I wonder what happens when there's lots of noise in the estimation of quality or fit (or whatever underlies the preference ranking). I imagine this has been done?
I learned about this in the book Algorithms to Live By. They talk about it in the context of dating; How do you know if you've met the right one? IIRC you have to go on x amount of dates knowing you won't commit to this person.
The book has an interesting way of looking at life, recommend.
>> is simple and selects the single best candidate about 37% of the time
Not a mathematician but 1 / e = 0.367, so is this just a way to ensure a 37% percentile of accuracy across normal distribution? Like Anchorman Sex Panther, "37% of the time it works every time"?
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[ 4.6 ms ] story [ 30.8 ms ] threadSounds to me like the actual objective is to hire the hottest-looking secretary.
This will ensure that all the remaining applicants are luckier than the average applicant.
The book has an interesting way of looking at life, recommend.
Not a mathematician but 1 / e = 0.367, so is this just a way to ensure a 37% percentile of accuracy across normal distribution? Like Anchorman Sex Panther, "37% of the time it works every time"?