2024/12/27 by Zexun Jiang, Jiang, Zexun, Yafang Shi +13 · 1 voice · 6 citations
Computer Science · Decision Sciences · Social Sciences · #Complex Systems and Decision Making #Evolutionary Game Theory and Cooperation #FOS: Computer and information sciences #Multi-Agent Systems and Negotiation #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2412.19498
openalex publication_date 2024/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we introduce a multi-agent simulation framework Casevo (Cognitive Agents and Social Evolution Simulator), that integrates large language models (LLMs) to simulate complex social phenomena and decision-making processes. Casevo is designed as a discrete-event simulator driven by agents with features such as Chain of Thoughts (CoT), Retrieval-Augmented Generation (RAG), and Customizable Memory Mechanism. Casevo enables dynamic social modeling, which can support various scenarios such as social network analysis, public opinion dynamics, and behavior prediction in complex social systems. To demonstrate the effectiveness of Casevo, we utilize one of the U.S. 2020 midterm election TV debates as a simulation example. Our results show that Casevo facilitates more realistic and flexible agent interactions, improving the quality of dynamic social phenomena simulation. This work contributes to the field by providing a robust system for studying large-scale, high-fidelity social behaviors with advanced LLM-driven agents, expanding the capabilities of traditional agent-based modeling (ABM). The open-source code repository address of casevo is https://github.com/rgCASS/casevo.