Inducing Approximately Optimal Flow Using Truthful Mediators

Ryan M. Rogers, Aaron Roth, Jonathan Ullman, Zhiwei Steven Wu
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引用次数: 20

Abstract

We revisit a classic coordination problem from the perspective of mechanism design: how can we coordinate a social welfare maximizing flow in a network congestion game with selfish players? The classical approach, which computes tolls as a function of known demands, fails when the demands are unknown to the mechanism designer, and naively eliciting them does not necessarily yield a truthful mechanism. Instead, we introduce a weak mediator that can provide suggested routes to players and set tolls as a function of reported demands. However, players can choose to ignore or misreport their type to this mediator. Using techniques from differential privacy, we show how to design a weak mediator such that it is an asymptotic ex-post Nash equilibrium for all players to truthfully report their types to the mediator and faithfully follow its suggestion, and that when they do, they end up playing a nearly optimal flow. Notably, our solution works in settings of incomplete information even in the absence of a prior distribution on player types. Along the way, we develop new techniques for privately solving convex programs which may be of independent interest.
使用真实介质诱导近似最佳流
我们从机制设计的角度重新审视了一个经典的协调问题:在具有自私参与者的网络拥塞博弈中,我们如何协调社会福利最大化的流量?将收费作为已知需求的函数来计算的经典方法,在机制设计者不知道需求的情况下就失败了,天真地推导它们并不一定会产生真实的机制。相反,我们引入了一个弱中介,它可以为玩家提供建议的路线,并根据报告的需求设置收费。然而,玩家可以选择忽略或错误地向这个中介报告他们的类型。使用微分隐私技术,我们展示了如何设计一个弱中介,使其成为一个渐进的事后纳什均衡,所有参与者都如实向中介报告他们的类型并忠实地遵循其建议,当他们这样做时,他们最终会玩一个接近最优的流。值得注意的是,我们的解决方案适用于不完整信息的设置,即使没有玩家类型的先验分布。在此过程中,我们开发了一些新的技术,用于私下求解凸规划,这可能是独立的兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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