Bidding and Allocation in Combinatorial Auctions

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Bidding and Allocation in Combinatorial Auctions (2000)

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by Noam Nisan

Venue:	IN ACM CONFERENCE ON ELECTRONIC COMMERCE
Citations:	274 - 11 self

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@INPROCEEDINGS{Nisan00biddingand,
    author = {Noam Nisan},
    title = {Bidding and Allocation in Combinatorial Auctions},
    booktitle = {IN ACM CONFERENCE ON ELECTRONIC COMMERCE},
    year = {2000},
    pages = {1--12},
    publisher = {}
}

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Abstract

When an auction of multiple items is performed, it is often desirable to allow bids on combinations of items, as opposed to only on single items. Such an auction is often called "combinatorial", and the exponential number of possible combinations results in computational intractability of many aspects regarding such an auction. This paper considers two of these aspects: the bidding language and the allocation algorithm. First we consider which kinds of bids on combinations are allowed and how, i.e. in what language, they are specified. The basic tradeoff is the expressibility of the language versus its simplicity. We consider and formalize several bidding languages and compare their strengths. We prove exponential separations between the expressive power of different languages, and show that one language, "OR-bids with phantom items", can polynomially simulate the others. We then consider the problem of determining the best allocation -- a problem known to be computationally intractable. We suggest an approach based on Linear Programming (LP) and motivate it. We prove that the LP approach finds an optimal allocation if and only if prices can be attached to single items in the auction. We pinpoint several classes of auctions where this is the case, and suggest greedy and branch-and-bound heuristics based on LP for other cases.

Keyphrases

combinatorial auction single item many aspect exponential separation lp approach bidding language computational intractability allocation algorithm linear programming possible combination result several bidding language exponential number several class branch-and-bound heuristic basic tradeoff phantom item multiple item expressive power optimal allocation different language

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