[amalcon]

Here's the thing: you're both right. It's both radically underspecified and overfitted. The information-theoretic argument demonstrate that a model cannot exactly match the reality unless it's as complex as the reality.

This article speaks of the separate problem that economic models are not evaluated in any sort of experiments, and thus are prone to overfitting. This makes them unlikely to even approximate well.

Consider a basic multilayer perceptron-style neural network. Overfitting is a well-understood problem in training an MLP. We work around it by training on a part of the data, and then measuring its accuracy on another part -- much as Carter did in his analysis. If the accuracy is poor, something is adjusted: the size of the hidden layer can be increased, the training set expanded, the duration of the training increased or decreased, or the MLP model discarded entirely.

If increase of the training set or reduction of the duration improves accuracy against the test set, this means we had an overfitting problem.

[jeffdavis]

"It's both radically underspecified and overfitted."

He used a perfect model (of a hypothetical world) which had exactly the right parameters, and then he calibrated it using exactly correct data.

So I don't see how this could be underspecified or overfitted. Can you please explain?

"The information-theoretic argument demonstrate that a model cannot exactly match the reality unless it's as complex as the reality."

In this case he defined his model to be reality.

[amalcon]

Those particular statements referred to some representative economic model, not the experiment in question. In the experiment in question, the model is fully specified by definition.

As far as overfitting goes, that applies when you have a parameterized general model and need to discover the correct parameters. You probably won't get the exact correct parameters; instead, you'll (hopefully) get parameters that approximate reality well.

More closely matching the training data can actually make it a worse approximation in the general case.

[iwwr]

"The information-theoretic argument demonstrate that a model cannot exactly match the reality unless it's as complex as the reality."

What if reality is self-similar at certain scales? You could generate something that resembles the whole from one part of it.

[angelbob]

Then the reality is, information-theoretically, less complex and you can use a less complex model to represent it.