Incentivized Exploration via Filtered Posterior Sampling

Anand Kalvit; Aleksandrs Slivkins; Yonatan Gur

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arXiv · 2402.13338

Incentivized Exploration via Filtered Posterior Sampling

Anand Kalvit·Aleksandrs Slivkins·Yonatan Gur

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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 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.

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Anand Kalvit, Aleksandrs Slivkins, Yonatan Gur. 2024-02-20. Incentivized Exploration via Filtered Posterior Sampling. https://arxiv.org/abs/2402.13338

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