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.
We thank Chiara Aina, Francesca Bastianello, Alessandro Bonatti, Tristan Gagnon-Bartsch, Ben Enke, Simone Galiperti, Robert Gibbons, Sam Hanson, Sendhil Mullainathan, Matthew Rabin, Kunal Sangani, Andrei Shleifer, Mario Small, Jeremy Stein, and seminar participants at UC San Diego, Stanford GSB, Stanford Economics, the NBER Organizational Economics Meetings, the University of Zurich, SITE, Harvard, and UC Santa Barbara for helpful comments. We thank John-Henry Pezzuto and Emma Ronzetti for outstanding research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.