Difference between revisions of "CATS-Nov-16-2012"
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== Abstract == | == Abstract == | ||
| + | What do the theory of computation, economics and related fields have to say about the emerging phenomena of crowdsourcing and social computing? Most successful applications of crowdsourcing to date have been on problems we might consider "embarrassingly parallelizable" from a computational perspective. But the power of the social computation approach is already evident, and the road cleared for applying it to more challenging problems. | ||
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| + | In part towards this goal, for a number of years we have been conducting controlled human-subject experiments in distributed social computation in networks with only limited and local communication. These experiments cast a number of traditional computational problems --- including graph coloring, consensus, independent set, market equilibria, biased voting and network formation --- as games of strategic interaction in which subjects have financial incentives to collectively "compute" global solutions. I will overview and summarize the many behavioral findings from this line of experimentation, and draw broad comparisons to some of the predictions made by the theory of computation and microeconomics. | ||
Latest revision as of 16:43, 11 October 2012
Title[edit]
Experiments in Social Computation
Speaker[edit]
Since 2002 Dr. Kearns has been a professor in the Computer and Information Science Department 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, with secondary appointments in the Statistics and Operations and Information Management (OPIM) departments of the Wharton School. He is an active member of Penn's machine learning community PRiML, and is an affiliated faculty member of Penn's Applied Math and Computational Science graduate program. Until July 2006 he was the co-director of Penn's interdisciplinary Institute for Research in Cognitive Science.
Abstract[edit]
What do the theory of computation, economics and related fields have to say about the emerging phenomena of crowdsourcing and social computing? Most successful applications of crowdsourcing to date have been on problems we might consider "embarrassingly parallelizable" from a computational perspective. But the power of the social computation approach is already evident, and the road cleared for applying it to more challenging problems.
In part towards this goal, for a number of years we have been conducting controlled human-subject experiments in distributed social computation in networks with only limited and local communication. These experiments cast a number of traditional computational problems --- including graph coloring, consensus, independent set, market equilibria, biased voting and network formation --- as games of strategic interaction in which subjects have financial incentives to collectively "compute" global solutions. I will overview and summarize the many behavioral findings from this line of experimentation, and draw broad comparisons to some of the predictions made by the theory of computation and microeconomics.
