Robust sequential search
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 robust. The search literature employs optimal rules based on cutoff strategies, and these rules are not robust. We derive robust rules and show that their performance exceeds 1/2 of the optimum against binary independent and identically distributed (i.i.d.) environments and 1/4 of the optimum against all i.i.d. 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.
Citation
Schlag , K H & Zapechelnyuk , A 2021 , ' Robust sequential search ' , Theoretical Economics , vol. 16 , no. 4 , pp. 1431-1470 . https://doi.org/10.3982/te3994
Publication
Theoretical Economics
Status
Peer reviewed
ISSN
1933-6837Type
Journal article
Rights
Copyright © 2021 The Authors. Open Access article. Licensed under the Creative Commons Attribution-NonCommercial License 4.0.
Collections
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.