  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.
