# Incentivizing Exploration with Selective Data Disclosure

arXiv: https://arxiv.org/abs/1811.06026 · HTML: https://mlsanity.com/paper/1811.06026/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 24 of 24)

- [Fair Exploration via Axiomatic Bargaining](https://mlsanity.com/paper/2106.02553/) (2021)
- [Fundamentals of Task-Agnostic Data Valuation](https://mlsanity.com/paper/2208.12354/) (2022)
- [Exploration and Incentives in Reinforcement Learning](https://mlsanity.com/paper/2103.00360/) (2021)
- [Incentivizing Combinatorial Bandit Exploration](https://mlsanity.com/paper/2206.00494/) (2022)
- [Bandit Social Learning: Exploration under Myopic Behavior](https://mlsanity.com/paper/2302.07425/) (2023)
- [Incentivizing Exploration in Linear Bandits under Information Gap](https://mlsanity.com/paper/2104.03860/) (2021)
- [Incentive-Aware Recommender Systems in Two-Sided Markets](https://mlsanity.com/paper/2211.15381/) (2022)
- [Algorithmic Censoring in Dynamic Learning Systems](https://mlsanity.com/paper/2305.09035/) (2023)
- [Incentivizing Exploration with Linear Contexts and Combinatorial Actions](https://mlsanity.com/paper/2306.01990/) (2023)
- [When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making](https://mlsanity.com/paper/2308.11721/) (2023)
- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [(Almost) Free Incentivized Exploration from Decentralized Learning Agents](https://mlsanity.com/paper/2110.14628/) (2021)
- [Multi-Round Human-AI Collaboration with User-Specified Requirements](https://mlsanity.com/paper/2602.17646/) (2026)
- [Incentivized Exploration via Filtered Posterior Sampling](https://mlsanity.com/paper/2402.13338/) (2024)
- [Learning from Imperfect Human Feedback: a Tale from Corruption-Robust Dueling](https://mlsanity.com/paper/2405.11204/) (2024)
- [Misalignment, Learning, and Ranking: Harnessing Users Limited Attention](https://mlsanity.com/paper/2402.14013/) (2024)
- [Exploration and Persuasion](https://mlsanity.com/paper/2410.17086/) (2024)
- [Truthful mechanisms for linear bandit games with private contexts](https://mlsanity.com/paper/2501.03865/) (2025)
- [Incentivized Collaboration in Active Learning](https://mlsanity.com/paper/2311.00260/) (2023)
- [Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration](https://mlsanity.com/paper/2312.16307/) (2023)
- [Learning with Limited Shared Information in Multi-agent Multi-armed Bandit](https://mlsanity.com/paper/2502.15338/) (2025)
- [Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts](https://mlsanity.com/paper/2506.01685/) (2025)
- [Prior-Agnostic Incentive-Compatible Exploration](https://mlsanity.com/paper/2602.20465/) (2026)
- [On Incentivized Exploration beyond Bayesianism and Full-Information](https://mlsanity.com/paper/2607.18300/) (2026)

## Abstract (arXiv)

We propose and design recommendation systems that incentivize efficient exploration. Agents arrive sequentially, choose actions and receive rewards, drawn from fixed but unknown action-specific distributions. The recommendation system presents each agent with actions and rewards from a subsequence of past agents, chosen ex ante. Thus, the agents engage in sequential social learning, moderated by these subsequences. We asymptotically attain optimal regret rate for exploration, using a flexible frequentist behavioral model and mitigating rationality and commitment assumptions inherent in prior work. We suggest three components of effective recommendation systems: independent focus groups, group aggregators, and interlaced information structures.