通胀让AI定价算法更易形成隐性合谋，监管需警惕。
Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents
- 在强化学习模型中引入通胀冲击，模拟市场动态
- 通胀下算法利润持续上升，但缺乏有效惩罚机制
- 适合关注算法合谋与宏观政策影响的研究者
算法定价正日益影响市场竞争，引发对竞争动态被削弱的担忧。尽管已有研究显示基于强化学习（RL）的定价算法可能导致隐性合谋，但宏观经济因素的作用仍较少被关注。本研究考察了通胀如何影响竞争市场中的算法合谋。通过在RL定价模型中引入通胀冲击，分析代理是否能调整策略以维持超额利润。结果表明，通胀降低了市场竞争力，促使代理间形成隐性协调，即使无直接合谋。尽管实现持续高利润，代理未能发展出可靠的惩罚机制以遏制偏离均衡策略的行为。研究揭示，通胀加剧了算法定价中的非竞争性动态，凸显在人工智能驱动定价普遍存在的市场中加强监管的重要性。
原文摘要 · Abstract (English)
Algorithmic pricing is increasingly shaping market competition, raising concerns about its potential to compromise competitive dynamics. While prior work has shown that reinforcement learning (RL)-based pricing algorithms can lead to tacit collusion, less attention has been given to the role of macroeconomic factors in shaping these dynamics. This study examines the role of inflation in influencing algorithmic collusion within competitive markets. By incorporating inflation shocks into a RL-based pricing model, we analyze whether agents adapt their strategies to sustain supra-competitive profits. Our findings indicate that inflation reduces market competitiveness by fostering implicit coordination among agents, even without direct collusion. However, despite achieving sustained higher profitability, agents fail to develop robust punishment mechanisms to deter deviations from equilibrium strategies. The results suggest that inflation amplifies non-competitive dynamics in algorithmic pricing, emphasizing the need for regulatory oversight in markets where AI-driven pricing is prevalent.
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