by From Lars Syll One can generally develop a theoretical model to produce any result within a wide range. Do you want a model that produces the result that banks should be 100% funded by deposits? Here is a set of assumptions and an argument that will give you that result. That such a model exists tells us very little … Being logically correct may earn a place for a theoretical model on the bookshelf, but when a theoretical model is taken off the shelf and applied to the real world, it is important to question whether the model’s assumptions are in accord with what we know about the world. To be taken seriously models should pass through the real world filter. Chameleons are models that are offered up as saying something significant about the real world even though they do not pass
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from Lars Syll
One can generally develop a theoretical model to produce any result within a wide range. Do you want a model that produces the result that banks should be 100% funded by deposits? Here is a set of assumptions and an argument that will give you that result. That such a model exists tells us very little …
Being logically correct may earn a place for a theoretical model on the bookshelf, but when a theoretical model is taken off the shelf and applied to the real world, it is important to question whether the model’s assumptions are in accord with what we know about the world. To be taken seriously models should pass through the real world filter.
Chameleons are models that are offered up as saying something significant about the real world even though they do not pass through the filter. When the assumptions of a chameleon are challenged, various defenses are made … In many cases the chameleon will change colors as necessary, taking on the colors of a bookshelf model when challenged, but reverting back to the colors of a model that claims to apply the real world when not challenged.
Pfleiderer’s absolute gem of an article reminds me of what H. L. Mencken once famously said:
There is always an easy solution to every problem — neat, plausible and wrong.
Pfleiderer’s perspective may be applied to many of the issues involved when modelling complex and dynamic economic phenomena. Let me take just one example — simplicity.
‘Simple’ macroeconom(etr)ic models may of course be an informative heuristic tool for research. But if practitioners of modern macroeconom(etr)ics do not investigate and make an effort of providing a justification for the credibility of the simplicity-assumptions on which they erect their building, it will not fullfil its tasks. Maintaining that economics is a science in the ‘true knowledge’ business, yours truly remains a skeptic of the pretences and aspirations of ‘simple’ macroeconom(etr)ic models and theories. So far, I can’t really see that e. g. ‘simple’ microfounded models have yielded very much in terms of realistic and relevant economic knowledge.
All empirical sciences use simplifying or unrealistic assumptions in their modelling activities. That is not the issue – as long as the assumptions made are not unrealistic in the wrong way or for the wrong reasons.
Being able to model a ‘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 — as Pfleiderer acknowledges — 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.
If we cannot show that the mechanisms or causes we isolate and handle in our models are stable, in the sense that what when we export them from are models to our target systems they do not change from one situation to another, then they — considered ‘simple’ or not — only hold under ceteris paribus conditions and a fortiori are of limited value for our understanding, explanation and prediction of our real world target system.
The obvious ontological shortcoming of a basically epistemic — rather than ontological — approach, is that ‘similarity’ or ‘resemblance’ tout court do not guarantee that the correspondence between model and target is interesting, relevant, revealing or somehow adequate in terms of mechanisms, causal powers, capacities or tendencies. If the simplifications made do not result in models similar to reality in the appropriate respects (such as structure, isomorphism, etc), the surrogate system becomes a substitute system that does not bridge to the world but rather misses its target.
Many of the model assumptions standardly made in mainstream macroeconomics — simplicity being one of them — are restrictive rather than harmless and could a fortiori anyway not in any sensible meaning be considered approximations at all. If economists aren’t able to show that the mechanisms or causes that they isolate and handle in their ‘simple’ models are stable in the sense that they do not change when exported to their ‘target systems,’ they do only hold under ceteris paribus conditions and are a fortiori of limited value to our understanding, explanations or predictions of real economic systems.
That Newton’s theory in most regards is simpler than Einstein’s is of no avail. Today Einstein has replaced Newton. The ultimate arbiter of the scientific value of models cannot be simplicity.