To all your points about the overloading of “Bayesian”, fair enough. I guess I just don’t see why that overloading is necessary.
We lack a good framework for doing this. Bayes rule is the answer to that problem that provides the promise of a solution. The solution to wait a few years and then read a meta review is unsatisfying.
Sure Bayes rule provides a formalization of updating beliefs based on evidence, but you can still be dead wrong. In particular, setting a prior on any given issue isn’t enough. You have to be prepared to update for evidence of the form “I am really bad at setting priors”. And really, priors are just a (possibly arbitrary) way of digesting existing evidence. Sometimes they can be very useful (avoiding privileging the hypothesis) but sometimes they are quite arbitrary.
There are issues such an the Bem paper about porno-precognition where frequentist techniques did suggest that porno-precognition is real but analysing Bems data with Bayesian methods suggested it’s not.
According to the Slate Star Codex article Bem’s results stand up to bayesian analysis quite well (that is, it has a strong Bayes factor). The only exception he mentioned was “I begin with a very low prior for psi phenomena, and a higher prior for the individual experiments and meta-analysis being subtly corrupt”; but there’s nothing especially helpful about this in actually fixing the experimental design and meta-analysis.
Part of LW is that it’s a place to discuss how an AGI could be structured. As such we care about the philosophic level of how you come to know that something is true. As such there an interest into going as basic as possible when looking at epistemology.
How you get from AGI to epistemology eludes me. As long as the AGI can accurately model its interactions with the environment, that’s really all it needs (or can hope) to do.
That sentence is quite easy to say but it effectively means there no such thing as pure absolute objective truth. If you use tools A you get truth X and if you use tools B you get truth Y. Neither X or Y are “more true”. That’s not an appealing conclusion to many people.
One of them is more useful for prediction and inference. They can guide you towards observing mechanisms useful for future hypothesis generation. That’s all you can hope for. Especially in the case of “are low-salt diets healthy”. A “Yes” or “No” to that question will never be truthful, because “health” and “for what segments of the population” and “in conjunction with what other lifestyle factors” are left underspecified. And you’ll never get rid of the kernel of doubt that the low-sodium lobby has been the silent force behind all the anti-salt research this whole time.
The best you can do is provide enough evidence that anyone who points out your hypothesis is not truth can be reasonably called a pedant or conspiracy theorist, but not 100% guaranteed wrong.
As you might see, I am a fan of the idea of Dissolving epistemology.
To all your points about the overloading of “Bayesian”, fair enough. I guess I just don’t see why that overloading is necessary.
Sure Bayes rule provides a formalization of updating beliefs based on evidence, but you can still be dead wrong. In particular, setting a prior on any given issue isn’t enough. You have to be prepared to update for evidence of the form “I am really bad at setting priors”. And really, priors are just a (possibly arbitrary) way of digesting existing evidence. Sometimes they can be very useful (avoiding privileging the hypothesis) but sometimes they are quite arbitrary.
According to the Slate Star Codex article Bem’s results stand up to bayesian analysis quite well (that is, it has a strong Bayes factor). The only exception he mentioned was “I begin with a very low prior for psi phenomena, and a higher prior for the individual experiments and meta-analysis being subtly corrupt”; but there’s nothing especially helpful about this in actually fixing the experimental design and meta-analysis.
How you get from AGI to epistemology eludes me. As long as the AGI can accurately model its interactions with the environment, that’s really all it needs (or can hope) to do.
One of them is more useful for prediction and inference. They can guide you towards observing mechanisms useful for future hypothesis generation. That’s all you can hope for. Especially in the case of “are low-salt diets healthy”. A “Yes” or “No” to that question will never be truthful, because “health” and “for what segments of the population” and “in conjunction with what other lifestyle factors” are left underspecified. And you’ll never get rid of the kernel of doubt that the low-sodium lobby has been the silent force behind all the anti-salt research this whole time.
The best you can do is provide enough evidence that anyone who points out your hypothesis is not truth can be reasonably called a pedant or conspiracy theorist, but not 100% guaranteed wrong.
As you might see, I am a fan of the idea of Dissolving epistemology.