  Large language models (LLMs), in conjunction with various reasoning
reinforcement methodologies, have demonstrated remarkable capabilities
comparable to humans in fields such as mathematics, law, coding, common sense,
and world knowledge. In this paper, we delve into the reasoning abilities of
LLMs within complex human systems. We propose a novel reasoning framework,
termed ``Mosaic Expert Observation Wall'' (MEOW) exploiting
generative-agents-based simulation technique. In the MEOW framework, simulated
data are utilized to train an expert model concentrating ``experience'' about a
specific task in each independent time of simulation. It is the accumulated
``experience'' through the simulation that makes for an expert on a task in a
complex human system. We conduct the experiments within a communication game
that mirrors real-world security scenarios. The results indicate that our
proposed methodology can cooperate with existing methodologies to enhance the
reasoning abilities of LLMs in complex human systems.
