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.
Which model is least wrong?