Prediction and Modularity in Dynamical Systems

Artemy Kolchinsky1,2 and Luis M. Rocha1,2

1 School of Informatics and Computing, Indiana University, 919 East Tenth Street, Bloomington IN 47408, USA
2FLAD Computational Biology Collaboratorium, Instituto Gulbenkian de Ciencia, Portugal

Citation: A. Kolchisnky and L.M. Rocha [2011]. "Prediction and Modularity in Dynamical Systems". In: Advances in Artificial Life, Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems (ECAL 2011). August 8 12, 2011, Paris, France,. MIT Press, pp. 423-430.

The pre-print is available from the Due to mathematical notation and graphics, only the abstract is presented here.


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.

Keywords:Modularity, Complex Systems, Dynamical Systems, Information Theory, Statistical Modeling, Complex Networks, Artificial Life, Cognitive Science.

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Last Modified: August 26, 2011