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Robust Sequential Search

Karl Schlag () and Andriy Zapechelnyuk

No 201803, Discussion Paper Series, School of Economics and Finance from School of Economics and Finance, University of St Andrews

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 cuto 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.

Keywords: Sequential search; search without priors; robust control; competitive ratio; dynamic consistency (search for similar items in EconPapers)
JEL-codes: D83 D81 C44 (search for similar items in EconPapers)
Date: 2017-12-04, Revised 2020-03-05
New Economics Papers: this item is included in nep-mic
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