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Incentivized Exploration via Filtered Posterior Sampling

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  • Anand Kalvit
  • Aleksandrs Slivkins
  • Yonatan Gur

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.

Suggested Citation

  • Anand Kalvit & Aleksandrs Slivkins & Yonatan Gur, 2024. "Incentivized Exploration via Filtered Posterior Sampling," Papers 2402.13338, arXiv.org.
    • Handle: RePEc:arx:papers:2402.13338
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    References listed on IDEAS

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    3. Yeon-Koo Che & Johannes Hörner, 2018. "Recommender Systems as Mechanisms for Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 871-925.
    4. Yiangos Papanastasiou & Kostas Bimpikis & Nicos Savva, 2018. "Crowdsourcing Exploration," Management Science, INFORMS, vol. 64(4), pages 1727-1746, April.
    5. Hamsa Bastani & Mohsen Bayati & Khashayar Khosravi, 2021. "Mostly Exploration-Free Algorithms for Contextual Bandits," Management Science, INFORMS, vol. 67(3), pages 1329-1349, March.
    6. Daniel Russo & Benjamin Van Roy, 2014. "Learning to Optimize via Posterior Sampling," Mathematics of Operations Research, INFORMS, vol. 39(4), pages 1221-1243, November.
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