LLM-Based Social Simulations Require a Boundary

• URL: http://arxiv.org/abs/2506.19806v1
• Date: Tue, 24 Jun 2025 17:14:47 GMT
• Title: LLM-Based Social Simulations Require a Boundary
• Abstract summary: This position paper argues that large language model (LLM)-based social simulations should establish clear boundaries.<n>We examine three key boundary problems: alignment (simulated behaviors matching real-world patterns), consistency (maintaining coherent agent behavior over time), and robustness.
• Score: 3.351170542925928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This position paper argues that large language model (LLM)-based social simulations should establish clear boundaries to meaningfully contribute to social science research. While LLMs offer promising capabilities for modeling human-like agents compared to traditional agent-based modeling, they face fundamental limitations that constrain their reliability for social pattern discovery. The core issue lies in LLMs' tendency towards an ``average persona'' that lacks sufficient behavioral heterogeneity, a critical requirement for simulating complex social dynamics. We examine three key boundary problems: alignment (simulated behaviors matching real-world patterns), consistency (maintaining coherent agent behavior over time), and robustness (reproducibility under varying conditions). We propose heuristic boundaries for determining when LLM-based simulations can reliably advance social science understanding. We believe that these simulations are more valuable when focusing on (1) collective patterns rather than individual trajectories, (2) agent behaviors aligning with real population averages despite limited variance, and (3) proper validation methods available for testing simulation robustness. We provide a practical checklist to guide researchers in determining the appropriate scope and claims for LLM-based social simulations.

