Strategic Usage in a Multi-Learner Setting

arXiv:2401.16422v2 Announce Type: replace
Abstract: Real-world systems often involve some pool of users choosing between a set of services. With the increase in popularity of online learning algorithms, these services can now self-optimize, leveraging data collected on users to maximize some reward such as service quality. On the flipside, users may strategically choose which services to use in order to pursue their own reward functions, in the process wielding power over which services can see and use their data. Extensive …

abstract algorithms arxiv cs.gt cs.lg data online learning pool quality service services set systems type usage world