Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253510 
Year of Publication: 
2021
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 16 [Issue:] 4 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 1431-1470
Publisher: 
The Econometric Society, New Haven, CT
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 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.
Subjects: 
Sequential search
search without priors
robustness
dynamic consistency
competitive ratio
JEL: 
C44
D81
D83
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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