Robust sequential search

Karl Schlag, Andriy Zapechelnyuk

    Research output: Working paperDiscussion paper


    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.
    Original languageEnglish
    Place of PublicationSt Andrews
    PublisherUniversity of St Andrews
    Number of pages58
    Publication statusPublished - 4 Aug 2020

    Publication series

    NameSchool of Economics and Finance Discussion Paper
    PublisherUniversity of St Andrews
    ISSN (Print)0962-4031
    ISSN (Electronic)2055-303X


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


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