Robust sequential search
- Author(s)
- Karl Schlag, Andriy Zapechelnyuk
- 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.
- Organisation(s)
- Department of Economics
- External organisation(s)
- University of St. Andrews
- Journal
- Theoretical Economics
- Volume
- 16
- Pages
- 1431 - 1470
- No. of pages
- 40
- ISSN
- 1933-6837
- DOI
- https://doi.org/10.3982/TE3994
- Publication date
- 2020
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 502021 Microeconomics
- Keywords
- ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/robust-sequential-search(11193145-5b41-4d14-85ff-11ce81743f7b).html