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Agent-Based Modelling — A Methodology for the Analysis of Qualitative Development Processes

In: Agent-Based Computational Modelling

Author

Listed:
  • Andreas Pyka

    (University of Augsburg)

  • Thomas Grebel

    (GREDEG-IDEFI)

Abstract

Summary In economics numerical approaches are increasingly used for the analysis of dynamic phenomena of economic development since almost 30 years. The first generation of simulation models was rather focused on stylized empirical phenomena. With the emergence of agent-based modelling the last 10 years, however, the trade-off between simplicity and abstracting 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

  • Andreas Pyka & Thomas Grebel, 2006. "Agent-Based Modelling — A Methodology for the Analysis of Qualitative Development Processes," Contributions to Economics, in: Francesco C. Billari & Thomas Fent & Alexia Prskawetz & Jürgen Scheffran (ed.), Agent-Based Computational Modelling, pages 17-35, Springer.
    • Handle: RePEc:spr:conchp:978-3-7908-1721-8_2
    DOI: 10.1007/3-7908-1721-X_2
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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. Fagiolo G. & Roventini A., 2004. "Animal Spirits, Lumpy Investment, and the Business Cycle," Computing in Economics and Finance 2004 109, Society for Computational Economics.

    More about this item

    Keywords

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    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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