Dynamic Benefit Allocation in Public Technology Service Platform Coalitions in China: A Multi-Agent Shapley-Adjusted Q-Learning Approach

Public technology service platforms (PTSPs) increasingly rely on coalition structures to deliver complex services, yet achieving equitable benefit allocation remains a critical challenge for sustained collaboration. To address the allocation challenges across different evolutionary phases of PTSP coalitions, this study develops a stage-contingent governance framework that maps allocation mechanisms onto the platform lifecycle: transitioning from classical Shapley values during initial coalition formation, to Owen values under sub-coalition emergence, and ultimately to a Shapley-adjusted Q-Learning (SHAQ) framework during dynamic scaling. SHAQ integrates cooperative behavioral Q-learning with coalition-based allocation rules, enabling members to dynamically align incentives, reward high contributors, and discourage disengagement. MATLAB2023a simulations demonstrate that SHAQ consistently outperforms static allocation methods in terms of distributional fairness, core stability, and economic efficiency. By offering a robust and adaptive allocation mechanism, this study presents a practical pathway to strengthen trust and sustainability in PTSP coalitions.