Summary of Incentivized Exploration Via Filtered Posterior Sampling, by Anand Kalvit et al.
Incentivized Exploration via Filtered Posterior Sampling
by Anand Kalvit, Aleksandrs Slivkins, Yonatan Gur
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Theoretical Economics (econ.TH)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper explores “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. By identifying posterior sampling, an algorithmic approach from the multi-armed bandits literature, as a general-purpose solution for IE, the paper expands its scope in several practically-relevant dimensions, including private agent types, informative recommendations, and correlated Bayesian priors. The authors provide a general analysis of posterior sampling in IE, allowing them to subsume these extended settings as corollaries while recovering existing results as special cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers looked at how a recommendation algorithm can encourage agents to explore new options when they don’t have all the information. They found that an existing algorithm called posterior sampling is effective in achieving this goal. The paper shows how this algorithm works well in different scenarios and recovers previous results as special cases. |
