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Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks


  • Andreas Pyka

    (University of Augsburg, Department of Economics)

  • Nigel Gilbert

    (School of Human Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom)

  • Petra Ahrweiler

    (Research Center Media and Politics, Institute for Political Science, University of Hamburg, Germany)


An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach al-lows the representation of heterogeneous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.

Suggested Citation

  • Andreas Pyka & Nigel Gilbert & Petra Ahrweiler, 2006. "Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks," Discussion Paper Series 287, Universitaet Augsburg, Institute for Economics.
  • Handle: RePEc:aug:augsbe:0287

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    References listed on IDEAS

    1. d'Aspremont, Claude & Jacquemin, Alexis, 1988. "Cooperative and Noncooperative R&D in Duopoly with Spillovers," American Economic Review, American Economic Association, vol. 78(5), pages 1133-1137, December.
    2. Cantner, Uwe & Pyka, Andreas, 1998. "Absorbing Technological Spillovers: Simulations in an Evolutionary Framework," Industrial and Corporate Change, Oxford University Press, vol. 7(2), pages 369-397, June.
    3. Petra Ahrweiler & Andreas Pyka & Nigel Gilbert, 2004. "Simulating Knowledge Dynamics In Innovation Networks (Skin)," World Scientific Book Chapters,in: Industry And Labor Dynamics The Agent-Based Computational Economics Approach, chapter 14, pages 284-296 World Scientific Publishing Co. Pte. Ltd..
    4. Cohen, Wesley M & Levinthal, Daniel A, 1989. "Innovation and Learning: The Two Faces of R&D," Economic Journal, Royal Economic Society, vol. 99(397), pages 569-596, September.
    5. Horst Hanusch & Andreas Pyka, 2007. "Principles of Neo-Schumpeterian Economics," Cambridge Journal of Economics, Oxford University Press, vol. 31(2), pages 275-289, March.
    6. Amable, Bruno, 2003. "The Diversity of Modern Capitalism," OUP Catalogue, Oxford University Press, number 9780199261147, June.
    7. Nigel Gilbert, 1997. "A Simulation of the Structure of Academic Science," Sociological Research Online, Sociological Research Online, vol. 2(2), pages 1-3.
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    Cited by:

    1. Olivier Barreteau & Christophe Le Page, 2011. "Using Social Simulation to Explore the Dynamics at Stake in Participatory Research," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(4), pages 1-12.
    2. Dan Farhat, 2013. "An Agent-based Model of Interdisciplinary Science and the Evolution of Scientific Research Networks," Working Papers 1302, University of Otago, Department of Economics, revised Jan 2013.
    3. Bruce Edmonds & Nigel Gilbert & Petra Ahrweiler & Andrea Scharnhorst, 2011. "Simulating the Social Processes of Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(4), pages 1-14.
    4. Blom, Martin & Castellacci, Fulvio & Fevolden, Arne, 2012. "Defence firms facing liberalization: innovation and export in an agent-based model of the defence industry," MPRA Paper 35702, University Library of Munich, Germany.
    5. Kurt Dopfer, 2011. "Economics in a Cultural Key: Complexity and Evolution Revisited," Chapters,in: The Elgar Companion to Recent Economic Methodology, chapter 14 Edward Elgar Publishing.
    6. Flavio Lenz-Cesar & Almas Heshmati, 2010. "Agent-based Simulation of Cooperative Innovation," TEMEP Discussion Papers 201052, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jan 2010.

    More about this item


    innovation networks; agent-based modelling; scale free networks;

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • L22 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Organization and Market Structure

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