Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders

Alexey Drutsa


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

[ Abstract ]
[ Slides
Wed 15 Jul noon PDT — 12:45 p.m. PDT
Thu 16 Jul 1 a.m. PDT — 1:45 a.m. PDT

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.

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