Strategic Observational Learning

TL;DR

This paper analyzes how forward-looking agents learn socially in repeated settings, revealing conditions under which they behave myopically or fail to reach equilibrium, depending on signal symmetry and patience levels.

Contribution

It characterizes the equilibrium behavior of forward-looking agents in social learning, highlighting the impact of signal symmetry and patience on strategic learning outcomes.

Findings

Myopic equilibrium is unique under symmetric signals.

Agents behave myopically regardless of patience in symmetric settings.

No equilibrium exists in myopic strategies with asymmetric signals in infinite games.

Abstract

We study learning by privately informed forward-looking agents in a simple repeated-action setting of social learning. Under a symmetric signal structure, forward-looking agents behave myopically for any degrees of patience. Myopic equilibrium is unique in the class of symmetric threshold strategies, and the simplest symmetric non-monotonic strategies. If the signal structure is asymmetric and the game is infinite, there is no equilibrium in myopic strategies, for any positive degree of patience.