Bounded incentives in manipulating the probabilistic serial rule
Bounded incentives in manipulating the probabilistic serial rule
复制标题
操纵概率序列规则的有限激励
DOI：
10.1016/j.jcss.2023.103491
复制
发表时间：
2024
影响因子：
1.1
通讯作者：
Huang H
中科院分区：
文献类型：
--
作者：
Huang H
概率序列机制在解决随机分配问题上的公平性和效率受到重视。然而，它缺乏真实性，这意味着只有当代理人的陈述偏好与真实偏好相匹配时，它才能很好地工作。从战略行动中获得的重大效用收益可能会导致自私的代理人操纵机制，破坏其实际采用。为了衡量潜在的操纵，我们探索了一个极端的情况下，操纵者有其他代理的报告和无限的计算资源，以找到他们的最佳策略的完整知识。我们建立了紧的激励比例界限的机制。此外，我们补充这些最坏情况下的保证进行实验，以评估代理的平均效用增益通过操纵。研究结果表明，操纵的动机是非常小的。这些结果提供了洞察机制的弹性对战略操纵，超越承认其缺乏激励兼容性。
The Probabilistic Serial mechanism is valued for its fairness and efficiency in addressing the random assignment problem. However, it lacks truthfulness, meaning it works well only when agents' stated preferences match their true ones. Significant utility gains from strategic actions may lead self-interested agents to manipulate the mechanism, undermining its practical adoption. To gauge the potential for manipulation, we explore an extreme scenario where a manipulator has complete knowledge of other agents' reports and unlimited computational resources to find their best strategy. We establish tight incentive ratio bounds of the mechanism. Furthermore, we complement these worst-case guarantees by conducting experiments to assess an agent's average utility gain through manipulation. The findings reveal that the incentive for manipulation is very small. These results offer insights into the mechanism's resilience against strategic manipulation, moving beyond the recognition of its lack of incentive compatibility.
DOI：
--
发表时间：
2013
期刊：
Games Econ. Behav.
影响因子：
--
作者：
David Hugh;Morimitsu Kurino;C. Vanberg
通讯作者：
C. Vanberg
超越随机优先级的最坏情况分析：机制设计中的平滑和平均情况近似比率
影响因子：
1
作者：
Xiaotie Deng;Yan Gao;Jie Zhang
通讯作者：
Jie Zhang
分配问题中的大小与公平性
DOI：
--
发表时间：
2015
期刊：
Games Econ. Behav.
影响因子：
--
作者：
Anna Bogomolnaia;H. Moulin
通讯作者：
H. Moulin