Learning from Shared News: When Abundant Information Leads to Belief Polarization
We study 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, we 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. We find that news aggregators can curb polarization caused by news sharing. Our results hold without media bias or fake news, so eliminating these is not sufficient to reduce polarization. When fake news is included, it can lead to polarization but only through misperceived selective sharing. We apply our theory to shed light on the evolution of public opinions about climate change in the US.
We thank S. Nageeb Ali, Myles Ellis, Harry Pei, Jacopo Perego, Joel Sobel, and participants in conferences and seminars at UCSD, PhDEI, SIOE, PennState, NBER POL, UC Berkeley, UC Davis, NYU, MEDS, Harvard, Chicago, WEAI, NASMES, CETC, and Stony Brook for helpful comments and suggestions. All remaining errors are ours. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
T Renee Bowen & Danil Dmitriev & Simone Galperti, 2023. "Learning from Shared News: When Abundant Information Leads to Belief Polarization," The Quarterly Journal of Economics, vol 138(2), pages 955-1000.