We study decentralized markets for goods whose utility perishes in time, with compute as a primary motivation. Recent advances in reproducible and verifiable execution allow jobs to pause, verify, and resume across heterogeneous hardware, which allow us to treat compute as time indexed capacity rather than bespoke bundles. We design an automated market maker (AMM) that posts an hourly price as a concave function of load--the ratio of current demand to a "floor supply" (providers willing to work at a preset floor). This mechanism decouples price discovery from allocation and yields transparent, low latency trading. We establish existence and uniqueness of equilibrium quotes and give conditions under which the equilibrium is admissible (i.e. active supply weakly exceeds demand). To align incentives, we pair a premium sharing pool (base cost plus a pro rata share of contemporaneous surplus) with a Cheapest Feasible Matching (CFM) rule; under mild assumptions, providers optimally stake early and fully while truthfully report costs. Despite being simple and computationally efficient, we show that CFM attains bounded worst case regret relative to an optimal benchmark.


翻译:本文研究效用随时间衰减的商品去中心化市场,以计算资源为主要应用场景。近期可复现与可验证执行技术的进展使得计算任务能够在异构硬件间暂停、验证并恢复执行,这使我们能够将计算资源视为时间索引的容量而非定制化捆绑服务。我们设计了一种自动做市商(AMM)机制,该机制以负载率(当前需求与'基础供给'的比值,其中基础供给指愿意按预设底价工作的供应商)的凹函数形式发布每小时价格。该机制将价格发现与资源分配解耦,实现了透明且低延迟的交易。我们证明了均衡报价的存在性与唯一性,并给出了均衡状态可接受(即有效供给不低于需求)的条件。为激励相容,我们将溢价共享池(基础成本加同期盈余按比例分配)与最廉价可行匹配(CFM)规则相结合;在温和假设下,供应商最优策略为尽早全额质押并如实申报成本。尽管CFM规则简单且计算高效,我们证明其相对于最优基准的最坏情况遗憾具有有界性。

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