by DeLong, Summers & Krugman on models Larry Summers, Brad DeLong, and Paul Kugman are having an extended discussion on the role of models in economics on their blogs this week. That’s good. Since the model is the message in economics today, that is actually the most important discussion possible to have in economics. Krugman is arguing that models are not ‘always the right guide for policy, but still necessary for disciplining our policy preferences.’ According to Krugman, ‘the discipline of thinking things through in terms of models is really important.’ This emphasis on the value of modeling should come as no surprise. Paul Krugman has always — although sometimes admitting that economists have a tendency to use ‘excessive math’ and ‘equate hard math with quality’ — vehemently defended the formalization and mathematization that comes with the insistence of using a model building strategy in economics. But if these math-is-the-message-modelers aren’t able to show that the mechanisms or causes that they isolate and handle in their mathematically formalized macromodels are stable in the sense that they do not change when we ‘export’ them to our ‘target systems,’ these mathematical models do only hold under ceteris paribus conditions and are consequently of limited value to our understandings, explanations or predictions of real economic systems.
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DeLong, Summers & Krugman on models
Larry Summers, Brad DeLong, and Paul Kugman are having an extended discussion on the role of models in economics on their blogs this week.
That’s good. Since the model is the message in economics today, that is actually the most important discussion possible to have in economics.
Krugman is arguing that models are not ‘always the right guide for policy, but still necessary for disciplining our policy preferences.’ According to Krugman, ‘the discipline of thinking things through in terms of models is really important.’
This emphasis on the value of modeling should come as no surprise. Paul Krugman has always — although sometimes admitting that economists have a tendency to use ‘excessive math’ and ‘equate hard math with quality’ — vehemently defended the formalization and mathematization that comes with the insistence of using a model building strategy in economics.
But if these math-is-the-message-modelers aren’t able to show that the mechanisms or causes that they isolate and handle in their mathematically formalized macromodels are stable in the sense that they do not change when we ‘export’ them to our ‘target systems,’ these mathematical models do only hold under ceteris paribus conditions and are consequently of limited value to our understandings, explanations or predictions of real economic systems. Building models only to show Krugmanian ‘self-dicipline’ is setting the aspiration level too low.
According to Keynes, science should help us penetrate to ‘the true process of causation lying behind current events’ and disclose ‘the causal forces behind the apparent facts.’ We should look out for causal relations. But models — mathematical, econometric, or what have you — can never be more than a starting point in that endeavour. There is always the possibility that there are other (non-quantifiable) variables – of vital importance and although perhaps unobservable and non-additive not necessarily epistemologically inaccessible – that were not considered for the formalized mathematical model.
The kinds of laws and relations that ‘modern’ economics has established, are laws and relations about mathematically formalized entities in models that presuppose causal mechanisms being atomistic and additive. When causal mechanisms operate in real world social target systems they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. If economic regularities obtain they do it (as a rule) only because we engineered them for that purpose. Outside man-made mathematical-statistical ‘nomological machines’ they are rare, or even non-existant. Unfortunately that also makes most of contemporary mainstream endeavours of mathematical economic modeling rather useless. And that also goes for Krugman and the rest of the ‘New Keynesian’ family.
The DeLong-Summers-Krugman discussion is certainly a question of methodology. And it shows the danger of neglecting methodological issues — issues mainstream economists regularly have almost put an honour in neglecting.
Being able to model a ‘disciplined’ credible world, a world that somehow could be considered real or similar to the real world, is not the same as investigating the real world. Even though all theories are false, since they simplify, they may still possibly serve our pursuit of truth. But then they cannot be unrealistic or false in any way. The falsehood or unrealisticness has to be qualified (in terms of resemblance, relevance, etc.). At the very least, the minimalist demand on models in terms of credibility has to give away to a stronger epistemic demand of appropriate similarity and plausibility. One could of course also ask for a sensitivity or robustness analysis, but the credible world, even after having tested it for sensitivity and robustness, can still be a far way from reality – and unfortunately often in ways we know are important. Robustness of claims in a model does not per se give a warrant for exporting the claims to real world target systems.
Questions of external validity are important more specifically also when it comes to microfounded macromodels. It can never be enough that these models somehow are regarded as internally consistent. One always also has to pose questions of consistency with the data. Internal consistency without external validity is worth nothing.
Yours truly has for many years been urging economists to pay attention to the ontological foundations of their assumptions and models. Sad to say, economists have not paid much attention — and so modern economics has become increasingly irrelevant to the understanding of the real world.