Information Flows, the Accuracy of Opinions, and Crashes in a Dynamic Network

This paper develops a model showing how information spreads in networks. People with more accurate information tend to remain in the network longer. The authors show that highly connected networks where most individuals have accurate information can collapse into ones with few connections and broadly inaccurate information. The model helps explain how financial markets can fail in their information processing role.


Markets coordinate the flow of information in the economy, aggregating it through the price mechanism. We develop a dynamic model of information transmission and aggregation in financial and other social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity in which agents’ opinions are widely dispersed. We add varying degrees of homophily through a model parameter called tribal preference and find that crash frequency is decreasing in the degree of homophily. Our results suggest that the information aggregation function of markets can fail solely because of the dynamics of information flows, irrespective of shocks or news.

Keywords: social networks, DeGroot learning, dynamic network formation, information transmission, nonlinear dynamical systems, crashes
JEL codes: D83, D85, Z13