Network structure and naive sequential learning

Krishna Dasaratha and Kevin He

Theoretical Economics, 2020, vol. 15, issue 2

Abstract: We study a sequential-learning model featuring a network of naive agents with Gaussian information structures. Agents apply a heuristic rule to aggregate predecessors' actions. They weigh these actions according the strengths of their social connections to different predecessors. We show this rule arises endogenously when agents wrongly believe others act solely on private information and thus neglect redundancies among observations. We provide a simple linear formula expressing agents' actions in terms of network paths and use this formula to characterize the set of networks where naive agents eventually learn correctly. This characterization implies that, on all networks where later agents observe more than one neighbor, there exist disproportionately influential early agents who can cause herding on incorrect actions. Going beyond existing social-learning results, we compute the probability of such mislearning exactly. This allows us to compare likelihoods of incorrect herding, and hence expected welfare losses, across network structures. The probability of mislearning increases when link densities are higher and when networks are more integrated. In partially segregated networks, divergent early signals can lead to persistent disagreement between groups.

Keywords: Network structure; sequential social learning; naive inference; mislearning; disagreement (search for similar items in EconPapers)
JEL-codes: D83 D85 D90 (search for similar items in EconPapers)
Date: 2020-05-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Working Paper: Network Structure and Naive Sequential Learning (2020)
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