Common Identification and Common Learning

TL;DR

This paper explores what is commonly learned in scenarios where the conditions for individual learning to ensure common learning are not met, extending understanding beyond the finite states and signals case.

Contribution

It characterizes the nature of common learning when the standard sufficient conditions do not hold, filling a gap in the existing literature.

Findings

Identifies the types of common knowledge that can emerge without finite states or signals.

Provides a framework for understanding learning dynamics in more general settings.

Extends previous results to broader conditions.

Abstract

Cripps, Ely, Mailath and Samuelson (2008) showed that if there are finitely many states, and the signals are i.i.d and finite, then individual learning is sufficient for common learning. In this note we describe what is commonly learned when this sufficient condition does not hold.