# Overabundant Information and Learning Traps

arXiv: https://arxiv.org/abs/1805.08134 · HTML: https://mlsanity.com/paper/1805.08134/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 1 of 1)

- [Exploration and Persuasion](https://mlsanity.com/paper/2410.17086/) (2024)

## Abstract (arXiv)

We develop a model of social learning from overabundant information: Short-lived agents sequentially choose from a large set of (flexibly correlated) information sources for prediction of an unknown state. Signal realizations are public. We demonstrate two starkly different long-run outcomes: (1) efficient information aggregation, where the community eventually learns as fast as possible; (2) "learning traps," where the community gets stuck observing suboptimal sources and learns inefficiently. Our main results identify a simple property of the signal correlation structure that separates these outcomes. In both regimes, we characterize which sources are observed in the long run and how often.