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Computer simulations offer a robust toolset for exploring complex systemsacross various disciplines. A particularly impactful approach within this realmis Agent-Based Modeling (ABM), which harnesses the interactions of individualagents to emulate intricate system dynamics. ABM's strength lies in itsbottom-up methodology, illuminating emergent phenomena by modeling thebehaviors of individual components of a system. Yet, ABM has its own set ofchallenges, notably its struggle with modeling natural language instructionsand common sense in mathematical equations or rules. This paper seeks totranscend these boundaries by integrating Large Language Models (LLMs) like GPTinto ABM. This amalgamation gives birth to a novel framework, Smart Agent-BasedModeling (SABM). Building upon the concept of smart agents -- entitiescharacterized by their intelligence, adaptability, and computation ability --we explore in the direction of utilizing LLM-powered agents to simulatereal-world scenarios with increased nuance and realism. In this comprehensiveexploration, we elucidate the state of the art of ABM, introduce SABM'spotential and methodology, and present three case studies (source codesavailable at https://github.com/Roihn/SABM), demonstrating the SABM methodologyand validating its effectiveness in modeling real-world systems. Furthermore,we cast a vision towards several aspects of the future of SABM, anticipating abroader horizon for its applications. Through this endeavor, we aspire toredefine the boundaries of computer simulations, enabling a more profoundunderstanding of complex systems.

Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations