Prediction and modularity in dynamical systems

Identifying and understanding modular organizations is centrally important in the study of complex systems. Several approaches to this problem have been advanced, many framed in information-theoretic terms. Our treatment starts from the complementary point of view of statistical modeling and prediction of dynamical systems. It is known that for finite amounts of training data, simpler models can have greater predictive power than more complex ones. We use the trade-off between model simplicity and predictive accuracy to generate optimal multiscale decompositions of dynamical networks into weakly-coupled, simple modules. State-dependent and causal versions of our method are also proposed.

• Publication year

  2011

• Venue

  European Conference on Artificial Life

• Publication date

  2011-06-19

• Fields of study

  Biology, Mathematics, Physics, Computer Science

• Identifiers
  DOI 10.7551/978-0-262-29714-1-ch065arXiv 1106.3703
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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