It still misses the biggest challenge though--defining "best", and ensuring you're actually measuring it and not something else. It's useful as long as your de

It still misses the biggest challenge though--defining "best", and ensuring you're actually measuring it and not something else. It's useful as long as your definition is good enough and your measurements and randomizations aren't biased. Are you monitoring this over time to ensure that it continues to hold? If you don't, you risk your MAB converging on something very different from what you would consider "the best". When it converges on the right thing, it's better. When it converges on the wrong thing, it's worse. Which will it do? What's the magnitude of the upside vs downside?