[论文解读] Online Truthful Mechanisms for Multi-sided Markets

Moran Feldman, Rica Gonen|arXiv (Cornell University)|Apr 17, 2016

Auction Theory and Applications参考文献 19被引用 5

一句话总结

本文提出了首个在线 truthful机制，适用于多方市场，同时保证真实性、持续个体理性、预算平衡以及近似交易收益最大化。该机制引入了一种新颖的动态定价方案，用户可获得最高至上限的持续支付，从而在具有信息中介的动态、注重隐私的在线广告环境中实现真实激励。

ABSTRACT

The study of mechanisms for multi-sided markets has received an increasingly growing attention from the research community, and is motivated by the numerous examples of such markets on the web and in electronic commerce. Many of these examples represent dynamic and uncertain environments, and thus, require, in fact, online mechanisms. Unfortunately, as far as we know, no previously published online mechanism for a multi-sided market (or even for a double-sided market) has managed to (approximately) maximize the gain from trade, while guaranteeing desirable economic properties such as incentivizing truthfulness, voluntary participation and avoiding budget deficit. In this work we present the first online mechanism for a multi-sided market which has the above properties. Our mechanism is designed for a market setting suggested by [Feldman and Gonen (2016)]; which is motivated by the foreseeable future form of online advertising. The online nature of our setting motivated us to define a stronger notion of individual rationality, called "continuous individual rationality", capturing the natural requirement that a player should never lose either by participating in the mechanism or by not leaving prematurely. Satisfying the requirements of continuous individual rationality, together with the other economic properties our mechanism guarantees, requires the mechanism to use a novel pricing scheme where users may be paid ongoing increments during the mechanism's execution up to a pre-known maximum value. As users rarely ever get paid in reality, this pricing scheme is new to mechanism design. Nevertheless, the principle it is based on can be observed in many common real life scenarios such as executive compensation payments and company acquisition deals. We believe both our new dynamic pricing scheme concept and our strengthened notion of individual rationality are of independent interest.

研究动机与目标

• 设计一种在线机制，用于多方市场，确保在动态、不确定环境中具备真实性、自愿参与及预算平衡。
• 解决现有机制在在线多方市场中缺乏近似最大化交易收益同时保持关键经济属性的问题。
• 提出并形式化‘持续个体理性’——一种更强的概念，确保参与者无论是否提前加入或退出，均不会受损。
• 开发一种新颖的动态定价方案，使用户可获得最高至预知上限的持续增量支付，从而在注重隐私的市场中实现真实激励。
• 将该机制应用于涉及广告商、用户和信息中介的前瞻性在线广告模型，增强用户对数据共享的控制权。

提出的方法

• 该机制采用随机抽样方法，通过独立概率 r 从中介和广告商中随机选取子集，形成初始分配的核心集合。
• 引入一个关键的阈值函数 ℓ(P,B)，平衡买家估值与卖家成本，确保交易收益非负。
• 采用两阶段分配过程：首先在抽样实体上计算核心分配 S_c；其次根据核心的价值结构分配剩余实体。
• 机制通过概率 r 和 16r⁻¹·α⁻¹/³ 分别将实体随机划分为集合 T（训练）和 L（学习），以确保鲁棒性与公平性。
• 关键组件是利用集中不等式对预期交易收益进行下界分析，以控制失败概率。
• 通过保证所有玩家的效用始终非负，即使提前退出，机制确保了持续个体理性。

实验结果

研究问题

• RQ1 能否设计一种在线机制，在多方市场中同时实现真实性、持续个体理性、预算平衡以及近似交易收益最大化？
• RQ2 如何设计一种动态定价方案，使用户可获得最高至上限的持续支付，同时仍确保经济效率与激励相容性？
• RQ3 在具有隐私保护信息中介和动态到达的多方市场中，该在线机制的竞争比是多少？
• RQ4 如何确保玩家在不确定的在线环境中，无论是否提前加入或退出，均不会变得更差？
• RQ5 当机制必须在所有参与者全部到达前做出不可撤销决策时，对交易收益最大化的理论保证是什么？

主要发现

• 该机制 OPM 在预期情况下，对交易收益最大化的竞争比至少为 (1 - r - 22r⁻¹·α⁻¹/³ - 10e⁻²/α⁻¹/³)。
• 该机制保证了持续个体理性，确保任何玩家在加入或提前退出时均不会损失效用。
• 该机制是真实的，即任何参与者都无法通过虚报估值或成本而获益。
• 该机制避免预算赤字，在所有执行阶段均保持预算平衡。
• 动态定价方案使用户可获得最高至已知上限的增量支付，这是机制设计中的一个新特性。
• 分析表明，预期交易收益至少为最优离线交易收益的 (1 - r - 22r⁻¹·α⁻¹/³ - 10e⁻²/α⁻¹/³) 倍。