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 
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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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

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