A Unified Approach for Solving Sequential Selection Problems
Probability
2020-01-27 v5 Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.1901.04183,
title = {A Unified Approach for Solving Sequential Selection Problems},
author = {Alexander Goldenshluger and Yaakov Malinovsky and Assaf Zeevi},
journal= {arXiv preprint arXiv:1901.04183},
year = {2020}
}