Best of Both Worlds Ad Contracts: Guaranteed Allocation and Price with Programmatic Efficiency

共同价值拍卖 微观经济学 投标 收入 风险厌恶(心理学) 经济 收入等值 荷兰式拍卖 拍卖理论 期望效用假设 金融经济学 财务
作者
Maxime C. Cohen,Antoine Désir,Nitish Korula,Balasubramanian Sivan
出处
期刊:Management Science [Institute for Operations Research and the Management Sciences]
卷期号:69 (7): 4027-4050
标识
DOI:10.1287/mnsc.2022.4542
摘要

Buying display ad impressions via real-time auctions comes with significant allocation and price uncertainties. We design and analyze a contract that mitigates this uncertainty risk by providing guaranteed allocation and prices while maintaining the efficiency of buying in an auction. We study how risk aversion affects the desire for guarantees and how to price a guaranteed allocation. We propose to augment the traditional auction with a programmatic purchase option (which we call a Market-Maker contract) that removes allocation and price uncertainties. Instead of participating in the auction, advertisers can secure impressions in advance at a fixed premium price offered by the Market-Maker. It is then the responsibility of the Market-Maker to procure these impressions by bidding in the auction. We model buyers as risk-averse agents and analyze the equilibrium outcome when buyers face two purchase options (auction and Market-Maker contract). We derive analytical expressions for the Market-Maker price that reveal insightful relationships with uncertainties in the auction price and buyers’ risk levels. We also show the existence of a Market-Maker price that simultaneously improves the seller’s revenue and the sum of buyers’ utilities. As a building block to our analysis, we establish the truthfulness of the multiunit auction when buyers have nonquasilinear utilities because of risk aversion. Recently, the Google’s Display & Video 360 platform started offering a product akin to Market-Maker called “Guaranteed Packages,” which was inspired by this paper. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: The authors thank Google Research for its generous support.
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