The Perils of Bogus Quantification

The British government recently sent a letter to every household saying it would do whatever was necessary to beat the coronavirus “if that is what the scientific and medical advice tells us we must do.” At the same time, numerous organizations — commercial, national and international — produced forecasts of the path of the economy over the next two years. But science, and especially models, cannot tell us what to do. In particular, using black-box models, in which the key assumptions are not laid out explicitly and debated, too often becomes an exercise in “bogus quantification,” a common policy failure John Kay and I describe in our new book, “Radical Uncertainty.”

In the context of Covid-19, there are two reasons for caution before rushing to judgment about the way forward. First, the science is uncertain — and how to take those uncertainties into account is not a question of fact. Second, the cost of the economic shutdown is not just a matter of forgone wages and gross domestic product, but also includes the harm the restrictions cause to the health and well-being of the tens of millions of people affected.