DanielLC comments on Open thread, Jan. 19 - Jan. 25, 2015 - Less Wrong Discussion
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General question: I've read somewhere that there's a Bayesian approach to at least partially justifying simplicity arguments / Occam's Razor. Where can I find a good accessible explanation of this?
Specifically: Say you're presented with a body of evidence and you come up with two sets of explanations for that evidence. Explanation Set A consists of one or two elegant principles that explain the entire body of evidence nicely. Explanation Set B consists of hundreds of separate explanations, each one of which only explains a small part of the evidence. Assuming your priors for each individual explanation is about equal, is there a Bayesian explanation for our intuition that we should bet on Explanation Set A?
What about if your prior for each individual explanation in Set B is higher than the priors for the explanations in Set A?
Example:
Say you're discussing Bible Criticism with a religious friend who believes in the traditional notion of complete Mosaic authorship but who is at least somewhat open to alternatives. To your friend, the priors for Mosaic authorship are much higher than the priors for a documentary or fragmentary hypothesis. (If you want numbers, say that your friend's priors are .95 in favor of Mosaic authorship.)
Now you present the arguments, many of which (if I understand them correctly) boil down to simplicity arguments:
The question is, is your friend justified in rejecting your simplicity-based arguments based on his high priors? What about if his priors were lower, say .6 in favor of Mosaic authorship? What about if he held 50-50 priors?
Do you mean your prior for A is about your prior for B, or your priors for each element are about the same?
If you mean the first, then there is no reason to favor one over the other. Occam's razor just says the more complex explanation has a lower prior.
If you mean the second, then there is a very good reason to favor A. If A has n explanations, B has m, all explanations are independant and of probability p, then P(A) = p^n and P(B) = p^m. A is exponentially more likely than B. In real life, assuming independence tends to be a bad idea, so it won't be quite so extreme, but the simpler explanation is still favored.