High Dimensional Decision Making, Upper and Lower Bounds
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.
 Publication:

arXiv eprints
 Pub Date:
 May 2021
 DOI:
 10.48550/arXiv.2105.00545
 arXiv:
 arXiv:2105.00545
 Bibcode:
 2021arXiv210500545P
 Keywords:

 Economics  Theoretical Economics;
 Statistics  Machine Learning
 EPrint:
 Economics Letters, 2021, Elsevier