Self-Sustaining Innovation in Regions: A Complex-Adaptive Systems Approach

Purpose - The aim of this paper is to present a computational laboratory to explore how to support the development of Regional Innovation Systems (RISs) in so-called lagging regions. Over the years, models and tools to define effective innovation policies have been developed. Notwithstanding, there is a strong discrepancy among proposed theoretical frameworks, innovation policies and related regional performance. The research questions we attempt to answer are: i) what are critical masses of resources and competencies necessary to sustain the growth of RISs? ii) how much effective are current innovation policies; iii) what are the most effective policies to reassess their current pattern? Design/methodology/approach - To address the research questions we adopt an approach grounded on complexity science and we consider RISs as Complex Adaptive Systems (CASs) (Squazzoni and Boero, 2002). Agent-Based Modeling is one of the most suited methodological approaches to analyze CASs (Heath et al, 2009) and it has been increasingly recognized as a useful tool to support policy-making in different fields and at different levels (OECD, 2009; Brenner and Werker, 2009). Therefore, according to this, we propose an agent-based computational laboratory to support policy-makers in assessing and defining the most adequate regional innovation policies. Originality/value - The proposed lab introduces the CAS approach in the analysis of RISs by integrating the key concepts of traditional perspectives on territorial innovation systems with new ones. Although the complexity has been recognized as a distinctive feature of territorial innovation systems, it has been poorly used to develop innovation policies to support the competitiveness of regions. Additionally, while the agent-based models proposed in the literature are used mainly with the aim of theory building and are poorly validated against reality, the CARIS lab has been built to be a simulation tool for policy advice (Brenner and Werker, 2009). Practical implications - Once fully developed, the CARIS laboratory should help researchers and practitioners to better investigate what are critical masses of resources and competencies necessary to sustain the growth of RISs, how much effective are current innovation policies and what are the most effective policies to reassess the current pattern. According to the European Commission indications, such topic is very relevant, in particular, for lagging Regions, which, despite conspicuous policy interventions, have been unable to develop significant innovation patterns. As the validation process will be completed, the computational laboratory could be used as a policy advice tool.