Stationary social learning in a changing environment

TL;DR

This paper analyzes how social learning functions in a changing environment, highlighting the balance between responsiveness to new information and the formation of consensus, with insights into inertia and signal precision effects.

Contribution

It introduces a model of social learning in dynamic settings, revealing how inertia and signal precision influence consensus formation and responsiveness.

Findings

Consensus often emerges in persistent environments

Inertia can prolong after-effects of state changes

Higher signal precision can reduce welfare due to correlated actions

Abstract

We consider social learning in a changing world. Society can remain responsive to state changes only if agents regularly act upon fresh information, which limits the value of social learning. When the state is close to persistent, a consensus whereby most agents choose the same action typically emerges. The consensus action is not perfectly correlated with the state though, because the society exhibits inertia following state changes. Phases of inertia may be longer when signals are more precise, even if agents draw large samples of past actions, as actions then become too correlated within samples, thereby reducing informativeness and welfare.