Evolution of Social Norms in LLM Agents using Natural Language

TL;DR

The experiments demonstrate that through dialogue, LLM agents can form complex social norms, such as metanorms-norms enforcing the punishment of those who do not punish cheating-purely through natural language interaction.

Abstract

Recent advancements in Large Language Models (LLMs) have spurred a surge of interest in leveraging these models for game-theoretical simulations, where LLMs act as individual agents engaging in social interactions. This study explores the potential for LLM agents to spontaneously generate and adhere to normative strategies through natural language discourse, building upon the foundational work of Axelrod's metanorm games. Our experiments demonstrate that through dialogue, LLM agents can form complex social norms, such as metanorms-norms enforcing the punishment of those who do not punish cheating-purely through natural language interaction. The results affirm the effectiveness of using LLM agents for simulating social interactions and understanding the emergence and evolution of complex strategies and norms through natural language. Future work may extend these findings by incorporating a wider range of scenarios and agent characteristics, aiming to uncover more nuanced mechanisms behind social norm formation.

Figures (8)

• Figure 1: Agent commands in a norms game
• Figure 2: Number of 'punish' commands within discussions for each group
• Figure 3: Progression of vengefulness and boldness by epoch when introducing natural selection based on payoffs
• Figure 4: Visualization of punishment network in high-vengefulness, high-boldness
• Figure 5: Embedding plot across all five trials. Arrows indicate the progression of means for each epoch.