# Incentivizing Exploration in Linear Bandits under Information Gap

arXiv: https://arxiv.org/abs/2104.03860 · HTML: https://mlsanity.com/paper/2104.03860/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Bayesian Incentive-Compatible Bandit Exploration](https://mlsanity.com/paper/1502.04147/) (2015)
- [Impact of Representation Learning in Linear Bandits](https://mlsanity.com/paper/2010.06531/) (2020)
- [The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits](https://mlsanity.com/paper/1610.04491/) (2016)
- [Incentivizing Exploration with Selective Data Disclosure](https://mlsanity.com/paper/1811.06026/) (2018)
- [Stochastic Linear Bandits with Hidden Low Rank Structure](https://mlsanity.com/paper/1901.09490/) (2019)
- [Multi-armed Bandits with Compensation](https://mlsanity.com/paper/1811.01715/) (2018)
- [Fairness Incentives for Myopic Agents](https://mlsanity.com/paper/1705.02321/) (2017)
- [Incentivized Exploration for Multi-Armed Bandits under Reward Drift](https://mlsanity.com/paper/1911.05142/) (2019)
- [Learning User Preferences to Incentivize Exploration in the Sharing Economy](https://mlsanity.com/paper/1711.08331/) (2017)
- [Incentivising Exploration and Recommendations for Contextual Bandits with Payments](https://mlsanity.com/paper/2001.07853/) (2020)

## Built on by (most cited first, 5 of 5)

- [Incentivizing Exploration with Linear Contexts and Combinatorial Actions](https://mlsanity.com/paper/2306.01990/) (2023)
- [(Almost) Free Incentivized Exploration from Decentralized Learning Agents](https://mlsanity.com/paper/2110.14628/) (2021)
- [MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning](https://mlsanity.com/paper/2304.04668/) (2023)
- [Learning with Limited Shared Information in Multi-agent Multi-armed Bandit](https://mlsanity.com/paper/2502.15338/) (2025)
- [Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation](https://mlsanity.com/paper/2507.18756/) (2025)

## Abstract (arXiv)

We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring. In order to maximize the long-term reward, the system offers compensation to incentivize the users to pull the exploratory arms, with the goal of balancing the trade-off among exploitation, exploration and compensation. We consider a new and practically motivated setting where the context features observed by the user are more informative than those used by the system, e.g., features based on users' private information are not accessible by the system. We propose a new method to incentivize exploration under such information gap, and prove that the method achieves both sublinear regret and sublinear compensation. We theoretical and empirically analyze the added compensation due to the information gap, compared with the case that the system has access to the same context features as the user, i.e., without information gap. We also provide a compensation lower bound of our problem.