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Dynamically robust sequential search

Research output: Working paperDiscussion paper

Author(s)

Karl Schlag, Andriy Zapechelnyuk

School/Research organisations

Abstract

We study sequential search without priors. Our interest lies in decision rules that are close to being optimal under any prior, at each point in time, 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 uncover general principles of dynamically robust decision making, such as the history independence and the necessity to randomize. We then derive dynamically robust rules and show that their performance exceeds 1/4 of the optimum against all environments and 1/2 of the optimum against binary environments.
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Details

Original languageEnglish
Place of PublicationSt Andrews
PublisherUniversity of St Andrews
Number of pages45
Publication statusPublished - 4 Dec 2017

Publication series

NameSchool of Economics and Finance Discussion Paper
PublisherUniversity of St Andrews
No.1803
ISSN (Print)0962-4031
ISSN (Electronic)2055-303X

    Research areas

  • Sequential search, Search without priors, Robust control, Competitive ratio, Dynamic consistency

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