Why it is better to be roughly right than precisely wrong

from Lars Syll

When applying 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. When addressing real economies, the idealizations necessary for the deductivist machinery to work, simply don’t hold.

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.

For Keynes the world in which we live is inherently uncertain and quantifiable probabilities are the exception rather than the rule. To every statement about it is attached a “weight of argument” that makes it impossible to reduce our beliefs and expectations to a one-dimensional stochastic probability distribution. If “God does not play dice” as Einstein maintained, Keynes would add “nor do people”. 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 organic parts prevent the possibility of treating it as constituted by “legal atoms” with discretely distinct, separable and stable causal relations. Our knowledge accordingly has to be of a rather fallible kind.

To search for precision and rigour in such a world is self-defeating, at least if precision and rigour are supposed to assure external validity. The only way to defend such an endeavour is to take a blind eye to ontology and 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. We have to at least justify our disregard for the gap between the nature of the real world and our theories and models of it.

Keynes once wrote that economics “is a science of thinking in terms of models joined to the art of choosing models which are relevant to the contemporary world.” Now, if the real world is fuzzy, vague and indeterminate, then why should our models build upon a desire to describe it as precise and predictable? Even if there always has to be a trade-off between theory-internal validity and external validity, we have to ask ourselves if our models are relevant.

Models preferably ought to somehow reflect/express/correspond to reality. I’m not saying that the answers are self-evident, but at least you have to do some philosophical under-labouring to rest your case. Too often that is wanting in modern economics, just as it was when Keynes in the 1930s complained about Tinbergen’s and other econometricians lack of justifications of the chosen models and methods.

“Human logic” has to supplant the classical, formal, logic of deductivism if we want to have anything of interest to say of the real world we inhabit. Logic is a marvellous tool in mathematics and axiomatic-deductivist systems, but a poor guide for action in real-world systems, in which concepts and entities are without clear boundaries and continually interact and overlap. In this world I would say we are better served with a methodology that takes into account that “the more we know the more we know we don’t know”.

The models and methods we choose to work with have to be in conjunction with the economy as it is situated and structured. Epistemology has to be founded on ontology. Deductivist closed-system theories, as all the varieties of the Walrasian general equilibrium kind, could perhaps adequately represent an economy showing closed-system characteristics. But since the economy clearly has more in common with an open-system ontology we ought to look out for other theories – theories who are rigorous and precise in the meaning that they can be deployed for enabling us to detect important causal mechanisms, capacities and tendencies pertaining to deep layers of the real world.

Rigour, coherence and consistency have to be defined relative to the entities for which they are supposed to apply. Too often they have been restricted to questions internal to the theory or model. But clearly the nodal point has to concern external questions, such as how our theories and models relate to real-world structures and relations. Applicability rather than internal validity ought to be the arbiter of taste.