Bayesian Mechanism Design Secures Blockchain Transaction Fee Allocation ∞ Research

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Briefing

This paper addresses the fundamental challenge in blockchain transaction fee mechanisms ∞ simultaneously achieving non-zero miner revenue and user incentive compatibility while preventing collusion. It proposes a foundational breakthrough by shifting from Dominant Strategy Incentive Compatibility (DSIC) to Bayesian-Nash Incentive Compatibility (BNIC) within a Bayesian game setting. This theoretical re-framing, coupled with an auxiliary mechanism method and a multinomial logit choice model, culminates in a transaction fee mechanism that breaks the long-standing “zero-revenue barrier” for miners, ensuring network stability and security through robust economic incentives.

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Context

Prior to this research, a significant theoretical limitation in blockchain mechanism design was the established impossibility of constructing collusion-proof transaction fee mechanisms that simultaneously guaranteed both non-zero miner revenue and Dominant Strategy Incentive Compatibility (DSIC) for users. This meant that designers faced a trade-off ∞ either ensure users had clear optimal strategies regardless of others’ actions (DSIC) or ensure miners were sufficiently incentivized, often at the cost of vulnerability to collusion or suboptimal user behavior. This dilemma posed a foundational challenge to the long-term economic stability and security of decentralized networks.

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Analysis

The core mechanism proposed by this paper is a new Transaction Fee Mechanism (TFM) rooted in Bayesian mechanism design. It fundamentally differs from previous approaches by relaxing the stringent requirement of Dominant Strategy Incentive Compatibility (DSIC) to Bayesian-Nash Incentive Compatibility (BNIC). This means users act optimally given their beliefs about others’ actions, rather than independently of them. The paper introduces an “auxiliary mechanism method” to bridge the gap between BNIC and DSIC, enabling the construction of a robust TFM.

This TFM integrates a multinomial logit (MNL) choice model, allowing it to approximate optimal miner revenue asymptotically while preserving both BNIC and collusion-proof properties. The breakthrough lies in demonstrating that by carefully modeling user beliefs and choices, a system can be designed to provide strong incentives without succumbing to the limitations of prior impossibility results.

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Parameters

Core Concept ∞ Bayesian Mechanism Design
Key Authors ∞ Xi Chen, David Simchi-Levi, Zishuo Zhao, Yuan Zhou
New Primitive ∞ Auxiliary Mechanism Method
Incentive Compatibility ∞ Bayesian-Nash Incentive Compatibility (BNIC)
Economic Model ∞ Multinomial Logit (MNL) Choice Model
Problem Addressed ∞ Zero-Revenue Barrier for Miners
Publication Venue ∞ Operations Research (2025)

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Outlook

This research opens new avenues for designing economically sustainable and secure blockchain protocols. The adoption of Bayesian mechanism design and the demonstrated ability to achieve non-zero miner revenue with truthfulness suggests future transaction fee markets could be significantly more robust. Potential real-world applications within 3-5 years include next-generation Layer 1 and Layer 2 blockchain designs that can implement more efficient and fair fee markets, reducing miner centralization risks and enhancing overall network health. Academically, it encourages further exploration into relaxing strong incentive compatibility constraints in complex distributed systems, leveraging more realistic behavioral assumptions.

This research decisively advances blockchain economics by demonstrating a practical, theoretically sound mechanism for sustainable transaction fee allocation, ensuring both miner incentives and user truthfulness.

Signal Acquired from ∞ arXiv.org