Before revision:  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.
After revision:  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 . 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 . Generations after the first contribute very little additional information , even when generations are arbitrarily large.