  Computer simulations offer a robust toolset for exploring complex systems
across various disciplines. A particularly impactful approach within this realm
is Agent-Based Modeling (ABM), which harnesses the interactions of individual
agents to emulate intricate system dynamics. ABM's strength lies in its
bottom-up methodology, illuminating emergent phenomena by modeling the
behaviors of individual components of a system. Yet, ABM has its own set of
challenges, notably its struggle with modeling natural language instructions
and common sense in mathematical equations or rules. This paper seeks to
transcend these boundaries by integrating Large Language Models (LLMs) like GPT
into ABM. This amalgamation gives birth to a novel framework, Smart Agent-Based
Modeling (SABM). Building upon the concept of smart agents -- entities
characterized by their intelligence, adaptability, and computation ability --
we explore in the direction of utilizing LLM-powered agents to simulate
real-world scenarios with increased nuance and realism. In this comprehensive
exploration, we elucidate the state of the art of ABM, introduce SABM's
potential and methodology, and present three case studies (source codes
available at https://github.com/Roihn/SABM), demonstrating the SABM methodology
and validating its effectiveness in modeling real-world systems. Furthermore,
we cast a vision towards several aspects of the future of SABM, anticipating a
broader horizon for its applications. Through this endeavor, we aspire to
redefine the boundaries of computer simulations, enabling a more profound
understanding of complex systems.
