Publications
Publications
- 2024
- HBS Working Paper Series
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 the following phenomena: disagreements persist as new information arrives, popular theories link seemingly unrelated events, ideological bubbles need not be hermetically sealed, and firms and politicians can benefit from preemptively framing news.
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." Harvard Business School Working Paper, No. 25-011, August 2024. (Revised August 2024.)