Complex Adaptive Systems: Using a Free-Market Simulation to Estimate Attribute Relevance

  • Christopher N. Eichelberger
  • Mirsad Hadžikadić
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


The authors have implemented a complex adaptive simulation of an agent-based exchange to estimate the relative importance of attributes in a data set. This simulation uses an individual, transaction-based voting mechanism to help the system estimate the importance of each variable at the system/aggregate level. Two variations of information gain – one using entropy and one using similarity – were used to demonstrate that the resulting estimates can be computed using a smaller subset of the data and greater accommodation for missing and erroneous data than traditional methods.


Information Gain Complex Adaptive System Vote Mechanism Gain Method Purchasing Strategy 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arteconi, Hales, D.: Greedy cheating liars and the fools who believe them. Technical Report UBLCS-2005-21, University of Bologna, Dept. of Computer Science, Bologna, Italy (December 2005), also available at:
  2. 2.
    Dooley, K.: A Complex Adaptive Systems Model of Organization Change. Nonlinear Dynamics, Psychology, & Life Science 1(1), 69–97 (1997)CrossRefMATHGoogle Scholar
  3. 3.
    Dorigo, M., Nolfi, S., Trianni, V.: Cooperative Hole-Avoidance in a Swarm-bot. Robotics & Autonomous Systems (2006)Google Scholar
  4. 4.
    Hadzikadic, M., Bohren, B.F.: Learning to Predict: INC2.5. IEEE Trans. Knowl. Data Eng. 9(1), 168–173 (1997)CrossRefGoogle Scholar
  5. 5.
    Hair Jr., J., Anderson, R., Tatham, R., Black, W.: Multivariate Data Analysis, 5th edn. Prentice Hall, Upper Saddle River (1998)Google Scholar
  6. 6.
    Holland, J.H.: Hidden Order: How Adaptation Builds Complexity. Basic Books, New York (1995)Google Scholar
  7. 7.
    Grunwald, P.D., Vitanyi, P.M.B.: Kolmogorov complexity and information theory. With an interpretation in terms of questions and answers. J. Logic, Language, and Information 12(4), 497–529 (2003)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Mandelbrot, B.: Fractal aspects of the iteration of zλz (1 − z) for complex λ and z. Non-Linear Dynamics, New York (1979); Edited by Robert H. G. Helleman. Annals of the New York Academy of Sciences 357, 249–259Google Scholar
  9. 9.
    Mushroom records drawn from The Audubon Society Field Guide to North American Mushrooms, G. H. Lincoff (Pres.), New York: Alfred A. Knopf. Donated to the UCI Machine Learning repository by Jeff Schlimmer ( (1981) (April 27, 1987), URL
  10. 10.
    Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  11. 11.
    Weisstein, E.W.: Rule 110. From MathWorld–A Wolfram Web Resource,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christopher N. Eichelberger
    • 1
  • Mirsad Hadžikadić
    • 1
  1. 1.College of Information TechnologyThe University of North Carolina at CharlotteCharlotteUSA

Personalised recommendations