Bayesian Mechanism Design for Blockchain Transaction Fee Allocation
Xi Chen, David Simchi-Levi, Zishuo Zhao, Yuan Zhou

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBlockchain Technology Applications and Security · Auction Theory and Applications · Supply Chain and Inventory Management
