S3: Social-network Simulation System with Large Language Model-Empowered Agents

By Chen Gao, Xiaochong Lan, Zhi-jie Lu, J. Mao, Jinghua Piao, Huandong Wang, Depeng Jin, Yong Li. Social Science Research Network, 2023

331 citations recorded by the source corpus.

Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and behaving, and utilize these qualities to construct the S$^3$ system (short for $\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$ystem). Adhering to the widely employed agent-based simulation paradigm, we employ prompt engineering and prompt tuning techniques to ensure that the agent's behavior closely emulates that of a genuine human within the social network. Specifically, we simulate three pivotal aspects: emotion, attitude, and interaction behaviors. By endowing the agent in the system with the ability to perceive the informational environment and emulate human actions, we observe the emergence of population-level phenomena, including the propagation of information, attitudes, and emotions. We conduct an evaluation encompassing two levels of simulation, employing real-world social network data. Encouragingly, the results demonstrate promising accuracy. This work represents an initial step in the realm of social network simulation empowered by LLM-based agents. We anticipate that our endeavors will serve as a source of inspiration for the development of simulation systems within, but not limited to, social science.

View Paper: https://www.semanticscholar.org/paper/221a72a3631ebf8b555c27bc864338390611feb1

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Citations in the corpus

Large language models empowered agent-based modeling and simulation: a survey and perspectives (2023, unknown): “This involves understanding and implementing the mechanisms by which these arti ﬁ cial agents can retain and utilize past experiences (memory) (Gao et al., 2023; Park et al., 2023; Zhu et al., 2023c), introspect and adjust their behavior based on their outcomes (re ﬂ ection) (Park et al., 2023;…”
A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, unknown): “To show the importance of the agent’s memory, we also present many typical applications, where the memory module plays an important role.”
A survey on large language model based autonomous agents (2023, background): “Social Simulation Social Simulacra [122], Generative Agents [121], SocialAI School [83], AgentSims [99], S(3) [55], Williams et al.”
Cognitive Architectures for Language Agents (2023, background): “Recent work also investigates interaction among multiple language agents for social simulation (Park et al., 2023; Jinxin et al., 2023; Gao et al., 2023), debate (Chan et al.”
Large Language Model based Multi-Agents: A Survey of Progress and Challenges (2024, background): “The study compares four communication frameworks, centralized, decentralized, and two hybrid models, to evaluate their effectiveness in coordinating complex multi-agent tasks.”