Robust sequential search

Karl Schlag and Andriy Zapechelnyuk

Theoretical Economics, 2021, vol. 16, issue 4

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 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.

Keywords: Sequential search; search without priors; robustness; dynamic consistency; competitive ratio (search for similar items in EconPapers)
JEL-codes: C44 D81 D83 (search for similar items in EconPapers)
Date: 2021-11-08
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Robust Sequential Search (2020)
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