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Neo-Schumpeterian Simulation Models

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  • Windrum, Paul

    (MERIT)

Abstract

The use of simulation modelling techniques by neo-Schumpeterian economists dates back to Nelson and Winter’s 1982 book “An Evolutionary Theory of Economic Change”, and has rapidly expanded ever since. This paper considers the way in which successive generations of models have extended the boundaries of research (both with respect to the range of phenomena considered and the different dimensions of innovation that are considered), and while simultaneously introducing novel modelling techniques. At the same time, the paper will highlight the distinct set of features that have emerged in these neo-Schumpeterian models, and which set them apart from the models developed by other schools. In particular, they share a distinct view about the type of world in which real economic agents operate, and a invariably contain a generic set of algorithms. In addition to reviewing past models, the paper considers a number of pressing issues that remain unresolved and which modellers will need to address in future. Notable amongst these are the methodological relationship between empirical studies and simulation (e.g. ‘history friendly modelling’), the development of common standards for sensitivity analysis, and the need to further extend the boundaries of research in order to consider important aspects of innovation and technical change.

Suggested Citation

  • Windrum, Paul, 2004. "Neo-Schumpeterian Simulation Models," Research Memorandum 002, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
  • Handle: RePEc:unm:umamer:2004002
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    File URL: https://www.merit.unu.edu/publications/rmpdf/2004/rm2004-002.pdf
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    References listed on IDEAS

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    1. Silverberg, Gerald & Lehnert, Doris, 1993. "Long waves and 'evolutionary chaos' in a simple Schumpeterian model of embodied technical change," Structural Change and Economic Dynamics, Elsevier, vol. 4(1), pages 9-37, June.
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    7. Gerald Silverberg & Giovanni Dosi & Luigi Orsenigo, 2000. "Innovation, Diversity and Diffusion: A Self-Organisation Model," Chapters, in: Innovation, Organization and Economic Dynamics, chapter 14, pages 410-432, Edward Elgar Publishing.
    8. Malerba, Franco & Nelson, Richard & Orsenigo, Luigi & Winter, Sidney, 2001. "Competition and industrial policies in a 'history friendly' model of the evolution of the computer industry," International Journal of Industrial Organization, Elsevier, vol. 19(5), pages 635-664, April.
    9. Brock, W A, 1999. "Scaling in Economics: A Reader's Guide," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 8(3), pages 409-446, September.
    10. Windrum, Paul & Birchenhall, Chris, 1998. "Is product life cycle theory a special case? Dominant designs and the emergence of market niches through coevolutionary-learning," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 109-134, March.
    11. Windrum, Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    12. Soete, Luc & Turner, Roy, 1984. "Technology Diffusion and the Rate of Technical Change," Economic Journal, Royal Economic Society, vol. 94(375), pages 612-623, September.
    13. Brian Arthur, W. & Ermoliev, Yu. M. & Kaniovski, Yu. M., 1987. "Path-dependent processes and the emergence of macro-structure," European Journal of Operational Research, Elsevier, vol. 30(3), pages 294-303, June.
    14. Malerba, Franco, et al, 1999. "'History-Friendly' Models of Industry Evolution: The Computer Industry," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 8(1), pages 3-40, March.
    15. G. Silverberg & B. Verspagen, 1995. "Evolutionary Theorizing on Economic Growth," Working Papers wp95078, International Institute for Applied Systems Analysis.
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    Cited by:

    1. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Safarzynska, Karolina & van den Bergh, Jeroen C.J.M., 2011. "Beyond replicator dynamics: Innovation-selection dynamics and optimal diversity," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 229-245, May.
    3. Cevikarslan, Salih, 2013. "Optimal patent length and patent breadth in an R&D driven market with evolving consumer preferences: An evolutionary multi-agent based modelling approach," MERIT Working Papers 2013-020, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    4. Garavaglia, Christian, 2010. "Modelling industrial dynamics with "History-friendly" simulations," Structural Change and Economic Dynamics, Elsevier, vol. 21(4), pages 258-275, November.
    5. Cevikarslan S., 2015. "Research joint ventures in an R&D driven market with evolving consumer preferences: An evolutionary multi-agent based modelling approach," MERIT Working Papers 2015-007, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    6. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
    7. Frank Beckenbach & Maria Daskalakis & David Hofmann, 2018. "Agent-Based Analysis of Industrial Dynamics and Paths of Environmental Policy: The Case of Non-renewable Energy Production in Germany," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 953-994, October.
    8. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    9. Tilmann Rave & Ursula Triebswetter, 2006. "Economic impacts of environmental regulations," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 30, October.
    10. Paul Windrum & Chris Birchenhall, 2005. "Structural change in the presence of network externalities: a co-evolutionary model of technological successions," Journal of Evolutionary Economics, Springer, vol. 15(2), pages 123-148, January.
    11. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-8.
    12. Malerba, Franco & Nelson, Richard & Orsenigo, Luigi & Winter, Sidney, 2008. "Public policies and changing boundaries of firms in a "history-friendly" model of the co-evolution of the computer and semiconductor industries," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 355-380, August.
    13. Sun Hi Yoo & DongKyu Won, 2018. "Simulation of Weak Signals of Nanotechnology Innovation in Complex System," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    14. Cevikarslan, Salih, 2013. "Heterogeneity in innovation strategies, evolving consumer preferences and market structure: An evolutionary multi-agent based modelling approach," MERIT Working Papers 2013-019, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).

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