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:
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arXiv e-prints
- Pub Date:
- May 2021
- DOI:
- 10.48550/arXiv.2105.00545
- arXiv:
- arXiv:2105.00545
- Bibcode:
- 2021arXiv210500545P
- Keywords:
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- Economics - Theoretical Economics;
- Statistics - Machine Learning
- E-Print:
- Economics Letters, 2021, Elsevier