We consider a Bayesian game of pure informational externalities, in which a group of agents learn a binary state of the world from conditionally independent private signals, by repeatedly observing the actions of their neighbors in a social network. We show that the question of whether or not the agents learn the state of the world depends on the topology of the social network. In particular, we identify a geometric "egalitarianism" condition on the social network graph that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium of the game. We give examples of non-egalitarian networks with equilibria in which learning fails.
We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals, and by observing the actions of their neighbors in a social network. We show that the question of whether or not the agents learn efficiently depends on the topology of the social network. In particular, we identify a geometric "egalitarianism" condition on the social network that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium . We also give examples of non-egalitarian networks with equilibria in which learning fails.
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Bayesian game of pure informational externalities, in which a group of agents learn a binary state of the world from conditionally independent
group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial
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the state of the world
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