DocumentCode :
2723428
Title :
Bayesian Combinatorial Auctions: Expanding Single Buyer Mechanisms to Many Buyers
Author :
Alaei, Saeed
Author_Institution :
Dept. of Comput. Sci., Univ. of Maryland, College Park, MD, USA
fYear :
2011
fDate :
22-25 Oct. 2011
Firstpage :
512
Lastpage :
521
Abstract :
For Bayesian combinatorial auctions, we present a general framework for approximately reducing the mechanism design problem for multiple buyers to the mechanism design problem for each individual buyer. Our frame- work can be applied to any setting which roughly satisfies the following assumptions: (i) the buyer´s types must be distributed independently (not necessarily identically), (ii) the objective function must be linearly separable over the set of buyers, and (iii) the supply constraints must be the only constraints involving more than one buyer. Our framework is general in the sense that it makes no explicit assumption about any of the following: (i) the buyer´s valuations (e.g., submodular, additive, etc), (ii) The distribution of types for each buyer, and (iii) the other constraints involving individual buyers (e.g., budget constraints, etc). We present two generic ra-buyer mechanisms that use 1- buyer mechanisms as black boxes. Assuming that we have an α-approximate 1-buyer mechanism for each buyer and assuming that no buyer ever needs more than 1/k of all copies of each item for some integer k ≥ 1, then our generic n- buyer mechanisms are γk · α-approximation of the optimal n-buyer mechanism, in which γk is a constant which is at least 1 - 1/√(k+3). Observe that γk is at least1/2 (for k = 1) and approaches 1 as k increases. As a byproduct of our construction, we improve a generalization of prophet inequalities. Furthermore, as applications of our main theorem, we improve several results from the literature.
Keywords :
Bayes methods; combinatorial mathematics; commerce; Bayesian combinatorial auctions; budget constraints; buyers valuation; expanding single buyer mechanisms; many buyers; mechanism design; objective function; supply constraints; Approximation methods; Bayesian methods; Benchmark testing; Mechanical factors; Pricing; Random variables; Resource management; Approximation; Bayesian Combinatorial Auction; Mechanism Design; Reduction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Foundations of Computer Science (FOCS), 2011 IEEE 52nd Annual Symposium on
Conference_Location :
Palm Springs, CA
ISSN :
0272-5428
Print_ISBN :
978-1-4577-1843-4
Type :
conf
DOI :
10.1109/FOCS.2011.90
Filename :
6108212
Link To Document :
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