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Modular Technical Change and Genetic Algorithms

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  • Birchenhall, Chris
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    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.

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    Bibliographic Info

    Article provided by Society for Computational Economics in its journal Computational Economics.

    Volume (Year): 8 (1995)
    Issue (Month): 3 (August)
    Pages: 233-53

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    Handle: RePEc:kap:compec:v:8:y:1995:i:3:p:233-53

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    Cited by:
    1. Tesfatsion, Leigh, 1997. "How Economists Can Get Alife," Staff General Research Papers 1685, Iowa State University, Department of Economics.
    2. Riechmann, Thomas, 1997. "Learning and Behavoiral Stability - An Economic Interpretation of Genetic Algorithms," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover dp-209, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    3. 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.
    4. 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.
    5. 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.
    6. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
    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. 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.
    9. Clemens, Christiane & Riechmann, Thomas, 1996. "Evolutionäre Optimierungsverfahren und ihr Einsatz in der ökonomischen Forschung," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover dp-195, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    10. Tesfatsion, Leigh S., 1998. "Teaching Agent-Based Computational Economics to Graduate Students," Staff General Research Papers 1199, Iowa State University, Department of Economics.
    11. Sándor Karajz, 2007. "Genetic Algorithms as Optimalisation Procedures," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 4(01), pages 37-41.
    12. 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).

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