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的动态机制，尤其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，传统上通过人类受试者实验或基于智能体的建模方法进行研究。我们提出了一种创新框架——智能体建模（SABM），其中由GPT-4技术支持的智能体代表企业并相互交互。我们开展了受控实验，研究企业在不同条件下的价格竞争与合谋行为。与人类受试者实验相比，SABM更具成本效益和灵活性。智能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ABM智能体。此外，智能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工具。我们的结果表明，在缺乏沟通的情况下，智能体持续达成默契合谋，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，智能体实现更高层次的合谋，价格接近卡特尔水平。合谋在有沟通时形成更快，而价格收敛在无沟通时更平滑。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移以探索更高层次共赢机会，并降低触发价格战的可能性。我们还为企业分配了不同人格角色以分析行为差异，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洞见。