IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

Interpreted and generated signals

  • Hong, Lu
  • Page, Scott
Registered author(s):

    Private information is typically modeled as signals. A joint probability distribution captures relationships between signals and between signals and relevant variables. In this paper, we define and contrast two types of signals: generated and interpreted. We demonstrate that even though the standard assumption of conditional independence is a reasonable benchmark assumption for generated signals, it imposes a specific, and unlikely structure on interpreted signals. We also show that independent interpreted signals are negatively correlated in their correctness, but generated signals can be independent. Our findings may limit the contexts in which many models of information aggregation and strategic choices in auctions, markets, and voting apply.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://www.sciencedirect.com/science/article/B6WJ3-4VP4TRN-1/2/72382703967c9b22c976389c8932525f
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Elsevier in its journal Journal of Economic Theory.

    Volume (Year): 144 (2009)
    Issue (Month): 5 (September)
    Pages: 2174-2196

    as
    in new window

    Handle: RePEc:eee:jetheo:v:144:y:2009:i:5:p:2174-2196
    Contact details of provider: Web page: http://www.elsevier.com/locate/inca/622869

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as in new window
    1. Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2005. "Fact-Free Learning," American Economic Review, American Economic Association, vol. 95(5), pages 1355-1368, December.
    2. Timothy Feddersen & Wolfgang Pesendorfer, 1997. "Voting Behavior and Information Aggregation in Elections With Private Information," Levine's Working Paper Archive 1560, David K. Levine.
    3. Al-Najjar, Nabil I. & Casadesus-Masanell, Ramon & Ozdenoren, Emre, 2003. "Probabilistic representation of complexity," Journal of Economic Theory, Elsevier, vol. 111(1), pages 49-87, July.
    4. Kenneth Judd & Scott E. Page, 2004. "Computational Public Economics," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 195-202, 05.
    5. Paul Klemperer, 2004. "Introduction to Auctions: Theory and Practice
      [Auctions: Theory and Practice]
      ," Introductory Chapters, Princeton University Press.
    6. Paul Klemperer, 2004. "Auctions: Theory and Practice," Economics Series Working Papers 2004-W09, University of Oxford, Department of Economics.
    7. Fryer Roland & Jackson Matthew O., 2008. "A Categorical Model of Cognition and Biased Decision Making," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 8(1), pages 1-44, February.
    8. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-71, May.
    9. Hong, Lu & Page, Scott E., 2001. "Problem Solving by Heterogeneous Agents," Journal of Economic Theory, Elsevier, vol. 97(1), pages 123-163, March.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:eee:jetheo:v:144:y:2009:i:5:p:2174-2196. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

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

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.