Working Paper: NBER ID: w25497
Authors: Abhijit Banerjee; Emily Breza; Arun G. Chandrasekhar; Markus Mobius
Abstract: The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naive learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent's social influence in this generalized DeGroot model is essentially proportional to the number of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. We then simulate the modeled learning process on a set of real world networks; for these networks there is on average 21.6% information loss. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real world network data show that with clustered seeding, information loss climbs to 35%.
Keywords: social learning; information aggregation; networks; generalized degroot model; uninformed agents
JEL Codes: D8; D83; D85; O1; O12; Z13
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| informed agent's influence (D82) | uninformed agents becoming informed (D82) |
| initial signals clustered (C38) | information loss increases (D89) |
| network geometry (D85) | effectiveness of information aggregation (D83) |
| network's configuration (D85) | belief dictatorship (D72) |