  The theoretical study of social learning typically assumes that each agent's
action affects only her own payoff. In this paper, I present a model in which
agents' actions directly affect the payoffs of other agents. On a discrete time
line, there is a community containing a random number of agents in each period.
Before each agent needs to take an action, the community receives a private
signal about the underlying state of the world and may observe some past
actions in previous communities. An agent's payoff is higher if her action
matches the state or if more agents take the same action as hers. I analyze two
observation structures: exogenous observation and costly strategic observation.
In both cases, coordination motives enhance social learning in the sense that
agents take the correct action with significantly higher probability when the
community size is greater than a threshold. In particular, this probability
reaches one (asymptotic learning) with unbounded private beliefs and can be
arbitrarily close to one with bounded private beliefs. I then discuss the issue
of multiple equilibria and use risk dominance as a criterion for equilibrium
selection. I find that in the selected equilibria, the community size has no
effect on learning under exogenous observation, facilitates learning under
endogenous observation and unbounded private beliefs, and either helps or
hinders learning under endogenous observation and bounded private beliefs.
