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

  • Duffie, Darrell

    (Stanford University)

  • Malamud, Semyon

    (ETH Zurich and Swiss Finance Institute)

  • Manso, Gustavo

    (MIT)

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.

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Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number 2023.

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Date of creation: Mar 2009
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Handle: RePEc:ecl:stabus:2023
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