I was thinking the other day it would be interesting to know something about the karma I was getting. Particularly the quality, to the extent that is possible to assess.
I suspect everyone would prefer to have those they respect up voting posts and comments but that type of transparency might not be desirable. I'm not sure if some type of weighting system would work or not -- how may karma points do the account giving an up vote have so you can get some average gauge of quality of the feedback.
I would not think that should be on the main page but something each person can look at under their own account.
Wonder what others think or if this has been suggested before?
It gives a reasonably rigorous way of predicting how many upvotes and downvotes a post will get, given the history of the user who wrote it. Specifically, it defines a probabilistic model: for each user, we can specify a Beta distribution with various unknown parameters, and then learn those parameters from the user's post history. The details of that learning are rather charming if you're a statistician, or aspire to be one, but don't translate very well.
mr-hire would like to know what his particular Beta distribution looks like. To find out, we have to adapt Moulton's method to the LW karma system. This turns out to be a little difficult, and requires some additional modeling choices:
Moulton models votes on individual posts with a Binomial distribution, which is used for sequences of binary outcomes. In this case each voter either upvotes the post (with probability p) or downvotes it (with probability 1-p) -- we ignore non-voters since it's hard to know how many of them there are. But a LessWrong voter has four choices: they can vote Up or Down, and they can vote Normal or Strong, so the Binomial distribution is no longer appropriate.
This is fixable with a different choice of distributions, but then you run into another problem. In LW, even normal votes vary in value: an upvote from a high-karma user is worth more than one from a low-karma user. Do we wish to model this effect, and if so how?
If you were willing to treat all user votes equally I think you could get away with using the Dirichlet-multinomial. If not, I think you have to give up on modeling individual votes and try to model karma directly, without breaking it down into its component upvotes and downvotes.