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JCSS
2008
138views more  JCSS 2008»
14 years 9 months ago
Reducing mechanism design to algorithm design via machine learning
We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a broad class of re...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin...
WINE
2009
Springer
93views Economy» more  WINE 2009»
15 years 4 months ago
On Stackelberg Pricing with Computationally Bounded Consumers
In a Stackelberg pricing game a leader aims to set prices on a subset of a given collection of items, such as to maximize her revenue from a follower purchasing a feasible subset o...
Patrick Briest, Martin Hoefer, Luciano Gualà...
75
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SODA
2003
ACM
132views Algorithms» more  SODA 2003»
14 years 10 months ago
Online learning in online auctions
We consider the problem of revenue maximization in online auctions, that is, auctions in which bids are received and dealt with one-by-one. In this note, we demonstrate that resul...
Avrim Blum, Vijay Kumar, Atri Rudra, Felix Wu
NIPS
2004
14 years 10 months ago
Approximately Efficient Online Mechanism Design
Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make valu...
David C. Parkes, Satinder P. Singh, Dimah Yanovsky
STOC
2010
ACM
194views Algorithms» more  STOC 2010»
15 years 2 months ago
Multi-parameter mechanism design and sequential posted pricing
We study the classic mathematical economics problem of Bayesian optimal mechanism design where a principal aims to optimize expected revenue when allocating resources to self-inte...
Shuchi Chawla, Jason Hartline, David Malec and Bal...