  With the rapid advancement of large language models (LLMs) for handling
complex language tasks, an increasing number of studies are employing LLMs as
agents to emulate the sequential decision-making processes of humans often
represented as Markov decision-making processes (MDPs). The actions in MDPs
adhere to specific probability distributions and require iterative sampling.
This arouses curiosity regarding the capacity of LLM agents to comprehend
probability distributions, thereby guiding the agent's behavioral
decision-making through probabilistic sampling and generating behavioral
sequences. To answer the above question, we divide the problem into two main
aspects: sequence simulation with known probability distribution and sequence
simulation with unknown probability distribution. Our analysis indicates that
LLM agents can understand probabilities, but they struggle with probability
sampling. Their ability to perform probabilistic sampling can be improved to
some extent by integrating coding tools, but this level of sampling precision
still makes it difficult to simulate human behavior as agents.
