In the context of a debate between economists Brad DeLong and Tyler Cowen on the “IS-LM model” [no, I don’t know what it is, either!], Kaiser writes:
Since a model is an abstraction, a simplification of reality, no model is above critique.
I [Kaiser] consider the following types of critique not deserving:
1) The critique that the modeler makes an assumption
2) The critique that the modeler makes an assumption for mathematical convenience
3) The critique that the model omits some feature
4) The critique that the model doesn’t fit one’s intuition
5) The critique that the model fails to make a specific prediction
Above all, a serious critique must include an alternative model that is provably better than the one it criticises. It is not enough to show that the alternative solves the problems being pointed out; the alternative must do so while preserving the useful aspects of the model being criticized.
I have mixed feelings about Kaiser’s rules. On one hand, I agree with his point that a model is a practical tool and that an imperfection is no reason to abandon a useful model. On the other hand, I think that much can be learned from rejection of a model, even without reference to any alternative.
Let me put it this way: That a model makes assumptions, even that a model makes wrong assumptions, is not news. If “wrong” is enough to kill, then all our models are dead on arrival anyway. But it’s good to understand the ways in which a model disagrees with the data at hand, or with other aspects of reality. As Kuhn and Lakatos knew, highlighting, isolating, and exploring anomalies are crucial steps in moving toward improvement—even if no alternative model is currently in the picture.