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Interpreted and generated signals

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  • Hong, Lu
  • Page, Scott
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    Abstract

    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.

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    Bibliographic Info

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

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

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    Handle: RePEc:eee:jetheo:v:144:y:2009:i:5:p:2174-2196

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    Web page: http://www.elsevier.com/locate/inca/622869

    Related research

    Keywords: Private information Signals Interpretations Attributes Independence Correlation;

    References

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    1. Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2004. "Fact-Free Learning," Cowles Foundation Discussion Papers 1491, Cowles Foundation for Research in Economics, Yale University.
      • Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2003. "Fact-Free Learning," PIER Working Paper Archive 03-023, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
      • Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2003. "Fact-Free Learning," PIER Working Paper Archive 05-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Dec 2004.
    2. Timothy Feddersen & Wolfgang Pesendorfer, 1994. "Voting Behavior and Information Aggregation in Elections with Private Information," Discussion Papers 1117, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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    5. Hong, Lu & Page, Scott E., 2001. "Problem Solving by Heterogeneous Agents," Journal of Economic Theory, Elsevier, vol. 97(1), pages 123-163, March.
    6. Paul Klemperer, 2004. "Auctions: Theory and Practice," Online economics textbooks, SUNY-Oswego, Department of Economics, number auction1, Spring.
    7. Paul Klemperer, 2004. "Introduction to Auctions: Theory and Practice
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    8. 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.
    9. 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.
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