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. (JEL C70, D11, D82, D83, D91)

中文翻译：

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基于邻域的信息成本

我们在理性注意力不集中问题中推导出新的信息成本，即基于邻域的成本函数，从观察到许多设置都涉及具有拓扑结构的外生状态。这些成本函数是一致的后分可分的，并捕获了感知距离的概念。第二个属性确保基于邻域的成本与互信息不同，可以准确预测感知实验中的行为。我们将基于邻域的成本函数的含义与一系列应用中的互信息的含义进行了比较：感知判断、二元选择的一般环境、制度变化博弈和线性二次高斯设置。(JEL C70, D11, D82, D83, D91)