When complexity does not mean chaos: nonlinear dynamics of entrepreneurial ecosystems

Abstract

Entrepreneurial ecosystems (EE) have received increasing attention from scholars, practitioners, and policymakers. Recently, EE are being conceptualized as complex adaptative systems (CAS), with ontological and epistemological implications for the field. Despite such conceptualization, scarce empirical evidence exists on whether EE can indeed be considered CAS. Building on established methods from complexity research, this study aims at answering the following research question: Do EE exhibit complex and/or chaotic behavior? Using time-series data from U.S. Metropolitan Statistical Areas (MSAs), we apply three techniques from chaos and complexity theory, already utilized in the field of EE—the Pointwise D2 (PD2), the Brock–Dechert–Scheinkman (BDS) test, and Local Largest Lyapunov Exponents (LLLEs). While prior work has applied these methods at the national level, our study extends the analysis to a more granular regional dataset, substantially increasing the empirical evidence available on the complex nature of EE. Results reveal that EE consistently display significant structural complexity, but deterministic chaos is not the dominant dynamic. Instead, EE evolve through nonlinear dependencies and structured complexity without widespread chaotic instability. Our findings open new conversations in the field of EE by demonstrating that EE can be understood as complex, but not necessarily chaotic, systems. This enhances the possibility of studying generalizable dynamics rather than relying solely on case-specific explanations, with important consequences for theory, empirical research, and policy. In particular, this implies that regional EE policy is not fundamentally constrained to purely bespoke, case-by-case interventions, but can instead draw on comparative evidence, transferable mechanisms, and adaptive policy design grounded in recurrent systemic patterns.