# COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents

arXiv: https://arxiv.org/abs/2505.23720 · HTML: https://mlsanity.com/paper/2505.23720/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 52 references that are in the map.
- First posted on arXiv 29 May 2025.

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

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Finite-Time Analysis of Kernelised Contextual Bandits](https://mlsanity.com/paper/1309.6869/) (2013)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [Neural Thompson Sampling](https://mlsanity.com/paper/2010.00827/) (2020)
- [Online Learning with Feedback Graphs: Beyond Bandits](https://mlsanity.com/paper/1502.07617/) (2015)
- [Scalable Generalized Linear Bandits: Online Computation and Hashing](https://mlsanity.com/paper/1706.00136/) (2017)
- [PAC-Learning for Strategic Classification](https://mlsanity.com/paper/2012.03310/) (2020)
- [On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials](https://mlsanity.com/paper/1903.07082/) (2019)
- [Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses](https://mlsanity.com/paper/2107.05762/) (2021)
- [Multi-armed Bandit Problems with Strategic Arms](https://mlsanity.com/paper/1706.09060/) (2017)
- [Online Learning with Gaussian Payoffs and Side Observations](https://mlsanity.com/paper/1510.08108/) (2015)
- [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)
- [Strategic Apple Tasting](https://mlsanity.com/paper/2306.06250/) (2023)
- [Stochastic Multi-Armed Bandits with Control Variates](https://mlsanity.com/paper/2105.03962/) (2021)
- [Exploiting Correlated Auxiliary Feedback in Parameterized Bandits](https://mlsanity.com/paper/2311.02715/) (2023)
- [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)
- [Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation](https://mlsanity.com/paper/2311.15647/) (2023)
- [Multi-armed Bandit Algorithm against Strategic Replication](https://mlsanity.com/paper/2110.12160/) (2021)
- [Strategic Linear Contextual Bandits](https://mlsanity.com/paper/2406.00551/) (2024)
- [Partially Observable Contextual Bandits with Linear Payoffs](https://mlsanity.com/paper/2409.11521/) (2024)
- [Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies](https://mlsanity.com/paper/2408.12845/) (2024)
- [Contextual Dynamic Pricing with Strategic Buyers](https://mlsanity.com/paper/2307.04055/) (2023)
- [Learning Payment-Free Resource Allocation Mechanisms](https://mlsanity.com/paper/2311.10927/) (2023)
- [Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting](https://mlsanity.com/paper/2501.16018/) (2025)
- [Online Allocation and Learning in the Presence of Strategic Agents](https://mlsanity.com/paper/2209.12112/) (2022)
- [Truthful mechanisms for linear bandit games with private contexts](https://mlsanity.com/paper/2501.03865/) (2025)

## Built on by (most cited first, 1 of 1)

- [MESHA: Mechanism-Enforced Sequential Halving for Strategic Linear Bandits](https://mlsanity.com/paper/2607.14706/) (2026)

## Abstract (arXiv)

This paper considers a contextual bandit problem involving multiple agents, where a learner sequentially observes the contexts and the agent's reported arms, and then selects the arm that maximizes the system's overall reward. Existing work in contextual bandits assumes that agents truthfully report their arms, which is unrealistic in many real-life applications. For instance, consider an online platform with multiple sellers; some sellers may misrepresent product quality to gain an advantage, such as having the platform preferentially recommend their products to online users. To address this challenge, we propose an algorithm, COBRA, for contextual bandit problems involving strategic agents that disincentivize their strategic behavior without using any monetary incentives, while having incentive compatibility and a sub-linear regret guarantee. Our experimental results also validate the different performance aspects of our proposed algorithm.