Friedman's "Prediction vs. Explanation"

Eliezer Yudkowsky

We do ten experiments. A scientist observes the results, constructs a theory consistent with them, and uses it to predict the results of the next ten. We do them and the results fit his predictions. A second scientist now constructs a theory consistent with the results of all twenty experiments.

The two theories give different predictions for the next experiment. Which do we believe? Why?

One of the commenters links to Overcoming Bias, but as of 11PM on Sep 28th, David's blog's time, no one has given the exact answer that I would have given. It's interesting that a question so basic has received so many answers.

David D. Friedman asks:

We do ten experiments. A scientist observes the results, constructs a theory consistent with them, and uses it to predict the results of the next ten. We do them and the results fit his predictions. A second scientist now constructs a theory consistent with the results of all twenty experiments.

The two theories give different predictions for the next experiment. Which do we believe? Why?

Assuming both of them can produce values (are formulated in such a way that they can produce a new value with just the past values + the environment)

The second theory has the risk of being more descriptive than predictive. It has more potential of being fit to the input data, including all its noise, and to be a (maybe complex) enumeration of its values.

The first one has at least proven it could be used to predict, while the second one can only produce a new value.

I would thus give more credit to the first theory. At least it has won against ten coin flips without omniscience.

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Friedman's "Prediction vs. Explanation"

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Friedman's "Prediction vs. Explanation"

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