Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning

Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies aimed at addressing challenges posed by complex systems and achieving various objectives. This process demands labor-intensive endeavors and multidisciplinary expertise. Large language models (LLMs) encapsulating cross-domain knowledge and programming proficiency could potentially alleviate the difficulty of this process. However, LLMs excel in handling sequential information, making it challenging for analyzing the intricate interactions and nonlinear dynamics inherent in ABMs. Additionally, due to the lack …

agent challenges complex systems context cs.ai cs.cl cs.lg cs.se domain domain knowledge expertise in-context learning iterative knowledge labor language language models large language large language models llms paradigm process programming solution solutions systems