Fairness in Multi-Agent Sequential Decision-Making
Author(s)Zhang, Chongjie; Shah, Julie A
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We define a fairness solution criterion for multi-agent decision-making problems, where agents have local interests. This new criterion aims to maximize the worst performance of agents with consideration on the overall performance. We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy. This game-theoretic approach formulates this fairness optimization as a two-player, zero-sum game and employs an iterative algorithm for finding a Nash equilibrium, corresponding to an optimal fairness policy. We scale up this approach by exploiting problem structure and value function approximation. Our experiments on resource allocation problems show that this fairness criterion provides a more favorable solution than the utilitarian criterion, and that our game-theoretic approach is significantly faster than linear programming.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Neural Information Processing Systems Foundation Inc.
Zhang, Chongjie and Shah, Julie A. "Fairness in Multi-Agent Sequential Decision-Making." Advances in Neural Information Processing Systems 27 (NIPS 2014), December 8-13 2014 Palais des Congrès de Montréal, Montréal, Canada, Neural Information Processing Systems Foundation Inc., December 2014 © 2014 Neural Information Processing Systems Foundation Inc.
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