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If you’re still single after considering all the possibilities—as Kepler was—then go back to the best one that got away.

  The symmetry between
strategy and outcome holds in this case once again, with
your chances of ending up with the best applicant under
this second-chances-allowed scenario also being 61%.
For Kepler, the difference between reality and the
classical secretary problem brought with it a happy ending.
In fact, the twist on the classical problem worked out well
for Trick, too. After the rejection, he completed his degree
and took a job in Germany. There, he “walked into a bar,
fell in love with a beautiful woman, moved in together
three weeks later, [and] invited her to live in the United
States ‘for a while.”’ She agreed—and six years later, they
were wed.
Knowing a Good Thing V\7hen You See It: Full Information
The first set of variants we considered—rejection and
recall—altered the classical secretary problem’s
assumptions that timely proposals are always accepted,
and tardy proposals, never. For these variants, the best
approach remained the same as in the original: look
noncommittally for a time, then be ready to leap.
But there’s an even more fundamental assumption of
the secretary problem that we might call into question.

Namely, in the secretary problem we know nothing about
the applicants other than how they compare to one
another. We don’t have an objective or preexisting sense of
what makes for a good or a bad applicant; moreover, when
we compare two of them, we know which of the two is
better, but not by how much. It’s this fact that gives rise to
the unavoidable “look” phase, in which we risk passing up
a superb early applicant while we calibrate our
expectations and standards. Mathematicians refer to this
genre of optimal stopping problems as “no-information
games.”
This setup is arguably a far ciy from most searches for
an apartment, a partner, or even a secretary. Imagine
instead that we had some kind of objective criterion—if
eveiy secretary, for instance, had taken a typing exam
scored by percentile, in the fashion of the SAT or GRE or
LSAT. That is, every applicant’s score will tell us where
they fall among all the typists who took the test: a 51st-
percentile typist is just above average, a 75th-percentile
typist is better than three test takers out of four, and so on.
Suppose that our applicant pool is representative of the
population at large and isn’t skewed or self-selected in any
way. Furthermore, suppose we decide that typing speed is
the only thing that matters about our applicants.
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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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