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David C. Parkes
Researcher at Harvard University
Publications - 407
Citations - 17022
David C. Parkes is an academic researcher from Harvard University. The author has contributed to research in topics: Common value auction & Combinatorial auction. The author has an hindex of 59, co-authored 396 publications receiving 15788 citations. Previous affiliations of David C. Parkes include IBM & University of Freiburg.
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Patent
Secure data interchange
TL;DR: A secure data interchange system enables information about bilateral and multilateral interactions between multiple persistent parties to be exchanged and leveraged within an environment that uses a combination of techniques to control access to information, release of information, and matching of information back to parties as mentioned in this paper.
Patent
Location enhanced information delivery system
TL;DR: The Location Enhanced Information Deliver System Architecture (LEIA) as mentioned in this paper customizes the information that is displayed to an information recipient based on optimizing a match between information purveyors such as advertisers, and the information recipients who are local to the information delivery system.
Proceedings Article
Iterative Combinatorial Auctions: Theory and Practice
David C. Parkes,Lyle H. Ungar +1 more
TL;DR: iBundle is introduced, the first iterative combinatorial auction that is optimal for a reasonable agent bidding strategy, in this case myopic best-response bidding, and its optimality is proved with a novel connection to primal-dual optimization theory.
Research priorities for robust and beneficial artificial intelligence
Stuart Russell,Daniel Dewey,Max Tegmar,Anthony Aguirre,Erik Brynjolfsson,Ryan Calo,Thomas G. Dietterich,Dileep George,Bill Hibbard,Demis Hassabis,Eric Horvitz,Leslie Pack Kaelbling,James Manyika,Luke Muehlhauser,Michael Osborne,David C. Parkes,Heather R. Perkins,Francesca Rossi,Bart Selman,Murray Shanahan +19 more
TL;DR: This article gives numerous examples of worthwhile research aimed at ensuring that AI remains robust and beneficial.
Journal ArticleDOI
Computational-mechanism design: a call to arms
TL;DR: This work states that game theory has developed powerful tools for analyzing, predicting, and controlling the behavior of self-interested agents and decision making in systems with multiple autonomous actors provide a foundation for building multiagent software systems.