Agent-Based Computational Macro-economics: A Survey

Conference paper


While by all standards the macroeconomic system is qualified to be a complex adaptive system, mainstream macroeconomics is not capable of demonstrating this feature. Recent applications of agent-based modeling to macroeconomics define a new research direction, which demonstrates how the macroeconomic system can be modeled and studied as a complex adaptive system. This paper shall review the development of agent-based computational modeling in macroeconomics.


Complex adaptive system Agent-based computational economics Adaptive economic agents Rational expectations equilibrium 


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  1. Arifovic J. (1994) Genetic algorithms learning and the cobweb model. Journal of Economic Dynamics and Control 18(1), 3–28.zbMATHCrossRefGoogle Scholar
  2. Arifovic J. (1995) Genetic algorithms and inflationary economies. Journal of Monetary Economics 36(1), 219–243.CrossRefGoogle Scholar
  3. Arifovic J. (1996) The behavior of the exchange rate in the genetic algorithm and experimental economies. Journal of Political Economy 104(3), 510–541.CrossRefGoogle Scholar
  4. Arifovic J. (2001) Evolutionary dynamics of currency substitution. Journal of Economic Dynamics and Control 25, 395–417.zbMATHCrossRefGoogle Scholar
  5. Arifovic J. (2002) Exchange Rate Volatility in the Artificial Foreign Exchange Market. In: Chen S.-H. (Ed.), Evolutionary Computation in Economics and Finance, Physica-Verlag. 125–136.Google Scholar
  6. Arifovic J., Gencay R. (2000) Statistical properties of genetic learning in a model of exchange rate. Journal of Economic Dynamics and Control 24, 981–1005.zbMATHCrossRefGoogle Scholar
  7. Arthur B. (1992) On learning and adaptation in the economy. SFI Economics Research Program, 92-07-038.Google Scholar
  8. Arthur W. B., Holland J., LeBaron B., Palmer R., Tayler P. (1997) Asset pricing under endogenous expectations in an artificial stock market. In: Arthur W. B., Durlauf S., Lane D. (Eds.), The Economy as an Evolving Complex System II. Addison-Wesley, Reading, MA, 15–44.Google Scholar
  9. Azariadis C, Guesnerie R. (1986) Sunspots and cycle. Review of Economic Studies LIII, 725–737.MathSciNetCrossRefGoogle Scholar
  10. Bell R., Beare S. (2002) Emulating trade in emissions permits: An application of genetic algorithms. In: Chen S.-H. (Ed.), Evolutionary Computation in Economics and Finance, Heidelberg: Physica-Verlag, 161–175.Google Scholar
  11. Birchenhall, C. R., Lin J.-S. Lin (2002) Learning and Convergence to Pareto Optimality. In: Chen S.-H. (Ed.), Genetic Algorithms and Geentic Programming in Computational Finance, Kluwer, 419–440.Google Scholar
  12. Bullard J. (1992) Samuelson’s model of money with n-period of lifetimes. Federal Reserve Bank of St. Louis Review, May/June, 67–82.Google Scholar
  13. Bullard J., Duffy J. (1998) A model of learning and emulation with artificial adaptive agents. Journal of Economic Dynamics and Control 22, 179–207.MathSciNetCrossRefGoogle Scholar
  14. Bullard J., Duffy J. (1998) Learning and the stability of cycles. Macroeconomic Dynamics 2(1), 22–48.zbMATHGoogle Scholar
  15. Bullard J., Duffy J. (1999) Using genetic algorithms to model the evolution of heterogeneous beliefs. Computational Economics 13(1), 41–60zbMATHCrossRefGoogle Scholar
  16. Chan, N. T., LeBaron B., Lo, A. W. and Poggio T. (1999). Agent-based models of financial markets: A comparison with experimental markets. Unpublished Working Paper, MIT Artificial Markets Project, MIT, MA.Google Scholar
  17. Chen S.-H. (1997) On the artificial life of the general economic system (I): the role of selection pressure. In: Hara F., Yoshida K. (Eds.), Proceedings of International Symposium on System Life, 233–240.Google Scholar
  18. Chen S.-H. (2001) On the relevance of genetic programming to evolutionary economics. In: Aruka Y. (Ed.), Evolutionary Controversies in Economics: A New Transdisciplinary Approach. Springer-Verlag, Tokyo, 135–150.Google Scholar
