High Dimensional Decision Making, Upper and Lower Bounds

Farzad Pourbabaee

Papers from arXiv.org

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.

Date: 2021-05
New Economics Papers: this item is included in nep-upt
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Published in Economics Letters, 2021, Elsevier

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