An Expressive Mechanism for Auctions on the Web

Paul Dütting, M. Henzinger, Ingmar Weber
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引用次数: 4

Abstract

Auctions are widely used on the Web. Applications range from sponsored search to platforms such as eBay. In these and in many other applications the auctions in use are single-/multi-item auctions with unit demand. The main drawback of standard mechanisms for this type of auctions, such as VCG and GSP, is the limited expressiveness that they offer to the bidders. The General Auction Mechanism (GAM) of Aggarwal et al. [2009] takes a first step toward addressing the problem of limited expressiveness by computing a bidder optimal, envy-free outcome for linear utility functions with identical slopes and a single discontinuity per bidder-item pair. We show that in many practical situations this does not suffice to adequately model the preferences of the bidders, and we overcome this problem by presenting the first mechanism for piecewise linear utility functions with nonidentical slopes and multiple discontinuities. Our mechanism runs in polynomial time. Like GAM it is incentive compatible for inputs that fulfill a certain nondegeneracy assumption, but our requirement is more general than the requirement of GAM. For discontinuous utility functions that are nondegenerate as well as for continuous utility functions the outcome of our mechanism is a competitive equilibrium. We also show how our mechanism can be used to compute approximately bidder optimal, envy-free outcomes for a general class of continuous utility functions via piecewise linear approximation. Finally, we prove hardness results for even more expressive settings.
网络拍卖的表达机制
拍卖在网络上被广泛使用。应用范围从赞助搜索到eBay等平台。在这些和许多其他应用中,使用的拍卖是具有单位需求的单/多项目拍卖。这类拍卖的标准机制(如VCG和GSP)的主要缺点是它们提供给竞标者的表达能力有限。Aggarwal等人[2009]的一般拍卖机制(GAM)通过计算具有相同斜率和每个投标人-物品对的单一不连续的线性效用函数的投标人最优、无嫉妒的结果,向解决有限表达性问题迈出了第一步。我们表明,在许多实际情况下,这不足以充分模拟竞标者的偏好,我们通过提出具有不相等斜率和多个不连续的分段线性效用函数的第一种机制来克服这个问题。我们的机制在多项式时间内运行。与GAM一样,它对满足一定非简并性假设的输入是激励相容的,但我们的要求比GAM的要求更一般。对于非退化的不连续效用函数和连续效用函数,我们的机制的结果是一个竞争均衡。我们还展示了我们的机制如何通过分段线性逼近来计算一般类型的连续效用函数的近似竞价最优、无嫉妒结果。最后,我们证明了更有表现力的设置的硬度结果。
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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