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Robust sequential search

Research output: Working paperDiscussion paper

Author(s)

Karl Schlag, Andriy Zapechelnyuk

School/Research organisations

Abstract

A searcher explores alternatives sequentially and is unaware of the distribution of values of unexplored alternatives. We are interested in decision rules that perform close to Bayesian optimal ones under any prior, at each point of time, and after each history of observations. We call such rules dynamically robust. Standard rules used in the search literature are based on cutoff strategies and are not dynamically robust. We uncover general principles that make this rich setting tractable. We derive a dynamically robust decision rule, which involves randomized behavior and can be approximated by a rule with a linear stopping probability.
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Details

Original languageEnglish
Place of PublicationSt Andrews
PublisherUniversity of St Andrews
Number of pages45
StatePublished - 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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