Policy-driven or ecosystem-autonomous? Evolutionary game and regulatory effects of digital innovation in future industries

The cultivation of new-quality productive forces centers on future industries, with the key lying in digital innovation within knowledge ecosystems. Existing research lacks a systematic analysis of how policy regulations dynamically guide the evolution of innovation ecosystems, leading to mismatches between policy provisions and industrial upgrading needs, as well as unclear mechanisms governing the interaction between policy regulation and ecosystem evolution. This paper establishes a tripartite evolutionary game model involving the government, knowledge producers, and knowledge consumers, combining MATLAB simulations with the Deepwise Smart Healthcare case study to systematically elucidate the interaction dynamics among policies, ecosystems, and digital technologies. The findings reveal: (1) Digitalization exhibits a critical threshold effect, where knowledge sharing transitions from localized participation to ecosystem emergence upon surpassing the threshold; (2) Incentive-based policies enhance stakeholder engagement through incentive-compatible mechanisms, demonstrating significantly superior long-term efficacy and cost-effectiveness compared to mandatory policies; (3) System evolution follows a transition path from policy-driven to ecosystem autonomy, with digitalization serving as the pivotal moderating variable determining this transformation. The study proposes coordinated strategies across three dimensions—government regulation, knowledge systems, and digital empowerment—to provide a theoretical foundation for optimizing innovation governance and accelerating the development of new-quality productive forces.