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U.S. users are leaving Facebook by the millions, Edison Research says

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1293 points rmason | 1 comments | 06 Mar 19 20:30 UTC | HN request time: 0.206s | source
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peteretep ◴[07 Mar 19 05:44 UTC] 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 ◴[07 Mar 19 05:53 UTC] 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 ◴[07 Mar 19 10:22 UTC] 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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Yhippa ◴[07 Mar 19 13:47 UTC] No.19327933[source]▶
This is interesting but I don't think I fully understand. Do you mind dumbing it down for me?
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1. JorgeGT ◴[07 Mar 19 19:25 UTC] No.19331260[source]▶
Typical example: you arrive at a crossroads A where both ways work for you, so you choose the one with less traffic. Then at crossroad B you do the same, and then at C, and finally you arrive at destination D.

However, it turns out the heavy traffic at the other A branch was just for a few miles and then it was actually empty after that --- you took optimum local decisions at each step but since you weren't able to look at the big picture, you didn't actually choose the globally optimal route.

As others have pointed, this is related to the mathematical concepts of local and global maxima: sometimes your optimization algorithm happily stops when it finds a local maximum, ignoring the much better global maximum because it didn't actually traversed the whole search domain.