Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915327639552000 |
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| author | Xu, Bihan Zhao, Shiwei Wu, Runze Huang, Zhenya Wang, Jiawei Hu, Zhipeng Wang, Kai Liu, Haoyu Lv, Tangjie Li, Le Fan, Changjie Tong, Xin Han, Jiangze |
| author_facet | Xu, Bihan Zhao, Shiwei Wu, Runze Huang, Zhenya Wang, Jiawei Hu, Zhipeng Wang, Kai Liu, Haoyu Lv, Tangjie Li, Le Fan, Changjie Tong, Xin Han, Jiangze |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04699 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling Xu, Bihan Zhao, Shiwei Wu, Runze Huang, Zhenya Wang, Jiawei Hu, Zhipeng Wang, Kai Liu, Haoyu Lv, Tangjie Li, Le Fan, Changjie Tong, Xin Han, Jiangze Artificial Intelligence 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. |
| title | Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.04699 |