by In mainstream economics, there is a lot of talk about ‘economic laws.’ The crux of these laws that allegedly do exist in economics, is that they only hold ceteris paribus. That fundamentally means that these laws only hold when the right conditions are at hand for giving rise to them. Unfortunately, from an empirical point of view, those conditions are only at hand in artificially closed nomological models purposely designed to give rise to the kind of regular associations that economists want to explain. But — since these laws do not exist outside these socio-economic machines, what is the point in constructing thought experimental models showing these non-existent laws? When the almost endless list of narrow and specific assumptions necessary to allow the ‘rigorous’
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In mainstream economics, there is a lot of talk about ‘economic laws.’ The crux of these laws that allegedly do exist in economics, is that they only hold ceteris paribus. That fundamentally means that these laws only hold when the right conditions are at hand for giving rise to them. Unfortunately, from an empirical point of view, those conditions are only at hand in artificially closed nomological models purposely designed to give rise to the kind of regular associations that economists want to explain. But — since these laws do not exist outside these socio-economic machines, what is the point in constructing thought experimental models showing these non-existent laws? When the almost endless list of narrow and specific assumptions necessary to allow the ‘rigorous’ deductions are known to be at odds with reality, what good do these models do?
Deducing laws in theoretical models is of no avail if you cannot show that the models — and the assumptions they build on — are realistic representations of what goes on in real life.
Conclusion? Instead of restricting our methodological endeavours at building ever more rigorous and precise deducible models, we ought to spend much more time improving our methods for choosing models!
There is a difference between having evidence for some hypothesis and having evidence for the hypothesis relevant for a given purpose. The difference is important because scientific methods tend to be good at addressing hypotheses of a certain kind and not others: scientific methods come with particular applications built into them … The advantage of mathematical modelling is that its method of deriving a result is that of mathematical proof: the conclusion is guaranteed to hold given the assumptions. However, the evidence generated in this way is valid only in abstract model worlds while we would like to evaluate hypotheses about what happens in economies in the real world … The upshot is that valid evidence does not seem to be enough. What we also need is to evaluate the relevance of the evidence in the context of a given purpose.