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Paul Milgrom, R. Weber (1982)
A theory of auctions and competitive biddingEconometrica, 50
M. Saar-Tsechansky, F. Provost (2001)
Active Learning for Class Probability Estimation and Ranking
R. McAfee, Daniel Vincent (1993)
The Declining Price AnomalyJournal of Economic Theory, 60
L. Kaelbling (1993)
Learning in embedded systems
Vincent Conitzer, T. Sandholm (2004)
Self-interested automated mechanism design and implications for optimal combinatorial auctions
M. Rothschild (1974)
A two-armed bandit theory of market pricingJournal of Economic Theory, 9
A. Byde (2003)
Applying evolutionary game theory to auction mechanism designEEE International Conference on E-Commerce, 2003. CEC 2003.
D. Parkes, L. Ungar (2001)
Iterative combinatorial auctions: achieving economic and computational efficiency
Chris Jones, F. Menezes, F. Vella (2004)
Auction Price Anomalies: Evidence from Wool Auctions in AustraliaCapital Markets: Market Microstructure eJournal
M. Saar-Tsechansky, F. Provost (2004)
Active Sampling for Class Probability Estimation and RankingMachine Learning, 54
P. Cramton (1997)
The FCC Spectrum Auctions: An Early AssessmentJournal of Economics and Management Strategy, 6
Peter Wurman, Michael Wellman, W. Walsh (2001)
A Parametrization of the Auction Design SpaceGames Econ. Behav., 35
N. Fehr (1994)
PREDATORY BIDDING IN SEQUENTIAL AUCTIONS, 46
D. Cliff (2002)
Evolution of market mechanism through a continuous space of auction-typesProceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2
R. Weber (1997)
Making More from Less: Strategic Demand Reduction in the FCC Spectrum AuctionsJournal of Economics and Management Strategy, 6
O. Ashenfelter (1989)
How Auctions Work for Wine and ArtJournal of Economic Perspectives, 3
R. Sutton, A. Barto (1998)
Reinforcement Learning: An IntroductionIEEE Trans. Neural Networks, 9
R. Sutton, A. Barto (1998)
Introduction to Reinforcement Learning
Mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, these assumptions may not hold, making bidder behavior difficult to model and complicating the design process. To address this issue, we propose a different approach to mechanism design. Instead of relying on analytic methods that require specific assumptions about bidders, our approach is to create a self-adapting mechanism that adjusts auction parameters in response to past auction results. In this paper, we describe our approach and then present an example of its implementation to illustrate its efficacy.
ACM SIGecom Exchanges – Association for Computing Machinery
Published: Apr 1, 2005
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