We study a sequential learning model featuring naive agents on a network. Agents wrongly believe their predecessors act solely on private information, so they ignore correlation between observed actions. We provide a simple linear formula expressing agents' actions in terms of network paths and use this formula to completely characterize the set of networks allowing eventual correct learning. Disproportionately influential early agents can cause herding on incorrect beliefs; we compute comparative statics of the probability of incorrect herding with respect to network parameters . The probability of mislearning increases when link densities are higher and when networks are more integrated. In  segregated networks, divergent early signals can lead to persistent disagreement between groups. We conduct an experiment and find that the accuracy gain from social learning is twice as large on sparser networks, which is consistent with our behavioral assumption but inconsistent with the rational learning model.
We study a sequential learning model featuring naive agents on a network. Agents wrongly believe their predecessors act solely on private information, so they neglect redundancies among observed actions. We provide a simple linear formula expressing agents' actions in terms of network paths and use this formula to completely characterize the set of networks guaranteeing eventual correct learning. This characterization shows that on almost all networks, disproportionately influential early agents can cause herding on incorrect actions. Going beyond existing social-learning results, we compute the probability of such mislearning exactly. This lets us 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. We conduct an experiment and find that the accuracy gain from social learning is twice as large on sparser networks, which is consistent with naive inference but inconsistent with the rational-learning model.
R
ignore correlation between
neglect redundancies among
R
allowing
guaranteeing
R
Disproportionately
This characterization shows that on almost all networks, disproportionately
R
beliefs; we compute comparative statics of
actions. Going beyond existing social-learning results, we compute
R
incorrect herding with respect to network parameters
such mislearning exactly. This lets us compare likelihoods of incorrect herding, and hence expected welfare losses, across network structures
A
partially
R
our behavioral assumption
naive inference
R
rational learning
rational-learning