You can use collaborative learning for other purposes. For example, suppose I wanted to show a user posts which Eliezer Yudkowsky would upvote (a “Things EY would Upvote” tab...), rather than posts they personally would upvote. This allows a moderator to implicitly choose which component of users has the “right” taste, without having to explicitly upvote/downvote every individual post.
I don’t know if imposing one individual’s taste is such a good idea, but it is an option. It seems like you should think for a while about what exactly you want, rather than just proposing mechanisms and then evaluating whether you like them or not. Once you know what you want, then we have the theoretical machinery to build a mechanism which implements your goal well (or, we can sit down for a while and develop it).
Also, it is worth pointing out that you can do much better than just weighting votes by similarity factors. In general, it may be the case that Alice and Bob have never voted on the same comment, and yet Alice still learns interesting information from Bob’s vote. (And there are situations where weighting by similarity breaks down quite explicitly.) My point is that instead of doing something ad-hoc, you can employ a predictor which is actually approximately optimal.
It seems like you should think for a while about what exactly you want, rather than just proposing mechanisms and then evaluating whether you like them or not.
Fair enough. Apologies for wasting your time with undirected musings.
In terms of what I want, everything I can think of shares the property of being more useful in a more heterogenous environment. I put together a wishlist along these lines some months ago.But within an environment as homogenous as LW, none of that seems worth the effort.
That said, I would find it at least idly interesting to be able to switch among filters (e.g., “Things EY would upvote”, “Things Yvain would upvote”, etc.), especially composite filters (e.g., “Things EY would upvote that aren’t things Yvain would upvote,” “90% things EY would upvote and 10% things he wouldn’t”, etc.).
You can use collaborative learning for other purposes. For example, suppose I wanted to show a user posts which Eliezer Yudkowsky would upvote (a “Things EY would Upvote” tab...), rather than posts they personally would upvote. This allows a moderator to implicitly choose which component of users has the “right” taste, without having to explicitly upvote/downvote every individual post.
I don’t know if imposing one individual’s taste is such a good idea, but it is an option. It seems like you should think for a while about what exactly you want, rather than just proposing mechanisms and then evaluating whether you like them or not. Once you know what you want, then we have the theoretical machinery to build a mechanism which implements your goal well (or, we can sit down for a while and develop it).
Also, it is worth pointing out that you can do much better than just weighting votes by similarity factors. In general, it may be the case that Alice and Bob have never voted on the same comment, and yet Alice still learns interesting information from Bob’s vote. (And there are situations where weighting by similarity breaks down quite explicitly.) My point is that instead of doing something ad-hoc, you can employ a predictor which is actually approximately optimal.
Fair enough. Apologies for wasting your time with undirected musings.
In terms of what I want, everything I can think of shares the property of being more useful in a more heterogenous environment. I put together a wishlist along these lines some months ago.But within an environment as homogenous as LW, none of that seems worth the effort.
That said, I would find it at least idly interesting to be able to switch among filters (e.g., “Things EY would upvote”, “Things Yvain would upvote”, etc.), especially composite filters (e.g., “Things EY would upvote that aren’t things Yvain would upvote,” “90% things EY would upvote and 10% things he wouldn’t”, etc.).