Social learning and expectational stability

Abstract

Stability features of social learning (SL) dynamics are examined. We show SL can be formulated as a stochastic recursive algorithm, making it possible to analyze asymptotics using the familiar differential-equation approach. For a simple univariate model, this approach reduces to the E-stability principle, though in prominent instability cases divergence is exceedingly slow compared to adaptive learning (AL). We locate differing fitness criteria as the source of the slower evolution rates of SL compared to AL. Modified AL and SL learning dynamics models are developed and used to illustrate the different implications of policy change in a standard New Keynesian model. We anticipate that the central question going forward will be how best to combine the two approaches when modeling adaptation to structural change.