How can LLMs help experts build better agent-based models?
Different Facets for Different Experts: A Framework for Streamlining The Integration of Qualitative Insights into ABM Development*
August 29, 2024
https://arxiv.org/pdf/2408.15725This paper proposes a new framework to make it easier for experts from different fields (like sociology or economics) to contribute to the creation and use of agent-based models (ABMs), even if they don't know how to program.
Key points for LLM-based multi-agent systems:
- Separation of expertise: The framework separates the technical ABM creation from defining agent behaviors. This means experts can design how agents make decisions using visual tools and without coding.
- Dynamic behavior: LLMs can be used to power the "BehaviourFlows," where domain experts define how agents respond to situations using conditional logic and adjustable triggers, leading to more nuanced agent actions.
- "What-if" scenarios: The system allows creating various scenarios with different policies applied to the agents, making it ideal for testing how LLMs would react to specific interventions in a simulated environment.