mlsanityData to 7 Oct 2026

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

arXiv 2412.03563 ↗First posted 4 Dec 2024cs.CL cs.CYcited by 76

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

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 urlhttps://github.com/FudanDISC/SocialAgent.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 229 of its 338 references that are in the map. First posted on arXiv 4 Dec 2024.

Builds on

229 of 338 references.

Closest ancestors · 35

Same direction, newest first

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  12. 2024SocialBench: Sociality Evaluation of Role-Playing Conversational Agents20 Mar
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Other references · 194

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50 most cited of 194 All 194 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 43; 2026: 33.

76 papers.

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  1. 2025Agentic Large Language Models, a surveycited by 105
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  8. 2025Group size effects and collective misalignment in LLM multi-agent systemscited by 16
  9. 2025Reasoning Does Not Necessarily Improve Role-Playing Abilitycited by 14
  10. 2025Simulating Society Requires Simulating Thoughtcited by 12
  11. 2025LLM-Based Social Simulations Require a Boundarycited by 11
  12. 2026PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebracited by 11
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50 most cited of 76 All 76 (JSON)

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  5. 2024Sense and Sensitivity: Evaluating the simulation of social dynamics via Large Language Models
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  8. 2025Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research