The Stopping Problem

The 37% Rule derives from optimal stopping’s most famous puzzle, which has come to be known as the “secretary problem.” Its setup is much like the apartment hunter’s dilemma that we considered earlier. Imagine you’re interviewing a set of applicants for a position as a secretary, and your goal is to maximize the chance of hiring the single best applicant in the pool. While you have no idea how to assign scores to individual applicants, you can easily judge which one you prefer. (A mathematician might say you have access only to the ordinal numbers—the relative ranks of the applicants compared to each other—but not to the cardinal numbers, their ratings on some kind of general scale.) You interview the applicants in random order, one at a time. You can decide to offer the job to an applicant at any point and they are guaranteed to accept, terminating the search. But if you pass over an applicant, deciding not to hire them, they are gone forever.


In your search for a secretary, there are two ways you can fail: stopping early and stopping late. When you stop too early, you leave the best applicant undiscovered. When you stop too late, you hold out for a better applicant who doesn’t exist. The optimal strategy will clearly require finding the right balance between the two, walking the tightrope between looking too much and not enough.


...the optimal solution takes the form of what we’ll call the Look-Then-Leap Rule: You set a predetermined amount of time for “looking”—that is, exploring your options, gathering data—in which you categorically don’t choose anyone, no matter how impressive. After that point, you enter the “leap” phase, prepared to instantly commit to anyone who outshines the best applicant you saw in the look phase.


...look at the first 37% of the applicants,* choosing none, then be ready to leap for anyone better than all those you’ve seen so far.


Folksonomies: computational thinking

/law, govt and politics (0.757461)
/education/homework and study tips (0.697358)
/business and industrial/business operations/human resources/compensation and benefits (0.694388)

Cardinal number (0.933232): dbpedia_resource
Decision theory (0.783278): dbpedia_resource
Natural number (0.773420): dbpedia_resource
Ordinal number (0.678983): dbpedia_resource
Georg Cantor (0.648585): dbpedia_resource

 Algorithms to Live By
Books, Brochures, and Chapters>Book:  Christian, Brian (April 19th 2016), Algorithms to Live By, Henry Holt and Co., Retrieved on 2021-09-27
Folksonomies: computer science algorithms optimization optimal living