We study a sequential learning model featuring naive agents on a network. The key behavioral assumption is that agents wrongly believe their predecessors act based only on private information, so  correlation between observed actions is ignored . We provide a simple linear formula characterizing agents' actions in terms of network paths and use this formula to determine when society eventually learns correctly . Disproportionately influential early agents can cause herding on incorrect beliefs and we compute comparative statics of the probability of  herding with respect to network parameters. When networks are segregated , divergent early signals can lead to persistent disagreement between groups  .
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 .
R
The key behavioral assumption is that agents
Agents
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based only
solely
A
they ignore
D
is ignored
R
characterizing
expressing
R
determine when society eventually learns correctly
completely characterize the set of networks allowing eventual correct learning
R
and
;
A
incorrect
R
When networks are segregated
The probability of mislearning increases when link densities are higher and when networks are more integrated. In segregated networks
A
. 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