System Efficiency

  • Tim Gooding


It is frequently said that the market economy is the most efficient economic organisation ever devised by human kind. However, numerous complexity experiments indicate that agent computational effort is inversely correlated with system efficiency . The market economy puts consider computation pressure on consumers because of the wide range of choice and prices available in a market economy. The Toy Trader model is used to test whether normal complexity characteristics hold true in monetary trade systems.


Efficiency Perfect information Computational power El Farol Tit for Tat Prisoner’s dilemma Evolution Agent-based model Netlogo Toy Trader model 


  1. Arthur, W. (1994). Inductive reasoning and bounded rationality. The American Economic Review, 84(2), 406–411.Google Scholar
  2. Axelrod, R., & Hamilton, W. D. (1981, March 27). The evolution of cooperation. Science, 211(4489), 1390–1396.Google Scholar
  3. Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S. A., & Karplus, M. (1983). CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry, 4(2), 187–217.Google Scholar
  4. Collard, F., Dellas, H., & Smets, F. (2009). Imperfect information and the business cycle. Journal of Monetary Economics, 56, S38–S56.CrossRefGoogle Scholar
  5. Dedeo, S. (2017, September 17). Is tribalism a nature malfunction? What computers teach us about getting along. Nautalus. Retrieved from
  6. De Grauwe, P. (2008). DSGE-modelling: When agents are imperfectly informed (European Central Bank Working Paper No. 897).Google Scholar
  7. Dymski, G. A. (1993). Keynesian uncertainty and asymmetric information: Complementary or contradictory? Journal of Post Keynesian Economics, 16(1), 49–54.Google Scholar
  8. Fontana, G., & Gerrard, B. (2004). A post Keynesian theory of decision making under uncertainty. Journal of Economic Psychology, 25(5), 619–637.CrossRefGoogle Scholar
  9. Helman, C. (2016, June 28). Berkeley lab: It takes 70 billion kilowatt hours a year to run the internet. Retrieved October 28, 2017, from
  10. Hogg, T., & Huberman, B. A. (1991). Controlling chaos in distributed systems. IEEE Transactions on Systems, Man, and Cybernetics, 21(6), 1325–1332.CrossRefGoogle Scholar
  11. Rauch, J. E. (1993). Productivity gains from geographic concentration of human capital: Evidence from the cities. Journal of Urban Economics, 34(3): 380–400.Google Scholar
  12. Lorenzoni, G. (2005). Imperfect information, consumers’ expectations and business cycles. Cambridge: MIT Mimeo.Google Scholar
  13. Rand, W., & Stonedahl, F. (2007). The El Farol bar problem and computational effort: Why people fail to use bars efficiently. Evanston, IL: Northwestern University Press.Google Scholar
  14. Rust, J., Miller, J. H., & Palmer, R. (1994). Characterizing effective trading strategies: Insights from a computerized double auction tournament. Journal of Economic Dynamics and Control, 18, 61–96.CrossRefGoogle Scholar
  15. Van Ees, H., & Garretsen, H. (1993). Financial markets and the complementarity of asymmetric information and fundamental uncertainty. Journal of Post Keynesian Economics, 16(1), 37–48.CrossRefGoogle Scholar
  16. Wilhite, A. (2001). Bilateral trade and ‘small-world’ networks. Computational Economics, 18(1), 49–64.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Authors and Affiliations

  • Tim Gooding
    • 1
  1. 1.Kingston UniversityKingston upon ThamesUK

Personalised recommendations