  We study social learning dynamics motivated by reviews on online platforms.
The agents collectively follow a simple multi-armed bandit protocol, but each
agent acts myopically, without regards to exploration. We allow a wide range of
myopic behaviors that are consistent with (parameterized) confidence intervals
for the arms' expected rewards. We derive stark learning failures for any such
behavior, and provide matching positive results. As a special case, we obtain
the first general results on failure of the greedy algorithm in bandits, thus
providing a theoretical foundation for why bandit algorithms should explore.
