Social Simulations: from Agent-Based Modeling to Digital Twins

• URL: http://arxiv.org/abs/2607.13693v1
• Date: Wed, 15 Jul 2026 10:42:25 GMT
• Title: Social Simulations: from Agent-Based Modeling to Digital Twins
• Abstract summary: Social simulation has evolved from classical agent-based models to AI-enhanced simulations based on Large Language Models.<n>Social Digital Twins are high-fidelity, data-driven representations of real-world socio-technical systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly defined behavioral rules, to AI-enhanced simulations based on Large Language Models and, ultimately, Social Digital Twins: high-fidelity, data-driven representations of real-world socio-technical systems. Along this trajectory, we discuss the main methodological foundations, applications, advantages, and limitations of each paradigm, highlighting the progressive shift from abstract models designed to investigate general social mechanisms toward increasingly realistic computational representations of specific social systems.

Related papers

• Social World Model-Augmented Mechanism Design Policy Learning [58.739456918502704]
  We introduce SWM-AP (Social World Model-Augmented Mechanism Design Policy Learning), which learns a social world model hierarchically to enhance mechanism design.<n>We show that SWM-AP outperforms established model-based and model-free RL baselines in cumulative rewards and sample efficiency.
  arXiv  Detail & Related papers  (2025-10-22T06:01:21Z)
• 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)
• Simulating Generative Social Agents via Theory-Informed Workflow Design [11.992123170134185]
  We propose a theory-informed framework that provides a systematic design process for social agents.<n>Our framework is grounded in principles from Social Cognition Theory and introduces three key modules: motivation, action planning, and learning.<n>Experiments demonstrate that our theory-driven agents reproduce realistic human behavior patterns under complex conditions.
  arXiv  Detail & Related papers  (2025-08-12T08:14:48Z)
• Towards Simulating Social Influence Dynamics with LLM-based Multi-agents [0.0]
  We investigate whether multi-agent simulations can reproduce core human social dynamics observed in online forums.<n>Our findings indicate that smaller models exhibit higher conformity rates, whereas models optimized for reasoning are more resistant to social influence.
  arXiv  Detail & Related papers  (2025-07-30T08:14:40Z)
• DynamiX: Large-Scale Dynamic Social Network Simulator [101.65679342680542]
  DynamiX is a novel large-scale social network simulator dedicated to dynamic social network modeling.<n>For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances.<n>For ordinary users, we construct an inequality-oriented behavior decision-making module.
  arXiv  Detail & Related papers  (2025-07-26T12:13:30Z)
• 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)
• Social Genome: Grounded Social Reasoning Abilities of Multimodal Models [61.88413918026431]
  Social reasoning abilities are crucial for AI systems to interpret and respond to multimodal human communication and interaction within social contexts.<n>We introduce SOCIAL GENOME, the first benchmark for fine-grained, grounded social reasoning abilities of multimodal models.
  arXiv  Detail & Related papers  (2025-02-21T00:05:40Z)
• From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents [47.935533238820334]
  Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns.<n>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.<n>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) Simulation Society, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics.
  arXiv  Detail & Related papers  (2024-12-04T18:56:37Z)
• 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.<n>Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts.<n>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)
• Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence [0.5898893619901381]
  We discuss the emerging new opportunity for building feedback-rich computational models of social systems using generative artificial intelligence. Referred to as Generative Agent-Based Models (GABMs), such individual-level models utilize large language models such as ChatGPT to represent human decision-making in social settings. We provide a GABM case in which human behavior can be incorporated in simulation models by coupling a mechanistic model of human interactions with a pre-trained large language model.
  arXiv  Detail & Related papers  (2023-09-20T16:43:05Z)

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.