Neighborhood-Based Information Costs
We propose a new measure of the cost of information structures in rational inattention problems, the "neighborhood-based" cost functions, given that many applications involve states with a topological structure. These cost functions summarize the results of a sequential information sampling problem, and also capture a notion of perceptual distance. This second property allows neighborhood-based cost functions, unlike mutual information, to make accurate predictions about behavior in perceptual experiments. We compare the implications of our neighborhood-based cost functions with those of a mutual-information cost function in a series of applications: security design, global games, modeling perceptual judgments, and linear-quadratic-Gaussian problems.
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Document Object Identifier (DOI): 10.3386/w26743