Learning when to say no
Abstract
We consider boundedly-rational agents in McCall's model of intertemporal job search. Agents update over time their perception of the value of waiting for an additional job offer using value-function learning. A first-principles argument applied to a stationary environment demonstrates asymptotic convergence to fully optimal decision-making. In environments with actual or possible structural change our agents are assumed to discount past data. Using simulations, we consider a change in unemployment benefits, and study the effect of the associated learning dynamics on unemployment and its duration. Separately, in a calibrated exercise we show the potential of our model of bounded rationality to resolve a frictional wage dispersion puzzle.
Citation
Evans , D , Evans , G W & McGough , B 2021 , ' Learning when to say no ' , Journal of Economic Theory , vol. 194 , 105240 . https://doi.org/10.1016/j.jet.2021.105240
Publication
Journal of Economic Theory
Status
Peer reviewed
ISSN
0022-0531Type
Journal article
Description
Financial support from National Science Foundation Grant No. SES-1559209 is gratefully acknowledged.Collections
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