Learning from Shared News

The paper presented in this Virtual Market Design Seminar studies learning via shared news. Each period agents receive the same quantity and quality of first-hand information and can share it with friends. Some friends (possibly few) share selectively, generating heterogeneous news diets across agents akin to echo chambers. Agents are aware of selective sharing and update beliefs by Bayes’ rule. Contrary to standard learning results, the authors show that beliefs can diverge in this environment leading to polarization. This requires that (i) agents hold misperceptions (even minor) about friends’ sharing and (ii) information quality is sufficiently low. Polarization can worsen when agents’ social connections expand. When the quantity of first-hand information becomes large, agents can hold opposite extreme beliefs resulting in severe polarization. Their results hold without media bias or fake news, so eliminating these is not sufficient to reduce polarization. When fake news is included, the authors show that it can lead to polarization but only through misperceived selective sharing. News aggregators can curb polarization caused by shared news.