, " an essay I recently came across a talk by Roderick Long in which he criticizes my father's methodological position, in particular the argument in his essay " The Methodology of Positive Economics " an essay which defends the use of unrealistic models in economics, such as perfect competition, on the grounds that the ultimate test of a model is not its descriptive accuracy but its ability to make correct predictions. The talk struck me as an attempt to make sense of the position without understanding it. Hence this post, in which I will attempt to explain the Chicago school methodology practiced by (among many others) both my father and myself.





Roderick starts his argument by imagining a theory of Harry Potter movies according to which some invisible force builds up over time to produce a Harry Potter movie every year. That theory predicts that each year there will be a new movie. For some years the prediction is correct, but eventually it fails. Someone with a more realistic theory would have produced a more correct prediction—that the series of movies would end, probably at the point when it had covered all of the books.





The first theory successfully predicts a reasonably likely event, a successful movie having a sequel, several times. That is evidence that it is a good theory, but not very much evidence. A theory built on a more realistic model of the process, in which successful movies are likely to have sequels but a series of movies based on a popular series of books is likely to end when it runs out of books, successfully predicts more facts, so is a superior theory by the criterion of prediction. Roderick's own example is one where the criterion of prediction and the criterion of realism lead to the same result—the more realistic theory is also the better predictor, so is to be preferred on either criterion.





His fundamental mistake, if I understand it correctly, is to imagine that all that is going on in the Chicago approach is blind curve fitting, looking for patterns in the observed data and assuming that those patterns will continue. The problem with that approach is that a body of data can be fitted with an infinite number of different curves. In selecting among the possible patterns that could explain the data, one uses whatever information is available to form a theory. The theory cannot be entirely realistic, since that would require including every feature of the situation that could conceivably be relevant. The test of whether one has done a good job of figuring out what simplified model includes the important factors and excludes the unimportant ones is the ability of the model to make correct predictions.





that distinguishes prediction from explanation is that humans have some ability to correctly perceive patterns, making correct predictions evidence of something more than a lucky guess. You can find a more detailed explanation Crucial to this view of the process is the distinction between explaining facts you already know and predicting facts you do not know, a point that is emphasized in my father's essay but, I think, entirely ignored in Roderick's lecture. Explanation of known facts can be blind curve fitting—but unless you have succeeded in choosing the right model, your predictions of facts that did not go into constructing it are unlikely to be correct. The crucial assumptionfrom explanation is that humans have some ability to correctly perceive patterns, making correct predictions evidence of something more than a lucky guess. You can find a more detailed explanation here





a priori approach associated with Ludwig Von Mises and some of his followers. There is a much simpler explanation. The problem with that approach, at least in its extreme version, is that pure a priori argument is unable to predict anything of economic interest. If one is completely agnostic about the facts, including both utility functions and production technology, any physically possible pattern of human behavior is consistent with the theory. As I put it long ago in my Roderick offers an elaborate philosophical explanation of why my father rejects what Roderick views as the correct approach to doing economics, theapproach associated with Ludwig Von Mises and some of his followers. There is a much simpler explanation. The problem with that approach, at least in its extreme version, is that pureargument is unable to predict anything of economic interest. If one is completely agnostic about the facts, including both utility functions and production technology, any physically possible pattern of human behavior is consistent with the theory. As I put it long ago in my Price Theory , explaining why the assumption of rationality is empty unless combined with some knowledge of what humans value:

Why did I stand on my head on the table while holding a burning $1,000 bill between my toes? I wanted to stand on my head on the table while holding a burning $1,000 bill between my toes.

a priori theory with evidence. You form plausible conjectures on the basis of theory and evidence, where part of forming them is deciding what simplifications, what unrealistic features of the model, assume away inessential complications while retaining the essential features of what you are trying to understand. You find out how good a job you have done by using the conjectures to make predictions and seeing whether the predictions are correct. An added benefit of that process, as I discovered in the course of writing my I conclude that the correct way of doing economics combinestheory with evidence. You form plausible conjectures on the basis of theory and evidence, where part of forming them is deciding what simplifications, what unrealistic features of the model, assume away inessential complications while retaining the essential features of what you are trying to understand. You find out how good a job you have done by using the conjectures to make predictions and seeing whether the predictions are correct. An added benefit of that process, as I discovered in the course of writing my first published journal article in economics, is that finding real world predictions of your model may force you to think through the model itself more clearly.





That is the Chicago School methodology as I understand and practice it.