Predicting agent strategy mix of evolving populations
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Mechanisms for making crowds truthful
Journal of Artificial Intelligence Research
Towards the design of a robust incentive mechanism for e-marketplaces with limited inventory
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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A general assumption for incentive mechanisms is that all agents are rational and seek to maximize their utility. When some agents are irrational and launch various attacks, these mechanisms may fail to work. To address the issue of evaluating the robustness of incentive mechanisms, we propose a robustness metric in this paper. It is inspired by the studies of the evolutionary game theory and defined as the maximum percentage of irrational agents existing in the system while it is still better off for rational agents to perform desired strategies. Then a simulation framework is designed to measure the robustness of incentive mechanisms, and is verified to be able to produce the same results as those by theoretical analysis. Finally, we demonstrate the usage of our simulation framework in evaluating and comparing the robustness of two incentive mechanisms where irrational agents adopt different attacking strategics.