  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.
