Three-tier Stackelberg game framework with evolutionary stability and blockchain incentives for collaborative governance of college digital aesthetic education platforms
Year: 2026
Type: article
Abstract: Collaborative governance of digital aesthetic education platforms in higher education is limited by fragmented resources, inefficient inter-institutional coordination and weak incentive mechanisms. Based on the above problems, this paper proposes a three-tier Stackelberg game-based governance mechanism to improve resource allocation efficiency and incentive coordination in college digital aesthetic education platforms. The three entities in the framework are as follows: the policy supervision layer, the institutional collaboration layer, and the teacher-student participation layer; thus, the structure of the traditional two-tier game has been extended to a multi-level educational governance system. To enhance the dynamic adaptability and long-term stability of the game model, evolutionary stability strategies and Lyapunov stability analysis have been combined. An improved Shapley value algorithm is used to determine the contribution of each participant, and a blockchain-based smart contract mechanism is employed to ensure transparency, automation, and controlled execution costs in incentive disbursement. Based on the empirical data and numerical experiments, the proposed model demonstrates improved resource utilisation efficiency, faster equilibrium convergence, more responsive policy updates, and enhanced overall system stability compared with the conventional two-tier game model. Sensitivity and anti-interference tests also show that the framework can still function normally under parameter changes, packet loss, node failures, malicious attacks and prolonged mixed disturbances. The findings provide a quantitative and implementable governance approach for improving coordination efficiency, incentive fairness, and sustainable operation of digital aesthetic education platforms in colleges and universities. Not applicable.
Source: Discover Computing
Authors: Dandan Cao, Xinhong Yu
Institution: Hefei University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,520
Funder: Hefei University
OpenAlex ID: W7222125505