IDEAS home Printed from https://ideas.repec.org/a/aea/aejmic/v14y2022i4p1-24.html
   My bibliography  Save this article

Screening Dominance: A Comparison of Noisy Signals

Author

Listed:
  • David Lagziel
  • Ehud Lehrer

Abstract

This paper studies the impact of noisy signals on screening processes. It deals with a decision problem in which a decision-maker screens a set of elements based on noisy unbiased evaluations. Given that the decision-maker uses threshold strategies, we show that additional binary noise can potentially improve a screening, an effect that resembles a "lucky coin toss." We compare different noisy signals under threshold strategies and optimal ones, and we provide several characterizations of cases in which one noise is preferable over another. Accordingly so, we establish a novel method to compare noise variables using a contraction mapping between percentiles.

Suggested Citation

  • David Lagziel & Ehud Lehrer, 2022. "Screening Dominance: A Comparison of Noisy Signals," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 1-24, November.
    • Handle: RePEc:aea:aejmic:v:14:y:2022:i:4:p:1-24
    DOI: 10.1257/mic.20200284
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/mic.20200284
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/mic.20200284.ds
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    File URL: https://libkey.io/10.1257/mic.20200284?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aejmic:v:14:y:2022:i:4:p:1-24. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.