was 50 percent certain that it was Bin Laden. That only makes sense if you think that there are 100 episodes like this and 100 Bin Ladens to capture, and 50 percent of the time the guy turns out to be Bin Laden and 50 percent of the time he doesn't. This was a one-off, unique situation. President Obama said that probabilities confused the situation rather than helped it because the only honest answer was: we don't know. He had to make that decision knowing that there was no answer to the question. What he did was to probe the individuals from the CIA and asked them, "Why do you think it might be Bin Laden?" He tried to accumulate the evidence and then reach an overall judgment, but expressing it in terms of a probability did not in any way help him to reach the decision. We think, and we give many examples in the book, that lots of decisions in business and finance are exactly of that kind. They are unique decisions. They're not simply a replay of past events when you can just carry over observed frequencies of what's happened in the past to tell you what will happen in the future.
OR: Why do the ideas of quantification and of modeling based on it have such a hold on us?
King: I think it's because if you believe literally in the model as a description of the world, you can turn what is in fact a mystery into a puzzle. Puzzles have well-defined solutions. Chess or Go or other games are puzzles, as are crossword puzzles. We know what the answer is at the end.
Most decisions are not like that. You may not know after you've made your decision, or even several years later, whether you did make the right decision. The answer is never fully revealed. I think there is this great temptation of decision-makers and politicians to believe that they must convey a degree of certainty far greater than that which they can possibly have — and also a matching wish on the part of the public to believe in that degree of certainty.
We all want to be certain about things. I think that's a terrible mistake.
We have to recognize that some situations pose problems to us that are completely wrapped up with radical uncertainty. There is no simple answer, but there is an intelligent way to go about finding the information you may need to make a good decision.
One of my favorite examples in this is that, early on, during the spread of AIDS in southern Africa, the World Health Organization built large demographic models of the different countries in southern Africa and from these they built a very complex regional model. It was, in the end, almost a black-box model about how to predict the spread of AIDS in Southern Africa.
One of the variables in their model was the average number of sexual contacts per person per year, and they put a number in, turned the handle, and got out predictions for the spread of AIDS. The most sensible thing they did was to ask Bob May, our former chief government scientist here in the U.K. He said to them, "Look, stop turning the handle and thinking that you've got a model that predicts. You have a variable here — the average number of sexual contacts per person per year. Don't you realize that it makes an enormous difference weather that is 100 contacts with the same person or 100 contacts with 100 different people? You get absolutely different answers."