Sustaining cooperation in resource-constrained populations requires allocation mechanisms that balance individual incentives, resource sustainability, and distributional fairness. This paper proposes a network common-pool resource game in which individuals are embedded in complex networks, participate in multiple overlapping local resource pools, and face endogenous resource constraints during strategy evolution. Within this framework, we first examine two representative allocation mechanisms, equal allocation and proportional allocation. The results show that equal allocation produces fair but inefficient outcomes by weakening contribution incentives, whereas proportional allocation can temporarily promote cooperation but amplifies accumulated advantages and leads to severe inequality. To overcome these limitations, we develop a graph neural network-based reinforcement learning framework in which a learned social planner allocates local pool resources without directly controlling individual strategies. Simulation results under four representative network topologies show that the learned planner sustains higher cooperation levels and average accumulated resources, and reduces inequality compared with the baselines. Furthermore, we interpret the learned policy and distill it into two simpler mechanisms: a resource-dependent mixture mechanism for regular networks and a degree-conditioned mixture mechanism for heterogeneous networks. These mechanisms reveal that effective allocation should adapt to both local resource states and structural positions, providing an interpretable route from reinforcement learning policy search to mechanism design in networked resource-sharing systems.

翻译：在资源受限群体中维持合作，需要既能平衡个体激励、资源可持续性与分配公平性的分配机制。本文提出一种网络化公共池资源博弈模型，其中个体嵌入复杂网络中，参与多个重叠的本地资源池，并在策略演化过程中面临内生资源约束。在该框架下，我们首先审视两种代表性分配机制：平均分配与按比例分配。结果表明，平均分配通过削弱贡献激励导致公平但低效的结果，而按比例分配虽能暂时促进合作，却会放大累积优势并引发严重不平等。为克服上述局限，我们开发了一种基于图神经网络的强化学习框架，其中学习型社会规划者在不直接控制个体策略的前提下分配本地池资源。在四种代表性网络拓扑结构下的仿真结果显示，与基线方法相比，学习型规划者能够维持更高的合作水平与平均累积资源量，并降低不平等程度。此外，我们对学得策略进行解析，并将其提炼为两种更简化的机制：适用于规则网络的资源依赖性混合机制，以及适用于异质性网络的度条件混合机制。这些机制表明，有效的分配应同时适应本地资源状态与结构位置，为网络化资源共享系统中从强化学习策略搜索到机制设计提供了可解释路径。