Mainstream economics — the art of building fantasy worlds

20 Jun, 2022 at 15:50 | Posted in Economics | 2 Comments

Mainstream macroeconomic models standardly assume things like rational expectations, Walrasian market clearing, unique equilibria, time invariance, linear separability and homogeneity of both inputs/outputs and technology, infinitely lived intertemporally optimizing representative household/ consumer/producer agents with homothetic and identical preferences, etc., etc. At the same time, the models standardly ignore complexity, diversity, uncertainty, coordination problems, non-market clearing prices, real aggregation problems, emergence, expectations formation, etc., etc.

How to Build a Fantasy Economy - YouTubeBehavioural and experimental economics — not to speak of psychology — show beyond doubt that “deep parameters” — peoples’ preferences, choices and forecasts — are regularly influenced by those of other economic participants. And how about the homogeneity assumption? And if all actors are the same — why and with whom do they transact? And why does economics have to be exclusively teleological (concerned with intentional states of individuals)? Where are the arguments for that ontological reductionism? And what about collective intentionality and constitutive background rules?

These are all justified questions — so, in what way can one maintain that these models give workable microfoundations for macroeconomics? Science philosopher Nancy Cartwright gives a good hint at how to answer that question:

Our assessment of the probability of effectiveness is only as secure as the weakest link in our reasoning to arrive at that probability. We may have to ignore some issues to make heroic assumptions about them. But that should dramatically weaken our degree of confidence in our final assessment. Rigor isn’t contagious from link to link. If you want a relatively secure conclusion coming out, you’d better be careful that each premise is secure going on.

Avoiding logical inconsistencies is crucial in all science. But it is not enough. Just as important is avoiding factual inconsistencies. And without showing — or at least warranted arguing — that the assumptions and premises of their models are in fact true, mainstream economists aren’t really reasoning, but only playing games. Formalistic deductive ‘Glasperlenspiel’ can be very impressive and seductive. But in the realm of science, it ought to be considered of little or no value to simply make claims about the model and lose sight of reality.

Instead of making the model the message, I think we are better served by economists who more than anything else try to contribute to solving real problems. And then the motto of John Maynard Keynes is more valid than ever:

It is better to be vaguely right than precisely wrong


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  1. Lars regularly makes the argument that there are stable structures underlying reality, including economic reality.
    If such is the case, then model making should be considered a viable means of understanding these structures. If these structures are stable there must be ways of detecting them and understanding them. By assertion they are stable.
    Any given model may or may not be an accurate representation of these underlying structures. This is a matter for testing. The fact that any particular model may or may not be representative of these underlying structures is not an argument for abandoning model making.
    I argue it is a contradiction on the one hand to assert that there are stable underlying structures in economics and then disband model making.
    It is not entirely clear from Lars’ above written comments that he favours total abandonment of model making. He most seems to be making the case for models but ones whose assumptions have some correspondence to reality. However, he ends his essay with “Instead of making the model the message…”, which undermines the line he was developing earlier in his essay.
    I think there is enough model phobia on the part of Lars to argue that the contradiction exists.
    Lars asks the rhetorical question: “in what way can one maintain that these models give workable microfoundations for macroeconomics?” He proceeds to provide an answer.
    So is Lars now proposing that macroeconomics needs microfoundations?
    I argue that general equilibrium theory used by the mainstream as microfoundation for macroeconomics is irrelevant. GET is a theory of optimal resource allocation under constraint. This cannot be developed into a theory of macroeconomics as GET assumes away the very subject matter of macroeconomics, i.e. involuntary unemployment and less than full employment output.
    If Lars is now arguing that microfoundations is the pathway to understanding macroeconomics, then he has meandered further off the track.

  2. 《Avoiding logical inconsistencies is crucial in all science.》
    But as Dalibor Wijas on The geometry of Bayes theorem hinted at, am I not free to use inconsistent heuristic sets in my everyday life? Did Gödel not show that if science insists on consistency, completeness will elude it?
    Second, regarding lesdomes comment expressing physics envy, may I cite an overly optimistic Feynman, in “Cargo Cult Science”?
    《Millikan measured the charge on an electron by an experiment with falling oil drops and got an answer which we now know not to be quite right. It’s a little bit off, because he had the incorrect value for the viscosity of air. It’s interesting to look at the history of measurements of the charge of the electron, after Millikan. If you plot them as a function of time, you find that one is a little bigger than Millikan’s, and the next one’s a little bit bigger than that, and the next one’s a little bit bigger than that, until finally they settle down to a number which is higher.
    Why didn’t they discover that the new number was higher right away? It’s a thing that scientists are ashamed of—this history—because it’s apparent that people did things like this: When they got a number that was too high above Millikan’s, they thought something must be wrong—and they would look for and find a reason why something might be wrong. When they got a number closer to Millikan’s value they didn’t look so hard. And so they eliminated the numbers that were too far off, and did other things like that. We’ve learned those tricks nowadays, and now we don’t have that kind of a disease.》
    Is all measurement a social act?

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