2024/03/13 by Yang Cai, Cai, Yang, Yingkai Li +3
Computer Science · Decision Sciences · #Auction Theory and Applications #Blockchain Technology Applications and Security #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #FOS: Economics and business #Privacy-Preserving Technologies in Data #Theoretical Economics (econ.TH)
paper · pdf · doi:10.48550/arxiv.2403.08145
openalex publication_date 2024/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper studies a joint design problem where a seller can design both the signal structures for the agents to learn their values, and the allocation and payment rules for selling the item. In his seminal work, Myerson (1981) shows how to design the optimal auction with exogenous signals. We show that the problem becomes NP-hard when the seller also has the ability to design the signal structures. Our main result is a polynomial-time approximation scheme (PTAS) for computing the optimal joint design with at most an ε multiplicative loss in expected revenue. Moreover, we show that in our joint design problem, the seller can significantly reduce the information rent of the agents by providing partial information, which ensures a revenue that is at least 1 - (1)/(e) of the optimal welfare for all valuation distributions.