On the Blending of Machine Learning and Economics
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Managing such sharing is one of the classical goals of microeconomics, and it is given new relevance in the modern setting of large, humanfocused datasets, and in data-analytic contexts such as classifiers and recommendation systems. Michael Jordan will discuss several recent projects that aim to explore the interface between machine learning and microeconomics, including the study of explorationexploitation trade-offs for bandit learning algorithms that compete over a scarce resource, leader/follower dynamics in strategic classification, and the robust learning of optimal auctions.
Michael I. Jordan ist Pehong Chen Distinguished Professor im Department of Electrical Engineering and Computer Sciences und im Department of Statistics an der University of California, Berkeley.
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