Expanding on Repeated Consumer Search Using Multi-Armed Bandits and Secretaries

TL;DR

This paper develops a new approach to the repeated consumer search problem by integrating multi-armed bandit methods and secretary problem techniques, removing the need for prior knowledge of price distributions.

Contribution

It introduces a novel framework combining MAB and secretary problem concepts to derive optimal search policies without assuming known price distributions.

Findings

Derives an optimal stopping policy using Bellman equations.

Breaks down the problem into period-specific stopping problems.

Solves the dynamic optimization via forward induction.

Abstract

We seek to take a different approach in deriving the optimal search policy for the repeated consumer search model found in Fishman and Rob (1995) with the main motivation of dropping the assumption of prior knowledge of the price distribution $F(p)$ in each period. We will do this by incorporating the famous multi-armed bandit problem (MAB). We start by modifying the MAB framework to fit the setting of the repeated consumer search model and formulate the objective as a dynamic optimization problem. Then, given any sequence of exploration, we assign a value to each store in that sequence using Bellman equations. We then proceed to break down the problem into individual optimal stopping problems for each period which incidentally coincides with the framework of the famous secretary problem where we proceed to derive the optimal stopping policy. We will see that implementing the optimal stopping policy in each period solves the original dynamic optimization by `forward induction' reasoning.