An Experiment on Network Density and Sequential Learning

Krishna Dasaratha and Kevin He

Papers from arXiv.org

Abstract: 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.

Date: 2019-09, Revised 2021-05
New Economics Papers: this item is included in nep-exp, nep-net and nep-ure
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Published in Games and Economic Behavior, Vol. 128, July 2021, 182-192

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Journal Article: An experiment on network density and sequential learning (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1909.02220

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