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Identifiable information structures

Author

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  • Arieli, Itai
  • Babichenko, Yakov
  • Smorodinsky, Rann

Abstract

Consider a setting where many individuals forecast the (unknown) state of nature based on signals they receive independently. We refer to the joint distribution over the states and signals as an “information structure.” An information structure is deemed identifiable if the distribution of forecasts is sufficient to determine the state of nature, even without knowing the underlying information structure. We characterize the set of identifiable information structures and propose a scheme that uniquely identifies the state of nature for the finite case.

Suggested Citation

  • Arieli, Itai & Babichenko, Yakov & Smorodinsky, Rann, 2020. "Identifiable information structures," Games and Economic Behavior, Elsevier, vol. 120(C), pages 16-27.
    • Handle: RePEc:eee:gamebe:v:120:y:2020:i:c:p:16-27
    DOI: 10.1016/j.geb.2019.12.006
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    References listed on IDEAS

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    Cited by:

    1. Arieli, Itai & Babichenko, Yakov & Shlomov, Segev, 2021. "Virtually additive learning," Journal of Economic Theory, Elsevier, vol. 197(C).
    2. Yakov Babichenko & Dan Garber, 2021. "Learning Optimal Forecast Aggregation in Partial Evidence Environments," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 628-641, May.
    3. Gradwohl, Ronen & Heller, Yuval & Hillman, Arye, 2022. "Social Media and Democracy," MPRA Paper 113609, University Library of Munich, Germany.
    4. Ronen Gradwohl & Yuval Heller & Arye Hillman, 2022. "Social Media and Democracy," Papers 2206.14430, arXiv.org.
    5. Itai Arieli & Yakov Babichenko & Fedor Sandomirskiy & Omer Tamuz, 2020. "Feasible Joint Posterior Beliefs," Papers 2002.11362, arXiv.org, revised Dec 2020.

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