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Sequential Search with Adaptive Intensity

Lee, J.; Li, D.

Authors

J. Lee

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Daniel Li daniel.li@durham.ac.uk
Associate Professor



Abstract

This paper studies sequential search problems, where a searcher chooses search intensity adaptively in each period. We fully characterize the optimal search rule and value, decomposing the inter-temporal change of search intensity into the fall-back value effect and the deadline effect. We show that the optimal search intensity (value) is submodular (supermodular) in fall-back value and time. It follows that the fall-back value effect increases when the deadline approaches, and the deadline effect decreases when a searcher’s fall-back value gets higher. We further identify the connection between search with full and no recall to quantify the value of recall

Citation

Lee, J., & Li, D. (2022). Sequential Search with Adaptive Intensity. International Economic Review, 63(2), 803-829. https://doi.org/10.1111/iere.12551

Journal Article Type Article
Acceptance Date Sep 10, 2021
Online Publication Date Oct 25, 2021
Publication Date 2022-05
Deposit Date Sep 13, 2021
Publicly Available Date Mar 29, 2024
Journal International Economic Review
Print ISSN 0020-6598
Electronic ISSN 1468-2354
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 63
Issue 2
Pages 803-829
DOI https://doi.org/10.1111/iere.12551
Public URL https://durham-repository.worktribe.com/output/1240802

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Accepted Journal Article (379 Kb)
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Copyright Statement
This is the peer reviewed version of the following article: Lee, J. & Li, D. (2022). Sequential Search with Adaptive Intensity. International Economic Review 63(2): 803-829, which has been published in final form at https://doi.org/10.1111/iere.12551. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.




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