  Large Language Models (LLMs) have increasingly been utilized in social
simulations, where they are often guided by carefully crafted instructions to
stably exhibit human-like behaviors during simulations. Nevertheless, we doubt
the necessity of shaping agents' behaviors for accurate social simulations.
Instead, this paper emphasizes the importance of spontaneous phenomena, wherein
agents deeply engage in contexts and make adaptive decisions without explicit
directions. We explored spontaneous cooperation across three competitive
scenarios and successfully simulated the gradual emergence of cooperation,
findings that align closely with human behavioral data. This approach not only
aids the computational social science community in bridging the gap between
simulations and real-world dynamics but also offers the AI community a novel
method to assess LLMs' capability of deliberate reasoning.
