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Robust Sequential Search

Author

Listed:
  • Karl H. Schlag
  • Andriy Zapechelnyuk

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 cutoff 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.

Suggested Citation

  • Karl H. Schlag & Andriy Zapechelnyuk, 2020. "Robust Sequential Search," Papers 2008.00502, arXiv.org.
    • Handle: RePEc:arx:papers:2008.00502
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    References listed on IDEAS

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    2. Kasberger, Bernhard & Woodward, Kyle, 2025. "Bidding in multi-unit auctions under limited information," Journal of Economic Theory, Elsevier, vol. 226(C).

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    More about this item

    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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