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：摘要：企业竞争与合谋涉及复杂动态，尤其是考虑企业间沟通时。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。我们提出一种创新框架——智能体建模（SABM），其中由GPT-4技术支撑的智能体代表企业并相互交互。我们通过受控实验研究不同条件下企业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，超越传统ABM智能体。此外，智能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。结果表明：在无沟通条件下，智能体持续达成默契合谋，价格收敛至高于伯川德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，促使频繁的小幅价格偏离以探索更高水平双赢机会，并降低触发价格战的可能性。我们还为企业分配不同人格以分析行为差异，并在多种市场结构下测试变体模型。研究结果证明了SABM的有效性和鲁棒性，并为竞争与合谋提供了有趣的洞见。