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“Multi-armed bandit” A/B testing optimality proved?

Correct me if I’m wrong, but it seems that this paper proves optimality of “multi-armed bandit” approach to A/B testing. The latter one was described in this post earlier this year.

For those who do not understand what it is about: A/B testing requires investment in the form of sample size (usually it is equal to number of unique users), which is time and money. “Multi-armed bandit” approach is about optimising this investment.

I wouldn’t say you’re ancient if you aren’t doing it already, but it’s interesting to see how abstract science creates new opportunities for business.