@inproceedings{
buening2024strategic,
title={Strategic Linear Contextual Bandits},
author={Thomas Kleine Buening and Aadirupa Saha and Christos Dimitrakakis and Haifeng Xu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=apPHMfE63y}
}
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.
Strategic Linear Contextual Bandits
Thomas Kleine Buening Aadirupa Saha Christos Dimitrakakis Haifeng Xu