Behavioral Experiments in Strategic Networks
published: June 29, 2010, recorded: May 2010, views: 4256
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For four years now, we have been conducting “medium-scale” experiments in how human subjects behave in strategic and economic settings mediated by an underlying social network structure. We have explored a wide range of networks inspired by generative models from the literature, and a diverse set of collective strategic problems, including biased voting, graph coloring, consensus, and networked trading. These experiments have yielded a wealth of both specific findings and emerging general themes about how populations of human subjects interact in strategic networks. Kearns will review these findings and themes, with an emphasis on the many more questions they raise than answer. Michael Kearns is a professor of computer and information science at the University of Pennsylvania, where he holds the National Center Chair in Resource Management and Technology. He is the founding director of Penn Engineering’s new Market and Social Systems Engineering (MKSE) program. Kearns has secondary appointments in the Statistics and Op erations and Information Management (OPIM) departments of the Wharton School, and is an affiliated faculty member of Penn’s Applied Math and Computational Science graduate program. Kearns also serves as an advisor to Yodle, kaChing, Invite Media, and Kwedit. His research interests include topics in machine learning, algorithmic game theory, social networks, computational finance, and artificial intelligence. Most recently, he has been conducting human-subject experiments on strategic and economic interaction in social networks. Kearns received his B.S in mathematics and computer science from the University of California at Berkeley in 1985, and his Ph.D. in computer science from Harvard University in 1989. He has served as the program chair of NIPS, AAAI, COLT, and ACM EC. He is a member of the NIPS Foundation and the steering committee for the Snowbird Conference on Learning, and serves on the editorial board of The MIT Press series on adaptive computation and machine learning.
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