Intelligent Data Analysis of Intelligent Systems

  • David C. Krakauer
  • Jessica C. Flack
  • Simon Dedeo
  • Doyne Farmer
  • Daniel Rockmore
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6065)


We consider the value of structured priors in the analysis of data sampled from complex adaptive systems. We propose that adaptive dynamics entails basic constraints (memory, information processing) and features (optimization and evolutionary history) that serve to significantly narrow search spaces and candidate parameter values. We suggest that the property of “adaptive self-awareness”, when applicable, further constrains model selection, such that predictive statistical models converge on a systems own internal representation of regularities. Principled model building should therefore begin by identifying a hierarchy of increasingly constrained models based on the adaptive properties of the study system.


Sparse Code Complex Adaptive System Adaptive Dynamic Double Auction Structure Prior 
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 2010

Authors and Affiliations

  • David C. Krakauer
    • 1
  • Jessica C. Flack
    • 1
  • Simon Dedeo
    • 1
  • Doyne Farmer
    • 1
  • Daniel Rockmore
    • 1
  1. 1.Santa Fe InstituteSanta Fe, New MexicoUSA

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