A Unified Approach for Solving Sequential Selection Problems

Author/Creator ORCID





Citation of Original Publication

Alexander Goldenshluger, Yaakov Malinovsky, Assaf Zeevi, A Unified Approach for Solving Sequential Selection Problems, Probability , 2019, https://arxiv.org/abs/1901.04183


This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.


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.