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with more fish in the sea, raise them. In both cases, crucially, the math tells you exactly by how much

It also makes clear the converse: with more fish in the sea,
raise them. In both cases, crucially, the math tells you exactly
by how much.
The easiest wa_y to understand the numbers for this
scenario is to start at the end and think backward. If you’re
down to the last applicant, of course, you are necessarily
forced to choose her. But when looking at the next-to—last
applicant, the question becomes: is she above the 50th
percentile? If yes, then hire her; if not, it’s worth rolling
the dice on the last applicant instead, since her odds of
being above the 50th percentile are 50/50 by definition.
Likewise, you should choose the third-to—last applicant if
she’s above the 69th percentile, the fourth—to-last
applicant if she’s above the 78th, and so on, being more
choosy the more applicants are left. No matter what, never
hire someone who’s below average unless you’re totally out
of options. (And since you’re still interested only in finding
the very best person in the applicant pool, never hire
someone who isn’t the best you’ve seen so far.)
The chance of ending up with the single best applicant
in this full-information version of the secretary problem
comes to 58%—still far from a guarantee, but considerably
better than the 37% success rate offered by the 37% Rule in
the no-information game. If you have all the facts, you can
succeed more often than not, even as the applicant pool
grows arbitrarily large.

The full-information game thus offers an unexpected
and somewhat bizarre takeaway. Gold digging is more
likely to succeed than a questfor love. If you’re evaluating
your partners based on any kind of objective criterion-
say, their income percentile—then you’ve got a lot more
information at your disposal than if you’re after a nebulous
emotional response (“love”) that might require both
experience and comparison to calibrate.
Of course, there’s no reason that net worth—or, for that
matter, typing speed—needs to be the thing that you’re
measuring. Any yardstick that provides full information on
where an applicant stands relative to the population at
large will change the solution from the Look-Then-Leap
Rule to the Threshold Rule and will dramatically boost
your chances of ļ¬nding the single best applicant in the
group.


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About Unknown

ZAKARIA AL BAZZAR, 19 yo, university student. love everything about new tech, and I'm sharing it with you :)
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