New Frontiers of Automated Mechanism Design for Pricing and Auctions
Tutorial at the International Joint Conferences on Artificial Intelligence (IJCAI)
Mechanism design is a field of game theory with significant real-world impact, encompassing areas such as pricing and auction design. Mechanisms are used in sales settings ranging from large-scale internet marketplaces to the US government's radio spectrum reallocation efforts. A powerful and prominent approach in this field is automated mechanism design, which uses optimization and machine learning to design mechanisms based on data. This automated approach helps overcome challenges faced by traditional, manual approaches to mechanism design, which have been stuck for decades due to inherent computational complexity challenges: the revenue-maximizing mechanism is not known even for just two items for sale! This workshop is focused on the rapidly growing area of automated mechanism design for revenue maximization. This encompasses both the foundations of batch and online learning (including statistical guarantees and optimization procedures), as well as real-world success stories.
Tuomas Sandholm is Angel Jordan Professor of Computer Science at Carnegie Mellon University. He is Founder and Director of the Electronic Marketplaces Laboratory. He has published over 500 papers. With his student Vince Conitzer, he initiated the study of automated mechanism design in 2001. In parallel with his academic career, he was Founder, Chairman, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world's largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over \$6 billion in generated savings. He is Founder and CEO of Optimized Markets, Strategic Machine, and Strategy Robot. Also, his algorithms run the UNOS kidney exchange, which includes 69% of the transplant centers in the US. He has developed the leading algorithms for several general classes of game. The team that he leads is the two-time world champion in computer Heads-Up No-Limit Texas Hold'em poker, and Libratus became the first and only AI to beat top humans at that game. Among his many honors are the NSF Career Award, inaugural ACM Autonomous Agents Research Award, Sloan Fellowship, Carnegie Science Center Award for Excellence, Edelman Laureateship, Newell Award for Research Excellence, Computers and Thought Award, and Marvin Minsky Medal. He is Fellow of the ACM, AAAI, and INFORMS. He holds an honorary doctorate from the University of Zurich.
Ellen Vitercik is a PhD student in computer science at Carnegie Mellon University. Her primary research interests are artificial intelligence, machine learning, theoretical computer science, and the interface between economics and computation. Among other honors, she is a recipient of the IBM PhD Fellowship and the NSF Graduate Research Fellowship.