What is a good model?
from Lars Syll
Whereas increasing the difference between a model and its target system may have the advantage that the model becomes easier to study, studying a model is ultimately aimed at learning something about the target system. Therefore, additional approximations come with the cost of making the correspondence between model and target system less straight- forward. Ultimately, this makes the interpretation of results on the model in terms of the target system more problematic. We should keep in mind the advice of Whitehead: “Seek simplicity and distrust it.”
A ‘good model’ is to be understood as a model that achieves an equilibrium between being useful and not being too wrong. The usefulness of a model is clearly context-dependent; it may involve a combination of desired features such as being understandable (for students, researchers, or others), achieving computational tractability, and other criteria. ‘Not being too wrong’ is to be understood as ‘not being too different from reality’.
An interesting article underlining the fact that all empirical sciences use simplifying or unrealistic assumptions in their modeling activities, and that that is not the issue – as long as the assumptions made are not unrealistic in the wrong way or for the wrong reasons.
Theories are difficult to directly confront with reality. Economists therefore build models of their theories. Those models are representations that are directly examined and manipulated to indirectly say something about the target systems.
But models do not only face theory. They also have to look to the world. 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.
Some of the standard assumptions made in neoclassical economic theory – on rationality, information handling and types of uncertainty – are not possible to make more realistic by “de-idealization” or “successive approximations” without altering the theory and its models fundamentally.
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 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.
No matter how many convoluted refinements of concepts made in the model, if the “successive approximations” 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.