Bayesian Mechanism Design Secures Miner Revenue with Truthful Fees ∞ Research

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Briefing

This paper addresses a critical challenge in blockchain systems ∞ designing transaction fee mechanisms that simultaneously ensure miner motivation and user truthfulness. A prior impossibility result stated that collusion-proof mechanisms could not achieve both non-zero miner revenue and Dominant-Strategy-Incentive-Compatibility (DSIC) for users. This research introduces an auxiliary mechanism method within a Bayesian game setting, relaxing the user requirement to Bayesian-Nash-Incentive-Compatibility (BNIC), to construct a new transaction fee mechanism. This mechanism guarantees positive miner revenue and collusion-proof properties, fundamentally altering the economic landscape for blockchain sustainability and participant incentives.

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Context

Before this research, a foundational theoretical limitation in blockchain mechanism design established an impossibility ∞ achieving both non-zero miner revenue and user Dominant-Strategy-Incentive-Compatibility (DSIC) within collusion-proof transaction fee mechanisms. This created a dilemma, as robust miner incentives are vital for network security and liveness, while DSIC is crucial for predictable and fair user interaction. The prevailing academic challenge centered on how to reconcile these competing objectives to foster a stable and economically viable decentralized system.

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Analysis

The core innovation lies in the proposed transaction fee mechanism (TFM), which operates under a Bayesian game setting. This TFM relaxes the strict DSIC requirement for users to Bayesian-Nash-Incentive-Compatibility (BNIC), a more practical assumption in real-world scenarios. The authors introduce an auxiliary mechanism method, creating a conceptual bridge between BNIC and DSIC properties.

Utilizing a multinomial logit (MNL) choice model, the TFM dynamically sets fees. This design ensures that both users and miners act truthfully in expectation, while providing an asymptotic constant-factor approximation of optimal miner revenue, thus breaking the previously acknowledged “zero-revenue barrier.”

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Parameters

Core Concept ∞ Bayesian Mechanism Design
New System/Protocol ∞ Multinomial Logit Transaction Fee Mechanism (MNL-TFM)
Key Properties ∞ Bayesian-Nash-Incentive-Compatibility (BNIC), Collusion-Proofness
Key Authors ∞ Chen, X. et al.
Valuation Model ∞ i.i.d. bounded valuations

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Outlook

This breakthrough in transaction fee mechanism design opens new avenues for enhancing the economic stability and security of blockchain networks. Future research will likely explore the practical implementation of Bayesian mechanisms in diverse blockchain environments, focusing on robust parameter estimation for the MNL model. This theory could unlock new models for sustainable network growth, fostering fairer and more predictable transaction markets, ultimately benefiting both protocol developers and end-users by aligning economic incentives within decentralized architectures.

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Verdict

This research represents a pivotal theoretical advancement, fundamentally reshaping the understanding of sustainable economic incentives within foundational blockchain protocols.

Signal Acquired from ∞ arXiv.org