Recommendation systems often face exploration-exploitation tradeoffs: the system can only learn about the desirability of new options by recommending them to some user. Such systems can thus be modeled as multi-armed bandit settings; however, users are self-interested and cannot be made to follow recommendations. We ask whether exploration can nevertheless be performed in a way that scrupulously respects agents' interests---i.e., by a system that acts as a fiduciary. More formally, we introduce a model in which a recommendation system faces an exploration-exploitation tradeoff under the constraint that it can never recommend any action that it knows yields lower reward in expectation than an agent would achieve if it acted alone. Our main contribution is a positive result: an asymptotically optimal, incentive compatible, and ex-ante individually rational recommendation algorithm.
@article{arxiv.1905.07043,
title = {Fiduciary Bandits},
author = {Gal Bahar and Omer Ben-Porat and Kevin Leyton-Brown and Moshe Tennenholtz},
journal= {arXiv preprint arXiv:1905.07043},
year = {2020}
}
Comments
Published in The Thirty-seventh International Conference on Machine Learning (ICML 2020)