High Dimensional Decision Making, Upper and Lower Bounds
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A decision maker's utility depends on her action a∈ A ⊂ℝ^d and the payoff relevant state of the world θ∈Θ. 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.
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