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Sequential Search With Adaptive Intensity

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  • Joosung Lee
  • Daniel Li

Abstract

This article 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 intertemporal 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 investigate the connection between search with full and no recall to quantify the value of recall.

Suggested Citation

  • Joosung Lee & Daniel Li, 2022. "Sequential Search With Adaptive Intensity," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(2), pages 803-829, May.
    • Handle: RePEc:wly:iecrev:v:63:y:2022:i:2:p:803-829
    DOI: 10.1111/iere.12551
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    References listed on IDEAS

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