Incentivized Exploration via Filtered Posterior Sampling
<|reference_start|>Incentivized Exploration via Filtered Posterior Sampling: 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.<|reference_end|>
@article{kalvit2024incentivized,
    title={Incentivized Exploration via Filtered Posterior Sampling},
    author={Anand Kalvit, Aleksandrs Slivkins, Yonatan Gur},
    journal={arXiv preprint arXiv:2402.13338},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13338},
    primaryClass={cs.LG econ.TH}
}