First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction where it is no longer optimal to bid one's private value truthfully and hard to know the others' bidding behaviors? To answer this question, we study online learning in repeated first-price auctions, and consider various scenarios involving different assumptions on the characteristics of the other bidders' bids, of the bidder's private valuation, of the feedback structure of the auction, and of the reference policies with which our bidder competes. For all of them, we characterize the essentially optimal performance and identify computationally efficient algorithms achieving it. Experimentation on first-price auction datasets from Verizon Media demonstrates the promise of our schemes relative to existing bidding algorithms.
This is based on joint work with Aaron Flores, Erik Ordentlich, Tsachy Weissman, and Zhengyuan Zhou.
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