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Optimal and Myopic Information Acquisition

Author

Listed:
  • Annie Liang

    (University of Pennsylvania)

  • Xiaosheng Mu

    (Columbia University)

  • Vasilis Syrgkanis

    (Microsoft Research)

Abstract

A decision-maker (DM) faces an intertemporal decision problem, where his payoff depends on actions taken across time as well as on an unknown Gaussian state. The DM can learn about the state from different (correlated) information sources, and allocates a budget of samples across these sources each period. A simple information acquisition strategy for the DM is to neglect dynamic considerations and allocate samples myopically. How inefficient is this strategy relative to the optimal information acquisition strategy? We show that if the budget of samples is sufficiently large then there is no inefficiency: myopic information acquisition is exactly optimal.

Suggested Citation

  • Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2019. "Optimal and Myopic Information Acquisition," Working Papers 2019-25, Princeton University. Economics Department..
    • Handle: RePEc:pri:econom:2019-25
    as

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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Ehud Lehrer & Tao Wang, 2024. "The value of information in stopping problems," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 78(2), pages 619-648, September.
    2. Ehud Lehrer & Tao Wang, 2022. "The Value of Information in Stopping Problems," Papers 2205.06583, arXiv.org.

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    Keywords

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    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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