We consider a sequential social-learning environment with rational agents and Gaussian private signals , focusing on how the observation network affects the speed of learning. Agents learn about a binary state and take turns choosing actions based on own signals and network neighbors' behavior. Equilibrium learning may be slow when agents do not observe all predecessors, as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers. We show that on any network, equilibrium actions are a log-linear function of observations and each agent's accuracy admits a signal-counting interpretation . Adding links to the observation network can harm agents even without introducing new confounds. We then consider a network structure where agents move in generations and observe some members of the previous generation. When this observation structure is sufficiently symmetric, the additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals , even when generations are arbitrarily large . When agents observe all predecessors from the previous generation, social learning aggregates no more than three signals per generation starting from the third generation, and the long-run learning rate is slower when generations are larger .
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 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 .
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We consider a sequential
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environment with rational agents and Gaussian private signals , focusing on how the observation network affects the speed of learning. Agents learn about a binary state and take turns choosing actions based on own signals and
settings where individuals receive private signals and observe
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behavior. Equilibrium learning may be slow when agents do not observe all predecessors, as agents compromise between incorporating the signals of the observed neighbors and not over-counting the confounding signals of the unobserved early movers. We show that on any network, equilibrium actions are a log-linear function of observations and each agent's accuracy admits a
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
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. Adding links to the observation network can harm agents even without introducing new confounds. We then consider a network structure
of accuracy, which lets us compare the aggregative efficiency of social learning across networks. Learning is very inefficient in a class of networks
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When this observation structure is sufficiently symmetric, the additional information aggregated by each generation is asymptotically equivalent to fewer than two independent signals
Generations after the first contribute very little additional information
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. When agents observe all predecessors from the previous generation, social learning aggregates no more than three signals per generation starting from the third generation, and the long-run learning rate is slower when generations are larger