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Comment by juanre | Hacker News Reader

I took Caltech/Coursera class "Principles of Economics for Scientists" this winter. I was struck by how lean and clear the concepts are once you bother to write them down mathematically. Much better than the hand-wavy explanations I had heard before.

I also took it a step further, and wrote them down as code. When you can explain something to a computer you know you have understood it; and you may even end up with something that other people can understand and play with. I believe something along the lines of what I ended up with (https://github.com/juanre/econopy) might be profitably used to teach economics to CS-type students.

As far as economics math not representing reality: I think it's obvious enough that humans don't behave the way these equations assume. However, they are a great framework from which to think about how humans actually behave.

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There's a problem: the only reason you would build a model is to predict things from it. If the model can't do that for you what you are doing has stopped being science and had turned into something else. Again: if the "framework from which to think about how humans ... behave" can't predict how humans behave in actuality looking at such frameworks is an exercise in futility.

A classic example of irrationality is mental accounting. Say you are going to the theater. You have a $100 ticket and a $100 bill, and on the way to the theater you lose the ticket. Would you spend your $100 to buy another one? Most people wouldn't, because they would be assigning a $200 cost to the theater department.

Now imagine that you only have a $100 bill with which you are going to pay the ticket, and you lose it on the way to the theater. Would you take out your credit card and pay for the ticket anyway? Most people would.

We know that if people were rational the two scenarios would be identical. But they differ, and this is interesting because we have a framework from which to look into it, and we can see it doesn't fit the framework, and therefore we can infer new and unexpected facts about the economic agents. Models inform in interesting ways how we go about trying to understand reality, even when they are poor predictors.

But those aren't identical. If I lose the $100 bill, I'm out those hundred bucks whether I buy a ticket or not. Whereas if I lose the ticket, I get to keep the money if I don't buy a replacement.

Scenario A: The setup is (1) lose $100, gain 1 ticket (current state is -$100, +1 ticket); (2) lose 1 ticket (current state is -$100, 0 tickets). The choices are (a) lose $100, gain 1 ticket (yields state -$200, +1 ticket) or (b) do nothing (yields state -$100, 0 tickets).

Scenario B: The setup is (1) lose $100 (current state is -$100, 0 tickets). The choices are the same as above.

The final 'state' at the end of each setup is the same, and the choices are the same, so the argument is that a rational actor would make the same choice in both situations. Maybe there's an argument that the state should include more than just $ and tickets though?

Ah, indeed! That'll teach me to think myself exempt from cognitive biases.

This explains a particular heuristic bias, i.e., it's a finding of cognitive psychology. The comment you're responding to talks about whether the mathematical models of economics have any correspondence to reality, and if anything, this finding shows that it's difficult to make such a model (rationality cannot be assumed).

Although that might be true in a roundabout way, I don't think that all models are required to be directly prognostic. Models can be useful without having much predictive power if they nevertheless lend insights into how different concepts (physical entities, energies, presumably something equivalent in economics...) interact, even if only in the idealized model world.

I don't think that's quite true. You create a model not necessarily to predict, but to understand. A good model - which is by definition not intended to be exactly right - helps one to come to insights that can in turn help to come up with predictions that can be tested.

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