arXiv AI
Modeling Social Dynamics with an LLM-Enabled Agent Based Network-Dynamic (LAND) Model
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arXiv AI
Jun 30
Modeling Earth-Scale Human-Like Societies with One Billion Agents
arXiv:2506. 12078v2 Announce Type: replace-cross Abstract: Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations.
arXiv AI
Jul 22
Learning to Make Friends: Coaching LLM Agents toward Emergent Social Ties
arXiv:2510. 19299v2 Announce Type: replace Abstract: Can large language model (LLM) agents reproduce the complex social dynamics that characterize human online behavior -- shaped by homophily, reciprocity, and social validation -- and what memory and learning mechanisms enable such dynamics to emerge?
arXiv AI
Jul 16
Social Simulations: from Agent-Based Modeling to Digital Twins
arXiv:2607. 13693v1 Announce Type: cross 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.
arXiv AI
Jun 16
MiroBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions
arXiv:2606. 14715v1 Announce Type: cross Abstract: LLM agents are increasingly used to simulate real world interactions, but it remains unclear whether simulated behaviors preserve the content patterns and interaction dynamics of real human behaviors.
arXiv Computation and Language
2d ago
Social Simulacra in the Wild: AI Agent Communities on Moltbook
arXiv:2603.16128v3 Announce Type: replace Abstract: As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for bot...
arXiv AI
Sep 10
LLMs for Social Network Modeling: From Network Generation to Dynamic Processes
The paper reviews how large language models (LLMs) are being used to model social networks, highlighting their ability to represent users, relationships, and interactions through natural language. It categorizes existing work into network generative models—split into selection‑based and interaction‑based approaches—and dynamic process models, which cover opinion dynamics, information diffusion, and rumor propagation. The survey also discusses the advantages of LLMs for realistic, context‑aware social behavior, while noting limitations such as social biases and prompt sensitivity, and outlines open research challenges and future directions.