The review titled Innovation System Policy Analysis presents a comprehensive analysis of how feedback mechanisms, policy interventions, and computational modeling shape innovation ecosystems. The paper categorizes applications of system dynamics into key areas, primarily R&D policy, innovation diffusion, science and technology policies, and regional innovation clusters. Each of these areas highlights the role of feedback loops in reinforcing or balancing innovation growth.
One of the primary contributions of this review is its emphasis on system dynamics models as exploratory tools rather than purely predictive ones. This perspective aligns with the need for adaptive policy design, where AI can play a crucial role in enhancing simulations and decision support. By integrating AI with system dynamics, it becomes possible to incorporate real-time data, refine policy scenarios, and reduce uncertainty in decision-making processes within startup accelerators and broader innovation ecosystems.
Moreover, the review provides compelling evidence that system dynamics and AI can mutually enhance each other’s roles in decision support. System dynamics offers a structured way to understand complex interdependencies, while AI introduces advanced pattern recognition and predictive capabilities. Together, they can create more dynamic and responsive decision-support systems, improving strategic planning in rapidly changing innovation environments.
The study also discusses the challenges of modeling innovation policies, particularly regarding data limitations and the need for interdisciplinary approaches. The review suggests that combining qualitative insights with quantitative models enhances the robustness of system dynamics applications. This observation is particularly relevant for AI-augmented decision frameworks, where machine learning can supplement system dynamics by identifying hidden patterns in complex innovation networks.
Beyond direct insights, this review also references several foundational works that provide further direction for research. Wieczorek and Hekkert’s framework on systemic policy failures offers a structured way to assess gaps in innovation ecosystems, while Lundvall’s work on national and regional innovation systems provides historical and theoretical perspectives on policy evolution.
Overall, the review underscores the growing relevance of system dynamics in innovation policy analysis and decision support. As AI continues to advance, its integration with system dynamics presents new opportunities for refining policy strategies, enhancing simulation capabilities, and better understanding the nonlinear behaviors of innovation ecosystems.