From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents

Fuente: arXiv

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Bibliographic Details
Main Authors:	Mou, Xinyi, Ding, Xuanwen, He, Qi, Wang, Liang, Liang, Jingcong, Zhang, Xinnong, Sun, Libo, Lin, Jiayu, Zhou, Jie, Huang, Xuanjing, Wei, Zhongyu
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Computers and Society
Online Access:	Acceder al recurso
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_version_	1866916507690205184
author	Mou, Xinyi Ding, Xuanwen He, Qi Wang, Liang Liang, Jingcong Zhang, Xinnong Sun, Libo Lin, Jiayu Zhou, Jie Huang, Xuanjing Wei, Zhongyu
author_facet	Mou, Xinyi Ding, Xuanwen He, Qi Wang, Liang Liang, Jingcong Zhang, Xinnong Sun, Libo Lin, Jiayu Zhou, Jie Huang, Xuanjing Wei, Zhongyu
contents	Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and facilitating studies on many interdisciplinary studies. In this paper, we conduct a comprehensive survey of this field, illustrating the recent progress in simulation driven by LLM-empowered agents. We categorize the simulations into three types: (1) Individual Simulation, which mimics specific individuals or demographic groups; (2) Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and (3) Society Simulation, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics. These simulations follow a progression, ranging from detailed individual modeling to large-scale societal phenomena. We provide a detailed discussion of each simulation type, including the architecture or key components of the simulation, the classification of objectives or scenarios and the evaluation method. Afterward, we summarize commonly used datasets and benchmarks. Finally, we discuss the trends across these three types of simulation. A repository for the related sources is at {\url{https://github.com/FudanDISC/SocialAgent}}.
format	Preprint
id	arxiv_https___arxiv_org_abs_2412_03563
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents Mou, Xinyi Ding, Xuanwen He, Qi Wang, Liang Liang, Jingcong Zhang, Xinnong Sun, Libo Lin, Jiayu Zhou, Jie Huang, Xuanjing Wei, Zhongyu Computation and Language Computers and Society Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and facilitating studies on many interdisciplinary studies. In this paper, we conduct a comprehensive survey of this field, illustrating the recent progress in simulation driven by LLM-empowered agents. We categorize the simulations into three types: (1) Individual Simulation, which mimics specific individuals or demographic groups; (2) Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and (3) Society Simulation, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics. These simulations follow a progression, ranging from detailed individual modeling to large-scale societal phenomena. We provide a detailed discussion of each simulation type, including the architecture or key components of the simulation, the classification of objectives or scenarios and the evaluation method. Afterward, we summarize commonly used datasets and benchmarks. Finally, we discuss the trends across these three types of simulation. A repository for the related sources is at {\url{https://github.com/FudanDISC/SocialAgent}}.
title	From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents
topic	Computation and Language Computers and Society
url	https://arxiv.org/abs/2412.03563