基于随机收益分布的机制防策略性量化
Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions
摘要：
策略证明机制提供了鲁棒的平衡与最小的假设知识和合理性，但可能无法实现与其他理想的属性，如不确定性平衡，稳定性对联盟的偏差，计算的易处理性。在寻找最大的strategyproof机制，同时满足其他理想的属性，我们引入了一个新的指标来量化的strategyproof的机制，比较的回报分布的基础上，给出真实的报告，对strategyproof的“参考”机制，解决了问题的放松。专注于组合交流，我们证明了该指标是信息的最终均衡，简单的regretbased指标不，并可用于在线选择的有效机制。
英文摘要：
Strategyproof mechanisms provide robust equilibrium with minimal assumptions about knowledge and rationality but can be unachievable in combination with other desirable properties such as budget-balance, stability against deviations by coalitions, and computational tractability. In the search for maximally-strategyproof mechanisms that simultaneously satisfy other desirable properties, we introduce a new metric to quantify the strategyproofness of a mechanism, based on comparing the payoff distribution, given truthful reports, against that of a strategyproof "reference" mechanism that solves a problem relaxation. Focusing on combinatorial exchanges, we demonstrate that the metric is informative about the eventual equilibrium, where simple regretbased metrics are not, and can be used for online selection of an effective mechanism.
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