Coding day in and out on LessWrong 2.0
Huh, I notice they don't show up in search, probably because they are marked as stubs, or something (somewhat surprised by this). I think we imported them to not break old links, but I am not sure whether I would want to have them actually show up in search and in other wiki-tag lists (both their current state, but even in a more fully completed state).
The key thing is something like "do we want to have lots of content organized by people?". Currently the wiki has basically no entries for people (we have one for Eliezer, but none for Scott Alexander or Lukeprog for example), and while I haven't though super much about this question, the status quo seems at least mildly good to me, because there is both an ethos of the site of focusing on ideas instead of people, and because writing about people is often kind of dicey and will push the whole wiki towards a much more defensible style.
I would have very little objection to having an article like this that tries to summarize some of his core ideas, or focuses on some kind of intellectual culture around him, but it feels like if we have this article, then we open up a hole of writing hundreds of articles about everyone who is vaguely related to rationality stuff.
Hmm, I don't currently feel on the margin super excited about wiki pages like this, but maybe I am wrong? Not sure what our content policy should be here, and seems like a good time to discuss.
Yep, on this page you can see all nominations and reviews, plus all the posts with at least two nominations: https://lesswrong.com/reviews
(I confirm this is not a moderator, and does not have any special arrangement with moderators)
Yeah, this seems reasonable. One of the nice things about this migration is that it's now very easy for us to adjust the rules and then just rerun the history again. So now is a pretty good time for suggestions for how the rules should change.
(I am promoting this site-meta post to the frontpage, so that users who have personal blogposts filtered out aren't surprised that suddenly a lot of scores are different. But generally we try to keep most site-meta stuff that isn't crucial for people to know about off the frontpage.)
This also strikes me as backwards, and the literature seems to back this up. Learning rates seem to differ a lot between different people, and also be heavily g-loaded.
I think in-practice there are lots of situations where you can confidently create a kind of pocket-universe where you can actually consider hypotheses in a bayesian way.
Concrete example: Trying to figure out who voted a specific way on a LW post. You can condition pretty cleanly on vote-strength, and treat people's votes as roughly independent, so if you have guesses on how different people are likely to vote, it's pretty easy to create the odds ratios for basically all final karma + vote numbers and then make a final guess based on that.
It's clear that there is some simplification going on here, by assigning static probabilities for people's vote behavior, treating them as independent (though modeling some subset of independence wouldn't be too hard), etc.. But overall I expect it to perform pretty well and to give you good answers.
(Note, I haven't actually done this explicitly, but my guess is my brain is doing something pretty close to this when I do see vote numbers + karma numbers on a thread)
So I'm not sure how we distinguish what's ruled out from what isn't.
Well, it's obvious that anything that claims to be better than the ideal bayesian update is clearly ruled out. I.e. arguments that by writing really good explanations of a phenomenon you can get to a perfect understanding. Or arguments that you can derive the rules of physics from first principles.
There are also lots of hypotheticals where you do get to just use Bayes properly and then it provides very strong bounds on the ideal approach. There are a good number of implicit models behind lots of standard statistics models that when put into a bayesian framework give rise to a more general formulation. See the Wikipedia article for "Bayesian interpretations of regression" for a number of examples.
Of course, in reality it is always unclear whether the assumptions that give rise to various regression methods actually hold, but I think you can totally say things like "given these assumption, the bayesian solution is the ideal one, and you can't perform better than this, and if you put in the computational effort you will actually achieve this performance".