Computer simulations offer a robust toolset for exploring complex systems
across various disciplines. A particularly impactful approach within this realm
is Agent-Based Modeling (ABM), which harnesses the interactions of individual
agents to emulate intricate system dynamics. ABM's strength lies in its
bottom-up methodology, illuminating emergent phenomena by modeling the
behaviors of individual components of a system. Yet, ABM has its own set of
challenges, notably its struggle with modeling natural language instructions
and common sense in mathematical equations or rules. This paper seeks to
transcend these boundaries by integrating Large Language Models (LLMs) like GPT
into ABM. This amalgamation gives birth to a novel framework, Smart Agent-Based
Modeling (SABM). Building upon the concept of smart agents -- entities
characterized by their intelligence, adaptability, and computation ability --
we explore in the direction of utilizing LLM-powered agents to simulate
real-world scenarios with increased nuance and realism. In this comprehensive
exploration, we elucidate the state of the art of ABM, introduce SABM's
potential and methodology, and present three case studies (source codes
available at this https URL), demonstrating the SABM methodology
and validating its effectiveness in modeling real-world systems. Furthermore,
we cast a vision towards several aspects of the future of SABM, anticipating a
broader horizon for its applications. Through this endeavor, we aspire to
redefine the boundaries of computer simulations, enabling a more profound
understanding of complex systems.

通过将大型语言模型（LLMs）如 GPT 集成到智能智能体建模（SABM）中，我们在这篇综述性研究中阐明了 ABM 的现状，介绍了 SABM 的潜力和方法，并提出了三个案例研究，验证了其在模拟现实系统中的有效性，同时展望了 SABM 未来应用的广阔前景，旨在重新定义计算机仿真的边界，实现对复杂系统的更深入理解。

智能基于代理的建模：关于在计算机模拟中使用大型语言模型

Smart Agent-Based Modeling: On the Use of Large Language Models in  Computer Simulations

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.

通过使用智能代理建模方法，模拟了企业间的竞争、勾结，以及通信对价格收敛的影响。结果表明，没有通信时智能代理往往达到默契勾结，价格收敛在柏特朗均衡价格之上但低于垄断或卡特尔价格；而允许通信时，智能代理能够实现更高程度的勾结，价格接近卡特尔价格，勾结形成更快，价格收敛更平稳。这些结果表明，通信可以增强企业之间的信任，鼓励频繁小幅度的价格偏差，以探索更高级别的双赢机会，减少引发价格战的可能性。