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Punoxysm comments on Open Thread, April 27-May 4, 2014 - Less Wrong Discussion
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There are a bunch of issues involved. It hard to speak about them because the term Bayesianism is encompasses a wide array of ideas and everytime it's used it might refer to a different subset of that cluster of ideas.
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. There are issues about objective knowledge versus "subjective" Bayesian priors that are worth thinking about.
We live at a time where up to 70% of scientific research can't be replicated. Frequentism might not be to blame for all of that, but it does play it's part. 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.
There are further issues that a lot of additional assumptions are loaded into the word Bayesianism if you use that word on LessWrong. What Bayesianism taught me speaks about a bunch of issues that only have indirectly something to do with Bayesian tools vs. Frequentist tools.
Let's say I want to decide how much salt I should eat. I do follow the consensus that salt is bad and therefore have some prior that salt is bad. Then a new study comes along and says that low salt diets are unhealthy. If I want to make good decisions I have to ask: How much should I update? There no good formal way for making such decisions. 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.
In the absence of a formal way to do the reasoning, many people do use informal ways of updating towards new evidence. Cognitive bias research suggest that the average person isn't good at this.
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.
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.