Alex Bloedel from UCLA will present "Modeling information acquisition: divergences, duality, and discrete choice" (joint with Tommaso Denti and Luciano Pomatto).

Abstract: 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 costs. We characterize its behavioral predictions by deriving optimality conditions that extend those of Matějka and McKay (2015) and Caplin, Dean, and Leahy (2019) beyond mutual information. Using these tools, we study the implications of f-information in a range of canonical decision problems. The framework can be analyzed using familiar methods of microeconomics—convex duality and the Arrow-Pratt approach to expected utility—and yields connections to well-known models of discrete choice.