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Methodology for Designing Reasonably Expressive Mechanisms with Application to Ad Auctions

机译:设计合理表达机制的方法论在广告拍卖中的应用

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Mechanisms (especially on the Internet) have begun allowing people or organizations to express richer preferences in order to provide for greater levels of overall satisfaction. In this paper, we develop an operational methodology for quantifying the expected gains in economic efficiency associated with different forms of expressiveness. We begin by proving that the sponsored search mechanism (GSP) used by Google, Yahoo!, MSN, etc. can be arbitrarily inefficient. We then experimentally compare its efficiency to a slightly more expressive variant (PGSP), which solicits an extra bid for a premium class of positions. We generate random preference distributions based on published industry knowledge. We determine ideal strategies for the agents using a custom tree search technique, and we also benchmark using straightforward heuristic bidding strategies. The GSP's efficiency loss is greatest in the practical case where some advertisers ("brand advertisers") prefer top positions while others ("value advertisers") prefer middle positions, and that loss can be dramatic. It is also worst when agents have small profit margins. While the PGSP is only slightly more expressive (and thus not much more cumbersome), it removes almost all of the efficiency loss in all of the settings we study.
机译:机制(尤其是在Internet上)已经开始允许人们或组织表达更丰富的偏好,以提供更高水平的总体满意度。在本文中,我们开发了一种操作方法,用于量化与不同形式的表现形式相关的经济效率的预期收益。我们首先证明Google,Yahoo!,MSN等使用的赞助搜索机制(GSP)可能会效率低下。然后,我们通过实验将其效率与更具表现力的变体(PGSP)进行比较,该变体为优质职位要求额外的出价。我们根据已发布的行业知识生成随机偏好分布。我们使用自定义树形搜索技术确定代理商的理想策略,并使用简单的启发式出价策略进行基准测试。在实际情况下,如果某些广告客户(“品牌广告客户”)偏爱最高职位,而另一些广告客户(“价值广告客户”)偏爱中间职位,则GSP的效率损失最大,这种损失可能是巨大的。当代理商的利润率很低时,这也是最糟糕的情况。尽管PGSP的表达能力稍强(因此不那么麻烦),但它消除了我们研究的所有设置中几乎所有的效率损失。

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