A reinforcement learning framework for modeling cultural inertia in public welfare resource allocation

Inefficiencies in corporate participation in public welfare have long been an issue, characterized by delayed responses, high resource mismatches, and increasing costs. To address these challenges, a Multi-Domain Reinforcement Learning Framework (MDRLE) inspired by advanced optimization techniques is proposed. To address these challenges, a quantum-inspired Multi-Domain Reinforcement Learning Framework (MDRLE) is proposed. The framework integrates variational quantum-circuit-based state encoding with classical optimization and behavioral modeling to account for cultural inertia through structured, high-dimensional representations. All quantum components are implemented through classical simulation. These social parameters are embedded into a classical optimization framework, enhancing the allocation of enterprise resources and strategies for poverty alleviation. Empirical results from 72 villages across three provinces demonstrate a 95.7% resource matching accuracy, a 35.8% reduction in relief costs, and an 84.8% decrease in poverty reversion rates. The framework has proven generalizable across six industrial sectors, including manufacturing and photovoltaic poverty alleviation. Task processing capacity increased by 37 times, and task latency was reduced to 12.8ms, providing an efficient and scalable solution for intelligent governance in public welfare. The integration of social behavior modeling with advanced optimization techniques demonstrates a promising and practically relevant approach for enabling dynamic, real-time management of corporate social responsibility initiatives under the evaluated settings.