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Comparison Between Different Methods Of Level Identification

Author

Listed:
  • OLIVER PFANTE

    (Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany)

  • NILS BERTSCHINGER

    (Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany)

  • ECKEHARD OLBRICH

    (Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany)

  • NIHAT AY

    (Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany;
    Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA)

  • JÜRGEN JOST

    (Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103 Leipzig, Germany;
    Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA)

Abstract

Levels of a complex system are characterized by the fact that they admit a closed functional description in terms of concepts and quantities intrinsic to that level. Several ideas have come up so far in order to make the notion of a closed description precise. In this paper, we present four of these approaches and investigate their mutual relationships. Our study is restricted to the case of discrete dynamical systems, where the different levels are linked by a coarse-graining of variables and states of the system.

Suggested Citation

  • Oliver Pfante & Nils Bertschinger & Eckehard Olbrich & Nihat Ay & Jürgen Jost, 2014. "Comparison Between Different Methods Of Level Identification," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-21.
    • Handle: RePEc:wsi:acsxxx:v:17:y:2014:i:02:n:s0219525914500076
    DOI: 10.1142/S0219525914500076
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    Citations

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    Cited by:

    1. Robin Lamarche-Perrin & Sven Banisch & Eckehard Olbrich, 2016. "The Information Bottleneck Method For Optimal Prediction Of Multilevel Agent-Based Systems," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(01n02), pages 1-45, February.
    2. Oliver Pfante & Nils Bertschinger, 2019. "Information-Theoretic Analysis Of Stochastic Volatility Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-21, February.
    3. Usó-Doménech, J.L. & Nescolarde-Selva, J.A. & Lloret-Climent, M. & Gash, H., 2016. "Semantics of language for ecosystems modelling: A model case," Ecological Modelling, Elsevier, vol. 328(C), pages 85-94.

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