Incentivized Exploration via Filtered Posterior Sampling

arXiv:2402.13338v1 Announce Type: new
Abstract: 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 …

abstract agents algorithm arxiv cs.lg econ.th exploration exploratory general identify information literature multi-armed bandits posterior recommendation recommendation algorithm sampling social solution study type via