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Machine Learning , Game Theory , and Mechanism Design for a Networked World

Many of the key algorithmic challenges in the context of the internet require considering the objectives and interests of the different participants involved. These include problems ranging from pricing goods and resources, to improving search, to routing, and more generally to understanding how incentives of participants can be harnessed to improve the behavior of the overall system. As a result, Mechanism Design and Algorithmic Game Theory, which can be viewed as “incentive-aware algorithm design,” have become an increasingly important part of algorithmic research in recent years. Along different lines, the area of Machine Learning has made continued progress in developing methods that can generalize from data, adapt to changing environments, and improve performance with experience, as well as progress in understanding fundamental underlying issues. It is clear these capabilities will be critical as well in the context of such large decentralized systems. While these areas seem quite distinct, recent results of the PIs have developed a number of important connections between them: fundamental questions in Mechanism Design and Algorithmic Game Theory turn out to have a strong relation to issues in Machine Learning and vice versa, and techniques from each seem well-poised to help with key problems of the other. The aim of this proposal is to further develop these connections in order to produce powerful mechanisms for adaptive and networked environments, and improve the experience of users of the Web and internet.