Computationally Manageable Combinational Auctions
Management Science
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Winner determination in combinatorial auction generalizations
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Expressive negotiation over donations to charities
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
ICE: an iterative combinatorial exchange
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Combinatorial Auctions
On the complexity of combinatorial auctions: structured item graphs and hypertree decomposition
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Combinatorial auctions with tractable winner determination
ACM SIGecom Exchanges
Charity auctions on social networks
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Externalities in online advertising
Proceedings of the 17th international conference on World Wide Web
A Cascade Model for Externalities in Sponsored Search
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Sponsored Search Auctions with Markovian Users
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
On the Equilibria and Efficiency of the GSP Mechanism in Keyword Auctions with Externalities
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Approximate mechanism design without money
Proceedings of the 10th ACM conference on Electronic commerce
Combinatorial auctions with structured item graphs
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Combinatorial auctions with k-wise dependent valuations
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A theory of expressiveness in mechanisms
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Taming the computational complexity of combinatorial auctions: optimal and approximate approaches
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Bidding languages and winner determination for mixed multi-unit combinatorial auctions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Achieving budget-balance with Vickrey-based payment schemes in exchanges
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Making decisions based on the preferences of multiple agents
Communications of the ACM
Northern exposure: a field experiment measuring externalities between search advertisements
Proceedings of the 11th ACM conference on Electronic commerce
Combinatorial auctions with externalities
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Money for nothing: exploiting negative externalities
Proceedings of the 12th ACM conference on Electronic commerce
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When a decision must be made based on the preferences of multiple agents, and the space of possible outcomes is combinatorial in nature, it becomes necessary to think about how preferences should be represented, and how this affects the complexity of finding an optimal (or at least a good) outcome. We study settings with externalities, where each agent controls one or more variables, and how these variables are set affects not only the agent herself, but also potentially the other agents. For example, one agent may decide to reduce her pollution, which will come at a cost to herself, but will result in a benefit for all other agents. We formalize how to represent such domains and show that in a number of key special cases, it is NP-complete to determine whether there exists a nontrivial feasible solution (and therefore the maximum social welfare is completely inapproximable). However, for one important special case, we give an algorithm that converges to the solution with the maximal concession by each agent (in a linear number of rounds for utility functions that additively decompose into piecewise constant functions). Maximizing social welfare, however, remains NP-hard even in this setting. We also demonstrate a special case that can be solved in polynomial time using linear programming.