How do norms emerge in multi-agent systems?
A systematic review of norm emergence in multi-agent systems
December 17, 2024
https://arxiv.org/pdf/2412.10609This paper reviews how norms emerge in multi-agent systems (MAS), drawing parallels with human societies. It explores how social structures, individual behaviors (influenced by cognitive abilities and emotions), and propagation mechanisms affect norm creation, spread, and evolution. The review classifies approaches to norm implementation in MAS as prescriptive (top-down), emergent (bottom-up), or hybrid.
Key takeaways for LLM-based multi-agent systems include:
- Norm representation: Norms can be explicitly coded (like rules) or implicitly learned through agent interactions (like social conventions). LLMs can facilitate both by generating explicit norm descriptions or by learning implicit norms from communication patterns.
- Propagation mechanisms: LLMs can act as "norm advisors" by providing explanations or suggestions, or as "role models" by demonstrating desired behaviors. Interaction learning, where agents adjust behavior based on feedback and observation, can be realized through LLM-mediated communication and evaluation.
- Cognitive abilities: LLMs enhance cognitive abilities of agents, enabling more nuanced reasoning about norms. Agents can evaluate context, prioritize norms, and adapt behavior accordingly.
- Emotions and values: While not explicitly modeled in most current systems, incorporating emotions and values can enrich agent interactions and influence norm adoption. LLMs can be used to model emotional responses and values-based decision-making.
- Network Topology: The structure of communication between agents influences how norms emerge and spread. LLMs can be used to analyze and adapt these structures.
- Online vs. Offline Norm Creation: Online norm creation, where norms emerge through interaction, aligns with LLM strengths in learning and adaptation. LLMs can also be used in offline norm design by simulating interactions and generating initial norms.
The paper highlights the need for integrated approaches that consider the interplay of these factors to build robust and adaptable normative multi-agent systems. LLMs offer a promising avenue for advancing this field.