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The Relative Contributions of Private Information Sharing and Public Information Releases to Information Aggregation

Author

Listed:
  • Darrell DUFFIE

    (Graduate School of Business, Stanford University and NBER)

  • Semyon MALAMUD

    (EPF Lausanne and Swiss Finance Institute)

  • Gustavo MANSO

    (Sloan School of Business, MIT)

Abstract

We calculate learning rates when agents are informed through both public and private observation of other agents’ actions. We provide an explicit solution for the evolution of the distribution of posterior beliefs. When the private learning channel is present, we show that convergence of the distribution of beliefs to the perfect-information limit is exponential at a rate equal to the sum of the mean arrival rate of public information and the mean rate at which individual agents are randomly matched with other agents. If, however, there is no private information sharing, then convergence is exponential at a rate strictly lower than the mean arrival rate of public information.

Suggested Citation

  • Darrell DUFFIE & Semyon MALAMUD & Gustavo MANSO, "undated". "The Relative Contributions of Private Information Sharing and Public Information Releases to Information Aggregation," Swiss Finance Institute Research Paper Series 09-33, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp0933
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Heng Chen & Yang K. Lu & Wing Suen, 2016. "The Power Of Whispers: A Theory Of Rumor, Communication, And Revolution," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57, pages 89-116, February.
    2. Mikhail Golosov & Guido Lorenzoni & Aleh Tsyvinski, 2014. "Decentralized Trading With Private Information," Econometrica, Econometric Society, vol. 82(3), pages 1055-1091, May.
    3. Amador, Manuel & Weill, Pierre-Olivier, 2012. "Learning from private and public observations of othersʼ actions," Journal of Economic Theory, Elsevier, vol. 147(3), pages 910-940.
    4. Attanasi, Giuseppe & Centorrino, Samuele & Moscati, Ivan, 2016. "Over-the-counter markets vs. double auctions: A comparative experimental study," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 63(C), pages 22-35.
    5. Duffie, Darrell & Malamud, Semyon & Manso, Gustavo, 2015. "Reprint of: Information percolation in segmented markets," Journal of Economic Theory, Elsevier, vol. 158(PB), pages 838-869.
    6. Duffie, Darrell & Malamud, Semyon & Manso, Gustavo, 2014. "Information percolation in segmented markets," Journal of Economic Theory, Elsevier, vol. 153(C), pages 1-32.
    7. Moscarini, Giuseppe & Wright, Randall, 2010. "Introduction to Search Theory and Applications," Journal of Economic Theory, Elsevier, vol. 145(4), pages 1319-1324, July.
    8. Andrei, Daniel & Cujean, Julien, 2017. "Information percolation, momentum and reversal," Journal of Financial Economics, Elsevier, vol. 123(3), pages 617-645.
    9. Manela, Asaf, 2014. "The value of diffusing information," Journal of Financial Economics, Elsevier, vol. 111(1), pages 181-199.

    More about this item

    Keywords

    information percolation; search; learning rates.;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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