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

Author

Listed:
  • 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)

Abstract
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
    as

    Download full text from publisher

    File URL: https://opus.bibliothek.uni-augsburg.de/opus4/files/71152/287.pdf
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    References listed on IDEAS

    as
    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 and the Associazione ICC, vol. 7(2), pages 369-397, June.
    3. Alice Lam, 2003. "Organizational Learning in Multinationals: R&D Networks of Japanese and US MNEs in the UK," Journal of Management Studies, Wiley Blackwell, vol. 40(3), pages 673-703, May.
    4. Petra Ahrweiler & Andreas Pyka & Nigel Gilbert, 2004. "Simulating Knowledge Dynamics In Innovation Networks (Skin)," World Scientific Book Chapters, in: Roberto Leombruni & Matteo Richiardi (ed.), Industry And Labor Dynamics The Agent-Based Computational Economics Approach, chapter 14, pages 284-296, World Scientific Publishing Co. Pte. Ltd..
    5. 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.
    6. N. Gilbert, 1997. "A Simulation of the Structure of Academic Science," Sociological Research Online, , vol. 2(2), pages 91-105, June.
    7. Horst Hanusch & Andreas Pyka, 2007. "Principles of Neo-Schumpeterian Economics," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 31(2), pages 275-289, March.
    8. Amable, Bruno, 2003. "The Diversity of Modern Capitalism," OUP Catalogue, Oxford University Press, number 9780199261147.
    9. Richard C. Levin & Peter C. Reiss, 1988. "Cost-Reducing and Demand-Creating R&D with Spillovers," RAND Journal of Economics, The RAND Corporation, vol. 19(4), pages 538-556, Winter.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Kurt Dopfer, 2011. "Economics in a Cultural Key: Complexity and Evolution Revisited," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 14, Edward Elgar Publishing.
    5. 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.
    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.

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    More about this item

    Keywords

    innovation networks; agent-based modelling; scale free networks;
    All these keywords.

    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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