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 environment ? First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity,  Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient . Third, good aggregation requires anti-imitation; without it, agents ' estimates are inefficiently confounded by "echoes." Our stationary equilibrium 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 resulting tractability can facilitate structural estimation of equilibrium learning models and testing against behavioral alternatives , as well as the analysis of welfare and influence .
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 information aggregation . Second, without such diversity, there are cases in which Bayesian information aggregation necessarily falls far short of efficient benchmarks . Third, good aggregation can be achieved only if agents "anti-imitate" some neighbors: otherwise, equilibrium estimates are inefficiently confounded by "echoes." Agents' stationary equilibrium 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. We discuss how the resulting tractability is useful for structural estimation of equilibrium learning models and testing against behavioral alternatives .
R
environment
state
R
private signal distributions are diverse enough across agents
each agent has access to neighbors with sufficiently diverse kinds of signals
D
as long as individuals observe sufficiently many others
A
there are cases in which
R
can fall
necessarily falls
R
good aggregation benchmarks, and can be Pareto-inefficient
efficient benchmarks
R
requires anti-imitation; without it, agents '
can be achieved only if agents "anti-imitate" some neighbors: otherwise, equilibrium
R
Our
Agents'
R
The resulting tractability can facilitate
We discuss how the resulting tractability is useful for
D
, as well as the analysis of welfare and influence