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This content will become publicly available on January 1, 2025

Title: Bayesian Calibrated Click-Through Auctions
We study information design in click-through auctions, in which the bidders/advertisers bid for winning an opportunity to show their ads but only pay for realized clicks. The payment may or may not happen, and its probability is called the click-through rate (CTR). This auction format is widely used in the industry of online advertising. Bidders have private values, whereas the seller has private information about each bidder’s CTRs. We are interested in the seller’s problem of partially revealing CTR information to maximize revenue. Information design in click-through auctions turns out to be intriguingly different from almost all previous studies in this space since any revealed information about CTRs will never affect bidders' bidding behaviors - they will always bid their true value per click - but only affect the auction’s allocation and payment rule. In some sense, this makes information design effectively a constrained mechanism design problem. Our first result is an FPTAS to compute an approximately optimal mechanism under a constant number of bidders. The design of this algorithm leverages Bayesian bidder values which help to "smooth" the seller’s revenue function and lead to better tractability. The design of this FPTAS is complex and primarily algorithmic. Our second main result pursues the design of "simple" mechanisms that are approximately optimal yet more practical. We primarily focus on the two-bidder situation, which is already notoriously challenging as demonstrated in recent works. When bidders' CTR distribution is symmetric, we develop a simple prior-free signaling scheme, whose construction relies on a parameter termed optimal signal ratio. The constructed scheme provably obtains a good approximation as long as the maximum and minimum of bidders' value density functions do not differ much.  more » « less
Award ID(s):
2303372
PAR ID:
10528617
Author(s) / Creator(s):
; ; ;
Editor(s):
Bringmann, Karl; Grohe, Martin; Puppis, Gabriele; Svensson, Ola
Publisher / Repository:
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Date Published:
Volume:
297
ISSN:
1868-8969
ISBN:
978-3-95977-322-5
Page Range / eLocation ID:
297-297
Subject(s) / Keyword(s):
information design ad auctions online advertising mechanism design Theory of computation → Algorithmic game theory and mechanism design
Format(s):
Medium: X Size: 18 pages; 2129164 bytes Other: application/pdf
Size(s):
18 pages 2129164 bytes
Right(s):
Creative Commons Attribution 4.0 International license; info:eu-repo/semantics/openAccess
Sponsoring Org:
National Science Foundation
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