Norm Evolution in Multi-Agent Systems Using LLMs as Agents

This research investigates the emergence and evolution of social norms in multi-agent systems by leveraging Large Language Models (LLMs) as sophisticated agents. Moving beyond traditional reinforcement learning approaches, the project focuses on how LLM agents acquire norms through social learning mechanisms, drawing inspiration from the cognitive gadget framework. A central experiment, designed within the Concordia simulation environment, compares the effectiveness of explicit norm instruction versus implicit learning through observation and environmental consequences. The study examines the impact of agent architectures augmented with cognitive-gadget-inspired components, such as social memory and norm processing modules, on norm acquisition and adherence. Expected outcomes will provide insights into the dynamics of norm formation in artificial societies, inform the design of more socially intelligent and aligned AI agents, and demonstrate the potential of LLM-based generative agent-based modeling for social science research.