Shared Models in Networks, Organizations, and Groups

To understand new information, we exchange models or interpretations with others. This paper provides a framework for thinking about such social exchanges of models. The key assumption is that people adopt the interpretation in their network that best explains the data, given their prior beliefs. An implication is that interpretations evolve within a network. For many network structures, social learning mutes reactions to data: the exchange of models leaves beliefs closer to priors than they were before. Our results shed light on why disagreements persist as new information arrives, as well as the goal and structure of meetings in organizations