Firm competition and collusion involve complex dynamics, particularly when considering communication among firms. Such issues can be modeled as problems of complex systems, traditionally approached through experiments involving human subjects or agent-based modeling methods. We propose an innovative framework called Smart Agent-Based Modeling (SABM), wherein smart agents, supported by GPT-4 technologies, represent firms, and interact with one another. We conducted a controlled experiment to study firm price competition and collusion behaviors under various conditions. SABM is more cost-effective and flexible compared to conducting experiments with human subjects. Smart agents possess an extensive knowledge base for decision-making and exhibit human-like strategic abilities, surpassing traditional ABM agents. Furthermore, smart agents can simulate human conversation and be personalized, making them ideal for studying complex situations involving communication. Our results demonstrate that, in the absence of communication, smart agents consistently reach tacit collusion, leading to prices converging at levels higher than the Bertrand equilibrium price but lower than monopoly or cartel prices. When communication is allowed, smart agents achieve a higher-level collusion with prices close to cartel prices. Collusion forms more quickly with communication, while price convergence is smoother without it. These results indicate that communication enhances trust between firms, encouraging frequent small price deviations to explore opportunities for a higher-level win-win situation and reducing the likelihood of triggering a price war. We also assigned different personas to firms to analyze behavioral differences and tested variant models under diverse market structures. The findings showcase the effectiveness and robustness of SABM and provide intriguing insights into competition and collusion.

翻译：企业竞争与合谋涉及复杂动态，尤其是在考虑企业间沟通时。这类问题可建模为复杂系统问题，传统上通过人类受试者实验或基于主体的建模方法进行研究。我们提出了一种名为“智能主体建模”（SABM）的创新框架，其中由 GPT-4 技术支持的智能主体代表企业并相互交互。我们开展了一项受控实验，研究企业在不同条件下的价格竞争与合谋行为。与使用人类受试者进行实验相比，SABM 更具成本效益和灵活性。智能主体拥有广泛的决策知识库，并展现出类似人类的战略能力，超越了传统的基于主体的建模主体。此外，智能主体能够模拟人类对话并实现个性化，使其成为研究涉及沟通的复杂情境的理想选择。我们的结果表明，在没有沟通的情况下，智能主体始终达成默契合谋，导致价格收敛于高于伯特兰均衡价格但低于垄断或卡特尔价格的水平。当允许沟通时，智能主体实现更高水平的合谋，价格接近卡特尔价格。有沟通时合谋形成更快，而无沟通时价格收敛更平滑。这些结果表明，沟通增强了企业间的信任，鼓励频繁的小幅价格偏离以探索更高水平双赢局面的机会，并降低了触发价格战的可能性。我们还为企业分配了不同角色以分析行为差异，并在多样化的市场结构下测试了变体模型。研究结果展示了 SABM 的有效性和稳健性，并为竞争与合谋提供了有趣的见解。