High Dimensional Decision Making, Upper and Lower Bounds

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.