[Funny] Even Clippy can be blamed on the use of non-Bayesian methods

lukeprog

[Funny] Even Clippy can be blamed on the use of non-Bayesian methods — LessWrong

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I communicated poorly. I don't think "should have a score of +2" should enter into the decision to upvote, downvote, or not vote. Instead, I'd rather voting algorithms which, when implemented individually, have results which can be meaningfully summed. For example, suppose everyone upvotes exactly when they think a comment is in the top 5% of comments in "everyone should read this" ordering and downvotes for the bottom 5%. Then the sum reflects the number of people who read the comment x (the average percentage of people who thought it was in the top 5% - bottom 5%). That's something I can understand.

If I think a comment should end up with a score of +2, too bad, I have no direct way of controlling that. The resulting score is a reflection of the community's votes, not something I try to game by altering my voting decision based on whether the score gets closer to +2.

I mean, do people downvote comments that they would have otherwise not voted on if they think the comment has too many upvotes? If not, why do they decline to upvote when they otherwise would have upvoted? The two look the same from everyone else's perspective, right?

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JoshuaZ15y30

I mean, do people downvote comments that they would have otherwise not voted on if they think the comment has too many upvotes? If not, why do they decline to upvote when they otherwise would have upvoted?

I often (although) not always will upvote a comment simply if it deserves it. I only very rarely downvote or don't vote a comment if I think it is too high but should be positive. Declining to upvote a too high comment is something I do much more frequently than downvoting a too high comment. This is a passive rather than active decision. In general de... (read more)

6AdeleneDawner15y

I'm not saying that your proposed algorithm is wrong - not exactly, anyway. I am pointing out something that I think is a flaw. Putting the same point a different way: Consider two comments. One is posted early, and is seen by 50 people. It's slightly good - good enough that each of those people would, by your algorithm, upvote it, but no better than that. The other is posted late, and is only seen by 10 people, but it's very, very good. According to your algorithm, the first one would get a score of +50 and the second one would get a score of +10. By the methods currently in use, the first one will get a low score - probably +1 or +2 - and the second one will still get +10. The first comment got many more points than the second, by your algorithm, because its author was able to quickly put together something good enough to be upvoteable, and because they were at the right place at the right time to post it early in the conversation, which implies either luck or lots of time spent lurking on LW. I don't think these are things we want to incentivise - at least not more than we want to incentivise putting time into crafting well-thought-out comments. Also: [...] I do this. Not very often, but it happens.

2wedrifid15y

Ok, but that's your self handicapping and I want no part of it myself. My decision to vote shall be determined by whatever vote I predict has the best consequences.

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[Funny] Even Clippy can be blamed on the use of non-Bayesian methods

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[Funny] Even Clippy can be blamed on the use of non-Bayesian methods

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