Agents learn about a  state using private signals and the past actions of their neighbors . In contrast to most models of social learning in a network , the target being learned about is moving around. We ask: when can a group aggregate information quickly, keeping up with the changing state? First, if each agent has access to neighbors with sufficiently diverse kinds of signals, then Bayesian learning achieves good informationaggregation. Second, without such diversity, there are cases in which Bayesian information aggregation necessarily falls far short of efficient benchmarks. Third, good aggregation requires agents who understand correlations in neighbors' actions with the sophistication needed to concentrate on recent developments and filter out older, outdated information. In stationary equilibrium, agents' learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations .
Agents learn about a changing state using private signals and past actions of neighbors in a network . We characterize equilibrium learning and social influence in this setting. We then examine when agents can aggregate information well, responding quickly to recent changes. A key sufficient condition for good aggregation is that each individual's neighbors have sufficiently different types of private information. In contrast, when signals are homogeneous, aggregation is suboptimal on any network. We also examine behavioral versions of the model, and show that achieving good aggregation requires a sophisticated understanding of correlations in neighbors' actions . The model provides a Bayesian foundation for a tractable learning dynamic in networks, closely related to the DeGroot model, and offers new tools for counterfactual and welfare analyses .
A
changing
D
the
R
their neighbors . In contrast to most models of social learning
neighbors
R
, the target being learned about is moving around. We ask: when can a group aggregate information quickly, keeping up with the changing state? First, if each agent has access to neighbors with sufficiently diverse kinds of signals, then Bayesian learning achieves good informationaggregation. Second, without such diversity, there are cases in which Bayesian information aggregation necessarily falls far short of efficient benchmarks. Third,
. We characterize equilibrium learning and social influence in this setting. We then examine when agents can aggregate information well, responding quickly to recent changes. A key sufficient condition for good aggregation is that each individual's neighbors have sufficiently different types of private information. In contrast, when signals are homogeneous, aggregation is suboptimal on any network. We also examine behavioral versions of the model, and show that achieving
R
agents who understand
a sophisticated understanding of
R
with the sophistication needed to concentrate on recent developments and filter out older, outdated information. In stationary equilibrium, agents' learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations
. The model provides a Bayesian foundation for a tractable learning dynamic in networks, closely related to the DeGroot model, and offers new tools for counterfactual and welfare analyses