Abstract
We develop a model of social learning from complementary information: short-lived agents sequentially choose from a large set of flexibly correlated information sources for prediction of an unknown state, and information is passed down across periods. Will the community collectively acquire the best kinds of information? Long-run outcomes fall into one of two cases: (i) efficient information aggregation, where the community eventually learns as fast as possible; (ii) "learning traps," where the community gets stuck observing suboptimal sources and information aggregation is inefficient. Our main results identify a simple property of the underlying informational complementarities that determines which occurs. In both regimes, we characterize which sources are observed in the long run and how often.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 389-448 |
| Number of pages | 60 |
| Journal | Quarterly Journal of Economics |
| Volume | 135 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 1 2020 |
Funding
∗We are grateful to Nageeb Ali, Aislinn Bohren, Tilman Börgers, Drew Fu-denberg, Ben Golub, David Hirshleifer, Emir Kamenica, George Mailath, Paul Milgrom, Andrew Postlewaite, Ilya Segal, Carlos Segura, Rajiv Sethi, Andrzej Skrzypacz, Vasilis Syrgkanis, and Yuichi Yamamoto for comments and suggestions that improved this article. We also thank four anonymous referees and the editor for very helpful suggestions. Xiaosheng Mu acknowledges the hospitality of the Cowles Foundation at Yale University, which hosted him during parts of this research.
ASJC Scopus subject areas
- Economics and Econometrics