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I think this is related to my fundamental problem with A/B testing: when people A/B test individual variables they'll take a greedy optimization that gets stuck
by inlined 10y ago
I think this is related to my fundamental problem with A/B testing: when people A/B test individual variables they'll take a greedy optimization that gets stuck in local minima.
The other problem is that you only understand the change in metrics you measure and things like "satisfaction" are often lost.
- bad_user 10y ago> greedy optimization that gets stuck in local minima I think you wanted to say local maxima, but yes I agree.
- deleted 10y ago[deleted]
- TeMPOraL 10y agoIt's customary to state the optimization problem such that you're minimizing a function - hence "local minima". If you want to maximize something, you simply negate the function.