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Computer Simulation Methods to Model Macroeconomics

  • Armand de Callataÿ
Chapter
Part of the Methodos Series book series (METH, volume 1)

Abstract

After an introduction on design and Simulation (section 1), reverse engineering, functional equivalence (section 2) and computerized large-scale Simulation models (section 3), an example of macroeconomic model is described (section 4). The specificity of the method for constructing and validating models is described in section 5. Its relevance for econometric models in social science is discussed in section 6. The interactions of the model components are briefly explained in section 7. The predictive and the explanatory power are assessed in section 8. In section 9, it is suggested that the large-scale models can solve problems that reductionist approaches do not address. The qualitative and quantitative aspects of the Simulation models are studied in section 10. Models are used as learning tools in educative games (section 11). The Validation of econometric models is still poorly done (section 12).

Keywords

Civil Servant Econometric Model Reverse Engineering Economic Rule Business Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. de Callataÿ, A. (1972). A Simulation game of local govemments in competitive cities. In J. Dreyfus (Ed.), Rationalisation des choix en urbanisme. Paris: Dunod.Google Scholar
  2. de Callataÿ, A. (1991). Paradigm shifts in artificial intelligence. In P. A. Flach & R. Meersman (Eds.), Future directions in artificial intelligence (pp. 37–52). Amsterdam: Elsevier-North HollandGoogle Scholar
  3. de Callataÿ, A. (1992). Natural and artificial intelligence: misconceptions about brains and neural networks. Amsterdam: Elsevier-North Holland.Google Scholar
  4. de Callataÿ, A. (1996). Are rule-based neural networks biologically plausible? Connection Science, 8, 115–151.CrossRefGoogle Scholar
  5. de Callataÿ, A. (1997a). Reverse engineering methods to study the brain (Research report XLKL-97.014). Bruxelles: XL Knowledge Lab.Google Scholar
  6. de Callataÿ, A. (1997b). Cost of education and productivity improvements. Communication and Cognition, 14(4), 267–290.Google Scholar
  7. Feynman, R. (1965). The characters ofthephysical laws. Cambridge, MA: M.I.T. Press.Google Scholar
  8. Forrester, J. (1969). Urban dynamics. Cambridge, MA: M.I.T. Press.Google Scholar
  9. Franck, R. (1997). Individualisme et holisme obsolètes, l’analyse en multiples niveaux. Note de synthèse, Université Catholique de Louvain.Google Scholar
  10. Hopfield, J.J. (1982). Neural networks and physical Systems with emergent collective computational abilities. Proceedings National Academy of Science USA, 79, 2554–2558.CrossRefGoogle Scholar
  11. Kauffman, S.A (1993). The origins oforder: seif Organization and selection in evolution. Oxford: Oxford University Press.Google Scholar
  12. Minsky, M. (1981). Criticism of the logistic approach, Appendix to “A framework for representing knowledge”. In J. Haugeland (Ed.), Mind Design. Cambridge, MA: M.I.T. Press.Google Scholar
  13. Minsky, M. (1986). The society of mind. New York: Simon & Schuster.Google Scholar
  14. Minsky, M. (1990). Logical vs analogical or symbolic vs connectionist or neat vs scruffy. In P. H. Winston & S. A. Shellard (Eds.), Artificial Intelligence at MIT: Expanding frontiers (vol. 1, pp. 218–243). Cambridge, MA: M.I.T. Press.Google Scholar
  15. International Monetary Fund (IMF) Statistics 1995–1996.Google Scholar
  16. Organization for Economic Cooperation and Development (OECD) Statistics 1994–1995.Google Scholar
  17. Romer, D. (1996). Advanced macroeconomics. New York: McGraw-Hill.Google Scholar
  18. Samuelson, P.A., & Nordhaus, W.D. (1995). Economics. New York: McGraw-Hill.Google Scholar
  19. Simon, H.A. (1991). Models qfmy life. New York: Basic Books.Google Scholar
  20. Simon, H.A. (1996). The sciences of the artificial. Cambridge, MA: M.I.T. Press. (Original work published 1969.)Google Scholar
  21. World Almanac Books (2001). The World Almanac. Mahwah, N.J.Google Scholar

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© Springer Science+Business Media New York 2002

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  • Armand de Callataÿ

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