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Agency and structure: a social simulation of knowledge-intensive industries

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  • Petra Ahrweiler

    (University College Dublin)

  • Nigel Gilbert

    (University of Surrey)

  • Andreas Pyka

    (University of Hohenheim)

Abstract

Modern knowledge-intensive economies are complex social systems where intertwining factors are responsible for the shaping of emerging industries: the self-organising interaction patterns and strategies of the individual actors (an agency-oriented pattern) and the institutional frameworks of different innovation systems (a structure-oriented pattern). In this paper, we examine the relative primacy of the two patterns in the development of innovation networks, and find that both are important. In order to investigate the relative significance of strategic decision making by innovation network actors and the roles played by national institutional settings, we use an agent-based model of knowledge-intensive innovation networks, SKIN. We experiment with the simulation of different actor strategies and different access conditions to capital in order to study the resulting effects on innovation performance and size of the industry. Our analysis suggests that actors are able to compensate for structural limitations through strategic collaborations. The implications for public policy are outlined.

Suggested Citation

  • Petra Ahrweiler & Nigel Gilbert & Andreas Pyka, 2011. "Agency and structure: a social simulation of knowledge-intensive industries," Computational and Mathematical Organization Theory, Springer, vol. 17(1), pages 59-76, March.
  • Handle: RePEc:spr:comaot:v:17:y:2011:i:1:d:10.1007_s10588-010-9081-3
    DOI: 10.1007/s10588-010-9081-3
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    References listed on IDEAS

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    Cited by:

    1. Davide Secchi & Raffaello Seri, 2017. "Controlling for false negatives in agent-based models: a review of power analysis in organizational research," Computational and Mathematical Organization Theory, Springer, vol. 23(1), pages 94-121, March.
    2. Paredes-Frigolett, Harold & Pyka, Andreas & Leoneti, Alexandre Bevilacqua, 2021. "On the performance and strategy of innovation systems: A multicriteria group decision analysis approach," Technology in Society, Elsevier, vol. 67(C).
    3. Liang Chen & Guy G. Gable & Haibo Hu, 2013. "Communication and organizational social networks: a simulation model," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 460-479, December.
    4. Martin Blom & Fulvio Castellacci & Arne Fevolden, 2014. "Defence firms facing liberalization: innovation and export in an agent-based model of the defence industry," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 430-461, December.
    5. Hyoung-Goo Kang & Richard M. Burton & Will Mitchell, 2021. "How firm boundaries and relatedness jointly affect diversification value: trade-offs between governance and flexibility," Computational and Mathematical Organization Theory, Springer, vol. 27(1), pages 1-34, March.
    6. Blom, Martin & Castellacci, Fulvio & Fevolden, Arne Martin, 2013. "The trade-off between innovation and defense industrial policy: A simulation model analysis of the Norwegian defense industry," Technological Forecasting and Social Change, Elsevier, vol. 80(8), pages 1579-1592.
    7. William Martin Tracy & M. V. Shyam Kumar & William Paczkowski, 2013. "Parametric interdependence, learning-by-doing, and industrial structure," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 580-600, December.

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