Proceedings of the Annual Meeting of the Cognitive Science Society

Opinion averaging is a common means of judgment aggregation that is employed in the service of crowd wisdom effects. In this paper, we use simulations with agent-based models to highlight contexts in which opinion averaging leads to poor outcomes. Specifically, we illustrate the conditions under which the optimal posterior prescribed by a normative model of Bayesian argument exchange diverges from the mean belief that would be arrived at via simple averaging. The theoretical and practical implications of this are discussed.