Effective social intelligence simulation requires language agents to dynamically adjust the depth of reasoning, a capability conspicuously absent in current methods. Existing methods either lack such reasoning capabilities or enforce a uniform long-chain thinking reasoning in all scenarios, leading to excessive token usage and inappropriate social simulations. This paper proposes a Self-Adaptation Mindset Learning (AML) framework that strategically selects from four mindsets (intuitive reaction → deep thinking) based on real-time context. The core innovation of this framework, the Self-Adaptation Mindset Policy Optimization (AMPO) algorithm, achieves three breakthroughs compared to existing methods: (1) multi-granularity mindset design, (2) context-aware mindset switching in social interactions, and (3) token-efficient reasoning through deep self-adaptation. Extensive experiments on social intelligence tasks show that AML outperforms the current state-of-the-art method by 15.6%. Notably, while shortening the reasoning chain length by 32.8%, our method still outperforms GRPO by 7.0%. These results demonstrate that the context-sensitive mindset selection achieved by AMPO is closer to human self-adaptation thinking characteristics than the fixed-depth reasoning approach of GRPO.