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Regional Innovation Systems: An Agent-Based Laboratory for Policy Advice

  • Cristina PonsiglioneEmail author
  • Ivana Quinto
  • Giuseppe Zollo
Chapter
  • 833 Downloads
Part of the Economic Complexity and Evolution book series (ECAE)

Abstract

The chapter presents a computational model for the development of a self-sustaining Regional Innovation System (RIS). The computational agent-based model is the core of a virtual laboratory, called CARIS (Complex Adaptive Regional Innovation System) aiming at (1) introducing the CAS (Complex Adaptive System) approach in the analysis of RISs; (2) enabling the development of effective innovation policies able to foster the growth and innovativeness of regions. This topic is particularly relevant for the so-called lagging regions, which, despite conspicuous policy interventions, have been unable to develop a significant capability to innovate. According to the European Union, lagging regions are those regions which show a GDP per capita less than 75 % of the European average. In this chapter, the methodological approach to verify the internal coherence of the model, as well as the simulation outputs are thoroughly discussed. Results show that the code is free of evident bugs, that it works coherently with the meta-model and that the agent-based computational model is able to reproduce some stylized representations characterizing the system under investigation. Finally, the first steps of the calibration activities and some preliminary results are described. Once fully validated, the CARIS laboratory should help researchers and practitioners to better investigate what critical mass of local resources and competencies are necessary to sustain the growth of RISs and, how effective current innovation policies are and what are the most effective measures to improve the current pattern.

Keywords

Innovation System Competitive Environment Innovation Policy Aerospace Industry Complex Adaptive System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Cristina Ponsiglione
    • 1
    Email author
  • Ivana Quinto
    • 1
    • 2
  • Giuseppe Zollo
    • 1
  1. 1.Department of Industrial EngineeringUniversity of Naples Federico IINaplesItaly
  2. 2.Online Pegaso UniversityNaplesItaly

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