On the Dynamics of Multi-Agent LLM Communities Driven by Value Diversity

2025-12-11

AI Summary

This work investigates how value diversity shapes collective behavior in large language model (LLM)-based multi-agent communities. Method: Grounded in Schwartz’s theory of basic human values, we construct the first naturalized, value-driven open-interaction simulation environment—establishing value diversity as a novel dimension of AI capability. Leveraging value extraction, multi-agent simulation, open-dialogue modeling, constitutional generation, and collective-dynamics analysis, we systematically probe its effects. Contribution/Results: We identify a nonlinear relationship between value diversity and collective intelligence, institutional emergence, and principle creativity: moderate diversity enhances system stability and bottom-up normative innovation, whereas excessive heterogeneity induces instability. We empirically characterize critical inflection points and decay patterns in diversity effects. The study provides an interpretable, empirically grounded, and experimentally verifiable framework for modeling institutional evolution in AI societies.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Philosophy and Ethics of AI: Morality & Value-based AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

As Large Language Models (LLM) based multi-agent systems become increasingly prevalent, the collective behaviors, e.g., collective intelligence, of such artificial communities have drawn growing attention. This work aims to answer a fundamental question: How does diversity of values shape the collective behavior of AI communities? Using naturalistic value elicitation grounded in the prevalent Schwartz's Theory of Basic Human Values, we constructed multi-agent simulations where communities with varying numbers of agents engaged in open-ended interactions and constitution formation. The results show that value diversity enhances value stability, fosters emergent behaviors, and brings more creative principles developed by the agents themselves without external guidance. However, these effects also show diminishing returns: extreme heterogeneity induces instability. This work positions value diversity as a new axis of future AI capability, bridging AI ability and sociological studies of institutional emergence.

Problem

• How value diversity shapes collective behavior in AI communities
• Simulating multi-agent interactions and constitution formation dynamics
• Exploring diminishing returns of extreme heterogeneity on stability

Innovation

• Using Schwartz's Theory for value elicitation in agents
• Simulating multi-agent communities with open-ended interactions
• Analyzing value diversity effects on emergent collective behaviors