Computational Economics

, Volume 8, Issue 3, pp 233–253 | Cite as

Modular technical change and genetic algorithms

  • Chris Birchenhall


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 in introduced; a highly simplified version of the model is used to assess the use of the GA approach, particularly Arifovic's augmented version.

Key words

Genetic Algorithm Technical Change Distributed Learning 


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  1. Aho, A. V. Hopcroft, J. E. and Ullman, J. D., (1983)Data Structures and Algorithms, Addison Wesley.Google Scholar
  2. Arifovic, Jasmina (1994) “Genetic algorithm learning and the cobweb model”,Journal of Economic Dynamics and Control, Special Issue on Computer Science and Economics, 18, No 1, 3–28.Google Scholar
  3. Binmore, K. (1992)Fun and Games, D. C. Health & Co., Lexington.Google Scholar
  4. Birchenhall, C. R. (1994)Evolutionary Games and Genetic Algorithms, School of Economics Discussion Paper, University of Manchester, forthcoming inComputational Methods in Economics and Finance, edited by Manfred Gilli, Kluwer.Google Scholar
  5. Booch, G., (1991)Object-Orientated Design with Applications, Benjamin/Cummings, Redwood.Google Scholar
  6. Dahl, O.-J., Dijkstra, E. W. and Hoare, C. A. R., (1972)Structured Programming, Academic Press.Google Scholar
  7. Dasgupta, P. & Stoneman, P. (eds) (1987)Economic Policy and Technological Performance, CUP.Google Scholar
  8. David, P. (1987) “Some new standards for the economics of standardization in the information age”, in (Dasgupta and Stoneman, 1987).Google Scholar
  9. Games and Economic Behaviour (1993) special issues on Adaptive Dynamics Vol 5, Nos 3 & 4.Google Scholar
  10. Goldberg, David E. (1989)Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley.Google Scholar
  11. Hayek, F. A., (1978) “Competition as a Discovery Procedure”, Chapter Twelve ofNew Studies in Philosophy, Economics and the History of Ideas, Routledge & Kegan Paul; reprinted in (Witt, 1993).Google Scholar
  12. Holland, J. H., (1992)Adaptation in Natural and Artificial Systems, A Bradford Book, MIT Press. References are to the 1992 edition; the first edition was published in 1975.Google Scholar
  13. Holland, J. H., Holyoak K. J., Nisbett R. E. & Thagard, P. R. (1989)Induction: Processes of Inference, Learning, and Discovery, MIT Press.Google Scholar
  14. Journal of Economic Theory (1992) special issue on Evolutionary Game Theory, Vol 57, No 2.Google Scholar
  15. Johnson-Laird, P. N. (1988)The Computer and the Mind, Fontana.Google Scholar
  16. Kinnear, K. E. (1994) editorAdvances in Genetic Programming, A Bradford Book, MIT Press.Google Scholar
  17. Koza, J. R. (1992)Genetic Programming, A Bradford Book, MIT Press.Google Scholar
  18. Koza, J. R. (1994)Genetic Programming II, A Bradford Book, MIT Press.Google Scholar
  19. Koza, J. R. (1994) “Introduction to Genetic Programming”, in Kinnear (Kinnear, 1994).Google Scholar
  20. Kreps, D. M., (1990)Game Theory and Economic Modelling, Clarendon Press, Oxford.Google Scholar
  21. Langlois, R. N. & Robertson P. L., (1992) “Networks and innovation in a modular system: Lessons from the microcomputer and stereo component industries”,Research Policy, Vol 21, pp 297–313.Google Scholar
  22. Marimon, R. & McGrattan, E., (1993)On Adaptive Learning in Strategic Games, Economic Theory Discussion Paper No. 190, Department of Applied Economics, Cambridge.Google Scholar
  23. Journal of Economic Perspectives, Winter 1994, Vol 8 No 1, pp 3–72.Google Scholar
  24. Romer, P. (1992)Two Strategies for Economic Develpment: Using Ideas vs. Producing Ideas, World Bank Annual Conference on Development Economics 1992.Google Scholar
  25. Sargent, T. J., (1993)Bounded Rationality in Macroeconomis, Clarendon Press, Oxford.Google Scholar
  26. Simon, H. A. (1981)The Sciences of the Artificial, MIT Press.Google Scholar
  27. Struostrup, B., (1991)The C ++ Programming Language, 2nd Edition, Addison-Wesley.Google Scholar
  28. Witt U. (ed) (1993)Evolutionary Economics, Edward Elgar.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Chris Birchenhall
    • 1
  1. 1.School of Economic StudiesUniversity of ManchesterManchesterUK

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