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Regional Innovation Systems in Policy Laboratories

Author

Listed:
  • Andreas Pyka

    () (Institute for economics, University of Hohenheim, 70599 Stuttgart, Germany)

  • Matthias Mueller

    () (Institute for economics, University of Hohenheim, 70599 Stuttgart, Germany)

  • Muhamed Kudic

    () (Faculty of Business Studies and Economics, University of Bremen, 28359 Bremen, Germany)

Abstract

Innovation policy and business strategy often expect that investing in private and public research and development will immediately produce a flow of products and processes with high commercial and social returns. Policymakers and managers implicitly follow the logic underlying most linear innovation models assuming a well-defined and uni-directional relationship between R&D spending as input and innovation rents as output of the innovation process. Modern innovation economics dismisses the simplified approximation of knowledge by R&D investment and, instead, considers complex knowledge generation and diffusion processes in innovation networks. From this angle, the disappointing performance of traditional approaches is traced back to strong limits of conventional steering, control, and policy instruments. In this paper, we show that the new view of knowledge generation and diffusion in innovation networks allows for an alternative and has led to systemic approaches in innovation analyses. Combined with computational approaches like agent-based modeling, this new view enables today innovative tools in policy consulting. Using the example of regional innovation policy, we introduce a policy laboratory in which innovation processes can be analyzed in depth to see the impact of different innovation policy instruments in-silico. This ex-ante evaluation helps considerably to improve the understanding of innovation processes and with it the performance of innovation policy.

Suggested Citation

  • Andreas Pyka & Matthias Mueller & Muhamed Kudic, 2018. "Regional Innovation Systems in Policy Laboratories," Journal of Open Innovation: Technology, Market, and Complexity, MDPI, Open Access Journal, vol. 4(4), pages 1-18, September.
  • Handle: RePEc:gam:joitmc:v:4:y:2018:i:4:p:44-:d:171406
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    References listed on IDEAS

    as
    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Gilbert, Nigel & Ahrweiler, Petra & Pyka, Andreas, 2007. "Learning in innovation networks: Some simulation experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 100-109.
    3. Mansfield, Edwin & Schwartz, Mark & Wagner, Samuel, 1981. "Imitation Costs and Patents: An Empirical Study," Economic Journal, Royal Economic Society, vol. 91(364), pages 907-918, December.
    4. Andreas Pyka & Muhamed Kudic & Matthias Mueller, 2019. "Systemic interventions in regional innovation systems: entrepreneurship, knowledge accumulation and regional innovation," Regional Studies, Taylor & Francis Journals, vol. 53(9), pages 1321-1332, September.
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    More about this item

    Keywords

    ex-ante policy evaluation; policy laboratory; agent-based modelling; regional innovation system; knowledge diffusion;

    JEL classification:

    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics

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