A unified approach for solving sequential selection problems

Alexander Goldenshluger, Yaakov Malinovsky, Assaf Zeevi

Research output: Contribution to journalArticlepeer-review


In this paper we develop a unified approach for solving a wide class of sequential selection problems. This class includes, but is not limited to, selection problems with no-information, rank-dependent rewards, and considers both fixed as well as random problem horizons. The proposed framework is based on a reduction of the original selection problem to one of optimal stopping for a sequence of judiciously constructed independent random variables. We demonstrate that our approach allows exact and efficient computation of optimal policies and various performance metrics thereof for a variety of sequential selection problems, several of which have not been solved to date.

Original languageEnglish
Pages (from-to)214-256
Number of pages43
JournalProbability Surveys
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2020 Institute of Mathematical Statistics.


  • Full information problems
  • No-information problems
  • Optimal stopping
  • Relative ranks
  • Secretary problems
  • Sequential selection

ASJC Scopus subject areas

  • Statistics and Probability


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