Bayesian Mechanism Design for Blockchain Transaction Fee Allocation

Xi Chen (), David Simchi-Levi (), Zishuo Zhao () and Yuan Zhou ()
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Xi Chen: Leonard N. Stern School of Business, New York University, New York, New York 10012
David Simchi-Levi: Institute for Data, Systems and Society, Operations Research Center, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Zishuo Zhao: Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801
Yuan Zhou: Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China; and Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China; and Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China

Operations Research, 2025, vol. 73, issue 4, 1944-1964

Abstract: 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.

Keywords: Markets; Platforms; and Revenue Management; blockchain; mechanism design; Bayesian game (search for similar items in EconPapers)
Date: 2025
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