Beyond mathematical modelling

by
Lars Pålsson Syll
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from Lars Syll

Mathematical modelling has now dominated the economics academy for so long that younger people that emerge from economic studies who are dissatisfied with what they are taught, cannot think beyond the modelling. They have been immersed in it so long that it is a kind of common sense to them. The idea that modelling is bound to be almost always irrelevant just does not compute for many. Yet they recognize that modern academic economics mostly does not provide any insights. So, they assume that the fault lies in the sorts of topics covered, or conclusions drawn etc. with the solution to be found by way of doing the modelling differently. It is all quite dire …

The only diversity the mainstream advocate is that which remains consistent with the mathematical modelling emphasis. So, it is more or less all irrelevant, because it all carries an unrealistic ontology. Different accounts or ways of modelling of isolated atoms … The result is that academic economics has been and remains a big failure in terms of providing anything of relevance … The modelling project in economics, as it turns out, has in fact not produced a single insight into the real world – as opposed, of course, to occasionally tagging on insights determined independently of modelling. If that assessment were wrong, it would be so easy to provide a counterexample. Yet so far none has ever been seriously suggested in defence of the methods …

I love mathematics. But everything has a context of relevance. Mathematical modelling methods are just irrelevant to the analysis of most social situations; I suspect you have as much chance of cutting the grass with your armchair as generating insight by way of addressing human behaviour using the methods in question … The problem is not mathematical methods in themselves but their employment in conditions where doing so is simply not appropriate.

Tony Lawson

Using known to be false assumptions, mainstream modellers can derive whatever conclusions they want. Wanting to show that ‘all economists consider austerity to be the right policy,’ just e.g. assume ‘all economists are from Chicago’ and ‘all economists from Chicago consider austerity to be the right policy.’  The conclusions follow by deduction — but is of course factually wrong. Models and theories building on that kind of reasoning is nothing but, as argued by Lawson, a pointless waste of time and resources.

Mainstream economics today is mainly an approach in which you think the goal is to be able to write down a set of empirically untested assumptions and then deductively infer conclusions from them. When applying this deductivist thinking to economics, economists usually set up ‘as if’ models based on a set of tight axiomatic assumptions from which consistent and precise inferences are made. The beauty of this procedure is of course that if the axiomatic premises are true, the conclusions necessarily follow. The snag is that if the models are to be relevant, we also have to argue that their precision and rigour still holds when they are applied to real-world situations. They often don’t do for the simple reason that empty theoretical exercises of this kind do not tell us anything about the world. When addressing real economies, the idealizations necessary for the deductivist machinery to work, simply don’t hold.

From a methodological point of view one can, of course, also wonder, how we are supposed to evaluate tests of theories and models building on known to be false assumptions. What is the point of such tests? What can those tests possibly teach us?

From falsehoods anything logically follows. Modern expected utility theory is a good example of this. Leaving the specification of preferences without almost any restrictions whatsoever, every imaginable evidence is safely made compatible with the all-embracing ‘theory’ — and a theory without informational content never risks being empirically tested and found falsified. Used in mainstream economics ‘thought experimental’ activities, it may of course be very ‘handy’, but totally void of any empirical value.

So how should we evaluate the search for ever-greater precision and the concomitant arsenal of mathematical and formalist models? To a large extent, the answer hinges on what we want our models to perform and how we basically understand the world.

The world as we know it has limited scope for certainty and perfect knowledge. Its intrinsic and almost unlimited complexity and the interrelatedness of its parts prevent the possibility of treating it as constituted by atoms with discretely distinct, separable and stable causal relations. Our knowledge accordingly has to be of a rather fallible kind. To search for deductive precision and rigour in such a world is self-defeating. The only way to defend such an endeavour is to restrict oneself to prove things in closed model-worlds. Why we should care about these and not ask questions of relevance is hard to see.