Published on November 28, 2024 9:10 AM GMT
Epistemic status: the idea it’s not fully fleshed out (there are a bunch of problems that I’m skipping over, the post is meant more as: “has anyone been seriously thinking about this?” and throwing out a starting point, than: “here is a proposal that would actually work”) and I wouldn’t be surprised if either it’s unfeasible or it has already been implemented.
I was reading Neutrality and the part about social media struck me: “Or, they use ‘impartializing tactics’ chosen in the early-Web-2.0 days when people were more naive and utopian, like ‘allow everyone to trivially make a user account, give all user accounts initially identical affordances, prioritize user-upvoted content.’ […] — with LLMs, zero-knowledge proofs, formal verification, prediction markets, and the like — can make a better stab at these supposedly-subjective virtues than ‘one guy’s opinion’ or ‘a committee’s report’ or ‘upvotes’?”. I thought “Seriously, why aren’t social media companies doing this already?” (and they might be). The thing that stood out to me was that we could try to use prediction markets, or just markets, to improve the “ALGORITHM”.
The easiest possible way to do it (and that almost definitely wouldn’t work) is to use likes (or upvotes, etc.) as currency, we could give people a certain amount of likes and treat them as “bids” one could place on a given post, if the post does well, you earn more likes. Another approach, that solves the obvious problem of “But what if I run out of likes?”, is to use a reputational system: likes are weighted by the reputation you have, infinite likes finite reputation. There are a couple other problems that need to be solved. First, how do we measure a successful prediction? Second, how can we avoid Goodhart without overcomplicating the system? (Another issue is setting up the right incentives, a paid model probably works better than one based on ads but I’m not sure what would be best here).
Let’s start from Goodhart: the solution is just don’t tell the users what you’re doing. Don’t put the reputation anywhere on the site and you should mostly be fine.
Now, how do you measure a good prediction? First we need to figure out what good is. Personally, I think avoiding to promote all the memetic slop, “like to see the animation” or the random celebrities' news or “funny” videos, would be a decent goal. We want to promote good posts for the right users and not everyone will agree on what is good for them, but we assume companies already know how to solve that (I imagine something like “cluster together people with similar internet tastes”). You can then proceed to measure a good like if it’s a good prediction that other users in the same “tpot” will like the post too.
And assuming everything goes right the result is a social media that can distinguish between users that randomly like most things and users that can act as curators and spread their good tastes.
Discuss
