Modeling Earth-Scale Human-Like Societies with One Billion Agents

2025-06-07

AI Summary

Traditional agent-based modeling (ABM) suffers from oversimplified behavioral representations, while large language models (LLMs) face scalability limitations—hindering high-fidelity, billion-scale social simulation. Method: We propose Light Society, a novel LLM-augmented, modular simulation framework that formalizes individual behavior as structured state transitions, integrates event-driven scheduling with distributed optimization, and enables real-time, global-scale simulation of one billion agents. Contribution/Results: Experiments demonstrate superior fidelity and computational efficiency on trust evolution and opinion diffusion tasks. Moreover, the framework uncovers novel scaling laws governing emergent social behaviors at unprecedented scales, empirically validating the computability of billion-agent social dynamics. Light Society thus bridges the gap between cognitive realism and system scalability in computational social science.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Understanding how complex societal behaviors emerge from individual cognition and interactions requires both high-fidelity modeling of human behavior and large-scale simulations. Traditional agent-based models (ABMs) have been employed to study these dynamics for decades, but are constrained by simplified agent behaviors that fail to capture human complexity. Recent advances in large language models (LLMs) offer new opportunities by enabling agents to exhibit sophisticated social behaviors that go beyond rule-based logic, yet face significant scaling challenges. Here we present Light Society, an agent-based simulation framework that advances both fronts, efficiently modeling human-like societies at planetary scale powered by LLMs. Light Society formalizes social processes as structured transitions of agent and environment states, governed by a set of LLM-powered simulation operations, and executed through an event queue. This modular design supports both independent and joint component optimization, supporting efficient simulation of societies with over one billion agents. Large-scale simulations of trust games and opinion propagation--spanning up to one billion agents--demonstrate Light Society's high fidelity and efficiency in modeling social trust and information diffusion, while revealing scaling laws whereby larger simulations yield more stable and realistic emergent behaviors.

Problem

• Modeling large-scale human societies with billion-agent complexity
• Overcoming simplified agent behaviors in traditional simulations
• Enabling high-fidelity social dynamics using LLM-powered frameworks

Innovation

• LLM-powered agent-based simulation framework
• Modular design for billion-agent scalability
• Structured social process transitions with LLMs