  This work studies social learning under non-stationary conditions. Although
designed for online inference, classic social learning algorithms perform
poorly under drifting conditions. To mitigate this drawback, we propose the
Adaptive Social Learning (ASL) strategy. This strategy leverages an adaptive
Bayesian update, where the adaptation degree can be modulated by tuning a
suitable step-size parameter. The learning performance of the ASL algorithm is
examined by means of a steady-state analysis. It is shown that, under the
regime of small step-sizes: i) consistent learning is possible; ii) an accurate
prediction of the performance can be furnished in terms of a Gaussian
approximation.
