Radical uncertainty
12 Nov, 2025 at 19:07 | Posted in Economics | Leave a comment
The financial crisis of 2007-2008 took both laypeople and economists by surprise. What went wrong with our macroeconomic models, since they clearly did not foresee the collapse, or even make it conceivable?
Many have attempted to answer that question, offering a range of explanations, from the excessive mathematization of economics to irrational and corrupt politicians.
But the root of our problem runs much deeper. It ultimately comes down to how we view the data we handle. In ‘modern’ macroeconomics — Dynamic Stochastic General Equilibrium, New Synthesis, New Classical, and New ‘Keynesian’ — variables are treated as if drawn from a known ‘data-generating process’ that unfolds over time, for which we therefore have access to vast amounts of historical time-series. If we do not assume we know the ‘data-generating process’ — if we do not have the ‘true’ model — the entire structure collapses. And of course, it has to. I mean, who honestly believes we should have access to this mythical Holy Grail, the data-generating process?
‘Modern’ macroeconomics clearly failed to anticipate the magnitude of the problems created by unregulated ‘efficient’ financial markets. Why? Because it is based on the myth that we know the ‘data-generating process’ and that we can describe the variables of our evolving economies as if drawn from an urn containing stochastic probability functions with known means and variances.
This is akin to saying you are going on a holiday trip and that you know the chance of the weather being sunny is at least 30%, and that this is enough for you to decide whether or not to bring your sunglasses. You are supposed to be able to calculate the expected utility based on the given probability of sunny weather and make a simple decision of either-or. Uncertainty is reduced to risk.
But, as Keynes convincingly argued in his monumental Treatise on Probability (1921), this is not always possible. Often, we simply do not know. We cannot put exact numbers on the assessments we make of future happenings. We cannot calculate means and variances. There are no given probability distributions we can rely on.
In the end, this is what it all boils down to. We all know that many activities, relationships, processes, and events fall into the category of Keynesian uncertainty. The data do not unequivocally point to one decision as the only ‘rational’ one. Neither the economist nor the decision-maker can fully pre-specify how people will decide when facing uncertainties and ambiguities that are ontological facts of the way the world works.
Some macroeconomists, however, still want to be able to use their hammer. So, they decide to pretend the world is a nail and pretend that uncertainty can be reduced to risk. They build their mathematical models on that assumption. The result: financial crises and economic ruin.
How much better — how much more likely we are to avoid falling into the comforting thought that we know everything, that everything is measurable, and that we have everything under control — if, instead, we could simply admit that we often do not know, and that we have to live with that uncertainty as best we can.
Fooling people into believing that one can cope with an unknown economic future in a way similar to playing roulette is a sure recipe for only one thing — economic disaster.
The disregard of radical uncertainty by a generation of economists condemned modern macroeconomics to near irrelevance … Keynes’ critique of ‘getting on the job’ without asking ‘whether the job is worth getting on with’ would prove to be as true of the new macroeconomic theorising as of the older econometric modeling …
Over forty years, the authors have watched the bright optimism of a new, rigorous approach to economics dissolve into the failures of prediction and analysis which were seen in the global financial crisis of 2007-08. And it is the pervasive nature of radical uncertainty which is the source of the problem.
