Computationally Manageable Combinational Auctions
Management Science
Bidding and allocation in combinatorial auctions
Proceedings of the 2nd ACM conference on Electronic commerce
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
Solving Combinatorial Auctions Using Stochastic Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Solving concisely expressed combinatorial auction problems
Eighteenth national conference on Artificial intelligence
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Combinatorial Auctions
A Branch-and-Price Algorithm and New Test Problems for Spectrum Auctions
Management Science
Bidding languages for combinatorial auctions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Algorithms for Recognizing Economic Properties in Matrix Bid Combinatorial Auctions
INFORMS Journal on Computing
Algorithms for Recognizing Economic Properties in Matrix Bid Combinatorial Auctions
INFORMS Journal on Computing
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In a combinational auction in which bidders can bid on any combination of goods, bid data can be of exponential size. We describe an innovative new combinatorial auction format in which bidders submit “matrix bids.” The advantage of this approach is that it provides bidders a mechanism to compactly express bids on every possible bundle. We describe many different types of preferences that can be modeled using a matrix bid, which is quite flexible, supporting additive, subadditive, and superadditive preferences simultaneously. To utilize the compactness of the matrix bid format in a more general preference environment, we describe a logical language with matrix bids as “atoms” and show that matrix bids compactly express preferences that require an exponential number of atoms in other bidding languages and are as expressive as the most sophisticated languages in the literature. We model the NP-hard winner-determination problem as a polynomially sized integer program, specifically an assignment problem with side constraints. We show the strength of this formulation with which we rapidly solve winner-determination problems with 72 unique items, indicating that this model may be well suited for practical implementation.