Social learning for the masses

Abstract

I consider a plausible role for social learning as implemented in the work of Jasmina Arifovic in a complex macroeconomic environment. The model is DSGE with considerable heterogeneity, enough to approach Gini coefficients for income, wealth, and consumption in the U.S. data. The economy has an ambient stochastic structure, and I consider transition dynamics following exceptionally large shocks like the global financial crisis or the global pandemic. These shocks are large enough to plausibly perturb the economy out of the rational expectations equilibrium associated with more ordinary shocks. How is equilibrium re-established? I argue that a social learning construct may be more appropriate in this environment, as opposed to the econometric learning constructs often used to analyze departures from rational expectations in the literature. I also argue that a “DNA” feature of social learning may have led to relatively fast convergence to rational expectations observed following these large shocks in the U.S. data.