A simulation framework for measuring robustness of incentive mechanisms and its implementation in reputation systems

• Published in: Autonomous agents and multi-agent systems 2016-07, Vol.30 (4), p.581-600
• Main Authors: Liu, Yuan, Zhang, Jie, An, Bo, Sen, Sandip
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computer Science; Computer Systems Organization and Communication Networks; Software Engineering/Programming and Operating Systems; User Interfaces and Human Computer Interaction
• ISSN: 1387-2532; 1573-7454
• DOI: 10.1007/s10458-015-9296-2

In game theoretical analysis of incentive mechanisms, all players are assumed to be rational. Since it is likely that mechanism participants in the real world may not be fully rational, such mechanisms may not work as effectively as in the idealized settings for which they were designed. Therefore, it is important to evaluate the robustness of incentive mechanisms against various types of agents with bounded rational behaviors. Such evaluations would provide us with the information needed to choose mechanisms with desired properties in real environments. In this article, we first propose a general robustness measure, inspired by research in evolutionary game theory, as the maximal percentage of invaders taking non-equilibrium strategies such that the agents sustain the desired equilibrium strategy. We then propose a simulation framework based on evolutionary dynamics to empirically evaluate the equilibrium robustness. The proposed simulation framework is validated by comparing the simulated results with the analytical predictions based on a modified simplex analysis approach. Finally, we implement the proposed simulation framework for evaluating the robustness of incentive mechanisms in reputation systems for electronic marketplaces. The results from the implementation show that the evaluated mechanisms have high robustness against a certain non-equilibrium strategy, but is vulnerable to another strategy, indicating the need for designing more robust incentive mechanisms for reputation management in e-marketplaces.