Information Costs and Sequential Information Sampling
Benjamin Hebert and
Michael Woodford ()
Research Papers from Stanford University, Graduate School of Business
We propose a new approach to modeling the cost of information structures in rational inattention problems: the â€œneighborhood-basedâ€ cost functions. These cost functions have two properties that we view as desirable: they summarize the results of a sequential evidence accumulation problem, and they capture notions of â€œperceptual distance.â€ The first of these properties is connected to an extensive literature in psychology and neuroscience, and the second ensures that neighborhood-based cost functions, 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: security design, global games, modeling perceptual judgments, and linear-quadratic-Gaussian settings.
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Working Paper: Information Costs and Sequential Information Sampling (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:3751
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