Shared Models in Networks, Organizations, and Groups

Why did the market rise yesterday? What are the implications of the latest school shooting? Why did a particular employee get promoted? To answer such questions, we often exchange models, stories, narratives, and interpretations with others. This paper provides a framework for thinking about such social exchanges of models. The key assumption, following Schwartzstein and Sunderam (2021), is that when people are exposed to multiple interpretations they adopt the one that best explains the data. A key implication is that within a network interpretations evolve. This evolution driven by social learning hardens reactions to data that are open to interpretation: following the exchange of models, people are more convinced they are able to explain the data. Thus, people in different networks can not only end up with vastly different beliefs, but in a sense be puzzled by the fact that others outside their network have different beliefs. For certain network structures, we show that social learning also mutes reactions to data that are open to interpretation: the exchange of models leaves beliefs closer to priors than they were before. Beyond studying fixed networks, we also consider how firm managers, politicians, and other agents are able to influence patterns of communication to their advantage. Agents who benefit from muting or broadly shared understandings will encourage a robust exchange of interpretations; agents who instead want new data to change behaviors will try to limit the exchange of interpretations, especially interpretations that suggest the data are not surprising. We apply the framework to consider the goal and structure of meetings in organizations, as well as the evolution and persistence of myths in social networks.