Bayesian agents learn about a moving target, such as a commodity price, using private signals and their network neighbors' estimates. The weights agents place on these sources of information are endogenously determined by the precisions and correlations of the sources; the weights, in turn, determine future correlations. We study stationary equilibria-ones in which all of these quantities are constant over time. Equilibria in linear updating rules always exist, yielding a Bayesian learning model as tractable as the commonly-used DeGroot heuristic. Equilibria and the comparative statics of learning outcomes can be readily computed even in large networks. Substantively, we identify pervasive inefficiencies in Bayesian learning.In any stationary equilibrium where agentsput positive weights on neighbors' actions, learning is Pareto inefficient in a generic network: agents rationally overuse social information and underuse their private signals .
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 .
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Bayesian agents
Agents
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moving target, such as a commodity price,
state
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their network neighbors' estimates. The weights agents place on these sources of information are endogenously determined by the precisions and correlations of the sources; the weights, in turn, determine future correlations. We study stationary equilibria-ones in which all of these quantities are constant over time. Equilibria in linear updating rules always exist, yielding a Bayesian learning model as tractable as the commonly-used DeGroot heuristic. Equilibria and the comparative statics of learning outcomes can be readily computed even in large networks. Substantively, we identify pervasive inefficiencies in Bayesian learning.In any stationary equilibrium where agentsput positive weights on neighbors' actions, learning is Pareto inefficient in a generic network: agents rationally overuse social information and underuse their private signals
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