On the complexity of cooperative solution concepts
Mathematics of Operations Research
An automated meeting scheduling system that utilizes user preferences
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Voting for movies: the anatomy of a recommender system
Proceedings of the third annual conference on Autonomous Agents
NP-completeness for calculating power indices of weighted majority games
Theoretical Computer Science
A heuristic technique for multi-agent planning
Annals of Mathematics and Artificial Intelligence
Junta distributions and the average-case complexity of manipulating elections
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Anyone but him: the complexity of precluding an alternative
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Power and stability in connectivity games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
A tractable and expressive class of marginal contribution nets and its applications
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Power in threshold network flow games
Autonomous Agents and Multi-Agent Systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Power Indices in Spanning Connectivity Games
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Variable Influences in Conjunctive Normal Forms
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Solving coalitional resource games
Artificial Intelligence
Simple coalitional games with beliefs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Approximating power indices: theoretical and empirical analysis
Autonomous Agents and Multi-Agent Systems
Honor among thieves: collusion in multi-unit auctions
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Collusion in VCG path procurement auctions
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Cooperative games with overlapping coalitions
Journal of Artificial Intelligence Research
Mechanisms for multi-level marketing
Proceedings of the 12th ACM conference on Electronic commerce
The least-core of threshold network flow games
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
Bribery in path-disruption games
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
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Preference aggregation is used in a variety of multiagent applications, and as a result, voting theory has become an important topic in multiagent system research. However, power indices (which reflect how much "real power" a voter has in a weighted voting system) have received relatively little attention, although they have long been studied in political science and economics. The Banzhaf power index is one of the most popular; it is also well-defined for any simple coalitional game. In this paper, we examine the computational complexity of calculating the Banzhaf power index within a particular multiagent domain, a network flow game. Agents control the edges of a graph; a coalition wins if it can send a flow of a given size from a source vertex to a target vertex. The relative power of each edge/agent reflects its significance in enabling such a flow, and in real-world networks could be used, for example, to allocate resources for maintaining parts of the network. We show that calculating the Banzhaf power index of each agent in this network flow domain is #P-complete. We also show that for some restricted network flow domains there exists a polynomial algorithm to calculate agents' Banzhaf power indices.