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Deep information from limited observation of robust yet fragile systems

Author

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  • LaViolette, Randall A.
  • Glass, Kristin
  • Colbaugh, Richard

Abstract

We show how one can completely reconstruct even moderately optimized configurations of the Forest Fire model with relatively few observations. We discuss the relationship between the deep information from limited observations (DILO) to the robust-yet-fragile (RYF) property of the Forest Fire model and propose that DILO may be a general property of RYF complex systems.

Suggested Citation

  • LaViolette, Randall A. & Glass, Kristin & Colbaugh, Richard, 2009. "Deep information from limited observation of robust yet fragile systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3283-3287.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:17:p:3283-3287
    DOI: 10.1016/j.physa.2009.05.019
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    References listed on IDEAS

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    1. Paul Ormerod & Rich Colbaugh, 2006. "Cascades of Failure and Extinction in Evolving Complex Systems," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(4), pages 1-9.
    2. Ormerod, Paul, 2007. "Extracting deep information from limited observations on an evolved social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 48-52.
    3. Drossel, B. & Schwabl, F., 1992. "Self-organized criticality in a forest-fire model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 191(1), pages 47-50.
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