Dynamic Tripartite Governance in data security: An evolutionary game model with cross-level government supervision

As digital governance transitions to a decentralized architecture, data security has become a key driver of institutional modernization. This study constructs a three-party evolutionary game framework, integrating the grassroots government (GRG), local government (LG), and third-party regulator (TPR), and empirically calibrates the parameters using financial records from municipal data projects. Using replicator dynamics and Lyapunov stability analysis, we identify three key mechanisms in cross-level governance: 1. While reward systems enhance GRG compliance, excessive rewards, due to resource misallocation, weaken LG's regulatory rigor. 2. When the overall penalties systematically exceed opportunistic gains, rent-seeking behavior is effectively suppressed, reducing the incentives for collusion between GRG and TPR. 3. Increasing the rent-seeking costs of TPR and enhancing social accountability benefits promote GRG's sustained commitment to data security supervision. Simulations conducted in MATLAB illustrate the nonlinear interactions between governance parameters and reveal that dynamic reward and punishment mechanisms can accelerate convergence towards a stable regulatory equilibrium. This study proposes optimizing China's grassroots data governance framework through dynamically adjusted reward and penalty mechanisms and increased rent-seeking costs, providing practical guidance for enhancing regulatory effectiveness. Index Terms data security governance, evolutionary game theory, incentive mechanisms, grassroots regulation, rent-seeking.