The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Economy as a Complex System

  • Alan Kirman
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2503

Abstract

Complex systems are composed of particles or agents which interact directly with each other. The rules for this interaction may be very simple and may not reflect the sort of rationality associated with standard economic models. Interaction is not through some exogenously given market, nor does it depend on the complicated reasoning involved in game theory. A complex system exhibits emergent aggregate properties as it organizes itself, and these can explain important phenomena such as bubbles, herding behaviour, and segregation. In each case the aggregate state of the economy or market could not be predicted from the average behaviour of the individuals.

Keywords

Agent-based models Bubbles Chaos Clusters Complex systems Complexity Computational complexity Coordination Deterministic chaos Economy as a complex system Econophysics Emergence Equilibrium Ergodicity Evolution Excess volatility Financial market contagion Forecasting Game theory Herding Imperfect competition Law of large numbers Minority game Neighbourhood effects Prisoner’s Dilemma Punctuated equilibria Residential segregation Social interaction Social networks Statistical mechanics Statistical physics Steady state Tit for tat Tournaments 

JEL Classifications

D85 
This is a preview of subscription content, log in to check access.

Bibliography

  1. Anderson, P.W., K.J. Arrow, and D. Pines. 1988. The economy as an evolving complex system. Redwood City: Addison-Wesley.Google Scholar
  2. Arthur, W.B. 1994. Inductive reasoning and bounded rationality. American Economic Review 84: 406–411.Google Scholar
  3. Arthur, W.B., S.N. Durlauf, and D.A. Lane, ed. 1997. The economy as an evolving complex system II. Reading: Addison-Wesley.Google Scholar
  4. Axelrod, R. 1997. The complexity of cooperation: Agent-based models of competition and collaboration. Princeton: Princeton University Press.Google Scholar
  5. Bak, P., K. Chen, J. Scheinkman, and M. Woodford. 1993. Aggregate fluctuations from independent sectoral shocks: Self-organized criticality in a model of production and inventory dynamics. Ricerche Economiche 47: 3–30.CrossRefGoogle Scholar
  6. Blume, L., and S.N. Durlauf. 2006. The economy as an evolving complex system III. Oxford: Oxford University Press.Google Scholar
  7. Brock, W.A., and C. Hommes. 1997. A rational route to randomness. Econometrica 65: 1059–1095.CrossRefGoogle Scholar
  8. Crane, E. 1999. The world history of beekeeping and honey hunting. London: Duckworth.Google Scholar
  9. De Grauwe, P., and M. Grimaldi. 2006. The exchange rate in a behavioral finance framework. Princeton: Princeton University Press.Google Scholar
  10. Durlauf, S.N., and H.P. Young. 2001. Social dynamics: Economic learning and social evolution. London: MIT Press.Google Scholar
  11. Eldredge, N., and S.J. Gould. 1972. Punctuated equilibria: An alternative to phyletic gradualism. In Models in paleobiology, ed. T.J.M. Schopf. San Francisco: Freeman, Cooper and Co.Google Scholar
  12. Foellmer, H. 1974. Random economies with many interacting agents. Journal of Mathematical Economics 1: 51–62.CrossRefGoogle Scholar
  13. Foellmer, H., U. Horst, and A. Kirman. 2005. Equilibrium in financial markets with heterogeneous agents: A new perspective. Journal of Mathematical Economics 41: 123–155.CrossRefGoogle Scholar
  14. Grandmont, J.-M. 1985. On endogenous competitive business cycles. Econometrica 53: 995–1046.CrossRefGoogle Scholar
  15. Johnson, N., P. Jeffries, and P.M. Hui. 2003. Financial market complexity. Oxford: Oxford University Press.CrossRefGoogle Scholar
  16. Kirman, A. 1992. What or whom does the representative individual represent? Journal of Economic Perspectives 6(2): 117–136.CrossRefGoogle Scholar
  17. Kirman, A., and G. Teyssiere. 2005. Testing for bubbles and change points. Journal of Economic Dynamics and Control 29: 765–799.CrossRefGoogle Scholar
  18. Lindgren, K. 1991. Evolutionary phenomena in simple dynamics. In Artificial life II, ed. C.G. Langton, C. Taylor, J.D. Farmer, and S. Rasmussen. Redwood City: Addison Wesley.Google Scholar
  19. Lux, T., and M. Marchesi. 1999. Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397: 498–450.CrossRefGoogle Scholar
  20. Mitchell, M. 1996. An introduction to genetic algorithms. Cambridge, MA: MIT Press.Google Scholar
  21. Pancs, R., and N.J. Vriend. 2007. Schelling’s spatial proximity model of segregation revisited. Journal of Public Economics 91: 1–24.CrossRefGoogle Scholar
  22. Pollicott, M., and H. Weiss. 2001. The dynamics of Schelling-type segregation models and a nonlinear graph Laplacian variational problem. Advances in Applied Mathematics 27: 17–40.CrossRefGoogle Scholar
  23. Rabin, M. 1998. Psychology and economics. Journal of Economic Literature 36: 11–46.Google Scholar
  24. Schelling, T. 1978. Micromotives and macrobehavior. New York: W.W. Norton and Co.Google Scholar
  25. Simon, H.A. 1957. Models of man: Social and rational. New York: Wiley.Google Scholar
  26. Vinkovic, D., and A. Kirman. 2006. A physical analogue of the Schelling model. Proceedings of the National Academy of Sciences 103: 19261–19265.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Alan Kirman
    • 1
  1. 1.