In the rapidly evolving landscape of eCommerce, Artificial Intelligence (AI)
based pricing algorithms, particularly those utilizing Reinforcement Learning
(RL), are becoming increasingly prevalent. This rise has led to an inextricable
pricing situation with the potential for market collusion. Our research employs
an experimental oligopoly model of repeated price competition, systematically
varying the environment to cover scenarios from basic economic theory to
subjective consumer demand preferences. We also introduce a novel demand
framework that enables the implementation of various demand models, allowing
for a weighted blending of different models. In contrast to existing research
in this domain, we aim to investigate the strategies and emerging pricing
patterns developed by the agents, which may lead to a collusive outcome.
Furthermore, we investigate a scenario where agents cannot observe their
competitors' prices. Finally, we provide a comprehensive legal analysis across
all scenarios. Our findings indicate that RL-based AI agents converge to a
collusive state characterized by the charging of supracompetitive prices,
without necessarily requiring inter-agent communication. Implementing
alternative RL algorithms, altering the number of agents or simulation
settings, and restricting the scope of the agents' observation space does not
significantly impact the collusive market outcome behavior.

电子商务中基于人工智能的定价算法，特别是利用强化学习的算法，正变得越来越普遍。这项研究通过实验性寡头垄断模型的多次价格竞争，系统地改变环境以涵盖从基础经济理论到主观消费者需求偏好的各种情况，探究了代理人开发的策略和新兴定价模式，可能导致共谋结果。此外，我们还调查了代理无法观察到竞争对手价格的情景，并对所有情景进行了综合法律分析。我们的研究发现，基于强化学习的人工智能代理会趋于一种共谋的状态，其特点是收取超竞争价格，而无需进行代理间通信。实施其他强化学习算法，改变代理数量或模拟设置，以及限制代理的观察范围不会显著影响共谋市场的结果行为。