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arXiv 1104.2872cs.GTcs.DM
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基于稳定匹配的无货币机制设计

Mechanism Design without Money via Stable Matching

Ning Chen, Nick Gravin, Pinyan Lu

摘要:

没有金钱的机制设计在社会选择文献中有着丰富的历史。由于Gibbard和Satterthwaite的强不可能定理,探索存在主导策略机制的领域是该领域的核心问题之一。我们提出了一个通用的框架,称为广义装箱问题(\gpp),研究机制设计问题没有支付。\gpp \具有丰富的结构,并包括许多作为特殊情况的经过充分研究的模型,例如,拟阵、匹配、背包、独立集和广义分配问题。我们采用近似机制设计的议程,其目标是设计一个真实的(或strategyproof)的机制,没有钱,可以在多项式时间内实现,并产生一个很好的近似社会最优解。研究了\gpp的几种特殊情况,给出了拟阵、匹配、背包和广义指派问题的常数逼近机制。我们对广义指派问题的结果解决了\cite{DG10}中提出的一个公开问题。我们的主要技术贡献是利用稳定匹配的方法,这是一个基本的解决方案的概念,在匹配市场的背景下,在应用到机制设计。稳定匹配,虽然概念上很简单,但提供了一套强大的工具来管理和分析参与代理的自利行为。我们的机制使用一个稳定的匹配算法作为一个关键组成部分,并采用其他方法,如随机抽样和在线机制。我们的工作也丰富了稳定匹配理论与一个新的背包约束匹配模型。

英文摘要:

Mechanism design without money has a rich history in social choice literature. Due to the strong impossibility theorem by Gibbard and Satterthwaite, exploring domains in which there exist dominant strategy mechanisms is one of the central questions in the field. We propose a general framework, called the generalized packing problem (\gpp), to study the mechanism design questions without payment. The \gpp\ possesses a rich structure and comprises a number of well-studied models as special cases, including, e.g., matroid, matching, knapsack, independent set, and the generalized assignment problem. We adopt the agenda of approximate mechanism design where the objective is to design a truthful (or strategyproof) mechanism without money that can be implemented in polynomial time and yields a good approximation to the socially optimal solution. We study several special cases of \gpp, and give constant approximation mechanisms for matroid, matching, knapsack, and the generalized assignment problem. Our result for generalized assignment problem solves an open problem proposed in \cite{DG10}. Our main technical contribution is in exploitation of the approaches from stable matching, which is a fundamental solution concept in the context of matching marketplaces, in application to mechanism design. Stable matching, while conceptually simple, provides a set of powerful tools to manage and analyze self-interested behaviors of participating agents. Our mechanism uses a stable matching algorithm as a critical component and adopts other approaches like random sampling and online mechanisms. Our work also enriches the stable matching theory with a new knapsack constrained matching model.

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。

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