Multi-Stakeholder Agile Governance Mechanism of AI Based on Credit Entropy

Lei Cheng, Wenjing Chen (), Ruoyu Li and Chen Zhang
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Lei Cheng: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Wenjing Chen: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Ruoyu Li: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Chen Zhang: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China

Sustainability, 2025, vol. 17, issue 20, 1-24

Abstract: Driven by the rapid evolution of AI technology, compatible management mechanisms have become a systematic project involving the participation of multiple stakeholders. However, constrained by the rigidity and lag of traditional laws, the “one-size-fits-all” regulatory model will exacerbate the vulnerability of the complex system of AI governance, hinder the sustainable evolution of the AI ecosystem that relies on the dynamic balance between innovation and responsibility, and ultimately fall into the dilemma of “chaos when laissez-faire, stagnation when over-regulated”. To address this challenge, this study takes the multi-stakeholder collaborative mechanism co-established by governments, enterprises, and third-party technical audit institutions as its research object and centers on the issue of “strategic fluctuations” caused by key factor disturbances. From the perspective of the full life cycle of technological development, the study integrates the historical compliance performance of stakeholders and develops a nonlinear dynamic reward and punishment mechanism based on Credit Entropy. Through evolutionary game simulation, it further examines this mechanism as a realization path to promote the transformation from passive campaign-style AI supervision to agile governance of AI, which is characterized by rapid response and minimal intervention, thereby laying a foundation for the sustainable development of AI technology that aligns with long-term social well-being, resource efficiency, and inclusive growth. Finally, the study puts forward specific governance suggestions, such as setting access thresholds for third-party institutions and strengthening their independence and professionalism, to ensure that the iterative development of AI makes positive contributions to the sustainability of socio-technical systems.

Keywords: collaborative governance; full life cycle; credit entropy; tripartite evolutionary game; dynamic reward and punishment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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