Decentralized cryptocurrencies are payment systems that rely on aligning the incentives of users and miners to operate correctly and offer a high quality of service to users. Recent literature studies the mechanism design problem of the auction serving as a cryptocurrency's transaction fee mechanism (TFM). We find that a non-myopic modelling of miners falls close to another well-known problem: that of online buffer management for packet switching. The main difference is that unlike packets which are of a fixed size throughout their lifetime, in a financial environment, user preferences (and therefore revenue extraction) may be time-dependent. We study the competitive ratio guarantees given a certain discount rate, and show how existing methods from packet scheduling, which we call "the undiscounted case", perform suboptimally in the more general discounted setting. Most notably, we find a novel, simple, memoryless, and optimal deterministic algorithm for the semi-myopic case, when the discount factor is up to ~0.770018. We also present a randomized algorithm that achieves better performance than the best possible deterministic algorithm, for any discount rate.

翻译：去中心化加密货币是依赖用户与矿工激励一致性以实现正确运行并提供高质量服务的支付系统。近期文献聚焦于作为加密货币交易费用机制（TFM）的拍卖机制设计问题。我们发现，矿工的非短视建模与另一经典问题——数据包交换中的在线缓冲区管理——高度相似。其核心区别在于：在金融环境中，用户偏好（进而影响收入抽取）可能存在时间依赖性，而数据包在其生命周期内具有固定大小。本文研究了给定折扣率下的竞争比保证，并揭示了来自数据包调度的现有方法（即"无折扣情形"）在更具一般性的折扣设定中表现欠佳。值得关注的是，我们针对贴现因子不超过~0.770018的半短视情形，提出了一种新颖、简洁、无记忆且最优的确定性算法。此外，我们针对任意折扣率，给出了一种性能超越最优确定性算法的随机化算法。