Economics as science?

Mainstream economics aspires to be a science in the sense of being able to make predictions, at least probabilistically. Some (mostly before 2007/8) claimed that it achieved this, because its methods were ‘scientific’. But are they? Keynes coined the term ‘pseudo-mathematical’ for the then mainstream practices, whereby mathematics was applied without due regard for the soundness of the application. Then, as now, the mathematics in itself is as much beyond doubt as anything can be. The problem is a ‘halo effect’ whereby the application is regarded as ‘true’ just because the mathematics is. It is like physics before Einstein, whereby some (such as Locke) thought that classical geometry must be ‘true’ as physics, largely because it was  true as mathematics and they couldn’t envisage an alternative. Alternatively, perhaps it would be like having confidence in a novel airplane design simply because it was designed and developed using a reliable computer.

From a logical perspective, all that the use of scientific methods can do is to make probabilistic predictions that are contingent on there being no fundamental change. In some domains (such as particle physics, cosmology) there have never been any fundamental changes (at least since soon after the big bang) and we don’t expect any, that we do not know for sure that they wont happen. But economics, as life more generally, seems full of changes. It is like much of engineering, that pushes on the bounds of what is known and often finds flaws in its previous understanding. Except that there seems little recognition of the flaws, and hence – unlike engineering –  little progress in understanding key areas.

Popper famously noted that proper science is in principle falsifiable. Many practitioners in science and science-like fields regard the aim of their domain as to produce ‘scientific’ predictions. They have had to change their theories in the past, and may have to do so again. But many still suppose that there is some ultimate  theory, to which their theories are tending. But according to Popper’s test this is not a ‘proper’ scientific belief. Following Keynes we may call it an example of ‘pseudo-science’: something that masquerades a science but goes beyond its bounds. Instead, as physicists do, we should only ever regard our results as ‘true’ in the sense that they are true to the domain in which they have been tested, and better than the other theories that we happen to have thought of.

One approach to mainstream economics, then, is to disregard the pseudo-scientific ideology and just take its scientific content. Thus we may regard its predictions as mere extrapolations, and look out for circumstances in which they may not be valid. (As Eddington did for cosmology.)

For example, mainstream economics depends heavily on a notion of stochasticity:

  1. That there is some pre-ordained state space.
  2. That transitions accord to some fixed conditional probabilities.

For most of us, most of the time, fortunately, these seem credible locally and in the short term, but not globally in space-time. (At the time of writing it seems hard to belive that just after the big bang there were in any meaningful sense state spaces and conditional probabilities that are now being realised.) We might adjust the usual assumptions:

The ‘real’ state of nature is unknowable, but one can make reasonable observations and extrapolations ‘as if’ reality were stochastic that will be ‘good enough’ most of the time for most routine purposes, or at least the best we can do given our current toolkit of ideas and methods.

This is true for hard and soft sciences, and for economics. What varies is the balance between the routine and the exceptional.

Keynes observed that some economic models work because people expect them to. For example, gold tends to rise in price because people think of it as being relatively sound. But why gold and not other precious materials? But anything that has a huge effect on expectations can undermine any prior extrapolations. This could be when the price of gold reaches the point where it can b made, or a large new source founds. More generally, it could be  a new product or service, an independence movement, a conflict or a cyber failing. These all have a structural impact on economies that can cascade. But will the effect dissipate as it spreads, or may it result in a noticable shift to ‘the system as a whole’? A mainstream economist would argue that all such impacts are probabilistic, and hence all that was happening was that we were observing new parts of the state space and new transitions. If we suppose for a moment that it is true, it is not a scientific belief, and hardly seems a useful way of thinking about potential and actual crises. It seems more natural to suppose that there can be genuine innovation, and this can be significant.

Mainstream economists suppose that people are ‘rational’, by which they mean that they act as if they are maximizing some utility, which is something to do with value and probability. But even if the world is probabilistic, being rational is not necessarily scientific. For example, when a levee is built  to withstand a ‘100 year storm’, this claim may  scientific if it is clear that the claim is based on past storm data. But it is unscientific and misleading if there is an implicit claim that the climate wont change. When building a levee it may be ‘rational’ to build it to withstand all but very improbable storms, but it is more sensible to add a margin and make contingency arrangements and monitor the situation (as engineers normally do). In much of life it is common experience that the ‘scientific’ results aren’t entirely reliable, so it is unscientific to totally rely on them.

Much of this is bread-and-butter in disciplines other than economics, and I am not sure that what economists mostly need is to improve is their mathematics: they need to improve their sciencey-ness, to set a better context for their mathematics. This will entail different mathematics. But I do think that they need somehow to come to a better appreciation of the mathematics of uncertainty, beyond basic probability  theory and its ramifications. Hence my blog.

Dave Marsay

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