Solution-oriented Agent-based Models Generation with Verifier-assisted
  Iterative In-context Learning
Tong Niu Weihao Zhang Rong Zhao
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 of self-evaluation capability of LLMs, relying solely on LLMs
is insufficient to effectively accomplish this process. In this paper, we
present SAGE, a general solution-oriented ABM generation framework designed for
automatic modeling and generating solutions for targeted problems. Unlike
approaches reliant on expert handcrafting or resource-intensive neural network
training, SAGE establishes a verifier-assisted iterative in-context learning
process employing large language models (LLMs) to leverages their inherent
cross-domain knowledge for tackling intricate demands from diverse domain
scenarios. In SAGE, we introduce an semi-structured conceptual representation
expliciting the intricate structures of ABMs and an objective representation to
guide LLMs in modeling scenarios and proposing hypothetical solutions through
in-context learning. To ensure the model executability and solution
feasibility, SAGE devises a two-level verifier with chain-of-thought prompting
tailored to the complex interactions and non-linear dynamics of ABMs, driving
the iterative generation optimization. Moreover, we construct an evaluation
dataset of solution-oriented ABMs from open sources.It contains practical
models across various domains.