Optimal Echo Chambers

TL;DR

This paper demonstrates that echo chambers can be a rational strategy under uncertainty, improving learning by focusing on similar views, but expanding exposure to diverse views may hinder learning.

Contribution

It models how rational agents form echo chambers as an optimal response to source uncertainty, balancing accuracy and diversity in information sources.

Findings

Echo chambers can enhance learning under uncertainty.

Expanding exposure to diverse views may slow learning.

Rational formation of echo chambers depends on source credibility.

Abstract

When learning from others, people tend to focus their attention on those with similar views. This is often attributed to flawed reasoning, and thought to slow learning and polarize beliefs. However, we show that echo chambers are a rational response to uncertainty about the accuracy of information sources, and can improve learning and reduce disagreement. Furthermore, extending the range of views someone is exposed to can backfire, slowing their learning by making them less responsive to information from others. We model a Bayesian decision maker who chooses a set of information sources and then observes a signal from one. With uncertainty about which sources are accurate, focusing attention on signals close to one's own expectation can be beneficial, as their expected accuracy is higher. The optimal echo chamber balances the credibility of views similar to one's own against the usefulness of those further away.