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Dynamically Aggregating Diverse Information

Author

Listed:
  • Annie Liang

    (Department of Economics, University of Pennsylvania)

  • Xiaosheng Mu

    (Columbia University)

  • Vasilis Syrgkanis

    (Microsoft Corporation - Microsoft Research New England)

Abstract

An agent has access to multiple data sources, each of which provides information about a different attribute of an unknown state. Information is acquired continuously where the agent chooses both which sources to sample from, and also how to allocate resources across them until an endogenously chosen time. We show that the optimal information acquisition strategy proceeds in stages, where resource allocation is constant over a fixed set of providers during each stage, and at each subsequent stage a new provider is added to the set. We additionally apply this characterization to derive results regarding: (1) equilibrium information provision by competing data providers, and (2) endogenous information acquisition in a binary choice problem.

Suggested Citation

  • Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2019. "Dynamically Aggregating Diverse Information," PIER Working Paper Archive 19-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:19-005
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    References listed on IDEAS

    as
    1. Jakub Steiner & Colin Stewart & Filip Matějka, 2017. "Rational Inattention Dynamics: Inertia and Delay in Decision‐Making," Econometrica, Econometric Society, vol. 85, pages 521-553, March.
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    4. Yeon-Koo Che & Konrad Mierendorff, 2019. "Optimal Dynamic Allocation of Attention," American Economic Review, American Economic Association, vol. 109(8), pages 2993-3029, August.
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    Cited by:

    1. Drew Fudenberg & Whitney Newey & Philipp Strack & Tomasz Strzalecki, 2020. "Testing the drift-diffusion model," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(52), pages 33141-33148, December.
    2. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2022. "Best Arm Identification with Contextual Information under a Small Gap," Papers 2209.07330, arXiv.org, revised Jan 2023.

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