When usefulness is more important than precision

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Lars Pålsson Syll
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When usefulness is more important than precision

This is a bit like a physicist saying, “Throwing a cricket ball at a window doesn’t cause it to break. Rather, giving an object of approximately spherical shape with a given density a certain velocity in a fluid medium of a certain density and viscosity within a gravitational field of a certain magnitude causes a silicone-based compound in a certain quantity of certain dimensions with certain reflective and refractive properties to break into a certain, much larger number of pieces.” This (or something like it) may be a more precise representation of the physics of the situation. However, the complex claim does not falsify the claim that throwing a cricket ball at a window causes it to break. It is more precise to say that I am in Johannesburg than to say that I am in South Africa, but the former claim clearly does not falsify the latter; showing that, in general, precision improvements need not falsify their less precise forbears (even if they sometimes can). Moreover, it is not always more useful to assert a more precise claim. Indeed, there are many contexts in which it is more useful to say that the cricket ball broke the window than to resort to complex physics, and there are contexts in which it is more useful to say that I am in SouthAfrica than that I am inJohannesburg. Likewise, saying something as vague as “Smoking causes lung cancer” or “Obesity is a cause of death” may be both true and useful, even if there are ways to make these claims more precise. The fact that it is possible to be more precise does not show that these claims are false or that they are useless. In order even to know whether to try lowering tar in cigarettes, one needs to at least entertain the possibility that smoking causes lung cancer. “Does smoking cause lung cancer?” was a useful question. Likewise, the question whether obesity is a cause of mortality is a useful question, even if in fact, because it may turn out that the way one reduces BMI affects mortality.

Alex Broadbent

Instead of making the model the message, I think we are better served by scientists who more than anything else try to contribute to solving real problems.

Fixation on constructing models — “implying the existence of greater precision in the data than the questions admit of” — showing the certainty of logical entailment — realiter simply collapsing the necessary ontological gap between model and reality — has since the days of Jevons and the marginalist revolution been detrimental to the development of a relevant and realist economics. Insisting on formalistic (mathematical) modelling forces the economist to give up on realism and substitute axiomatics for real-world relevance. The price for rigour and precision is far too high for anyone who is ultimately interested in using economics to pose and (hopefully) answer real-world questions and problems.

This deductivist orientation is the main reason behind the difficulty that mainstream economics has in terms of understanding, explaining, and predicting what takes place in our societies. But it has also given mainstream economics much of its discursive power — at least as long as no one starts asking tough questions on the veracity of — and justification for — the assumptions on which the deductivist foundation is erected.  Asking these questions is an important ingredient in a sustained critical effort to show how nonsensical the embellishing of a smorgasbord of models is founded on wanting (and often hidden) methodological foundations.

The mathematical-deductivist straitjacket used in mainstream economics presupposes atomistic closed systems — i.e., something that we find very little of in the real world, a world significantly at odds with an (implicitly) assumed logic world where deductive entailment rules the roost. Ultimately then, the failing of modern mainstream economics has its root in a deficient ontology. The kind of formal-analytical and axiomatic-deductive mathematical modelling that makes up the core of mainstream economics is hard to make compatible with a real-world ontology. It is also the reason why so many critics find mainstream economic analysis patently and utterly unrealistic and irrelevant.

If we want theories and models to confront reality, there are obvious limits to what can be said rigorously in economics. In the deductivist approach, model consistency trumps coherence with the real world. That is surely getting the priorities wrong. Creating models for their own sake is not an acceptable scientific aspiration — impressive-looking formal-deductive (mathematical) models should never be mistaken for truth.

To construct and use an economic model you have to start by establishing that the phenomena modeled are ontologically compatible with the model. The rigour and precision in models have a devastatingly important trade-off: the higher the level of rigour and precision, the smaller the range of real-world applications. So the more mainstream economists insist on formal logic validity, the less they have to say about the real world. And to think we solve the problem by reforms to mathematical modelling is like looking for a spoon when what is needed is a knife.

The motto of John Maynard Keynes is more valid than ever:

It is better to be vaguely right than precisely wrong