On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX

arXiv:2404.05155v1 Announce Type: new
Abstract: In one view of the classical game of prediction with expert advice with binary outcomes, in each round, each expert maintains an adversarially chosen belief and honestly reports this belief. We consider a recently introduced, strategic variant of this problem with selfish (reputation-seeking) experts, where each expert strategically reports in order to maximize their expected future reputation based on their belief. In this work, our goal is to design an algorithm for the selfish experts …

abstract advice arxiv belief binary cs.gt cs.lg expert feedback game online learning prediction price reports stat.ml type view