Robust Sequential Search

TL;DR

This paper introduces dynamically robust sequential search rules that perform well across various priors and histories, outperforming traditional cutoff strategies and achieving significant fractions of the optimal performance in binary and general environments.

Contribution

It derives new dynamically robust decision rules for sequential search that outperform traditional cutoff strategies in worst-case scenarios.

Findings

Performance exceeds 1/2 of the optimum in binary environments.

Performance exceeds 1/4 of the optimum in all environments.

Performance improves with higher outside option value, surpassing 2/3 of the optimum when outside option exceeds 1/6 of the highest alternative.

Abstract

We study sequential search without priors. Our interest lies in decision rules that are close to being optimal under each prior and after each history. We call these rules dynamically robust. The search literature employs optimal rules based on cutoff strategies that are not dynamically robust. We derive dynamically robust rules and show that their performance exceeds 1/2 of the optimum against binary environments and 1/4 of the optimum against all environments. This performance improves substantially with the outside option value, for instance, it exceeds 2/3 of the optimum if the outside option exceeds 1/6 of the highest possible alternative.