Working Paper: CEPR ID: DP10493
Authors: Markus Mobius; Tuan Phan; Adam Szeidl
Abstract: We seed noisy information to members of a real-world social network to study how information diffusion and information aggregation jointly shape social learning. Our environment features substantial social learning. We show that learning occurs via diffusion which is highly imperfect: signals travel only up to two steps in the conversation network and indirect signals are transmitted noisily. We then compare two theories of information aggregation: a naive model in which people double-count signals that reach them through multiple paths, and a sophisticated model in which people avoid double-counting by tagging the source of information. We show that to distinguish between these models of aggregation, it is critical to explicitly account for imperfect diffusion. When we do so, we find that our data are most consistent with the sophisticated tagged model.
Keywords: Information aggregation; Information diffusion; Networks; Social learning
JEL Codes: C91; C93; D83
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| imperfect diffusion (F12) | social learning (C92) |
| distance of information transmission (L96) | learning outcomes (A21) |
| social learning (C92) | improvement in correctness (C52) |
| conversations (Y70) | improvement in correctness (C52) |
| tagging sources (Y90) | accurate information aggregation (D83) |
| social distance (Z13) | influence of conversation partners (C92) |
| frictions in information transmission (D83) | weight of indirect partners' signals (C69) |