# A Unified Approach for Solving Sequential Selection Problems

## TL;DR

This paper introduces a unified framework for solving diverse sequential selection problems by reducing them to optimal stopping problems, enabling exact and efficient computation of optimal policies across various scenarios.

## Contribution

The paper presents a novel, unified approach that handles multiple types of sequential selection problems, including those with no-information and rank-dependent rewards, many of which were previously unsolved.

## Key findings

- Exact computation of optimal policies for various problems
- Efficient solution method applicable to a broad class of problems
- Extension to problems with no-information and random horizons

## 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.