In social-learning settings where individuals receive private signals and observe network neighbors' actions, the network structure often obstructs information aggregation . We consider sequential social learning with rational agents and Gaussian signals  and ask how the  efficiency of signal aggregation changes with the network . Rational actions in our model  admit a signal-counting interpretation of accuracy , which lets us compare the aggregative efficiency of social learning across networks . Learning is very inefficientin a class of networks where agents move in generations and observe the previous generation  . Generations after the first contribute very little additional information  , even when generations are arbitrarily large.
When individuals in a social network learn about an unknown state from private signals and neighbors' actions, the network structure often causes information loss . We consider rational agents and Gaussian signals in the canonical sequential social-learning problem and ask how the network changes the efficiency of signal aggregation . Rational actions in our model are a log-linear function of observations and admit a signal-counting interpretation of accuracy . This generates a fine-grained ranking of networks based on their aggregative efficiency index. Networks where agents observe multiple neighbors but not their common predecessors confound information, and we show confounding can make learning very inefficient. In a class of networks where agents move in generations and observe the previous generation , aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding . Generations after the first contribute very little additional information due to confounding , even when generations are arbitrarily large.
R
In social-learning settings where individuals receive
When individuals in a social network learn about an unknown state from
D
observe network
R
obstructs information aggregation
causes information loss
D
sequential social learning with
A
in the canonical sequential social-learning problem
A
network changes the
D
changes with the network
A
are a log-linear function of observations and
R
, which lets us compare the aggregative efficiency of social learning across networks . Learning is very inefficientin
. This generates a fine-grained ranking of networks based on their aggregative efficiency index. Networks where agents observe multiple neighbors but not their common predecessors confound information, and we show confounding can make learning very inefficient. In
A
, aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding
A
due to confounding