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This seems like a trivial thing to fix by presenting optimal with limited noise. Let's say it picks the optimal choice x% of the time (some really high number), and when additional changes are made or automatically detected, this percentage drops. If you pick the next most optimal down the line through all of your options, and make x proportional to the period of time since the last change, it should make it reasonably resistant to this kind of biasing in the first place, and can ramp back up at a reasonable rate.

Better yet, make x dependent in some way on time since the last change, and relative change in performance of all options from before and after the change.




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