2022
AAAI
AAAI 2022
Characterization of Incentive Compatibility of an Ex-ante Constrained Player
Abstract
Abstract We consider a variant of the standard Bayesian mechanism, where players evaluate their outcomes and constraints in an ex-ante manner. Such a model captures a major form of modern online advertising where an advertiser is concerned with her/his expected utility over a time period and her/his type may change over time. We are interested in the incentive compatibility (IC) problem of such Bayesian mechanism. Under very mild conditions on the mechanism environments, we give a full characterization of IC via the taxation principle and show, perhaps surprisingly, that such IC mechanisms are fully characterized by the so-called auto-bidding mechanisms, which are pervasively fielded in the online advertising industry.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
🧭
Keyword Pioneer
— auto-bidding mechanism
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy