Neighborhood-Based Information Costs
We derive a new cost of information in rational inattention problems, the neighborhood-based cost functions, starting from the observation that many settings involve exogenous states with a topological structure. These cost functions are uniformly posterior-separable and capture notions of perceptual distance. This second property ensures that neighborhood-based costs, unlike mutual information, make accurate predictions about behavior in perceptual experiments. We compare the implications of our neighborhood-based cost functions with those of the mutual information in a series of applications: perceptual judgments; the general environment of binary choice; regime-change games; and linear-quadratic-Gaussian settings.
Published Versions
Benjamin Hébert & Michael Woodford, 2021. "Neighborhood-Based Information Costs," American Economic Review, vol 111(10), pages 3225-3255. citation courtesy of 
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