Comment by crazygringo
6 days ago
I'm not talking about the literature -- I'm talking about the extremely simplistic and sub-optimal procedure described in the post.
If you want to get sophisticated, MAB properly done is essentially just A/B testing with optimal strategies for deciding when to end individual A/B tests, or balancing tests optimally for a limited number of trials. But again, it doesn't "beat" A/B testing -- it is A/B testing in that sense.
And that's what I mean. You can't magically increase your reward while simultaneously getting statistically significant results. Either your results are significant to a desired level or not, and there's no getting around the number of samples you need to achieve that.
I am talking about the literature which solves MAB in a variety of ways, including the one in the post.
> MAB properly done is essentially just A/B testing
Words are only useful insofar as their meanings invoke ideas, and in my experience absolutely no one thinks of other MAB strategies when someone talks about A/B testing.
Sure, you can classify A/B testing as one extremely suboptimal approach to solving MAB problem. This classification doesn’t help much though, because the other MAB techniques do “magically increase the rewards” compared this simple technique.
> Sure, you can classify A/B testing as one extremely suboptimal approach to solving MAB problem. This classification doesn’t help much though, because the other MAB techniques do “magically increase the rewards” compared this simple technique.
You are quite simply wrong. There is nothing suboptimal about an A/B test between two choices performed until desired statistical significance. There is nothing you can do to magically increase anything.
If you think there is, you'll have to describe something specific. Because nowhere in the academic MAB literature does anyone attempt to state the contrary. And which, again, is why this blog post is so flawed.