Robust sequential search

Karl H. Schlag, Andriy Zapechelnyuk*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)1431-1470
    Number of pages40
    JournalTheoretical Economics
    Issue number4
    Early online date6 Dec 2020
    Publication statusPublished - 11 Nov 2021


    • Sequential search
    • Search without priors
    • Robustness
    • Dynamic consistency
    • Competitive ratio


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