Automated Market Making for Goods with Perishable Utility
Chengqi Zang, Gabriel P. Andrade, Oğuzhan Ersoy
TL;DR
This work develops a decentralized two‑sided market for time‑bound compute with perishable utility, enabled by reproducible execution that allows time‑indexed resource units and verifiable matching. An automated market maker posts hourly quotes $P^t = f^t(\boldsymbol{\alpha}^t)$ based on load, while a pool‑sharing scheme and a Cheapest‑Feasible Matching rule ensure incentive compatibility and efficient use of capacity, yielding bounded regret relative to online benchmarks. The paper proves existence and uniqueness of equilibrium quotes, characterizes admissibility conditions, and analyzes welfare via competing matching rules (GCM/GSM) and the incentive‑compatible CFM, with regret guarantees in single‑period and robustness results in two‑provider, multi‑period settings. It also extends to incomplete information with verifiable checking and proposes floor‑price updating to maintain market health, offering a scalable, transparent mechanism for decentralized compute markets with practical implications for utilization and pricing in dynamic, heterogeneous environments.
Abstract
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.