  Computational experiments have emerged as a valuable method for studying
complex systems, involving the algorithmization of counterfactuals. However,
accurately representing real social systems in Agent-based Modeling (ABM) is
challenging due to the diverse and intricate characteristics of humans,
including bounded rationality and heterogeneity. To address this limitation,
the integration of Large Language Models (LLMs) has been proposed, enabling
agents to possess anthropomorphic abilities such as complex reasoning and
autonomous learning. These agents, known as LLM-based Agent, offer the
potential to enhance the anthropomorphism lacking in ABM. Nonetheless, the
absence of explicit explainability in LLMs significantly hinders their
application in the social sciences. Conversely, computational experiments excel
in providing causal analysis of individual behaviors and complex phenomena.
Thus, combining computational experiments with LLM-based Agent holds
substantial research potential. This paper aims to present a comprehensive
exploration of this fusion. Primarily, it outlines the historical development
of agent structures and their evolution into artificial societies, emphasizing
their importance in computational experiments. Then it elucidates the
advantages that computational experiments and LLM-based Agents offer each
other, considering the perspectives of LLM-based Agent for computational
experiments and vice versa. Finally, this paper addresses the challenges and
future trends in this research domain, offering guidance for subsequent related
studies.
