Abstract for: System Dynamics of Emerging Competitive Analysis: A Comparative Analysis of AI and Biotech Innovation Ecosystems

The AI and Biotechnology sectors in Taiwan are both key drivers of technological innovation but follow distinct development trajectories. AI benefits from rapid iteration and data-driven growth, while Biotech faces long R&D cycles and regulatory constraints. This study adopts a Multi-Level Perspective (MLP) as a main framework. Combining Scientometrics, Muti Criteria Decision-Making, Thematic analysis, and System Dynamics to examine how feedback loops and transition dynamics shape their innovation ecosystems. This study combines Scientometrics, Muti Criteria Decision-Making, Thematic analysis, and System Dynamics modeling to compare AI and Biotech innovation ecosystems in Taiwan, focusing on reinforcing and balancing feedback loops. The Multi-Level Perspective (MLP) is used to analyze how niche, regime, and landscape factors influence each sector's evolution. Data is drawn from policy reports, industry case studies, and expert interviews to map key transition dynamics. AI innovation is characterized by reinforcing feedback loops (e.g., government incentives, rapid adoption), enabling faster integration into the market. In contrast, Biotech experiences strong balancing loops (e.g., regulatory delays, high R&D costs), leading to slower transitions. AI startups thrive in data-rich sectors, whereas Biotech firms require strong academia-industry collaboration to scale. AI and Biotech innovation ecosystems exhibit different system dynamics due to regulatory, financial, and technological factors. While AI benefits from fast-cycle innovation and global competitiveness, Biotech requires long-term policy support and public-private partnerships. Understanding these dynamics through System Dynamics and MLP framework can help policymakers design targeted interventions to enhance industry growth and sustainability.