Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
k-order additive discrete fuzzy measures and their representation
Fuzzy Sets and Systems - Special issue on fuzzy measures and integrals
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
Contract Type Sequencing for Reallocative Negotiation
ICDCS '00 Proceedings of the The 20th International Conference on Distributed Computing Systems ( ICDCS 2000)
Logical Preference Representation and Combinatorial Vote
Annals of Mathematics and Artificial Intelligence
Combinatorial Auctions
Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics)
How equitable is rational negotiation?
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Extremal behaviour in multiagent contract negotiation
Journal of Artificial Intelligence Research
Negotiating socially optimal allocations of resources
Journal of Artificial Intelligence Research
The complexity of contract negotiation
Artificial Intelligence
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In distributed approaches to multiagent resource allocation, the agents belonging to a society negotiate deals in small groups at a local level, driven only by their own rational interests. We can then observe and study the effects such negotiation has at the societal level, for instance in terms of the economic efficiency of the emerging allocations. Such effects may be studied either using theoretical tools or by means of simulation. In this paper, we present a new simulation platform that can be used to compare the effects of different negotiation policies and we report on initial experiments aimed at gaining a deeper understanding of the dynamics of distributed multiagent resource allocation.