In several of my recent posts, I laid out my case for how it might not undermine all of economic thinking to find that people aren’t necessarily rational. One of my key points was that you can model irrationality as rationality, but this does raise the question: What good are models in the first place? I get the impression that many people think that a model is only useful if it can make a prediction – meaning that the test of a model is how well it holds up in “the real world”. I would strongly disagree with this idea, and I might go so far as to say that modeling “the real world” is so hard that in many cases this is actually at best an ancillary benefit of having a good model.

In my view, what a good model does is reveal the symmetry in a class or between classes of problems. What this does is allow you to solve many problems at the same time – if you know how to solve a fifteen puzzle with numbers on it, you know how to solve a fifteen puzzle with a picture on it, because even though the goal is different (in one case you are trying to order the numbers and in another case you are trying to make an image), the “hard part” is identical. In the same way, you can form a model for any sort of problem by separating out the “hard parts” from the “easy parts”, then solving the hard parts. Any time you come across something with the same hard parts, you can apply the same solution.

Russell Roberts at EconTalk has frequently said that he is coming to the view that economics is less valuable as an empirical science than as a framework for how to look at the world. I tend to sympathize with this view, but I am not sure that this is limited to economics. The same can be said for physics and chemistry as well – the empirical side is important there, but the bigger focus tends to be on developing ways to think about the world that are easier to parse. We find some problems and solve them, then run around trying to transform other problems into the ones we’ve already solved by modeling those new problems as the old ones plus something else. In fact, in computer science, the entire field of complexity theory is more or less devoted to finding isomorphisms between problems that we’d like to solve (and they do a damn good job of it, too).

This is why I think that how agents are modeled is the relevant part of the question of behavioral economics. If the models are the same either way, the puzzles are all still the same and still have the same solutions. If the models have to be adjusted, then you still have those earlier solutions, they just happen to be solutions to a different problem.

Similar Posts:

• Thinking about logic and information, Part II
• Review of Why Popcorn Costs So Much at the Movies
• Symmetry and Information, Part II

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Economics, Information, Logic, Symmetry by Paul Ganssle