Fresh from slashdot: A smart application of Bayes' rule to web-voting.

http://engineering.foursquare.com/2014/01/03/the-mathematics-of-gamification/

[The results] are exactly the equations for voting you would expect. But now, they’re derived from math!

The Benefits

• Efficient, data-driven guarantees about database accuracy. By choosing the points based on a user’s accuracy, we can intelligently accrue certainty about a proposed update and stop the voting process as soon as the math guarantees the required certainty.
• Still using points, just smart about calculating them. By relating a user’s accuracy and the certainty threshold needed to accept a proposed update to an additive point system (2), we can still give a user the points that they like. This also makes it easy to take a system of ad-hoc points and convert it over to a smarter system based on empirical evidence.
• Scalable and easily extensible. The parameters are automatically trained and can adapt to changes in the behavior of the userbase. No more long meetings debating how many points to grant to a narrow use case.
  So far, we’ve taken a very user-centric view of pk (this is the accuracy of user k). But we can go well beyond that. For example, pk could be “the accuracy of user k’s vote given that they have been to the venue three times before and work nearby.” These clauses can be arbitrarily complicated and estimated from a (logistic) regression of the honeypot performance. The point is that these changes will be based on data and not subjective judgments of how many “points” a user or situation should get.

I wonder whether and how this could be applied to voting here as LW posts are not 'correct' per se.

One rather theoretical possibility would be to assign prior correctness to some posts e.g. the sequences and then use that to determine the 'accuracy' of users based on that.