Briefing
The foundational challenge in transaction fee mechanism design is the established impossibility result proving that collusion-proof systems cannot simultaneously guarantee Dominant-Strategy-Incentive-Compatibility for users and non-zero miner revenue. This research addresses the fundamental incentive misalignment by proposing a novel Transaction Fee Mechanism (TFM) that relaxes the incentive constraint to Bayesian-Nash-Incentive-Compatibility (BNIC). The core breakthrough is the introduction of an auxiliary mechanism method, which establishes a formal connection between BNIC and the stronger DSIC mechanisms, allowing the new TFM to break the zero-revenue barrier. The single most important implication is the creation of a theoretically sound economic model that guarantees strong truthfulness and collusion-proofness while providing an asymptotic constant-factor approximation of optimal miner revenue, ensuring long-term system stability and validator participation.
Context
Prior to this work, the design of reliable, long-term sustainable transaction fee mechanisms was constrained by a critical game-theoretic impossibility theorem. This theorem demonstrated that any mechanism designed to be collusion-proof → preventing miners and users from profiting from off-chain agreements → could not simultaneously achieve both Dominant-Strategy-Incentive-Compatibility (DSIC) for users and a non-zero revenue guarantee for miners. The DSIC property, which dictates that truthful bidding is a user’s best strategy regardless of what others do, was considered the gold standard for robust mechanism design. The resulting zero-revenue barrier presented a foundational dilemma for blockchain economics, as positive miner revenue is essential for network security and incentive alignment.
Analysis
The paper’s core mechanism operates by shifting the incentive framework from the absolute certainty of Dominant-Strategy-Incentive-Compatibility (DSIC) to the probabilistic guarantee of Bayesian-Nash-Incentive-Compatibility (BNIC). This means users are incentivized to bid truthfully when they assume other users are also playing their optimal, rational strategy based on a shared understanding of transaction valuation distribution. The key primitive is the auxiliary mechanism method , a formal technique that decomposes the problem to establish a direct link between the BNIC and DSIC properties.
The resulting TFM, which is constructed using the Multinomial Logit (MNL) choice model to represent user behavior, bypasses the original impossibility result. This design ensures that even with the relaxed BNIC condition, the mechanism retains collusion-proof properties and provides a provable, non-zero revenue stream to the block producer.
Parameters
- Incentive Compatibility Standard → BNIC (Bayesian-Nash-Incentive-Compatibility) – The relaxed truthfulness condition for users, requiring optimal bidding given the distribution of others’ bids.
- Revenue Guarantee Metric → Asymptotic constant-factor approximation – The mechanism’s revenue is guaranteed to be within a constant ratio of the theoretical optimal revenue as the number of users grows.
- Core Modeling Primitive → Auxiliary Mechanism Method – The innovative mathematical technique used to connect the BNIC and DSIC properties.
- User Behavior Model → Multinomial Logit (MNL) Choice Model – The economic model used to simulate user preferences and transaction inclusion probabilities.
Outlook
This research opens a new, viable avenue for the design of stable, long-term transaction fee markets across all decentralized architectures. The auxiliary mechanism method is a versatile theoretical tool that extends beyond fee allocation, offering a general framework for designing optimal BNIC mechanisms in various resource allocation problems across distributed systems. In the next three to five years, this foundational work will likely inform the next generation of fee mechanisms for Layer 1 and Layer 2 solutions, particularly those seeking to mitigate Maximal Extractable Value (MEV) by aligning validator incentives with system fairness.
Future research will focus on extending these BNIC guarantees to dynamic, non-i.i.d. (independent and identically distributed) user valuation environments.
