用大模型打造更像人的游戏经济模拟代理，能自适应决策并催生真实市场现象。
Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling
- 用大语言模型构建具备角色扮演、感知、记忆和推理能力的智能代理
- 模拟中出现角色分工与符合市场规律的价格波动等涌现现象
- 适合研究游戏经济机制或想提升仿真真实性的学者与开发者
在大型多人在线（MMO）经济研究领域，基于代理的建模（ABM）已成为分析游戏经济的有力工具，已从规则驱动代理发展为借助强化学习增强决策能力的代理。然而，现有方法在模拟代理间类人经济行为时仍面临可靠性、社交性和可解释性不足的挑战。本研究首次探索将大语言模型（LLMs）引入MMO经济仿真。利用LLMs的角色扮演能力、生成能力和推理能力，设计具备角色扮演、感知、记忆和推理功能的LLM驱动代理，有效解决了上述问题。聚焦游戏内经济活动的仿真实验表明，这些代理能够产生符合市场规则的角色专业化与价格波动等涌现现象。
原文摘要 · Abstract (English)
Within the domain of Massively Multiplayer Online (MMO) economy research, Agent-Based Modeling (ABM) has emerged as a robust tool for analyzing game economics, evolving from rule-based agents to decision-making agents enhanced by reinforcement learning. Nevertheless, existing works encounter significant challenges when attempting to emulate human-like economic activities among agents, particularly regarding agent reliability, sociability, and interpretability. In this study, we take a preliminary step in introducing a novel approach using Large Language Models (LLMs) in MMO economy simulation. Leveraging LLMs' role-playing proficiency, generative capacity, and reasoning aptitude, we design LLM-driven agents with human-like decision-making and adaptability. These agents are equipped with the abilities of role-playing, perception, memory, and reasoning, addressing the aforementioned challenges effectively. Simulation experiments focusing on in-game economic activities demonstrate that LLM-empowered agents can promote emergent phenomena like role specialization and price fluctuations in line with market rules.
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