95 lines
3.2 KiB
TeX
95 lines
3.2 KiB
TeX
\section{The Secretary}
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\definition{The secretary problem}
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Say we need to hire a secretary. We have exactly one position to fill,
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and we must fill it with one of $n$ applicants. These $n$ applicants,
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if put together, can be ranked unambiguously from \say{best} to \say{worst}.
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\vspace{2mm}
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We interview applicants in a random order, one at a time. \par
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At the end of each interview, we either reject the applicant (and move on to the next one), \par
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or select the applicant (which fills the position and ends the process).
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\vspace{2mm}
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Each applicant is interviewed at most once---we cannot return to an applicant we've rejected. \par
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In addition, we cannot reject the final applicant, as doing so will leave us without a secretary.
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\vspace{2mm}
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For a given $n$, we would like to maximize our probability of selecting the best applicant. \par
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This is the only metric we care about---we do not try to maximize the rank of our applicant. \par
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Hiring the second-best applicant is no better than hiring the worst.
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\problem{}
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If $n = 1$, what is the best hiring strategy, and what is the probability that we hire the best applicant?
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\vfill
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\problem{}
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If $n = 2$, what is the best hiring strategy, and what is the probability that we hire the best applicant? \par
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Is this different than the probability of hiring the best applicant at random?
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\vfill
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\pagebreak
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\problem{}
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If $n = 3$, what is the probability of hiring the best applicant at random? \par
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Come up with a strategy that produces better odds.
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\vfill
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\problem{}<bestyet>
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Should we ever consider hiring a candidate that \textit{isn't} the best we've seen so far? \par
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Why or why not? \hint{Read the problem again.}
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\vfill
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\remark{}
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\ref{bestyet} implies that we should automatically reject any applicant that isn't
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the best we've seen. We can take advantage of this fact to restrict the types of
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strategies we consider.
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\vspace{2mm}
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We'll transform our sequence of $n$ secretaries into a sequence of $n$ random variables $I_1, I_2, ..., I_n$,
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each producing values in $\{\texttt{0}, \texttt{1}\}$. Each $I_x$ will produce \texttt{1} if the $x^\text{th}$
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secretary we interview is the best we've seen so far, and \texttt{0} otherwise.
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\problem{}
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What is the probability distribution of $I_1$? \par
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That is, what are $\mathcal{P}(I_1 = \texttt{0})$ and $\mathcal{P}(I_1 = \texttt{1})$?
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\vspace{1cm}
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\problem{}
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Convince yourself that the largest $x$ where $I_x = 1$ is the \par
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position of the best candidate in our list of applicants.
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\vspace{1cm}
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\remark{}
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Recall that we only know the \textit{relative} ranks of our applicants. \par
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We have no absolute metric by which to judge each candidate.
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\vspace{2mm}
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Thus, all $I_x$ defined above are independent: \par
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the outcome of any $I_a$ does not influence the probabilities of any other $I_b$.
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\vspace{2mm}
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We can therefore ignore any strategy that depends on the outcomes of
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previous $I_x$. Since all random variables in this sequence are independent,
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the results of past $I_x$ cannot possibly provide information about future $I_x$.
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\vspace{2mm}
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Given the above realizations, we are left with only one kind of strategy: \par
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We blindly reject the first $k$ applicants, and select the first \say{best-seen} applicant we encounter afterwards.
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All we need to do now is pick the optimal $k$. |