LARS P. SYLL

Economic modeling — a constructive critique

If we have independent reasons to believe that the phenomena under investigation are mechanical in Mill’s sense, well and good: mathematical modeling will prove an apt mode of representation … But if we have independent reasons to believe that there is more going on in the phenomena under investigation than a mathematical model can suggest – that is, that the phenomena in question are not in fact mechanical in the required sense – then mathematical modeling will prove misleading … Moreover, as will be discussed, the empirical assessment of such models using econometric methods will not be sufficient to reveal that mismatch.

These problems cannot themselves be addressed through reforms to mathematical methods. That would simply be to produce a more refined version of the wrong tool for the job, like sharpening one’s knife when what is needed is a spoon. Rather than striving to improve the quality of mathematical models given the assumption that the subject matter under investigation is mechanical in Mill’s sense and therefore susceptible of mathematical analysis, we need to ask a prior question, which is whether there is sufficient reason to feel confident that the subject matter under investigation is mechanical in the first place. That means scrutinizing the subject matter in the first instance in non-mathematical ways … We as scientists must remain sensitive to information about the phenomena in which we are interested that lies outside our models’ conceptual maps. In the case of economics, what this requires is a new field dedicated to qualitative empirical methods that would play a similar role to that played by econometrics in the matter of quantitative empirical methods.

Highly recommended reading!

Using formal mathematical modeling, mainstream economists sure can guarantee that the conclusions hold given the assumptions. However, the validity we get in abstract model worlds does not warrantly transfer to real-world economies.

In their search for validity, rigour and precision, mainstream macro modellers of various ilks construct microfounded DSGE models that standardly assume 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.

Behavioural and experimental economics — not to speak of psychology — show beyond any doubt that ‘deep parameters’ — peoples’ preferences, choices and forecasts — are regularly influenced by those of other participants in the economy. 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?

The rigour and precision in formal logic focus has a devastatingly important trade-off: the higher the level of rigour and precision, the smaller the range of real-world application. So the more mainstream economists insist on formal logic validity, the less they have to say about the real world.

And as Spiegler has it — to think we solve the problem by reforms to mathematical modeling is nothing but “a more refined version of the wrong tool for the job, like sharpening one’s knife when what is needed is a spoon.

My philosophy of economics

A critique yours truly sometimes encounters is that as long as I cannot come up with some own alternative to the failing mainstream theory, I shouldn’t expect people to pay attention.

This is however to misunderstand the role of philosophy and methodology of economics!

As John Locke wrote in An Essay Concerning Human Understanding:

The Commonwealth of Learning is not at this time without Master-Builders, whose mighty Designs, in advancing the Sciences, will leave lasting Monuments to the Admiration of Posterity; But every one must not hope to be a Boyle, or a Sydenham; and in an Age that produces such Masters, as the Great-Huygenius, and the incomparable Mr. Newton, with some other of that Strain; ’tis Ambition enough to be employed as an Under-Labourer in clearing Ground a little, and removing some of the Rubbish, that lies in the way to Knowledge.

That’s what philosophy and methodology can contribute to economics — clear obstacles to science.

Every now and then yours truly also gets some upset comments from people wondering why I’m not always ‘respectful’ of people like Eugene Fama, Robert Lucas, Greg Mankiw, and others of the same ilk.

But sometimes it might actually, from a Lockean perspective, be quite appropriate to be disrespectful.

New Classical and ‘New Keynesian’ macroeconomics is rubbish that ‘lies in the way to Knowledge.’

And when New Classical and ‘New Keynesian’economists resurrect fallacious ideas and theories that were proven wrong already in the 1930s, then I think a less respectful and more colourful language is called for.

How to ensure that models serve society

• Mind the assumptions — assess uncertainty and sensitivity.

• Mind the hubris — complexity can be the enemy of relevance.

• Mind the framing — match purpose and context.

• Mind the consequences — quantification may backfire.

• Mind the unknowns — acknowledge ignorance.

Andrea Saltelli, John Kay, Deborah Mayo, Philip B. Stark, et al.

Five principles I think modern times “the model is the message” economists would benefit much from pondering. And especially when it comes to the last principles, they would benefit enormously from reading.

More than a hundred years after John Maynard Keynes wrote his seminal A Treatise on Probability (1921), it is still very difficult to find economics and statistics textbooks that seriously try to incorporate his far-reaching and incisive analysis of induction and evidential weight.

The standard view in statistics and economics — and the axiomatic probability theory underlying it — is to a large extent based on the rather simplistic idea that “more is better.” But as Keynes argues — “more of the same” is not what is essential when making inductive inferences. It’s rather a question of “more but different.”

Variation, not replication, is at the core of induction. Finding that p(x|y) = p(x|y & w) doesn’t make w “irrelevant.” Knowing that the probability is unchanged when w is present gives p(x|y & w) another evidential weight (“weight of argument”).

According to Keynes we live in a world permeated by unmeasurable uncertainty — not quantifiable stochastic risk — which often forces us to make decisions based on anything but “rational expectations.” Keynes rather thinks that we base our expectations on the confidence or “weight” we put on different events and alternatives. To Keynes, expectations are a question of weighing probabilities by “degrees of belief,” beliefs that often have preciously little to do with the kind of stochastic probabilistic calculations made by the rational agents as modelled by “modern” social sciences. And often we “simply do not know.” 

Science according to Keynes should help us penetrate “the true process of causation lying behind current events” and disclose “the causal forces behind the apparent facts.” Models can never be more than a starting point in that endeavour. He further argued that it was inadmissible to project history on the future. Consequently, we cannot presuppose that what has worked before, will continue to do so in the future. Getting hold of correlations between different “variables” is not enough. If our models cannot get at the causal structure that generated the data, they are not really “identified.”

How strange that writers of economics and statistics textbooks as a rule do not even touch upon these aspects of scientific methodology that seem so fundamental and important for anyone trying to understand how we learn and orient ourselves in an uncertain world. An educated guess on why this is a fact would be that Keynes’s concepts are not possible to squeeze into a single calculable numerical “probability.” In the quest for quantities, one puts a blind eye to qualities and looks the other way – but Keynes’s ideas keep creeping out from under the carpet.

It’s high time that economics and statistics textbooks give Keynes his due.