Publications
Publications
- 2023
Sharing Models to Interpret Data
By: Joshua Schwartzstein and Adi Sunderam
Abstract
To understand new data, we share models or interpretations with others. This paper studies
such exchanges of models in a community. The key assumption is that people adopt the interpretation in their community that best explains the data, given their prior beliefs. An implication is that interpretations evolve within communities to better fit prior knowledge, potentially
making final reactions less accurate than initial reactions. When people entertain a rich set of
possible interpretations, social learning often mutes reactions to data: the exchange of models
leaves beliefs closer to priors than they were before, untethering beliefs from data. Our results
shed light on why disagreements persist as new information arrives, why ideological bubbles
need not be hermetically sealed, and the structure of meetings in organizations.
Keywords
Social Learning Theory; Theory; Social Issues; Cognition and Thinking; Social and Collaborative Networks; Attitudes
Citation
Schwartzstein, Joshua, and Adi Sunderam. "Sharing Models to Interpret Data." NBER Working Paper Series, No. 30642, November 2022.