# High Dimensional Decision Making, Upper and Lower Bounds

arXiv: https://arxiv.org/abs/2105.00545 · HTML: https://mlsanity.com/paper/2105.00545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 2 May 2021.

## Builds on (most cited first, 1 of 1)

- [Robust Experimentation in the Continuous Time Bandit Problem](https://mlsanity.com/paper/2104.00102/) (2021)

## Abstract (arXiv)

A decision maker's utility depends on her action a∈ A subset ℝd and the payoff relevant state of the world θ∈ 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 → ∞, by using tools from the theory of (sub)-Guassian processes and generic chaining.