Two key features describe ideological differences in society: (i) individuals persistently disagree about objective facts; (ii) individuals also disagree about which sources can be trusted to provide reliable information about these facts. We develop a model in which these patterns arise endogenously as the result of Bayesian learning with small biases. Individuals learn the accuracy of information sources by comparing their reports about a sequence of states to noisy feedback about the truth. Arbitrarily small biases in this feedback can lead them to trust biased sources more than unbiased sources and create persistent divergence in beliefs. Entry of partisan sources into a market causes trust in unbiased sources to fall, beliefs about the truth to become more polarized, and biased agents to become overconfident. These patterns arise whether agents select a subset of sources or whether they are exposed to all sources.
Matthew Gentzkow is Professor of Economics at Stanford University. He studies empirical industrial organization and political economy, with a focus on media industries. He received the 2014 John Bates Clark Medal, given by the American Economic Association to the American economist under the age of forty who has made the most significant contribution to economic thought and knowledge. He is a fellow of the American Academy of Arts and Sciences and the Econometric Society, a senior fellow at the Stanford Institute for Economic Policy Research, and a former co-editor of American Economic Journal: Applied Economics.