Crosscutting Bayesian Mechanism Design for Blockchain Transaction Fee Allocation

• 成果类型: Article
• 署名作者: Chen, Xi; Simchi-Levi, David; Zhao, Zishuo; Zhou, Yuan
• 署名单位: New York University; Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Urbana-Champaign; Tsinghua University; Tsinghua University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2024.0865
• 发表日期: 2025
• 页码: 1944-1964
• 关键词: assortment optimization; CHOICE; MODEL

摘要

In blockchain systems, the design of transaction fee mechanisms (TFMs) is essential for stability and satisfaction for both miners and users. A recent work has proven the impossibility of collusion-proof mechanisms that achieve both nonzero miner revenue and Dominant Strategy Incentive Compatibility (DSIC) for users. However, a positive miner revenue is important in practice to motivate miners. To address this challenge, we consider a Bayesian game setting and relax the DSIC requirement for users to Bayesian Nash Incentive Compatibility (BNIC). In particular, we propose an auxiliary mechanism method that makes connections between BNIC and DSIC mechanisms. With the auxiliary mechanism method, we design a TFM based on the multinomial logit (MNL) choice model, and prove that the TFM has both BNIC and collusion-proof properties with an asymptotic constant-factor approximation of optimal miner revenue for i.i.d. bounded valuations. Our result breaks the zero-revenue barrier while preserving truthfulness and collusion-proof properties.