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Robust sequential search

Author

Listed:
  • Schlag, Karl H.

    (Department of Economics, University of Vienna)

  • Zapechelnyuk, Andriy

    (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 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.

Suggested Citation

  • Schlag, Karl H. & Zapechelnyuk, Andriy, 2021. "Robust sequential search," Theoretical Economics, Econometric Society, vol. 16(4), November.
    • Handle: RePEc:the:publsh:3994
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    References listed on IDEAS

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    Cited by:

    1. Kasberger, Bernhard & Woodward, Kyle, 2021. "Bidding in Multi-Unit Auctions under Limited Information," MPRA Paper 111185, University Library of Munich, Germany.

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    More about this item

    Keywords

    Sequential search; search without priors; robustness; dynamic consistency; competitive ratio;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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