Revenue Maximization Envy-Free Pricing for Homogeneous Resources
Revenue Maximization Envy-Free Pricing for Homogeneous Resources
复制标题
收入最大化同质资源的无嫉妒定价
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Qiang Zhang
中科院分区：
文献类型：
--
作者：
G. Monaco;P. Sankowski;Qiang Zhang
基于定价的资源分配机制在多智能体系统中得到了广泛的研究和发展。这些研究的主要目标之一是避免代理之间的嫉妒，即保证公平分配。然而，即使是这个问题的最简单的组合情况也没有得到很好的理解。在此，我们试图填补这些空白，设计多项式收益最大化定价机制，在购买者之间以无嫉妒的方式分配同质资源。特别地，我们考虑了所有购买者效用最大化的无嫉妒结果。我们还考虑了配对无嫉妒的结果，即所有买家都更喜欢自己的分配，而不是其他代理获得的分配。对于没有嫉妒的两个概念，我们考虑了项目和捆绑定价方案。我们的研究结果清楚地展示了这两种不同的“无嫉妒”概念在收入方面的局限性和优势。
Pricing-based mechanisms have been widely studied and developed for resource allocation in multi-agent systems. One of the main goals in such studies is to avoid envy between the agents, i.e., guarantee fair allocation. However, even the simplest combinatorial cases of this problem is not well understood. Here, we try to fill these gaps and design polynomial revenue maximizing pricing mechanisms to allocate homogeneous resources among buyers in envy-free manner. In particular, we consider envy-free outcomes in which all buyers' utilities are maximized. We also consider pair envy-free outcomes in which all buyers prefer their allocations to the allocations obtained by other agents. For both notions of envy-freeness, we consider item and bundle pricing schemes. Our results clearly demonstrate the limitations and advantages in terms of revenue between these two different notions of envy-freeness.