{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T17:02:30Z","timestamp":1783702950465,"version":"3.55.0"},"reference-count":297,"publisher":"Association for Computing Machinery (ACM)","issue":"11","funder":[{"name":"AI for Science Program, Shanghai Municipal Commission of Economy and Informatization","award":["2025-GZL-RGZN-BTBX-02028"],"award-info":[{"award-number":["2025-GZL-RGZN-BTBX-02028"]}]},{"name":"CFFF platform of Fudan University"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,8,30]]},"abstract":"\n 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 article, 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)\n Individual Simulation<\/jats:italic>\n , which mimics specific individuals or demographic groups; (2)\n Scenario Simulation<\/jats:italic>\n , where multiple agents collaborate to achieve goals within specific contexts; and (3)\n Society Simulation<\/jats:italic>\n , 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, benchmarks and tools. Finally, we discuss the risks and challenges across these three types of simulation. A repository for the related sources is at\n https:\/\/github.com\/FudanDISC\/SocialAgent<\/jats:ext-link>\n .\n <\/jats:p>","DOI":"10.1145\/3800683","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:56:20Z","timestamp":1776077780000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9255-8164","authenticated-orcid":false,"given":"Xinyi","family":"Mou","sequence":"first","affiliation":[{"name":"School of Data Science, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2598-2365","authenticated-orcid":false,"given":"Xuanwen","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University","place":["Shanghai, China"]},{"name":"School of Data Science, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8719-444X","authenticated-orcid":false,"given":"Qi","family":"He","sequence":"additional","affiliation":[{"name":"School of Data Science, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3892-0785","authenticated-orcid":false,"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science, Harbin Institute of Technology Shenzhen","place":["Shenzhen, China"]},{"name":"School of Data Science, Fudan University","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2488-2683","authenticated-orcid":false,"given":"Jingcong","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Data Science, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8791-3308","authenticated-orcid":false,"given":"Xinnong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University","place":["Shanghai, China"]},{"name":"Shanghai Innovation Institute","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3705-7656","authenticated-orcid":false,"given":"Libo","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Data Science, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2740-5401","authenticated-orcid":false,"given":"Jiayu","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Data Science, Fudan University","place":["Shanghai, China"]},{"name":"Shanghai Innovation Institute","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2589-0164","authenticated-orcid":false,"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9197-9426","authenticated-orcid":false,"given":"Huang","family":"Xuanjing","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3789-8507","authenticated-orcid":false,"given":"Zhongyu","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Data Science, Fudan University","place":["Shanghai, China"]},{"name":"Shanghai Innovation Institute","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Conversational health agents: A personalized llm-powered agent framework","author":"Abbasian Mahyar","year":"2023","unstructured":"Mahyar Abbasian, Iman Azimi, Amir M. 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Using large language models to simulate multiple humans and replicate human subject studies. In International Conference on Machine Learning. PMLR, 337\u2013371."},{"key":"e_1_3_1_5_2","first-page":"3354","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Ahn Jaewoo","year":"2023","unstructured":"Jaewoo Ahn, Yeda Song, Sangdoo Yun, and Gunhee Kim. 2023. Mpchat: Towards multimodal persona-grounded conversation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3354\u20133377."},{"key":"e_1_3_1_6_2","first-page":"321","volume-title":"Proceedings of the Aristotelian Society","volume":"57","author":"Anscombe Gertrude Elizabeth Margaret","year":"1956","unstructured":"Gertrude Elizabeth Margaret Anscombe. 1956. Intention. In Proceedings of the Aristotelian Society, Vol. 57. 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