We conduct a sequential social learning experiment where subjects guess a hidden state after observing 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. Later agents' accuracy gains from social learning are twice as large in the sparse treatment compared to the dense treatment . Models of naive inference where agents ignore correlation between observations predict this comparative static in network density, while the result is difficult to reconcile with rational-learning models.
We conduct a sequential social-learning experiment where subjects take turns guessing 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.
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social learning
social-learning
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guess
take turns guessing
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after observing
based on
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Later agents' accuracy
Accuracy
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in the sparse treatment compared to the dense treatment
on sparse networks compared to dense networks
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result
finding