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High dimensional decision making, upper and lower bounds

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  • Pourbabaee, Farzad

Abstract

A decision maker’s utility depends on her action a∈A⊂Rd and the payoff relevant state of the world θ∈Θ. One can define the value of acquiring new information as the difference between the maximum expected utility pre- and post information acquisition. In this paper, I find asymptotic results on the expected value of information as d→∞, by using tools from the theory of (sub)-Gaussian processes and generic chaining.

Suggested Citation

  • Pourbabaee, Farzad, 2021. "High dimensional decision making, upper and lower bounds," Economics Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001713
    DOI: 10.1016/j.econlet.2021.109894
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    References listed on IDEAS

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    1. Hamsa Bastani & Mohsen Bayati, 2020. "Online Decision Making with High-Dimensional Covariates," Operations Research, INFORMS, vol. 68(1), pages 276-294, January.
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    4. Al-Najjar, Nabil I. & Pai, Mallesh M., 2014. "Coarse decision making and overfitting," Journal of Economic Theory, Elsevier, vol. 150(C), pages 467-486.
    5. Luciano Pomatto & Philipp Strack & Omer Tamuz, 2018. "The Cost of Information: The Case of Constant Marginal Costs," Papers 1812.04211, arXiv.org, revised Feb 2023.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Decision making; Information valuation; High dimensional vectors;
    All these keywords.

    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

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