# Incentivized Exploration via Filtered Posterior Sampling

arXiv: https://arxiv.org/abs/2402.13338 · HTML: https://mlsanity.com/paper/2402.13338/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [An Information-Theoretic Analysis of Thompson Sampling](https://mlsanity.com/paper/1403.5341/) (2014)
- [Mostly Exploration-Free Algorithms for Contextual Bandits](https://mlsanity.com/paper/1704.09011/) (2017)
- [Bayesian Incentive-Compatible Bandit Exploration](https://mlsanity.com/paper/1502.04147/) (2015)
- [Matroid Bandits: Fast Combinatorial Optimization with Learning](https://mlsanity.com/paper/1403.5045/) (2014)
- [The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity](https://mlsanity.com/paper/2002.00558/) (2020)
- [Prior-free and prior-dependent regret bounds for Thompson Sampling](https://mlsanity.com/paper/1304.5758/) (2013)
- [Incentivizing Exploration with Selective Data Disclosure](https://mlsanity.com/paper/1811.06026/) (2018)
- [Greedy Algorithm almost Dominates in Smoothed Contextual Bandits](https://mlsanity.com/paper/2005.10624/) (2020)
- [Incentivizing Combinatorial Bandit Exploration](https://mlsanity.com/paper/2206.00494/) (2022)
- [Economic Recommendation Systems](https://mlsanity.com/paper/1507.07191/) (2015)
- [Incentivizing Exploration with Linear Contexts and Combinatorial Actions](https://mlsanity.com/paper/2306.01990/) (2023)

## Built on by (most cited first, 2 of 2)

- [Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System](https://mlsanity.com/paper/2406.04374/) (2024)
- [Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts](https://mlsanity.com/paper/2506.01685/) (2025)

## Abstract (arXiv)

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.