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Sequential Search and Learning from Rank Feedback: Theory and Experimental Evidence

Author

Listed:
  • Asa B. Palley

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Mirko Kremer

    (Management Department, Frankfurt School of Finance and Management, 60314 Frankfurt am Main, Germany)

Abstract

This paper studies the effect of limited information in a sequential search setting where a single selection is to be made from a set of random potential options. We consider both a full-information problem, where the decision maker observes the exact value of each option as she searches, and a partial-information problem, in which the decision maker only learns the rank of the current option relative to the options that have already been observed. We develop a model that allows for a sharp contrast between search behavior in the two information settings, both theoretically and empirically. We present the results of an experiment that tests, and supports, the key prediction of our model analysis---limited information induces longer search. Our data further suggest systematic deviations from the theoretical benchmarks in both informational settings. Importantly, subjects in our partial-information conditions are prone to stop prematurely during early stages of the search process and to suboptimally continue the search during late stages. We propose a simple model that succinctly captures the interplay of two symmetric choice and judgment biases that have asymmetric (but opposing) effects on the length of search.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1902 . This paper was accepted by Teck-Hua Ho, behavioral economics.

Suggested Citation

  • Asa B. Palley & Mirko Kremer, 2014. "Sequential Search and Learning from Rank Feedback: Theory and Experimental Evidence," Management Science, INFORMS, vol. 60(10), pages 2525-2542, October.
    • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:10:p:2525-2542
    DOI: 10.1287/mnsc.2014.1902
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    Cited by:

    1. Amnon Rapoport & Darryl A. Seale & Leonidas Spiliopoulos, 2023. "Progressive stopping heuristics that excel in individual and competitive sequential search," Theory and Decision, Springer, vol. 94(1), pages 135-165, January.
    2. Mirko Kremer & Francis de Véricourt, 2022. "Mismanaging diagnostic accuracy under congestion," ESMT Research Working Papers ESMT-22-01, ESMT European School of Management and Technology.
    3. Vincent Mak & Darryl A. Seale & Amnon Rapoport & Eyran J. Gisches, 2019. "Voting Rules in Sequential Search by Committees: Theory and Experiments," Management Science, INFORMS, vol. 65(9), pages 4349-4364, September.
    4. Song-Hee Kim & Jordan Tong & Carol Peden, 2020. "Admission Control Biases in Hospital Unit Capacity Management: How Occupancy Information Hurdles and Decision Noise Impact Utilization," Management Science, INFORMS, vol. 66(11), pages 5151-5170, November.
    5. Daniel G. Goldstein & R. Preston McAfee & Siddharth Suri & James R. Wright, 2020. "Learning When to Stop Searching," Management Science, INFORMS, vol. 66(3), pages 1375-1394, March.
    6. Yu‐Chin Hsiao & Simon Kemp & Maroš Servátka, 2020. "On the Importance of Context in Sequential Search," Southern Economic Journal, John Wiley & Sons, vol. 86(4), pages 1510-1530, April.
    7. Kinneret Teodorescu & Ke Sang & Peter M. Todd, 2018. "Post-decision search in repeated and variable environments," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(5), pages 484-500, September.
    8. Hsiao, Yu Chin & Kemp, Simon & Servátka, Maroš, 2019. "On the Importance of Context in Sequential Decision-Making," MPRA Paper 94027, University Library of Munich, Germany.
    9. Francesco P. Appio & Mario G. C. A. Cimino & Alessandro Lazzeri & Antonella Martini & Gigliola Vaglini, 2018. "Fostering distributed business logic in Open Collaborative Networks: an integrated approach based on semantic and swarm coordination," Information Systems Frontiers, Springer, vol. 20(3), pages 589-616, June.
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