Bayesian Mechanism Design for Blockchain Transaction Fee Allocation

Reliable Blockchain Transaction Fee Mechanisms with Revenue Guarantees In the paper “Bayesian Mechanism Design for Blockchain Transaction Fee Allocation” by Xi Chen, David Simchi-Levi, Zishuo Zhao, and Yuan Zhou, the authors address the critical challenge of designing reliable blockchain transaction fee mechanisms (TFMs) that ensure truthfulness, collusion-proofness, and desirable miner revenues. By proposing an innovative “auxiliary mechanism method,” the authors establish connections between Bayesian Nash Incentive Compatible (BNIC) and Dominant Strategy Incentive Compatible (DSIC) mechanisms through an “auxiliary-variation decomposition.” This breakthrough allows the proposed mechanism to bypass previous limitations in TFM design via the Bayesian game setting, achieving constant-ratio approximations of optimal miner revenues while providing strong incentive guarantees. The research not only advances the theoretical understanding of blockchain fee mechanisms but also demonstrates the potential versatility of the auxiliary mechanism method for designing and optimizing BNIC mechanisms across broader applications. This work represents a significant contribution to both blockchain economics and mechanism design, offering a pathway toward more reliable and efficient blockchain ecosystems.