Solving optimal stopping problems with statistical algorithms

Enough Is Enough

Article from Issue 190/2016
Author(s):

When is it statistically favorable to conclude, say, the search for a suitable employee with a chosen one? Solid algorithms point the way to success.

The saying goes that you should quit when you're ahead. Making a decision on when to conclude a selection process is often not that easy in real life. In mathematical statistics, a puzzle known as the "secretary problem" [1] sums this up nicely. A number of candidates are applying for a secretary position at a company. In the selection process, the employer must decide after each interview, whether to accept the candidate or reject them and hope for a more suitable applicant later on. The decision is final, the employer is not allowed to invite back any rejected applicants.

An employer who is thinking logically should carefully check the first few candidates and not simply take the first one. Later, when the line starts to come to an end, the employer will possibly take on a more-or-less suitable candidate for fear of being left with only unsuitable candidates, and – not having any other options – grudgingly just bite the bullet, and grab whoever is left.

Ideal: 37 Percent

How should the employer proceed to mathematically ensure the best possible odds of finding an above-average candidate? How long should the try-before-buy phase last, in which the employer explores the unknown capabilities of candidates before immediately snapping up the one that is better than all the previous ones and hopefully scores favorably against future candidates further down the line? Mathematicians have been tackling stop problems [2] for thousands of years.

[...]

Use Express-Checkout link below to read the full article (PDF).

Buy this article as PDF

Express-Checkout as PDF
Price $2.95
(incl. VAT)

Buy Linux Magazine

SINGLE ISSUES
 
SUBSCRIPTIONS
 
TABLET & SMARTPHONE APPS
Get it on Google Play

US / Canada

Get it on Google Play

UK / Australia

Related content

  • Linux Jobs

    We look at some recent reports about the Linux job market and interview experts from Red Hat and SUSE.

  • OSCON 2007

    More than 2500 open source enthusiasts met in Oregon for O'Reilly's annual OSCON convention.

  • Neural networks learn from mistakes and remember successes

    The well-known Monty Hall game show problem can be a rewarding maiden voyage for prospective statisticians. But is it possible to teach a neural network to choose between goats and cars with a few practice sessions?

  • Calculating Probability

    To tackle mathematical problems with conditional probabilities, math buffs rely on Bayes' formula or discrete distributions, generated by short Perl scripts.

  • Free Software Projects

    Free software covers such a diverse range of utilities, applications, and other assorted projects that it can be hard to find the perfect tool. We pick the best of the bunch. This month’s column covers file management with Xfe, IRC, the Sync2cd backup tool, and email printing with Muttprint.

comments powered by Disqus
Subscribe to our Linux Newsletters
Find Linux and Open Source Jobs
Subscribe to our ADMIN Newsletters

Support Our Work

Linux Magazine content is made possible with support from readers like you. Please consider contributing when you’ve found an article to be beneficial.

Learn More

News