  We study "incentivized exploration" (IE) in social learning problems where
the principal (a recommendation algorithm) can leverage information asymmetry
to incentivize sequentially-arriving agents to take exploratory actions. We
identify posterior sampling, an algorithmic approach that is well known in the
multi-armed bandits literature, as a general-purpose solution for IE. In
particular, we expand the existing scope of IE in several practically-relevant
dimensions, from private agent types to informative recommendations to
correlated Bayesian priors. We obtain a general analysis of posterior sampling
in IE which allows us to subsume these extended settings as corollaries, while
also recovering existing results as special cases.
