  Economic experiments offer a controlled setting for researchers to observe
human decision-making and test diverse theories and hypotheses; however,
substantial costs and efforts are incurred to gather many individuals as
experimental participants. To address this, with the development of large
language models (LLMs), some researchers have recently attempted to develop
simulated economic experiments using LLMs-driven agents, called generative
agents. If generative agents can replicate human-like decision-making in
economic experiments, the cost problem of economic experiments can be
alleviated. However, such a simulation framework has not been yet established.
Considering the previous research and the current evolutionary stage of LLMs,
this study focuses on the reasoning ability of generative agents as a key
factor toward establishing a framework for such a new methodology. A
multi-agent simulation, designed to improve the reasoning ability of generative
agents through prompting methods, was developed to reproduce the result of an
actual economic experiment on the ultimatum game. The results demonstrated that
the higher the reasoning ability of the agents, the closer the results were to
the theoretical solution than to the real experimental result. The results also
suggest that setting the personas of the generative agents may be important for
reproducing the results of real economic experiments. These findings are
valuable for the future definition of a framework for replacing human
participants with generative agents in economic experiments when LLMs are
further developed.
