Incentivized Exploration via Filtered Posterior Sampling

• URL: http://arxiv.org/abs/2402.13338v1
• Date: Tue, 20 Feb 2024 19:30:55 GMT
• Title: Incentivized Exploration via Filtered Posterior Sampling
• Authors: Anand Kalvit, Aleksandrs Slivkins, Yonatan Gur
• Abstract summary: We study "incentivized exploration" (IE) in social learning problems where the principal can leverage information asymmetry to incentivize 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.
• Score: 51.32577788466152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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