When should we trust science?

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Lars Pålsson Syll
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When should we trust science?

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Using formal mathematical modelling, mainstream economists sure can guarantee that the conclusions hold given the assumptions. However the validity we get in abstract model-worlds does not warrant transfer to real-world economies. Validity may be good, but it isn’t — as Nancy Cartwright so eloquently argues — enough. From a realist perspective, both relevance and soundness are sine qua non.

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.

The predominant strategy in mainstream macroeconomics today is to build ‘rigorous’ models and make things happen in these ‘analogue-economy models.’ But although macro-econometrics may have supplied economists with rigorous replicas of real economies, if the goal of theory is to be able to make accurate forecasts or explain what happens in real economies, this ability to — ad nauseam — construct toy models, does not give much leverage.

‘Rigorous’ and ‘precise’ New Classical models — and that goes for the ‘New Keynesian’ variety too — cannot be considered anything else than unsubstantiated conjectures as long as they aren’t supported by evidence from outside the theory or model. To my knowledge no in any way decisive empirical evidence has been presented.

Mainstream economists are proud of having an ever-growing smorgasbord of models to cherry-pick from (as long as, of course, the models do not question the standard modelling strategy) when performing their analyses. The ‘rigorous’ and ‘precise’ deductions made in these closed models, however, are not in any way matched by a similar stringency or precision when it comes to what ought to be the most important stage of any research — making statements and explaining things in real economies. Although almost every mainstream economist holds the view that thought-experimental modelling has to be followed by confronting the models with reality — which is what they indirectly want to predict/explain/understand using their models — they all of a sudden become exceedingly vague and imprecise. It is as if all the intellectual force has been invested in the modelling stage and nothing is left for what really matters — what exactly do these models teach us about real economies.

No matter how precise and rigorous the analysis, and no matter how hard one tries to cast the argument in modern mathematical form, they do not push economic science forwards one single millimetre if they do not stand the acid test of relevance to the target. No matter how clear, precise, rigorous or certain the inferences delivered inside these models are, they do not per se say anything about real-world economies.

Proving things ‘rigorously’ in mathematical models is at most a starting point for doing an interesting and relevant economic analysis. Forgetting to supply export warrants to the real world makes the analysis an empty exercise in formalism without real scientific value.