发表机构
Northwestern University（西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文用多智能体强化学习研究共享频谱市场中需求未知时的竞争，考察价格与数量竞争模式下是否出现默契合谋，为无线市场设计提供启示。
AI 中文摘要
本文研究了使用共享频谱为客户提供服务的无线服务提供商（SP）之间的市场竞争。先前的工作通过具有拥塞资源的竞争模型分析了此类市场，既捕捉了无线频谱的拥塞敏感特性，也捕捉了频谱共享对服务质量的影响。这些模型通常假设市场需求函数是已知的，使得SP能够在伯特兰或古诺竞争下优化定价或数量决策。相比之下，我们考虑需求函数最初未知且必须随时间学习的环境。我们使用多智能体强化学习（MARL）对此学习过程进行建模，允许竞争的SP在学习市场动态的同时调整其竞争策略。尽管MARL在各种经济环境中表现出强大的性能，但近期研究表明，它也可能在自私智能体之间引发默契合谋。因此，我们考察类似的合谋行为是否会在共享频谱市场中出现，以及其普遍性如何取决于竞争模式（价格与数量）和MARL算法的选择。我们的结果为学习动态、市场结构和频谱共享之间的相互作用提供了见解，对无线市场设计以及支持学习的决策系统的部署具有重要意义。
英文摘要
This paper investigates market competition among wireless service providers (SPs) that serve customers using shared spectrum. Prior work has analyzed such markets through models of competition with congestible resources, capturing both the congestion-sensitive nature of wireless spectrum and the effects of spectrum sharing on service quality. These models typically assume that the market demand function is known, enabling SPs to optimize pricing or quantity decisions under either Bertrand or Cournot competition. In contrast, we consider a setting in which the demand function is initially unknown and must be learned over time. We model this learning process using multi-agent reinforcement learning (MARL), allowing competing SPs to learn market dynamics while adapting their competitive strategies. Although MARL has shown strong performance in a variety of economic settings, recent work has demonstrated that it can also give rise to tacit collusion among self-interested agents. We therefore examine whether similar collusive behavior emerges in shared-spectrum markets and how its prevalence depends on the mode of competition (price versus quantity) and the choice of MARL algorithm. Our results provide insight into the interaction between learning dynamics, market structure, and spectrum sharing, with implications for both wireless market design and the deployment of learning-enabled decision-making systems.
