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JGWeissman comments on The Optimizer's Curse and How to Beat It - Less Wrong

44 Post author: lukeprog 16 September 2011 02:46AM

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Comment author: JGWeissman 17 September 2011 07:06:08PM 3 points [-]

That is a good example of how the optimizer's curse causes an overestimate of the maximum expected value, and even reliably causes a wrong choice to be associated with the maximum expected value. But how do I apply the correction mathematically, so I can know for which expected values on the high uncertainty boxes I should expect their best of them to be better or worse than the low uncertainty box? Even better, how can I deal with situations where the uncertainties of the expected values are not so conveniently categorized (and whose actual values aren't conveniently uniform)?

Comment author: Manfred 19 September 2011 10:52:04AM *  2 points [-]

Oh - I learned how, by the way. You start with some prior over how you expect the actual coins to be distributed, and then you convolute in the noise distribution of each box to get the combined distribution for each box. Then, given where the number on the outside of each box falls on the combined distribution, you can assign how much of that you expect to be signal and how much you expect to be noise by distributing improbability equally between signal and noise. Then you subtract out the expected noise.

Comment author: Manfred 17 September 2011 07:48:50PM 0 points [-]

I'm not sure. It's probably in the paper.