Collaborative Research: CIF: Medium: An Information-Theoretic Foundation for Adaptive Bidding in First-Price Auctions

合作研究：CIF：媒介：一价拍卖中自适应出价的信息理论基础

• 批准号: 2106508
• 负责人: Zhengyuan Zhou
• 金额: $ 45万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106508&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Medium; Information

With the advent and increasing consolidation of e-commerce, digital advertising has very recently replaced traditional advertising as the main marketing force in the economy. In the past two years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads. This shift immediately motivated the intellectually challenging question of how to bid in first-price auctions, because unlike in second-price auctions, bidding one's private value truthfully is no longer optimal. Furthermore, this shift has two unique modern characteristics: 1) the auctions are occurring repeatedly at a very high frequency and the bidding decisions must be made on that (milliseconds) timescale; second, there is exchange-dependent feedback information that one can and should leverage to inform one's sequential bidding decisions. These two characteristics expose drawbacks in the existing game-theoretical approaches and call for novel and principled developments in sequential bidding. The methodological and algorithmic innovation established in this project could also potentially help various organizations with advertising needs to navigate in the new and challenging landscape of display ads bidding.The broad goal of this project is to develop a methodological framework that intelligently and adaptively leverages past information to construct bidding strategies that are both computationally and statistically efficient. This requires developing information-theoretic tools to understand the fundamental learning limits for bidding in first-price auctions, where the reward function is neither convex nor continuous but has a special structure of its own that needs to be exploited. Further, it requires developing computationally efficient bidding and private value estimation algorithms for repeated first-price auctions that could meet the demanding nature of real-time bidding and large-scale historical bidding dataset, as well as learning-theoretical tools that enable the analysis and rigorous characterization of the algorithms' performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着电子商务的出现和日益整合，数字广告最近取代了传统广告，成为经济中的主要营销力量。过去两年，数字广告行业一个特别重要的发展是在线展示广告从二价拍卖向一价拍卖的转变。这种转变立即引发了如何在第一价格拍卖中投标的智力挑战问题，因为与第二价格拍卖不同，真实地投标一个人的私人价值不再是最佳选择。此外，这种转变具有两个独特的现代特征：1）拍卖以非常高的频率重复发生，并且投标决策必须在该（毫秒）时间尺度上做出；其次，存在依赖于交易所的反馈信息，人们可以而且应该利用这些信息来为后续的投标决策提供信息。这两个特征暴露了现有博弈论方法的缺陷，并要求顺序投标中新颖且有原则的发展。该项目中建立的方法和算法创新还可能帮助有广告需求的各种组织在展示广告竞价的新的和具有挑战性的领域中导航。该项目的总体目标是开发一个方法框架，智能地、自适应地利用过去的信息来构建计算和统计上高效的竞价策略。这需要开发信息论工具来理解最高价拍卖中出价的基本学习限制，其中奖励函数既不是凸的也不是连续的，而是有一个需要利用的特殊结构。此外，它还需要开发计算效率高的投标和私人价值估计算法，用于重复的第一价格拍卖，以满足实时投标和大规模历史投标数据集的要求，以及能够对算法性能进行分析和严格表征的学习理论工具。该奖项反映了 NSF 的法定使命，并被认为值得通过使用基金会的评估来支持 智力价值和更广泛的影响审查标准。

项目成果

MEOW: A Space-Efficient Nonparametric Bid Shading Algorithm

MEOW：一种节省空间的非参数投标着色算法

• 发表时间: 2021
• 期刊: KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Wei Zhang, Brendan Kitts