Reducing mechanism design to algorithm design via machine learning

DOI10.1016/j.jcss.2007.08.002zbMath1157.68055OpenAlexW2075208090MaRDI QIDQ959896

Maria-Florina Balcan, Yishay Mansour, Jason D. Hartline, Avrim L. Blum

Publication date: 12 December 2008

Published in: Journal of Computer and System Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcss.2007.08.002

zbMATH Keywords

mechanism designmachine learningcovering numbersprofit maximizationstructural risk minimizationcombinatorial auctionssample complexityattribute auctionsdigital good auctionunlimited supply

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Microeconomic theory (price theory and economic markets) (91B24) Auctions, bargaining, bidding and selling, and other market models (91B26)

Related Items (16)

The complexity of optimal multidimensional pricing for a unit-demand buyer ⋮ Stackelberg network pricing games ⋮ Prior-free multi-unit auctions with ordered bidders ⋮ Incentive compatible regression learning ⋮ Making the Most of Your Samples ⋮ On stackelberg pricing with computationally bounded customers ⋮ Optimal bundle pricing with monotonicity constraint ⋮ Envy freedom and prior-free mechanism design ⋮ Full surplus extraction from samples ⋮ Competitive auctions ⋮ Automated Design of Revenue-Maximizing Combinatorial Auctions ⋮ Unnamed Item ⋮ Incentive-Compatible Learning of Reserve Prices for Repeated Auctions ⋮ Selling multiple correlated goods: revenue maximization and menu-size complexity ⋮ Adversarial behavior in network games ⋮ Learning in Repeated Auctions

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