Pacing Equilibrium in First Price Auction Markets

Author:

Conitzer Vincent12ORCID,Kroer Christian34ORCID,Panigrahi Debmalya2,Schrijvers Okke3,Stier-Moses Nicolas E.3ORCID,Sodomka Eric3,Wilkens Christopher A.5

Affiliation:

1. Econorithms LLC, Chapel Hill, North Carolina 27517;

2. Computer Science Department, Duke University, Durham, North Carolina 27708;

3. Core Data Science, Meta, Menlo Park, California 94025;

4. Industrial Engineering and Operations Research Department, Columbia University, New York, New York 10027;

5. Tremor Technologies, Boston, Massachusetts 02110

Abstract

Mature internet advertising platforms offer high-level campaign management tools to help advertisers run their campaigns, often abstracting away the intricacies of how each ad is placed and focusing on aggregate metrics of interest to advertisers. On such platforms, advertisers often participate in auctions through a proxy bidder, so the standard incentive analyses that are common in the literature do not apply directly. In this paper, we take the perspective of a budget management system that surfaces aggregated incentives—instead of individual auctions—and compare first and second price auctions. We show that theory offers surprising endorsement for using a first price auction to sell individual impressions. In particular, first price auctions guarantee uniqueness of the steady-state equilibrium of the budget management system, monotonicity, and other desirable properties, as well as efficient computation through the solution to the well-studied Eisenberg–Gale convex program. Contrary to what one can expect from first price auctions, we show that incentives issues are not a barrier that undermines the system. Using realistic instances generated from data collected at real-world auction platforms, we show that bidders have small regret with respect to their optimal ex post strategy, and they do not have a big incentive to misreport when they can influence equilibria directly by giving inputs strategically. Finally, budget-constrained bidders, who have significant prevalence in real-world platforms, tend to have smaller regrets. Our computations indicate that bidder budgets, pacing multipliers, and regrets all have a positive association in statistical terms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: D. Panigrahi was supported in part by the National Science Foundation [Awards CCF 1535972, CCF 1750140, and CCF 1955703]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4310 .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Strategy and Management

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. PPAD-Membership for Problems with Exact Rational Solutions: A General Approach via Convex Optimization;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10

2. Strategic Budget Selection in a Competitive Autobidding World;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10

3. Liquid Welfare Guarantees for No-Regret Learning in Sequential Budgeted Auctions;Mathematics of Operations Research;2024-05-14

4. Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study;Proceedings of the ACM Web Conference 2024;2024-05-13

5. Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints;Proceedings of the ACM Web Conference 2024;2024-05-13

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3