利用真中介诱导近似最优流
Inducing Approximately Optimal Flow Using Truthful Mediators
摘要：
我们从机制设计的角度重新审视了一个经典的协调问题：在一个具有自私参与者的网络拥塞博弈中，如何协调一个社会福利最大化的流量？经典的方法，计算通行费作为已知需求的函数，失败时的需求是未知的机制设计者，天真地引出他们不一定会产生一个真实的机制。相反，我们引入了一个弱中介，可以提供建议的路线，球员和设置收费作为报告的需求的函数。然而，玩家可以选择忽略或误报他们的类型。使用技术从差分隐私，我们展示了如何设计一个弱调解人，使它是一个渐近事后纳什均衡的所有球员如实报告自己的类型的调解人，并忠实地按照其建议，当他们这样做，他们最终发挥了近最优流。值得注意的是，我们的解决方案适用于不完整信息的设置，即使在没有先验分布的球员类型。在此过程中，我们开发了新的技术，私下解决凸规划，这可能是独立的利益。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
