As well as the algorithm, there are also structural things.
For example tweets limited to 160 characters favor simplistic solutions, which the far right provide. An endless stream of random unrelated nuggets of ideas create a fugue of confusion, perfect for injecting disinformation. Ruined attention spans can only grasp simplistic solutions. Video-based media means surface appearance matters more than substance. And so on.
Yeah I really couldn’t agree more. I really harped on the importance of other properties of the medium, like brevity, when I reviewed the book #HashtagActivism, and how those too are structurally right wing. There’s a lot of scholars doing these kinds of network studies and imo they way too often emphasize user-user dynamics and de-emphasize, if not totally omit, the fact that all these interactions are heavily mediated. Just this week I watched a talk that I thought had many of these same problems.
I feel enlightened now that you called out the self-reinforcing nature of the algorithms. It makes sense that an RL agent solving the bandits problem would create its own bubbles out of laziness.
Maybe we can take advantage of that laziness to incept critical thinking back into social media, or at least have it eat itself.
Thanks!
I feel enlightened now that you called out the self-reinforcing nature of the algorithms. It makes sense that an RL agent solving the bandits problem would create its own bubbles out of laziness.
You’re totally right that it’s like a multi-armed bandit problem, but maybe with so many possibilities that searching is prohibitively expensive, since the space of options to search is much bigger than the rate that humans can consume content. In other ways, though, there’s a dissimilarity because the agent’s reward depends on its past choices (people watch more of what they’re recommended). It would be really interesting to know if anyone has modeled a multi-armed bandit problem with this kind of self-dependency. I bet that, in that case, the exploration behavior is pretty chaotic. @abucci@buc.ci this seems like something you might just know off the top of your head!
Maybe we can take advantage of that laziness to incept critical thinking back into social media, or at least have it eat itself.
If you have any ideas for how to turn social media against itself, I’d love to hear them. I worked on this post unusually long for a lot of reasons, but one of them was trying to think of a counter strategy. I came up with nothing though!
@theluddite@lemmy.ml @kersplomp@programming.dev I have a lot to say on this subject but unfortunately do not have the time right now to write out anything worth reading! I will return perhaps tomorrow.
@theluddite@lemmy.ml @kersplomp@programming.dev I didn’t fully follow the connection between the social media post and multi-armed bandit problems. Is the idea that a user has k options about what to view, chooses one, and experiences some kind of payoff from the choice? If so I’m not sure the situation is well-modeled by bandits, since the typical social media user is presented with a smallish set of options chosen for them by an algorithm, with each user choice resulting in an algorithm presenting them with another smallish set of options that might be of different size and comprise different options. That kind of situation might be better modeled as an extensive form game of user against “the algorithm” with a finite but variable set of choices for the player at each ply. It’s common in a turn-taking game for both player’s and opponent’s choice to affect the choices available to player next ply, which is why this feels like a better model to me than k-armed-bandits or the POMDP type setups usually explored in RL.
If what the algorithm does can be approximated that way (as a reward-maximizing player in a multi-ply game that chooses what category of content to show a user at each turn), then you can get partway towards understanding how it works functionally by understanding how the tradeoffs between monetization, data gathering, and maximizing surprisal (learning) in its reward function are struck. I suspect that splitting the bins/categories more and more finely sometimes makes the tradeoffs look better, which might explain why social media companies tend to do this (if you have one bin of stuff with red and blue objects, and people choose randomly from it, they’ll be less happy on average than if you have a bin of red objects and a bin of blue objects and are able to direct red-preferring and blue-preferring users to the appropriate bin better than a coin flip would).
People are not static utility maximizers, but these types of algorithms assume we are. So I think they tend to get stuck in corners both because of how they strike tradeoffs (you get manosphere content because that’s what’s most monetizable) and because people’s preferences aren’t expressed consistently in their actions and change through time (you keep getting shown scifi content because you looked at a few scifi videos in a row awhile ago when you were feeling nostalgic but you don’t usually prefer it).
That’s what I have for now. Sorry for length.
No need to apologize for length with me basically ever!
I was thinking how you did it in the second paragraph, but even more stripped down. The algorithm has N content buckets to choose from, then, once it chooses, the success is how much of the video the user watched. Users have the choice to only keep watching or log off for simplicity. For small N, I think that @kersplomp@programming.dev is right on that it’s the multi-armed bandit problem if we assume that user preferences are static. If we introduce the complexity that users prefer familiar things, which I think is pretty fair, so users are more likely to keep watching from a bucket if it’s a familiar bucket, I assume that exploration gets heavily disincentivized and exhibits some pretty weird behavior, while exploitation becomes much more favorable. What I like about this is that, with only a small deviation from a classic problem, it would help explain what you also explain, which is getting stuck in corners.
Once you allow user choice beyond consume/log off, I think your way of thinking about it, as a turn based game, is exactly right, and your point about bin refinement is great and I hadn’t thought of that.
I knew you were the person to call :)
