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Agent-based modelling - A methodology for the analysis of qualitative development processes

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Abstract

The tremendous development of an easy access to computational power within the last 30 years has led to the widespread use of numerical approaches in almost all scientific disciplines. The first generation of simulation models was rather focused on stylized empirical phenomena. With agent-based modelling, however, the trade-off between simplicity in modelling and taking into account the complexity of the socio-economic reality has been enhanced to a large extent. This paper serves as a basic instruction on how to model qualitative change using an agent-based modelling procedure. The necessity to focus on qualitative change is discussed, agent-based modelling is explained and finally an example is given to show the basic simplicity in modelling.

Suggested Citation

  • Thomas Grebel & Andreas Pyka, 2003. "Agent-based modelling - A methodology for the analysis of qualitative development processes," Discussion Paper Series 251, Universitaet Augsburg, Institute for Economics.
    • Handle: RePEc:aug:augsbe:0251
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    File URL: https://opus.bibliothek.uni-augsburg.de/opus4/files/71206/251.pdf
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    1. Thomas Grebel & Andreas Pyka & Horst Hanusch, 2004. "An evolutionary approach to the theory of entrepreneurship," Chapters, in: John Foster & Werner Hölzl (ed.), Applied Evolutionary Economics and Complex Systems, chapter 7, Edward Elgar Publishing.
    2. Evans, David S & Jovanovic, Boyan, 1989. "An Estimated Model of Entrepreneurial Choice under Liquidity Constraints," Journal of Political Economy, University of Chicago Press, vol. 97(4), pages 808-827, August.
    3. Boulding, K E, 1991. "What Is Evolutionary Economics?," Journal of Evolutionary Economics, Springer, vol. 1(1), pages 9-17, January.
    4. Cantner, Uwe & Pyka, Andreas, 1998. "Technological evolution -- an analysis within the knowledge-based approach," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 85-107, March.
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    Cited by:

    1. Kurt Dopfer, 2016. "Evolutionary economics," Chapters, in: Gilbert Faccarello & Heinz D. Kurz (ed.), Handbook on the History of Economic Analysis Volume III, chapter 14, pages 175-193, Edward Elgar Publishing.
    2. 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).
    3. Leonardo Augusto Amaral Terra & João Luiz Passador, 2018. "Strategies for the Study of Complex Socio-Economic Systems: an Approach Using Agent-Based Simulation," Systemic Practice and Action Research, Springer, vol. 31(3), pages 311-325, June.
    4. Fagiolo G. & Roventini A., 2004. "Animal Spirits, Lumpy Investment, and the Business Cycle," Computing in Economics and Finance 2004 109, Society for Computational Economics.
    5. G. Blind & A. Pyka, 2014. "The rule approach in evolutionary economics: A methodological template for empirical research," Journal of Evolutionary Economics, Springer, vol. 24(5), pages 1085-1105, November.
    6. Giovanni Dosi & Giorgio Fagiolo & Andrea Roventini, 2005. "Animal Spirits, Lumpy Investment, and Endogenous Business Cycles," LEM Papers Series 2005/04, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. 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).
    8. Gabriele Tedeschi & Stefania Vitali & Mauro Gallegati, 2014. "The dynamic of innovation networks: a switching model on technological change," Journal of Evolutionary Economics, Springer, vol. 24(4), pages 817-834, September.
    9. Gianpaolo Abatecola & Alberto Surace, 2020. "Discussing the Use of Complexity Theory in Engineering Management: Implications for Sustainability," Sustainability, MDPI, vol. 12(24), pages 1-24, December.

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    More about this item

    Keywords

    agent-based modelling; methodology; evolutionary economics; qualitative change;
    All these keywords.

    JEL classification:

    • B52 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Historical; Institutional; Evolutionary; Modern Monetary Theory;
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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