Motivated by the phenomenon of strategic agents gaming a recommendation 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 strategically misreport privately observed contexts to the learner. % under strategic context manipulation. We treat the algorithm design problem as one of emph{mechanism design} under uncertainty and propose the Optimistic Grim Trigger Mechanism (OptGTM) that minimizes regret while simultaneously incentivizing the agents to be approximately truthful. We show that… Motivated by the phenomenon of strategic agents gaming a recommendation 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 strategically misreport privately observed contexts to the learner. % under strategic context manipulation. We treat the algorithm design problem as one of emph{mechanism design} under uncertainty and propose the Optimistic Grim Trigger Mechanism (OptGTM) that minimizes regret while simultaneously incentivizing the agents to be approximately truthful. We show that… Read More