On models and simplicity

20 Nov, 2022 at 14:44 | Posted in Economics | 5 Comments

When it comes to modelling yours truly does see the point emphatically made time after time by e. g. Paul Krugman about simplicity — at least as long as it doesn’t impinge on our truth-seeking. ‘Simple’ macroeconomic models may of course be an informative heuristic tool for research. But if practitioners of modern macroeconomics 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 fulfil its tasks. Maintaining that economics is a science in the ‘true knowledge’ business, yours truly remains a sceptic of the pretences and aspirations of  ‘simple’ macroeconomic 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 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.

Explanation, understanding, and prediction of real-world phenomena, relations, and mechanisms therefore cannot be grounded on simpliciter assuming simplicity. If we cannot show that the mechanisms or causes we isolate and handle in our models are stable, in the sense that 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 does 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. No matter how many convoluted refinements of concepts are made in the model, 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.

Constructing simple macroeconomic models somehow seen as ‘successively approximating’ macroeconomic reality, is a rather unimpressive attempt at legitimizing using fictitious idealizations for reasons more to do with model tractability than with a genuine interest in understanding and explaining features of real economies. Many of the model assumptions standardly made by 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.

As scientists, we have to get our priorities right. Ontological under-labouring has to precede epistemology.