Incentivized Exploration via Filtered Posterior Sampling

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Tác giả: Yonatan Gur, Anand Kalvit, Aleksandrs Slivkins

Ngôn ngữ: eng

Ký hiệu phân loại: 003.83 Discrete-time systems

Thông tin xuất bản: 2024

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 201722

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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