In the aftermath of large-scale disasters, the scarcity of resources and the paralysis of infrastructure raise severe challenges to effective post-disaster recovery. Efficient coordination between shelters and victims plays a crucial role in building community resilience, yet the evolution of two-layer behavioral feedback between these two groups through network coupling remains insufficiently understood. Here, this study develops a two-layer network to capture the cross-layer coupling between shelters and victims. The upper layer uses a post-disaster emergency resource redistribution model within the framework of the public goods game, while the lower layer adopts a cooperative evolutionary game to describe internal victim interactions. Monte Carlo simulations on scale-free networks reveal threshold effects of incentives: moderate public goods enhancement and subsidies promote cooperation, whereas excessive incentives induce free-riding. In contrast, credible and well-executed punishment effectively suppresses defection. Targeted punishment of highly connected shelters significantly enhances cooperation under resource constraints. A comparative analysis using a network generated from the actual coordinates of Beijing shelters confirms the model's generality and practical applicability. The findings highlight the importance of calibrated incentives, enforceable sanctions, and structural targeting in fostering robust cooperation across organizational and individual levels in post-disaster environments.

翻译：大规模灾害发生后，资源稀缺与基础设施瘫痪对有效灾后恢复构成严峻挑战。避难所与受灾者之间的高效协调对构建社区韧性至关重要，然而这两类群体之间通过网络耦合形成的双层行为反馈演化机制尚未得到充分理解。本研究构建了一个双层网络来刻画避难所与受灾者之间的跨层耦合关系。上层网络在公共物品博弈框架下建立灾后应急资源再分配模型，下层网络采用合作演化博弈描述受灾者内部互动。在无标度网络上进行的蒙特卡洛模拟揭示了激励措施的阈值效应：适度的公共物品增强与补贴能促进合作，而过度激励反而诱发搭便车行为。相比之下，可信且执行到位的惩罚能有效抑制背叛行为。在资源约束条件下，针对高连接度避难所的定向惩罚能显著提升合作水平。基于北京实际避难所坐标生成的网络对比分析验证了模型的普适性与实际适用性。研究结果强调了精准化激励、可执行制裁及结构针对性对于在灾后环境中培育跨组织与个体层面稳健合作的重要性。