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Modular Technical Change and Genetic Algorithms

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  • Birchenhall, Chris

Abstract

Given knowledge is distributed across the economic population, it is appropriate to consider technical change as a process of distributed learning. This leads naturally to an evolutionary perspective. Noting the work of cognitive sciences, which uses a computational model of the mind, we are drawn to models based on genetic algorithms (GAs). Using the concept of modular technologies we are able to offer an interpretation of the GA as a model of population learning. A model involving the coevolution of implemented technologies and technological models is introduced; a highly simplified version of the model is used to assess the use of the GA approach, particularly Arifovic's augmented version. Citation Copyright 1995 by Kluwer Academic Publishers.

Suggested Citation

  • Birchenhall, Chris, 1995. "Modular Technical Change and Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 8(3), pages 233-253, August.
  • Handle: RePEc:kap:compec:v:8:y:1995:i:3:p:233-53
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    Cited by:

    1. Sándor Karajz, 2007. "Genetic Algorithms as Optimalisation Procedures," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 4(01), pages 37-41.
    2. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
    3. Roos, Michael W. M., 2015. "The macroeconomics of radical uncertainty," Ruhr Economic Papers 592, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Tesfatsion, Leigh S., 1998. "Teaching Agent-Based Computational Economics to Graduate Students," Staff General Research Papers Archive 1199, Iowa State University, Department of Economics.
    5. Thomas Riechmann, 1999. "Learning and behavioral stability An economic interpretation of genetic algorithms," Journal of Evolutionary Economics, Springer, vol. 9(2), pages 225-242.
    6. Leigh TESFATSION, 1995. "How Economists Can Get Alife," Economic Report 37, Iowa State University Department of Economics.
    7. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
    8. van den Bergh, Jeroen C.J.M., 2008. "Optimal diversity: Increasing returns versus recombinant innovation," Journal of Economic Behavior & Organization, Elsevier, vol. 68(3-4), pages 565-580, December.
    9. Sylvie Geisendorf, 2016. "The impact of personal beliefs on climate change: the “battle of perspectives” revisited," Journal of Evolutionary Economics, Springer, vol. 26(3), pages 551-580, July.
    10. Frenken, Koen & Windrum, Paul, 2000. "Product differentiation and product complexity: A conceptual model and an empirical application to microcomputers," Research Memorandum 019, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    11. Clemens, Christiane & Riechmann, Thomas, 1996. "Evolutionäre Optimierungsverfahren und ihr Einsatz in der ökonomischen Forschung," Hannover Economic Papers (HEP) dp-195, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    12. Chapman, Lisa & Beare, Stephen, 2001. "Optimal fisheries management innstruments under biological uncertainty," 2001 Conference (45th), January 23-25, 2001, Adelaide 171991, Australian Agricultural and Resource Economics Society.
    13. 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.
    14. Bulat Sanditov, 2005. "Patent Citations, the Value of Innovations and Path-Dependency," KITeS Working Papers 177, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Nov 2005.
    15. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
    16. Andrea Bonaccorsi, 2011. "A Functional Theory of Technology and Technological Change," Chapters,in: Handbook on the Economic Complexity of Technological Change, chapter 12 Edward Elgar Publishing.

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