False Information and Disagreement in Social Networks

Disagreement, including on matters of fact, is a pervasive phenomenon, yet this is incompatible with existing work on social learning. I propose a model of information processing with two key features: (i) the agent encounters false information, and (ii) the agent cannot distinguish true propositions from false ones. I study two families of axioms for update rules, finding that ``willingness-to-learn'' axioms are incompatible with ``non-manipulability'' axioms. I also provide an axiomatic characterization of several update rules. In a simple social learning model, disagreement is not just possible, but generic. I characterize the influence of each agent on steady-state beliefs and apply the framework to study echo chambers and belief manipulation.