Fast Core Pricing for Rich Advertising Auctions
Rad Niazadeh, Jason Hartline, Nicole Immorlica, Mohammad Reza Khani,, Brendan Lucier

TL;DR
This paper introduces a fast algorithm for core pricing in complex ad auctions, significantly increasing revenue and enabling practical real-time implementation in online advertising.
Contribution
It presents a novel, efficient combinatorial algorithm for approximate bidder optimal core points with theoretical guarantees, improving revenue in rich ad auction settings.
Findings
Core pricing yields 26% more revenue than VCG.
Algorithm is faster than previous heuristics.
Core pricing approaches GSP revenue levels.
Abstract
Standard ad auction formats do not immediately extend to settings where multiple size configurations and layouts are available to advertisers. In these settings, the sale of web advertising space increasingly resembles a combinatorial auction with complementarities, where truthful auctions such as the Vickrey-Clarke-Groves (VCG) can yield unacceptably low revenue. We therefore study core selecting auctions, which boost revenue by setting payments so that no group of agents, including the auctioneer, can jointly improve their utilities by switching to a different outcome. Our main result is a combinatorial algorithm that finds an approximate bidder optimal core point with almost linear number of calls to the welfare maximization oracle. Our algorithm is faster than previously-proposed heuristics in the literature and has theoretical guarantees. We conclude that core pricing is…
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Taxonomy
TopicsAuction Theory and Applications · Consumer Market Behavior and Pricing · Digital Platforms and Economics
