Bayesian Calibrated Click-Through Auction

by   Junjie Chen, et al.

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 for a click – but only affect the auction's allocation and payment rule. This makes information design effectively a (constrained) mechanism design problem. We primarily focus on the two-bidder situation, which is already notoriously challenging as demonstrated in recent works, and adopt the algorithmic lens of developing approximate algorithms. Our first result is an FPTAS to compute an approximately optimal mechanism. The design of this algorithm leverages Bayesian bidder values which help to “smooth” the seller's revenue function and lead to better tractability. Our second result seeks to design “simple” and more practical signaling schemes. When bidders' CTR distribution is symmetric, we develop a simple prior-free signaling scheme, whose construction relies on a single parameter called 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.


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