Too Much Information & The Death of Consensus

Modern society is increasingly polarized, even on purely factual questions, despite greater access to information than ever. In a model of sequential sociallearning, I study the impact ofmotivated reasoningon information aggregation. This is a belief formation process in which agents trade-off accuracy against ideological convenience. I find that even Bayesian agents only learn in very highly connected networks, where agents have arbitrarily large neighborhoods asymptotically. This is driven by the fact that motivated agents sometimes reject information that can be inferred from their neighbors’ actions when it refutes their desired beliefs. Observing any finite neighborhood, there is always some probability that all of an agent’s neighbors will have disregarded information thus. Moreover, I establish thatconsensus, where all agents eventually choose the same action, is only possible with relatively uninformative private signals and low levels of motivated reasoning.