# Strategic Linear Contextual Bandits

arXiv: https://arxiv.org/abs/2406.00551 · HTML: https://mlsanity.com/paper/2406.00551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 55 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Builds on (most cited first, 30 of 34)

- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [Stochastic Linear Bandits Robust to Adversarial Attacks](https://mlsanity.com/paper/2007.03285/) (2020)
- [Bayesian Incentive-Compatible Bandit Exploration](https://mlsanity.com/paper/1502.04147/) (2015)
- [Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions](https://mlsanity.com/paper/2205.06811/) (2022)
- [PAC-Learning for Strategic Classification](https://mlsanity.com/paper/2012.03310/) (2020)
- [A Model Selection Approach for Corruption Robust Reinforcement Learning](https://mlsanity.com/paper/2110.03580/) (2021)
- [Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits](https://mlsanity.com/paper/2002.00287/) (2020)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Adversarial Attacks on Linear Contextual Bandits](https://mlsanity.com/paper/2002.03839/) (2020)
- [Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks](https://mlsanity.com/paper/2106.02978/) (2021)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses](https://mlsanity.com/paper/2107.05762/) (2021)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [Linear Contextual Bandits with Adversarial Corruptions](https://mlsanity.com/paper/2110.12615/) (2021)
- [First- and Second-Order Bounds for Adversarial Linear Contextual Bandits](https://mlsanity.com/paper/2305.00832/) (2023)
- [Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits](https://mlsanity.com/paper/2309.00814/) (2023)
- [Multi-armed Bandit Problems with Strategic Arms](https://mlsanity.com/paper/1706.09060/) (2017)
- [The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation](https://mlsanity.com/paper/1906.01528/) (2019)
- [No-Regret and Incentive-Compatible Online Learning](https://mlsanity.com/paper/2002.08837/) (2020)
- [A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems](https://mlsanity.com/paper/2305.06055/) (2023)
- [One-Shot Strategic Classification Under Unknown Costs](https://mlsanity.com/paper/2311.02761/) (2023)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Strategic Apple Tasting](https://mlsanity.com/paper/2306.06250/) (2023)
- [Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?](https://mlsanity.com/paper/2306.07893/) (2023)
- [User Welfare Optimization in Recommender Systems with Competing Content Creators](https://mlsanity.com/paper/2404.18319/) (2024)
- [Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments](https://mlsanity.com/paper/2208.11040/) (2022)
- [Poisoning Attacks against Recommender Systems: A Survey](https://mlsanity.com/paper/2401.01527/) (2024)
- [Linear Regression from Strategic Data Sources](https://mlsanity.com/paper/1309.7824/) (2013)
- [Combinatorial Bandits under Strategic Manipulations](https://mlsanity.com/paper/2102.12722/) (2021)

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

- [Strategyproof Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2503.09561/) (2025)
- [COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents](https://mlsanity.com/paper/2505.23720/) (2025)
- [A Practical Algorithm for Feature-Rich, Non-Stationary Bandit Problems](https://mlsanity.com/paper/2603.16755/) (2026)
- [Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning](https://mlsanity.com/paper/2605.15692/) (2026)
- [MESHA: Mechanism-Enforced Sequential Halving for Strategic Linear Bandits](https://mlsanity.com/paper/2607.14706/) (2026)

## Abstract (arXiv)

Motivated by the phenomenon of strategic agents gaming a recommender system to maximize the number of times they are recommended to users, we study a strategic variant of the linear contextual bandit problem, where the arms can strategically misreport privately observed contexts to the learner. We treat the algorithm design problem as one of mechanism design under uncertainty and propose the Optimistic Grim Trigger Mechanism (OptGTM) that incentivizes the agents (i.e., arms) to report their contexts truthfully while simultaneously minimizing regret. We also show that failing to account for the strategic nature of the agents results in linear regret. However, a trade-off between mechanism design and regret minimization appears to be unavoidable. More broadly, this work aims to provide insight into the intersection of online learning and mechanism design.