Incentivized Exploration via Filtered Posterior Sampling
Anand Kalvit Aleksandrs Slivkins Yonatan Gur
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.