Robust sequential search

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Theoretical Economics ; ISSN: 1555-7561 ; Volume: 16 ; Year: 2021 ; Issue: 4 ; Pages: 1431-1470 ; New Haven, CT: The Econometric Society

Classification and Topics

• Classification: Wirtschaft ; Operations Research; Statistical Decision Theory ; Criteria for Decision-Making under Risk and Uncertainty ; Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
• Subject: Sequential search ; search without priors ; robustness ; dynamic consistency ; competitive ratio

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Schlag, Karl H. ; Zapechelnyuk, Andriy
• Event: Veröffentlichung
• (who): The Econometric Society
• (where): New Haven, CT
• (when): 2021

Further information

• DOI: doi:10.3982/TE3994
• Handle: http://hdl.handle.net/10419/253510
• Last update: 12.03.2025, 8:40 PM CET