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1293 points rmason | 1 comments | | HN request time: 0.206s | source
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peteretep ◴[] No.19325816[source]
By far the biggest factor that had me stopping checking Facebook, and indeed LinkedIn, is number of utterly fictitious notifications they generate. There was a time a few years back when that red dot made me drop everything to check FB, but these days it’ll be some completely bullshit message they’ve made a notification out of. Feels like they got greedy for my attention and killed the golden goose there. I check it about once a day now, and in the browser not the app. If the notifications were still meaningful I’d probably still have the app and all the metadata that sent them.
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Matheus28 ◴[] No.19325847[source]
It really feels like some companies like Facebook are flying blind by using A/B testing everywhere and ignoring the long term effects of the changes they do.
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arethuza ◴[] No.19326938[source]
Isn't A/B testing pretty much going to behave like a steepest ascent hill climbing? At each micro decision point you take what looks like the 'best' option but that means you can get stuck in local maxima?
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1. Matheus28 ◴[] No.19332210[source]
Yes, but it gets more complicated because there's a time factor too.

If you're A/B testing each change for 2 weeks, but the negative impacts of it only happen after a few months (like what the parent post mentioned [1]), then while you're in a local maxima right now, it'll slowly sink, along with all your neighborhood of choices.

You can think of it as a function that returns the current value and another function that you have to use for the next time step. Sorta like f(a, b, c, ...) = (y, \a_2 b_2 c_2 ... -> ...). Steepest ascent hill climbing doesn't work well for finding good long term local maxima, since you don't know how long it takes until it stabilizes (or if it ever does). The best you can do is guess it'll stabilize in X amount of time, but if X is too small, you might end up stuck in a really bad local maxima.

[1] https://news.ycombinator.com/item?id=19325816