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Machine Learning-based Combinatorial Auctions

Market designers study how to set the "rules of a market" such that the market works well. However, some markets are so complex that they put a high cognitive or computational burden on the participants which may lead to inefficiencies. In combinatorial auctions (CAs), for example, the value space grows exponentially in the number of goods, which can make it impossible for bidders to express their full value function. In this talk, I will present a new design paradigm for CAs using machine learning to help the auctioneer identify the most relevant information to elicit from the bidders. We design CAs based on this paradigm that are (1) computationally tractable, (2) provide bidders with good incentives to report their true values and (3) achieve high allocative efficiency.

Type of Seminar:
Control Seminar Series
Prof. Sven Seuken
University of Zurich
Dec 04, 2017   17:15 h

Contact Person:

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Biographical Sketch:
Sven Seuken is a tenured Associate Professor of Computation and Economics at the University of Zurich. His research lies at the intersection of Computer Science and Game Theory, with a focus on (electronic) market design. He received his PhD in Computer Science at Harvard University. Since joining the University of Zurich in 2011 he has received multiple awards and research grants, including three grants from the SNF, a Google Faculty Research Award, and he was recently ranked as one of the "Top 40 under 40" in the category "society and science" by the German business magazine Capital. He is also interested in practical applications of market design: he is the Chief Economist at BandwidthX (USA); he works as a market designer for Tremor Technologies, Inc. (USA); and in 2017, he co-founded a Swiss start-up in the legal tech sector, where he is also in charge of market design.