Some uncertainties are resolvable. The insurance industry’s actuarial tables and the gambler’s roulette wheel both yield to the tools of probability theory. Most situations in life, however, involve a deeper kind of uncertainty, a radical uncertainty for which historical data provide no useful guidance to future outcomes. Radical uncertainty concerns events whose determinants are insufficiently understood for probabilities to be known or forecasting possible.
... a rant. An eloquent, highbrow, entertaining and enlightening rant, but a rant all the same. The authors’ bugbear is the standard approach to uncertainty in economics and related disciplines, which requires a comprehensive list of possible outcomes with well-defined numerical probabilities attached ... This is a necessary critique and the [authors] make it with verve, knowledge and a wealth of stories ... In that wealth of anecdote, however, the exact object of their criticism gets a little blurred. Are they aiming at the use of incorrect probabilities, such as when investors valued mortgage-backed securities on the basis that US house prices could not fall nationwide as never before? Or at thinking that a complex economy can be adequately captured by stylised mathematical models? Or at any attempt to put numbers on an uncertain future? ... Their alternative to probability models seems to be, roughly, experienced judgment informed by credible and consistent 'narratives' in a collaborative process. They say little about how those exercising such judgment would be held to account. The argument would be more convincing if it also explained, say, how this approach enabled the Bank of England to leave the UK well-prepared for the financial crisis, or why it wasn’t employed.
... a clear book, and it lands its argument that assuming the future could be predicted with great accuracy using known data was foolish. Certainly, the impact of the over-reliance by the financial services industry on dodgy numbers has been felt by all of us ... But it could reasonably be objected that it lands its argument more than once. It doesn’t require 444 pages plus endnotes to make the point that lots of numerical models are poorly specified and don’t account sufficiently for things we don’t know. It could have been said at half the length ... The book is well written, though, and is often entertaining. It is just that it goes on entertaining for quite a while. It can be read by the general reader, but I think its core audience is people who calculate financial risk. It’s not so much that the lay reader wouldn’t understand it, but more that it spends a lot of time attacking an error — the false precision of numbers and forecasting — that most people don’t encounter a great deal.
This illuminating work presents compelling arguments about the distinction between the concepts of risk and uncertainty, and the impact of both on modern finance, economics, and decision-making ... This informative book will appeal to readers wishing to delve further into the processes of modern finance and economic forecasts.