Algorithms and the Internet are revolutionizing how society allocates its resources. Examples range from wireless spectrum and electricity to online advertising and carpooling opportunities. A fundamental question is how to allocate such resources efficiently by designing robust computational markets.
In this talk I will demonstrate recent progress on this question by considering a problem crucial for major industry players like Google: how to design revenue-maximizing allocation mechanisms. Most existing designs hinge on “getting the price right” – selling goods to buyers at prices low enough to encourage a sale, but high enough to garner non-trivial revenue. This approach is difficult to implement when the seller has little or no a priori information about buyers’ valuations, or when the setting is sufficiently complex, as in the case of markets with heterogeneous goods.
I will show a robust approach to designing auctions for revenue, which “lets the market do the work” by allowing prices to emerge from enhanced competition for scarce goods. This work provides guidelines for a seller in choosing among data acquisition and sophisticated pricing, and investment in drawing additional buyers.
Inbal Talgam-Cohen is a Marie Curie postdoctoral researcher at HUJI and a visiting postdoctoral researcher at TAU. She holds a PhD from Stanford (2015) supervised by Tim Roughgarden, an MSc from Weizmann and a BSc from TAU in computer science, as well as a law LLB. Her research is in algorithmic game theory, including computational and data aspects of market design and applications to Internet economics. Her awards include Best Doctoral Dissertation Award of ACM SIGecom, the Stanford Interdisciplinary Graduate Fellowship, and the Best Student Paper Award at EC’15.