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Ignacio Esponda is a Professor of Economics at the University of California - Santa Barbara.
PAPER TOPIC: "Berk-Nash Rationalizability", joint with Demian Pouzo (UC Berkeley)
ABSTRACT: Misspecified learning—where agents rely on simplified or biased models—offers a unifying framework for analyzing behavioral biases, cognitive constraints, and system-atic misperceptions. We introduce Berk–Nash rationalizability, a new solution concept for such settings that parallels rationalizability in games. Our main result shows that, with probability one, every limit action—any action played or approached infinitely often—is Berk–Nash rationalizable. This holds regardless of whether behavior con-verges and offers a tractable way to bound long-run behavior without solving complex learning dynamics. We illustrate this advantage with a known example and identify
general classes of environments where the rationalizable set can be easily characterized.
Zoom link available upon request. Please contact vadams@arizona.edu for information.