Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/306545 
Year of Publication: 
2024
Series/Report no.: 
Working Paper No. 460
Publisher: 
University of Zurich, Department of Economics, Zurich
Abstract: 
We study maximum-likelihood estimation and updating, subject to computational, cognitive, or behavioral constraints. We jointly characterize constrained estimates and updating within a framework reminiscent of a machine learning algorithm. Without frictions, the framework simplifies to standard maximum-likelihood estimation and Bayesian updating. Our central finding is that under certain intuitive cognitive constraints, simple models yield the most effective constrained fit to data - more complex models offer a superior fit, but the agent may lack the capability to assess this fit accurately. With some additional structure, the agent's problem is isomorphic to a familiar rational inattention problem.
Subjects: 
Bayesian updating
cognitive constraints
belief formation
machine learning in economics
Bayesian networks
JEL: 
D8
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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