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arXiv 2211.02004cs.GTcs.DS
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具有在线物品与离线代理的真实匹配

Truthful Matching with Online Items and Offline Agents

Michal Feldman, Federico Fusco, Stefano Leonardi, Simon Mauras, Rebecca Reiffenhäuser

AI总结:

研究在线物品与离线买家场景下的二分匹配真实机制,刻画了不同代理与支付设定下的竞争比边界,并扩展了经典 $e/(e-1)$ 竞争比。

摘要:

我们研究在线二分匹配中用于最大化社会福利的真实机制。在我们的(多参数)设定中,每个买家都与一个(可能是私有的)期望物品集相关联,并且对于被分配到其期望集中的物品具有私有估值。与大多数代理在线到达的在线匹配设定不同,在我们的设定中,物品以对抗性顺序在线到达,而买家在整个过程期间始终存在。由于买家有能力在未来的轮次中进行策略操作,这给真实机制的设计带来了重大挑战。我们提供了不同场景下竞争比的全景图,包括近视与非近视代理、延迟与即时支付、以及私有与公开期望集。在诸多结果中,我们确定了边界,使得 Karp、Vazirani 和 Vazirani 提出的著名的 $e/(e-1)$ 顶点加权在线匹配竞争比能够扩展至真实代理与在线物品场景。

英文摘要:

We study truthful mechanisms for welfare maximization in online bipartite matching. In our (multi-parameter) setting, every buyer is associated with a (possibly private) desired set of items, and has a private value for being assigned an item in her desired set. Unlike most online matching settings, where agents arrive online, in our setting the items arrive online in an adversarial order while the buyers are present for the entire duration of the process. This poses a significant challenge to the design of truthful mechanisms, due to the ability of buyers to strategize over future rounds. We provide an almost full picture of the competitive ratios in different scenarios, including myopic vs. non-myopic agents, tardy vs. prompt payments, and private vs. public desired sets. Among other results, we identify the frontier for which the celebrated $e/(e-1)$ competitive ratio for the vertex-weighted online matching of Karp, Vazirani and Vazirani extends to truthful agents and online items.

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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