Aggregative Efficiency of Bayesian Learning in Networks

Understand

When individuals in a social network learn about an unknown state from private signals and neighbors' actions, the network structure often causes information loss.

• We consider rational agents and Gaussian signals in the canonical sequential social-learning problem and ask how the network changes the efficiency of signal aggregation.
• Rational actions in our model are log-linear functions of observations and admit a signal-counting interpretation of accuracy.
• Networks where agents observe multiple neighbors but not their common predecessors confound information, and even a small amount of confounding can lead to much lower accuracy.