When beautiful models go wrong

No, not those types of models! We mean abstract, mathematical models. Celebrity economist Paul Krugman writes:

Suppose you’re making a prediction — and every assertion about how the world works has to involve at least an implicit prediction of something, because otherwise it’s empty. This prediction comes from some kind of model — if you don’t think you have a model, you’re kidding yourself, and your model is all the worse because you imagine that you aren’t using it. For the sake of argument, let’s say that your model takes the form

y = a + b*x + u

where y is what you’re predicting, x is some kind of explanatory variable, a and b are parameters, and u represents random stuff (not necessarily really random, but stuff that isn’t part of your model). That last term is important: nobody, and nobody’s model, gets things totally right.

So, suppose your prediction about y ends up having been pretty far off. What does that tell you?

It could say simply that, as the bumper stickers don’t quite say, Stuff Happens. There could have been a random shock; or for that matter your explanatory variables may not have done what you expected them to. But it could also say that your underlying model was just all wrong, requiring a rethink.

And here’s the thing: over the course of your life, you’re going to make both kinds of mistakes. The question is whether to hold em or fold em — to stick with your basic story, or realize that the story is wrong.

What’s your favorite or most beautiful mistaken model? (Ours is the Coase theorem.) In the meantime, while you think about this, you can read Paul Krugman’s full post here. (Hat tip to Tyler Cowen.)

 Which model is least wrong?

About F. E. Guerra-Pujol

When I’m not blogging, I am a business law professor at the University of Central Florida.
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