The Secretary Problem

You have to fill one job. The applicants arrive one at a time, in random order, and you can rank any two you have seen. The catch: right after each interview you must say hire or pass, on the spot. Pass, and that person is gone forever. Hire, and the search ends. You want the single best applicant of them all.

How do you even start? Hire the first person and you are almost certainly settling. Wait too long and the best one may have already walked out the door. Every step is a bet with no take-backs.

This is the secretary problem, and it has a clean, surprising answer. There is a single rule that maximizes your odds — and it wins far more often than instinct says it should.

Here is the rule. Look, don't leap for the first r applicants — interview them, hire none, just remember the best score you saw. After that, hire the first applicant who beats everyone in that look phase. Below, set how many candidates and what fraction to skip, then run many random trials and see how often each strategy lands the true best.

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<label class="row">{{label_f}} <b id="fVal">37%</b>
  <input id="f" type="range" min="0" max="90" value="37"></label>
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Drag the skip fraction toward 37% and watch the win rate climb to its peak. Compare it with "always hire the first" and "always hire the last" — both crawl along near 1/n. The magic number is no accident: as the pool grows, the best cutoff converges to 1/e ≈ 0.368, and the win rate converges to the very same 1/e ≈ 37%.

So how good is the 37% rule, really? Not "pretty good" — it is provably the best possible.

• It is solved exactly. The strategy is: reject the first n/e applicants (about 37%), then hire the first one who is better than all of those. As n grows, this rule wins the very best candidate with probability approaching 1/e ≈ 0.368.
• No strategy beats it. Among all rules that must decide on the spot with no look-back, none can win more often than ≈ 1/e. The bound is tight — this is the famous 1/e law of optimal stopping.
• It is cheap to compute. Each step is one comparison; the whole policy is just a threshold. The hard part was never speed — it was proving that this simple threshold cannot be improved.
• Author and year. The clean solution was published by D. V. Lindley (1961) and E. B. Dynkin (1963), building on the puzzle that circulated in the late 1950s.

That is the punchline: most "decide as you go" problems are messy and only approximately solvable, but this one is closed. Unlike the open frontier of P vs NP, here we know the optimal algorithm and a matching proof that nothing can do better.

"Commit now or keep looking, with no rewind" is the shape of countless real decisions, and the secretary problem is its cleanest model:

• Hiring and admissions: interviewing in rounds where strong candidates accept other offers if you hesitate.
• Apartment and house hunting: in hot markets a listing is gone if you sleep on it — look-then-leap gives a principled cutoff.
• Selling under deadlines: take this offer or wait for a better one that may never come.
• Online algorithms and auctions: ad systems and matching markets must accept or reject each arriving item before the rest are revealed; secretary-style guarantees underpin many of them.

Understand the 37% rule and you have met optimal stopping — the same engine behind sequential decisions in optimal testing and many online, commit-as-you-go problems.

The secretary problem hides a rare gift: a real-world, decide-as-you-go choice that mathematics has completely solved. Spend the first 37% of your search just learning, then grab the first candidate who beats everything you have seen. You will land the very best about 37% of the time — and no strategy, however clever, can do better.

So the next time you must choose without a rewind — an apartment, an offer, a hire — remember that "looking before you leap" is not just folk wisdom. It is a theorem, with the number 1/e stamped right on it.