  19. Chen S.-H. (2002) Fundamental issues in the use of genetic programming in agentbased computational economics. In: Namatame A., Terano T., Kurumatani K. (Eds), Agent-based Approaches in Economic and Social Complex Systems, IOS Press, 208–220.Google Scholar
  20. Chen S.-H., Hwang Y.-C. (2002) Simulating the evolution of portfolio behavior in a multiple-asset agent-based artificial stock market. AI-ECON Research Center Working Paper, National Chengchi University.Google Scholar
  21. Chen S.-H., Kuo T.-W. (1999) Towards an agent-based foundation of financial econometrics: an approach based on genetic-programming artificial markets. In: Banzhaf W., Daida J., Eiben A. E., Garzon M. H., Honavar V., Jakiela M., Smith R. E. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, Vol. 2. Morgan Kaufmann, 966–973.Google Scholar
  22. Chen S.-H, Liao C.-C. (2002a) Price discovery in agent-based computational modeling of artificial stock markets. In Chen S.-H. (Ed), Genetic Algorithms and Genetic Programming in Computational Finance, Kluwer. 333–354Google Scholar
  23. Chen S.-H., Liao C.-C. (2002b) Testing for Granger causality in the stock-price volume relation: A perspective from the agent-based model of stock markets. Information Sciences, forthcoming.Google Scholar
  24. Chen S.-H., Liao C.-C. (2002c) Understanding sunspots: An analysis based on agent-based artificial stock markets. AI-ECON Research Center Working Paper, National Chengchi University.Google Scholar
  25. Chen S.-H., Yeh C.-H. (1996) Genetic programming learning and the cobweb model. In: Angeline P. (Ed.), Advances in Genetic Programming, Vol. 2, Chap. 22. MIT Press, Cambridge, MA, 443–466.Google Scholar
  26. Chen S.-H., Yeh C.-H. (1997) Modeling speculators with genetic programming. In: Angeline P., Reynolds R. G., McDonnell J. R., Eberhart R. (Eds.), Evolutionary Programming VI, Lecture Notes in Computer Science, Vol. 1213. Springer-Verlag, Berlin, 137–147.Google Scholar
  27. Chen S.-H., Yeh C.-H. (1999) Modeling the expectations of inflation in the OLG model with genetic programming. Soft Computing 3(2), 53–62.CrossRefGoogle Scholar
  28. Chen S.-H., Yeh C.-H. (2000a) Simulating economic transition processes by genetic programming. Annals of Operation Research 97, 265–286.zbMATHCrossRefGoogle Scholar
  29. Chen S.-H., Yeh C.-H. (2000b) On the Role of Intensive Search in Stock Markets: Simulations Based on Agent-Based Computational Modeling of Artificial Stock Markets. In: Proceedings of the Second Asia-Pacific Conference on Genetic Algorithms and Applications. Global Link Publishing Company, Hong Kong, 397–402.Google Scholar
  30. Chen S.-H., Yeh C.-H. (2000c) On the Consequence of “Following the Herd”: Evidence from the Artificial Stock Market. In: Arabnia H. R. (Ed.) Proceedings of the International Conference on Artificial Intelligence, Vol. II, CSREA Press, 388–394.Google Scholar
  31. Chen S.-H., Yeh C.-H. (2001) Evolving traders and the business school with genetic programming: a new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control 25, 363–393.zbMATHCrossRefGoogle Scholar
  32. Chen S.-H., Yeh C.-H. (2002) On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis. Forthcoming in Journal of Economic Behavior and Organization.Google Scholar
  33. Chen S.-H., Yeh C.-H., Liao C.-C. (2002) On AIE-ASM: Software to simulate artificial stock markets with genetic programming, in Chen S.-H. (Ed.), Evolutionary Computation in Economics and Finance, Heidelberg: Physica-Verlag. 107–122.Google Scholar
  34. Dawid H. (1996) Learning of cycles and sunspot equilibria by genetic algorithms. Journal of Evolutionary Economics 6(4), 361–373.MathSciNetCrossRefGoogle Scholar
  35. Dawid H., Kopel M. (1998) On economic applications of the genetic algorithm: a model of the cobweb type. Journal of Evolutionary Economics 8(3), 297–315.CrossRefGoogle Scholar
  36. Duffy J. (2001) Learning to speculate: Experiments with artificial and real agents. Journal of Economic Dynamics and Control 25, 295–319.MathSciNetzbMATHCrossRefGoogle Scholar
