Bayesian Mechanism Design for Blockchain Transaction Fee Allocation

TL;DR

This paper introduces a Bayesian mechanism design for blockchain transaction fees, achieving collusion-proofness and positive miner revenue by relaxing incentive constraints and using an auxiliary mechanism approach.

Contribution

It proposes a novel Bayesian game framework and a multinomial logit-based transaction fee mechanism that breaks the zero-revenue barrier while maintaining truthfulness and collusion-proofness.

Findings

The proposed TFM achieves asymptotic constant-factor approximation of optimal miner revenue.

The mechanism is both BNIC and collusion-proof.

It overcomes the impossibility of collusion-proof, revenue-positive mechanisms.

Abstract

In blockchain systems, the design of transaction fee mechanisms 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 non-zero miner revenue and Dominating-Strategy-Incentive-Compatible (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 transaction fee mechanism (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.