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机制，尤其是在企业间存在沟通的情况下。此类问题可建模为复杂系统问题，传统上需开展受试者实验或基于智能体建模方法进行研究。我们提出了一种名为智能主体建模（SABM）的创新框架，该框架中由GPT-4技术支持的主体代表企业并进行交互。我们通过控制实验研究了不同条件下企业的价格竞争与合谋行为。与受试者实验相比，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，展现出类人战略能力，超越了传统ABM主体。此外，智能主体可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工具。研究结果表明：在无沟通条件下，智能主体始终达成默契合谋，使价格收敛至高于伯特兰均衡价格但低于垄断或卡特尔价格的水平；允许沟通时，智能主体能实现更高水平的合谋，价格接近卡特尔水平。有沟通时合谋形成速度更快，而无沟通时价格收敛过程更平滑。这些结果表明，沟通增强了企业间的信任，促使企业频繁进行小幅价格偏离以探索更高水平的共赢机会，并降低了触发价格战的可能性。我们还为企业赋予了不同人格特征以分析行为差异，并在多种市场结构下测试了变体模型。研究结果验证了SABM的有效性与鲁棒性，并为理解竞争与合谋提供了富有洞见的启示。