2019/06/21 by Alexey Drutsa, Drutsa, Alexey · 2 citations
Business, Management and Accounting · Decision Sciences · #68T05 #68W27 #91A05 #91A20 #91A26 #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Consumer Market Behavior and Pricing #F.2.2 #FOS: Computer and information sciences #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Supply Chain and Inventory Management
paper · pdf · doi:10.48550/arxiv.1906.09331
openalex publication_date 2019/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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(loglog 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.