Learning when to say no

David Evans, George W. Evans, Bruce McGough

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)
    2 Downloads (Pure)

    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.
    Original languageEnglish
    Article number105240
    Number of pages33
    JournalJournal of Economic Theory
    Volume194
    Early online date6 Apr 2021
    DOIs
    Publication statusPublished - Jun 2021

    Keywords

    • Search and unemployment
    • Learning
    • Dynamic optimization
    • Bounded rationality
    • Wage dispersion

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