In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile.

翻译：在本文中,我们提出并评估了设计真实拍卖的总体框架,以便在一个动态的双面市场中为匹配代理人设计匹配代理人。一种单一的商品,如一种资源或任务,由多个随着时间的推移而来和离开的买主和卖主购买和出售。我们的算法链提供了第一个框架,允许从真实的、单一阶段(即静态的)双重拍卖规则中构建真实的动态双重拍卖(DA)。链条结构的定价和匹配方法在采用同一建筑块的动态拍卖规则中是独一无二的。我们实验性地研究了在根据各种单一时期规则,包括卡通式麦卡菲双拍卖规则同时出现时,链条的配给效率。对于基线而言,我们还考虑到由零智能加”式学习代理人组成的非真实性的双重拍卖(DA)。与其他计划相比,基于链条的拍卖运作良好,特别是由于到达强度下降和代理人估值变得更加不稳定。