Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling

2025/06/05 by Bihan Xu, Shiwei Zhao, Xu, Bihan +23 · 1 citation
Computer Science · Economics, Econometrics and Finance · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Language and cultural evolution

paper · pdf · doi:10.48550/arxiv.2506.04699

openalex publication_date 2025/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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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• Beyond Playtesting: A Generative Multi-Agent Simulation System for Massively Multiplayer Online Games

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