  We conduct a sequential social-learning experiment where subjects each guess
a hidden state based on private signals and the guesses of a subset of their
predecessors. A network determines the observable predecessors, and we compare
subjects' accuracy on sparse and dense networks. Accuracy gains from social
learning are twice as large on sparse networks compared to dense networks.
Models of naive inference where agents ignore correlation between observations
predict this comparative static in network density, while the finding is
difficult to reconcile with rational-learning models.
