Economic Agents as Imperfect Problem Solvers
We develop a tractable model of limited cognitive perception of the optimal policy function, with agents using costly reasoning effort to update beliefs about this optimal mapping of economic states into actions. A key result is that agents reason less (more) when observing usual (unusual) states, producing state- and history-dependent behavior. Our application is a standard incomplete markets model with ex-ante identical agents that hold no a-priori behavioral biases. The resulting ergodic distribution of actions and beliefs is characterized by “learning traps”, where locally stable dynamics of wealth generate “familiar” regions of the state space within which behavior appears to follow past-experience-based heuristics. We show qualitatively and quantitatively how these traps have empirically desirable properties: the marginal propensity to consume is higher, hand-to-mouth status is more frequent and persistent, and there is more wealth inequality than in the standard model.
We would like to thank our discussants George-Marios Angeletos, Paolo Bonomolo, Tarek Hassan and Luigi Iovino, as well as Ian Dew-Becker, Ryan Chahrour, Guido Lorenzoni, Filip Matejka, Kristoffer Nimark, Philipp Sadowski, Todd Sarver and Mirko Wiederholt, and seminar and conference participants at Bank of Finland, CERGE, CEU, EEA Congress, Green Line Macro Meetings, NBER EFBEM and EFCE Groups, Northwestern Macro Conference, Society for Economic Dynamics and Computing in Economics and Finance for helpful discussions and comments. This project is supported by NSF Award SES-1824367. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Cosmin Ilut & Rosen Valchev, 2022. "Economic Agents as Imperfect Problem Solvers," The Quarterly Journal of Economics, vol 138(1), pages 313-362.