Robust sequential search

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...

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Bibliographic Details
Published in:Theoretical economics 2021-11, Vol.16 (4), p.1431-1470
Main Authors: Schlag, Karl H, Zapechelnyuk, Andriy
Format: Article
Language:English
Subjects:
C44
competitive ratio
Control theory
D81
D83
Decision making
dynamic consistency
Economic theory
Performance evaluation
robustness
search without priors
Sequential search
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Summary: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.
ISSN:1555-7561
1933-6837
1555-7561
DOI:10.3982/TE3994