Related papers

• Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits [11.902271500463934]
  Generative agents bring new expressive power to agent-based modeling.<n>Small perturbations that appear minor to researchers can cascade into macro-level outcomes.<n>We introduce TRAILS, a robustness-audit taxonomy spanning three levels of simulation design.
  arXiv  Detail & Related papers  (2026-05-17T00:21:53Z)
• We Need Strong Preconditions For Using Simulations In Policy [0.0]
  Two critical challenges remain understudied: the dual-use potential of accurate models of individual or population-level human behavior and the difficulty of validating simulation outputs.<n>We propose and discuss three preconditions for societal-scale LLM agent simulations.
  arXiv  Detail & Related papers  (2026-04-09T05:39:35Z)
• Position: AI Agents Are Not (Yet) a Panacea for Social Simulation [62.891898598784415]
  Recent advances in large language models (LLMs) have spurred growing interest in using LLM-integrated agents for social simulation.<n>This position paper argues that LLM-based agents are not (yet) a panacea for social simulation.
  arXiv  Detail & Related papers  (2026-02-19T06:35:07Z)
• Leveraging LLM-based agents for social science research: insights from citation network simulations [132.4334196445918]
  We introduce the CiteAgent framework, designed to generate citation networks based on human-behavior simulation.<n>CiteAgent captures predominant phenomena in real-world citation networks, including power-law distribution, citational distortion, and shrinking diameter.<n>We establish two LLM-based research paradigms in social science, allowing us to validate and challenge existing theories.
  arXiv  Detail & Related papers  (2025-11-05T08:47:04Z)
• SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors [58.87134689752605]
  We introduce SimBench, the first large-scale, standardized benchmark for a robust, reproducible science of LLM simulation.<n>We show that even the best LLMs today have limited simulation ability (score: 40.80/100), performance scales log-linearly with model size.<n>We demonstrate that simulation ability correlates most strongly with deep, knowledge-intensive reasoning.
  arXiv  Detail & Related papers  (2025-10-20T13:14:38Z)
• Integrating LLM and Diffusion-Based Agents for Social Simulation [28.21329943306884]
  We propose a hybrid simulation framework that strategically integrates large language model (LLM)-driven agents with diffusion model-based agents.<n>Our framework outperforms existing methods in prediction accuracy, validating the effectiveness of its modular design.
  arXiv  Detail & Related papers  (2025-10-18T06:23:22Z)
• Population-Aligned Persona Generation for LLM-based Social Simulation [58.84363795421489]
  We propose a systematic framework for synthesizing high-quality, population-aligned persona sets for social simulation.<n>Our approach begins by leveraging large language models to generate narrative personas from long-term social media data.<n>To address the needs of specific simulation contexts, we introduce a task-specific module that adapts the globally aligned persona set to targeted subpopulations.
  arXiv  Detail & Related papers  (2025-09-12T10:43:47Z)
• Integrating LLM in Agent-Based Social Simulation: Opportunities and Challenges [0.7739037410679168]
  The paper reviews recent findings on the ability of Large Language Models to replicate key aspects of human cognition.<n>The second part surveys emerging applications of LLMs in multi-agent simulation frameworks.<n>The paper concludes by advocating for hybrid approaches that integrate LLMs into traditional agent-based modeling platforms.
  arXiv  Detail & Related papers  (2025-07-25T15:15:35Z)
• Modeling Earth-Scale Human-Like Societies with One Billion Agents [54.465233996410156]
  Light Society is an agent-based simulation framework.<n>It formalizes social processes as structured transitions of agent and environment states.<n>It supports efficient simulation of societies with over one billion agents.
  arXiv  Detail & Related papers  (2025-06-07T09:14:12Z)
• MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework [53.82097200295448]
  Mean-Field LLM (MF-LLM) is first to incorporate mean field theory into social simulation.<n>MF-LLM models bidirectional interactions between individuals and the population through an iterative process.<n> IB-Tune is a novel fine-tuning method inspired by the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-04-30T12:41:51Z)
• SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users [70.02370111025617]
  We introduce SocioVerse, an agent-driven world model for social simulation.<n>Our framework features four powerful alignment components and a user pool of 10 million real individuals.<n>Results demonstrate that SocioVerse can reflect large-scale population dynamics while ensuring diversity, credibility, and representativeness.
  arXiv  Detail & Related papers  (2025-04-14T12:12:52Z)
• From ChatGPT to DeepSeek: Can LLMs Simulate Humanity? [32.93460040317926]
  Large Language Models (LLMs) have become a promising method for exploring complex human social behaviors.<n>Recent studies highlight discrepancies between simulated and real-world interactions.
  arXiv  Detail & Related papers  (2025-02-25T13:54:47Z)
• GenSim: A General Social Simulation Platform with Large Language Model based Agents [111.00666003559324]
  We propose a novel large language model (LLMs)-based simulation platform called textitGenSim. Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts. To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform.
  arXiv  Detail & Related papers  (2024-10-06T05:02:23Z)
• Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents [18.961470450132637]
  This paper emphasizes the importance of spontaneous phenomena, wherein agents deeply engage in contexts and make adaptive decisions without explicit directions. We explored spontaneous cooperation across three competitive scenarios and successfully simulated the gradual emergence of cooperation.
  arXiv  Detail & Related papers  (2024-02-19T18:00:53Z)
• LLM-driven Imitation of Subrational Behavior : Illusion or Reality? [3.2365468114603937]
  Existing work highlights the ability of Large Language Models to address complex reasoning tasks and mimic human communication. We propose to investigate the use of LLMs to generate synthetic human demonstrations, which are then used to learn subrational agent policies. We experimentally evaluate the ability of our framework to model sub-rationality through four simple scenarios.
  arXiv  Detail & Related papers  (2024-02-13T19:46:39Z)
• Systematic Biases in LLM Simulations of Debates [12.933509143906141]
  We study the limitations of Large Language Models in simulating human interactions.<n>Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases.<n>These results underscore the need for further research to develop methods that help agents overcome these biases.
  arXiv  Detail & Related papers  (2024-02-06T14:51:55Z)
• CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations [61.9212914612875]
  We present a framework to characterize LLM simulations using four dimensions: Context, Model, Persona, and Topic. We use this framework to measure open-ended LLM simulations' susceptibility to caricature, defined via two criteria: individuation and exaggeration. We find that for GPT-4, simulations of certain demographics (political and marginalized groups) and topics (general, uncontroversial) are highly susceptible to caricature.
  arXiv  Detail & Related papers  (2023-10-17T18:00:25Z)
• Training Socially Aligned Language Models on Simulated Social Interactions [99.39979111807388]
  Social alignment in AI systems aims to ensure that these models behave according to established societal values. Current language models (LMs) are trained to rigidly replicate their training corpus in isolation. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions.
  arXiv  Detail & Related papers  (2023-05-26T14:17:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.