Modeling information acquisition via f-divergence and duality

Fuente: arXiv
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Main Authors: Bloedel, Alex, Denti, Tommaso, Pomatto, Luciano
Format: Preprint
Published: 2025
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author Bloedel, Alex
Denti, Tommaso
Pomatto, Luciano
author_facet Bloedel, Alex
Denti, Tommaso
Pomatto, Luciano
contents We introduce a new cost function over experiments, f-information, based on the theory of multivariate statistical divergences, that generalizes Sims's classic model of rational inattention as well as the class of posterior-separable cost functions. We characterize its behavioral predictions by deriving optimality conditions that extend those of Matejka and McKay (2015) and Caplin, Dean, and Leahy (2019) beyond mutual information. Using these tools, we study the implications of f-information in a number of canonical decision problems. A strength of the framework is that it can be analyzed using familiar methods of microeconomics: convex duality and the Arrow-Pratt approach to expected utility.
format Preprint
id arxiv_https___arxiv_org_abs_2510_03482
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Modeling information acquisition via f-divergence and duality
Bloedel, Alex
Denti, Tommaso
Pomatto, Luciano
Theoretical Economics
We introduce a new cost function over experiments, f-information, based on the theory of multivariate statistical divergences, that generalizes Sims's classic model of rational inattention as well as the class of posterior-separable cost functions. We characterize its behavioral predictions by deriving optimality conditions that extend those of Matejka and McKay (2015) and Caplin, Dean, and Leahy (2019) beyond mutual information. Using these tools, we study the implications of f-information in a number of canonical decision problems. A strength of the framework is that it can be analyzed using familiar methods of microeconomics: convex duality and the Arrow-Pratt approach to expected utility.
title Modeling information acquisition via f-divergence and duality
topic Theoretical Economics
url https://arxiv.org/abs/2510.03482