Working Paper: NBER ID: w30642
Authors: Joshua Schwartzstein; Adi Sunderam
Abstract: 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.
Keywords: social learning; belief formation; interpretation exchange; organizational behavior; network effects
JEL Codes: D83; D85; D9; G40
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| social learning (C92) | hardening reactions to data (Y10) |
| social learning (C92) | mutes reactions to data (Y10) |
| exposure to multiple interpretations (C90) | convergence on a model (C52) |
| convergence on a model (C52) | increased confidence in interpretations (D80) |
| adopting models that fit data well (C52) | belief they fully understand data (D83) |
| social learning processes (C92) | convergence on a model suggesting election was likely unfair (D79) |
| exposure to models explaining data as unsurprising (C29) | less movement in beliefs (Z12) |
| posterior beliefs closer to prior beliefs (D80) | less movement in beliefs in response to new data (D80) |
| initial reactions to data (Y10) | beliefs revert to prior views over time (G41) |