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On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX
In this work, our goal is to design an algorithm for the selfish experts problem that is incentive-compatible (IC, or \emph{truthful}), meaning each expert's best strategy is to report truthfully, while also ensuring the algorithm enjoys sublinear regret with respect to the expert with the best belief.
Best-Case Lower Bounds in Online Learning
Much of the work in online learning focuses on the study of sublinear upper bounds on the regret.
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