Skip to yearly menu bar Skip to main content


Poster

Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders

Alexey Drutsa

Virtual

Keywords: [ Game Theory and Mechanism Design ] [ Learning Theory ] [ Online Learning / Bandits ]


Abstract: We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that has strategic regret upper bound of $O(\log\log T)$ for worst-case valuations. This pricing is based on our novel transformation that upgrades an algorithm designed for the setup with a single buyer to the multi-buyer case. We provide theoretical guarantees on the ability of a transformed algorithm to learn the valuation of a strategic buyer, which has uncertainty about the future due to the presence of rivals.

Chat is not available.