Learning Market Competition in Shared Spectrum: A Multi-Agent Reinforcement Learning Approach

2026-09-15

AI Summary

本文研究了无线服务提供商在共享频谱市场中的竞争问题，通过多智能体强化学习方法来学习未知的市场需求函数，并探讨了该方法可能引发的默契合谋行为。

Abstract

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.

Problem

• Shared Spectrum
• Market Competition
• Multi-Agent Reinforcement Learning
• Demand Function
• Collusion

Innovation

• Multi-Agent Reinforcement Learning
• Shared Spectrum
• Market Competition
• Demand Function Learning
• Tacit Collusion