Evolutionary Game Analysis of Digital Product Copyright Protection in the Context of Generative Artificial Intelligence

Abstract

The rapid development of generative artificial intelligence (AI) poses new challenges to digital product copyright governance. Considering unauthorized training-data use, infringing AI-generated outputs, nonzero infringement costs, and uncertain enforcement outcomes, this paper constructs a two-population evolutionary game model between generative AI agents and digital copyright holders. The model incorporates infringement costs, probabilistic enforcement, and static and dynamic punishment mechanisms to examine the evolution of compliance, infringement, active enforcement, and passive tolerance strategies. The results show that under static punishment, the system may fall into widespread infringement, periodic oscillations, or passive tolerance. To overcome these limitations, a dynamic punishment mechanism is introduced, in which penalty intensity adjusts according to the AI side’s compliance rate. MATLAB simulations and sensitivity analyses based on normalized but industry-informed parameters verify the theoretical results. The findings indicate that dynamic punishment can improve system stability and guide periodic fluctuations toward a stable mixed–strategy equilibrium, thereby promoting a more predictable balance between AI compliance and copyright enforcement. This study provides theoretical support for adaptive copyright governance in the era of generative AI.