  37. Franke R. (1998) Coevolution and stable adjustments in the cobweb model. Journal of Evolutionary Economics 8(4), 383–406.MathSciNetCrossRefGoogle Scholar
  38. Grandmont J.-M. (1985) On endogeneous competitive business cycles. Econometrica 53, 995–1045.MathSciNetzbMATHCrossRefGoogle Scholar
  39. Grossman S. (1976) On the efficiency of competitive stock markets where traders have diverse information. Journal of Finance 31, 573–585.CrossRefGoogle Scholar
  40. Grossman S. Stiglitz J. (1980) On the impossibility of informationally efficient markets. American Economic Review 70, 393–408.Google Scholar
  41. Kareken J., Wallace N. (1981) On the indeterminacy of equilibrium exchange rate. Quarterly Journal of Economics 96, 207–222.CrossRefGoogle Scholar
  42. Krugman P. (1996) The Self-Organizing Economy, Blackwell.Google Scholar
  43. LeBaron, B. (1999). Building financial markets with artificial agents: Desired goals and present techniques.” In: G. Karakoulas (ed.), Computational Markets, MIT Press.Google Scholar
  44. LeBaron, B. (2001) Evolution and time horizons in an agent based stock market. Macroeconomic Dynamics 5, 225–254.zbMATHCrossRefGoogle Scholar
  45. LeBaron B., Arthur W. B., Palmer R. (1999) Time series properties of an artificial stock market. Journal of Economic Dynamics and Control 23, 1487–1516.zbMATHCrossRefGoogle Scholar
  46. Leijonhufvud A. (1993) Towards a not-too-rational macroeconomics. Southern Economic Journal 60(1), 1–13.CrossRefGoogle Scholar
  47. Lucas R. (1986) Adaptive behaviour and economic theory. In: Hogarth R. Reder M. (eds) Rational choice: the contrast between economics and psychology. University of Chicago Press, 217–242.Google Scholar
  48. Mandlebrot B. (1963) The variation of certain speculative prices. Journal of Business 36, 394–419.CrossRefGoogle Scholar
  49. Muth J. F. (1961) Rational expectations and the theory of price movements. Econometrics 29, 315–335.CrossRefGoogle Scholar
  50. Palmer, R. G., Arthur W. B., Holland J. H., LeBaron B., and Tayler P.(1994). Artificial economic life: a simple model of a stock market. Physica D, 75, 264–274.zbMATHCrossRefGoogle Scholar
  51. Smith V. L., Suchanek G. L., Williams A. W. (1988) Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica 56(6), 1119–1152.CrossRefGoogle Scholar
  52. Tay, N., Linn S. (2001) Fuzzy inductive reasoning, expectation formation and the behavior of security prices. Journal of Economic Dynamics and Control 25, 321–361.zbMATHCrossRefGoogle Scholar
  53. Tayler P. (1995) Modelling artificial stock markets using genetic algorithms. In Goonatilake S., Treleaven P. (Eds.), Intelligent Systems for Finance and Business. Wiley, New York, NY, 271–287.Google Scholar
  54. Tirole, J. (1982) On the possibility of speculation under rational expectations. Econometrica, 50, 1163–1182.zbMATHCrossRefGoogle Scholar
  55. Vriend, N. (2001) On two types of GA-Learning. In: Chen S.-H. (Ed), Evolutionary Computation in Economics and Finance, Heidelberg: Physica-Verlag, 233–243.Google Scholar
  56. Yang J. (2001) The efficiency of an artificial double auction stock market with neural learning agents. In: Chen S.-H. (Ed), Evolutionary Computation in Economics and Finance, Physica Verlag. 87–107.Google Scholar
  57. Yeh C.-H., Chen S.-H. (2001a) Toward an integration of social learning and individual learning in agent-based computational stock markets: The approach based on population genetic programming. Journal of Management and Economics 5.Google Scholar
  58. Yeh C.-H., Chen S.-H. (2001b) Market diversity and market efficiency: The approach based on genetic programming. Journal of Artificial Simulation of Adaptive behavior, Vol. 1, No. 1. 147–167.Google Scholar

Copyright information

© Springer Japan 2003

Authors and Affiliations

  1. 1.AI-ECON Research Center, Department of EconomicsNational Chengchi UniversityTaipeiTaiwan

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