XiXiDu comments on Confidence levels inside and outside an argument - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (174)
The problem I was specifically asking to solve is "what if Bayesian updating is flawed", which I thought was an appropriate discussion on an article about not putting all your trust in any one system.
Bayes theorem looks solid, but I've been wrong about theorems before. So has the mathematical community (although not very often and not for this long, but it could happen and should not be assigned 0 probability). I'm slightly sceptical of the uniqueness claim, given I've often seen similar proofs which are mathematically sound, but make certain assumptions about what it allowed, and are thus vulnerable to out-of-the-box solutions (Arrow's impossibility theorem is a good example of this). In fact, given that a significant proportion of statisticians are not Bayesians, I really don't think this is a good time for absolute faith.
To give another example, suppose tomorrow's main page article on LW is about an interesting theorem in Bayesian probability, and one which would affect the way you update in certain situations. You can't quite understand the proof yourself, but the article's writer is someone whose mathematical ability you respect. In the comments, some other people express concern with certain parts of the proof, but you still can't quite see for yourself whether its right or wrong. Do you apply it?
Assign a probability 1-epsilon to your belief that Bayesian updating works. Your belief in "Bayesian updating works" is determined by Bayesian updating; you therefore believe with 1-epsilon probability that "Bayesian updating works with probability 1-epsilon". The base level belief is then held with probability less than 1-epsilon.
As the recursive nature of holding Bayesian beliefs about believing Bayesianly allows chains to tend toward large numbers, the probability of the base level belief tends towards zero.
There is a flaw with Bayesian updating.
I think this is just a semi-formal version of the problem of induction in Bayesian terms, though. Unfortunately the answer to the problem of induction was "pretend it doesn't exist and things work better", or something like that.
I'd love to see someone like EY tackle the above comment.
On a side note, why do I get an error if I click on the username of the parent's author?
I'm actually planning on tackling it myself in the next two weeks or so. I think there might be a solution that has a deductive justification for inductive reasoning. EY has already tackled problems like this but his post seems to be a much stronger variant on Hume's "it is custom, and it works" - plus a distinction between self-reflective loops and circular loops. That distinction is how I currently rationalise ignoring the problem of induction in everyday life.
Also - I too do not know why I don't have an overview page.