How do LLMs diffuse info in asymmetric networks?
Understanding Dynamic Diffusion Process of LLM-based Agents under Information Asymmetry
February 20, 2025
https://arxiv.org/pdf/2502.13160This paper explores how LLM-powered agents spread information in scenarios where some agents have more information than others. It focuses on how this information asymmetry affects group dynamics, communication patterns, and individual actions.
Key points for LLM-based multi-agent systems:
- Dynamic Attention Mechanism: The paper introduces a custom attention mechanism to help agents prioritize important information from multiple sources, mimicking how humans filter information. This addresses limitations of standard LLM attention in complex social simulations.
- Open Environment: The simulation allows agents to interact with a growing number of other agents, creating a more realistic and dynamic social environment than fixed group sizes.
- Information Diffusion Patterns: The study analyzes how different types of information (gossip, policy, legal cases, etc.) and distribution methods affect how quickly and widely information spreads, highlighting the emergence of information gaps and echo chambers.
- Social Behaviors: The agents demonstrate social motivations in their interactions, including cooperation, support, and discussion, offering insights into how social factors influence information spread.
- Social Capital and Information Cocoons: The simulation observes how some agents accumulate social capital by connecting with others, while others become trapped in information cocoons, reinforcing existing beliefs. These observations connect the simulation to existing social theories.
- Agent Action Prompting: The provided prompts clearly define how agents should make decisions based on their attention, relationships, and the information they receive, illustrating a practical approach to controlling agent behavior.