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Dynamically Aggregating Diverse Information

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  • Annie Liang
  • Xiaosheng Mu
  • Vasilis Syrgkanis

Abstract

An agent has access to multiple information sources, each modeled as a Brownian motion whose drift provides information about a different component of an unknown Gaussian state. Information is acquired continuously—where the agent chooses both which sources to sample from, and also how to allocate attention across them—until an endogenously chosen time, at which point a decision is taken. We demonstrate conditions on the agent's prior belief under which it is possible to exactly characterize the optimal information acquisition strategy. We then apply this characterization to derive new results regarding: (1) endogenous information acquisition for binary choice, (2) the dynamic consequences of attention manipulation, and (3) strategic information provision by biased news sources.

Suggested Citation

  • Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2022. "Dynamically Aggregating Diverse Information," Econometrica, Econometric Society, vol. 90(1), pages 47-80, January.
    • Handle: RePEc:wly:emetrp:v:90:y:2022:i:1:p:47-80
    DOI: 10.3982/ECTA18324
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    References listed on IDEAS

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    Cited by:

    1. Karun Adusumilli, 2022. "How to sample and when to stop sampling: The generalized Wald problem and minimax policies," Papers 2210.15841, arXiv.org, revised Feb 2024.
    2. Yingkai Li & Jonathan Libgober, 2023. "Optimal Scoring for Dynamic Information Acquisition," Papers 2310.19147, arXiv.org.
    3. Benjamin Davies, 2024. "Learning about a changing state," Papers 2401.03607, arXiv.org.

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