This paper introduces the Minimum Price Markov Game (MPMG), a theoretical model that reasonably approximates real-world first-price markets following the minimum price rule, such as public auctions. The goal is to provide researchers and practitioners with a framework to study market fairness and regulation in both digitized and non-digitized public procurement processes, amid growing concerns about algorithmic collusion in online markets. Using multi-agent reinforcement learning-driven artificial agents, we demonstrate that (i) the MPMG is a reliable model for first-price market dynamics, (ii) the minimum price rule is generally resilient to non-engineered tacit coordination among rational actors, and (iii) when tacit coordination occurs, it relies heavily on self-reinforcing trends. These findings contribute to the ongoing debate about algorithmic pricing and its implications.

翻译：本文提出了最低价马尔可夫博弈（MPMG），该理论模型能够合理近似遵循最低价规则的真实世界第一价格市场，例如公开拍卖。在当前对在线市场中算法性合谋日益担忧的背景下，本研究旨在为研究人员和实践者提供一个框架，用以研究数字化与非数字化公共采购过程中的市场公平性与监管问题。通过采用多智能体强化学习驱动的人工智能体，我们证明：（i）MPMG是第一价格市场动态的可靠模型；（ii）最低价规则通常能够抵御理性参与者之间非人为设计的默示协调；（iii）当默示协调发生时，其高度依赖于自我强化的趋势。这些发现为当前关于算法定价及其影响的持续讨论提供了新的见解。