Joint Computer Science Speaking Skills Talk / AI Seminar

— 1:00pm

In Person and Virtual - ET - ASA Conference Room, Gates Hillman 6115 and Zoom

SIDDHARTH PRASAD , Ph.D. Student, Computer Science Department, Carnegie Mellon University

Bicriteria Multidimensional Mechanism Design with Side Information

We develop a versatile new methodology for multidimensional mechanism design that incorporates side information about agent types with the bicriteria goal of generating high social welfare and high revenue simultaneously. Side information can come from a variety of sources---examples include advice from a domain expert, predictions from a machine-learning model trained on historical agent data, or even the mechanism designer's own gut instinct---and in practice such sources are abundant. In this work we adopt a prior-free perspective that makes no assumptions on the correctness, accuracy, or source of the side information. 

First, we design a meta-mechanism that integrates input side information with an improvement of the classical VCG mechanism. The welfare, revenue, and incentive properties of our meta-mechanism are characterized by a number of novel constructions we introduce based on the notion of a \emph{weakest competitor}, which is an agent that has the smallest impact on welfare. We then show that our meta-mechanism---when carefully instantiated---simultaneously achieves strong welfare and revenue guarantees that are parameterized by errors in the side information. When the side information is highly informative and accurate, our mechanism achieves welfare and revenue competitive with the total social surplus, and its performance decays continuously and gradually as the quality of the side information decreases. 

Finally, we apply our meta-mechanism to a setting where each agent's type is determined by a constant number of parameters. Specifically, agent types lie on constant-dimensional subspaces (of the potentially high-dimensional ambient type space) that are known to the mechanism designer. We use our meta-mechanism to obtain the first known welfare and revenue guarantees in this setting. 

Siddharth Prasad is a fourth-year PhD student in the Computer Science Department at Carnegie Mellon University advised by Nina Balcan and Tuomas Sandholm. His research interests span machine learning, integer programming, mechanism design, algorithms, and their various interactions. He was a student researcher at Google Research during Summer 2022, hosted by Craig Boutilier and Martin Mladenov. He received a B.S. in math and computer science from Caltech in 2019.

Presented as part of the AI Seminar

Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.

In Person and Zoom Participation. See announcement.

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