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High Dimensional Decision Making, Upper and Lower Bounds

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  • Farzad Pourbabaee

Abstract

A decision maker's utility depends on her action $a\in A \subset \mathbb{R}^d$ and the payoff relevant state of the world $\theta\in \Theta$. 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 $d \to \infty$, by using tools from the theory of (sub)-Guassian processes and generic chaining.

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  • Farzad Pourbabaee, 2021. "High Dimensional Decision Making, Upper and Lower Bounds," Papers 2105.00545, arXiv.org.
    • Handle: RePEc:arx:papers:2105.00545
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    References listed on IDEAS

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    1. Al-Najjar, Nabil I. & Pai, Mallesh M., 2014. "Coarse decision making and overfitting," Journal of Economic Theory, Elsevier, vol. 150(C), pages 467-486.
    2. 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.
    3. Nabil I. Al-Najjar & Luca Anderlini & Leonardo Felli, 2006. "Undescribable Events," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 849-868.
    4. Hamsa Bastani & Mohsen Bayati, 2020. "Online Decision Making with High-Dimensional Covariates," Operations Research, INFORMS, vol. 68(1), pages 276-294, January.
    5. Li, Jian, 2019. "The K-armed bandit problem with multiple priors," Journal of Mathematical Economics, Elsevier, vol. 80(C), pages 22-38.
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